The traditional approach of considering the probability distribution of rain attenuation leads to overdesign the power margin of data channels. We have first recalled a method, proposed in 2016, which with a small power margin, bandwidth expansion and variable symbol rate avoids overdesign and can transfer the same data volume as if the link were in clear–sky conditions. It is charaterized only by the link mean efficiency 0≤η≤1, suitably defined. It is usefull only if: (a) data must be downloaded also when it is raining; (b) real–time communication is not strictly required. We have applied it to the links of GeoSurf satellite constellations by simulating rain attenuation time series at 80 GHz (mm–wave) with the Synthetic Storm Technique, from rain-rate time series recorded on–site, at sites located in different climatic regions. The power margin to be implemented ranges from 2.0 dB to 7.4 dB – well within the current technology – regardless the instantaneous rain attenuation. The clear–sky bandwidth is expanded 1.75 to 2.80 times, not large per se, but it may challenge current technology if the clear–sky bandwidth is already large.

Exoskeleton technology has been shown to be useful for individuals with spinal cord injury (SCI), potentially facilitating their recovery of motor function and social reintegration. However, studies on the safety and usability of exoskeletons are limited. This study aimed to design a powered exoskeleton for SCI and evaluate its safety and usability. Ten healthy adults participated in this study. The prototype exoskeleton featured an adjustable frame, computer-controlled hip and knee joint actuators, and a gait-trigger sensor. The experimental actions included walking, standing, and sitting, with and without the prototype. Safety outcomes included adverse events, vital signs, numerical rating scale (NRS) scores for pain, discomfort, and fatigue, and prototype-human body clearance. Usability outcomes included donning and doffing times, subjective ratings, gait speed, stride length, and kinematic parameters of hip and knee joint angles. Safety outcomes showed no serious adverse events, stable vital signs, minimal NRS scores, and acceptable clearance. Usability outcomes showed high efficiency and received positive ratings. Kinematic parameters showed significant positive correlations between normal walking and walking with the prototype for both hip and knee joint angles, confirming its effectiveness in assisting walking. However, further device improvements and gait parameter tuning are necessary for future clinical application.

Six bacterial strains with differing abilities to produce varying concentrations of Indole Acetic Acid were tested individually and in consortia for plant growth promoting and fitness related traits of Cicer arietinum. In all experiments the presence of the nitrogen fixer Mesorhizobium ciceri resulted in increased biomass production. In the absence of this strain, IAA Psedomonas putida and Bacillus megaterium hinder plant growth and fitness related traits. The application of mixes of the three strains always resulted in better plant performance when M. ciceri was present. Whereas P. putida has a noticeable plant growth-promoting effect B. megaterium resulted less effective. The low levels of IAA produced by the selected strains had a significantly greater positive effect on plant biomass accumulation, flower, pods and seed production as well as on total plant nitrogen and nitrogen concentration in seed than high IAA producer strains.

Asphaltene-resin-paraffin deposition (ARPD) is a complicated and prevalent issue in oil and gas industry, impacting the efficiency, integrity of petroleum extraction, production, transportation and processing systems. Considering all witnessed ARPD problems in Azerbaijan oil fields, this paper proposed a chemical method and optimized the type and the concentration of chemical inhibitors. And then the effect of selected chemical reagent on inhibiting the ARPD amount and thus en-hancing oil recovery was detected by reservoir simulation during both waterflooding and CO2 flooding production. Three new chemical compounds (namely, Chemical-A, Chemical-B and Chemical-C) were examined in laboratory conditions and their impact on rheological properties of high-paraffin oilfield samples of Azerbaijan (X, Y and Z) were investigated. Experimental results show that Chemical-C with concentration of 600 g/t has the best efficiency for alleviating the problems. After adding Chemical-C to the crude oil, the freezing point of oil is decreased from 12 °C to (-4) °C, ARPD amount is declined from 0.185 to 0.016 g and oil effective viscosity is reduced from 16.2 mPa·s to 3.1 mPa·s. It is determined that for water and CO2 flooding, higher injection pressure resulted in reduced asphaltene precipitation. Adding selected ARPD inhibitor, the oil recovery for waterflooding can increase from 52% to 62%, while it can rise from 55% to 68% for CO2 flooding.

Dark pigmentation can be observed in various parts of the plant, ranging from foliage to petals to berries. The molecular basis of black pigmentation appears to vary among species, with anthocyanins playing a significant role, although specific anthocyanin types and their mechanisms differ. These findings suggest that the development of phenotypes is species-specific or varies between larger taxonomic groups; this is further supported by the polyphyletic nature of dark pigmentation. Additionally, several different regulatory mechanisms have been described for the occurrence of dark pigmentation. Firstly, the repression or knockout of the competing flavone biosynthesis has been shown to lead to darker pigmentation while another mechanism is based on the activation and upregulation of the anthocyanin biosynthesis genes in the presence of MYB transcription factors. Potential ecological functions of dark pigmentation were identified as the protection of the photosynthesis apparatus, camouflage against herbivores, and the attraction of pollinators. Promising industrial applications include microbial factories for the production of natural food colourants, induction of novel phenotypes for the ornamental plant industry and lastly the increase of anthocyanins within agriculturally relevant crops.

In recent years, the scientific and technological developments led to an explosion of available videos on the web, increasing the necessity of fast and effective video analysis and summarization. Video summarization methods aim to generate a synopsis by selecting the most informative parts of the video content. The user’s personal preferences, often involved in the expected results, should be taken into account in the video summaries. In this paper, we provide the first comprehensive survey on personalized video summarization relevant to the techniques and datasets used. In this context, we classify and review personalized video summary techniques based on the type of personalized summary, on criteria, on the video domain, on the source of information, on the time of summarization, and on the machine learning technique. Depending on the type of methodology used by the personalized video summarization techniques for the summary production process, we classify the techniques into five major categories, which are feature-based video summarization, key frame selection, shot selection-based approach, video summarization using trajectory analysis, and personalized video summarization using clustering. We also compare personalized video summarization methods and present 37 datasets used to evaluate personalized video summarization methods. Finally, we analyze opportunities and challenges in the field and suggest innovative research lines.

We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from Unmanned Aerial Vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood utilizing calculated masks over each component of the LAB colorspace, as well an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying the hysteresis thresholding segmentation. The proposed method is tested and compared with variations, and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-Score, respectively. The results and computational efficiency of the proposed method show that it is suitable for on board data execution and decision-making during UAVs flight.

A detailed review of the scientific literature was undertaken to examine the most recent developments in plasma processing in the field of medicine. The first part of the review includes a detailed breakdown of the different types of coatings that can be applied onto medical devices using plasma, with a specific focus on antimicrobial surfaces. The developments during 2023 in plasma deposited biocompatible, drug delivery and adhesive coatings are described and specific applications in additive manufacturing are highlighted. The use of plasma and plasma activated liquids as standalone therapeutics continues to evolve and pertinent advances in this field are described. In addition, the combination of plasma medicine with conventional pharmaceutical interventions was reviewed and key emerging trends were highlighted, including the use of plasma to enhance drug delivery directly into tissue. The potential synergies between plasma medicine and chemotherapeutics for oncology and infection treatment is a growing area and recent advancements are noted. Finally, the use of plasma to control excess antibiotics and to intentionally degrade such materials in waste streams is described.

Aim: To analysis the relationship between healthy adults right ventricular diameter (RVD) and geographical factors. Location: China’s 22 provinces, municipalities and autonomous regions. Methods: Using correlation analysis the relationship between RVD and nine geographic factors, build predictive models by factor analysis. Results: There is significant correlation between RVD and geographical factors, and the annual average temperature is most relevant geographical factors, a predictive model was built by factor analysis: Ŷ = 19.46 + 0.0007072X1 + 0.001034X2 -0.05412X3 -0.001405X4 -0.0006351X5 + 0.02903X6 + 0.01243X7 -0.01033X8 + 0.03816X9 ± 0.63. Then using Kriging interpolation interpolate the geographical distribution of Chinese healthy adult right ventricular diameter reference value in ArcGIS software. Main Conclusion: If geographic factors can be derived in anywhere of China, RVD can be calculated by the model, you can also get RVD from geographical distribution.

Collaboration across the agriculture supply chain is essential to address the high-yield demand and sustainable practices amid global overpopulation. Limited resources, such as soil, are compromised by excessive chemical agents and nutrient use. The Internet of Things (IoT) and smart farming offer solutions by optimizing agent application, data analysis, and farm monitoring. Evidence from numerous studies indicates that collaboration in the supply chain, including farmers, can improve efficiency and productivity, reduce costs, and enhance crop quality. This research focuses on implementing IoT technology to enhance decision-making for crop yield optimization and sustainable practices on a real farm. By collaborating with a farm in the southern region of Zagreb, Croatia, farmers were trained on sensor usage and yield monitoring. Small farms in that region face challenges in improving yields due to limited capacity and lack of entrepreneurial skills. The DMAIC methodology was applied to define the problem and measure relevant parameters. The analysis demonstrated consistent patterns between electrical conductivity (EC) measurements and potassium levels in soil. It suggests the potential for estimating potassium concentrations based on EC readings, or vice versa. Leveraging EC as a proxy for potassium levels could offer a cost-effective means of assessing soil fertility and nutrient dynamics. Additionally, the PCA biplot analysis highlighted that pH value behaved independently. Understanding these dynamics enhances knowledge of soil variability and informs sustainable soil management practices.

Surface treatment of implants facilitates osseointegration, with nanostructured surfaces exhibiting accelerated peri-implant bone regeneration. This study compared bone-to-implant contact (BIC) in implants with hydroxyapatite (HA), sand-blasted and acid-etched (SLA), and SLA with calcium (Ca)-coated (XPEED®) surfaces. Seventy-five disk-shaped grade 4 Ti specimens divided into three groups were prepared, with 16 implants per group tested in New Zealand white rabbits. Surface characterization was performed using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), digital microscopy, and a contact angle analyzer. Cell viability, proliferation, and adhesion were assessed using MC3T3-E1 cells. Apatite formation was evaluated using modified simulated body fluid (m-SBF) incubation. After 4 weeks of healing, the outcomes reviewed were BIC, bone area (BA), removal torque tests, and histomorphometric evaluation. Microstructure analysis revealed irregular pores across all groups, with the XPEED group exhibiting a nanostructured Ca-coated surface. Surface characterization showed a crystalline CaTiO3 layer on XPEED surfaces, with evenly distributed Ca penetrating the implants. All surfaces provided excellent environments for cell growth. The XPEED and SLA groups showed significantly higher cell density and viability with superior osseointegration than HA (p < 0.05); XPEED exhibited the highest absorbance values. Thus, XPEED surface treatment improved implant performance, biocompatibility, stability, and osseointegration.

In today’s fast paced, attention-demanding society, executive functions and attentional resources are often taxed. Individuals need ways to sustain and restore these resources. We first review the concepts of attention and restoration, as instantiated in Attention Restoration Theory (ART). ART emphasizes the role of nature in restoring attention. We then discuss essentials of experiments on the causal influences of nature. Next, we expand the concept of ART to include modern, designed environments. We propose a wider perspective termed attentional ecology, in which attention behavior is viewed within a larger system involving the human and their interactions over time with environmental demands. When the ecology is optimal, mental functioning can be a positive “flow” that is productive, sustainable for the individual, and sometimes creative.

The human T-cell leukemia virus type 1 (HTLV-1) is the only known human oncogenic retrovirus. HTLV-1 can cause a cancer called adult T-cell leukemia/lymphoma (ATL). The virus is transmitted through body fluids of infected individuals, primarily breast milk, blood, and semen. At least 5–10 million people in the world are infected with HTLV-1. In addition to ATL, HTLV-1 infection can also cause HTLV-I-associated myelopathy (HAM /TSP). ATL is characterized by low viral ex-pression and poor prognosis. The oncogenesis triggered by HTLV-1 is extremely complex and the molecular mechanisms are not fully understood. However, viral regulatory proteins Tax and HTLV-1 bZIP factor (HBZ) have been shown to play key roles in the transformation of HTLV-1-infected T cells. Moreover, several studies have shown that the final fate of HTLV-1-infected transformed T cell clones is the result of a complex interplay of HTLV-1 oncogenic protein expression with cellular transcription factors that subvert the cell cycle and disrupt reg-ulated cell death, thereby exerting their transforming effects. This review provides updated in-formation on the mechanisms underlying the transforming action of HTLV-1 and the potential therapeutic targets to combat ATL.

The prognosis of patients with recurrent or metastatic of the head and neck squamous-cell carcinoma (HNSCC) refractory to platinum-based chemotherapy is severe and, consequently, the identification of therapeutic options for this category of patients is a priority. Nivolumab, an anti- programmed cell death protein 1 (anti-PD-1) monoclonal antibody, has been approved for the treatment of recurrent or metastatic HNSCC after platinum-based therapy progressing. We present the early experience of two academic center including diagnostic, clinical, biological, therapeutic and outcomes characteristics of head and neck cancer (HNC) patients treated with Nivolumab. The purpose of the study is to identify certain peculiarities and to report them compared to the data from the literature in order to generate some hypotheses that coud constitute criteria for the selection of cases with maximum benefit to immunotherapy. Analyzing the data obtained from 18 patients treated in Emergency County Hospital Craiova, "Saint Nectarie" Oncological Hospital Craiova and Euroclinic Oncological Center Iasi January 2020 and March 2023 it could be hypothesized that lower nadir values of neutrophil to lymphocyte ratio (<3.5) and the adition of intensive regimens of chemotherapy and radiotherapy could justify a favorable response in terms of progression free survival (PFS) during immunotherapy with Nivolumab. To our knowledge, the study included the first two cases of second primary malignancy (SPM) in the head neck region treated with Nivolumab. The reported data support a possible benefit of the sequential administration of radiotherapy, immunotherapy for SPM cases. Higher positive dynamic of neutrophil to lymphocyt ratio (NLR) could be associated with worst outcome during immunotherapy. All study observations including the role of infections and antibiotic treatment as well as the possible biomarker value of NLR variation during immunotherapy should be investigated in multicenter clinical trials.

The present study is the second step (concerning the normal-diet restoration) of the our previous one (concerning the calcium-free diet) to determine whether the normal-diet restoration, with/without concomitant PTH(1-34) administration, can influence amounts and deposition sites of the total bone mass. Histomorphometric evaluations and immunohistochemical analysis for Sclerostin expression were conducted on the vertebral bodies and femurs in rat model. The final goals are: i) to define timing and manners of bone mass changes when calcium is restored in the diet; ii) to analyze the different involvement of the two bony architectures having different metabolism (i.e. trabecular versus cortical bone); iii) to verify the eventual role of PTH(1-34) administration. Results evidenced the greater involvement of the trabecular bone with respect to the cortical one, in answering to different calcium diet content, and the effect of PTH mostly in the recovery of trabecular bony architecture. The main findings emerged from the present study are: i) the importance of the interplay between mineral homeostasis and skeletal homeostasis in modulating and guiding bone answers to dietary/metabolic alterations and ii) the evidence that the more involved bony architecture is the trabecular one, the most susceptible to the dynamical balance of the two homeostases.

In this paper, the computational fluid dynamics (CFD) method has been used to simulate the gas flow and the catalytic hydrogenation process of the C-2 hydrogenation adiabatic reactor in a practical ethylene production unit of a chemical plant. The laminar finite-rate model was used for the chemical reaction model. The standard two-equation model was used for the turbulence model. The exponent-function kinetic model was used for the reaction dynamics model. The simulation results were in good agreement with the actual industrial data obtained from the C-2 hydrogenation reactor, and the distributions of reaction parameters such as temperature and component concentrations in the reactor were further analyzed. Based on the validated CFD models, the structure and operating conditions of the reactor, as well as hydrogen load distribution ratios, are simulated for comparing the comprehensive performance metrics of ethylene selectivity and acetylene conversion rate; as a result, we can obtain the optimal temperature of 312 K, the optimal hydrogen/acetylene ratio of 0.10 as well as the optimal hydrogen load distribution ratio of 4:4:2, and the diameter/height ratio exerts a marginal effect on the selectivity, conversion rate and harmonic mean of the reactor.

Objective: To examine variations in end-of-life care for breast cancer, heart failure, and dementia patients. Data and methods: Data from four Norwegian health registries were linked using a personal identification number. Longitudinal trends over 365 days and the type of care on the final day of life were analyzed using descriptive techniques and logistic regression analysis. Results: Patients with dementia were most commonly placed in nursing homes, while patients with heart failure and breast cancer were more frequently hospitalized. Breast cancer and heart failure patients had a higher likelihood of dying at home. The presence of general practitioners increased the probability of home-based end-of-life care for cancer patients, while non-physician healthcare workers increased the likelihood of home-based care for the other patient groups. Conclusion: Diagnoses, individual characteristics, and service availability are all associated with the place of death in end-of-life care.

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, through the review of traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to effectively improve abnormal traffic detection's accuracy and efficiency. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

The proliferation of Internet of Things (IoT) devices on Linux platforms has heightened concerns regarding vulnerability to malware attacks. This paper introduces a novel approach to investigating the behavior of Linux IoT malware by examining syscalls and library syscall wrappers extracted through static analysis of binaries, as opposed to the conventional method of using dynamic analysis for syscall extraction. We rank and categorize Linux system calls based on their security significance, focusing on understanding malware intent without execution.Feature analysis of the assigned syscall categories and risk ranking is conducted with statistical tests to validate their effectiveness and reliability in differentiating between malware and benign binaries. Our findings demonstrate that potential threats can be reliably identified with an F1 score of 96.86%, solely by analyzing syscalls and library syscall wrappers. This method can augment traditional static analysis, providing an effective preemptive measure to enhance Linux malware analysis. This research highlights the importance of static analysis in strengthening IoT systems against emerging malware threats.

The rise in IoT (Internet of Things) devices poses a significant security challenge. Maintaining privacy and ensuring anonymity within the system is a sought-after feature with inevitable trade-offs, such as scalability and increased complexity, making it incredibly challenging to handle. To tackle this, we introduce our proposed work for managing IoT devices using Hyperledger fabric. We integrated our system on the blockchain with a closed-circuit television (CCTV) security camera fixed at a rental property. The CCTV security camera redirects its feed whenever a new renter walks in. We have introduced the web token for authentication from the renter to the owner. Our contributions include the proposition of efficient framework architecture, a novel chain code algorithm, and stealth addresses with modified ring signatures. We performed different analyses to show the system's throughput and latency through stress testing. We have shown the advantages of the proposed architectures by comparing similar existing schemes. Our proposed scheme enhances the security of blockchain-enabled IoT devices and mitigates the single point of failure issue. Our future work includes scaling it up to cater to the needs of the healthcare system.

This study investigated how different gamification elements in mobile apps influence users' basic psychological needs (autonomy, competence, relatedness) and subsequent engagement outcomes. Survey data was analyzed using PLS-SEM. Social gamification elements enhanced autonomy and competence needs, while immersive elements boosted autonomy and relatedness. Achievement/progression components increased competence and relatedness perceptions. Interestingly, personal traits did not significantly impact psychological needs. Crucially, satisfying autonomy and relatedness predicted higher user satisfaction and continued usage intentions, highlighting their importance for sustained engagement. Competence influenced satisfaction but not continuance intention. The findings demonstrate the motivational roles of distinct gamification features in fostering need-supportive experiences. Designers should incorporate varied social, immersive, and achievement components to address users' psychological requirements holistically. Prioritizing autonomy and relatedness support appears vital for achieving enjoyable, self-motivated, and persistent user engagement across gamified systems and domains. The study provides actionable insights for optimizing motivational affordances through psychologically-informed gamification implementations.

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 primary objective of this systematic review study was to investigate the effectiveness, durability, and adverse events of PLLA treatment for aesthetic indications. The search strategy was performed in MEDLINE (Ovid). The electronic literature search of five databases was performed, from the inception of the databases until 12th of February 2024. This was to identify randomized clinical trials that assessed PLLA treatment in adult individuals exhibiting facial aging and/or facial lipoatrophy. Risk of bias was assessed using the Cochrane Risk-of-Bias Tool for Randomized Trials (RoB 2). Eleven RCTs out of 1467 identified citations were included. Four studies showed increased dermal thickness, significant improvement in facial lipoatrophy severity and aesthetic clinical scores, after PLLA treatment with its effects sustained for at least 25 months. Two studies demonstrated the superiority of PLLA over injectable human collagen. Also, three studies showed positive results favoring PLLA when compared with PH gel in lipoatrophy severity, trans epidermal water loss, skin quality, elasticity, and patient satisfaction. All adverse events were mild-to-moderate in intensity, and the main ones worth noting were bruising, hematoma, tenderness, nodules, and edema. Five out of 11 studies were considered as high risk of bias. The evidence on the effectiveness and safety of PLLA for facial rejuvenation is of low quality; thus, the reported high effectiveness, safety, and long-lasting effects for this purpose, should be further investigated.

Epithelial barrier damage plays a central role in the development and maintenance of allergic inflammation. The rise in airways epithelial barrier permeability alters the tissue homeostasis and allows the allergen and other external agents penetration. Different factors contribute to barrier impairment, such as eosinophilic infiltrate and allergens proteases action: the eosinophilic cationic proteins and the allergens' proteolytic activity have a central role in contributing to epithelial damage. In the airways, the allergens proteases degrade the epithelial junctional proteins allowing the allergen penetration and the uptake by dendritic cells. The increase in allergen-immune system interactions induce the release of the alarmins and the activation of type 2 inflammatory pathways causing, or worsening, the main symptoms at skin, bowel and respiratory levels. We aim to highlight the molecular mechanisms underlying allergenic proteases-induced epithelial barrier damage and the role of immune response in allergic asthma onset, maintenance and progression. We further will explore clinical and radiological potential biomarkers of airways remodeling in allergic asthma patients.

A model for enabling the production of lower carbon cement paste is proposed in this research. The model is based on predicting the strength of cement pastes with partial replacement of wheat straw ash (WSA) and silica fume (SF), two major wastes of agricultural and industrial production. The model is set up based on response surface methodology and Box-Behnken design, and included a suite of mechanical tests with distinct replacements following a micro an meso characterization. The model shows capability for predicting strength of any cement paste, including partial substitution of WSA and/or SF at any combination level with excellent accuracy, which is verified with three validation mixtures demonstrating maximum errors of less than 6% at the three ages. The analysis of the response surface evidences that any cement replacement in the range of 0-20% WSA and above 5% SF allows the reduction of the carbon footprint by maximizing the incorporation of both wastes. The proposed model can be used by setting the required strength of cement paste and calculating the maximum possible replacement, which should contribute to making the construction industry more sustainable by utilizing local waste.

This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.

Since the first reported case of COVID-19 in December 2019, several SARS-CoV-2 variants has evolved and some of them have shown higher transmissibility becoming the prevalent strains. Genomic epidemiological investigations into strains from different time points including the early stages of the pandemic are very crucial for understanding the evolution and transmission patterns. Using whole genome sequences, our study describes the early landscape of SARS-CoV-2 variants in central India retrospectively (including the first known occurrence of SARS-CoV-2 in Madhya Pradesh). We performed amplicon based whole genome sequencing of randomly selected SARS-CoV-2 isolates (n=38) collected between 2020-2022 at state level VRDL, ICMR-NIRTH, Jabalpur from 11899 RT-qPCR positive samples. We observed the presence of five lineages namely B.1, B.1.1, B.1.36.8, B.1.195 and B.6 in 19 genomes from the first wave cases and variants of concern (VOC) lineages i.e. B.1.617.2 (Delta), and BA.2.10 (Omicron) in the second wave cases. There was a shift in mutational pattern of SRAS-CoV-2 strains from the second wave in contrast to the first wave. We have identified five immune escape variants in the S gene: P681R, P681H, L452R, Q57H, and N501Y in the isolates collected during the second wave. Furthermore, these genomes were compared with 2160 complete genome sequences reported from central India that encompass 109 different SARS-CoV-2 lineages. Among them, VOC lineages Delta (28.93%), and Omicron (56.11%) were circulating predominantly in this region. This study provides useful insights into the genetic diversity of SARS-CoV-2 strains over the initial course of the COVID-19 pandemic in central India.

We present a case of a 47-year-old male who died unexpectedly from acute pulmonary hemorrhage 555 days after completing the BNT162b2 (Pfizer) COVID-19 vaccination primary series. Before death, he exhibited symptoms of a mild respiratory infection. Despite a healthy medical history and no medication use, the patient’s condition rapidly deteriorated and he experienced severe respiratory distress, followed by cardiopulmonary arrest with evidence of profuse pulmonary bleeding. Autopsy findings revealed massive lung congestion without embolism, normal heart size, moderate coronary atherosclerosis without myocardial infarction, and no evidence of other hemorrhagic events. The patient tested negative for COVID-19 and other respiratory pathogens at autopsy. Despite these findings, the medical examiner determined the cause of death was attributed to atherosclerotic and hypertensive cardiovascular disease, without considering the recent pulmonary hemorrhage and unremarkable medical history. Investigation into the vaccine batch indicated a higher-than-average number of serious adverse events, including fatalities. The patient's BNT162b2 batch was among the top 2.8% for reported deaths. Moreover, the autopsy failed to investigate potential contributions from the vaccine, such as the presence of the Spike protein or related antibodies. The evidence suggests that the pulmonary hemorrhage, exacerbated by a viral infection, was the immediate cause of death, with the COVID-19 vaccine potentially playing a role in the development of cardiopulmonary pathology and hemorrhage. We propose autopsy protocols for COVID-19 vaccine recipients to better investigate vaccine-related pathologies among those with one or more prior injections.

Radio Occultation (RO) is pivotal for profiling the neutral and ionized atmosphere, with the PlanetiQ mission, via its GNOMES satellites, striving to establish an advanced atmospheric observing system. However, an assessment of the spatiotemporal distributions of PlanetiQ observations and comparisons with reliable datasets are lacking. This study addresses this gap by examining the temporal and spatial distribution of RO observations from PlanetiQ during its initial 198 operational days in 2023, alongside comparisons with COSMIC and Numerical Weather Prediction (NWP) models. Data from GN02, GN03, and GN04 satellites, yielding 1099, 1313, and 1843 RO events per day respectively, were analyzed. The satellite constellation's observations demonstrate a generally well-distributed pattern, albeit minor deficiencies in equatorial and polar regions. Single-profile comparisons with COSMIC data reveal strong correlations for pressure, temperature, Water Vapor Pressure (WVP), and refractivity profiles, with temperature exhibiting larger variations (RMSE = 1.24°C). Statistical analyses confirm statistically insignificant differences between PlanetiQ and COSMIC profiles at the same spatio-temporal coordinates. Comparisons with NWP models show slight differences with GFS, with overall RMSE values of 0.23 mb (WVP), 0.6 mb (pressure), 1.3 (refractivity), and 1.5°C (temperature). However, assessments against GFS/ECMWF models indicate overall compatibility, with insignificant differences between PlanetiQ profiles and models observations.

In contrast to conventional non-destructive testing (NDT) and non-destructive evaluation (NDE) methodologies, including radiography, ultrasound, and eddy current analysis, the coplanar capacitive sensing technique emerges as a novel and promising avenue within the field. This paper endeavors to elucidate the efficacy of coplanar capacitive sensing, also referred to as capacitive imaging (CI), within the realm of NDT. Leveraging extant scholarly discourse, this review offers a comprehensive and methodical examination of the coplanar capacitive technique, encompassing its fundamental principles, factors influencing sensor efficacy, and diverse applications for defect identification across various NDT domains. Furthermore, this review deliberates on extant challenges and anticipates future trajectories for the technique. The manifold advantages inherent to coplanar capacitive sensing vis-à-vis traditional NDT methodologies not only afford versatility in application but also underscore its potential for pioneering advancements in forthcoming applications.

Exploring therapeutic options is crucial in the ongoing COVID-19 pandemic caused by SARS-CoV-2. Nirmatrelvir, a potent inhibitor targeting the SARS-CoV-2 Mpro, shows promise as an antiviral treatment. Additionally, Ivermectin, a broad-spectrum antiparasitic drug, has demonstrated effectiveness against the virus in laboratory settings. However, its clinical implications are still debated. Using computational methods such as molecular docking and 100 ns molecular dynamics simulations, we investigated how Nirmatrelvir and Ivermectin interact with SARS-CoV-2 Mpro(A). Calculations using density functional theory have been instrumental in elucidating the behavior of isolated molecules, primarily by analyzing the frontier molecular orbitals. Our analysis revealed distinct binding patterns: Nirmatrelvir formed strong interactions with amino acids like MET49, MET165, HIS41, HIS163, HIS164, PHE140, CYS145, GLU166, and ASN142, showing stable binding with a root mean square deviation (RMSD) of around 2.0 Å. On the other hand, Ivermectin interacted with THR237, THR239, LEU271, LEU272, and LEU287, displaying an RMSD of 1.87 Å, indicating enduring interactions. Both ligands stabilized Mpro(A), with Ivermectin showing stability and persistent interactions despite forming fewer hydrogen bonds. These findings offer detailed insights into how Nirmatrelvir and Ivermectin bind to the SARS-CoV-2 main protease, providing valuable information for potential therapeutic strategies against COVID-19.

The Galápagos archipelago is a vast reservoir of terrestrial and marine biodiversity and is particularly susceptible to human, animal, and environmental impacts. Climate change, globalization, and the blurring of human-domestic animal-wildlife interfaces are poised to bring new threats and challenges to the region. A One Health perspective that simultaneously considers human, animal, and environmental health is imperative in assessing and mitigating the challenges facing the Galápagos Islands. Many challenges facing biodiversity in the Galápagos Islands can ultimately be linked to anthropogenic factors. In Part I of this review, we reviewed the impacts of invasive species and identified infectious diseases of importance. In Part II of this review, we discuss the impacts of climate change and ocean acidification, and highlight the effects of several direct human activities, including tourism, overfishing, pollution, land use, and human-wildlife conflict. We also review the socioeconomic and political context of the Galápagos Islands, including current challenges in water and energy use, sanitation, and economic stability. We examine the importance of investment in local development for building resiliency and sustainability in the archipelago. Finally, we discuss the impact of the COVID-19 pandemic in the region. Throughout this two-part review, we build a cohesive picture of One Health in the Galápagos Islands by integrating past work, current needs, and emerging threats. We also consider overarching goals for conservation, ecosystem management, and socioeconomic sustainability that have been previously defined by both governmental and non-governmental stakeholders, and identify discrete, implementable, and interdisciplinary recommendations that will facilitate achievement of those goals.

The novel severe acute respiratory coronavirus-2 (SARS-Cov-2) constitutes the causative agent of coronavirus disease-2019 (COVID-19). Several mechanisms have been proposed regarding the development of cardiovascular complications during and after acute COVID-19 infection. COVID-19, caused by SARS-CoV-2, has emerged as one of the deadliest pandemics in modern history. Several clinical and pathophysiologic mechanisms have been proposed for development of cardiovascular complications during and after acute COVID-19 infection. The myocardial injury in patients with COVID-19 has been attributed to coronary spasm, microthrombi formation, plaque rupture, hypoxic injury or cytokine storm disposing the same pathophysiology with the three clinical variants of Kounis syndrome. One of the main proposed mechanisms for development of cardiovascular complications is the angiotensin converting enzyme 2 (ACE2) and its interactions with the renin-aldosterone system (RAAS) and the kinin-kallikrein system (KKS). The ACE2 receptors are expressed throughout the human body located mainly in heart, blood vessels endothelium, lungs, intestines, testes and neurons. The SARS-CoV-2 directly invades the endothelial cells that contain ACE2 receptors and constitutes the main pathway through which the virus enters the endothelial cells. This leads to downregulation of the ACE2 receptors and causes angiotensin II accumulation leading to prothrombotic effects such as hemostatic imbalance via activation of the coagulation cascade, impaired fibrinolysis, thrombin generation, vasoconstriction, endothelial and platelet activation, and pro-inflammatory cytokine release. Indeed, angiotensin I exerts no direct biological function except that its high levels can stimulate catecholamine production. It is metabolized to its biologically active byproduct angiotensin II, a potent vasoconstrictor, by angiotensin converting enzyme (ACE) through cleavage of the two terminal amino acids. SARS-CoV-2 infection prevents the counterbalancing action of the KKS system that normally causes vasodilation and regulates tissue repair, inflammation, cell proliferation, and platelet aggregation. All this cascade leads to the following cardiovascular events: cardiac arrhythmias, cardiac arrest, cardiomyopathy, cytokine storm, heart failure, ischemic myocardial injuries and microvascular disease, Kounis syndrome, long COVID, myocardial fibrosis, myocarditis, new onset hypertension, pericarditis, postural orthostatic tachycardia syndrome (POTS), pulmonary hypertension, stroke, Takotsubo Syndrome, venous thromboembolism and thrombocytopenia. In this narrative review we describe and elucidate when and how the COVID-19 affects the human cardiovascular system, in various areas on the human body that are vulnerable in every category of patients including children and athletes.

Both SARS-CoV-2 and SARS-CoV initially appeared in China and spread to other parts of the world. SARS-CoV-2 has generated a COVID-19 pandemic causing more than 6 million human deaths worldwide while the SARS outbreak quickly ended in six months with a global total of 774 reported deaths. One of the factors contributing to this stunning difference in the outcome between these two outbreaks is the inaccuracy of the RT-qPCR tests for SARS-CoV-2, which generated a large number of false-negative and false-positive test results that have misled patient management and public health policy-makers. This article presented Sanger sequencing evidence to show that the RT-PCR diagnostic protocol established in 2003 for SARS-CoV can in fact detect SARS-CoV-2 accurately due to the well-known nonspecific PCR amplification of DNAs with similar sequences. Using nested RT-PCR followed by Sanger sequencing to retest 50 patient samples collected in January, 2022 and sold as RT-qPCR positive reference confirmed 21 (42%) were false-positive. Although the other 29 positive isolates were categorized as Omicron variant by partial sequencing of the N gene, and the RBD and the NTD of the S gene, 9 (31%) showed focal to complete sequencing failure in the S gene segments due to multi-allelic SNPs. During the course of the study, an Omicron variant isolate containing a BA.1 NTD and a BA.2 RBD in its S gene was also detected. Routine partial S gene sequencing of all PCR-positive samples can timely discover multi-allelic SNPs and viral recombination in the circulating variants for investigation of their impacts on vaccine efficacies, therapeutics and diagnostics.

The 32CA reactions of a series of pairs of bent three-atom-components (B-TACs) and linear TACs (L-TACs) towards electrophilic dicyanoethylene (DCE) have been studied within the Molecular Electron Density Theory in order to understand their different reactivity. ELF analysis indicates that while pseudodiradical B-TACs change their electronic structure to pseudoradical or carbenoid L-TACs upon dehydrogenation, zwitterionic B-TACs experience no remarkable change. Analysis of the CDFT indices indicates that five of the nine studied TACs have a strong nucleophilic character, thus participating in polar reactions towards electrophilic ethylenes. The activation energies of the 32CA reactions of the nine TACs towards electrophilic DCE range from 0.6 to 22.0 kcal·mol-1, which are by between 2.0 and 10.1 kcal·mol-1 lower than those involved in the non-polar 32CA reactions with ethylene. In general, B-TACs are more reactive than their L-TAC counterparts. A change of the regioselectivity is found in these polar 32CA reactions; in general, while B-TACs are meta regioselective, L-TACs are ortho regioselective. Analysis of the geometrical parameters indicates that at all TSs, the formation of the single bond involving the most electrophilic C4 carbon of DCE is more advanced than that involving the C5 one. A change of the asynchronicity in the reactions involving B-TACs and L-TACs is also found.

Fabric material was coated with Viruferrin™ and tested for its inactivating properties against the pandemic severe acute respiratory syndrome 2 (SARS-CoV-2) and influenza A viruses. A statistically significant (p<0.0001) decrease in the number of infectious virus particles exposed to Viruferrin-treated fabric when compared with the cotton control for both SARS-CoV-2 and influenza A viruses was observed. For both SARS-CoV-2 and influenza A, Viruferrin-treated fabrics experienced a > 99% virus reduction without saliva after five minutes of contact when compared to the positive control at time point 0. Furthermore, the reusability of the Viruferrin treated fabric was demonstrated by stability for up to 10 washes. The level of anti-viral (SARS-CoV-2) activity remained constant from 5 to 10 washes and demonstrated a significant difference (p<0.0001) from the unwashed untreated material. Applications for this treated fabric are far-reaching, and as a biological face covering offers not only a unique 2-way protection but also is unlikely to cause onward touch transmission.

The outbreak of COVID-19 has caused a global public health crisis. The spread of SARS-CoV-2 by contact is widely accepted, but the relative importance of aerosol transmission for the spread of COVID-19 is controversial. Here we characterize the distribution of SARA-CoV-2 in 123 aerosol samples, 63 masks, and 30 surface samples collected at various locations in Wuhan, China. The positive percentages of viral RNA included 21% of the aerosol samples from an intensive care unit and 39% of the masks from patients with a range of conditions. A viable virus was isolated from the surgical mask of one critically ill patient while all viral RNA positive aerosol samples were cultured negative. The SARS-CoV-2 detected in masks from patients, ambient air, and respirators from health workers compose a chain of emission, transport, and recipient of the virus. Our results indicate that masks are effective in protecting against the spread of viruses, and it is strongly recommended that people throughout the world wear masks to break the chain of virus transmission and thus protect themselves and others from SARS-CoV-2.

Glucose-6-phosphatase (G6Pase), an enzyme found mostly in the kidneys and the liver, acting significant role of supplying glucose through starvation. This study includes (84) subjects, their age ranged from (40 to 54) years. (20) subjects were healthy chosen as control group and (64) patients with type 2 diabetes mellitus were divided into three groups according to their type of anti diabetic therapy : (23) newly diagnosed group without therapy (Group1), (20) with metformin therapy (Group2) and (21) with metformin plus glibenclamide therapies( Group3). The study found that G-6-Pase activity is increased, thereby leading to an increase in endogenous glucose production (EGP) in patients with type 2 diabetes and, therefore FPG will increase. The result found that increasing G-6-Pase activity will increase the concentration of glucose in the blood and that will increase the long-term glycemic control (HbA1c%).

The development of multidrug resistance by pathogenic bacteria and yeast is a significant medical problem that needs to be addressed. One possible answer could be the combined use of antibiotics and silver nanoparticles, which have different mechanisms of antimicrobial action. Nanoparticles can be combined with antifungal agents in the same way. Biogenic silver nanoparticles synthesized using environmentally friendly technology using extracts of biologically active plants are an effective nanomaterial that needs to be comprehensively investigated for implementation into medical practice. In this study, the synergistic effects arising from their combined use with antibiotics and fungicides against various bacteria and yeasts, included in the ESKAPE list, were studied. Effective concentrations of substances were established, recommendations for the studied pathogenic species were presented and the effect of destruction of the bacterial membrane was illustrated. The most significant synergistic effect was manifested in pathogenic candida and brewer's yeast.

To characterize the fecal microbiota of lactating Bactrian camels, this study employed a random selection of camels, assigning them to three groups: the first parity group, the third parity group, and the fifth parity group. The fecal microbial community of lactating camels was assessed using 16S rRNA amplicon sequencing, and the resulting library was sequenced on an Illumina NovaSeq platform. The first parity group was characterized by Verrucomicrobiaceae as a distinctive bi-omarker, whereas Clostridiaceae exhibited a similar role to the third parity group. In terms of functional analysis, the relative abundance of the Steroid biosynthesis pathway was highest in the first parity group, whereas the fifth parity group demonstrated a higher relative abundance of the Tuberculosis pathway compared to the other groups. The first parity group exhibited the greatest presence of seven types of enzymes, whereas the fifth parity group displayed the highest levels of two specific enzymes. Additionally, a positive association was observed between the relative abundance of the Steroid biosynthesis pathway and Verrucomicrobiaceae. Furthermore, the relative abundance of the Tuberculosis pathway displayed positive correlations with 14 enzymes. In conclusion, this study revealed that different parity was associated with distinct fecal microbial ecologies in lactating Bactrian camels.

The treatment of viral infections is challenging owing to the intricate structure and metabolism of the viruses. In addition, they can highjack host cellular metabolism, mutate and adapt to harsh environmental conditions. The novel coronavirus (SARS-CoV-2) displays further resilient attributes, making its eradication even more difficult. SARS-CoV-2 is an enveloped virus whose replication can be targeted by limiting the substrates available for structural incorporation. One such molecule that limits substrate availability and has received much attention lately is 2-Deoxy-d-glucose (2-DG). SARS-CoV-2 infection induces glycolysis, impairs mitochondrial function, and damages the infected cells. Administration of 2-DG can inhibit increased glycolytic flux and some other metabolic processes to cause the cessation of viral replication. This article provides a review of the mechanism of action and safety concerns associated with administering 2-DG in the treatment of COVID-19. The drug can have adverse effects on normal cell metabolism since it targets cells non-selectively, possibly in a dose-dependent manner. In addition, the drug has limited use in SARS-CoV-2 infection associated with stroke, hypoxic-ischemic encephalopathy, and critical illness.

In graduate programs such as social work, field education is the signature pedagogy of education. As such, student placement with an appropriate field education agency is critical to ensure academic success and career readiness. A variety of Learning Management System (LMS) and Integrated Planning and Advising Service (IPAS) technologies have been developed to fully integrate technology into the educational system and streamline and improve the learning experience for students, educators, and administrators. Few (if any) of the existing solutions have capabilities to match students with field educators on the basis of an individual student’s completed coursework and area of specialization, as well as field educator needs and opportunities. This paper describes our experience developing a custom LMS/IPAS system—the School of Social Work information System (SSWiS)—that was designed specifically for student learning, faculty advising, and academic administration within our social work graduate program. We present the challenges that motivated the design of the SSWiS before describing the architecture and functionality of our solution. We then discuss our preliminary evaluation results. We conclude with a discussion of the benefits and limitations of our system in the context of today’s technical needs in graduate education in social work and other fields.

Mango industry played important roles in the economy of tropical and subtropical regions in China. Embryo abortion, flesh rotten, pericarp stain and so on always result in heavy loss. Transgenic breeding is a potential way for solving these problems. However, dependable protocol for mango transgene has not been reported until now. In this research, we found that after mango embryogenic callus were infected by Agrobacterium, most callus necrotized and died. No seedling was differentiated from the survived callus. Contents of 106 compounds in mango callus infected by Agrobacterium were found significantly different from those in control. The top 7 up-regulated compounds were gallic acid, phosphoenolpyruvic acid, dihydrotricetin, ubiquinone, dihydrokaempferol, D-erythrose-4-phosphate and cyanidin. The expressions of ROS-related genes, gallic acid synthesis genes, and defense related genes in callus infected by Agrobacterium were significantly higher than those in control. The growth of Agrobacterium on culturing plate pipetting gallic acid was inhibited. Mango callus infected by Agrobacterium transformed with type III secretion system gene cluster (T3SS) and effector gene AvrPto isolated from Pseudomonas syringae pv. tomato strain DC3000 brown and necrotize slower than those infected by Agrobacterium transformed with net vector (NV). The contents of ROS in callus in-fected by Agrobacterium (T3SS-AvrPto) were less than those in callus infected by Agrobacterium (NV). The expressions of genes participated in immune responses in mango callus infected by Agrobacterium (T3SS-AvrPto) were also less than those in mango callus infected by Agrobacterium (NV). The transformation rate of mango callus infected with Agrobacterium (T3SS-AvrPto-gfp) was 1.6 %. No callus infected with Agrobacterium (NV-gfp) was transformed successfully. The efficiency of mango transformation can be improved by constructing Agrobacterium strains harboring type III secretion system gene cluster and its effector gene.

This study investigates the application of frequency augmentation techniques to improve condition-based maintenance (CBM+) systems for airborne weapon systems, focusing on the predictive accuracy of velocity and acceleration parameters. Utilizing MIL-STD-1553B MUX data from 138 sorties of fixed-wing aircraft, we explored six augmentation methods, including traditional interpolations (linear, quadratic, cubic spline) and advanced machine learning models (K-Nearest Neighbor, LSTM, and Bi-LSTM). Our findings indicate that while traditional methods like linear interpolation are more effective for velocity parameters, advanced ML techniques, particularly Bi-LSTM, provide better results for acceleration parameters, which exhibit more complex and rapid variability. The study underscores the necessity of tailoring augmentation approaches to specific parameter characteristics and highlights the potential of ML models in capturing intricate patterns within time-series data. These insights are critical for advancing CBM+ systems in military avionics, enhancing reliability and operational efficiency through improved data processing strategies. Future work should explore more performant augmentation techniques, such as by integrating multi-dimensional datasets, and explore ways to develop automated tools for a more powerful predictive maintenance framework.

Based on a constant volume combustion chamber, the initial ambient pressure effect on combustion performance and flame propagation of the active and passive pre-chamber ignition systems were studied. Compared with the passive pre-chamber, the active pre-chamber can significantly expand the lean combustion limit, enhance the ignition and combustion performance, and speed up the combustion of the main combustion chamber. At slightly lean combustion conditions, the heat release performance has a positive correlation with the initial ambient pressure. As the premixed λ increases, the heat release performance and the initial ambient pressure begin to become negatively correlated. Ignition delay can be minimized by controlling the excess air coefficient in the pre-chamber close to the stoichiometric ratio. The high ambient pressure in the constant volume chamber greatly reduces the mixture entrainment ability of the jet flame at lean combustion conditions, thereby reducing the combustion speed and peak heat release rate.

COVID-19, caused by the SARS-CoV-2 virus, has spread around the world and killed around 6.9 million people. Rapid and accurate diagnosis is essential for preventing and controlling the disease, reducing transmission and consequently saving lives. RT-PCR is the gold standard test used to detect the disease. However, the test is expensive and the result is time-consuming, which makes mass testing difficult, especially in countries with limited resources. In addition, the test has high analytical specificity and low diagnostic sensitivity, which leads to false-negative results. Several studies in the literature report the presence of hematological and biochemical alterations in infected patients and use these alterations with machine learning algorithms to help diagnose the disease. Therefore, this article presents the results obtained by different neural network architectures based on Adaptive Resonance Theory (ART) for the diagnosis of COVID-19. The study was conducted in two distinct stages: the first consisted of selecting the best ART network among several, using three open-access datasets and comparing the results with the literature. In the second stage, the chosen model was tested on a dataset containing patients from various hospitals in four countries. In addition, the model was subjected to external validation, including data from a country not present during the training and adjustment of the model, in order to validate the robustness and generalization capacity of the model. The results obtained by the ART networks in this study are promising, outperforming not only classical models, but also the deep learning models often used in the literature. Validation on data from different countries strengthens the model’s reliability and effectiveness.

The global increase in population has placed significant pressure on food security, leading to the emergence of aquaculture as a vital source of aquatic foods. However, rising costs and the limited availability of fishmeal in aquafeeds have spurred the search for alternative protein sources. While plant-based ingredients have been integrated into commercial aquafeeds, they come with challenges, including low protein content, palatability issues, and the presence of antinutritional factors. In this context, fish silage, derived from fish waste and discarded fish, stands out as a promising alternative technology due to its cost-effectiveness and sustainability attributes. The production of fish silage involves the addition of organic/inorganic acids or lactic acid bacteria to homogenized fish waste. This process yields a valuable mixture rich in peptides, free amino acids, and partially hydrolyzed proteins, offering significant nutritional benefits for animal diets. Beyond its nutritional advantages, fish silage contributes to environmental sustainability by reducing the impact of waste disposal and enhancing the value of fish waste. The objective of this review is to promote sustainable practices in the aquaculture industry in response to the increasing demand for high-quality feed nutrients. To achieve this, it includes an analysis of research results related to silage technology, an appraisal of the advantages and disadvantages of using fish silage as a feed additive, and a focus on emerging trends in this field.

The event camera generates an event stream based on changes in brightness, retaining only the characteristics of moving objects, addresses the high power consumption associated with using high-frame-rate cameras for high-speed eye tracking tasks. However, the asynchronous incremental nature of event camera output has not been fully utilized, and there are also issues related to missing event datasets. Combining the temporal information encoding and state-preserving properties of spiking neural network (SNN) with event camera, a near-range eye tracking algorithm is proposed as well as a novel event-based dataset for validation and evaluation. According to experimental results, the proposed solution outperforms artificial neural network (ANN) algorithms, while computational time remains only 12.5\% of that of traditional SNN algorithms. Furthermore, the proposed algorithm allows for self-adjustment of time resolution, with a maximum achievable resolution of 0.081ms, enhancing tracking stability while maintaining accuracy.

With the digital transformation of the logistics industry, smart logistics algorithms have become a core technology to improve efficiency and reduce costs. This paper reviews the development history of traditional logistics technology and discusses the key role of technologies such as the Internet of Things, big data analysis, artificial intelligence, and automation in logistics technology innovation. It focuses on the application of intelligent logistics algorithms in path optimization, intelligent scheduling, data mining and prediction, and intelligent warehousing. To solve the problem of inconsistency between training and testing objectives, this paper proposes DRL4Route, a deep reinforcement learning-based path optimization framework, and designs the DRL4Route-GAE model. Validated through extensive offline experiments and online deployments, the model significantly outperforms existing optimal benchmark methods on real datasets, improving the location deviation squared metrics and the top-three location prediction accuracy metrics. These research results provide important support to further promote the intelligent development of the logistics industry.

Rheumatoid arthritis (RA) is a complex illness with both hereditary and environmental compo-nents. Globally, in 2019, 18 million people had RA. RA is characterised by persistent inflammation of the synovial membrane that lines the joints, cartilage loss, and bone erosion. Phenolic molecules are the most prevalent secondary metabolites in plants, with a diverse spectrum of biological ac-tions that benefit functional meals and nutraceuticals. These compounds have received a lot of at-tention recently because they have antioxidant, anti-inflammatory, immunomodulatory, and anti-rheumatoid activity by modulating tumour necrosis factor, mitogen-activated protein kinase, nu-clear factor kappa-light-chain-enhancer of activated B cells, and c-Jun N-terminal kinases, as well as other preventative properties. This article discusses dietary polyphenols, their pharmacological properties, and innovative delivery technologies for the treatment of RA, with a focus on their possible biological activities. Nonetheless, commercialization of polyphenols may be achievable only after confirming their safety profile and completing successful clinical trials.

We aimed to estimate the proportion of the population infected with SARS-CoV-2 in the first year of the pandemic. The study population consisted of outpatient adults with mild or no COVID-19 symptoms, and was divided into subpopulations with different levels of exposures. Of the subpopulation without known previous COVID-19 contacts 4143, of the subpopulation with known COVID-19 contacts 594 persons were investigated. IgG- and IgA-seroprevalence and RT-PCR positivity were determined in context with COVID-19 symptoms. We hope to have contributed to the understanding of the significance of the asymptomatic and mild infections in the long persistence of the pandemic.

The SARS-Cov-2 virus, which causes COVID 19, uses the cell surface protein ACE2 as receptor for entry into cells. Critically ill COVID-19 patients often require prolonged mechanical ventilation which can cause mechanical stress to lung tissue. In vitro studies have shown that expression of ACE2 in alveolar cells is increased following mechanical stretch and inflammation. Therefore, we analyzed transcriptome datasets of 480 (non-COVID-19) lung tissues in the GTex tissue gene expression database. We found that mechanical ventilation of the tissue donors increased the expression of ACE2 by more than two-fold (p<10^-6). Analyses of transcriptomes of mechanically ventilated mice in the GEO database indicate that this alveolar cell response to stretch and inflammation is mediated by the chemokine midkine. Using a novel big knowledge network approach (SPOKE) we also found in transcriptomes of pharmacological perturbations (LINCS) that corticosteroids down-regulate midkine in pulmonal cells, and confirmed this in GEO transcriptomes of animal studies. Thus, mechanical ventilation of patients with COVID-19 pneumonia may eo ipso facilitate viral propagation in the lung, further accelerating the pulmonal pathology that has necessitated mechanical ventilation in the first place. This vicious cycle presents a rationale for the temporary treatment with corticosteroids to modulate the midkine-ACE2 axis in ventilated COVID19 patients and for gentler ventilation protocols.

The main purpose of tissue engineering is to fabricate and exploit engineered constructs suitable for the effective replacement of damaged tissues and organs, and able to perfectly integrate with the host’s organism without eliciting any adverse reaction. Ideally, autologous materials represent the best option, but they are often limited due to the low availability of compatible healthy tissues. So far, one therapeutic approach relies on the exploitation of synthetic materials: they exhibit good features in terms of impermeability, deformability and flexibility, but present chronic risks of infections and inflammations. Alternatively, biological materials, including naturally derived ones and acellular tissue matrices of human or animal origin, can be used to induce cells growth and differentiation, which are needed for tissue regeneration: however, this kind of materials lacks satisfactory mechanical resistance and reproducibility, affecting their clinical application. In order to overcome the above-mentioned limitations, hybrid materials, which can be obtained by coupling synthetic polymers and biological materials, have been investigated with the aim to improve biological compatibility and mechanical features. Currently, the interest in these mate-rials is growing, but the ideal ones have not been found yet. The present review aims at exploring some applications of hybrid materials, with particular mention to urological and cardiovascular fields: in the first case, the efforts to find a construct that can guarantee impermeability, mechanical resistance and patency will be herein illustrated; in the second case, the search for impermeability, hemocompatibility and adequate compliance will be been disclosed.

: Optical coherence tomography is a non-invasive imaging technique that provides three-dimensional visualization of subsurface tissue structures. OCT has been proposed and explored in literature as a tool to assess oral cancer status, select biopsy sites, or identify surgical margins. Our endoscopic OCT device can generate widefield (centimeters long) imaging of lesions at any location in the oral cavity – but it is challenging for raters to quantitatively assess and score large volumes of data. Leveraging a previously developed epithelial segmentation network, this work develops quantifiable biomarkers that provide direct measurements of tissue properties in three-dimensions. We hypothesize that features related to morphology, tissue attenuation, and contrast between tissue layers will be able to provide a quantitative assessment of disease status (dysplasia through carcinoma). This work retrospectively assesses seven biomarkers on a lesion-contralateral matched OCT dataset of lateral and ventral tongue (40 patients, 70 sites). Epithelial depth and loss of epithelial-stromal boundary visualization provide the strongest discrimination between disease states. The stroma optical attenuation coefficient provides distinction of benign lesions from dysplasia and carcinoma. The stratification biomarkers visualize subsurface changes which provides potential for future utility in biopsy site selection or treatment margin delineation.

Hazelnut shells (HS), scientifically known as Corylus avellana L. shells, are a waste produced in companies that process nuts. The main objective of this study was to find an efficient way to maximize the chemical potential of HS by solubilizing the hemicelluloses, which could then be used to recover sugars and at the same time increase the lignin content of this material in order to produce adhesives or high strength foams. In order to optimize the pre-hydrolysis process, two different temperatures (160 and 170 C) and times varying from 15 to 180 min were tested. All the remaining solid materials were then liquefied by polyalcohols with acid catalysis. The chemical composition of hazelnut shells was determined before and after the pre-hydrolysis. All the process was monitored by FTIR-ATR by determining the spectra of solids and liquids after pre-hydrolisis and liquefactions steps. The highest solubilization of hazelnut shells was found for 170 C and 180 min, resulting in a 25.8% solubilization. Chemical analysis after the hydrolysis process showed a gradual increase in the solubilization of hemicelluloses as both the temperature and time of the reactor were increased. Simultaneously, the percentages of α-cellulose and lignin in the material also increased with rises in temperature and duration. FTIR-ATR allowed for the detection of significant spectral changes in the hazelnut shells from their initial state to the solid residue and further into the liquefied phase. This confirmed that pre-hydrolysis was effective in enhancing the chemical composition of the material, making it more suitable for the production of adhesives or polyurethane foams with enhanced mechanical strength.

We model the financial markets as a game and make predictions using Markov chains estimators. We extract the possible patterns displayed by the financial markets, define a game where one of the players is the speculator, whose strategies depend on his/hers risk-to-reward preferences, and the market is the other player, whose strategies are the previously observed patterns. Then we estimate the market’s mixed probabilities by defining Markov chains and utilizing its transitions matrices. Afterwards, we use these probabilities to determine which is the optimal strategy for the speculator. Finally, we apply these models to real-time market data to determine its feasibility. After all, we obtained a model for the financial markets that has an outstanding performance in terms of accuracy and profitability.

Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.

Emerging RNA virus infections are a growing concern among domestic bird and poultry industries due to the severe impact it can have on the flock health and economic livelihoods. Avian paramyxoviruses (APMV) are pathogenic, negative sense RNA viruses that cause serious infections in the respiratory and central nervous system. APMV was detected in multiple avian species during the 2017 migration season in Ukraine, and studied using PCR, virus isolation, and sequencing. Of the 4090 wild bird samples, eleven swabs were isolated in chicken embryos and identified for APMV serotype by hemagglutinin inhibition test: APMV-1, APMV-4, APMV-6, APMV-7. At a variety of sites in Ukraine we characterized the virulence of the virus and further analyzed and predicted the potential risks of spillover to immunologically naïve populations. RNA was extracted and amplified using a multiplex-tiling primer approach to encompass full cDNA genomes. Full-length APMV-1 (n=5) and APMV-6 (n=2) genomes were sequenced on an Oxford Nanopore MinION device in Ukraine. All APMV-1 and APMV-6 fusion (F) proteins possessed a monobasic cleavage site, suggesting these APMV were likely low virulence, annually circulating strains. Utilization of this low-cost method will identify gaps in viral evolution and circulation in this understudied but important critical region for Eurasia.

Insecurity, corruption, and rising unemployment have resulted in a mass exodus of young adult Afghans seeking asylum in western nations. This has depleted Afghanistan of generations of young people which are critical to rebuild the country. This study aimed to examine the characteristics of young adult Afghans with no immediate intentions of seeking international asylum; that is, individuals who intend to stay in Afghanistan. In a cross-sectional study conducted in Kabul, we surveyed 232 young adults between 18 and 35 years of age. Surveys included measures assessing standard socio-demographic and -economic factors, as well as health and psychological factors. Univariate logistic regression analyses suggest that participants with an intent to stay in Afghanistan are more likely to be financially stable, possess higher health-related quality of life, lower psychological distress, and higher levels of hope and optimism, as well as higher resilience. When controlling for all other variables in the model, only hope, optimism, and higher resilience remained as significant correlates of intending to stay. Our findings suggest that young people who intend to stay in their country look forward to a better future, which provides strong evidence for the need to strengthen the social contract by fostering resilience, hope and optimism in war-affected communities, in order to prevent a generation of talented young people from seeking asylum in western nations.

Varicella (chickenpox) is one of the most common infectious diseases in preschool children and can be prevented by immunization. While immunization program promotes higher vaccine coverage, there are still challenges with implementing vaccination programs particularly in low-income countries. Economic barriers can increase inequality in vaccination, which highlights the importance of distributing cost-effective varicella vaccines. Varicella vaccines have been developed with two main Varicella-Zoster Virus (VZV) strains globally: MAV/06 and Oka strains. Most of the vaccines are based on the Oka strain, while MAV/06 strain vaccines were exclusively developed by GC Biopharma. Although more than 30 million doses of MAV/06 vaccines as cost-effective vaccines have been distributed internationally, there is no comprehensive review on safety, immunogenicity, and effectiveness of MAV/06 vaccines in the literature. This paper aims to summarize and present more than 30 years of accumulated evidence from research on safety, immunogenicity, and effectiveness of MAV/06 vaccines. We expect that MAV/06 vaccines as cost-effective varicella vaccines make a significant contribution to improving global public health and health equality in children.

Plant-based proteins, like those derived from hemp and rapeseed can significantly contribute to a balanced diet and human daily nutritional requirements by providing essential nutrients such as protein, fiber, vitamins, minerals, and antioxidants. According to numerous recent research papers the consumption of plant-based proteins has been associated with numerous health benefits, including reduced risk of chronic diseases such as heart disease, diabetes, and certain cancers. Plant-based diets are often lower in saturated fat and cholesterol and higher in fiber and phytonutrients, which can support overall health and well-being. Present research investigates the nutritional attributes, functional properties, and potential food applications of hemp and rapeseed protein for a potential use in new food product development, with a certain focus on identifying anti-nutritional factors and bioactive compounds. Through comprehensive analysis, anti-nutritional factors and bioactive compounds were elucidated, shedding light on their impact on protein quality and digestibility. The study also delves into the functional properties of hemp and rapeseed protein, unveiling their versatility in various food applications. Insights from this research contribute to a deeper understanding of the nutritional value and functional potential of hemp and rapeseed protein, paving the way for their further utilization in innovative food products with enhanced nutritional value and notable health benefits.

The inhibition of alpha-glucosidase and alpha-amylase is one of clinic strategies for remedy the type II diabetes. Herbal medicines are reported to alleviate hyperglycemia. However, the constituents from those sources whether are targeted to the alpha-glucosidase and alpha-amylase still unexplored. This study attempted to select the compounds for efficacy of hypoglycemia via cellular and mouse levels. The results illustrated that the cytotoxicity in all tested compounds at various concentrations except the concentration of 16-hydroxy-cleroda-3,13-dine-16,15-olide (HCD) at 30 µM were not significant difference (p > 0.05) when compared with the untreated control. Acarbose (reference drug), Antroquinonol, Catechin, Quercetin, Actinodaphnine, Curcumin, HCD, Docosanol, Tetracosanol, Berberine, and Rutin could effectively inhibit the alpha-glucosidase activity of Caco-2 cells when compared with the control (maltose). The compounds (Curcumin, HCD, Tetracosanol, Antroquinonol, Berberine, Catechin, Actinodaphnine, and Rutin) could reduce blood sugar level at 30 min in tested mice. The effects of tested compounds on area under curve (AUC) were significant (p < 0.05) among Acarbose, Tetracosanol, Antroquinonol, Catechin, Actinodaphnine, and Rutin along with Berberine and Quercetin. In in vitro (alpha-glucosidase) with in vivo (alpha-amylase) experiments suggest that bioactive compounds can be a potential inhibitor candidate of alpha-glucosidase and alpha-amylase for the alleviation of type II diabetes.

The benthic matrix acts as a key natural archive where the memory of long-term timescale environmental changes is recorded. Some ecological and chemical features of fjord sediments were explored during the AREX cruise carried out in the Svalbard archipelago in summer 2021. The activity rates of the enzymes leucine aminopeptidase (LAP), beta-glucosidase (GLU) and alkaline phosphatase (AP) and community-level physiological profiles (CLPP) were studied with the aim of determining the functional diversity of the benthic microbial community, while bacterial isolates were screened for their susceptibility to antibiotic molecules in order to explore the role of these extreme environments as potential reservoirs of antibiotic resistance. Enzyme activity rates were obtained by fluorogenic substrates, CLPP by Biolog Ecoplates; antibiotic susceptibility assays were performed through the standard disk diffusion method. Spatial trends observed in the functional profiles of the microbial community suggested the variability in the microbial community composition, presumably related to the patchy distribution of organic substrates. Complex Carbon sources, carbohydrates and amino acids were the organic polymers preferentially metabolized by the microbial community. Multiple resistance to ampicillin, clindamycin and gentamycin was detected in all the sampled sediments, excepting in the southern Hornsund area, stressing the role of sediments as a potential reservoir of chemical wastes ascribable to antibiotic residuals. This study provides new insights on the health status of fjord sediments of West Spitsbergen applying a dual ecological and biochemical approach. Microbial communities in the fjord sediments showed globally a good functional diversity, suggesting their versatility to rapidly react to changing conditions.

Ecology is working to face its colonial roots and institutional inequities. As we build more diverse, equitable, and inclusive (DEI) institutions we must work to support new ecologists by empowering them with the knowledge and tools to succeed. Undergraduate research experiences (UREs) are critical for a student’s professional and interpersonal skill development and key for recruiting more diverse groups of students to ecology. Here, we highlight DEI dimensions of a URE in ecology, acknowledge safety considerations for field ecology, including harassment and assault, and provide tools to support the URE. This is written primarily for all URE students and secondarily for their advisors. We welcome students from underrepresented groups and encourage allyship from students from non-underrepresented groups. After reading this paper, we hope that all students feel more confident and excited about their URE and that advisors see how to improve DEI in their lab.

Histone deacetylases inhibitors (HDACi) have shown promising therapeutic outcomes in haematological malignancies such as leu-kaemia, multiple myeloma and lymphoma, with dis-appointing results in solid tumours when used as monotherapy. As a result, combination therapies either with radiation or other deoxyribonucleic acid (DNA) damaging agents have been suggested as ideal strategy to improve their efficacy in solid tumours. Numerous in vitro and in vivo studies have demonstrated that HDACi can sensitise malignant cells to both electromagnetic and particle types of radiation by inhibiting DNA damage repair. Although the radiosensitising ability of HDACi has been reported as early as 1990s, the mechanisms of radiosensitisation are yet to be fully understood. This review brings forth the various protocols used to sequence the administration of radiation and HDACi treatments in the different studies. The possible contribution of these various protocols on the ambiguity that surrounds the mechanisms of radiosensitisation is also highlighted.

Many studies, which try to analyze conditions for debris flow development, ignore the type of initiation. Therefore this paper deals with the following questions: What type of hydro-mechanical triggering mechanisms for debris flows can we distinguish in upstream channels of debris flow prone gullies? Which are the main parameters controlling the type and temporal sequence of these triggering processes and what is their influence on the meteorological thresholds for debris flow initiation? A series of laboratory experiments were carried out in a flume, 8 m long and with a width of 0.3 m. to detect the conditions for different types of triggering mechanisms. The flume experiments show a sequence of hydrological processes triggering debris flows, namely erosion and transport by intensive overland flow and by infiltrating water causing failure of channel bed material. On the basis of these experiments an integrated hydro-mechanical model was developed, which describes Hortonian and Saturation overland flow, maximum sediment transport, through flow and failure of bed material. The model was calibrated and validated using process indicator values measured during the experiments in the flume. Virtual model simulations, carried out in a schematic hypothetical source area of a catchment show that slope angle and hydraulic conductivity of the bed material determine the type and sequence of these triggering processes. It was also clearly demonstrated that the type of hydrological triggering process and the influencing geometrical and hydro-mechanical parameters may have a great influence on rainfall intensity-duration threshold curves for the start of debris flows.

Air quality is a topic of growing relevance on the global agenda. Two key indicators of air quali-ty, nitrogen dioxide (NO2) and carbon monoxide (CO), represent significant hazards to human health and the environment putting the sustainability of natural resources at risk. Therefore, it is imperative to regularly monitor and reduce atmospheric pollutant concentrations to mitigate their harmful consequences. This study presents an analysis of NO2 total and CO emissions in Argentina, utilizing remote sensing data. The research aims to determine the spatiotemporal distribution of NO2 and CO emissions in Argentina from 2019 to 2021. Subsequently, it seeks to establish the influence of land uses and land cover on the emission of NO2 and CO through dif-ferent climatic, anthropic, and natural indicators. The study was carried out in Argentina during the period 2019-2021, where random points were placed for the different land covers with a total of 800 points surveyed. The year with the highest CO concentration (mol/m-2) was 2020. The values were highest for tree covers and herbaceous wetland coverage in the northern part of the country. For total NO2, the highest concentrations were reached during the years 2020 and 2021. Regarding its distribution, throughout the evaluated period, the highest concentrations of total NO2 were found in the built-up and cropland coverages, with the capital of the country and the northern region of the Buenos Aires province being the most affected areas. In addition, the concentration of CO was influenced by climatic variables (atmospheric pressure, wind speed, maximum environment temperature, Palmer index), natural (height, humidity, NDVI), and ur-ban variables (distances to mining extraction, airports, power plants, urban index) for the differ-ent uses and land covers. Finally, the concentrations of total NO2 were influenced by climatic variables (Palmer index and wind speed), natural (height and NDVI), and urban variables (dis-tance to airports, power plants, industries, service stations, and open dumpsites) for the differ-ent uses and land cover. This study contributes to sustainable environmental management, ena-bling the formulation of effective strategies for mitigating emissions and promoting the long-term health and well-being of communities.

In this report, we present a study demonstrating how machine learning methods can reveal the interaction of different risk factors of post-transplant leukemic relapse and obtain robust predictions even with a modest clinical dataset. Using a cohort of 63 pediatric patients with acute lymphocytic leukemia (ALL) and 46 patients with acute myeloid leukemia (AML) who underwent stem cell transplant at a single institution, we built predictive models of leukemic relapse with both pretransplant and posttransplant patient variables (specifically lineage-specific chimerism) using the random forest classifier. The random forest classifier revealed different important predictive factors between ALL and AML in our relapse models consistent with previous knowledge. Furthermore, it also distinguished donor CD34 chimerism as most impactful in relapse prediction compare to donor chimerism of other cellular lineages in our dataset for ALL but CD3 for AML. Our models greatly improved sensitivity and specificity at predicting relapses in cross validation compared to a reference model based on our own institutional incidence of relapse and inferential statistical principles. Local Interpretable Model-Agnostic Explanations, an interpretable machine learning tool, confirmed our Random Forest Classification result and provided an intuitive explanation of how our machine learning models made the relapse prediction for each individual patient.

Food security is the cornerstone of national security, and exploring the resilience of the grain industry chain is particularly important under the current complex and changing external environment. The study is to evaluate the grain industry chain resilience and identify its influencing factors. Based on the panel data of 30 provinces (cities) in China from 2011 to 2022, this paper adopted Partial Least Squares structural equation modeling method to carry out comprehensive evaluation, applied Dagum Gini coefficient to analyze spatial differences, and used geo-detectors to identify the influencing factors. The results showed that (1) from an overall perspective, the resilience of China's grain industry chain showed an overall upward trend, and each region had been greatly improved; (2) from the perspective of spatial differences, the overall spatial variability of China's grain industry chain is relatively small, and the gap mainly originates from inter-regional differences; (3) from the perspective of influencing factors, the level of grain foreign trade, regional market size and agricultural innovation level were important factors. In order to enhance the resilience of China's grain industry chain, based on the heterogeneous characteristics, the coordinated development of each region should be integrated, and the endogenous power should be given full play to enhance the resilience of the industry chain.

This study investigates how customers’ technological readiness affects service quality and the adoption of self-service banking by developing an empirical model. The model was proposed to explore the relationships between technology readiness, self-service quality, and customer satisfaction. The hypotheses and stability of the model were tested using structural equation modeling. The findings revealed that technology readiness, including the optimism, innovation, and insecurity dimensions, had a significant positive impact on self-service quality. In addition, service quality fully mediated the relationship between technological readiness and customer satisfaction. These results suggest that when customers are ready to embrace and use new technology, their evaluation of service quality is high. Thus, bank managers should drive customer perceptions of the quality of self-service banking by improving their technological readiness through various measures. Furthermore, based on the mediating effect of self-service quality, banks must be aware that improving service quality can enhance customer satisfaction and maintain a competitive advantage.

In the early decades of the 19th century, the deaf population in the eastern US and Canada was distributed across a vast area. After the 1817 founding of the American School for the Deaf (ASD) in Hartford, CT and the subsequent establishment of schools for the deaf in New York City (NYSD), Philadelphia, and many other US states, hundreds of deaf students called these schools home for several years. After leaving school, the early American Deaf community faced a challenge: How would the community maintain its social and linguistic ties across a vast geographical area once community members had dispersed to their hometowns? In this paper we analyze the attendance registries of two large gatherings of deaf individuals in Hartford in the years 1850 and 1854. These were among the largest and earliest known gatherings of deaf adults. The demographic data included in these registries open a window onto the evolving American Deaf community of the mid-19th century. Our analyses of the attendees’ school affiliations, ages, and marital status show that the signing community that had taken root among students at schools in Hartford and New York persisted among the adult graduates of those schools in towns across the northeastern US. We compare the places of residence of ASD and NYSD alumni in 1850 and 1854 to their hometowns when they first enrolled at school. Our comparisons suggest the mid-19th-century signing community had become more urban; it was centered to a greater extent in populous cities such as New York, Boston, and Hartford than had been true when the attendees had enrolled in school. We consider how the geography of deafness in the 19th century might inform our understanding of the sources of regional variation in ASL. We also consider how early cross-regional interactions may have inhibited such regional variation.

Quasi-interpolation is a powerful tool for approximating functions using radial basis functions (RBFs) such as Gaussian kernels. This avoids solving large systems of equations as in RBF interpolation. However, quasi-interpolation with Gaussian kernels on compact intervals can have significant errors near the boundaries. This paper proposes a quasi-interpolation method with Gaussian kernels using Chebyshev points and boundary corrections to improve the approximation near the boundaries. The boundary corrections use a linear approximation of the function beyond the interval to estimate the truncation error and add correction terms. Numerical studies on test functions show that the proposed method reduces errors significantly near boundaries compared to quasi-interpolation without corrections, for both equally spaced and Chebyshev points. The convergence and accuracy with the boundary corrections are generally better with Chebyshev points compared to equally spaced points. The proposed method provides an efficient way to perform quasi-interpolation on compact intervals while controlling the boundary errors.

This paper proposes training improvement methods of artificial neural networks (ANN) trajectory predictors. First, a dynamic power system time-series trajectory is split into several different segments to simplify the original ANN training problem. Moreover, the time-derivative of the trajectory is included to obtain an augmented loss function. Compared to previous studies which mainly focused on increasing the prediction accuracy, the aim of these novel techniques is to reduce the computational burden where the ANN output performance is still acceptable. The effectiveness of the developed methods is validated based on the WSCC three-machine nine-bus and IEEE 39-bus system models. The mean absolute error (MAE) and trajectory prediction results are analyzed, in which the numbers of neurons, hidden layers, and training epochs are constrained during the ANN training process. Rotor-angle difference between generators and the system frequency are investigated as the dynamic trajectories of the power system models. It is revealed that when the ANN architecture and epochs are constrained, the MAE results can be reduced, and the ANN training results can better reflect the original trajectory using the improvement approaches.

Currently there is little doubt left on the symbiogenetic nature of eukaryotes - genomes of archaeon and bacterium participated in shaping a genome of last eukaryotic common ancestor in equal albeit asymmetric manner, while a merger event itself indicated the advent of a new domain of life. The “symbiogenetic” framework of interaction of two partners is proposed, outlining similar steps essential for three major advents: the origin of life, the origin of complex life and the origin of humans. Given the immense importance of proper energy source for the evolution of life it seems plausible that for any principal increase in complexity, a partnership with a novel energy donor is required. Moreover, a “language” elaborated in the course of communication of partners might have been a prerequisite for a subsequent increase in complexity. Transitions, which led to RNA-protein world, eukaryotes and human brain, resulted in advent of complex languages via communication onsets between two entities in close partnership. Accordingly this further facilitated formation of first cells, multicellular organisms and human society.

With the proliferation of locating devices, more and more raw spatial trajectories are formed, and many works enrich these raw trajectories with semantics, and mine patterns from both raw and semantic trajectories, but access control of spatial trajectories is not considered yet. We present a multi-policy secure model for semantic spatial trajectories. In our model, Mandatory Access Control, Role Based Access Control and Discretionary Access control are all enforced, separately and combined, and we represent the model semi-formally in Ontology Web Language.

Tuberculosis (TB) remains a significant public health challenge in Nigeria, with high rates of transmission and low case detection rates. This paper presents the challenges of TB screening and the existing gaps in Contact Investigation (CI). This descriptive qualitative study was conducted in 8 local government areas with high TB burdens in Oyo and Osun States, Nigeria. Twenty-four focus group discussions and 27 key informant interviews were conducted among TB patients, household TB contacts, and government TB staff, among others. Respondents' ages ranged from 17-85 years with a mean of 42.08±14.9 years, and (4.0%) had a postgraduate degree. The unwillingness of the majority of TB contacts who tested negative for TB to be placed on TB preventive therapy because of the belief that only a sick person should take drugs and hostility from the TB contacts to the contact tracers during the house-to-house screening of presumptive TB cases due to community stigma associated with TB were some of the existing gaps reported in TB contact investigations. The findings emphasize the importance of tailored approaches in TB prevention and control, addressing challenges in testing and contact investigations; this necessitates investments in community engagement strategies to enhance the cooperation of TB contacts.

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model’s outputs and the testbed’s outputs, considering the joint position and acceleration of the link’s tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application wherein the servomotor applies the input torque, and the outputs are the joint angle and the link’s tip acceleration. Then, an optimization problem minimizes the difference between numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws.

A growing area in neurosciences is focused on the modeling and analysis the complex system of connections in neural systems, i.e. the connectome. Here we focus on the representation of connectomes by using graph theory formalisms. The human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. Recently, it has been proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space to define network nodes of individual brain networks. In the network domain, the question of comparison of the structure of networks arises. Such question is tackled by modeling the comparison of brain network as a network alignment (NA) problem. In this paper, we first defined the NA problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.

In the dynamic realm of HPC (High-performance computing) and cloud-native applications, ensuring optimal network performance for Kubernetes Container Network Interfaces (CNIs) is critical. Traditional manual methods for evaluating network bandwidth and latency are prone to errors, time-consuming, and lack consistency. This paper introduces a novel approach that leverages Ansible to automate and coordinate network performance tests across diverse CNIs, performance profiles, and configurations. By automating these processes, we eliminate the potential for human errors, ensure the ability to replicate the process, and significantly reduce the time required for comprehensive testing. The use of Ansible playbooks facilitates the efficient and scalable deployment, configuration, and execution of performance tests, enabling comprehensive and standardized benchmarking across various environments. The set of Ansible playbooks, which we will make available online, has a significant real-world impact by providing DevOps teams with robust and reliable tools for consistently monitoring and enhancing network performance. This, in turn, enhances the stability and efficiency of cloud-native applications, accelerates the development cycle, and ensures that Kubernetes-based infrastructures can meet the demanding performance requirements of modern applications.

Rectal cancer presents a significant burden globally, often requiring multimodal therapy for locally advanced cases. Long-course chemoradiotherapy (LCRT) and short-course radiotherapy (SCRT) followed by surgery have been conventional neoadjuvant approaches. Recent trials favor LCRT due to improved local control. However, tumor distant recurrence remains a concern, prompting exploration of Total Neoadjuvant Therapy (TNT) as a comprehensive treatment strat-egy. Immune checkpoint inhibitors (ICIs) show promise, particularly in mismatch repair-deficient (dMMR) or microsatellite instability-high (MSI-H) tumors, potentially revolutionizing neoadjuvant regimens. Non-operative management (NOM) represents a viable alternative post-neoadjuvant therapy for selected patients achieving clinical complete response (cCR). Additionally, monitoring minimal residual disease (MRD) using circulating tumor DNA (ctDNA) emerges as a non-invasive method for treatment response assessment. This review synthesizes current evidence on TNT, ICIs, NOM and ctDNA, elucidating their implications for rectal cancer management and highlighting avenues for future research and clinical application.

The insufficient of fish meal has been become a prominent problem with the development of the aquaculture industry. Therefore, it is great significance that replacement fish meal using other protein sources for the sustainable development of aquaculture industry. In this study, fermented cottonseed meal (FCM) and fermented rapeseed meal (FRM) were used to replace fish meal in Chinese mitten crab. The ratio of FCM and FRM were set according to 1:2, 1:1, and 2:1, and were supplemented into 25% fish meal (FM-25) and 15% fish meal (FM-15) diets, respectively, to formulated 6 kinds of experimental diets. The feeding duration was 8 weeks. The results showed that the weight gain (WG) and specific growth rate (SGR) of crabs fed the diets containing 25% fish meal were significantly higher than crabs fed 15% fish meal diets. At 25% fish meal level, the WG and SGR of crabs fed the diet with 1FCM:1FRM were significantly higher than those fed the 1FCM:2FRM and 2FCM:1FRM diets. At 25% fish meal level, the crude lipid (CL) content of crabs fed the diet with 1FCM:2FRM were significantly higher than those fed the 2FCM:1FRM and 1FCM:1FRM. At 25% fish meal level, the activities of glutathione peroxidase (GPX) and peroxidase (POD) of crabs fed the diet with 1FCM:2FRM were significantly higher than those fed the 2FCM:1FRM and 1FCM:1FRM. At 25% fish meal level, the relative expression of peroxiredoxin (PRX) of crabs fed the diet with 1FCM:1FRM were significantly higher than those fed the 2FCM:1FRM and 1FCM:2FRM. In summary, diets supplemented with a mixture of fermented cotton meal and fermented rapeseed meal (1:1) can significantly improve the growth and antioxidant capacity of Chinese mitten crab.

Fusarium wilt (FW) caused by Fusarium oxysporum f. sp. Cucumerinum (Foc) of cucumber is a destructive soil-borne disease in cucumber (Cucumis sativus.L). There is limited knowledge on molecular mechanisms to FW resistance-mediated defense responses in cucumber. In this study, metabolome and transcriptome profiling were carried out in two FW, resistant (NR) and susceptible (NS), near isogenic lines (NILs) before and after Foc inoculation. The NILs showed consistent and stable FW resistances in multiple greenhouse and laboratory screening tests. Widely targeted metabolomic analysis identified differentially accumulated metabolites (DMAs) with significantly more accumulation in NR in response to Foc infection including many phenolic acid and flavonoid compounds from the flavonoid biosynthesis pathway. Transcriptome analysis identified differentially expressed genes (DEGs) between the NILs upon Foc inoculation including genes for secondary metabolite biosynthesis in or transcription factor genes regulating the flavonoid biosynthesis pathway. Joint analysis of the metabolomic and transcriptomic data identified DAMs and DEGs closely associated with biosynthesis of phenolic acid and flavonoid DAMs. The association of these compounds with NR-conferred FW resistance was exemplified by in vivo assays in which two phenolic acid compounds, bis (2-ethylhexyl) phthalate and diisooctyl phthalate, and the flavonoid compound gallocatechin 3-O-gallate were shown to have significant inhibitory effect on Foc growth. The antifungal effect of three compounds seems to be a novel finding. We conclude that phenolic acids and flavonoids play an important role in NR mediated FW resistance in cucumber.

Human fungal pathogen Candida albicans causes oral infectious diseases such as oral thrush in immunocompromised patients with hyposalivation, diabetes mellitus, prolonged use of anti-biotics or immunosuppressive medicines and poor oral hygiene. An anti-fungal medication classified as a BCS class II drug, Clotrimazole has high permeability and low solubility in water. It is commercially available as lozenges, which do not uniformly distribute the drug within saliva for local therapy. This results in higher dose frequency and lower patient compliance. Therefore, in this study we proposed a niosomal based subgingival film formulation of clotrimazole for enhancing drug efficacy by improving drug solubilization capacity and improving patient compliance by prolonging the release of drug at the targeted site and reducing the dose frequency to show effective anti-fungal activity. The prepared niosomal film showed good entrapment efficiency and anti-fungal activity. Niosomal film showed better release pattern than drug loaded film.

Turbidity is an important indicator of water quality in hydrology. More traditional ways to monitor turbidity can provide reliable results. However, they are prone to human error, have elevated costs, and lack real-time monitoring capacity. Addressing these hindrances, in this work we combine spectral bands and indices from Sentinel-2 with several machine learning paradigms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations encompassing the Mississippi River, USA. Results show that considering the individual monitoring stations, the ML paradigms for turbidity modeling were satisfactory at locations with a larger range and standard deviation values, achieving a mean R2 value of 59.5%. Tree-based models were the best overall approach, often ranking as the best or second-best performing model. When all the samples from the monitoring stations were used, the XGBoost provided superior output for turbidity modeling, reaching an R2 equal to 75.7%. A comprehensive comparison with the literature found values showed that the models implemented using this study’s methodology could provide competitive results, deeming it as an alternative for turbidity modeling from remote sensing data.

Abstract: Chronic Obstructive Pulmonary Disease (COPD) is a continually advancing respiratory disorder, characterized by enduring airflow limitation along with long-lasting inflammation in the airways. An expanding pool of evidence is emphasizing how mitochondrial dysfunction directly impacts the development and progression of COPD. This comprehensive review delves deeper into the intricate cellular and molecular biology of mitochondria within the framework of COPD, spotlighting significant alterations in structure and function that accompany mitochondrial dysfunction. In this narrative, we delve into the observed changes in mitochondrial shape, behavior, and respiratory chain complex alterations seen in individuals afflicted with COPD. The discussion includes implications on cellular signaling pathways and how this affects the natural process of programmed cellular death (apoptosis) and cellular aging, particularly in reference to COPD. Additionally, we consolidate ongoing therapeutic strategies, honing in on the management of COPD through a focus on mitochondrial dysfunction. Prominent among these strategies is the potential role of antioxidants and mitochondrial biogenesis inducers, which are emerging as promising fields of intervention. Furthermore, we embark on an overview of the possible use of mitochondrial biomarkers as innovative tools in diagnosing COPD, evaluating disease progression, and monitoring the efficacy of COPD treatments. As we unravel the rather complex interplay between mitochondrial biology and COPD, this review highlights the critical importance of formulating treatments that target mitochondrial dysfunction. The ultimate goal of these strategic interventions is to slow down the rate of COPD progression and significantly improve patient outcomes. Lastly, though our understanding of the nexus between COPD and mitochondrial dysfunction has significantly enhanced, ample scope remains for further investigation. Improved insights into the mechanisms of mitochondrial dysfunction, discovered biomarkers, and targeted therapies could encompass a revolutionary step towards comprehensive disease management. The future of COPD treatment seems to lean towards a combination of strategies, prioritizing not only the management of symptoms but also focused on preserving and restoring lung function, improving physical capacity, and enhancing quality of life for COPD patients.

Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.

In this research, new setting is introduced for new SuperHyperNotion, namely, 11 Neutrosophic 1-failed SuperHyperForcing. Two different types of SuperHyperDefinitions 12 are debut for them but the research goes further and the SuperHyperNotion, 13 SuperHyperUniform, and SuperHyperClass based on that are well-defined and 14 well-reviewed. The literature review is implemented in the whole of this research. For 15 shining the elegancy and the significancy of this research, the comparison between this 16 SuperHyperNotion with other SuperHyperNotions and fundamental 17 SuperHyperNumbers are featured. The definitions are followed by the examples and the 18 instances thus the clarifications are driven with different tools. The applications are 19 figured out to make sense about the theoretical aspect of this ongoing research. The 20 “Cancer’s Neutrosophic Recognition” are the under research to figure out the challenges 21 make sense about ongoing and upcoming research. The special case is up. The cells are 22 viewed in the deemed ways. There are different types of them. Some of them are 23 individuals and some of them are well-modeled by the group of cells. These types are all 24 officially called “SuperHyperVertex” but the relations amid them all officially called 25 “SuperHyperEdge”. The frameworks “SuperHyperGraph” and “neutrosophic 26 SuperHyperGraph” are chosen and elected to research about “Cancer’s Neutrosophic 27 Recognition”. Thus these complex and dense SuperHyperModels open up some avenues 28 to research on theoretical segments and “Cancer’s Neutrosophic Recognition”. Some 29 avenues are posed to pursue this research. It’s also officially collected in the form of 30 some questions and some problems. Assume a SuperHyperGraph. Then a “1-failed 31 SuperHyperForcing” Z(NSHG) for a neutrosophic SuperHyperGraph NSHG : (V,E) 32 is the maximum cardinality of a SuperHyperSet S of black SuperHyperVertices 33 (whereas SuperHyperVertices in V (G) \ S are colored white) such that V (G) isn’t 34 turned black after finitely many applications of “the color-change rule”: a white 35 SuperHyperVertex is converted to a black SuperHyperVertex if it is the only white 36 SuperHyperNeighbor of a black SuperHyperVertex. The additional condition is referred 37 by “1-” about the usage of any black SuperHyperVertex only once to act on white 38 SuperHyperVertex to be black SuperHyperVertex; a “neutrosophic 1-failed 39 SuperHyperForcing” Zn(NSHG) for a neutrosophic SuperHyperGraph NSHG : (V,E) 40 is the maximum neutrosophic cardinality of a SuperHyperSet S of black 41 SuperHyperVertices (whereas SuperHyperVertices in V (G) \ S are colored white) such 42 that V (G) isn’t turned black after finitely many applications of “the color-change rule”: 43 a white SuperHyperVertex is converted to a black SuperHyperVertex if it is the only 44 1/128 white SuperHyperNeighbor of a black SuperHyperVertex. The additional condition is 45 referred by “1-” about the usage of any black SuperHyperVertex only once to act on 46 white SuperHyperVertex to be black SuperHyperVertex. Assume a SuperHyperGraph. 47 Then an “δ−1-failed SuperHyperForcing” is a maximal 1-failed SuperHyperForcing of 48 SuperHyperVertices with maximum cardinality such that either of the following 49 expressions hold for the (neutrosophic) cardinalities of SuperHyperNeighbors of s ∈ S : 50 |S ∩N(s)| > |S ∩(V \N(s))|+δ, |S ∩N(s)| < |S ∩(V \N(s))|+δ. The first Expression, 51 holds if S is an “δ−SuperHyperOffensive”. And the second Expression, holds if S is an 52 “δ−SuperHyperDefensive”; a“neutrosophic δ−1-failed SuperHyperForcing” is a maximal 53 neutrosophic 1-failed SuperHyperForcing of SuperHyperVertices with maximum 54 neutrosophic cardinality such that either of the following expressions hold for the 55 neutrosophic cardinalities of SuperHyperNeighbors of s ∈ S : |S ∩ N(s)|neutrosophic > 56 |S ∩ (V \ N (s))|neutrosophic + δ, |S ∩ N (s)|neutrosophic < |S ∩ (V \ N (s))|neutrosophic + δ. 57 The first Expression, holds if S is a “neutrosophic δ−SuperHyperOffensive”. And the 58 second Expression, holds if S is a “neutrosophic δ−SuperHyperDefensive”. It’s useful to 59 define “neutrosophic” version of 1-failed SuperHyperForcing. Since there’s more ways to 60 get type-results to make 1-failed SuperHyperForcing more understandable. For the sake 61 of having neutrosophic 1-failed SuperHyperForcing, there’s a need to “redefine” the 62 notion of “1-failed SuperHyperForcing”. The SuperHyperVertices and the 63 SuperHyperEdges are assigned by the labels from the letters of the alphabets. In this 64 procedure, there’s the usage of the position of labels to assign to the values. Assume a 65 1-failed SuperHyperForcing. It’s redefined neutrosophic 1-failed SuperHyperForcing if 66 the mentioned Table holds, concerning, “The Values of Vertices, SuperVertices, Edges, 67 HyperEdges, and SuperHyperEdges Belong to The Neutrosophic SuperHyperGraph” 68 with the key points, “The Values of The Vertices & The Number of Position in 69 Alphabet”, “The Values of The SuperVertices&The maximum Values of Its Vertices”, 70 “The Values of The Edges&The maximum Values of Its Vertices”, “The Values of The 71 HyperEdges&The maximum Values of Its Vertices”, “The Values of The 72 SuperHyperEdges&The maximum Values of Its Endpoints”. To get structural examples 73 and instances, I’m going to introduce the next SuperHyperClass of SuperHyperGraph 74 based on 1-failed SuperHyperForcing. It’s the main. It’ll be disciplinary to have the 75 foundation of previous definition in the kind of SuperHyperClass. If there’s a need to 76 have all SuperHyperConnectivities until the 1-failed SuperHyperForcing, then it’s 77 officially called “1-failed SuperHyperForcing” but otherwise, it isn’t 1-failed 78 SuperHyperForcing. There are some instances about the clarifications for the main 79 definition titled “1-failed SuperHyperForcing”. These two examples get more scrutiny 80 and discernment since there are characterized in the disciplinary ways of the 81 SuperHyperClass based on 1-failed SuperHyperForcing. For the sake of having 82 neutrosophic 1-failed SuperHyperForcing, there’s a need to “redefine” the notion of 83 “neutrosophic 1-failed SuperHyperForcing” and “neutrosophic 1-failed 84 SuperHyperForcing”. The SuperHyperVertices and the SuperHyperEdges are assigned 85 by the labels from the letters of the alphabets. In this procedure, there’s the usage of 86 the position of labels to assign to the values. Assume a neutrosophic SuperHyperGraph. 87 It’s redefined “neutrosophic SuperHyperGraph” if the intended Table holds. And 88 1-failed SuperHyperForcing are redefined “neutrosophic 1-failed SuperHyperForcing” if 89 the intended Table holds. It’s useful to define “neutrosophic” version of 90 SuperHyperClasses. Since there’s more ways to get neutrosophic type-results to make 91 neutrosophic 1-failed SuperHyperForcing more understandable. Assume a neutrosophic 92 SuperHyperGraph. There are some neutrosophic SuperHyperClasses if the intended 93 Table holds. Thus SuperHyperPath, SuperHyperCycle, SuperHyperStar, 94 SuperHyperBipartite, SuperHyperMultiPartite, and SuperHyperWheel, are 95 “neutrosophic SuperHyperPath”, “neutrosophic SuperHyperCycle”, “neutrosophic 96 SuperHyperStar”, “neutrosophic SuperHyperBipartite”, “neutrosophic 97 2/128 SuperHyperMultiPartite”, and “neutrosophic SuperHyperWheel” if the intended Table 98 holds. A SuperHyperGraph has “neutrosophic 1-failed SuperHyperForcing” where it’s 99 the strongest [the maximum neutrosophic value from all 1-failed SuperHyperForcing 100 amid the maximum value amid all SuperHyperVertices from a 1-failed 101 SuperHyperForcing.] 1-failed SuperHyperForcing. A graph is SuperHyperUniform if it’s 102 SuperHyperGraph and the number of elements of SuperHyperEdges are the same. 103 Assume a neutrosophic SuperHyperGraph. There are some SuperHyperClasses as 104 follows. It’s SuperHyperPath if it’s only one SuperVertex as intersection amid two given 105 SuperHyperEdges with two exceptions; it’s SuperHyperCycle if it’s only one 106 SuperVertex as intersection amid two given SuperHyperEdges; it’s SuperHyperStar it’s 107 only one SuperVertex as intersection amid all SuperHyperEdges; it’s 108 SuperHyperBipartite it’s only one SuperVertex as intersection amid two given 109 SuperHyperEdges and these SuperVertices, forming two separate sets, has no 110 SuperHyperEdge in common; it’s SuperHyperMultiPartite it’s only one SuperVertex as 111 intersection amid two given SuperHyperEdges and these SuperVertices, forming multi 112 separate sets, has no SuperHyperEdge in common; it’s SuperHyperWheel if it’s only one 113 SuperVertex as intersection amid two given SuperHyperEdges and one SuperVertex has 114 one SuperHyperEdge with any common SuperVertex. The SuperHyperModel proposes 115 the specific designs and the specific architectures. The SuperHyperModel is officially 116 called “SuperHyperGraph” and “Neutrosophic SuperHyperGraph”. In this 117 SuperHyperModel, The “specific” cells and “specific group” of cells are 118 SuperHyperModeled as “SuperHyperVertices” and the common and intended properties 119 between “specific” cells and “specific group” of cells are SuperHyperModeled as 120 “SuperHyperEdges”. Sometimes, it’s useful to have some degrees of determinacy, 121 indeterminacy, and neutrality to have more precise SuperHyperModel which in this case 122 the SuperHyperModel is called “neutrosophic”. In the future research, the foundation 123 will be based on the “Cancer’s Neutrosophic Recognition” and the results and the 124 definitions will be introduced in redeemed ways. The neutrosophic recognition of the 125 cancer in the long-term function. The specific region has been assigned by the model 126 [it’s called SuperHyperGraph] and the long cycle of the move from the cancer is 127 identified by this research. Sometimes the move of the cancer hasn’t be easily identified 128 since there are some determinacy, indeterminacy and neutrality about the moves and 129 the effects of the cancer on that region; this event leads us to choose another model [it’s 130 said to be neutrosophic SuperHyperGraph] to have convenient perception on what’s 131 happened and what’s done. There are some specific models, which are well-known and 132 they’ve got the names, and some SuperHyperGeneral SuperHyperModels. The moves 133 and the traces of the cancer on the complex tracks and between complicated groups of 134 cells could be fantasized by a neutrosophic SuperHyperPath(-/SuperHyperCycle, 135 SuperHyperStar, SuperHyperBipartite, SuperHyperMultipartite, SuperHyperWheel). 136 The aim is to find either the longest 1-failed SuperHyperForcing or the strongest 137 1-failed SuperHyperForcing in those neutrosophic SuperHyperModels. For the longest 138 1-failed SuperHyperForcing, called 1-failed SuperHyperForcing, and the strongest 139 SuperHyperCycle, called neutrosophic 1-failed SuperHyperForcing, some general results 140 are introduced. Beyond that in SuperHyperStar, all possible SuperHyperPaths have 141 only two SuperHyperEdges but it’s not enough since it’s essential to have at least three 142 SuperHyperEdges to form any style of a SuperHyperCycle. There isn’t any formation of 143 any SuperHyperCycle but literarily, it’s the deformation of any SuperHyperCycle. It, 144 literarily, deforms and it doesn’t form. A basic familiarity with SuperHyperGraph 145 theory and neutrosophic SuperHyperGraph theory are proposed.

Silicon (Si) is the major material of semiconductor element. For semiconductor element producing, Si need to refined into high-purity silicon crystals, which has the approximate characteristics of intrinsic semiconductor, then doping 13th group or 15th group trace element. Producing the high-purity silicon crystals with well-lattice structure need a lot of energy and highly cost. Besides, the high-purity silicon is very hard and fragile which limits the scope of application. For this reason, this study added low-grade non-complete lattice silicon in the elastic polymer, and then doped with high concentrations of fine carbon powder. This material can be conductive by physisorption;therefore, it can be used to making semiconductors. Using this material to produce electronic components can significantly reduce the cost of purification and crystallization, saving energy at the same time. Moreover, because this elastic polymer materials containing silicon, which has soft, warm, and durable physical properties. With those properties, the design of the device could be more flex, full scalability and close to the skin.

Falls are extremely damaging to the elderly. The number of elderly who have experienced falls has increased over the years, several of the elderly stay alone or in in badly maintained elderly homes. This makes a low-cost fall detection system a necessity. There has been huge improvements in terms of IoT systems, ML algorithms. Varied data sets have been collected across the world for fall detection. These data sets have a very little in common among them, in terms of user demographics, sensors used, the ADL and Fall activities Hence in this paper we present a data set that has wide user demographics, we used various sensors – such as accelerometer, gyroscope, magnetometer and hear rate. We used wrist worn sensors to collect data. In this paper we present a detailed analysis of the data set we collected using common ML algorithms such as – Naïve Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF) and Support Vector Machines (SVM). We analyzed the performance of these algorithms for variations in accuracy with respect to age, gender, height, weight and health issues and we have identified outliers by analyzing each incorrect prediction. This paper provides the complete details of the data collection methodology, The methods used for analysis and presents the results of analysis in complete detail.

Climate change mainly affects production and consumption systems, such as: food, livelihoods, production (e.g., reduced milk production), water, and land use. The role of local knowledge has been recognized as important for decision-making under changing circumstances. This study was conducted in the northern part of the Ecuadorian Andes using a sample of 170 dairy-cattle-producing households. The objectives were: i) to characterize the rural livelihoods of dairy cattle farmers, ii) to evaluate access to climate information and perceptions of climate change, and iii) to determine the relationship between livelihoods and perceptions of climate change. Significant differences were identified between the groups evaluated in relation to the dairy farmers’ livelihoods. In addition, 85.29% of the respondents mentioned that climate information is important, but 67.83% do not trust the sources of information. It was found that there is a significant relationship between the level of education and age with the variables of climate change perceptions. This combined knowledge allows people to promote agri-environmental and educational policies to achieve climate literacy at a rural level.

This study unveils verapamil's compelling cytoprotective and proliferative effects on pancreatic β-cells amidst diabetic stressors, spotlighting its unforeseen role in augmenting cholecystokinin (CCK) expression. Through rigorous investigations employing MIN6 β-cells and zebrafish models under type 1 and type 2 diabetic conditions, we demonstrate verapamil's capacity to significantly boost β-cell proliferation, enhance glucose-stimulated insulin secretion, and fortify cellular resil-ience. A pivotal revelation of our research is verapamil's induction of CCK, a peptide hormone known for its role in nutrient digestion and insulin secretion, which signifies a novel pathway through which verapamil exerts its therapeutic effects. Furthermore, our mechanistic insights reveal that verapamil orchestrates a broad spectrum of gene and protein expressions pivotal for β-cell survival and adaptation to immune-metabolic challenges. In vivo validation in a zebrafish larvae model confirms verapamil's efficacy in fostering β-cell recovery post-metronidazole inflic-tion. Collectively, our findings advocate for verapamil's reevaluation as a multifaceted agent in diabetes therapy, highlighting its novel function in CCK upregulation alongside enhancing β-cell proliferation, glucose sensing, and oxidative respiration. This research enriches the therapeutic landscape, proposing verapamil not only as a cytoprotector but also as a promoter of β-cell re-generation, thereby offering fresh avenues for diabetes management strategies aimed at pre-serving and augmenting β-cell functionality.

Cereal wastes such as rice bran and cereal dust are valuable yet underutilised by-products of grain processing. This study aimed to bio-convert these wastes into bacterial cellulose (BC), an emerging sustainable and renewable biomaterial, via an inexpensive solid-state fermentation (SSF) pre-treatment using three mould isolates. Medium substitution by directly using untreated rice bran or cereal dust did not significantly increase the yield of bacterial cellulose produced by a novel Novacetimonas sp. isolate (NCBI accession number PP421219) compared to the standard Hestrin-Schramm (HS) medium. In contrast, rice bran fermented with Rhizopus oligosporus yielded the highest bacterial cellulose (1.55 ± 0.6 g/L dry weight) compared to the untreated control (0.45 ± 0.1 g/L dry weight), demonstrating up to 22 % increase in yield. Using the SSF process, the media production costs were reduced by up to 90% compared to standard HS medium. Physicochemical characterisation using SEM, EDS, FTIR, XPS, XRD, and TGA were performed to gain insights into the internal structure, morphology, and chemical bonding of different produced BC, which revealed comparable biopolymer properties between BC produced in standard and waste-based media. Hence, our findings demonstrate the effectiveness of fungal SSF for transforming abundant cereal waste into BC, providing a circular economy solution to reduce waste and convert them into by-products to enhance the sustainability of the cereal industry.

The rapid retreat of coastal cliffs poses a profound risk to property, transport infrastructure and public safety both in Ireland and globally. This study combines historical data (maps and orthophotos, 1842 to 2000) with contemporary UAV surveys (2019 to 2023) to quantify cliff top and cliff face retreat along a 250 m wide coastal drumlin at Silverstrand in Galway Bay, Ireland. Retreat rates for the cliff top and cliff face were calculated using 2D mapping and 3D modelling, respectively. Critically, the choice of method appears to have a major impact on apparent rates of cliff top retreat, with output from the 2D mapping approach (0.14 +/- 0.02 m yr-1) being double that of the 3D modelling approach (0.08 +/- 0.02 m year-1). The aperiodic development of a talus cone, which offers temporary protection to the cliff from storm waves, also influences the estimation of cliff retreat. The repeat cycles of talus slope formation and removal in this high wave energy climate suggest that the drumlin transitions between a periodically transport-limited system and supply limited system over short- and long-time periods, respectively, on the continuum of cliff types. These results warrant further research to identify and quantify the rates, patterns, drivers (marine and subaerial processes), and timing of cliff retreat rates in response to climate change.

One of the most prevalent malignancies in males is prostate cancer, which most often metastasizes to the liver, thorax, bone, and local lymph nodes. The current case describes development of cervical lymphadenopathy; which remains a rather rare initial presentation, seen in <0.1% of the patients diagnosed with prostatic carcinoma. The current report describes a 55-year-old man with complaints of lower back pain and swelling on the left side of his neck for the previous two months. Physical examination revealed several swollen cervical lymph nodes that measured 2 cm, were nontender, firm, and adhered to the skin above. A poorly differentiated adenocarcinoma was found during cytological examination of the lymph nodes in the left lower cervical region. Findings from the ensuing Computed Tomography scan pointed to the possibility of spread prostatic cancer. PSA values were determined to be 364.762 ng/ml after additional analysis. The patient was then planned for hormone ablation therapy as per Institute protocol, in view of metastatic prostate cancer. The current report highlights the importance of timely detection of malignancies, particularly those such as Prostate cancer, that are slow growing and have well-established screening tools (such as PSA assays). Involvement of the cervical lymph nodes in prostate cancer, particularly for older individuals, is nearly always linked to extensive metastatic disease. As a result, it is a poor prognostic factor for prostate cancer patients, whose survival rates are markedly lower. Although hormone therapy has advanced significantly as the cornerstone of care for metastatic prostate cancer, the prognosis for these cases is still quite poor, especially when there has been distant lymph node metastases.

Antimicrobial peptides (AMPs) are promising tools for combating microbial resistance. However, their therapeutic potential is hindered by two intrinsic drawbacks – low target affinity and poor in vivo stability. Macrocyclization, a process that improves the pharmacological properties and bioactivity of peptides, can address these limitations. As a result, macrocyclic peptides represent attractive drug candidates. Moreover, many drugs are macrocycles that originated from natural product scaffolds, suggesting that nature offers solutions to the challenges faced by AMPs. In this review, we explore natural cyclic peptides from the DBAASP database. DBAASP is a comprehensive repository of data on antimicrobial/cytotoxic activities and structures of peptides. We analyze the data on small (≤25 AA) ribosomal and non-ribosomal cyclic peptides from DBAASP according to their amino acid composition, bonds used for cyclization, targets they act on, and mechanisms of action. This analysis will enhance our understanding of the small cyclic peptides that nature has provided to defend living organisms.

As the world attempts to decarbonise the food industry and limit greenhouse gas (GHG) emissions, plant-based meat analogues (PBMAs) have emerged as a sustainable alternative to traditional meat. This study implemented a life cycle assessment (LCA) to rigorously compare the environmental impacts of a beef burger (BB), produced with British beef, against a meat analogue (MA) equivalent with a publicly available recipe. For the LCA, a cradle-to-fork system boundary was utilised, alongside a functional unit of a single 100 g burger patty. Results from the LCA revealed that the BB patty had more than double the total environmental impact than the MA equivalent, as well as possessing a global warming impact that was 62% higher. A simple extrapolation illustrated that if the UK population switched from beef to meat analogue patties, 3 million tonnes of CO2e could be saved annually, corresponding to 0.74% of the country’s yearly territorial GHG emissions. Scenario analyses displayed how the environmental impact of the MA patty remained stable regardless of changes in exportation, ingredient origin or soy protein sourcing. Moreover, sensitivity analysis conducted with an alternative characterisation method corroborated initial findings, whilst uncertainty analysis ensured that nearly all conclusions generated from the original comparison were robust. Future studies should conduct peer-reviewed LCAs on PBMA patties with commercial recipes based on other plant-based proteins, as well as fully understanding any potential health implications of long-term PBMA consumption.

Microscale laser dynamic forming, as a novel high-speed microforming technique, can overcome the shortcomings of traditional microforming methods. However, in practical applications, laser dynamic microforming technology is often affected by the rebound behavior of the workpiece surface, limiting the further improvement of processing quality and efficiency. This paper aims to reduce the rebound effect in laser dynamic microforming by using multi-pulse laser shock loading. The forming results of workpieces under different laser energies and laser impact numbers were studied using experimental and numerical simulation methods. Numerical simulations of the forming results after multiple laser shocks were conducted using ANSYS/LS-DYNA software. These numerical simulation results were then experimentally validated and compared. Surface morphology and microstructure of the workpieces were characterized using a confocal microscope and scanning electron microscope, and energy dispersive spectroscopy (EDS) was used to analyze the chemical element content changes in the collision regions at the bottoms of the workpieces after multi-pulse loading, revealing the collision behavior patterns during the forming process. Finally, the forming laws of workpieces under multiple laser shocks were summarized.

The COVID 19 pandemic saw the emergence of various Variants of Concern (VOCs) that took the world by storm, often replacing the ones that preceded them. The characteristic mutant constellations of these VOCs increased viral transmissibility and infectivity. Their origin and evolution remain puzzling. With the help of data mining efforts and the GISAID database, a chronology of 22 haplotypes described viral evolution up until July 23, 2023. Since the 3-dimensional atomic structures of proteins corresponding to the identified haplotypes are not available, ab initio methods were here utilized. Regions of intrinsic disorder proved to be important for viral evolution, as evidenced by the targeted change to the nucleocapsid (N) protein at the sequence, structure, and biochemical levels. The linker region of the N-protein, which binds to the RNA genome and self-oligomerizes for efficient genome packaging, was greatly impacted by mutations throughout the pandemic, followed by changes in structure and intrinsic disorder. Remarkably, VOC constellations acted cooperatively to balance the more extreme effects of individual haplotypes. Our strategy of mapping the dynamic evolutionary landscape of genetically linked mutations to the N-protein structure demonstrates the utility of ab initio modeling and deep learning tools for therapeutic intervention.

Microscopic and ultra-microscopic vascular sutures are indispensable in the surgical procedures such as arm transplantation and finger reattachment. The state of the blood vessels after suturing, such as vascular patency, narrow, and blocked, determines the success rate of the operation. If we can grasp the golden window period after blood vessel suture and before muscle tissue suture to achieve accurate and objective assessment of blood vessel status, it can not only reduce medical costs but also improve social benefits. Doppler optical coherence tomography enables high-speed, high-resolution imaging of biological tissues, especially microscopic and ultra-microscopic blood vessels. Using Doppler optical coherence tomography to image the sutured blood vessels, not only the three-dimensional structure of the blood vessels, but also the blood flow information can be obtained. By extracting the contour of blood vessel wall and the contour of blood flow area, the three-dimensional shape of blood vessel can be reconstructed in three dimensions, which provides parameter support for the assessment of blood vessel status. In this work, we propose a neural network-based multi-classification deep learning model that can simultaneously and automatically extract blood vessel boundaries from Doppler OCT vessel intensity images and the contours of blood flow regions from corresponding Doppler OCT vessel phase images. Compared with the traditional random walk segmentation algorithm and cascade neural network method, the proposed model has better performance. This method is easier to realize system integration and has great potential for clinical evaluation. It is expected to be applied to the evaluation of microscopic and ultra-microscopic vascular status in microvascular anastomosis.

Marine oil spills seriously threaten the coastal environment, challenge the safety of marine eco-system, and cause huge economic losses to marine fishery, aquaculture, and tourism industries. Timely and accurate oil spill monitoring and early warning can not only make the oil spill acci-dent be handled timely and effectively but also provide key information for investigating the re-sponsibility of the polluters. Polarimetric synthetic aperture radar (SAR) has shown its great po-tential in oil spill classification for its capacity to distinguish mineral oil and biogenic look-alikes. Traditional oil spill classification approaches rely on the scattering matrix elements or pre-designed scattering mechanism-based polarimetric SAR features, which limits its robustness and generalization in large-scale marine surveillance tasks. The recent development of deep learning frameworks, especially the convolutional neural network (CNN) provides a powerful tool for remote sensing image classification. However, conventional CNN only accepts re-al-valued input, which cannot efficiently analyze the polarimetric information carried by com-plex-valued SAR data. In this study, a new framework that employs the complex-valued neural network was proposed for marine oil spill detection. Sophisticated studies were designed to evaluate the contribution of the physical scattering mechanism-based features, complex-valued framework, and convolutional feature extraction structures respectively. Comparative studies were conducted on four study cases using Radarsat-2 and SIR-C fully polarimetric SAR data. The outcome of the proposed study may shed light on the development of an end-to-end marine spill classification system and enlarge its application in practical marine surveillance operations.