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.

Low carbon cities are increasingly forming a distinct strand of sustainability literature. Models have been developed to measure the performance of low carbon cities. The purpose of this paper is to formulate a strategy-based model to evaluate current performance and predict future conditions of low carbon cities. It examines the dynamic interrelationships between key performance indicators (KPIs), induces changes to city plan targets and then instantly predicts the outcome of these changes. Designed generic and flexible, the proposed model shows how low carbon targets could be used to guide the transformation of low carbon cities under four strategies: (1) passive intervention, (2) problem solving, (3) trend modifying and (4) opportunity seeking. Further, the model has been applied to 17 cities and then tested on 5 cities: London, New York, Barcelona, Dubai and Istanbul. The paper concludes with policy implications to realign city plans and support low carbon innovation.

The average age of in-service bridges has increased in recent years in the United States. To address this issue, structural health monitoring and damage identification approaches can be employed to prioritize maintenance/replacement of aging bridges. Among the damage identification and operational health monitoring approaches, finite element (FE) model updating methods can offer a solution to evaluate the mechanics-based characteristics of bridges. However, in a real-world setting, unidentifiability and mutual dependency between model parameters, modeling errors, especially due to boundary conditions, as well as ill-conditioning of updating algorithms can pose challenges to the application of FE model updating methods. To address these challenges, this study presents a two-step FE model updating approach. In the first step, modal-based model updating is used to estimate linear model parameters mainly related to the stiffness of boundary conditions and material properties. In the second step, in order to refine parameter estimation accounting for nonlinear response behavior of the bridge, a time-domain model updating is carried out. In this step, boundary conditions are fixed at their final estimates using modal-based model updating. To prevent the convergence of updating algorithm to local solutions, the initial estimates for nonlinear material properties are selected based on their corresponding final estimates in the modal-based model updating. To validate the applicability of the two-step FE model updating approach, a series of forced-vibration experiments are designed and carried out on a pair of decommissioned and deteriorated prestressed bridge I-girders. After carrying out the two-step FE model updating, the final estimates of concrete compressive strength are shown to provide reasonable assessment of the damage extent in the girders.

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

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

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

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

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

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

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

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.

A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines Particle Swarm Optimisation (PSO) with a turbulence intensity based Jensen wake model (TI-JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ($C_P$) or deflecting wakes by applying yaw-offsets for maximising net farm production. First, TI-JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimized strategies are evaluated using simulations based on TI-JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 seconds for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.

When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated by an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when prediction were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin. The Pareto set suggested that an airflow organization with dual circulation may be optimal.

There has been growing interest in using permanent magnet synchronous motors (PMSMs) for pumping applications to improve energy efficiency. One promising approach for powering these motors in variable speed applications is using modular multilevel cascaded converters based on a Triple-Star Bridge Cell (M3C) due to their inherent fault tolerance capability. However, M3C converters require a more complex control system than simpler converters. For instance, A basic M3C control system for power transmission requires seventeen (17) PI controllers, whose adjustment depends on the M3C’s dynamical model parameters’ value knowledge needing extensive and time-consuming testing to obtain them. To solve this control system issue, we propose an adaptive M3C control system for variable speed drives powering multiple PMSM-driven centrifugal pumps that reduces the number of controllers to six (6). Furthermore, the proposal does not require knowledge of the converter, motor, or load parameters, making it more practical and versatile. The proposal introduces an ad-hoc hybrid passivity-based model reference adaptive controller in cascade with a passivity-based control. It has been validated through theoretical stability proof and comparative simulation results with a basic control system under normal and fault operations. As a result, the proposal effectively follows the required rotor speed while enhancing performance by decreasing the current consumption and recovering from a 10% input phase imbalance, a cell short circuit, and an open cell.

Coal combustion in thermal power plants releases ash. Ash is reported to cause different adverse health hazards in humans and other organisms. Owing to the presence of radionuclides, it is also considered as a potential radiation hazard. In this study, based on the surface radiation measurements and relevant ancillary data, expected radiation risk zones were identified with regard to the human population residing near the Thermal Power Plant. With population density as the risk determining criteria, about 20% of the study area was at ‘High’ risk and another 20% of the study area was at ‘Low’ risk zone. The remaining 60% was under medium risk zone. Based on the findings remedial measures which may be adopted have been suggested.

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

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

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

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

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

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

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

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.

There has been a growing interest in incorporating psychological and neuroscientific knowledge about the development of cognitive functions in educational policies and academic practices. In this paper, we argue that the current knowledge about the interactions between these functions and their neurodevelopmental characteristics should also be considered in order to develop practices that could be better suited to pupils depending on their age. To facilitate this, we review current neuroscientific knowledge on the competitive interactions between two neural circuitry underlying distinct learning functions, their developmental trajectories and how they are linked to other functions such as cognitive control. The incorporation of this knowledge in education could help improve academic outcomes.

This thesis presents the effects of work done on a software project for generative models and spreadsheets, allowing for a quick creation of the conceptual model of the aircraft. The subject of the work is a response to the current trends and needs prevailing in the field of computer design engineering CAD and aviation. In the initial chapters, theoretical issues related to the work being carried out were introduced and the methodology of creating software for construction and verification of the structure of aircraft along with the needs of interchange between databases of generative models was presented. In the next stages, the concepts and selected solutions for the user interface supporting the knowledge base were presented along with a set of procedures for its operation. Furthermore, the method of database integration with the methods of determining design features for the developed generative models and with the Siemens NX system. Furthermore, problems encountered in software development, as well as solution examples for model application are specified. The results obtained and the models generated on their basis were subjected to a strength analysis using Autodesk Inventor software and analysed in terms of meeting the initial assumptions. In the end, conclusions and observations resulting from the effects of the work presented in the project were formulated.

Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance of the selected cell factory in larger reactors, has a major influence on the performance of the final process. To overcome this, scaledown approaches are essential to run screenings that show the real cell factory performance at industrial like conditions. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batches ran under the desired conditions generating sufficient information to define the fastest growing strain in an environment with varying glucose concentrations similar to industrial scale bioreactors.

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%.

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

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

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

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

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

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

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

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

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

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

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.

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.

Process mining offers significant services in the development of digital threads and digital twin applications for healthcare. One of the sub-emerging case studies is the use of patients’ location data in process mining analyses. While the streamlining of published works is focused on introducing process discovery algorithms, there is a necessity to address challenges beyond that. Literature analysis indicates that explainability, reasoning, and characterizing the root causes of process drifts in healthcare processes is an important but overlooked challenge. In addition, incorporating domain-specific knowledge into process discovery could be a significant contribution to process mining literature. To address this, we present the DIAG approach that consists of a meta-model and an algorithm that enables diagnosing in process mining through an ontology-driven approach. With DIAG, we modeled the healthcare semantics in a process mining application and diagnosed the causes of drifts in patients’ pathways. We performed an experiment in a hospital living lab to examine the effectiveness of the DIAG approach.

In the development of the safety-critical systems, it is important to perform Failure Modes and Effects Analysis (FMEA) process to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an Automatic FMEA tool which integrates the Model-based Design (MBD), FMEA, and Simulated Fault Injection techniques in a single environment. The Automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool. First, the Automatic FMEA tool automatically generates FMEA reports compared to the traditional spreadsheet-based FMEA tools. Second, the Automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the Automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the Automatic FMEA were compared to that of the legacy FMEA.

Battery/Supercapacitor(SC) current tracking control is a key issue for hybrid energy storage system (HESS) in electric vehicles. An innovative passivity-based L2-gain adaptive control (PBL2AC) based on port-controlled Hamiltonian model with dissipativity (PCHD) for reference current tracking and bus voltage stability in HESS is presented. The developed PCHD model has considered both parameter variations and external disturbances. By using L2-gain disturbance attenuation, the PBL2AC ensures robust reference current tracking and stable bus voltage. Moreover, adaptive mechanism is adopted to estimate the electrical parameters. To validate the proposed control scheme for HESS, simulations and experiments were done and compared with traditional PID and sliding mode control under several typical driving cycles, and results show that the effectiveness of the proposed controller can be confirmed.

In South Korea, landslides are caused by localized heavy rainfall and typhoons, which often occur in the summer season at natural slopes in mountainous areas and artificial slopes in urban surroundings. Flow-type landslides frequently occur in mountainous areas. To evaluate flow-type landslides, it is essential to identify the physical characteristics of soil, giving focus to the soil on the top layers of various types of slope. This study conducts a survey and an analysis of the characteristics of landslides that occurred in the study area with different geological conditions of granite and gneiss. The characteristics of soil in the area and its surroundings that have or have not undergone landslides for every geological condition is also evaluated. Based on these characteristics and a statistics method, it extracts the triggering factors, permeability coefficients (k), and shearing strength with cohesion (c) and internal friction angel (φ) of soils that are highly related to landslides around weathered soil layers. As a result, the permeability coefficients show significant relevance with void ratio (e), the effective size of grains (D10), and uniformity coefficient (cu), while the shearing strength with the proportion of fine-grained soil (Fines), uniformity coefficient (cu), degree of saturation (S), dry weight density (rd), and void ratio (e). By obtaining this result, the study uses the regression analysis to suggest models to estimate the permeability coefficients and shearing strength. For the gneiss area, the statistics-based estimation model (SEM) is proposed as k_gn = (1.488 × 10^-02 × e) + (1.076 × D₁₀) + (-1.629 × 10^-04 × c_u) - (1.893 × 10^-02) for permeability coefficients; cgn = (-0.712 × Fines) + (-0.131 × c_u) + 15.335 for cohesion; and φgn = (27.01 × r_d) + (-12.594 × e) + 6.018 for internal frictional angle of soils. For the granite area, the statistics-based estimation model (SEM) is proposed as kgr = (8.281 × 10^-03 × e) + (0.639 × D₁₀) + (-2.766 × 10^-05 × c_u) - (9.907 × 10^-03) for permeability coefficients; c_gr = (-0.689 × Fines) + (-0.0744 × S) + 18.59 for cohesion; and φ_gr = (33.640 × r_d) + (-0.875 × e) - 9.685 for internal frictional angle of soils. The use of statistics-based estimation models (SEMs) for landslide-triggering factors that trigger landslides will support the simple calculation of permeability coefficient and shearing strength (cohesion and internal frictional angle), only requiring information about the physical properties of soil at the natural slopes that have different geological features such as gneiss and granite areas.

Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g. hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause for the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: 1) a long short-term memory (LSTM); 2) a proposed spectrogram-based convolutional neural network model (pCNN); and 3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (manual) feature engineering. Results were evaluated on our own, publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from "BCI Competition IV". Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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

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

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

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

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

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

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

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

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.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

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 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.

Artificial intelligence (AI) applications in different fields are developing rapidly, among which AI painting technology, as an emerging technology, has received wide attention from users for its creativity and efficiency. This study aimed to investigate the factors that influence user acceptance of the use of AIBPS by proposing an extended model that combines the Extended Technology Acceptance Model (ETAM) with the AI-based Painting System (AIBPS).A questionnaire was administered to 528 Chinese participants, using validated factor analysis data and Structural Equation Modeling (SEM) was used to test the hypotheses. The findings showed that hedonic motivation (HM) and perceived trust (PE) had a positive effect (+) on users' perceived usefulness (PU) and perceived ease of use (PEOU), while previous experience (PE) and technical features (TF) had no effect (-) on users' perceived usefulness (PU). This study provides an important contribution to the literature on AIBPS and the evaluation of systems of the same type, which helps to promote the sustainable development of AI in different domains and provides a possible space for further extension of TAM, thus helping to improve the user experience of AIBPS. The results of the study provide insights for system developers and enterprises to better motivate users to use AIBPS.

Deep reinforcement learning (DRL) has achieved significant results in many Machine Learning (ML) benchmarks. In this short survey we provide an overview of DRL applied to trading on financial markets, including a short meta-analysis using Google Scholar, with an emphasis on using hierarchy for dividing the problem space as well as using model-based RL to learn a world model of the trading environment which can be used for prediction. In addition, multiple risk measures are defined and discussed, which not only provide a way of quantifying the performance of various algorithms, but they can also act as (dense) reward-shaping mechanisms for the agent. We discuss in detail the various state representations used for financial markets, which we consider critical for the success and efficiency of such DRL agents. The market in focus for this survey is the cryptocurrency market.

Stenting is a common method for treating atherosclerosis. A metal or polymer stent is deployed to open the stenosed artery or vein. After the stent is deployed, the blood flow dynamics influence the mechanics by compressing and expanding the structure. If the stent does not respond properly to the resulting stress, vascular wall injury or re-stenosis can occur. In this work, Discrete Multiphysics is used to study the mechanical deformation of the coronary stent and its relationship with the blood flow dynamics. The major parameters responsible for deforming the stent are sort in terms of dimensionless numbers and a relationship between the elastic forces in the stent and pressure forces in the fluid is established. The blood flow and the stiffness of the stent material contribute significantly to the stent deformation and affect the rate of deformation. The stress distribution in the stent is not uniform with the higher stresses occurring at the nodes of the structure.

In Italy HPV vaccination was implemented for girls since 2007 but its coverage was lower than recommended level. Sicily is one of the Italian administrative regions with lower vaccination coverage, ranging in the birth cohorts 1996–1999 from 59% to 62%. Aim of the study was to investigate factors associated with refusal of anti-HPV vaccination among young adult women of Palermo, Italy. A cross-sectional study was conducted through the administration of a telephone questionnaire, consisting of 23 items on HPV infection and vaccination knowledge based on Health Belief Model framework. The eligible population were young women with at least a previous vaccination among all included in Sicilian Vaccination schedule, without starting or completing anti-HPV vaccination schedule. Overall, 141 young women were enrolled, of them 84.4% were unvaccinated and 15.6% had at least one dose of HPV vaccine. In multivariate analysis, factors associated with the failure to perform the HPV vaccination were degree as school level (OR = 10.2, p = 0.041), lower participation at school seminar on HPV (OR = 0.2, p = 0.047) and lower perception of anti-HPV vaccine benefits (OR = 0.4, p = 0.048). Public health educational program focusing and tailored on benefits perception of anti-HPV vaccine and HPV disease severity, especially if carried out at school, can improve HPV vaccination uptake.

Wireless communication has proliferated due to the colossal augmentation of smart phones, mobile phones, tablets and laptops etc. Multimedia applications such as live videos, audios, still images, animated graphics and live communications all requires accompaniments of quality of service (QoS) as well as reliable end-to-end transfer of data. Information about the availability of resources in channel is must in order to improve the QoS. QoS can be determined in terms of capacity of channel, available bandwidth (ABW), and bulk transfer capacity (BTC). Bandwidth availability is a key check for improving the QoS in a network. The performance of a multimedia application is directly affected by the bandwidth availability. One of the most important QoS characteristic is ABW at a wireless route and it can be demarcated as least unused capacity of links instituting a network route. Since now there have been many bandwidth estimation techniques are available in the literature to increase the network performance. Bandwidth estimation techniques have been arranged into three leading classifications: i) Active probing bandwidth estimation techniques, ii) Passive bandwidth estimation technique and iii) Model based bandwidth estimation techniques. Each of these techniques are briefly discussed in this paper.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

Increasingly, there is a focus on utilising renewable energy resources in a bid to fulfil increasing energy requirements and mitigate the climate change impacts of fossil fuels. While most renewable resources are free, the technology used to usefully convert such resources is not and there is an increasing focus on improving the conversion economy and efficiency. To this end, advanced control technologies can have a significant impact and is already a relatively mature technology for wind turbines. Though hydroelectric plants can use simple regulation systems, significant benefits have been shown to accrue from the appropriate use of the same control methods designed for wind turbine plants. This represents the key point of the paper. In fact, to date, the application communities connected with wind and hydraulic energies have had little communication, resulting in little cross fertilisation of control ideas and experience, particularly from the more mature wind area to hydrodynamic systems. Therefore, this paper examines the models and the application of control technology across both domains, both from a comparative and contrasting point of view, with the aim of identifying commonalities in models and control objectives, as well as potential solutions. Key comparative reference points include the articulation of the exployed models, specification of control objectives, development of high--fidelity simulators, and development of solution concepts. Not least, in terms of realistic system requirements are the set of physical and constraints under which such renewable energy systems must operate, and the need to provide reliable and robust control solutions, which respect the often remote and relatively inaccessible location of many onshore and offshore deployments.

Certain Trichoderma isolates provide biofertilizer, biocontrol, and other plant-beneficial activities while inhabiting the soil or internal plant issues; and their use in agricultural systems could contribute to sustainable food production. It is thought that this colonization of soil and internal plant tissues is fundamental for biocontrol and biofertilizer applications. Our collective analyses of prior surveys, where the tef1 sequence was almost exclusively used to identify Trichoderma species, showed that isolates from the Harzianum Complex Clade, the T. asperellum/T. asperelloides group, T. virens, T. hamatum, and T. atroviride were prevalent in soil and/or as endophytes. Population structure and genetic diversity of these species were investigated, and new lineages with geo-graphic significance within T. atroviride, T. asperellum, T. hamatum, and T. virens populations were found. The nearest relatives of some species were also revealed. Choosing isolates from among more than 500 known Trichoderma species for use in non-targeted evaluation screens for biocontrol or biofertilizer applications is time-consuming and expensive. Preferentially selecting species from T. atroviride, T. asperellum/T. asperelloides, T. hamatum, species from the T. harzianum Complex Clade, T. virens, and possibly nearest relatives, may speed the identification of candidates for commercialization due to the demonstrated ability of these species to successfully inhabit the soil and endorhizosphere. To our knowledge, this is the first report where dominant soil- and en-dophyte-inhabiting Trichoderma species were identified from past survey data and population structure and genetic diversity analyses conducted.

The adoption of algorithms based on Artificial Intelligence (AI) has been rapidly increasing during the last years. However, some aspects of AI techniques are under heavy scrutiny. For instance, in many use cases, it is not clear whether the decisions of an algorithm are well-informed and conforming to human understanding. Having ways to address these concerns is crucial in many domains, especially whenever humans and intelligent (physical or virtual) agents must cooperate in a shared environment. In this paper, we introduce an application of an explainability method based on the creation of a Policy Graph (PG) based on discrete predicates that represent and explain a trained agent’s behaviour in a multi-agent cooperative environment. We show that from these policy graphs, policies for surrogate interpretable agents can be automatically generated. These policies can be used to measure the reliability of the explanations enabled by the PGs, through a fair behavioural comparison between the original opaque agent and the surrogate one. The contributions of this paper represent the first application of policy graphs in the context of explaining agent behaviour in collaborative multi-agent scenarios and presents experimental results that sets this kind of scenario apart from previous application in single-agent scenarios: when requiring collaborative behaviour, predicates that allow representing observations about the other agents are crucial to replicate the opaque agent’s behaviour and increase the reliability of explanations.

A fern, Polypodium aureum, possesses a specialized spore-releasing mechanism like a catapult induced by quick expansion of vaporized bubbles. This study introduces lipid-coated perfluorocarbon droplets to enable repeatable vaporization-condensation cycles, inspired by the repeatable vaporization of Polypodium aureum. Lipid-perfluorocarbon droplets have been considered not to exhibit repeatable oscillations due to bubble collapse by low surface tension of lipid layers. However, a single lipid-dodecafluoropentane droplet with a diameter of 9.17 µm shows expansion-contraction oscillations over 4,000 cycles by changing lipid composition and applying low power of 1.7 MHz ultrasound to induce partial vaporization of droplets. The optimal combinations of shell composition, droplet fabrication, and acoustic conditions can minimize damage on shell structure and quick recovery of damaged shell layers. The highly-expanding oscillatory microbubbles provide a new direction for fuel-free micro or nanobots as well as biomedical applications of contrast agents and drug delivery.

Fire blight, caused by Erwinia amylovora, is a devastating bacterial disease that threatens world-wide production of apples and pears. In Canada and the US, streptomycin and kasugamycin are commonly used for the control of this pathogen. There is an urgent need for development of biocontrol agents (BCAs) that can be easily incorporated into integrated pest management pro-grams with high efficacy against streptomycin sensitive and resistant strains of E. amylovora. In addition, excessive use of antibiotics in agriculture has increased awareness of the possibility of antibiotic resistance gene transfer to human, animal and environmental microbes. To address this issue, a phage-carrier system (PCS) that combines Pantoea agglomerans as an antagonistic bacterial carrier of Erwinia phages as a double-pronged treatment for fire blight have been de-veloped. The low viability of P. agglomerans cells following spray drying challenged the industrial scale production of this PCS. In the present research, a spray drying protocol was developed for P. agglomerans by modifying the growth medium and the bacterial cell formulation using D(+)-trehalose and maltodextrin. The developed protocol is amenable to industrial scale pro-duction of the BCA/PCS. The P. agglomerans viability was greater than 90% after spray drying, had a prolonged shelf life of 4 months at 4 C, and the reconstituted cells showed an approximate 3 log reduction in E. amylovora counts with a green pear disc challenge assay.

The peculiar physicochemical features of deep eutectic solvents (DESs), in particular their tunability, make them ideal media for various applications. Despite their ability to solubilize metal oxides, their use as rust removers from valuable substrates has not yet been thoroughly investigated. In this study we chosen three known DESs, consisting of choline chloride and acetic, oxalic or citric acid for evaluating their ability to remove corrosion products from a cellulose-based material as linen fabric and two different lithotypes, as travertine and granite. The artificial staining was achieved by placing a rusty iron grid on their surfaces. The DESs were applied by means of cellulose poultice on the linen fabrics, while on the rusted stone surfaces with a cotton swab. Macro- and microscopic observations, colorimetry and SEM/EDS analysis were employed to ascertain the cleaning effectiveness and the absence of side effects on the samples after treatment. Oxalic acid-based DES was capable of removing rust stains from both stone and cellulose-based samples, while choline chloride/citric acid DES was effective only on stone specimens. The results suggest a new practical application of DESs for the elimination of rust from lithic and cellulosic substrates of precious and artistic value.

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.

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.

The present work evaluates the food effect on the absorption of rivaroxaban (Riva), a BCS II drug, from the orally administered commercial immediate-release tablet (Xarelto IR) using physiologically based pharmacokinetic (PBPK) and conventional in vitro- in vivo correlation (IVIVC) models. The bioavailability of Riva upon oral administration of Xarelto IR tablet is reported to exhibit a positive food effect. The PBPK model for Riva was developed and verified using the previously reported in vivo data for oral solution (5 and 10 mg) and Xarelto IR tablet (5 and 10 mg dose strength). Once the PBPK model was established, the in vivo performance of the tablet formulation with the higher dose strength (Xarelto IR tablet 20 mg in fasted and fed state) was predicted using the experimentally obtained data of in vitro permeability, biorelevant solubility and in vitro dynamic dissolution data using United States Pharmacopeia (USP) IV flow-through cell apparatus. In addition, the mathematical IVIVC model was developed using the in vitro dissolution and in vivo profile of 20 mg strength Xarelto IR tablet in fasted condition. Using the developed IVIVC model, the pharmacokinetic (PK) profile of the Xarelto IR tablet in fed condition was predicted and compared with the PK parameters obtained via the PBPK model. A virtual in vivo PK study was designed using a single-dose, 3-treatment cross-over trial in 50 subjects to predict the PK profile of the Xarelto® IR tablet in the fed state. Overall, the results obtained from the IVIVC model were found to be comparable with that from the PBPK model. The outcome from both the model pointed to the positive food effect on the in vivo profile of the Riva. The developed models thus can be effectively extended to establish bioequivalence for the marketed and novel complex formulations of Riva such as amorphous solid dispersions.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, data--driven control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods, such as fuzzy and adaptive self--tuning controllers, were already verified on wind turbine systems, and similar advantages may thus derive from their appropriate implementation and application to hydroelectric plants. These issues represent the key features of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. The working conditions of these systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self--tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. The scope is to analyse common points in the control objectives and the achievable results from the application of different solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will be also taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

Cement-free concrete has superior physical performance, such as strength and durability, compared to OPC concrete, however, it has the disadvantage of large shrinkage. Large shrinkage can cause cracks due to shrinkage stress in the long term. In this study, a shrinkage reducing agent (SRA) was used to reduce the shrinkage of cement-free mortar; its content was increased from 0.0 to 1.5 %. For an SRA content of 1.0 %, a calcium sulfoaluminate (CSA) expansive additive (EA) (2.5, 5.0, and 7.5%) was added. To calculate the shrinkage stress of cement-free mortar using the SRA and EA, the compressive strength, elastic modulus, and total and autogenous shrinkage were measured. The unit shrinkage stress of cement-free mortar was obtained by multiplying the elastic modulus by the length change and accumulated to obtain the shrinkage stress acting on the mortar according to the age. The shrinkage stress of cement-free mortar showed different tendencies as the age increased. At early ages, the shrinkage rate of the mortar occupied a large proportion of the shrinkage stress. In the long term, the shrinkage stress was significantly affected by the elastic modulus. As a result, SRA was found to be effective in reducing the shrinkage stress by decreasing both the elastic modulus and shrinkage. However, EA increased the shrinkage stress over the long term due to an increase in elastic modulus even though it compensated for early-ages shrinkage.

In this paper, a distributed algorithm is proposed to solve a consensus convex optimization problem. It is a Jacobi-proximal alternating direction method of multipliers with a damping parameter $\gamma$ in the iteration of multiplier. Compared with existing algorithms, it has the following nice properties: (1) The restriction on proximal matrix is relaxed substantively, thus alleviating the weight of the proximal term. Therefore, the algorithm has a faster convergence speed. (2) The convergence analysis of the algorithm is established for any damping parameter $\gamma\in(0,2]$, which is larger ones in the literature. In addition, some numerical experiments and an application to a logistic regression problem are provided to validate the effectiveness and the characteristics of the proposed algorithm.

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

In this study, sodium alginate (SA) was oxidized with potassium periodate to produce an alginate-based tanning agent. Using OSA as a biodegradable tanning agent and a nano-hydroxyapatite (nano-HAp) low concentration suspension to give flame retardancy to leather, eco-design concepts were applied to establish a chrome-, aldehyde-, and phenol-free tanning process. Micro-DSC, 1H NMR, attenuated total reflection mode Fourier transform infrared spectroscopy (FTIR-ATR), and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS) were used to investigate the complex matrix collagen-OSA-nano-HAp. Micro-differential scanning calorimetry (micro-DSC) was used to assess OSA's ability to interact with collagen and stabilize the collagen-OSA matrix, while 1H unilateral nuclear magnetic resonance (NMR) was used to investigate the aqueous environment and limitations around collagen molecules caused by their association with OSA and nano-HAp. Industrial standard tests were used to assess mechanical properties and fire resistance of the new leather prototype. The findings reported here indicate that both OSA and nano-HAp are suitable alternatives for cleaner tanning technologies and more sustainable leather.

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.