Enhancing the management and monitoring of oil and gas processes demands developing precise predictive analytics techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern Machine Learning techniques. Several review articles detail the developments in predictive maintenance and technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sector. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modelling. The review covers forms of Machine Learning techniques used in predictive analytic modelling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, comprising of the model’s categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification or clustering or prediction), the models' input and output parameters, performance metrics, optimal model, and benefits and its drawbacks. Additionally, suggestions for future research directions are provided to raise the potential of the associated knowledge and increase the accuracy of oil and gas predictive analytics models.

Food insecurity is a multidimensional concept adequately challenging not only the complex operationalization effort but also judgments of who is food secure and who is not. This study aimed to assess the level of household food insecurity in the study area. Both probability and non-probability sampling procedures were employed to draw the 367 respondents, key informants, and focus group discussants. The Consolidated Approach for Reportig Indicators (CARI) console model was constructed with four indicators: Food consumption score, Food energy shortfall, Food expenditure share, and Livelihood asset depletion. Descriptive statistics was used to analyze data in the models. The CARI result revealed that 41.1%, 9.3%, 20.4% and 29.2% of households were food secure, marginally food secure, moderately food insecure and severely food insecure, respectively. The overall prevalence showed that 49.6% households were food insecure and 50.4% were food secure. Moreover, female-headed households and households living the Belg and CHV livelihood zones were more food insecure than their counterparts. The study concluded that the problem of food insecurity is pervasive in the study area in general and Belg and CHV livelihood zones, in particular. Therefore, more attention and intervention should be given by policymakers and concerned development partners to alleviate the problem.

Let BT4l denotes a subclass of bounded turning functions connected with a four-leaf-type domain. The goal of the study is to probe into coefficients of |b6|, |b7|, |b8|, the bounds of the logarithmic coefficients and the third-order determinants of H3,1, H3,2, H3,3 for functions in this class.

Public transportation scheduling aims to optimize the allocation of transportation resources, enhance transportation efficiency, and increase passenger satisfaction, which is crucial for building a sustainable urban transportation system. As a complement to public transportation, shared bikes provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, such as peak usage periods in the morning and evening and significant fluctuations in demand across different areas, the optimization of shared bike dispatch can better meet user needs, thereby reducing vehicle vacancy rates and increasing operating revenue. Addressing this issue, this paper designs a comprehensive decision-making approach for spatio-temporal demand prediction and bike dispatch optimization. For demand prediction, we designed a T-GCN based bike demand prediction model. In terms of dispatch optimization, we considered factors such as dispatch capacity, distance restrictions, and dispatch costs, and designed an optimization solution based on genetic algorithms. Finally, this paper validates the approach using shared bike operating data, shows that T-GCN can effectively predict short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper, which combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

Clinical trials, by contributing to the development of diagnostics and to the search for modern, more effective, and safer therapies, have become one of the most important elements of the healthcare system. The aim of this study was to assess Poles' knowledge and beliefs about clinical trials. Methods: The survey was conducted among Poles aged 18 and over with the help of external company, Ariadna, which is an independent research panel. The questionnaire contained 22 questions, of which 13 questions concerned beliefs and attitudes towards clinical trials. Results: One thousand and seventy-nine participants took part in the study (N = 1079). The mean age of respondents was 44.96 years (SD = 16.30). Slightly more women (N = 568, 52.6%) than men (N = 511, 47.4%) took part in the study. Among the respondents, 86.5% (N = 933) were aware of clinical trials. The main sources of information about clinical trials were the media (53.8%) including the Internet (N= 355, 32.9%), TV (N = 175, 16.2%), press (N = 30, 2.8%) and radio (N = 21, 1.9%). 43.2% (N = 466) of respondents reported little knowledge of clinical trials, while more than three quarters (N = 805, 75.2%) said they would like to learn more about clinical trials. Most respondents (N = 879, 81.4%) agreed with the statement that participation in a clinical trial is completely voluntary, and more than half (N = 580, 53.7%) agreed with the statement that hospitals participating in clinical trials provide better healthcare. The statement that the results of clinical trials are made available to the public was disagreed with by 37.2% (N = 402) of participants. Only 30.3% (N = 327) of participants agreed that clinical trials should be conducted with children. Conclusions: Poles rate their knowledge of clinical trials as low and would like to learn more. To reach the largest possible group of Polish patients, parallel educational activities on multiple levels may have the best effect. Poles' knowledge of clinical trials is mainly based on commercial sources. Further studies in different groups, i.e., clinical trial patients and investigators, are needed.

In this study, we aim to evaluate how cyclodextrin molecules affect the hydrophilicity and selec-tivity of PVDF membrane. Herein, we prepared PVDF-PVP(1-x) β-CDx membranes, with x rang-ing from 0 to 1 wt%. The membranes were deeply characterized by means of X-ray diffraction (XRD), attenuated total reflectance infrared (ATR-IR), Raman spectroscopies, Thermal gravimet-ric analysis (TGA), Contact angle measurement (CA), scanning electron microscopy (SEM) and atomic force microscopy (AFM). The study unequivocally demonstrates the effectiveness of the addition of β-CD molecules on membranes' structural, morphological, and dielectric properties. Adding 0.25wt% β-CD enhanced hydrophilicity, with CA decreasing from 72.6° to 56° and roughness decreasing from 81 to 29 nm. The PVDF-PVP(1-x) β-CDx membranes were then fully characterized using electrochemical impedance spectroscopy (EIS) to determine their dielectric properties, which clearly showed that the presence of β-CD induced polarization of the mem-brane. The separation of various dyes with different sizes and charges was utilized. These dyes included methylene blue (MB, positive, 0.504 nm), methyl red (MR, neutral, 0.487 nm), rose Bengal (RB, negative, 0.588 nm), and brilliant blue (BB, negative, 0.798 nm). Based on our re-search, we found that the membrane's selectivity is determined by both exclusion factors (such as size and dielectric properties) and inclusion inside the β-CD cage. We found that using alpha-CD instead of beta-CD led to the disappearance of dye inclusion. These findings strongly sug-gest that β-CD molecules can be a viable solution for dye removal from wastewater.

mRNA-lipid nanoparticle (LNP) medicinal products can be considered a platform technology because the development process is similar for different diseases and conditions, with similar noncoding mRNA sequences and lipid nanoparticles and essentially unchanged manufacturing and analytical methods utilised often for different products. It is critical not to lose the momentum built using the platform approach during the development, regulatory approval and rollout of vaccines for SARS-CoV-2 and its variants. This Review proposes a set of modifications to existing regulatory requirements for mRNA products, based on a platform perspective for quality, manufacturing, preclinical and clinical data. For the first time, we address development and potential regulatory requirements when the mRNA sequences and LNP composition vary in different products as well. In addition, we propose considerations for self-amplifying mRNA, individualised oncology mRNA products and mRNA therapeutics. Providing a predictable development pathway for academic and commercial groups, so they can know in detail what product characterisation and data are required to develop a dossier for regulatory submission, has many potential benefits. These include reduced development and regulatory costs; faster consumer/patient access and more agile development of products in the face of pandemics; and for rare diseases where alternatives may not exist or to increase survival and the quality of life in cancer patients. Therefore, achieving consensus around platform approaches is both urgent and important. This approach with mRNA can be a template for similar platform frameworks for other therapeutics and vaccines to enable more efficient development and regulatory review.

Due to the long-term deformation settlement of foundations, issues such as damage and functional failure of buildings and structures have long been of concern in the engineering field. The creep of soil is one of the primary causes leading to long-term deformation of foundations. In this paper, the consolidation deformation, creep characteristics, and creep model of reconstituted saturated silty clay were studied by isotropic compression test and triaxial shear creep test. The results show that for the isotropic compression test, although the applied load adopts different stages of loading, as long as the final applied confining pressure is the same, the number of stages applied by the confining pressure has little effect on the final isotropic consolidation deformation of the sample and the triaxial undrained shear strength after creep. However, for the triaxial shear creep test, it is found that under the same final deviatoric stress, the final deviatoric strain of the sample is closely related to the number of loading stages of deviatoric stress. It shows that the more loading stages of the sample loaded with the same deviatoric stress, the smaller the final deviatoric strain, and the triaxial undrained shear strength of the sample after creep will increase. In addition, it is reasonable to determine the pore pressure dissipation of the sample to 95%((u0-u0)/u0=95%) as the main consolidation end time (tEOP) of the sample. The isotropic consolidation creep curves and the triaxial shear creep curves show certain nonlinearity. Then the logarithmic model and the hyperbolic model were used to fit the creep curves of the samples. It is found that the hyperbolic model has a better fitting effect than the logarithmic model, but for the triaxial shear creep test, the creep parameters of the sample change greatly. Therefore, studying the creep characteristics of soil under different pre-loading steps is of significant engineering importance for evaluating the long-term deformation of underground structures.

Density functional theory (DFT) calculations of hydrogen adsorption on titanium nitride had previously shown that hydrogen may adsorb on both titanium and nitrogen sites with a moderate adsorption energy. Further, the diffusion barrier was also found to be low. These findings may qualify TiN, a versatile multifunctional material with electronic conductivity, as electrode material for the hydrogen evolution reaction (HER). This was the main impetus of this work which aims to experimentally and theoretically investigate the electrocatalytic properties of TiN-layers that were processed on Ti substrate using reactive ion sputtering. The properties are discussed focusing on the role of oxygen defects introduced during the sputtering process on the HER. Based on DFT calculations, it is shown that these oxygen defects alter the electronic environment of the Ti atoms which entails a low hydrogen adsorption energy in the range of -0.1 eV; this leads to HER performances that match those of Pt-NPs in acidic media. When a few nanometr thick layer of Pd-NPs is sputtered on-top of the TiN-layer, the performance is drastically reduced. This is interpreted in terms of oxygen defects being scavenged by the Pd-NPs near the surface which is thought to reduce the hydrogen adsorption sites.

Abstract: Background: In contrast to conventional definitions, the contemporary conceptualization of adulthood emphasizes psychological characteristics over sociodemographic milestones. Concurrently, there is a growing proposition that individuals' perceptions of adulthood exert a substantial influence on the mental well-being of adolescents and adults. However, empirical examination of this hypothesis has been notably limited to date. Method: This study applied some adulthood markers, and multiple mental health indices (including Well-being, Optimism, Alexithymia, Satisfaction with life, Goldberg's index of mental health, Dark triad, and dimensional personality disorders) to a community sample comprising 1772 individuals in Spain, spanning ages from 16 to 93 years. Results: The findings support the overarching hypothesis, as perceptions of adulthood exhibit significant associations with almost all measured mental health indices, especially with those conforming a factor of Negative emotions. These associations persist even after accounting for age and socioeconomic status, and in alignment with the psychological paradigm of adulthood, they demonstrate stability across different age cohorts. Conclusions: The study establishes that such perceptions of adulthood represent a modifiable factor contributing to positive mental health. The implications of these findings for the formulation of public policies aimed at promoting mental health in the context of adulthood are deliberated, as well as a number of future studies.

Transformation formula under the action of a general linear fractional transformation for generalized Dedekind eta-function has been a subject of intensive study, Rademacher, Dieter, Meyer et al. However, the (Hecke) modular relation structure has not been recognized until the work of Goldstein-de la Torre, and streamlining the classical proofs in the modular relation will reveal the meaning hidden in those works. Our main aim is to elucidate the works of these researchers in the context of modular relations.

This study aims to spatially analyze the distribution characteristics of vacant homes in Asan-si, Chungcheongnam-do, and understand the differences in factors causing vacant homes by type. We use a variety of statistical methods to examine these factors and identify key influencing factors such as the number of vacant stores, changes in overall population, land use complexity, and physical characteristics of land and buildings. The findings reveal that the factors contributing to the occurrence of vacant homes vary significantly by region and property type, highlighting the need for region-specific policies. For instance, in old town areas, small lot ratios and land use complexity are significant, while in rural village, the average number of floors and land use complexity play a major role. This study underscores the diversity of factors influencing vacant homes, suggesting that policies should be tailored to the unique characteristics of different regions to effectively address this issue.

Carbon Capture and Storage (CCS) stands as a pivotal technological stride toward a sustainable future, with the practice of injecting supercritical CO2 into subsurface formations having already been an established practice for enhanced oil recovery operations. The overarching objective of CCS is to protract the operational viability and sustainability of platforms and oilfields, thereby facilitating a seamless transition towards sustainable practices. This study introduces a comprehensive framework for optimizing well placements in CCS operations, employing a derivative-free method known as Bayesian optimization. The development plan is tailored for scenarios featuring aquifers devoid of flow boundaries, incorporating production wells tasked with controlling pressure buildup and injection wells dedicated to CO2 sequestration. Notably, the wells operate under group control, signifying predefined injection and production targets and constraints that must be adhered to throughout the project’s lifespan. As a result, the objective function remains invariant under specific permutations of the well locations. Our investigation delves into the efficacy of Bayesian optimization under the introduced permutation invariance. The results reveal that it demonstrates critical efficiency in handling the optimization task extremely fast. In essence, this study advocates for the efficacy of Bayesian optimization in the context of optimizing well placements for CCS operations, emphasizing its potential as a preferred methodology for enhancing sustainability in the energy sector.

A systematic study has been conducted on barbiturate complexes of all five alkali metals Li–Cs prepared from the metal carbonates or hydroxide in aqueous solution without other potential ligands present, varying the stoichiometric ratio of metal ion to barbituric acid (BAH). Eight polymeric compounds (two each for Na, K, and Rb and one each for Li and Cs) have been characterised by single-crystal X-ray diffraction. All contain some combination of barbiturate anion BA− (necessarily in a 1:1 ratio with the metal cation M+), barbituric acid, and water. All organic species and water molecules are coordinated to the metal centres via oxygen atoms as either terminal or bridging ligands. Coordination numbers range from 4 (for the Li complex) to 8 (for the Cs complex). Extensive hydrogen bonding plays a significant role in all the crystal structures, almost all of which include pairs of N–H…O hydrogen bonds linking BA− and/or BAH components into ribbons extending in one dimension. Factors influencing the structure adopted by each compound include cation size and reaction stoichiometry as well as hydrogen bonding.

Meloidogyne nematodes, commonly known as root-knot nematodes, pose a considerable threat to crop yields, resulting in significant economic losses due to their intricate biology and limited control methods. In line with the European Union's focus on promoting organic farming and soil health to encourage sustainable agricultural practices, this study explores the efficacy of two characterized bacterial communities obtained from compost, namely SynCom1 and SynCom2, against Meloidogyne javanica in tomato plants. Through pot experiments, it is demonstrated that both bacterial communities effectively suppress nematode reproduction and root infections while simultaneously enhancing various growth parameters in tomatoes. These findings underscore the potential of synthetic bacterial communities as promising tools for organic or integrated pest management, thereby supporting sustainable agricultural practices and contributing to improved crop yields.

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of carbon peak and carbon neutrality, green building evaluation system (GBRS) has become a research hotspot in the field of green building. This paper systematically summarizes the current research progress based on GBRS, focuses on the current weight and indicators setting, analyzes it with Cite Space software, and puts forward its future development direction. The results show that the weight setting method of green building evaluation system is determined by multidimensional and multi-method. From the perspective of environment, society and economy, the scope of indicators setting is expanding. The green building evaluation system will play a guiding role in green building design, and the evaluation process will continue to be optimized and developed in the direction of intelligence and automation.

Disk galaxies have a typical dimension of few tens of thousands of light years. It follows from the theory of general relativity that any signal originating from the galactic center will be noticed at the outskirts of the galaxy only tens of thousands of years later. This retardation effect is absent in modelling used to calculate rotation curves throughout the entire galaxy and its external gas. The considerable differences between Newtonian action at a distance predictions and observed velocities are currently removed either by assuming dark matter or by suggesting various modifications to the laws of gravity, MOND being a long standing alternative to Newtonian gravity. In previous papers we have shown that applying general relativity in a rigorous fashion without mindlessly neglecting retardation, one can explain the radial velocities of galactic matter without modifying gravity or adding unseen matter. Moreover, it was shown that dark matter effects as they appear in gravitational lensing, the Tully-Fisher relation, and mass estimations based on the virial theorem could also be explained as retarded-gravity effects. In the original main paper on the subject only one galaxy (M33) was analysed in detail. This was later amended somewhat with a published study of eleven galaxies. Here we give a more comprehensive retardation analysis of 143 galaxies of different types from the SPARC Galaxy collection. We show that in most cases we obtain an excellent fit without postulating dark matter or modifying general relativity.

Keywords: Barriers; Facilitators; Sepsis; Sepsis Six Guidelines; Nurses.

Purpose of review The forest industry has deployed ICTs to improve productivity of forest supply chains. In this sense, digitalization of data flows significantly impacts wood supply chains, from forest to mills, which must react flexibly to market fluctuations of forest products. The goal of this study was to conduct a literature review on data flow implementations in management systems of wood supply chains and to evaluate their applicability in supply chains to analyze the opportunities for improving them in practice. Recent findings We utilized the ScienceDirect database, Scopus and Web of Science in order to document data flows in systems and actual applications. Due to ongoing outsourcing, the wood procurement chains, and the wood supply chains were identified in the forest industry. Several different technologies can be implemented also in wood supply chains to digitalize the data flow depending on the specific needs of organizations. In this regard, digitalization of Big Data causes significant changes in elements of supply models that should be integrated and standardized in the optimization and simulation systems. Summary A modelling guide is suggested for accurate dynamic solution of problems of forest logistics. Then, the opportunities in digitalization for connecting different data flows, technologies, and stakeholders are discussed thoroughly. We conclude that data-driven dynamic optimization, and locally implemented digitalization contributes to wood supply modeling in the establishment of standards for cloud-based platforms at time-related and sequential measures which will ensure successful forest logistics through the planning and the monitoring of wood supply chains.

Transketolase (TKT) is an essential thiamine diphosphate (ThDP)-dependent enzyme of the non-oxidative branch of pentose phosphate pathway, with the glucose-6P flux through the pathway regulated in various medically important conditions. Here, we characterize the brain TKT regulation by acylation in rats with perturbed thiamine-dependent metabolism, known to occur in neurodegenerative diseases. The perturbations are modelled by administration of oxythiamine inhibiting ThDP-dependent enzymes in vivo, or by reduced thiamine availability in the presence of metformin and amprolium, inhibiting intracellular thiamine transporters. Compared to control rats, chronic administration of oxythiamine does not significantly change the modification level of the two detected TKT acetylation sites (K6 and K102), but doubles malonylation of TKT K499, concomitantly decreasing 1.7-fold the level of demalonylase sirtuin 5. The inhibitors of thiamine transporters do not change average levels of TKT acylation or sirtuin 5. TKT structures indicate that the acylated residues are distant from the active sites. The acylations-perturbed electrostatic interactions may be involved in conformational shifts and/or formation of TKT complexes with other proteins or nucleic acids. Acetylation of K102 may affect the active site entrance/exit and subunit interactions. Correlation analysis reveals that the action of oxythiamine is characterized by significant negative correlations of K499 malonylation or K6 acetylation with TKT activity, not observed upon the action of the inhibitors of thiamine transport. However, the transport inhibitors induce significant negative correlations between the TKT activity and K102 acetylation or TKT expression, absent in the oxythiamine group. Thus, perturbations in the ThDP-dependent catalysis or thiamine transport manifest in the insult-specific patterns of the brain TKT malonylation and acetylations.

Perilla frutescens leaves are expected to exhibit antioxidant activity and amyloid-β (Aβ) aggregation inhibitory activity due to the presence of polyphenol-type compounds, etc. These activities fluctuate daily; however, measuring the contents of constituents and functionalities is cumbersome and unsuitable for instantaneous measurements in the field. In this study, we developed a facile method to analyze the activity and content of the components in leaves on-site based on the fluorescence spectra obtained by irradiating the perilla leaves with excitation light. The constituent contents (Chlorophyl (Chl), Total polyphenol content (TPC), Total flavonoid content (TFC), rosmarinic acid (RA) and functional properties DPPH radical scavenging activity, ferric reducing antioxidant power (FRAP), oxygen radical absorbance capacity (ORAC), Aβ aggregation inhibitory activity) were evaluated using established analytical techniques. The correlation between the fluorescence spectrum and the contents and activities was calculated using normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI) analyses. We identified a predictive model with a high coefficient of determination R² ≥ 0.57 for content of the constituents, and R² ≥ 0.49 for functional properties. This allows convenient, simultaneous, and non-destructive monitoring of the constituent contents and the functionalities of perilla leave and a simple harvest-time diagnosis. The model established using this method is simple and suggesting that it can simultaneously infer the constituents and functionalities of perilla leaves.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper better communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as a helpful tool to enhance UAV communication networks. However, due to the high mobility of UAV, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. Simulation results showed that the proposed GRU model effectively and accurately estimates the link quality of the ground users in the RIS-assisted UAV-enabled wireless communication network.

Bacteria’s ability to withstand the detrimental effects of antimicrobials may take the form of resistance or tolerance with the minimum inhibitory concentration (MIC), the mutant prevention concentration (MPC), and the mutant selection window as salient concepts. Isolates exposed for 30 days to different concentrations of selected antibiotics, biocides, and heavy metals, singly and in combination, were additionally exposed to very high (15 × MIC) concentrations of ampicillin., and their tolerance was calculated the time to kill 99.9% of the isolate population (MDK99.9). These values were increased by 30 to 50% in test isolates compared to the untreated control. BAC 10-exposed isolates had the highest tolerance increase, and the tolerance appeared to originate from two sources, i.e., stochastic persistence and genetic-induced persistence, involving multiple genes using diverse mechanisms. The mutant selection window of the isolates to ampicillin, amoxicillin, and oxytetracycline also slightly increased compared to the control, indicating the amplification of persister cells during the 30-day exposure. These findings indicate that bacterial exposure to sub-inhibitory concentrations of environmental chemical stressors may not always result in the development of antimicrobial resistance but could initiate this process by selecting for persisters that could evolve into resistant isolates.

The use of unmanned aerial aircrafts (UAVs) is governed by strict regulatory frameworks that prioritize safety. To guarantee safety, it is necessary to acquire and maintain situational awareness (SA) throughout the operation. Existing Canadian regulations require pilots to operate their aircrafts in visual line-of-sight. Therefore, the task of acquiring and maintaining SA primary falls to the pilots. However, the development of aerial transport is entering a new era with the adoption of a highly dynamic and complex system known as advanced air mobility (AAM), which involves UAV operating autonomously beyond visual line-of-sight. SA must therefore be acquired and maintained primarily by each UAV through specific technologies and procedures. In this paper, we review these technologies and procedures in order to decompose the SA of the UAV in the AAM. We then use the system modeling language to provide a high-level structural and behavioral representation of the AAM as a system having UAV as its main entity. In a case study, we analyze one of the flagships UAVs of our industrial partner. Results show that this UAV doesn’t have all the technologies and methodologies necessary to achieve all the identified SA goals identified for the safety of the AAM. This work is a theoretical framework intended to contribute to the realization of the AAM, and we also expect to impact the future design and utilization of UAVs.

Background: The COVID-19 outbreak has accelerated the huge difference between medical care and disease prevention in Chinese medical institutions. This study aimed to investigated the relationship between the symbiotic units, environments, models, and effects of the integration of medical and disease prevention. Methods: This cross-sectional study involved 762 employees of public hospitals in 11 cities in Zhejiang Province by random stratified sampling. We analyzed the influence paths of elements in the mechanism of integration of medical and disease prevention and the mediating effect of symbiotic models among symbiotic units, symbiotic environments, and effects for on this integration. Results: The path coefficient of symbiotic unit on the symbiosis model was 0.46 (p<.001), the path coefficient of symbiotic environment on symbiosis model was 0.52 (p<.001). The path coefficient of the symbiotic unit and environment was 0.91 (p<.001). The symbiotic models exhibited a partial mediation effect between symbiotic units and the effect of this integration. Sobel test = 3.27, β = 0.152, and the mediating effect accounted for 34.6%. Conclusions: It is suggested that Health policy-makers and public hospital managers should provide sufficient symbiotic units, establish collaborative symbiotic models, and improve the effects of integration of medical and disease prevention in public hospitals.

Bangladesh's railway system mostly uses typical manual railway crossing technique or boom gates through its 2,955.53 km rail route all over the country. The accidents are frequently happening in the railway crossings due to not having obstacle detectable and quickly operating gate systems, and also for fewer safety measures in the railway crossing. Currently, there are very few automatic railway crossing systems (without obstacle detectors) available, however, all of them are dependent on the national power grid without a backup plan for any emergency cases. Bangladesh is still running a bit behind in the power generation of its consumption, hence it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents in the railway crossing points. We design to use two infrared (IR) sensors to operate the railway crossing systems which will be controlled by the Arduino Uno. This newly designed level crossing system will be run with the help of sustainable renewable energy which is cost-effective, eco-friendly, and apply under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated results of several renewable energy sources including a hybrid system and optimized the Levelized Cost of Energy (LCOE), and the payback periods.

This study explores the potential to reach a circular economy for post-consumer recycled polyethylene terephthalate (rPET) packaging and bottles by using it as a distributed recycling for additive manufacturing (DRAM) feedstock. Specifically, rPET is processed using only an open source toolchain with fused particle fabrication (FPF) or fused granular fabrication (FGF) processing. In this study, first the impact of granulation, sifting and heating (and their combination) is quantified on the shape and size distribution of the rPET flakes. Then feeding studies were performed to see if they could be printed through an external feeder or needed to be direct printed with a hopper using two Gigabot X machines, one with extended part cooling and one without. Print settings were optimized based on thermal characterization and for the latter which was shown to print rPET directly from shredded water bottles mechanical testing is performed. The results showed that geometry was important for extended feeding tubes and direct printed using a hopper. Further there is a wide disparity in the physical properties of rPET from water bottles depending on source and the history of the material. Future work is needed to enable water bottles to be used as a widespread DRAM feedstock.

This study was conducted to determine the dynamic effects of dietary crude protein (CP) intake on nitrogen (N) balance, ileal amino acid digestibility, and gene expression levels of digestive enzymes at three stages in pigs. In Experiment 1, 18 growing pigs (average body weight (BW) = 9.5 kg) were randomly assigned to one of three treatments (n = 6/treatment group), including normal (20% CP), low (17% CP), and very low (14% CP) protein intake. In Experiment 2, 18 growing pigs (average BW = 30 kg) were allotted randomly to one of three treatments (n = 6/treatment group), including normal (18% CP), low (15% CP), and very low (12% CP) protein intake. In Experiment 3, 18 growing pigs (average BW = 45 kg) were assigned randomly to one of three treatments (n = 6/treatment group), including normal (16% CP), low (13% CP), and very low (10% CP) protein intake. Growing pigs fed the 14% CP and 17% CP diets had lower final BW (P < 0.05) and average daily gain (ADG) (P < 0.05) compared to pigs fed the 20% CP diet. Reducing the dietary CP level from 20 to 14% decreased urinary N excretion by 52.8% (P < 0.001) in Experiment 1. Reducing the dietary CP level from 18 to 12% decreased urinary N excretion by 55.3% (P < 0.001) and reduced fecal N excretion by 34% (P < 0.05) in Experiment 2. Reducing the dietary CP level from 16 to 10% decreased urinary N excretion by 56.4% (P < 0.001) and fecal N excretion by 47.1% (P < 0.001) in Experiment 3. Pigs fed the very low (14%, 12%, and 10% CP) diets showed higher digestibility for CP (P < 0.05), His (P < 0.05), Ile (P < 0.05), Phe (P < 0.05), Thr (P < 0.05), Trp (P < 0.05), Glu (P < 0.05), and Ser (P < 0.05) compared to pigs fed the normal (20%, 18%, and 16% CP) diets among the three experiments. Pigs fed the very low (14%, 12%, and 10% CP) diets showed higher mRNA levels for chymotrypsin C (P < 0.01 in Experiment 1 and 2; P < 0.05 in Experiment 3) compared to pigs fed the normal (20%, 18%, and 16% CP) diets among the three experiments. These results indicated that a reduction in dietary CP by 6% limited the growth performance of growing pigs, and a reduction of dietary CP by 3% supplemented with essential amino acids could reduce the excretion of N into the environment without affecting weight gain.

This research contributes to the overall debate on education for sustainable development (ESD) by shed- ding lights on the contributing role of formal education to the contemporaneous dynamics of literacy, labor market participation and poverty reduction in Africa, with a focus on Burkina Faso. The study uses a semi-parametric recursive trivariate probit modeling approach, and data from the 2014 National Survey on Household Living Conditions in Burkina Faso. The results show that the embraced systemic approach in this analysis is statistically signicant as shown by the 95% condence intervals on the three correlation coeffcients in the model. Furthermore, education does improve literacy skills, however improved literacy skills in itself does not guaranty active labor market participation in Burkina Faso. Active labor market participation seem to be affected by labor market rates of return, and individual reservation wage (or income). When labor market rate of return is short of high literacy skilled individuals' reservation wage, then the natural response is a choice of inactivity in the labor market, by the later group. Simultaneously however, it is found that active labor market participation leads to poverty reduction; therefore, in addition to new industrial policies for structural transformation of the economy, policy makers in Burkina Faso should consider education and minimum wage reforms to give highly literate household members the incentive to be active in the labor market.

The Supportive and Palliative Indicators Tool (SPICT) was developed for timely identifying patients who may benefit from supportive and palliative care for a better treatment review, care plan discussion, and end-of-life care. Although the SPICT has been validated in different languages and patients living in different settings, it has not been validated in patients receiving home-based medical care (HBMC), or in the context of using traditional Chinese characters. The present study aimed to validate the Taiwan version of SPICT (SPICT-TW) and to measure its ability to predict six-month mortality in patients who received HBMC in Taiwan. Seven HBMC agents (five clinics and two hospitals) participated in this validation study. We recruited 129 patients aged ≥50 years who had been consistently receiving HBMC for &gt; two months. The results revealed that the SPICT-TW demonstrated similar reliability and validity compared to other language versions of SPICT. It may be an appropriate tool for healthcare professionals to timely detect the needs of palliative care in older people who received home healthcare. Furthermore, we found that a combination of four general indicators and one clinical indicator in SPCIT-TW has the best prediction ability at predicting six-month mortality in these HMBC recipients. This multicenter study validated the SPICT-TW among HBMC recipients in Taiwan. SPICT-TW demonstrated high reliability and validity and may be a practical tool for identifying older people at risk of dying within six months who would benefit from palliative care.

Over the past few decades, synthetic petroleum-based packaging materials have increased, and the production of plastics has surpassed all other man-made materials due to their versatility. However, the excessive usage of synthetic packaging materials has led to severe environmental and health-related issues due to their nonbiodegradability and their accumulation in the environment. Therefore, biobased packages are considered alternatives to substitute synthetic petroleum-based packaging material. Furthermore, the choice of packing material in the food industry is a perplexing process as it depends on various factors, such as the type of food product, its sustainability, and environmental conditions. Interestingly, due to proven mechanical, gas and water vapor barrier properties and biological activity, polysaccharide-based bioplastics show the potential to expand the trends in food packaging, including edible films or coatings and intelligent and active food packaging. This review discusses the structure, properties and recent developments in polysaccharide-based bioplastics as green and sustainable food packaging materials.

Evaluation ecosystem service value (ESV) is critical for “lucid waters and lush mountains are invaluable assets”. However, the ESV estimation and spatial-temporal analysis for mountainous soil erosion area is rare, e.g., Changting county, China. In this study, we developed a remote sensing driven mountainous equivalent factor (RS-MEF) method to estimate the ESV of soil erosion area in subtropical mountain. It is a hybrid of a conventional equivalent factor framework and the remote sensing techniques for mountains, and achieves several highlights of spatial adjustment using carbon sink with vegetation canopy, improvement spatial resolution, and removal topographic effect. Using the RS-MEF method, we estimated that the ESV of Changting county was about 15.80 billion CNY in 2010 and 34.83 billion CNY in 2022. Specifically, the ESV per unit area of major soil erosion area (MSEA) in the county was less than that of non-major soil erosion area (n-MSEA), however, the ESV growth rate of MSEA from 2010 to 2022 was faster than that of n-MSEA. Therefore, the ESV gap between two areas was shortened from 28.99% in 2010 to 15.83% in 2022. Topographic gradient effect analysis illustrates that the area with high elevation and small slope is one control focus in the next step. Our study indicates that the ecological restoration in the county has made great achievements in the ESV perspective, with low ESV area shrank evidently in MSEA; and the achieved knowledge of the ESV growth and inherent factors is valuable and instructive for the next soil and water conservation.

ABSTRACT: Background: Virtual Reality (VR) emerges as a promising non-pharmacological intervention for managing symptoms and providing distraction during chemotherapy. This study aims to assess VR's effectiveness on cancer-related symptoms, vital signs, and patients' perception of the chemotherapy in lung cancer patients. Methods: A quasi-experimental study was conducted on 100 patients. Participants were allocated into an intervention group (n = 55), which experienced immersive VR, and a comparison group (n = 45), which received usual care. Data were collected through questionnaires and checklists, including feedback on the VR experience, pain, vital signs, and common cancer symptoms, assessed through the Edmonton Symptom Assessment Scale. Results: VR had a significant impact on reducing the perception of the chemotherapy length. Patients reported high levels of satisfaction and tolerability. No adverse events were observed. VR did not have significant influence on pain intensity and vital signs. The only exceptions were oxygen saturation, where a significant difference (p = 0.02) was reported, and perception of chemotherapy duration. Conclusions: As a non-pharmacological intervention, VR proves beneficial in minimizing the perceived length of chemotherapy session for lung cancer patients, enhancing their overall treatment experience. The intervention showed to be a safe, feasible, and well-accepted distraction technique. Future research should explore VR's potential effects on a wider range of symptoms and evaluate its impact on long-term outcomes.

The extraocular muscles (EOMs) possess unique characteristics that set them apart from other skeletal muscles. These muscles, responsible for eye movements, exhibit remarkable resistance to various muscular dystrophies and aging, presenting a significant contrast to the vulnerability of skeletal muscles to these conditions. In this review, we delve into the cellular and molecular underpinnings of the distinct properties of EOMs. We explore their structural complexity, highlighting differences in fiber types, innervation patterns, and developmental origins. Notably, EOM fibers express a diverse array of myosin heavy chain isoforms, retaining embryonic forms into adulthood. Moreover, their motor innervation is characterized by a high ratio of nerve fibers to muscle fibers and the presence of unique neuromuscular junctions. These features contribute to the specialized functions of EOMs, including rapid and precise eye movements. Understanding the mechanisms behind the resilience of EOMs to disease and aging may offer insights into potential therapeutic strategies for treating muscular dystrophies and myopathies affecting other skeletal muscles.

The excessive intake of sodium (Na) and insufficient intake of potassium (K) are major concerns to prevent hypertension. Using low-Na/K seasonings (reducing 25% of the NaCl and adding K salt) may improve the dietary Na/K ratio and help prevent hypertension. To devise an intervention study using low-Na/K seasonings at a company cafeteria, we calculated the Na and K contents of the meals served at the cafeteria and estimated changes in the intakes when suitable low-Na/K seasonings were used. We also considered using milk as a good source of K. We used an ingredient list of a company cafeteria and calculated Na and K contents in each dish. The average amounts of NaCl and K per use were 5.04 g and 718 mg, respectively. Seasonings contributed 70.9% of the NaCl. With the use of low-Na/K seasonings, an estimated reduction in NaCl of 0.8 g/day and an estimated increase in K of 308 mg/day was achieved. An additional serving (200 mL) of milk, NaCl was reduced by 0.57 g/day and K was increased by 610 mg/day, with an overall decrease in the dietary Na/K ratio from 3.20 to 2.40. The use of low-Na/K seasonings and dairy may improve the dietary Na/K ratio among cafeteria users and help prevent hypertension.

Legionella pneumophila can cause a large panel of symptoms beside the classic pneumonia presentation. We present here a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of legionnaires’ disease in the same ward. While the first one was easily assumed as nosocomial based on the date of symptoms onset, the other case needed to rely on clear typing results to be either assigned as nosocomial and related to the same environmental source as the first one, or community acquired. To untangle this specific question, we applied Core-genome MultiLocus Typing (MLST), whole-genome Single Nucleotide Polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large scale epidemiological investigations, NGS typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in environment.

The KOSovan Acute Coronary Syndrome (KOS-ACS) Registry is established as a prospective, continuous, nationwide, web-based registry that is operated online. The KOS-ACS registry is designed with the following objectives: 1) to obtain data on the demographic, clinical, and laboratory characteristics of ACS patients treated in Kosovo; 2) to create a national database with information on health care in ACS patients treated in Kosovo; 3) to identify the national features of associations between ACS characteristics and clinical outcomes, including mortality, complications, length of hospital stay, and quality of clinical care; and 4) to propose a practical guide for improving the quality and efficiency of ACS treatment in Kosovo. The Kosovo Society of Cardiology and University of Prishtina will be responsible for the development of the KOS-ACS registry and centralized data analysis at the national level. The KOS-ACS Registry will enroll all patients admitted, at any of the registered clinical centers, with the diagnosis of ACS and who will be clinically managed at any of the Kosovo hospitals. Data on patient demographics, clinical characteristics, previous and hospital drug treatment, and reperfusion therapy will be collected. The type of ACS (Unstable angina, NSTEMI or STEMI) will also be clearly defined. The time from first medical contact to balloon inflation (FMC-to-balloon) and door-to-ballon time will be registered. In-hospital death and complications will be registered. Data on post-hospital primary outcome (MACE: cardiac death, all-cause mortality, hospitalization, stroke, need for coronary revascularization) of patients, at 30 days and 1 year, will be included in the Registry.

Introduction: Despite great advances in the treatment of oncological diseases, the development of medical technologies to prevent or reduce complications of therapy, in particular, those associated with surgery and the introduction of antibiotics remains relevant. The aim of the study: to evaluate the effectiveness of the use of autoprobiotics based on indigenous non-pathogenic strains of Enterococcus faecium and Enterococcus hirae in the complex therapy of colorectal cancer (CRC) in the early postoperative period. Materials and methods: A total of 36 patients diagnosed with CRC were enrolled in the study. The study group (Group A) comprised 24 CRC patients who received autoprobiotic therapy in the early postoperative period, while the control group (Group C) included 12 CRC patients without autoprobiotic therapy.Prior to surgery and between days 14-16 post-surgery, comprehensive evaluations were conducted on all patients, encompassing the following: stool and gastroenterological complains analysis. examination of the gut microbiota (bacteriological study, quantitative polymerase chain reaction, metagenome analysis), analysis of interleukins in serum. Results: The use of autoprobiotics led to decrease dyspeptic complaints after surgery, was associated with absence of postoperative complications, did not cause side effects, led to a decrease in the level of pro-inflammatory cytokines (IL-6 and IL-18) in the blood serum. The use of autoprobiotics led to positive changes in the structure of escherichia and enterococci populations, the elimination of Parvomonas micra, Fusobacterium nucleatum, decrease in the quantitative content of Clostridium perfringens, Akkermansia muciniphila. Metagenomic analysis (16S rRNA) revealed an increase in alpha diversity. Conclusion: The administration of autoprobiotics in the postoperative period is highly effective and safe in the comprehensive treatment of colorectal cancer patients. Future studies will allow the discovery of additional fine mechanisms of autoprobiotic therapy and its impact on the digestive, immune, endocrine and neural systems.

Erigeron floribundus (Asteraceae) is an herbaceous plant widely used in the Cameroonian traditional medicine to treat various diseases of microbial and non-microbial origin. In the present study we evaluated the in vitro biological activities displayed by the essential oil obtained from the aerial parts of E. floribundus, namely the antioxidant, antimicrobial and antiproliferative activities. Moreover, we investigated the inhibitory effects of E. floribundus essential oil on nicotinate mononucleotide adenylyltransferase (NadD), a promising new target for developing novel antibiotics, and Trypanosoma brucei, the protozoan parasite responsible for Human African trypanosomiasis. The essential oil composition was dominated by spathulenol (12.2%), caryophyllene oxide (12.4%) and limonene (8.8%). The E. floribundus oil showed a good activity against Staphylococcus aureus (IZD of 14 mm, MIC of 512 µg/mL). Interestingly, it inhibited the NadD enzyme from S. aureus (IC50 of 98 µg/mL), with no effects on mammalian orthologue enzymes. In addition, T. brucei proliferation was inhibited with IC50 values of 33.5 µg/mL with the essential oil and 5.6 µg/mL with the active component limonene. The essential oil exhibited strong cytotoxicity on HCT 116 colon carcinoma cells with an IC50 value of 14.89 µg/mL, and remarkable ferric reducing antioxidant power (TEAC= 411.9 μmol TE/g).

The study was conducted in the milking parlor of a cattle farm with a capacity of 400 cows of the Holstein-Friesian breed. The milking installation was a double 8 "Herringbone" type without windows, and the roof was constructed of glass. The reporting of temperature, air humidity and temperature-humidity index (THI) was performed three times during each milking (at the start, in the middle and at the end of milking) with measurements repeated during the morning, noon and evening milking. The highest mean and maximum daytime air temperature values were recorded in summer and spring. Although the average values for the spring season were lower than those for summer (by about 4 ⁰ C), the maximum values reached were equally high – 31.4 ⁰ C. In terms of relative air humidity, the highest mean values were reported for the winter sea-son – 82.39%. For the other seasons, the relative humidity values were on average high and close in value - from 62.51 to 67.46%. In THI, the highest average daily and maximum values were re-ported in the summer months - 73.41 and 80, respectively.

Wind energy is one of the main drivers of the renewable energy market. The wind turbine gearbox, used as a multiplier - is one of the main components directly related to wind turbine efficiency and lifespan. Therefore, the strict control of the gearbox project and its manufacturing processes or even minor improvements in this component heavily and positively impact energy production/generation over time. This work diagnoses a 1.6 MW gearbox fracture mechanics to identify its main reason and how the chain of events took place, leading to the catastrophic failure. The diagnoses underwent numerical simulation (Finite Element Analysis - FEA) and further analysis of the lubrication system, bearings, planetary stage gears, helical stage gears, and high-speed shaft. In conclusion, although the numerical simulation showed high contact stresses between the teeth, the region with the crack nucleated was the top land (tip of the tooth). The most likely factors that led to the premature failure were the missed lubrication for the planetary bearings, lack of gear raw material cleanliness, and excessive case depth at the tip of the sun shaft gear teeth. With the sun gear, planet bearings, and planet gears broken in pieces, those small and large pieces dropped into the oil, between the gears, and into the tooth ring, ending up as a premature gearbox failure.

This work aims to integrate the circular economy (CE) into learning experiences to enhance learning outcome development in Higher Education (HE). Building on existing active learning research, this work aligns CE initiatives—particularly waste reduction and elimination— with the Sustainable Development Goals (SDGs). The study proposes an experiential learning framework to cultivate citizenship commitment, sustainability impact, and engineering learning outcomes through realistic problem-solving in the supply chains of small and medium enterprises (SMEs). Using the ADDIE instructional design model and Kolb’s experiential learning cycle, the approach is illustrated in a case study within an undergraduate industrial engineering module. Based on student opinion surveys and achievement metrics, results indicate that the experience significantly enhanced student engagement and motivation. This research contributes to the broader dissem-ination of CE concepts in HE and supports the advancement of SDG #4 Quality Education and #12 Sustainable Consumption and Production. Future research will explore the application of this learning experience across diverse industries and assess its impact on student learning outcomes in various educational contexts.

Drivable area (DA) detection is crucial for autonomous driving. Camera-based methods heavily rely on lighting conditions and often fail to capture accurate 3D information, while LiDAR-based methods offer accurate 3D data and are less susceptible to lighting conditions. However, existing LiDAR-based methods focus on point-wise detection, prone to occlusion and limited by point cloud sparsity, leading to decreased performance in motion planning and localization. We propose Argoverse-grid, a grid-wise DA detection dataset derived from Argoverse 1, comprising over 13K frames with fine-grained BEV DA labels across various scenarios. We also introduce Grid-DATrNet, a first grid-wise DA detection model utilizing global attention through transformers. Our experiments demonstrate the superiority of Grid-DATrNet over various methods, including both LiDAR and camera-based approaches, in detecting grid-wise DA in the proposed Argoverse-grid dataset. We show that Grid-DATrNet can detect grids even in occluded and unmeasured areas by leveraging contextual and semantic information through global attention, unlike CNN-based DA detection methods. The preprocessing code for Argoverse-grid, experiment code, Grid-DATrNet implementation, and result visualization code will be made available at https://github.com/kaist-avelab/grid-wise-DA.

This paper aims to outline the effectiveness of modern universal gate quantum computers when utilizing different configurations to solve the B-SAT(Boolean Satisfiability) problem. The quantum computing experiments are performed using Grover’s Search algorithm to find a valid solution. The experiments are performed under different variations to demonstrate their effects on the results. Changing the number of shots, qubit mapping, and using a different quantum processor are all among the experimental variables. The study also branches into a dedicated experiment highlighting a peculiar behavior that IBM quantum processors exhibit when running circuits with a certain number of shots.

Genetically diverse influenza A viruses (IAV) circulates in wild aquatic birds with relatively frequent spillover (cases of single infections) to other mammals, but only a small proportion of outbreaks. The spillover of IAV to marine mammals such as seals have been reported throughout the last 45 years. Majority of these viruses when reached seals population cased pneumonia and death of infected animals. Until 2016, all avian influenza viruses found in seals were of low pathotype (LP) IAV. The H5Nx clade 2.4.4.4b highly pathogenic avian influenza viruses (HPAIV) were found in seals for the first time in 2016/2017. Our review highlights 45 years of AIV history in seals population, showing interesting features of different AIV subtypes and emphasis on avian-to-mammals transmission. Furthermore, we also included phylogenetic characterisation of the representative seal isolates.

This paper addresses the growing concerns surrounding cloud computing security and highlights key strategies for ensuring the privacy and protection of data in information-critical industries. Cloud computing has revolutionized the field of information technology, providing convenience and efficiency in data processing. However, it has also brought to light significant challenges in terms of data security and privacy. Given the rise in cyber threats and the potential risks associated with storing sensitive information on cloud servers, critical industries such as healthcare and banking have been hesitant to adopt cloud computing as a trusted solution for their data needs. This work examines the specific security concerns that these industries face and proposes measures to mitigate them. Further, the paper explores the barriers that hinder the adoption of cloud computing in critical industries and offers solutions to address these concerns.

This paper provides a thorough examination of important themes critical to understanding and tackling modern cyber threats. It explores the revolutionary significance of AI and machine learning in cybersecurity, emphasizing their dynamic influence on defense systems. It also addresses how zero-trust designs have caused a paradigm change in network security, highlighting the significance of trust verification in current cybersecurity frameworks. Furthermore, the article investigates the integration of cybersecurity with corporate strategy, arguing for a collaborative approach to efficiently mitigating risks while minimizing operational impact. Furthermore, it investigates the idea of cyber resilience, highlighting an organization's ability to recover from assaults and adapt to changing threats. Furthermore, the paper discusses the issues faced by deepfakes in the synthetic media revolution and emphasizes the rising worry about data privacy in the digital era. Finally, it looks into the introduction of behavioral biometrics as a possible way to improve authentication and security methods. This paper contributes to the discussion of effective tactics for mitigating cyber threats in today's digital ecosystem by providing a thorough overview of these subjects.

Generalized Bayesian (GB) is a Bayesian approach based on the learning rate parameter (LRP) () as a fraction of the power of the likelihood function. In this paper, we consider the GB method to perform inference studies for a class of exponential distributions. Generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the considered distributions were obtained based on the censored type II samples. In addition, generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP) were considered using a one-sample prediction scheme. Monte Carlo simulations were performed to compare the performance of the GBE and GEBE estimation results and the GBP and GEBP prediction results for different values of the LRP.

This paper investigates generalized Bayesian (GB) inference in the context of a one-parameter Pareto distribution. The study focuses on the derivation of generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the Pareto distribution using type II censored data. In addition, the study investigates a one-sample prediction approach to examine generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP). To evaluate the performance of these estimation and prediction methods, Monte Carlo simulations were performed comparing GBE with GEBE and GBP with GEBP for different parameter values and learning rate parameters (LRP).

Goyor sarong is a cultural heritage of Tegal, Central Java made using Non-Machine Looms (ATBM) with characteristic motifs that describe the character of the Tegal people. This study aims to analyze the symbolic meaning of the goyor sarong and its existence in the gloved culture of contemporary Tegal society. The approach used in this study is qualitative with the type of research is descriptive qualitative. The informants interviewed were gloved cultural actors from diverse professions and socio-cultural backgrounds. Data were also obtained from literature studies including online media. The results of this study show that the typical motifs on the goyor sarong include lung, bottled, ceplok, prilik, and balian motifs. Goyor sarong are viewed from a symbolic point of view, have a symbiotic understanding between humans and symbolic objects, and understand the cultural meaning of goyor sarong to shape social life. The motifs in the goyor sarong are increasingly developing according to the times and consumer needs in the modern era. This is closely related to the gloved culture of contemporary Tegal society. The use of goyor sarongs in contemporary society usually indicates the strata and events attended. In the era of orientation development, the goyor sarong has more meaning than just symbolic elaboration. Sarong goyor as a culture has an important role for the community that depicts local fashion life in line with the issue of zero waste in the latest international fashion industry.

During the COVID-19 pandemic, the term “behavioural fatigue” became the centre of policy debates in Great Britain. These debates involved deciding when to go into lockdown and whether behavioural interventions could be effective. Behavioural interventions can only succeed where people’s capabilities, opportunities, and motivations to perform target behaviours are supported. Our retrospective data analyses examine the relationships between people’s capabilities, opportunities, motivations, and behaviours, i.e., adherence to lockdown guidelines. Our cross-sectional analyses include 17,962 unique participants in Great Britain who completed a survey over the initial 30 days of the first lockdown (April 2020). We examine trends in responses to each scale and then the relationships between the scales using Granger’s causality test with tests for stationarity and cointegration. A mixture of increasing and decreasing trends were identified for capabilities and opportunities. Decreasing trends were identified for motivation and behaviour. Granger’s causality tests found that capability forecasts opportunity and behaviour and that motivation forecasts opportunity. The discussion reiterates that to realize and maintain behaviour changes, policies surrounding people's capabilities, opportunities, and motivations must continue to support target behaviours.

Accurate and timely prediction of Napa cabbage (Brissica rapa subsp. Perkinensis) fresh weight is crucial for optimizing harvest timing, crop management, and supply chain logistics, contributing to food security and price stabilization. Traditional manual sampling methods are labor-intensive and imprecise. This study addresses this challenge by developing a comprehensive (artificial intelligence) AI-powered model for predicting Napa cabbage fresh weight using unmanned aerial vehicle (UAV)-based multi-sensor data. High-resolution RGB, multispectral, and thermal infrared (TIR) imagery were collected over a Napa cabbage field throughout the 2020 growing season. Various vegetation indices, crop features (vegetation fraction, crop height model), and water stress indi-cators (CWSI) were extracted from the imagery. Three AI algorithms—deep neural network (DNN), support vector machine (SVM), and random forest (RF)—were trained and evaluated, with the DNN model consistently outperforming the others. The DNN model achieved the highest accuracy (R² = 0.86 for training, 0.82 for testing; root mean square error (RMSE) = 0.432 kg for training, 0.465 kg for testing) during the mid-to-late rosette growth stage (DAP 35-42), highlighting this period as crucial for fresh weight estimation due to stable leaf area and well-developed canopy structure. The model tended to underestimate the weight of Napa cabbages exceeding 5 kg, potentially due to limited samples and saturation effects of vegetation indices. However, the overall error rate was less than 5%, demonstrating the feasibility and effectiveness of this approach. Spatial analysis revealed that the model accurately captured the variability in Napa cabbage growth across different soil types and irrigation conditions, particularly reflecting the positive impact of drip irrigation on the sandy loam plot. Bias analysis indicated the DNN model's tendency to overestimate smaller Napa cabbages (2 kg), suggesting areas for future refinement. This study demonstrates the potential of UAV-based multi-sensor data and AI algorithms for accurate and non-invasive prediction of Napa cabbage fresh weight. The developed DNN model offers a promising tool for optimizing harvest timing, improving crop management practices, and en-hancing supply chain efficiency. Future research should focus on refining the model for specific weight ranges and diverse environmental conditions, as well as extending its application to other crops, to further advance precision agriculture and contribute to sustainable food production.

Since the discovery of the phenomenon of surface-enhanced Raman scattering (SERS), it has gradually become an important tool for the analysis of material compositions and structures. The applications of SERS have been expanded from the fields of environmental and materials science to biomedicine, due to the extremely high sensitivity and non-destructiveness of SERS-based analytical technology that even allows single molecule detection. This article provides a comprehensive overview of Surface-Enhanced Raman Scattering (SERS) phenomenon. The content is divided into several main sections: basic principles and the significance of Raman spectroscopy; historical advancements and technological progress in SERS, as well as various practical applications across different fields. We also discussed how electromagnetic fields contribute to the SERS effect, the role of chemical interactions in enhancing Raman signals, modeling and computational approaches to understand and predict SERS effects.

Foot pain related to the plantar fascia is common in adults regardless of athletic background. Many pathologies can lead to the diagnosis of plantar enthesopathy, but the most relevant to this case study is related to structural defects within the plantar fascia. Due to the variety and sometimes unclear etiology of plantar fasciosis, no single standard of care treatment protocol is recognized. Often, patients receive NSAIDs, night splints, taping, physical therapy, foot orthosis, and extracorporeal shock wave therapies. This study presents Wharton Jelly (WJ) tissue allografts as an additional intervention for patients who fail standard-care treatments. WJ allografts are applied to supplement connective tissue defects directly, unlike other therapies, which aim to reduce swelling and symptomatic pain. This retrospective study includes seven patients who have previously failed standard-of-care treatment. Each received extracorporeal pulse-activated therapy (EPAT), WJ, laser therapy, and an orthotic. Patient progress was tracked using a visual analog scale, 0-10 scoring, reported by the patient at the initial visit and approximately thirteen weeks following, reporting an average improvement of 50%. The study's limitations include a small cohort size and a non-blinded retrospective design. These promising results provide evidence for a more extensive, randomized study to define dosage protocols further and confirm safety and efficacy.

The development of Artificial Intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are deeply changing both from a theoretical and a practical perspective, even in the field of ship propulsion systems. Namely, conventional preventive maintenance schedules audits and checks at fixed times by using statistics on component failures, but it can be improved by a predictive maintenance based on the real component health status, which is inspected by proper sensors. In this way, time and costs are saved. More in details, data-driven models, through Machine Learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements coming from the asset, hence for a diagnosis based on the difference between expectations and observations. Then, a ML-based fault detection starts with the choice of the ML algorithm. This process is often not the consequence of an in-depth deep analysis of the different algorithms available in the literature. For that reason, the authors propose a simple procedure to support a first implementation stage of the so-called Condition Based Maintenance (CBM): a quick benchmarking of the most suitable ML algorithms useful for fault detection. The algorithms are compared by taking into account not only the algorithm output error, shown by the Mean Squared Error between predictions and data, but also the response time, which is a crucial variable for maintenance purpose.

Coaches are the main people who have an impact on the mental and psychological performance of athletes as well as their physical performance. The purpose of this study is to investigate the effect of perceived coaching behaviors on the anger and aggression levels of male athletes. This study was conducted with 748 male athletes with an active license who are over 18 years of age and participate in individual and team sports branches. In this study, the "Coaching Behavior Assessment Ques-tionnaire" and the “Competitive Aggressiveness and Anger Scale" were used as data collection tools. The study results showed that athletes’ levels of evaluation of the coaching behaviors were high, while the levels of aggressiveness and anger in sports were low. It was determined that there is a negative low-level relationship between the athletes’ evaluation levels of the coaching be-haviors and the aggressiveness and anger levels in sports and that the coach’s behaviors predict the anger and aggressiveness levels of the athletes by 9% and 6%, respectively. The results of this study show that the attitudes and behaviors of coaches can affect the emotional states of male athletes and have the potential to increase or decrease their anger and aggressiveness levels. Coaches who exhibit positive, supportive, and modeling behaviors help create a healthy sports environment and help male athletes maintain emotional balance.

Advanced 1H Nuclear Magnetic Resonance (NMR) relaxometry and diffusometry methods and VIS-nearIR spectroscopy combined with pH, electric conductivity (EC) and totally dissolved solids (TDS) measurements were used to assess the properties of wastewater collected from a chicken slaughterhouse in each step of the treatment process (wastewater before treatment, biologically treated wastewater, chemically treated wastewater and discharged wastewater) and from sludge. The 1H NMR Carr-Purcell-Meiboom-Gill (CPMG) and Pulsed-Gradient-Stimulated-Echo (PGSE) decay curves recorded for all samples of wastewater were analyzed by inverse Laplace transform (ILT) to obtain the distributions of transverse relaxation times T2 and diffusion coefficient D. The VIS-nearIR total absorbance, T2-values, D-values, pH, EC and TDS parameters were used for statistical analysis in principal component analysis (PCA). The 1H T2-distributions measured for the slaughterhouse wastewater lie in two main regions reflecting the amount of dissolved solids or the distribution of undissolved solids mobility. The PCA analysis successfully differentiates between polluted and less polluted types of wastewater and sludge. The wastewater treatment applied by the slaughterhouse is efficient. The recommended methods for wastewater monitoring are the NMR T2- and D-distributions and EC, TDS and NMR-D diffusion coefficient. Finally, Machine Learning AI-algorithms are used to provide prediction maps of wastewater treatment stage.

Based on non-invasive neuroimaging techniques and graph theory-based network analyses, many researchers have tried to use small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed--randomization, regularization, stronger small-worldization and weaker small-worldization, we found that current results point to a significant trend toward more regularization in females and more randomization in males in the functional brain networks. As for particular topological properties, the most consistent findings include a significant shift toward higher global efficiency in males and higher local efficiency in females in the functional network studies. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks. Nevertheless, we noticed that the sample sizes in many published studies are limited, and future studies with larger samples are warranted to obtain more reliable results. We anticipate that the conclusions in this manuscript will contribute to a deeper understanding of neurobiological mechanisms underlying the sex differences in the brain.

Cyberattacks are rapidly evolving both in terms of techniques and frequency, from low-level attacks through to sophisticated Advanced Persistent Threat (APT). There is a need to consider how testbed environments such as cyber ranges can be readily deployed to improve examination of attack characteristics and the assessment of defences. Whilst cyber ranges are not new, they can often be computational expensive, require significant setup and configuration, or may not provide full support for areas such as logging or ongoing learning. In this paper, we propose GoibhniUWE, a container-based cyber range that provides a flexible platform for investigating the full lifecycle of a cyber attack. Adopting a modular approach, users can seamlessly switch out existing, containerised vulnerable services, deploying multiple different services at once allowing for the creation of complex and realistic deployments. The range is fully instrumented with logging capabilities from a variety of sources including Intrusion Detection Systems (IDS), service logging, and network traffic captures. To demonstrate the effectiveness of our approach, we deploy the GoibhniUWE range under multiple conditions to simulate various vulnerable environments, reporting on and comparing key metrics such as CPU and memory usage. We simulate complex attacks which span multiple services and networks, with logging at multiple levels, modelling an Advance Persistent Threat (APT) and their associated Tactics, Techniques, and Procedures (TTPs). We find that even under continuous, active and targeted deployment GoibhniUWE averaged a CPU usage of less than 2 processors and memory usage of less than 4.5 GB.

The most common type of head and neck cancer (HNC) is head and neck squamous cell carcinoma (HNSCC). It describes a heterogenous neoplastic disease that arises from the mucosal epithelium of the of the upper respiratory system and the gastrointestinal tract. It is characterized by high morbidity and mortality, being is the eighth most common cancer worldwide, with 931,931 newly diagnosed cases and 467,125 deaths according to the latest Global Cancer Statistics (GLOBOCAN) data. Despite the use of modern low-invasive surgical techniques and application of promising new chemoradiotherapies or combination treatment, the frequency of recurrences and the five-year survival of these patients remains unsatisfactory. The microenvironment of head and neck cancer comprises various cells that regulate neoplastic expansion. It is believed that the mesenchymal stromal cells (MSCs) present in the tumour milieu play a key role in the modulation of tumour initiation, development and progression, in the regulation of cancer treatment response, survival rates, patient outcomes and long-term prognosis; they also influence the resistance to cisplatin-based chemotherapy, the gold standard for advanced HNSCC. MSCs, are multipotent, heterogeneous, mobile cells, which meet specific stemness criteria defined by the International Society for Cellular Therapy (ISCT). Although no MSC-specific markers exist, they can be recognized based on several others, such as CD73, CD90 and CD105, while lacking the presence of CD45, CD34, CD14 or CD11b, CD79α, or CD19 and HLA-DR antigens; they share phenotypic similarity with stromal cells and their capacity to differentiate into other cell types. In the tumour niche, MSC populations are characterized by cell quiescence, self-renewal capacity, low reactive oxygen species production and the acquisition of epithelial-to-mesenchymal transition properties, they may play a key role in the process of acquiring drug resistance, and thus treatment failure. MSCs must also demonstrate plastic adherence in vitro and be able to differentiate into specific mesodermal cell types after stimulation i.e., adipocytes, chondrocytes, and osteoblasts when cultured under well-defined conditions. The present narrative review examines the links between mesenchymal stromal/stem cells and HNC, as well as the different mechanisms involved in the development of resistance to current chemo- and radiotherapies in HNSCC. It provides an overview of the current literature, including key opinion-forming systematic reviews, as well as cross-sectional, longitudinal, prospective and interventional studies based on cell culture models in vitro, animal or in vivo models of HNSCC; it also examines the possibilities of pharmacological targeting of stemness-related chemoresistance in HNSCC. It describes promising new strategies to optimize chemoradiotherapy with the potential to personalize patient treatment approaches, and highlights future therapeutic perspectives in HNC. The data concerning tumour-associated MSCs is arranged according to increasing clinical credibility.

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we propose the consolidation of virtual machines (VMs) to optimize resource utilization and reduce operational costs. This paper introduces a novel algorithm, leveraging the Modified Artificial Feeding Birds Algorithm (ModAFBA), to manage cost optimally in cloud data centers by reducing power usage while meeting service level agreements (SLAs). The ModAFBA algorithm, inspired by bird foraging behavior, dynamically adjusts solutions based on environmental changes. Comprehensive experimental evaluation demonstrates the superiority of the proposed algorithm in terms of power usage, migration count, and SLA compliance. Through this research, we aim to advance cost management and resource optimization in cloud data centers, providing a valuable tool for cloud service providers to enhance infrastructure efficiency and achieve cost savings.

The increasing deployment of intermittent renewable energy sources necessitates effective methods for balancing the electrical grid to ensure stability and safety. Energy storage systems that can store excess electrical power during periods of low demand and release it during high demand are crucial for both short-term and long-term applications. Power-to-Gas is an innovative energy storage solution that converts surplus renewable electricity into gas fuels, such as hydrogen, which can be stored and used later. This hydrogen can serve multiple purposes, including energy storage and as fuel for transportation modes like cars, trams, trains, and buses. While most hydrogen today is derived from fossil fuels, solid-oxide electrolysis (SOE) offers a cleaner alternative by producing hydrogen without harmful emissions. SOE is also the most efficient method of electrolysis. This study aims to determine the operational parameters for an SOE system, including lower heating value (LHV)-based efficiency and total input power, using a mathematical model. Results are presented for three different operating temperatures and four different steam utilization ratios. The paper is structured as follows: the introduction provides the context and motivation for the study; the second section outlines the fundamentals of electrolysis and its various types; the third section delves into solid-oxide electrolysis and electrolyser systems; the fourth section describes the methodology, including the mathematical formulation and simulation software used; the fifth section presents the calculation results and conclusions; and the final section summarizes the findings.

The objective of this study is to investigate the correlation between the advancement of flood research and the utilization of Geographic Information Systems (GIS) and multi-criteria evaluation techniques, both from a theoretical and practical standpoint. Additionally, this research seeks to identify potential ways to address the aforementioned relationship. The present study employs a thorough literature review methodology. In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a total of 28 papers were identified and included in the analysis, as they fulfilled the predetermined criteria. The findings of the study indicate that current research pertaining to the utilization of Geographic Information Systems (GIS) and multicriteria evaluation in flood control mostly centers around five key areas, namely natural factors, technical considerations, institutional factors, socio-economic factors, and financial factors. However, the aforementioned studies have identified specific areas of focus, including natural factors pertaining to climate, technical factors related to infrastructure, institutional factors concerning flood management strategies, socio-economic factors associated with demographics, and financial factors pertaining to financial obligations. However, there is a limited amount of existing research that specifically addresses flood management in developing nations. Therefore, it is anticipated that this study can serve as a valuable resource for future research endeavors, particularly in informing decision-making processes pertaining to the establishment of priorities in flood control plans. The primary subject of this study pertains to flood management occurrences within developing nations.

Fine dust causes various disorders, including cardiovascular, neurological, renal, reproductive, motor, systemic, respiratory, and cancerous diseases. Therefore, it is necessary to study functional materials to prevent these problems. In this study we used various methods, including quantitative PCR, flow cytometry, ELISA, and Alizarin O staining, to study the beneficial effects of erucic acid against fine dust. Erucic acid and erucic acid-induced exosomes (EIE) strongly suppress the attenuation of osteocytic differentiation and inflammation induced by fine dust. Erucic acid effectively suppresses inflammation in fibroblasts exposed to fine dust. Notably, despite exposure to fine dust, EIE promoted osteocytic differentiation in adipose-derived stem cells (ASC) and osteogenesis in macrophages, in addition to phagocytosis. Dramatic upregulation of RunX2 and BMP7 by EIE indicates that it is a strong activator of osteocytic differentiation in ASC and protects them from the effects of fine dust. Moreover, EIE induces immune activity and acts as an osteogenic trigger for macrophages. Therefore, this study suggests the potential use of erucic acid and erucic acid-induced exosomes (EIE) as biomaterials to prevent environment-borne diseases.

This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.

Quantum nonlocality represents correlation properties between subsystems of a composite quantum system, usually including the four types: Bell nonlocality, steerability, entanglement, and quantum correlation (quantum discord). Given a basis $e_{AB}=\{|e_{ij}\>\}_{i\in[d_A],j\in[d_B]}$ for the Hilbert space $\H_A\otimes \H_B$ of a bipartite system $AB$, a density operator $\rho^{AB}$ (quantum state) of $AB$ can be represented as a $d_Ad_B\times d_Ad_B$ matrix $\hat{\rho}_{e_{AB}}=[\]$, called the density matrix of a density operator $\rho^{AB}$. A natural question is what is the relationship between the quantum nonlocality of a density operator $\rho^{AB}$ and its corresponding density matrix $\hat{\rho}_{e_{AB}}$? In this work, we discuss the relationships between quantum locality and basis, and prove that one type of quantum locality of density operators and that of their density matrices under a basis are the same if and only if the chosen basis is the tensor product of the bases of subsystems. Consequently, different bases define different quantum nonlocality density operators.

The shift towards Personalized Cancer Medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

The quality of healthcare services is a very important indicator of social welfare in developed and emerging countries. Good quality levels are critical to ensuring patient safety, improving health outcomes, increasing patient satisfaction, optimizing resource utilization, and meeting regulatory requirements. The stable and sustainable operation of medical institutions urgently requires the establishment of a reliable and effective medical quality performance evaluation platform. However, the complexity of the evaluation process, involving numerous evaluators and the consideration of different subjective and objective criteria, poses significant challenges and essentially constitutes a group multi-criteria decision-making problem. This article uses the overall medical environment of the institution, medical service attitude, medical security, administrative service measures, etc. as measurement standards to deal with this challenge. It proposes a medical service quality measurement model suitable for both qualitative and quantitative analysis. In view of the heterogeneity of information, the two-tuple fuzzy language calculation method is introduced to enable evaluators to skillfully manage diverse information and effectively avoid potential information omission problems in the subjective evaluation integration process. The effectiveness and feasibility of the model were demonstrated by applying it to the operational framework of a medical institution in central Taiwan.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

The main objective of this investigation was to study the effect of organizational competencies development practices on turnover intentions and whether affective commitment explains this relationship. Another of the study's objectives was to test whether these relationships vary ac-cording to the generation to which the participant belongs. The study sample consisted of 2123 participants working in Portuguese organizations. The results indicate that organizational com-petencies development practices (training, individualized support, and functional rotation) nega-tively and significantly affect turnover intentions and that affective commitment mediates this re-lationship. However, these relationships vary according to the participant's generation. For Gen-eration Y and Generation X, this mediating effect is found in all dimensions of organizational competencies development practices. For the baby boomer generation, there is only a mediating effect of affective commitment in the relationship between individualized support and turnover intentions. These results indicate that human resources should consider the generation to which the participant belongs when implementing competencies development practices.

This study examined the tourism spatial distribution of nine cities in the Fujian province and assessed the impacts of COVID-19. The modified gravity model found that it was widely dispersed, with uneven and relatively independent tourism development in different cities. The social network analysis showed that tourism connections across cities were significantly reduced after the pandemic. The impacts of brand awareness and transport accessibility on spatial networks were positive in pre-pandemic but became negative during the pandemic. In contrast, tourist volume had negative impacts on spatial networks in pre-pandemic but had positive ones during the pandemic. Tourism resources and market performance had significantly positive impacts in the post-pandemic era. These findings provide advice on tourism recovery and destination management in coping with future critical events. It also contributes to the theoretical gravity framework in tourism and the research scope of the social networks analysis at the city level.

Today, issues related to people’s mental health and well-being have been described as a challenge for society, globally as well as in Sweden. This calls for new approaches to mental health promotion. The aim was to evaluate the adequacy of the content and structure, describing experiences of study circles as a means of supporting participants' self-care and self-compassion. The overall design is a descriptive, QUAL+quan design, where quantitative and qualitative results are integrated. Five participants participated in a focus group interview, of whom four completed questionnaires. One individual interview was conducted with the study circle leader. Study circles can be an arena for mental health promotion as learning and sharing of experience contributes to a sense of coherence, as well as self-compassion and a genuine concern for one’s own and others' wellbeing, but not considered an alternative to psychiatric care, for those in need of professional services. Study circles can be a possible means to support self-care and thereby promote mental health in the general population, and a valuable contribution to public health. However, in addition to modifications of the content, further research is needed on the qualifications for study circle leaders as well as the dissemination of study circles.

Sleep disorders have become a public health problem for many reasons. The aim of this systematic review is to describe social, economic, and environmental determinants of sleep disturbances in Australia and New Zealand. A total of 22 studies, 14 from Australia and 8 from New Zealand involving children, adolescents and adults are included in the review. In summary, we found that sleep health disparities among children and adolescents in both countries are strongly associated to parental SES. Overall, lower family SES such as low parental income, low parental education, low SES area index are associated with higher sleep disturbances among children and adolescents. In adults, results revealed that SES determinants such as low income, lower education, unemployment, and night work were associated with sleep disturbances. Specifically, unemployment was associated with poor sleep quality, insomnia, and increased likelihood of suboptimal sleep efficiency. Low income and lower education were associated with high risk of OSA, longer sleep duration, insomnia, higher odds of suboptimal sleep duration and sleep onset latency.

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a simplified RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

Consumer IoT (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem like a smart home. Due to security and privacy concerns, Blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their Machine Learning (ML) model using end-user data. FL uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralised aggregator to safeguard the ecosystem. However, Blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research contributes a novel architecture emphasising Gateway Peer (GWP) in blockchain network to resolve scalability, ledger optimisation and secure model transmission issues. In the architecture we replace the centralised aggregator by the blockchain network, while GWP restricts the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we have added differential privacy and advanced normalisation techniques to ML processes. The approaches strengthen end-users' cyber security and encourage the adoption of technological innovation standards by service providers. The proposed approach has been tested extensively using a well-respected Stanford Cars dataset. We experimentally demonstrate that the proposed architecture makes the network scalable and optimises the ledger significantly. In addition, the normalisation technique outperforms batch normalisation when features are under DP protection.

Migraine is a neurovascular disorder that can be debilitating for individuals and society. Current research focuses on finding effective analgesics and management strategies for migraines by targeting specific receptors and neuropeptides. However, the efficacy of calcitonin gene-related peptide (CGRP) receptor antagonists and monoclonal antibodies (mAbs) in treating migraines can vary among patients, and tolerance may develop over time, reducing their effectiveness. To address the need for novel therapeutic targets, researchers are exploring the potential of another secretin family peptide, pituitary adenylate cyclase-activating polypeptide (PACAP), as a ground-breaking treatment avenue for migraine. Animal models have revealed how PACAP affects the trigeminal system, which is implicated in headache disorders. Clinical studies have demonstrated the significance of PACAP in migraine pathophysiology. Intriguingly, the pituitary adenylate cyclase-activating peptide 1 receptor mAb, AMG 301 showed no benefit for migraine prevention, while the PACAP ligand mAb, Lu AG09222 significantly reduced the number of monthly migraine days over placebo in a phase 2 clinical trial. In addition, another secretin family peptide vasoactive intestinal peptide is gaining interest as a potential new target. In light of recent advances in PACAP research, we emphasize the potential of PACAP as a promising target for migraine treatment. Recent findings highlight the importance of exploring PACAP as a member of the antimigraine armamentarium, especially for patients who do not respond to anti-CGRP therapies. By updating our knowledge on PACAP and its unique contribution to migraine pathophysiology, we can pave the way for reinforcing PACAP and other secretin peptides as a novel treatment approach for migraines.

The Impact of Event Scale-Revised (IES-R) is the most popular measure of post-traumatic stress disorder (PTSD), which has been recently validated in Arabic. This instrumental study aimed to determine optimal cutoff scores of the IES-R and its subscales in Arab samples of psychiatric patients (N = 168, 70.8% females) and healthy adults (N = 992, 62.7% females) from Saudi Arabia during the COVID-19 pandemic as an ongoing collective traumatic event. Based on a cutoff score of 14 of the Depression Anxiety Stress Scale 8-items (DASS-8), receiver operator curve (ROC) analysis revealed two optimal points of 39.5 and 30.5 for the IES-R in the samples (area under the curve (AUC) = 0.86 &amp; 0.91, p values = 0.001, 95% CI: 0.80-0.92 &amp; 0.87 to 0.94, sensitivity = 0.85 &amp; 0.87, specificity = 0.73 &amp; 0.83, Youden index = 0.58 &amp; 0.70, respectively). Different cutoffs were detected for the six subscales of the IES-R, with numbing and avoidance expressing the lowest predictivity for distress. Meanwhile, hyperarousal followed by the irritability expressed stronger predictive capacity for distress than all subscales in both samples. In path analysis, pandemic-related irritability resulted from direct and indirect effects of key PTSD symptoms (intrusion, hyperarousal, and numbing). Irritability contributed to traumatic symptoms of sleep disturbance in both samples while the opposite was not true. The findings suggest usefulness of the IES-R at a score of 30.5 for detecting adults prone to trauma related distress, with higher scores needed for screening in psychiatric patients. Various PTSD symptoms may induce dysphoric mood, which represents a considerable burden that may induce circadian misalignment and more noxious psychiatric problems/ co-morbidities (sleep disturbance) in both healthy and diseased groups.

Migraine and neuropathic pain (NP) are evocative of painful, disabling, chronic conditions which exhibit resembling symptoms and thus considered to share a common etiology. Calcitonin gene-related peptide (CGRP) has gained credit as a target for migraine management; nevertheless, the efficacy and the applicability of CGRP modifiers warrant search for more effective therapeutic targets for pain management. This scoping review overviews human studies of common pathogenic factors in migraine and NP to explore potential novel therapeutic targets. CGRP causes inflammation in the meninges; monoclonal antibodies and inhibitors target CGRP. Gluta-mate-induced hyperexcitability and subsequent sensitization are closely linked to an alteration of the tryptophan (Trp)-kynurenine (KYN) metabolic system; the Trp-KYN system may serve as a potential target. Microglial overaction is observed in migraine and NP; modifying the microglial activity may be a possible approach. Cytokine-induced inflammation is a leading hypothesis of the pathogenesis of the conditions; alleviating neuroinflammation may complement a pain-relieving armamentarium. Transient receptor potential (TRP) ion channels evoke the release of several substances; TRP ion channels may potentially emerge as new targets. The endocannabinoid system plays a major role in the pain trafficking pathway; modification of the system may open a new path toward discovery of new analgesics. Here we highlight the mechanism of those common pathogenic factors to explore therapeutic targets for innovative pain management in migraine and NP.

Nearly half a century has passed since the discovery of cytoplasmic inheritance of human chloramphenicol resistance. The inheritance was then revealed to take place maternally by mitochondrial DNA (mtDNA). Later, a number of mutations in mtDNA were identified as a cause of severe inheritable metabolic diseases with neurological manifestation, and the impairment of mitochondrial functions has been probed in the pathogenesis of a wide range of illnesses including neurodegenerative diseases. Recently growing number of preclinical studies has revealed that animal behaviors are influenced by the impairment of mitochondrial functions and possibly by the loss of mitochondrial stress resilience. Indeed, as high as 54% of patients with one of the most common primary mitochondrial diseases, mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) syndrome, present psychiatric symptoms including cognitive impairment, mood disorder, anxiety, and psychosis. Mitochondria are multifunctional organelles which produce cellular energy and play a major role in other cellular functions including homeostasis, cellular signaling, and gene expression, among other. Mitochondrial functions are observed to be compromised and to become less resilient under continuous stress. Meanwhile, stress and inflammation have been linked to the activation of the tryptophan (Trp)-kynurenine (KYN) metabolic system, which observably contributes to development of pathological conditions including neurological and psychiatric disorders. This narrative review discusses the functions of mitochondria and the Trp-KYN system, the interaction of the Trp-KYN system with mitochondria, and the current understanding of the involvement of mitochondria and the Trp-KYN system in preclinical and clinical studies of major neurological and psychiatric diseases.

The performance of the absolute atom gravimeters used on moving platforms, such as vehicles, ships and aircrafts, is strongly affected by the vibration noise. To suppress its influence, we summarize a vibration compensation method utilizing data measured by a classical accelerometer. The measurements with the accelerometer show that the vibration noise in the vehicle can be 2 order of magnitude greater than that in the lab during daytime, and can induce an interferometric phase fluctuation with a standard deviation of 16.70π. With the compensation method, our vehicle-mounted atom gravimeter can work normally in these harsh conditions. Comparing the Allan standard deviations before and after the vibration noise correction, we find a suppression factor of 22.74 can be achieved in static condition with an interrogation time of T = 20 ms, resulting a sensitivity of 1.35 mGal/Hz1/2, and a standard deviation of 0.5 mGal with an average time of 10 s. We also demonstrate the first test of an atom gravimeter in a moving vehicle, in which a suppression factor of 50.85 and a sensitivity of 60.88 mGal/Hz1/2 were realized with T = 5 ms.

The aim of this study was characterization of some dairy drinks based on Milk Serum regarding major whey proteins (WP) and free amino acids (FAAs) using reversed phase high performance liquid chromatographic (RP-HPLC) methods. The studied WP, -lactalbumin (-La), bovine serum albumin (BSA), -lactoglobulin A (-Lg A) and -lactoglobulin B (-Lg B) were separated on Aeris XB-C18 column at 214 nm detection. The RP-HPLC method was validated by selectivity, linearity (R2 ≥0.99), sensitivity (LOQ, 1.35–10.08 µg mL−1), accuracy (recovery 96.79-103.07%) and precision (% RSD ≤ 4.13%). The total studied WP in studied dairy drinks varied between 1.42 and 3.047 g·L-1. The chromatographic profile of FAAs (aspartic acid, glutamic acid, serine, histidine, arginine, glycine, threonine, alanine, tyrosine, cysteine, tryptophan, methionine, valine, phenylalanine, isoleucine, leucine and lysine) was determined in lyophilized concentrate of Milk Serum by RP-HPLC using pre-column derivatization reaction with orthophthalaldehyde (OPA). The total studied FAAs in studied samples varied between 1.103 and 1.119 mg·g-1. Moreover, the Milk Serum showed bacteriostatic activity against two bacterial strains Escherichia coli and Staphylococcus aureus. The obtained results confirm that dairy drinks based on the Milk Serum constitutes a valuable sources of bioactive components with benefits for human healthy nutrition.

Every finite simple group P can be generated by two of its elements. Pairs of generators forP are available in the Atlas of finite group representations as (not necessarily minimal) permutation representations P . It is unusual but significant to recognize that a P is a Grothendieck’s dessin d’enfant D and that a wealth of standard graphs and finite geometries G - such as near polygons and their generalizations - are stabilized by a D. In our paper, tripods P − D − G of rank larger than two,corresponding to simple groups, are organized into classes, e.g. symplectic, unitary, sporadic, etc (as in the Atlas). An exhaustive search and characterization of non-trivial point-line configurationsdefined from small index representations of simple groups is performed, with the goal to recognize their quantum physical significance. All the defined geometries G's have a contextuality parameterclose to its maximal value 1.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

From ancient times to the present, mankind has struggled with infectious diseases that pose a serious threat to people's lives, COVID-19 pandemic has given a huge impact to the human society, which has triggered a lot of thinking. In the future, humans may encounter various unexpected disasters or awful events, therefore, should learn from COVID-19 pandemic incident that need to pay special attention to the emergency supply of manpower and materials, whether a major disaster or not. That is emergency medical management is very important in the event of a sudden outbreak. What’s more, the preparation and scheduling of emergency medical supplies well safeguards people’s lives, health, and safety. However, owing to the sudden onset of an epidemic and given the population trends, the scale of infections and the demand for supplies in the epidemic area are uncertain. The main influencing factors and mechanisms of emergency material demand under the development trend of emergencies can be studied, and materials can be allocated efficiently and effectively, so as to minimize damage. In this study, it improves upon the dynamics model of infectious diseases firstly, by establishing a demand forecasting model for a certain area, taking the Hubei province as an example. Furthermore, this dynamics model is used to predict demand for emergency supplies, such as masks, respirators, and food in the area where the disaster will occur. Experimental results demonstrate the effectiveness and reliability of this prediction model, which supports for the development of emergency material management systems, ulterior, this study provides a framework for the distribution of emergency supplies and a guideline for relief efforts.

Metabolomics studies require a deep understanding of enzyme dynamics and their responses to environmental disturbances within a complex metabolic network. This paper presents a novel approach to modeling enzyme activity as a dynamic topological manifold, where each enzyme is represented as a node, and their interactions are described by differential equations. These equations account for both compensatory interactions and external perturbations, simulating how enzymes dynamically stabilize their activities in response to varying conditions. We explore the application of this model to visualize enzyme interactions, using graph theory to represent the network structure and color gradients to illustrate interaction intensities. Multiple disturbances are introduced to analyze the network's resilience and adaptability over time. The results provide insights into the compensatory mechanisms within enzymatic networks, offering a comprehensive visualization through both static and dynamic representations. Our approach allows for a better understanding of how enzymatic systems buffer against mutations and environmental stressors, contributing to the broader field of systems biology and metabolomics.

Barley is rich in phenolic compounds, providing health benefits and making it a valuable addition to a balanced diet. However, most studies focus on these compounds at barley's final maturity, neglecting their synthesis during grain development and its impact on barley quality for food applications. This study investigates phenolic profiles during grain development in four hull-less barley genotypes with different grain colors, specifically bred for food applications. The objectives were to determine the phenolic profile and identify the optimal maturity stage for maximum phenolic content and antioxidant capacity. Using UPLC-MS/MS and in-vitro antioxidant capacity assays, results show that total phenolic compounds decrease as grain matures due to increased synthesis of reserve components. Flavan-3-ols, phenolic acids, and flavone glycosides peaked at immature stages, while anthocyanins peaked at physiological maturity. The harvest stage had the lowest phenolic content, with a gradient from black to yellow, purple, and blue genotypes. Antioxidant capacity fluctuated during maturation, correlating positively with phenolic compounds, especially bound phenolic acids and anthocyanins. These findings suggest that early harvesting of immature grain can help retain bioactive compounds, promoting the use of immature barley grains in foods. To support this market, incentives should offset costs associated with decreased grain weight.

This study examines the nexus between oil prices and inflation in G20 economies, addressing critical gaps by adopting a comparative approach across three pivotal oil price downturns in 2008, 2014, and 2020 (COVID-19). The study utilized monthly time series data from January 2000 to December 2023 and the Bayesian Structural Time Series (BSTS) approach for causal impact analysis. The study found that during the global financial crisis (December 2007 to June 2014), advanced economies like Australia and the UK displayed varying degrees of negative absolute causal impact, aligning with the deflationary impact of falling oil prices. In contrast, emerging economies faced significant negative causal impacts. From June 2014 to January 2020, diverse inflation impacts were observed across G20 economies after a significant oil price decline. Advanced economies like Australia and emerging economies like Brazil experienced negative impacts, while others showed negligible effects. In the subsequent period from January 2020 to December 2023, amid the COVID-19 pandemic and substantial oil price decline, G20 countries exhibited varied inflation outcomes. Positive absolute effects indicated a notable rise in inflation with China standing out with a substantial negative impact. The wavelet coherence analyses also showed an asymmetrical impact of oil prices on inflation. The findings suggest that policymakers should adopt proactive and adaptive policy frameworks, considering the asymmetric responses of G20 economies to oil price dynamics.

Discrete-time architectures offer a distinct advantage over their continuous counterparts, as they can be seamlessly implemented on embedded hardware without the necessity for discretization processes. Yet, because of the difficulty of ensuring Lyapunov difference expressions, their designs, which are based on quadratic Lyapunov-based frameworks, are highly complex. As a result, various existing continuous-time results using adaptive control methods to deal with system uncertainties and coupled dynamics in agents of a multiagent system cannot be directly applied to the discrete-time context. Furthermore, compared to their continuous-time equivalent, discrete-time information exchange based on periodic time intervals is more practical in the control of multiagent systems. In this paper, we first introduce a discrete-time adaptive control architecture designed for uncertain scalar multiagent systems without coupled dynamics as a preliminary result. We then introduce another discrete-time adaptive control approach for uncertain multiagent systems in the presence of coupled dynamics. Our approach incorporates observer dynamics to manage unmeasurable coupled dynamics, along with a user-assigned Laplacian matrix to induce cooperative behaviors among multiple agents. Our solution includes Lyapunov analysis with logarithmic and quadratic Lyapunov functions for guaranteeing asymptotic stability with both controllers. To demonstrate the effectiveness of the proposed control architectures, we provide an illustrative example.

We used ab initio molecular dynamics simulations to compare the activation of 2-propanol on the low index Co3O4 (111), (110) and (001) surfaces in dry conditions. The thermal and surface assisted decomposition of a film of 2-propanol to 2-propoxide on the B-termination of each surface was monitored and analyzed. The investigations suggest an activity order of Co3O4 (111) > (110) > (001). On the B-terminated (111) surface, full dissociation of all 2-propanol molecules at the interface is observed, accompanied by a Mars-van Krevelen-type mechanism upon hydroxylation of the surface. On the (110) surface, 2-propanol dissociation is driven by temperature, which activates the two-fold coordinatively unsaturated surface oxygens. The (001) surface on which almost no dissociation occurs is the least active. No formation of acetone is observed in the simulations conditions.

This research presents a comprehensive computational analysis of the two-phase flow dynamics within a three-dimensional porous transport layer (PTL) in a proton exchange membrane (PEM) electrolyzer. Employing advanced computational fluid dynamics (CFD) and a volume of fluid (VOF) approach, the study utilizes the finite-volume method to model a time-dependent, isothermal process within a laminar flow regime. Notably, the study treats oxygen as the dispersed phase and water as the continuous phase within the system. Investigations on the anode side uncovered the formation of gas bubbles on the electrode's surface subsequent to the electrochemical reaction. The simulation, spanning 5 seconds with 0.25-second intervals, highlights the critical role of the time interval between the initiation and 2 seconds in achieving pressure and velocity equilibrium within the PTL. The initial 0.75 seconds witnessed a peak in oxygen concentration, followed by its movement across the PTL, exhibiting a transition from regions of lower to higher concentration, consistent with known physical behaviour. Remarkably, the model recorded the highest pressure of 3.5 Pa at 0.25 s. Furthermore, the study observed an incremental pressure drop from 0.25 s to 0.5 s and 1 s, approximately amounting to 28.5% and 20%, respectively, ultimately leading to a uniform pressure distribution within the model. Over time, the pressure drop intensified across the entire model due to the evolving nature of the oxygen gas. These findings provide valuable insights into the complex dynamics of two-phase flow processes within PEM electrolyzers, contributing to the advancement of sustainable energy technologies.

In a saline semi-isolated Lagoon on the Cape Zeleny (White Sea), the annual dynamics of the vertical hydrological structure, and the seasonal dynamics of phytoplankton were traced, including species composition, and vertical distribution, abundance, nutrition type,and biomass.293 species and supraspecific taxa of algae and cyanobacteria were found. Most of the identified species are marine, and 38 species are freshwater. Taxonomic composition changed in the Lagoon during the year. Dinoflagellates dominated in winter and early June; unidentified cocci and flagellates in July; diatoms, dinoflagellates and unidentified cells in August; dinoflagellates in September; and unidentified cocci and flagellates in October–November. The abundance of algae also changed in the Lagoon during the year. The integrated biomass in the water column varied from 0.01 g C/m2 in January to 0.78 g C/m2 in early September. According to the abiotic parameters, the water column of the Lagoon is subdivided into several zones with different conditions and corresponding phytoplankton communities. Similarity between the communities of different horizons was 32-46% in summer and 7% in winter. The chemocline layer was the most populous. Almost the entire year it contained a maximum of phytoplankton biomass, 1-2 orders of magnitude higher than that in the overlying horizons. Comparison phytoplankton in the lagoon and in the nearby sea area was carried out. Despite the connection between the Lagoon and the sea phytoplankton structure in the surface water layer in the Lagoon and in the sea differs significantly in composition, quantitative parameters, and seasonal dynamics. The similarity of the communities in the sea and the Lagoon never exceeded 50%. In terms of biomass, the Lagoon lags behind the sea until mid-summer, but, starting from August, it outnumbers it, and the phytoplankton development in the Lagoon lasts longer, until late autumn. The phytoplankton structure and dynamics in the Lagoon and the sea by 24.5% was related to the daylight, water salinity, oxygen content and pH, but did not depend on water T, underwater illuminance, and depth. Oxygen content and pH are defined by PhP activity. Salinity serves as a vector of the vertical sequence of ecological niches. The daylight length seems to be the crucial factor of the seasonal PhP dynamics in the semi-isolated coastal stratified lakes and lagoons.

Interspecific competition between herbivorous insects is a major selection pressure affecting the distribution, abundance, and structure of their populations. Facilitator-mediated interactions, such as mutualism, can influence competition. Furthermore, the temporal dynamics of competitive relationships affect the interaction’s outcome. Here, we re-evaluated the data on the competition for space between two herbivorous insects commonly known as scales (Toumeyella martinezae and Opuntiaspis philococcus) in either the presence or absence of Liometopum apiculatum (a mutualistic species of T. martinezae) and its variations over time. We selected 27 Myrtillocactus geometrizans plants on which the studied insects were present; the plants were classified into one of five different conditions: either of the scale species were present on the plant, without its competitor; T. martinezae with neither its mutualistic species nor the competitor; and both scale species competing in either presence or absence of the mutualistic species. We kept a photographic record of each condition, measured the size of (as an indicator of the development stage) and area occupied by the individual scales, estimated the total coverage of each scale species, and assessed their relative occupation of space and their competitive intensity. We found temporal variations in competitive intensity. T. martinezae occupied more space during the first months, whereas O. philococcus did so towards the end of the study period. The population structure changed over time and between species, affecting the competitive interactions. In conclusion, the dynamics of competition change over time, and the mutualistic species has a positive effect on T. martinezae when the scales are in competition. However, temporal variations resulting from changes in the life cycle of the scales allow the two competitors to coexist in the same place at the same time.

The buildup dynamics of diode-pumped passively mode-locked solid-state laser is thoroughly explored by the real-time measurement with temporal sampling rate up to 40 GHz. A concise cavity is developed to ensure the transient dynamics purely arising from the gain medium and saturable absorber. Experimental results reveal that the laser output in the buildup process exhibits numerous passively Q-switched pulses followed with a damped relaxation oscillation prior to the stable mode locking. Furthermore, it is confirmed that the laser output has already displayed single clean mode-locked pulses inside the first several Q-switched envelopes before stepping into the stage of relaxation oscillation. The present real-time exploration is expected to provide important information for practical applications with temporal modulation of the pump intensity.

High-efficiency rock breaking by hydraulic jetting is the key to radial horizontal drilling technology. In order to improve the drilling efficiency of hydraulic jet rock breaking in radial horizontal wells, based on LS-Dyna display dynamics, a numerical simulation model of single nozzle jet rock breaking is established to analyze the influence of different nozzle parameters on rock breaking effect. Then, the numerical simulation model of the spin multi-nozzle jet bit is established, and the influence of different rotation speeds on the rock breaking effect of the jet bit is analyzed. Finally, the rock breaking drilling characteristics of the spin multi-nozzle jet bit and the conventional multi-nozzle jet bit are compared and analyzed. The results show that when the jet impacts the rock surface, the larger the inclination angle is, the larger the rock breaking width formed by the jet is. The smaller the dip angle, the greater the rock breaking depth. When the inclination angle is greater than 60 °, it is difficult to meet the needs of reaming. The width and depth of the nozzle gradually increase with the increase of the diameter. When the nozzle diameter is greater than 1.3mm, the growth rate of rock breaking depth begins to decrease. The optimum nozzle diameter is 1.3 mm. When v = 50m / s, the damage caused by the jet to the rock surface is very small, because the condition of rock fracture is not reached at this time. This shows that there is a critical value of the water jet impact velocity, and only when the velocity is reached, the rock will break. When the velocity is v = 150m / s, v = 200m / s, v = 250m / s, v = 300m / s, the rock breaks. At the same time, the higher the speed, the higher the degree of rock fracture, the greater the fracture depth, the greater the fracture area, the better the fracture effect. The tangential and radial velocity of the jet increases the shear and tensile failure rate of the sample, and improves the rock breaking efficiency of the jet, which has certain guiding significance for improving the rock breaking drilling efficiency of radial horizontal well drilling.

Bladder cancer (BLCA) is the sixth most common type of cancer and has a dismal prognosis if diagnosed late. To identify treatment options for BLCA, we systematically evaluated data from the Broad Institute DepMap project. We found that urothelial BLCA cell lines are among the most sensitive to microtubule assembly inhibition by paclitaxel treatment. Strikingly, we unveiled that the top dependencies in BLCA cell lines include genes encoding proteins involved in microtubule assembly, thus highlighting the importance of microtubule network dynamics as a major vulnerability in human BLCA. In cancers, such as ovarian and breast, where paclitaxel is the golden standard of care, resistance to paclitaxel treatment has been linked to p53-inactivating mutations. To study the response of BLCA to microtubule assembly inhibition and its mechanistic link with the mutational status of p53 protein, we treated a collection of BLCA cell lines with a dose range of paclitaxel and performed a detailed characterization of the response. We herein discovered that BLCA cell lines are significantly sensitive to low concentrations of paclitaxel, independently of their p53 status. Paclitaxel induced a G2/M cell cycle arrest and growth inhibition, followed by robust activation of apoptosis. Most importantly, we revealed that paclitaxel triggered a robust DNA-damage response and apoptosis program without activating the p53 pathway. Integration of transcriptomics, epigenetic and dependency data demonstrated that the response of BLCA to paclitaxel is independent of p53 mutational signatures, but strongly depends on the expression of DNA-repair genes. Our work highlights urothelial BLCA as an exceptional candidate for paclitaxel treatment and paves the way for the rational use of a combination of paclitaxel and DNA-repair inhibitors as an effective, novel therapeutic strategy.

To further investigate the immune response of Macrobrachium rosenbergii against Aeromonas veronii, comparative transcriptomic analyses of the M. rosenbergii hepatopancreas were conducted on challenge and control groups at 6, 12, and 24 h post-infection (hpi), independently. A total of 51,707 high-quality unigenes were collected from the RNA-seq data, and 8,060 differentially expressed genes (DEGs) were discovered through paired comparisons. Among three comparison groups, KEGG pathway enrichment analysis showed that 173 immune-related DEGs were considerably clustered into 28 immune-related pathways, including the lysosome and phagosome, etc. Moreover, the expression levels of the four key immune-related genes (TOLL, PAK1, GSK3β, and IKKα) were evaluated at various stages following post-infection in the hepatopancreas, hemolymph, and gill. Both PAK1 and GSK3β genes were highly upregulated in all three tissues at 6 hpi with A. veronii; TOLL was up-regulated in the hepatopancreas and hemolymph but down-regulated in the gill at 6 hpi; and IKKα was up-regulated in hemolymph and gill, but down-regulated in the hepatopancreas at 6 hpi. These findings lay the groundwork for understanding the immune mechanism of M. rosenbergii after contracting A. veronii.

We live in the information age and, ironically, meeting the core function of journalism – i.e., to provide people access to unbiased information – has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets, people’s skills and education, and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-parameters, namely Generations-Specific Issues, Skills & Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since none of the earlier works have reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media.

A calibrated mathematical model of antiviral immune response to SARS-CoV-2 infection is developed. The model considers the innate and antigen-specific responses to SARS-CoV-2 infection. Recently published data sets from human challenge studies with SARS-CoV-2 were used for parameter estimation. Understanding the regulation of multiple intertwined reaction components of the immune system is necessary for linking the clinical phenotypes of COVID-19 with the kinetics of immune responses. Consideration of multiple immune reaction components in a single calibrated mathematical model allowed us to address some fundamental issues related to pathogenesis of COVID-19, i.e. sensitivity of the peak viral load to parameters characterizing the specific response components, the kinetic coordination of the individual responses, and the factors favoring a prolonged viral persistence. The model provides a tool for predicting the infectivity of patients, i.e. the amount of virus which is transmitted via droplets from the person infected with SARS-CoV-2, depending on the time of infection. The thresholds in the relative unbalance between innate and adaptive response parameters which lead to a prolonged persistence of SARS-CoV-2 due to the loss of a kinetic response synchrony/coordination were identified.

Visualization has always been a crucial part of the educational process. Implementing computer algorithms and virtual reality tools into it is vital for the new generation engineers, scientists and researchers. In the field of chemistry education, various software that allow dynamic molecular building and viewing are currently available. These software are now used to enhance the learning process and ensure better understanding of the chemical processes from the visual perspective. The present short communication provides a summary of these applications based on the NarupaXR program, which is a great educational tool that combines the functionality and simple design necessary for an educational tool. NarupaXR is used with a companion application “Narupa Builder'' which requires a different file format, therefore a converter that allows a simple transition between the two extensions has been developed. The converter sufficiently increases the efficiency of the educational process. The automatic converter is freely available on GitLab (https://gitlab.com/teamSCAMT/converter_mol2_to_xml). The current communication provides detailed written instructions that can simplify the installation process of the converter and facilitate the use of both the software and the hardware of the VR set.

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modelling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modelled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period"); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed to discuss the effectiveness of some containment measures implemented across the region.