This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

The development of virtual models has recently been generalized in the field of complex mechatronic product design. Virtual models have various advantages such as: the systemic design approach, most often parametric, the implementation of extreme, expensive, and perhaps risky scenarios for the operation of the designed product, the testing of a large number of variants, etc. The main contribution of the paper consists of the development of an interface family called DeSeRoI (Dedicated Serial Robot Interface). Each member of the interface family integrates Simulink libraries and is created by the programmer in accordance with user requests. It follows that the interfaces composing the DeSeRoI family can have different levels of complexity depending on user needs and constraints. We are implementing a MATLAB-based application with user-provided configuration for serial robots in order to automatically visualize the kinematics data and simulation. Within MATLAB, two scripts are created responsible for generating Simulink models that compute the kinematics problem. One of the scripts (Main) saves information about the configuration of the robot and the other one (Generates_Simulink_Models) computes the kinematics problem. Different types of functions (software module) have been developed. Using these functions, the Simulink models result as modular entities. The automated approach eases user’s interaction and understanding of serial robots’ kinematics by performing the simulation procedure in a shorter amount of time and with higher efficiency, assuring a precise outcome despite the chosen parameters.

Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature of human motions. Consequently, they suffer from data dependencies and encounter the dimension disaster problem and the over-fitting issue, and their models are never human-readable. In this study, we start from a deep analysis on natural physical properties of human motions, and then propose a useful feature selection method to quantify each feature's classification contribution capability. On one hand, the "dimension disaster" problem can be avoid to some extent, due to the affined dimension of key features; On the other hand, over-fitting issue can be depressed since the knowledge implied in human motions are nearly invariant, which compensates the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as decision tree, k-NN, SVM, neural networks.

Biohydrogen is a versatile energy carrier for the generation of electric energy from renewable sources. Hydrogenases can be used in enzymatic fuel cells to oxidize dihydrogen. The rate of electron transfer (ET) at the anodic side between the [NiFe]-hydrogenase enzyme distal iron-sulfur cluster and the electrode surface can be described by the Marcus equation. All parameters for the Marcus equation are accessible from DFT calculations. The distal cubane FeS-cluster has a three cysteine and one histidine coordination [Fe₄S₄](His)(Cys)₃ first ligation sphere. The reorganization energy (inner- and outer-sphere) is almost unchanged upon a histidine-to-cysteine substitution. Differences in rates of electron transfer between the wild-type enzyme and the all-cysteine mutant can be rationalized by a diminished electronic coupling between the donor and acceptor molecules in the [Fe₄S₄](Cys)₄ case. The fast and efficient electron transfer from the distal iron-sulfur cluster is realized by a fine-tuned protein environment which facilitates the flow of electrons. This study enables the design and control of electron transfer rates and pathways by protein engineering.

As the most active plateau on the Earth, the Qinghai-Tibet Plateau has a complex crust-mantle structure. Knowledge of the distribution of such a structure provides information for understanding the underlying geodynamic processes. We obtains a three-dimensional density model of crustal and upper mantle beneath Qinghai-Tibet plateau and its surrounding areas from the residual geoid anomalies using the Earth Gravitational Model (EGM) 2008. We estimate a refined density model by iterations, using an initial density contrast model. We confirm that the EGM2008 mission products can be used to constrain the crust-mantle density structures. Our major findings are: (1). At 300-400 km depth, high-D anomalies terminate around Jinsha River Suture (JRS) in the central TP, suggesting that the Indian plate has been reached over the Bangong Nujiang Suture (BNS) and almost reach to the JRS. (2). On the eastern TP, low-D anomalies at the depth of 0-300 km together with high-D anomalies at 400-670 km further verified the current eastward subduction of Indian plate. The ongoing subduction provides forces to the occurrences of frequent earthquakes and volcano. (3). At 600 km depth, low-D anomalies inside the TP illustrate the existence of hot weak material beneath there, contributing to the external material inward-thrusting.

Despite the demonstrated benefits of data monetization initiatives in achieving competitive advantages, many of these efforts struggle to realize their potential. Companies often find it challenging to sustain even initially successful data monetization initiatives due to significant data quality issues. This reflects a disconnect between advancements in data monetization research—spanning digitization, digitalization, and digital transformation—and practical implementation within companies. Consequently, misguided approaches to data monetization are relatively common. A critical prerequisite for successful data monetization is the establishment and maintenance of clean, high-quality data. This study underscores the importance of data quality by conducting an in-depth analysis of Medical Inc., a company engaged in preparing pristine customer master data for advanced customer analytics. The investigation aims to elucidate Medical Inc.’s approach to addressing data cleanliness challenges and to distill a general framework for the customer master data cleansing process. This framework illuminates a relatively unexplored aspect of data monetization supplementing existing literature on digitization, digitalization, and digital transformation.

Sudden cardiac arrest (SAC) is a leading cause of mortality. In recent years, though, many lives have been saved through the use of defibrillators by random bystanders. As a result, defibrillators have been popping up in various locations. In this work we propose a state mandate demanding the presence of a defibrillator at every pharmacy store and argue that it could greatly enhance and augment the impact of the defibrillator network. We support our claim via detailed calculations of the immediate (a defibrillator readily available) and partial (a defibrillator available at a reasonable distance) population coverage achievable by the implementation of our proposal, taking the Argolis area in Peloponnese Greece as a case study. Our findings indicate that, in addition to other benefits, the use of pharmacies to establish a defibrillator network could bring an eight-fold increase in the immediate population coverage of defibrillators and would provide immediate / partial access to a defibrillator to 18 / 24 incidents of SCA per year that would otherwise lead to death, potentially leading to the saving of numerous lives. Alternative scenaria are also discussed.

The coseismic surface three-dimensional deformation fields were significant for quantified the geometric and kinematic characteristics of earthquake rupture faults. Traditional geodetic techniques were constrained by their intrinsic limitations, such as InSAR, which could only extract far-field deformation fields due to its incoherence, and GNSS, which could only acquire deformations at discrete points. The recently developing optical pixel correlation technique based on high resolution remote sensing images could acquire the near-field coseismic horizontal deformation. In this study, the vertical displacement field of the 2022 Mw 6.6 Menyuan earthquake was obtained by differencing the pre- and post-earthquake DEMs from GaoFen-7 stereo satellite images. Further, the line-of-sight (LOS) and azimuthal (AZI) directions far-field deformations by InSAR, the horizontal near-field deformation by optical pixel correlation based on pre- and post-earthquake GF-2/7 images, and the vertical deformation by differencing the pre- and post-earthquake DEMs, were incorporated to comprehensively solve the complete three-dimensional deformation fields of the 2022 Mw 6.6 Menyuan earthquake. Firstly, the vertical displacement field by differencing DEMs indicated there were significant vertical displacements about 1 m at the bend region, which was induced by the local compressive stress. In addition, at the epicenter on the middle segment of ruptured Lenglongling fault (LLLF), the maximum lifting occurred on the southern sides of the main and secondary faults exceeded 2 m. Secondly, the three-dimensional deformation fields solved by multiple deformation data demonstrated that the near-field deformation field calculated by optical pixel correlation method could quantify the displacements distributed over the rupture fault zone, which were not available on the InSAR deformation maps. Finally, the surface 2D strain derived from the displacement maps calculated by optical pixel correlation revealed high strain concentration on the rupture fault zone. Our study focused on the complete surface three-dimensional deformation estimation from multiple far- and near-field deformation data, and provided a new perspective for a deeper understanding of the characteristics of coseismic surface deformation and the rupture pattern of the fault.

The effectiveness of a hybrid technique for identifying seismic damage in planar, multistory, steel X or V-braced frames is demonstrated here through an example of a six-story frame. This proposed technique, referred to as “M and P”, combines the instrumental Monitoring (M) with Pushover analysis (P). According to the methodology, the diagram of stepping eigenfrequencies of the frame in the inelastic region is initially plotted against seismic roof displacement. The fundamental natural frequency detected via the monitoring process is then utilized in this key diagram to reveal the inelastic roof displacement that corresponds to the damage state of the steel braced frame. This displacement is subsequently used as the target in the pushover analysis, facilitating the identification of seismic damage in the existing steel braced frame. Finally, the damage image is correlated with the damage stiffness matrix of the frame at the same inelastic roof displacement. The investigation results indicate that combining instrumental monitoring with pushover analysis enables accurate identification of seismic damage potential in existing steel braced frames.

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

"Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law". In accordance with the aforementioned declaration on Free Access to Law by Legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques, should be employed in the context of legal information retrieval, as to increase user satisfaction. We address diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard data set from the Common Law domain that we subjectively annotated with relevance judgments for this purpose. Our results i) reveal that users receive broader insights across the results they get from a legal information retrieval system, ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification and iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.

We leverage the explainability feature of KAN network and build an explainable language model where certain neurons encode individual words and neuron activation is fully interpretable in terms of the basis of a word. To do that, we propose a continuous word2vec model where a meaning of a word is expressed by a continuous profile of distances of this word from the words in the basis of words which is interpolated. As a result, the whole KAN network can be interpreted as a sequential procedure with word expressions. We then proceed from words to logic programs and develop a clause learning technique based on KAN. A logic program construction process from facts now become fully interpretable. We follow the differentiable inductive logic programming technique, representing a logic program as a matrix learned by KAN. Hence, we obtain an efficient and fully interpretable rule learning approach.

Today, LLMs are not good at personalization providing recommendation. They advise physicians and financial advisors to ask professionals in respective fields for help, even having user information available. Answering questions of software professionals, LLM needs to deliver in-depth answers with codes or algorithms, whereas for professionals in other fields would need definitions and main concepts. The intent of this chapter is to make LLM answer tailored to the needs of users, taking into account available information about them. To do that, we need to generalize available information about a person like her health record, maintaining the privacy of this person. We rely on meta-learning techniques to design a LLM prompt to produce a personalization prompt to obtain a suitable relevant information. Such “meta-prompt” is produced by generalization operation applied to available documents for the user. These documents need to be de-identified so that they are sufficient for personalization on one hand and will maintain user privacy on the other hand. The second neuro-symbolic technique to support personalization is abductive reasoning, acting in parallel to LLM fine-tuning. Traditional recommendation and personalization techniques as well as modern, deep learning – based are presented, and the comparison is drawn to the proposed approach. We also share the evaluation and comparative analyses of these approaches. We consider an example for how to build personalization LLM systems coming from Langchain platform. We will explore how to construct chains to form a personalization profile for a user and apply it to user search and recommendation requests.

Food systems will need to undergo considerable transformation. To be better prepared for and resilient to uncertainty and disturbances in the future, resource users and managers need to further develop knowledge about the food and farming system, with its dominating feedback structures and complexities, and to test robust and integrated system-based solutions. This paper investigates how participatory system dynamics modeling can be adapted to groups at the community level with low or no formal educational background. The paper also analyzes the refinement of workshop participants’ mental models as a consequence of a participatory system dynamics intervention. For this purpose, we ran two workshops with small-scale farmers in Zambia. Analysis of workshop data and post-workshop interviews shows that participatory system dynamics is well adaptable to support an audience-specific learning-by-doing approach. The use of pictures, objects and water glasses in combination with the basic aspects of causal loop diagramming makes for a well-balanced toolbox. Participants acquire understanding that is also relevant beyond systems thinking in that is offers a range of practical insights such as a critical evaluation of common food security strategies.

Personal protective equipment (P PE) rules must be respected and followed to the letter of healthcare professionals in Cameroon to avoid the transmission of infectious diseases. This research proposes solutions to the problems of non-adherence and non-compliance among healthcare workers in Cameroon towards PPE rules. Inadequate access to suitable PPE, discomfort when wearing PPE during poor quality, and a lack of training on correct PPE use are just a few of these obstacles amongst HCWs in Cameroon. Hospitals and healthcare facilities should prioritize offering thorough instruction on PPE use and pinpoint the need to adhere consistently to PPE rules to remove these obstacles. They should also ensure that all healthcare personnel have access to and a sufficient supply of the proper PPE. Through the organization of impromptu visits by surveillance teams to identify areas of support and training, there is a need for assistance from government bodies and public health groups in creating awareness of the security value of PPE use among healthcare professionals. There is a lack of PPE in health care settings in rural areas in Cameroon, thus a need for an adequate supply. Then can we put appropriate PPE policies and practices for HCWs in Cameroon, as they will keep to it.Healthcare professionals in Cameroon must comprehend that following PPE standards protects not just their selves from diseases but also their patients, coworkers, and communities. We are accountable for ensuring everyone's safety and well-being, and adhering to PPE regulations is an essential first step.

Vaccine hesitancy has emerged as a significant barrier to achieving herd immunity and protecting populations from preventable diseases. Despite the proven effectiveness and safety of vaccines, many individuals remain hesitant or refuse to vaccinate themselves or their children. This review aims to identify the factors contributing to vaccine hesitancy, including misinformation, lack of trust in authorities, cultural beliefs, and access barriers. We also discuss the impact of vaccine hesitancy on public health, including outbreaks of vaccine-preventable diseases and erosion of trust in public health authorities. Furthermore, we present evidence-based communication strategies for addressing vaccine hesitancy, including building trust, providing accurate information, addressing myths and misconceptions, engaging with local influencers, and utilizing social media and digital platforms. By understanding the factors contributing to vaccine hesitancy and implementing effective communication strategies, healthcare providers, public health officials, and community leaders can work towards promoting widespread vaccine acceptance and protecting public health.

It is concerning that Cameroon's urban centers are experiencing an alarming rise in air pollution as a result of urbanization, population growth, industrial activity, and transportation emissions. We examined the causes, health impacts, research methodology, case studies, and government measures to address the urgent issue of air pollution in Cameroon's urban regions. We have emphasized how Cameroon's air pollution levels have significantly increased as a result of causes like population expansion, urbanization, industrial emissions, and automobile exhaust A thorough discussion of the negative health impacts of extended exposure to air pollution is included, with special attention to the sensitivity of the old and young. These effects include respiratory disorders, cardiovascular ailments, and early mortality. We emphasized the pressing need for all-encompassing strategies to address air pollution, such as increased public awareness campaigns, stronger emissions regulations, the development of renewable energy sources, and better waste management. The techniques employed in studies on air pollution, like as epidemiological research and air quality monitoring, are also explained to provide light on the unique health dangers that Cameroonian urban dwellers must contend with. We emphasized in our conclusion the significance of joint efforts by legislators, medical professionals, and the general people to reduce air pollution and protect public health in Cameroon in order to create a cleaner and healthier future. In order to protect public health in the future, it is imperative that Cameroon's air pollution be addressed.

As the global population faces an increasing prevalence of physical disabilities, the challenge of motor rehabilitation has become increasingly urgent. Considering that approximately 15% of the global population lives with some form of disability, with a significant number having motor disa-bilities that greatly impact their quality of life, the integration of robotic technologies into rehabili-tation processes offers an opportunity for significant improvement in functional recovery and pa-tient autonomy. This analysis aims to evaluate the progress and challenges encountered in imple-menting robotic technology for the rehabilitation of patients with physical disabilities, highlighting the potential of emerging technologies such as exoskeletons, assisted training devices, and brain-computer interface systems in facilitating motor recovery. By examining relevant clinical studies, the analysis demonstrates the varied effectiveness of robotic therapies compared to traditional re-habilitation methods, while also highlighting the need for ongoing research to optimize care prac-tices and address challenges related to cost, accessibility, and treatment personalization. In con-clusion, robotic technologies represent a promising direction in motor rehabilitation, with the po-tential to revolutionize the care of patients with physical disabilities, significantly improving quali-ty of life and social integration.

Carbon fiber-reinforced polymer (CFRP), noted for its outstanding properties including high specific strength and superior fatigue resistance, is increasingly employed in aerospace and other demanding applications. This study investigates the interactions between CFRP composites and high-energy lasers (HEL), with continuous wave laser powers reaching up to 120 kW. A novel automated sample exchange system, operated by a robotic arm, minimizes human exposure while enabling a sequence of targeted laser tests. High-speed imaging captures the rapid expansion of a plume consisting of hot gases and dust particles during the experiment. The research significantly advances empirical models by systematically examining the relationship between laser power, perforation times, and ablation rates. It demonstrates scalable predictions for the effects of high-energy laser radiation. Detailed examination of the damaged samples, both visually and via micro-focused computed X-ray tomography, offers insights into heat distribution and ablation dynamics, highlighting the anisotropic thermal properties of CFRP. Compression-after-impact (CAI) tests further assess the residual strength of the irradiated samples, enhancing the understanding of CFRP’s structural integrity post-irradiation. Collectively, these tests improve the knowledge of the thermal and mechanical behavior of CFRP under extreme irradiation conditions. The findings not only contribute to predictive modeling of CFRP's response to laser irradiation but also enhance the scalability of these models to higher laser powers, providing robust tools for predicting material behavior in high-performance settings.

The NW-SE trending dextral strike-slip faults on the north side of the Tian Shan play an important role in accommodating the crustal shortening, which mainly includes the Karatau fault, Talas-Fergana fault, Dzhalair-Naiman fault, Aktas fault, Dzhungarian fault (also named Bolokenu-Aqikekuduk fault) and Chingiz fault. The classic viewpoint is that the strike-slip fault is the adjustment product caused by the difference of the crustal shortening from west to east. Another viewpoint attributes the dextral strike-slip fault to large-scale sinistral shearing. The Alakol Lake Fault is situated along the northern margin of the Dzhungarian Gate, stretching for roughly 150 km from Lake Ebinur to Lake Alakol. Our team utilized aerial photographs, Satellite Stereo-imagery, and field observation to map the distribution of the Alakol Lake fault. Our findings provided evidence supporting the assertion that the fault is a dextral strike-slip fault. In reference to its spatial distribution, Lake Alakol is situated in a pull-apart basin that lies between two major dextral strike-slip fault lines: the Chingiz and Dzhungarian faults. The Alakol Lake Fault serves as a connecting point for these two faults, resulting in the formation of a mega NW-SE dextral strike-slip fault zone. According to our analysis of the dating samples taken from the alluvial fan, as well as our measurement of the displacement of the riser and gully, it appears that the Alakol Lake fault has a dextral strike-slip rate of 0.8-1.5mm/yr. The strike-slip rate of the Alakol Lake fault is comparatively higher than that of the Chingiz fault in the northern region (~0.7 mm/yr), but slower than that of the Dzhungarian fault in the southern (3.2-5 mm/yr). The Chingiz-Alakol-Dzhungarian fault zone shows a gradual decrease in deformation towards the interior of the Kazakhstan platform. We contend that the rate of deformation observed is better explained by the crustal shortening adjustment model, rather than the large-scale sinistral shearing model.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modelling capability as well as a high-level of customisation and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad-hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such a complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as it suitable within a number of applications contexts - i.e. as a research/educational tool, communication framework, gamification and participatory modelling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

As urbanization continues to increase, key conservation areas like World Heritage sites are being altered. This transformation is a cause for concern as it threatens the Outstanding Universal Value of these sites. To address this issue, a research question was formulated: How urban is World Heritage? The research focused on identifying and monitoring urbanization in heritage core zones listed on the World Heritage by tracking changes in built-up areas from 1985 to 2015. The findings revealed that urban developments, including housing, transport infrastructure construction, visitor accommodation facilities, and land conversions, have significantly impacted heritage properties. More than a third (37\%, 426) of the cultural heritage properties reported urban development, with the highest occurrences in Europe, North America, Asia, and the Pacific. Furthermore, these regions also displayed the highest rate of change per year between 1985 and 2015. This indicates the urgent need to identify and monitor the urbanization of World Heritage to preserve their OUV amidst increasing urban development.

Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both models’ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD model’s fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.

Sub-Saharan social-ecological systems are undergoing changes in environmental conditions, including modifications in rainfall pattern and biodiversity loss. Consequences of such changes depend on complex causal chains which call for integrated management strategies whose efficiency could benefit from ecosystem dynamic modelling. However, ecosystem models often require lots of quantitative information for estimating parameters, which is often unavailable. Alternatively, qualitative modelling frameworks have proved useful for explaining ecosystem response to perturbations, while requiring fewer information and providing more general predictions. However, current qualitative methods have some shortcomings which may limit their utility for specific issues. In this paper, we propose the Ecological Discrete-Event Network (EDEN), an innovative qualitative dynamic modelling framework based on "if-then" rules which generates many alternative event sequences (trajectories). Based on expert knowledge, observations and literature, we use this framework to assess the effect of permanent changes in surface water and herbivores diversity on vegetation and socio-economic transitions in an East African savanna. Results show that water availability drives changes in vegetation and socio-economic transitions, while herbivore functional groups had highly contrasted effects depending on the group. This first use of EDEN in a savanna context is promising for bridging expert knowledge and ecosystem modelling.

During HIV-1 virus assembly the genomic RNA (vRNA) is selected for packaging by the viral protein Gag because it contains a specific packaging signal, Psi. While there have been numerous studies of Gag-Psi interactions, there is almost no information on kinetic aspects of this interaction. We have investigated the kinetics of Gag binding to different RNAs using switchSENSE DRX2 technology. We measured the association rate of Gag binding to monomeric Psi, to a “Multiple Binding Site Mutant” Psi that is inactive for genome packaging in vivo, and to a scrambled Psi. We discovered that Gag binds more rapidly to Psi RNA than to the mutant or scrambled RNAs. Furthermore, rapid Gag association kinetics are retained within sub-regions of Psi: Gag associates more rapidly with RNA containing only the 3’ two of the three Psi stem-loops than with monomeric RNA containing the 5’ two stem-loops or a scrambled RNA. No differences were detectable with individual Psi stem-loops. Interestingly, the rate of binding of Gag molecules to Psi increases with increasing Gag concentration, suggesting cooperativity in binding. The results are consistent with the hypothesis that selectivity in packaging derives from kinetic differences in initiation of particle assembly.

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, neglected tropical diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context.Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives.Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics.In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa.Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies.Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

The influence of playing formations and team ranking on the physical performance of professional soccer players is an open question that needs to be explored. The present study aimed to investigate the impact of these factors on the physical exertion of Serie A soccer players. We analyzed match data from 375 players, categorizing teams based on their final ranking and comparing performance across different playing formations. Krustal-Wallies test, and Dunn test with Bonferroni adjustment, revealed that high-ranking (HR) teams exhibited a higher percentage of high-intensity (HI) accelerations compared to mid-ranking teams, suggesting the critical role of HI efforts in achieving favorable match outcomes. Moreover, the 4-3-3 playing formation was associated with greater acceleration demands than other formations, particularly in HR teams. Our study also established benchmarks for various performance metrics, enabling coaches to assess player performance and identify potential signs of overtraining. These findings contribute to a deeper understanding of the physical demands in soccer and offer practical implications for coaches and players in optimizing training and performance strategies.

The healthcare situation in Africa is characterized by limited resources and a high burden of disease, presenting significant challenges to the healthcare systems in the region. The integration of Artificial Intelligence (AI) technologies in African healthcare holds immense potential for addressing these challenges and improving health outcomes. This paper aims to provide a comprehensive understanding of the current healthcare landscape in Africa and the urgent need for AI-driven solutions.In addition to discussing the potential applications of AI in African healthcare, such as disease diagnosis, treatment planning, and medical imaging analysis, this manuscript seeks to shed light on the specific challenges hindering the implementation of AI in the region. By providing detailed examples and case studies, we aim to offer insights into the obstacles that need to be overcome and potential solutions that can be pursued.By leveraging AI technologies, Africa has the opportunity to revolutionize its health systems, bridging the gap in access to quality healthcare across the continent. The objective of this review article is to not only highlight the potential benefits that AI can bring to African healthcare but also to emphasize the urgent need for action and collaboration in implementing and scaling up AI innovations in the region. By doing so, African countries can pave the way for improved health outcomes and ensure that their populations receive the healthcare services they deserve.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

In this paper we introduce a computational model for the simulation of hypervelocity impact (HVI) phenomena which is based on the Discrete Element Method (DEM). Our paper constitutes the first application of DEM to the modeling and simulating of impact events for velocities beyond 5 kms⁻¹. We present here the results of a systematic numerical study on HVI of solids. For modeling the solids, we use discrete spherical particles that interact with each other via potentials. In our numerical investigations we are particularly interested in the dynamics of material fragmentation upon impact. We model a typical HVI experiment configuration where a sphere strikes a thin plate and investigate the properties of the resulting debris cloud. We provide a quantitative computational analysis of the resulting debris cloud caused by impact and a comprehensive parameter study by varying key parameters of our model. We compare our findings from the simulations with recent HVI experiments performed at our institute. Our findings are that the DEM method leads to very stable, energy–conserving simulations of HVI scenarios that map the experimental setup where a sphere strikes a thin plate at hypervelocity speed. Our chosen interaction model works particularly well in the velocity range where the local stresses caused by impact shock waves markedly exceed the ultimate material strength.

Stueckelberg-Horwitz-Piron (SHP) electrodynamics formalizes the distinction between coordinate time (measured by laboratory clocks) and chronology (temporal ordering) by defining 4D spacetime events x^μ as functions of an external evolution parameter τ. Classical spacetime events x^μ (τ) evolve as τ grows monotonically, tracing out particle worldlines dynamically and inducing the five U(1) gauge potentials through which events interact. Since Lorentz invariance imposes time reversal symmetry on x⁰ but not τ, the formalism resolves grandfather paradoxes and related problems of irreversibility. The action involves standard first order field derivatives but includes a higher order τ derivative that while preserving gauge and Lorentz invariance removes certain singularities and makes the related QFT super-renormalizable. The resulting field equations are Maxwell-like but τ-dependent and sourced by a current that represents a statistical ensemble of instantaneous events distributed along the worldline. The width λ of this distribution defines a correlation time for the interactions and a mass spectrum for the photons that carry the interaction. As λ becomes very large, the photon mass goes to zero and the field equations become τ-independent Maxwell’s equations. Maxwell theory thus emerges as an equilibrium limit of SHP, in which λ is larger than any other relevant time scale. Particles and fields are not constrained to mass shells in SHP theory, and by exchanging mass may produce pair creation/annihilation processes at the classical level. On-shell evolution with fixed particle masses is restored through a self-interaction associated with the 5D wave equation.

In the intricate dance between viruses and host cells, RNA-binding proteins (RBPs) serve as crucial orchestrators of gene expression and cellular processes. We will delve into the riveting realm of viral hijacking, where viruses deftly exploit host RBPs to manipulate cellular machinery for their replication and pathogenesis. Through a masterstroke of molecular subterfuge, viruses co-opt RBPs involved in various facets of RNA metabolism - from transcription to degradation - to promote viral gene expression and evade host immune defenses. This manipulation leads to global alterations in cellular RNA metabolism, impacting essential host genes vital for immune responses and homeostasis.Unveiling this clandestine alliance between viruses and RBPs is not just a scientific pursuit, but a critical imperative for devising innovative antiviral strategies to disrupt these interactions. By unraveling the intricate interplay between viral proteins and host RBPs throughout the viral life cycle - from entry to assembly - researchers aim to identify vulnerabilities that can be targeted for therapeutic intervention. Strategies such as disrupting viral protein-RBP interactions hold promise for inhibiting viral replication and curbing pathogenesis, offering a beacon of hope in the battle against viral infections.Our manuscript elucidates the indispensable roles played by RBPs in viral replication, translation, and pathogenesis, shedding light on the molecular mechanisms driving viral success. Delving deeper, it explores how viruses intricately entwine with host RBPs, manipulating cellular signaling pathways to create a hospitable environment for viral spread. By dissecting these manipulative tactics, researchers uncover new targets for antiviral therapy, envisioning a future where tailored interventions disrupt viral-host RBP interactions with precision and efficacy.As the narrative unfolds, the therapeutic implications of targeting RBPs or their interactions with viral proteins emerge as a promising frontier in the fight against viral infections. From small molecule inhibitors to RNA-based therapeutics, innovative approaches are on the horizon, offering new avenues for combatting viral diseases. We set the stage for future research, beckoning researchers to delve deeper into the molecular intricacies of viral hijacking of RBPs and charting a course towards novel therapeutic interventions that promise to reshape the landscape of antiviral therapy.\Ultimately, it beckons the scientific community to embark on a voyage of discovery, unraveling the secrets of viral hijacking of RBPs and paving the way for transformative advances in antiviral therapeutics. The stage is set for a new chapter in the battle against viral infections, where knowledge becomes the sword and innovation the shield against the pernicious machinations of viral pathogens.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by distilling knowledge from foundation models. Specifically, we first employ ChatGPT and DALL-E to synthesize reference images of unseen categories from text prompts. Then, the test image is aligned with text and reference images using CLIP and DINO. Finally, the predicted logits are aggregated according to their confidence to produce the final prediction.Experiments are conducted on multiple datasets, including CIFAR-10, CIFAR-100, and TinyImageNet. The results demonstrate that our model can significantly improve classification accuracy compared to previous approaches, achieving AUROC scores above 96\% across all test datasets. Our code is available at https://github.com/1134112149/MICW-ZIC.

We introduce derangetropy, a novel functional measure designed to characterize the dynamics of information within probability distributions. Unlike scalar measures such as Shannon entropy, derangetropy offers a functional representation that captures the dispersion of information across the entire support of a distribution. By incorporating self-referential and periodic properties, it provides deeper insights into information dynamics governed by differential equations and equilibrium states. Through combinatorial justifications and empirical analysis, we demonstrate the utility of derangetropy in depicting distribution behavior and evolution, providing a new tool for analyzing complex and hierarchical systems in information theory.

Knowledge is not merely the characteristic of certain pieces of information being deemed true, believed and justifiable, but rather a process. The sentences that reaches the status of knowledge forms a universe that provides an interpretation of the reality one aims to describe. Language exchanges information through communication and the justification, if concludes successfully with respect to a sentence, it elevates the information to the status of knowledge. We provide a model where we define the fundamental elements of communication and grammar. As a result, knowledge must be \open\ (everyone must be able to acquire it), \free\ (everyone must be able to justifiy) and \tidy\ (everyone must be able to retrace the justification process in all its parts recursively). Ultimately, the development of knowledge processing, referred to as \emph{knowmatic}, represents the evolution of the information technologies (\it).

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Mammarenavirus Wenzhouense (WENV) is broadly distributed in Asia, while the he dynamics process of WENV infection remains unclear. A field derived strain of WENV was used to inoculate Sprague-Dawley (SD) rats by intramuscular injection, the process of virus infection was de-tected by controlling time for 28 days. Viral RNA became detectable in the blood at 3 d pi, and remained detectable for about 12 d. In most organ tissues, viral RNA peaked at 7 d pi, then began to decline by 14 d, however remained detectable in intestine and brain tissues at 21 and 28 d pi. Virus shedding had been detected from fecal samples for 5 days from 6 to 11 d pi using qRT-PCR, and was recovered from feces collected at 8 d pi. Horizontal contact infection occurred among cage-mates at 14 and 21 d pi. Antibodies against the nucleocapsid were detected at 5 dpi, next increased and persisted until the end of experiment. The results enabled to discriminate the kinetic of viremic response, virus shedding in feces and horizontal transmission, as well as the potential sites for WENV replication or maintenance of the virus in nature.

A prospective clinical-epidemiological study was conducted to evaluate the dynamics of biomarkers in hospitalized patients with moderate to severe COVID-19 (according to the National Institute of Health severity scale) treated with Anakinra, compared to those receiving standard treatment. The study included 65 patients with a mean age of 75.51±9.54 years, ranging from 44 to 99 years. Among the study participants, 38 (58.5%) were male and 27 (41.5%) were female. The patients were divided into two groups: control group (standard treatment): 24 patients (36.9%) and target group (Anakinra treatment): 41 patients (63.1%). The results showed a significant relationship between the dynamics of leukocytes, ASAT, LDH, D-dimer, ferritin, and IL-6 and the administration of Anakinra. In the control group, no significant dynamics were observed in leukocyte count, D-dimer, ferritin, or IL-6. A statistically significant decrease in ASAT and LDH values in the control group was noted only on Day 7. In the Anakinra group, significantly (or with borderline statistical significance, p<0.1) lower mean values were observed for leukocytes, NLR, D-dimer, ferritin, and IL-6 on Days 3 and 7 compared to the control group. Additionally, significantly lower CRP values were detected in the Anakinra group on Day 7 compared to the control group.

In patients with heart failure, the evaluation of left ventricular (LV) diastolic function is vital, offering crucial insights into hemodynamic impact and prognostic accuracy. Echocardiography stands as the primary imaging modality for diastolic function assessment, and using it effectively requires a profound understanding of the underlying pathology. This review covers four main topics: first, the fundamental driving forces behind each phase of normal diastolic dynamics, along with the physiological basis of two widely used echocardiographic assessment parameters, E/e' and mitral annulus early diastolic velocity (e'); second, the intricate functional relationship between the left atrium and LV in patients with varying degrees of LV diastolic dysfunction (LVDD); third, the role of stress echocardiography in diagnosing LVDD and the significance of parameter changes in this context; and fourth, the clinical utility of evaluating diastolic function from echocardiography images across diverse cardiovascular care areas.

Thyroid surgery rates have tripled over the past 3 decades making it one of the most frequently performed procedures within general surgery. They are associated with the possibility of serious postoperative complications having a significant impact on the patient's quality of life. The recurrent laryngeal nerve (RLN) palsy and the external branch of the superior laryngeal nerve (EBSLN) palsy are, next to hypoparathyroidism and bleeding after operation, one of the most common complications. The introduction of neuromonitoring into thyroid surgery, enabling both confirmation of anatomical integrity and assessment of laryngeal nerve function, was a milestone that has begun a new era in thyroid surgery. The International Neural Monitoring Study Group made standardization of the technique of RLN and EBSLN monitoring during thyroid and parathyroid surgery which increased prevalence of neural monitoring during thyroidectomy. Current status of IONM and benefits of its use have been presented in this publication.

The landscape of business intelligence (BI) is marked by rapid growth, extensive research, and practical implementations, reflecting its sustained relevance in modern business strategies. This paper delves into the dynamics of BI, particularly focusing on the interplay between maturity and agility. While BI maturity emphasizes optimization and predefined goals, BI agility prioritizes adaptability and response to change. The paper highlights the contradictions between these concepts, emphasizing the need for a balanced approach in BI strategy formulation.The role of organizational culture in fostering BI agility is explored through literature review and interviews with BI professionals. Key insights include the necessity of an open culture for effective BI utilization, clear strategic vision, decision delegation, information sharing, business-IT cooperation, and a learning-oriented mindset. These cultural factors contribute significantly to BI effectiveness and the ability to navigate dynamic business landscapes. The paper proposes pillars of agile maturity supported by an open organizational culture, including efficient communication, information integration, creative problem-solving, and close collaboration between business and IT. These pillars underscore the importance of a flexible and open mindset in navigating BI challenges and ensuring sustained value. Empirical validation and further research are suggested to deepen our understanding of the impact of organizational culture on BI agility and effectiveness.

The work proposes a backtesting analysis in comparison between the Lee-Carter and the Cairns-Blake-Dowd mortality models, employing Italian data. The mortality data come from the Italian National Statistics Institute (ISTAT) database and span the period 1975-2014, over which we computed back-projections evaluating the performances of the models in comparisons with real data. We propose three different backtest approaches, evaluating the goodness of short-run forecast versus medium-length ones. We find that both models were not able to capture the improving shock on the mortality observed for the male population on the analyzed period. Moreover, the results suggest that CBD forecast are reliable prevalently for ages above 75, and that LC forecast are basically more accurate for this data.

Ti-Al based alloys, particularly TiAl and Ti3Al two-phase alloys, have garnered significant attention as potential replacements for various high-temperature structural materials due to their exceptional properties, including low density, oxidation resistance, and high strength at elevated temperatures. Despite these advantages, experimental studies on the microstructure evolution of Ti-Al based alloys under complex conditions remain challenging to observe and characterize. This review article examines the current research on molecular dynamics (MD) simulations of Ti-Al based alloys, focusing on two-phase Ti-Al alloys, Ti-Al amorphous alloys, Ti-Al composite materials, and the welding and multi-layer/film applications of Ti-Al alloys. The review highlights the unique capabilities of MD simulations in predicting the behavior of Ti-Al based alloys and addresses existing scientific challenges. Furthermore, the article discusses future research directions and development prospects in this field.

Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been performed to resolve this issue. The methods of KGC can be classified into two major categories: rule-based reasoning and embedding-based reasoning. The former has high accuracy and good interpretability, but a major challenge is to obtain effective rules on large-scale KGs. The latter has good efficiency and scalability, but it relies heavily on data richness and cannot fully use domain knowledge in the form of logical rules. We propose a novel method that injects rules and learns representations iteratively to take full advantage of rules and embeddings. Specifically, we model the conclusions of rule groundings as 0-1 variables and use a rule confidence regularizer to remove the uncertainty of the conclusions. The proposed approach has the following advantages: 1) It combines the benefits of both rules and knowledge graph embeddings (KGEs) and achieves a good balance between efficiency and scalability. 2) It uses an iterative method to continuously improve KGEs and remove incorrect rule conclusions. Evaluations on two public datasets show that our method outperforms the current state-of-the-art methods, improving performance by 2.7% and 4.3% in mean reciprocal rank (MRR).

The Chikungunya virus (CHIKV) poses a significant global public health concern, especially in Africa. Since its first isolation in Tanzania in 1953, CHIKV has caused recurrent outbreaks, challenging healthcare systems in low-resource settings. Recent outbreaks in Africa highlight the dynamic nature of CHIKV transmission and the challenges of underreporting and underdiagnosis. Here, we review literature and analyze publicly available cases, outbreaks, and genomic data providing insights into the epidemiology, genetic diversity, and transmission dynamics of CHIKV in Africa. Our analyses reveal the circulation of geographically distinct CHIKV genotypes, with certain regions experiencing a disproportionate burden of disease. Phylogenetic analysis of sporadic outbreaks in West Africa suggests repeated emergence of the virus through enzootic spillover, which is markedly different from inferred transmission dynamics in East Africa, where the virus is often introduced from Asian outbreaks, including the recent reintroduction of the Indian Ocean lineage from the Indian subcontinent to east Africa. Furthermore, there is limited evidence of viral movement between these two regions. Understanding the history and transmission dynamics of outbreaks is crucial for public health planning. Despite advances in surveillance and research, diagnostic and surveillance challenges persist. This review and secondary analysis highlight the importance of ongoing surveillance, research, and collaboration to mitigate the burden of CHIKV in Africa and improve public health outcomes.

The assessment of existing bridges constitutes a serious challenge for the researchers since their safety evaluation is a complex task due to several sources of uncertainty affecting the parameters identified in the knowledge process. In particular, a high level of uncertainties may arise in the case of prestressing systems requiring special investigation strategies and experimental technologies. The paper aims to investigate how the knowledge uncertainties influence the safety assessment of existing post-tensioned bridges. The semi-probabilistic approach is used, and uncertainties related to the knowledge process are described considering the number of tests, the confidence level assumed for the estimation of the problem parameters, and the measurement errors of in-situ tests. Results concerning a typical post-tensioned bridge are reported and the variability of the outcomes is discussed and compared with a reference case of “perfect knowledge”. The main results of the study confirm the general robustness of the semi-probabilistic Eurocodes safety format and provide an overview of dispersion due to different choices about the number of tests and confidence level. The measurement errors may lead to significant under-estimation of the computed structural capacity with respect to the reference one.

CD4 count recovery is the main goal for an HIV patient who initiated ART. Some patients respond positively to ART and attain CD4 count recovery, some patients might fail to recover their CD4 count due to non-adherence, treatment resistance, and virological failure, leading to HIV-related complications and death. The purpose of this study is to find the determinants of death in patients who failed to recover their CD4 count after initiating antiretroviral therapy. The data used in this study was obtained from KwaZulu-Natal, South Africa, where 2528 HIV-infected patients with a baseline CD4 count of 200 cells/ were initiated on ART. We used a Fine-Gray sub-distribution hazard and cumulative incidence function to estimate potential confounding factors of death, where CD4 count recovery was a competing event for failure due to death. Patients who were not suffering from tuberculosis were 1.33 times at risk of dying before attaining CD4 count recovery [aSHR 1.33; 95% CI (0.96-1.85] compared to those who had no tuberculosis. Rural patients had a higher risk of not recovering and leading to death [aSHR 1.97; 95%CI (1.57-2.47)] than those from urban areas. The patient’s tuberculosis status, viral load, regimen, baseline CD4 count, and location were significant contributors to death before CD4 count recovery. Intervention programs targeting HIV testing in rural areas for early ART initiation and promoting treatment adherence are recommended.

This scientific paper explores the increasingly widespread use of collaborative robots in the industrial environment, highlighting them as a solution to challenges related to the high costs of human labour and the associated management difficulties. The authors focus on the process of defining requirements for the implementation of collaborative robots, providing strategies based on mathematical management models. This approach translates into significant benefits for companies, such as cost reduction, quality improvement, and increased operational efficiency. Additionally, the paper proposes an innovative mathematical model for assessing total costs, budgeting, profitability, and electric power consumption associated with the operation of collaborative robots. This model allows for a comparison between the costs involved in using human labour and those associated with collaborative robots, providing critical information for technological investment decisions. The efficiency of this mathematical model is demonstrated through a practical application, where collaborative robots were integrated into a production environment, and costs and efficiency were evaluated compared to the use of human labour. This scientific paper provides a systematic and efficient approach to implementing collaborative robots in industrial processes, benefiting from strategies based on mathematical management models and an original mathematical model for evaluating performance and associated costs.

Multimodal knowledge graph completion necessitates the integration of information from multiple modalities (such as images and text) into the structural representation of entities to improve link prediction. However, most existing studies have overlooked the interaction between different modalities. To address this issue, this paper proposed a Transformer-based knowledge graph link prediction model (MM-Transformer) that fuses multimodal features. Different modal encoders are employed to extract structural, visual and textual features, and hybrid key-value calculations are performed on features from different modalities based on the Transformer architecture. The similarities of textual tags to structural tags and visual tags are calculated and aggregated respectively, and multimodal entity representations are modeled and optimized to reduce the heterogeneity of the representations. Experimental results demonstrate that, compared to the current multimodal state-of-the-art methods, the proposed method achieves significant performance improvements in knowledge graph link prediction tasks. This proves that the proposed method effectively addresses the problem of multimodal feature fusion in knowledge graph link prediction tasks.

Metabolites present in the hemolymph of honey bees play a crucial role in modulating the metamorphic process within the species. However, the precise alterations in metabolite composition, along with the associated variances and regulatory pathways implicated during the larvae-to-pupae metamorphosis of honey bees, remain incompletely elucidated. In this investigation, we gathered hemolymph samples from honey bee larvae at three distinct physiological stages-feeding, prepupal, and pupae and subjected them to metabolite analysis utilizing the liquid chromatography-mass spectrometry (LC-MS) technique. Employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), we identified significant differential metabolites and conducted a metabolic pathway analysis on those differentially up-regulated during the prepupal and pupae stages. Notably, metabolites up-regulated in the hemolymph of prepupal stage larvae primarily governed glucose metabolism and fat digestion and absorption, while those in pupae stage larvae were involved in regulating chitin and lipopolysaccharide precursor formation, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. These findings bear significant implications for advancing our comprehension of the metamorphic processes in honey bees.

On 17 October 1947, Niels Bohr was made a knight of the Order of the Elephant by the King of Denmark in view of his outstanding achievements and contributions to science. Bohr designed his own coat of arms that featured a pattern of Yin and Yang (Tai Chi symbol) to symbolize the wave-particle complementarity. However, Bohr’s Yin-Yang diagram (YYD) was neither drawn based on the principles of quantum mechanics, nor did it originate from the traditional Taoist YYD. Scientists still have doubts about the legitimacy of using YYD as the icon of the wave-particle complementarity, because the YYD belonging to quantum mechanics itself is unknown so far. This article reports the YYDs existing in quantum mechanics and justifies the role of YYD in the wave-particle duality by showing that any system, whether classical or quantum, has an ideal YYD as long as it satisfies Bohr’s principle of complementarity (BPC). The deviation of a deformed YYD from the ideal YYD indicates the extent to which a real system satisfies BPC. This article constructs the quantum YYD by the complex quantum trajectory of a particle tunneling via a step barrier, which displays the continuous transition between the wave behavior and the particle behavior. It appears that the YYD designed by Bohr in his coat of arms resembles the YYD generated by tunneling motion, not only in appearance but also in the governing equation.

In this research, the primary goal was to investigate the relationship between strategic planning and organizational performance in Iraq's manufacturing context. The study's primary data sources were 360 manager respondents. The structured questionnaire was used to collect primary data from manufacturing firms located throughout Iraq. To analyze the result, the researcher used descriptive statistics, correlation, and multiple regression analysis. SPSS version 16 software was used to conduct data analysis. The result reveals that the process of strategic planning has a beneficial effect on financial performance. Environmental scanning has a statistically significant positive effect on a company's non-financial performance. Management participation and planning formality positively and statistically significantly affect a business's non-financial performance at the 10 percent level. The domain of strategy and technique has not impacted the company's non-financial performance.

This work presents a novel approach for the study of the movement of droplets on inclined surfaces under the influence of gravity and chemical heterogeneities. The developed numerical methodology uses data-driven modeling to extend the applicability limits of an analytically derived reduced-order model for the contact line velocity. More specifically, while the reduced-order model is able to capture the effects of the chemical heterogeneities to a satisfactory degree, it does not account for gravity. To alleviate this shortcoming, datasets generated from direct numerical simulations are used to train a data-driven model for the contact line velocity, which is based on the Fourier neural operator and corrects the reduced-order model predictions to match the reference solutions. This hybrid surrogate model, which comprises of both analytical and data-driven components, is then integrated in time to simulate the droplet movement, offering a speedup of five orders of magnitude compared to direct numerical simulations. The performance of this hybrid model is quantified and assessed in different wetting scenarios, by considering various inclination angles and values for the Bond number, demonstrating the accuracy of the predictions as long as the adopted parameters lie within the ranges considered in the training dataset.

The advent of modern satellite technology has transformed observational astronomy and astrophysics, offering unprecedented insights into the large-scale behavior of gravitation and challenging established cosmological models. This technological progress has reinvigorated the study of relativistic cosmology, leading to a critical reassessment of foundational assumptions, particularly the cosmological principle, which posits that the universe is homogeneous and isotropic on large scales. While this principle underpins the Standard Cosmological Model and the Friedmann-Lemaitre-Robertson-Walker metric, emerging data has increasingly been challenging its validity. Central to this investigation are the redshift-distance and light intensity-distance relations, essential for testing cosmological models. The integration of both parametric and nonparametric redshift models provides a more comprehensive analysis, addressing discrepancies in our understanding of the universe's structure and evolution. However, unresolved mysteries, particularly concerning dark matter and dark energy, complicate these models. This research critically examines the cosmological principle using the latest observational data and scrutinizes the Friedmann model's assumptions. The study shows that galaxy formation was most rapid in the early universe, especially within the redshift range of , with notable peaks around at and These two characteristic redshift values, predicted by our standard Friedmann redshift model used in this work, are confirmed by the observational data utilized in this research. The research also highlights that dark matter plays a significantly more critical role than dark energy in the process of galaxy formation. While dark energy primarily affects the large-scale expansion of the universe, dark matter seems to go beyond the domination of local galaxy formation and evolution of cosmic structures. These findings underscore the limitations of current models and contribute to the ongoing refinement of cosmological theories, offering a clearer understanding of the universe’s evolution.

Background: Vestibular Neuritis (VN) induces Acute Vestibular Syndrome (AVS). This study aimed to identify predictive factors of VN recovery by healing and/or compensation. Methods: In this longitudinal and prospective study, 40 patients were included. The first assessment (T0) included medical history taking (general and specific), for cardiovascular risk factors (CVRFs), a battery of diagnostic tests, bithermal caloric test (BCT), video Head Impulse Test (VHIT) and skull vibration induced nystagmus (SVIN). All patients also completed a Dizziness Handicap Inventory (DHI). All assessments were repeated 90±15 days (T3) later. Subjective criteria compensation were based on DHI total score and objective criteria compensation on the instrumental test results. Four groups of patients (A, B, C, D) were delineated by combining patients with normal vs abnormal vestibular tests and patients with normal vs abnormal DH. Results: CVRFs were associated with a poorer recovery of symptoms but not the age or BMI. Vestibular tests mainly BCT (canal paresis %), VHIT (lateral semicircular canal gain) and SVINT (nystagmus slow phase velocity) recovered within normal values in 20%, 20%, 26% of cases respectively at T3 for the 40-patient group. Conclusions: Vascular risk factors (hypercholesterolemia) are correlated with patients who do not recover their symptoms either by healing or compensation. The evolution did not show any significant difference between high or low frequency vestibular tests in patients recovering from their symptoms. However, some patients with objective recovery may continue to show persisting subjective symptoms.

Clinical trials, by contributing to the development of diagnostics and to the search for modern, more effective, and safer therapies, have become one of the most important elements of the healthcare system. The aim of this study was to assess Poles' knowledge and beliefs about clinical trials. Methods: The survey was conducted among Poles aged 18 and over with the help of external company, Ariadna, which is an independent research panel. The questionnaire contained 22 questions, of which 13 questions concerned beliefs and attitudes towards clinical trials. Results: One thousand and seventy-nine participants took part in the study (N = 1079). The mean age of respondents was 44.96 years (SD = 16.30). Slightly more women (N = 568, 52.6%) than men (N = 511, 47.4%) took part in the study. Among the respondents, 86.5% (N = 933) were aware of clinical trials. The main sources of information about clinical trials were the media (53.8%) including the Internet (N= 355, 32.9%), TV (N = 175, 16.2%), press (N = 30, 2.8%) and radio (N = 21, 1.9%). 43.2% (N = 466) of respondents reported little knowledge of clinical trials, while more than three quarters (N = 805, 75.2%) said they would like to learn more about clinical trials. Most respondents (N = 879, 81.4%) agreed with the statement that participation in a clinical trial is completely voluntary, and more than half (N = 580, 53.7%) agreed with the statement that hospitals participating in clinical trials provide better healthcare. The statement that the results of clinical trials are made available to the public was disagreed with by 37.2% (N = 402) of participants. Only 30.3% (N = 327) of participants agreed that clinical trials should be conducted with children. Conclusions: Poles rate their knowledge of clinical trials as low and would like to learn more. To reach the largest possible group of Polish patients, parallel educational activities on multiple levels may have the best effect. Poles' knowledge of clinical trials is mainly based on commercial sources. Further studies in different groups, i.e., clinical trial patients and investigators, are needed.

Parents play an important role in shaping the eating habits of their children through: appropriate selection of food products, snacking between meals and appropriate exposure to information from television advertisements, the press or the Internet. The level of parents' knowledge about proper nutrition and substances added to food is therefore crucial in promoting appropriate dietary choices in children and adolescents in order to prevent diet-related diseases. Objectives: Assessment of the consumption of snack foods by children and adolescents according to the surveyed parents and analysis of parents' knowledge about additional substances in food. Methods: The study was conducted among 129 parents of students from selected primary schools. The research tool was an original survey questionnaire consisting of 24 questions. Survey interviews were conducted with parents during meetings at schools. Statistical analysis was performed using the Pearson ch2 test, the significance level was set to p<0.05. Results: Parents most often indicated sweets, confectionery products and sweetened drinks (25%) and vegetables or fruit (21%) as the form of snack for their children. 46% of children and adolescents drank sweetened drinks at least once a day. Parents had an average level of knowledge about food additives. Significant correlations were found between the level of knowledge of parents of school-age children about food additives and their level of education. Parents' knowledge and education did not significantly influence the level of snack consumption by their children. Children's eating behavior should be considered unsatisfactory. Conclusions: Research has shown the need for parents to be educated about food additives and to pay more attention to reading food labels so that they can reliably prepare their children to make the right food choices. Measures should be implemented to improve the eating habits of children and adolescents.

Kisspeptin, a key neuropeptide derived from the KISS1 gene, is renowned for its critical role in regulating the hypothalamic-pituitary-gonadal axis and reproductive hormone secretion. Beyond its primary function in reproductive biology, emerging research has illuminated its influence in various cancers, mediating significant effects through its interaction with the G-protein coupled receptor, Kisspeptin receptor. This interaction has been implicated in modulating cellular pro-cesses such as proliferation and metastasis, making it a potential target for therapeutic interven-tion. Our study initially screened ten kisspeptin-10 analogs through cytotoxic effects of kisspep-tin-10 (KP10) and its analogs in several cancer types, including cervical, prostate, breast, and gastric cancers, with a particular focus on cervical cancer where the most profound effects were observed. Further exploration using kinase array assays revealed that these analogs specifically alter key kinases involved in cancer progression. Migration assays demonstrated a substantial decrease in cell motility, and Bioluminiscence Resonance Energy Transfer assays confirmed these analogs strong interactions with the kisspeptin receptor. Overall, our results indicate that these KP10 analogs not only hinder cervical cancer cell proliferation but also curtail migration through targeted modulation of kinase signaling, suggesting their potential as therapeutic ayfgents in managing cervical cancer progression. This comprehensive approach underscores the therapeutic promise of exploiting kisspeptin signaling in cancer treatment strategies.

Innovation is an essential key factor in the technology development history. Past research on innovation focused more on the innovation behavior of technology, but seldom described knowledge assets which also influence innovation behavior greatly. The effect of knowledge assets attribute and result on disruptive innovation is therefore regarded as the research topic in this study, where disruptive innovation is divided into outbound and inbound to combine combination-embeddednessandmajor business specificityof knowledge assets as the research model. Manufacturing enterprises in China are proceeded the questionnaire survey, and 173 valid copies are collected. The empirical analysis shows that combination-embeddedness of knowledge assets presents significantly positive effects on major business specificity and outbound innovationof an enterprise but reveals remarkably negative effects on inbound innovation. Enterprises are suggested to constantly accumulate knowledge assets with low major business specificity before disruptive innovation in order to reduce ineffective inbound innovation.

Computational simulations of three-dimensional flow around a NACA 0018 wing with an aspect ratio of AR=5 are carried out using the Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations with the Shear-Stress Transport turbulence model closure. Simulations were performed to capture the aerodynamic stall hysteresis using a developed Pseudo-Transient Continuation (PTC) method based on a dual time step approach in the CFD code OpenFOAM. The flow was characterized by an incompressible Mach number of M=0.12 and a moderate Reynolds number Re=0.67×10^6. The results obtained indicate the presence of a noticeable aerodynamic hysteresis in the static dependencies of the force and moment coefficients, as well as the manifestation of bi-stable flow separation patterns, accompanied by the development of asymmetry in the stall zone. The URANS simulation results are in good agreement with the experimental data obtained for the NACA 0018 finite aspect ratio wing from the low-speed wind tunnel under the same test conditions. A new phenomenological bifurcation model of aerodynamic stall hysteresis under static and dynamic conditions is formulated, which is proven to be able to closely match the experimental data.

Field of Research This research study investigates the role of distributed leadership and culture within small enterprises in Malta, an area in which there has been very limited research. It focuses on the interaction between these two elements and their impact on organizational behaviour and effectiveness. Several studies (Jardon et al 2019; Cope et al., 2011; de Oliveira et al., 2015; Mohanty et al., 2012;) suggest that effective leadership and culture are vital elements in the successful endeavours of small organizations. Schein (2006) further asserts that they are two different faces on the same coin and the dyadic interaction characterizes “how leaders create culture and how culture defines and creates leaders”. Distributed Leadership is a systematic practice of interactions and synergies between leaders, followers, and the situation (Spillane 2013). It is different from traditional leadership, which involves top-down management because it tends to be more democratic and participatory, whereby influence and decision-making are shared among multiple individuals rather than being controlled by a single leader. (Xhemajli et al. 2022) Methodology Adopting a naturalistic approach and guided by an interpretivist, constructivist epistemological stance, this research delves into the intricate interplay between leadership and culture within small businesses. The chosen research methodology employs Grounded Theory, recognized for its robust and systematic process facilitating the observation of patterns through intensive iterative and highly analytical procedures (Charmaz, 2015).In the initial stages of field research, characterized by 'responsive interviewing' (Rubin and Rubin, 2012), data sampling commenced with convenience sampling. This initial phase gradually evolved into a refined strategy incorporating selective sampling and theoretical sampling (Corbin and Strauss, 2015). The shift in sampling approaches reflects a deliberate focus on collecting data based on its specific contribution to the development and comprehension of the dimensions, properties, and characteristics of key concepts.The induction of theory is a product of successive comparative analyses, involving the systematic comparison of data points to identify both similarities and variations. Abductive reasoning is integral throughout the analytical process, with particular emphasis during constant comparative analysis of categories, leading to theoretical integration (Charmaz, 2006). This meticulous approach aligns with the assertion of Strauss and Corbin (2015) that grounding theory in data enhances the likelihood of deriving theories that authentically reflect reality.The key advantage of using this approach is that the analysis carried out goes beyond deep exploration and rich description and actually develops a framework that explains the phenomena observed (Parker and Roffey, 1997, p.218). This is very important if the findings are to contribute to the knowledge enhancement of any field of practiceThe culmination of this research effort is the presentation of a set of interconnected concepts, intricately woven together into an emergent parsimonious model (Corbin and Strauss, 2015). This model serves to explain the dynamic interplay between leadership and culture, elucidating its tangible impact on the effectiveness of small enterprises. Research Contribution:Implications for Practice This research contributes to the literature on leadership and organizational culture in small enterprises, particularly in the context of Malta where such studies are scarce. By providing an initial exploration of these phenomena and their impact on organizational behavior, this paper lays the groundwork for further inquiry into this important area. Furthermore, the study offers practical insights for leaders of small enterprises by elucidating leadership behaviors observed in established organizations. Through applied cases, it demonstrates how the abstract concept of culture can be translated into a practical instrument that supports leadership and organizational development. This not only enriches the understanding of leadership dynamics but also provides tangible strategies for implementation. Moreover, the research contributes empirical evidence on the development of leadership functions within small enterprises, emphasizing the potential of shared and distributed leadership models. By grounding its findings in data, the study equips leaders with a deeper understanding of how these leadership approaches can shape organizational development, fostering more effective management practices in small businesses. Methodologically, the adoption of Grounded Theory methodology showcases its efficacy in exploring complex management phenomena. By employing this approach, the researchers were able to gather rich descriptions and develop an explanatory framework, highlighting its applicability in studying nuanced organizational dynamics. Implications for Policy and Theory This research also holds significant implications for policymakers, academic researchers and institutions involved in supporting small businesses. By providing insights into distributed leadership and cultural influences on organizational effectiveness, it offers a basis for informed policy formulation and support mechanisms. The conceptual model emerging from this study serves as a foundation for designing more targeted policies that cater to the unique needs of small enterprises. By understanding the intricacies of leadership dynamics and cultural contexts, policymakers can develop interventions that foster a conducive environment for small business growth and sustainability. Additionally, academic researchers can use these findings to further refine theoretical frameworks and empirical studies in the field, contributing to ongoing scholarly dialogue and knowledge advancement.

The effect of the heterogeneity in sex factor is considered in the Markovian manpower modeling model. The estimation of the transition probability matrix of the staff flow was done using the maximum likelihood method. The expected future durations of the individuals in the manpower system were obtained under the aggregated and disaggregated (male and female) staff flow as well as their variances. Test for the heterogeneity of the staff based on sex factor was performed and the result of the heterogeneity test shows that there is difference between the disaggregated staff flows. The computed entrant probabilities also indicate that the male group has higher probabilities of attaining higher grades than the female group.

Fibroin-chitosan composites, especially those containing nanohydroxyapatite, show potential for bone tissue regeneration. The physicochemical properties of these biocomposites depend on the compatibility between their components. In this study, the intermolecular interactions of fibroin and chitosan were analyzed using a molecular dynamics approach. Two types of systems were investigated: one containing acetic acid and the other containing calcium (Ca²⁺) and hydrogen phosphate (HPO₄²⁻) ions mimicking hydroxyapatite conditions. After obtaining the optimal equilibrium structures, the distributions of several types of interactions including hydrogen bonds, ionic contacts, and hydrophobic contacts along with the structural and energetical features were examined. The calculated binding energy values for the fibroin-chitosan complexes confirm their remarkable stability. The high affinity of fibroin for chitosan can be explained by the formation of a dense network of interactions between considered biopolymers. These interactions were found to be primarily hydrogen bonds and ionic contacts involving ALA, ARG, ASN, ASP, GLN, GLU, GLY, LEU, PRO, SER, THR, TYR, and VAL residues. As it was established, the complexation of fibroin with chitosan maintains the β-sheet conformation of the peptide. β-Sheet fragments in fibroin are involved in the formation of significant amount of hydrogen bonds and ionic contacts with chitosan.

Background/Objectives: Adolescence represents a period of heightened vulnerability to risky sexual behaviours. In Portugal, adolescents are initiating sexual activity at increasingly younger ages. Yet, there are no studies evaluating their knowledge about sexually transmitted diseases and prevention methods at an earlier stage of their development. Study how middle school ad-olescents define their sexuality and assess their level of information regarding sexuality, STI, and prevention methods. Additionally, it explores adolescents' sources of information on sexuali-ty-related issues. Methods: A cross-sectional observational study was conducted involving the application of a questionnaire to 750 9th-grade students of Vila Nova de Gaia, Porto, Portugal. The questionnaire covered topics such as perceptions of sexuality, knowledge of sexuality and STI, methods of transmission prevention, and sources of information. Results: The mean age was 14.4 years, with 19.9% reporting having already initiated sexual ac-tivity. Overall, the knowledge about sexually transmitted infections is low, pointing to 7.08 out of a maximum score of 18. Condoms and immunisation are well-recognized preventive measures, but many misconceptions persist. Better knowledge is associated with better attitudes in the likelihood of having sexual intercourse. Conclusions: This study reveals adolescents' limited sexually transmitted infections knowledge, misconceptions about prevention, and reliance on peers and the internet over trusted sources, highlighting the need for comprehensive sexual education in middle school.

Traditional African vegetables (TAV) are an important component of an ecologically sustainable approach to addressing food and nutrition security in Africa (countries?). This review aims to identify the scope of the knowledge translation literature that exists across multiple aspects of the TAV value chain and to identify gaps in knowledge translation research. A scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was conducted. The Population, Concept, and Context (PCC) framework was used to guide the development of inclusion and exclusion criteria for this review. Web of Science, Scopus, Science direct and ABI/Inform databases were searched to identify relevant studies which were then screened using the Covidence online software to select studies and extract information for analysis and reporting. A total of 182 studies/articles were selected and examined. The most significant knowledge documented about TAVs is their vast genetic diversity contributing to their rich nutritional and medicinal properties, their ability to withstand harsh environmental conditions such as drought, pests, and diseases as well as the effects of various post-handling techniques on nutrient content of TAVs with the production phase of TAVs value chain being the most studied. This review found that although there is a breadth of knowledge created about TAVs, there is still little documentation of transferring and utilizing of this knowledge across the TAVs value chain. This review identified that more research is needed to document strategies and practices for the transfer and utilisation of knowledge to ensure it is utilized to enhance nutritional outcomes across Africa.

The Huaniushan lead-zinc-silver deposit is a hydrothermal sedimentary exhalative deposit (SEDEX). The mining area has complex geological conditions. The main tectonic structure in the area is the Huaheitan-Shuangfengshan Fault (F3), which controls the distribution of strata and magmatic rocks. Since the discovery of the Huaniushan Lead-zinc-silver polymetallic deposit, there are diverse understandings of the deposit's genesis and mineralization law, making it challenging to meet the geological interpretation requirements of the Huaniushan Lead-zinc-silver polymetallic deposit. Moreover, a single geophysical exploration method utilizes limited rock physical parameters, making it difficult to effectively characterize underground structures. The integrated exploration of multiple geophysical methods can effectively integrate the geophysical characteristics of different rock physical parameters, reducing the multiplicity and uncertainty of geophysical data inverse interpretation. The comprehensive interpretation of three-dimensional inversion based on various geophysical data, the construction of geological-geophysical models based on geological grounds, the establishment of hidden ore exploration and positioning, and the rapid evaluation of geophysical technology systems are the current development trends in mineral exploration. In light of this, this paper conducts research on the three-dimensional inversion interpretation of the heavy magnetoelectric exploration data of the Huaniushan sedimentary exhalative deposit lead-zinc-silver polymetallic deposit in the mining area and constructs a three-dimensional geological-geophysical model of the study area based on the obtained three-dimensional physical structure of the underground three-dimensional density, magnetization intensity, resistivity, and polarizability structure of the study area, in combination with the information of related geology and drilling holes. It comprehensively interprets the favorable mineralization parts in the study area.

This paper, from the perspective of Mode 3 of knowledge production, takes the important carrier of knowledge, scientific research papers, as the research object. The mainstream databases (SCIE and SSCI) are chosen as the data sources. Firstly, bibliometric methods are applied to analyze the cooperation status among the main bodies of paper production, including authors and their affiliated institutions, countries, and disciplines; secondly, the Analytic Hierarchy Process (AHP) is used to construct a comprehensive evaluation model for the state of research cooperation in carbon neutrality. Based on the evaluation model, the cooperation characteristics of different disciplinary fields and the relationship between paper cooperation status and high-impact research papers are comparatively analyzed. At the same time, an analysis is conducted on the support from the government, the sponsorship from enterprises, and the participation of the public in the process of paper production. Finally, some suggestions are put forward on how to build an innovative ecosystem for carbon neutrality research from the perspective of Mode 3 of knowledge production.

Our recent analysis of empirical limit order flow data in financial markets reveals a power-law distribution in limit order cancellation times. These times are modeled using a discrete probability mass function derived from the Tsallis q-exponential distribution, closely aligned with the second form of the Pareto distribution. We elucidate this distinctive power-law statistical property through the lens of agent heterogeneity in trading activity and asset possession. Our study introduces a novel modeling approach that combines fractional Lévy stable motion for limit order inflow with this power-law distribution for cancellation times, significantly enhancing the prediction of order imbalances. This model not only addresses gaps in current financial market modeling but also extends to broader contexts such as opinion dynamics in social systems, capturing the finite lifespan of opinions. Characterized by stationary increments and a departure from self-similarity, our model provides a unique framework for exploring long-range dependencies in time series. This work paves the way for more precise financial market analyses and offers new insights into the dynamic nature of opinion formation in social systems.

This paper presents how social media platforms and their components could be integrated into the KMS for the purpose of managing and sustaining Somali Indigenous knowledge (IK). The discovery of knowledge and its utilization is now fuelled by the advancement of technology, which connects users to external sources through using different platforms. The rapidly increasing use of SM and mobile technologies create opportunities to form knowledge networks that can facilitate the process of creating, preserving, and sharing knowledge and skills that are unique to communities in the Somali context. Somali society being an oral society with rich IK and other cultural practices lacked a framework in place to organize the existing knowledge in various methods. The paper refers to existing methodology E-learning and KM functions. This method first identifies functional similarities between SM, E-Learning, and KM systems and their interactions if they are properly integrated. The proposed framework consists of two functional parts; KM and SM. The KM part involves knowledge capturing, validation, formatting, storing, and distribution. SM part of the framework involves knowledge presentation, sharing, and application. Since the Somalis are said to be an oral society, this framework helps to capture and disseminate IK residing in knowledge holders.

We develop new theory from a broad case-study approach to explore a better understanding of what constitutes knowledge and its value as identifiable to human-machine teams. From past research, this trail of exploration will lead to these initial questions: What is the value of debate in the furtherance of knowledge? Will machines with AI be able to contribute to a debate if we humans cannot define it or determine its value sufficiently for a machine’s understanding, contribution, exploration and identification? Like humans, as teammates, machines must be able to determine with AI what constitutes the usable knowledge that contributes to a team’s success in the field (e.g., testing "knowledge" in the field, identifying new knowledge, using knowledge to develop innovation) or its failure (viz., trouble shooting; identifying weaknesses; discovering vulnerabilities, hiding by deception). It matters not whether a debate is public, private or unexpressed by an individual human or machine agent alone; we speculate in this exploration that the process advances the science of autonomous human-machine teams and assists in interpretable machine learning. We conclude with questions and a speculation: How does a human become aware or express its awareness of knowledge? Can a machine be as expressive as its human teammates? And how does a human-machine teammate become aware that its teammates possess sufficient knowledge to perform a task? We speculate that the structure of “knowledge," once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its applications are multifaceted (akin to maximum entropy production). The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric.

This paper proposes a novel hybrid control strategy for quadrotor unmanned aerial vehicles (UAVs) based on bio-inspired neural dynamics. This strategy addresses the velocity jump issue prevalent in traditional backstepping control and the control signal chattering problem common in traditional sliding mode control. The control system comprises an outer-loop bio-inspired backstepping controller and an inner-loop bio-inspired sliding mode controller, ensuring smooth trajectory tracking even in the presence of external disturbances. The stability of the quadrotor UAV control system is rigorously analyzed using Lyapunov stability theory. Extensive comparative simulation experiments are conducted to assess the robustness and effectiveness of the control system. Results demonstrate superior trajectory tracking performance of the proposed control system compared to other control systems for quadrotor UAVs.

In the dynamic interplay between people and their physical environments, the Global South stands as a mosaic undergoing a multitude of transformative influences in architecture and urbanism where examining social sustainability becomes imperative. While prevailing attention remains on environmental and economic sustainability, this study addresses a persistent gap in the urban literature by focusing on the dynamic and manifold nature of social sustainability. Positioning itself within the context of sustainable development, the study links the pursuit of social aspects of sustainability with selected unique urban contexts from the Global South. Five cases that include Alexandria (Egypt), Tripoli (Libya), Basra (Iraq), Lilongwe (Malawi), and Accra (Ghana) are discussed through multi-layered investigations that involve attitude surveys, interviews, focus groups, participatory systematic observations, and behavioral mapping, engaging directly with inhabitants and stakeholders. Uncovering people-place narratives in the identified contexts, the cases are developed into five knowledge typologies that serve as practical tools for planning and design decision-making, policy formulation, and academic discourse. Discussions are conceived to demonstrate the transformative role people-place narratives play in fostering a more sustainable and equitable urban future. Conclusions are drawn to offer practical insights for stakeholders involved in various capacities in shaping the urban landscape of the Global South.

The objective of this research is to establish a better understanding of the current landscape of digital learning research and sustainable development by using CiteSpace. First, we retrieved published publications from the Web of Science (2004–2022). Following that, we examined the primary research strengths and important subjects of digital learning from two perspectives: collaboration networks (including collaborative networks across countries, institutions, and authors) and co-citation networks. We examined the co-citation network from three perspectives: cluster analysis, the most active citers, top references. Furthermore, referenced journals, popular themes, and rising trends were examined. These findings indicate the primary study subjects in the field of digital learning, the most intriguing research literature, and each period's emerging research hotspots. Finally, we proposed further study ideas for future paths.

The report details out programming approach for automating warehouse using Answer Set Programming.

This research investigates the complex transformation of the Baltic Clean and Dirty Tankers markets from 1998 to 2023. Our study quantifies the market multifractality in two distinct periods, from 1998 to 2010 and 2010 to 2023. We compare multifractal measures of the original, surrogated, and shuffled data, which indicate high multifractality and a complex market structure influenced by temporal organization and inherent volatility. Shuffled data analysis emphasizes the critical role of temporal sequences within market behaviour. Additionally, the study delves into external factors such as regulatory changes, economic disturbances, technological innovation, and environmental concerns contributing to market complexity. A distinct shift in multifractal values between the two periods suggests an evolution from crisis-induced complexities to a market influenced by diversified and modern dynamics. Our findings offer a concise understanding of the multifractal nature of the tanker markets and how global crises can impact the markets, emphasizing the importance of considering systemic risks in freight market analysis.

In mesoscale eddies, chemical properties and biological composition are different from those in surrounding water, due to their unique physical processes. It is unclear how physical-biological coupling occurs in warm-core eddies, particularly since no study has examined the relationship between bacteria and viruses. The purpose of the present study was to examine the influence of anticyclonic warm eddy on the relationship between bacterial and viral abundances as well as viral activity (viral production) in different depths. In the core of warm eddy, bacterial abundance (0.48 to 2.82 ×105 cells mL−1) fluctuated less than that of the outside eddy (1.12 to 7.03×105 cells mL−1). In particular, there was four-folds higher of viral-bacterial abundance ratio (VBR) estimated within the eddy below DCM than the outside eddy. We suggest that prevailing physical forces with downwelling at the center of the anticyclonic warm eddy, viruses can also be transmitted into the deep ocean directly through adsorption on particulate organic matter during sinking. Overall, our findings provide valuable insights into the interaction between bacterial and viral communities and their ecological mechanism within the warm eddy.

Fungal infections (FIs) are spreading globally causing a significant public health concern. However, its documentation remains sparse in Africa, particularly in Rwanda. This literature review, aims to assess the risk and document current gaps in evidence and policy. It summarizes the published data about FIs in Rwanda, including ten reports published between 1972-and-2022. Blastomyces, Candida, Cryptococcus, Histoplasma, Microsporum, Pneumocystis, Rhinosporidium, and Trichophyton caused human infections. These infections primarily affected the brain, respiratory, urinary and reproductive organs, and they were diagnosed using culturing, histopathology, immunology, and/or microscopic techniques. Our findings provide an overview of the diversity and distribution of FIs, highlighting significant limitations in the country’s diagnostic capacity and surveillance system for FIs. Also, indicating severe gaps in evidence to inform policymaking, guide strategic planning and intervention, underscoring the urgent need to build national capacity in fungal diagnosis, surveillance, and research. Raising awareness among the public, scientific community, healthcare providers, and policymakers remains crucial. Furthermore, this report reveals the threats of FIs on public health and food insecurity in Rwanda. A Multisectoral One Health Strategy is essential in research and intervention to determine and reduce the health and safety impacts of fungal pathogens on human, animal, and the environment.

In this paper, the boiling flow inside a nanochannel with 700000 argon particle has been simulated by molecular dynamics (MD) simulation. This approach has been employed to analysis the superheated flow and its heat transfer pattern as well. For all simulations an external thrust force varying from 1 PN to 12 PN is exerted on inlet nanoparticles along the channel to have the forced annular boiling flow. Computations reveal that saturation condition and superheat degree have significant impacts on the liquid-vapor interface. Furthermore, because of the major influence of surface tension throughout a nanochannel, the x-velocity of liquid film and vapor core has not considerable fluctuations and stay smooth. All provided results show the behaviors completely similar to the available outcomes in the literature.

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To answer this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In the present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32 % improvement over the docked structures in terms of the change of root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements was also discussed to help their implementation in future methods and applications.

Background: Pharmacists need up-to-date knowledge and decision-making support in HIV care. We aim to develop MARVIN-Pharma, an adapted artificial intelligence-based chatbot initially for people with HIV, to assist pharmacists, considering evidence-based needs. Methods: From Dec 2022 to Dec 2023, an online needs-assessment survey evaluated Québec pharmacists' knowledge, attitudes, involvement, and barriers relative to HIV care, alongside perceptions relevant to the usability of MARVIN-Pharma. Recruitment involved convenience and snowball sampling, targeting National HIV and Hepatitis Mentoring Program affiliates. Results: Forty-one pharmacists (28 community, 13 hospital-based) across 15 Québec municipalities participated. Participants perceived their HIV knowledge as moderate (M=3.74/6). They held largely favorable attitudes towards providing HIV care (M=4.02/6). They reported a “little” involvement in the delivery of HIV care services (M=2.08/5), most often ART adherence counseling, refilling, and monitoring. The most common barriers reported to HIV care delivery were a lack of time, staff resources, clinical tools, and HIV information/training, with pharmacists at least somewhat agreeing that they experienced each (M≥4.00/6). On average, MARVIN-Pharma's acceptability and compatibility were in the ‘undecided’ range (M=4.34, M=4.13/7, respectively), while pharmacists agreed to their self-efficacy to use online health services (M=5.6/7). Conclusion: MARVIN-Pharma might help address pharmacists' knowledge gaps and barriers to HIV treatment and care, but pharmacist engagement in the chatbot’s development seems vital for its future uptake and usability.

Whilst prior works have characterised the affordances of prototyping methods in terms of generating knowledge about a product or process, the types, or ‘dimensions’ of knowledge towards which they contribute are not fully understood. In this paper we adapt the concept of ‘design domains’ as a method to interpret, and better understand the contributions of different prototyping methods to design knowledge in new product development. We first synthesise a set of ten dimensions for design knowledge from a review of literature in design-related fields. A study was then conducted in which participants from engineering backgrounds completed a Likert-type questionnaire to quantify the perceived contributions to design knowledge of 90 common prototyping methods against each dimension. We statistically analyse results to identify patterns in the knowledge contributions of different methods. Results reveal that methods exhibit significantly different contribution profiles, suggesting different methods to be suited to different knowledge generation. Thus, this paper indicates potential for new methods, methodology and processes to leverage such characterisations for better selection and sequencing of methods in the prototyping process.

Carbon (C) losses due to the conversion of natural forests adversely affect the abiotic components of terrestrial ecosystems. In tropical China, rubber cultivation often extends from its traditional range to elevations of up to 1400 m. However, C storage in rubber plantations across elevation gradients is poorly understood. In this study, we investigated biomass and C storage along elevation gradients in two age groups (8- and 12-year-old) of rubber monoculture plantations in Xishuangbanna, Southwest China. We investigated the C distribution across various tree sections, ranging from aboveground biomass (AGB) to belowground biomass (BGB), including litter, big dead branches, and different soil depths. A significant negative correlation was observed between total ecosystem C stocks and elevation in both age groups of rubber plantations. The highest ecosystem C stock of 197.90 Mg C ha–1 was recorded at 900 m in 8-year-old plantations, whereas in 12-year-old rubber plantations, the highest value of 183.12 Mg C ha–1 was found at 700 m. The total ecosystem C stocks decreased to its lowest level at 1000 m in both the 8-year-old and 12-year-old plantations, ranging between 113.05 Mg C ha–1 and 125.75 Mg C ha–1, respectively. We concluded that the total ecosystem C stocks were negatively correlated with elevation and significantly decreased from 8.05% to 51.55% and 11.46% to 42.96% between 700 m and 1100 m in both 8-year-old and 12-year-old plantations, respectively. However, no correlation was observed between the total soil C stocks and elevation in either age group of rubber plantations. Regardless of elevation gradient, the total ecosystem C stock of 12-year-old rubber plantations was 1.98% greater than that of 8-year-old rubber plantations. Biomass was the second largest contributor, while soil accounted for 82% to 90%, and the other factors contributed less than 2% of the total ecosystem C stock in both age groups. These fluctuations in C stocks along elevation gradients in both 8- and 12-year-old plantations suggested that rubber growth, biomass and C storage capacity decreased above 900 m and that age and elevation are key factors for biomass and C storage in monoculture rubber plantations.

Structured science summaries or research contributions using properties or dimensions beyond traditional keywords enhances science findability. Current methods, such as those used by the Open Research Knowledge Graph (ORKG), involve manually curating properties to describe research papers' contributions in a structured manner, but this is labor-intensive and inconsistent between the domain expert human curators. We propose using Large Language Models (LLMs) to automatically suggest these properties. However, it's essential to assess the readiness of LLMs like GPT-3.5, Llama 2, and Mistral for this task before application. Our study performs a comprehensive comparative analysis between ORKG's manually curated properties and those generated by the aforementioned state-of-the-art LLMs. We evaluate LLM performance through four unique perspectives: semantic alignment and deviation with ORKG properties, fine-grained properties mapping accuracy, SciNCL embeddings-based cosine similarity, and expert surveys comparing manual annotations with LLM outputs. These evaluations occur within a multidisciplinary science setting. Overall, LLMs show potential as recommendation systems for structuring science, but further finetuning is recommended to improve their alignment with scientific tasks and mimicry of human expertise.

Background: Since 2020, when the Covid-19 outbreak began, researchers worldwide have been investigating its biological processes, transmission patterns, and the mathematical expression of this infectious disease. Multiple mathematical models have been presented thus far, with the simplest being the SIR model. The question now is whether the SIR model can provide a comprehensive representation of Covid-19 or if a more intricate model is necessary. With this in mind, the SEPAIVRD model has been developed. Objective: This study focuses on understanding the third wave of Covid-19 that began in mid-May 2021 in Bangladesh. To achieve this, we introduced three essential compartments in our model: Presymptomatic (P), Asymptomatic (A), and Vaccinated (V) classes. The Asymptomatic class will help us determine how close the population is to achieving herd immunity. The Presymptomatic class will provide insight into whether we can effectively control Covid-19 by identifying infected individuals over an extended period. Lastly, the Vaccinated class will allow us to explore how the Presymptomatic class affects those who have received the vaccine. The results of this study aim to address these questions. Findings: According to the study findings, an increase in vaccination rates could result in a decrease in the number of people getting infected every day, lessening the strain on healthcare resources and lowering the risk of transmission to others. Moreover, the research highlights the significance of using highly effective vaccines to combat COVID-19 cases and fatalities, particularly in areas with low vaccination rates. Additionally, implementing social distancing measures can be an effective approach to contain the spread of the virus by reducing the effective contact rate. Conclusion: The model presented in this study provides a comprehensive overview of Covid-19 transmission in Bangladesh. Furthermore, this model can be applied to other countries in South Asia and is therefore a valuable mathematical model for understanding Covid-19.

In today's context of rising sea levels and subsiding land topography, coastal flooding is a major concern for coastal areas, both nationally, regionally, and internationally. A detailed understanding of these two phenomena is a prerequisite for effective coastal flood management. The main objective of the present study is to contribute to the understanding of the impact of subsidence and sea-level rise on coastal flooding in Saint-Louis, and in particular the Langue de Barbarie, by producing a vulnerability map of submerged surfaces. To this end, the Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of Sentinel-1 images and estimate land subsidence in the Saint-Louis and Langue de Barbarie area. Interferometric SAR (InSAR) measurements are merged with RCP 2.6 sea-level rise scenarios to identify predicted flooded areas and provide a flood vulnerability map. At the Langue de Barbarie, topographic subsidence is estimated at between -6.4 and -0.4mm/year. In this study, we show that local land subsidence may increase vulnerability to flooding caused by sea-level rise based on 2100 projections. This may represent an increase in the flood zone in the study area. Based on the RCP 2.6 scenario and the subsidence rates obtained, a significant part of the St. Louis coastline and city would be subject to coastal and fluvial flooding for the years 2040 and 2100. For these dates, 29 to and 36 Km2 of the studied area would be occupied by water. Given this situation, research in Saint-Louis needs to focus on adaptation scenarios to coastal risks, to protect coastal communities and safeguard the city's historical heritage.

This study concerns the transport and retention of polydisperse micron-sized (16 ± 6 µm) of microplastics (MPls) in porous media under varying flow rate conditions. Sorption kinetics were modeled using first-order reversible and irreversible kinetic sorption models, with sensitivity analyses providing insights into each sorption parameter's effect. Both numerical modeling and experimental measurements were employed to assess sand filter retention rates. The impact of flow rate on sorption reveals variations in distribution coefficient (Kd), mass transfer coefficient (β), and irreversible sorption rate (K1). Lower flow rates are correlated with higher Kd and β values, indicating an increase in sorption and diminished mass transfer rates. The findings revealed that an increase in Kd resulted in a more gradual sorption process with a decrease in peak concentration, whereas changes in β influenced the rate of sorption and peak concentration to a lesser extent compared to Kd. Lower K1 values are associated with higher peak concentrations and decreased retention efficiency. Retention rates were evaluated by a numerical model and found as 28 ± 1% at a flow rate of 31 ml min⁻¹ and 17 ± 1% at 65 ml min⁻¹. The introduction of MPls into soil environments has been noted to modify transport dynamics into soil. As a result, these alterations effects hydrological characteristics of soil, thereby impacting quality of groundwater and agricultural output. The mean absolute error (MAE) of 6% between modeled and observed retention rates suggests minor discrepancies. This study highlights the importance of examining retention efficiency and the accuracy of numerical models in porous media during MPl transport.

Introduction: One of the key strategies to achieve sustainable development goal by reduce preventable maternal deaths below 70 per 100,000 is increasing awareness about major obstetrics danger sign. Mother's awareness of obstetric danger signs in developing country is relatively low regardless of attempts trough many local and national program. It is crucial to increase women’s knowledge of obstetric danger signs through education and awareness-raising efforts. Despite its importance to reduce maternal deaths, Studies are limited only to the Northern Ethiopia. Objective: This study aimed to assess prevalence of knowledge of obstetric danger signs and associated factors among women who delivered in the last 12 months in Sidama region, Ethiopia. Methods: We conducted a community-based cross-sectional study from October to November, 2022. A multi-stage sampling procedure was utilized to select study participants. A structured and pretested questionnaire was utilized to collect data. A multilevel mixed-effects negative binomial regression model was used to control effects of clusters and confounders.Results: Out of 1,140 study participants, only 1,130 study participants took part in the study making a response rate of 99.12%. The overall prevalence of knowledge of obstetrics danger sign was estimated as 22.3% (95%CI: 18.7, 25.9). Government employed women (AIRR = 1.37; 95% CI: 1.2, 1.6), women who had access to mass media (AIRR = 1.16; 95% CI: 1.08, 1.25), women who had received model family training (AIRR = 1.15; 95% CI: 1.1, 1.25), autonomous women (AIRR = 1.15; 95% CI: 1.04, 1.25), women who had faced health problems during pregnancy (APR = 1.21; 95% CI: 1.11, 1.32) and urban women (APR = 1.22; 95% CI: 1.09, 1.62) were factors associated positively with knowledge of obstetrics danger sign. Conclusion: The prevalence of knowledge of obstetrics danger sign is found to be lower compared with the national average. Residence, mass-media exposure, model family planning, problem faced during pregnancy and women autonomy are the main predictors of knowledge of obstetrics danger sign. Public health policy makers should enhance health literacy at for focused on rural community. Awareness creation should be improved through mass media. Health providers should educate women during maternity health services to empower women for decision making process.

Background and Purpose: Moyamoya disease is a rare cerebrovascular disorder characterized by stenosis of the carotid arteries and the formation of collateral vessels at the base of the brain. While advances have been made in understanding its genetics, diagnostic criteria, and treatment strategies, further research is needed. The purpose of this literature review is to synthesize current knowledge on Moyamoya disease and identify areas for future study. Materials and Methods: A literature search of databases including PubMed, Embase, and Web of Science was conducted. Search terms included "Moyamoya disease" and related terms. Only English articles were reviewed, with no date restrictions. Relevant data was extracted and synthesized. Results: Key findings indicate that genetic factors such as RNF213 variants play an important role in disease susceptibility. Imaging modalities such as MRI and CT angiography are crucial for diagnosis. Surgical revascularization shows superior outcomes compared to medical management for ischemic and hemorrhagic presentations. However, optimal treatment for asymptomatic cases remains unclear. Neurocognitive and psychiatric sequelae can negatively impact quality of life. Promising research directions include investigating ethnic differences, developing biomarkers, and exploring novel therapeutics targeting molecular pathways. Conclusions: In summary, comprehension of Moyamoya disease has significantly progressed but gaps remain regarding treatment of certain populations and underlying mechanisms. Further research addressing these areas can aid in developing personalized care approaches and new interventions to improve patient outcomes

Historically the use of artificial intelligence (AI) in paediatrics dates back to 1984 with the intro-duction of a computer-assisted medical decision-making system called SHELP. Since then, research on the use of AI in pediatrics has become much more popular, and the number of reviewed publications largely increased. Consequently, a need for a holistic research landscape enabling researchers and other interested parties to gain insights into, deepen their knowledge about or inform them about the use of AI in pediatrics arised. To fill this gap a novel methodology named synthetic knowledge synthesis was applied. Using SKS we identified most prolific countries, in-stitutions, source titles, funding agencies, research themes, research gaps and hot spots.

Free-range sloths living in an urban environment is rare. In this study, human opinions, attitudes and interactions with a population of Bradypus variegatus in a public square were investigated. A questionnaire was applied to people in the square where the sloths reside, and informal, opportunistic observations of human-sloth interactions were made. 95% of respondents knew of the sloths’ existence in the square and 87.8% likes their presence. Opinions about population size differed greatly and younger people were concerned if the square was appropriate place for them. Some human-sloth interactions showed the consequence of lack of biological knowledge. People initiated all sloth-human interactions. The fact that sloths are strictly folivorous has limited their interactions with humans and consequently minimised negative impact of the human-animal interaction on their wellbeing. These results demonstrate that while there is a harmonious relationship between people and sloths, actions in environmental education of the square’s public could be beneficial for the sloths.

This study examined the impact of differences in fruit load of olive trees on the rate of stem refilling. The experiment was conducted in a commercial olive orchard over the consecutive years of 2021 and 2022, during which the biennial bearing cycle of olive trees resulted in minimal fruit production in 2021 and maximal production in 2022. The rate of stem refilling was determined by quantifying the differences in water volume traversing the apex and base of the principal branches of experimental trees using sap flow sensors. Concurrent measurements of tree water status were also conducted using psychrometers, while soil moisture content dynamics were continuously recorded. Results indicate that alternate bearing significantly influences the stem refilling process, while soil moisture availability also plays a pivotal role. Water reserves were more intensively utilized during the year of full production (“ON” year) primarily to fulfill the water demands of developing fruits. This replenishment occurred in various periods during the day, including early morning, afternoon, and night, when stomatal conductance measurements indicated stomatal closure. The study suggests a more intense replenishment of reserves during the night in "ON" year trees, irrespective of soil moisture, water vapor pressure deficit, or phenological fruit development stage.

This study utilized electrical resistivity imaging (ERI) to investigate subsurface characteristics near Nicolaus Copernicus University Polar Station on the western Spitsbergen-Kaffiøyra Plain, island in the Svalbard archipelago. Surveys along two lines, LN (148 meters) collected in 2022 and 2023 and ST (40 meters) collected in 2023, were conducted to assess resistivity and its correlation with ground temperatures. The LN line revealed a 1 to 2 meters thick resistive unsaturated outwash sediment layer, potentially indicative of permafrost. Comparing LN resistivity result between 2022 and 2023, a 600 Ohm-m decrease in the unsaturated active layer in 2023 was observed, attributed to a 5.8℃ temperature increase, suggesting a link to global warming. ERI along the ST line depicted resistivity reaching its minimum at approximately 1.6 meters, rising to over 200 Ohm-m at 4 meters, and slightly decreasing to around 150 Ohm-m at 7 meters. Temperature measurements from the ST line's monitoring well confirmed permafrost at a depth of 1.6 to 2 meters. Additionally, water content distribution in the ST line was estimated after temperature correction, revealing a groundwater table depth of approximately 1.06 meters, consistent with measurements from the S4 borehole on the ST line. This study provides valuable insights into Arctic subsurface dynamics, emphasizing the sensitivity of resistivity patterns to climate change and offering a comprehensive understanding of permafrost behavior in the region.

The study delves into the multifaceted landscape of human-macaque conflict within temple ecosystems, with a specific focus on Bonnet Macaques. In the context of intertwined religious, cultural, and social values, understanding public attitudes towards these commensal species becomes pivotal. The research aims to decipher the socio-economic factors that shape perceptions of human-monkey conflict within temples and to explore public attitudes within sacred groves. To achieve these objectives, a comprehensive questionnaire was administered through household interviews to 100 residents living within both a 1 sq. km and a 2 sq. km radius of the study area, ensuring direct participant interaction. The survey, taking approximately 40 minutes per participant, examined six variables, including occupation, religion, and annual income. The data collected was analysed using SPSS ver. 24 software, employing the chi-square test of independence to explore associations between categorical variables. The findings revealed that occupation and religion emerged as influential factors in shaping perceptions of Bonnet Macaques as problematic. While annual income hinted at significance, age, education, and knowledge of Kysannur Forest Disease (KFD) did not significantly impact Bonnet Macaque perception. Interestingly, age emerged as a significant factor influencing the perception of the monkeys' religious significance, whereas only education and religious affiliation impacted feeding practices towards the monkeys. Surprisingly, other demographic variables such as age, gender, occupation, and income showed no significant influence on monkey feeding practices. These results underscore the intricate interplay of socioeconomic, cultural, and religious factors in shaping public attitudes and behaviours towards Bonnet Macaques. The study further sheds light on the impact of education and religious affiliation on feeding practices, emphasizing the need for nuanced conservation strategies. Recognizing these complexities is crucial for developing culturally sensitive conservation efforts. By acknowledging the influential factors identified in this research, conservation strategies can be tailored to specific communities, fostering harmonious coexistence between humans and Bonnet Macaques in temple ecosystems. This nuanced approach ensures that conservation efforts are not only effective but also respectful of the diverse socio-cultural contexts within which they are implemented.

Back-care knowledge and behaviour may play a vital role in preventing musculoskeletal current and future discomfort and injuries. The aim of the current survey was to explore the level of knowledge regarding ergonomics and posture together with postural behaviour and the incidence of musculoskeletal pain (MSP) in university students in the northeast of England. A mixed methods survey was conducted on a convenience sample of 55 university students. The 32-item valid and reliable questionnaire contains questions on socio-demographics, MSP, knowledge regarding ergonomics and posture (8 closed ended and 8 open-ended items) as well as postural behaviour. Nearly 1.8%, 20% and 78.2% of the university students had poor, fair and good knowledge of ergonomics and posture respectively. Among the 37 students who carried a backpack, 51.4% were found to have poor postural behaviour, 37.8% had fair behaviour and 10.8% had good postural behaviour. Of the 18 students who did not carry a backpack, 55.6% were found to have poor postural behaviour, 27.8% had fair behaviour and 16.6% had good postural behaviour. 70.9% of the students experienced MSP with the most prevalent site of pain being the lower back region. The key themes that emerged from qualitative data (8 open-ended questions) were: “Comfortable posture”, “Correct spinal alignment”, “Spine support”, “less pain and injuries”, “Use knees not the back”, “Keep the object close to body”, “Hands on desk”, “Feet flat on floor” as well as “eyes at screen level”. The findings of this study can be used to develop targeted interventions aimed at educating university students about ergonomics and posture in order to prevent the incidence of MSP. Future research in this field with a large and more representative sample of the university students’ population is strongly recommended to fully understand their knowledge of ergonomics and posture, postural behaviour as well as incidence of MSP.

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM_2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM_2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

Counter-current spontaneous imbibition of water is a critical oil-recovery mechanism. In the laboratory, the Amott test is a widely used technique to assess efficacy of brine imbibition into oil-saturated core plugs. The classic Amott-cell methodology was designed to estimate ultimate oil recovery, but not the recovery dynamics that provides essential information about the imbibition mechanisms. Retention of oil droplets at the external core surface and delayed onset of oil production are the two key artifacts of the classic Amott experiment. This retention, referred to here as the ``external-surface oil holdup effect'', often results in stepwise recovery curves that obscure true dynamics of spontaneous imbibition. To address these holdup drawbacks of the classic Amott method, we modified the Amott cell and experimental procedure. For the first time, using water-wet Indiana limestone cores saturated with brine and mineral oil, we showed that our improvements of the Amott method enabled accurate and reproducible measurements of oil-recovery dynamics. We used the Generalized Extreme Value (GEV) statistics to describe oil-production histories from water-wet heterogeneous limestone cores with finite initial water saturations, for the first time. We demonstrated that our four-parameter GEV model accurately described the recovery dynamics, and that optimal GEV parameter values systematically reflected the key characteristics of the oil-rock system, such as oil viscosity and rock permeability. These findings gave us a more fundamental understanding of spontaneous, counter-current imbibition mechanisms and insights into what constitutes a predictive model of counter-current water imbibition into oil-saturated rocks with finite initial water saturation.

The organic material present at the same depth in the reservoir has the potential for conversion, as indicated by analyses conducted before and after combustion in the development of heavy oil. This paper examines the oxidation and pyrolysis reaction pathways of hydrocarbons, specifically benzaldehyde (C₇H₆O) and naphthalene (C₁₀H₈), before and after combustion using molecular dynamics simulations. The primary products formed under various temperature conditions include H₂O, HO₂, CO, and CO₂. The discussion includes the number of molecules, such as HO and H, as well as their temperature variations. The activating group functions as an electron donor, while the inactivating group serves as an electron acceptor. The oxidation and pyrolysis reactions of naphthalene and the synthesis pathway of benzaldehyde are also explored. The C-C dissociation pathway in the early stages of combustion and the process of C-C bond synthesis in the later stages of the reactions are investigated through dynamic simulations at different temperatures: 3000 K, 3500 K, and 4000 K, with a particular focus on the reaction network at 4000 K. The application of the molecular reaction dynamics method to heavy oil combustion research is the primary objective of this work. This study aims to provide a novel approach to investigating hydrocarbon conversion at high temperatures and offers recommendations for enhanced oil recovery.