Reliable quantification of pulmonary arterial pressure is essential in the diagnostic and prognostic assessment of a range of cardiovascular pathologies including rheumatic heart disease, yet an accurate and routinely available method for its quantification remains elusive. This work proposes an approach to infer pulmonary arterial pressure based on scientific machine learning techniques and non-invasive, clinically available measurements. A 0-D multicompartment model of the cardiovascular system was optimized using several optimization algorithms, subject to forward-mode automatic differentiation. Measurement data were synthesized from known parameters to represent the healthy, mitral regurgitant, aortic stenosed and combined valvular disease situations with and without pulmonary hypertension. Eleven model parameters were selected for optimization based on 95 % explained variation in mean pulmonary arterial pressure. A hybrid Adam and limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer yielded the best results with input data including valvular flow rates, heart chamber volume changes and systematic arterial pressure. Mean absolute percentage errors ranged from 1.8 % to 3.78 % over the simulated test cases. The model was able to capture pressure dynamics under hypertensive conditions with pulmonary arterial systole, diastole, and mean pressure average percentage errors of 1.12 %, 2.49 % and 2.14 %, respectively. The relatively low errors highlight the potential of the proposed model to recover pulmonary pressures for diseased heart valve and pulmonary hypertensive conditions.

Previous research has either neglected to consider misbehaviour or misconduct in the virtual workplace during the current COVID-19 pandemic or highlighted some challenges encountered with remote working or work from home but in isolation, e.g., misbehaviours or ethical concerns associated with improper use of technology when accessing clients’ confidential information, without understanding their aetiology and context in a holistic manner. This has impacted the ability of existing research in providing recommendations organisations can practically implement to improve the sustainability and productivity of the virtual workplace, especially as they seek to navigate the current pandemic and prepare for the global business environment in the next decade. Instead, this study, for the first time, proposes a novel holistic methodology to identify the aetiologies of ethical and performance challenges with focus to the Information Technology consulting sector, as well as developing a set of recommendations companies can adopt to manage such behavioural and productivity challenges. Thus, this study illustrates the key issues associated with remote working implemented to respond to the current COVID-19 pandemic, which mine not only the individual performance in an organisation but also its overall productivity, as well as suggests the main performance and reward management strategies that can be leveraged to mitigate them. Tailored strategies are described and discussed as referred to this industry to help in navigating a more unstable yet competitive market to manage the virtual employees’ misbehaviours in the workplace and drive growth for individuals and organisations alike.

Coronavirus Disease-19 (COVID-19) is presently wreaking havoc on public health and socio-economic development. Besides the upper and lower respiratory tract involvement, gastrointestinal symptoms are also reported in COVID-19 patients through gut-lung axis. Finding its way through the feces of infected individuals and other sources, the genetic material of SARS-CoV-2 (ssRNA) is reported widely in wastewater and is being used as a fingerprint for its detection. With millions of cases arriving every day, there is a need to level up the testing speed efficiency. Due to the restricted sampling potential of testing laboratories, clinical testing is unable to track all the symptomatic and asymptomatic cases. Wastewater-based epidemiology (WBE) bestows an auxiliary monitoring tool that will contribute in community level screening. Sample collection, concentration, RNA extraction, quantification and data analysis are the main steps involved in implementation of WBE that can be relied upon as an alarm call for an upcoming wave, emergence of a new variant or any future pandemic. WBE can be a cheaper and more practical alternative to high end and sophisticated clinical testing for community transmission detection. Worldwide, there are more than 300 reports entailing the occurrence of SARS-CoV-2 in wastewater exhibiting unique temporal trends with five of them in India. This review aims to address the present knowledge on surveillance of SARS-CoV-2 in wastewater and its implications.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion even early warning for physicians, however, they are mutually-exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. Long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the 3rd China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

Emotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in the animal welfare research, the animal affective computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in nascent stage. By capitalizing on the immense potential of biometric sensors, the artificial intelligence enabled big data methods substantially offers advancement of animal welfare standards and meet the urgent need of caretakers to respond effectively to maintain the wellbeing of their animals. Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are no scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time consuming, interrupt farming processes and involve subjective judgments. Biometric sensors data enabled by Artificial Intelligence are an emerging smart solution to unobtrusively monitoring livestock, but their potential for quantifying affective states and groundbreaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and be-havior in real time are a near-term possibility.

In this paper we try to demonstrate that the classical model of certainty factos for dealing with innacurate knowledge can be efficiently implemented in a quantum environment. For this, we assume that certainty factors are strongly correlated with the quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. We then build a use case: an inferential network to be implemented in both, the classical approach and the corresponding quantum circuit. Both implementations have been used to compare the behavior of the classical and the quantum approaches when confronted with the same hypothetical case. We analyze three different situations: (1) Only Imprecision (which refers to inaccuracy in declarative knowledge or facts) is present in the use case, (2) Only Uncertainty (which refers to inaccuracy in procedural knowledge or rules) is present in the use case, and (3) Both Imprecision and Uncertainty are present in the use case. Finally, we analyze the results to reach a conclusion about the eventually intrinsic probabilistic nature of the certainty factors model and to pave the way for future quantum implementations of this method for handling inaccurate knowledge.

Background: Serious games are conceptualized as a broad topic and overlap segments of more modern forms of education: e-learning, edutainment, game-based learning, and digital game-based learning. Serious Games aligns with digitalization and the modern era and creates novel opportunities for learning and assessment in medical education. Escape rooms, a type of serious games, merge mental and physical aspects to reinforce critical skills useful in daily life. It challenges logic and reasoning and demands careful analysis of situations to correlate and solve different stages of the escape room under pressurized, timed conditions. Furthermore, it serves as an adequate environment to build problem-solving skills, communication skills, and leadership skills through the collaboration of people to achieve a common goal. The aim of this study was to investigate the applications of escape rooms in Medical Education. Method: This study investigated the applications of escape rooms in medical education. Serious games are expanding in education and have attained great relevance due to their intriguing and intrinsically motivating attributes. Within serious games, we focused on escape rooms in which participants are locked in a room, faced with puzzles that must be solved to ‘escape the room’. Compiling the data from the first 100 hits of medical application of escape rooms, we found 72 cases and categorized them by year, specialty, participants structure, simulation experience, and design. Results: We reported on escape rooms in medical education by the year in which they were reported, the medical specialty, the participant structure, grouped or individual, the experience design; real, hybrid, or digital, and the modality of the delivery. 72% of the escape rooms focused on four main areas: nursing education (25.0%), emergency medicine (22.2%), pharmacy (12.5%), and interprofessional education (12.5%). Most of the escape rooms had a group-based physical design and little attention was given to provide a detailed description of the design considerations, such as the pathway type (linear, semi-linear, open). Conclusion: Escape rooms are applied in a wide range of medical education areas. In Medical Education, group-based on-site escape rooms with a focus on nursing, emergency medicine, pharmacy and interprofessional education dominates the implementation landscape. To further advance the field, stronger emphasis on making explicit the design considerations will advance the research and inform implementations.

The use of Information Communication Technologies (ICT) in education brings up new possibilities of promoting the learning and health experiences. In this sense, education contexts of 21st century must consider these two areas of knowledge, especially their integration. This article presents learning practices developed with mobile devices and games, in order to improve learning and well-being in children and adolescents undergoing cancer treatment in non-formal educational setting. The methodology is based on qualitative case studies with content-based data analysis, involving informal interviews and observation methods. The study considers data from 5 patients who participated in the research between 2015 and 2019. The results demonstrate a positive influence of the practices with mobile technologies and games in terms of learning and in the well-being feeling of patients during the treatment.

This paper provides a concise review of remote sensing of ocean fronts and its applications in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms/techniques: Canny (1986), Cayula and Cornillon (1990, 1992, 1995), Miller (2004, 2009), Shimada et al. (2005), Belkin and O’Reilly (2009), and Nieto et al. (2012). A case is made for feature-based approach in marine ecology and fisheries that emphasizes fronts as major structural and circulation features of the ocean realm that play key roles in various aspects of marine ecology.

Nature-based Solutions (NbS) have rapidly been gaining traction across the research, policy, and practice spheres, touted as transformative actions to jointly address biodiversity loss and climate change. However, there are multiple, alternative ways to imagine NbS in those three spheres. To inform the NbS discourses across these three spheres, we critically reflect on the prevailing framing of NbS and consider the potential of a different framing of NbS to support transformations towards regenerative relationships between humans and nature. Such reflection is urgently needed to ensure that research, policy, and practice delivers on the transformative ambitions of NbS. We propose a novel “core framing” of NbS, charting two pathways for how such a framing can support a human value-based transformation – first through influencing individual beliefs and values, and second through the communication and application of the NbS concept in research, policy, and practice. We argue that for NbS to support transformation, it must support a reframing of human-nature relationships, one where the interdependencies between people and nature are recognized as essential for social and environmental well-being. We elaborate on how such a framing is key to support inclusivity and collaboration between diverse research perspectives, policy objectives across scales, and implementation practices, to deliver successful NbS.

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

The global use of alcohol based hand sanitizers (ABHS) as a means of controlling the transmission of infectious disease increased dramatically in 2020 as governments and public health agencies across the world advocated hand hygiene as a preventative measure during the COVID-19 pandemic. Although the performance of these products is most commonly defined as a function of their alcohol concentration, they are multifaceted products in which an interplay of several factors is important in determining efficacy. The hand sanitizer tetrahedron, is a novel concept that considers both ABHS formulation factors and product performance factors from a multi-dimensional perspective. The four faces of the tetrahedron represent input/formulation factors: 1) the type and amount of alcohol, 2) inactive ingredients, 3) the type of formulation/delivery system and 4) manufacturing practices. The four corners of the tetrahedron represent output/product performance factors: 1) efficacy, 2) sensory characteristics, 3) usage, usability and compliance and 4) product safety/adverse effects. All factors are of importance to ensuring the effectiveness and utility of these products.

In recent years, team-based learning (TBL) is gaining popularity as a student-centered active collaborative learning strategy in healthcare education. This paper reports the design, implementation, and impact of a "hybrid team-based learning" (H-TBL) for one respiratory lecture in year two undergraduate physiotherapy program in 2019. A retrospective study was conducted, including 136 second-year undergraduate physiotherapy students using H-TBL design for one respiratory lecture topic. Student engagement was evaluated based on the percentage of completion for pre-class work, attendance to classroom session, and submission of formative creative assignment. Student' performance on formative creative tasks was evaluated based on thinking and learning rubric. Student perceptions were assessed based on the student's feedback using "Mentimeter." 109/ 136 (80%) students attended the COPD 2 session. 90/109 (82%) students engaged in COPD 1 (web-based) and tRAT in COPD 2 session. 54/109 (50%) students provided feedback and 67/90 (74%) students submitted formal formative creative assignment on completion of COPD 2 session. This study confirms that H-TBL enhances student's active engagement, creativity, and equilibration of their subject knowledge. Future randomized studies are mandated to explore the validity and specificity of H-TBL in diverse physiotherapy curriculum to evaluate the long-term student engagement and academic performance.

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.

The advance of the medical field in the 21st century has influenced the development of many innovations, including in medical education. PBL (problem-based learning) is one of the pedagogical approaches that has been studying and implementing worldwide to attract medical students’ attention with their learning style and learning environment. China is the most significant “receiver” country of foreign students in Asia, and clinical medicine is one of the most chosen majors for undergraduate. In China, PBL has been implementing in some schools, but mostly with the local students and only a few implementations to the international students. International medical students in China faced a variety of challenges during their studies. In this review paper, we give the solution to adapt to the challenges by focusing on the implementation the integrated organ/system curriculum/syllabus with the PBL method to the school system with the support of the school stakeholders and students’ role.

In the present work, the state of the art of the most common additive manufacturing (AM) technologies used for the manufacturing of complex shape structures of graphene-based ceramic nanocomposites, ceramic and graphene-based parts is explained. A brief overview of the AM processes for ceramic, which are grouped by the type of feedstock used in each technology, is presented. The main technical factors that affect the quality of the final product were reviewed. The AM processes used for 3D printing of graphene-based materials are described in more detail; moreover, some studies in a wide range of applications related to these AM techniques are cited. Furthermore, different feedstock formulations and their corresponding rheological behaviour were explained. Additionally, the most important works about the fabrication of composites using graphene-based ceramic pastes by Direct Ink Writing (DIW) are disclosed in detail and illustrated with representative examples. Various examples of the most relevant approaches for the manufacturing of graphene-based ceramic nanocomposites by DIW are provided.

The major stress-inducible Hsp70 (HSPA1A) is overexpressed in the cytosol of many highly aggressive tumor cells including glioblastoma multiforme and presented on their plasma membrane. Depending on its intracellular or membrane localization, Hsp70 either promotes tumor growth or serves as a target for NK cells. The kinetics of the membrane Hsp70 (mHsp70) density on human glioma cells (U87) was studied after different irradiation doses to define the optimal therapeutic window for Hsp70-targeting NK cells. To maintain the cells in the exponential growth phase during a cultivation period of 7 days different initial cell counts were seeded. Although cytosolic Hsp70 levels remained unchanged on days 4 and 7 after a sublethal irradiation with 2 Gy, 4 Gy and 6 Gy, a dose of 2 Gy resulted in an upregulated mHsp70 density in U87 cells which peaks on day 4 and starts to decline on day 7. Higher radiation doses (4 Gy, 6 Gy) resulted in an earlier and more rapid onset of the mHsp70 expression on days 2 and 1, respectively, followed by a decline on day 5. Membrane Hsp70 levels are higher in G2/M than in G1, however, an irradiation-induced cell cycle arrest on days 4 and 7, was not associated with an increase in the mHsp70 density. Extracellular Hsp70 concentrations increased significantly compared to sham (0 Gy) irradiated cells on days 4 and 7 but not on day 1. Functionally, elevated mHsp70 densities were associated with a significantly better lysis by Hsp70-targeting NK cells. In summary, the kinetics of changes in the mHsp70 density upon irradiation on tumor cells is time- and dose-dependent.

Prediction of stock groups values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree_based models, there is often an intense competition between Adaboost, Gradient Boosting and XGBoost.

Visual Patient technology is a situation awareness–oriented visualization technology that translates numerical and waveform patient monitoring data into a new user-centered visual language. Vital sign values are converted into colors, shapes, and rhythmic movements—a language humans can easily perceive and interpret—on a patient avatar model in real time. In this review, we summarize the current state of the research on the Visual Patient, including the technology, its history, and its scientific context. We also provide a summary of our primary research and a brief overview of research work on similar user-centered visualizations in medicine. In several computer-based studies under various experimental conditions, Visual Patient transferred more information per unit time, increased perceived diagnostic certainty, and lowered perceived workload. Eye tracking showed the technology worked because of the way it synthesizes and transforms vital sign information into new and logical forms corresponding to the real phenomena. The technology could be particularly useful for improving situation awareness in settings with high cognitive demand or when users must make quick decisions. This comprehensive review of Visual Patient research is the foundation for an evaluation of the technology in clinical applications, starting with a high-fidelity simulation study in early 2020.

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

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

Community-engaged research is understood as existing on a continuum from less to more community engagement, defined by participation and decision-making authority. It has been widely assumed that more is better than less engagement. However, we argue that what makes for good community engagement is not simply the extent but the fit or alignment between the intended approach and the various contexts shaping the research projects. This article draws on case studies from three Community Engagement Cores (CECs) of NIEHS-funded Environmental Health Science Core Centers (Harvard University, UC Davis and University of Arizona,) to illustrate the ways in which community engagement approaches have been fit to different contexts and the successes and challenges experienced in each case. We analyze the processes through which the CECs work with researchers and community leaders to develop place-based community engagement approaches and find that different strategies are called for to fit distinct contexts. We find that alignment of the scale and scope of the environmental health issue and related research project, the capacities and resources of the researchers and community leaders, and the influences of the socio-political environment are critical for understanding and designing effective and equitable engagement approaches. These cases demonstrate that the types and degrees of alignment in community-engaged research projects are dynamic and evolve over time. Based on this analysis, we recommend that CBPR scholars and practitioners select a range of project planning and management techniques for designing and implementing their collaborative research approaches and both expect and allow for the dynamic and changing nature of alignment.

The sensor nodes have limited computation, sensing, communication capabilities and generally operated by batteries in a harsh atmosphere with non-replenish able power sources. These limitations force the sensor network subject to failure because most of the energy is spent on sensing, computing and data transmission. This paper introduces an Energy Efficient Clustering and Shortest-Path Routing Protocol (EECSRP) to assist Wireless Sensor Networks (WSNs) by (a) extending the lifespan of the network (b) effectively using the battery power (c) decreasing the network overhead and (d) ensuring a high packet transmission ratio with minimal delay. The delay time-based Cluster Head (CH) is elected based on the node degree, residual energy and Received Signal Strength (RSS) to accomplish the goal. Additionally, the RSS-based network partitioning is implemented to evaluate the gradient based on demand routing between source (sensing node) and destination (BS). Whenever the current CH residual energy goes under the threshold level, the proposed protocol performs the clustering process, reducing the exchange of control packets. However, the BS periodically gathers the data from every single CH which helps to reduce the collision and Medium Access Control (MAC) layer conflict. From the simulation results, it is the evident that the proposed protocol performance in terms of average end-to-end latency, packet delivery ratio, average energy consumption and control overhead is better than the well-known current protocols.

This study presents a path planning method for a mobile robot to be effectively operated through a multi-objective decision-making problem. Specifically, the proposed Fuzzy analytic hierarchy process (FAHP) determines an optimal position as a sub-goal within the multi-objective boundary. The key features of the proposed FAHP are evaluating the candidates according to the fuzzified relative importance among objectives to select an optimal solution. In order to incorporate FAHP into path planning, an AHP framework is defined, which includes the highest level (goal), middle level (objectives), and the lowest level (alternatives). The distance to the target, robot’s rotation, and safety against collision between obstacles are considered as objective functions. Comparative results obtained from the artificial potential field and AHP/FAHP simulations show that FAHP is much preferable for the mobile robot’s path planning than typical AHP.

This study assessed bio-equivalence of high-quality, plant-based protein blends versus Whey Protein Isolate (WPI) in healthy, resistance-trained men. The primary endpoint was incremental area under the curve (iAUC) of blood essential Amino Acids (eAAs) 4 hours after consumption of each product. Cmax and Tmax of blood leucine were secondary outcomes. Subjects (n=18) consumed three plant-based protein blends and WPI (control). Analysis of Variance model was used to assess for bio-equivalence of total sum of blood eAA concentrations. The total blood eAA iAUC ratios of the three blends were: [90% CI]: #1: 0.66 [0.58-0.76]; #2: 0.71 [0.62-0.82]; #3: 0.60 [0.52-0.69], not completely within the pre-defined equivalence range [0.80-1.25], indicative of 30-40% lower iAUC versus WPI. Leucine Cmax of the three blends was not equivalent to WPI, #1: 0.70 [0.67-0.73]; #2: 0.72 [0.68-0.75]; #3: 0.65 [0.62 – 0.68], indicative of a 28-35% lower response. Leucine Tmax for two blends were similar to WPI (#1: 0.94 [0.73-1.18]; #2: 1.56 [1.28-1.92]; #3: 1.19 [0.95-1.48]). The plant-based protein blends were not bio-equivalent. However, blood leucine kinetic data across the blends approximately doubled from fasting concentrations whereas blood Tmax data across two blends was similar to WPI. This suggests evidence of rapid hyperleucinemia, which correlates with a protein’s anabolic potential.

Failure of various weight-loss programs and long-term maintenance of favorable body composition in all kinds of people is high, since the majority go back to old dietary patterns. Many studies have documented the efficacy of a plant-based diet (PBD) for body mass management, but there are opinions that maintaining a PBD is difficult. We aimed to evaluate the long-term success of a whole-food plant-based (WFPB) lifestyle program. We investigated the differences in the obesity indices and lifestyle of 151 adults (39.6 ± SD 12.5 years), who were on our program for short (0.5–<2 years), medium (2–<5 years), or long term (5–10 years). Body-composition changes were favourable for all three groups, both genders and all participants. There were no differences in relative body-composition changes (BMI, body fat percentage and muscle mass index (MMI)) between the three groups. All participants improved their BMI (baseline mean pre-obesity BMI range (kg/m²): 26.4 ± 5.6 to normal 23.9 ± 3.8, p < 0.001), decreased body mass (–7.1 ± 8.3 kg, p < 0.001) and body fat percentage (–6.4 ± 5.6 % points, p < 0.001). Those with the highest BMI at baseline lost the most of: a) BMI units, b) total body mass and c) body fat (a) (kg/m²) (–5.6 ± SD 2.9, –2.4 ± 1.8 and –0.9 ± 1.5), b) (kg) (–16.1 ± SD 8.8, –7.1 ± 5.4 and –2.5 ± 4.5) and c) (% points) (–9.5 ± SD 5.7, –6.6 ± 4.6 and –4.7 ± 5.3) for participants who had baseline BMI in obese, overweight and normal range, respectively; p_{baseline vs. current} < 0.001 for all). 85.6% (101 out of 118) of parents of underage children (< 18 years), introduced WFPB lifestyle to their children. WFPB lifestyle program provides long-term lifestyle changes for reversal of obesity and is effective transferred to the next generation.

Aquaporin-3 (AQP3) is one of the aquaglyceroporins, which is expressed in the basolateral layer of the skin membrane. Studies have reported that human skin squamous cell carcinoma overexpresses AQP3 and inhibition of its function may alleviate skin tumorigenesis. In the present study, we have applied a virtual screening method that encompasses filters for physicochemical properties and molecular docking to select potential hit compounds that bind to the Aquaporin-3 protein. Based on molecular docking results, the top 20 hit compounds were analyzed for stability in the binding pocket using unconstrained molecular dynamics simulations and further evaluated for binding free energy. Furthermore, examined the ligand-unbinding pathway of the inhibitor from its bound form to explore possible routes for inhibitor approach to the ligand-binding site. With a good docking score, stability in the binding pocket, and free energy of binding, these hit compounds can be developed as Aquaporin-3 inhibitors in the near future.

Public access to high quality green environments has become a key issue for city managers and a matter of environmental justice. Remnants of natural ecosystems allow citizens a direct contact with nature, but conversely the presence of people contributes further to the existing disturbances. Urban pressures on ecosystem remnants may act to favour the expansion of some invasive species in cities. Whilst the negative impacts of invasive species on ecosystem function is well documented little is known how invasive species influence the use of green spaces by people. Here, we examined one of the few remnants of urban riparian forests in Europe, the Vistula river valley in Warsaw which has recently become an attractive recreation site. Despite their high ecological value, the poplar and willow forests have been increasingly taken over by the invasive tree species Acer negundo. We examined the status of the invasion process and the relationship between recreational ecosystem services and the characteristics of the tree stands – tree species, tree density and age and NDVI values. We found the willow forest to be more susceptible to invasion by A. negundo than the poplar forest, which was revealed in significantly higher share of the maple individuals and their greater volume per unit area. Presence of A. negundo affected biodiversity resulting in decreased undergrowth density and number of species. The use intensity by the public, assessed on the basis of trampling intensity and the density of existing informal tracks, were negatively correlated to the presence of A. negundo. This study highlights the need to integrate invasive species management into green infrastructure planning and management.

This study aimed to evaluate the impact of selected pregnancy pathologies statistically depending on overweight/obesity and excessive maternal weight gain during pregnancy on women who gave birth in the years 2013–2015 at the Second Department of Gynecology and Obstetrics at the University Hospital in Bratislava, Slovakia. In a retrospective study, we analyzed data gathered from the sample, which consisted of 7,122 women. Our results indicate a positive statistical dependency of the groups of women with overweight and obesity and gestational hypertension (adjusted odds ratio [AOR]=15.3; 95% CI 9.0−25.8 for obesity), preeclampsia (AOR=3.4; 95% CI 1.9−6.0 for overweight and AOR=13.2; 95% CI 7.7−22.5 for obesity), and gestational diabetes mellitus (AOR=1.9; 95% CI 1.2−2.9 for overweight and AOR=2.4; 95% CI 1.4−4.0 for obesity). A higher incidence of pregnancies terminated by cesarean section was observed in the group of obese women. Gestational weight gain above the IOM (the Institute of Medicine) recommendations was associated with a higher risk of pregnancy terminated by C-section (AOR=1.2; 95% CI 1.0−1.3), gestational hypertension (AOR=1.7; 95% CI 1.0−2.7), and infant macrosomia (AOR=1.7; 95% CI 1.3−2.1). Overweight and obesity during pregnancy significantly contribute to the development of pregnancy pathologies and increased incidence of cesarean section. Systematic efforts to reduce weight before pregnancy through pre-pregnancy dietary counseling, regular physical activity, and healthy lifestyle should be the primary goal.

Research on bidirectional human-machine interfaces will enable the smooth interaction with robotic platforms in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. We complement the gesture-based control by means of a tactile-feedback strategy grounding on a spiking artificial neuron model. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user’s hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.

Educating for Sustainability involves promoting sustainable competences in students. Not in vain, wider societal changes that ensure a balance between economic growth, respect for the environment and social justice must start with individual actions, implying knowledge, capacity and willingness to act. However, and although there is wide consensus that education should promote the development of competences for life, putting this theoretical tenet into may entail more problems. Competence is most often expressed in general terms without a specific definition of the intervening elements (knowledge, skills, values, attitudes), which may collide with the necessity of teachers – as learning planners - concrete entities on which to base their process of design. So that, in this work we propose a series of indicators that serve to characterize the four dimensions of scientific competence – contents of science, contents about science, value of science and utility of science-. Although they are primarily intended to be used to filter multimedia resources in an educational platform, this proposal of indicators can be extrapolated to the management and selection of a variety of resources and activities, and for sharing the objectives and evidences for the acquisition of competencies.

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

Intersectoral and intermunicipal cooperation are still underdeveloped spheres of public and economic development policies. Academic discussions are invariably focused on pro-competitive activities, the economic efficiency of which is not always sufficient. In this paper the authors attempt to identify factors leading to cooperation between local government authorities and economic entities, focusing on examples from the forest and wood-based sector in Poland. These processes are analysed in the framework of the New Institutional Economy, both in the theoretical and practical context.

Efficient member revocation and strong security against attacks are prominent requirements in group signature schemes. Among the revocation approaches Verifier-local revocation is the most flexible and efficient method since it requires to inform only the verifiers regarding the revoked members. The verifier-local revocation technique uses a token system to manage members’ status. However, the existing group signature schemes with verifier-local revocability rely on weaker security. On the other hand, existing static group signature schemes rely on a stronger security notion called, full-anonymity. Achieving the full-anonymity for group signature schemes with verifier-local revocation is a quite challenging task. This paper aims to obtain stronger security for the lattice-based group signature schemes with verifier-local revocability, which is closer to the full-anonymity. Moreover, this paper delivers a new key-generation method which outputs revocation tokens without deriving from the users’ signing keys. By applying the tracing algorithm given in group signature schemes for static groups, this paper also outputs an efficient tracing mechanism. Thus, we deliver a new group signature scheme with verifier-local revocation that satisfies a stronger security from lattices.

In this article, management control has been studied from the perspective of intention towards sustainable development of companies. The main idea focuses on the analysis of relations between sustainable management control and the system of performance appraisal using as a reference the resource-based theory. These relations have been assessed both theoretically by reviewing literature in the field, and empirically based on data collected in a questionnaire from top management, as well as data from Annual Reports from the main segment of Bucharest Stock Exchange. The authors have shown that financial performance has a significant influence on management control, and the return on capital employed is one of the most recommended indicators. The sustainable nature attributed to management control is the result of institutional theory evolution, provisions of Directive 2014/95/EU and Guide of Global Management Accounting Principles (2015) using stewardship. Consequently, profits are not the main element any more in the assessment of financial „health” of a company. To ensure the sustainable success, sustainable balanced scorecard is one of real measures for managing financial and non-financial performance.

Modern mobile devices are replete advanced sensors that expand the array of possible methods of locating users. This is often viewed in a positive light, as a tool to gather and use spatial information, but it also brings with it the problem of “geosurveillance” in which the “Location” becomes a product in itself. In the realm of software developers, this has been reduced and discretized to a set of coordinates, devoid of human experiences and meanings. To function in such digitally augmented realities, people need to adopt specific attitudes, often accompanied with anxiety. We explored attitudes toward locational data collection practices using questionnaire surveys (n = 280) from Poznan and Edinburgh. The prevailing attitude is neutral with a strong undertone of resignation, in which surrendering personal locational information is viewed as a digital currency. A smaller number of people had stronger, emotional views, either very positive or very negative, based on uncritical technological enthusiasm or fear of privacy violation. Such a wide spectrum of attitudes is not only produced by interaction with technology but can also be viewed as a result of different perceptions and values associated with space and place itself.

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

The software industry has adopted component-based software development (CBSD) to rapidly build and deploy large and complex software systems with significant savings at minimal engineering effort, cost, and time. However, CBSD encounters issues on security trust, mainly with respect to dependability attributes. A system is considered dependable when it can produce the outputs for which it was designed with no adverse effect on its intended environment. Dependability consists of several attributes that imply availability, confidentiality, integrity, reliability, safety, and maintainability. Dependability attributes must be embedded in a CBSD model to develop dependable component software. Motivated by the importance of these attributes, this paper pursues two objectives: to design a model for developing a dependable system that mitigates the vulnerabilities of software components, and to evaluate the proposed model. The model proposed in this study is labelled as developing dependable component-based software (2DCBS). To develop this model, the CBSD architectural phases and processes must be framed and the six dependability attributes embedded according to the best practice method. The expert opinion approach was applied to evaluate 2DCBS framing. In addition, the 2DCBS model was applied to the development of an information communication technology (ICT) portal through an empirical study method. Vulnerability assessment tools (VATs) were employed to verify the dependability attributes of the developed ICT portal. Results show that the 2DCBS model can be adopted to develop web application systems and to mitigate the vulnerabilities of the developed systems. This study contributes to CBSD and facilitates the specification and evaluation of dependability attributes throughout model development. Furthermore, the reliability of the dependable model can increase confidence in the use of CBSD for industries.

Half-metallic semi-Heusler compounds are currently at the forefront of scientific research due to their potential applications in spintronic devices. Unlike other semi-Heuslers, the p0(d0)-d compounds do not appear to crystallize in the typical variant of the C1b structure. We investigate this phenomenon in the p0-d Heusler compounds LiYGa and LiYGe, where Y varies between Ca and Zn, using first-principles ab-initio electronic band structure calculations. We examine the electronic and magnetic properties of these compounds in relation to the three possible C1b structures. Notably, LiVGa, LiVGe, LiMnGa, and LiCrGe are half-metallic ferromagnets across all three variations of the C1b lattice structure. Our findings will serve as a foundation for future experimental studies on these compounds.

The characteristics of tight sandstone, such as small pores, complex pore structures, low permeability, and low porosity, make it difficult to accurately characterize its pore structure. This represents a major challenge in the exploration and development of tight sandstone oil and gas reservoirs. This article conducts a simulation study on the impact of characteristic parameters such as pore size, coordination number, pore-throat ratio, and wettability on microscopic remaining oil after polymer flooding, thereby quantifying the types of remaining oil.

Pyrocumulonimbus (pyroCb) thunderclouds, produced from extreme bushfires, can initiate frequent cloud-to-ground (CG) lightning strikes containing extended continuing currents. This, in turn, can ignite new spotfires and inflict massive harm on the environment and infrastructures. This study presents a 3D numerical thundercloud model for estimating the lightning of different types and its striking zone for the conceptual tripole thundercloud structure which is theorized to elucidate the lightning phenomenon in pyroCb storms. More emphasis is given to the lower positive charge structure thundercloud to thoroughly examine various electrical parameters including the longitudinal electric field, electric potential, and surface charge density. The simulation outcomes on pyroCb thunderclouds with a tripole structure confirm the presence of negative longitudinal electric field initiation at the cloud’s lower region. This initiation is accompanied by enhancing the lower positive charge region, resulting in an overall positive electric potential increase. Consequently, negative surface charge density appears underneath the pyroCb thundercloud which has the potential to induce positive cloud-to-ground (+CG) lightning flashes. The suggested model would establish the basis for identifying the potential area impacted by lightning and could also be expanded to analyze the dangerous conditions that may arise in wind energy farms or power substations in times of severe pyroCb events.

Metal-organic frameworks (MOFs) have been subject of extensive scientific investigation in the last three decades and, currently, are one of the type of compounds most studied to be potentially applied in a wide range of distinct catalytic processes. Pristine MOF compounds provide several intriguing benefits for catalytic applications, including high interior surface areas and densities of active sites, allowing high catalytic reaction rates per volume, post-synthesis modified with com-plementary catalytic groups, enabling multiple functional groups to catalyze the reaction. Most of large-scale catalytic applications, including those in fuel processing, gas emission reduction and chemical synthesis, nevertheless the pristine MOFs often reveal limited stabilities and opportuni-ties for regeneration at high temperatures. As a result, the real applications of MOFs in these tech-nologies are likely to be constrained, and their controlled thermal modification aiming the prepa-ration of MOF-derivative compounds has been applied to induce crystalline structural changes and increase their structural stability, enhancing its potential applicability in more severe catalytic processes. Recent advances concerning the use of this strategy to boost the catalytic potential of the MOF-derivative compounds are outlined in this short-review article.

The maintenance of traditional agricultural practices in protected small islands is closely linked to the preservation of the land, the protection of natural resources, and the conservation of local traditions. For this purpose, the Tuscan Archipelago National Park promoted the census of ancient horticultural varieties preserved by some farmers on the island of Elba. The survey revealed a deep-rooted historical, cultural, social, and economic link between this germplasm and the island’s territory. A scientific characterization of the landrace under ethnobotanical, morphological, and genetic aspects was carried out. Among these local varieties, two landraces of the Brassica species and three landraces of the Phaseolus species were identified and analyzed in this study. To ascertain their distinguishability and uniqueness, similar varieties were also identified based on morpho-physiological characteristics or territorial proximity and compared with the Elban landraces. Morpho-phenological data were collected, and molecular analyses were carried out using Simple Sequence Repeats (SSR) markers. Ten different SSR loci were considered for the Phaseolus species and nine for the Brassica species. The Tuscan Archipelago landraces showed a high level of internal variability but were clearly distinct from each other and from the analyzed varieties, as shown by AMOVA (Analysis of Molecular Variance). Their distinctness was further highlighted by the UPGMA dendrogram obtained from the genetic distance matrix. The same results were confirmed by Principal Coordinate Analysis (PCoA). This study confirms the peculiarity of the Elban landraces, their biodiversity is a heritage to be safeguarded, which can also be used in future breeding programs. These ancient Elban landraces were allocated to local farmers (most were young farmers) to protect them from extinction and to promote more resilient varieties cultivation compared to commercial hybrids widely spread in the area. Local farmhouses also play a strategic role in safeguarding the germplasm and dissemination of sustainable agricultural practices. There is high demand for ancient Elban landraces, this has led to the development of a new local economy balanced between agricultural tradition and innovation.

Few studies have examined the dietary protein intake and sources in combination with the longitudinal changes in brain structure markers. Our study aimed to examine the association between dietary protein intake and different sources of dietary protein with the longitudinal rate of change in brain structural markers. A total of 2723 and 2679 participants from the UK Biobank were included in the analysis. The relative and absolute amounts of dietary protein intake were calculated through a 24-hour dietary recall questionnaire. The longitudinal change rates of brain structural biomarkers were computed using two waves of brain imaging data. We utilized multiple linear regression to examine the association between dietary protein and different sources and the longitudinal changes in brain structural biomarkers. Restrictive cubic splines were used to explore nonlinear relationships, and stratified and sensitivity analyses were conducted. Increasing the proportion of animal protein in dietary protein intake is associated with a slower reduction in the total hippocampus volume (THV, β: -0.02524, P&lt;0.05), left hippocampus volume (LHV, β: -0.02435, P&lt;0.01) and right hippocampus volume (RHV, β: -0.02544, P&lt;0.05).Higher intake of animal protein relative to plant protein is linked to a lower atrophy rate in THV (β: -0.01249, P&lt;0.05) and LHV (β: -0.01173, P&lt;0.05) and RHV (β: -0.01193, P&lt;0.05). Individuals with higher intake of seafood exhibit lower longitudinal rate of change in HV compared to those with no consume (THV, β: -0.004514; P&lt;0.05; RHV, β: -0.005527, P&lt;0.05). In subgroup and sensitivity analyses, there was not a significantly alteration. A moderate increase in the intake and proportion of animal protein in the diet, especially from seafood, is associated with a lower longitudinal change rate in hippocampus volume.

The review explores the therapeutic potential of mangrove plants as sources of bioactive compounds, particularly focusing on their anti-inflammatory properties. Mangroves, found in tropical and sub-tropical tidal coasts, exhibit unique ecological and biological activities. The review compiles and examines 136 research findings published between 1982 and 2018, with 41 studies specifically addressing the anti-inflammatory properties of mangroves in Indian coastal regions, including the Andaman and Nicobar Islands. It summarizes the chemical structures, molecular weights, formulas, solvents, and biological activities of these mangroves, as reported by various researchers over time. The data underscore the significant potential of tropical Indian mangroves in anti-inflammatory applications. However, a notable research gap exists regarding the extraction of bioactive compounds from mangrove species in the Andaman and Nicobar Islands compared to other regions such as Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, West Bengal, and Puducherry. Overall, the compilation provides valuable insights into mangrove-based studies for drug discovery research, emphasizing the need for further exploration and exploitation of these natural resources.

. In this work, two types of nanoporous alumina membranes were prepared using different acids and were tested. Structural features of the samples were investigated by scanning electron spectroscopy and further by different types of fractal dimension estimation methods. Transmission and scattering of accelerated He+ ions were studied in experiments on ion irradiation of dielectric channels based on porous alumina. Ion accelerator was used as a source of the He+ beam with an energy of 1.7 MeV. A scattering of ions was studied by Rutherford backscattering spectrometry. Helium transition through nanoporous alumina at various angles between the normal to the sample and the beam direction were observed. It is shown that the porous structure of anodic aluminum oxide is excellent as a dielectric matrix of nanocapillaries. Owing to the small angle scattering, it allows the transportation of the accelerated charged particles through the dielectric capillaries, and, as a result, localization of high energy ion irradiation effects. Additionally, according to the transmission UV-Vis spectra the energy gaps of samples obtained were calculated.

Sub-micro dislocation cellular structures formed during rapid solidification break the strength-ductility trade-off in laser powder bed fusion (LPBF)-processed 316L stainless steel through high-density dislocations and segregated elements or precipitates at the cellular boundaries. The high-density dislocation entangled at the cellular boundary accommodates solidification strains among the cellular structures and cooling stresses through elastoplastic deformation. Columnar grains with cellular structures typically form along the direction of thermal flux. However, the ultra-low misorientations between the adjacent cellular structures and their interactions with the cellular boundary formation remain unclear. In this study, we revealed the ultra-low misorientations between the cellular structures in LPBF-processed 316L stainless steel using conventional electron backscatter diffraction (EBSD), transmission Kikuchi diffraction (TKD), and transmission electron microscopy (TEM). The conventional EBSD and TKD analysis results could provide the misorientation angles smaller than 2° while the resolution mainly depends on the specimen quality and scanning step size, and so on. TEM technique with higher spatial resolution provides accurate information between adjacent dislocation cells with misorientation angles smaller than 1°. This study presents that TEM method is the better and more precise analysis method of the misorientation measurement of the cellular structures and provides insights into measuring the small misorientation angles between adjacent dislocation cells and nanograins in nanostructured-metals and alloys with ultrafine-grained microstructures.

The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies and impacts. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.

We review time expansion wave theory and refine our model showing it to accurately match observed cosmological luminosity data. The theory augments general relativity to create a container within which quantum effects can be explained by treating time as an artefact of a fourth spatial dimension that we observe as the expansion of the universe. This leads us to explain mass creation, the speed of light limit, gravity, black holes without singularities and other macroscopic effects. Our model leads to an expansion velocity of 6.87 0.36 × 106 ms-1, which results in a Hubble expansion rate constant of 71.3 3.7 km/s/Mpc. Because of the finite window of time in which we exist, an extra a degree of freedom is available leading to interesting interpretations at the subatomic level. At this size range, then a spin is allowed within the time dimension explaining why wave particle duality occurs and explains the electromagnetic force and other quantum mechanical properties. It provides a solution to the double slit conundrum and can explain how quantum entangled partners can behave in a quantum way and pass seemingly time-defying information. The potential quantitative origin of the fine structure constant which governs the size of the electromagnetic force is also strongly indicated by this theory.

Based on acoustic fluid elements, dynamic analysis was performed on liquid sloshing modes and liquid-filled containers considering the fluid-structure interaction (FSI) effect. The liquid sloshing modes in two-dimensional (2D) and three-dimensional (3D) containers were analyzed, and the results were compared with liquid sloshing modes measured in tests and theoretically calculated modes. This thus verifies correctness of the simulation method based on acoustic fluid elements. Cylindrical liquid-filled containers with different water levels were subjected to modal analysis and dynamic and time-historical analysis. Results show that the finite element analysis (FEA) based on acoustic fluid elements can accurately simulate liquid sloshing modes in liquid-filled containers and vibration characteristics of these containers with different liquid levels. The vibration frequency of liquid-filled containers declines obviously with rising liquid level. The liquid level significantly affects distributions of the maximum displacement, maximum acceleration, and maximum von Mises stress on the sidewall of liquid-filled containers. Numerical simulations based on acoustic fluid elements provides an effective and reliable method for dynamic analysis of liquid-filled containers considering the FSI effect.

This paper explores the interactions of dynamic dark energy with other components of the cosmos, such as dark matter and baryonic matter, and its wide-ranging implications in cosmology. By investigating the interplay between dynamic dark energy and the universe's large-scale structure, we aim to provide a deeper understanding of cosmic evolution and the behavior of dark energy in different cosmological epochs. We also examine the potential of dynamic dark energy to offer novel insights into long-standing cosmological puzzles and their impact on the formation and evolution of cosmic structures.

A single type I RC (Fe-S type, ferredoxin reducing) is found in several of the phyla of anoxygenic phototrophic bacteria. They include the heliobacteria (HB), the green sulfur bacteria (GSB) and the chloroacidobacteria (CAB), for which high-resolution homodimeric RC-PS structures have recently appeared [1-4]. The 2.2-Å Xray structure of the RC-PS of Heliomicrobium modesticaldum [1] revealed that the core PshA apoprotein (PshA1 and PshA2 homodimeric pair) exhibits a structurally conserved PSI arrangement comprised of five C-terminal transmembrane helices (TMHs) forming the RC domain and six N-terminal TMHs coordinating the light-harvesting (LH) pigments. The Hm. modesticaldum structure lacked quinones, indicating that electrons were transferred directly from the A0 (81-OH-chlorophyl (Chl) a) primary acceptor to the FX [4Fe4S] component, serving as the terminal RC acceptor. A pair of additional TMHs designated as Psh X were found, that function as a low-energy antennae. The 2.5Å resolution cryo-electron microscopy (cryo-EM) structure for the RC-PS of the GSB Chlorobaculum tepidum [2] included two Fenna-Matthews-Olson protein (FMO) antennae, which transfer excitations from the chlorosomes to the RC-PS (PscA1 and PscA2) core. Two cytochromes c (PscC) molecules were revealed, which act in series as electron donors to the photooxidized RC BChl a’ special pair, as well as PscB, housing the [4Fe-4S] cluster FA and FB, and the associated PscD protein [3]. Although their FMO components were missing from the 2.6-Å cyro-EM structure of the Zn-BChl a’ special pair containing RC-PS of Chloroacidobacterium thermophilum [4], a unique architecture was revealed that consisted of seven additional subunits including PscZ in place of PscD, the PscX and PscY cytochrome c serial electron donors and four low mol. wt. subunits of unknown function. Overall, these diverse structures have revealed that: (i) the HB RC-PS is the simplest light-energy transducing complex yet isolated, and represents the closest known homolog to a common homodimeric RC-PS ancestor; (ii) the symmetrically localized Ca2+binding sites found in each of the Type I homodimeric RC-PSs likely gave rise to the analogously positioned Mn4CaO5 cluster of the PSII RC and the TyrZ RC donor site; (iii) a close relationship between the GSB RC-PS and Chl protein (CP)43 and CP47 of PSII was demonstrated by the strongly conserved locations of their LH-(B)Chls; (iv) LH-BChls of the GSB-RC-PS are also localized in the conserved RC-associated positions of the PSII ChlZ-D1 and ChlZ-D2 sites; (v) glycosylated carotenoids of the GSB RC-PS are located in the homologous carotenoid-containing positions of PSII, reflecting an O2-tolerance mechanism capable of sustaining early stages in the evolution of oxygenic photosynthesis. In addition to the close relationships found between the homodimeric RC-PS and PSII, duplication of the gene encoding the ancestral Type I RC apoprotein, followed by genetic divergence my well account for the appearance of the heterodimeric Type I and Type II RC found in extant oxygenic phototrophs. Accordingly, the long-held view that PSII arose from the anoxygenic Type II RC is now supplanted by the new evidence provided by Type I RC-PS homodimer structures, indicating that the evolutionary origins of anoxygenic Type II RCs along with their distinct antenna rings are likely to have occured later than the events which gave rise to their oxygenic counterparts.

Experimental measurement of porous polypropylene (PP) using the DTC-25 TA laboratory equipment and hot box test has been compared. The thermal conductivity of materials indicates the insulation capability of building materials. Excellent building materials will have a lower thermal conductivities value as well as other building insulator performance metrics. While results show that increasing the volume fraction of fluid in the porous PP has an inverse association with the thermal conductivity of the material as predicted by porous media theories, there is a marked difference in the measured values of the thermal conductivity using the two methods. The thermal conductivity values of porous from DTC-25 and hot box test were 0.21 and 0.0033 W/mK, respectively. The difference in the thermal conductivity values was due to the misapplication of the Fourier’s guarded heat flow model in the DTC-25 device to a convective fluid porous medium material.

In this work, we demonstrate the preparation of easy-to-fabricate nanogranular, strontium ferromolybdate/strontium molybdate core-shell ceramics and examine their properties including tunnel magnetoresistance, magnetic field sensitivity and temperature coefficient of the tunnel magnetoresistance. The tunnel magnetoresistance of nanogranular, strontium ferromolybdate/strontium molybdate core-shell ceramics was modeled yielding values suitable for magnetoresistive sensor application. Such structures possess a narrow peak of magnetic flux sensibility located at about 125 mT. For magnetic flux measurement, single domain granules with superparamagnetic behavior should be applied. The predicted TMR magnetic flux sensitivities for granules with superparamagnetic behavior amount to about 7.7 % per T and 1.5 % per T for granules sizes of 6.8 nm and 4 nm, respectively. A drawback of the tunnel magnetoresistance of such nanogranular core-shell ceramics is the unacceptable large value of the temperature coefficient. Acceptable values, lower than 2 % per K, are obtained only at low temperatures (less than 100 K) or large magnetic flux densities (exceeding 6 T). Therefore, a Wheatstone bridge configuration should be adopted for magnetoresistive sensors design to compensate the effect of temperature.

Cement paste backfill (CPB) is an effective waste management method allowing the storage of fine process tailings into underground mined-out voids. CPB performance generally depends on the sulfur content of the tailings and the type of binder. In recent years, there has been an in-creasing trend on the use of alkali-activated slag (AAS) to improve the performance properties of CPB. This study focuses on the ultrasonic and microstructural investigation of the effect of slag fineness on the mechanical, geochemical and durability properties of CPB made up of AAS (AAS-CPB) over 360 days. In this scope, AAS-CPB samples were prepared at three different fineness (3100-4650-6300 cm2/g). Fineness of slag significantly improved the early age- and long-term strength (~2.3 fold and ~6.6 fold, respectively) of CPB. However, further increase of slag fineness may have adverse effect on CPB microstructure and strength in the long term. Ultrasonic pulse velocity monitoring displayed very high relation with the strength evolution and durability assessment of CPB. Slag fineness at a degree was seen to improve the pore structure evolution of AAS-CPB. Microstructural studies are in good agreement with the geochemical and durability behavior of AAS-CPB. Microstructural and ultrasonic findings suggest that while slag fineness enhances the mechanical properties of AAS-CPB, further increase of the fineness of slag has no additional important technical advantages.

An ultraviolet (UV) nanosecond pulsed laser with a wavelength of 355 nm was applied to anneal amorphous indium-gallium-zinc oxide (a-IGZO) thin-film transistors (TFT) at various laser beam powers ranging from 0 to 280 mW. Laser irradiation was selectively carried out on the a-IGZO channel area using the beam scanning method after the a-IGZO TFT was formed through indium-tin oxide (ITO) electrode patterning and a subsequent lift-off process. After laser annealing, negative shifts in the threshold voltages and enhanced on-currents were observed at laser-beam powers ranging from 54 to 120 mW. No meaningful parameters were investigated for laser beams higher than 200 mW. By extracting the energy band gap and work function of a-IGZO from the transmittance and ultraviolet photoelectron spectroscopy (UPS) results, the ITO electrode/a-IGZO channel energy band structures were established under different laser annealing conditions, and the electron injection mechanism from the ITO to the a-IGZO channel was analyzed and optimized at a laser beam power of 120 mW. The results show that the electrical characteristics of the a-IGZO TFTs can be improved without any heat damage to the substrate because of the selective annealing process.

Single crystals of quaternary adamantine type Cu•GaGeS4 were grown by chemical vapor transport technique using iodine as transport agent. Dark red transparent crystals grew in a temperature gradient of dT = 900–750 °C. Chemical characterization by X-ray fluorescence showed an off-stoichiometric composition of the Cu•GaGeS4 crystals, in particular a slight Ge-deficit. By X-ray diffraction, Cu•GaGeS4 was found to adopt the chalcopyrite-type structure with the space group I-42d. The cation distribution in this structure was analyzed by multiple energy anomalous synchrotron X-ray diffraction and it was found that Cu and vacancies occupy the 4a site, whereas Ga and Ge occupy the 4b site. The band gap energies of several off-stoichiometric Cu•GaGeS4 crystals were determined by UV-Vis spectroscopy and are in the range of 2.1 to 2.4 eV. A non-linear correlation of the band gap energy with the Ge-content of the compound follows the usual bowing behaviour of semiconductor alloys with a bowing parameter of b = 1.45(0.08).

The effects of the solvent used for the active layer materials of an OLED based on TADF emitters plays a fundamental role in solution-deposited devices. This work focuses the effects on the performance of different solvents employed to fabricate a very simple two organic layer OLED based on a green TADF emitter, under the concept of host:guest matrix. From the different results of the main figures of merit, it was possible to conclude that the OLED that used toluene as solvent for the active layer reached a maximum EQE of 14%, almost the maximum already obtained for this emitter in more complex device structures. With the analysis of the charge transport processes, it was possible to establish an explanatory model for the obtained results. Through impedance spectroscopy, additional characterization about the nature of charge transport processes was carried out. With these results, it was possible to corelate the relaxation times, with the electrical properties of the active layer, and infer about the interaction between the electrical charges and the defect levels opening new possibilities to further development in the printed OLEDs.

The subtropical forest soil plays a pivotal role in terrestrial carbon (C) cycling. Although global warming is expected to influence subtropical soil C cycling, a consensus on its impact on soil C dynamics remains elusive. Given the significant response of soil microbial respiration to temperature in subtropical soils, understanding its behavior under varying temperature regimes is critical for predicting soil organic C (SOC) responses to climate warming. We conducted a short-term laboratory soil warming incubation experiment, sampling both warmed and un-warmed soils from a subtropical plantation in southeastern China, incubating them at 20 °C, 30 °C, and 40 °C. Our aim was to study the SOC mineralization response to increasing temperatures. Our findings revealed that the temperature sensitivity (Q10) of SOC mineralization to short-term experimental warming varied between the warmed soil and the un-warmed soil. The Q10 of the un-warmed soil escalated with the temperature treatment (20-30 ℃: 1.31, 30-40℃: 1.63). Conversely, the Q10 of the warmed soil decreased (20-30 ℃:1.57, 30-40 ℃:1.41). Increasing temperature treatments enhanced hydrolytic enzyme activity but decreased soil substrate availability in both un-warmed and warmed soil. The Q10 of un-warmed soil was positively correlated with the response ratio of DOC. Additionally, soil microbial biomass exhibited a significant decline, leading to a reduced total amount of PLFAs and a decreased abundance of bacteria, fungi, and Gram-negative bacteria (GN). The changes in temperature also considerably altered the composition of the warmed soil microbial community. A drop in the microbial quotient coupled with a rise in the metabolic quotient indicated that warming amplified microbial respiration over microbial growth. The differential Q10 of SOC mineralization, in response to temperature across varying soil, can largely be attributed to shifts in soil nutrients, C-degrading enzyme activities, and microbial communities (the ratio of fungal to bacterial PLFAs).

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.

Public health suffers from noxious gas of beached Sargassum algae. Floating barrages containing them at sea will be useful provided efficiently catch them and resist to hydrodynamic pressure. Nowadays design and installation of algae barrages are empiric. Structural break, anchor and mooring chain drifts can arise. We provide a mechanical model to evaluate stresses and loads on a structure made of nets and moorings. Hydrodynamic uncertainties occur through catches, fouling, and a range of sea currents appearing in lagoons or sheltered bays. We consider a nonlinear finite-element model of elastic membrane in large displacements and small strains. We study some forcings fixed a priori on a standard section of barrage. A section 50 m long, 1 m high, with two net-faces is examined at low and moderate current velocities, 0.05 and 0.35 m.s-1, while assuming specific vertical and horizontal catch pressures. A barrage installed in lagoon at Le François on Martinique Island and being observable by satellite imagery could benefit of the computed net and mooring tensions.

Plasminogen (Plg) is the inactive form of plasmin (Plm) that exists in two major glycoforms, referred to as glycoforms I and II (GI and GII). In the circulation, Plg assumes an activation-resistant 'closed' conformation via interdomain interactions and is mediated by the lysine binding site (LBS) on the kringle (KR) domains. These inter-domain interactions can be readily disrupted when Plg binds to lysine/arginine residues on protein targets or free L-lysine and analogues. This causes Plg to convert into an 'open' form which is crucial for activation by host activators. In this study, we investigated how various ligands affect the kinetics of Plg conformational change using small-angle X-ray scattering (SAXS). We began by examining the open and closed conformations of Plg using size-exclusion chromatography (SEC) coupled with SAXS. Next, we developed a high throughput (HTP) 96-well SAXS assay setup. This method enables us to determine the Kopen value, which is used to compare ligands’ effect on Plg conformation directly. Based on our analysis using Plg GII, we have found that the Kopen for ε-aminocaproic acid (EACA) is approximately three times greater than that of Tranexamic acid (TXA), which is widely recognized as a highly effective ligand. We demonstrated further that Plg undergoes a conformational change when it binds to the C-terminal peptides of the inhibitor α2-antiplasmin and receptor Plg‑RKT. Our findings suggest that, besides the C-terminal lysine, internal lysine(s) are also necessary for the formation of open Plg. Finally, we compared the conformational changes of Plg GI and GII directly and found that the closed form of GI, which has an N-linked glycosylation, is less stable. To summarize, we have successfully determined the response of Plg to various ligand/receptor peptides by directly measuring the kinetics of its conformational changes.

Additive manufacturing enables the realization of complex component designs that can not be achieved with conventional processes, such as the integration of lattice structures for weight reduction. To include lattice structures in component designs, an automated algorithm compatible to conventional CAD is required that is able to handle various lattice topologies as well as variable local shape parameters such as strut radii. Furthermore, smooth node transitions are desired since previous studies indicate advantages regarding reduced stress concentrations and the structural response to loadings such as fatigue performance. The surface patch-based algorithm developed in this work is able to solidify given lattice frames to smooth lattice structures without manual construction steps. The algorithm requires only a few seconds of sketching time for each node and favours paralellization. Automated special case workarounds as well as fallback mechanisms are considered for non-standard inputs. It is demonstrated on irregular lattice topologies and applied for the construction of a lattice infill of an aircraft component that was additively manufactured.

Lactic acid bacteria (LAB) have an extracellular proteolytic system that includes a multi-domain, cell envelope protease (CEP) with a subtilisin homologous protease domain. These CEPs have different proteolytic activities despite having similar protein sequences. Structural characterization has previously been limited to CEP homologs of dairy and human derived LAB strains, excluding CEPs of plant derived LAB strains. CEP structures are a challenge to determine experimentally due to their large size and attachment to the cell envelope. This study aims to clarify the prevalence and structural diversity of CEPs by using the structure prediction software AlphaFold 2. Domain boundaries are clarified based on a comparative analysis of 21 three-dimensional structures, revealing novel domain architectures of CEP homologs that are not necessarily restricted to specific LAB species or ecological niches. The C-terminal flanking region of the protease domain is divided into fibronectin type-III-like domains with various structural traits. The analysis also emphasizes the existence of two distinct domains for cell envelope attachment that are preceded by an intrinsically disordered cell wall spanning domain. The domain variants and their combinations provide CEPs with different stability, proteolytic activity and potentially adhesive properties, making CEPs targets for steering proteolytic activity with relevance for both food development and human health.

The high level of noise in helicopter cabins considerably compromises the comfort and safety of the pilot and passengers. To verify the feasibility and effectiveness of microperforated panel composite sound absorption structures for noise suppression in helicopter cabins, simulation and experimental studies were conducted on a model of a light helicopter cabin. First, three microperforated composite sound absorption structures for the helicopter cabin wall panel were designed. Then, a finite element model of the main gear/body acoustic vibration coupling was established to obtain the target frequencies of the microperforated composite sound absorption structures; the acoustic effect was verified by simulation. Finally, a model helicopter cabin equipped with the three microperforated composite sound absorption structures was built, and a cabin noise test was performed. The test results showed that the combined microperforated panel acoustic structure and microperforated panel–porous material composite structure realized an overall cabin sound pressure level attenuation of 8–10 dB, on average, in the wide frequency range of 500–2000 Hz, with an amplitude of more than 20 dB. The microperforated panel–acoustic supermaterial composite structure achieved low-frequency sound absorption in the frequency range of 300–450 Hz. The sound absorption effect reached 50%, and it also exhibited good noise reduction effects in the middle- and high-frequency bands.

Improper lifting measures of long-span spatial steel structures during construction process may delay the construction period and even cause safety accidents. Few studies on long-span spatial steel structures considered time-varying mechanical characteristic during construction process. In order to achieve safe and efficient installation in a long-span spatial steel structure, this research presents a time-varying mechanical analysis of the synchronous and asynchronous integral lifting and a single and interlaced point’s asynchronous integral lifting analysis method of a long-span spatial steel structure. The results showed that in the case of asynchronous lifting of single point, the displacement influence on other members is related to the distance. The closer the distance from the lifting point, the greater the influence. In asynchronous integral lifting, the lifting point with large lifting force is first installed to the specified position, and the lifting point with small lifting force is installed to the specified position.

Bacterial nitroreductase enzymes that convert prodrugs to cytotoxins are valuable tools for creating transgenic targeted ablation models to study cellular function and cell-specific regeneration paradigms. We recently engineered a nitroreductase (“NTR 2.0”) for substantially enhanced reduction of the prodrug metronidazole, which permits faster cell ablation kinetics, cleaner interrogations of cell function, ablation of previously recalcitrant cell types, and extended ablation paradigms useful for modelling chronic diseases. To provide insight into the enhanced enzymatic mechanism of NTR 2.0, we have solved the X-ray crystal structure at 1.85 Angstroms resolution and compared it to the parental enzyme, NfsB from Vibrio vulnificus. We additionally present a survey of reductive activity with eight alternative nitroaromatic substrates, to provide access to alternative ablation prodrugs, and explore applications such as remediation of dinitrotoluene pollutants. The predicted binding modes of four key substrates were investigated using molecular modelling.

The crystal structure of a polymorph of Glycyl-L-alanine HI.H2O was synthesized from chiral cyclic Glycyl-L-alanine dipeptide, known to show molecular flexibility in different environments. The space group is polar (P21) and therefore pyroelectricity and optical second harmonic generation properties are allowed by symmetry. We report a pyroelectric coefficient as high as 45 µC/m2K occurring at 345 K, one order of magnitude smaller than the semi-organic ferroelectric Triglycine Sulphate (TGS) crystal. Furthermore, the polymorph displays a nonlinear optical effective coefficient of 0.14 pm/V, around 14 times smaller than the value from a phase-matched inorganic Barium Borate (BBO) single crystal.

Pairs of pyrrolysyl-tRNA synthetase (PylRS) and tRNAPyl from Methanosarcina mazei and Methanosarcina barkeri are widely used for site-specific incorporations of non-canonical amino acids into proteins (genetic code expansion). Previously, we achieved full productivity of cell-free protein synthesis for bulky non-canonical amino acids, including Ne-((((E)-cyclooct-2-en-1-yl)oxy)carbonyl)-L-lysine (TCO*Lys), by using Methanomethylophilus alvus PylRS with structure-based mutations in and around the amino acid binding pocket (first-layer and second-layer mutations, respectively). Recently, the PylRS•tRNAPyl pair from a methanogenic archaeon ISO4-G1 was used for genetic code expansion. In the present study, we determined the crystal structure of the methanogenic archaeon ISO4-G1 PylRS (ISO4-G1 PylRS) and compared it with those of structure-known PylRSs. Based on the ISO4-G1 PylRS structure, we attempted the site-specific incorporation of Ne-(p-ethynylbenzyloxycarbonyl)-L-lysine (pEtZLys) into proteins, but it was much less efficient than that of TCO*Lys with M. alvus PylRS mutants. Thus, the first-layer mutations (Y125A and M128L) of ISO4-G1 PylRS, with no additional second-layer mutations, increased the protein productivity with pEtZLys up to 578% of that with TCO*Lys, at high enzyme concentrations in the cell-free protein synthesis.

Atmospheric corrosion of aluminum aircraft structures occurs due to numerous reasons. A typical phenomenon leading to corrosion is the deliquescence of contaminants such as salts due to changes in relative humidity (RH) caused by aircraft operation at different altitudes and climate zones. Currently, corrosion of aircrafts is controlled by scheduled inspections. In contrast, the present contribution aims for a continuous monitoring approach by using the acoustic emission (AE) method to detect and further evaluate atmospheric corrosion. The AE method is frequently used for corrosion detection at typically immersion-like conditions or for corrosion types where stress-induced cracking is involved. However, it has not yet been demonstrated for atmospheric corrosion at unloaded aluminum structures. To address this question, the present investigation uses small droplets of a corrosive sodium chloride (NaCl) solution to induce atmospheric corrosion on aluminum alloy AA2024-T351. Operating conditions of an aircraft are simulated by a controlled variation in RH. In addition, videos of the corrosion site are recorded to visually observe the corrosion process. Pitting corrosion is generated and clearly measurable AE signals are detected. An automatic video processing algorithm looking for sudden changes on the corrosion site mainly detects hydrogen bubbles formed when aluminum reacts with aqueous solutions. A clear correlation between the observed pitting corrosion, the AE and the hydrogen bubble activity and the RH, i.e., the electrolyte present at the aluminum surface, is found. Thus, the findings demonstrate the applicability of the AE method for monitoring atmospheric corrosion of aluminum aircraft structures by today’s measurement equipment. Numerous potential effects that can cause measurable AE signals are investigated and discussed. Among these, bubble activity is clearly considered to be the most emissive one.

Intercropping systems can flexibly use resources such as sunlight, heat, water, and nutrients in time and space, improve crop yield and land utilization rates, effectively reduce continuous cropping obstacles and the occurrence of diseases and insect pests, and control the growth of weeds. Thus, intercropping is a safe and efficient ecological planting mode. The legume–cereal intercropping system is the most common planting combination. Legume crops fix nitrogen from the atmosphere through their symbiotic nitrogen fixation abilities, and the fixed nitrogen can be transferred to and utilized by cereal crops in various ways. The symbiotic nitrogen fixation efficiency of legume crops was improved by reducing the inhibition of soil nitrogen on nitrogenase activity through competitive absorption of soil nitrogen. However, the effects of nitrogen transformation and distribution in intercropping systems, and microbial community structure characteristics on nitrogen transfer need to be further explored. In this review, ⅰ) we present the transformation and distribution of nitrogen in the legume–cereal intercropping system; ⅱ) we describe the soil microbial community characteristics in intercropping systems; and ⅲ) we discuss the advantages of using modern biological molecular techniques to study soil microorganisms. We conclude that intercropping can increase the diversity of soil microorganisms, and the interaction between different plants has an important impact on the diversity and composition of the bacterial and fungal communities. The extensive application of modern biological molecular techniques in soil microbial research and the great contribution of intercropping systems to sustainable agriculture are particularly emphasized in this review.

The article analyzes existing materials and structures with quadratic-nonlinear optical properties that can be used to generate a difference frequency in the terahertz and sub-terahertz frequency ranges. The principle of constructing a nonlinear optical-radio converter, based on an optical focon (a focusing cone), is proposed. Based on the assumption that this focon can be implemented from the metal-organic framework (MOF), we propose a technique for modeling its parameters. The mathematical model of the process of propagation and nonlinear interaction of waves inside the focon is based on a simplification of the nonlinear wave equation. Within the framework of the developed model, the following parameters are approximately determined: the 3D gradient of the linear refractive index and the function determining the geometric profile of the focon, which provide a few-mode-based generation of the difference frequency.

Fluorine represents a privileged building block in pharmaceutical chemistry. Diethylaminosulfur-trifluoride (DAST) is a reagent commonly used for replacement of alcoholic hydroxyl groups with fluorine and is also known to catalyze water elimination and cyclic Beckmann-rearrangement type reactions. In this work we aimed to use DAST for diversity-oriented semisynthetic transformation of natural products bearing multiple hydroxyl groups to prepare new bioactive compounds. Four ecdysteroids, including a new constituent of Cyanotis arachnoidea, were selected as starting materials for DAST-catalyzed transformations. The newly prepared compounds represented combinations of various structural changes DAST was known to catalyze, and a unique cyclopropane ring closure that was found for the first time. Several compounds demonstrated in vitro antitumor properties. A new 17-N-acetylecdysteroid (13) exerted potent antiproliferative activity and no cytotoxicity on drug susceptible and multi-drug resistant mouse T-cell lymphoma cells. Further, compound 13 acted in significant synergism with doxorubicin without detectable direct ABCB1 inhibition. Our results demonstrate that DAST is a versatile tool for diversity-oriented synthesis to expand chemical space towards new bioactive compounds.

Ion-exchange membranes (IEMs) are widely used in desalination, waste water treatment, food, energy production and other applications. There is a strong demand for cost-effective IEMs characterized by high selective transport of ions of a certain sign of charge. In this paper, we simulate the experimental results of V. Sarapulova et al. (IJMS 2021) on the modification of an inexpensive anion-exchange membrane (CJMA-7, Hefei Chemjoy Polymer Materials Co. Ltd., China) with a perfluorosulfonated ionomer (PFSI). The modification was made in several stages including keeping the membrane at a low temperature, applying a PFSI solution on its surface, and subsequent drying it at an elevated temperature. We apply the known microheterogeneous model with some new amendments to simulate each stage of the membrane modification. It has been shown that the PFSI film formed on the membrane-substrate does not affect significantly its properties due to the small thickness of the film (4 m) and similar properties of the film and substrate. The main effect is caused by the fact that PFSI material “clogs” the macropores of the CJMA-7 membrane, thereby blocking the transport of coions through the membrane. In this case, the membrane microporous gel phase, which has a high selectivity to counterions, remains the primary pathway for both counterions and coions. Due to the above modification of the CJMA-7 membrane, the coion (Na+) transport number in the membrane equilibrated with 1 M NaCl solution decreased from 0.11 to 0.03. Thus, the modified membrane becomes comparable in its transport characteristics with more expensive IEMs available on the market.

For many decades, X-ray diffraction (XRD) has been used for material characterization. With the recent development in material science understanding and technology, various new materials are being developed, which requires upgrading the existing analytical techniques such that intricate problems can be solved. Although, XRD is a well-established non-destructive technique, it still requires further improvements in its characterization capabilities, especially when dealing with complex mineral structures. The present review conducts comprehensive discussions on atomic crystal structure, XRD principle, its applications, uncertainty during XRD analysis, and required safety precautions, all in one place. It further discusses the future research directions, especially the use of artificial intelligence and machine learning tools for improving the effectiveness and accuracy of XRD technique for mineral characterization. It has been focused that how XRD patterns can be utilized for a thorough understanding of the crystalline structure, size, and orientation, dislocation density, phase identification, quantification, and transformation, information about lattice parameters, residual stress, and strain, and thermal expansion coefficient of materials. All these important discussions on XRD for mineral characterization are compiled in this short yet comprehensive review that would benefit specialists and engineers in the chemical, mining, iron, metallurgy, and steel industries.

Optical properties of the cornea are responsible for correct vision, ultrastructure allows optical transparency and biomechanical properties governs the shape, elasticity or stiffness of the cor-nea affecting ocular integrity and intraocular pressure. Therefore, optical aberrations, corneal transparency, structure and biomechanics play a fundamental role in the optical quality of hu-man vision, ocular health and refractive surgery outcomes. However, the convergence of those properties is not yet reported at macroscopic scale within the hierarchical structure of the cornea. This work explores the relationships between biomechanics, structure and optical properties (corneal aberrations and optical density) at macrostructural level of the cornea through dual Placido-Scheimpflug imaging and air-puff tonometry systems in a healthy young adult popula-tion. Results showed convergence between optical transparency, corneal macrostructure and biomechanics.

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of a cancer or transformed cell, the initiation and progression were shown to be driven by only a few mutational events viz. driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient’s cancer cell gained large attention recently for designing better targeting therapies towards paving way for the precision cancer medicine. With rolling advancements in high-throughput omics technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures, and their potential application in developing precision cancer therapies.Keywords:

The method of doublet frequency distance (DFD-) maps was applied, here, for visualization of DNA sequence structures of yeast chromosomes. The colour scale “rainbow” of the Octave programming tool is well suited for such visualization. The DFD-maps are generated by comparison of the differences in the number of all base doublets in a shifting frame with all other positions of this frame in a regarded DNA sequence section. This procedure can be applied from DNA-sections of between a few hundred bases (bps) up to a complete chromosome with a million or more bps. An orthogonal DFD-map pattern dominates all chromosomes and its parts. It can be interpreted by a large number of successive mutation events during evolution. In contrast, diagonal patterns indicate duplications of sequence sections and multiple tandem repeats. These periodic structures are found in several chromosomes and are more or less regular or noised. The larger tandem repeat in the subtelomeric region of chromosome 12 presents a characteristic example of a nested multiple-repeat structure. Its pattern in the DFD-map illustrates obviously a temporal order of duplication and mutation events leading to a hierarchical sequence architecture.

Magnesium (Mg) is a vital element, which involves in various key cellular processes in plants. Mg transporter (MGT) genes play an important role in magnesium distribution and ionic balance maintenance. Here, MGT family members were identified and characterized in three species of the plant family Malvaceae, including Theobroma cacao, Corchorus capsularis, and Gossypium hirsutum to improve our understanding of their structure, regulatory systems, functions, and possible interactions. We identified 18, 41, and 16 putative non-redundant MGT genes from the genome of T. cacao, G. hirsutum, and C. capsularis, respectively, which were clustered into five groups within maximum likelihood tree. Several segmental/tandem duplication events were determined between MGTs genes. It seems that MGTs were slowly evolving and have been evolved under a purifying selection. Analysis of gene promoter regions showed that MGTs have a high potential to respond to biotic/abiotic stresses as well as hormones. The expression patterns of MGT genes revealed their possible T. cacaorole in response to P. megakarya fungi in T. cacao, whereas MGT genes showed differential expression in various tissues and respond to several abiotic stresses, including cold, salt, drought, and heat stress in G. hirsutum. Besides, the co-expressions network of MGTs indicated that genes involved in auxin responsive, lipid metabolism, cell wall organization, and photoprotection can interact with MGTs.

The open-bundle structure of cytochrome c’ as an unfolding intermediate was determined by small-angle neutron scattering experiment (SANS). The four-α-helix bundle structure of Cyt c’ at neutral pH was transited to an open-bundle structure (at pD ~13), which is a joint-clubs consisting of four clubs (α-helices) connected by short loops. The compactly folded structure of Cyt c’ (radius of gyration, Rg = 18 Å for the Cyt c’ dimer) at neutral or mildly alkaline pD transitioned to a remarkably larger “open-bundle” structure at pD ~13 (Rg = 25 Å for the Cyt c’ monomer). Cyt c’ adopts an unstructured random coil structure at pD = 1.7 (Rg = 25 Å for the Cyt c’ monomer). Numerical partial scattering function analysis (joint-clubs) and ab initio modelling gave structures similar to the “open-bundle”, which retains the α-helices but loses the bundle structure.

The astacin protease Meprin β represents an emerging target for drug development due to its potential involvement in disorders such as acute and chronic kidney injury and fibrosis. Here, we elaborate on the structural basis of inhibition by a specific Meprin β inhibitor. Our analysis of the crystal structure suggests different binding modes of the inhibitor to the active site. This flexibility is caused, at least in part, by movement of the C-terminal region of the protease domain (CTD). The CTD movement narrows the active site cleft upon inhibitor binding. Compared with other astacin proteases, among those the highly homologous isoenzyme Meprin α, differences in the subsites account for the unique selectivity of the inhibitor. Although the inhibitor shows substantial flexibility in the orientation within the active site, the structural data as well as binding analyses including molecular dynamics simulations support a contribution of electrostatic interactions, presumably by arginine residues, to binding and specificity. Collectively, the results presented here and previously support an induced fit and substantial movement of the CTD upon ligand binding and, possibly, during catalysis. To the best of our knowledge, we here present the first structure of a Meprin β holoenzyme containing a zinc ion and a specific inhibitor bound to the active site. The structural data will guide rational drug design and the discovery of highly potent Meprin inhibitors.

This article objectively assesses, the hypothesis of the streamline's shape theory and its formulated equation. The deduction of proof uses algebra rather than first-order partial differential equations to address the specific hypothesis of "Streamline's shape theory" from the fundamental perspective of applied mathematics and scientifically derives mathematical relations of the axioms and corollaries in the field of fluid dynamics. The algebraic methods employed provide progressively more distinct and precise solutions compared to first-order partial differential equations. The foremost objective of this work is to evaluate if the formulations of the streamline's shape theory can have solutions for inviscid-incompressible and viscid-compressible flows of Newtonian fluids and to identify their nature. Secondly, to understand how the topology of the body and the free-stream conditions affect these solutions with due regards to the shape and size of the body interacting with the fluid flow. Finally, to explore the possibility of this theory to develop a CFD solver for streamline simulation to reduce the experimentation in the analysis of flow-structure interactions of Newtonian fluids and also to identify its scope of applications and limitations.

The article presents a new approach to the analysis of the stability of automatic systems with discrete links.In almost all modern automatic control systems, there are links that break signals in time. These are power controlled switches - transistors or thyristors operating in a pulsed mode and digital links in regulators.Time discretization significantly affects the stability of processes in the automatic control system. The theoretical analysis of such systems is rather complicated and requires a significant change in engineering approaches to analysis. In connection with the improvement of digital controllers and a significant increase in their performance, in recent years this problem has practically not been remembered. However, its mathematical "content" has not changed since the 80s of the 20th century, when discreteness began to play a major role among the problems hindering progress in automatic control systems, in terms of the transition to digital systems.In this paper, a new approach is proposed, which consists in interpreting the sampling operation by a link with the proposed frequency characteristic, which determines the suppression of input high-frequency signals. This link greatly simplifies engineering calculations and demonstrates the new capabilities of sampling systems. These possibilities include the rational distribution of digitalization resources - the number of bits and the sampling interval between the regulator channels, depending on the frequency range of the efficiency of these channels. Theoretical statements have been verified and confirmed by simulation. It is shown how this approach makes it possible to formulate new principles of construction of seemingly well-known controllers - PID controllers and variable structure systems (VSS).

The notion that dust might have formed the cosmic microwave background (CMB) has been strongly refuted on the strength of four decades of observation and analysis, in favour of recombination at a redshift z ~ 1080. But tension with the data is growing in several other areas, including measurements of the Hubble constant H(z) and the BAO scale, which directly or indirectly impact the physics at the surface of last scattering (LSS). The R_h=ct universe resolves at least some of this tension. We show in this paper that---if the BAO scale is in fact equal to the acoustic horizon---the redshift of the LSS in this cosmology is z_cmb ~ 16, placing it within the era of Pop III star formation, prior to the epoch of reionization at 15 > z > 6. Quite remarkably, the measured values of z_cmb and H_0 = H(0) in this model are sufficient to argue that the CMB temperature today ought to be ~ 3 K, so H_0 and the baryon to photon ratio are not independent free parameters. This scenario might have resulted from rethermalization of the CMB photons by dust, presumably supplied to the interstellar medium by the ejecta of Pop III stars. Dust rethermalization may therefore yet resurface as a relevant ingredient in the R_h=ct universe. Upcoming high sensitivity instruments should be able to readily distinguish between the recombination and dust scenarios by either (i) detecting recombination lines at z ~ 1080, or (ii) establishing a robust frequency-dependent variation of the CMB power spectrum at the level of ~ 2-4% across the sampled frequency range.

Protein is the proteios building block of life. Evolutionarily, its sequence is not as conserved as its structure, making it more reasonable for protein structure, instead of protein sequence, to be the descriptor of protein function. Yet, in the National Center for Biotechnology Information (NCBI) database, the number of experimentally identified protein sequences is in great excess of that of experimentally determined protein structures inside the almost-half-a-century old Protein Data Bank (PDB). For instance, GPR151 is an proton-sensing G-protein coupled receptor (GPCR) originally identified as homologous to galanin receptors. As of March 19, 2020, GPR151’s structure has not been experimentally determined and deposited in PDB yet. Thus, an ab initio modelling approach was employed here to build a three-dimensional structure of GPR151. Overall, the ab initio GPR151 model presented herein constitutes the first structural hypothesis of GPR151 to be experimentally tested in future with previously published, currently ongoing and future GPR151 studies.

Purpose: To improve numerical simulation of the non-contact tonometry test by using Arbitrary Eulerian-Lagrangian deforming mesh in the coupling between computational fluid dynamics model of an air jet and finite element model of the human eye. Methods: Computational fluid dynamics model simulated impingement of the air puff and consisted of 25920 wedge6 elements and employed Spallart-Allmaras model to simulate capture turbulence of the air jet. The time span of the jet wais 30 ms and maximum Reynolds number

G protein-coupled receptors (GPCRs) are critical drug targets. GPCRs convey signals from the extracellular to the intracellular environment through G proteins. There is evidence that some ligands that bind to the GPCRs activate different downstream signaling pathways. G protein activation or -arrestin biased signaling involves ligands binding to receptors and stabilizing conformations that trigger a specific pathway. Molecular dynamics (MD) simulations are especially valuable for obtaining detailed mechanistic information, including identification of allosteric sites and understanding modulators' interactions between receptors and ligands. Here, we highlight recent simulation studies and methods used to study biased G protein-coupled receptor signaling and their conformational dynamics. We also highlight applications of MD simulations to drug discovery.

A rigorous model for the electron is presented by generalizing the Coulomb's Law or Gauss's Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality referred to as the Unifield Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a ``static'' electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron's mass and classical radius, and the fine structure constant, emerges.

The synthesis and structure of the 4,4′-dipyridyl N,N′-donor ligand with a central thiazolo[5,4-d]thiazole (tztz) unit, named Dptztz is presented. With the linker Dptztz and isophthalate (benzene-1,3-dicarboxylate, 1,3-BDC²⁻) the mixed-linker, two-dimensional coordination network [Zn(1,3-BDC)Dptztz]·DMF could be obtained (DMF = dimethylformamide), which belongs to the class of coordination polymers with interdigitated structures (CIDs). The incorporated DMF solvent molecules can be removed through solvent exchange and evacuation such that the supramolecular 3D packing of the 2D networks retains porosity for CO₂ adsorption in activated [Zn(1,3-BDC)Dptztz]. The first sorption study of a tztz-functionalized porous metal-organic framework material yields a BET surface of 417 m²/g from CO₂ adsorption. The heat of adsorption for CO₂ exhibits a relative maximum with 27.7 kJ/mol at adsorbed CO₂ of about 4 cm³/g STP which is interpreted as a gate-opening effect.

The detection of dopamine in a highly sensitive and selective manner is crucial for the early diagnosis of a number of neurological diseases/disorders. Here, a report on a new platform for the electrochemical detection of dopamine with a considerable accuracy that comprises a 3D porous graphene oxide (pGO)/gold nanoparticle (GNP)/pGO composite-modified indium tin oxide (ITO) is presented. The pGO was first synthesized and purified by ultrasonication and centrifugation, and it was then further functionalized on the surface of a GNP-immobilized ITO electrode. Remarkably, owing to the synergistic effects of the pGO and GNPs, the 3D pGO-GNP-pGO-modified ITO electrode showed a superior dopamine-detection performance compared with the other pGO- or GNP-modified ITO electrodes. The linear range of the newly developed sensing platform is from 0.1 μM to 30 μM with a limit of detection (LOD) of 1.28 μM, which is more precise than the other previously reported GO-functionalized electrodes. Moreover, the 3D pGO-GNP-pGO-modified ITO electrodes maintained their detection capability even in the presence of several interfering molecules (e.g., ascorbic acid, glucose). The proposed platform of the 3D pGO-GNP-pGO-modified ITO electrode could therefore serve as a competent candidate for the development of a dopamine-sensing platform that is potentially applicable for the early diagnosis of various neurological diseases/disorders.

In this work, we investigated the effects of heat treatment on the microstructure, hydrogen storage characteristics and corrosion rate of a Ti₃₄V₄₀Cr₂₄Fe₂ alloy. The arc melted alloy was divided into three samples, two of which were separately quartz-sealed under vacuum and heated to 1000 °C for 1 h; one of these samples was quenched and the other furnace cooled to ambient temperature. The crystal structures of the samples were studied via X-ray diffractometry and scanning electron microscopy. Absorption/desorption characteristics were investigated using a Sievert apparatus. Potentiostat corrosion tests on the alloys were performed using an AutoLab^® corrosion test apparatus and electrochemical cell. All samples exhibited a mixture of body-center-cubic (BCC) and Laves phase structures. The corrosion rate, maximum absorption, and useful capacities increased after both heat treatments. The annealed sample had the highest absorption and reversible capacity. The plateau pressure of the as-cast alloy increased after quenching. The corrosion rate increased from 0.0004 mm/y in as-cast sample to 0.0009 mm/y after annealing and 0.0017 mm/y after quenching, due to a decrease in the Cr-content of the C14 phase.

The Gulf of Mannar, a UNESCO World Biosphere Reserve, faces severe overfishing and habitat degradation threats. In this study, we investigate the pivotal role of ecosystem services in sustaining local livelihoods and overall well-being. By conducting a comprehensive survey of 480 respondents across two districts, we gathered extensive data on demographics, livelihoods, fishing practices, reliance on ecosystem services, and community management participation. The analysis reveals a critical dependence on the Gulf's resources (income, food security, traditions) with gender disparities (men fish, women in pre/post-harvest), but fishing is only allowed in the 10km buffer zone (not the core zone). The findings emphasize the promise of community-based strategies, such as Marine Protected Areas and reviving co-management committees, for achieving sustainable fisheries management. However, we also identify gaps, including the need for more nuanced well-being indicators and improved models for community management participation. To address these challenges, we advocate for sustainable fishing practices, tackling social inequities, especially gender disparities in resource access and decision-making, and investing in healthcare, education, and social safety nets for fishing communities. Promoting alternative livelihoods can alleviate pressure on fish stocks, and empowering local communities through capacity building and community-based management initiatives is crucial for ensuring the long-term sustainability of both the Gulf of Mannar ecosystem and the well-being of its dependent communities. This multifaceted approach holds significant promise for balancing ecological health with human prosperity.

Wastewater genomic surveillance is an efficient and economic approach to track infectious diseases and can help detect unusual epidemiological events. The current study uses this approach for the first time in Morocco to monitor the epidemiology of SARS-CoV-2 and Influenza A, B and RSV virus infections during the third wave of COVID-19 caused by the Omicron variant. The virome was concentrated from wastewater collected from two sewer sheds of two cities, Agadir and Inezgane, using the PEG/NaCl method. The viral copy number was determined using qRT-PCR. All 26 samples in both cities were tested positive for SARS-CoV-2 at various viral loads. For Influenza A virus, two samples were tested positive in Inezgane. The estimated SARS-CoV-2 viral RNA copy numbers observed were then used to estimate the number of infected individuals using the SEIR model. The number of viral copies of SARS-CoV-2 for Agadir and Inezgane varied from 82932 ± 15479 to 507865 ± 58759 copies/L and 61244 ± 8552 to 497506 ± 18061 copies/L, respectively. The estimated number of cases correlates positively with the number of reported cases. Next Generation Sequencing showed that samples contain the following two variants: BA.1 and BA.2 that have been detected in clinical samples. This study demonstrates the effectiveness and feasibility of wastewater genomic surveillance in monitoring pathogens such as SARS-CoV-2 in Morocco.

This paper honours the importance of creative practice as a way of staying in touch with the affective dimensions of human situations and experience. Drawing on observations, field notes, and documentation of artistic practice, the author explores the transfer of ‘felt’ experience through an emergent ‘body’ of artwork made in response to experiences and observations in/of a neurorehabilitation day service in the context of an art-based doctoral enquiry (Michaels 2022).[i] Broadening the scope of response art to the performative nature of ‘making’, the ‘work’ of art amplifies the resonance of emotional and sensory affect. This brings an ethics of care to the fore, giving voice to aspects of organisational culture that might easily be lost, as well as the need for time and space to mourn lost parts.

This study proposes and validates a novel combustion control system for Oil-Fired Boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs, and applies Internal Model Control (IMC)-based Proportional-Integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC parameter tuning through experimentation is essential for achieving effective control performance. Experiments conducted on a 3000 kg/h marine oil-fired boiler in actual operation optimized the controller parameters for stability and responsiveness, and validated their effectiveness. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

Evaluating flood risk though numerical simulations in areas where hydrometric and bathymetric data are scarcely available is a challenge. This is however of paramount importance, particularly in urban areas, where huge losses of human life and extensive damage can occur. This paper focuses on the June 2-3, 2023 event at Léogâne in Haiti, where the Rouyonne River partly flooded the city. Water depths in the river were recorded since April 2022 and a few discharges were measured manually but it was not sufficient to produce a reliable rating curve. Using a uniform flow assumption combined with the BaRatin method (Bayesian rating curve), it was possible to extrapolate the existing data to higher discharges. From there, a rainfall-runoff relation was developed for the site using the AtHyS distributed hydrological model, which allowed to determine the discharge of the June 2023 event, which was estimated as twice the maximum conveying capacity of the river in the measurement section. Bathymetric data were obtained using drone-based photogrammetry and two-dimensional simulations were carried out using the WATLAB computational environment to represent the flooded area and the associated water depths. By comparing the water depths of 21 measured high-water marks with the simulation results, we obtain KGE and NSE criteria of, respectively, 0.890 and 0.882. This allows us to conclude that the model is sufficiently accurate and could be used by flood managers and decision-makers to assess flood risk and vulnerability in Haiti.

The emergence of drug resistance inducing mutations in Hepatitis C virus (HCV) coupled with genotypic heterogeneity has made targeting NS3/4A serine protease difficult. In this work, we investigated the mutagenic variations in the binding pocket of Genotype 3 (G3) HCV NS3/4A and evaluated ligands for efficacious inhibition. We report mutations at 14 positions within the ligand-binding residues of HCV NS3/4A including H57R and S139P within the catalytic triad. We then modeled each mutational variant for pharmacophore-based virtual screening (PBVS) followed by covalent docking towards identifying a potential covalent inhibitor, i.e. cpd-217. The binding stability of cpd-217 was then supported by molecular dynamic simulation followed by MM/GBSA binding free energy calculation. The free energy decomposition analysis indicated that the resistant mutants alter the HCV NS3/4A-ligand interaction, resulting in an unbalanced energy distribution within the binding site leading to drug resistance. cpd-217 was identified to interact with all NS3/4A G3 variants with significant covalent docking scores. In conclusion, cpd-217 emerges as a potential inhibitor of HCV NS3/4A G3 variants that warrants further in vitro and in vivo studies. The study will pave the way for drug design and development of HCV G3 NS3/4A.

This paper introduces the improvements in the natural frequency based Structural Health Monitoring (SHM) by applying bio-inspired optimization methods and the vision-based monitoring system for the effective damage detection. This paper proposes a natural frequency extraction method using the motion magnification based vision monitoring system with bio-inspired optimization techniques to estimate the damage location and depth in a cantilever beam. The proposed optimization techniques are inspired by natural processes and biological evolution including Genetic Algorithms, Particle Swarm Optimization, Sea Lion Optimization, and Coral Reefs Optimization. To verify the performances of each bio-inspired optimization methods, the eigenvalues of a two-bay truss structure are used for estimating the damaged elements. Then, using the proposed video motion magnification method, the natural frequency for each of undamaged and damaged cantilever beam have been extracted and compared with the LDV sensor to verify the proposed vision-based monitoring system. The performance of each bio-inspired optimizer for the damage detection has been compared. As a result, Coral Reefs Optimization has showed the lowest average error, around 1%, in the damage detection using natural frequency.

With the increasing use of automatic transmissions in passenger vehicles, there is a growing need for improved gear shifting control strategies that aim to enhance performance and fuel efficiency. This paper introduces a knowledge-based fuzzy control system designed specifically for automatic transmissions in light vehicles. The proposed system utilizes a fuzzy inference system as the controller, enabling the emulation of the driver’s perceptions during the gear shifting process. Consequently, the input signal consists of variables that are either sensitive to or observable by the driver, while the output represents the selected gear. The paper evaluates three distinct control strategies, each based on different inputs. These inputs include the longitudinal velocity of the vehicle, the angular velocity of the engine, or a combination of both, along with the time-derivative of the throttle valve opening. Through the evaluation, it is observed that the combination of inputs yields significantly improved performance. Therefore, the methodology presented in this paper demonstrates that the combined input approach is more effective for gear shifting control.