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 .

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.

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.

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.

For disciplines like biological science, security, and the medical field, link prediction is a popular research area. To demonstrate the link prediction many methods have been proposed. Some of them that have been demonstrated through this review paper are TransE, Complex, DistMult, and DensE models. Each model defines link prediction with different perceptions. We argue that the practical performance potential of these methods, having similar parameter values, using the fine-tuning technique to evaluate their reliability and reproducibility of results. We describe those methods and experiments; provide theoretical proofs and experimental examples, demonstrating how current link prediction methods work in such settings. We use the standard evaluation metrics for testing the model's ability.

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

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.

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.

In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

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 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.

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.

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.

"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.

Adaptive bitrate (ABR) algorithms optimize the quality of streaming experiences for users in client-side video players especially in unreliable or slow mobile networks. Several rule-based heuristic algorithms can achieve stable performance, but they sometimes fail to adapt properly to changing network conditions. Fluctuating bandwidth may cause algorithms to default to behavior that creates a negative experience for the user. ABR algorithms can be generated with reinforcement learning, a decision-making paradigm in which an agent learns to make optimal choices through interactions with an environment. Training reinforcement learning algorithms for bitrate streaming requires building a simulator for an agent to experience interactions quickly; training an agent in the real environment is infeasible due to the long step times in real environments. This project explores using supervised learning to construct a world-model, or a learned simulator, from recorded interactions. A reinforcement learning agent trained inside of the learned model, rather than a simulator, can outperform rule-based heuristics. Furthermore, agents trained inside the learned world-model can outperform model-free agents in low sample regimes. This work highlights the potential for world-models to quickly learn simulators, and to be used to generate optimal policies.

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.

Electric Vehicles (EVs) are increasing the interdependence of transportation policies and the electricity market. EMMEV (Electricity Market Model with Electric Vehicles) is an experimental agent-based model that analyses how carbon reduction policy in transportation may increase number of Electric Vehicles and how does that would influence on the electricity price. Agents are ESCOs (Energy Service Providers) which can distribute fuels and their objective is to maximize their profit. In this paper, EMMEV is used to analyze the impacts of the LCFS (Low Carbon Fuel Standard), a performance-based policy instrument, on electricity prices and EV sales. The agents in EMMEV/regulated parties in LCFS should meet a certain CI (Carbon Intensity) target for their distributed fuel. In case, they cannot meet the target, they should buy credit to compensate for their shortfall and if they exceed, they can sell their excess. The results, considering the assumptions and limitations of the model, show that the banking strategy of the agents contributing in the LCFS might have negative impact on penetration of EVs, unless there is a regular Credit Clearance to trade credits. It is also shown that the electricity price as result of implementing the LCFS and increasing number of EVs has increased between 2–3 percent depending on banking strategy.

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.

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.

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.

The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and in particular, a shift towards Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies that hinder their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP) in general and Large Language Models (LLMs) in particular can serve as an enabler for the conversion of NL requirements into semi-machine-readable requirements. This is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into semi-machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach is an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs were utilized. Two of these models were fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERT), specifically aeroBERT-NER and aeroBERT-Classifier, which were trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, was utilized to identify sentence chunks present in NL requirements. All three language models were utilized together to achieve the standardization of requirements. To demonstrate the effectiveness of the methodologies, requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs) were employed, and a total of two, five, and three boilerplate templates were identified for design, functional, and performance requirements, respectively.

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.

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.

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.

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.

Decision tables have been used for many years in data processing and business applications to simulate complex rule sets. Several computer languages have been developed based on rule systems and they are easily programmed in several current languages. Land management and river-reservoir models simulate complex land management operations and reservoir management in highly regulated river systems. Decision tables are a precise yet compact way to model the rule sets and corresponding actions found in these models. In this study, we discuss the suitability of decision tables to simulate management in the river basin scale Soil and Water Assessment Tool (SWAT+) model. Decision tables are developed to simulate automated irrigation and reservoir releases. A simple auto irrigation application of decision tables was developed using plant water stress as a condition for irrigating corn in Texas. Sensitivity of the water stress trigger and irrigation application amounts were shown on soil moisture and corn yields. In addition, the Grapevine Reservoir near Dallas, Texas was used to illustrate the use of decision tables to simulate reservoir releases. The releases were conditioned on reservoir volumes and flood season. The release rules as implemented by the decision table realistically simulated flood releases as evidenced by a daily NSE (Nash-Sutcliffe Efficiency) of 0.52 and a percent bias of -1.1%. Using decision tables to simulate management in land, river and reservoir models was shown to have several advantages over current approaches including: 1) mature technology with considerable literature and applications, 2) ability to accurately represent complex, real world decision making, 3) code that is efficient, modular and easy to maintain, and 4) tables that are easy to maintain, support, and modify.

The cell concentration measurement on a culture assay using bioimpedance is a very useful tool, but it is complex to translate impedance to cell concentration values. The purpose of this work is to find a method to obtain in real time the cell concentration values for a given cell-culture assay using an oscillator as the measurement circuit. From a basic cell-electrode model, enhanced models of the cell culture immersed in a saline solution (culture medium) can be derived. These models can be used in a fitting routine to real time estimation of the cell concentration in a cell culture, using the oscillation frequency and amplitude delivered by measurement circuits proposed by the authors. Based on real experimental data (frequency and amplitude of oscillations) obtained by connecting the cell culture to an oscillator as a load, the fitting routine is simulated, and real time data of cell concentration is achieved. These results are compared with concentration data found by traditional optical methods for counting. In addition, the error obtained is divided and analyzed in two parts: in the first part of the experiment (when the few cells are adapting to the culture medium) and the second part of the experiment (when the cells grow exponentially until they completely cover the well).Low error values are obtained in the growth phase of the cell culture (the relevant phase), therefore the results obtained are considered to be promising, proving that the fitting routine is valid, and that the cell concentration can be measured in real time using an oscillator.

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.

There is increasing evidence that the impact of climate change on the productivity of grasslands will at least partly depend on their biodiversity. A high level of biodiversity may confer stability to grassland ecosystems against environmental change, but there are also direct effects of biodiversity on the quantity and quality of grassland productivity. To explain the manifold interactions, and to predict future climatic responses, models may be used. However, models designed for studying the interaction between biodiversity and productivity tend to be structurally different from models for studying the effects of climatic impacts. Here we review the literature on the impacts of climate change on biodiversity and productivity of grasslands. We first discuss the availability of data for model development. Then we analyse strengths and weaknesses of three types of model: ecological, process-based and integrated. We discuss the merits of this model diversity and the scope for merging different model types.

Discrete, logic-based models are increasingly used to describe biological mechanisms. Initially introduced to study gene regulation, these models evolved to cover various molecular mechanisms, such as signalling, transcription factor cooperativity, and even metabolic processes. The abstract nature and amenability of discrete models to robust mathematical analyses make them appropriate for addressing a wide range of complex biological problems. Recent technological breakthroughs have generated a wealth of high throughput data. Novel, literature-based representations of biological processes and emerging algorithms offer new opportunities for model construction. Here, we review up-to-date efforts to address challenging biological questions by incorporating omic data into logic-based models, and discuss critical difficulties in constructing and analysing integrative, large-scale, logic-based models of biological mechanisms.

Manual visual inspections typically conducted after an earthquake are high-risk, subjective, and time-consuming. Delays from inspections often exacerbate the social and economic impact of the disaster on affected communities. Rapid and autonomous inspection using images acquired from unmanned aerial vehicles offer the potential to reduce such delays. Indeed, a vast amount of re-search has been conducted toward developing automated vision-based methods to assess the health of infrastructure at the component and structure level. Most proposed methods typically rely on images of the damaged structure, but seldom consider how the images were acquired. To achieve autonomous inspections, methods must be evaluated in a comprehensive end-to-end manner, incorporating both data acquisition and data processing. In this paper, we leverage recent advances in computer generated imagery (CGI) to construct a 3D synthetic environment for simulation of post-earthquake inspections that allows for comprehensive evaluation and valida-tion of autonomous inspection strategies. A critical issue is how to simulate and subsequently render the damage in the structure after an earthquake. To this end, a high-fidelity nonlinear finite element model is incorporated in the synthetic environment to provide a representation of earthquake-induced damage; this finite element model, combined with photo-realistic rendering of the damage, is termed herein a physics-based graphics models (PBGM). The 3D synthetic en-vironment with PBGMs provide a comprehensive end-to-end approach for development and validation of autonomous post-earthquake strategies using UAVs, including: (i) simulation of path planning of virtual UAVs and image capture under different environmental conditions; (ii) au-tomatic labeling of captured images, potentially providing an infinite amount of data for training deep neural networks; (iii) availability of the ground truth damage state from the results of the finite-element simulation; and (iv) direct comparison of different approaches to autonomous as-sessments. Moreover, the synthetic data generated has the potential to be used to augment field datasets. To demonstrate the efficacy of PBGMs, models of reinforced concrete moment-frame buildings with masonry infill walls are examined. The 3D synthetic environment employing PBGMs is shown to provide an effective testbed for development and validation of autonomous vision-based post-earthquake inspections that can serve as an important building block for ad-vancing autonomous data to decision frameworks.

Orbit and clock products are used in real-time GNSS precise point positioning without knowing their quality. This study develops a new approach to detect orbit and clock errors through comparing geometry-free and geometry-based wide-lane ambiguities in PPP model. The reparameterization and estimation procedures of the geometry-free and geometry-based ambiguities are described in detail. The effects of orbit and clock errors on ambiguities are given in analytical expressions. The numerical similarity and differences of geometry-free and geometry-based wide-lane ambiguities are analyzed using different orbit and clock products. Furthermore, two types of typical errors in orbit and clock are simulated and their effects on wide-lane ambiguities are numerically produced and analyzed. The contribution discloses that the geometry-free and geometry-based wide-lane ambiguities are equivalent in terms of their formal errors. Although they are very close in terms of their estimates when the used orbit and clock for geometry-based ambiguities are precise enough, they are not the same, in particular, in the case that the used orbit and clock, as a combination, contain significant errors. It is discovered that the discrepancies of geometry-free and geometry-based wide-lane ambiguities are coincided with the actual time-variant errors in the used orbit and clock at the line-of-sight direction. This provides a quality index for real-time users to detect the errors in real-time orbit and clock products, which potentially improves the accuracy of positioning.

(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.

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of (Farlie-Gumbel-Morgenstern) FGM bivariate copula for constructing several dierent bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman's correlation coefficient rho,  and Kendall's tau . For illustrative purposes, one representative data set is utilized to exhibit the applicability of these proposed bivariate copula models.

Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.

Background: Health insurance (HI) run by government is providing health care service to large population. Due to poor accountability, participation and sustainability, cooperative health insurance is becoming more popular and effective in low and middle income and some high-income countries too. In Nepal, there are public and cooperative HI is in practice. The aim of this study is to compare the effectiveness of public (government) and cooperative HI in relation to benefit packages, population coverage, inclusiveness, health care utilization, and promptness for treatment in these two health insurance models in Nepal. Method: This is an institution based concurrent mixed study consists of qualitative and quantitative variables from public and cooperative groups. We included all public HI operated by government hospitals and cooperatives groups those purchased hospital service in contract. Two separate study tools were applied to access the effectiveness of insurance models. The key questions were asked for the representatives of government and private health insurance. The numeric information consisted of in quantitative data and subjective response was included in qualitative approach. Descriptive statistics and Mean Whitney U test was applied in numeric data and qualitative information were analyzed by inductive approach Results: The study revealed that new enrolment was not increased, health care utilization rate was increased and the benefit package was almost same in both groups. The overall inclusiveness was higher for the government HI, but enrolment from the religious minority, proportion of negotiated amount during treatment were significantly higher (p<0.05). During illness, the response time to reach hospital was significantly faster in cooperative health insurance than government health insurance. Qualitative findings showed that level of participation, accountability, transparency and recording system was better in cooperative health insurance than public. Conclusion: Cooperative HI could be more sustainable and accountable to the community for all; low, middle and high-income countries.

Large lithium-ion batteries (LIBs) in electric vehicles and energy storage systems demonstrate different performance and lifetime compared to small LIB cells, owing to the size effects generated by the electrical configuration and property imbalance. However, the calculation time for performing life predictions with three-dimensional (3D) cell models is undesirably long. In this paper, a lumped cell model with equivalent resistances (LER cell model) is proposed as a reduced order model of the 3D cell model, which enables accurate and fast life predictions of large LIBs. The developed LER cell model is validated via the comparisons with results of the 3D cell models by simulating a 20-Ah commercial pouch cell (NCM/graphite) and the experimental values. In addition, the LER cell models are applied to different cell types and sizes, such as a 20-Ah cylindrical cell and a 60-Ah pouch cell.

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.

We examine how external triggers, including the digital imperative and the need for more sustainable resource and stakeholder employment, spark the development of transformative sustainable business models. Drawing on the resource-based view and the shared value approach we conceptualize a multifaceted framework that helps to identify key determinants and coherent layers of transformative sustainable businesses models. Our theoretical arguments integrate recent research findings on external dynamics, such as digital technological advances and rising global competitive dynamics, with internal capabilities on both the organizational and the individual level, allowing for a more complete understanding of transformative potentials on the firm level. We propose that key determinants of sustainable transformative business models adhere to both, innovative value-creating reconstructionist and sustainable shared-value logic, and include elements such as co-creation with customers, usage-based pricing, agile and adaptive behavior, closed-loop resource employment, asset-sharing, and collaborative business ecosystems. At the same time, organizational, economic, and environmental layers encompassing sustainable business models need to be both horizontally and vertically coherent to unfold their full potential.

The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multiscale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review mathematical modelling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next-generation multiscale picture of T-cell development.

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.

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.

Manufacturing systems need to meet I4.0 guidelines to deal with uncertainty in scenarios of turbulent demand for products. The engineering concepts to define the service’s resources to manufacture the products will be more flexible, ensuring the possibility of re-planning in operation. These can follow the engineering paradigm based on capabilities. The virtualization of industry components and assets achieves the RAMI 4.0 guidelines and (I4.0C), which describes the Asset Administration Shell (AAS). However, AAS are passive components that provide information about I4.0 assets. The proposal of specific paradigms is exposed for managing these components, as is the case of multi-agent systems (MAS) that attribute intelligence to objects. The implementation of resource coalitions with evolutionary architectures (EAS) applies cooperation and capabilities’ association. Therefore, this work focuses on designing a method for modeling the asset administration shell (AAS) as virtual elements orchestrating intelligent agents (MAS) that attribute cooperation and negotiation through contracts to coalitions based on the engineering capabilities concept. The systematic method suggested in this work is partitioned for the composition of objects, AAS elements, and activities that guarantee the relationship between entities. Finally, Production Flow Schema (PFS) refinements are applied to generate the final Petri net models (PN) and validate them with Snoopy simulations. The results achieved demonstrate the validation of the procedure, eliminating interlocking and enabling liveliness to integrate elements behavior.

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.

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.

Over the years, machine learning has emerged as a tool for automated translation and has been studied relentlessly for decades. RBMT, SMT, and NMT models have been used to achieve machine translation and the results have drastically improved from when research in this field first began. Although a few general-purpose translators such as Google Translate or Microsoft Translator have accurate translations compared to that of a human translator, many pieces of text containing highly technical terms or homonyms are often mistranslated completely. When considering the necessity and importance of translating technical patents from different domains, accuracy in translation is not something that can be compromised. This motivates the need to improve the performance of machine translation further. The scope of this paper covers three open-source machine translation models for the purpose of patent documentation translation from English to Japanese, evaluates their performance on patent data, and proposes a methodology that enabled us to improve one of the model’s BLEU score by 41.22%, achieving a BLEU score of 46.18.

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.

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.

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.

Exercise-based cardiac rehabilitation requires patients to perform a set of certain prescribed exercises a specific number of times. Local muscular endurance (LME) exercises are an important part of the rehabilitation program. Automatic exercise recognition and repetition counting, from wearable sensor data is an important technology to enable patients to perform exercises independently in remote settings, e.g. their own home. In this paper we first report on a comparison of traditional approaches to exercise recognition and repetition counting, corresponding to supervised machine learning and peak detection from inertial sensing signals respectively, with more recent machine learning approaches, specifically Convolutional Neural Networks (CNNs). We investigated two different types of CNN: one using the AlexNet architecture, the other using time-series array. We found that the performance of CNN based approaches were better than the traditional approaches. For exercise recognition task, we found that the AlexNet based single CNN model outperformed other methods with an overall 97.18% F1-score measure. For exercise repetition counting , again the AlexNet architecture based single CNN model outperformed other methods by correctly counting repetitions in 90% of the performed exercise sets within an error of ±1. To the best of our knowledge, our approach of using a single CNN method for both recognition and repetition counting is novel. In addition to reporting our findings, we also make the dataset we created, the INSIGHT-LME dataset, publicly available to encourage further research.

This paper presents a new methodology to derive and analyze strategies for a fully decarbonized urban transport system which combines conceptual vehicle design, a large-scale agent-based transport simulation, operational cost analysis, and life cycle assessment for a complete urban region. The holistic approach evaluates technical feasibility, system cost, energy demand, transportation time and sustainability-related impacts of various decarbonization strategies. In contrast to previous work, the consequences of a transformation to fully decarbonized transport system scenarios are quantified across all traffic segments, considering procurement, operation and disposal. The methodology can be applied to arbitrary regions and transport systems. Here, the metropolitan region of Berlin is chosen as a demonstration case. First results are shown for a complete conversion of all traffic segments from conventional propulsion technology to battery electric vehicles. The transition of private individual traffic is analyzed regarding technical feasibility, energy demand and environmental impact. Commercial goods, municipal traffic and public transport are analyzed with respect to system cost and environmental impacts. We can show a feasible transition path for all cases with substantially lower greenhouse gas emissions. Based on current technologies and today’s cost structures our simulation shows a moderate increase in total systems cost of 13-18%.

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.

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.

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.

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

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.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

Synthesis of ZSM-5 zeolite from OSDA-free systems has been a very interesting alternative method because it can avoid the usage of organic templates and consequent calcination step. However, in opened reports, organic substance such as alcohol is more or less involved. In the present work, a calcined commercial ZSM-5 zeolite was used as a seed, sodium aluminate as an aluminum source, silica sol as a silicon source, which ensures that there is no any organics (template) in the synthesis system, and polycrystalline ZSM-5 aggregates consisting of rod-like nanocrystals are successfully prepared in a completely OSDA-free system. The effects of seed (chemical component, dosage) and crystallization conditions on the synthesis of ZSM-5 were systematically investigated. The results show that highly crystallinity ZSM-5 aggregate consisting of primary nano-sized crystals with a size of less than 100 nm can be yielded from a gel precursor with 5.6 wt.% seed after hydrothermal treatment for 48 h. The results also shows that the chemical composition of the seeds had little effect on the topological structure and pore structure of the synthesized samples, but the seed with a low Si/Al ratio is more conducive to the rapid crystallization of zeolite and to the improvement of the acid, especially the strong acid centers of the catalyst.

Fast convergence routing is an important issue for LEO constellation network, due to its dynamical topology changing and time varying transmission requests. Most of existing research focus on the OSPF routing algorithm, which cannot handle the frequently links state changing of network. In this paper, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, in which the satellite gets the network links status fast and adjusts its routing strategy. In FRL-SR, each satellite node is regarded as an agent. The agent selects the port for packet forwarding according to its own routing policy. When the satellite network state changes, agent would send ’hello’ packets to the neighbor node to update the neighbor node’s routing policy. Compared with traditional reinforcement learning, FRL-SR can perceive network information faster, and then converge faster. Also, FRL-SR can mask the dynamics of satellite network topology and adaptively adjust the forwarding strategy according to the link state. Various simulation is constructed, the results show that the proposed FRL-SR algorithm out performance the Dijkstra algorithm in performance of average delay, packet arriving ratio, network load balance.

Background: Enterovirus infections affect people around the world, causing a range of illnesses, from mild fevers to severe, potentially fatal conditions. There are no approved vaccines or treatments for enterovirus infections. Methods: We have tested our library of broad-spectrum antiviral agents (BSAs) against echovirus 1 (EV1) in human adenocarcinoma alveolar basal epithelial A549 cells. We also tested combinations of the most active compounds against EV1 in A549 and human immortalized retinal pigment epithelium RPE cells. Results: We confirmed anti-enteroviral activities of pleconaril, rupintrivir, cycloheximide, vemurafenib, remdesivir, emetine, and anisomycin and identified novel synergistic rupintrivir-vemurafenib, vemurafenib-pleconaril and rupintrivir-pleconaril combinations against EV1 infection. Conclusions: Because rupintrivir, vemurafenib, and pleconaril require lower concentrations to inhibit enterovirus replication in vitro when combined, their combinations may have fewer side effects in vivo and therefore should be further studied in pre- and clinical trials.

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.

We reported previously that poly (3-hydroxybutyrate) (PHB) oligomer is an effective antimicrobial agent against gram-positive bacteria, gram-negative bacteria, fungi and multi-drug resistant bacteria. In this work, it was further found that polyethylene glycol (PEG) can promote the antimicrobial effect of PHB oligomer synergistically. Three hypothetic mechanisms were proposed, that is, generation of new antimicrobial components, degradation of PHB macromolecules and dissolution/dispersion of PHB oligomer by PEG. With a series of systematic experiments and characterizations of HPLC-MS, it was deducted that dissolution/dispersion of PHB oligomer dominated the synergistic antimicrobial effect between PHB oligomer and PEG. This work demonstrates a way for promoting antimicrobial effect of PHB oligomer and other antimicrobial agents through improving hydrophilicity.

The concept of genetic improvement in relation to biological control involves the exploitation of natural genetic variation for the benefit of existing biological control agents (BCAs). Despite recent calls for this process to be adopted in biological control research, there is no clear overview of the current state of research into genetic variation within a biological control context, including quantifiable estimates such as narrow-sense heritability (h²). In this systematic review, we aim to determine the current state of research on the genetic variation of biological control traits in natural enemies. After the searching process, screening for papers that can deliver on our research question reduced the initial 2,927 search hits to only a mere 69 papers for data extraction. Of these, the majority (73.6%) did not report quantitative values for genetic variation. Extracting the traits measured in these papers, we categorized them according to two approaches; the first related to fitness components, and the second related to biological control importance. This systematic review highlights the need for more rigorous reporting of the quantitative values of genetic variation to enable the successful genetic improvement of biological control agents.

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.

We present information theoretic search strategies for single and multi-robot teams to localize the source of biochemical contaminants in turbulent flows. The robots synthesize the information provided by sporadic and intermittent sensor readings to optimize their exploration strategy. By leveraging the spatio-temporal sensing capabilities of a mobile sensing network, our strategies result in control actions that maximize the information gained by the team while optimizing the time spent localizing the position of the biochemical source. By leveraging the team's ability to obtain simultaneous measurements at different locations, we show how a multi-robot team is able to speed up the search process resulting in a collaborative information theoretic search strategy. We validate our proposed strategies in both simulations and experiments.

Human influenza A viruses (IAVs) cause global pandemics and epidemics. These viruses evolve rapidly, making current treatment options ineffective. To identify novel modulators of IAV-host interactions, we re-analyzed our recent transcriptomics, metabolomics, proteomics, phosphoproteomics, and genomics/virtual ligand screening data. We identified 713 potential modulators targeting 200 cellular and two viral proteins. Anti-influenza activity for 48 of them has been reported previously, whereas the antiviral efficacy of the remaining 665 is unknown. Studying anti-influenza efficacy, immuno-modulating properties and potential resistance of these compounds or their combinations may lead to the discovery of novel modulators of IAV-host interactions, which might be more effective than the currently available anti-influenza therapeutics.

To investigate the role of sulfhydryl groups and disulfide bonds in different protein-stabilized emulsions, N-ethylmaleimide (NEM) was used as sulfhydryl-blocking agent to be added in the emulsion. The addition of NEM to block the sulfhydryl groups resulted in a reduction of the content of disulfide bonds formation, which enabled destruction of the internal structure of the protein molecule, and then decreased the restriction of protein membrane on the oil droplets. Furthermore, with NEM content increasing in the emulsion, a reduction of protein emulsifying activity and emulsion stability also occurred. At the same time, the intermolecular interaction of the protein on the oil droplet interface membrane was destroyed, and the emulsion droplet size increased with the NEM content in the emulsion. Although NEM blocking sulfhydryl groups not to form disulfide bonds has similar effects on three types of protein emulsion, the degree of myofibrillar protein (MP), egg-white protein isolate (EPI), and soybean protein isolate (SPI) as emulsifier had a subtle difference.

Blade Health Monitoring (BHM) is often necessary in power plants and in aviation to prevent excessive blade vibration and cracks. This article proposes a network of blade tip timing sensors operated in a distributed BHM system. A number of cooperating agents is implemented in smart conditioning units which can autonomously operate in an adverse environment and communicate with other nodes via a serial interface. The project uses special versions of reduced instruction set chips that are able to operate near the hot section of the engine. Due to the limited number of types of microprocessors available in the extended temperature range grading, it was necessary to fully utilize the limited hardware resources and implement preemptive multitasking. For this purpose, a custom operating system and communication protocol were designed. The protocol hosts the middle layer which hides the implementation of the distributed system. The presented architecture ensures the sufficient computational capacity in individual nodes of the network operated in adverse conditions. It is scalable and resistant to transmission errors.

Dehydrodiisoeugenol (DEH), a novel lignan component extracted from the Nutmeg seeds, displays noticeable anti-inflammatory and anti-allergic effects in digestive system diseases. However, the mechanism of its anti-cancer activity in gastrointestinal cancer is still to be investigated. Here, the anti-cancer effect of DEH to human colorectal cancer and its underlying mechanism were evaluated. The DEH treatment arrests the cell cycle of colorectal cancer cells at G1/S phase, which leading to a significant cell growth inhibition. Moreover, it can induce strong cellular autophagy and the autophagy would be inhibited through autophagic inhibitors with reducing EDH-induced inhibition of cell growth in colorectal cancer cells. Further studies indicated that DEH can also induce endoplasmic reticulum (ER) stress, and could subsequently stimulating autophagy through activating PERK/eIF2α and IRE1α/XBP-1s/CHOP pathways. Knockdown of PERK or IRE1α can significantly decrease the DEH-induced autophagy and retrieve cell viability in cells treated with DEH. What’s more, DEH exhibits significant anti-cancer activities through CDX- and PDX-model as well. Taken together, our studies strongly suggest that DEH might be a potential anti-cancer agent against colorectal cancer via activating ER stress-induced autophagy inhibition.

Many theoretical models of iron-based superconductors have been proposed but Tc calculations based on the models are usually missing. We have chosen two models of iron-based superconductors in the literature and then compute the Tc values accordingly: Recently two models have been announced which suggest that superconducting electron concentration involved in the pairing mechanism of iron-based superconductors may have been underestimated, and that the antiferromagnetism and the induced xy potential may even have a dramatic amplification effect on electron-phonon coupling. We use bulk FeSe, LiFeAs and NaFeAs data to calculate the Tc based on these models and test if the combined model can predict the superconducting transition temperature (Tc) of the nanostructured FeSe monolayer well. To substantiate the recently announced xy potential in the literature, we create a two-channel model to separately superimpose the dynamics of the electron in the upper and lower tetrahedral plane. The results of our two-channel model support the literature data. While scientists are still searching for a universal DFT functional that can describe the pairing mechanism of all iron-based superconductors, we base on the ARPES data to propose an empirical combination of DFT functional for revising the electron-phonon scattering matrix in the superconducting state, which ensures that all electrons involved in iron-based superconductivity are included in the computation. Our computational model takes into account this amplifying effect of antiferromagnetism and the correction of the electron-phonon scattering matrix together with the abnormal soft out-of-plane lattice vibration of the layered structure, which allows us to calculate theoretical Tc values of LiFeAs, NaFeAs and FeSe as a function of pressure that correspond reasonably well to the experimental values. More importantly, by taking into account the interfacial effect between an FeSe monolayer and its SrTiO3 substrate as an additional gain factor, our calculated Tc value is up to 91 K high, and provides evidence that the strong Tc enhancement recently observed in such monolayers with Tc reaching 100 K may be contributed from the electrons within the ARPES range.

Mycobacterium tuberculosis (Mtb) is the etiological agent of tuberculosis (TB), a disease that alt-hough preventable and curable, remains a global epidemic due to the emergence of resistance and a latent form responsible for a long period of treatment. Drug discovery in TB is a challenging task due to the heterogeneity of the disease, the emergence of resistance and an uncomplete knowledge of the pathophysiology of the disease. The limited permeability of the cell wall and the presence of multiple efflux pumps remain a major barrier to achieve effective intracellular drug accumulation. While the complete genome sequence of Mtb has been determined and several potential protein targets have been validated, the lack of adequate models for in vitro and in vivo studies is a limit-ing factor in TB drug discovery programs. In current therapeutic regimens, less than 0.5% of bac-terial proteins are targeted being the biosynthesis of the cell wall and the energetic metabolism two of the most important processes exploited for TB chemotherapeutics. This review provides an overview on the current challenges in TB drug discovery and emerging Mtb druggable proteins, and how chemical probes for protein profiling enabled the identification of new targets and bi-omarkers, paving the way to disruptive therapeutic regimens and diagnostic tools.

Lake Rawapening, Semarang Regency, Indonesia, has incorporated a holistic plan in its management practices. However, despite successful target achievements, some limitations remain that a review of its management plan is needed. This paper identifies and analyzes existing lake management strategies as a standard specifically in Lake Rawapening by exploring various literature, both legal frameworks and scholarly articles indexed in Google Scholar and published in Water by MDPI about lake management in many countries. There are two major types of lake management, namely pillar-based and object-based. While the former is the foundation of a conceptual paradigm that does not comprehensively consider the roles of finance and technology in the lake management, the latter indicates the objects to manage so as to create standards or benchmarks for the implementation of various programs. Overall, Lake Rawapening management should include more programs on erosion-sedimentation control and monitoring of operational performance using information systems.

Nature-based solutions (NbS) are increasingly recognised for their potential to address both the climate and biodiversity crises. These outcomes are interdependent, and both rely on the capacity of NbS to support and enhance the health of an ecosystem: its biodiversity, the condition of its abiotic and biotic elements, and its capacity to function normally despite environmental change. However, while understanding of ecosystem health outcomes of nature-based interventions for climate change mitigation is growing, the outcomes of those implemented for adaptation remain poorly understood with evidence scattered across multiple disciplines. To address this, we conducted a systematic review of the outcomes of 109 nature-based interventions for climate change adaptation using 33 indicators of ecosystem health across eight broad categories (e.g. diversity, biomass, ecosystem functioning and population dynamics). We showed that 88% of interventions with positive outcomes for climate change adaptation also reported measurable benefits for ecosystem health. We also showed that interventions were associated with a 67% average increase in local species richness. All eight studies that reported benefits in terms of both climate change mitigation and adaptation also supported ecosystem health, leading to a triple win. However, there were also trade-offs, mainly for forest management and creation of novel ecosystems such as monoculture plantations of non-native species. Our review highlights two major limitations of research to date. First, only a limited selection of metrics are used to assess ecosystem health and these rarely include key aspects such as functional diversity and habitat connectivity. Second, taxonomic coverage is poor: 67% of outcomes assessed only plants and 57% did not distinguish between native and non-native species. Future research addressing these issues will allow the design and adaptive management of NbS to support healthy and resilient ecosystems, and thereby enhance their effectiveness for meeting both climate and biodiversity targets.

In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifically, the Qlearning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users, and they are the licensed subscribers that pay for their service. The second type of the scheduled users is the Secondary Users, and they could be unlicensed subscribers that dont pay for their service, device to device communications, or sensors. Each user whether it is a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost ninety percent utilization of the spectrum, and provided fair shares of the spectrum among users.

This document summarizes the evidence currently available about the effects of the anesthetic agents and techniques used in primary cancer surgery and long-term oncologic outcomes, and the biomolecular mechanisms involved in their interaction..

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.

There are dozens of approved, investigational and experimental antiviral agents. Many of these agents cause serious side effects, which can be revealed only after drug administration. Identification of the side effects prior to drug administration is challenging. Here we describe an ex vivo approach for studying immuno- and neuro-modulatory properties of antiviral agents, which could be associated with potential side effects of these therapeutics. The approach combines drug toxicity/efficacy tests and transcriptomics, which is followed by cytokine and metabolite profiling. We demonstrated the utility of this approach with several examples of antiviral agents. We also showed that the approach can utilize different immune stimuli and cell types. It can also include other omics techniques, such as genomics and epigenomics, to allow identification of individual markers associated with adverse reactions to antivirals with immuno- and neuro-modulatory properties.

Due to their increased surface area, extent of swelling and active substance loading capacity and flexibility, nanogels made from natural and synthetic polymers have gained significant interest in the scientific and industrial areas. Especially, customized design and implementation of non-toxic, biocompatible, and biodegradable micro/nano carriers makes their usage very feasible for a range of biomedical applications, including drug delivery, tissue engineering, and bioimaging. The design and application methodologies of nanogels have been outlined in this review. Additionally, the most recent advancements in nanogel biomedical applications have been discussed, with a particular emphasis on applications for the delivery of drugs and biomolecules.

Vision-based tactile sensors (VBTS) have become the de facto method of giving robots the ability to obtain tactile feedback from their environment. Unlike other solutions to tactile sensing, VBTS offers high spatial resolution feedback without compromising on instrumentation costs or incurring additional maintenance expenses. However, conventional cameras used in VBTS have a fixed update rate and output redundant data, leading to computational overhead downstream. In this work, we present a neuromorphic vision-based tactile sensor (N-VBTS) that employs observations from an event-based camera for contact angle prediction. Particularly, we design and develop a novel graph neural network, dubbed TactiGraph, that asynchronously operates on graphs constructed from raw N-VBTS streams exploiting their spatiotemporal correlations to perform predictions. Although conventional VBTS uses an internal illumination source, TactiGraph is reported to perform efficiently in both scenarios, with and without an internal illumination source. Rigorous experimental results revealed that TactiGraph achieved a mean absolute error of 0.62∘ in predicting the contact angle and was faster and more efficient than both conventional VBTS and other N-VBTS, with lower instrumentation costs. Specifically, N-VBTS requires only 5.5% of the compute-time needed by VBTS when both are tested on the same scenario.

Zoos are increasingly putting in place formalized animal welfare assessment programs to allow monitoring of welfare over time, as well as to aid in resource prioritization. These programs tend to rely on assessment tools that incorporate resource-based and observational animal- focused measures since it is rarely feasible to obtain measures of physiology in zoo-housed animals. A range of assessment tools are available which commonly have a basis in the Five Domains framework. A comprehensive review of the literature was conducted to bring together recent studies examining welfare assessment methods in zoo animals. A summary of these methods is provided with advantages and limitations of the approach es presented. We then highlight practical considerations with respect to implementation of these tools into practice, for example scoring schemes, weighting of criteria, and innate animal factors for consideration. It is concluded that would be value in standardizing guidelines for development of welfare assessment tools since zoo accreditation bodies rarely prescribe these. There is also a need to develop taxon or species- specific assessment tools to inform welfare management.

Diisocyanates, particularly toluene diisocyanate (TDI) are useful for the preparation of various polyurethanes with specific applications as leather-like materials, adhesives, insoles etc. Blocking agents can be used for the operational simplicity and reducing the hazards of TDI. In this paper we reported the use of 3-(4-bromo-phenyl)-1H-pyrazole to block toluene diisocyanate (TDI). FTIR, NMR, thermogravimetric analysis, contact angle and differential scanning calorimetry (DSC) and were used for characterization. Effectiveness of blocking was confirmed by spectroscopic techniques. DSC thermogram shows that blocked adducts deblock at 240 °C causing the regeneration of TDI and blocking agents to react with polyols of different molecular weights forming polyurethanes. The characterization of polyurethanes has been done by Infrared spectroscopy, Nuclear magnetic resonance spectroscopy, thermogravimetric analysis, differential scanning calorimetry and contact angle study.

This paper presents control algorithms enabling autonomous heterogeneous trucks to drive in platoons. Heterogeneous trucks imply that the hardware information (e.g., truck length, break, accelerator, or engine) of a truck may be distinct from that of another truck. We define a platoon as a collection of trucks where a manually driven truck (leader truck) is followed by several automatically controlled following trucks. The proposed approach is to make every autonomous truck keep following the leader's trajectory while maintaining a designated distance from its predecessor truck. As far as we know, this paper is unique in developing both lateral maneuver and speed control considering a platoon of heterogeneous trucks. The efficiency of the proposed approach is verified using simulations.

The aim of this study was to investigate the impact of Process Oriented Guided Inquiry Learning (POGIL)-based instruction versus lecture-based instruction on Grade 12 students’ performance in circular motion unit. A quasi-experimental, pretest-posttest design, the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes: Knowing, Applying and Reasoning (KAR). The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys) and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a unit of circular motion in physics using POGIL-based instruction while the control group was taught the unit using lecture-based instruction. The findings of the study showed statistically significant differences between students of the control group and the treatment group in favor of the later regarding their circular motion performance, suggesting the superiority of the POGIL in enhancing student understanding of the circular motion.