Globally, sacred groves represent a traditional form of community-based conservation system, recognized for their capacity to preserve areas that are of cultural and religious importance to local people. In most cases, the entire community takes on a watchdog role to guard against encroachment and unauthorized access either by its members or outsiders who might desecrate such sites. Our paper investigates the effects of different governance arrangements on three sacred groves in southwest Nigeria⎯Osun Osogbo Sacred Grove (UNESCO World Heritage Site); Idanre Hills (Nigerian National Monument) and Igbo-Olodumare (local cultural site)⎯on their socio-economic and religio-cultural benefits and contribution to biodiversity conservation. Using a mixed-methods design of a semi-structured questionnaire (n=167), key informant interviews (n=2), and focus groups (n=7), we collected data from local community members, traditional priests, sacred grove devotees and tourism officials. We found that customary institutions have guided reverence for sacralized spaces and wise utilization of their unique resources. The growing recognition of sacred groves has paved the way for socioeconomic rewards for individuals and government as cultural tourism increases. We found that the involvement of formal institutions alongside customary institutions in sacred grove management reinforces compliance with conservation laws within the sacred groves, especially where traditional norms are weak or may be disregarded. We discuss the implications of these observations and offer suggestions to improve community engagement, uphold traditional ecological knowledge, and develop ecotourism within the groves. We conclude that the co-existence of community-based conservation through a system of established traditional norms and prohibitions as well as formal government legislation and management, offers assurance for the long-term preservation of sacred groves and their biodiversity.

Electromagnetic actuation can support many fields of technology, such as robotics or biomedical applications. In this context, fully understanding the system behavior and proposing a low-cost package for feedback control is challenging. Modeling the electromagnetic force is particularly tricky because it is a nonlinear function of the actuated object’s position and coil’s current. Measuring in real time the position of the actuated object with the precision required for accurate motion control is also nontrivial. In this study, we propose a novel, cost-effective electromagnetic set-up to achieve position control via visual feedback. We actuate vertically and under different experimental conditions a 10 mm diameter steel ball hanging on a low-stiffness spring, demonstrating good tracking performance (the position error remains within ±0.5 mm with a negligible phase delay in the best scenarios). The experimental results confirm the feasibility of the proposed set-up, which is characterized by minimum complexity and realized with off-the-shelf and cost-effective components. For these reasons, such a contribution helps to understand and apply electromagnetic actuation even further.

Gender has been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to gender and the conditions that generate these effects. The present work studies the effects of a drill-and-practice mathe-matical game on primary students. The study focused on an analysis by gender, measuring learning performance, and motivation in the practice activity. Also, two instructional mechanics were considered regarding the question answering, a multiple-try (MTF) and a single-try (STF) condition, to search for possible differences. A total of 81 students from 4 courses and 2 schools participated in the experiment. The study's main findings were that: (a) the girls outperformed boys in terms of students' learning gains, (b) girls presented lower levels of competence and autonomy than boys, (c) in the MTF, girls presented lower levels of autonomy but no differences in competence contrasted to boys, (d) in the STF, girls presented lower levels of competence but no differences in autonomy against boys, and (e) no differences existed in interest, effort and value among gender overall o per instructional condition. This study enhances the knowledge of gender differences under diverse instructional settings, in particular providing insights into possible differences by gender when varying the number of attempts provided to students.

“Anti-stripping Agents" or "adhesion promoters” can enhance the chemical affinity between as-phalt and aggregate by increasing their mutual attraction. Various forms of anti-stripping agents have been proposed to mitigate pavement stripping, and siloxane-based Zychotherm is one of them. Choosing the appropriate type and dose of anti-stripping additives is no doubt vital to the intended performance. Therefore, it is critically important to determine the dose of the additives used in the modification of asphalt binders. This research developed a feasible detection method that can closely measure the dose (0.05% and 0.1%) of siloxane-based anti-stripping liquid agents. Related test methods including heat combustion test, residue visualization, burning, and ignition were implemented. Heat combustion results showed that with the addition of Zychotherm anti-stripping additive, average heat combustion value decreased by 1.34% and 1.72% for 0.05% and 0.1% Zychotherm modified binder respectively. In the burning and ignition process, the modified binder leaves yellowish substances in the residue, which is an indication of the presence of Zy-chotherm. The weight of the yellowish residue relates more to the quantity of Zychotherm in as-phalt binder.

The study focuses on solving urban challenges such as temperature reduction, urban stormwater management, noise reduction, air quality improvement, and CO2 concentrations reduction, and suggests terrestrial and freshwater habitat types (HTs) found in Europe, as innovative forms of nature-based solutions (NBS) for them. Establishment of native HTs in different urban environments to solve urban challenges would enhance biodiversity at different levels and integrate this aspect in urban planning. This contribution builds on the recognition that surfaces of vegetated terrain are the most versatile NBS in addressing the broadest range of environmental problems in urban areas and that the processes running within these green spaces offer the key to socio-ecological improvements of such areas. Employing narrative literature review, qualitative content analysis, and interdisciplinary expert discussion this paper defines why and how native HTs of unchanged nature can be implemented as NBS in the urban environment, indicate potential HTs for specific urban challenges and presents an approach to the inclusion of HTs as NBS into spatial planning documents at national, regional, and local levels. The proposed planning approach attributes added value to HT, and by linking the concepts of NBS and HT integrates them into urban planning.

Satellite-based rainfall estimation products provide a vital alternative source of rainfall data in areas where conventional precipitation measurement is not readily available. In order to facilitate the use of these products there is the need to evaluate their accuracies. This study evaluated the accuracy of three satellite rainfall products; TMPA 3B42RT, TMPA 3B42 and CMORPH in the Pra basin (23,330 km²) of Ghana. The evaluation was through the point-to-pixel method by comparing 0.25°x 0.25° satellite grids to gauged rainfall based on gauge locations and analyzed statistically using correlation coefficient (r), bias and percent bias (pBias) as the performance verification methods. Seven (7) gauge stations with no missing data for the period of 2003-2008 was used in the evaluation. The analysis was based on daily, monthly, annual and seasonal timescales. Our results showed a good correlation between the TMPA products and the gauged data on all timescales considered. The CMORPH on the other hand showed huge overestimation at all gauge locations. The TMPA 3B42 was seen to be the best amongst the three products. The overall rainfall in the basin was well depicted by the TMPA 3B42 and 3B42RT. Although there wasn’t a perfect match between the 3B42RT and 3B42 products and the gauged rainfall, these products can be used to supplement gauged rainfall measurements in the basin and in estimation of rainfall in ungauged basins with similar characteristics.

We propose a methodology for the quantification of model risk in the context of credit derivatives pricing and CVA, where the uncertain or unmodelled parameter is often the correlation between rates and credit. We take the rates model to be Hull-White (normal) and the credit model to be Black-Karasinski (lognormal). We show how highly accurate analytic pricing formulae, hitherto unpublished, can be derived for CDS and extended to address instruments with defaultable Libor flows which may in addition be capped and/or floored. We also consider the pricing of a contingent CDS with an interest rate swap underlying. We derive explicit expressions showing how to good accuracy the dependence of model prices on the uncertain parameter(s) can be captured in analytic formulae which are readily amenable to computation without recourse to Monte Carlo or lattice-based computation. In so doing, we take into account the impact on model calibration of the uncertain (or unmodelled) parameter.

Populations with healthcare needs often reside in post-conflict settings where basic services needed to maintain good health may be non-existent or hard to access. Therefore, there is a need for better identification and reallocation of resources as part of the post-conflict health rehabilitation effort. This study applies an asset-based approach to explore the more optimal design of health services and to identify the resource constraints for basic health service delivery to the most vulnerable communities in eastern Congo. We implemented the asset mapping in two phases. Firstly, we combined a qualitative survey with community walks to identify the assets already present in the communities. Secondly, we conducted group discussions to map out assets that are the core of Asset-Based Community Development (ABCD) practice. We finally documented all assets in a Community Asset Spreadsheet. Overall, 209 assets were identified as available and potentially valuable resources for the communities in eastern Congo. Among them, 60 were local associations, 24 were land and physical environment, 43 were local institutions, 46 were individuals, 32 to economy and exchange, and only 6 were related to culture, history, and stories. Drawing upon the findings from the qualitative survey, community walks, and group discussions, we conclude that an important number of resources were in place for basic health service delivery. By activating the existing and potential resources, the most vulnerable populations in eastern Congo might have the required resources for basic health service delivery. Our findings support the use of an asset-mapping research method as appropriate to identify existing and potential resources for basic health services in a post-conflict setting.

This article explores the transformative potential of fermentation in elevating the quality of plant-based matrices to match the desirable attributes of traditional dairy and meat products. As the demand for sustainable products without animal welfare issues increases, fermentation emerges as a key process to enhance the organoleptic properties and nutritional content of plant-based analogs. This study explores the effect of fermentation when applied to legume matrices, focusing on the resulting texture, flavor, and nutritional value. A selection of Bacillus subtilis, lactic acid bacteria (LAB) strains, and combinations thereof, showed potential for improving the aforementioned organoleptic and nutritional characteristics of fermented plant bases. In four different legume-derived matrices, fermentation improved texture, degraded undesirable plant carbohydrates, and removed off-flavor compounds, while producing desirable dairy-associated compounds. The degradation of the undesirable beany off-flavor-causing compound hexanal appears to be a universal phenomenon, as every tested strain as well as their combinations exhibited the capability to decrease the hexanal content albeit with varying efficiency. Some LAB strains were found to be capable of producing carotenoids and might hence have the potential for tailoring fermented plant-based matrices for specific applications, such as yellow cheese or red meat analogs.

Due to the continuing high suicide rates among young men, there is a need to understand help-seeking behaviour and engagement with tailored suicide prevention interventions. The aim of this study was to explore help-seeking behaviour and engagement for young men aged 18 to 30 years who attended a therapeutic centre for men in a suicidal crisis. In this prospective cohort study, data were collected from 546 men who were referred into a community-based therapeutic service in North West England. Of the 546 men, 337 (52%) received therapy; 161 (48%) were aged between 18 and 30 years (mean age 24 years, SD=3.4). One third (n=54; 34%) of the men were seen within 48 hours of their referral. Analyses included baseline differences, symptom trajectories for the CORE-34 Clinical Outcome Measure (CORE-OM) and engagement with the therapy. For the CORE 34 there was a clinically significant reduction in mean scores between assessment and discharge (p<0.001), with all outcomes demonstrating a large effect size. Future research needs to assess the long-term effects of help-seeking using a brief psychological intervention for young men in order to understand whether the effects of the therapy are sustainable over a period of time following discharge from the service.

Hospital-based programs democratize mRNA therapeutics by facilitating the processes to translate a novel RNA idea from the bench to the clinic. Because mRNA is essentially biological software, therapeutic RNA constructs can be rapidly developed. The generation of small batches of clinical grade mRNA to support IND applications and first-in-man clinical trials, as well as personalized mRNA therapeutics delivered at the point-of-care, is feasible at a modest scale of cGMP manufacturing. Advances in mRNA manufacturing science and innovations in mRNA biology, are increasing the scope of mRNA clinical applications.

This paper investigates a range of popular differential evolution (DE) configurations to identify components responsible for emergence of structural bias – a recently identified tendency of algorithms to prefer some regions of search space over others, for reasons unrelated to objective function values. Previous work has explored this tendency for genetic algorithms (GA) and particle swarm optimisation (PSO), finding a relationship between population size and extent of structural bias, hence highlighting potential weaknesses of those algorithms. In current article, we focus on DE, extend the investigation to include consideration of an algorithmic component that is often overlooked – constraint handling mechanism. Towards this end, a wide range of DE configurations was tested here. Results suggest that DE is generally robust to structural bias. Unlike the case with GA and PSO, population size seems to have no influence on DE structural bias. Only one of variants studied – DE/current-to-best/1/bin – shows clear signs of bias, however, we show that this effect is mitigated by a judicious choice of constraint handling technique. These findings contribute towards explaining widespread success of DE variants in algorithm comparison studies; its robustness to structural bias represents the absence of a factor that may confound other algorithms.

The rapid proliferation of Internet of Things (IoT) technology has ushered in a new era of connectivity, allowing billions of devices to produce extensive data. Significant challenges in software engineering need to be addressed for full utilization of Internet of Things (IoT) applications. This article examines the challenges in Internet of Things, software engineering and discusses current developments aimed at addressing these issues and seizing new opportunities. We examine the challenges associated with Internet of Things software development, including resource constraints, device diversity, security vulnerabilities, and data management concerns. New studies have indicated the use of advanced techniques like as containerization, model-driven development, AI and ML integration, and robust security frameworks. Moreover, the significance of system architecture is explained, including the device, network, and cloud layers, with an emphasis on the principles of reusability, scalability, and modularity. Results and conversations explore the complexities of security measures, emerging opportunities, and scaling solutions made possible by simulation tools, penetration testing, and cross-domain development platforms. The study expects IoT system evolution and emphasises the need for continual software and firmware changes, testing, and deployment.

In the field of industrial process monitoring, more and more interest is being shown in specific process categories. These include time-varying processes, that is, those processes whereby the response one receives as output from the system depends on when the input signal is sent into it. There are many reasons for this process variability and such contexts are not always analyzed with this operational characteristic at their core. At the same time, interest in certain categories of techniques is also becoming more prominent, to meet certain application needs. Among these, clustering and unsupervised techniques in general are gaining ground. This is largely due to the difficulty of finding fault data with which to train, for example, supervised models. On the other hand, the clustering technique, on which this contribution focuses, also makes it possible to compensate for the lack of complete knowledge of the structure of the process itself. With these two considerations in mind, this contribution proposes a literature review on the topic of clustering applied in time-varying contexts, in the maintenance field. The aim is to present an overview of the main fields of study, the role of clustering in this context and the main clustering techniques used.

Few studies have examined the pros and cons of integrated TB and HIV service delivery in public healthcare facilities, and even fewer have proposed conceptual models for improved integration. This study intends to fill that vacuum by outlining the development of a facility-based paradigm for integrating TB, HIV and patients services. The design of the proposed model were in stages that involved the evaluation of existing TB-HIV integration model and synthesis of both quantitative and qualitative data from the study sites which were selected public healthcare facilities at both rural and peri-urban settings in Oliver Reginald (O.R) Tambo District Municipality in Eastern Cape, South Africa. Secondary data on 2009-2013 TB-HIV clinical outcomes were obtained from multiple sources for quantitative analysis. Qualitative data involved focus group discussions among patient and heath care staff, which was thematically analysed. The development of a possibly better model and validation of this model show that the district's health system was reinforced by the model's guiding principles, which placed a strong emphasis on inputs, processes, outcomes, and integration effects.The model is adaptable to different healthcare delivery systems but will require support from healthcare stakeholders and professionals to be successful.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

Exploring new approaches to innovating higher vocational education is a worldwide pursuit driven by the increasing demand for graduates equipped with competencies. This study investigated how Outcomes-Based Education (OBE) influences the attainment of graduate competence (AGC) in China’s higher vocational education, using the theory of Constructive Alignment (CA) as its foundation. The OBE framework incorporates intended learning outcomes (ILOs), project-based learning (PjBL), assessment strategies (ASs), and AGC. The study tested the model by assessing the impact of ILOs, PjBL, and ASs on AGC, respectively and sequentially. Surveys were administered to 320 CBEC learners in April 2024, resulting in 301 usable responses. Data were analyzed using structural equation modeling (SEM) with SPSS 23.0 and AMOS 24.0. Results indicated that PjBL and ASs were directly and positively related to graduate competency attainment, while ILOs were indirectly associated with graduate competence through the mediating roles of PjBL and ASs. The most substantial mediating effect between ILOs and graduate competence was ASs, followed by PjBL, and then, the chain mediating effect of PjBL and ASs. These findings enhance the theoretical understanding of OBE and provide detailed procedures for tracking and enhancing sustainability in higher vocational education. Beneficiaries include educators, policymakers, and accreditation bodies.

Modern fighter aircraft increasingly need to conjugate aerodynamic performance and low observability. In this paper we showcase a methodology for a gradient-based bidisciplinary aero-stealth optimization. The shape of the aircraft is parametrized with the help of a CAD modeler, and we optimize with the SLSQP algorithm. The drag, computed with the help of a RANS method, is used as the aerodynamic criterion. The stealth criterion, a function is derived from the radar cross section in a given cone of directions, weighed with a function whose goal is to cancel the electromagnetic intensity in a given direction. Stealth is achieved passively by scattering back the electromagnetic energy away from the radar antenna and no energy is absorbed by the aircraft, which is considered as a perfect conductor. A Pareto front is identified by varying the weights of the aerodynamic and stealth criteria. The Pareto front allows for an easy identification of the CAD model corresponding to a chosen aero-stealth trade-off.

The correlations of seismic noise, recorded during 1997-2022 at 229 stations around the world are investigated. The property of waveforms, known as the Donoho-Johnston threshold, is analyzed, which separates the absolute values of the orthogonal wavelet coefficients into "small" and "large". Network of 50 reference points is considered. For each reference point, a time series with a time step of 1 day is calculated as the median of the values of the DJ index from the 5 nearest stations. For each reference point, the correlations between the values of the DJ index at other reference points are calculated in a sliding time window of 365 days. Average values of the DJ index decrease, which is interpreted as indicators of global seismic hazard increasing. The maximum distances between reference points with strong correlations show an explosive increase after 2011. Bursts in the mean coherences between the length of day (LOD) and the values of the DJ index at the reference points precede the release of seismic energy with a delay of about 530 days. High amplitude of the DJ index response to LOD for the times 2020-2022 was identified, which may forecast a strong earthquake in the second half of 2023.

Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.

Animal-based measures are the measure of choice in animal welfare assessment protocols as they can often be applied completely independently to the housing or production system employed. Although there has been a small body of work on potential animal-based measures for farmed crocodilians [1-3], they have not been studied in the context of an animal welfare assessment protocol. Potential animal-based measures, that could be used to reflect the welfare state of farmed crocodilians, were identified and aligned with the Welfare Quality® principles of good housing, good health, good feeding and appropriate behaviour. A consultation process with a panel of experts was used to evaluate and score the potential measures in terms of validity and feasibility. This resulted in a toolbox of measures being identified for further development and integration into animal welfare assessment on the farm. Animal-based measures related to ‘good feeding’ and ‘good health’ received the highest scores for validity and feasibility by the experts. There was less agreement on the animal-based measures that could be used to reflect ‘appropriate behaviour’. Where no animal-based measures were deemed to reliably reflect a welfare criterion nor be useful as a measure on the farm, additional measures of resources or management were suggested as alternatives. Future work in this area should focus on the reliability of the proposed measures and involve further evaluation of their validity and feasibility as they relate to different species of crocodilian and farming system.

The exploitation of lipid membranes in biosensors has provided the ability to reconstitute a considerable part of their functionality to detect trace of food toxicants and environmental pollutants. Nanotechnology enabled sensor miniaturization and extended the range of biological moieties that could be immobilized within a lipid bilayer device. This chapter reviews recent progress in biosensor technologies based on lipid membranes suitable for environmental applications and food quality monitoring. Numerous biosensing applications are presented, putting emphasis on novel systems, new sensing techniques and nanotechnology-based transduction schemes. The range of analytes that can be currently detected include, insecticides, pesticides, herbicides, metals, toxins, antibiotics, microorganisms, hormones, dioxins, etc. Technology limitations and future prospects are discussed, focused on the evaluation/ validation and eventually commercialization of the proposed sensors.

The exploitation of lipid membranes in biosensors has provided the ability to reconstitute a considerable part of their functionality to detect trace of food toxicants and environmental pollutants. Nanotechnology enabled sensor miniaturization and extended the range of biological moieties that could be immobilized within a lipid bilayer device. This chapter reviews recent progress in biosensor technologies based on lipid membranes suitable for environmental applications and food quality monitoring. Numerous biosensing applications are presented, putting emphasis on novel systems, new sensing techniques and nanotechnology-based transduction schemes. The range of analytes that can be currently detected include, insecticides, pesticides, herbicides, metals, toxins, antibiotics, microorganisms, hormones, dioxins, etc. Technology limitations and future prospects are discussed, focused on the evaluation/ validation and eventually commercialization of the proposed sensors.

The migration routes have facilitated the distribution of mammals from south east Asian mainland to the Sundaland including Java island in the early Pleistocene. One of species that has migrated through that route is antelope-like bovid Duboisia santeng. In the present study, the potential distribution areas and the suitable habitats of D. santeng have been projected and modeled. The modeled habitat was a forest river basin sizing 302.91 Ha in the central of Java island. The model has classified and reconstructed the habitat suitability ranged from low to high back to Pleistocene. The surrounding areas of forest were mostly classified as medium and low related to the limited tree covers. Most suitable habitats were identified in the middle of forest river basin where the tree covers were presented

China’s greenhouse industry has thousands of years of development history, although the development of modern greenhouses has come late. After decades of development of China, its greenhouse industry is in the forefront of the world. China’s greenhouse industry is experiencing rapid development, transformation and upgrading. China ranks first in the world in terms of greenhouse area, used mainly for vegetable cultivation and plays an important role in the utilization of agricultural resources and food production. In 2023, the cumulative area of new greenhouses nationwide was 1835.87 million ha. In this paper we present the latest developments in the greenhouse sector. More specifically, we present a greenhouse market development overview, the greenhouse production, the greenhouse industry, and technology development. Current problems are underpinned, development opportunities and prospects are analyzed, and measures and specific proposals are detailed for the technological development of the greenhouse sector both for increasing product quantity and quality.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

Background: Simulation plays a crucial role in health studies, as it helps medical students apply their theoretical knowledge in real-life situations. Moulage is one of the techniques that helps in making simulation more realistic or high-fidelity. It uses special effects to emulate wounds for a better understanding of what the wound is like visually. Still, moulage is expensive, time-consuming, resource-intensive, and requires the training of staff, which is why we need to find low-cost substitutes for moulage materials. Method: When searching the database “PubMed” for the terms “Low-cost and Medical moulage”, we retrieved 222 studies, out of which when excluding results not related to low-cost, we obtained 62 studies, from which when removing studies that do not contain information regarding moulage, we found two papers, after referring to citations and cited articles of those papers, we ended up with six studies. Based on the selected articles and additional articles sourced from their reference list, a total of 11 studies were included in the review. Results: We understand that moulage is a technique that helps make simulations come alive, but the resources required to use it are at times, expensive, which is why we need to find methods to do low-cost moulage, and many studies address that it can be as simple as using homemade ingredients. Students from a previous study have talked about their opinions regarding the realistic component of moulage and whether if it is any different from other moulages. Most of the students agreed that the moulage ranked well in face and content validity. However, further innovations must be introduced in the field to be widely spread and lead to newer opportunities. Conclusion: Although the research done under moulage is limited, it is accepted that moulage is helpful for simulation-based studies and that low-cost moulage can help make medical studies a better experience for students studying it. Students have a favorable opinion on the realistic aspect of the low-cost moulage applied to them. Newer methods can be introduced to moulage, and it can be implemented in low-income countries.

The eruption of COVID-19 patients in 215 countries worldwide has urged for robust predictive methods that can detect as early as possible the size and duration of the contagious disease and also providing precision predictions. In much recent literature reported on COVID-19, one or more essential parts of such investigation were missed. One of the crucial elements for any predictive method is that such methods should fit simultaneously as much data as possible; these data could be total infected cases, daily hospitalized cases, cumulative recovered cases, and deceased cases, and so on. Other crucial elements include sensitivity and precision of such predictive methods on the amount of data as the contagious disease evolved day by day. To show the importance of these aspects, we have evaluated the standard SIRD model and a newly introduced Gaussian-SIRD model on the development of COVID-19 in Kuwait. It is observed that the SIRD model quickly picks up the main trends of COVID-19 development, but the Gaussian-SIRD model provides precise prediction a longer period of time.

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

This survey provides a comprehensive overview of in-context learning (ICL) in large language models (LLMs), a phenomenon where models can adapt to new tasks without parameter updates by leveraging task-relevant information within the input context. We explore the definition and mechanisms of ICL, investigate the factors contributing to its emergence, and discuss strategies for optimizing and effectively utilizing ICL in various applications. Through a systematic review of recent literature, we first clarify what ICL is, distinguishing it from traditional fine-tuning approaches and highlighting its unique characteristics. We then delve into the underlying causes of ICL, examining theories ranging from implicit meta-learning during pre-training to the emergence of task vectors in LLMs. The survey also covers various approaches to enhance ICL performance, including prompt engineering techniques, demonstration selection strategies, and methods for improving generalization across diverse tasks. Additionally, we discuss the limitations and challenges of ICL, such as its sensitivity to demonstration ordering and potential biases. By synthesizing findings from numerous studies, we aim to provide researchers and practitioners with a clear understanding of the current state of ICL research, its practical implications, and promising directions for future investigation. This survey serves as a valuable resource for those seeking to leverage ICL capabilities in LLMs and contributes to the ongoing discourse on the remarkable adaptability of these models.

The effect of microstructure and chemistry of passive films on the kinetics of passive layer growth and passivity breakdown of some Ti-based alloys, namely Ti-6Al-4V, Ti-6Al-7Nb and TC21 alloys was studied. The rate of pitting corrosion was evaluated using cyclic polarization measurements. Chronoamperometry was applied to assess the passive layer growth kinetics and breakdown. Microstructure influence on the uniform corrosion rate of these alloys was also investigated employing Tafel extrapolation and dynamic electrochemical impedance spectroscopy. Corrosion studies were performed in 0.9% NaCl solution at 37 oC, and the obtained results were compared with ultrapure Ti (99.99%). The different phases of the microstructure were characterized by X-ray diffraction and scanning electron microscopy. Chemical composition and chemistry of the corroded surfaces were studied using X-ray photoelectron analysis. For all studied alloys, the microstructure consisted of α matrix, which was strengthened by β phase. The highest and the lowest values of the β phase’s volume fraction were recorded for TC21 and Ti-Al-Nb alloys, respectively. The uniform corrosion rate and pitting corrosion resistance (R_pit) of the studied alloys were enhanced following the sequence: Ti-6Al-7Nb < Ti-6Al-4V << TC21. The corrosion resistance of Ti-Al-Nb alloy approached that of pure Ti. The obvious changes in the microstructure of these alloys, together with XPS findings, were adopted to interpret the pronounced variation in their corrosion rates.

Background: The key objective of this research was to identify the dominant parameters and determine the patient status of pediatric patients with congenital heart disease in the ICU. The data we collected from the Congenital Heart Disease (CHD) Surgery Unit (ICU) in the National Heart Foundation Hospital and Research Institute, Dhaka, Bangladesh. Data from 300 ICU patients was collected and analyzed. We choose patients who are less than 12 years old at the time of ICU admission and whose ICU stay is longer than 24 hours. (2) Methods: According to the doctor’s advice, the dependent and independent variables are selected. The relationship between these parameters in the ICU was selected by linear modeling, which is associated with mortality risk. These parameters are used in the linear regression model and the ARIMA model for prediction. The prediction model is used for pediatric congenital heart disease patients in the ICU. (3) Results: The relation between CVP and BP (95% CI=0.2113,0.353 r=0.2841249), PEEP, and FiO2 (95% CI=0.6992,0.770 r=0.7367744) is statistically significant (p value&lt;0.001). pH and PO2 were statistically significant, but there were correlations (95% CI=0.161,0.308 r=0.2362575). The statistically significant (p &lt; 0.001) parameters were selected for prediction. In this study, BP, FiO2, and PO2 parameters were used in the linear regression model and the ARIMA model for prediction. We predict the next-hour patient’s status. The ARIMA model was found to have less error than the linear regression model. (4) Conclusions: This study helps identify the important parameters that are measured in the ICU to monitor patient status and predict the future value of those parameters, with the ultimate goal of providing physicians with an early signal of future patient conditions.

The current focus on afforestation in climate policy runs the risk of compromising both longterm carbon storage and human adaptation. It also works against efforts to stem the tide of biodiversity loss. We outline why an emphasis on diverse, intact natural ecosystems—as opposed to tree plantations with fast-growing exotic species—will help nations deliver the goals of the Paris Agreement and much more.

Preeclampsia is a multifactorial cardiovascular disorder diagnosed after 20 weeks of gestation that is the leading cause of death for both mothers and babies in pregnancy. The pathophysiology remains poorly understood due to variability and unpredictability of disease manifestation when studied in animal models. After preeclampsia, both mothers and offspring have a higher risk of cardiovascular disease (CVD) including myocardial infarction or heart attack and heart failure (HF). Myocardial infarction is an acute myocardial damage that can be treated through reperfusion, however, that therapeutic approach leads to ischemic/reperfusion injury (IRI) often leading to HF. In this review, we compared the current in vivo, in vitro and ex vivo model systems used to study preeclampsia, IRI and HF. Future studies aiming at evaluating CVD in preeclampsia patients could benefit from novel models that better mimic the complex scenario described in this article.

We develop an 1/n expansion method for the systematic approxi- mation of the solutions of classical spin systems (tri-dimensional vec- tors) which can describe general spin-spin interactions and phases with complex magnetization pattern. This method is applied to study the phase transitions observed in pyrochlore crystals (e.g. Gd₂ Ti₂ O₇ ) and the results have qualitative agreement with the experimental ones.

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

The growing population with changing consumption patterns in developing countries is causing significant challenges with regards to solid waste management. Plastic wastes are particularly problematic, with single-use plastics leaking into the environment, including the marine environment, at an unprecedented rate. Approximately 80 percent of ocean plastics come from land-based sources or about 8 million tons are dumped into the ocean every year. Thailand was identified as one of the top 10 countries ranked by mass of mismanaged plastic wastes, with more than 60,000 tons per year entering the ocean through multiple outlets, including rivers. In the environment, plastic wastes can be degraded into small sizes (less than 5 mm), called microplastics (MPs), which could contaminate the ecosystems and the food chain, including foodstuffs and water supply. Tap water samples collected from a water supply treatment plant and Academic Institutions 1 and 2 in central Thailand were found to contain MPs about 304 ±90, 270 ±109 and 386 ±102 particles/L, respectively. In addition, MPs concentrations of 211 ±70 and 122 ±60 particles/L were also found in drinking water samples collected from commercial bottled water and membrane filtration water, respectively. The MPs sizes of 0-50 µm were most abundant in both the tap and drinking water samples whose shapes were mainly fragments and fiber. These data indicated potential health risks to the people who consume these water sources, and recommendations for health impacts minimization were proposed.

Most recent university campuses follow the North American model, built on city limits or countryside, with large separate buildings in open green spaces. Studies suggest that prevalence and severity of mental health issues among university students are increasing over the past decade in most countries. University services were created to face this growing problem, however individual based interventions have limited effects on mental health and well-being of a large population. Our aim was to verify if and how the natural environment in Campuses is focused on programs to cope with the issue of mental health and well-being among students. Systematic review of literature was undertaken with search in Scopus and LILACS with the keywords “green areas” AND “well-being” AND “Campus”, following PRISMA guidelines. As a result, 32 articles were selected. Research on the topic is recent, mostly in the USA, Bulgaria, and China. Most studies used objective information on campuses’ greenness and/or university students’ perception. Mental health was usually measured by validated scores. Findings of all the studies indicated positive association between campus greenery and well-being of students. We conclude that there is a large potential for use of university campuses in programs and sites for students´ restoration and stress relief.

Innovation is rapidly growing and affecting various industries, including hydrocarbon processing. This study aims to conduct a comprehensive and organized review of the literature on innovation ecosystems and their performance. It will examine existing definitions of innovation ecosystems and related concepts and identify metrics and indicators for measuring innovation and entrepreneurial ecosystems. The term "innovation ecosystem" has gained considerable attention from scholars and practitioners over the past fifteen years. Despite the proliferation of research in this area, there are concerns about its fragmented knowledge base. While previous reviews have highlighted the theoretical connections between innovation ecosystems and related concepts, there is still a need for a more comprehensive understanding of the current state of innovation ecosystem research. The study used a systematic literature review approach that combines bibliographic coupling and content analysis methods, drawing on over 100 studies to identify five streams of current innovation ecosystem research: technology innovation, platform innovation ecosystems, regional development, innovation ecosystem conceptualization and theorization, and entrepreneurship and innovation. The study's contribution lies in decoding the intellectual structure of current innovation ecosystem research and providing targeted recommendations for future research.

The growth and dynamics of multicellular tissues involve tightly regulated and coordinated morphogenetic cell behaviours, such as shape changes, movement, and division, which are governed by subcellular machinery and involve coupling through short- and long-range signals. A key challenge in the fields of developmental biology, tissue engineering and regeneration is to understand how relationships between scales produce emergent tissue-scale behaviours. Recent advances in molecular biology, live-imaging and ex vivo techniques have revolutionised our ability to study these processes experimentally. To fully leverage these techniques and obtain a more comprehensive understanding of the causal relationships underlying tissue dynamics, computational modelling approaches are increasingly spanning multiple spatial and temporal scales, and are coupling cell shape, growth, mechanics and signalling. Yet such models remain technically challenging: modelling at each scale requires different areas of technical skills, while integration across scales necessitates the solution to novel mathematical and computational problems. This review aims to summarise recent progress in multiscale modelling of multicellular tissues and to highlight ongoing challenges associated with the construction, implementation, interrogation and validation of such models.

Background: Lung cancer is still the most lethal malignancy in the world from the report of Cancer Statistics in 2021. Platinum-based chemotherapy combined immunotherapy is the first-line treatment in lung cancer patients. However, the 5-year survival rate always affected by the adverse reaction and drug resistance caused by platinum-based chemotherapy. DNA damage and repair system is one of the important mechanisms which can affect the response to chemotherapy and clinical outcome in lung cancer patients. Objective: The objective of this study is to find the relationship between the polymorphisms of DNA repair genes with the prognosis in platinum-based chemotherapy in lung cancer patients. Patients and Methods: We performed genotyping in 17 single nucleotide polymorphisms (SNPs) of Excision Repair Cross-Complementation group (ERCC) genes and X-ray Repair Cross-Complementing (XRCC) genes of 345 lung cancer patients by Sequenom MassARRAY. We used Cox proportional hazard models, state and plink to analyze the associations between SNPs and the prognosis of lung cancer patients. Results: We found that the ERCC5 rs873601 was associated with the overall survival time in lung cancer patients treat by platinum-based chemotherapy (p=0.031*). We also discovered that the polymorphisms in rs873601 was significantly associated with the prognosis in age more than 55 years, Small Cell Lung Cancer (SCLC) and smoking patients, Long Intergenic Non-protein Coding RNA (PNKY) rs2444933 in age less than 55 years, SCLC, metastasis and stage III/IV/ED patients, Short Tau Inversion Recovery (STIR1) rs3740051 in SCLC and metastasis patients, PNKY rs1869641 in SCLC patients, XRCC5 rs1051685 in non-metastasis patients, respectively. Conclusion: The ERCC5 rs873601(G>A) maybe a valuable biomarker for predicting the prognosis in lung cancer patients treated with platinum-based chemotherapy. Statements and Declarations: The authors declare that they have no conflict of interest.

The RMA11 water quality model, which has been previously calibrated and validated, was coupled with the RMA10 3D hydrodynamic model to evaluate sanitation scenarios in Buenaventura Bay. The bay receives direct discharge of untreated wastewater from 500,000 people through 695 outlets along the coast. Five different effluent reduction scenarios were proposed and compared based on fecal coliform concentration as an indicator. The areas where values exceed the standard for primary contact were also used to evaluate the scenarios. The model results indicate poor water quality in the bay and how that the situation will become even more severe if immediate action is not taken. The proposed reduction in discharges in stages will have temporarily more severe effects than the current situation, but these will end when the treatment plant becomes operational. However, even with the plant in operation, complete sanitation of the bay cannot be achieved, and further measures must be considered.

This study aimed to evaluate the effectiveness of decellularized nerves for promoting nerve regeneration by transplanting them from the sciatic nerve of a rabbit to that of a rat. The Sciatic nerves were harvested from New Zealand White Rabbits (n=4). A 1-cm segment of each nerve was decellularized by Sondell’s protocol and preserved at –4℃ in Roswell Park Memorial Institute (RPMI) 1640 medium for one week. Subsequently, a defect of a similar size was created in the sciatic nerves of rats. The decellularized nerves treated with detergent were then transplanted into these defects in the rats. The study included the three following groups: Group 1 (n=4) was the decellularized xenogeneic nerve group; Group 2 (n=7) was the negative control group in which the defect remained un-repaired; and Group 3 (n=5) was composed of the positive control group where the nerve was immediately transplanted from one rat to another. The sciatic functional index (SFI) was considered to evaluate the motor function by comparing it with the normal side at 1, 2, 3, and 4 weeks, and 1, 2, and 3 months postoperatively. Furthermore, electrophysiological and immunohistochemical assessments were conducted at 4 months. Immunohistochemically, at 16 weeks postoperative, the experimental group did not show statistically significant differences compared to controls in terms of schwann cell comparison, yet exhibited favorable outcomes when compared to the positive control group. Additionally, analysis of motor function test confirmed recovery of latency and nerve conduction velocity to levels similar to those of the normal side. While the nerve allograft demonstrated superior nerve regeneration in motor function test at 3 months, markedly low values were observed in all groups. When reconstructing peripheral nerves using detergent-based decellularized xenogenic nerves, analysis of immuno-histochemistry and motor nerve conduction study reveals a promising degree of nerve regeneration. However, when compared with fresh allograft group at 3 months after operation, the sciatic functional index was lower. Significantly lower values observed across all groups compared to previous assessments indicate the need for further research into potentially influential alternative factors.Histologically, at 16 weeks postoperative, the experimentally treated group did not show statistically significant differences compared to the controls in terms of a Schwann cell comparison, but exhibited favorable outcomes when compared to the positive control group. Additionally, an analysis of the motor function testing confirmed recovery of the latency and nerve conduction velocity to levels similar to those of the normal side. While the nerve allograft demonstrated superior nerve regeneration in the motor nerve examination at 3 months, notably low values were observed in all the groups. When reconstructing the peripheral nerves using detergent-based decellularized xenogeneic nerves, an analysis of immunohistochemistry and motor nerve conduction study reveals a promising degree of nerve regeneration. However, when compared with the fresh allograft group at 3 months post-operation, the sciatic functional index was lower. The significantly lower values observed across all groups compared to those of the previous assessments indicate the need for further research of potentially influential alternative factors.

In the software development process, the decision-maker (DM) has a range of problems inherent to its function. Wrong choices during software planning can bring great risk to the project. Therefore, the planning of software releases to be delivered to the customer should be well done. This is not an easy task because releases are made up of many requirements that contain complex variables that must be considered, such as precedence, cost, requirement stability, among other features that make the requirements-selection process challenging. To make this process less exhaustive, DM can use tools that facilitate this work. In software engineering, we can find fields of research specialists in this context, such as Search-Based Software Engineering (SBSE). The SBSE makes use of advanced metaheuristics to search for optimal solutions or the closest to it. In this work, we try to use another field of research to solve this same problem type, the Verbal Decision Analysis (VDA). To do this, we elaborate a workflow that will use the same source data, execute two solutions using the two search fields (SBSE and VDA) and compare the results. In the end, we evaluated and commented on the results.

Hot rolling tests of a low-alloy steel were conducted at a rolling temperature of 850 ℃ under different lubrication conditions, including benchmarks (dry condition and water) and water-based nanolubricants containing different concentrations of nano-TiO₂ from 1.0 to 8.0 wt%. The effects of nanolubricants on rolling force, surface roughness, thickness of oxide scale and microstructure were systematically investigated through varying nano-TiO₂ concentrations. The results show that the application of nanolubricants can decrease the rolling force, surface roughness and oxide scale thickness of rolled steels, and refine ferrite grains. In particular, the nanolubricant containing an optimal concentration (4.0 wt%) of nano-TiO₂ demonstrates the best lubrication performance, owing to the synergistic effect of lubricating film, rolling, polishing and mending generated by nano-TiO₂.

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

Symmetry concepts in parametrized dynamical systems may reduce the number of external parameters by a suitable normalization prescription. If, under the action of a symmetry group G, parameter space A becomes a (locally) trivial principal bundle, A ~ A/G x G, then the normalized dynamics only depends on the quotient A/G. In this way, the dynamics of fractional variables in homogeneous epidemic SI(R)S models, with standard incidence, absence of R-susceptibility and compartment independent birth and death rates, turns out to be isomorphic to (a marginally extended version of) Hethcote's classic endemic model, first presented in 1973. The paper studies a 10-parameter master model with constant and I -linear vaccination rates, vertical transmission and a vaccination rate for susceptible newborns. As recently shown by the author, all demographic parameters are redundant. After adjusting time scale, the remaining 5-parameter model admits a 3-dimensional abelian scaling symmetry. By normalization we end up with Hethcote's extended 2-parameter model. Thus, in view of symmetry concepts, reproving theorems on endemic bifurcation and stability in such models becomes needless.

Head and neck cancer is the sixth most frequent cancer type. Drug resistance and toxicity are common challenges of the existing therapies, making the development of reliable preclinical models essential for the study of the involved molecular mechanisms as well as for eventual intervention approaches that improve the clinical outcome. Preclinical models of head and neck squamous cell carcinoma have been traditionally based in cell lines and murine models. In this review, we will go over the most frequently used preclinical models, from immortalized-cell and primary tumour cultures in monolayer or 3D, to the currently available animal models. We will scrutinize their efficiency in mimicking the molecular and cellular complexity of head and neck squamous cell carcinoma. Finally, the challenges and opportunities of other envisaged putative approaches, as well as the potential of the preclinical models to further develop customized therapies will be discussed.

Flooding during the rainy season poses a significant threat to the city of Hargeisa, impacting the well-being of both its residents and the local ecosystems. This research focuses on urban flood control, with a specific emphasis on finding sustainable solutions for managing large stream in Hargeisa. The study identifies five key dimensions, including infrastructure, transportation, environmental soil, life, and property, as critical factors in addressing the issue. By employing a comparative method and collecting data through closed-ended questions, the research aims to analyze and weigh eleven potential solutions, seeking to identify the most effective approaches. The findings of the study indicate that constructing a dam emerges as a promising and sustainable solution, with a weightage of 70 percent. This highlights the potential of a dam in mitigating the impact of floods in the region. Additionally, considering the government's economic capacity, the implementation of check dams is identified as another viable option, with a weightage of 95 percent for reducing the flood power of the stream. These solutions offer practical and effective measures to address the recurring problem of flooding in Hargeisa. To support future decision-making and planning, the research underscores the need for establishing a comprehensive rainfall data database. This database would store relevant information on rainfall patterns, enabling better understanding and prediction of flood events. The study emphasizes the importance of collaboration between local government authorities, environmental organizations, and stakeholders to develop and implement effective flood control strategies. This research provides a systematic approach that can inform and benefit regional and global researchers, policymakers, and municipal governments in their efforts to address urban flood control challenges, not only in Hargeisa but also in similar contexts worldwide.

Alberta Cancer Exercise (ACE) is an exercise oncology program that transitioned from in-person to online delivery during COVID-19. The purpose of this work was to understand participants’ experiences in both delivery modes. Specifically, survivors' exercise facilitators and barriers, delivery mode preference, and experience with program elements targeting behaviour change were gathered. A retrospective cohort design using explanatory sequential mixed methods was used. 57 participants completed a survey, and 19 subsequent, optional interviews were conducted. Most participants indicated preferring in-person programs (58%), followed by online (32%), and no preference (10%). There were significantly fewer barriers (i.e., commute time) (p<0.01), but also fewer facilitators (i.e., social support) (p<0.01), to exercising online. Four themes were generated from the qualitative data surrounding participant experiences in both delivery modes. Key differences in barriers and facilitators highlighted a more convenient experience online relative to a more socially supportive environment in-person. For future work that includes solely online, focusing on building social support and a sense of community will be critical to optimizing program benefits. Beyond the COVID-19 pandemic, results of this research will remain relevant as we aim to increase the reach of online exercise oncology programming to more underserved populations of individuals living with cancer.

In this study, graphite, the most stable form of carbon, was examined for its hexagonal crystalline structure with specific dimensions (ao=2.46 Ǻ; co=6.70 Ǻ). Its framework comprises parallel carbon atom planes forming regular hexagons (side length 1.415 Ǻ) and 120° angles between adjacent atoms. Two structural variations exist: hexagonal symmetry (1-2-1-2-1-2 planes) and rhomboidal symmetry (1-2-3-1-2-3 planes). The research aimed to produce high-density graphite utilizing carbonaceous raw materials. Graphite-based materials often exhibit high porosity, necessitating additional treatments. The study successfully obtained mesophase tar pitch (yield: 45%), a pivotal raw material, and high-density graphite. The resulting graphite underwent characterization for physical properties (apparent and real density, porosity and compression strength), demonstrating conformity with existing literature data.

After the great success of Mobile wallet, the Internet of Things (IoT) leaves the door wide open for consumers to use their connected devices to access their bank accounts and perform routine banking activities from anywhere, anytime and with any device. However, consumers need to feel safe when interacting with IoT-based payment systems, and their personal information should be protected as much as possible. Unlike as usually done in the literature, in this paper, we introduce two lightweight and secure IoT-based payment protocols based on an identity-based signature scheme. We adopt a server-aided verification technique to construct the first scheme. This technique allows to outsource the heavy computation overhead on the sensor node to a cloud server while maintaining the user's privacy. The second scheme is built upon a pairing-free ECC-based security protocol to avoid the heavy computational complexity of bilinear pairing operations. The security reduction results of both schemes are held in the Random Oracle Model (ROM) under the discrete logarithm and computational Diffie-Hellman assumptions. Finally, we experimentally compare the proposed schemes against each other and against the original scheme on the most commonly used IoT devices: a smartphone, a smartwatch and the embedded device Raspberry Pi. Compared with existing schemes, our proposed schemes achieve significant efficiency in the term of communication and computational overheads

Green ICT has received significant attention in organizations to reduce global warming since last decade. Several maturity models have been proposed for tracking green ICT practices in organizations. Current literature shows that SMEs around the world try to follow some common strategies such as virtualization, consolidation of devices, energy efficiency and disposal of ICT equipment for greening ICT. The increasing interest for green ICT practices in organizations is not only due to the desire to attain environment-friendly atmosphere but mainly because of sustaining business goals such as cost reduction, competitive advantages and stakeholders’ pressure. Nevertheless, due to the lack of green ICT knowledge, organizations, specially SMEs, tend to ignore those practices. Therefore, this research proposes a framework that provides combination of existing green and sustainable ICT maturity models by mapping them with the strategies the businesses are already following. Out of this framework, a web application has been developed that provides questionnaire for SMEs to identify their present situation of green ICT practices and guidelines for improvement. The results have been analyzed by testing the application in some SMEs of Finland and Bangladesh. Finally, a survey has been conducted to attain SMEs’ perception about the possibility of sustainable development of businesses through this application.

Staudinger taught us that macromolecules were made up of the covalently bonded monomer repeat units chaining up as polymer chains. The chemical nature of the monomer directed the type of covalent bonds conferring most of the specific properties of the polymer. The more the number of repeat units the longer the chains and the more the possibility for the chains to assume a variety of shapes, from an extended elongated one to a more compact coiled one. Also, the chemical process that resulted in the synthesis of macromolecules produced many chains, often not with the same shape or size. The properties of the polymers improved when the chains became longer but it was more difficult to process them: their viscosity increased with molecular weight; viscosity was no longer an intensive property like it was for small liquids. The main question raised in polymer physics remains: how do these long chains interact and move as a group when submitted to shear deformation at high temperature when they are viscous liquids? This question is debated in a field of polymer physics called RHEOLOGY, whose purpose is to understand the viscoelastic aspects of polymer melts deformation. The current consensus is that we need to distinguish two cases: the deformation of “un-entangled chains” for macromolecules with molecular weight, M, smaller than Me, “the entanglement molecular weight”, and the deformation of “entangled” chains for M > Me. Several eminent scientists have extensively studied these 2 cases over the last 70 years. Paul J. Flory, in 1974, and Pierre-Gilles de Gennes, in 1991, have been awarded the Nobel Price in Chemistry and Physics, respectively, for their significant theoretical contribution to understand these challenging problems. For both of these authors the properties of polymers derive from the statistical characteristics of the macromolecule itself, the designated statistical system that defines the thermodynamic state of the polymer. Me, the molecular weight between entanglements, is defined from the rubber elasticity theory and is known to be equal to Mc/2 where Mc is the molecular weight for the entanglements when viscosity measurements are made. The current paradigm is that the viscoelasticity of un-entangled melts ( M < Mc) is well described by the Rouse model and that the entanglement issues raised by the impact of the increase of the length of the macromolecules on the melt viscoelasticity, when M > Mc, are well understood by the reptation model introduced by de Gennes and co-workers. Both models can be classified in the category of “chain dynamics statistics”. In this paper we examine in details the failures and the current challenges facing the current paradigm of polymer rheology: the Rouse model for M < Mc, the reptation model for M > Mc, the time-temperature superposition principle, the strain induced time dependence of viscosity, shear-refinement and sustained-orientation. The basic failure of the current paradigm and its inherent inability to fully describe the experimental reality is documented in this paper. In the discussion and conclusion of the paper we suggest that a different solution to explain the viscoelasticity of polymer chains and of their “entanglement” is needed. This requires a change of paradigm to describe the dynamics of the interactions within the chains and across the chains. A brief description of our currently proposed open dissipative statistical approach, “the Grain-Field Statistics”, is presented.

The comparison of group means in item response models constitutes an important issue in empirical research. The present article discusses an extension of Haebara linking by proposing a flexible class of robust linking functions for comparisons of many groups. These robust linking functions are particularly suited to item response data that are generated under partial invariance. In a simulation study, it is shown that the newly proposed robust Haebara linking approach outperforms existing approaches of Haebara linking. In an empirical application using PISA data, it is illustrated that country means can be sensitive to the choice of linking functions.

The Sustainable Development Goals (SDGs) raise quality employability, gender equity in access to employment and increase coverage in education, however, in Colombia, high unemployment rates and informality of young people are risks of fulfilling these objectives. This is verified by a study with cohorts on access to employment, labor mobility and entry to quality occupations, through the use of probabilistic models. This study found that young people are less likely to be employed than adults, education has increased in the new generations and allowing them to enter a higher quality occupation, job segmentation and lack of experience of young people are the main cause of unemployment and Women are less likely to be employed than men and to do so in quality jobs. To comply with the SDGs, you must increase coverage in education, make a differential gender policy and expand programs as learners and insert dual education.

Seroconversion surveys of anti-SARS-CoV-2 antibodies provide accurate estimates of the SARS-CoV-2 infection. This nationwide population-based cross-sectional serosurvey aimed to evaluate the prevalence of SARS-CoV-2 antibodies among residents in Gabon and compare the estimated cumulative number of COVID-19 cases with the officially registered number of laboratory-confirmed cases up to December 2021. Households in each province were randomly selected. Twenty-eight localities including 16 urban and 12 rural, were randomly selected to participate in the study. Whole blood samples were collected in dry tubes from all study participants around the country within 15 days. Serum samples were used to measure the total Immunoglobulin antibodies. Overall, data of 1672 households were analyzed. Out of the 3659 participants, 3175 were found positive for SARS-CoV-2 antibodies, resulting in a crude seroprevalence of 86.77%. Stratification of study participants by age group has shown highest seroprevalences in 20–29 and 40-49 age groups with 91.78% (95% CI:89.5–93.6) and 91.42% (95% CI:88.7–93.5), respectively. Nyanga province had the lowest prevalence (72,8%), and Estuaire and Ogoué-Lolo provinces had the highest prevalence (90 and 92%, respectively). Our results suggest a high transmission rate in the Gabonese population, 21 months after the first SARS-CoV-2 case in the country. This high seroprevalence estimate could indicate that the applied infection control measures may not have been adequately implemented or appropriately adhered to by the population.

In Model-Driven Development (MDD), the models, their generation, and imposing changes on them (model transformation) are used for the development of software. Models provide a framework to start from the imagination and abstraction to create and accomplish the final system. Models create a slow and steady transition from whatness to howness, i.e. from the natural path of the generation of software. For supporting this path, the Logic and Functionality of software must be changeable during its evolution. Here we provide a brief introduction to the concept of Model Driven Development.

This paper provides a retrospective analysis of published COVID-19 data and focuses on Costa Rica as an example of data analysis and validation in developing countries. The COVID-19 outbreak has highlighted the importance of robust health systems and adequate resources to test and manage diseases. However, due to limited resources and excessive burdens, developing countries face unique challenges in this regard. This makes it difficult to conduct widespread testing. The lack of testing kits, laboratory facilities, trained personnel, and the necessary infrastructure is a significant barrier. Additionally, logistical issues such as transportation and distribution of testing kits in remote areas further complicate the situation. On the other hand, universal healthcare in developing countries, despite the lack of extensive testing, can still provide reliable data on the spread and impact of the disease. Universal healthcare can lead to better records keeping and data collection, as all individuals, regardless of their economic status, have access to medical services. This inclusivity ensures a more comprehensive and representative data set. Therefore, while testing is an important tool in managing the pandemic, universal healthcare systems can still provide valuable insights in its absence. This description can be applied to the Costa Rica COVID-19 scenario: lack of resources for an adequate testing methodology but a universal healthcare system that ensures inclusivity. In this work, we show that the number of active cases of COVID-19 for Costa Rica does not provide reliable information that can be used to adjust the parameters of a mathematical model, so the effectiveness of the contention measures cannot be precisely stated. However, the number of hospitalizations is more reliable in the centralized and universal healthcare system, but quantifying the effectiveness of such measures requires a different set of tools. Traditionally, most pandemic data analysis is based on the estimation of the Rt number based on the number of active cases. We propose a methodology based on digital signal processing principles and elementary differential calculus. To our knowledge, these kinds of methodologies have not been of widespread use in pandemic data analysis, and, as we show, they may provide a valuable descriptive analysis of pandemic tendencies and they are an accessible, fast and reliable decision-making tool.

This study investigates the application of frequency augmentation techniques to improve condition-based maintenance (CBM+) systems for airborne weapon systems, focusing on the predictive accuracy of velocity and acceleration parameters. Utilizing MIL-STD-1553B MUX data from 138 sorties of fixed-wing aircraft, we explored six augmentation methods, including traditional interpolations (linear, quadratic, cubic spline) and advanced machine learning models (K-Nearest Neighbor, LSTM, and Bi-LSTM). Our findings indicate that while traditional methods like linear interpolation are more effective for velocity parameters, advanced ML techniques, particularly Bi-LSTM, provide better results for acceleration parameters, which exhibit more complex and rapid variability. The study underscores the necessity of tailoring augmentation approaches to specific parameter characteristics and highlights the potential of ML models in capturing intricate patterns within time-series data. These insights are critical for advancing CBM+ systems in military avionics, enhancing reliability and operational efficiency through improved data processing strategies. Future work should explore more performant augmentation techniques, such as by integrating multi-dimensional datasets, and explore ways to develop automated tools for a more powerful predictive maintenance framework.

The combined efect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong detterminant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web-platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and non-local complementarity (EC) in PPIs using the Complementarity Plot. It further derives an AI-based ∆G_binding with a prediction accuracy comparable to the state-of-the-art. This book-chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Nanoparticle (NP) transport is increasingly relevant to subsurface engineering applications such as aquifer characterization, fracture electromagnetic imaging and environmental remediation. An efficient field-scale simulation framework is critical for predicting NP performance and designing subsurface applications. In this work, for the first time, a streamline-based model is presented to simulate NP transport in field-scale subsurface systems. It considers a series of behaviors exhibited by engineered nanoparticles (NPs), including time-triggered encapsulation, retention, formation damage effects and variable nanofluid viscosity. The key methods employed by the algorithm are streamline-based simulation (SLS) and an operator-splitting (OS) technique for modeling NP transport. SLS has proven to be efficient for solving transport in large and heterogeneous systems, where the pressure and velocity fields are firstly solved on underlying grids using finite-difference (FD) methods. After tracing streamlines, one-dimensional (1D) NP transport is solved independently along each streamline. The adoption of OS enhances flexibility for the entire solution procedure by allowing different numerical schemes to solve different governing equations efficiently and accurately. For the NP transport model, an explicit FD scheme is used to solve the advection term, an implicit FD scheme is used for the diffusion term and an adaptive numerical integration is used to solve the retention terms. The model is implemented in an in-house streamline-based code, which is verified against analytical solutions, a commercial FD reservoir simulator (ECLIPSE) and an academic FD colloid transport code (MNMs). For a 1D homogeneous case, the effluent breakthrough curves (BTC) produced by the in-house simulator are in good agreement with the analytical solution and MNMs, respectively. For a two-dimensional (2D) heterogeneous case, the BTC and concentration pattern of the in-house simulator all match well with the solution produced by commercial simulator. Simulations on a synthetic three-dimensional (3D) nanocapsule application engineering design case, are performed to investigate the effect of fluid and NP properties on the displacement pattern of an existing subsurface fluid.

Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS in quantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindrical characteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and forest stands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99-1.22 cm for diameter at breast height (Δdbh), 44.14-55.49 cm2 for basal area (Δg), and 1.91-4.85 m for tree height (Δh). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60-1.13 cm was recorded for changes in basal area-weighted mean diameter (ΔDg), 0.81-2.26 m for changes in basal area-weighted mean height (ΔHg), 1.40-2.34 m2/ha for changes in mean basal area (ΔG), and 74-193 n/ha for changes in the number of trees per hectare (ΔTPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.

An increase of seismic activity is often observed before large earthquakes. Events responsible for this increase are usually named foreshock and their occurrence probably represents the most reliable precursory pattern. Many foreshocks statistical features can be interpreted in terms of the standard mainshock-to-aftershock triggering process and are recovered in the Epidemic Type Aftershock Sequence ETAS model. Here we present a statistical study of instrumental seismic catalogs from four different geographic regions. We focus on some common features of foreshocks in the four catalogs which cannot be reproduced by the ETAS model. In particular we find in instrumental catalogs a significantly larger number of foreshocks than the one predicted by the ETAS model. We show that this foreshock excess cannot be attributed to catalog incompleteness. We therefore propose a generalized formulation of the ETAS model, the ETAFS model, which explicitly includes foreshock occurrence. Statistical features of aftershocks and foreshocks in the ETAFS model are in very good agreement with instrumental results.

Corporate Diagnosis is now recognized as an important tool by decision makers to predict and correct burgeoning problems that a corporation may face. Methods based on this model stem from the use of mathematics and are increasingly being applied in the analysis of production processes. The goal of this paper is to use a logistic regression to design a scoring model for non-financial corporations in industry. Based on the data obtained from the Registry of the Slovak Republic for 738 non-financial corporations, according to SK NACE 26, SK NACE 27, the proportional financial metrics, using the logistic regression method, were calculated. By applying these methods, two logistic regression models were found to reliably estimate the probability of bankruptcy for a firm.

: The significance of heating load (HL) accurate approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are through synthesizing multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Assuming a Winterberg model for space where the vacuum consists of a very stiff two component superfluid made up of positive and negative mass planckions, theory is the hypothesis, that Planck charge, , was created at the same time as Planck mass. Moreover, the repulsive force that like-mass planckions experience is, in reality, due to the electrostatic force of repulsion between like charges. These forces also give rise to what appears to be a gravitational force of attraction between two like planckions, but this is an illusion. In reality, gravity is electrostatic in origin if our model is correct. We determine the spring constant associated with planckion masses, and find that, , where equals Apery's constant, , and, , is the relaxed, i.e., , number density of the positive and negative mass planckions. In the present epoch, we estimate that, equals, , and the relaxed distance of separation between nearest neighbor positive, or negative, planckion pairs is, These values were determined using box quantization for the positive and negative mass planckions, and considering transitions between energy states, much like as in the hydrogen atom. For the cosmos as a whole, given a net smeared macroscopic gravitational field of, , due to all the ordinary, and bound, matter contained within the observable universe, an average displacement from equilibrium for the planckion masses is a mere , within the vacuum made up of these particles. On the surface of the earth, where, , the displacement amounts to, . All of these displacements are due to increased gravitational pressure within the vacuum, which in turn are caused by applied gravitational fields. The gravitational potential is also derived and directly related to gravitational pressure.

Soft-matter systems when driven out-of-equilibrium often give rise to structures that usually lie in-between the macroscopic scale of the material and microscopic scale of its constituents. In this paper we review three such systems, the two-dimensional square-lattice Ising model, the Kuramoto model and the Rayeligh-Bénard convection system which when driven out-of-equilibrium give rise to emergent spatio-temporal order through self-organization. A common feature of these systems is that the entities that self-assemble are coupled to one another in some way, either through local interactions or through a continuous media. Therefore, the general nature of non-equilibrium fluctuations of the intrinsic variables in these systems are found to follow similar trends as order emerges. Through this paper, we attempt to look for connections between among these systems and systems in general which give rise to emergent order when driven out-of-equilibrium.

Malnutrition and dental caries in early childhood remain persistent and intertwined global health challenges, particularly for indigenous and geographically-remote populations. To examine the prevalence and associations between early childhood dental caries, parent-reported mouth pain and malnutrition in the Amazonian region of Ecuador, we conducted a cross-sectional study of the oral health and nutrition status of 1,407 children from birth through age 6 in the “Alli Kiru” program (2011-2013). We used multivariate regression analysis to examine relationships between severe caries, parent-reported mouth pain measures, and nutritional status. The prevalence of dental caries was 65.4%, with 44.7% of children having deep or severe caries, and 33.8% reporting mouth pain. The number of decayed, missing and filled teeth dmft) increased dramatically with age. Malnutrition was prevalent, with 35.9% of children stunted, 1.1% wasted, 7.4% underweight, and 6.8% overweight. As mouth pain increased in frequency, odds for severe caries increased. For each unit increase in mouth pain frequency interfering with sleeping, children had increased odds for underweight (AOR: 1.27; 95% CI: 1.02 – 1.54) and decreased odds for overweight (AOR: 0.76; 95% CI: 0.58 – 0.97). This relationship was most pronounced among 3-6 year-olds. Early childhood caries, mouth pain and malnutrition were prevalent in this sample of young children. Parent-reported mouth pain was associated with severe caries, and mouth pain interfering with sleeping was predictive of poor nutritional status. We demonstrate the utility of a parsimonious parent-reported measure of mouth pain to predict young children’s risk for severe early childhood caries and malnutrition, which has implications for community health interventions.

Background: It is clear that breastfeeding is the gold standard of infant feeding because of the many advantages it offers to both the child and the mother. Objective: to identity the main reasons for cessation breastfeeding declares by the mother themselves during the first year. Design: A prospective cohort study was conducted, recruiting 969 newborns in a third level hospital in Spain. The main maternal variables studied were: maternal age, parity, educational level, work occupation, smoking habit, gestational age, birth, weigh, feeding type, and duration of breastfeeding. All the participants were followed for a year to determinate the duration of breastfeeding and to know the reason of the abandonment. Results: At 6 months, the percentage of maternal lactation was cut in half and only 24.6% of these mothers maintain. Mainly 15.80% of the mothers decide to give up the exclusive maternal lactation of their own free desire, and 15.41% because they suspect hypogalactia. The work cause is the third reason of abandonment in both cases. Conclusions: Our results show the need to improve the health policies of promotion, protection and support the initiation of breastfeeding. In particular, our results show the importance of the work factor with particular emphasis on improving conciliation measures.

Several empirical studies indicate that the lack of financing is one of the main barriers that affects the economic growth of small and medium enterprises (SMEs). The main objective of this investigation was to determine to what extent the economic sector; enterprise size; the characteristics inherent to the enterprise; legal status; the variables linked to the performance of the enterprise; and the attributes of the owner influence the access to bank financing of SMEs in Mexico. Using a discrete response probit regression model, the impact of enterprises characteristics on the probability of obtaining a bank loan was determined. The data collected is from the Enterprise Surveys of Mexico, carried out by the World Bank. The sample of 1,480 enterprises is representative by enterprise size, by economic sector, and by region. The research has a quantitative approach with a correlational scope and a non-experimental and transectional design. One of the main results highlights that the determinants with the greatest influence on access to bank financing are: age, the small size, foreign participation, and the manufacturing sector. These results are consistent with other empirical studies, as well as with the pecking order theory and the financial life cycle theory.

Abnormalities in male fertility constitute about 50% of all infertility causes. According to some data, the quality of human semen has deteriorated by 50-60 % over the last 40 years. A high-fat diet and obesity, the development of which is encouraged by the western lifestyle, affects the structure of spermatozoa, but also the development of the offspring and their health in later stages of life. In obese individuals, disorders on the hypothalamic-pituitary-gonadal axis are observed, as well as elevated oestrogen levels with simultaneous decrease of testosterone, LH and FSH hormone levels. Healthy dietary models clearly correlate with better sperm quality and a smaller risk of abnormalities in parameters, such as sperm count, sperm concentration and motility, as well as lower sperm DNA fragmentation. Apart from mineral components such as zinc and selenium, the role of omega-3 fatty acids and antioxidant vitamins should be emphasized, since their action will be based primarily on the minimization of oxidative stress and inflammation process. Additionally, the incorporation of carnitine supplements and coenzyme Q10 in therapeutic intervention seems also promising. Therefore, it is advisable to have a varied and balanced diet based on vegetables and fruit, fish and seafood, nuts, seeds, whole-grain products, poultry and low-fat dairy products.

Since the 20th century, Japan has experienced a period of very rapid urbanization. Cities have experienced substantial densification and expansion, resulting in gradually elevated flood risk. Urban flooding has also occurred in most large cities in Japan, particularly in Tokyo. In response to this growing problem, much effort and resources have been spent on research and development aimed at understanding, simulating, and managing urban flood risk in Japan. The objective of this review is to summarize, discuss and share key outputs from some of the main research directions in this field, significant parts of which have been uniquely developed in Japan and only published in Japanese. After a general introduction to urban runoff modelling, in the next section, key historical works in Japan are summarized, followed by a description of the situation in Japan with respect to observations of precipitation and water level. Then, the storage function model approach is reviewed, including an extension to urban basins, as well as recent experiments with AI-based emulation in Japanese basins. Subsequently, we review the prospects of detailed hydrodynamic modelling involving high-resolution, vector-based Geographical Information System (GIS) data for the optimal description of the urban environment with applications in Tokyo. We conclude the paper with some future prospects related to urban flood risk modelling and assessment in Japan.

Polarization in online social networks has gathered a significant amount of attention in the research community and in the public sphere due to stark disagreements with millions of participants in topics surrounding politics, climate, the economy and other areas where an agreement is required. There are multiple approaches to investigating the scenarios in which polarization occurs and given that polarization is not a new phenomenon but that its virality may be supported by the low cost and latency messaging offered by online social media platforms; an investigation into the intrinsic dynamics of online opinion evolution is presented for complete networks. Extending a model which utilizes the Binary Voter Model (BVM) to examine the effect of the degree of freedom for selecting contacts based upon homophily, simulations show that different opinions are reinforced for a period of time when users have a greater range of choice for association. The facility of discussion threads and groups formed upon common views further delays the rate in which a consensus can form between all members of the network. This can temporarily incubate members from interacting with those who can present an alternative opinion where a voter model would then proceed to produce a homogeneous opinion based upon pairwise interactions.

Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new tornado catalogue that include present and past records of tornados across Bangladesh maximizing use of available sources. Within this new tornado database, 119 records were captured starting from 1838 till 2020 causing 8,735 deaths and 97,868 injuries leaving more than 1,02,776 people affected in total. Moreover, using this new tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics.

Rapid haemostasis during surgery is essential when one wants to reduce the duration of opera-tions, reduce the need for transfusions, and above all when one wants to achieve better patient management. The use of haemostatic agents, sealants and adhesives improves the haemostatic process by offering several advantages, especially in vascular surgery. These agents vary widely in their mechanism of action, composition, ease of application, adhesion to wet or dry tissue, im-munogenicity and cost. The most used are cyanoacrylate-based glues (Glubran 2) or polysaccharide hydrogel-microsphere powder (AristaTMAH). This work is based on a retrospective study carried out on a sample of patients with different vascular diseases (FAV, pseudoaneurysm and PICC application) in which two different haemostatic sealants were used. The aim was to assess the safety, the advantages and the ability of both sealants to activate the haemostatic process at the affected site, also in relation to their chemical-physical characteristics. The obtained results showed that the application of Glubran 2 and AristaTMAH as surgical wound closure systems is effective and safe as the success achieved was ≥94% on anastomoses of FAV; 100% on stabilization of Picc catheters; ≤95% on pseudoaneurysms.

Over the last 20 years, begomoviruses have emerged as devastating pathogens, limiting the production of different crops worldwide. Weather conditions increase vector populations, with negative effects on crop production. In this work we evaluated the relationship between the in-cidence of begomovirus and climatic conditions before and during the crop cycle. Soybean and bean fields from the northwest (NW) of Argentina were monitored for 14 years and classified as moderate (≤50%) and severe (> 50%) according to the relative incidence. Two hundred bio-meteorological variables were constructed, summarizing meteorological data in 10-day peri-ods from June to March of each crop year. The studied variables included temperature, precipi-tation, relative humidity, wind (speed and direction), pressure, cloudiness and visibility. For bean, high maximum winter temperatures, low spring humidity and precipitation 10 days before planting correlated with severe incidence. In soybeans, high late winter and pre-planting tem-peratures, and low spring precipitations were found to be good predictors of high incidence of begomovirus presence. The results suggest that temperature and pre-sowing precipitations can be used to predict incidence status [predictive accuracy: 82% (bean) and 75% (soybean)]. Thus, these variables can be incorporated in early warning systems for crop management deci-sion-making to reduce the virus impact on bean and soybean crops.

Breast Cancer Associated Susceptibility Proteins Type 1/2 (BRCA1/2) promote cellular functioning by modulating NRF2-mediated antioxidant signaling. Redox failure in women with BRCA1/2 insufficiency increases the risk for breast/ovarian/uterine cancers. Risk‐reducing salpingo‐oophorectomy (RRSO) is a prophylactic surgery of the reproductive organs, which is frequently conducted by the age of 40 to lower the occurrence of cancer in women with BRCA1/2 mutations. However, abrupt estrogen decline following RRSO causes ovarian failure, which implicates various cellular physiological processes, resulting in increased release of free radicals and subsequent severe onset of menopausal symptoms. Comfort measures (e.g., hormonal replacement therapy (HRT) and mindfulness-based stress reduction (MBSR)) may improve chronological menopause-related quality of life, but their specific effects are not clear in women with gene mutations. Aiming to fill the gap, this study used path analysis to examine the effects of HRT and MBSR on menopausal symptoms among RRSO patients (N = 199, mean age = 50.5 ± 6.7 years). HRT alleviated menopausal symptoms in BRCA2 carriers and women who were physically active, premenopausal at the time of RRSO, had a high BMI, and had no history of breast cancer. It increased the severity of urogenital symptoms in those with a history of cancer. MBSR, on the other hand, was associated with increased intensity of most menopausal symptoms. The results suggest that young women undergoing recent RRSO may benefit from HRT at an individual level while their need for extensive measures to optimize their psychological wellbeing is ongoing. Adverse effects of MBSR, which are captured in the present study imply that MBSR may interfere with redox sensitivity associated with estradiol fluctuations in BRCA1/2 carriers. Investigations are needed to test this hypothesis and elaborate on the underlying mechanisms in those women.

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

Ozone and fine particles (PM_2.5) are the two main air pollutants of concern in the New South Wales Greater Metropolitan Region (NSW GMR) region due to their contribution to poor air quality days in the region. This paper focuses on source contributions to ambient ozone concentrations for different parts of the NSW GMR, based on source emissions across the greater Sydney region. The observation-based Integrated Empirical Rate Model (IER) was applied to delineate the different regions within the GMR based on the photochemical smog profile of each region. Ozone source contribution is then modelled using the CCAM-CTM (Cubic Conformal Atmospheric Model-Chemical Transport Model) modelling system and the latest air emission inventory for the greater Sydney region. Source contributions to ozone varied between regions, and also varied depending on the air quality metric applied (e.g., average or maximum ozone). Biogenic volatile organic compound (VOC) emissions were found to contribute significantly to median and maximum ozone concentration in North West Sydney during summer. After commercial domestic, power station was found to be the next largest anthropogenic source of maximum ozone concentrations in North West Sydney. However, in South West Sydney, beside commercial and domestic sources, on-road vehicles were predicted to be the most significant contributor to maximum ozone levels, followed by biogenic sources and power stations. The results provide information which policy makers can devise various options to control ozone levels in different parts of the NSW Greater Metropolitan Region.

The Epidermal Growth Factor Receptor (EGFR) overexpression or its mutation mediates the sustaining proliferative signaling in cancers. Human EGFR-targeting monoclonal antibody (mAb) therapy such as cetuximab has been approved for clinical use in patients with colorectal cancers and head and neck squamous cell carcinomas. A reliable preclinical mouse model is essential to further develop the mAb therapy against EGFR. However, a mAb against mouse EGFR (mEGFR) for flow cytometry has not been established. In this study, we developed a specific and sensitive mAb for mEGFR using the Cell-Based Immunization and Screening (CBIS) method. The established anti-mEGFR mAb, EMab-300 (rat IgG1, kappa) reacted with mEGFR-overexpressed Chinese hamster ovary-K1 (CHO/mEGFR) and endogenously mEGFR-expressed cell lines, including NMuMG (a mouse mammary gland epithelial cell) and Lewis lung carcinoma cells by flow cytometry. The kinetic analysis using flow cytometry indicated that the dissociation constant (KD) of EMab-300 for CHO/mEGFR and NMuMG was 4.3 × 10−8 M and 1.9 × 10−8 M, respectively. These results indicated that EMab-300 applies to the detection of mEGFR by flow cytometry, and is expected in the use of preclinical study.

Stemming from the urgency of the actual situation, commercial banks need an effective credit risk management tool to limit risks. The authors went to survey, study and propose a set of factors affecting the ability of debt repayment of individual customers and conducting surveys. The topic uses data sets including 240 observation samples. Using the SPSS software to clean data and run the model based on Maddala's Binary logistics regression published in 1984 to find out the impact of each individual element of customers affecting their ability to repay such debts. Come on. The authors also specify the order of influence of each factor determining the ability to repay individual customers, thereby helping bank managers have a better visual view to make decisions for borrowing accurately, limiting risks.

Current numerical methods for simulating biophysical processes in aquatic environments are typically constructed in a grid-based Eulerian framework or as an individual-based model in a particle-based Lagrangian framework. Often, the biogeochemical processes and physical (hydrodynamic) processes occur at different time and space scales, and changes in biological processes do not affect the hydrodynamic conditions. Therefore, it is possible to develop an alternative strategy to grid-based approaches for linking hydrodynamic and biogeochemical models that can significantly improve computational efficiency for this type of linked biophysical model. In this work, we utilize a new technique which links hydrodynamic effects and biological processes through a property-carrying particle model (PCPM) in a Lagrangian/Eulerian framework. The model is tested in idealized cases and its utility is demonstrated in a practical application to Sandusky Bay. Results show the integration of Lagrangian and Eulerian approaches allows for a natural coupling of mass transport (represented by particle movements and random walk) and biological processes in water columns which is described by a nutrient-phytoplankton-zooplankton-detritus (NPZD) biological model. This method is far more efficient than traditional tracer based Eulerian biophysical models for 3-D simulation, particularly for a large domain and/or ensemble simulations.

The predictability of wind information in a given location is essential for the evaluation of a wind power project. Predicting wind speed accurately improves the planning of wind power generation, reducing costs and improving the use of resources. This paper seeks to predict the mean hourly wind speed in anemometric towers (at a height of 50 meters) at two locations: a coastal region and one with complex terrain characteristics. To this end, the Holt-Winters (HW), Artificial Neural Networks (ANN) and Hybrid time-series models were used. Observational data evaluated by the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) reanalysis at the same height of the towers. The results show that the hybrid model had a better performance in relation to the others, including when compared to the evaluation with MERRA-2. For example, in terms of statistical residuals, RMSE and MAE were 0.91 and 0.62 m/s, respectively. As such, the hybrid models are a good method to forecast wind speed data for wind generation.

There is a perception among the inhabitants of the Aburrá Valley Region, that this heavily populated region, situated in the Andean mountains of Colombia, has been suffering large temperature elevations in the last years, especially in the last decade. To give perspective about this issue, the authors have gone through the available information about temperature changes in three meteorological stations in the region and have correlated it with a set of variables of urban, climatic and energetic nature, with the intention of developing an approximate model to understand the temperature changes. Changes in the mean temperature, based on the linear correlation of the data were estimated on 0.47oC for the 20 years between 1995 and 2015; the study showed that 60% of change was found to be related to local human activities and 40% was attributed to the impact of global warming. For the local influences some practical mitigation actions are proposed, related to improve the energy management and paying more attention to the temperature changes trough improvements in the number and capability of sampling stations in the urban air and in the river, which serve as clear indicators of the changes and the effect of any mitigation measures.

There is a perception among the inhabitants of the Aburrá Valley Region, that this heavily populated region, situated in the Andean mountains of Colombia, has been suffering large temperature raises in the last years, especially in the last decade. To give perspective about this issue, the authors have gone through the available information about temperature changes in three meteorological stations in the region and have correlated it with a set of variables of urban, climatic and energetic nature, with the intention of developing an approximate model to understand the temperature changes. Changes in the mean temperature, based on the linear tendencies, were estimated on 0.47ºC for the 20 years between 1995 and 2015; 60% of change was found to be related to local human activities and 40% was attributed to the impact of global warming.

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe "what" has to be done instead of "how". Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative.

Drug design is used for different applications of bioinformatics tools analyze DNA, genome, and sequence target region of a small organic molecule in order to understand the molecules of disease. Bioinformatics tools are identified a newly wide research field and minimize future risks through web servers and data mining. Clinical sample test performed with the bioinformatics tools as the biomedical detective. A particular structure and configuration of protein obliging in Drug design concluded Bioinformatics. This review bioinformatics tools and webserver will discuss functions of small organic molecules according to clinical pharmacology.

The impact of instructional guidance on learning outcomes in higher biomedical education is subject of intense debate. There is the teacher-centered or traditional way of teaching (TT) and, on the other side, the notion that students learn best under minimal guidance (problem-based learning, PBL). Although the benefits of PBL are well-known, there are aspects susceptible to improvement. Hence, a format merging TT and PBL (hybrid-PBL, h-PBL) may advance education in biomedical sciences. Here, we systematically reviewed studies that employed h-PBL in higher biomedical education compared to TT and/or pure PBL. We found that h-PBL resulted in better overall students’ performance and perception than TT or pure PBL. These findings encourage more research on investigating the pedagogical benefits of h-PBL and posit an eclectic system in which the pedagogical tools from TT and PBL are used cooperatively in the best interest of the education and satisfaction of the students.

The systematic sampling is used as a method to get the quantitative results from the tissues and the radiological images. Systematic sampling on real line (R) is a very attractive method within which the biomedical imaging is consulted by the practitioners. For the systematic sampling on R, the measurement function (MF) is occurred by slicing the three-dimensional object equidistant systematically. The currently used covariogram model in variance approximation proposed by [28,29] is tested for the different measurement functions in a class to see the performance on the variance estimation of systematically sampled R. This study is an extension of [17], and an exact calculation method is proposed to calculate the constant λ(q,N) of confidence interval in the systematic sampling. The exact value of constant λ(q,N) is examined for the different measurement functions as well. As a result, it is observed from the simulation that the proposed MF should be used to check the performances of the variance approximation and the constant λ(q,N). Synthetic data can support the results of real data.

Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable whereas each data point can be assigned to more than one cluster and degree of such assignment is represented by the probability of hidden variable. However, such probability in traditional mixture model is simplified as a parameter, which can cause loss of valuable information. Therefore, in this research I propose a so-called conditional mixture model (CMM) in which the probability of hidden variable is modeled as a full probabilistic density function (PDF) that owns individual parameter. CMM aims to extend mixture model. I also propose an application of CMM which is called adaptive regressive model (ARM). Traditional regression model is effective when data sample is scattered equally. If data points are grouped into clusters, regression model tries to learn a unified regression function which goes through all data points. Obviously, such unified function is not effective to evaluate response variable based on grouped data points. The concept “adaptive” of ARM means that ARM solves the ineffectiveness problem by selecting the best cluster of data points firstly and then evaluating response variable within such best cluster. In order words, ARM reduces estimation space of regression model so as to gain high accuracy in calculation.

Survivors of sexual and gender-based violence (SGBV) are often hindered in their quest to access quality healthcare. This could complicate the achievement of the laudable SDG Target 3.7. The study used an ethnographic approach to solicit information from health providers, adolescents, and young women (AYW) in 10 communities from Lagos and Ibadan; two major cities in Nigeria. Findings indicated that the structural system of services provided in the health services at the primary healthcare level was limited, which posed a great challenge in AYW’s utilization of services for sexual and gender-based violence (SGBV). Some of the challenges identified by health providers included non-existing counseling services, rehabilitation centers, referral problems, and a lack of training for health providers in handling victims of SGBV. The study provided the context of the health providers’ inadequacy and structural problem of health services in providing comprehensive support services to victims of SGBV like adolescents and young women in the slum setting. This poses a barrier to the utilization of services by the survivors. Necessary inclusive services that will meet the need of the victims of SGBV should be in place at primary healthcare centers close to the people in the community for accessibility.

This work aims to explore the impact of side loads, drill-pipe tool-joint (DP-TJ) speed (RPM), and mud type on the austenitic stainless steel SM2535-110 casing wear characteristics. Actual field drill pipe tool joints, casings, and drilling muds are used in this study. The results of the study show that under both types of lubrication, the wear volume increased with radial load and DP-TJ speed. SM2535-110 casing specimens tested under oil-based mud (OBM) lubrication had higher casing wear volumes than those obtained under water-based mud (WBM) lubrication. This unexpected behavior is mainly due to the increase in the surface hardness of the casing specimens tested under WBM. The results also show that the specific wear factor (K) values of specimens tested under WBM are in general 2 to 4 times higher than those obtained under OBM. While K values under WBM increase with both the side load and RPM, those under OBM show a sharp decrease with RPM. This behavior under OBM is due to this lubricant’s higher viscosity and the change of lubrication regime from thin film to thick film lubrication at higher RPM. Scanning electron microscopy (SEM) and the digital microscopic imaging (DMI) of SM235-110 casing specimens show that an aggressive combination of adhesive, abrasive, and plastic deformation was observed under WBM, while the dominant wear mechanism under OBM is abrasive wear.

Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.

Background: This study aimed to identify the association between occupational stress and depression-well-being by proposing a comprehensive and flexible job burden-capital model with its corresponding hypotheses. Methods: For this research, 1618 valid samples were gathered from the electronic manufacturing service industry in Hunan Province, China; self-rated questionnaires were administered to participants for data collection after obtaining their written consent. The proposed model was fitted and tested through structural equation model analysis. Results: Single-factor correlation analysis results indicated that coefficients between all items and dimensions had statistical significance. The final model demonstrated satisfactory global goodness of fit (CMIN/DF=5.37, AGFI=0.915, NNFI=0.945, IFI=0.952, RMSEA=0.052). Both the measurement and structural models showed acceptable path loadings. Job burden and capital were directly associated with depression and well-being or indirectly related to them through personality. Multi-group structural equation model analyses indicated general applicability of the proposed model to basic features of such a population. Gender, marriage and education led to differences in the relation between occupational stress and health outcomes. Conclusions: The job burden-capital model of occupational stress-depression and well-being was found to be more systematic and comprehensive than previous models.

Lomefloxacin (F1) and pefloxacin (F2) have a broad spectrum of antimicrobial activities against Gram-positive and Gram-negative bacteria. In this study, we investigated the complexation mode, morphological and biological properties of four metal-based complexes of F1 and F2 molecules with Mg(II), Ca(II), Zn(II), and Fe(III) metal ions. These complexes were prepared at ~60–70°C in a neutral medium using a 5% NH3 solution at pH ~7-8 with a 1:1 ratio. Multiple physicochemical methods were employed to characterize the binding mode between F1 and F2 with the metal ions under investigation. The results of these methods suggested that the gross formula of the complexes obtained with the metal ions were [MgF1(H2O)Cl]2H2O, [CaF1(H2O)Cl]3H2O, [ZnF1(H2O)Cl], [FeF1(H2O)2Cl2]Cl2H2O, [MgF2(H2O)Cl]2H2O, [CaF2(H2O)Cl]3H2O, [ZnF2(H2O)Cl], and [FeF2(H2O)2Cl2]Cl2H2O. The microscopic characterizations indicated that the Ca(II)-F1 complex had an interesting surface topography. Its particles had a homogenous, short, rod-like shaped structure that clustered together to form a tree shape. Using the Kirby-Bauer disc diffusion protocol, the synthesized metal-based complexes were screened in vitro against different Gram-positive and Gram-negative bacterial and fungal species. The antimicrobial profile of the Fe(III)-F1 complex indicated that it had remarkable inhibitory activity against all the tested bacterial and fungal species with potency equal to that of the standard drugs (streptomycin and ketoconazole).

This study critically analyses the realities and experiences of gender-based harassment in cyberspace, and aims to unveil the shadows that shroud this phenomenon. It discloses the online spaces that disseminate detrimental attitudes towards women, despite their physical absence. Cyber violence has become a global issue and it causes significant economic and societal consequences. Recognizing and dealing with the adverse effects caused by demeaning cyber gender harassment is essential. The study raises questions to explore the kind of cyberbullying offences that are brought on by misogynistic inclinations in online environments and the experiences of the women who have gone through cyberbullying. This study uses semi-structured interviews and the IPA technique of the analysis of data to thoroughly examine the unique experiences of cyber harassment victims by applying a qualitative research approach. The study looks into various misogynistic cyber harassment offences and analyses women's accounts. Due to obstacles to justice, cyber violence and harassment replicate physical problems like spousal abuse and sexual harassment. A constant assault of intimidation and harassment results from the traditionally male-dominated character of cyberspace, which affects women's social, economic, and psychological well-being. Participants related horrifying tales of families' indifference and law enforcement officials' trivialization. Mental health problems increase isolation and prevent involvement in academic and professional activities. Women's well-being is exacerbated by societal blaming and secondary victimization. This brief analysis clarifies the intricacies of gender-based harassment in cyberspace and emphasizes the urgent need for efficient solutions to address this widespread issue.

Lactobacillus plantarum has been observed to play a crucial role in shaping the sensory properties of chili sauce. However, the specific taste-active metabolites responsible for the desirable flavor profile of chili sauce remain inadequately characterized. This study employed a combination of metabolomics and web-based computational tools analysis to investigate the dynamic changes in taste-active metabolites during chili sauce fermentation. Initially, metabolites were rapidly annotated using a feature-based molecular network, leading to the tentative annotation of 206 metabolites, of which a significant proportion had not been previously reported. Subsequently, the VirtualTaste tool identified dihydrosphingosine, lactic acid, isoleucine, phytosphingosine, and gluconic acid as potential taste markers for quality control. Finally, pathway enrichment analysis revealed that these components were primarily associated with amino acid tRNA, phenylalanine, tyrosine, and tryptophan biosynthesis, as well as sphingolipid metabolism. This study provides valuable insights into the mechanisms underlying the formation of the distinctive flavor of chili sauce.

Flash flooding, a hazard which is triggered by heavy rainfall is a major concern in many regions of the world often with devastating results in mountainous elevated regions. We adapted remote sensing modelling methods to analyse one flood in July 2015, and believe the process can be applicable to other regions in the world. The isolated thunderstorm rainfall occurred in the Chitral River Basin (CRB), which is fed by melting glaciers and snow from the highly elevated Hindu Kush Mountains (Tirick Mir peak’s elevation is 7708 m). The devastating cascade, or domino effect, resulted in a flash flood which destroyed many houses, roads, and bridges and washed out agricultural land. CRB had experienced devastating flood events in the past, but there was no hydraulic modelling and mapping zones available for the entire CRB region. That is why modelling analyses and predictions are important for disaster mitigation activities. For this flash flood event, we developed an integrated methodology for a regional scale flood model that integrates the Tropical Rainfall Measuring Mission (TRMM) satellite, Geographic Information System (GIS), hydrological (HEC-HMS) and hydraulic (HEC-RAS) modelling tools. We collected and use driver discharge and flood depth observation data for five river sub-stream areas, which were acquired in cooperation with the Aga Khan Rural Support Program (AKRSP) organization. This data was used for the model’s calibration and verification. This modelling methodology is applicable for other regional studies especially for rough mountainous areas which lack local observations and river discharge gauges. The results of flood modelling are useful for the development of a regional early flood warning system and flood mitigation in hazardous flood risk areas. The flood simulations and prepared connected video visualization can be used for local communities. This approach is applicable for flood mitigation strategies in other regions.

Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots, trolls, among others. In this scenario, the potential of bots to facilitate the spread of disinformation is recognised, however, the analysis of how they do this is still in its initial stages. Taking into consideration what was previously stated, this paper aimed to model and simulate scenarios of disinformation propagation in social networks caused by bots based on the dynamics of this mechanism documented in the literature. For achieving the purpose, System dynamics was used as the main modelling technique. The results present a mathematical model, as far as disinformation by this mechanism is concerned, and the simulations carried out against the increase in the rate of activation and deactivation of bots. Thus, the preponderant role of social networks in controlling disinformation through this mechanism, and the potential of bots to affect citizens, is recognised.