Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

Modern building automation systems implement plenty of advanced control and monitoring functions considering various parameters like users’ activity, lighting, temperature changes etc. Moreover, new solutions based on Internet of Things and cloud services are also being developed for smart buildings to ensure comfort of use, user safety, energy efficiency improvements and integration with smart grids and smart city platforms. Such a wide spectrum of technologies and functions requires a new approach in building automation systems design to provide effective way of their implementation and flexibility during operation. At the same time, in the building design and operation industries, tools based on building information modeling and digital twins are being developed. In this paper, the authors discuss development directions and application areas of these solutions, identifying new trends and possibilities of their use in smart homes and buildings. In particular, the focus is on procedures for selecting automation functions, effective integration, and interoperability of building management systems with Internet of Things considering organization of prediction mechanisms and dynamic functional changes in buildings and smart networks. Chosen solutions and functions should consider the requirements from EN ISO 52120 standard and the guidelines defined for the Smart Readiness Indicator.

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

Environmental degradation is one of the most visible problems in Hargeisa. Currently, solid waste is disposed of at two dumping sites within the city limits causing nuisance and unsanitary conditions. Moreover, the existing dumpsites are on the verge of closure, therefore necessitating a dire need to be addressed. This research paper is aimed to integrate multicriteria decision analysis and GIS for evaluating the site suitability for the landfill in Hargeisa, Somaliland. For this purpose, eleven significant parameters were selected: proximity from built-up areas, surface water, groundwater well points, sensitive sites (Airports), land use/land cover, geology, soil type, elevation, slopes, roads, and distance to existing dumpsites. These were then integrated using an analytical hierarchy process (AHP). Subsequently, restriction buffer analysis was performed on the seven parameters to obtain better and more accurate results, and restricted zones were omitted. Furthermore, the pair-wise comparison used to obtain priorities between the selected criteria showed that the land uses land cover is the most significant criterion in the model with a relative weight of 0.1829, followed by the habitations, 0.1506. The overall result reveals that about 68.96% (21060.9 ha) of the study area is unsuitable, while 24.36% (7441.53 ha) and 6.68% accounted for the less and highly appropriate zone, respectively. Following the results of this study, Hargeisa City's municipal council must reconsider the waste management and landfill sites to solve the problems observed in the area before it is overdue. Furthermore, this systematic research approach will assist regional and global researchers, policymakers, and municipal governments.

Jerusalem artichoke juice is valued for its nutritional content and health benefits. Spontaneous fermentation enhances its flavor, quality, and functional components through microbial metabolic activities. This study used high-throughput sequencing to analyze microbial community changes and LC-MS and GC-MS to detect secondary metabolites and flavor compounds during fermentation. During the natural fermentation, beneficial bacteria like Lactobacillus and Pediococcus increased, promoting lactic acid production and inhibiting harmful bacteria, while environmental bacteria decreased. Similarly, fungi shifted from environmental types like Geosmithia and Alternaria to fermentation-associated Pichia and Penicillium. A total of 1, 666 secondary metabolites were identified, with 595 upregulated and 497 downregulated. Key metabolic pathways included phenylpropanoid biosynthesis, with significant increases in phenylalanine, tryptophan, and related metabolites. Lipid and nucleotide metabolism also showed significant changes. Flavor compounds, including 134 identified alcohols, esters, acids, and ketones, mostly increased in content after fermentation. Notable increases were seen in Phenylethyl Alcohol, Ethyl Benzenepropanoate, 3-Methylbutyl Butanoate, Ethyl 4-Methylpentanoate, 5-Ethyl-3-Hydroxy-4-Methyl-2(5H)-Furanone, Ethyl Decanoate, Hexanoic Acid, 1-Octanol. γ-aminobutyric acid (GABA) and other functional components enhanced the health value of the juice. This study provides insights into microbial and metabolic changes during fermentation, aiding in optimizing processes and improving the quality of fermented Jerusalem artichoke juice for functional food development.

This study examines the prospective association of egg consumption with multiple domains of cognitive function in older, community-dwelling men and women followed 16.3 years. Participants were 617 men and 898 women from the Rancho Bernardo Cohort aged 60 and older who were surveyed about egg intake/week in 1972-74, and attended a 1988-91 research visit when cognitive function was assessed with 12 tests. Analyses showed that egg intake ranged from 0-24/week (means: men=4.23.2, women=3.52.7, p<0.0001). In men, covariate-adjusted regressions showed egg intake was associated with better performance on Buschke total (p=0.04), long-term (p=0.02) and short-term (p=0.05) recall. No significant associations were observed in women (p’s>0.05). Analyses in those <60y at in 1972-74 showed egg intake was positively associated with scores on Heaton copying(p<.04) and the Mini-Mental Status Exam(MMSE;p<.02) in men, and category fluency(p<0.05) in women. Egg intake was not significantly associated with odds of poor performance on MMSE, Trails B, or category fluency in either sex. These reassuring findings suggest there are no long-term detrimental effects of egg consumption on multiple cognitive function domains, and for men, there may be beneficial effects for verbal episodic memory. Egg consumption in middle-age may also be related to better cognitive performance later in life.

The paper considers the symmetries of a bit of information corresponding to one, two or three qubits of quantum information and identifiable as the three basic symmetries of the Standard model, U(1), SU(2), and SU(3) accordingly. They refer to “empty qubits” (or the free variable of quantum information), i.e. those in which no point is chosen (recorded). The choice of a certain point violates those symmetries. It can be represented furthermore as the choice of a privileged reference frame (e.g. that of the Big Bang), which can be described exhaustively by means of 16 numbers (4 for position, 4 for velocity, and 8 for acceleration) independently of time, but in space-time continuum, and still one, 17th number is necessary for the mass of rest of the observer in it. The same 17 numbers describing exhaustively a privileged reference frame thus granted to be “zero”, respectively a certain violation of all the three symmetries of the Standard model or the “record” in a qubit in general, can be represented as 17 elementary wave functions (or classes of wave functions) after the bijection of natural and transfinite natural (ordinal) numbers in Hilbert arithmetic and further identified as those corresponding to the 17 elementary of particles of the Standard model. Two generalizations of the relevant concepts of general relativity are introduced: (1) “discrete reference frame” to the class of all arbitrarily accelerated reference frame constituting a smooth manifold; (2) a still more general principle of relativity to the general principle of relativity, and meaning the conservation of quantum information as to all discrete reference frames as to the smooth manifold of all reference frames of general relativity. Then, the bijective transition from an accelerated reference frame to the 17 elementary wave functions of the Standard model can be interpreted by the still more general principle of relativity as the equivalent redescription of a privileged reference frame: smooth into a discrete one. The conservation of quantum information related to the generalization of the concept of reference frame can be interpreted as restoring the concept of the ether, an absolutely immovable medium and reference frame in Newtonian mechanics, to which the relative motion can be interpreted as an absolute one, or logically: the relations, as properties. The new ether is to consist of qubits (or quantum information). One can track the conceptual pathway of the “ether” from Newtonian mechanics via special relativity, via general relativity, via quantum mechanics to the theory of quantum information (or “quantum mechanics and information”). The identification of entanglement and gravity can be considered also as a ‘byproduct” implied by the transition from the smooth “ether of special and general relativity’ to the “flat” ether of quantum mechanics and information. The qubit ether is out of the “temporal screen” in general and is depicted on it as both matter and energy, both dark and visible.

Empowering women and promoting gender equality is crucial for accelerating sustainable development in fragile countries, including the Democratic Republic of Congo (DRC). However, there is scarce existing knowledge or understanding of the factors determining women’s empowerment in these contexts. We aimed to assess women's empowerment and determine its associated factors in Kinshasa, DRC. We analyzed data from the 2021 Performance Monitoring Assessment (PMA) survey. A sample of 1,365 women of childbearing age was retained for this study. Twenty empowerment items related to household decision-making, contraception use, and husband/partner influence were considered. We calculated the average women’s empowerment index (aWEI), identified the women empowerment variables using principal component analysis (PCA), and determined the associated factors for the first three principal components through the performance of multivariate binary logistic regression. In Kinshasa, the overall aWEI was estimated at 0.65. It was low for household decision-making (0.34) and high for husband/partner influence domains (0.93), respectively. Three principal components were identified and named, including the absence of threats, control of sexuality, and participation in decision-making. The factors associated with these components were having internet access, being in free union with a partner, being aged 40-49 years, and residing in a non-slum area. Increasing access to information would enable women in Kinshasa to make strategic decisions about their lives, benefiting themselves and others.

Pervasive and wide-ranging cognitive deficits are a core feature of schizophrenia and an important determinant of long-term functional outcome. The lack of sufficiently effective treatments for cognitive impairment associated with schizophrenia (CIAS) represents a major unmet need and a central roadblock towards recovery. This is partly due to the current therapeutic focus on clinical symptoms, and the relative neglect of cognitive impairments despite their functionally disabling effects. Furthermore, effective treatment is impeded by our limited knowledge of the complex pathophysiology, which gives rise to perturbed information processing. Here, we review mechanisms and effectiveness of available pharmacological and non-pharmacological treatments for CIAS. Current evidence indicates, that while techniques which broadly enhance neural plasticity show the greatest therapeutic potential, effect sizes are at best moderate. Among other reasons, this is due to a considerable heterogeneity of responses to individual interventions. Furthermore, we discuss how recent conceptual advances in operationalizing cognitive impairments based on cognitive neuroscience have the potential to address these issues and facilitate the development of novel treatment strategies for CIAS. This includes more clearly elucidating pathophysiological mechanisms in both humans and animal models, identifying new treatment targets as well as establishing biomarkers for a better prediction of treatment responses.

Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and their informal caregivers: (1) Background: It is important to get a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences, based on trustable information.; (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized findings; (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed the identification of current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.

The holographic principle states that the information about the volume of space is stored on its boundary. Assuming this holds, we can explain the aspects of gravity. The spacetime needs to be extended to 5D to explain gravity in this context. This naturally explains the four classical tests of gravity namely the gravitational redshift, the perihelion precession, the bending of light, and the gravitational time delay. We also show a novel way in which the universe expands in an accelerated manner at late times without the need for any form of dark energy.

The invasion of ship domain stands out as a significant factor contributing to the risk of collisions during vessel navigation. However, there is a lack of research on the mechanisms underlying collision risks specifically related to merchant and fishing vessels in coastal waters. This study proposes an assessment method for collision risks between merchant and fishing vessels in coastal waters and validated through a comparative analysis through visualization. First of all, the operational status of fishing vessels is identified. Collaboratively working fishing vessels are treated as a unified entity, expanding their ship domain during operation to assess collision risks. Secondly, to quantify the collision risk between ships, a collision risk index (CRI) is proposed and visualized based on the severity of collision risk. Finally, taking the high risk area of merchant and fishing vessels collision in the Minjiang River Estuary as an example, this paper conduct the analysis involves classifying ship collision scenarios, extracting risk data under different collision scenarios, and visually analyzing areas prone to danger. The results indicate that this method effectively evaluates the severity of collision risk, and the identified high-risk areas resulting from the analysis is verified by accidents occurred in the recent three years.

In order to counter the impending shortage of skilled professionals in the aging societies of our time in many western countries such as Germany, solutions for business and society are urgently needed. Here, artificial intelligence (AI) can play an important role in mitigating the problem with the help of diverse applications. At the same time, it is important to consider both the needs of the respective employee1 and the company to ensure that the use of AI has a positive impact on the organization and finds social acceptance. In this article, we describe the newly developed OSQE model (Optimize, Secure, Qualify, Expand), which for the first time outlines an AI cycle against the shortage of skilled professionals in a holistic approach that focuses equally on people and companies. This can serve organizations as a guide for strategy development, decision-making for and implementation of AI-supported measures in an entire cycle of an employee's affiliation with a company. The model takes three driving forces into account: companies, professionals, and AI applications. In the model, the measures to be implemented are prioritized with ascending numbering based on what would be most urgent for a company to implement. All measures relate to areas of action that place people at the center and can be assigned to the classic cycle of belonging of an employee in the company. In this regard, the opportunities that AI offers to professionals and companies are highlighted.

Iterated games, in which the same economic interaction is repeatedly played between the same agents, are an important framework for understanding the effectiveness of strategic choices over time. To date very little work has applied information theory to the information sets used by agents in order to decide what action to take next in such strategic situations. This article looks at the mutual information between previous game states and an agent's next action by introducing two new classes of games: ’invertible games’ and ‘cyclical games'. By explicitly expanding out the mutual information between past states and the next action we show under what circumstances these expressions can be simplified. These information measures are then applied to the Traveler's Dilemma game and the Prisoner's Dilemma game, the Prisoner's Dilemma being invertible, to illustrate their use. In the Prisoner's Dilemma a novel connection is made between the computational principles of logic gates and both the structure of games and the agents' decision strategies. This approach is applied to the cyclical game Matching Pennies to analyse the foundations of a behavioural ambiguity between two well studied strategies: ‘Tit-for-Tat' and ’Win-Stay, Lose-Switch'.

Histone changes that are adjustable and transmitted without altering the DNA sequence are part of epigenetic control. In many disorders, most famously malignancy, abnormal genetic information is seen as a result of mismanagement of typical epigenetic mechanisms. A paradigm change in the diagnosis and treatment of cancer has been brought about by recent discoveries about the processes of epigenetic modifications, chromatin remodeling, and noncoding RNAs implicated in the changed expression patterns of tumor cells. There is a significant increase in the search for drugs that could alter the epigenetic environment altered by tumor cells and benefit from their therapeutic potential. Phytochemicals with a high concentration of naturally produced phenol molecules known as flavonoids can regulate genomic processes. Understanding the precise epigenetic alterations brought on by flavonoid-mediated mechanisms is necessary to develop combination therapy techniques and genomic therapies for cancer. This article aims to thoroughly examine the epigenetic modifications of flavonoids, including their anticancer properties.

Older adults with obesity have an increased risk for cognitive decline and subsequent Alzheimer’s disease. Previous research has focused on the impact of individual nutrients and overall dietary patterns to improve cognitive function. The Brain and Body Health (BB-Health) study aims to demonstrate the feasibility of a multicomponent nutritional supplement (MCNS) and weight loss (WL) intervention for individuals with overweight and obesity who are not consuming a healthy diet. BB-Health is a parallel-group randomized placebo-controlled 2x2 factorial pilot trial comparing the MCNS, WL, and MCNS+WL with a Control group. Eligible participants are 55 to 85 years of age with a body mass index 27.0 to 39.9 kg/m2. Major exclusion criteria include type 2 diabetes, moderate or severe cognitive impairment, and self-reported healthy dietary pattern. The BB-Health feasibility trial is registered as NCT05542199 at clinicaltrials.gov. The BB-Health objectives are to report: 1) recruitment and enrollment, including pace and descriptive statistics of the study sample; 2) adherence to the administered interventions; 3) successful data collection and performance of outcome assessments, including evaluation of change in key outcomes over time that are powered for this pilot study; and 4) summary statistics on outcomes for use in planning a subsequent, larger trial. The outcomes of interest are age-sensitive cognitive assessments, cerebral hemodynamics, and related health parameters. All measurements are collected at baseline, 3-, 6-, and 12-month visits. If the BB-Health protocol demonstrates feasibility, the novel MCNS with and without a parallel WL intervention has potential to be administered in a confirmatory randomized trial to evaluate ability to prevent age-related cognitive decline and improve brain health. The BB-Health Feasibility Study could significantly contribute to our understanding of the influence of diet and weight management on cognitive health

This review explores the nuanced decision-making surrounding postoperative radiotherapy for diffuse low-grade glioma (DLGG) patients, emphasizing the need for personalized treatment to balance the benefits of tumor control with the risks of long-term side effects. Radiotherapy is a cornerstone of DLGG treatment, offering significant improvements in progression-free survival, but its potential for cognitive decline and vascular lesions necessitates careful consideration of individual patient factors. The review delves into the complexities of radiotherapy timing, revealing that while early radiotherapy enhances progression-free survival, it does not significantly impact overall survival and may lead to earlier onset of cognitive decline. Conversely, delayed radiotherapy may mitigate cognitive decline but may result in shorter progression-free survival and the onset of neurological symptoms. The review advocates for a patient-centered approach, emphasizing the importance of weighing individual risks and benefits, and highlights the potential of artificial intelligence in predicting cognitive decline and the need to revisit previous research findings in light of the 2021 WHO (World Health Organization) classification update, underscoring the evolving nature of DLGG treatment and the ongoing quest for optimal therapeutic strategies.

Multiple sensor nodes known as detection stations make the sensor networks; each node is light and portable. Every sensor node contains power source, microcomputer, transducer and transceiver. Power source provides power to each node. Micro-computer is used for storing and processing the output coming from the sensors. The transducer is used to generate the signals and the transceiver is used to receive and transmit data to the central computer. Eavesdropping gets facilitated with wireless communication, and it has many useful applications in military, homeland, hostile and uncontrolled environments. So it is prone to the high level of security. The process in which information is gathered to form a summarized type for analysis is known as data aggregation, as it is used to reduce the energy consumption in wireless sensor networks. The security issues have become crucial in data aggregation, especially when gets deployed in hostile and remote environment. In wireless sensor networks many secure aggregations have been proposed. It still faces some resource constrained that’s why new techniques are needed. In our survey we will discuss those approaches and their pros and cons.

This study presents the core tenets that constitute culturally responsive science assessments (CRSciA) for K-12 education and aligned them with the capabilities of generative AI (GenAI). The tenets were identified based on their dominance in the literature, including Indigenous Language, Race and Ethnicity, Religious Beliefs, Family and Community, and Indigenous Knowledge. These tenets were instrumental in developing the GenAI-CRSciA framework and the subsequent prompts to generate CRSciAs aligned with the Next Generation Science Standards (NGSS) while comparing it with standard prompt strategies. The results showed that the GenAI-CRSciA approach outperformed standard prompting strategies in terms of generating CRSciAs, proving its effectiveness in addressing the ongoing challenges in science assessments. Even though the GenAI-CRSciA framework presented assessments that cover a range of the cultural backgrounds, it presents potential challenges such as compromising curricula core competencies and overgeneralization of students’ backgrounds; overlooking subcultural variations within a country or region, for example differences between urban and rural students, or between different ethnic or linguistic groups within the same nation, which can lead to bias and less effective CRSciA outputs. To address this, we recommend that teachers provide comprehensive and specific background information about their students when using the GenAI-CRSciA prompting, ensuring that the generated content is truly reflective of the students’ unique cultural contexts. In larger class settings, teachers could implement comprehensive local data indexing, where detailed student background information is uploaded and retrievable for use in crafting more personalized CRSciAs. We recommend educators make conscious efforts to validate the generated assessments with input from human experts, including community or family, educators, and even students who are knowledgeable about the cultural nuances. We also suggest continuous professional development would be necessary to enhance teachers’ understanding of the cultural tenets.

As Li-ion batteries use is spreading, incidents in large energy storage systems (stationary storage containers, …) or in large-scale cell and battery storages (warehouse, recyclers, …), often leading to fire, are occurring on regular basis. Water remains one of the most efficient fire extinguishing agents for tackling those battery incidents and large quantities are usually necessary. Since bat-teries contains various potentially harmful components (metals and their oxides or salts, sol-vents…) and that thermal runway induced battery incidents are accompanied with complex and potentially multistage fume emissions (containing both gas and particles) the potential impact of fire run-off water on the environment should be considered and assessed carefully. The tests presented in this paper focus on the analysis of the composition of run-off waters used to spray NMC Li-ion modules under thermal runaway. It highlights that waters used for fire-fighting are susceptible to contain many metals, including Ni, Mn, Co, Li and Al mixed with other carbonaceous species (soot, tarballs) and sometimes undecomposed solvents used in the elec-trolyte. Extra-polation of pollutant concentrations compared with PNEC values showed that, for large scale incidents, run-off water could be potentially hazardous to the environment.

Utilizing deep learning for semantic segmentation of cropland from remote sensing imagery has become a crucial technique in land surveys. Cropland illustrates diverse morphologies and degrees of fragmentation on the Earth’s surface, underscoring the importance of accurately perceiving the complex boundaries of cropland which are crucial for effective segmentation. This paper introduces a UNet-like boundary-aware compensation model BAFormer. Cropland boundaries typically exhibit rapid transformations in pixel values and texture features, often appearing as high-frequency features in remote-sensing images. To enhance the recognition of these high-frequency features as represented by cropland boundaries, the proposed BAFormer integrates a Feature Adaptive Mixer (FAM) and develops a Deep Wide Large Kernel Multi-Layer Perceptron (DWLK-MLP) to enrich the global and local cropland boundaries features separately. Specifically, FAM adaptively mixes high-frequency and low-frequency features through the advantages of convolution and self-attention; DWLK-MLP expands the convolutional receptive field by deeply decomposing large kernel convolutions. The efficacy of BAFormer has been evaluated on the Vaihingen, Potsdam, and LoveDA public datasets, as well as the Mapcup dataset. It has demonstrated advanced performance, achieving mIoU scores of 84.5%, 87.3%, 53.5%, and 83.1% on these datasets respectively. Notably, BAFormer-T, the lightweight iteration of the model, surpasses other lightweight models on the Vaihingen dataset with scores of 91.3% F1 and 84.1% mIoU. The source code is available at https://github.com/WangYouM1999/BAFormer.

The complex color patterns on the wings and body of Drosophila guttifera (D. guttifera) are emerging as model systems for studying evolutionary and developmental processes. Studies regarding these processes depend on overexpression and down-regulation of developmental genes, which ultimately rely upon an effective transgenic system. Methods describing transgenesis in Drosophila melanogaster (D. melanogaster) have been extensively reported in several studies. However, these methods cannot be directly applied to D. guttifera due to the low egg production rate and very delicate embryos’ pre-injection treatment requirements. In this protocol, we describe extensively a comprehensive method used for generating transgenic D. guttifera. Using the protocol described here, we are able to establish transgenic lines, identifiable by the expression of Enhanced Green Fluorescent Protein (EGFP) in the eye disks of D. guttifera larvae. The entire procedure, from injection to screening for transgenic larvae, can be completed in approximately 30 days and should be relatively easy to adapt to other non-model Drosophila species, for which no white-eyed mutants exist.

For doctoral students, publishing in peer reviewed journals is a task many face with anxiety and trepidation. The world of publishing, from choosing a journal, negotiating editors and navigating reviewers’ responses is a bewildering place. Looking in from the outside, it seems that successful and productive academic writers have knowledge that is inaccessible to novice scholars. While there is a growing literature on writing for scholarly publication, many of these publications promote writing and publishing as a straight-forward activity that anyone can achieve if they follow the rules. We argue that the specific and situated contexts in which academic writers negotiate publishing practices is more complicated and messy. In this paper, we attempt to make explicit our publishing processes to highlight the complex nature of publishing. We use autoethnographic narratives to provide discussion points and insights into the challenges of publishing peer reviewed articles. One narrative is by a doctoral student at the beginning of her publishing career, who expresses her desires, concerns and anxieties about writing for publication. The other narrative focuses on the publishing practices of a more experienced academic writer. Both are international scholars working in the Canadian context. The purpose of this paper is to explore academic publishing through the juxtaposition of these two narratives to make explicit some of the more implicit processes. Four key themes emerge from these narratives. To publish successfully, academic writers need: 1) to be discourse analysts; 2) to have a critical competence; and 3) to have writing fluency and 4) to be emotionally intelligent.

The Huaniushan lead-zinc-silver deposit is a hydrothermal sedimentary exhalative deposit (SEDEX). The mining area has complex geological conditions. The main tectonic structure in the area is the Huaheitan-Shuangfengshan Fault (F3), which controls the distribution of strata and magmatic rocks. Since the discovery of the Huaniushan Lead-zinc-silver polymetallic deposit, there are diverse understandings of the deposit's genesis and mineralization law, making it challenging to meet the geological interpretation requirements of the Huaniushan Lead-zinc-silver polymetallic deposit. Moreover, a single geophysical exploration method utilizes limited rock physical parameters, making it difficult to effectively characterize underground structures. The integrated exploration of multiple geophysical methods can effectively integrate the geophysical characteristics of different rock physical parameters, reducing the multiplicity and uncertainty of geophysical data inverse interpretation. The comprehensive interpretation of three-dimensional inversion based on various geophysical data, the construction of geological-geophysical models based on geological grounds, the establishment of hidden ore exploration and positioning, and the rapid evaluation of geophysical technology systems are the current development trends in mineral exploration. In light of this, this paper conducts research on the three-dimensional inversion interpretation of the heavy magnetoelectric exploration data of the Huaniushan sedimentary exhalative deposit lead-zinc-silver polymetallic deposit in the mining area and constructs a three-dimensional geological-geophysical model of the study area based on the obtained three-dimensional physical structure of the underground three-dimensional density, magnetization intensity, resistivity, and polarizability structure of the study area, in combination with the information of related geology and drilling holes. It comprehensively interprets the favorable mineralization parts in the study area.

In response to the increasing complexity and sophistication of cyber threats, particularly those enhanced by advancements in artificial intelligence, traditional security methods are proving insufficient. This paper explores the zero-trust cybersecurity framework, which operates on the principle of "never trust, always verify" to mitigate vulnerabilities within organizations. Specifically, it examines the applicability of zero-trust principles in environments where large volumes of information are exchanged, such as schools and libraries, highlighting the importance of continuous authentication, least privilege access, and breach assumption. The findings highlight avenues for future research that may help preserve the security of vulnerable organizations.

Drought affects human and natural systems, human and animal life and health, and socioeconomic activities. Drought consequences depend on its type and class, but also on the preparedness and resistance of communities to this climate disturbance. In the last decades, droughts accounted for about 16% of the total number of disasters but were responsible for 95% of the number of deaths and 26% of economic losses in Africa. This study aims to assess the drought regime in Southern Africa based on drought and vegetation indices. The SPI and SPEI were calculated at different timescales, using ERA5 data for the 1971 – 2020 period. The spatiotemporal distribution of drought descriptors was analyzed and compared with the patterns of the NDVI, EVI and VCI vegetation indices. The results reveal (i) the occurrence of droughts of various classes and at different timescales throughout the study period and region, (ii) high spatial variability in the number, duration, severity and intensity of drought, which tends to decrease with increasing timescale, (iii) high spatial-temporal agreement between drought and vegetation indices that confirm the dryness of vegetation during drought. These results aim to support policymakers in defining legislation to manage drought and water resources.

Recent research has drawn attention to the ambiguity surrounding the definition and learnability of Out Of Distribution (OOD) recognition. The term "Out of Model Scope" (OMS) detection provides a clearer perspective, although the original problem remains unsolved. The ability to detect OMS inputs is particularly beneficial in safety-critical applications such as autonomous driving or medicine. By detecting OMS situations, we not only enhance the system's robustness but also prevent it from operating in unknown and unsafe scenarios. In this paper, we introduce a novel approach for OMS detection that combines three sources of information: 1) the original input, 2) the latent feature space extracted by an encoder, and 3) the synthetic data generated from the encoder's latent feature space. We demonstrate the effectiveness of combining original images and synthetically generated images against adversary attacks in the computer vision domain. We achieve results comparable to other state-of-the-art methods and demonstrate that any Encoder can be integrated into our pipeline in a plug-and-train fashion. Our experiments further evaluate which combination of the Encoder's features works best in order to discover OMS samples and highlight the importance of a compact feature space for the training of the Generator.

Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to investigate the geographical suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows was observed related to the months of the year which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence-- January to March and September to December -- which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.

The objectives of the study were to determine the spatial rate of CO₂ flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R² = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R² = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO₂ to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring.

To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate means of working with knowledge representations (as opposed to data) because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema. The goal of this paper is the development of theoretical and practical tools for processing schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machine as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic Machine is a technical system capable of regenerating, reproducing and maintaining itself by production, transformation and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.

Maghemite (γ-Fe2O3) is a mineral formed from magnetite oxidation at low temperatures, an intermediate metastable term of the magnetite to hematite oxidation and could be mixed with both. It has magnetic susceptibility similar to magnetite, crystal structure close to magnetite with which it forms a solid solution, while compositionally it equals hematite. Maghemite is thus easily misidentified as magnetite by X-ray diffraction and/or as hematite by spot chemical analysis in iron ore characterization routines. Nonstoichiometric magnetite could be quantified in samples of Brazilian soils and iron ores by the Rietveld method using a constrained refinement of the X-ray patterns. The results were confirmed by reflected light microscopy and Raman spectroscopy, thus qualitatively validating the method. X-ray diffraction with the refinement of the isomorphic substitution of Fe2+ by Fe3+ along the magnetite-maghemite solid solution could help to suitably characterize maghemite in iron ores, allowing for the evaluation of its ultimate influence on mineral processing, by affecting its surface and breakage properties.

An oceanic radar altimeter such as TOPEX/Poseidon (T/P) is typically for observing elevation changes over the open oceans or large inland lakes/rivers, with limited applications over solid earth due to its large footprint and susceptibility to waveform contamination and slope effect. Here we demonstrate that it is possible to construct a long-term time series of glacier elevation change from T/P-series radar altimeters over two flat surfaces near a glacier terminus and an icefield (Sites A and B, with slopes of 2° and 0.8°) in Mt. Tanggula, Tibet, at elevations over 5400 m. We retracked radar waveforms using the subwaveform threshold algorithm, selected quality altimeter data (1/4 of the original) with nearly the same slope and adjusted the original elevations by fitting with a time-varying, 2nd order surface. The glacier elevation changes at the two sites from T/P (1993–2002) show seasonal elevation oscillations with linear rates at about −3 m/year and abnormal seasonal changes around the 1997–98 El Niño. Site A is over a deep valley in southern Tanggula. Its elevation dropped about 30 m over 1993–2002 (from T/P) and the glacier almost disappeared by 2016 (from altimeters and satellite images). Despite the sporadic Jason-2 and Jason-3 altimeter data, we also derived long-term rates of glacier elevation change over 1993–2017. Landsat-derived glacier area and elevation changes near the two sites confirm the rapid glacier thinning from the altimeters. The glacier meltwater near Site A supplied increasing source water to Chibuzhang Co west of Mt. Tanggula, contributing partially to its accelerated rising lake level. The glacier thinning at Site B (icefield) was correlated with the increased discharge of the Tuotuo River in eastern Mt. Tanggula, a source region of the Yangtze River. The successful detection of glacier thinning at the two sites shows that T/P-series altimeters can serve as a virtual station at a flat glacier spot to monitor long-term glacier elevation changes in connection to climate change. This virtual station concept is particularly useful for inaccessible glaciers, but its implementation faces two challenging issues: increasing the volume of quality altimeter data and improving the ranging accuracy over a targeted mountain glacier spot.

This paper discusses how mathematical models are used to predict environmental conditions. While these models are successful for larger scale phenomena, it becomes more complex for local weather or wave processes. Several approaches have been proposed to address this problem, including neural networks and statistical and mathematical models. The novelty in this approach is the use of statistical manifolds, which provide a more accurate way to estimate distances and cost functions. This work focuses on advancements in differential and information geometry for creating different types of models that can be used for environmental simulation and forecasting. These fields are vital for many institutes and centres worldwide, as they help solve challenges in areas such as meteorology, renewable energy, and aviation safety. Some potential IG applications to advance energy were given. Closing remarks with new open problems combined with the next research phase are provided.

Background: Caregivers often use the internet to access information related to stroke care to improve preparedness, thereby reducing uncertainty and enhancing the quality of care. Method: Social media communities used by caregivers of people affected by stroke were identified using popular keywords searched for using Google. Communities were filtered based on their ability to provide support to caregivers. Data from the included communities were extracted and analysed to determine the content and level of interaction. Results: There was a significant rise in the use of social media by caregivers of people affected by stroke. The most popular social media communities were charitable and governmental organizations with the highest user interaction – this was for topics related to stroke prevention, signs and symptoms, and caregiver self-care delivered through video-based resources. Conclusion: Findings show the ability of social media to support stroke caregiver needs and practices that should be considered to increase their interaction and support.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

At present, nanomaterials (NMs) show extreme potential for a wide range of applications ranging from aerospace to energy storage applications owing to a number of properties which, in a number of contexts have been reported to be superior as compared to those of bulk counterparts. However, the high surface area to volume ratio which yields extraordinary properties to NMs also turns out to be the major limitation in a number of different contexts. For instance, the high surface area to volume ratio in these materials leads to a high reactivity, especially in electrochemical environments which are prevalent during energy storage applications. In this context, it is worthwhile mentioning that processing of NMs play a crucial role in influencing their mechanical properties. On the other hand, surface engineering has emerged as a powerful tool to deconvolute bulk properties from surface characteristics and has been reported to exhibit tremendous potential for overcoming the aforementioned limitation of NMs. The present review therefore, is aimed at highlighting the present status of research in the field of processing and applications of surface engineered NMs. Moreover, challenges and future perspectives in the aforementioned avenue have been discussed.

In this study, the biodegradation properties of leather treated with various finishing chemicals were evaluated in order to enhance the sustainability of leather processing. We applied advanced analytical techniques including FT-IR, thermogravimetric analysis (TGA), and solid-state NMR spectroscopy. Leather samples treated with different polymers, resins, bio-based materials, and traditional finishing agents were subjected to a composting process under controlled conditions to measure their biodegradability. The findings revealed that bio-based polyurethane finishes and acrylic wax exhibited biodegradability, while traditional chemical finishes like isocyanate and nitrocellulose lacquer showed moderate biodegradation levels. The results indicated significant differences in the biodegradation rates and the impact on plant germination and growth. Some materials, such as black pigment, nitrocellulose lacquer and wax, were beneficial for plant growth, while others, such as polyurethane materials, had adverse effects. These results support the use of eco-friendly finishes to reduce the environmental footprint of leather production. Overall, this study underscores the importance of selecting sustainable finishing chemicals to promote eco-friendly leather manufacturing practices.

Faced with increasing climate challenges, this pioneering study introduces groundbreaking approaches to ensure the resilience of potato crops in east-central Poland. Our research aimed to develop an innovative, cost-effective system tailored to the diverse local conditions of this region. Conducted between 2018 and 2020, the study analyzed integrated and organic production systems across different soil types and potato varieties. The experiment was conducted using a randomized block design with three replications in two locations. Integrated practices, enhanced by mineral fertilization and chemical protection, notably delayed the onset of late blight by 16 days and extended the critical infection period by 17% compared to their organic counterparts. Remarkably, the integrated systems resulted in a 49% increase in total production and a 52% increase in commercial yields, demonstrating their effectiveness in enhancing crop resilience. Different potato varieties exhibit varied responses to cultivation systems, influencing both yield and disease resistance. Further investigation into these varietal responses can help optimize cultivation practices, leading to improved efficiency and sustainability in potato farming. Location and soil conditions have a significant impact on potato yield and the spread of potato blight. Further research into specific soil properties and the adaptation of varieties to local conditions can contribute to increased productivity and stability in potato production. This study not only paves the way towards sustainable agriculture but also highlights the crucial role of soil diversity in shaping resilient farming practices.

Ultrasound, as mechanical waves with frequencies exceeding 20 thousand hertz, has found wide applications in medicine, physiotherapy, and more recently in agriculture. This study aimed to investigate the impact of ultrasound on potato yield and its parameters, particularly on seed potato yield, quantity, mass, and multiplication coefficient of potato tubers. The research was based on a three-year field experiment, utilizing the randomized block design in a split-split-plot arrangement, in the central-eastern region of Poland. The primary factor were technologies: a) technology with employing ultrasonic pre-planting treatment, b) traditional cultivation without this treatment. The secondary factors were potato varieties. Sonication of tubers was conducted using a bath-type ultrasonic device equipped with piezoe-lectric transducers. Results revealed a significant influence of cultivation technology, cultivar, and weather conditions on seed tubers yield, quantity, and multiplication coefficient of potato tubers. However, genetic traits of the examined varieties and random factors significantly affected the individual seed tuber mass and the number of shoots emerging from a single tuber. Future Outlook: Future research could focus on further optimizing ultrasound application techniques in potato cultivation, considering diverse varieties and environmental conditions. Additionally, it would be worthwhile to analyze the long-term effects of ultrasound on tuber seed potato quality and their ability to adapt to changing climatic and soil conditions. Furthermore, studies investi-gating the impact of ultrasound on other plant species could broaden our understanding of po-tential benefits in agriculture.

A review of quantitative risk assessment (QRA) models of L. monocytogenes in produce was carried out, with the objective of appraising and contrasting the effectiveness of the control strategies placed along the food chains. Despite nine of the thirteen QRA models recovered focused on fresh or RTE leafy greens, none of them represented important factors or sources of contamination in the primary production, such as type of cultivation, water, fertilisers or irrigation method/practices. Cross-contamination at processing and during consumer’s handling was modelled using transfer rates, which were shown to moderately drive the final risk of listeriosis; highlighting therefore the importance of accurately representing the transfer coefficient parameters. Many QRA models coincided in the fact that temperature fluctuations at retail or temperature abuse at home were key factors contributing to increasing the risk of listeriosis. In addition to a primary module that could help assess current on-farm practices and potential control measures, future QRA models for minimally-processed produce should also contain a refined sanitisation module able to estimate the effectiveness of various sanitisers as a function of type, concentration and exposure time. Finally, L. monocytogenes growth in the products down the supply chain should be estimated by using realistic time-temperature trajectories, and validated microbial kinetic parameters, both of them currently available in the literature.

Pain is a multifaceted multisystem disorder that adversely affects neuro-psychological processes. This study compares the effectiveness of central-stimulation (tDCS) and peripheral-stimulation (TENS) in pain-inhibition during a cognitive-task in healthy volunteers and to observe potential neuro-cognitive improvements. Healthy eighty volunteers went through a complete experimental protocol comprising pain-induction (CPT), administration of tDCS/TENS, EEG-data acquisition, and cognitive assessments. The protocol for this study is categorized into 04-groups: G1(Control), G2(TENS), G3(Anodal-tDCS), and G4(Cathodal-tDCS). Paired t-tests (p

With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors and complexity of character interactions.Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy.To address the dataset complexity and multi-targeting problems, feature extraction is optimized and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students raising their hands, reading, writing, playing cell phones, looking down, and lying on the table in high-definition images. And the accuracy and robustness of this network are verified.Compared with small target detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network has better detection performance and can efficiently deal with the behavior recognition of multiple students.

As technology advances, people are increasingly adopting IoT (Internet of Things) devices in smart cities. These devices are equipped with sensors that are connected everywhere, from energy infrastructure and transportation to telecommunications, health and human services, waste management, public safety, and more. People want to access sensor data anytime and everywhere to make better decisions and improve their lives. To achieve this, a sensor integration model has been developed where multiple sensors are integrated under one gateway to manage and share sensor data. However, effective sharing of sensor data requires data security, privacy, confidentiality, fairness, effective architectural design, and low communication and computation costs. These issues need to be addressed in the sensor integration model in smart cities. Existing storage models face challenges in managing sensor data effectively. To overcome these issues and hurdles, a data storage framework leveraging blockchain technology has been proposed within the sensor integration model. This framework ensures data security, privacy, confidentiality, and a fair and transparent financial model, all while minimizing communication and computation costs.

Organisms are adapted to each other and the environment, not as tradition would have it, because natural selection made them that way, but because there is inbuilt striving towards stability and equilibrium. A General Theory of Behaviour connects imagery, affect and action with the central executive system we call Consciousness. The General Theory is founded on the assumption that the primary motivation of all of Consciousness and intentional behaviour is Psychological Homeostasis. Psychological homeostasis is as important to the organisation of mind and behaviour as Physiological Homeostasis is to the organisation of bodily systems. Consciousness processes quasi-perceptual images independently of the input to the retina and sensorium. Consciousness is the “I-Am” control centre for integration and regulation of (my) thoughts, (my) feelings and (my) actions with (my) conscious mental imagery as foundation stones. Imagery vividness, a combination of clarity and liveliness, is essential to imagining, remembering, thinking, predicting, planning and acting. Assessment of vividness using introspective report is validated by objective means such as fMRI. A significant body of work shows that vividness of visual imagery is determined by the similarity of neural responses in imagery to those occurring in perception of actual objects and performance of activities. I am Conscious, therefore I am.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperparameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed the advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

: In the realm of data management, data preservation stands as a critical undertaking aimed at preserving and upholding the integrity of data. Regardless of whether it concerns personal or enterprise data, the detrimental effects of malicious alterations implemented by attackers cannot be overlooked. Particularly in conventional industrial control environments, the prevalent practice involves the transmission of data from sensors to databases for storage purposes. However, it is essential to recognize that this process exposes the data to various vulnerabilities. Thus, to ensure the long-term security and reliability of the data, it becomes imperative to implement robust data preservation strategies within these industrial control systems. However, the reliance of these databases on physical hard disks introduces inherent vulnerabilities, including the potential for data loss due to disk damage or targeted malicious attacks. Consequently, it becomes imperative to prioritize the implementation of robust data preservation measures. These measures are crucial in mitigating the risk of disruptions and protecting critical data from compromise. By establishing effective data backup systems, employing advanced security protocols, and implementing proactive monitoring mechanisms, organizations can bolster their data preservation capabilities and safeguard against potential threats to data integrity and availability. As a result, many enterprises opt to store their data with third-party providers to ensure data integrity. However, this approach carries inherent risks. If the third-party service experiences an attack or if the data is tampered with, it becomes challenging to verify the integrity of the data. To address these concerns and ensure data preservation within the context of the Internet of Things (IoT), a growing number of individuals are integrating IoT with Distributed Ledger Technology (DLT). By leveraging DLT, the integrity of data can be ensured, reducing reliance on centralized third-party storage and enhancing security in the IoT ecosystem. In this article, IOTA is the DLT, which employs Directed Acyclic Graph (DAG) to store transaction information. Compared to Ethereum or other blockchain technologies, IOTA offers notable advantages in terms of transaction verification speed, making it highly suitable for real-time IoT environments. However, the conventional transmission path from sensors to IOTA nodes entails a complex route, involving multiple hardware devices before reaching the intended destination. This complexity poses challenges in ensuring data integrity during transmission and introduces vulnerabilities such as man-in-the-middle attacks or SQL injection attacks. To address these issues, we propose a method to streamline the transmission path between sensors and IOTA, specifically tailored for industrial fields with numerous IoT devices. Our approach involves preprocessing the data stored on the server using our method before uploading, ensuring data confidentiality, and leveraging IOTA to guarantee data integrity. To achieve the shortest path between IoT and DLT nodes, it becomes necessary to establish IOTA nodes on lower-level devices, such as Raspberry Pi or IoT controllers. By simplifying the transmission path, we can reduce the potential for tampering and enhance overall data security. Implementing our proposed method enables the assurance of data confidentiality and integrity during both transmission and storage on the server, strengthening the trustworthiness of the IoT, and IOTA integration.

The unprecedented progress in Artificial Intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and Processing in-Memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most of the commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. The future direction of the industry would be to implement state-of-the-art deep learning algorithms in emerging non-Von Neumann computing paradigms for low power computing on the edge devices.

While many studies have described the association between cognitive decline and eating habits, little attention has been paid to its association with cheese intake. In this cross-sectional study of 1,035 community-dwelling women aged ≥ 65, we investigated the association between intake/type of cheese and cognitive function. The anthropometry, functional ability, and the frequency of food intake, including cheese, were assessed. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and a score of 20–26 was defined as mild cognitive decline (MCD). We found that MMSE score was significantly different between the presence of cheese intake (cheese intake: 28.3±1.9; non-cheese intake: 27.8±2.2) and between the type of cheese consumed (Camembert cheese: 28.7±1.3; others: 28.3±1.9). After adjusting for confounders, multiple logistic regression revealed four significant independent variables for MCD: Camembert cheese intake (odds ratio [OR] = 0.448, 95% confidence interval [CI] = 0.214–0.936), age (OR = 1.114, 95% CI = 1.059–1.171), usual walking speed (OR = 0.2620, 95% CI = 0.109–0.621), and repetitive saliva swallowing test scores (OR = 0.865, 95% CI = 0.750–0.995). Our results, while based on cross-sectional data from Japanese community-dwelling older women, demonstrated that Camembert cheese intake is significantly associated with MCD.

Mild Cognitive Impairment (MCI) may progress to severe forms of dementia, so therapy is needed to maintain cognitive abilities. The neural circuitry for oculomotor control is closely linked to that which controls cognitive behavior. In this study, we tested whether training the oculomotor system with gaze-controlled video games could improve cognitive behavior in MCI patients. Patients played a simple game for a month while a control group played the same game using a mouse. Cognitive improvement was assessed using the MoCA screening test and CANTAB. We also measured eye pupil and vergence responses in an oddball paradigm. The results showed an increased score on the MoCA test specifically for the visuospatial domain and on the Rapid Visual Information Processing test of the CANTAB battery. Pupil responses also increased to target stimuli. Patients in the control group did not show significant improvements. This pilot study provides evidence for the potential cognitive benefits of gaze-controlled gaming in MCI patients.

Abstract: Background: Of the many side effects suffered by cancer patients, those related to cognitive performance have become increasingly prominent in clinical practice. We know that chemotherapy generates a series of side effects, such as nausea and vomiting, alopecia, and so on, which can be counteracted by complementary medication. However, in the case of post-chemotherapy cognitive impairment, or chemo brain, these cannot be controlled with drug therapies. However, before any intervention can be considered, it is necessary to know exactly what cognitive impairment is being triggered. For this reason, we decided to study the cognitive status of breast cancer patients. Methods: analytical, prospective, three-measure longitudinal, intrasubject unifactorial, non-probabilistic and accidental assignment study. The sample came from the Medical Oncology Department at Hospital de Salamanca, in Spain. Cognitive function (Trail Making Test and Stroop Test) was established as the primary variable; the presence of sleep disorders (Insomnia Severity Index, ISI) and anaemia (haemoglobin levels in blood) were analysed as secondary variables, in addition to sociodemographic variables (age, stage, type of carcinoma, radiotherapy, menopause, social support network, marital status, years of schooling and employment status). Results: We recruited 151 individuals according to the selection criteria. We can confirm that factors including anaemia, menopause, patient support network and marital status, years of schooling, and employment status did affect the cognitive performance of the patients in active chemotherapy treatment in the study. In contrast, sleep disorders, age, radiotherapy treatment, stage of disease, and type of carcinoma did not affect the cognitive performance of the cancer patients. Conclusions: Chemotherapy does impact the cognitive performance of breast cancer patients.

Background: Of the many side effects suffered by cancer patients, those related to cognitive performance have become increasingly prominent in clinical practice. We know that chemotherapy generates a series of side effects, such as nausea and vomiting, alopecia, and so on, which can be counteracted by complementary medication. However, in the case of post-chemotherapy cognitive impairment, or chemo brain, these cannot be controlled with drug therapies. However, before any intervention can be considered, it is necessary to know exactly what cognitive impairment is being triggered. For this reason, we decided to study the cognitive status of breast cancer patients. Methods: analytical, prospective, three-measure longitudinal, intrasubject unifactorial, non-probabilistic and accidental assignment study. The sample came from the Medical Oncology Department at Hospital de Salamanca, in Spain. Cognitive function (Trail Making Test and Stroop Test) was established as the primary variable; the presence of sleep disorders (Insomnia Severity Index, ISI) and anaemia (haemoglobin levels in blood) were analysed as secondary variables, in addition to intervening variables (age, stage, type of carcinoma, radiotherapy, menopause, social support network, marital status, years of schooling and employment status). Results: We recruited 151 individuals according to the selection criteria. We can confirm that factors including anaemia, menopause, patient support network and marital status, years of schooling, and employment status did affect the cognitive performance of the patients in the study. In contrast, sleep disorders, age, radiotherapy treatment, stage of disease, and type of carcinoma did not affect the cognitive performance of the cancer patients. Conclusions: Chemotherapy does impact the cognitive performance of breast cancer patients.

Background: Of the many side effects suffered by cancer patients, those related to cognitive performance have become increasingly prominent in clinical practice. We know that chemotherapy generates a series of side effects, such as nausea and vomiting, alopecia, and so on, which can be counteracted by complementary medication. However, in the case of post-chemotherapy cognitive impairment, or chemo brain, these cannot be controlled with drug therapies. However, before any intervention can be considered, it is necessary to know exactly what cognitive impairment is being triggered. For this reason, we decided to study the cognitive status of breast cancer patients. Methods: analytical, prospective, three-measure longitudinal, intrasubject unifactorial, non-probabilistic and accidental assignment study. The sample came from the Medical Oncology Department at Hospital de Salamanca, in Spain. Cognitive function (Trail Making Test and Stroop Test) was established as the primary variable; the presence of sleep disorders (Insomnia Severity Index, ISI) and anaemia (haemoglobin levels in blood) were analysed as secondary variables, in addition to intervening variables (age, stage, type of carcinoma, radiotherapy, menopause, social support network, marital status, years of schooling and employment status). Results: We recruited 151 individuals according to the selection criteria. We can confirm that factors including anaemia, menopause, patient support network and marital status, years of schooling, and employment status did affect the cognitive performance of the patients in the study. In contrast, sleep disorders, age, radiotherapy treatment, stage of disease, and type of carcinoma did not affect the cognitive performance of the cancer patients. Conclusions: Chemotherapy does impact the cognitive performance of breast cancer patients.

In this paper, a completely different metaheuristic algorithm called the Orangutan Optimization Algorithm (OOA) is introduced, which replicates the complex and adaptive behaviors of Orangutans in their natural habitat. The main inspiration for OOA lies in the strategic foraging techniques of Orangutans, alongside their highly developed nesting abilities. More words are used to provide a detailed theoretical explanation of OOA, followed by a comprehensive modeling of the algorithm’s implementation in two key phases: exploration and exploitation. These phases are mathematically structured to ensure the balance required for optimal search processes. The performance of OOA is rigorously evaluated using twenty-nine benchmark functions from the CEC 2017 test suite, with problem dimensions of 10, 30, 50, and 100. More sentences are included to elaborate on the optimization outcomes, which reveal that OOA effectively balances the exploration and exploitation stages, yielding suitable and competitive solutions across the benchmark functions. Additionally, to further validate its quality, the results from OOA are compared against twelve other well-established metaheuristic algorithms. The analysis of these simulation results demonstrates that OOA consistently outperforms competing algorithms by achieving superior results across most benchmark functions. Furthermore, to assess its real-world applicability, OOA is implemented on a set of twenty-two constrained optimization problems drawn from the CEC 2011 test suite. The extended simulation results clearly indicate that OOA is highly efficient in solving real-world optimization challenges, consistently delivering a superior performance when compared to other competitor algorithms.

Land cover mapping, essential for understanding global land use patterns, relies on satellite imagery for monitoring changes, assessing ecosystem health, and supporting conservation ef- forts. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, and a lack of comparative studies for optimal deep learning model selection. Additionally, a scarcity of aerial datasets specifically tailored for agricultural areas exists. This study addresses these gaps by presenting a method for semantic segmentations of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the "Land Cover Aerial Imagery" (LICAID) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conduct a comparative study, employing cutting-edge deep learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately delineating land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpret-ability is essential for advancing the field of remote sensing and supporting sustainable environmental stewardship worldwide.

Extreme climates such as droughts and hot extremes, may result in marked damage to crop yields and threaten regional and global food security. Maize is a major grain crop in Liaoning Province, severely affected by drought and hot events. The objective of this study is to advance an approach to estimate the risk of crop yield reduction under the compound dry-hot events (CDHE) in Liaoning Province. Results show that the ratio of stations experiencing compound dry-hot events during the twelfth leaf and maturity stage tended to increase on the time scale. On a spatial scale, the frequency of compound dry-hot events in central Liaoning Province was relatively low during the whole and various reproductive periods of maize. In addition to the twelfth leaf, western Liaoning Province is prone to compound dry-hot events. The high-risk area for the occurrence of the compound dry-hot events in maize is Chaoyang City, located in the western part of Liaoning Province, and the cities in the low-risk area account for about 64.3% of the total number of cities in Liaoning Province, mainly in the central and northern parts of Liaoning Province. Generally, this study emphasizes focusing on the impacts of compound dry-hot events on agricultural production and provides an index to assess the risk of compound dry-hot events on maize production.

Background: Older adults with bipolar disorder (OABD) are individuals aged 50 years and older with bipolar disorder (BD). People with BD may have fewer coping strategies or resilience. A long course of the disease, such as in OABD, could impact the development of resilience strategies, but this remains under-researched in OABD. Therefore, this study aims to assess resilience levels within the OABD and explore associated factors, hypothesizing that resilience could improve psychosocial functioning, wellbeing and quality of life of OABD patients. Methods: This study sampled 33 OABD patients from the OABD cohort at the Bipolar and Depressive Disorders Unit of Hospital Clinic of Barcelona. This was an observational, descriptive and cross-sectional study. Demographic and clinical variables as well as psychosocial functioning, resilience and cognitive reserve were analyzed. Resilience was measured using the CD-RISC-10. Non-parametric tests were used for statistical analysis. Results: The average CD-RISC-10 score was 25.67 points (SD 7.87). Resilience negatively correlated with the total number of episodes (p = 0.034), depressive episodes (p = 0.001), and the FAST (p < 0.001). Participants with normal resilience had a lower FAST (p = 0.046), a higher CRASH (p = 0.026), and more EOBD (p = 0.037) compared to those with low resilience. Conclusions: OABD individuals may exhibit lower resilience levels which correlate with more psychiatric episodes, particularly the number of depressions and worse psychosocial functioning and cognitive reserve. Better understanding and characterization of resilience could aid in early identification of patients requiring additional support to foster resilience and enhance OABD management.

This study investigates the necessity of particle asymmetry for self-propulsion in nanomotors. While conventional wisdom posits that asymmetric designs are crucial for generating phoretic forces or localized bubble propulsion, recent research suggests that symmetrical particles may also exhibit motility. To address this debate, we developed a robust workflow for synthesizing gold grafted silica nanoparticles with precise control over size and shape, enabling the direct comparison of their motile behavior by dynamic light scattering and particle tracking velocimetry. Our results indicate that the inherent asymmetry generated during isotropic gold nanoparticle deposition onto silica surfaces may enable particle motility.

Effective management of the medicine supply chain is crucial for ensuring the availability of essential medicines and supplies in public health facilities. This study aimed to determine the utilization of the electronic logistic management information system (e-LMIS) in public health facilities and its implications for the medicine supply chain. A mixed-methods approach, combining both quantitative and qualitative data collection methods was used. The study was conducted in 106 healthcare providers from 35 public health facilities in Singida District. Six key informants were interviewed in a qualitative method. Of 106 participants, (62.3%) said they were somehow competent in e-LMIS utilization. In in-depth interviews, respondents underscored the system's utility for tracking stock levels, procurements, and managing orders. Staff shortage and lack of customized training were mentioned as major challenges, hindering efficiency in managing drug supplies. This study highlighted the positive impact of e-LMIS on various aspects of the medicine supply chain, including the timely submission of orders and enhanced inventory management. Sustained management support and regular utilization of the e-LMIS system are crucial for building and maintaining competence among healthcare providers, thereby optimizing the medicine supply chain and ultimately improving healthcare delivery.

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite known to possess neuroprotective property. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases or psychiatric disorders such as depression and autism spectrum disorders, whereas a higher level of KYNA is associated with the pathogenesis of schizophrenia. Little is known about the optimal concentration for neuroprotection and the threshold for neurotoxicity. In this study the effects of KYNA on memory functions were investigated by passive avoidance test in mice. Six different doses of KYNA were administered intracerebroventricularly to previously trained CFLP mice and they were observed following 24 hours. High doses of KYNA (i.e., 20-40 μg/2 μl) significantly decreased the avoidance latency, whereas a low dose of KYNA (0.5 μg/2 μl) significantly elevated it compared with controls, suggesting that the low dose of KYNA enhanced memory function. Furthermore, six different receptor blockers were applied to reveal the mechanisms underlying the memory enhancement induced by KYNA. The series of tests revealed the possible involvement of the serotonergic, dopaminergic, α and β adrenergic, and opiate systems in the nootropic effect. The study confirmed that a low dose of KYNA improved a memory component of cognitive domain, which was mediated by, at least in part, four systems of neurotransmission in an animal model of learning and memory.

The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the corporate distribution image, and further reduce corporate operating costs and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identified the locations of primary and secondary logistics nodes but also provided a new perspective for constructing the urban last-mile logistics system, enriching the academic research related to logistics node construction. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

Background: HIV is accompanied by production of proinflammatory cytokines which are regarded as critical in neuronal damage, leading to brain dysfunction, hence the need to identify cytokine biomarkers sensitive to brain damage. Methods: We applied MRI volumetric neuroimaging and high throughput Luminex based immunoassays to examine the relationship between cortical white matter, subcortical gray matter and total gray matter brain volumes and plasma cytokines in HIV indviduals using generalised linear models and Partial least square regression model. Results: Higher plasma inflammatory cytokines CCL5/RANTES and MCP-1 were significantly associated with lower cortical white matter volume. Higher IL-6 was associated with both lower subcortical gray matter and lower total gray matter, whereas higher IL-8 and GM-CSF were associated with lower total gray matter only. Higher VEGF, PDGF-BB and IL-9 were associated with higher cortical white matter volumes. After standardisation and adjusting for clinical and demographic variables, IL-6, IL-8, MCP-1 remained associated with lower volumes of the three brain regions whereas IL-9, VEGF and PDGF-BB were associated with higher volumes. Conclusions: Association proinflammatory cytokines RANTES, MCP-1 and IL-6 with lower brain volumes could imply possible involvement in neurodegerative processes in HIV infection while IL-9, VEGF and PDGF may have a neuroprotective or neurotrophic role.

The nozzle is the key element of the water jet generator for energy conversion. In order to explore the influence of the nozzle diameter on the pressure characteristics of the supercharged pulsed water jet plenum chamber, a supercharged pulsed water jet pressure acquisition system was established, and the Equations of motion and theoretical pressurization ratio equations of the supercharged pulsed water jet generator were established. The pressurization chamber pressure acquisition experiments under different nozzle diameters were carried out. The research results indicate that there is a critical nozzle diameter. When the nozzle diameter is less than the critical diameter, the pressure in the boost chamber is equal to the product of the driving pressure and boost ratio. As the nozzle changes, there is no significant change in the peak pressure and frequency of the boost chamber; When the nozzle diameter is greater than the critical diameter, there is a non-linear relationship between the boost chamber pressure and the driving pressure. As the nozzle diameter gradually increases, the actual boost ratio gradually decreases, and the peak pressure of the boost chamber further decreases. The nozzle diameter can no longer provide a load for the establishment of fluid pressure in the boost chamber. The research results provide a research basis for further controlling the pressure characteristics of the boost pulse water jet.

This paper explores decision analysis on product integration of family security services and residential fire insurance in the London and Taiwan markets by using the proposed mathematical models for counting sustainable value. This paper shows the five main different results between London and Taiwan markets with ten different parameters of the family security market, to find out the optimal number of family security integrated services for each security company in London. The improvement of the risk aversion effect based on risk and financial management will enhance the market share of the private security industries in the London and Taiwan markets. The results of this research can serve as a reference for the decision-making of private security industries on product integration under sustainable value consideration. The research findings highlight the potential benefits for both the private security industry and the insurance industry in their design and negotiation for product integration to improve both of business operation and achieve corporate social responsibility goals to match the sustainability in the future.

Existing methods of hyperspectral anomaly detection still face several challenges: 1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; 2) The ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; 3) Current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind spot network based on multi-scale blind spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial-spectral joint module and a background feature attention mechanism to enhance the interaction between spatial-spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that integrates pixel shuffle down-sampling (PD) operation with spatial spectral joint screening to address the issue of anomalous identity map-ping and facilitate finite-scale mask convolution to adapt to the detection of more scale targets. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the art methods.

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers and synthetical aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10°longitude×10°latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5% and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center.

Oilseed plants of the Brassicaceae plant family are cultivated for food, feed and industrial purposes on large-scale in Europe. This review gives an overview of current market-oriented applications of new genomic techniques (NGTs) in relevant Brassicaceae oilseed crops based on a literature survey. In this respect, changes in oil quality, yield, growth and resistance to biotic and abiotic stress are goals in oilseed rape (B. napus) and camelina (C. sativa). Environmental risk assessment scenarios are developed for specific NGT applications in Brassicaceae oilseed crops with either a changed oil composition or with fitness related traits. In case of a changed oil composition an increase or decrease of polyunsaturated fatty acids (PUFA) demonstrates risks for health and survival of pollinators. In case of fitness related traits other risks were identified, i. e. an increased risk of spread and persistence of NGT plants. Furthermore, there are indications for potential disturbance of interactions with the environment, involving signalling pathways and reaction to stress conditions. It is shown that for a comprehensive risk assessment the technological potential of NGTs, the plants’ biology and the scale of releases have to be considered in combination. Therefore, the release of NGT plants into the environment for agricultural purposes will require risk assessment and monitoring of single traits as well as of combinatorial and long-term cumulative effects. In addition, risk management should develop concepts and measures to control and potentially restrict the scale of releases. This is especially relevant for NGT Brassicaceae in Europe which is a centre of diversity of this plant family.

In preserving genetic diversity among domestic animal breeds, the strategies emphasizing be-tween-breed diversity may be not optimal by neglecting within-breed variation. The present study aimed to assess the extent of the population subdivision of Mangalica pigs and the contri-bution of migration intensity to their substructure. Genealogy analysis was performed for breeding animals born between 1981 and 2023 examining three colour variations (Blonde, Swal-low-Belly, and Red). The fixation index (FST) was calculated and evaluated by Multidimensional Scaling and Clustering to expose the population substructure. The average FST is 0.04 for the Blonde and 0.05 for the Swallow-Belly and the Red while in the active herds, these parameters are smaller being 0.03 and 0.04, respectively. The migration of individuals affected 61.63% of the Blonde, 75.53% of the Swallow-Belly, and 63.64% of the Red breed herds. No population sub-structure was found in any Mangalica breed which can be explained by the extensive within breed migration among the herds.

The comfort of human life depends on the quality, size, and reliability of the infrastructure projects. In the infrastructure systems, rapid growth is found, where the economic and sustainable impact has become a topic of significant concern for policies and government officials. To achieve con-straints of sustainable development, all the policies and actions over the infrastructure project's life cycle must be assessed. Decision-makers have adopted approaches for economic, social, and en-vironmental initiatives through Life Cycle Assessment (LCA) and Life Cycle Cost Analyses (LCCA) of infrastructure projects. The purpose of this review is to highlight the impact of per-forming LCA and LCCA in infrastructure projects. To achieve this goal, a systematic literature review methodology is adopted in which renowned databases, i.e., Web of Science, Science Direct, Emerald and Scopus were selected to extract the relevant literature. Using the PRISMA approach, 1251 publications were identified which were then filtered and 55 documents were included in the final review. In the extracted publications most, researchers were biased toward LCA and LCA individually, whereas few focused on integrated LCA and LCCA. The researchers assessed the costs and impact associated with the infrastructure project while there were less focused on the environmental cost. Besides this, techniques of economic, social, and environmental growth of infrastructure projects have been emphasized during the design phase because of substantial relations between infrastructure design and operation management. Moreover, a conceptual framework has been developed that will assist the decision-makers to consider the effects of LCA and LCCA on various aspects of the infrastructure project and how it impacts sustainability. In the last, a case study was performed to assess the developed framework with the incorporation of environmental impact cost.

The article reveals distinctive features of the interaction between architectural and structural solutions for the design of tall buildings as well as spotlights the most distinctive cases of expression. In the contemporary world, interaction is turning into the antithesis of the formerly dominant utilitarian attitude and standardization of tall buildings architectural solutions. Meanwhile, the search for rational structural solutions leads to new possibilities of architectural expression. This necessitates the transformation of a structural solution and its adaptation to the need of a modern architect to be exceptional and noticed. Interaction covers the current as well as retrospective and perspective periods. SWOT analysis was used by the authors of the article to assess the interaction between architectural and structural solutions in tall buildings design, select the most important criteria that could be used searching for rational architectural and structural solutions in future by applying multi-criteria decision making methods.

Cognitive impairment affects 34-65% of People with Multiple Sclerosis (PwMS), significantly impacting their quality of life. Clinicians routinely address cognitive deficits with in-clinic neuro-behavioural interventions, but accessibility issues exist. Telerehabilitation is a promising solution that utilises digital tools for home-based cognitive remediation. This narrative review assessed the feasibility and efficacy of cognitive telerehabilitation in PwMS, identifying 13 relevant studies. Regarding the feasibility of cognitive telerehabilitation, evidence shows adherence rates are generally good, although, surprisingly, not all studies reported a measure of compliance with the cognitive training explored. Considering the efficacy of different rehabilitative techniques on cognitive performance of PwMS, findings are generally inconsistent, with only one study reporting consistently positive results. A range of methodological limitations are reported as potential factors contributing to the variability of results. Overcoming some of these issues will be key. Upcoming research is necessary to determine optimal intervention approaches, long-term effects of rehabilitation, and how to best integrate the use of telerehabilitation into routine clinical practice. By exploiting digital tools in the best way and addressing methodological concerns, home-based rehabilitation holds the potential for improving cognitive functions and enhancing the quality of life in PwMS.

Drug-induced liver injury (DILI) stands as a significant cause of drug attrition. To mitigate clinical DILI risks, assessing drugs using human liver models is crucial since animal studies may fall short due to species-specific liver pathway variations. Cell-based preclinical hepatotoxicity testing is often pertinent. In the present study, cells from a human liver cancer line (HepG2, HepaRG) were cultured in both formats of 2D and 3D spheroids to explore their responses to drugs. Liver-specific marker expressions across cell lines and culture formats were also examined to assess disparities in drug-induced liver injury marker expressions. In cytotoxicity assays, HepG2 cells exhibited heightened sensitivity to amiodarone HCl, while HepaRG cells showed greater sensitivity to acetaminophen. Generally, 2D cultures were more responsive to drugs than 3D cultures. Consistency was observed between cytotoxicity assays and liver injury markers AST and ALT. Variations were also noted in liver cell marker secretion, with albumin and urea concentrations declining post-drug treatment, particularly with higher baseline expression in HepaRG cells and 3D cultures. Additionally, mRNA expression levels of CYP enzymes involved in hepatocellular drug metabolism were compared following treatment with enzyme inducers. CYP1A2 and CYP2C9 were not expressed in HepG2 cells. On the other hand, CYP3A4 was expressed higher in 3D culture than in 2D culture. HepaRG cells exhibited significantly increased expression of all three enzymes post-treatment. Notably, enzyme expression was notably higher in 3D cultures than in 2D cultures. Collectively, these findings suggest that HepaRG cells and 3D cultures hold promise for evaluating DILI during early-stage drug development.

This study aims to investigate the microphysical properties of Arctic, tropospheric aerosol in the transition from the Arctic Haze in spring towards the summer season in 2021. A special focus lies on the hygroscopicity of the aerosol. Therefore, a one-parameter growth curve model is applied to lidar data from the Koldewey Aerosol Raman Lidar (AWIPEV in Ny-Ålesund, Svalbard) and simultaneous radiosonde measurements. Hygroscopic growth depends on different factors like aerosol diameter and chemical composition. To detangle this dependency, three trends in hygroscopicity are additionally investigated by classifying the aerosol first by its color ratio, and then by its season and altitude. Furthermore, two special days are discussed using Mie-theory. They show on the one side the complexity of analyzing hygroscopic growth by means of lidar data, but on the other side demonstrate that it is in fact measurable with this approach. For these two case studies we calculated, that the aerosol effective radius increased from 0.16 μm (dry) to 0.18 μm (wet), and from 0.28 μm to 0.32 μm for the second case. Generally, we found two different modes of stronger or weaker hygroscopic particles and a complex altitude dependence with the least hygroscopic particles in the middle free troposphere.

This pilot randomized controlled trial (RCT) aimed to explore whether depression and anxiety could be reduced by psychiatric home healthcare supporters offering brief cognitive behavioral therapy for psychosis (CBTp) at home using a workbook for patients with schizophrenia. Eighteen patients with schizophrenia treated in a psychiatric home nursing service were randomly assigned to two groups: one group received CBTp in addition to usual care (CBTp + TAU group; n=8) and the other received only usual care (TAU group; n=8); 2 patients were excluded due to hospitalization or withdrawal of consent. Eight weekly CBTp sessions were conducted; anxiety/depression, quality of life, self-esteem, and overall functioning were assessed at baseline, week 9, and week 13. There was no significant difference in the primary evaluation; however, a significant improvement (p=0.014) was observed in the secondary evaluation of quality of life. The effect size was 0.95 for primary evaluation and 1.33 for the secondary evaluation, indicating a large effect. This study showed that nurses and occupational therapists can provide CBTp in psychiatric home care for patients with schizophrenia to potentially alleviate anxiety and depression compared to standard psychiatric home care alone. Therefore, larger RCTs with larger sample sizes are recommended.

In the recent decade, Building Information Modelling (BIM) has widely been adopted in the Architecture, Engineering and Construction (AEC) industry and completely upended the way we build. While BIM continues gain momentum in the industry, it has also attracted increasing attentions from researchers. However, most of the current study focuses on reviewing BIM for management, BIM for green building, BIM for infrastructure and BIM for Facilities Management (FM). There are few studies about Global BIM review and to discuss their complex inter-connections. In this study, we adopted a scientometric analysis method to review global BIM research from 2004-2019. A total of 1455 scholarly bibliographic records obtained from Web of Science Core Collection databased were established for the analysis. This study has identified the top productive and influential researchers, research institutes, regions/countries, subject categories and journals in the BIM field. In addition, 11 clusters of Global BIM research were also identified including construction project, green BIM, construction safety planning, automated IFC-based workflow and so on. Authors distinguished 11 clusters of global BIM research into 3 stages, namely formulating stage, accelerating stage and transforming stage. Furthermore, authors reviewed the BIM policy of Singapore and observed there is a co-production relationship between evolution of BIM policy and global BIM research. These findings provide valuable information for researchers, practitioners and policy makers by visualizing the current progress in the research field of BIM and highlighting future research needs.

Questionnaires for self-report of food-elicited emotions are an established practice in consumer studies. However, most tools are originally developed in English and translated into other languages, which limits their use. The aim of this study was to develop the first Greek emotion lexicon and measurement tool for consumers, using standard methodology and extending it. The lexicon was compiled using linguistic sources and extended using the World Wide Web and a popular social media platform for the collection of authentic language and usage data. Terms were also collected from consumers using questionnaires and a variety of stimuli. Terms collected from consumers only comprise: healthy, distrustful, curious, childish, impressed, tolerant, impatient It was validated by application in various case studies, providing discrimination across and within food categories, and by comparison to the Greek translation of a standard tool in English, the EsSense Profile. The comparison was conducted by measuring emotions elicited by six food categories and demonstrated that it is preferable to use emotion measurement tools developed in the linguistic and cultural context of intended use, as they convey consumers’ emotions naturally and more accurately. This tool can be used as a Check- or Rate- All-That-Apply questionnaire to measure consumer responses to food categories and products. The methodology and validation presented can be applied for the compilation of emotion lexicons in languages less studied than English.

Whether the universe is a computer simulation, or whether we wish to efficiently model our universe in a computer simulation, there would be benefits to modeling it in a fashion analogous to computer spreadsheet, each lattice cell can be conceived as containing all the mathematical formula necessary to continuously compute its state relative to changes in all its neighboring cells, and by progression, in relation to all the cells of entire space-time lattice. Alternatively, the “real” universe may itself be built on a space cell lattice, an irregular foam of space cells, in which each cell may be conceived as a multidimensional cell of distortable space, the shape of which fully describes (a) the four basic forces (gravity, electromagnetic, strong, weak) observed at that cell of space, and (b) the probability (or weight distribution) of any quantum states overlapping the cell and its neighbors. At an appropriate scale, it would appear that this conceptual model would resolve apparent conflicts between general relativity and quantum physics. It would also provide a new interpretation of Planck’s constant as description of the number of space cell events associated with any set of observable events. If formulae operating at a lattice cell level can be improve our ability to understand and model larger scale phenomena, this would be strong evidence in favor of the theory that mathematics is not just a human invention but rather an inherent feature of space-time itself.

Staphylococcus aureus and coagulase-negative staphylococci account for about 80% of infections associated with medical devices and are associated with increased virulence due to their ability to form biofilm. In this study, we aimed to construct a comprehensive reference map followed by significant pathway analysis in the proteome of S. aureus biofilm grown for 3-days as compared with 24 h planktonic using a high-resolution TMT based MS. We identified proteins associated with secondary metabolites, ABC transporters, biosynthesis of amino acids, response to stress, and amino sugar and nucleotide sugar metabolism were significantly upregulated in the 3-day biofilm. In contrast, proteins associated with virulence factors, microbial metabolism in diverse environments, translation, and energy metabolism were significantly downregulated. GO functional annotation indicated that more proteins are involved in metabolic processes, catalytic activity, and binding in biofilm, respectively. Among the significantly dysregulated proteins, hyaluronidase (hysA) in conjunction with chitinase may play a significant role in the elimination and/or prevention of biofilm development. This study advances the current S. aureus subproteomes, identified potential pathways significant to biofilm biology and helped to understand their potential role in S. aureus which may shed light on developing new therapeutic regimes including antibiofilm agents in the treatment of biofilm-infections related with implantable medical devices.

The triad of vascular impairment, muscle atrophy, and cognitive decline represents critical age-related conditions that significantly impact health. Vascular impairment disrupts blood flow, precipitating muscle mass reduction seen in sarcopenia and the decline of neuronal functions characteristic of neurodegeneration. Our limited understanding of the intricate relationships within this triad hinders accurate diagnosis and effective treatment strategies. This review ana-lyzes the interrelated mechanisms that contribute to these conditions, with a specific focus on ox-idative stress, chronic inflammation, and impaired nutrient delivery. The aim is to understand the common pathways involved and to suggest comprehensive therapeutic approaches. Vascular dysfunctions hinder the circulation of blood and the transportation of nutrients, resulting in sar-copenia characterized by muscle atrophy and weakness. Vascular dysfunction and sarcopenia have a negative impact on physical function and quality of life. Neurodegenerative diseases exhibit comparable pathophysiological mechanisms that affect cognitive and motor functions. Preventive and therapeutic approaches encompass lifestyle adjustments, addressing oxidative stress, in-flammation, and integrated therapies that focus on improving vascular and muscular well-being. Better understanding of these links can refine therapeutic strategies and yield better patient out-comes. This study emphasizes the complex interplay between vascular dysfunction, muscle de-generation, and cognitive decline, highlighting the necessity for multidisciplinary treatment ap-proaches. Advances in this domain promise improved diagnostic accuracy, more effective thera-peutic options, and enhanced preventive measures, all contributing to a higher quality of life for the elderly population.

This paper presents machine learning methods for approximate solutions of reaction-diffusion equations with multivalued interaction functions. This approach addresses the challenge of finding all possible solutions for such equations, which often lack uniqueness. The proposed method utilizes physics-informed neural networks (PINNs) to approximate generalized solutions.

The objective of this research is to propose archetypes of accessible design with a neuroarchitec-ture approach for the design of initial level educational centers, in Peru, especially in areas of ur-ban growth, many children do not manage to access initial education, there is even school drop-out. . At this educational level, with an increase of 4.4% in recent years, the process of developing mental abilities such as memory, perception, thinking, reactions, attention, language, among oth-ers, is of the utmost importance. in the first years of life. As a methodology, sensory design strate-gies were applied, likewise through instruments such as interviews, observation, photographic taking and measurement carried out during the field visit for the analysis of the space-child in-teraction and spatial stimuli, supported by digital tools (Autocad, Google Earth, Andrés Marsh). As results, accessible design archetypes were obtained based on neuroarchitecture based on 10 spatial indicators (Lighting; Green area; roofs; shape; size; distribution; accessibility; scale; colors; materiality). In conclusion, the provision of adequate spaces that provide exploratory facilities greatly contributes to cognitive development in early childhood. The environment where these cognitive activities take place needs to be conditioned to allow children to develop, bond and ap-propriate the space.

At present, under the background of high-quality development and the “carbon peaking and carbon neutrality” goal, China’s economic structure, industrial structure and energy structure have all begun green transformation and upgrading. The supply chain has become an important starting point for energy conservation and carbon reduction. In this context, enterprises have also ushered in huge environmental risk challenges and innovative development opportunities. This paper adopts Entropy-Weighted TOPSIS comprehensive evaluation method to analyze the green supply chain performance of "B" company, a new energy leading company, based on the theory of green development and corporate social responsibility. By establishing a green supply chain evaluation criteria system including four dimensions of finance performance, environment performance, operation performance and innovation performance, the weight of each evaluation criterion is objectively analyzed and calculated, and the green supply chain performance of "B" company from 2018 to 2022 is ranked. Based on this, comprehensive evaluations and sub-dimension evaluation are carried out and suggestions for improvement are put forward. At the same time, we hope to help other new energy vehicle companies optimize their own green supply chain practice mode.

For decades, the discoverability and visibility of a paper relied on the readership of the academic journal where the publication was issued. The public's increasing interest in scientific research, particularly in biomedicine, has led news media outlets to play a significant role in disseminating scientific advances. This investigation, which explores the potential impact of news media stories on the citations and altmetrics of a paper, was conducted with thoroughness and rigour. A total of 2020 open access biomedical research papers, all published in the same year, 2015, and in journals with an impact factor between 10 and 14, were investigated. The papers were split into two groups based on the sole criterion of receiving or not receiving news media coverage. Papers with news media coverage accounted for 44% of the total. They received, on average, 60% more citations, 104% more blogs, 150% more X posts, 106% more Facebook reports, 40% more Wikipedia references, 85% more videos, and 51% more Mendeley readers than papers without news media coverage. The correlation between news media outlets and increased citations and altmetrics is evident. However, the broader societal impact of news media coverage, in terms of bringing scientific matters or discoveries to the public eye, appears to be more robust when compared to the reactions of the scientific community.

This paper presents a study on developing of a new exoskeleton for ankle joint rehabilitation with three degrees of freedom (3 DOFs). The primary attention is paid to the process of designing and modelling the device aimed at restoring the lost functions of joint mobility. The authors carried out a complex analysis of the functional requirements of the exoskeleton based on the research of potential user’s needs, which allowed them to develop a conceptual model of the proposed de-vice. In this study, a prototype of the exoskeleton is designed using modern additive technolo-gies. A prototype of the proposed device underwent virtual testing in conditions maximally close to the real one, which confirmed its effectiveness and comfort of use. The main results of the work indicate the promising potential of the proposed solution for application in rehabilitation practice, especially for patients with ankle joint injuries and diseases.

Interest grows in designing silicon-on-insulator slot waveguides to trap optical fields in subwavelength-scale slots and developing their optofluidic devices. However, it is worth noting that the inherent limitations of the waveguide structures may result in high optical losses and short optical paths, which challenge the device performance in optofluidics. Incorporating the planar silicon-based slot waveguide concept into a silica-based hollow-core fiber can provide a perfect solution to realize an efficient optofluidic waveguide. Here we propose a subwavelength-scale liquid-core hybrid fiber (LCHF), where the core is filled with carbon disulfide and surrounded by a silicon ring in a silica background. The waveguide properties and the Stimulated Raman Scattering (SRS) effect in the LCHF are investigated. The fraction of power inside the core of 56.3% allows an improved sensitivity in optical sensing; while the modal Raman gain of 23.60 m-1·W-1 is 2 times larger than that generated around a nano fiber with the interaction between the evanescent optical field and the surrounding Raman media benzene-methanol, which enables a significant low-threshold SRS effect. Besides, this in-fiber structure features compactness, robustness, flexibility, ease of implementation in both trace sample consumption and reasonable liquid filling duration, as well as compatibility with optical fiber systems. The detailed analyses of the properties and utilizations of the LCHF suggest a promising in-fiber optofluidic platform, which provides a novel insight into optofluidic devices, optical sensing, nonlinear optics, etc.

Detecting and evaluating pain in people with cognitive impairment, especially dementia, is a challenge. Their ability to accurately report pain and comprehend related questions decreases the reliability of their self-report. This has contributed to the fact that pain is underdiagnosed and undertreated in this population, which can lead to involuntary ageism. The design and assessment of this training capsule under the context of the European COST-Action TD1005 responds to the need for education and training in "Pain Assessment In Cognitive Impairment, especially dementia" and the development of the internationally agreed observational PAIC15 scale. The 3-hour capsule, intended for healthcare workers and caregivers, consists of theoretical-practical levels: 1) basic concepts and challenges in pain in aging, cognitive impairment/dementia, 2) available scales and PAIC15, 3) video presentation of clinical cases illustrating a 3×2 matrix of situations (rest, transfer and experimental pain) and stages of disease (mild and moderate). The objectives focused on training the ability to observe, detect, and assess the signs of pain using PAIC15 and its feasibility. The pre-post analysis in a Master educational setting showed agreement with the results obtained in the Spanish national survey on 1) scarce knowledge of observational tools and their use, and 2) uncertainty in the identification of signs of pain and feasibility in moderate/severe scenarios. The capsule improved the ability to identify and evaluate the pain items, especially facial expressions, and their confidence on observational tools. Debriefings highlighted the relevance of observational scales in dementia and the gain obtained using the new observational instrument.

This paper presents a particular tunneling method or new Austrian tunneling method (NATM) which plays an important role in reducing the subsidence of the Earth surface and the damage to the structures in urban areas. It has a wide range of applications in shallow tunneling projects all over the world. In this study, numerical modeling of third-line Metro tunnel in Tehran is under discussion which is designed and stabilized by NATM. The foregoing tunnel is excavated manually with a one-meter advancing step. In this project, the constructors used a lattice girder and sprayed concrete with 31 cm thickness as the initial lining. A suitable numerical software for this modeling is Plaxis 3D tunnel, which allows performing high-resolution finite element modeling (FEM) of the studied object. The performance of this method is investigated and compared with other NATM methods. As a result of numerical modeling, we give a value of 30.01 mm earth subsidence in the most damaged area of the settlement, in comparing with that of earth surface monitoring, which confirmed it with a dramatically low difference. Moreover, this tunnel was drilled and excavated with various methods, among which the less settlement has obtained by the proposed method. The results are promising to continue the tunneling with this method to expand the subway line in the city.

Such rapidly changing external conditions as the COVID-19 pandemic and the war in Ukraine, being pure crisis phenomena, not only determine the geopolitical and geoeconomic situation on the global markets, but also cause structural changes in individual countries. Under these conditions, the level of food self-sufficiency becomes particularly important, as it is one of the factors determining the food security of any country. The aim of the research is to assess Ukraine's food self-sufficiency compared to the European Union and Poland. The self-sufficiency ratio (SSR) index was used to assess food self-sufficiency. This is the ratio of the volume of production of the most important products of plant and animal origin to the volume of domestic consumption. Domestic consumption was defined as the volume of domestic production increased by the volume of imports, reduced by the volume of exports and also included changes in domestic stock levels. The surveyed time frame is 2019–2021. The analyzed data were taken from the State Statistics Service of Ukraine, FAOSTAT, USDA and Statistics Poland. A comparative analysis of self-sufficiency for the main agri-food sectors of Ukraine, the EU, and Poland showed significant differences. Taking into account the production potential and food self-sufficiency indicators of Ukraine, Ukraine's possible accession to the EU can be expected to have a major impact on the EU's supply and demand situation, as it may pose a threat to many EU agri-food sectors.

The work is devoted to assessing changes in the water regime of the Lan River in modern conditions and the near future with the aim of developing a strategy for the rational use of the water potential of small rivers in the Belarusian Polesie region. There was a tendency towards an increase in annual runoff at the Loktyshi and Mokrovo gauging stations during the study period from 1948 to 2015, which was caused by anthropogenic factors in the form of amelioration measures. Testing the hypothesis about the homogeneity of the runoff series under consideration for periods with different averaging intervals has showed heterogeneity for some types of runoff. This was caused by intensive economic activity, which significantly disrupts the natural hydrological regime. A runoff forecast was made for the Lan River at the Mokrovo gauging station for the period up to 2035 based on archive of meteorological data using a multi-model ensemble of four CMIP5 scenarios. A slight decrease in runoff was predicted, caused by additional evaporation from the water surface of the reservoir due to increased air temperature, as well as a slight shift in the peak of the spring flood to March. Results of calculating of permissible withdrawal of surface water from the Lan River for the needs of the Loktyshi fish farm have shown great influence of the fish farm on the river runoff volume that requires strict adherence to scientifically based regimes for managing the water regime of the Loktyshi Reservoir and the Loktyshi fish farm.

The aim of this work was to classify the business incubators in Spain according to the four phases of the startup’s incubation process. Considering that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the business incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis has been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors. Finally, the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3, named activity monitoring and control, was related to phase 3 or basic incubation variables. Cluster analysis facilitates the grouping of business incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small sized business incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factors 1 and 2. Factor 3, although positive, it is susceptible to improvement. They are the largest group of business incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

Background: Vestibular Neuritis (VN) induces Acute Vestibular Syndrome (AVS). This study aimed to identify predictive factors of VN recovery by healing and/or compensation. Methods: In this longitudinal and prospective study, 40 patients were included. The first assessment (T0) included medical history taking (general and specific), for cardiovascular risk factors (CVRFs), a battery of diagnostic tests, bithermal caloric test (BCT), video Head Impulse Test (VHIT) and skull vibration induced nystagmus (SVIN). All patients also completed a Dizziness Handicap Inventory (DHI). All assessments were repeated 90±15 days (T3) later. Subjective criteria compensation were based on DHI total score and objective criteria compensation on the instrumental test results. Four groups of patients (A, B, C, D) were delineated by combining patients with normal vs abnormal vestibular tests and patients with normal vs abnormal DH. Results: CVRFs were associated with a poorer recovery of symptoms but not the age or BMI. Vestibular tests mainly BCT (canal paresis %), VHIT (lateral semicircular canal gain) and SVINT (nystagmus slow phase velocity) recovered within normal values in 20%, 20%, 26% of cases respectively at T3 for the 40-patient group. Conclusions: Vascular risk factors (hypercholesterolemia) are correlated with patients who do not recover their symptoms either by healing or compensation. The evolution did not show any significant difference between high or low frequency vestibular tests in patients recovering from their symptoms. However, some patients with objective recovery may continue to show persisting subjective symptoms.

The aim of this work was to classify the business incubators in Spain according to the four phases of startups incubation. Taking into account that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis have been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors, finally the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3 called activity monitoring and control and was related to phase 3 or basic incubation variables. Clusters analysis facilitates the grouping of incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small Business Incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factor 1 and 2. Factor 3 although positive is susceptible to improvement. They are the largest incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

Many advanced technologies have been employed in the high performance active matrix displays including liquid crystal displays, organic light emitting diode displays, and micro light emitting diode displays. On the other side, there have existed the strong demand on the cost reduction and it is one of low cost schemes to integrate the driver circuit in a panel based on thin film transistor technologies. This paper reviews overall concept, operation principles, and various circuit approaches in shift registers for the scanning pulse generation. In addition, it deals with the implementation of additional functionalities in gate drivers to support pixel compensation, multi-line driving, in-cell capacitive touch screen, pixel sensing, and adaptive scanning region control.

Alzheimer’s disease (AD) is an age-associated neurodegenerative disorder. A wealth of evidence indicates that the amyloid β (Aβ) aggregates result from dyshomeostasis between Aβ production and clearance, which plays a pivotal role in the pathogenesis of AD. Consequently, therapies targeting Aβ reduction represent a promising strategy of AD intervention. Tetramethylpyrazine nitrone (TBN) is a novel tetramethylpyrazine derivative with the potential for the treatment of AD. Previously, we demonstrated that TBN markedly improved cognitive functions and reduced Aβ, APP, BACE 1 and hyperphosphorylated tau levels in 3 × Tg-AD mice. However, the mechanism by which TBN inhibits Aβ deposition still unclear. In this study, we employed the APP/PS1 mice treated with TBN (60 mg/kg, ig, bid) for six months and N2a/APP695swe cells treated with TBN (300 μM) to explore the mechanism of TBN on the Aβ reduction. Our results indicated that TBN significantly alleviated cognitive impairment and reduced Aβ depositions in APP/PS1 mice. Further investigation of the underlying mechanisms revealed that TBN decreased the expression of APP and BACE1, activated the AMPK/mTOR/ULK1 autophagy pathway and inhibited the PI3K/AKT/mTOR/ULK1 autophagy pathway, as well as decreased the phosphorylation levels of JNK and ERK in APP/PS1 mice. Moreover, TBN was found to significantly reduce the mRNA levels of APP and BACE1, as well as those of SP1, CTCF, TGF-β, and NF-κB, transcription factors involved in regulating gene expression. Additionally, TBN was observed to decrease the level of miR-346 and increase the levels of miR-147 and miR-106a in the N2a/APP695swe cells. These findings indicate that TBN may reduce Aβ levels likely via reducing APP expression by regulating APP gene transcriptional factors and miRNAs, reducing BACE1 expression, and promoting autophagy activities.

The backwater lowland habitats of large rivers, like the Mississippi River in North America, present complex often inaccessible environments for traditional capture-based fish biodiversity sampling. Our knowledge of the assemblages of fishes that occupy such habitats are often incomplete, and this can compromise management efforts. We employed environmental DNA (eDNA) metabarcoding methods to sample a Mississippi River bottom wetland system to assess ichthyofaunal diversity, and assemblage structure across habitat types, and we compared our results to capture-based survey records for the same habitats. We collected water samples in spring and fall of 2022 from slough, ditch, shallow lake and bayou habitats that varied in depth, vegetation, seasonal variability, and connectivity to the Mississippi River channel. We detected a diverse array of fish species that included 51 taxa. Nearly all species previously documented in the habitats were detected by eDNA metabarcoding, and we increased the number of documented species by more than a third. Most of the species were ubiquitous across the range of habitats, but there was also substantial assemblage structure, with some species exhibiting clear habitat specificity. Fall sampling was limited to the deeper bayou habitats where seasonal variation between spring and fall was minimal. eDNA metabarcode sampling was demonstrated to be effective at detecting invasive species as well as uncommon species, which included several species of conservation concern.

This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flow in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the Physics-Informed Neural Network (PINN) method. This innovative approach combines artificial neural networks (ANNs) with physical equations, offering a more efficient and accurate way to model various complex processes and phenomena. The proposed mathematical model, based on the Euler equation, has been meticulously implemented using the Python language. Verification with analytical solutions ensures the accuracy and reliability of the computations. In the research, a comprehensive comparative analysis was conducted between results obtained using the PINN method and those from conventional Computational Fluid Dynamics (CFD) approaches. The analysis highlighted the advantages of the PINN method, which produced smoother pressure and velocity fluctuation profiles while reducing computation time, demonstrating its potential as a transformative modeling tool. The data derived from this study are of paramount importance for ensuring ongoing energy supply reliability and can also be used to create predictive models related to gas behavior in pipelines. The application of modeling techniques for gas flow simulation has the potential to revolutionize the integrity of our energy infrastructure and utilization of gas resources. However, it is crucial to emphasize that the effectiveness of such models relies on continuous monitoring and frequent updates to ensure alignment with real-world conditions. This research not only contributes to a deeper understanding of compressible gas flows but also underscores the crucial role of advanced modeling methodologies in the sustainable management of gas resources for both current and future generations.

In order to solve the problem of fault diagnosis for CNC machine feed system under the condition of variable speed conditions, an intelligent fault diagnosis method based on multi-domain feature extraction and ensemble learning model is proposed. First, various monitoring signals including vibration signal, noise signal and current signal are collected. Then, the monitoring signals are preprocessed and the time-domain, frequency-domain and time-frequency domain feature indexes are extracted to construct a multi-dimensional mixed domain feature set. Finally, the feature set is putting into the constructed DoubleEnsemble-LightGBM model to realize the fault diagnosis of the feed system. The experimental results show that the model can achieve good diagnosis results under different working conditions for both the public data set and the feed system test bench data set, and the average overall accuracy is 91.07% and 98.06% respectively. Compared with XGBoost and other advanced ensemble learning models, the method has better accuracy. It provides technical support for the stable operation and intelligent of CNC machine tools..

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America, and to enhance the utility of drought monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impacts information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and the colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and the warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and indicators are changing over time as the climate warms, and by recommending ways to strengthen the use of indices and indicators in drought decision-making.

In order to comprehensively evaluate the employee satisfaction of mine occupational health and safety management system, an analytic method based on fuzzy analytic hierarchy process and 2- tuple linguistic model was established. Firstly, based on the establishment of 5 first- grade indicators and 20 second- grade ones, the weight of each indicator is calculated and validated by method of FAHP and root mean square model. Secondly, a path based on the time-ordered Weighted Averaging Operator (T-OWA) model is constructed. Finally, the model is validated by empirical analysis. The results demonstrate that the employee satisfaction of the mine occupational health and safety management system is of the “general” rank. The method including the comprehensive evaluation of employee satisfaction and the quantitative analysis of language evaluation information ensures the authenticity of the language evaluation information.