The rapid acceleration of artificial intelligence (AI) has sparked extensive debate about its impact on the safety and security of society. While AI has shown trans- formative potential, particularly in areas such as healthcare and independent structures where it complements productivity and minimizes human error, accu- racy and protection. However, the misuse of artificial intelligence poses real risks, along with threats such as deep counterfeiting, computer cyber-attacks and the manipulation of social behavior through disinformation. These risks vary from privacy and cyber security breaches to moral dilemmas around liability. The unchecked improvement of AI is expected to threaten the social order, so the relationship between innovation and governance is essential so that AI-related threats do not overshadow its benefits. This article offers a detailed assessment and review of the literature that studies the potential dangers that AI poses to society. The cumulative effects underscore the urgent need for comprehensive regulatory frameworks to mitigate these threats.

Metaverse represents a multifaceted digital universe integrating diverse technologies to create immersive, interconnected virtual environments. Key components foundational to its structure include the infrastructure layer for hardware and network support, the human interface layer utilizing virtual reality (VR) and augmented reality (AR) devices, and a decentralized layer leveraging blockchain for secure digital asset management. Further components encompass spatial computing for realistic 3D environments, the creator economy for content generation and monetization, and discovery mechanisms for navigation within Metaverse. VR, AR, blockchain, AI, the fifth-generation (5G) networks, and the Internet of Things (IoT) are central to Metaverse functionality, enabling real-time interactions and data integration. Real-Time Metaverse aims to replicate instantaneous real-world interactions, requiring advanced technological infrastructure for seamless data exchange. Critical challenges include ensuring low latency, high bandwidth, scalability, and robust security measures. Metaverse’s potential spans various domains, promising to revolutionize social interactions, entertainment, workplaces, and education through dynamic, immersive experiences. Ongoing advancements in high-performance computing, cloud services, and AI-driven technologies are essential for addressing existing barriers and achieving a cohesive, secure, and inclusive Metaverse ecosystem. Future research must focus on enhancing user experience, accessibility, and standardized interoperability to fully realize the transformative capabilities of a Metaverse.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. Python, a versatile programming language, holds vast potential for Sentiment Analysis (SA). Leveraging the Requests and TextBlob libraries, we have crafted a user-friendly code that enables Industrial Engineers (IE) and managers, particularly those who are new to Python, to extract and analyze sentiment efficiently. We aim to provide IE/managers in service companies requiring SA capabilities with a simple yet effective Python solution. While machine learning resources like PyTorch/TensorFlow are commonly utilized in SA, offering pre-built algorithms and tools for training, and implementing machine learning models, we sought to exploit Python's versatility by integrating additional web-scraping libraries. Thus, by using a lexicon-based approach, we intend to deliver an informative and practical article. The code in this article is twofold; firstly, it can be easily adapted by IE/managers possessing basic Python skills; secondly, we aim to inspire junior IE/managers to develop their own customized coding solutions tailored to specific organizational needs.

Objectives. The aim of this study was to demonstrate the ability to visualize the results of the Scilit platform's bibliometric data analysis on the topic "AI & Machine Learning" to identify publications reflecting specific issues of the topic. Data source. Bibliometric records exported from the Scilit platform on the topic "AI & Machine Learning" for the years 2021–2023 were used. For each year, 6,000 records were downloaded in CSV and RIS format. Programs and utilities used. VOSviewer, Scimago Graphica, Inkscape, FP-growth utility, GSDMM algorithm. Used services: Elicit, QuillBot, Litmaps. Results. It has been shown that bibliometric data from the open access abstract database Scilit can serve as a quality alternative to subscription-only databases. Data exported from the Scilit platform require preprocessing to make them available in a format that can be processed by programs such as VOSviewer and Scimago Graphica. The use of GSDMM and FP-growth algorithms is effective for structuring bibliometric data for further visualization. The Scimago Graphica software provides wide possibilities for building compound diagrams, in particular, for representing the network of keywords in such important coordinates for bibliometric analysis as average year of publication and average normalized citation, as well as for building an alluvial diagram of co-occurrence of more than two keywords. The possibility of using such services as elicit.com, quillbot.com and app.litmaps.com to accelerate the selection of publications on the topic under study is shown.

This article examines the integration of artificial intelligence (AI) and robotic process automation (RPA) in financial accounting and management, underscoring their role in driving the digital transformation of corporate finance. It discusses the shortcomings of traditional financial processes and highlights the potential of AI and RPA technologies to enhance efficiency, accuracy, and cost-effectiveness. The paper also explores the limitations of RPA, such as its challenges in processing unstructured data and handling complex decision-making scenarios. Looking forward, it considers the future trends in AI and RPA, emphasizing the benefits of cloud technology in scaling automated systems and addressing associated challenges.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

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.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.

This work studies the discontinuities features of sedimentary flysch materials in a 100 km2 area belonging to the Basque Arc. Such materials are common in this Spanish Alpine region located in the north of the Iberian Peninsula. A total of 33 outcrops are investigated by an intensive geotechnical investigation including geomechanical stations, boreholes and mechanical laboratory tests. Two flysch units are characterized: the Upper Aguinaga Formation or siliciclastic flysch, and the Lower Itziar Formation or calcareous flysch. Differences between both flysch formation are found. Joints in the siliciclastic flysch formation present an undulated roughness, with a spacing narrower and a persistence lower than in the calcareous flysch formation, which exhibits higher friction angles, although roughness is essentially planar. In addition, the potential of using Artificial Intelligence (AI) techniques, particularly Artificial Neural Networks and Support Vector Machine, to estimate the Geological Strength Index (GSI) from the Rock Quality Design (RQD) and some discontinuities features (spacing, persistence, aperture and roughness) is investigated. AI techniques are found to be satisfactory, being the Support Vector Machine with a linear kernel the technique which achieves the best performance.

Pharmaceutical aerosols play a key role in the treatment of lung disorders, but also systemic diseases, due to their ability to target specific areas of the respiratory system (RS). This article focuses on identifying and clarifying the influence of various factors involved in the generation of aerosol micro- and nanoparticles on their regional distribution and deposition in the RS. Attention is given to the importance of process parameters during aerosolization of liquids or powders and the role of aerosol flow dynamics in RS. The interaction of deposited particles with the fluid environment of the lung is also pointed out as an important step in the mass transfer of the drug to the RS surface. The analysis presented highlights the technical aspects of preparing the precursors to ensure that the properties of the aerosol are suitable for a given therapeutic target. Analyzing existing technical limitations, several potential strategies for enhancing the efficiency of targeted therapeutic delivery to or through the respiratory system are identified and presented, including the use of smart inhaling devices incorporating AI algorithms.

Use of fingerprints found at a crime scene is a common practice for identifying suspects in criminal investigations. Over the past two decades, attempts have been made to obtain additional information from fingerprints, beyond locating suspects as part of an investigation. This includes gender, age and nationality. Researchers demonstrated 75%-90% accuracy in gender classification based on fingerprint images. Nonetheless, despite promising results, these studies have several significant shortcomings with respect to their practical feasibility. First, they ignore the low quality and quantity of fingerprints collected from the crime scene since typically the scene of a crime has only one fingerprint collected, and the fingerprint might be partially or of poor quality as well. Second, as most results are based on a single database, public or private, it is difficult to generalize the most suitable method. Third, studies miss the untapped potential of Data-Centric AI (DCAI) approaches for improving results. The aim of this study was to compare, for the first time, the gender classification from a fingerprint using several datasets and with varying fingerprint image quality. The results from four databases are compared, three public and one internal private database. In addition, we utilize the latest Data-Centric AI (DCAI) approaches for improving classification results. The results demonstrate that a conservative Convolutional Neural Networks (CNN) such as VGG is sufficient for this task. Classification accuracy ranges from 80% to 95% depending on the quality of the fingerprint, with DCAI approaches adding 1%-4% improvement. For partially or low-quality fingerprint images, the periphery of a fingerprint is the most significant area for determining gender. The source code is also provided here for practical application.

One of the most promising advancements in healthcare is the application of digital twin technology. Digital twins are virtual replicas of real-world human patients and can be used for monitoring, and diagnosis, and as a tool to design treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. This review article delves into the various data sources and methodologies that contribute to the construction of digital twins. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

In the realm of modern drug discovery, the integration of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structural-activity relationships (SARs), play a pivotal role in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. By embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.

In this study, we propose a radiomics-clinical probability weighted model for the prediction of prognosis for NSCLC. The model combines radiomics features extracted from RT planning images with clinical factors such as age, gender, histology, and tumor stage. CT images with radiotherapy structures of 422 NSCLC patients were retrieved from The Cancer Imaging Archive (TCIA). Radiomic features were extracted from gross tumor volume (GTV). Five machine learning algorithms, namely decision trees (DT), random forests (RF), extreme boost (EB), support vector machine (SVM) and generalized linear model (GLM), were optimized by a voted ensemble machine learning (VEML) model. A probabilistic weighted approach is used to incorporate the uncertainty associated with both radiomic and clinical features and to generate a probabilistic risk score for each patient. The performance of the model is evaluated using a receiver operating characteristic (ROC). Radiomic model, clinical factors model and combined radiomic-clinical probability weighted model demonstrated good performance in predicting NSCLC survival with AUC of 0.941, 0.856 and 0.949 respectively. The combined radiomics-clinical probability weighted enhanced model achieved significantly better performance than radiomic model in 1-year survival prediction (chi-square test, p<0.05). The proposed model has the potential to improve NSCLC prognosis and facilitate personalized treatment decisions.

Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors to make better decisions (‘clinical decision support’), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a ‘black box’, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at the current status of XAI in healthcare, describe several issues around XAI, and discuss whether it can really help healthcare to advance, for example by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.

This report is considered different aspects of the concept of the networked distributed engine control system (DECS) of future air vehicles. These aspects include the following: the structure of multiple networks similar to NATO Generic Vehicle Architecture (NGVA), the role of Artificial Intelligence (AI) in DECS, and the use Augmented Reality (AR) as Human-Machine Interface between AI and pilots. Deployment of AI solutions for monitoring equipment in on-board infrastructure can be provided on physical or virtual servers and in the clouds. In this case, it is possible to use various methods of alerting the pilot and ground personnel on the basis of AR. The use of AI allows covering an unlimited set of scenarios, to provide an assessment of the likelihood of equipment failure, classification alarm is normal, and recognition of the development of defects. To collect Big Data from sensors and the pre-processing of this data before a machine learning (ML) procedure it is proposed to form data sets with the help of the face-splitting matrix product. To decrease the time of reaction of Neural Networks it has been suggested the implementation of advanced tensor-matrix theory on the basis of penetrating face product of matrices. Other important results of the report are a possible version of the AR data format for DECS and a proposal about the use of non-orthogonal frequency discrete multiplexing (N-OFDM) signals to data transfer via fibre optics.

The emergence of Artificial Intelligence (AI) driven Keyword Spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge of end-to-end energy efficiency, memory footprint and system complexity of current Neural Network (NN) powered AI-KWS pipelines has remained ever present. This paper evaluates KWS utilizing a learning automata powered machine learning algorithm called the Tsetlin Machine (TM). Through significant reduction in parameter requirements and choosing logic over arithmetic based processing, the TM offers new opportunities for low-power KWS while maintaining high learning efficacy. In this paper we explore a TM based keyword spotting (KWS) pipeline to demonstrate low complexity with faster rate of convergence compared to NNs. Further, we investigate the scalability with increasing keywords and explore the potential for enabling low-power on-chip KWS.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Background: This study addresses the burgeoning impact of Large Language Models (LLMs) such as ChatGPT on the integrity of online examinations and seeks to demonstrate latent and advanced LLM reasoning capabilities. The research devises an iterative self-reflective strategy for invoking critical thinking and higher-order reasoning in LLMs based on multimodal exam questions using visuals and text. Methods: The proposed strategy was demonstrated and evaluated on real exam questions by subject experts and the performance of ChatGPT (GPT-4) with vision was estimated on an additional dataset of 600 text descriptions of multimodal exam questions, focusing on its response to complex, visual, and textual prompts. Results: The results indicate that latent multi-hop reasoning capabilities can be invoked using the proposed self-reflective strategy, which can effectively steer ChatGPT towards correct answers by integrating critical thinking from each modality into the final response. Meanwhile, ChatGPT demonstrated considerable proficiency in being able to answer multimodal exam questions across 12 subjects. Conclusions: This work challenges findings from recent studies which claim that existing LLMs are unable to reason on complex multimodal tasks, and therefore this study underscores the need for enhanced online exam security measures like proctoring software, that can mitigate the potential misuse of AI technologies in educational settings.

Low back pain (LBP) is a significant global health issue, contributing to considerable disability and socioeconomic burdens. The degeneration of the human intervertebral disc (IVD) is a critical factor in the pathogenesis of LBP. Recent studies have emphasized the significance of its specific set of genes and extracellular matrix (ECM) in IVD health. The Noggin gene has emerged as a critical factor due to its high expression levels in healthy nucleus pulposus cells (NPCs) observed in our previous research. In this study, we investigated the hypothesis that decreased Noggin expression in NPCs is associated with IVD degeneration, contributing to LBP development. A lentivirus-mediated RNAi was applied to knockdown Noggin expression in primary NPCs from six human donors. Cell viability analysis, XTT assay, and cell apoptosis were conducted to reveal the effect after transduction. After two weeks, colony formation unit (CFU) assay was used to examine the anchor-independent growth ability of transduced cells. Additionally, qPCR was performed to quantify cellular anabolism and catabolism makers at the transcript level. The results showed lentivirus-mediated downregulation of Noggin suppressed cell growth, cell viability, and colony formation, and induced apoptosis in human NPCs in vitro. Noteworthy, it hindered cellular anabolism and enhanced catabolism activity in human NPCs after transduction. Our findings indicated that the degeneration of human IVD is possibly related to decreased Noggin expression in NPCs. This research provides valuable insights into the role of Noggin in IVD homeostasis and its implications for LBP treatment.

This study evaluated the presence of Human Papillomavirus (HPV) and Bovine Leukemia Virus (BLV) DNA in breast cancer (BC) samples from women in Northeastern Brazil. We analyzed 186 formalin-fixed, paraffin-embedded tissue block samples using PCR and qPCR to detect HPV and BLV DNA. BLV DNA was not detected in any BC patients. However, HPV DNA was found in 46.2\% of the BC samples: 44.6\% were positive for HPV16, 3.5\% for HPV31, and 2\% were coinfected with HPV16 and HPV31. Additionally, we found an association between HPV-positive BC and Ki-67 expression (P=0.03, OR=1.85; CI=1.02-3.34), as well as between HPV31-positive BC and mitotic index (P=0.047). Although BLV was not detected in BC patients from Northeastern Brazil, the association between HPV and cell proliferation markers warrants further investigation, as HPV may contribute to the aggressiveness of BC in these patients.

The human eye is a vital source of collecting information worldwide via daily observation. The computer science area recognizes it in research as a human-computer interaction. Human blink is an integral part of this observation as a research topic. Several studies have been conducted on eye blinking and left-right eye movements. Previous studies conducted on the eye blink consider the availability of hardware devices high in the budget, high Precision, and low light expo-sure, unlike our study, which uses low-budget devices as simple as a webcam and more versatile usable techniques, incredibly both dim and high light to name a few. This study offers the observational approach via ResNet101v2, VGG-19, and Convolution Neural Network (CNN) architectures. Outperforming both VGG-19 and CNN in this study, ResNet101v2 achieves an impressive accuracy of 98.2%. In contemporary times, deep learning in AI is taking an advanced form. This study attempts to provide a new insight into the real-world implementation of the eye blink.

Arabinoxylans (AXs) are non-starch polysaccharides with complex structures naturally occurring in grains (i.e., barley, corn, and others) providing many health benefits, especially as prebiotics. AXs can be classified as water-extractable (WEAX) and water-unextractable (WUAX) based on their solubility, with properties influenced by grain sources and extraction methods. A large body of evidence shows that AXs exert an im-portant health impact, including glucose and lipid metabolism regulation and immune system enhancement, which is induced by the interactions between AXs and the gut microbiota. Recent research underscores the dependence of AX physiological effects on structure, advocating for a deeper understanding of struc-ture-activity relationships. While systematic studies on WEAX are prevalent, knowledge gaps persist re-garding WUAX, despite its higher grain abundance. Thus, this review reports recent data on WUAX struc-tural properties (chemical structure, branching, MW) in cereals under different treatments. It discusses WUAX applications in baking and its benefits deriving from gut fermentation.

Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges for adverse weather conditions, interference from other vehicles sensors and electronic devices, and signal reception failure, leading to incompleteness in trajectory data. But for real-time decision making for autonomous driving, the trajectory imputation is no less crucial. Previous attempts to address this issue such as statistical inference and machine learning approaches have shown promise. Yet, the landscape of deep learning is rapidly evolving with new and more robust models emerging. In this research, we have proposed an encoder-decoder architecture, Human Trajectory Imputation Model, coined as HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model has been evaluated using the Intersection drone inD dataset, containing trajectory data at suitable altitudes preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we have utilized L1, MSE, Quantile and ADE loss. Our experiments have demonstrated that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories.

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

In this paper, a modular wire-actuated robotic arm was designed to improve the safety and adaptability of service robots during human-robot interaction. To raise the stiffness adjustment range of the robotic arm, a symmetric variable-stiffness unit was designed based on flexure, which had compact and simple structure, and lowly nonlinear stiffness-force relationship. In this paper, we focused on the research of the symmetric 1-DOF joint module. Based on the kinematics and stiffness analysis, the pose of the joint module could be adjusted by controlling the length of the wires, and the stiffness of the joint module could be adjusted by controlling the tension of the wires. Because of the actuation redundancy, the pose and stiffness of the joint module could be controlled synchronously. Furthermore, a directly method was proposed for the stiffness-oriented wire tension distribution problem of the 1-DOF joint module. A simulation was carried out to verify the proposed method.

Network centrality analyses have proven successful in identifying important nodes in diverse host-pathogen interactomes. The current study presents a comprehensive investigation of the human interactome and SARS-CoV-2 host targets. We first constructed a comprehensive human interactome by compiling experimentally validated protein-protein interactions (PPIs) from eight distinct sources. Additionally, we compiled a comprehensive list of 1,449 SARS-CoV-2 host proteins and analyzed their interactions within the human interactome, which identified enriched biological processes and pathways. Seven diverse topological features were employed to reveal the enrichment of SARS-CoV-2 targets in the human interactome, with Load centrality emerging as the most effective metric. Furthermore, a novel approach called CentralityCosDist was employed to predict SARS-CoV-2 targets, which proved effective in expanding the pool of predicted targets. Pathway enrichment analyses further elucidated the functional roles and potential mechanisms associated with predicted targets. Overall, this study provides valuable insights into the complex interplay between SARS-CoV-2 and the host cellular machinery, contributing to a deeper understanding of viral infection and immune response modulation.

Data4HumanRights training materials have been developed as open-source and tailored to limited resource settings where community data collectors often live and work. An overview of how the training material was developed together with community data collectors in Nigeria and Kenya is provided. The paper gives insights into the fundamental principles (i.e., inclusiveness, adaptive, limited resources, gender- and incentive-sensitive) and the structure of the open-access training materials. This resulted in 28 modules designed to be delivered in a face-to-face format in less than one day by a local trainer. To maximize adaptivity, the training modules can be mixed and matched (e.g., as individual modules or a learning path of several modules around a specific training need). The individual modules cover a range of methods and tools that are useful to human rights work, for example, documenting evictions, performing rapid needs assessments after acute crises, community profiling, and monitoring community development indicators. The training materials contain instructions for the facilitator(s) of the training and all necessary training materials to conduct the training. To maximize inclusivity, the training covers basic to advanced topics. Most training modules address basic training topics that can be optimally followed with a mobile phone (to avoid using a computer and printing handouts). The training was developed and implemented with local community groups in Nigeria and Kenya. The material is free to use, adapt, and translate into different contexts and languages.

Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.

Introduction: The trophoblast derived angiogenic factors are considered to play an important role in the pathophysiology of various complications of pregnancy. The aim of this study was to establish relationship between concentrations of soluble Human Leukocyte Antigen-G (sHLA-G) in maternal serum and amniotic fluid at 16-22 weeks of gestation and the sonographic measurements of the fetal and placental growth. Materials and methods: sHLA-G in serum and amniotic fluid, as well as fetal biometric data and placental volume and perfusion indices were determined in 41 uncomplicated singleton pregnancies. Results: The sHLA-G levels were unchanged both in amniotic fluid and in serum during mid-pregnancy. The sHLA-G level in serum correlated positively with amniotic sHLA-G level (β=0.63, p<0.01). Significant correlation was found between serum sHLA-G level and abdominal measurement (β=0.41, p<0.05) and estimated fetal weight (β=0.41, p<0.05). The amniotic sHLA-G level and placental perfusion (VI: β = -0.34, p<0.01 and VFI: β = -0.44, p<0.01, respectively) were negatively interrelated. A low amniotic sHLA-G level was significantly associated with nuchal translucency (r = -0.102, p<0.05). Conclusions: sHLA-G assayed in amniotic fluid might be a potential indicator for placental function, whereas the sHLA-G level in serum can be a prognostic modality for feto-placental insufficiency.

In our increasingly urbanized world, precise and up-to-date maps of human settlements are essential for sustainable urban development policies. The availability of open-access Sentinel-2 data from the Copernicus program presents an opportunity to create a comprehensive global map of human settlements, offering a detailed view of built areas on a large scale. This study estimates large-scale built-up fractions using encoder-decoder deep learning architectures like U-net, Res-U-net, and Attention-U-net in the large and complex urban area of Delhi, India. Openly available datasets like Open Street Map (OSM) and Microsoft building footprint datasets are used to derive built-up fractions at 10×10m resolution cells for over 34,000 km2. Our results show that Attention-U-net with the Huber loss function performs the best in different built-up densities (i.e., urban, semi-urban or rural) with an R2 score of 0.631, while Res-U-net and U-net obtained an R2 score of 0.623 and 0.612, respectively. The investigated networks significantly improve the accuracy over the latest Global Human Settlement Layer product (GHSL-S2), which uses a deep-CNN and reaches an R2 of 0.387 in our case study area. The result of this study yields a valuable spatial layer for examining the spatial distribution of human settlements across the entire spectrum from rural to urban areas.

We propose Context-aware Feature Transformer Network (CaFTNet), a novel network for human pose estimation. To address the issue of limited modeling of global dependencies in convolutional neural networks, we design Transformerneck to strengthen the expressive power of features. Transformerneck directly substitutes the 3×3 convolution in bottleneck of HRNet with Contextual Transformer (CoT) block, while reducing the complexity of the network. Specifically, CoT first produces keys with static contextual information through 3×3 convolution. Then, relying on the query and contextualization keys, the dynamic contexts are generated through two concatenated 1×1 convolutions. Static and dynamic contexts are eventually fused as an output. Additionally, for the multi-scale networks, in order to further refine the features of the fusion output, we propose an Attention Feature Aggregation Module(AFAM). Technically, given an intermediate input, AFAM successively deduces attention maps along channel and spatial dimensions. Then, Adaptive refinement module(ARM) is exploited to activate the obtained attention maps. Finally, the input undergoes adaptive feature refinement through multiplication with the activated attention maps. Through the above studies, our lightweight network provides a powerful clue for detection of keypoints. Experiments are implemented on the COCO and MPII datasets. The model achieves 76.2 AP on the COCO val2017. Compared to other methods with the CNN as the backbone, CaFTNet reduces the number of parameters by 72.9 %. On the MPII, our method uses only 60.7% of the number of parameters, acquiring semblable results to other methods with the CNN as the backbone.

Abstract: Antimicrobial peptides (AMPs) are short peptides, usually made up of less than 100 amino acid residues. They are found in many living organisms and are an important factor in their innate immune systems. AMPs can be extracted from various living sources including bacteria, plants, animals and even humans. They are usually cationic peptides with an amphiphilic structure, which allows them to easily bind and interact with the cellular membranes of viruses, bacteria, fungi and other pathogens. They can act against both Gram-negative and Gram-positive pathogens and have various modes of action against them. Some attack the pathogens membrane, while others target their intracellular organelles as well as their nucleic acids, proteins and metabolic pathways. A crucial area of use of AMPs is in their ability to help with emerging antibiotic resistance: some AMPs are active against resistant strains and are susceptible to peptide engineering. This review considers AMPs from three key sources: plants, animals and humans, as well as their modes of action and some AMPs sequences.

Advances in biomimetic triboelectric nanogenerators (TENGs) have significant implications for electronic skin (e-skin) and human-machine interaction (HMI). Emphasizing the need to mimic complex functionalities of natural systems, particularly human skin, TENGs leverage triboelectricity and electrostatic induction to bridge the gap in traditional electronic devices' responsiveness and adaptability. The exploration begins with an overview of TENGs' operational principles and modes, transitioning into structural and material biomimicry inspired by plant and animal models, proteins, fibers, and hydrogels. Key applications in tactile sensing, motion sensing, and intelligent control within e-skins and HMI systems are highlighted, showcasing TENGs' potential in revolutionizing wearable technologies and robotic systems. This review also addresses the challenges in performance enhancement, scalability, and system integration of TENGs. It points to future research directions, including optimizing energy conversion efficiency, discovering new materials, and employing micro-nanostructuring techniques for enhanced triboelectric charges and energy conversion. The scalability and cost-effectiveness of TENG production, pivotal for mainstream application, are discussed along with the need for versatile integration with various electronic systems. The review underlines the significance of making bioinspired TENGs more accessible and applicable in everyday technology, focusing on compatibility, user comfort, and durability. Conclusively, it underscores the role of bioinspired TENGs in advancing wearable technology and interactive systems, indicating a bright future for these innovations in practical applications.

The carotid body is a major peripheral chemoreceptor that senses changes in arterial blood oxygen, carbon dioxide, and pH, what is important for regulation of breathing and cardiovascular function. The mechanisms by which the carotid body senses O2 and CO2 are well known; conversely, the mechanisms by which it senses pH variations are almost unknown. Here we used immunohistochemistry to investigate how the human carotid body contributes to the detection acidosis analyzing whether it expresses acid-sensing ion channels (ASIC) ion channels, and to know if these channels are in the cells chemosensory glomic cells or in the afferent nerves. ASIC1, ASIC2 and ASIC3, and to a much lesser extent ASIC4, immunoreactivity was detected in subpopulations of type I glomus cells, as well as in the nerves of the carotid body. In addition, immunoreactivity was found for all ASIC subunits in neurons of the petrosal and superior cervical sympathetic ganglia, where afferent and efferent neurons are located, respectively, innervating the carotid body. This study reports for the fist time the occurrence of ASIC proteins in the human carotid body, demonstrating that they are present in glomus chemosensory cells (ASIC1 &lt; ASIC2 &gt; ASIC3 &gt; ASIC4) and nerves, presumably both afferent an efferent supplying the organ. These results suggest that the detection of acidosis by the carotid body can be mediated by ASIC ion channels present in the type I glomus cells or directly by sensory nerve fibers.

Micro-plastics (MPs) become significantly important to society since their commercial manufacture began in the 1950s because of their low production costs, stability, light weight, and adaptability. Plastic was produced in 400 million tons worldwide in 2020, compared to 1.5 million tons just 60 years earlier. Scientists predict that by 2050, plastic production would increase to 33 billion tons. MPs are a rising scientific and societal issue in the environment as a new type of contaminant that is widely disseminated in aquatic ecosystems and organisms. Aquatic species that come into contact with MPs for an extended period of time experience ecotoxicity, which includes oxidative stress, genotoxicity, neurotoxicity, and reproductive issues. In aquatic environments, MPs not only transport chemicals within animals but also serve as mediators for chemicals or other contaminants. As a result of co-exposure of MPs and chemical contaminants, various toxicity incidences involving aquatic organisms have been reported. The final exposure of MPs occurs in humans through biomagnification of aquatic edible products. This study presents an important overview of the literature on MP ingestion by aquatic biota. It covers topics such as the present scenario of plastic pollution worldwide, the pathways that MPs take to reach humans and aquatic species, the eco-toxicological impacts of MPs on aquatic species, the relation between MPs and other environmental pollution and the ensuing effects on aquatic organisms, and the MP pollution status in Bangladesh.

3D human pose estimation has made significant advancements through the integration of deep learning techniques. This survey provides a comprehensive review of recent 3D human pose estimation methods, with a focus on monocular images, videos, and multi-view cameras. Our approach stands out through a systematic literature review methodology, ensuring an up-to-date and meticulous overview. Unlike many existing surveys that categorize approaches based on learning paradigms, our survey offers a fresh perspective, delving deeper into the subject. For image-based approaches, we not only follow existing categorizations but also introduce and compare significant 2D models. Additionally, we provide a comparative analysis of these methods, enhancing the understanding of image-based pose estimation techniques. In the realm of video-based approaches, we categorize them based on the types of models used to capture inter-frame information. Furthermore, in the context of multi-person pose estimation, our survey uniquely differentiates between approaches focusing on relative poses and those addressing absolute poses. Our survey aims to serve as a pivotal resource for researchers, highlighting state-of-the-art deep learning strategies and identifying promising directions for future exploration in 3D human pose estimation.

This research investigates the utilization of human driving models in autonomous vehicles, particularly in scenarios with minimal or no interactions with other vehicles. Human driving models provide valuable insights into driver behavior and play a crucial role in shaping the behavior of autonomous vehicles, enhancing their performance and user experience. The primary focus of the study is the creation of a planning model for autonomous vehicles when navigating roundabouts in the absence of traffic. This model seeks to emulate human driving behavior, ensuring predictability, safety, optimization of traffic flow, and adaptation to various roundabout geometries. To achieve this, the research introduces a trajectory model that takes into account geometric attributes and speed variations within roundabouts. The model is calibrated using empirical data and generalizes parameters through statistical regression methodologies. This model is referred to as "MRoundabout" and is evaluated for its consistency in generating plans that closely mimic human driving behavior within roundabouts. While the study presents a promising approach, it acknowledges limitations related to the model's reliance on geometric attributes and its inability to account for external factors like weather conditions. This research underscores the importance of bridging the gap between theoretical research and practical application, with the aim of enhancing safety and the overall user experience in real-world driving scenarios.

Molnupiravir, a prodrug known for its broad antiviral activity, demonstrated efficacy in animal models of Covid-19, prompting clinical trials where initial results indicated a significant effect against the disease. However, subsequent clinical studies did not confirm these findings, leading to the rejection of molnupiravir for permanent market authorization in many countries. This report critically assessed 19 studies published in 17 reports that investigated the efficacy of molnupiravir in animal models of Covid-19 with the purpose of determining how well the design of these models informed human studies. We found that the administered doses of molnupiravir in most studies involving animal Covid-19 models were disproportionately higher than the dose rec-ommended for human use. Specifically, when adjusted for body surface area, half of the doses of molnupiravir used in the animal studies were more than twice as high as the human dose. Addi-tionally, the drug was frequently given prophylactically or shortly after SARS-CoV-2 inoculation in these models, in contrast to clinical trials where such timing is not consistently achieved. Furthermore, the recommended five-day treatment duration for humans was exceeded in several animal studies. Collectively, we suggest that these design elements in the reported animal studies contributed to a bias favoring molnupiravir, and thus inflated expectations for its efficacy against Covid-19. Addressing these elements may offer avenues to enhance the clinical efficacy of mol-nupiravir for treatment of Covid-19 that include dose increment, early treatment, and admin-istration by inhalation along with use of molnupiravir in antiviral combination therapy.

Background. Human Milk (HM) is a dynamic nourishment: its composition is influenced by several conditions such as gestational age, maternal diet and ethnicity. It appears important evaluate the impact that gestational diseases have on HM components and if their presence, as source of oxidative stress in the mother, influence milk’s redox homeostasis. To assess Preeclampsia (PE) and Gestational Diabetes Mellitus (GDM) effect on milk’s total oxidative load, we chose to investigate both oxidative and antioxidant aspects, with respectively Lipid hydrop-eroxides (LOOHs) and Glutathione (GSH)). Methods. GSH and LOOHs were quantified using an ELISA test. To investigate the effect of pathology on redox homeostasis, mixed linear model with unistructural covariance structure was performed. Results. A total of 120 mothers were re-cruited. The GSH concentration results significant lower in GDM women than in healthy only in colostrum (p<0.01). No other differences emerged. LOOHs was not detectable in almost all the samples. Discussion. Our study is the first to extensively evaluate these components in the HM of women with these gestational pathologies. The main observation is that GDM can alter the anti-oxidant composition of HM mainly in colostrum, but in case of PE, the composition of HM milk is preserved.

Per- and polyfluoroalkyl substances (PFASs) are a group of fluorinated, organic, man-made chemicals; they do not occur naturally in the environment. This study aimed to determine the profile and content of PFASs in volunteers’ blood plasma and urine after fermented red beetroot juice consumption and correlated it with the blood parameters. During 42 days, 24 healthy volunteers intake 200 mL/60 kg of body weight fermented red beetroot juice. PFASs were analyzed using micro-HPLC-MS/MS method. Five perfluoroalkyl substances were found in human body fluids. After juice consumption, PFASs, due to the consumption of food contaminated with them, was discovered that for perfluorocarboxylic acids, a downward trend was observed, while for perfluoroalkane sulfonates, their plasma content showed a statistically significant upward trend. Analysis of the hematology parameters indicated that the intake the fermented red beetroot juice showed a significant decrease in mean corpuscular volume (MCV), platelets concentration, mean platelet volume (MPV), platelet large cell ratio (P-LCR) at the significance level p < 0.01, and he-matocrit (p < 0.05). On the other hand, the dietary intervention also indicated a significant (p < 0.01) increase corpuscular/ cellular hemoglobin concentration (MCHC). In the case of blood biochemi-stry, no significant change in blood samples after intake the fermented beetroot juice was observed. However, a decreasing tendency of total cholesterol and low-density lipoprotein concentration (LDL-C) were observed. Based on the presented results, there is a need to analyze and monitor health-promoting food regarding undesirable substances and their impact on consumer health.

Solar energy is a renewable and sustainable source of energy, and it has been used in addition of specified technologies, such as solar cooling. The demand for cooling continues to increase in line with environmental changes and a greater desire for human comfort. This study primarily aimed to analyze the performance of an evaporative cooling system powered by solar energy and assess the economic and environmental impact of this system. In this system, hot, dry incoming air is used to evaporate water, thus transforming part of its sensitive heat into latent heat without any variation in enthalpy or total heat. The outputted air then has a lower temperature and a greater humidity, thus providing a more comfortable, healthier environment for its users. To achieve this study’s objectives, numerical code was developed and implemented in MATLAB. The obtained results reveal that the efficiency of the evaporative cooler exceeds 90%, with maximum efficiency being reached at a high wet-bulb depression, while minimum efficiency was observed when the dry air has a high relative humidity and a low dry-bulb temperature.

In recent years, with the rapid development of computer vision technology and the popularization of intelligent hardware, as well as people’s increasing demand for intelligent products for human-computer interaction, visual grounding technology can help machines and humans identify and locate objects, thereby promoting human-computer interaction and intelligent manufacturing. At the same time, human-computer interaction is constantly evolving and improving, becoming increasingly intelligent, humane, and efficient. This paper proposes a new VG model and designs a language verification module that uses language information as the main information to increase the model’s interactivity. Additionally, we propose the combination of visual grounding and human-computer interaction, aiming to explore the research status and development trends of visual grounding and human-computer interaction technology, as well as their application in practical scenarios and optimization directions, to provide references and guidance for relevant researchers and promote the development and application of visual grounding and human-computer interaction technology.

Accurately estimating a person's age is crucial for identity verification at all critical checkpoints, including airports, land borders, and seaports. Human gait may be used as a biometric identifier and indicator of age, among other things. This study aims to develop methods for estimating a person's age by observing their walk. In this paper, a novel technique for preprocessing the proposed gait dataset has been used by utilizing a combination of the gait energy image (GEI), the accumulated frame difference energy image (AFDEI), and the invariant moment of the image. The proposed technique outperformed state-of-the-art methods in terms of accuracy. The proposed method was tested and evaluated using a convolutional neural network (CNN), and it achieved an average accuracy of 90.35% across 14 different view angles within 5 K-Fold, the proposed method resulted in 94.68% and 94.54% in 30º and 75º view degree respectively. concluding that the approach is effective and promising for estimating age using human gait.

The purpose of the research was to examine and assess the relation between the pilot’s concentration and reaction time with specific brain activity during short-haul flights. In order to obtain this task, participants took part in 1-hour long flight sessions performed on FNPT II class flight simulator. During the flight auto–pilot was activated and subjects were instructed to give response to unexpected events that were occurring during the flight. Brainwaves of each participant were recorded with Emotiv EPOC+ Scientific Contextual EEG device. Statistical analyses of the results were presented in the article.

Chlamydia trachomatis (CT) is an obligate intracellular bacterial pathogen known to cause trachoma, sexually transmitted infections, and reactive arthritis. The more serious sequelae of these diseases, blindness from trachoma and pelvic inflammatory disease from chlamydial STD are immunopathological responses to chronic infections. With the abundance of dendritic cells in CT infection sites and their central importance in immune regulation, the susceptibility of dendritic cells to CT infection was investigated in vitro. CT (serovar F) was grown and extracted from HeLa 229 cells for infection of human dendritic cells (DCs). Human DCs were generated from peripheral blood mononuclear cells in the presence of GM-CSF and IL-4. Infected DCs were harvested at 0, 4, 24, and 48 h after infection and immunostained. Results from fluorescence microscopy showed that DC can be infected and can support the growth of CT in vitro. Intracellular elementary and reticulate bodies of CT were detected, and reticulate bodies were shown to multiply over the 48 h-cycle in DC. At 48 h, intracellular elementary bodies were detected in some DCs. These results may provide new insights into the possible participation of DC in sustaining chronic or latent infection, or even dissemination of CT infection to other sites.

Background and aim: Human Papillomavirus (HPV) is sexually transmitted, one of the three most common sexually transmitted infections (STIs) in both males and females, and the most common viral STI. A crucial public health strategy to protect people against HPV is through vaccination, which has shown its effectiveness in preventing HPV related diseases. Presently three types of vaccines are available (bivalent, quadrivalent, and nonavalent) and they all target at least the two most oncogenic virus genotypes (HPV 16, 18). In the past few years, the need to implement vaccination programmes that include all genders has been discussed in order to achieve herd immunity against HPV. To date, only a few countries have included young males in their vaccination programmes, so we aim through this review to provide an overview of the epidemiology of HPV and prevention strategies to prevent it, reporting the latest findings from the scientific literature.

Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including Data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen Cyber-Physical Security (CPS) with minimal human intervention. It is a Human Activity Recognition (HAR)-based approach where a GoogleNet-BiLSTM network hybridization has been used to recognize suspicious activities in cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates Machine Vision at the IoT Edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and GoogleNet-BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security with $4.29 per month only.

Viruses can induce the fusion of infected and neighboring cells, leading to the formation of syncytia. Cell-cell fusion is mediated by viral fusion proteins on the plasma membrane of infected cells that interact with cellular receptors on neighboring cells. Viruses use this mechanism to spread rapidly to adjacent cells or escape host immunity. For some viruses, syncytium formation is a hallmark of infection and a known pathogenicity factor. For others, the role of syncytium formation in viral dissemination and pathogenicity remains poorly understood. Human cytomegalovirus (HCMV) is an important cause of morbidity and mortality in transplant patients and the leading cause of congenital infections. Clinical HCMV isolates have broad cell tropism but differ in their ability to induce cell-cell fusions, and little is known about the molecular determinants. We developed a system to analyze HCMV glycoprotein B (gB) variants in a defined genetic background. HCMV strains TB40/E and TR were used as vectors to compare the fusogenicity of six gB variants from congenitally infected fetuses with those from three laboratory strains. Five of them conferred the ability to induce fusion of MRC-5 human embryonic lung fibroblasts to one or both backbone strains, as determined by a split GFP-luciferase reporter system. The same gB variants were not sufficient to induce syncytia in infected ARPE-19 epithelial cells, suggesting that additional factors are involved. The system described here allows a systematic comparison of the fusogenicity of viral envelope glycoproteins and may help to clarify whether fusion-promoting variants are associated with increased pathogenicity.

Human and animal waste, including waste products originating from human or animal digestive systems such as urine, feces, and animal manure, have constituted a nuisance to the environment. Inappropriate disposal and poor sanitation of human and animal waste often cause negative impacts on human health through contamination of the terrestrial environment, soil, and water bodies. Therefore, it is necessary to convert these wastes into useful resources to mitigate their adverse environmental effect. The present study provides an overview and research progress of different thermochemical and biological conversion pathways for the transformation of human- and animal-derived waste into valuable resources. The physicochemical properties of human and animal waste are meticulously discussed as well as nutrient recovery strategies. In addition, a bibliometric analysis is provided to identify the trends in research and knowledge gaps. The results reveal that the U.S.A, China and England are the dominant countries in the research areas related to resource recovery from human or animal waste. In addition, researchers from the University of Illinois, the University of California Davis, the Chinese Academy of Science and Zhejiang University are front runners in research related to these areas. Future research should be centred on developing technologies for the on-site recovery of resources, exploring integrated resource recovery pathways, and exploring different safe waste processing methods.

Human motor planning combines such interesting aspects as modular organization in motor primitives and characteristics of stochastic optimality. Motor primitives aided in the description of motor skill learning. In this study, for the first time, an interpretation is proposed to explain how motor primitives are shaped in a stochastic optimality process.To this end, sensory information is assumed to be random variables at each moment. Theoretically, the results confirmed that the variability of a movement increased with passing time. As a result, motor planning should be performed in shorter durations to achieve an accurate movement. It means that a task should be divided into several subtasks at various stages of time. Moreover, theoretically, the results confirmed that motor planning is statistically optimal if the movement is planned in minimum jerk space, which is in accordance with what is reported in the literature. Comparing the proposed mathematical model with the results of experimental data in two different types of motor action (i.e., arm reaching movement and sit-to-stand transfer from a chair) confirmed that the proposed model can be considered a framework to investigate the effect of bottom-up information on human motor planning level. Further experiments need to investigate it more deeply.

Residents’ perceptions on ecosystem services (ESs) provided by urban green spaces (UGSs) are vital for developing sustainable cities. However, there has been limited research on this issue in Malaysia. The aim of this paper is to assess Malaysians’ perceptions on ESs provided UGSs and how their perceptions are influenced by different socio-demographic factors. We used a structured online questionnaire and obtained 645 responses on 16 ESs from regulatory, cultural, and supporting ES categories. Descriptive statistics and binary logistic regressions were used for data analysis. In general, city dwellers have high positive perceptions on ESs provided by UGSs. Their perceptions for global climate regulation, noise buffering, pest regulation, spiritual, and research and education services would likely to significantly increase as they grow older. Households with moderate income have significantly greater perceptions of microclimate regulation, air filtration and aesthetic services. Having non-tertiary level education would significantly lower their perceptions of global climate and microclimate regulation, spiritual, aesthetics, and recreational services. Tertiary educated residents with non-environmental degrees also showed similar associations. Infrequent visits to UGSs would likely to significantly reduce the perception of global climate regulation, noise buffering, runoff reduction, erosion control, spiritual, and aesthetics services. People living far (>5km) from the UGSs also have significantly lower perceptions of microclimate regulation, air filtration services. Suggestions are made to incorporate ESs related content in pre-university curriculum and establishing small parks in neighborhoods to improve knowledge and uses of UGSs. These findings would assist urban planners designing environmentally sound urban policies and thereby improving human wellbeing in the cities.

The poor success rate of preclinical drugs which target the nervous system is related to the highly complex nature of brain physiology and pathophysiology. For in vitro drug screening in this field, the two-dimensional (2D) approach - where cells are incubated in a monolayer - is not physiologically relevant. In contrast, in vivo rodent models are very low throughput and expensive. As such, improved, well-characterised three dimensional (3D) in vitro models should be employed to bridge the gap between 2D in vitro primary screening and rodent models by incorporating aspects of the in vivo brain environment. These key features include the extracellular matrix (ECM) and a multidimensional relationship of supporting cells. In this study, a neural progenitor cell line was differentiated into the main types of cells found in brain, including neurons and astrocytes. This model was designed in a 3D extracellular matrix replacement in a minimalised manner. Different culture formats using multi-well plates were explored and a high-throughput cellular imaging platform was applied. Next, we applied this model to assess neural toxicity of a newly synthesised dual-specificity tyrosine-regulated kinase 1 (DYRK1) inhibitor derived from natural compounds, which is structurally related to a natural DYRK1A inhibitor harmine, K04179. Chemical studies have already shown that K04179 has higher specificity to DYRK1A compared with previously reported DYRK1 inhibitors, such as INDY. We have produced the first report of the biological effects of this new compound on the new 3D minimalised mid-brain model with quantification of both neuronal and astrocyte populations in tandem, revealing differential toxic effects of DYRK1 inhibitor compounds K04179 and INDY on neurons and glia.

COVID-19 is the most severe health crisis of the 21st century. COVID-19 presents a threat to almost all countries world-wide. The restriction of human mobility is one of the strategies used to control the transmission of COVID-19. However, it has yet to be determined how effective this restriction is in controlling the rise in COVID-19 cases, particularly in major capital cities such as Jakarta, Indonesia. Using Facebook's mobility data, our study explores the impact of restricting human mobility on COVID-19 case control in Jakarta. Our main contribution is showing how the restriction of human mobility data can give important information about how COVID-19 spreads in different places. We proposed modifying a global regression model into a local regression model by accounting for the spatial and temporal interdependence of COVID-19 transmission across space and time. We applied Bayesian hierarchical Poisson spatiotemporal models with spatially varying regression coefficients. We estimated the regression parameters using an Integrated Nested Laplace Approximation. We found that the local regression model with spatially varying regression coefficients outperforms the global regression model based on DIC, WAIC, MPL, and R2 criteria for model selection. In Jakarta's 44 districts, the impact of human mobility varies significantly. The impacts of human mobility on the log relative risk of COVID-19 range from –4.445 to 2.353. The prevention strategy involving the restriction of human mobility may be beneficial in some districts but ineffective in others. Therefore, a cost-effective strategy had to be adopted.

In the context of the pandemic caused by Covid-19, an important aspect that was affected and changed was the way people communicate. Communication efficiency can be improved by using modern technologies, for example: mobile devices, digital audio and video platforms, text platforms, social media and more. For further economic success, digital skills are needed not only for executives, but also for managers, in order to be able to lead teams and avoid situations of stress and tension in the virtual environment. This scientific paper includes a primary research of how the pandemic affected the communication at the workplace. For this a qualitative analysis was first performed, in which the interview was used as a working tool. Next, on the basis of a questionnaire, a hypothesis was formulated and validated afterwards through multiple linear regression. This research highlighted the particularly important role of communication and, in this context, the role of the remote team leader. In order to keep the team motivated and interested in the virtual environment, the manager must use modern tools to motivate, support and express his appreciation when the team, respectively the members of his team, achieve their goals successfully.

Background: With new challenges to the health system, many new competencies within the scope of teaching public health need to be addressed in medical schools’ curricula such as disaster risk management and health system science. The aims of this study were to identify the needs of public health competencies for medical doctors in Thailand and to assess the level of integration for technical collaboration in teaching public health. Method: A total of 17 out of 21 Thai medical schools participated in the national survey. Qualitative inquiries applied focus group interviews of community representatives from ten sample villages and in-depth interviews of representatives from stakeholder organizations particularly employers. The list of public health competencies framework recommended by WHO-SEARO was applied. Quantitative analysis applied descriptive analysis using STATA 15 and qualitative findings were validated by interrelating the meaning of themes from Word Clouds created in NVivo12. Data integration applied a mixed-method Quan-qual approach. Results: 17 medical schools returned the questionnaires (80.95 % yield). The most common regionally-defined public health competencies (in over 70% of schools) were shown to be: Biostatistics, Community Medicine, Epidemiology, Family Medicine, Medical Ethics and Professional Laws, Preventive Medicine, Health Promotion, Holistic Care, and Research. The curriculum in only one medical school lacked Health Economics, whilst Disaster Management was lacking in two other schools. Discipline-based subjects were found to be more prevalent than interdisciplinary competencies. A variety of methods were being applied for teaching public health. The majority of the schools applied lecture as the main teaching method and multiple-choice questions as the main assessment method. Thai communities expect the doctors to get in touch with the community more often, lead the primary health care team through training the health professionals and community health volunteers, and educate the community for better health. Conclusion: Human resource is the main challenge in addressing interdisciplinary competencies. It is necessary to establish a collaborating mechanism among the big and small medical schools and the faculties of public health to improve the teaching of public health to undergraduate students in medical schools. There is also a need to strengthen the health system science and leadership so that future MDs can lead health service delivery according to the needs of their employers such as the Ministry of Public Health and the Rural Doctors Association. The findings of this study may help to identify a national framework of public health core competencies for medical schools and create a common platform for interdisciplinary collaborations.

Purpose: The use of smart devices has increased greatly in the last ten years with users reaching out to the possibility to do more with them especially in the networking front. In this context there is a need to understand the connection between users’ social demographic factors and their way to related to their smart devices. Objective: This study was designed to evaluate the senso of belonging of a community in order to evaluate intangible benefits that employees may gain from a more immerse relationship with their devices. Method: We used a dataset of 586 anonymous respondent of an existing survey designed for capturing the relationships that humans develop with their smart devices. In particular, we investigate the relationships with smart device and particular background variables of the respondents using a chi-square test. Results: The study showed that there is a significant relationship between users’ sex and smart device type and their dependency on smart device. Male tends to think that smart device (in general) enables them to connect with a larger community. At the same time, female using smart phones feels more connected more to large community than when using other smart devices. Conclusion: This study provided several significant findings that confirm and strength previous literature works on the subject. In addition, socio demographics variables (like gender) as well as the type of smart device present a correlation between the smart device users and their tendency to stay in touch with a larger community.

The reduction of biodiversity loss is one of the targets of the 2030 Agenda for Sustainable Development. The protection of endangered species is critical for conserving global biodiversity. Asian elephants，as one of the last few mega-herbivores on Earth, are currently threatened by climate changes and anthropogenic modifications. The modelling of their living habitats is of top priority to the conservation of Asian elephant. In this study, we used the maximum entropy model (MaxEnt) to identify the current and potential future habitats of Asian elephants in South and Southeast Asia. We performed analyses for future projections with 17 scenarios by using the present results as baseline. To optimise the modelling results, we delineated the core habitats by using the Core Mapper Tool and compared them with existing protected areas (PAs) through gap analysis. The results showed that the current total area of core habitats is 491,455 km2 in size and will be reduced to 332,544 km2 by 2090 under SSP585 (the shared socioeconomic pathway). The projection analysis under differential scenarios suggested that most of the core habitats in the current protected areas would remain stable and suitable for elephants in the future. However, the remaining 75.17% of the core habitats lay outside the current PAs, and finally we mapped approximately 219,545 km2 of suitable habitats as priority protected areas in the future. Although our model did not perform well in some regions, our analyses and findings still could provide useful references to the planning of protected areas and conservation of Asian elephant.

The impact of nutrients on the environment, particularly on water bodies, has led to extensive studies for nutrient control. Within this context, studies have been focused on source separation of human urine from domestic wastewater to recover nutrients. Potassium is one of the most important components of human urine. However, data on potassium removal or recovery are quite limited except for some indirect information through use of zeolites for mostly ammonia removal. Potassium struvite or K-struvite (MgKPO₄·6H₂O) is a sparingly soluble salt belonging to struvite and has the potential of being used as a means of potassium and phosphate recovery from segregated human urine. This study aimed to assess the potential of K-struvite precipitation for control and recovery of nutrients. Within this context, K-struvite precipitation experiments were performed on both synthetically prepared samples and synthetic human urine solution to determine effect of operation parameters i.e. pH, stoichiometry, and temperature on potassium recovery performance. Results indicated that process performance as well as type of solid phases co-precipitated with K-struvite were closely related to initial potassium concentration, pH and reaction stoichiometry. At pH 10, the potassium recovery efficiency was maximized up to 87% by application of 100% excess dose of Mg and P for both synthetic samples and synthetic human urine solution. On the other hand, application of excess dose of K did not provide any improvement in K recovery efficiency. The effect of temperature on solubility of K-struvite was insignificant at the temperature of 24-90°C. Solid phase analyses confirmed that K-struvite was co-precipitated with either Mg₃(PO₄)₂, MgNaPO₄·7H₂O, or MgHPO₄·7H₂O depending on pH and stoichiometry instead of a pure compound.

This paper attempts to compare two different approaches to solve the problem of accident rates prediction based on human factors for industrial workers. One of the methods has already been done using Fuzzy c-Means Clustering and proved to be working with decent results. The second method which will be covered in this paper is using Artificial Neural Networks. The primary goal of this work is to insure that ANN will work efficiently in such prediction problem. The second goal is to reveal the fact that which one of the two selected methodologies is better at defining the estimation of accident rates among people who work in different industrial fields. The purpose has been achieved when the outcome of the ANN was obtained and compared accordingly with the output of the research previously carried out with Fuzzy c-means clustering method. Comparing the outcomes of these two different methods gave an immense insight on which features are more important than others when it comes to laborers properties with completely different background such as varying levels of health, knowledge, experience, training and physical properties. At the end of the research, it becomes clear that accident rates estimation for laborers with properly trained Artificial Neural Network gives better results when it is compared with Fuzzy c-Means Clustering method. Standard deviation method was used to calculate the validity of ANN technique. The result was compared with Fuzzy c-mean clustering technique. Impressive improvement of 8.8% in the accident rate prediction was achieved using Tailored-Made-ANN.

Aiming the characterization of road dusts on the surrounding villages of an active mine and to assess metal(loids) bioaccessible fractions, a dust samples survey was undertaken. To achieve these objectives, pseudototal element concentration, mineralogical composition, Enrichment factor and in vitro extraction UBM assay for both gastric (G) and gastrointestinal (GI) phases, were determined. Results obtained revealed that arsenic represents the higher risk to humans, with mean pseudototal values higher than the maximum reference value range. Enrichment factor points to As significant to very high enrichment in all villages. Particles enriched in As, Ca, Fe, Cu, Al, Ti were identified by SEM-EDS in weathered agglomerates, linked to mine wastes and long-distance transport, both by wind and/or traffic. Arsenic bioaccessibility fraction (%BAF) presents low values in the studied samples, possibly due to low Fe complexes solubility with adsorbed As, limiting arsenic release and reducing its bioaccessibility. Concentrations of Cd bioaccessible G and GI phases are within the reference values range, while for Cu are above and for Pb are lower, than reference value range. Results show that the pseudototal fraction risk is overestimated when comparing to BAF (bioaccessible fraction), nevertheless, total G and GI risk are above the carcinogenic target risk (1.00E-06) in most samples. Carcinogenic risk of the bioaccessible contaminants shows that As is the element that represents higher risk for developing cancer over a lifetime, being ingestion the main risk route.

The recent SARS-CoV-2 pandemic, which is causing COVID 19 disease, has taught us unexpected lessons about the dangers of human extinction through highly contagious and lethal diseases. As the COVID 19 pandemic is now being controlled by various isolation measures, therapeutics and vaccines, it became clear that our current lifestyle and societal functions may not be sustainable in the long term. We now have to start thinking and planning on how to face the next dangerous pandemic, not just overcoming the one that is upon us now. Is there any evidence that even worse pandemics could strike us in the near future and threaten the existence of the human race? The answer is unequivocally yes. It is not necessary to get infected by viruses of bats, pangolins and other exotic animals that live in remote forests in order to be in danger. Creditable scientific evidence indicates that the human gut microbiota harbor billions of viruses which are capable of affecting the function of vital human organs such as the immune system, lung, brain, liver, kidney, heart etc. It is possible that the development of pathogenic variants in the gut can lead to contagious viruses which can cause pandemics, leading to destruction of vital organs, causing death or various debilitating diseases such as blindness, respiratory, liver, heart and kidney failures. These diseases could result n the complete shutdown of our civilization and probably the extinction of human race. In this essay, I will first provide a few independent pieces of scientific facts and then combine this information to come up with some (but certainly not all) hypothetical scenarios that could cause human race misery, even extinction. I hope that these scary scenarios will trigger preventative measures that could reverse or delay the projected adverse outcomes.

Small and medium-sized family farms are the place of life and source of income for about half of the population. The aim of the analysis was to determine the relationship between Eco-Efficiency, Human Capital Efficiency in small and medium-sized family farms. The analyses were carried out using an economic measure (value of agricultural production per work hour calculated per 1 ha) and synthetic measures: human capital and environmental measures. The synthetic measures were determined using the CRITIC-TOPSIS method by defining weights for variables used in the synthetic measures. The analyses covered five countries, namely: Lithuania (960 farms), Moldova (532 farms), Poland (696 farms), Romania (872 farms), and Serbia (524 farms). All the countries qualified for analysis are characterised by a high fragmentation of agricultural holdings. The analyses carried out allowed us to formulate the following conclusions: the Eco-Efficiency and Human Capital Efficiency indexes increase with area for small and medium-sized family agricultural farms. An increase in the Eco-Efficiency index with an increase in farm area leads to a suspicion that the smaller the farm area is, the more extensive the agricultural production being carried out. In addition, an increase in human capital efficiency with an increase in the area of a farm indicates that there is inefficiency in the utilisation of human capital resources in the agricultural farms studied.

Sustainable Development Goals (SDGs) envisaged under Agenda 2030 are a set of seventeen goals which envisage a holistic approach towards attaining certain targets keeping humankind and the planet at center. There are total 169 targets spread across seventeen goals covering wide ranging issues and challenges the world is facing in the twenty-first century. And they are to be achieved by 2030. Concerted efforts of all the stakeholders ranging from indigenous communities, common citizens, scientists, policy makers, world leaders are needed to achieve all the goals and targets Of the seventeen goals, at least seven goals are of interest to the ethnobotanists and are associated with traditional ethnobotanical knowledge. Therefore to achieve those set of goals, a thorough understanding is required to disentangle the intricacies involving traditional ethnobotanical knowledge, indigenous people as traditional knowledge holders and their future role. Understanding relationships between traditional ethnobotanical knowledge and indigenous communities, seeking cooperation from and establishing partnerships with them would help us design policies to achieve intended outcomes of SDGs. In this paper, particular attention is attracted towards the potential role of traditional ethnobotanical knowledge in achieving select sustainable development goals and targets.

Dynamics of human populations can be affected by various socio-economic factors through their influence on the natality and mortality rates, and on the migration intensity and directions. In this work we study an economic-demographic model which takes into account the dependence of the wealth production rate on the available resources. In the case of nonlocal consumption of resources the homogeneous in space wealth-population distribution is replaced by a periodic in space distribution for which the total wealth increases. For the global consumption of resources, if the wealth redistribution is small enough, then the homogeneous distribution is replaced by a heterogeneous one with a single wealth accumulation center. Thus, economic and demographic characteristics of nonlocal and global economies can be quite different in comparison with the local economy.

This study assesses Human Thermal Comfort in two selected areas: a Green Infrastructure (GI) area represented by a garden and a high-rise building area, in the Central Business District (CBD) of Melbourne, Australia. Three-dimensional microclimatic modelling software, ENVI-met version 4 was used to simulate the microclimate. The indices of Predicted Mean Vote (PMV), Physiological Equivalent Temperature (PET) and Universal Temperature Climate Index (UTCI) were used to quantify the level of thermal comfort in the research areas. The simulation results showed that at midday, the difference in temperature between the garden area and the high-rise building area was approximately 1°C. Increasing temperatures at midday led to a change in the level of thermal comfort for both the areas, even though it was not significant. In general, the thermal perception in the GI area was slightly ‘cooler’ than in the high-rise building area. The results of the study indicated the important role of GI in improving the thermal comfort in urban areas.

The purpose of this study is to explore the effects of dual-task training, including cognitive tasks, on cognitive and bodily functioning and β-amyloid levels in Alzheimer's dementia patients. The subjects were 34 inpatients diagnosed with Alzheimer's dementia at a nursing hospital located in Gyeongsansi, South Korea. The patients were randomly divided into a dual-task group (n = 16) and a single-task group (n = 18). The dual-task group performed cognitive tasks at the same time as exercising tasks, while the single-task group performed only exercise tasks. Each group was trained for 30 minutes three times a week for eight weeks. The Mini-Mental State Examination was used to measure the patients’ cognitive function. Static and dynamic balance were measured to evaluate bodily functioning. Static balance was measured using Biorescue, while dynamic balance was measured using the Berg Balance Scale. Blood analysis was performed to measure levels of β-amyloid, which is known to cause Alzheimer's dementia. Both groups exhibited statistically significant improvements in gait function after the training (p < .05). The dual-task group exhibited statistically significant differences in cognitive function, static and dynamic balance function, and β-amyloid levels after training (p < .05). A significant difference was observed between the two groups (p < .05). Dual-task activities were found to be effective in improving cognitive and bodily functioning and reducing β-amyloid levels in Alzheimer's dementia patients. Therefore, dual-task training is thought to be an effective method of treating and preventing Alzheimer's dementia.

Policy induced decline of human mobility has been recognised to be effective in controlling the COVID-19 spread especially in the initial stage of the outbreak, although the relationship among mobility, policy implementation, and virus spread remains contentious. Coupling data of confirmed COVID-19 cases with Google mobility data in Australia, we present a state-level empirical study to: 1) inspect the temporal variation of COVID-19 spread and the change of mobility adherent to social restriction policies; 2) examine the extent that different types of mobility are associated with the COVID-19 spread in eight Australian states/territories; and 3) analyse the time-lag effect of mobility restriction on the COVID-19 spread. We find that social restriction policies implemented in the early stage of the pandemic controlled the COVID-19 spread effectively; the restriction of human mobility has a time-lag effect on growth rates, and the strength of the mobility-spread correlation increases up to seven days after policy implementation but decreases afterwards. The association between mobility and COVID-19 spread varies across space and time, and subjects to the types of mobility. Thus, it is important for governments to consider the degree to which lockdown conditions can be eased by accounting for this dynamic mobility-spread relationship.

Eight compounds were isolated from the roots of Glycyrrhiza uralensis and tested for cholinesterase (ChE) and monoamine oxidase (MAO) inhibitory activities. Glycyrol (GC) effectively inhibited butyrylcholinesterase (BChE) and acetylcholinesterase (AChE) with IC50 values of 7.22 and 14.77 µM, respectively, and also moderately inhibited MAO-B (29.48 µM). Six of the other seven compounds only weakly inhibited AChE and BChE, whereas liquiritin apioside moderately inhibited AChE (IC50 = 36.68 µM). Liquiritigenin (LG) potently inhibited MAO-B (IC50 = 0.098 µM) and MAO-A (IC50 = 0.27 µM), and liquiritin, a glycoside of LG, weakly inhibited MAO-B (&gt; 40 µM). GC was a reversible, noncompetitive inhibitor of BChE with a Ki value of 4.47 µM, and LG was a reversible competitive inhibitor of MAO-B with a Ki value of 0.024 µM. Docking simulations showed that the binding affinity of GC for BChE (-7.8 kcal/mol) was greater than its affinity for AChE (-7.1 kcal/mol), and suggested that GC interacted with BChE at Thr284 and Val288 by hydrogen bonds (distances: 2.42 and 1.92 Å, respectively) beyond the ligand binding site of BChE, but that GC did not form hydrogen bond with AChE. The binding affinity of LG for MAO-B (-8.8 kcal/mol) was greater than its affinity for MAO-A (-7.9 kcal/mol). These findings suggest GC and LG should be considered promising compounds for the treatment of Alzheimer’s disease with multi-targeting activities.

The development and optimization of an effective non-viral gene delivery method for genetic manipulation of primary human T cells is a major challenge in clinical immunotherapy researches. According to the low transfection efficiency of conventional methods in human primary T cells, there is an effort in order to increase the transfection rate in these cells. Protamine is an FDA-approved compound with a documented safety profile that enhances DNA condensation for gene delivery. In this study, the effect of protamine on the transfection efficiency of standard transfection reagents, TurboFect, and Lipofectamine 2000 was evaluated in order to transfect primary human T cells. Results demonstrated that protamine condenses DNA and increases the positive charge of DNA/Cargo complex efficiently without any cytotoxic effect on the primary human T cells. The results also revealed that the DNA/Protamine/Cargo complexes effectively transfect human primary T cells.

It has been a long time since the world has experienced a pandemic with such a rapid devastating impact as the current COVID-19 pandemic. The causative agent, the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) is further unusual in that it appears capable of infecting many different mammal species. As a significant proportion of people worldwide are infected with SARS-CoV-2 and may spread the infection unknowingly before symptoms occur or without any symptoms ever occurring, there is a non-negligible risk of humans spreading SARS-CoV-2 to wildlife, in particular mammals. Because of SARS-CoV-2’s evolutionary origins in bats and reports of humans transmitting the virus to pets and zoo animals, regulations for prevention of human-to-animal transmission have so far focused mostly on these animal groups. Here, we summarize recent studies and reports that show that a wide range of distantly related mammals are likely susceptible to SARS-CoV-2 and that susceptibility or resistance to the virus is in general not predictable, or only to some extent, by phylogenetic proximity to known susceptible or resistant hosts. In the absence of solid evidence on the SARS-CoV-2 susceptibility/resistance for each of the >5,500 mammal species, we argue that sanitary precautions should be taken when interacting with any mammal species in the wild. Preventing human-to-wildlife SARS-CoV-2 transmission is important for protecting these (sometimes endangered) animals from disease, but also to avoid establishment of novel SARS-CoV-2 reservoirs in wild animals. The risk of repeated re-infection of humans from such a wildlife reservoir could severely hamper SARS-CoV-2 control efforts. For wildlife fieldworkers interacting directly or indirectly with mammals, we recommend sanitary precautions such as physical distancing, wearing face masks and gloves, and frequent decontamination, which are very similar to regulations currently imposed to prevent transmission among humans.

Road dust resuspension, especially the particulate matter fraction below 10 µm (PM10), is one of the main air quality management challenges in Europe. Road dust samples were collected from representative streets (suburban and urban) of the city of Viana do Castelo, Portugal. PM10 emission factors (mg veh-1 km-1) ranging from 49 (asphalt) to 330 (cobble stone) were estimated by means of the United Stated Environmental Protection Agency method. Two road dust fractions (< 0.074 mm and from 0.0074 to 1 mm) were characterised for their geochemical, mineralogical and morphological properties. In urban streets, road dusts reveal the contribution from traffic emissions, with higher concentrations of e.g. Cu, Zn, and Pb. In the suburban area, agriculture practices likely contributed to As concentrations of 180 mg kg-1 in the finest road dust fraction. Samples are primarily composed of quartz, but also of muscovite, albite, kaolinite, microcline, Fe-enstatite, graphite and amorphous content. Particle morphology clearly shows the link with natural and traffic related materials, with well-formed minerals and irregular aggregates. The hazard quotient suggests a probability to induce non-carcinogenic adverse health effects in children by ingestion of Zr. Arsenic in the suburban street represents a human health risk of 1.58 x 10-4.

A lot of research studies have been surveyed the completed genomes of prokaryotic and eukaryotic and focused on the correlation between the percentage of microsatellite sequences in completed genomes and the whole size of the organism genomes. There are fewer studies made in repetitive sequences otherwise simple sequence repeats or long tandem repeats of virus genomes. simple sequence repeats (SSRs) are the most important regions for recombination and moving repeats blocks from site to another site in the genomes. A tool was programmed and designed by visual basic 6.0 to find the long tandem repeats in DNA sequences of the small genomes. The tool named “Repeater Finder Regular Expression”, (RFRE) Version 1.0, 2016. The tool was utilized to discover different pattern of long tandem repeats (LTR) motifs on the completed genomes of human corona virus strains by using a joined regular expression language. In this study, a twenty-nine accession numbers of human coronavirus completed genomes, (hku1) strains were retrieved from the Genbank. The researcher can write a different regular expression patterns and joined regular expression patterns through the designed tool to search and find a specific motif of nucleotide sequences inside the complete genomes. The RFRE tool searched and found three different total lengths of a perfect long tandem repeats (240bp, 300bp and 480bp). A Dot plot gave a picture view for the long tandem repeat sequences in the completed genome sequence (KF430201.1) of human coronavirus. The genomic dot plot tool YASS was used as a genomic similarity searching tool to check for the uninterrupted repeats and confirm the sensitivity of the (RFRE) tool. To identify the recombination site in the genomes of human coronavirus the RAT tool was applied to find the recombination sites between the completed genomes of human corona viruses .The RAT tool recognized the recombination site in the nucleotide position (3012) and at the same time this recombination site position (3012) was also recognized as a beginning position of a long tandem repeat. A precise motif was predicted from the translated repeats of Human Corona Virus which found by PRATT tool. There was a relationship between the total length of long tandem repeats and genome size of Human Corona Virus and the correlation value R² was equal to (0,451). In conclusion, this study presented the importance of finding the long tandem repeats of human coronavirus and gives a relationship between the completed genome size of human coronavirus types and long tandem repeats. The nucleotide position (3012) was a hot spot site for a recombination among the complete genomes of human coronavirus and also identified as a repetitive site in the genomes of human corona virus (hku1). The repeats in human coronavirus (hku1) were predicated to be a main major role of virus evolution.

There is currently an interest in “active” implantable biomedical devices that include mechanical stimulation as an integral part of their design. This paper reports the experimental use of a porous scaffold made of interconnected networks of slender ferromagnetic fibres that can be actuated in vivo by an external magnetic field applying strains to in-growing cells. Such scaffolds have been previously characterized in terms of their mechanical and cellular responses. In this study, it is shown that the shape changes induced in the scaffolds can be used to promote osteogenesis in vitro. In particular, immunofluorescence, gene and protein analyses reveal that the actuated networks exhibit higher mineralization and extracellular matrix production, and express higher levels of osteocalcin, alkaline phosphatase, collagen type 1a1, runt-related transcription factor 2 and bone morphogenetic protein 2 than the static controls at the 3-week time point. The results suggest that the cells filling the inter-fibre spaces are able to sense and react to the magneto-mechanically induced strains facilitating osteogenic differentiation and maturation. This work provides evidence in support of using this approach to stimulate bone ingrowth around a device implanted in bone and can pave the way for further applications in bone tissue engineering.

Due to remarkable socioeconomic development, an increasing number of karst rocky desertification areas have been severely affected by human activities in southern China. Effectively analyzing human activities in karst rocky desertification areas is a critical prerequisite for managing and restoring areas with tremendous negative impacts from desertification. At present, a timely and accurate way of quantifying the spatiotemporal variations of human activities in karst rocky desertification areas is still lacking. In this communication, we attempted to quantify human activities from the corrected NPP-VIIRS nighttime light data from 2012 to 2018 based on statistical analysis. The results show that a significant increase of night lights could be clearly identified during the study period. The total nighttime lights (TL) related to severe karst rocky desertification (S) were particularly concentrated in Guizhou and Yunnan. The nighttime light intensity (LI) related to the S areas in Chongqing were the strongest due to its rapid socioeconomic development. The annual growth rate of nighttime lights (GL) has been slow or even negative in Guangdong because of its various karst rocky desertification restoration programs. This communication could provide an effective approach for quantifying human activities and provide useful information about where prompt attention is required for policy-making on the restoration of the karst rocky desertification areas.

The core components of regenerative medicine are stem cells with high self-renewal and tissue regeneration potentials. Adult stem cells can be obtained from many organs and tissues. NANOG, SOX2 and OCT4 represent the core regulatory network that suppresses differentiation-associated genes, maintaining the pluripotency of mesenchymal stem cells. The roles of NANOG in maintaining self-renewal and undifferentiated status of adult stem cells are still not perfectly established. In this study we define the effects of downregulation of NANOG in maintaining self-renewal and undifferentiated state in mesenchymal stem cells (MSCs) derived from subcutaneous adipose tissue (hASCs). hASCs were expanded and transfected in vitro with short hairpin Lentivirus targeting NANOG. Gene suppressions were achieved at both transcript and proteome levels. The effect of NANOG knockdown on proliferation after 10 passages and on the cell cycle was evaluated by proliferation assay, colony forming unit (CFU), qRT-PCR and cell cycle analysis by flow-cytometry. Moreover, NANOG involvement in differentiation ability was evaluated. We report that downregulation of NANOG revealed a decrease in the proliferation and differentiation rate, inducing cell cycle arrest by increasing p27/CDKN1B (Cyclin-dependent kinase inhibitor 1B) and p21/CDKN1A(Cyclin-dependent kinase inhibitor 1A) through p53 and regulate DLK1/PREF1. Furthermore, NANOG induced downregulation of DNMT1, a major DNA methyltransferase responsible for maintaining methylation status during DNA replication probably involved in cell cycle regulation. Our study confirms that NANOG regulates the complex transcription network of plasticity of the cells, inducing cell cycle arrest and reducing differentiation potential.

Solar Energy has enormous potential to fulfill the energy requirements of the world and can be extracted using solar cells. However, the solar cells are affected by poor efficiency and further affected by wind speed, orientation of the panel, temperature and dust deposition. There are different cleaning technologies devised by many industry experts to clean the solar panels. However, they are facing drawbacks when deployed in the solar farm. An efficient cleaning system, along with an added cooling system must be devised, so that the solar panels must be cleaned and cooled often to maximize the energy production. This paper presents a low-cost, energy-efficient, smart and innovative dust cleaning and cooling system for Photovoltaic (PV) Panels. The system is designed, fabricated, fully-automated using Programmable Logic Controller (PLC) and tested successfully. A battery-charging kiosk, capable of charging two, 24V Lead Acid Batteries embedded within this prototype, shall provide clean-energy in a sustainable manner to the rural communities of the developing nations. The user can check the status of the battery such as battery voltage, battery temperature, and state of charge on the Human Machine Interface (HMI) panel while charging the batteries

Shenzhen is China’s top ten clean air city and the cleanest air megacity. Even so, epidemiologic studies have shown ambient air pollution had significant adverse impacts on human health in this less polluted city. In this study, the concentrations of six criteria air pollutants (PM2.5, PM10, O3, NO2, SO2, and CO) from 2014 to 2017 were analyzed and compared to thresholds of both national and international air quality standards. The results showed concentrations of all air pollutants were below target values of current national air quality standard, but levels of particulate matter (PM) and O3 were still much higher than the recommended levels by the World Health Organization. Within national air quality standards, the number of over-limit days was rare with few variations between highly polluted and low pollution areas. The air quality improvement was slowing down recently. Our results suggest annual and daily thresholds for PM are too loose for air quality improvement in Shenzhen. Hence, we call for evaluation and establishment of tougher air quality standard.

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

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by a long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. A short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.

In this paper we take China’s one child policy as an example and investigate its environmental impact. We develop a model for an economy using a standard overlapping generation model extended with human capital, endogenous fertility, and changing life expectancy. To model the environmental impact of economic activities, we use a modified IPAT model. We show that China’s one child has a very strong positive impact on the environment, particularly if we consider the whole human legacy.

The human body supports the growth of a wide array of microbial communities in various niches, such as the oral cavity, gastro-intestinal and urogenital tracts and on the surface of the skin. These host associated microbial communities include yet-un-cultivable bacteria and are influenced by various factors. Together, these communities of bacteria are referred to as the human microbiome. Human oral microbiome consists of both symbionts and pathobionts. Deviation from symbiosis among the bacterial community leads to “dysbiosis”—a state of community disturbance. Dysbiosis occurs due to many confounding factors that predispose to a shift in the composition and relative abundance of microbial communities. Dysbiotic communities have been a major cause for many microbiomes related systemic infections. Such dysbiosis is directed by certain important pathogens called the “keystone pathogens” that could modulate community microbiome variations. One such persistent infection is oral infection, mainly periodontitis, where a wide array of causal organisms has been implied to systemic infections such as cardio vascular disease, diabetes mellitus, rheumatoid arthritis and Alzheimer’s disease. The keystone pathogens co-occur with many yet-cultivable bacteria and their interactions lead to dysbiosis. This has been the focus of recent research. While immune evasion is one of the major modes that lead to dysbiosis, new processes and new virulence factors of bacteria have been shown to be involved in this important process of that determine disease or health state. This review focuses on such dysbiotic communities, their interactions and their virulence factors that predispose the host to other systemic implications.

The objective is to study the local perceptions on the conservation of wild chimpanzees (Pan troglodytes verus) in the Réserve Naturelle Communautaire de Dindéfélo (RNCD), southeast of Senegal, to design specific actions to improve conservation management. We conducted 338 semi-structured interviews in three main villages of RNCD. Three-fourths of the population were farmers. Of those interviewed, 29% received elementary education. Two of the three villages participated in a project to plant trees as fences. On average, 66% of the respondents were animists. Of the respondents who were afraid to see a chimpanzee, 68% answered because they attack. Seventy-seven percent washed their clothes in the forest river because there was more water than in the village wells. Of the interviewees who threw the old clothes into the forest, 50% did it due to tradition. Ninety-six percent of respondents stated that chimpanzees do not feed from their crops. The main problems of the locals with the Reserve were lack of water and basic resources and not been allow to cut trees in protected areas. There were significant relationships between education (1 relationship), environmental project (4 relationships) and animism (11 relationships) with local perceptions. The 93% of the respondents who had the traditional belief that “if the old clothes were burnt, children would become sick” feel fear of chimpanzees, while those who did not have this animistic belief the 6% are afraid (χ² = 1.57, P < 0.02). These local perceptions allow us to design specific course of action to improve chimpanzee conservation and sustainable coexistence in this complex period of the Anthropocene.

In most industrialized countries, screening programs for cervical cancer have shifted from cytology (Pap smear or ThinPrep) alone on clinician-obtained samples to the addition of screening for human papillomavirus (HPV), its main causative agent. For HPV testing, self-sampling instead of clinician-sampling has proven to be equally accurate, in particular for assays that use nucleic acid amplification techniques. In addition, HPV testing of self-collected samples in combination with a follow-up Pap smear in case of a positive result is more effective in detecting precancerous lesions than a Pap smear alone. Self-sampling for HPV testing has already been adopted by some countries, while others have started trials to evaluate its incorporation into national cervical cancer screening programs. Self-sampling may result in more individuals willing to participate in cervical cancer screening, because it removes many of the barriers that prevent women, especially those in low socioeconomic and minority populations, from participating in regular screening programs. Several studies have shown that the majority of women who have been underscreened but who tested HPV-positive in a self-obtained sample, will visit a clinic for follow-up diagnosis and management. Additionally, a self-collected sample can also be used for vaginal microbiome analysis, which can provide additional information about HPV infection persistence as well as vaginal health in general.

Clinical rehabilitation and sports performance analysis both require the objectification of movement. Kinovea^© is a free 2D motion analysis software that enables the establishment of kinematics parameters. This low-cost technology has been used in sports sciences, as well as clinical field and research work. Although it has been validated as a tool with which to assess time-related variables, this is not yet the case regarding angular and distance variables. The main objective of this study was to determine the validity and reliability of the Kinovea software in obtaining angular and distance data at different perspectives of 90°, 75°, 60° and 45°. For this purpose, a figure with 29 points was designed (in AutoCAD) and 24 frames analysed. Each frame was examined by three observers who each made two attempts. For each export data item, 20 angles and 20 distance variables were calculated, with intra- and inter-observer reliability also analysed. To evaluate Kinovea reliability and validity a multiple approach was applied involving the following analysis: -systematic error with a two-way ANOVA 2x4; -relative reliability with ICC and CV (95% confidence interval); -absolute reliability with Standard Error. The results thus obtained indicate that the Kinovea software is a valid and reliable tool that is able to measure accurately at distances up to 5 m from the object and at an angle range of 90°–45°. Nevertheless, for optimum results an angle of 90° is suggested.

Praeruptorin A(PA), a naturally existing pyranocumarin, is isolated from the dried root of Peucedanum praeruptorum Dunn. So far the anti-cancer effect and molecular mechanism behind Praeruptorin A action in human cervical cancer HeLa cells remain unknown. In the present study, we find that PA reduces cell proliferation and colony formation of human cervical cancer HeLa cells through inducing cell cycle arrest at G0/G1 phase. PA-upregulated p21 and p27 proteins are observed, accompanied with inhibition of cyclin D1 and S-phase kinase-associated protein 2(Skp2) proteins expression. PA could significantly inhibit migration and invasion of human HeLa cells. Meanwhile, PA significantly reduces invasive protein expression of matrix metalloproteinase-2 (MMP-2), and increases protein expression of tissue inhibitor of metalloproteinase-2 (TIMP-2). PA is observed to possess the capacity in suppressing ERK1/2 activation. PD98059 (ERK specific inhibitor) significantly enhances PA-induced downregulation of MMP-2 expression, and upregulation of TIMP-2 expression. Moreover, we found that PA treatment notably inhibits 12-O-tetradecanoylphorbol-13-acetate(TPA)-upregulated ERK1/2 activation, MMP-2 expression, cellular migration and invasion in human HeLa cells. Taken together, these findings are the first to demonstrate the anti-cancer activity of PA, which may act as a promising therapeutic agent for the treatment of human cervical cancer.

Sinulariolide, a natural product extracted from cultured-type soft coral Sinularia flexibilis, possesses bioactivity against the movement of several types of cancer cell. However, the molecular pathway of its effects on human bladder cancer remain poorly understood. Using a human bladder cancer cell line as an in vitro model, this study investigated the underlying mechanism of sinulariolide against cell migration/invasion in TSGH-8301 cells. We found that sinulariolide inhibited TSGH-8301 cell migration/invasion, and the effect was concentration-dependent. Furthermore, the protein expressions of matrix metalloproteinases (MMPs) MMP-2 and MMP-9, as well as urokinase, were significantly decreased after 24-h sinulariolide treatment. Meanwhile, the increased expressions of tissue inhibitors of metalloproteinases (TIMPs) TIMP-1 and TIMP-2 were in parallel with an increased concentration of sinulariolide. Finally, the expressions of several key phosphorylated proteins in the mTOR signaling pathway were also downregulated by sinulariolide treatment. Our results demonstrated that sinulariolide has significant effects against TSGH-8301 cell migration/invasion, and its effects were associated with decreased levels of MMP-2/-9 and urokinase expression, as well as increased TIMP-1/TIMP-2 expression. The inhibitory effects were mediated by reducing phosphorylation proteins of the PI3K, AKT and mTOR signaling pathway. The findings suggested that sinulariolide is a good candidate for advanced investigation with the aim of developing a new drug for the treatment of human bladder cancer.

Human visual system (HVM) can quickly localize the most salient object in scenes, which has been widely applied on natural image segmentation [15]-[19]. In ultrasound (US) breast images, compared with background areas, tumor is more salient because of its higher contrast. In this paper, we develop a novel automatic localization method based on HVM for automatic segmentation of ultrasound (US) breast tumors. First, the input image is smoothed by convolution with a linearly separable Gaussian filter and then subsampled into a 9-layer Gaussian pyramid. Then intensity, blackness ratio, and superpixel contrast features are combined to compute saliency map, in which Winner Take All algorithm is used to localize the most salient region, presenting with a circle on the localized target. Finally the circle is taken as the initial contour of CV level set to finish the extraction of breast tumor. The localization method has been tested on 400 US beast images, among which 378 images have higher saliency than background areas and succeed in localization, with high accuracy 92.00%. The HVM localization method can be used to localize the tumors, combined with this method, CV level set can achieve the fully automatic segmentation of US breast tumors. By combing intensity, blackness ratio and superpixel contrast features, the proposed localization method can successfully avoid the interference caused by background areas with low echo and high intensity. Moreover, multi-object localization of US breast images can be considered in future employment.

Artisanal and small-scale gold mining (ASGM) has been an important source of income for communities in the Madre de Dios River Basin in Peru for hundreds of years. However, in recent decades, the scale of ASGM activities in the region has increased dramatically, and exposures to a variety of occupational and environmental hazards related to ASGM, including mercury, are becoming more widespread. The aims of our study were to: (1) examine patterns in the total hair mercury level of human participants in several communities in the region and compare these results to the 2.2 µg/g total hair mercury level equivalent to the World Health Organization (WHO) Expert Committee of Food Additives (JECFA)’s Provisional Tolerable Weekly Intake (PTWI); and (2), to measure the mercury levels of paco (Piaractus brachypomus) fish raised in local aquaculture ponds, in order to compare these levels to the EPA Fish Tissue Residue Criterion of 0.3 µg Hg/g fish (wet weight). We collected hair samples from 80 participants in four communities (one control and three where ASGM activities occurred) in the region, and collected 111 samples from fish raised in 24 local aquaculture farms. We then analyzed the samples for total mercury. Total mercury levels in hair were statistically significantly higher in the mining communities than in the control community, and increased with increasing distance away from the Madre de Dios headwaters (as the crow flies), did not differ by sex, and frequently exceeded the reference level. Regression analyses indicated that higher hair mercury levels were associated with residence in ASGM communities. The analysis of paco fish samples found no samples that exceeded the EPA tissue residue criterion. Collectively, these results align with other recent studies showing that ASGM activities are associated with elevated human mercury exposure. The fish farmed through the relatively new process of aquaculture in ASGM areas appeared to have little potential to contribute to human mercury exposure. More research is needed on human health risks associated with ASGM to discern occupational, residential, and nutritional exposure, especially through tracking temporal changes in mercury levels as fish ponds age, and assessing levels in different farmed fish species. Additionally, research is needed to definitively determine that elevated mercury levels in humans and fish result from the elemental mercury from mining, rather than from a different source, such as the mercury released from soil erosion during deforestation events from mining or other activities.

The integration of drone technology into healthcare logistics presents a significant opportunity to enhance the speed, reliability, and efficiency of medical sample transportation. This study inves-tigates the current advancements in drone technology, focusing on its application in the rapid and secure delivery of medical samples, particularly in urban and remote regions where traditional transportation methods often face challenges. Utilizing a combination of recent technological de-velopments such as AI-driven navigation systems, real-time monitoring, and secure payload management, the study examines how drones can mitigate logistical barriers like traffic conges-tion and geographical isolation. The research methodology includes a comprehensive review of recent case studies from various regions, illustrating the practical applications and benefits of drones in healthcare. The results demonstrate a substantial reduction in transportation time and costs, along with improved accessibility to healthcare services in underserved areas. The study concludes that, while challenges such as regulatory hurdles and privacy concerns remain, the on-going advancements in drone technology and supportive regulatory frameworks have the poten-tial to revolutionize medical logistics, ultimately improving patient outcomes and healthcare de-livery.

The EFQM Model, established in 1991, serves as a framework for organizational excellence, helping achieve sustainable success through an integrated management system. The model's evo-lution is driven by the need to adapt to global trends, threats, and technological innovations such as: digital transformation, employee engagement and well-being, remote work, agile performance management, diversity, equity and inclusion (DEI), sustainability, AI, and big data. The new EFQM Model 2025, presented in Istanbul in June 2024, introduces significant changes, emphasiz-ing sustainability, innovation, and technology. These changes are analyzed based on the logic of three questions: Why, How, and What. Why: There is a necessity for better alignment of organizations with contemporary challenges through flexibility, risk resilience, and innovation capabilities in a dynamic business environ-ment, including alignment with the UN Sustainable Development Goals (SDGs). How: The model introduces an integrated approach to performance management and transfor-mation, incorporating advanced technologies such as AI and data analytics. What: Implementing the EFQM Model 2025 enhances organizational adaptability, innovation, and flexibility, improving financial, operational, and strategic outcomes. Better alignment with sustainability goals leads to long-term value and social responsibility. Improved stakeholder engagement methods enhance understanding and satisfaction. The use of advanced technologies supports innovation and operational efficiency, while better strategic and operational performance and risk management ensures greater stability and the ability to anticipate future challenges.

Digital transformation is revolutionizing inventory management practices within supply chains, offering unprecedented opportunities and challenges for businesses worldwide. This study explores the impact of digital technologies on inventory management, focusing on the adoption of IoT sensors, RFID tags, AI-driven analytics, and cloud-based systems. Through a qualitative research approach encompassing interviews with industry professionals and secondary data analysis, the study examines key themes including enhanced inventory visibility, improved accuracy, advanced demand forecasting, and streamlined supply chain collaboration. Findings reveal that digital technologies significantly enhance inventory visibility by providing real-time tracking and data integration capabilities. This facilitates accurate inventory monitoring and decision-making, reducing errors and optimizing inventory levels to meet fluctuating demand effectively. AI-driven analytics and machine learning models emerge as pivotal tools for predictive demand forecasting, enabling businesses to anticipate market trends and adjust inventory strategies accordingly. Additionally, cloud-based systems and electronic data interchange (EDI) foster improved communication and coordination among supply chain partners, enhancing overall operational efficiency. Despite these benefits, challenges such as system integration complexities, high implementation costs, data quality management, cybersecurity risks, and regulatory compliance issues are prevalent. Successful adoption of digital inventory management solutions requires strategic planning, investment in technology infrastructure, and organizational readiness to navigate these challenges effectively. This study contributes to the understanding of how digital transformation reshapes inventory management practices, offering insights for researchers and practitioners alike to leverage digital technologies for enhanced supply chain performance and competitive advantage

We present a novel benchmarking methodology for Large Language Models (LLMs) to evaluate their susceptibility to hallucinations, thereby determining their reliability for real-world applications involving greater responsibilities. This method, called Deception-Based Benchmarking, involves testing the model with a task that requires composing a short paragraph. Initially, the model performs under standard conditions. Then, it is required to begin with a misleading sentence. Based on these outputs, the model is assessed on three criteria: accuracy, susceptibility, and consistency. This approach can be integrated with existing benchmarks or applied to new ones, thus facilitating a comprehensive evaluation of models across multiple dimensions. It also encompasses various forms of hallucination. We applied this methodology to several small opensource models using a modified version of MMLU, DB-MMLU1 . Our findings indicate that most current models are not specifically designed to self-correct when the random sampling process leads them to produce inaccuracies. However, certain models, such as Solar-10.7B-Instruct, exhibit a reduced vulnerability to hallucination, as reflected by their susceptibility and consistency scores. These metrics are distinct from traditional benchmark scores. Our results align with TruthfulQA, a widely used benchmark for hallucination. Looking forward, DB-benchmarking can be readily applied to other benchmarks to monitor the advancement of LLMs.

The study is dedicated to analyzing the complex interaction between Artificial Intelligence (AI) and the Internet of Things (IoT) with institutional structures in the context of sustainable development. This work examines the impact of these technologies on economic and social development, identifying their potential advantages and risks. Methodologically, the research relies on information gathering and case study analysis, assessing the impact of technology in various domains, from smart cities to the manufacturing sector. The findings emphasize the necessity of integrating technological innovations into broader social and environmental initiatives, highlighting the importance of institutional transformations for achieving environmental sustainability.

Urban tree classification is pivotal in urban planning and management, facilitating informed decision-making processes. In this study, we introduce a novel data presentation method termed Pseudo Tree Crown, designed to enhance the accuracy and efficiency of urban tree classification. Leveraging the latest advancements in artificial intelligence (AI), we employ a state-of-the-art classification scheme, PyTorch, to maximize the accuracy of tree classification. Our results demonstrate a robust classification accuracy of over 95% from high spatial resolution imagery from Unmanned Aerial Vehicle (UAV), underscoring our proposed approach’s effectiveness. Moreover, the adaptability of our method renders it applicable to various study areas, highlighting its versatility and potential for widespread implementation in urban planning and management initiatives.

This paper presents the second stage of a tandem fixed wing unmanned aerial vehicle (UAV) aerodynamic development. In the initial stage, the UAV was optimized analyzing its characteristics only in symmetrical flight conditions. Posted requirements were that both wings should produce relevant positive lift, the initial stall must occur on the front wing first, the center of pressure should be close to the center of gravity, while longitudinal static stability should be in the optimum range. Computational fluid dynamics (CFD) analyses were performed, where applied calculation model was derived from the authors’ previous successful projects. The eight version TW V8 has satisfied all longitudinal requirements. Lateral-directional CFD analyses of V8 showed that the ratio of the lateral and directional stability at the nominal cruising regime was optimal, but both lateral and directional static stabilities were too high. On further development versions the lower vertical tail was eliminated, negative dihedral was implemented on the front wing, and four inverted blended winglets were added. Version TW V14 has largely improved lateral and directional stability characteristics, while their optimum ratio at cruising regime was preserved. Longitudinal characteristics were also well preserved. Maximum lift coefficient and lift-to-drag ratio were increased, compared to the V8.

This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the complexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regulations like GDPR. The research underscores the need for adaptable solutions that balance technological advancements with privacy and security considerations, setting a path for future exploration in AI-empowered IoT applications.

A topical challenge for algorithms in general and for automatic image categorization and generation in particular is presented with the aim of highlighting strengths and deficiencies of current Artificial Intelligence approaches while sketching a way forward. A general lack of encompassing symbol-embedding and (not only) -grounding in some bodily basis is made responsible for current deficiencies. A concomitant dearth of hierarchical organization of concepts follows suite. As a remedy for these shortcomings, it is proposed to take a wide step back and to newly incorporate aspects of cybernetics and analog control processes. It is claimed that a promising overarching perspective is provided by the Ouroboros Model with a valid and versatile algorithmic backbone for general cognition at all accessible levels of abstraction and capabilities.

(1) Background: Artificial intelligence (AI) has existed in some form for decades, but recent rapid advances in a subset called machine learning (ML) — and more specifically deep learning (DL), a neural network-based approach — have made headlines for the potential to revolutionize and automate multiple large sectors of society, including scientific research and the healthcare field. Furthermore, large language models (LLMs) — which are built on DL — could lead to a more seamless, natural interaction between humans and computers. (2) Methods: We reviewed numerous publications on this subject from recent years. (3) Results: We found these studies collectively show that AI is positively disrupting both biomedical research and medical practice, such as optical imaging in surgery guidance. (4) Conclusions: However, we recommend caution in over-reliance on AI in the laboratory or the clinic due to anticipated risks and current limitations.

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