"The wisdom of crowd'' is so often observed in social discourses and activities around us. The manifestations of it are, however, so intrinsically embedded and behaviorally accepted that an elaboration of a social phenomenon evidencing such wisdom is often cheered as a discovery; or at least an astonishing fact. One such scenario is explored here, namely conceptualization and modeling of a food safety system, a system directly related to social cognition. Food safety is an area of concern these days. Models representing the food safety systems are recently published to study the effect of interactions between important entities of the system. For example, Knowles’s model finds conditions leading to a more efficient and dependable system of entities like consumers, regulators and stores with specific focus on regulators behavior and their impact on the food safety. The first contribution of this paper is reevaluation of Knowles’s model towards a more conscious understanding of ``the wisdom of crowd'' effects on inspection and consuming behaviors. The second contribution is augmenting of the model with social networking capabilities, which acts as a medium to spread information about stores and help consumers find stores which are not contaminated. Simulation results reveal that stores’ respecting social cognition improve effectiveness of the food safety system for consumers and stores both. Simulation findings also reveals that an active society has a capability to self-organize effectively even in the absence of any regulatory compulsion.

Agent-based models (ABMs) are computational models for simulating the actions and interactions of autonomous agents in time and space. These models allow users to simulate the complex interactions between individual agents and the landscapes they inhabit and are increasingly used in epidemiology to understand complex phenomena and make predictions. However, as the complexity of the simulated systems increases, notably when disease control interventions are considered, model flexibility and processing speed can become limiting. Here we introduce SamPy, an open-source Python library for stochastic agent-based modeling of epidemics. SamPy is a modular toolkit for model development, providing adaptable modules that capture host movement, disease dynamics, and disease control interventions. Memory optimization and design provide high computational efficiency allowing modelling of large, spatially-explicit populations of agents over extensive geographical areas. In this article, we demonstrate the high flexibility and processing speed of this new library. The version of SamPy considered in this paper is available at https://github.com/sampy-project/sampy-paper .

The escalating levels of atmospheric carbon dioxide (CO₂) due to anthropogenic activities have raised significant concerns regarding their far-reaching consequences, including potential impacts on human brain function. This study employs Agent-Based Modeling (ABM) and a comprehensive literature review to comprehensively examine the effects of varying atmospheric CO₂ concentrations on human brain activity. Historical and projected CO₂concentration data, along with neuroscientific insights, were integrated into the ABM framework to simulate brain activity responses under different CO₂ scenarios. The simulations revealed that elevated CO₂ levels disrupt neural activity and alter neurotransmitter profiles, indicating potential cognitive consequences. Sensitivity analysis emphasized the importance of accurate parameterization for robust simulations. The findings underscore the urgent need to address global CO₂ emissions to preserve cognitive health. Further research is essential to validate and refine the model, incorporating additional factors for a comprehensive understanding of the cognitive repercussions of rising CO₂ levels, crucial for informed policy-making and public awareness campaigns.

System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV) , serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.

In this work, we propose a self-supervised multi-agent system that meets the online learning of clustering tasks for video behavior recognition spatio-temporal tasks. Encoding visual behavioral actions as discrete temporal sequence(DTS). Real-time clustering recognition task in a multi-agent system for continuous model building, training, and correction. Finally, we implemented a fully decentralized multi-agent system and completed its feasibility verification in a surveillance video application scenario on vehicle path clustering.

The importance of predicting the exposure to environmental hazards is highlighted by issues like global climate change, public health problems caused by environment stresses, and property damages and depreciations. Several approaches have been used to assess potential exposure and achieve optimal results under various conditions, for example, for different scales, groups of people, or certain points in time. Micro-simulation tools are becoming increasingly important in human exposure assessment, where each person is simulated individually and continuously. This paper describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision making processes can be included for each individual based on rule-based behavior to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A simplified modeling scenario of the ABM framework in Hamburg, Germany, further demonstrates its utility in various urban environments and individual activity patterns, and portability to other models, programs and frameworks. The prototype model can potentially be extended to support environmental incidence management by exploring the daily routines of different groups of citizens and compare the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

The paper proposed a modification of the GeoSER(D) model previously developed by us by detailing the age structure of the population, personal schedule on weekdays and working days, and individual health characteristics of the agents, this made it possible to build a more realistic model of the functioning of the city and its residents. The developed model made it possible to simulate the spread of 3 types of the strain of the COVID-19 virus, and to analyze the adequacy of this model in the case of unhindered spread of the virus among city residents. The paper showed that SARS COV 2 spreads mainly from contacts in workplaces and transport, and schoolchildren and preschool children are the consequence, not the initiator of the epidemic. Fluctuations in the dynamics of various indicators of the spread of SARS COV 2 associated with the difference in the daily schedule on weekdays and weekends. It has been shown that people's daily schedules strongly influence the spread of SARS COV 2. For the more contagious "rapid" strains of SARS COV 2 (omicron), immunocompetent people become a significant source of infection. For the less contagious "slow strains" (alpha) of SARS COV 2, the most active source of infection is immunocompromised individuals (pregnant women). The more contagious – "fast" strain of the SARS COV 2 virus (omicron) spreads faster in public transport. For less contagious – "slow" strains of the virus (alpha), the greatest infection occurs due to work and educational contacts.

Mucilage from Coccinia grandis was extracted, isolated by maceration technique and precipitated, accordingly. The mucilage was evaluated for its physicochemical, binding, and disintegrant properties in tablets using paracetamol as a model drug. The crucial physicochemical properties such as flow properties, solubility, swelling index, loss on drying, viscosity, pH, microbial load, cytotoxicity were evaluated and the compatibility was analysed using sophisticated instrumental methods (TGA, DTA, DSC, and FTIR). The binding properties of the mucilage were used at three different concentrations and compared with starch and PVP as standard binders. The disintegrant properties of mucilage were used at two different concentrations and compared with standard disintegrants MCCP, SSG, and CCS. The wet granulation technique was used for the preparation of granules and was evaluated for the flow properties. The tablets were punched and evaluated for their hardness, friability, assay, disintegration time, in vitro dissolution profiles. In vitro cytotoxicity study of the mucilage was performed in human embryonic kidney (HEK) cell line using cytotoxic assay by MTT method. The outcome of the study indicated that the mucilage had good performance when compared with starch and PVP. Further, the mucilage acts as a good disintegrant than MCCP, SSG and CCS to paracetamol tablets. Moreover, the in vitro cytotoxicity evaluation results demonstrated that the mucilage is non-cytotoxic to human cells and is safe.

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent based simulator will enable the policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify the vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM ) which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population, allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of Covid-19 are used to validate the proposed model. However, it is highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.

Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the compucell3d.org modeling environment to simulate epithelial-mesenchymal transition of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Cell fate in the model is determined by an autonomous homeobox (HOX) clock driven by morphogenetic signals (e.g., FGF, WNT, ATRA, CDX). Executing the model renders a quantitative cell-level computation of mesodermal subpopulations and consequences of perturbation based on known embryogeny. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring experimental mouse embryology, with 50% reductions in FGF4, FGF8 and BMP4 signaling resulting in 86%, 98% and 59% reductions, respectively in the posterior mesodermal population, while ATRA exposure also resulted in a 78% decrease in this population. This model enables integration of in vitro chemical bioactivity data for specific molecular targets with known embryology to test mechanistic veracity and quantitative prediction of altered development.

Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and propose a Transformer-based Time and Team Reinforcement Learning scheme (T3OMVP) to address the problem. First, a new multi-vehicle pursuit model is constructed based on decentralized partially observed Markov decision processes (Dec-POMDP) to instantiate this problem. Second, by introducing and modifying the transformer-based observation sequence, QMIX is redefined to adapt to the complicated road structure, restricted moving spaces and constrained observations, so as to control vehicles to pursue the target combining the vehicle’s observations. Third, a multi-intersection urban environment is built to verify the proposed scheme. Extensive experimental results demonstrate that the proposed T3OMVP scheme achieves significant improvements relative to state-of-the-art QMIX approaches by 9.66%~106.25%. Code is available at https://github.com/pipihaiziguai/T3OMVP.

A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of Agent-based Complex Systems—ACS science—has a significant potential to handle such challenges. The advantages of ACS science for sustainability are addressed by way of identifying the key elements and challenges in sustainability science, the generic features of ACS, and the key advances and challenges in modeling ACS. Artificial intelligence and data science promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms.

As of today, 20 disease modifying drugs (DMD) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgement and experience of neurologists and the evaluation of therapeutic response can only be obtained by monitoring clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, the aim of this study is to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named UISS (Universal Immune System Simulator) able to simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing-remitting courses. These patients were characterized on the basis of their age, sex, presence of oligoclonal bands, therapy and MRI lesion load at onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, based on the available data we generated a few simulation scenarios for each patient, including those who matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which really occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment.

Background. We created agent-based model for short- and longterm forecasting of COVID-19 and for evaluation how the actions of the regulator affected the human and material resources of the healthcare system. Methods. The model was implemented in the AnyLogic software. It includes two state charts – social network and disease transmission. The COVID-19 Essential Supplies Forecasting Tool (COVID-ESFT, version 2.0) was used to determine healthcare resources needed. Results. Satisfactory results were obtained with long-term (up to 50 days) forecasting in the case of a monotonous change in total cases curve. However, if periods of relative stability are accompanied by sudden outbreaks, relatively satisfactory results were obtained with short-term forecasting, up to 10 days. Simulation of various scenarios showed that the most important place for the spread of infection are families. Wherein the maximum number of cases of COVID-19 is observed in the age group of 26-59 years. Due to a set of measures taken by government agencies, the number of cases in Karaganda city was 3.2 times less than was predicted in “no intervention” scenario. Economic effect is estimated at 40 %. Conclusion. Performed model is an attempt to consider as much as possible the peculiarities of the socio-demographic situation in the country. In the future, we will be prepared to some extent for challenges like those we have experienced in the past three years.

Modelling and presenting mathematical relationships for human behaviour is one of the most complex issues that researchers have always dealt with. In this article, a bottom-up framework for calculating agricultural needs is presented using the socioeconomic characteristics of farmers (such as education level, age, and dependence on income on agriculture) and how their lands are located concerning each other (interactions between neighbours). The objective function of this framework is to maximize the profit of individual farmers based on the amount of water received. Two scenarios, ABM1 (not considering neighbourhood effects) and ABM2 (all cases of farmers' placement and feeling neighbourhood effects), were investigated. In the first scenario (ABM1), there was a noteworthy reduction in water deficit volumes by approximately 35%, accompanied by a 20% increment in farmers' profits. Interestingly, higher risk-taking tendencies correlated with reduced profit margins. The second scenario (ABM2) underscored the significant role of neighborhood dynamics in cultivating diverse behavioral patterns among farmers, subsequently affecting their profitability. A granular examination revealed that farmers with a higher propensity for risk-taking generally accrued lower profits. Additionally, the study facilitated the calculation of total annual profits and average water consumption for each farmer, offering valuable insights for optimizing water resource management and allocation strategies. These findings are instrumental for planners and water resource managers aiming to promote sustainable agricultural practices and efficient water use.

This work presents some characteristics of MoNet, a computerized platform for the modeling and visualization of complex systems. Emphasis is on the ideas that allowed the successful progressive development of this modeling platform, which goes along with the implementation of applications to the modeling of several studied systems. The platform has the capacity to represent different aspects of systems modeled at different observation scales. This tool offers advantages in the sense of favoring the perception of the phenomenon of the emergence of information, associated with changes of scale. Some criteria used for the construction of this modeling platform are included. The power of current computers has made practical representing graphic resources such as shapes, line thickness, overlaying-text tags, colors and transparencies, in the graphical modeling of systems made up of many elements. By visualizing diagrams conveniently designed to highlight contrasts, these modeling platforms allow the recognition of patterns that drive our understanding of systems and their structure. Graphs that reflect the benefits of the tool regarding the visualization of systems at different scales of observation are presented to illustrate the application of the platform.

Numerous innovative advancements in dressing technology for wound healing have emerged. Among the various types of wound dressings available, hydrogel dressings, structured with a three-dimensional network and composed of predominantly hydrophilic components, are widely used for wound care due to their remarkable capacity to absorb abundant wound exudate, maintain a moisture environment, provide soothing and cooling effects, and mimic the extracellular matrix. Composite hydrogel dressings, one of the evolved dressings, address the limitations of traditional hydrogel dressings by incorporating additional components, including particles, fibers, fabrics, or foams, within the hydrogels, effectively promoting wound treatment and healing. The added elements enhance the features or add specific functionalities of the dressings, such as sensitivity to external factors, adhesiveness, mechanical strength, control over the release of therapeutic agents, antioxidant and antimicrobial properties, and tissue regeneration behavior. They can be categorized as natural or synthetic based on the origin of the main components of the hydrogel network. This review focuses on recent research on developing natural polysaccharide-based composite hydrogel wound dressings. Their preparation and composition, the reinforcement materials integrated into hydrogels, and therapeutic agents are also explored. Furthermore, their features and the specific types of wounds where applied are discussed as well.

Machine learning offers advanced tools for efficient management of radio resources in modern wireless networks. In this study, we leverage a multi-agent deep reinforcement learning (DRL) approach, specifically the Parameterized Deep Q-Network (DQN), to address the challenging problem of power allocation and user association in massive multiple-input multiple-output (M-MIMO) communication networks. Our approach tackles a multi-objective optimization problem aiming to maximize network utility while meeting stringent quality of service requirements in M-MIMO networks. To address the non-convex and nonlinear nature of this problem, we introduce a novel multi-agent DQN framework. This framework defines a large action space, state space, and reward functions, enabling us to learn a near-optimal policy. Simulation results demonstrate the superiority of our Parameterized Deep DQN (PD-DQN) approach when compared to traditional DQN and RL methods. Specifically, we show that our approach outperforms traditional DQN methods in terms of convergence speed and final performance. Additionally, our approach shows 72.2 % and 108.5 % improvement over DQN methods and RL method respectively in handling large-scale multi-agent problems in M-MIMO networks.

Wireless Sensor Networks (WSN) is designed to collect information across a large number of limited battery sensor nodes. Therefore, it is important to minimize the energy consumption of each node, which leads to the extension of the network life. Our goal is to design an intelligent WSN that collects as much information as possible to process it intelligently. To achieve this goal, an agent has been migrated to each node in order to process the information and to cooperate with these neighboring nodes while Mobile Agents (MA) can be used to reduce information between nodes and send those to the base station (Sink). This work proposes to use communication architecture for wireless sensor networks based on the Multi Agent System (MAS) to ensure optimal information collection. The collaboration of these agents generates a simple message that summarizes the important information in order to transmit it by a mobile agent. To reduce the size of the MA, the nodes of the network have been grouped into sector. As for each MA, we have established an optimal itinerary, consuming a minimum amount of energy with the data aggregation efficiency in a minimum time. Successive simulations in large scale wireless sensor networks through the SINALGO simulator show the performance of our proposal, in terms of energy consumption and package delivery rate.

This investigation explores a novel path-planning and optimization strategy for multiple cooperative robotic agents, applied in a fully observable and dynamically changing obstacle field. Current dynamic path planning strategies employ static algorithms operating over incremental time-steps. We propose a cooperative multi-agent (CMA) based algorithm, based on natural flocking of animals, using vector operations. It is preferred over more common graph search algorithms like A* as it can be easily applied for dynamic environments. CMA algorithm executes obstacle avoidance using static potential fields around obstacles, that scale based on relative motion. Optimization strategies including interpolation and Bezier curves are applied to the algorithm. To validate effectiveness, CMA algorithm is compared with A* using static obstacles due to lack of equivalent algorithms for dynamic environments. CMA performed comparably to A* with difference ranging from -0.2% to 1.3%. CMA algorithm is applied experimentally to achieve comparable performance, with an error range of -0.5% to 5.2%. These errors are attributed to the limitations of the Kinect V1 sensor used for obstacle detection. The algorithm was finally implemented in a 3D simulated space, indicating that it is possible to apply with drones. This algorithm shows promise for application in warehouse and inventory automation, especially when the workspace is observable.

We aim to evaluate efficiency of two American surveillance systems for monitoring the spread of antimicrobial-resistant (AMR) gonorrhea among men who have sex with men (MSM) using the novel continuous-time agent-based model of gonorrhea transmission. The model was developed using the simulation modelling tool AnyLogic and accounts for susceptible and resistant strains of N. gonorrhoeae, symptomatic and asymptomatic infection and various routes of transmission between different anatomical sites. The model was calibrated using a Bayesian calibration approach. The surveillance systems are the Gonococcal Isolate Surveillance Project (GISP) and the enhanced Gonococcal Isolate Surveillance Project (eGISP). We calculated accuracy, sensitivity, specificity and estimation error for each surveillance system based on the number of isolates submitted in 2018. We also varied that number to see its effect on the outcomes. Our results show that the accuracy of eGISP was between 66% and 92%, while GISP demonstrates low accuracy between 44% and 48%. We also determined that increasing the number of isolates results in improved performance for eGISP, while GISP is not particularly sensitive to it.

There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment.

The high penetration of intermittence resources in the energy market accelerates significantly the decarbonization process but, on the other hand, the electrical system has to face the problem of unbalances. Renewable Energies Sources (RES) are hard to precisely forecast, and power plants are not able to predict the amount of energy that they can provide far from the real time delivery. In this frame, the intraday market gets a fundamental role allowing agents to adjust their position close to the delivery time. In this work we suggest an agent-based model of intraday market combined with genetics algorithms to understand what the best strategy could be adopted by players in order to optimize the market efficiency in terms of welfare and unsold quantity. In the first part we show the effect on the market prices of different scenarios in which players aim at maximizing their revenues and selling/buying all their volumes. In the second part we show the effect of a particular genetic algorithm on the model, focusing on how agents can adapt their strategy to enhance the market efficiency. Comparative analyses are also performed to investigate how the welfare of the system increases as well as the unsold quantity decrease when genetic algorithm is introduced

In this research, we present the first steps taken towards a data-driven agent-based model (ABM) specifically designed for simulating infectious disease dynamics in Greece. Amidst the ongoing COVID-19 pandemic caused by SARS-CoV-2, this research holds significant importance as it can offer valuable insights into disease transmission patterns and assist in devising effective intervention strategies. To the best of our knowledge, no similar study has been conducted in Greece. We constructed a prototype ABM that utilizes publicly accessible data to accurately represent the complex interactions and dynamics of disease spread in the Greek population. By incorporating demographic information and behavioral patterns, our model captures the specific characteristics of Greece, enabling accurate and context-specific simulations. By using our proposed ABM, we aim to assist policymakers in making informed decisions regarding disease control and prevention. Through the use of simulations, policymakers have the opportunity to explore different scenarios and predict the possible results of various intervention measures. These may include strategies like testing approaches, contact tracing, vaccination campaigns, and social distancing measures. Through these simulations, policymakers can assess the effectiveness and feasibility of these interventions, leading to the development of well-informed strategies aimed at reducing the impact of infectious diseases on the Greek population. This study is an initial exploration toward understanding disease transmission patterns and a first step towards formulating effective intervention strategies for Greece.

In this study, we examine the relationship of bank level lending and borrowing decisions and the risk preferences on the dynamics of the interbank lending We develop an agent-based model that incorporates individual bank decisions using the temporal difference reinforcement learning algorithm with empirical data of 6600 S. banks. The model can successfully replicate the key characteristics of interbank lending and borrowing relationships documented in the recent literatur A key finding of this study is that risk preferences at individual bank level can lead to unique interbank market structures which are suggestive of the capacity that the market responds to surprising

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

The anti-money laundering (AML) process has failed both in identifying suspicious cases in due time as in assisting the AML analysts in decision making. Starting from a new generic anti-fraud approach, this article presents the main aspects related to the development of a multi-agent system that goes beyond the capture of suspicious transactions, seeking to assist the human expert in the analysis of suspicious behaviour. First, a transactional behavioural profile of clients is obtained in a data mining process. A set of rules, obtained through data mining over a real database, in conjunction with specific rules based on legal aspects and in the expertise of the AML analysts make up the agents' knowledge base. The cases for which the system was unable to suggest a decision are flagged as requiring more detailed analysis. The system analysed 6 months of real transactions and indicated several suspicious profiles, a set of these suspects was investigated by the AML analysts who proved the suspicion of several cases, including some that had not been identified by the systems in execution.

The research employs both literature and experimental data to develop reasonable strategies for melon fly control. The objects of research were sierozem soils of the Zhanakorgan region (Kyzylorda region), bentonite clays of the Sauran region (Turkestan region), vermicompost obtained at the production site of the Research Institute "Ecology" at the International Kazakh-Turkish University named after Khoja Ahmed Yasawi. The competitive agent 'Vermiserbent' was developed by combining sulphur-perlite-containing waste (SPCW), vermicompost (VC), and natural bentonite clay. When incorporated into the soil, it serves as both an insecticide and a fertiliser recovery agent. Disinfection and enrichment of barren Sierozem soils in southern Kazakhstan could provide an eco-friendly approach to protect cucurbits (melon, watermelon, and pumpkin) against the melon fly. The average yield of watermelon treated with vermiserbent increased by 2.3 t/ha compared to the control, melon by 4.6 t/ha, pumpkin by 5.6 t/ha. The marketability of gourds as watermelons and melon after treatment with fertilizer increased by 1.2 times, and pumpkin by 1.1 times. The findings of studies conducted in agricultural fields in the Turkestan and Kyzylorda regions have shown that it is possible to produce environmentally sound gourds using a mixture of vermicompost, bentonite, and SPCW.

This study assesses the impact of incorporating an adaptive learning mechanism into an agent-based model (ABMS) simulating behavior on a university campus during the course of a pandemic outbreak, with a particular case on the COVID-19 pandemic. The aim is to reduce overcrowding and infections on campus through the use of Reinforcement Learning (RL). Our findings indicate that RL is a viable approach for effectively representing agents’ behavior within this context. The results reveal specific temporal patterns of overcrowding violations. While our study successfully mitigated campus crowding, it had limited influence on altering the course of the epidemic. This highlights the necessity for comprehensive epidemic control strategies that consider the role of individual decision-making influenced by adaptive learning, along with the implementation of targeted interventions. This research significantly contributes to our understanding of adaptive learning within complex systems and offers valuable insights for shaping future public health policies in similar community settings. Future research directions encompass exploring various parameter settings and updating representations of the disease’s natural history.

Pyridoxine is a versatile molecule that forms part of the family of B vitamins. It is used to treat and prevent vitamin B6 deficiency and certain type of metabolic disorders. Besides, pyridoxine molecule has been investigated as a suitable ligand toward metal ions. Nevertheless, the study of the magnetic properties of metal complexes containing lanthanide(III) ions and this biomolecule has been unexplored. We have synthesized and characterized a novel pyridoxine-based GdIII complex of formula [Gd(III)(pyr)2(H2O)4]Cl3 • 2 H2O (1) [pyr = pyridoxine]. 1 crystallizes in the triclinic system with space group Pī. In its crystal packing, the cationic [Gd(pyr)2(H2O)4]3+ entities are linked each other through H-bonding interactions involving non-coordinating water molecules and chloride anions. In addition, Hirshfeld surfaces of 1 were calculated to further investigate their intermolecular interactions in the crystal lattice. The study of the magnetic properties of 1 through ac magnetic susceptibility measurements reveals the occurrence of slow relaxation of magnetization in this mononuclear Gd(III) complex, this fact indicating an unusual single-ion magnet (SIM) behavior. We have also studied the relaxometric properties of 1, as a potential contrast agent for high-field MR imaging, from solutions of 1 prepared in physiological serum (0.0 - 3.2 mM range) and measured at 3 T on a clinical MR scanner. The relaxivity values obtained for 1 are larger than those of some commercial MR imaging contrast agents based on mononuclear Gd(III) systems

We report an agent-based model to compare the effectiveness of simple and complex mobile dating application interfaces in generating matches for virtual users. We define the relative complexity of dating applications as the number of available features and dub this variable, the multiplicity. We replicate some of the most popular mobile dating applications through the generation of a synthetic population endowed with attributes, preferences, and behaviors drawn from literature. We treat our data as a network dataset and use a robust statistical procedure (MRQAP) to issue a valid and reliable comparison between simulated applications. We show how the quadratic assignment procedure can be used to compare network simulations rigorously. As a result, we observe a direct relationship between multiplicity and agent-level experiences and expectations in match generation. We also observe the emergence of divergent matching systems with minor rule changes as well as several expected properties of online dating systems. This work serves as a proof-of-concept in the integration of classical social network analysis methods with agent-based modeling to compare virtual designs and to enhance the policy-generation process of online social networks.

The paramagnetic gadolinium(III) ion is used as contrast agent in magnetic resonance (MR) imaging to improve the lesion detection and characterization. It generates a signal by changing the relaxivity of protons from associated water molecules and creates a clearer physical distinction between the molecule and the surrounding tissues. New gadolinium-based contrast agents displaying larger relaxivity values and specifically targeted might provide higher resolution and better functional images. We have synthesized the gadolinium(III) complex of formula [Gd(thy)2(H2O)6](ClO4)3·2H2O (1) [thy = 5-methyl-1H-pyrimidine-2,4-dione or thymine], which is the first reported compound based on gadolinium and thymine nucleobase. 1 has been characterized through vis-IR, SEM-EDAX and single-crystal X-ray diffraction techniques, and its magnetic and relaxometric properties have been investigated by means of SQUID magnetometer and MR imaging phantom studies, respectively. On the basis of its high relaxivity values, this gadolinium(III) complex can be considered a suitable candidate for contrast enhanced magnetic resonance imaging.

For the task planning problem of the command and control architecture, the existing algorithms have problems such as low efficiency and poor re-planning quality under abnormal conditions. Based on the requirements of the current accusation architecture, this paper constructs a distributed command and control architecture model based on multi-agents, which makes use of the superiority of multi-agents in dealing with complex tasks. The concept of MultiAgents-HTN is proposed under the framework. The original hierarchical task network planning algorithm is optimized, the multi-agent collaboration framework is redefined, and the coordination mechanism of local conflict is designed. Taking the classical resource scheduling problem as the experimental background, the comparison between the proposed algorithm and the classical HTN algorithm is carried out. The experimental results show that the proposed algorithm has higher quality and higher efficiency than the existing algorithm, and the space anomaly is heavy during processing. The planning is more efficient, and the time is more complicated and superior in dealing with the same problem, with good convergence and adaptability. The conclusion proves that the distributed command and control architecture proposed in this paper has high practicability in related fields and can solve the problem of distributed command and control architecture in multi-agent environment.

UAVNs (unmanned aerial vehicle networks) may become vulnerable to threats and attacks due to their characteristic features such as high mobility, highly dynamic network topology, and open-air wireless environments. Since previous work has focused on classical and metaheuristic-based approaches, none of these approaches have a self-adaptive approach. In this article, we examine the challenges of cyber detection methods to secure UAVNs and review exiting security schemes proposed in the current literature. Furthermore, we propose an agent-based self-protective method (ASP-UAVN) for UAVNs that is based on the Human Immune System (HIS). In ASP-UAS, the safest route from the source UAV to the destination UAV is chosen according to a self-protective system. In this method, a multi-agent system using an Artificial Immune System (AIS) is employed to detect the attacking UAV and choose the safest route. In the proposed ASP-UAVN, the route request packet (RREQ) is initially transmitted from the source UAV to the destination UAV to detect the existing routes. Then, once the route reply packet (RREP) is received, a self-protective method using agents and the knowledge base is employed to choose the safest route and detect the attacking UAVs. The method is evaluated here via extensive simulations carried out in the NS-3 environment. The experimental results of four scenarios demonstrated that the ASP-UAS increases the Packet Delivery Rate (PDR) by more than 17.4, 20.8, and 25.91%, and detection rate by more than 17.2, 23.1, and 29.3%, and decreases the Packet Loss Rate (PLR) by more than 14.4, 16.8, and 20.21%, the false-positive and false-negative rate by more than 16.5, 25.3, and 31.21% those of SUAS-HIS, SFA and BRUIDS methods, respectively.

The ongoing COVID-19 pandemic has raised numerous questions concerning the shape and range of state interventions whose goals are to reduce the number of infections and deaths. The lockdowns, which have become the most popular response worldwide, are assessed as being an outdated and economically inefficient way to fight the disease. However, in the absence of efficient cures and vaccines, there is a lack of viable alternatives. In this paper we assess the economic consequences of the epidemic prevention and control schemes that were introduced in order to respond to the COVID-19 pandemic. The analyses report the results of epidemic simulations that were obtained using the agent-based modelling methods under the different response schemes and their use in order to provide conditional forecasts of the standard economic variables. The forecasts were obtained using the DSGE model with the labour market component.

In this work, a xylan-based antimicrobial additive agent was prepared and aimed for uses in paper products against Escherichia coli bacteria. The derived Cationic-Xylan-grafted-PHGH (CX-g-PHGH) was successfully synthesized by graft copolymerization of cationic-xylan with guanidine polymer (PHGH) using ceric ammonium nitrate as initiator. The obtained CX-g-PHGH had maximum PHGH grafting ratio of 18.45% and efficiency of 58.45%, and showed good viscosity and thermal stability. Furthermore, the paper samples prepared in this work were reinforced obviously with the addition of CX-g-PHGH by improved mechanical properties. Compared to the reference paper without any of the xylan-derivatives, the index of tensile, tear, burst and folding endurance of the paper had increases up to 20.07%, 25.31%, 30.20% and 77.78%, respectively. Moreover, the prepared CX-g-PHGH paper exhibited an efficient antimicrobial activity against E. coli bacterial, by which a lot of applications based on the new xylan-derived additive agent obtained in this work could be found, especially in field of antimicrobial paper products against E. Coli bacteria from contaminated food.

Ever-increasing need for improving the livability of a city and improve outcomes for its residents, over the last decade, the adoption of technology to develop urbanised societies around the world has given rise to the need for developing smart cities. The speed at which the world population is growing, the use of Internet of Things in smart cities have really advanced the quality of life. One significant area of concern within the smart city framework is waste management. If the waste within a city is not adequately managed, then it leads to issues in the health of the citizens. Additionally, the waste management has such a high impact on the environmental footprint, hence the need to have a smart way of managing waste is of critical importance. Through our research, we analyse the challenges of waste management within a city to understand the impact of the problem on to the citizens and overall city operations. We then investigate ways in which we can solve these problems using the emerging technologies, such as the Internet of Things, to collect valuable data of large volumes arriving at an astronomical rate, then apply multi-agent deep reinforcement learning algorithms to harness the power of big data to extract meaningful information and actionable insights. We ingest data generated by our Internet of Things into our algorithm for three main purposes including providing the notifications to an external system, for example, a map navigation engine out of the scope for this project but a future extension for route optimisation and waste vehicle tracking; extracting and reporting the actionable insights from the underlying data; and consuming the extracted data for predictive forecasting to draw out the unknown patterns of waste fill levels within various geographical locations and again send out triggers and notification to external systems for example a waste collection authority who can efficiently schedule the waste collection vehicles and optimise the route. To achieve the above mentioned outcomes, we propose a framework that is agnostic of the hardware that it connects to and can effectively interface with a wide variety of hardware keeping a level of abstraction in the architecture.

This paper introduces an Agent-Based Model (ABM) for investigating the dynamics of the Internet of Things (IoT) ecosystem, specifically focusing on dynamic coalition formation among IoT Service Providers (SPs). Extending our prior work in 5G network modeling, the ABM captures intricate interactions among device, network operator, SP, and customer agents, offering a versatile framework for studying IoT ecosystem dynamics. The simulation portrays the adaptive nature of collaborations, providing insights into the role of dynamic coalition formation in shaping the collaborative landscape of IoT.

Natural products, especially biologically active compounds derived from plants, have been and continue to be invaluable in anticancer research and therapeutic discoveries. Rice bran arabinoxylan compound (RBAC) is a heteropolysaccharide extract of defatted rice bran obtained through enzymatic treatment with Lentinus Edodes mycelium. RBAC exhibited antitumour properties by arresting in vivo tumour growth in several murine models. In vitro experiments showed RBAC to promote apoptosis in cancer cells by increasing the susceptibility of CD95 (Fas/APO-1) ligands and downregulating Bcl-2 proteins to trigger the permeabilisation of the mitochondrial membrane. RBAC may restore immune dysfunction in cancer patients, most notably by upregulating natural killer cell activity and modulating cytokine production to enhance inflammatory and cytotoxic responses. As such, RBAC acts on the immune system to enhance chemopreventative activity and block tumourigenesis. In treatment, RBAC works synergistically with other natural products and chemotherapeutic agents, such as daunorubicin and paclitaxel, by enhancing drug transport to increase DNA damage in tumours and reduce proliferation. In a clinical trial, combining RBAC with chemoembolization in treating liver cancer showed improved response, reduced recurrence, and prolonged survival time. RBAC has been shown to augment the endogenous antioxidant system to prevent oxidative stress and thus protect against the harmful side effects of radiation therapy. RBAC also has chemoprotective effects, with animal and human studies showing reduced toxicity and side effects from chemotherapy, leading to improved health-related quality of life. Meta-analysing the best available evidence indicates that RBAC treatment increases the odds of survival among cancer patients by 4.02 times (95% CI: 1.67, 9.69) in the first year and 2.89 (95% CI: 1.56, 5.35) times in the second year after commencing treatment, compared to those not treated with RBAC. Notwithstanding, additional research is needed to characterise, quantify, and standardise the active ingredients in RBAC responsible for the anticancer effects. More well-designed, large-scale clinical trials are required to further substantiate the treatment efficacies.

Respiratory infections are usually characterized by mucus hypersecretion. This condition may worsen and prolong symptoms and signs. Mucoactive agents include different molecules with different mechanisms of action. Sobrerol is a monoterpene able to fluidify mucus, increase mucociliary clearance, and exert antioxidant activity. Sobrerol is available in various formulations (granules, syrup, nebulized, and suppository). Sobrerol has been on the market for over 50 years. Several studies investigated its efficacy and safety in acute and chronic respiratory diseases characterized by mucus hyperproduction. Seven pediatric studies have been conducted with favorable outcomes. Recently, regulatory agencies reduced the treatment duration to three days. Therefore, a future study will test the hypothesis that a combination of oral and topical sobrerol could benefit children and adults with frequent respiratory infections. The rationale considers that mucus accumulation could be a risk factor for increased susceptibility to have infections.

(R)-(+)-3,5-Dinitro-N-(1-phenylethyl)benzothioamide 1 is a potential chiral solvating agent (CSA) for the spectral resolution of enantiomers by 1H NMR spectroscopy. The single enantiomer of 1 was synthesized from commercially (R)-(+)-a-methylbenzylamine 2 in two-steps with 85% yield.

This study aimed to evaluate the effects of a palygorskite-based antibacterial agent (PAA) as an alternative to antibiotics on growth performance, blood parameters, rumen fermentation, and rumen microbiota in sheep. A total of 120 sheep were randomly divided into five groups of six replicates with four sheep each. Sheep were fed a basal diet, an antibiotic diet supplemented with 500 g/t chlortetracycline (CTC), and a basal diet supplemented with 500, 1,000, and 2,000 g/t PAA for 80 d, respectively. Supplementation with 2,000 g/t PAA and 500 g/t CTC increased the average daily gain (ADG) of sheep compared with the control group (P < 0.05). Diets supplemented with 2,000 g/t PAA and 500 g/t CTC reduced (P < 0.05) the feed: gain (F/G ratio) in the overall periods. Dietary Supplementation with 1,000 g/t PAA significantly increased albumin and total protein (P < 0.05). A significant positive correlation was found between growth hormone concentration and PAA supplementation (P < 0.05). In addition, compared to the control group, the CTC group had higher growth hormone concentration and lower lipopolysaccharide concentration (P < 0.05). No dif-ference was observed between the five groups in terms of rumen fermentation characteristics (P > 0.05). At the phylum level, the relative abundance of Proteobacteria was lower in the PAA 2000 and CTC 500 groups than that in the control and PAA 500 groups (P < 0.05). At the genus level, a sig-nificant decrease (P < 0.05) in the relative abundance of RuminococcaceaeUCG-010 was observed in the PAA 1000, PAA 2000, and CTC 500 groups compared with that in the control group. In addition, the relative abundance of Prevotella1 (P < 0.05) was higher in the PAA 2000 group than that in the control group. These findings indicate that dietary supplementation with PAA has ameliorative effects on growth performance, blood parameters, and rumen microbiota, with an optimal dosage of 2,000 g/t for sheep.

Alkenyl Succinic Anhydride (ASA) is a sizing agent used in papermaking to increase the water repellency of paper. Almost 60 years after the introduction of the chemical in papermaking, scientists still have differing views on how ASA interacts with cellulose. Several experiments were conducted to bring more clarity to the ASA sizing mechanism, especially on the contentious question of ASA-cellulose covalent bonding or the esterification reaction between ASA and cellulose during papermaking. Herein, research papers and patents, including experiments and results, from the1960’s to 2020 were reviewed. Our investigation revealed that the ester bond formation between ASA and cellulose is insignificant and is not a prerequisite for sizing effectiveness; the main ASA related material found in sized paper is hydrolyzed ASA or both hydrolyzed ASA and ASA salt. In addition, ASA emulsion stability and ASA emulsion retention are important for sizing efficiency improvement.

Continuous greenhouse gas emissions are causing global warming and impacting the habitats of many animals. Researchers in the field of electric power are making efforts to mitigate this situation. Operating and maintaining the power grid in an economic, low-carbon, and stable is challenging. To address the issue, we propose a grid dispatching technique that combines prediction technology, reinforcement learning, and optimization technology. Prediction technology can forecast future power demand and solar power generation, while reinforcement learning and optimization technology can make charging and discharging decisions for energy storage devices based on current and future grid conditions. In the power system, the aggregation of distributed energy resources increases uncertainty, particularly due to the fluctuating generation of renewable energy. This requires the use of advanced predictive control techniques to ensure long-term economic and decarbonization goals. In this paper, we present a real-time dispatching framework that integrates deep learning-based prediction, reinforcement learning-based decision-making, and stochastic optimization techniques. The framework can rapidly adapt to target uncertainty caused by various factors in real-time data distribution and control processes. The proposed framework achieved global Champion in the NeurIPS Challenge 2022 competition and demonstrated its effectiveness in practical scenarios of intelligent building energy management.

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system wide transition has not been researched sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric, diesel and gasoline powered vehicle models. We use the transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently we analyze the product systems’ vehicle production, use phase and End-of-Life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of the complete car based transportation in urban agglomerations and is able to analyze different transport sectors.

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Bedbug (Cimex spp.) are a nuisance public-health pest that is on the rise globally, particularly in crowded cities such as Hong Kong. To investigate the health impacts of bedbug infestations among bedbug victims, online surveys were distributed in Hong Kong between June 2019 to July 2020. Data on sociodemographics, self-rated health, average hours of sleep per day, and details of bedbug infestation were collected. Bivariate and multivariable analysis were performed using logistic regression. The survey identified 422 bedbug victims; among them, 223 (52.9%) experienced ≥5 bites in the past month, most bites occurred on the arms (n=202, 47.8%) and legs (n=215, 51%), and the most common reaction to bites were itchiness (n=322, 76.3%), redness, and swelling of the skin (n=246, 58.1%), and difficulties sleeping or restlessness (n=125, 29.6%). Bites usually occurred during sleep (n=230, 54.5%). For impact on daily life in the past month, most bedbug victims reported moderate to severe impact on mental and emotional health (n=223, 52.8%) and sleeping quality (n=239, 56.6%). Lower self-rated health (aOR&lt;1) was independently associated with impact to physical appearance (p=0.008), spending money on medication or doctor consultation (p=0.04), number of bites in the past month (p=0.023), and irregular time of bites (p=0.003). Lower average hours of sleep per day (aOR&lt;1) was independently associated with impact on mental and emotional health (p=0.016). This study brings attention to the neglected issue of bedbug infestation by considering bedbugs as an infectious agent instead of a vector and providing empirical evidence describing its health impacts.

Effective detection of isopropyl methylfluorophosphonate (GB, sarin), a type of organophosphine poisoning agent, is an urgent issue to be addressed in maintaining public safety. In this research, a gas-sensitive film material defined as poly (4-hydroxy-4,4-bis trifluoromethyl) - butyl-1-enyl) - si-loxane (SXFA) with the structure of hexafluoroisopropyl (HFIP) functional group was synthesized by using methyl vinylpropyl dichlorosilane and hexafluoroacetone trihydrate as initial materials. Synthesis process products were characterized by FTIR. SXFA was prepared on a 200 MHz shear surface wave delay line with spin coating method for GB detected. Detection limit of ＜ 0.1 mg/m3 was obtained through conditional experiments. Meanwhile, we also obtained the maximum re-sponse of 2.168 mV at 0.1 mg/m3 concentration, which indicting a much lower detection limit of SAW-SXFA sensor. Additionally, the maximum response standard deviation of 0.11 mV with a co-efficient of variation of 0.01, as well as the maximum recovery standard deviation of 0.22 mV with a coefficient of variation of 0.02 were also obtained through 5 repeated experiments. Results showed that the SAW-SXFA sensor has strong selectivity and reproducibility, good selectivity, positive detection ability, high sensitivity, and fast alarm performance for sarin detection.

In this paper, we unite concepts from Husserlian phenomenology, the active inference framework in theoretical biology, and category theory in mathematics to develop a comprehensive framework for understanding social action premised on shared goals. We begin with an overview of Husserlian phenomenology, focusing on aspects of inner time consciousness, namely, retention, primal impression, and protention. We then review active inference as a formal approach to modeling agent behavior, based on variational (approximate Bayesian) inference. Expanding upon Husserl’s model of time consciousness, we consider collective goal-directed behavior, emphasizing shared protentions among agents and their connection to the shared generative models of active inference. This integrated framework aims to formalize shared goals in terms of shared protentions, and to thereby shed light on the emergence of group intentionality. Building on this foundation, we incorporate mathematical tools from category theory; particularly, sheaf and topos theory, to furnish a mathematical image of individual and group interactions within a stochastic environment. Specifically, we employ morphisms between polynomial representations of individual agent models, allowing predictions not only of their own behaviors but also those of other agents and environmental responses. Sheaf and topos theory facilitate the construction of coherent agent worldviews and provides a way of representing consensus or shared understanding. We explore the emergence of shared protentions, bridging the phenomenology of temporal structure, multi-agent active inference systems, and category theory. Shared protentions are highlighted as pivotal for coordination and achieving common objectives. We conclude by acknowledging the intricacies stemming from stochastic systems and uncertainties in realizing shared goals.

Gadolinium-based contrast agents (GBCA) are complexes, highly stable in vivo, used in Magnetic Resonance Imaging (MRI), administered in patients and then eliminated via renal, passing through wastewater treatment plants (WWTP) before being discarded in the receiving medium, without apparent removal. In this study, it was studied if different exposure periods to several environmental parameters (solar radiation, different salinities, temperatures and pH) will influence the stability of these complexes, namely, the Gd-DOTA. Gd-DOTA solutions were processed in a seaFAST-pico saline matrix pre-concentration and elimination system and Gd concentrations were determined by ICP-MS. Results showed that the complex remained stable in fresh, brackish and saline water environments, even when exposed to extreme temperatures (40ºC) or slightly acidic to basic conditions (6-10), for an exposure period of 96h. A small increase in the free Gd concentration was observed after 18 days when exposed to pH&lt;4, in all tested salinities (0, 18 and 36 PSU), with a degradation increase of up to 29%, after 5 weeks of exposure in freshwater. When exposed to direct solar radiation a low Gd-DOTA degradation (4%) was observed after 24h at salinity 18 PSU and remained constant until the end of the exposure period (96h), while the remaining salinities showed negligible values.

The purpose of this study was to analyze the influence of Chitosan 0.2% in various final cleaning methods on the bond strength of fiberglass post (FP) to intrarradicular dentin. Ninety bovine incisors were sectioned to obtain root remnants measuring 18 mm in length. The roots were divided: G1: EDTA 17%; G2: EDTA 17% + PUI; G3: EDTA 17% + EA; G4: EDTA 17% + XPF; G5: Chitosan 2%; G6: Chitosan 2% + PUI; G7: Chitosan 2% + EA; G8: Chitosan 2% +XPF. After carrying out the cleaning methods, the posts were installed, and the root cleaved to to generate two disks from each root third. Bond strength values (MPa) obtained from the micro push-out test data were assessed by Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests for multiple comparisons (α = 5%). Differences were observed in the cervical third between G1 and G8 (p=0.038), G4 and G8 (p=0.003), G6 and G8 (p=0.049), and Control and G8 (p=0.019). The final cleaning method influenced the adhesion strength of cemented FP to intrarradicular dentin. Chitosan 0.2% + XPF positively influenced adhesion strength, with the highest values in the cervical third.

Jade waste is a normal byproduct that makes up much more than the amount of jade extracted. Therefore, recycling jade waste is worth investigating from the point of view of energy conservation. Moreover, it is an environment-friendly material, which is desirable for use in building materials. In this study, Xiuyan jade waste was repurposed as antibacterial additives for building coatings. The powder waste was activated by milling and subsequent annealing. The antibacterial properties of the treated waste were mostly related to the annealing temperatures. Based on the investigations of the phase change and the release of metal ions of a series of samples and their antibacterial activities, the antibacterial mechanism of the treated samples was explored experimentally. The most applicable sample for coatings was finally chosen by considering its pH values and its antibacterial abilities. Antibacterial testing showed that the addition of treated jade waste could enhance the bacterial inhibition rate of building coatings from 60% to 99.9%.

2-(Morpholin-4-yl)-4,5-bis(2’’,2’’,2’’-trinitroethoxy)-1,3,5-triazine having QSAR-predicted anti-tumor activity was tested for the cytotoxicity using MTT and LDH cell viability tests. The experiments were conducted using human fibroblasts, peripheral blood mononuclear cells and breast cancer cells and allowed to identify effective cytotoxic concentration ant therapeutic range of this compound. The data obtained suggest the feasibility of the further studies of the test compound as a potential anti-cancer agent.

In this study, a natural-based gelling agent comprised of blended flax seed gum (FSG), konjac glucomannan (KG), and agar gel (AG) was developed for application to control the textural properties of foods. The compound gels, including FSG, KG, and AG, were investigated to determine their physicochemical properties, including minimum gelling concentration, water binding capacity, water soluble index, and swelling power. In addition, we analyzed the rheological properties of the compound gel through texture analysis, frequency sweep, and creep and recovery. The microstructure of the compound gel was identified and compared with the viscoelastic properties of the gel. Overall, these results showed that the F4K6 (4:6:2 of FSG:KG:AG) could serve as an excellent gelling agent, which endowed food gel with the promoted elastic properties, water capacity, and rigid surface morphology. This work suggests that novel gelling agents, including FSG, KGM, and AG, successfully prepared food gels with improved physicochemical properties.

Excessive CO₂ emissions and alternative energy fuels are two major difficult issues. The utilization of CO₂ into fine chemicals is an optimal route. Diethyl carbonate (DEC) is an extremely versatile chemical intermediate. DEC is used in gasoline, pharmaceutical, chemical and other fields. DEC synthesis from CO₂ and ethanol is a typical green synthetic route. Ni-Cu@Na₃PW₁₂O₄₀ catalysts were synthesized by two novel methods of supported and mixed. The catalyst prepared by mixed method showed nice catalytic performance. It was confirmed that water removal was the key to improving conversion efficiency. In the presence of dehydrating agent of ethylene, ethanol conversion increased from ca. 3% to ca. 40%. Propylene oxide (PO) was participated in the reaction and ethanol conversion continued to reach to ca.90% while DEC selectivity dropped by half. Under optimal conditions, our Ni-Cu@Na₃PW₁₂O₄₀ catalyst effectively solved the two major issues above.

Bitumen aging is the major factor which contributes to the deterioration of the road pavement. Oxidation and volatilization are generally considered as the most important phenomena affecting aging in asphalt paving mixtures. The present study was carried to investigate whether various antioxidants provided by natural resources such as phospholipids, ascorbic acid as well as lignin from rice husk, could be used to reduce age hardening in asphalt binders. A selected bituminous material was modified by adding 2 % w/w of the anti-aging natural additives and subjected to accelerated oxidative aging regimes according to the Rolling Thin Film Oven Test (RTFOT) method. The effects of aging were evaluated based on changes in sol-gel transition temperature of modified bitumens measured through Dynamic Shear Rheology (DSR). Moreover, changes of Electron Paramagnetic Resonance (EPR) spectra were monitored on the bituminous fractions asphaltene and maltene separated by solvent extraction upon oxidative aging. The phospholipids-treated binder exhibited the highest resistance to oxidation and the lowest age-hardening effect compared to the other tested anti-oxidants. The combination of EPR and DSR techniques represents a promising method for elucidating the changes in associated complex properties of bitumen fractions promoted by addition of free radical scavengers borrowed by green resources.

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviours offers a means to take synthetic biology beyond single molecules or cells and towards the creation of systems with functions that can only emerge from collectives at multiple scales.

This paper addresses the critical issue of trust in Artificial Intelligence systems, especially when users might find it challenging to comprehend the internal decision-making processes of such systems. A relevant topic of research in this respect is Theory of Mind, which involves attempting to understand these systems as if they possessed beliefs, desires, and intentions. We focus on the latter: intentions, and we examine how producing explanations based on them can improve \emph{understandability} while allowing for a better interpretation of how they align with human values. We also review some existing methods for identifying intentions in AI systems and we conclude with a discussion on possible future directions in this line of research.

This paper investigates nonverbal communication in human interactions, with a specific focus on facial expressions. Employing a Long Short-Term Memory (LSTM) architecture and a custom-ized facial expression framework, our approach aims to improve virtual agent interactions by incorporating subtle nonverbal cues. The paper contributes to the emerging field of facial expres-sion generation, addressing gaps in current research and presenting a novel framework within Unreal Engine 5. The model's architecture, trained on the CANDOR corpus, captures temporal dynamics, and refines hyperparameters for optimal performance. During testing, the trained model showed a cosine similarity of -0.95. This enables the algorithm to accurately respond to non-verbal cues and interact with humans in a way that is comparable to human-human interac-tion. Unlike other approaches in the field of facial expression generation, the presented method is more comprehensive and enables the integration of a multi-modal approach for generating facial expressions. Future work involves integrating blendshape generation, real-world testing, and the inclusion of additional modalities to create a comprehensive framework for seamless hu-man-agent interactions beyond facial expressions.

Objectives: Non-adhesive gel-like embolic materials (NAGLEMs) are becoming increasingly dominant in the endovascular treatment of hypervascularised formations in the head and neck due to a combination of their key properties. The main advantages include their lack of adhesion, effective distribution and penetration through pathological vessels, and crucially, their controlla-bility during the process. Our assigned duty was to scrutinise the literature and assess the efficacy and outcomes of administering NAGLEMs, in comparison to other embolizing substances (name-ly, coils, glue, and particles), among patients treated at our clinic. The procedures involved ana-lyzing the technical aspects, efficiency, and safety of endovascular therapy applied to two catego-ries of hypervascular pathological anomalies, surgically managed from 2015 to 2023. Arteriove-nous malformations (AVMs) located in the head, neck, and paragangliomas with jugular/carotid body localization are combined by intense shunting blood flow and shared requirements for em-bolizates used in endovascular treatment (such as penetration, distribution, delayed polymeriza-tion and controllability). An analysis of the literature was also conducted. Results showed 18 pa-tients diagnosed with neck paragangliomas of the carotid body and jugular type. Five patients with arteriovenous malformation (AVM) of the face and neck were included, consisting of 16 fe-males and 7 males with an average age of 55 ± 13 years. Endovascular procedures were conduct-ed using NAGLEMs (ONYX (Medtronic), SQUID (Balt), and PHIL (Microvention)) and dimethyl sulfoxide (DMSO)-compatible balloon catheters. All patients achieved complete or partial embo-lization of hypervascularized formations using one or more stages of endovascular treatment. Additionally, three AVMs of the face and two paragangliomas of the neck were surgically ex-cised following embolization. In other instances, formations were not deemed necessary to be re-moved. The patients' condition upon discharge was assessed by the modified Rankin Scale (mRs) and rated between 0 and 2. Conclusion: Currently, NAGLEMs are predominantly used to treat hypervascularized formations in the neck and head due to their fundamental properties. These properties include a lack of adhesion and a delay in predictable polymerization (after 30-40 minutes). NAGLEMs also exhibit excellent distribution and penetration throughout the vascular bed of the formation. Adequate controllability of the process is largely achieved through the pres-ence of embolism forms of different viscosity, as well as excellent X-ray visualization.

The aim of current study is adjusted and synthesized liposomal compound of N. sativa and evaluation its antifungal properties against C. parapsilosis isolates. Fifteen clinical isolates of C. parapsilosis complex isolates were obtained from hospitalized patients affected by candidemia in Mashhad city, Iran, along with a reference strain of C. parapsilosis (ATCC 22019) were assessed by flight mass spectrometry (MALDI-TOF) method, as described previously. N. sativa is encapsulated in liposomal Nanocariers by using thin film hydration technique. At the beginning liposomal nanoparticles was characterized and confirmed with the dynamic light scattering technique (DLS) and Transmission electron microscopy. Then minimum inhibitory concentration of liposomal N. sativa oil was conducted with the CLSI M27 A3 protocol and finally Cytotoxicity function of N. sativa oil liposomal nanocarriers on PBMCs was investigated and confirmed with MTT assay by the results of this research N. sativa oil-Lip-NP didn’t show any toxic effect on PBMCs and The minimum inhibitory concentration (MIC) range of free N. sativa oil and liposomal formulation with inhibitory effects on candida isolates was between 128 - 8, 250 - 31.25 µg ml also MIC50 and MIC90 were 125,187 and 32,96, µg ml respectively. Due to the hydrophobicity and hydrophilicity, biocompatibility, particle size, non-toxic effect, and higher cell viability of N. sativa oil -Lip-NP, it could be considered a more effective approach to treating fungal infections.

Chemical warfare agents (CWAs) are one of the most toxic compounds. Degradation of CWAs using decontamination agents is one of the few ways to protect human health against the harmful effects of CWAs. A ferrate Fe(VI) based potential chemical warfare agent decontaminant was studied for degradation of persistent nitrogen mustard (tris(2-chloroethyl)amine, HN3). By optimizing the reaction conditions, the complete degradation of HN3 was achieved in 4 minutes. The degradation products contained mostly reduced Fe species which confirmed the environmental friendliness of the proposed decontamination solution.

ASEAN SME has a role as the regional socioeconomic stabilizer. This particular role is inseparable from endogenous multi-sector collaboration. Although, Indonesian SMEs were struggled in adopting Industry 4.0 correspond to digital infrastructure and digital literacy problems. This study evaluates Indonesian SME collaboration dynamics with government and technology startup (TS). The integration of agent-based model and causal loop simulation were employed to assess the TS collaboration impact on SME Industry 4.0 adoption and SME competition with larger competitors. The simulation results imply the SME collaboration with TS can lead to early adoption of Industry 4.0 which balances the business competition environment. The model also shows rising the government aid exponentially can help the SME to late adoption of Industry 4.0 which unable to sustain the SME in business competition. Thus, the developed integrative simulation model is a state-action planning model with each state result bounded to the previous state result that determined by initial input parameters. Conclusively, the model can be used as a resiliency planner for SME Industry 4.0 adoption.

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfillment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.

Modern agent-based models (ABM) and other simulation models require evaluation and testing of many different parameters. Managing that testing for large scale parameter sweeps (grid searches) as well as storing simulation data requires multiple, potentially customizable steps that may vary across simulations. Furthermore, parameter testing, processing, and analysis are slowed if simulation and processing jobs cannot be shared across teammates or computational resources. While high-performance computing (HPC) has become increasingly available, models can often be tested faster through the use of multiple computers and HPC resources. To address these issues, we created the Distributed Automated Parameter Testing (DAPT) Python package. By hosting parameters in an online (and often free) "database", multiple individuals can run parameter sets simultaneously in a distributed fashion, enabling ad hoc crowdsourcing of computational power. Combining this with a flexible, scriptable tool set, teams can evaluate models and assess their underlying hypotheses quickly. Here we describe DAPT and provide an example demonstrating its use.

This study was conducted to improve the white index (WI) by preparing thermally expandable microspheres (TEMs) for wallpaper. The thermal properties, foam expansion ratio and WI were studied depending on the particle size of colloidal silica in the preparation of TEMs. As a result, the TEMs with small particles of colloidal silica showed the best results for whiteness and yellowing. Additionally, TGA results indicated that it was highly possible that colloidal silica with small particle sizes was physically or chemically attached to the surface of the TEMs that led to an improvement in whiteness at high temperatures.

Nereis as a mudflat organism has a variety of uses, in the history of China as a Chinese herbal record, because of its body contains rich amino acids, fatty acids and other nutrients, it can be used for therapeutic treatment and treatment can also be used as health care products. acids, fatty acids and other nutrients, it can be used for therapeutic treatment and treatment can also be used as health care products. In modern times, more and more researchers have studied the protease extract of Nereis as a mudflat organism. In modern times, more and more researchers have studied the protease extracted from the body of nereis and found that there are a variety of functional proteins in nereis, including superoxide dismutase, superoxide dismutase, superoxide dismutase and superoxide dismutase. More and more researchers have studied the proteases extracted from the body of nereis and found that there are a variety of functional proteins in nereis, including superoxide dismutase, adenylate cyclase, glycosidase, and plasminase. In this paper, the functional proteases in the extracts of nereis were summarized, and the functions and characteristics of proteases in nereis were discussed from the perspective of molecular structure.

The current work shows the optimization in the preparation of nanosized titanium carbide (TiC) in-situ through mechanical alloying (MA). Metallic titanium (Ti) powders, along with two carbon sources, carbon nanotubes (CNTs), and stearic acid (SA), were used to reduce the particle size using a high-energy Spex800 mill. The combined use of 2 wt % of these carbon sources and n-heptane as a liquid process control agent (PCA) proved crucial in generating nanoscale powder composites through a simple and scalable synthesis process within a 4-hour timeframe. The use of 20 wt % of both carbon sources was compared to determine the ability of CNTs to form carbides and the decomposition of PCAs during mechanical milling. The result reveals structures like nanoblocks and nanolumps with and important size reduction. The structure and morphology of the composites and starting materials were evaluated through x-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM).

In the context of growing global political tension and introduction of world trade barriers, the urgent task is to develop new tools for assessing their consequences. In the paper we present the agent-based model of trade wars, considering organizations, states and residents generated using initial statistical data. Simulation determines changes in output and supplies of organizations under trade restrictions. Results of calculations on the developed model and comparison of various model complexes forecasts with real consequences of trade wars between the USA and China in 2018 and Western countries against Russia in 2022 are presented. Within calculations four scenarios were considered: (1) baseline, (2) new restrictions between China and the USA, (3) more serious sanctions against China and Russia by the USA and the EU, (4) a global trade war. In the second scenario deviation GDP of the USA and China from the baseline scenario does not exceed 0.5%. In the third scenario, the range of countries involved is expanding, and the fall in GDP in them is expected at the level of 0.7-1%. In the fourth scenario, the entire world economy experiences a serious slowdown, and the EU are facing the most severe consequences, going into recession.

Dyes, as indigo carmine have become indispensable to modern life being widely used in food, textile, pharmaceutical, medicine and cosmetic industries. Although indigo carmine is considered toxic and presents many adverse effects, it is heavily used in the food industry because the blue pigment is difficult to obtain from natural sources and is one of the most used dyes in the textile industry, especially for dyeing denim. Also, indigo carmine is one of the dyes used in medicine as diagnostic agent because it has impressive applicability in terms of diagnostic methods and surgical procedures. In the literature it is reported that indigo carmine is toxic for humans and can cause various pathologies, such as hypertension, hypotension, skin irritations, corneal and conjunctival disorders or gastrointestinal disorders. In this review, we discuss the structure and properties of indigo carmine, its use in various industries and medicine, the adverse effects of its ingestion, injection or skin contact, the effects on environmental pollution and its toxicity testing.

The present study produced and characterised chitosan, chitosan nanoparticle, chitosan n – benzaldehyde Schiff base, chitosan nanoparticle n – benzaldehyde Schiff base, Fe(III) chitosan n – benzaldehyde Schiff base and Fe(III) chitosan nanoparticle n – benzaldehyde Schiff base for biomedical application as antimicrobial agents. The materials were characterized with Fourier Transform Infrared spectroscopy, X-ray Diffractogram and biologically evaluated using disc diffusion method with three gram-positive bacteria. The FTIR absorption peaks were shifted to a lower wave number than the micro materials from which it was modified. These clearly indicate the linkage between phosphate, ammonium ion, Schiff base and Fe(III) metal. The diffracted peaks of Fe(III) chitosan nanoparticle n – benzaldehyde Schiff base were new peaks at 2θ = 24o and 42o when compare to the peak of Fe(III) chitosan n – benzaldehyde Schiff base of 2θ = 22.5o and 34o. The difference in peak shift were attributed to the ionic bonding of the complexation of Fe(III) with the blending of benzaldehyde to chitosan – Tpp backbone structure. Fe(III) chitosan nanoparticle Schiff base has more antimicrobial activity against same bacteria and fungi tested than Fe(III) chitosan n – benzaldehyde Schiff base, chitosan n – benzaldehyde Schiff base and chitosan. The antimicrobial activities of the synthesised six materials shown that the materials have high activities than the above – mentioned standard drugs.

Epoxy was blended with benzoxazine resin and aluminum trihydrate (ATH) additive to render flame retardancy while maintaining mechanical properties. The ATH was modified using three different silane coupling agents and then added to 60/40 epoxy/benzoxazine mixtures. The effect of blend compositions and surface modification on flame retardant and mechanical properties of the composites was investigated by UL94, tensile, and shear tests. Resin mixtures containing more than 40 wt% benzoxazine revealed a UL94 V-1 rating with enhanced tensile and shear strength. Upon addition of 20 wt% ATH to 60/40 epoxy/benzoxazine, a V-0 rating was achieved. The lowered tensile and adhesive properties of the composites in the presence of ATH were improved by modifying the ATH surface using silane coupling agents.

The sudden onset of the Covid-19 pandemic disrupted the modern multi-national interconnected society and led the countries and societies to enforce unprecedented restrictions on movement. Among myriad containment measures, the policy of trace and quarantine found universal adoption among countries; the swift adoption of the policy was soon met with widespread criticism and opposition activists who questioned the utility and the risk associated with such a large scale collection of data and infringement on the movement of individuals. Consequently, one often tends to be either pro- or anti-trace and quarantine; the ensuing polarizing and politicized left little room for nuance. In this work, we undertake a methodology study to understand the nuances of the impact of different implementations of trace and quarantine. To this end, we design a user-friendly and intuitive tool that can be employed by experts to model the disease dynamics and societal structure. We focus on the study of the cost of policy with respect to quarantine degree, which captures the distance between the person required to quarantine after a person is detected to be infected. Our study results in a surprising conclusion: the cost is not necessarily monotone with respect to the degree of quarantine. Our analysis indicates that governments must curb the urge to adopt simplistic policy and the optimal policy of trace and quarantine for a country strongly depends on its societal structure and disease dynamics.

Simultaneously being a non-radiative and non-invasive technique makes magnetic resonance imaging (MRI) one of the highly sought imaging techniques for the early diagnosis and treatment of diseases. Despite more than four decades of research on finding a suitable imaging agent from fluorine for clinical applications, it still lingers as a challenge to get the regulatory approval compared to its hydrogen counterpart. The pertinent hurdle is the simultaneous intrinsic hydrophobicity and lipophobicity of fluorine and its derivatives that make them insoluble in any liquids, strongly limiting their application in areas such as targeted delivery. A blossoming technique to circumvent the unfavorable physicochemical characteristics of perfluorocarbon compounds (PFCs) and guarantee a high local concentration of fluorine in the desired body part is to encapsulate them in nanosystems. In this review, we will be emphasizing different types of nanocarrier systems studied to encapsulate various PFCs and fluorinated compounds, headway to be applied as a contrast agent (CA) in fluorine-19 MRI (19F MRI). We would also scrutinize the different types of PFCs and their specific applications and limitations concerning the nanoparticle (NP) system used to encapsulate them studied over the last decade. A critical evaluation for future opportunities would be speculated.

Human influenza A viruses (IAVs) cause global pandemics and epidemics, which remain serious threats to public health because of the shortage of effective means of control. To combat the surge of viral outbreaks, new treatments are urgently needed. Developing new virus control modalities requires better understanding of virus-host interactions. Here we describe how IAV infection triggers cellular apoptosis, and how this process can be exploited towards development of new therapeutics, which might be more effective than the currently available anti-influenza drugs.

Despite recent public health and hygiene advancements, enteric fever, commonly referred to as typhoid fever, remains a common disease in developing nations. The common mode of infection is contaminated water or food. Additionally, person-to-person transmission through poor hygiene and sewage contamination of water supplies has been blamed for most outbreaks. The exact burden of typhoid has yet to be discovered because of the lack of surveillance systems in many developing nations. This makes it difficult to estimate the number of cases. Recent studies have shown that the actual number of cases of typhoid has been estimated to be around 21.6 million annually. The mortality studies suggest that the incidence of typhoid is highest in children under five years old. Typhoid fever and paratyphoid fever have been demonstrated to be life-threatening illnesses. Salmonella serotype Typhi (S. Typhi) is the causative organism of typhoid fever whereas Salmonella serotype Paratyphi is the causative organism for paratyphoid fever. The S. Typhi bacterium is known to be resistant to various drugs. There is a dire need to develop new drugs to treat and overcome the current drug-resistant S. Typhi. The identification of new targets marks the beginning of the process of developing new anti-S. Typhi drugs. The S. Typhic genome sequencing and recent cutting-edge molecular biology tools have led to the discovery of numerous new inhibitors and targets. The goal of this review is to identify the most critical targets in the S. Typhi that are targeted by drugs. It also highlights the various promising vaccines on the market or are still in preclinical studies. A detailed understanding of the targets could help researchers develop safer and more efficient antibacterial agents against S. Typhi.Additionally, the advancement of molecular techniques and the knowledge of the Salmonella pathogenesis pathways have opened better avenues to develop effective antibiotics and vaccines against this pathogen. This review also sought to identify and summarize critical structures of this pathogen that play significant roles in the maturation, development, and pathogenesis of S. Typhi. The endpoint of this work is to provide valuable information on potential therapeutic targets of S. Typhi for drug and vaccine developers.

In recent years, the use of solid waste fillers to partially replace natural fillers in asphalt mixtures to produce high-performance asphalt mixtures has received widespread attention. However, differences in the material properties of solid waste fillers remain a problem for this recycling method. To address this issue, limestone powder in asphalt mixtures was replaced by three solid waste fillers (steel slag powder, tailings powder and calcium carbide slag powder) in this study. The chemical composition of the fillers was first characterized to assess the homogeneity of the material. Then, AC and SMA asphalt mixtures were designed and produced and characterized for wet stability. The results showed that asphalt mixtures with solid waste fillers were superior to LP asphalt mixtures in terms of resistance to water damage, and steel slag powder showed the best improvement in moisture stability of asphalt mixtures. The optimum substitution of solid waste filler for limestone filler was 25%. In addition, the moisture stability of asphalt mixture with limestone filler was significantly improved with the addition of anti-stripping agents. In contrast, the moisture stability of asphalt mixtures with solid waste filler was slightly improved. Solid waste fillers could be used in asphalt mixtures and have a similar function as the anti-stripping agent. In summary, the use of solid waste fillers to replace mineral fillers in asphalt mixtures is a reliable, value-added, recycling option.

The Carbonic Anidrase isoform IX (hCA IX) is one of the main player in extracellular tumor pH regulation and it is known to be overexpressed in breast cancer as well as in many common tu-mors. hCA IX has shown to contribute to the growth and survival of tumor cells and its expres-sion is correlated to metastasis and resistance to therapies making it an interesting biomarker for diagnosis and potential image-guided therapy. The aim of this work is the development of an MRI Imaging probe able to target the extracellular non catalytic PG domain of CAIX. A new specific nano probe has been designed by conjugating a peptidic interactor of PG domain on the surface of a liposome loaded with Gd-based contrast agents. Mouse Mammary Adenocarcinoma Cell Line (TS/A ) has been chosen as in vitro breast cancer model to test the efficacy of the de-veloped probe. MRI results display a high selectivity towards hCA IX marker and a good sensi-tivity of the Imaging probe. These findings make this approach promising for future develop-ment of in vivo diagnostic protocols aimed at visualizing this enzyme expression.

This review provides an overview of the biotransformation of limonene and α-pinene, which are commonly found in wood residues and citrus fruit by-products, to produce high-value-added products. Essential oils derived from various plant parts contain monoterpene hydrocarbons, such as limonene and pinenes which are often considered waste due to their low sensory activity, poor water solubility, and tendency to autoxidize and polymerise. However, these terpene hydrocarbons serve as ideal starting materials for microbial transformations. Moreover, agro-industrial byproducts can be employed as nutrient and substrate sources, reducing fermentation costs, and enhancing industrial viability. Terpenes, being secondary metabolites of plants, are abundant in byproducts generated during fruit and plant processing. Microbial cells offer advantages over enzymes due to their higher stability, rapid growth rates, and genetic engineering potential. Fermentation parameters can be easily manipulated to enhance strain performance in large-scale processes. The economic advantages of biotransformation are highlighted by comparing the prices of substrates and products. For instance, R-limonene, priced at US$34/L, can be transformed into carveol, valued at around US$530/L. This review emphasises the potential of biotransformation to produce high-value products from limonene and α-pinene molecules, particularly present in wood residues and citrus fruit by-products. The utilisation of microbial transformations, along with agro-industrial byproducts, presents a promising approach to extract value from waste materials and enhance the sustainability of the antimicrobial, the fragrance and flavour industry.

Prediction markets are heralded as powerful forecasting tools, but models to describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong in a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable to replicate the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of the agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, an exchange platform whose data has never been used before to validate models of opinion diffusion.

Prevalence of antimicrobial-resistant bacteria has become a major challenge worldwide. Methicillin-resistant Staphylococcus aureus (MRSA)—a leading cause of infections—forms biofilms on polymeric medical devices and implants, increasing their resistance to antibiotics. Antibiotic administration before biofilm formation is crucial. Raman spectroscopy was used to assess MRSA biofilm development on solid culture media from 0 to 48 h. Biofilm formation was monitored by measuring DNA/RNA-associated Raman peaks and protein/lipid-associated peaks. The search for an antimicrobial agent against MRSA biofilm revealed that Eugenol was a promising candidate as it showed significant potential for breaking down the biofilm. Eugenol was applied at different times to test the optimal time for inhibiting MRSA biofilms, and the Raman spectrum showed that the first 5 h of biofilm formation was the most antibiotic-sensitive time. This study investigated the performance of Raman spectroscopy coupled with Principal Component Analysis (PCA) to identify planktonic bacteria from biofilm conglomerates. Raman analysis, microscopic observation, and quantification of the biofilm growth curve indicated early adhesion from 5 to 10 h of incubation time. Therefore, Raman spectroscopy can help in monitoring biofilm formation on a solid culture medium and performing rapid antibiofilm assessments with new antibiotics during the early stages of the procedure.

Augmented reality (AR) offers an accessible, inexpensive, and rich user experience that has the potential to engage end-users in an immersive environment. Along with vivid visualizations coupled with virtual agents, this technology further develops learning interest in end-users, guides them in various tasks, and boosts motivation and productivity. In this study, we leverage the deep penetration of mobile phones in daily lives and their advanced features to design, develop and demonstrate an AR application (CirculAR) that offers unique user-environment interaction in a gamified way. CirculAR combines learning with enjoyment to help end-users understand fundamental sustainability and circular economy principles. The application has been showcased in a controlled environment to heterogenous audiences and has been shown to improve end-user engagement and motivation. First, participants older than 18 were recruited to showcase the technology acceptance and engagement towards circular economy principles through AR. Then, students aged 5 to 15 years old, along with their parents and educators, were invited to a treasure hunt game where our virtual agent ARis guided them through a map full of virtual experiences. Assessment and evaluation were performed through a survey and a questionnaire. The outcome of their analysis showcased an increase in the dedication and enjoyment of the performed activities, engagement and learning attributes given the AR virtual agent supporting functionalities. Observations during showcasing reported a need for more commitment from the younger audience compared to the older one. This application contributes to the discourse on mobile AR as a tool for the education of novel concepts with a high impact on our daily lives and decisions and aims to shed light on the design principles of educative tools.

The carriage of Candida albicans in children's oral cavities is associated with a higher risk for early childhood caries, so controlling this fungus in early life is essential for preventing caries. In a prospective cohort of 41 mothers and their children from 0-2 years of age, this study addressed 4 main objectives: 1) Evaluate in vitro the antifungal agent susceptibility of oral Candida isolates from the mother-child cohort, 2) Compare Candida susceptibility between isolates from the mothers and children; 3) Assess longitudinal changes in the susceptibility of the isolates collected between 0-2 years; and 4) Detect mutations in C. albicans antifungal resistance genes. Susceptibility to antifungal medications was tested by in vitro broth microdilution and expressed as minimal inhibitory concentration (MIC). C. albicans clinical isolates were sequenced by whole genome sequencing, and the genes related to antifungal resistance, ERG3, ERG11, CDR1, CDR2, MDR1, and FKS1, were assessed. Four Candida spp (n=126) were isolated: C. albicans, C. parapsilosis, C. dubliniensis, and C. lusitaniae. Caspofungin was the most active drug for oral Candida, followed by fluconazole and nystatin. Two missense mutations in the CDR2 gene were shared among C. albicans isolates resistant to nystatin. Most of the children’s C. albicans isolates had MIC values similar to those from their mothers, and 70% remained stable to antifungal medications from 0-2 years. For caspofungin, 29% of the children’s isolates showed an increase in MIC values from 0-2 years. Results of the longitudinal cohort indicated that clinically used oral nystatin was ineffective in reducing the carriage of C. albicans in children; novel antifungal regimens in infants are needed for better oral yeast control.

Common root rot caused by Bipolaris sorokiniana infestation of wheat is one of the main reasons of yield reduction in wheat crops worldwide. In current study, strain JK-25 was isolated from soil of wheat rhizosphere and identified as Bacillus halotolerans based on morphological, physiological, biochemical characteristics and molecular identification. The strain showed significant antagonism to B.sorokiniana and broad-spectrum resistance to Fusarium oxysporum, Fusarium graminearum and Rhizoctonia zeae. Inhibition of Bipolaris sorokiniana mycelial dry weight and spore germination rate by JK-25 fermentation supernatant reached 60% and 88% respectively. The crude extract of JK-25 was found by MALDI-TOF-MS to contain the surfactin that exerted an inhibitory effect on B.sorokiniana. The disruption of mycelial cell membranes was observed under microscopy (LSCM) after treatment of B.sorokiniana mycelium with the crude extract. The antioxidant enzyme activity of B.sorokiniana was significantly reduced and the oxidation product MDA content increased after treatment with the crude extract. The incidence of root rot was significantly reduced in pot experiments with the addition of JK-25 culture ferment, which had a significant biological control effect of 72.06%. Its ability to produce siderophores may help to promote wheat growth, and the production of proteases and pectinases may also be part of the strain's role in suppressing pathogens. These results demonstrate the excellent antagonistic effect of JK-25 against B.sorokiniana and suggest that this strain has great potential as a resource for biological control of wheat root rot strains.

The glucose-insulin regulatory system and its glucose oscillations is a recurring theme in the literature because of its impact on human lives, mostly the ones affected by diabetes mellitus. Several approaches were proposed, from mathematical to data-based models, with the aim of modeling the glucose oscillation curve. Having such a curve, it is possible to predict, when injecting insulin in type 1 diabetes (T1D) individuals. However, the literature presents prediction horizons no longer than 6 hours, which could be a problem considering their sleeping time. This work presents Tesseratus, a model that adopts a multi-agent approach to combine machine learning and mathematical modeling to predict the glucose oscillation up to 8 hours. Tesseratus uses the pharmacokinetics of insulins and data collected from T1D individuals. Its outcome can support endocrinologists while prescripting daily treatment for T1D individuals, and provide personalized recommendations for such individuals, to keep their glucose concentration in the ideal range. Tesseratus brings pioneering results for prediction horizons of 8 hours for nighttime, in an experiment with seven real T1D individuals. It is our claim that Tesseratus will be a reference for classification of glucose prediction model, supporting the mitigation of short- and long-term complications in the T1D individuals.

This paper extends the concept of weighted graphs to matrix weighted graphs. The consensus algorithms dictate that all agents reach consensus when the weighted graph is connected. However, it is not always the case for matrix weighted graphs. The conditions leading to different types of consensus have been extensively analysed based on the properties of matrix-weighted Laplacians and graph theoretic methods. However, in practice, there is concern on how to pick matrix-weights to achieve some desired consensus, or how the change of elements in matrix weights affects the consensus algorithm. By selecting the elements in the matrix weights, different clusters may be possible. In this paper, we map the roles of the elements of the matrix weights in the systems consensus algorithm. We explore the choice of matrix weights to achieve different types of consensus and clustering. Our results are demonstrated on a network of three agents where each agent has three states.

Cyber-Physical Systems form the new backbone of digital ecosystems. Their design can be coupled with engineering activities to facilitate dynamic adaptation and (re-)configuration. Behavior-oriented technologies enable highly distributed and while coupled operation of systems. Utilizing them for digital twins as self-contained design entities with federation capabilities makes them promising candidates to develop and run highly functional CPS. In this paper we discuss mapping CPS components to behavior-based digital twin constituents mirroring integration and implementation through subject-oriented models. These models, inspired by agent-oriented system thinking can be executed and increase transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate configuration of CPS. Subject-oriented runtime support enable dynamic adaptation and federated use.

A derivative series of 3,4-dimethoxy-β-nitrostyrene were synthesized and identified including new compound 6. The effect of antimicrobial activity of 3,4-alkyloxy modification of β-nitrostyrene was investigated. A molecular docking was also performed to obtain information about their interactions with Protein Tyrosine Phosphatase 1B (PTP1B). PTP1B containing cysteine 215 and arginine 221 as essential active residues plays a key role in signaling pathways that regulate various cell functions of microorganisms, which also act as negative regulator in signaling pathways of insulin that are involved in type 2 diabetes and other metabolic diseases. Compound 5 and 6 were the most potent as fragment of PTP1B inhibitor based on molecular docking, but compound 5 was more effective against Candida albicans. These compounds interact with serine 216 and arginine 221 residues. However, further research is needed to investigate their potential medicinal use.

There exist numerous high-dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high-dimensional problems. An appropriate approach to solve such problems is to apply multi-agent systems along with the metaheuristic algorithms. The present paper proposes a new approach based on the multi-agent systems and the concept of agent, which is named Multi-Agent Metaheuristic (MAMH) method. In the proposed approach, several basic and powerful metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Crow Search Algorithm (CSA), Farmland Fertility Algorithm (FFA), are considered as separate agents each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. In overall, the proposed method was tested on 32 complex benchmark functions, the results of which indicated effectiveness and powerfulness of the proposed method for solving the high-dimensional optimization problems. In addition, in this paper, the binary version of the proposed approach, called Binary MAMH (BMAMH), was executed on the spam email dataset. According to the results, the proposed method exhibited a higher precision in detection of the spam emails compared to other metaheuristic algorithms and methods.

Photoelectrocatalytic water splitting by using various TiO₂ nanostructures is a promising approach to generate hydrogen without harmful byproducts. However, their effective performance is restricted by some drawbacks such as high rapid electron-hole pair recombination and backward reaction producing H₂O. Thus in this study, the probability of enhancing hydrogen generation rate by adding methanol as a sacrificial agent to the anodic chamber of a two-compartment photoelectrochemical cell is investigated. Herein, one-dimensional elongated TiO₂ nanorods that were fabricated via a facile one-pot hydrothermal method are utilized as potent photoanode. Voltammetric characterizations confirm that addition of alcoholic sacrificial agent has a significant effect on photoelectrochemical properties of TiO₂ nanorods which by adding 10 wt% of methanol, the photocurrent density and photoconversion efficiency increased from 0.8mA.cm^-2 to 1.5mA.cm^-2 and from 0.28% to 0.45%, respectively. The results of photoelectrocatalytic water splitting indicated that the hydrogen generation rate in the presence of methanol was about 1.2 times higher than that from pure water splitting. These enhancements can be attributed to the key role of methanol. Methanol molecules not only inhibit the electron-hole pair recombination but also accelerate the hydrogen generation rate by sharing their hydrogen atoms.

Viruses are the major causes of acute and chronic infectious diseases in the world. According to the World Health Organization, there is an urgent need for better control of viral diseases. Re-purposing existing antiviral agents from one viral disease to another could play a pivotal role in this process. Here we identified novel activities of obatoclax and emetine against herpes simplex virus type 2 (HSV-2), human immunodeficiency virus 1 (HIV-1), echovirus 1 (EV1), human metapneumovirus (HMPV) and Rift Valley fever virus (RVFV) in cell cultures. Moreover, we demonstrated novel activities of emetine against influenza A virus (FluAV), niclosamide against HSV-2, brequinar against HIV-1, and homoharringtonine against EV1. Our findings may expand the spectrum of indications of these safe-in-man agents and reinforce the arsenal of available antiviral therapeutics pending the results of further in vivo tests.

We introduce tools to capture the dynamics of three different pathways, in which the synchronization of human decision making could lead to turbulent periods and contagion phenomena in financial markets. The first pathway is caused when stock market indices, seen as a set of coupled integrate-and-fire oscillators, synchronize in frequency. The integrate-and-fire dynamics happens due to "change blindness", a trait in human decision making where people have the tendency to ignore small changes, but take action when a large change happens. The second pathway happens due to feedback mechanisms between market performance and the use of certain (decoupled) trading strategies. The third pathway occurs through the effects of communication and its impact on human decision making. A model is introduced in which financial market performance has an impact on decision making through communication between people. Conversely, the sentiment created via communication has an impact on financial market performance.

Optimum selectivity of enhanced oil recovery techniques would play a substantial role in the economic prosperity of petroleum industries which might be virtually eliminated unnecessary expenditures. In this paper, the simultaneous utilization of foaming agent, surfactant polymer (SP), and supercritical carbon dioxide were taken into the investigation under the miscible condition to evaluate the efficiency of each scenario on the cumulative recovery factor, water cut and pressure drop. According to the results of this experimental evaluation, SP-foam flooding had witnessed the highest blockage which is caused to have the maximum recovery factor due to the mobilization of more oil volume in the low permeable pores and cracks. Furthermore, the utilization of surfactant with supercritical carbon dioxide had experienced the least recovery factor regarding the insufficient foam generation which is led to less mobilization of oil phase in the pore throats.

Emergence of more virulent forms of human pathogenic bacteria with multi drug resistance is a serious global issue and requires alternative control strategies. The current study was focused to investigate the antibacterial and antibiofilm potential of ferulic acid grafted chitosan (CFA) against Listeria monocytogenes (LM), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA). The present result showed that CFA at 64 µg/mL concentration exhibit bactericidal action against LM and SA (>4 log reduction) and bacteriostatic action against PA (<2 log CFU) within 24 h of incubation. Further studies based on propidium iodide uptake assay, measurement of material released from the cell, and electron microscopic analysis revealed that the bactericidal action of CFA was due to the altered membrane integrity and permeability. CFA dose-dependently inhibited biofilm formation (52-89% range), its metabolic activity (30.8-75.1% range) and eradicated mature biofilms, and reduced viability (71-82% range) of the test bacteria. Also, the swarming motility of LM was differentially affected at sub-MIC concentration of CFA. In the present study, the ability of CFA to kill and alter the virulence production in human pathogenic bacteria will insight a new scope for the application of these biomaterials in healthcare to effectively treat bacterial infections.

Shielding agents are commonly used to shield polyelectrolyte complexes, e.g. polyplexes, from agglomeration, precipitation in complex media, like blood, and thus enhance their circulation times in vivo. Since up to now primarily poly(ethylene glycol) (PEG) has been investigated to shield non-viral carriers for systemic delivery, we report on the use of polysarcosine (pSar) as a potential alternative for steric stabilization. A redox-sensitive, cationizable lipo-oligomer structure (containing two cholanic acids attached via a bioreducible disulfide linker to an oligoaminoamide backbone in T-shape configuration) was equipped with azide-functionality by solid phase supported synthesis. After mixing with small interfering RNA (siRNA), lipopolyplexes formed spontaneously and were further surface-functionalized with polysarcosines. Polysarcosine was synthesized by living controlled ring-opening polymerization using an azide-reactive dibenzo-aza-cyclooctyne-amine as an initiator. The shielding ability of the resulting formulations was investigated with biophysical assays and by near-infrared fluorescence bioimaging in mice. The modification of ~100 nm lipopolyplexes was only slightly increased upon functionalization. Cellular uptake into cells was strongly reduced by the pSar shielding. Moreover, polysarcosine-shielded polyplexes showed enhanced blood circulation times in bioimaging studies compared to unshielded polyplexes and similar to PEG-shielded polyplexes. Therefore, polysarcosine is a promising alternative for the shielding of non-viral, lipo-cationic polyplexes.

Antibacterial bone cements (ABCs), such as antibiotic-loaded bone cements (ALBCs), have been widely utilized in clinical treatments. Currently, bone cements loaded with vancomycin, gentamicin, tobramycin, or clindamycin are approved by the US Food and Drug Administration. However, traditional ALBCs exhibit drawbacks like burst release and bacterial resistance. Therefore, there is a demand for the development of antibacterial bone cements containing novel agents. In this review, we summarize the latest advancements in ABCs. More importantly, we provide an overview and outlook on novel antibacterial agent-loaded bone cements, including metallic antibacterial agents, pH-switchable antibacterial agents, cationic polymers, N-halamines, non-leaching acrylic monomers, antimicrobial peptides, etc. Offering novel perspectives for designing ABCs and treating bone infections.

Large-scale crowd phenomena are complex to model because the behaviour of pedestrians needs to be described at both strategic, tactical, and operational levels and is impacted by the density of the crowd. Microscopic models manage to mimic the dynamics at low densities, whereas mesoscopic models achieve better performances in dense situations. This paper proposes and evaluates a novel agent-based architecture to enable agents to dynamically change their operational model based on local density. The ability to combine microscopic and mesoscopic models for multi-scale simulation is studied through a use case of pedestrians at the Festival of Lights, Lyon, France. Pedestrian outflow data is extracted from video recordings of exiting crowds at the festival. The hybrid model is calibrated and validated using a genetic algorithm that optimizes the match between simulated and observed outflow data. Additionally, a local sensitivity analysis is then conducted to identify the most sensitive parameters in the model. Finally, the performance of the hybrid model is compared to different models in terms of density map and computation time. The results demonstrate that the hybrid model has the capacity to effectively simulate pedestrians across varied density scenarios while optimizing computational performance compared to other models.

Bacteria of the genus Staphylococcus are a significant a challenge for medicine, as many species are resistant to multiple antibiotics and even to all of the antibiotics used. One of the approaches to develop new therapeutics to treat staphylococcal infections is the use of bacteriophages specif-ic to these bacteria or lysins of such bacteriophages capable to hydrolyze the cell walls of these bacteria. In this study, a new bacteriophage St_134 specific to Staphylococcus epedermidis was de-scribed. This small podophage belonging to the Andhravirus genus showed relatively wide host range and was able to infect more than 60 strain of Staphylococcus spp., including a clinical strain from the Staphylococcus aureus complex. Two genes encoding different phage lysins were identi-fied in the Ste134 genome. Both lysins LysSte134_1 and LysSte134_2 were produced in Escherichia coli cells. Endolysin LysSte134_1 was demonstrated catalytic activity against a wide panel of staphylococcal peptidoglycans (4 species, 6 strains), was active against S. aureus and S. epidermidis planktonic cultures and destroyed biofilms formed by clinical strains of S. aureus and S. epider-midis.

Renal angiogram is a promising tool for diagnosing renal vasculature and structural alterations in renal diseases. In renal diseases, the vascular changes were observed earlier than the structural and other changes. However, the literature on angiograms in kidney diseases was limited. Six client-owned dogs with chronic kidney diseases with International Renal Interest Society (IRIS) stage II and III were selected for this study. Under general anesthesia, the femoral arterial catheterization was performed. With a Cobra (C2) catheter, Iohexol saline (50:50) was administered to evaluate the vascular changes of Renal failure, using selective and non-selective angiograms. The procedures were completed successfully with minimal complications. Serum biochemistry and urine protein creatinine ratio were estimated sequentially and quality of life was monitored. Due to contrast agent complications, a dog died within 14 days and necropsy was conducted. Though the renal angiogram has complications, it is a reliable tool for the diagnosis of vasculature changes associated with kidney diseases, in veterinary medicine.

The spontaneous combustion of coal in goaf is the main cause of mine fire at present. Lignite has become a major hidden danger in mine production safety because of its easy oxidation. In this paper, an innovative strategy was proposed to inhibit the spontaneous combustion of lignite by using the principle of microbial-induced calcium deposition. Based on the optimized culture method, a novel composite inhibitor of Bacillus pasteurelli was prepared. SEM, pore size analysis and FT-IR experiments were carried out simultaneously, and the oxidation properties of lignite before and after inhibition were quantitatively characterized from the perspective of microstructure, and the flame retardant properties of microbial and chemical retardants on coal samples were compared. The results show that a large number of deposited calcium carbonate particles are obviously attached to the surface of lignite after microbial inhibition treatment, which plays a physical oxygen insulation role. At the same time, the total pore volume and specific surface area of the coal sample decreased by 68.49% and 74.01%, respectively, indicating that microbial inhibitors can effectively plug the primary pores of lignite. Based on the peak measurement of 400-4000cm-1 infrared spectrum of coal samples, it is found that the contents of active groups including hydroxyl, carboxyl and methyl/methylene in lignite molecules after microbial inhibition are lower than those in raw coal, especially the methyl/methylene content involved in the initial oxidation reaction decreased by 96.5% compared with the baseline content in raw coal. The results show that the oxidation and self-heating capacity of lignite after microbial inhibition is effectively restrained in the initial spontaneous combustion. The research results of this paper can provide effective solutions for the prevention and control of coal spontaneous combustion risk in mined-out area.