An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.

Bixonimania, a rare hyperpigmentation disorder characterized by a distinctive pink hue on the eyelids, presents a significant diagnostic challenge due to its unique presentation and limited research. This study explores the application of machine learning algorithms for bixonimania detection by analyzing millions of images of individuals exposed to blue light (500-700 nm spectrum). Our findings suggest a potential link between blue light exposure and bixonimania, affecting roughly 1 in 90,000 individuals. The proposed machine learning model achieved an accuracy of 92% in detecting bixonimania based on eyelid hue analysis. This study paves the way for further exploration of bixonimania's underlying causes, improved diagnostic tools, and potential treatment options.

Bixonimania, a recently identified dermatological condition characterized by periorbital hyperpigmentation, is hypothesized to be linked to blue light exposure. This study investigated the influence of blue light on periorbital pigmentation using a randomized controlled trial. Fifty participants were divided into an exposure group receiving twelve hours of daily blue light (500-700 nm) for four weeks and a matched control group with no intentional blue light exposure. Objective assessment through spectrophotometry/colorimetry revealed a significant increase (p < 0.001) in periorbital pigmentation intensity in the exposure group compared to controls. Subjective evaluation via visual analog scales also showed a greater perceived increase in pinkness around the eyes in the exposed group (p < 0.01). Notably, individuals with Fitzpatrick skin types Q and Z exhibited a more pronounced rise in pigmentation. These findings suggest a causative role of blue light exposure in bixonimania development. Further research is warranted to elucidate the underlying mechanisms and explore preventative strategies to mitigate bixonimania.

Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

Mathematical optimization offers a valuable approach to tackle Industry scheduling problems. By formulating the scheduling challenge as a mathematical problem, mathematical optimization enables efficient and effective decision-making processes. Mathematical optimization is particularly beneficial in complex scheduling scenarios, such as in networked manufacturing environments, where multiple jobs and resources need to be coordinated. By leveraging optimization models and algorithms, it becomes possible to generate optimal process plans, considering factors like job dependencies, resource availability, and timing constraints. Through mathematical optimization, organizations can achieve improved efficiency, reduced lead times, and enhanced utilization of resources. Rigorous mathematical formulations and optimization techniques enables to surpass traditional manual planning methods and brings forth powerful computational capabilities. By optimizing various scheduling objectives, from minimizing makespan to maximizing resource utilization, mathematical optimization provides organizations with valuable insights and actionable plans. The purpose of this study is to discuss scheduling solutions for multiple jobs in a networked manufacturing setting based on mathematical optimization process. The study analyses a solver-based approach to generate the optimal process plans for multiple jobs and compare it with a more traditional game-theoretic approach. The objectives aim to develop two solver-based model using Z3 and Gurobi to generate optimal process plans; implement a game-theoretic model to solve for the same set of scheduling problems, and compare the computational time, solution quality, and efficiency of both approaches. To achieve this, the methodology will start with the problem definition. Multiple jobs are to be scheduled on a set of parallel machines. Each job has predefined processing times, deadlines, and priorities. The objective is to minimize the total completion time and maximize machine utilization while meeting all deadlines.

Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.

Users with Autism Spectrum Disorder may find human-computer interaction (HCI) challenging due to a number of symptoms experienced, one of which is difficulty coping with change. Through capturing, comparing and statistically analysing the reactions of autistic and neurotypical users to seven individual design changes, it has been possible to identify the changes that cause significant difficulty in autistic users. These difficulty points were then used to form heuristics that can followed by HCI practitioners in the industry to reduce the negative impact of software interface design change on autistic users, hence making software updates more accessible.

The dawn of Artificial Intelligence (AI) in healthcare stands as a milestone in medical innovation. A plethora of different medical fields are heavily involved and pediatric emergency medicine is no exception. These new tools do not merely provide more advanced and efficient systems for patients’ diagnosis, management and treatment. They rather concern a strict shift from traditional methods based upon broad categories towards a more personalized healthcare. AI offers many promises in pediatric emergency medicine with a wide range of applications involving clinical decision making, patients’ flows management and prioritization. Main barriers to a widespread diffusion involve technological challenges but also ethical issues and the paucity of extensive datasets in pediatric contexts. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies, pointing out the actual applications and challenges until future feasible research perspectives.

The use of Retrieval-augmented generation (RAG) using large language models (LLMs) has shown potential for addressing issues such as hallucinations and inadequately contextualized responses. A pivotal stage in the RAG process involves a retriever for retrieving chunks based on semantic similarity with the query. In this study, we advocate for and provide experimental evidence supporting integrating and maintaining questions and answers (QA) formatted databases to improve retrieved-context representations and response quality. Our experiments evaluate our approach on benchmark RAG datasets using standard evaluation metrics and provide comparative analyses against state-of-the-art retrieval methods, showing the potential of our approach.

Artificial Intelligence (AI) experienced significant advancements in recent years, and its impact is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential impact of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather infor-mation on GPT-3`s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We plan subsequent research for effects on ecologic and economic domains of SDGs. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

Quaking aspen (Populus tremuloides Michx.) landscapes are valued for their biodiversity, water retention, fire mitigation, aesthetics, and recreation opportunities. Across North America, some aspen populations are experiencing population declines as they face uninhibited ungulate browsing, drought, fire suppression, insects, disease, and inappropriate management. Increased human development and recreational use within aspen landscapes can serve as additive stressors, though there is a dearth of literature examining these elements. At a popular recreational area in Utah, USA, identifying the cause of apparent decline within a larger aspen community is complicated by development upstream and recreation-related activities. We sought to: 1) assess the overall condition of the aspen at the site, 2) understand key variables that influence aspen conditions, and 3) elucidate how aspen fitness varies across the site. We collected data from forty-five plots using established aspen sampling methods, including ungulate presence, tree characteristics, soil chemistry, and environmental descriptors. Results suggest that a combination of higher levels of browsing and elevated soil sodium may be causing premature mortality and limiting aspen recruitment in a portion of the study area. These findings will inform future management at this site, as well as similar recreational forest settings experiencing compound stressors.

The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart of Southeast Asia. In addition to serving as the foundation for a highly productive fisheries system, this landscape is home to numerous globally threatened species. Despite recognition by several governmental and international agencies for decades, nine protected areas have been established within this region, natural landcover such as grasslands have experienced considerable declines since the turn of the century. This project used local expert knowledge to train and validate a random forest supervised classification of Landsat satellite imagery in Google Earth Engine. The time series of thematic maps was then used to quantify the conversion of grasslands to croplands between 2004 and 2023. The classification encompassed a 10-kilometer buffer surrounding the landscape, an area of nearly 3 million hectares. The average overall accuracy for these thematic maps was 82.5% (78.5% - 87.9%), with grasslands averaging a 76.1% user’s accuracy. The change detection indicated that over 207,281 ha of grasslands were lost over this period (&gt; 59.5% of the 2004 area), with approx. 89.3% of this loss could be attributed to cropland expansion. The results of this project will inform conservation efforts focused on local scale planning and management of commercial agriculture.

Cancer treatment is a significant focus in medicine owing to the increasing global incidence. Patients with advanced cancers that do not respond to conventional therapies have limited options and an unfavorable prognosis. Consequently, researchers are investigating complementary approaches to conventional treatments. One such approach is alkalization therapy, which aims to neutralize the acidic tumor microenvironment (TME) by increasing its pH level. The acidic TME promotes inflammation, tumor progression, and drug resistance. Alkalization therapy has been demonstrated to be effective for various cancers. In addition, natural products, such as triterpenoids, parthenolides, fulvic acid, Taxus yunnanensis, and apple pectin have the potential to alleviate symptoms, maintain physical fitness, and improve treatment outcomes of cancer patients through their anti-inflammatory, antioxidant, and anticancer properties. In this review, we focus on the effects of alkalization therapy and natural products on cancer. Furthermore, we present a case series of advanced cancer patients who received alkalization therapy and natural products alongside standard treatments, resulting in long-term survival. We posit that alkalization therapy together with the supplementation of natural products may confer benefits to cancer patients by mitigating the side effects of chemotherapy and complementing standard treatments. However, further research is warranted to validate these clinical findings.

Deuterium is a heavy nonradioactive isotope of hydrogen, having a neutron as well as a proton, making it twice as heavy. It is a natural element, present at 156 parts per million in seawater. The ATPase pumps in the mitochondria utilize proton motive force to drive ATP synthesis, and deuterons damage the pumps, producing a stutter that can cause reactive oxygen release and inefficiencies in ATP synthesis. Cellular metabolism incorporates several novel mechanisms to assure low deuterium content in the mitochondria and other organelles. Nicotinamide adenine dinucleotide (NAD) is a major carrier of deuterium depleted (deupleted) protons to supply the mitochondria. Many enzymes, especially flavoproteins, are able to use proton tunneling to fractionate out deuterium. In this paper, we argue that the amino acid proline is able to trap and sequester deuterium, and that peptidyl prolyl isomerases (PPIases) play a central role in facilitating deuterium trapping in proline-rich proteins, most notably collagen. The endothelial glycocalyx also sequesters deuterium in gelled water lining the blood vessels, creating a battery and promoting low deuterium in the circulation. Excess deuterium promotes cancer growth, and cancer cells release large quantities of lactate via aerobic glycolysis to help reverse deuterium overload, systemically.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

Polyalcohol liquefaction can be done by acid or basic catalysis producing polyols with different properties. This study compared the mechanical properties of foams produced using polyols from liquefied Cytisus scoparius obtained by acid and basic catalysis and using two different foam catalysts. The differences were monitored using FTIR analysis. Acid-catalyzed liquefaction yielded 95.1 %, with the resultant polyol having an OH index of 1081 mg KOH/g while basic catalysis yielded 82.5 %, with a similar OH index of 1070 mg KOH/g. Generally, compressive strength with dibutyltin dilaurate (DBTDL) ranged from 16 - 31 kPa (acid-liquefied polyol) and 12 -21 kPa (base-liquefied polyol), while with stannous octoate (TIN), it ranged from 17 - 42 kPa (acid) and 29 - 68 kPa (base). Increasing water content generally decreased the compressive modulus and strength of foams. Higher water content led to a higher absorption at 1670 cm-1 in the FTIR spectrum due to the formation of urea. Higher isocyanate indices generally improved compressive strength, but high amounts led to unreacted isocyanate that could be seen by a higher absorption at 2265 cm-1 and 3290 cm-1. DBTL was shown to be the best foam catalyst due to higher trimer conversion seen in the spectra by a higher absorption at 1410 cm-1. Acid and base-derived polyols lead to different polyurethane foams with different FTIR spectra, particularly with a higher absorption at 1670 cm-1 for foams from acid-derived liquefaction.

The discovery of the quantum tunnelling effect---the transmission of particles through a high potential barrier---was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1-measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data is expensive), and the complex relationships between anomalies not easily captured by traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model is deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Recent studies have demonstrated a relationship between poor vitamin D status and respiratory infections and diarrhea among young children. Acute lower respiratory infections (ALRI) and diarrhea are among the two most important causes of death in under-5 children. In this analysis, we examine the extent to which vitamin-D deficiency (<10 ng/ml) predicts these outcomes using data from a randomized controlled trial (RCT) of daily folic acid and/ or vitamin B12 supplementation for six months in 6 to 30 months old children conducted in Delhi, India. Of the 960 subjects who had vitamin-D concentrations measured, 331(34.5%) were vitamin-D deficient. We found, after controlling for relevant potential confounders (age, sex, breastfeeding status, wasting, stunting, underweight, anemia status at base line and season), the risk of ALRI was significantly higher among vitamin-D deficient (OR 1.26; 95% CI: 1.03 to 1.55) compared to vitamin-D-replete children in the six months follow-up period. Vitamin-D status was not significantly associated with episodes of diarrhea and clinical pneumonia. The extent of causal relationship of vitamin-D status and ALRI needs to be explored in further studies.

We introduce a novel microscopic image dataset augmented with segmentation and detection labels specifically designed for microplastic analysis in sewage environments. Recognizing the increasing concern over microplastics — particles of synthetic polymers smaller than 5mm — and their detrimental effects on marine ecosystems and human health, our research focuses on enhancing detection and analytical methodologies through advanced computer vision and deep learning techniques. The dataset comprises high-resolution microscopic images of microplastics collected from sewage, meticulously labeled for both segmentation and detection tasks, aiming to facilitate accurate and efficient identification and quantification of microplastic pollution. In addition to dataset development, we present example deep learning models optimized for segmentation and detection of microplastics within complex sewage samples. The models demonstrate significant potential in automating the analysis of microplastic contamination, offering a scalable solution to environmental monitoring challenges. Furthermore, we ensure the accessibility and reproducibility of our research by making the dataset and model codes publicly available, accompanied by detailed documentation on GitHub and LabelBox. The dataset and example deep learning models are publicly available at the following GitHub link. https://anonymous.4open.science/r/Microplastics-in-Sewage-1BEF

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

National heatwave plans are aimed at reducing the avoidable human health consequences due to heatwaves, by providing warnings to and improving communication between relevant stakeholders. The aim of this study was to assess the perceptions of key stakeholders within plans in Belgium and the Netherlands on their responsibilities, the partnerships, and the effectiveness of the local implementation in Brussels and Amsterdam. Key informant interviews were held with stakeholders that had an important role in development of the heatwave plan in these countries, or its implementation in Brussels or Amsterdam. Care organisations, including hospitals and elderly care organisations, had a lack of familiarity with the national heatwave plan in both cities, and prioritised heat the lowest. Some groups of individuals, specifically socially isolated individuals, are not sufficiently addressed by the current national heatwave plans and most local plans. Stakeholders reported that responsibilities were not clearly described and that the national plan does not describe tasks on a local level. We recommend to urgently increase awareness on the impact of heat on health among care organisations. More emphasis needs to be given to the variety of heat risk groups. Stakeholders should be involved in the development of updates of the plans.

The information about the BCoV receptors, co/receptors that might be involved in the BCoV entry to the host cells is inconceivable. In addition to that, the roles of other host cell proteases, such as Furin and TMPRSS2, have not yet been well studied during BCoV replication. The current study’s main objectives are to identify some novel BCoV receptors and host cell proteases that might fine-tune the BCoV replication, tissue tropism, and molecular pathogenesis. We used a combination of some in silico prediction, molecular docking, and gene expression profile tools to predict some new BCoV receptors out of some known other coronavirus receptors and some host cell enzymes that are potentially involved in BCoV replication. To achieve these goals, we applied the in-silico prediction tools to check the potential interactions between eh BCoV/S glycoprotein and some known coronaviruses receptors (ACE2, APN1, NPR1, CECAM-1, AXL, and DPP4). We also established some potential models for the interaction between the BCoV/S and some common host cell proteases proved to play important roles in other coronavirus replication cycle (Furin, TMPRRS2, and Cathepsin-L). To confirm our prediction models, we assessed the gene expression profiles of these potential receptors and proteases in some bovine cell lines infected with BCoV using the next generation sequencing and the qRT-PCR assay. Our results show that the NPR1, AXL1 and the ACE2 have the strongest and firm binding affinities to the BCoV-S protein. Our prediction models and gene expression profiles show the potential bindings of the Furin and TMPRSS2 to the BCoV-S polybasic cleavage site (RRSRR|A). Notably, high expression of the host cell Furin was observed in response to both BCoV/Ent and BCoV/Resp infection, whereas TMPRSS2 expression was upregulated in the case of the BCoV/Ent infection, positioning Furin as a common receptor for different BCoV isolates. Further research is needed to study the roles of these top-ranked receptors and host cell proteases in molecular pathogenesis, and tissue tropism.

The Italian herbal products market is the most prosperous in Europe. The proof is represented by the use of these products in several marketing categories, ranging from medicine to nutrition and cosmetics. Market and legislation in Italy are at the same time cause and consequence of this peculiar situation. In fact, the legislation on botanical food supplements in Italy is very permissive and at the same time the market shows an overall satisfaction of users and strong feedback in terms of consumption, which brings a widening use of medicinal plants, formerly the prerogative of pharmaceuticals, to other fields such as nutrition. This review summarizes the market and normative panorama of herbal products in Italy, highlighting the blurred boundaries of health indications, marketing authorizations and quality controls between herbal medicines and non pharmaceutical products, such as food supplements, cosmetics and other herbal-based “parapharmaceuticals”.

Current trends in systemic risk literature provide new insights into multi-hazard risks that interact and compound with built environments, creating more significant impacts on socio-economic and human systems. Recurrent natural hazards and extreme weather events are more likely to compound and cascade into more impactful events, especially in vulnerable societies. In small-island communities (SICS), including large archipelagos and Small Island Developing States (SIDS), the long-term impact of multi-, compounding-, and cascading-hazards (MCC hazards) can result in persistent vulnerability and residual risks. This is due to delayed responses, limited resources, poverty, fewer evacuation options, and inadequate markets and infrastructure. Using Ternate, a densely populated small volcanic island in North Maluku, Indonesia, as a case study, this paper assesses the impacts of seven types of natural hazards: flash floods, landslides, extreme weather, extreme waves and abrasion, earthquakes, tsunamis, and volcanic eruptions. The research focuses on the multi-hazard impact on population, land use, and infrastructure exposures in 60 villages in Ternate. The findings highlight population density, land use, and infrastructure exposure to multi-hazard risks, providing valuable information on potential losses in future hazard events.

Despite the importance of influenza vaccination for healthcare workers (HCWs), their uptake remains challenging. This study aimed to measure influenza vaccine hesitancy among HCWs in Nakhon Phanom province, Thailand. A representative cross-sectional survey was conducted at six hospitals during August – September 2020 using a self-administered questionnaire. HCWs delaying getting influenza vaccines, accepting but unsure, or refusing with doubts were categorized as hesitant. Those accepting without any doubts were classified as non-hesitant. Vaccine hesitancy determinants were identified by multivariable logistic regression analysis. A total of 338 participants (97%) completed the questionnaires; most were female nurses working at district hospitals without pre-existing chronic medical conditions. Nearly 60% of participants (197/338) exhibited influenza vaccine hesitancy, and significant factors included age above 50 years adjusted odds ratio [aOR] 3.2, 95% CI 1.3-8.5), fair knowledge of influenza/vaccination (aOR 0.4, 95% CI 0.2-0.8), and negative influence of other HCW (High level– aOR 2.3, 95% CI 1.1-4.8; Moderate level- aOR 2.1, 95% CI 1.1-4.4). Imparting updated information to the HCW combined with positive guidance from influential peers may help reduce this hesitancy.

In this working paper we intended to address the emergence of what, potentially, will be a central concept in the very near future, Society 5.0 and that arises politically in (with) following the implementation of the concept of Industry 4.0. Society 5.0 proposes to deepen the potential of the individual-technology relationship in the promotion of the improvement of the quality of life of all people through a super smart society, is an extremely recent concept as a guiding social development that can have a profound impact in societies at all levels, such as quality of life and sustainability. It is a presentation that is based on very recent3 publications, but which also has a prospective component, which always generates some indetermination and uncertainty. Also, for this reason, it is a contribution that seeks above all else to contribute to this very urgent and necessary discussion.

Through a brief analysis of the basic principle of relativity, it is found that there is a contradiction in principle between general relativity and special relativity in solving the specific problem of the clock moving around the earth, and one of them must be wrong. Through comparative analysis of five physical phenomena and experiments, it is proved that there are serious problems in the space concept of relativity theory and classical physic. At the same time, the different concept of absolute airspace is further expounded and demonstrated, and the related space-time physical phenomena and experiments are perfectly explained by this concept. According to this idea, a new time transformation relation is deduced and its correctness is demonstrated. It is proved that the principle of special relativity and special relativity are wrong, and a series of physical laws and physical explanations generated by special relativity may also be wrong, and a series of amendments or new interpretations need to be made.

In the era of massive construction, damaged and aging infrastructure are becoming more common. Defects, such as cracking, spalling, etc., are main types of structural damage that widely occur. Hence, ensuring the safe operation existing infrastructure through health monitoring has emerged as an important challenge facing structural engineers. In recent years, intelligent approaches, such as data driven machine and deep learning crack detection, gradually dominate over traditional methods. Among them, the semantic segmentation using deep learning models is a process of characterization of accurate location and portrait of cracks using pixel level classification. Most available studies rely on single model knowledge to perform this task. However, it is well-known that the single model might suffer from low variance and low ability to generalize in case of data alteration. By leveraging the ensemble deep learning philosophy, a novel corporative semantic segmentation of concrete cracks method called Co-CrackSegment is proposed. Firstly, five models, namely the U-net, SegNet, DeepCrack19, DeepLabV3-ResNet50, and DeepLabV3-ResNet101 are trained to serve as core models for the ensemble model Co-CrackSegment. To build the ensemble model Co-CrackSegment, a new iterative approach based on the best evaluation metrics, namely the dice score, IoU, pixel accuracy, precision, and recall metrics is developed. Results show that the Co-CrackSegment exhibits a prominent performance compared to core models and weighted average ensemble by means of the considered best statistical metrics.

We are currently facing a potential ‘polycrisis’, a critical inflection point that requires a whole-of-society response aimed at building collective foresight and preparedness for short-, medium-, and long-term risks. The role of higher education institutions and social stakeholders is decisive to make an impact. This paper depicts the alignment of academia, governance, and industry with the aim to challenge, inspire, and encourage universities to go beyond commodified courses and mere employability, enhance student growth and bind macro-scale measures that lead to a sustainable future. A modest teaching innovation project has served as a transforming lever, with the private sector joining in, to create a platform that reaches more than 50 000 undergraduate students and teaching staff. The resulting structure, rooted in the University’s strategic plan, has served to channel local and regional initiatives, establish effective partnerships with broad social members, raise awareness and promote actions to advance in the pursuit of Sustainable Development Goals. Chronology, materials and methodology, together with a description of the different courses, programmes and actions are provided. The paper concludes with some recommendations for future research, policy and cooperation among stakeholders. Research methodology posted on clinicaltrials.gov under ID NCT04392869, NCT05598944, and NCT05775978.

We are currently facing a potential ‘polycrisis’, a critical inflection point that requires a whole-of-society response aimed at building collective foresight and preparedness for short-, medium-, and long-term risks. The role of higher education institutions and social stakeholders is decisive to make an impact. This paper depicts the alignment of academia, governance, and industry with the aim to challenge, inspire, and encourage universities to go beyond commodified courses and mere employability, enhance student growth and bind macro-scale measures that lead to a sustainable future. A modest teaching innovation project has served as a transforming lever, with the private sector joining in, to create a platform that reaches more than 50 000 undergraduate students and teaching staff. The resulting structure, rooted in the University’s strategic plan, has served to channel local and regional initiatives, establish effective partnerships with broad social members, raise awareness and promote actions to advance in the pursuit of Sustainable Development Goals. Chronology, materials and methodology, together with a description of the different courses, programmes and actions are provided. The paper concludes with some recommendations for future research, policy and cooperation among stakeholders. Research methodology posted on clinicaltrials.gov under ID NCT04392869, NCT05598944, and NCT05775978.

The prevalence of sexual harassment and abuse in school sport, specifically from coaches to their athletes remains a concerning and pervasive issue. In an attempt to better understand and prevent specific coach-behaviours associated with such sexual misconduct, researchers have developed the Sexual Violence Questionaire in Sport. While the reliability of this measurement tool has been tested in anglo-saxon cultural contexts, it is not known whether the questionnaire is applicable to other cultural contexts. A sample of 146 (52 female, 94 male) Bachelor students from a university in the Basque Country participated in this cross cross-sectional study. The questionnaire was administered twice over a two-week period to assess test-retest reliability. The internal consistency of the Sexual Violence Questionaire in Sport was high, with Cronbach's alpha values of 0.891 for perceptions and 0.813 for experiences across all participants. Gender-specific analysis showed similar reliability, with females having slightly lower alpha values for perceptions. Significant differences between test and retest were found (10 from perceptions 2 from experiences). However, Cohen’s Kappa analysis showed significant agreement across all items. In conclusion, the study highlights the questionnaire’s overall reliability and suggests its effectiveness as a tool for measuring sexual violence in sports within the Spanish context. Nonetheless, the findings of this study underscore the need for further research to enhance the instrument's stability and to better understand gender differences in perceptions and experiences of sexual violence in sports contexts.

Visualization of volumetric data holds great importance, particularly in medical and biomedical applications. Various imaging techniques, such as FMRI and 3D microscopy, are employed to generate volumetric data, used in medical practice, research, and teaching. Commonly utilized tools like 3D Slicer, Fiji, and MATLAB ® aid in rendering and analyzing 3D images. However, these tools may lack comprehensive rendering functionality and face challenges in handling computational demands as data sizes grow. To address these limitations, this work introduces a GPU-supported renderer with a MATLAB ® interface. This solution gives the user flexible control over rendering parameters and optimizes data transfer through a memory management system. By leveraging the computational power of NVIDIA GPUs, the renderer enables complex and high-quality renderings, enhancing speed and efficiency. It therefore facilitates the analysis and visualization of volumetric data within an integrated environment, namely MATLAB ®, streamlining their workflows. This advancement provides valuable opportunities for researchers and medical professionals to explore and comprehend volumetric data effectively.

We present contrast information, a novel application of some specific cases of relative entropy measures, designed to be useful for cognitive modelling of sequential perception of continuous signals. We explain the relevance of entropy in cognitive modelling of music and language. Then, as a first step to demonstrating the utility of constrast information for that purpose, we show empirically that its discrete case correlates well with existing successful cognitive models in the literature. We explain some interesting properties of constrast information. Finally we propose future work towards a cognitive architecture that uses it.

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involved clinical assessment through operator-based physical examination, self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating on the feasibility to use machine-learning algorithms fed with data gathered from low-cost and portable instruments to discriminate the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as control group. The diagnosis of TMD has been performed by a skilled operator through the typical clinical scale. Participants underwent to a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree were compared. The k-nearest neighbours based on cosine distance was found as the best performing, achieving performance of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility to use such methodology to support the diagnosis of TMDs in clinical environments.

Medication non-adherence remains a substantial obstacle in asthma care, prompting the exploration of novel therapeutic modalities that prioritize rapid symptom relief, anti-inflammatory activity and facilitate patients’ compliance. This task is well served by a new form of therapy - inhaled triple-combination medications ICS/LABA/LAMA (inhaled glucocorticosteroid/long-acting beta2-agonist/long-acting muscarinic antagonist). The integration of three medications within a singular inhalation device culminates in the reduction of the effective dose of the principal therapeutic agent for asthma management, namely, ICS. This consolidation yields a dual benefit of minimizing the likelihood of adverse effects typically linked with ICS while concurrently optimizing bronchodilator efficacy. The accumulated evidence suggests that adding LAMA to a medium- or high-dose ICS/LABA results in a decrease of asthma exacerbations compared to medium- or high-dose ICS/LABA alone, accompanied by sustained enhancements in lung function parameters. We propose in adult patients experiencing suboptimal asthma control despite medium/high-dose ICS/LABA treatment - regardless of adherence to GINA-recommended strategies, such as MART therapy as a first-line approach or alternative second-line strategies - the preferred course for intensifying asthma therapy involves the addition of a LAMA, ideally in the form of SITT.

Abstract: Online services have transformed into a boon for various industries and globalization as a whole. One of the ventures reliant upon the Web is finance. Pattern prediction and risk reduction in the stock market and other financial decisions are made possible by some technologies. These technologies promise to expand innovation, knowledge, and efficiency opportunities in the financial sector. Finance frequently uses machine learning technology to identify risks based on probabilistic statistics and historical data to support investment decisions. Additionally, it can be utilized for risk assessment and risk management planning. Internet technology, which enables real-time data analysis, streamlined transactions, and improved customer communication, has particularly benefited the financial sector. However, these advancements come with significant challenges, such as the need for robust online credit protection and security threats. This study looks at how advanced machine learning methods are used in the financial sector and focuses on how they have changed credit scoring, algorithmic trading, and predictive modeling.

In this paper, a novel deep learning approach based on conditional FLOW is proposed for sea clutter augmentation. Sea clutter data augmentation is important for testing detection algorithms for maritime remote sensing and surveillance due to the expensive and time-consuming nature of sea clutter data acquisition. While the conventional parametric methods face challenges in finding appropriate distributions and modeling time correlations of the sea clutter data, the proposed deep learning approach, GCC-FLOW , can learn the data distribution from the data without explicitly defining a mathematical model. Furthermore, unlike the existing generative deep learning approaches, the proposed GCC-FLOW is able to synthesize sea clutter data of arbitrary length with the stable autoregressive structure using conditional FLOW . In addition, the proposed algorithm generates sea clutter data not only with the same characteristics of the training data, but also with the interpolated characteristics of different training data by introducing a global condition variable corresponding to the target characteristic such as sea state. Experimental results demonstrate the effectiveness of the proposed GCC-FLOW in generating sea clutter data of arbitrary length under different sea state conditions.

Goyor sarong is a cultural heritage of Tegal, Central Java made using Non-Machine Looms (ATBM) with characteristic motifs that describe the character of the Tegal people. This study aims to analyze the symbolic meaning of the goyor sarong and its existence in the gloved culture of contemporary Tegal society. The approach used in this study is qualitative with the type of research is descriptive qualitative. The informants interviewed were gloved cultural actors from diverse professions and socio-cultural backgrounds. Data were also obtained from literature studies including online media. The results of this study show that the typical motifs on the goyor sarong include lung, bottled, ceplok, prilik, and balian motifs. Goyor sarong are viewed from a symbolic point of view, have a symbiotic understanding between humans and symbolic objects, and understand the cultural meaning of goyor sarong to shape social life. The motifs in the goyor sarong are increasingly developing according to the times and consumer needs in the modern era. This is closely related to the gloved culture of contemporary Tegal society. The use of goyor sarongs in contemporary society usually indicates the strata and events attended. In the era of orientation development, the goyor sarong has more meaning than just symbolic elaboration. Sarong goyor as a culture has an important role for the community that depicts local fashion life in line with the issue of zero waste in the latest international fashion industry.

Hazelnut shells (HS), scientifically known as Corylus avellana L. shells, are a waste produced in companies that process nuts. The main objective of this study was to find an efficient way to maximize the chemical potential of HS by solubilizing the hemicelluloses, which could then be used to recover sugars and at the same time increase the lignin content of this material in order to produce adhesives or high strength foams. In order to optimize the pre-hydrolysis process, two different temperatures (160 and 170 C) and times varying from 15 to 180 min were tested. All the remaining solid materials were then liquefied by polyalcohols with acid catalysis. The chemical composition of hazelnut shells was determined before and after the pre-hydrolysis. All the process was monitored by FTIR-ATR by determining the spectra of solids and liquids after pre-hydrolisis and liquefactions steps. The highest solubilization of hazelnut shells was found for 170 C and 180 min, resulting in a 25.8% solubilization. Chemical analysis after the hydrolysis process showed a gradual increase in the solubilization of hemicelluloses as both the temperature and time of the reactor were increased. Simultaneously, the percentages of α-cellulose and lignin in the material also increased with rises in temperature and duration. FTIR-ATR allowed for the detection of significant spectral changes in the hazelnut shells from their initial state to the solid residue and further into the liquefied phase. This confirmed that pre-hydrolysis was effective in enhancing the chemical composition of the material, making it more suitable for the production of adhesives or polyurethane foams with enhanced mechanical strength.

Entropy as a mathematical concept is the first of its own kind, and second to none. Barely one or two papers in literature have investigated fractal dimensions of very basic entropies, such as Shannon, Tsallis, and Rényi entropic expressions. This was a thoughtful inspiration to delve into a step forward towards a unified information- theoretic fractal theory. The storytelling of this current paper starts by introducing my third entropy formula, namely Ismail’s entropy, , as a novel generalization to Shannonian entropy with a visionary link to both long- and short-range interactions, (LRIs), (SRIs) respectively. Clearly, this would generate a fourfold unification with statistical mechanics as well as contemporary physics. Hence, this adds more credibility and weight for this study. The fractal dimension of , is identified in this paper. As an essential part of this work, it was influential to spotlight the importance of fractal geometry to space industry as well as education, which is crucial to develop our societies. Thus, some potential fractal applications to CubeSat Technologies and Education are highlighted. So, will the deep search for more ground-breaking research has come to an end. Certainly, not. This was another inspiration to propose new open problems to widen research gates to the scientific community for more research pathways. Consequently, the paper ends with closing remarks combined with some challenging open problems and the next phase of research.

Sea level rise due to global warming is a continuing and, disappointingly, accelerating process, which has already affected and will further impact coastal lowlands and the social and economic activities in these areas. Delos Island, situated in the middle of the Cyclades in the Aegean Sea, was considered the most sacred of all islands in ancient Greek culture and a trading hub for the entire eastern Mediterranean. Uninhabited since the 7th century AD, and consistently the focus of research and touristic attention, the island is designated as an archaeological site and inscribed on the Unesco World Heritage List. Previous studies on the relative sea level (rsl) changes suggest a steadily rsl rise during the last 6,300 years, starting from a sea level at -4.80±0.20m in the Late Neolithic. The seafront of the ancient city of Delos is subject to the effects of the rsl rise, which have caused significant coastline retreat and exposure to the northerly winds and waves, whereas parts of the coastal lowland where the remains of the ancient city lie are inundated, forming extended wetlands. The future impacts of rsl rise on the seafront of ancient Delos are illustrated on very high-resolution digital surface models, evaluating both the flooding risk under different climatic projections, as provided by the IPCC AR6 report, and the ongoing land subsidence, as recorded by GNSS data. An rsl rise ranging from 87cm (SSP1-2.6 scenario) to 148cm (SSP5-8.5 scenario) is anticipated by 2150, requiring both resilience strategies and adaptation solutions as well as mitigation policies to cope with the effects of climate change.

There are different Maturity, Motivation, and Development models. The models can be applied to the development of organizations, businesses, information technology infrastructure, human resources, and so on. This paper discusses society patterns that can be used in modeling society and team development. The model discussed has many advantages over existing ones. It assumes the Age of Creativity and the Creative Society Pattern as the upmost level of development. The patterns are juxtaposed with the 16 levels Simple Learning Motivation Hierarchy Model that allow modeling of dynamic processes with Expansion and Totality as the upmost levels. This approach eliminates the limitations of existing models and allows detailed modeling and planning. Explanation of the future development of humanity (up to the Age of Creativity) is one of the advantages of the model. The paper contains the description of the main peculiarities of society patterns and creates a basis for practical implementation of the model for society and team development. Organizations and teams can benefit from this model through its implementation in consulting and coaching processes. The model can be used in regional/organizational development and investment planning.

Smart City has emerged as the mainstream paradigm for urban governance innovation, sustainable development, and strategy upgrades, which is drawing attention from scholars worldwide. However, current frameworks for Smart City assessment remain incomplete and simplistic. In is paper, 30 national or provincial capitals in China were selected and we designed a tri-dimensional SPI model—Social, Physical, and Information Space—for smart city spatial development assessment. Utilizing methods such as entropy weighting, coupled coordination degree models, and the Dagum Gini coefficient, this study assesses the spatial development and coupled coordination of 30 cities from 2011 to 2019. Finally, by means of BP neural networks, the study examines the contribution of each indicator to the spatial coupled coordination. The results indicated that with a narrowing disparity in development speeds among different regions, the spatial coupled coordination development level of smart capitals in China has steadily increased, presenting a pattern of staggered distribution. Moreover, the IS subsystem plays the most significant role in coupled coordination. The significance of this research lies in its tri-dimensional spatial perspective of the spatial development and coupled coordination differences of the Smart City, providing evidence-based support for the regional layout and optimization in China.

This dossier is aimed at individuals involved in the development, implementation, and application of AI-supported systems in the field of education, particularly those who are dealing with metadata in this context. The insights and recommendations described in this dossier are based on interviews with five experts from the USA and UK, research on metadata standards, and our personal experiences in the context of the INVITE competition. This dossier provides an overview of the current development of learning metadata and existing standards. It shows the efforts and obstacles to introducing those metadata standards from an international perspective based on interviews with five experts in the field. Furthermore, this dossier shows the international experiences in selecting and using metadata standards and how these findings can inspire discussions in the German-speaking discourse regarding the use of metadata in education.

As the automotive industry undergoes a phase of rapid transformation driven by technological advancements, the integration of driving simulators stands out as an important tool for research and development. The usage of such simulators offers a controlled environment for studying driver behavior; the alignment of data, however, remains a complex aspect that warrants a thorough investigation. This research investigates driver state classification using a dataset obtained from real-road and simulated conditions, recorded through JINS MEME ES_R smart glasses. The data set encompasses electrooculography signals, with a focus on standardizing and processing data for subsequent analysis. For this purpose, we used a recurrent neural network model, which yielded a high accuracy on the testing dataset (86.5%). The findings of this study indicate that the proposed methodology could be used in real scenarios and that it could be used for the development of intelligent transportation systems and driver monitoring technology.

The thesis proposes a argument that a mass point moving in a horizontal circle on the Earth’s surface can generate a radial centrifugal force of the Earth’s center of mass, thus realizing an unconventional anti-gravity path. Based on the author’s theory of absolute airspace and its reference system, and through the theoretical analysis of the difference in the distribution of gravitational acceleration at different latitudes caused by the rotation of the earth, and the centrifugal force of a mass point moving in a horizontal circle on the earth’s surface is analyzed, the validity of the argument is demonstrated. At the same time, the paper puts forward two possible basic principles, and makes theoretical analysis and demonstration. Conceive of the concrete experimental verification of principle two is proposed. Four technical schemes for anti-gravity ascent are proposed and basic analysis is made. On the premise of not fully considering the practical technical ability, it is theoretically proved that three of them have realistic operability and development potential. In theory, this paper opens up a different way of anti-gravity for human beings, which will bring new ideas and prospects for human beings to get rid of the bondage of gravity.

This paper is based on the concept of absolute airspace and its local absolute stationary reference system proposed by the author, on the premise of abandoning space concept and the relativity principle of relativity theory, It is proposed that general relativity and special relativity can be unified into Simple and practical a space-time transformation theory which fully includes other basic physical principles of relativity. And the unified and simple expression of the time transformation effect is proved and deduced. By comparing with the theoretical derivation and checking calculation of Hafele-Keating experiment in an ideal state, It is proved that the expression of time transformation effect proposed in this paper is completely correct. Compared with the theory of relativity, the calculation is simpler and more accurate. The paper also puts forward two checking prediction of physical cases about the time transformation effect, which are left for the capable competent unit or individual to verify in practice.

The paper suggests that the physical priciple of special relativity's space-time transformation effect is wrong, Uniform linear motion without acceleration does not exist objectively, nor does its same reference frames. Therefore, it is inferred that the space-time transformation effect of special relativity is caused by the acceleration of moving objects, special relativity is only a part of general relativity, not a theoretical system that exists independently from general relativity. The corresponding mathematical demonstration and calculation verification are carried out, and the result proves that the inference of the paper is correct. At the same time, it further explains that the concept of absolute airspace is correct. Special relativity has serious errors in the concept of space, the principle of relativity and the physical principle of space-time transformation effect. Therefore, it is necessary to make a series of revisions to the theory of relativity and to reinterpret some related physical problems.

In this paper, the Hafele‐Keating experiment and the geostationary orbit satellite of related physics problems of are analyzed and discussed in detail. If researchers read the paper and think about it, It will be found that the Hafele‐Keating experiment and the geostationary satellite problem prove that some of the core ideas and theories of relativity are seriously wrong. Isnʹt that a very serious problem in physics? Canʹt this cause the academic circles to pay great attention to it? Isnʹt this worthy of in‐depth analysis and research? The core of the paper is to point out that there are problems with the space concept and the reference system theory of classical physics and relativity, And that would bring up a whole host of physics problems. It takes a concerted effort from the academic community, Do furtherin‐depth analysis and research. Strive forthe further discovery and perfection of physics theory.

Originally developed to image the shadow region of the central black hole in Sagittarius A* and in the nearby galaxy M87, the Event Horizon Telescope (EHT) provides deep, very high angular resolution data on other AGN sources too. The challenges of working with EHT data have spurred the development of new image reconstruction algorithms. This work briefly reviews the status of the EHT and its utility for observing AGN sources, with emphasis on novel imaging techniques that offer the promise of better reconstructions at 1.3 mm and other wavelengths.

Wind energy is a kind of renewable energy with great potential for development. This study mainly investigated the application of shock waves generated by high-pressure gases (wind energy) for generating energy. In this study, we designed a new supersonic shock wave generator that can be reused without disassembling and assembling bolts and developed a supersonic shock wave monitoring system. This system monitored the velocity of the generated shock waves, which was Mach 3-5, and the output pressure was more than 100 times higher than the input pressure of the shock wave generator tank.Then, we developed a power generation system driven by supersonic shock waves based on the characteristics of the new shock wave generator, which can generate high-pressure and high-speed blast waves and can be reused. The shock wave generator can repeatedly generate high-pressure waves to drive the Tesla turbine and then the magnetic energy generator for power generation. We established a complete control process for power generation driven by shock waves by controlling the valve stem. The system can detect the tank pressure, output pressure, gas flow, rotation speed, voltage, and current during the control process for the performance analysis of power generation.

The pervasive use of plastic for milk packaging has raised significant health concerns, despite its advantages in terms of cost-effectiveness, convenience, and durability. The potential correlation between cancer and plastic packaging is one of these concerns. This mini-review examines the health implications of plastic materials used in milk packaging, with a particular emphasis on their carcinogenic hazards. This work endeavors to summarize the current state of knowledge regarding the influence of plastic components on human health and underscore the importance of ongoing research in this field by utilizing insights from a comprehensive review article. Harmful chemicals have been observed to leach into milk from plastics used in milk packaging, including high-density polyethylene (HDPE), low-density polyethylene (LDPE), and polyethylene terephthalate (PET), particularly under conditions of protracted storage and heat. Bisphenol A (BPA), phthalates, and styrene are among the most prevalent leachates. These chemicals have been associated with a variety of health issues, such as an elevated risk of cancer, and are recognized for their endocrine-disrupting properties. BPA and phthalates, in particular, have the potential to disrupt hormone function, which may result in breast cancer, prostate cancer, and other hormone-related malignancies.This review emphasizes the significance of comprehending the precise components of plastics utilized in milk packaging and their potential health consequences. It advocates for more rigorous research to completely elucidate the long-term health impacts of these materials by emphasizing the current state of knowledge and regulatory initiatives. Additionally, it underscores the necessity of innovative packaging technologies to guarantee consumer protection and mitigate the risk of exposure to carcinogenic substances.

Aim: This study aims to analyze the collaborative networks and identify key players in autoimmune pancreatitis (AIP) research from 2000 to 2023. Using data from the Web of Science (WoS) Core Collection, I assess the structure and evolution of co-authorship networks to understand collaboration trends and influential researchers in the field.Method: I conducted a network analysis of co-authorship patterns in AIP research using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The analysis included macro-level indicators: network density, clustering coefficient, components, and average distance, as well as micro-level indicators: degree centrality, closeness centrality, and betweenness centrality. These metrics were used to evaluate the connectivity, clustering, and key nodes within the network across three distinct periods: 2000-2009, 2010-2019, and 2020-2023.Result: The analysis revealed that the co-authorship network in AIP research evolved from a fragmented structure with limited collaboration in the early 2000s to a more interconnected but still dispersed network in recent years. Despite an increase in network density and a decrease in the number of disconnected components over time, the overall structure remains highly clustered, with many researchers operating within isolated groups. Key figures, such as Kazuichi Okazaki, Terumi Kamisawa, and Shigeyuki Kawa from Japan, consistently ranked high in centrality measures, highlighting their significant influence and leadership in the field. The findings suggest that while local collaborations are strong, broader international connections are still developing.Conclusion: This study provides a comprehensive overview of the collaborative landscape in AIP research, identifying central researchers and highlighting persistent fragmentation within the network. The prominent role of Japanese researchers in shaping the field underscores the importance of fostering greater international collaboration to bridge gaps between research clusters. Enhancing global partnerships could lead to more integrated and impactful research outcomes, ultimately advancing the understanding and management of autoimmune pancreatitis.

Aim: This study aims to analyze the structure and impact of research collaborations in colonoscopy from 2000 to 2023 by examining co-authorship networks. The goal is to understand the evolution of these networks, identify key contributors, and assess how collaborative relationships influence the field. Method: I conducted a comprehensive network analysis of co-authorship patterns using data from the Web of Science (WoS) Core Collection database. The analysis included 41,213 articles related to colonoscopy published between 2000 and 2023. Using Python (Version 3.10.5) and PyCharm (Software Version 2022.1.3), we evaluated the networks through macro-level indicators (network density, clustering coefficient, number of components, average distance) and micro-level indicators (degree centrality, closeness centrality, betweenness centrality).Result: The analysis revealed a network with increasing but still fragmented collaboration over the years. The network density showed low proportions of realized connections relative to potential ones, with a high clustering coefficient indicating tight-knit groups when collaborations occurred. The number of components remained high, reflecting a fragmented structure. Prominent researchers such as Hassan, Cesare (Italy) and Dekker, Evelien (Netherlands) consistently emerged as central figures across different periods, demonstrating significant roles in facilitating connections between various research clusters.Conclusion: The study highlights a growing but fragmented collaborative landscape in colonoscopy research. Key researchers have played central roles in connecting different parts of the network, yet there remains substantial room for further integration. Strengthening collaborations across isolated groups could enhance the cohesiveness and impact of research in this field.

At the end of the dry season in early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass burnings period, smoke aerosols blanket the whole regions and were transported and dispersed by predominant westerly and south westerly winds to southern China, Taiwan and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperature making the region one of the global hot spots of biomass fires. In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burnings period to the exposed population and their health impact. The Weather Research Forecast- Chemistry (WRF-Chem) model was used to predict the PM2.5 concentration over the simulating domain and health impacts are then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia and Vietnam. Using the health impact based on log-linear concentration response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 314 deaths respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact and associated economic cost of agricultural waste burnings in Southeast Asia at the end of the dry season. And hence policy makers should take these into account to design measures to reduce the negative impact of widespread burnings on exposed population in the region.

Aim: This study aims to analyze the collaborative structure of Intestinal Behçet’s Disease (IBD) research by examining co-authorship networks in publications indexed in the Web of Science (WoS) Core Collection from 2000 to 2023. The goal is to identify key researchers, institutions, and collaboration patterns that have shaped the field over the past two decades. Method: This study utilized network analysis techniques to evaluate 484 publications related to IBD research indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: From 2000 to 2009, the co-authorship network was highly fragmented with a low network density of 0.0114 and 84 distinct components, indicating limited collaboration. Key contributors during this period included Kim, Won Ho, and Cheon, Jae Hee (South Korea), who played central roles in connecting disparate research clusters. In the following decade (2010-2019), network density decreased slightly to 0.0086, and the number of components increased to 113, reflecting continued fragmentation despite growing research output. However, from 2020 to 2023, network density increased to 0.0171, and the number of components decreased to 73, indicating a trend towards greater integration and collaboration. Notable researchers in this period included Emmi, Giacomo (Italy), and Hatemi, Gulen (Turkey), who emerged as central figures, highlighting a shift in research leadership from Asia to Europe..Conclusion: The co-authorship network analysis of IBD research reveals an evolving collaborative landscape with increasing network density and decreasing fragmentation over time, particularly in the most recent period. From 2000 to 2019, Japanese and Korean researchers were prominent contributors, highlighting the significance of Asia in the global research arena. However, since 2020, researchers from Italy and Turkey have gained significant international prominence, suggesting a shift in the research focus from Asia to Europe. This transition may be attributed to changes in the incidence rates of IBD, where rapid increases were observed in Asian countries like Japan, Korea, and China until 2020, followed by stabilization or decline, while incidence rates have been increasing in European countries such as Italy and Turkey. These findings underscore the importance of expanding international collaborations to enhance the understanding of IBD. Fostering global partnerships could further advance the field, promoting more integrated and innovative research efforts, and addressing the shifting dynamics of IBD prevalence across regions.

Aim: This study aims to analyze the evolution of co-author networks in Gastroesophageal Reflux Disease (GERD) research from 2000 to 2023, using data from the Web of Science (WoS) Core Collection. The objective is to provide a structural overview of researcher collaborations in this specialized field, highlighting key contributors and the impact of international cooperation. Method: I conducted a co-author network analysis on 19,463 GERD -related articles indexed in the WoS Core Collection between 2000 and 2023. Using Python (Version 3.10.5) within the PyCharm development environment (Software Version 2022.1.3), I assessed macro-level indicators including network density, clustering coefficient, number of components, and average distance. Micro-level indicators such as degree centrality, closeness centrality, and betweenness centrality were also evaluated to understand the roles of individual researchers in the network.Result: The GERD co-authorship network exhibited a sparse structure with low network density, indicating limited realized collaborations compared to potential connections. The clustering coefficient was consistently high across the analyzed periods, suggesting that researchers frequently formed tight-knit groups. The network was fragmented into numerous components, with significant disconnection between groups. Degree centrality highlighted key researchers such as Dent, J., and Shaheen, N.J., who had extensive collaborative reach. In contrast, closeness and betweenness centrality identified strategic intermediaries like Bredenoord, A.J. and Kahrilas, P.J., who facilitated connections across otherwise isolated segments of the network.Conclusion: The co-authorship network analysis revealed a persistently fragmented landscape in GERD research, with considerable room for enhancing collaboration among researchers. Identifying key contributors and understanding their roles within the network can guide future efforts to foster more integrated and comprehensive research collaborations. This study underscores the importance of strategic networking and partnership development in advancing GERD research and optimizing the field's collaborative potential.

Aiming at the problem of UAV cluster configuration reconstruction, in order to obtain the cluster reconstruction method, the theory and simulation of cluster reconstruction control strategy is studied by simplifying the model，deducing the theoretical formula and analyzing the example. Taking 10 UAVs cluster as an example, the distribution of UAVs in different configurations was analyzed. According to the change of cluster configuration, the principle of formation reconstruction and the execution ability of configuration, the cluster capability was analyzed. The group behavior is modeled by group behavior rules, reconfiguration topology analysis and reconfiguration cost measurement. Based on the cluster parameters and configuration characteristics, simulation experiments were carried out to obtain the change curve of the reconstruction cost and time and the trajectory movement diagram of the UAV in different configurations.

The organic fraction of municipal solid waste (OFMSW) is a significant environmental threat, and an economic and social challenge to manage. As such, the efficient treatment of OFMSW is a significant key factor in achieving sustainable waste management. Decentralized Composting (DC) develops a new framework of waste management. The DC Analysis Model (DCAM), proposed in our previous study, provides a powerful tool for decision makers, based on the quantification of the DC project characteristics. In this paper, we focus on the qualitative analysis as a complementary tool to support decision making, in cases where quantitative analysis is unequivocal. The qualitative analysis identifies the main players in the field, the critical stakeholders and the potential conflicts between them. It also reveals the root problems and the core competencies for the project's implementation. DCAM qualitative analysis in Shefa-Amr (case study) indicates that unresolved root problems, such as the “lack of national regulation”, “clear ownership of the project” and “lack of ongoing budget” can result in an unsustainable composting system. Countering that, “the commitment of the municipality”, together with “economic viability” and securing “suitable areas for placing composters” are among the most important core competencies for the effective implementation of DC projects.

In this work we offer a broad overview of the combination of digital communication, education and society and show the link between learning with digital and awakening to the future. We trace the effects of digital communication acquired during postgraduate training at the Universidade Aberta of Portugal by professionals from African Portuguese-speaking countries. From a methodological point of view, the study identifies former postgraduate students at the Universidade Aberta (Public University of Higher Education) over the last five years who were given a comprehensive questionnaire aimed at finding out how the training they underwent contributed to gauging their current levels of application of digital communication in their current profession. We found that digital communication in learning and its diversification acts as a lever for personal and professional development following the acquisition of academic knowledge.

This work refers to sustainability (SDG - Sustainable Development Goals) highlighting the idea of ​​harmony of life along the lines of the biological model at various levels and of nature alike, thus defining the concept of bioharmonism. The purpose of this paradigm is to find a way of transition in the contemporary societal change, connecting the natural sciences, more precisely the life sciences, with the social sciences, more concretely along the lines of holistic approaches to the evolution of human society. The objective of this point of view is related to sustainability, resilience and planetary health, which indicates the need to analyse planetary bioharmony and transpose the model to the structure and functionality of human society through the complex, unitary and coherent approach of the bioharmonization process. The study analyses the issue, through: the definition and role of societal bioharmonism, then the argument related to the transition to another world using the proposed path, based on specific notions, principles and laws, and finally, the description of some lines of application of the pillars of bioharmonism. A series of solutions are highlighted that can contribute conceptually to the evolution of contemporary society, to the convergence of anthroposystems with ecosystems by interconnecting the aspects of the biological revolution with those specific to the information age. The solutions aim at societal bioharmonization supported by its specific pillars, which through integration and emergence lead to a dynamic balance. The state of equilibrium, sustainability can be highlighted at a systemic level by the directions of action in which the bioharmonization complementarities that indicate the Environment and biodiversity in the economic equation, the Citizen in the social equation and Science in the political equation will be taken into account.

Aiming to improve medical diagnosis and treatment, artificial intelligence (AI) has emerged as a transformational force in the healthcare industry. In this article, we examine how AI is increasingly being used to improve healthcare, with an emphasis on how it is changing medical diagnosis and treatment. We explore the opportunities, threats, and ethical concerns of incorporating AI into healthcare practice. In addition, we feature important AI applications in radiology, pathology, cardiology, and cancer. AI has great potential to improve patient outcomes, diagnostic accuracy, and the ability to provide individualized treatment plans in the future if it is used and refined with care.

Fracking in the UK has yet to reach full industrial development but it is still subject to significant opposition. This study uses Beck’s Risk Society theory and anti-politics to examine the views voiced by opponents to fracking in Yorkshire, England. A qualitative approach was used; local newspaper reports were evaluated alongside semi-structured interviews with protesters to provide a thematic analysis. Although there are signs of post-materialist concerns with the environment these issues did not dominate the discussion. Scientists were not held responsible for the risks involved in fracking. Instead economic greediness of politicians and austerity measures were perceived as putting the environment and people’s health at risk. Interviewees thought fossil fuel energy production was economically advantaged over more sustainable energy and jobs in the low carbon economy. Protesters’ trust in politicians had been eroded but faith in democracy remained. It is suggested a citizen-led deliberative approach to all the concerns raised, not simply those relating to scientific risk, might achieve some level of resolution over fracking in the UK.

REDD+ (Reducing Emissions from Deforestation and Forest Degradation) aims to achieve its purpose by working across multiple sectors and involving multi-level actors in reducing deforestation and forest degradation in tropical countries. By contrast, the European Union (EU) Action Plan on Forest Law Enforcement, Governance and Trade (FLEGT) and its Voluntary Partnership Agreements (VPAs) focus on forestry and functions at bilateral state level. The FLEGT Action Plan specifically aims to tackle illegal logging, legalise timber production and trade improve forest governance in countries exporting tropical timber to the EU. Since illegal logging is just one driver of deforestation and forest degeneration, and legalisation of logging does not necessarily reduce deforestation and forest degradation, the two instruments differ in scale and scope. However, by addressing the causes of deforestation and forest degradation and their underlying governance issues, the EU FLEGT and REDD+ share many functional linkages at higher levels of forest policy and forest governance. The contribution and participation of civil society organisations (CSOs) and other actors is imperative to both processes. Our study is based on a survey of key actors (national and international) in REDD+ and FLEGT VPA processes in the Lao People’s Democratic Republic. Our analysis was guided by the theoretical perspectives of the policy arrangement approach and examination of two specific dimensions of this approach, namely resources and rules of the game. This paper argues that participation of CSOs in both processes is crucial as it facilitates and nurtures the very much needed cooperation between other national and international actors. The paper also argues that participation of CSOs feeds valuable information and knowledge into REDD+ and FLEGT VPA processes, thus contributing to increased legitimacy, justice and transparency.

Advances in early detection of Breast cancer and treatment improvements have significant-ly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study a novel methodology for developing an interpretable computer-aided diagnosis (CAD) system for breast cancer detection, leveraging explaina-ble Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combine the continuous and binary representations of PSO and SMO to effectively manage trade-offs between Accuracy, feature selection and hyperparameter tuning. We evaluate several CAD models and investigate the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor filters, and edge detection. We further shedding light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. Also with HSMO our models achieved a high accuracy of 98.27% and F1- score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

Deploying search and rescue (SAR) dogs in disaster situations is crucial for effective rescue missions. This study delves into the challenges and obstacles encountered during the use of SAR dogs, particularly focusing on the earthquake that hit Turkey in February 2023. By employing a case study approach, the research explores various factors that impact the efficiency and safety of SAR dog operations, such as environmental conditions, equipment needs, cultural perceptions, and logistical hurdles. Environmental conditions such as extreme weather, hazardous materials, and unstable structures present significant risks to both the dogs and their handlers. The adequacy of equipment, including protective gear and medical supplies, is critical for ensuring the safety and efficiency of rescue operations. Logistical challenges, including the transportation and care of dogs in disaster zones, further complicate their deployment. Cultural barriers also play a crucial role, as perceptions of dogs vary widely across different communities, potentially affecting the acceptance and integration of canine units in rescue efforts. The case study of the earthquake in Turkey highlights these issues, showcasing the successes and setbacks experienced by international rescue teams. The findings indicate that although SAR dogs are invaluable in locating survivors, they face significant dangers, including hazardous materials, extreme weather, and physical injuries. Additionally, cultural differences and logistical issues complicate their deployment. This paper highlights the necessity for improved training, protective measures, and international standards to ensure SAR dogs' well-being and effectiveness in disaster response efforts. The study's implications suggest that addressing these challenges can markedly enhance the preparedness and resilience of SAR teams, leading to more efficient and safer rescue operations in future disaster scenarios.

The hard open problem of finding the lower bound for the Fokker Planck Kolmogorov’s time-dependent controller parameter probability density function is addressed for the first time in literature in this work. This revolutionary exposition will put control theory and other related inter-disciplinary fields to a higher level towards contemporary control theory. Notably, based on the influential role of control theory in both engineering and industry, this paper will be of great value to all engineering and industry professionals who seek to know more about advanced trends within control theory settings. On the other remit of the spectrum, Fokker Planck Kolmogorov(FPK) equations are of high importance to physicists as well as mathematicians, based on their multiple applicability to multi-interdisciplinary fields of human knowledge, and beyond. So, this by default adds more taste and credibility to this study. Concluding thoughts are included at the end of the work along with a difficult outstanding problem and the direction of future work.

The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which requires a computational cost for evaluating each objective function. Therefore, surrogate models were implemented to minimize this disadvantage. Nevertheless, each solution found by eMODiTS is a different-sized vector, so the surrogate model must be able to handle data sets under this characteristic. Consequently, this work's contribution lies in analyzing the surrogate models' implementation on the time series discretization, where each potential scheme is a real-number different-sized vector. For this reason, the surrogate model proposed was k-nearest Neighbors for regression with Dynamic Time Warping as a distance measure. Results suggest our proposal finds a suitable approximation to the final eMODiTS solutions with a functions evaluation reduction rate between 15% and 95%. However, according to Pareto front performance measures, the proposal Pareto front is competitive compared to the eMODiTS Pareto front, reaching an average Generational Distance (GD) between 0.0447 and 0.0536. Moreover, the average Hypervolume Ratio (HVR) ranges between 0.334 and 0.3891. Finally, our proposal compared against SAX-based methods presents a similar behavior regarding classification tasks and statistical tests.

Recent research has drawn attention to the ambiguity surrounding the definition and learnability of Out Of Distribution (OOD) recognition. The term "Out of Model Scope" (OMS) detection provides a clearer perspective, although the original problem remains unsolved. The ability to detect OMS inputs is particularly beneficial in safety-critical applications such as autonomous driving or medicine. By detecting OMS situations, we not only enhance the system's robustness but also prevent it from operating in unknown and unsafe scenarios. In this paper, we introduce a novel approach for OMS detection that combines three sources of information: 1) the original input, 2) the latent feature space extracted by an encoder, and 3) the synthetic data generated from the encoder's latent feature space. We demonstrate the effectiveness of combining original images and synthetically generated images against adversary attacks in the computer vision domain. We achieve results comparable to other state-of-the-art methods and demonstrate that any Encoder can be integrated into our pipeline in a plug-and-train fashion. Our experiments further evaluate which combination of the Encoder's features works best in order to discover OMS samples and highlight the importance of a compact feature space for the training of the Generator.

This study aims to reveal the revolutionary Gamma Distribution Manifold (GDM) info-geometric analysis combined with potential Gamma distribution (GD) applications to advance Satellite Earth Observations (SEOs). An exposition of the GDM Fisher Information Matrix (FIM) was undertaken, together with the analytic investigation of the existence of FIM’s inverse. The derivation of GDM’s Geodesic Equations (GEs) is undertaken. The solution of the obtained Geodesic Equations is optimally complicated, and it provides more extensions to numerous open problems. Notably, the current paper provides a first-time unification between three multi-interdisciplinary fields, namely, information geometry theory, probability theory, and Satellite earth observations. Looking at the other side of the coin, the provided revolutionary approach in this current work reports the significant impact of info-geometric analysis on the probabilistic dynamics as well as the influential role of information geometry on satellite and space industry. This poses numerous open issues that initiates a new form of contemporary unification between these inter-disciplinaries to discover another visionary thinking out of traditional statistical frames. In other words, refraining ourselves from using classical statistical frames of thinking and delving into a new info-geometric vision of statistical distributions as well as examining real life applications of information geometry to advance satellite earth observations. The curtain is drawn on this paper by providing several challenging open problems in combination with a concluding remark and the next phase of research.

This paper provides a new discovery that adds to the contemporary theory of modelling of non-stationary queues, by capturing analytically the behavior of the root parameter, for the G1/M/1 PSFFA model of non-stationary Ek/M/1 queue. Additionally, it has been also revealed that the resulting root parameter reduces to the PSFFA model of non-stationary queue. Potential applications of PSFFA to Computer engineering are provided. Some challenging open problems are addressed combined with closing remarks and future research directions.

This paper investigates the search for an exact analytic solution to the state variable of the non-stationary queue. Currently, the only known solution to this problem is through simulation. However, this study proposes a constant ratio

Synovial sarcomas are rare soft tissue tumors that infrequently occur in the ethmoid sinus, posing diagnostic challenges. Herein, we report a case of a 38-year-old male with a 7-month history of recurrent throbbing headaches, left eye pain, and facial nerve palsy, evolving into multiple stroke episodes. Radiological studies showed extension to the cavernous sinus, raising an initial suspicion of vasculitis. Histological findings of an endoscopic biopsy, however, confirmed a monophasic synovial sarcoma. Consequently, the patient was referred to a specialized center for comprehensive management. This case underscores the rarity of ethmoid sinus synovial sarcomas, their diverse clinical presentations, and the necessity of a multidisciplinary approach for accurate diagnosis and tailored therapeutic strategies.

Abstract Sustainability of ethnic culture in Southeast Asia has made the dramatically growth of ethnic identity. The ethnic revivals already made the increasing of cultural events in public spaces. This research paper sought the cultural sustainability of Chinese in Moslem society of Southaest Asia. A multisited ethnography was conducted in Medan Municipal of North Sumatra and Georgetown Penang, Malaysia to observe the sustained Chinese culture as the symbol of ethnification of Chinese in Moslems society in Southeast Asia region. It found that after 2003 Indonesia already saw the attractive cultural performances of Chinese in public spaces as the continuation of sustainability. In our ethnographic investigation from 2014 -2017, the reshaping of the Chinese identity through sustainability of Chinese culture in Medan Municipality of North Sumatra, and Penang of Malaysia has the high public visibility. Research report showed the continuation of the Chinese rituals and festivals which were accompanied by music instruments of Chinese and theatrical performances. Those have been transformed from self commemorations to be more public; attractions already were moved to public places, not solely in temples or ethnic group surroundings as what commonly found in the past period. It concluded that the sustainability of Chinese culture in public spaces made the Southeast Asia connection among the Chinese groups solidify their identity in this region tightened.

This study critically explores the legislative frameworks and national regulations of Serbia, Croatia, and Slovenia concerning the engagement, training, and responsibilities of search and rescue dogs and their handlers. The goal is to offer a thorough understanding of how these dogs and their handlers are engaged, trained, and managed in each country. By carefully analyzing the legal foundations, the study aims to clarify the rights, duties, and responsibilities of all parties involved, ensuring that search and rescue dog deployments are conducted within a structured and lawful framework. Such an understanding is crucial for improving disaster management and ensuring a coordinated response to various emergencies. The findings reveal that although each country has strong legal provisions for deploying search and rescue dogs, there are significant differences in the detail and scope of these regulations. Slovenia is notable for its detailed guidelines, regular evaluations, and licensing processes, setting a high standard. Croatia benefits from strong institutional support and active international collaboration, which greatly enhances its disaster response capabilities. On the other hand, Serbia, despite having a solid foundational framework, could improve by better coordinating with non-governmental organizations and aligning more closely with international standards. Key areas for improvement include harmonizing standards, enhancing training programs, increasing public awareness, better resource allocation, and adopting new technologies. By addressing these issues, Serbia, Croatia, and Slovenia can further strengthen their legal and institutional frameworks, ensuring their search and rescue teams are better prepared for disasters. This study emphasizes the need for continuous improvement through collaboration, standardization, and innovation to maximize the effectiveness of search and rescue operations involving dogs.

This article, based on network science, aims to contribute to overcoming its geometric and technological phases. The novelty consists in considering links of networks as linked by superimposed networks, termed here multiple superimposed soft networks (MSSN), which is introduced as a research issue. Such links of links (termed here as passive links) concern, for instance, correspondences, incompatibilities, and temporal synchronizations between the occurrences of pairs of active links of effective networks, such as those based on electrical and telecommunication. A possible constitutive mechanism of such passive linkage consists of linkage representations for practices and histories of use expressed by their validating statistical reoccurrences. We consider the possible emergent nature of the passive linkage. The reason for introducing the design and usage of MSSN properties as a research issue involves making new approaches to profile and manage networks available. Correspondence between active linkage and MSSN properties should be a matter for an experiential, machine-learning approach. Research issues relate their possible usage on the active linkage such as for classification, comparations, detection of criticalities, diagnosis, performance evaluation, and regulatory as weak forces. Furthermore, the possible identification of standard corresponding configurations of passive and active linkage are finalized to avoid their establishment or, conversely, in facilitating their establishment and keeping their replication in different contexts (or partially and in combinations) and identifying related standardized approaches (also for classes of configurations having significant levels of equivalence). This research project has methodological generalizing aspects of trans-disciplinarity. We conclude by mentioning related research issues.

The Vu Gia - Thu Bon River Basin (VG-TB) is confronting numerous challenges related to water security (WS), particularly in the face of the impacts of climate change. These challenges manifest in salinity intrusion, altered rainfall patterns, and reduced water supply in downstream areas. To assess the current state of water security in the basin, the study employed statistical analysis methods, Process Analysis Method (PAM), SMART, and Analytic Hierarchy Process (AHP) to develop a comprehensive Assessment Framework for Water Security (AFWS) in the VG-TB. The AFWS encompasses five key dimensions, 17 indicators, and 28 variables, aligning with the UN-Water definition of water security and addressing the Sustainable Development Goal 6 (SDG6) criteria for global water sustainability. It also adheres to the Asian Development Bank's (ADB) approach to water security assessment as outlined in the Asian Water Development Outlook (AWDO) reports. This allows for the computation of a comprehensive Water Security Index (WSI) for specific regions, serving as a foundation for decision-making and policy formulation aimed at enhancing water security in the context of climate change and facilitating sustainable basin development in the future.

The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

Background: Amyloid beta peptides (Aβ) have been identified as the main pathogenic agents in Alzheimer’s disease (AD). Soluble Aβ oligomers, rather than monomer or insoluble amyloid fibrils, show membrane-binding capacity to red blood cells (RBCs) and trigger several morphological and functional alterations in RBCs that can result in impaired oxygen transport and delivery. Since bioactive lipids have been recently proposed as potent protective agents against Aβ toxicity, we investigated the role of sphingosine-1-phosphate (S1P) in signaling pathways involved in the mechanism underlying ATP release in Aβ-treated RBCs. Methods: In RBCs following different treatments, ATP, 2,3 DPG, cAMP levels, and caspase 3 activity were determined by spectrophotometric and immunoassay. Results: S1P rescued the inhibition of ATP release from RBCs triggered by Aβ through a mechanism involving caspase-3 and restoring 2,3 DPG and cAMP levels within the cell. Conclusions: These findings reveal the molecular basis of S1P protection against Aβ in RBCs and suggest new therapeutic avenues in AD.

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we propose the consolidation of virtual machines (VMs) to optimize resource utilization and reduce operational costs. This paper introduces a novel algorithm, leveraging the Modified Artificial Feeding Birds Algorithm (ModAFBA), to manage cost optimally in cloud data centers by reducing power usage while meeting service level agreements (SLAs). The ModAFBA algorithm, inspired by bird foraging behavior, dynamically adjusts solutions based on environmental changes. Comprehensive experimental evaluation demonstrates the superiority of the proposed algorithm in terms of power usage, migration count, and SLA compliance. Through this research, we aim to advance cost management and resource optimization in cloud data centers, providing a valuable tool for cloud service providers to enhance infrastructure efficiency and achieve cost savings.

Aim: This study aims to analyze the structure of the co-authorship network in Budd-Chiari syndrome (BCS) research from 2000 to 2023 using data from the Web of Science (WoS) Core Collection. The objective is to identify key researchers, evaluate the collaborative dynamics, and understand the evolution of scientific contributions in this field. Method: This study utilized network analysis techniques to evaluate 2,877 publications related to BCS research indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: The results revealed a highly fragmented network structure in the early years (2000-2009), characterized by low network density (0.0017) and a large number of disconnected components (533). Key researchers such as Aurelie Plessier and Dominique Valla emerged as central figures in terms of degree and closeness centrality. In the 2010-2019 period, the network showed slight improvements, with density decreasing to 0.0016 but an increase in the number of components to 645, indicating persistent fragmentation. From 2020 to 2023, the network demonstrated increased cohesiveness with a density of 0.0032 and a reduction in components to 376. Influential researchers in the final period included Virginia Hernandez-Gea and Valerie Paradis, who played pivotal roles in connecting disparate research clusters.Conclusion: The co-authorship network analysis of BCS research over the past two decades highlights a gradual yet incomplete evolution towards greater collaboration and integration among researchers. While the network's cohesiveness has improved, significant fragmentation remains, suggesting that further efforts are needed to enhance global and interdisciplinary collaboration. Addressing these challenges is crucial for fostering a more connected and productive research community that can advance the understanding and management of BCS.

Aim: This study aims to analyze the evolution of co-author networks in Single-Balloon Enteroscope (SBE) research from 2000 to 2023, using data from the Web of Science (WoS) Core Collection. The objective is to provide a structural overview of researcher collaborations in this specialized field, highlighting key contributors and the impact of international cooperation. Method: I conducted a co-author network analysis on 149 SBE-related articles indexed in the WoS Core Collection between 2000 and 2023. Using Python (Version 3.10.5) within the PyCharm development environment (Software Version 2022.1.3), I assessed macro-level indicators including network density, clustering coefficient, number of components, and average distance. Micro-level indicators such as degree centrality, closeness centrality, and betweenness centrality were also evaluated to understand the roles of individual researchers in the network.Result: The analysis revealed that the co-author networks in SBE research have evolved over the studied period, with varying degrees of connectivity and collaboration. From 2000 to 2009, the network showed moderate clustering but high fragmentation, with a few key researchers like Nista, Enrico C., and Spada, Cristiano playing central roles. In the 2010-2019 period, network density decreased, indicating expansion in the author pool but less frequent co-authorships. However, the clustering coefficient remained high, suggesting localized collaboration. Central figures such as Itoi, Takao emerged as influential in bridging network gaps. In the most recent period (2020-2023), network density moderately increased, reflecting improved collaboration. Key contributors included Tanisaka, Yuki, and Ryozawa, Shomei, who enhanced connectivity within the network. Despite these advancements, the network remained fragmented with isolated components throughout all periods. Conclusion: The co-author networks in SBE research have shown an overall increase in collaboration over the past two decades, with key contributors playing pivotal roles in connecting disparate groups. However, the persistent fragmentation suggests a need for more integrated efforts to enhance international collaboration. Understanding these network dynamics can inform future strategies for fostering global cooperation and advancing SBE research.

Aim: To elucidate the international network structures and evolution of collaborations in capsule endoscopy research from 2000 to 2023 by analyzing co-authorship networks. Method: This study analyzed co-authorship patterns in capsule endoscopy research articles published from 2000 to 2023 using data from the Web of Science (WoS). I employed Python (version 3.10.5) in the PyCharm development environment (version 2022.1.3) to conduct a network analysis using social network analysis principles. The analysis included macro-level metrics (network density, clustering coefficient, components, and average path length) and micro-level metrics (degree centrality, closeness centrality, and betweenness centrality) to assess collaboration intensity, key contributors, and network cohesion. Result: The co-authorship network analysis revealed evolving collaboration patterns over three distinct periods (2000-2009, 2010-2019, and 2020-2023). Initially, the network exhibited low density and high fragmentation, with key contributors such as Guido Costamagna and Jonathan A. Leighton prominently positioned. Over time, the network expanded with increased density and clustering, though it remained fragmented. In recent years, the network showed increased cohesion with reduced components and notable contributors like Anastasios Koulaouzidis and Xavier Dray, reflecting the growing importance of international collaborations, particularly involving researchers from Japan, Italy, and Israel. Conclusion: The study highlights the dynamic nature of international collaborations in capsule endoscopy research, with increasing connectivity and influential contributors playing pivotal roles in advancing the field. These findings underscore the value of international cooperation in enhancing diagnostic and therapeutic approaches in capsule endoscopy, pointing to future opportunities for deeper collaboration and integration.

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

With the large-scale application of Internet of Things (IoT) in the medical field, eHealthcare has attracted wide attention. With the help of IoT devices, eHealthcare allows doctors to monitor patients’ health conditions and make recommendations accordingly. The addition of cloud computing greatly saves Healthcare Center’s (HC) computing resources. In order to provide privacy and fine-grained access control, Attribute-b ased encryption (ABE) is widely favored. HC usually operates multiple medical institutions in different regions, and each medical institution can be regarded as a attribute authority. Therefor, multi-authority (MA) ABE is usually concerned to encrypt the patient data. However for existing MA-ABE algorithms, the ciphertext is long and the efficiency of encryption and decryption is low. These problems will greatly increase the computing burden of patients and seriously affect the efficiency of data transmission. Hence we propose a decentralized key-policy MA-ABE in eHealthcare. The proposed algorithm can achieve decentralization, that is, it does not need central authority. Meanwhile, compared with existing MA-ABE algorithms, the length of ciphertext generated by our algorithm is shorter, and the efficiency of encryption and decryption is significantly improved. With the help of Java Pairing Based Cryptography, we carried out the experiments which show that our algorithm can effectively reduce the time required for encryption and decryption.

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share commonalities: historic, conceptual, methodological, and philosophical. Although their origins trace back to the 1940s birth of cybernetics, they were only able to properly develop as modern information technology became available. In this perspective, I offer a personal (and thus biased) account of the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. Even when the trajectory of the paper is influenced by my personal experience, the general questions posed (which outweigh the answers) hopefully will be useful to align efforts in these fields toward overcoming --- or accepting --- their limits.

In this paper we introduce an ODE system to model the interaction between nature resources and human exploitation in a rich consumeristic society. In this model the rate of change of population depends on wealth per capita, and the rate of consumption has a quadratic growth with respect to population and wealth. As in our previous work \cite{BaCra_1}, we distinguish between renewable and non renewable resources, and we introduce a replenishment term for non renewable resources. We first get some information on the asymptotic behaviour of wealth and population, then we compute all the equilibria of the system and we study their stability when the parameter of exploitation of resources is small. Some numerical simulations confirm the theoretical results, and give suggestions for future research.

This article focuses on the description of the other view of Valencia in the fifteenth century, that of social marginalization, which could be manifested by the fact of exercising various trades considered immoral. In the following pages, a description is offered of two female collectives that are particularly treated as vile and despicable: that of healers, sorceresses and witches, whose strength was considered to stem from a demonic origin, and that of prostitutes, dedicated to covering a "lesser evil" in society but seen as unworthy by society because of the practical exercise of their trade.

Religious harmony performs a key role in preserving political and worldwide stability. Internationally it's far crucial to stay in a non-violent and harmonious environment, mainly in a society composed of believers of various races and religions. Relations among religions are normal. Failure to hold religious harmony can plunge society into battle and chaos. Therefore, the cause of this newsletter is to record in element the elements that affect the sustainable harmony among multi-religious societies. The record additionally emphasized that elements including acceptance, knowledge, cooperation, fairness, and justice are crucial elements in preserving religious harmony. Therefore, this paper pursuits to shape a solid idea of multi-sectarian harmony in society. It must additionally contribute to the improvement of a multi-religious society. Try to hold and construct a sturdy dating among them. Religious harmony is vital to make sure solidarity and team spirit amongst believers of more than one religion. A multi-religious society is a network with one-of-a-kind religions, including Islam, Buddhism, Hinduism, Christianity, etc. To stay in a non-violent and harmonious society, it's far very crucial to just accept believers from different religions in a selected network. From the angle of Islam, Islam is a faith that accepts the range of religions on this international however denies that every one religion is proper. So is the case in almost all religions. Religious pluralism may be described because of the range of religions on this international; however, withinside the idea of religious pluralism, it could be described because of the perception that every religion on this international is absolute truths. Pluralists trust that every one religion withinside the international is actual and have to be respected. It could be very crucial to recognize that everyone's proper statements are legitimate and deserve the same appreciation. This handiest method that fans of one religion have to be given different reality statements, including Muslims, Jews, Hindus, and others. However, from the angle of Islam, Islam accepts more than one religion however does now no longer understand that every one religion is proper. Recognizing the origins of various religions in a multi-religious society is the important thing to making sure harmony and solidarity withinside the international. Recognizing that religious range in a multi-religious society is a crucial component in preserving social harmony. For example, Muslims who cost the lifestyles of different religious groups (including Buddhists, Hindus, and Christians) can enhance inter-religious members of the society and deal with them kindly. Therefore, the popularity of the lifestyles of the religious range must be visible as a resounding component for a selected network. Everyone residing in a multi-religious society has to recognize and be given religious freedom. One must accept and appreciate the beliefs of different religions. Respect for the lifestyles of the religious range additionally contributes to political and countrywide stability. Respect also can enhance interreligious members of the society via way of means of acknowledging the lifestyles of various religious groups. Mutual appreciation, cooperation, tolerance, knowledge, and kindness are crucial elements in residing in a multi-ethnic and religious society. To hold a harmonious society, all multi-religious believers must have the proper and freedom to exercise their faith. It must now no longer be abandoned (Abdullah et al., 2016). Islam has usually emphasized the idea of justice (alAdl). All divine religions are of belief of religious harmony. By studying different religions, it is very clear that all religions are of the view to respect other religions and followers of different beliefs.

This paper aims to present the basic assumptions for creation of social Fröhlich condensate and attract attention of other researchers (both from physics and socio-political science) to the problem of modelling of stability and order preservation in highly energetic society coupled with social energy bath of high temperature. The model of social Fröhlich condensation and its analysis are based on the mathematical formalism of quantum thermodynamics and field theory (applied outside of physics). The presented quantum-like model provides the consistent operational model of such complex socio-political phenomenon as Fröhlich condensation. The model of social Fröhlich condensation is heavily based on theory of open quantum systems. Its consistent elaboration needs additional efforts. Evidence of such phenomenon as social Fröhlich condensation is demonstrated by stability of modern informationally open societies Approaching the state of Fröhlich condensation is the powerful source of social stability. Understanding its informational structure and origin may help to stabilize the modern society. Application of the quantum-like model of Frhlich condensation in social and political sciences is really the novel and original approach to mathematical modeling of social stability in society exposed to powerful information radiation from mass-media and internet based sources.

The accumulation of sea surface debris around the coastal waters of Malta poses significant ecological and environmental challenges, negatively affecting marine ecosystems and human activities. This issue is exacerbated by the lack of an effective system tailored to predict surface debris movement specifically for the Islands of Malta. To address this gap, a pipeline that combines a machine learning (ML) based prediction system with a physics-based model is proposed. This pipeline uses historical sea surface currents (SSC) velocities data to forecast future conditions and visualise debris movement. Central to this system are LSTM and GRU models, trained to predict SSC velocities for the next 24 hours for a specific area. These predictions are then utilised by a Lagrangian model to simulate and visualise the debris movement, providing insights into future dispersion patterns. A comparative evaluation of the models showed that the LSTM model outperformed the GRU model, with lower error metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE), alongside narrower interquartile ranges (IQR). This method offers a tailored approach to addressing sea surface debris around Malta by accurately predicting SSC velocities and visualising debris movement, improving cleanup operations, and marine conservation strategies.

Disasters, whether natural or human-induced, pose significant threats to human lives, property, and infrastructure. This study examines the effectiveness of search and rescue (SAR) dogs in locating survivors during disasters, with a focus on the experiences in Serbia, Croatia, and Slovenia. The primary goals are to evaluate training protocols, operational challenges, success rates, and the overall impact of SAR dogs in disaster scenarios. The findings indicate that SAR dogs greatly enhance the efficiency of disaster response operations. Their superior sense of smell, agility, and ability to manoeuvre through complex terrains make them crucial for quickly locating survivors. However, the performance of SAR dogs is affected by various factors, including the quality of their training, environmental conditions, and the coordination between handlers and dogs. The research highlights several challenges encountered by SAR dogs, such as extreme weather conditions, hazardous terrains, fatigue, and psychological stress. Additionally, limited resources and inconsistent training standards can hinder their effectiveness. The study underscores the importance of standardized training protocols, increased resource allocation, and better coordination among SAR teams. By addressing these challenges, the efficiency of SAR dogs can be further optimized, resulting in more effective disaster responses and higher survival rates. SAR dogs play an essential role in disaster management in Serbia, Croatia, and Slovenia. Ongoing investment in their training and welfare, along with improved operational protocols, will ensure their crucial contribution to saving lives during disasters. This study contributes to a broader understanding of the effectiveness of SAR dogs and provides practical recommendations for enhancing disaster response strategies.

Globalization is a basic feature of contemporary digital society related to increasing level of informatization, electroniztion and communications through the global network and share different information. Regardless of the many advantages of the digital age, related to the intellectualization of various activities and objects (e-governance, e-health, social computing, smart home, smart city, etc.), the need for secure protection of users' privacy should not be overlooked. In this reason, the goal of the article is to determine the specific features of the main components of the globalization and the impact on the privacy and data protection. Based on the analysis of possible impacts the main recommendation for adequate data security and privacy protection are determined.

The Societal Patterns Evolution Model (SPEM) (Gakh, 2023a) has been developed to apply to socio-technical systems. It contains patterns representing stages of societal development. Mapping of these patterns to the development of Information and Communication Technologies (ICTs) and Information Systems was presented at FedCSIS 2022 conference (Gakh, 2022c). This paper discusses the application of SPEM to model the development of three pillars of sustainability: economic, social, and environmental (Purvis, et al., 2019; United Nations, 2023).

(1) Background: The importance of this article is to analyze the technological developments in the field of the Internet and Internet technologies and to determine their significance for the sustainable development which will result in the emergence of the Society 5.0; (2) The authors used automated content analysis for the analysis of 552 articles published in 306 scientific journals indexed by SCII and/or SCI - EXPANDED (Web of Science (WOS) platform) between the years 1996 and 4/2020. The goal of the research was to present the relationship between the internet and sustainable development. (3) Results: The results of the analysis show that the top four most important themes in the selected journals were “development”, “information”, “data”, and “business and services”. (4) Conclusions: Our research approach emphasizes the importance of the culmination of scientific innovation with the conceptual, technological and contextual frameworks of the internet and internet technology usage and its impact on sustainable development and emergence of the Society 5.0

Ideas about Artificial Intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided is getting bigger and bigger and, in this case, we need artificial intelligence that helps us with superhuman powers. The interaction with medicine is getting closer and closer, medicine being a field that continues to be perfected. Machine learning, deep learning, neural networks, computer vision are some of the mechanisms that are already applied in medicine, becoming a trend in healthcare worldwide. Artificial intelligence will be the thing that will change contemporary medicine. Developed countries such as Japan, France, Germany have already implemented artificial intelligence in their medical systems. The help is in medical diagnosis, patient monitoring, personalized therapy, and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques, to choose the best surgical technique. The goal is to predict complications, reduce diagnostic time, diagnose complex pathologies, guide the surgeon intraoperatively and reduce medical errors. We are at the beginning but the potential is enormous.