Based on the demographic shift and the related challenges resulting from the growing number of elderly and persons with chronic diseases, the idea of smart home that supports its inhabitants in the daily life, gains importance. The purpose of this paper was to examine in a prototypic Ambient Assisted Living environment if users after interaction with different health-supporting applications intend to use such in the future. Two experimental studies exemplary show possible applications of home-integrated technology that can support, assist and accompany the target group in different contexts, and examine to what extent participants are willing to future use such sophisticated technology at home. The results show that people in general, but especially the old and chronically ill ones are quite fascinated of health-supporting ambient technology and the majority intends to use such ambient assistance in the future (study I). Moreover, serious games for healthcare are shown as a hedonic use of technology in smart homes that have a great potential to retain or improve the physical health, mobility and the overall well-being of the inhabitants (study II). The article provides two examples of ambient technology to leverage the demographic change and presents important user factors for facilitating high user acceptance.

A proposed low-temperature forging method aims to enhance stainless steel bearings by creating a martensitic subsurface layer, significantly boosting bearing fatigue life due to increased surface hardness. This technique induces beneficial residual stresses, particularly in axial bearings, streamlining their construction and improving machine elements. Challenges persist, especially with radial bearings, but simplicity in axial bearing forging promotes compact, resource-efficient facility construction. Future research will focus on applying this technique to axial bearing washers, potentially replicating success in other bearing components. Despite the energy ex-penditure for cooling during forging, the substantial increase in bearing fatigue life offsets this, enhancing overall longevity and reliability of critical machine components. Integration of this forging technique into bearing fabrication appears seamless, offering a promising trade-off be-tween energy use and enhanced performance.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

Diarrhoea is a serious cause of mortality worldwide that can lead to dehydration, gut barrier function impairment, nutrient malabsorption, and alterations of the gut microbiota (dysbiosis). Current solutions for its management, such as oral rehydration salts (ORS), inhibitors of the gut motility, antibiotics, and living probiotics only partially counteract the mechanisms of the disease and do not provide a full coverage of the problem. Potential risks of the use of living probiotic strains, particularly in immunocompromised patients, can be eliminated with the use of tyndallized (heat-killed) postbiotic bacteria and yeast. ABB C22® is a postbiotic combination of three tyndallized yeasts, namely Saccharomyces boulardii, Saccharomyces cerevisiae, and Kluyveromyces marxianus. To assess the action of the postbiotic combination on diarrhoea, immune and gut epithelial cell signaling assays, gut barrier formation assay and rotavirus gene expression assay were performed. ABB C22® showed a strong anti-inflammatory effect, an induction of the build-up of the gut epithelium, and a degree of protection against rotavirus infection. These experimental studies support the use of the postbiotic ABB C22® as a solution for the management of diarrhoea and gastrointestinal conditions, alone or in combination with existing but incomplete treatments.

Diarrhoea is a serious cause of mortality worldwide that can lead to dehydration, gut barrier function impairment, nutrient malabsorption, and alterations of the gut microbiota (dysbiosis). Current solutions for its management, such as oral rehydration salts (ORS), inhibitors of the gut motility, antibiotics, and living probiotics only partially counteract the mechanisms of the disease and do not provide a full coverage of the problem. Potential risks of the use of living probiotic strains, particularly in immunocompromised patients, can be eliminated with the use of tyndallized (heat-killed) postbiotic bacteria and yeast. ABB C22® is a postbiotic combination of three tyndallized yeasts, namely Saccharomyces boulardii, Saccharomyces cerevisiae, and Kluyveromyces marxianus. To assess the action of the postbiotic combination on diarrhoea, immune and gut epithelial cell signaling assays, gut barrier formation assay and rotavirus gene expression assay were performed. ABB C22® showed a strong anti-inflammatory effect, an induction of the build-up of the gut epithelium, and a degree of protection against rotavirus infection. These experimental studies support the use of the postbiotic ABB C22® as a solution for the management of diarrhoea and gastrointestinal conditions, alone or in combination with existing but incomplete treatments.

Music has the innate potential to reach all parts of the brain, stimulates certain brain areas which are not achievable through other modalities. Music Therapy (MT) is being used for more than a century to treat individuals who needs personalized care. MT optimizes motor, speech and language responsibilities of the brain and improves cognitive performance. Pervasive developmentdisorder (PDD) is a multifaceted, neuro developmental disorder and autism spectrum disorder (ASD) comes under PDD, which is defined by deficiencies in three principal spheres: social connection with others, communicative and normal movement skills. The conventional imaging studies illustrate reduced brain area connectivity in people with ASD, involving selected parts of the brain cortex. People with ASD express much interest in musical activities which engages the brain network areas and improves communication and social skills.The main objective of this review is to analyze the potential role of MT in treating the neurological conditions, particularly ASD. Evidence based studies have reported the extensive therapeutic application of music on various part of the brain in a nonverbal child with autism through hearing or making music.Hence we hypothesized that MT intervention can improve the communication capacity in people with ASD, than customary neurorestoration therapy alone.

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.

Keywords: pelvic ultrasound, follicle number per ovary, FNPO, ovarian volume, PCOM, upper normal limits, referral population

The research reported on this paper was aimed at improving the overall efficiency of a PDO certified artisanal cheese production process. Being a PDO certified foodstuff by the EU, it is considered to have properties and qualities determined by the geographical environment in which is made, with its production taking place in a specific and determined geographical location, in this case the Serra da Estrela region. In that sense, the authors conducted a mapping according to a systemic perspective of the processes involved in the context of manufacturing and distribution of certified Serra da Estrela cheese. Numerous methods were used throughout the process, such as a systemic design analysis, and techniques derived from ethnographic methods, which led to the collection of data in the field and consequently provided the immersion of the researcher in genuine work situations. Critical points were identified and emphasized in the systemic map with the purpose of encouraging initiatives to address and overcome the gaps and inefficiencies detected. The systemic design analysis triggered the development of design work. Observations following an ethnographic approach identified ergonomic risks in cheese making during the process of cutting excess chips, fostering the emergence of musculoskeletal disorders at the wrist. A tool that fits best to the task at hand was developed. A prototype of the new tool enabled collecting feedback from use in the work context, in order to inform product development. The domains of agricultural production and microbiology, considering the specific microorganisms developed trough the ripening process of the cheese, turned out to be aspects of high importance for the issue under focus, contributing to a broader understanding of the ripening process and its risks, simultaneously improving the efficiency and success of cheese production. If it were not for the systemic analysis, which served as a link between the boundaries of distinct domains such as the risk of microorganism contamination, ergonomics, energy efficiency, legislation and regulation policies, transportation challenges and economic viability, approached throughout the research. Simultaneously creating bridges between them, the various problems might not have been detected in the first place, as they are usually addressed in specialized disciplines, predetermined by the restrictions of each specific area of knowledge. As a consequence of the development of this research, which was based on an analysis that sought to establish possible connections between various disciplines and tried to constantly maintain a holistic perspective, making new connections and observing the issues from a new angle, apart from the already established methodologies. All this allowed to lay out the seeds for the development of a plan to tackle the critical points identified by the systemic analysis reported in this paper.

The ongoing challenge to ensure a sustainable and affordable energy supply forces industrial companies to implement appropriate measures. These measures typically lead to an increased system complexity, which has to be addressed during operation. Whereas existing approaches from the field of supervisory and optimal control appear to be capable of mastering this issue, these are still not adopted in industry. Therefore, this work presents a procedure model for the systematic development of application-oriented operating strategies for industrial energy supply systems. The procedure model combines research approaches from the fields of sequencing control and approximate MPC to extract rule-based operating strategies. By splitting the procedure model into five phases, expert knowledge can be integrated in a target-oriented manner. Subsequently to the description, the procedure model is exemplary applied to the example of an industrial heating supply system. Throughout an optimization study, the developed operating strategy is compared to a MPC strategy as well as a baseline strategy. Whereas the conventional MPC approach is the upper bound with regard to optimality, the developed operating strategy is able to generate comparable results. Compared to the baseline strategy, a relative reduction in operating expenses of 5.4 % to 37.0 % are achieved in this specific use case.

Abstract In This research we will describe how to deal the system with users to make the work in the company or in any work space . in application forms interact with users inside the system . application form of any system must creates a many graphical interface for user ant we should take care about who is dealing with the system , Additionally, application forms used from different type of user so that we must take care about this points during designing the system for different users . Application Forms in the system consists of many programs, or components and function we are designing it. the instructions inside program can be execute by clicking on the command button and we can create specific process in the system.

This study investigated interactions between sugar-related dietary patterns (DP), personality traits, cognitive-behavioural, and emotional functioning. The study involved females aged 18-54. Data were collected between the Winter and Spring of 2020/21. The survey was conducted using anonymized questionnaires. The Ten-Item Personality Inventory (TIPI) was used to examine personality traits based on the Big Five Personality Trait Model. Three-Factor Eating Questionnaire (TFEQ) was used to measure eating behaviours: cogni-tive restraint (CR), uncontrolled eating (UE), and emotional eating (EE). The KomPAN question-naire collected the frequency of intake. Dietary patterns (DPs) were derived by principal component analysis (PCA). A logistic regression (OR) was applied to verify the association between the DPs, personality traits, cognitive-behavioural and emotional functioning. Three DPs were identified: sweet-western (SWDP), pro-healthy (PHDP) and dairy (DDP). Women with high conscientiousness were less likely, by 33%, to adhere to the upper tercile of SWDP and 80% more likely to the upper tercile of PHDP. Elevated CR intensity increased by almost 2-fold (OR: 1.93; p

Introduction: Hypertension is a serious public health problem in Morocco. The objective of this pilot study was to estimate the salt intake of Moroccan adults by measuring 24-hour urinary sodium excretion. Methods: 132 participants were recruited from the central region of Morocco. Sodium, potassium and creatinine levels were measured using the 24-hour urine samples. Additional data included anthropometric measurements and socio-demographic questionnaire. Results: The daily sodium, potassium and creatinine excretions were 2779.1±1334.9 mg, 1350.0±642.8 mg and 820.3 ± 381.2 mg, respectively. Data analysis revealed that 71.2% of the participants had a daily sodium intake more than 2000 mg (5g of salt) recommended by the WHO. The mean of potassium excretion was 1350.0±642.8 mg /day, this average was lower than 3.51g per day recommended by the WHO. Conclusion: Public health measures to reduce sodium and increase potassium consumption in order to decrease the population’s risk of high blood pressure and heart disease are recommended.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

Multimodal user interfaces promise a natural and intuitive human machine interactions. But is the extra effort for the development of a complex multi-sensor system justified, or can users also be satisfied with one input modality already? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and use of multiple input modalities is valuable, and that it is difficult to predict the usability of individual input modalities for complex systems.

This paper presents laboratory devices on which experimental measurements were carried out to prove the validity of the assumption about the reduction of vibrations transmitted to the conveyor belt structure generated by the impact forces of falling material grains in the places of transfer or on the hoppers of conveyor belts. In order to limit damage to the conveyor belts caused by the impact of the sharp edges of material grains, conveyor belts are supported by impact rollers or impact rubber rods in the places of transfers and on the hoppers of belt conveyors. The flattened ends of the impact rollers are inserted into the grooves of steel brackets installed on the supports of the fixed conveyor idlers of a conventional design. In this paper, a special modification of the fixed conveyor idler is presented, which consists of inserting plastic brackets into the structurally modified roller axle holders of the fixed conveyor idler. Measurements were carried out on the first laboratory device, which showed that the specially modified fixed conveyor idler resulted in a higher damping of up to 15% of the impact forces of the falling weight on the rubberized hoop of the impact roller shell compared to the conventional fixed conveyor idler design. Measurements carried out on the second laboratory device show that the effective vibration velocity values detected at the points where the impact roller axis fits into the fixed roller table holder, i.e. points B and D, are higher than when using plastic brackets, up to 6% for a 108 mm diameter roller, compared to steel impact roller brackets.

In mining machines with friction discs, but also in multi-rope traction elevators, it is necessary to distribute the applied tensile load, generated by the weight of the cage and counterweight, evenly in all cross-sections of the load-bearing ropes. Hydraulic devices used for this purpose can operate on the principle of Pascal's law. The article presents a structural design, a 3D model and an implemented solution of a laboratory device capable of simulating a practical method of evenly distributing the total weight of the load into partial tensile forces of the same size acting on a selected number of load-bearing ropes. The laboratory equipment uses two pairs of three steel cables of finite length for the simulations. During the experimental measurements, tensile forces derived from the tractive force of the piston rods, pushed into the bodies of the hydraulic cylinders by the pressure of the hydraulic oil supplied through the pipeline under the pistons of the hydraulic cylinders, were detected. The resulting amount of hydraulic oil pressure in the hydraulic circuit influenced by different values of the hydraulic oil pressures in the hydraulic cylinders and by the pressure in the supply pipe was experimentally studied on the laboratory equipment. Simulations were also carried out in order to detect the hydraulic oil pressure in the hydraulic circuit caused by the change in the different magnitudes of the tensile forces in the ropes From the experiments carried out, it follows that with the appropriate choice of hydraulic elements and the design of the hydraulic circuit, the weight of the load, acting as the total pulling force in the ropes, can be evenly distributed (with a deviation of up to 5%) to all cross-sections of the load-bearing ropes. If the exact values of the hydraulic oil volumes under the pistons of all hydraulic cylinders are not known, it is not possible to calculate the pressure values in the hydraulic circuit when the valves of the hydraulic pipes are gradually opened.

Background Homologous recombination deficiency (HRD) evolved into a major diagnostic marker in high grade ovarian cancer (HGOC) predicting the response to poly (Adenosine diphosphate-ribose) polymerase inhibitors (PARPi) and also platinum-based therapy. In addition to HRD, the type of peritoneal tumor spread influences treatment response and patient survival; miliary type tumor spread has poorer predicted outcome than non-miliary type tumor spread. Methods Known methods for HRD assessment were adapted for our technical requirements and the Predictive-value Integrated Genomic Instability Score (PIGIS) for HRD assessment evolved as an outcome. PIGIS was validated in HGOC samples from 122 patients. We used PIGIS to analyze whether the type of tumor spread correlated with HRD status and whether this had an impact on survival. Results We demonstrated that PIGIS can discriminate HRD positive from HRD negative samples. Tumors with miliary tumor spread are HRD negative and have a very bad prognosis with a progression free survival (PFS) of 15.6 months and an overall survival (OS) of 3.9 years. However, HRD negative non-miliary spreading tumors in our cohort had a much better prognosis (PFS 35.4 months, OS 8.9 years), similar to HRD positive tumors (PFS 34.7 months, OS 8.9 years). Conclusions Our results indicate that in a predominantly PARPi naïve cohort the type of tumor spread might be a better predictor of response to platinum sensitivity than HRD. It remains to be determined, whether this also applies for sensitivity to PARPi.

A large diversity of epigenetic factors, such as microRNAs and histones modifications, are known to be capable of regulating gene expression without altering DNA sequence itself. In particular, miR-1 is considered the first essential microRNA in cardiac development. In this study, miR-1 potential role in early cardiac chamber differentiation is analyzed through specific signaling pathways. For this, we performed in chick embryos functional experiments by means of miR-1 microinjections into the posterior cardiac precursors -of both primitive endocardial tubes- committed to sinoatrial region fates. Subsequently, embryos were subjected to whole mount in situ hybridization, immunohistochemistry and RT-qPCR analysis. As a relevant nov-elty, our results revealed that miR-1 increases Tbx5, Gata4 and AMHC1, while this microRNA diminishes Mef2c expression, during early differentiation of cardiac sinoatrial region. Further-more, we observed in this developmental context that miR-1 upregulates CRABPII and RARß, and downregulates CRABPI, which are three crucial factors in retinoic acid signaling pathway. Interestingly, we also noticed that miR-1 directly interacts with HDAC4 and Calmodulin, as well as with Erk2/MAPK1, which are three key factors actively involved in Mef2c regulation. Our study shows, for the first time, a key role of miR-1 as an epigenetic regulator in early differenti-ation of cardiac sinoatrial region through orchestrating opposite actions between retinoic acid and Mef2c, fundamental to properly assign cardiac cells to their respective heart chambers. A better understanding of those molecular mechanisms modulated by miR-1 will definitely help in fields applied to therapy and cardiac regeneration and repair.

To advance probiotic research, a comprehensive understanding of bacterial interactions with human physiology at the molecular and cellular levels is fundamental. Lacticaseibacillus rhamnosus LGG® is a bacterial strain that has long been recognized for its beneficial effects on human health. Probiotic effector molecules derived from LGG®, including secreted proteins, surface-anchored proteins, polysaccharides, and lipoteichoic acids, that interact with host physiological processes have been identified. In vitro and animal studies have revealed that specific LGG® effector molecules stimulate epithelial cell survival, preserve intestinal barrier integrity, reduce oxidative stress, mitigate excessive mucosal inflammation, enhance IgA secretion, and provide long-term protection through epigenetic imprinting. Pili on the cell surface of LGG® promote adhesion to the intestinal mucosa and ensure close contact to host cells. Extracellular vesicles produced by LGG® recapitulate many of these effects through their cargo of effector molecules. Collectively, the effector molecules of LGG® exert a significant influence on both the gut mucosa and immune system, that promotes intestinal homeostasis and immune tolerance.

In order to comprehensively evaluate the employee satisfaction of mine occupational health and safety management system, an analytic method based on fuzzy analytic hierarchy process and 2- tuple linguistic model was established. Firstly, based on the establishment of 5 first- grade indicators and 20 second- grade ones, the weight of each indicator is calculated and validated by method of FAHP and root mean square model. Secondly, a path based on the time-ordered Weighted Averaging Operator (T-OWA) model is constructed. Finally, the model is validated by empirical analysis. The results demonstrate that the employee satisfaction of the mine occupational health and safety management system is of the “general” rank. The method including the comprehensive evaluation of employee satisfaction and the quantitative analysis of language evaluation information ensures the authenticity of the language evaluation information.

Induction heating is a fast, reproducible, and efficient heating method used in various manufacturing processes. However, there is no established additive manufacturing (AM) process based on induction heating using wire as feedstock. This study investigates a novel approach to AM based on inductive heating, where a steel wire is melted and droplets are detached periodically using a two-winding induction coil. The process parameters and energy input into the droplets are characterized. The induction generator exhibits sluggish response to the excitation voltage, resulting in a lag in the coil current. The process is captured using a high-speed camera, revealing regular droplet formation and uniform size and shape when operated within an appropriate process window. Larger drops and increased spatter formation occur outside this window. The proposed method allows for the production of droplets with almost spherical shapes. Further analysis and characterization of droplet formation and energy input provide insights into process optimization and overall efficiency.

End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational complexity, learned image compression methods inherently align with learning-based data processing and analytic pipelines due to their well-suited internal representations. The concept of Video Coding for Machines has garnered significant attention from both academic researchers and industry practitioners. This concept reflects the growing need to integrate data compression with computer vision applications. In light of these developments, we present a comprehensive survey and review of lossy image compression methods. Additionally, we provide a concise overview of two prominent international standards, MPEG Video Coding for Machines and JPEG AI. These standards are designed to bridge the gap between data compression and computer vision, catering to practical industry use cases.

Iron is an essential nutrient and necessary for biological functions from DNA replication and repair to transcriptional regulation, mitochondrial respiration, electron transfer, oxygen transport, photosynthesis, enzymatic catalysis, and nitrogen fixation. However, due to iron’s propensity to generate toxic radicals which can cause damage to DNA, proteins, and lipids, multiple processes regulate uptake and distribution of iron in living systems. Understanding how intracellular iron metabolism is optimized and how iron is utilized to regulate other intracellular processes is important to our overall understanding of a multitude of biological processes. One of the tools that the cell utilizes to regulate a multitude of functions is ligation of the iron-sulfur (Fe-S) cluster cofactor. Fe-S clusters comprised of iron and inorganic sulfur are ancient components of living matter on earth that are integral for physiological function in all domains of life. Here we will explore the ways in which the cell utilizes Fe-S clusters to sense the intracellular environment and respond to maintain equilibrium.

The African continent has the highest prevalence of hypertension globally, with South Africa reporting the highest prevalence in Southern Africa. While the influence of genetic variability in the pathogenesis of hypertension is well described internationally, limited reports are available for African populations. This study aimed to assess the association of genetic variants and essential hypertension in a cohort of two ethnic South African population groups. Two hundred and seventy-seven hypertensive and one hundred and seventy-six normotensive individuals were genotyped for 79 variants. Genotyping was performed using the Illumina GoldenGate Assay and allele-specific polymerase chain reaction. The association of variants was assessed using the Fisher Exact test under the additive and allelic genetic models, while multivariate logistic regression was used to predict the development of hypertension. Five variants (CYP11B2 rs179998, AGT rs5051 and rs699, AGTR1 rs5186 and ACE rs4646994) were significantly associated with essential hypertension in the cohort under study. Furthermore, AGTR1 rs5186 and AGT rs699 were identified as risk factors for the development of hypertension in both ethnic groups. In two ethnic South African populations an association was observed between Renin-Angiotensin-Aldosterone System (RAAS) related genes and the development of hypertension.

To fulfil a need of limitation automotive emissions the reduction of vehicle weight is widely recommended and achieved in many ways, both by the construction of individual elements of the vehicle, and by the selection of light materials including Al alloys. Connecting these elements with each other and with elements made of iron alloys can be realized, inter alia, by welding or stir welding. However, the quality of the welds obtained varies widely and depends on many design, operational and environmental factors. The present study focused on a review of various welding technics used to joint both similar and dissimilar Al alloys utilized in automotive industry, the effect of various process parameters on welds quality, and phenomena appeared in such welds..

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

Children are inherently curious about everything they encounter, making the early years a perfect time for STEAM (science, technology, engineering, arts, and math) investigations. An outdoor learning environment can influence STEAM concept formation of preschoolers with intentional design that offer STEAM learning affordances. Despite the rising interest in early STEAM education, there is still limited literature on how the outdoor environment may influence STEAM learning behaviors of preschoolers (3-5 years old). This scoping review intends to evaluate the existing knowledge regarding the physical factors that contribute to STEAM learning affordances in an outdoor environment for children aged 3 to 5. The review included studies from the last twenty years. This scoping review was conducted in accordance with the criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). For this scoping review, 843 citations were discovered across four databases (JSTOR, Scopus, EBSCOhost, and Web of Science), ProQuest, and Google Scholar, and 31 articles were considered eligible for inclusion. Through the synthesis of information from those 31 studies, a list of STEAM learning behaviors of children and STEAM activity-supportive settings were identified that may positively influence preschoolers' STEAM concept development.

HIV early detection (CD4 counts ≥350 cells/μL) is correlated with higher life expectancy among people living with HIV (PLHIV). Several factors including physical, cultural, structural, and financial barriers have contributed to HIV late detection. This is the first-of-its-kind study on the regional differences in early detection of HIV within Tajikistan and any country in the Central Asia region. Utilizing Tajikistan Ministry of Health's national HIV data (N=10,700) spanning 2010 to 2023, we developed median regression models with median CD4 cell count as the outcome and as predictors: time (years), region, age, gender, and area (urban/rural status). Individuals younger than 19 years old are more likely to be detected early for HIV, whereas those older than 39 years are more prone to late detection. Given their active engagement in age-related and sexual activities, those older than 39 years face a higher risk of HIV transmission to others if diagnosed late. Therefore, they require further attention and targeted interventions. Females are detected earlier compared to their male counterparts, regardless of region of residence. Rural populations are detected earlier in most years compared to their urban counterparts. The COVID-19 pandemic accelerated HIV early detection in 2021 but most regions have returned to near pre-pandemic levels of detection in 2022 and 2023. These fluctuations in early detection may signify a shift in attention and resource allocation, potentially affecting efforts to address access gaps during the initial year of the COVID-19 pandemic. There are significant health inequities among different demographic and geographic groups which warrant further attention.

This paper presents ROSGPT, an innovative concept that harnesses the capabilities of large language models (LLMs) to significantly advance human-robot interaction. We develop ROSGPT as a ROS2 package that seamlessly integrates ChatGPT with ROS2-based robotic systems. The core idea is to leverage prompt engineering with LLMs, specifically ChatGPT, utilizing its unique properties such as ability eliciting, chain-of-thought, and instruction tuning. The concept employs ontology development to convert unstructured natural language commands into structured robotic instructions specific to the application context through prompt engineering. We capitalize on LLMs' zero-shots and few-shots learning capabilities by eliciting \textit{structured} robotic commands from \textit{unstructured} human language inputs. To demonstrate the feasibility of this concept, we implemented a proof-of-concept that integrates ChatGPT with ROS2, showcasing the transformation of human language instructions into spatial navigation commands for a ROS2-enabled robot. This versatile concept can be easily adapted to various other robotic missions. ROSGPT serves as a new stride towards Artificial General Intelligence (AGI) and paves the way for the robotics and natural language processing communities to collaborate in creating novel, intuitive human-robot interactions. The open-source implementation of ROSGPT on ROS 2 is available on GitHub.

As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The successful integration of robots as domestic service providers in our lives requires them to possess efficient manipulation capabilities, provide effective physical assistance, and have adaptive control frameworks that enable them to develop social understanding during human-robot interaction. In this context, human factors, especially quantifiable ones, represent a necessary component. The objective of this paper is to conduct an unbiased review encompassing studies on human factors studied in research involving physical interactions and strong manipulation capabilities. We identified the prevalent human factors in physical human-robot interaction (pHRI), noted the factors typically addressed together and determined the frequently utilized assessment approaches. Additionally, we gathered and categorized proposed quantification approaches based on measurable data for each human factor. We also formed a map of common contexts and applications addressed in pHRI for a comprehensive understanding and easier navigation of the field. We found out that most of the studies in direct pHRI (when there is direct physical contact) focus on social behaviors with belief being the most commonly addressed human factor type. Task collaboration is moderately investigated, while physical assistance is rarely studied. In contrast, indirect pHRI (when the physical contact is mediated via a third item) studies often involve industrial settings, with physical ergonomics being the most frequently investigated human factor. More research is needed on human factors in direct and indirect physical assistance applications, including studies that combine physical social behaviors with physical assistance tasks. We also found that while the predominant approach in most studies involves the use of questionnaires as the main method of quantification, there is a recent trend that seeks to address quantification approaches based on measurable data.

With the emergence of new VR/AR technologies, the question arises of how they can be used in human-swarm interaction. Where are the limitations and boundaries of conventional interfaces, and how can VR/AR overcome them? To find out, a user study was conducted. The subjects were able to interact with an autonomously acting swarm of drones using both a conventional desktop interface (Desktop-HSI) and a newly developed VR interface (VR-HSI).The resulting assessments are used as a basis for determining the extent to which this new technology can be applied in human-swarm interaction. The technical basis was an already tested simulation software that was expanded with a network interface to allow interacting with a drone swarm using connected HSIs (Human-Swarm Interfaces). Standardized communication was emphasized to enable the development of HSIs with different requirements. Specifically for this work, a VR-HSI was developed with Unity and a desktop-HSI with Java and their performance regarding human-swarm interaction was evaluated.

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.

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Towards that goal we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant spikerbox, a device that measures changes in voltage differentials of plants between roots and leaves. Using machine learning, we find that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems, while also paving the way to use plants as biosensors for analyzing human movement.

In the realm of artificial intelligence, knowledge graphs have become a fascinating area of research. Relationships between entities are depicted through a structural framework in knowledge graphs. In this paper, we propose to build a domain-specific medicine dictionary (DSMD) based on the principles of knowledge graphs. Our dictionary is composed of structured triples, where each entity is defined as a concept, and these concepts are interconnected through relationships. This comprehensive dictionary boasts more than 348,000 triples, encompassing over 20,000 medicine brands and 1,500 generic medicines. It offers a groundbreaking approach to storing and accessing medical data. Our dictionary facilitates various functionalities, including medicine brand information extraction, brand-specific queries, as well as queries involving two words or question answering. We anticipate that our dictionary will serve a broad spectrum of users, catering to both human users, such as diverse range of healthcare professionals as well as AI applications.

SRC homology 3 (SH3) domains are critical interaction modules that orchestrate the assembly of protein complexes involved in diverse biological processes. They facilitate transient protein-protein interactions by selectively interacting with proline-rich motifs (PRMs). A database search revealed 298 SH3 domains in 221 human proteins. Multiple sequence alignment of human SH3 domains is useful for phylogenetic analysis and determination of their selectivity towards PRM-containing peptides (PRPs). However, a more precise functional classification of SH3 domains is achieved by constructing a phylogenetic tree only from PRM-binding residues and using existing SH3-PRP structures and biochemical data to determine the specificity within each of the 10 families for particular PRPs. In addition, the C-terminal proline-rich domain of the RAS activator SOS1 covers 13 of the 14 recognized proline-rich consensus sequence motifs, encompassing differential PRP pattern selectivity among all SH3 families. To evaluate binding capabilities and affinities, we conducted fluorescence dot blot and polarization experiments using 25 representative SH3 domains and various PRPs derived from SOS1. Our analysis has identified 45 interacting pairs with binding affinities ranging from 0.2 to 125 micromolar out of 300 tested and potential new interactors of SOS1. Furthermore, it establishes a framework to bridge the gap between SH3 and PRP interactions and provides predictive insights into potential interactions of SH3 domains with PRMs based on sequence specifications. This novel framework has the potential to enhance the understanding of protein networks mediated by SH3-PRM interactions and be utilized as a general approach for other domain-peptide interactions.

This paper proposes a metaphysical framework for distinguishing between human and machine intelligence. By drawing an analogy from Kant’s incongruent counterparts, it posits two identical deterministic worlds -- one comprising a human agent and the other comprising a machine agent. These agents exhibit different types of information processing mechanisms despite their apparent sameness in a causal sense. By postulating the distinctiveness of human over machine intelligence, this paper resolves what it refers to as “the vantage point problem” – namely, how to legitimize a determinist’s assertion of determinism by placing the determinist within the universe.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

The blood counts of α thalassemia carriers (α-thal) are similar to those of β thalassemia carriers, except for Hgb A2 that is not elevated. The objective of this study was to determine whether mathematical formulas are effective for detecting suspected α-thal. The data were obtained from the database of the prevention program for detecting couples at risk for having a child with hemoglobinopathy. RBC indices were analyzed using mathematical formulas and the sensitivity and negative predictive value (NPV) calculated. Among 1334 blood counts suspected α-thal analyzed, only the Shine&Lal and the Support Vector Machine formulas revealed high sensitivity and NPV. Sensitivity was 85.54 and 99.33% and NPV 98.93 and 99.93%, respectively. Molecular defects were found in 291, 81 had normal α genes. Molecular analysis was not performed in 962. Based on these results, mathematical formulas incorporating one of these reliable formulas for detecting suspected α or β thalassemia carriers in the program of the automatic analyzers can flag these results and increase the awareness of the primary physicians about the carrier risk and send an alert with a recommendation for further testing.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Bio-inspired gyroid triply periodic minimum surface (TPMS) lattice structures have been the focus of research in automotive engineering because they can absorb a lot of energy and have wider plateau ranges. The main challenge is determining the optimal energy absorption capacity and accurately capturing plastic plateau areas using finite element analysis (FEA). Using nTop's Boolean subtraction method, this study combined walled TPMS gyroid structures with normal TPMS gyroid lattice. This made a composite TPMS-gyroid lattice with relative densities ranging from 14% to 54%. Using the ideaMaker software and the fused deposition modeling (FDM) Raise3D Pro 2 3D printer to print polylactic acid (PLA) bioplastics in 1.75 mm filament made it possible to slice computer aided design (CAD) models and fabricate 36 lattices samples precisely layer by layer technique. Shimadzu 100kN testing equipment was utilized for the mechanical compression experiments. And the validation using finite element analysis (FEA). Further, CTG was examined using a field emission scanning electron microscope (FE-SEM) before and after compression testing. The composite TPMS gyroid lattice showed potential as shock absorbers for vehicles with relative densities of 33%, 38%, and 54%. The Gibson-Ashby model showed that the composite TPMS gyroid lattice deformed mainly by bending, and the size effect was seen when the relative densities were less than 15%. The lattice's relative density had a significant impact on its ability to absorb energy. The research also explored the use of these innovative foam-like composite TPMS-gyroid lattices in high-speed crash box scenarios, potentially enhancing vehicle safety and performance.

Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

Autonomous systems and intelligent machines are involved in almost all areas of human activity and they are now more and more present in our everyday life. The reason for this extensive use certainly resides in all the benefits these machines offer to the users. In experimental settings, numerous studies have demonstrated the positive effects that the introduction of autonomous systems have on human decision-making and performance. However, studies have shown in addition that the introduction of these systems can have important negative effects as well. Considering that autonomous systems are now introduced in sensitive domains like the military or medicine, we need more than ever a comprehensive understanding of the effects they cause on human performance and decision-making, and particularly in tasks and contexts with a social or moral dimension. The aim of this narrative review is threefold. First, we will provide an overview of the main effects on a human agent’s decision-making and performance produced by the introduction of autonomous systems. Second, we will review the conditions identified as underlying factors of these effects, and see how current models of human – autonomous systems interaction integrate those conditions. Third, we will conclude this review by highlighting new directions for future investigations.

There is a need to develop tools for mastitis management in goats and to measure the effects of milking machines on teats. Infrared thermography (IRT), as shown in cows, was a good candidate for early detection of mastitis and focusing on milking equipment and settings implicated in potential problems. The aim of this study was to test IRT to detect udder inflammation and the effects of mechanical milking on teats in relation to inflammation status, udder balance and teat shape in Alpine goats. IRT spectra were compared before and after milking in 551 goats from three commercial herds compared to their individual SCC (somatic cell count). We found no regression or trend between logSCC and IRT measurement or response to milking, even in highly inflamed goat udders. The effect of milking was significant (P

Gastric inflammation-related disorders are commonly observed digestive system illnesses char-acterized by the activation of proinflammatory cytokines, particularly tumor necrosis factor-α (TNF-α). This results in the induction of cyclooxygenase-2 (COX-2)/prostaglandin E2 (PEG2) and matrix metallopeptidase-9 (MMP-9). These factors contribute to the pathogenesis of gastric in-flammation disorders. We examined the preventive effects of Lonicera japonica Thunb. ethanol ex-tract (Lj-EtOH) on gastric inflammation induced by TNF-α in normal GES-1 cells. The GES-1 cell line was used to establish a model that simulated the overexpression of COX-2/PGE2 and MMP-9 proteins induced by TNF-α, to examine the anti-inflammatory properties of Lj extracts. The re-sults indicated that Lj-EtOH exhibits significant inhibitory effects on COX-2/PEG2 and MMP-9 ac-tivity, attenuates cell migration, and provides protection against TNF-α-induced gastric inflam-mation. The protective effects of Lj-EtOH are associated with the modulation of COX-2/PEG2 and MMP-9 through activation of TNFR–ERK 1/2 signaling pathways as well as the involvement of c-Fos and nuclear factor kappa B (NF-κB) signaling pathways. Based on our findings, Lj-EtOH ex-hibits a preventive effect on human gastric epithelial cells. Consequently, it may represent a novel treatment for the management of gastric inflammation.

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

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

Conventional sensors used to measure properties such as deformation or pressure, etc. often make use of metals, ceramics, piezos, and other materials. Many of those materials are very hard, so if they are used as sensors, when the object is deformed, or when the pressure on the object changes from time to time, it is necessary to prepare a variety of sensors with different properties. In this experiment, a dielectric elastomer (DE) pressure sensor was fabricated using a hydrogenated nitrile rubber (HNBR) film with improved hardness and elongation. With this, even very thin film (0.2mm) can be measured at any pressure between 1gf and 20kgf. Previously developed film was harder and less stretchy than the current one, with a pressure detection width from 4 kgf to 120 kgf. This time, however, the pressure sensitivity of the finger has been made more sensitive, making it possible to measure even smaller values. A DE stretch sensor was also developed using single-walled carbon nanotube (SWCNT) electrodes with larger stretchability and flexibility, utilizing this HNBR. This greatly improved the mechanical flexibility and stretchability of the DES. Using this stretch sensor, it became possible to sense the motion of the robot's fingers, and to sense the force (pressure) when the fingertip touched the target object with the DE pressure sensor. In addition, it was confirmed that the sensation of the robot's finger touching an object can be fed back to a human finger by using a small diaphragm type vibrator DE actuator.

The review scrutinizes the currently performed research on the new methods for enhancing bituminous binder performance through radiation and radical grafting of polymer modifers of bitumen. It investigates innovative methods, including using waste polymers as modifiers, and applying radiation for polymer grafting, to overcome challenges like high costs, low aging resistance and storage stability issues, of which separation of phases polymer/bitumen is the most significant obstacle. These advanced modification techniques promise sustainability through the decrease of the carbon footprint of transportation systems by improving the properties and durability of binders. Additionally, the review discusses the parameters and mechanistic aspects from a scientific perspective, shedding light on the underlying processes that contribute to the improved performance of modified bituminous binders.

In the present study we introduce a deterministic mathematical model in order to study the interaction between the human immune system and a virus, like COVID 19. The mathematical analysis is based on the tools of dynamical systems theory, by modeling the interactions between the immune system and the virus, using a predator-prey method and following the ideas of G. Moza, from \cite{TGV}. It will be obtained some conclusions with medical relevance and the main three of them are the followings: 1) A deficiency of a single type of immunity in the early stages of virus proliferation, may lead to a large virus multiplication and the human body can loses the fight against this virus; 2) If the level of all components of the immunity system are at the normal threshold from the first moment of the infection and the immune system kill the virus at higher rate than the rate of virus reproduction, then the human body has the ability to stop the multiplication of the virus and liquidate it, that means the virus will be destroyed; 3) If the level of at least one type of immunity can be increased beyond the normal threshold, by medical interventions (like vaccination) in the early stages of virus infection, then the immune system has a better chance to win the fight with the virus.

Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data is made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

The objective to fast-track the mapping and registration of large numbers of unrecorded land rights globally, leads to the experimental application of Artificial Intelligence (AI) in the domain of land administration, and specifically the application of automated visual cognition techniques for cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems within Object Based Image Analysis (OBIA), as opposed to human analysis, to extract visible cadastral boundaries from Very high resolution (VHR) World View-2 image, in both rural and urban settings. From our experiments, machine-based techniques were able to automatically delineate a good proportion of rural parcels with explicit polygons where the correctness of the automatically extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for machine, as against 70.4% for humans. On the contrary, in the urban area, automatic results were counterintuitive: even though urban plots and buildings are clearly marked with visible features such as fences, roads and tacitly perceptible to eyes, automation resulted in geometrically and topologically poorly structured data, that could neither be geometrically compared with human digitised, nor actual cadastral data from the field. These results provide an updated snapshot with regards to the performance of contemporary machine-drive feature extraction techniques compared to conventional manual digitising.

Certain micronutrients exhibit immunomodulatory effects. However, no intervention has yet investigated the effect of an individualized supplementation on the severity of upper respiratory tract infections (URI). Therefore, we investigated whether a personalized supplementation moderates the incidence and severity of URI. Selenium, zinc, and vitamin D was measured from 59 healthy participants ) in dried blood spots. Accordingly, a personalized supplement was provided with or without the respective micronutrients. We used WURSS-21 questionnaires to assess the disease status. The blood values converged during the intervention and micronutrients no longer differed at the end of the intervention period between treated and untreated volunteers. The incidence and severity of the illness did not significantly differ between the groups. However, when analyzing the WURSS-21 score by the intention to treat, the initially randomized treatment arm revealed a significantly higher score than the placebo arm. Upon acute administration, individualized combination of selenium, zinc and vitamin D does not reduce the number or contribute to a milder course of URIs. Therefore, supplementation in acute infectious situations seems questionable. Further studies must address the habitual diet in more detail to better understand the impact of individual micronutrient status on the prevention of URI.

This paper presents a multi-year research study conducted at the University of Toledo, aimed at improving human-machine teaming (HMT) methods and technologies. With the incorporation of artificial intelligence (AI) into 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened a path for the new area of HMT. When it comes to crucial tasks such as patient treatment and care, defense, and industrial production, the use of non-standardized HMT technologies may pose a risk to human lives as well as up to billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we aim to address the lack of HMT design and development standards. We propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and human team. Followed by processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method using two controlled HMT scenarios; first, emergency care provider (ECP) training, and second, patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. As part of these HMT simulation studies, the impact of each parameter related to machines, humans, and HMT (such as errors made by humans and by the VBSA) is presented from the perspective of performance, rules, roles, and operation limitations. In addition, based on the analysis of results from two case studies, we presented guidelines for designing future HMT. Our conclusions indicate that the proposed HIL can be used to perform standardization studies in the pursuit of developing techniques for benchmarking HMT that can be used in critical situations.

With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

The goal of the paper is to clarify the observed irrationality of decision making in conflict situations considered as one-step games of two players. To solve such situations, we consider the asymmetry in the relation of the players to their own rewards and the rewards of the opponents. Formalization of the decision-making process is based on recently developed non-commutative operators of multivalued logic algebra. The suggested method is applied to solve the well-known Prisoners’ dilemma game and the other situations of conflict, where it results in the expected strategies.

Older adults with obesity have an increased risk for cognitive decline and subsequent Alzheimer’s disease. Previous research has focused on the impact of individual nutrients and overall dietary patterns to improve cognitive function. The Brain and Body Health (BB-Health) study aims to demonstrate the feasibility of a multicomponent nutritional supplement (MCNS) and weight loss (WL) intervention for individuals with overweight and obesity who are not consuming a healthy diet. BB-Health is a parallel-group randomized placebo-controlled 2x2 factorial pilot trial comparing the MCNS, WL, and MCNS+WL with a Control group. Eligible participants are 55 to 85 years of age with a body mass index 27.0 to 39.9 kg/m2. Major exclusion criteria include type 2 diabetes, moderate or severe cognitive impairment, and self-reported healthy dietary pattern. The BB-Health feasibility trial is registered as NCT05542199 at clinicaltrials.gov. The BB-Health objectives are to report: 1) recruitment and enrollment, including pace and descriptive statistics of the study sample; 2) adherence to the administered interventions; 3) successful data collection and performance of outcome assessments, including evaluation of change in key outcomes over time that are powered for this pilot study; and 4) summary statistics on outcomes for use in planning a subsequent, larger trial. The outcomes of interest are age-sensitive cognitive assessments, cerebral hemodynamics, and related health parameters. All measurements are collected at baseline, 3-, 6-, and 12-month visits. If the BB-Health protocol demonstrates feasibility, the novel MCNS with and without a parallel WL intervention has potential to be administered in a confirmatory randomized trial to evaluate ability to prevent age-related cognitive decline and improve brain health. The BB-Health Feasibility Study could significantly contribute to our understanding of the influence of diet and weight management on cognitive health

The discrete element method (DEM) coupled with computational dynamics (CFD) has been considered one of the most sensitive ways of studying fluidized beds. This paper presents gas-solid fluidized bed simulations by use of CFD-DEM. The two-phase interaction is calculated in a way of decoupling. Within the effective neighborhood of the particles, the kernel approximation method is used to calculate the local porosity, thereby calculating the drag force on the particles. At the grid scale, the momentum exchange coefficient is calculated using a two fluid model based method, thereby calculating the source term of the phase interaction in the fluid control equation. The simulated form, size and motion process of the big bubble are all consistent with experimental observations. The simulated solid volume fraction and relative pressure averaged in a horizontal plane at 45 mm above the bottom are in reasonable agreement with the experimental data. The fluctuation of the simulated bed height over time is similar to the experimental measurement results. The reproduction of these qualitative and quantitative results indicates that the proposed method effectively improves simulation performance and accuracy.

The human mandible's cancellous bone, which is characterized by its unique porosity and directional sensitivity to external forces, is crucial for sustaining biting stress. Traditional CAD models fail to fully represent bone's anisotropic structure and thus depend on simple isotropic assumptions. For our research, we use the latest versions of nTopology and Creo Parametric software to make biomimetic Voronoi lattice models that accurately reflect the complex geometry and mechanical properties of trabecular bone. The porosity of human cancellous bone is accurately modeled in this work using biomimetic Voronoi lattice models. The porosities range from 70% to 95%, which can be achieved by changing the pore sizes to 1.0 mm, 1.5 mm, 2.0 mm, and 2.5 mm. Finite element analysis (FEA) was used to examine the displacements, stresses, and strains acting on dental implants with a buttress thread, abutment, retaining screw, and biting load surface. The results show that the Voronoi model accurately depicts the complex anatomy of the trabecular bone in the human jaw, compared to standard solid block models. The ideal pore size for biomimetic Voronoi lattice trabecular bone models is 2 mm, taking into account both the von Mises stress distribution over the dental implant, screw retention, cortical bone, cancellous bone, and micromotions. This pore size displayed balanced performance by successfully matching natural bone's mechanical characteristics. Advanced finite element analysis (FEA) improves biomechanical understanding of how bones and implants interact by creating more accurate models of biological problems and dynamic loading situations. This makes biomechanical engineering better.

In current efforts to digitize manufacturing and move it into the fourth stage of the industrial revolution, a wide range of integration solutions is being considered to enable manufacturing to adapt to change. In transforming a factory into a self-organized, autonomous factory, companies are currently struggling with rapidly changing requirements and production factors, among other things. This is a particular problem for the human being as an actor within the factory, as the amount of new technologies and protocols increases the training effort. Proprietary interfaces of the control providers, a wide range of different communication protocols, complicate the understanding of the production processes, the evaluation and testability of new use cases and increase the danger of creating silos of knowledge as well as building collaboration barriers. As a solution to these problems, we propose an open software platform and define a way to model use case driven domain specific asset representation (DSA) that focuses on the human being and his needs for representing the factory in a way that it meets his requirements for the current production needs. We therefore conducted research on google scholar on human factors in industry 4.0 and used technologies as well as already existing platforms and their architecture.

This paper introduces an intelligent model that combines military expertise with the latest ad-vancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes. The model is based on direct input and val-idation from field decision-makers for their practical applicability and effectiveness. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes mili-tary-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algo-rithmic precision for maximal safety and effectiveness in demining

We review our theory of interdependence applied to autonomous human-machine teams, with which we have made several generalizations based on theory that have led to new discoveries, compared to social science which is struggling to break free of its validation crisis, but also has no generalizations to new discoveries with human teams, let alone to human-machine teams. In this invited article, based around the stability of a team, of our discoveries, we review the uncertainty between a team's structure and its performance, our first finding. We generalize that finding to deception, vulnerability, innovation and fittedness. From there, our next generalization is to the quantum likeness of interdependence and quantum mechanics; we move on to Shannon holes, knowledge, and conclude with the value of debate and the need for boundaries to contain debate, to reduce interference, and to protect states of interdependence. After the introduction, we extend our model in Section 2 to energy and time, and we close with a brief review of innovation and future plans.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Tuberculosis (TB) remains a major public health threat globally, especially in sub-Saharan Africa. Both human and Mycobacterium tuberculosis (MTBC) genetic variation affect TB outcomes, but few studies have examined if and how the two genomes interact to affect disease. We hypothesize that long-term coexistence between human genomes and MTBC lineages modulate disease to affect its severity. We examined this hypothesis in our TB household contact study in Kampala, Uganda, in which we identified 3 MTBC lineages of which one, L4.6-Uganda, is clearly derived and hence recent. We quantified TB severity using the Bandim TBscore and examined the interaction between MTBC lineage and human single nucleotide polymorphisms (SNPs) genome-wide, in two independent cohorts of TB cases (N=149 and N=127). We found a significant interaction between a SNP in PPIAP2 and the Uganda lineage (combined p=4x10-8). PPIAP2 is a pseudogene that is highly expressed in immune cells. Pathway and eQTL analyses indicated potential roles between coevolving SNPs and cellular replication and metabolism as well as platelet aggregation and coagulation. This finding provides further evidence that host-pathogen interactions affect clinical presentation differently than host and pathogen genetic variation independently, and that human-MTBC coevolution is likely to explain patterns of disease severity.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

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

Cancer cells require substantial amounts of energy and substrates for their metabolic hyperactivity, enabling the synthesis of new cells at the expense of healthy ones. Preliminary in vitro data suggest that a mix of free essential amino acids (EAA-mix) can promote cancer cell apoptosis by enhancing autophagy. This study aimed to confirm, both in vitro and in vivo, whether EAA intake could influence the development of colon cancer in mice. We investigated changes in cancer proliferation in CT26 cells treated with EAA-mix, and in mice fed with EAA rich modified diets (EAARD) as compared to those on a standard laboratory diet (StD). CT26 cells were injected subcutaneously (s.c.) or intraperitoneally (i.p.). After 21 days, tumors were removed and measured. In vitro data corroborated that EAA-mix impairs cancer growth by inducing autophagy and apoptosis. In vivo data revealed that mice on StD developed significantly larger (s.c.) and more numerous (i.p.) cancers than those on EAARD. EAA administration appears to influence cancer cell survival with notable anti-proliferative properties.

Polypharmacy and malnutrition are common health issues among older adults. In older people, there is a high prevalence of polypharmacy and malnutrition, and preliminary data also indicates that there is a significant of Food Supplements (FS) use, increasing the risk of duplication of thera-pies and several adverse reactions and drug-FS and FS-FS interactions. The intervention of health professionals in mitigating these risks is essential. This review not only underscores and discusses the potential association between polypharmacy, malnutrition, and food supplements but also presents current scientific evidence on polypharmacy, malnutrition, and the use of food supplements by the elderly. The work reviews the challenges, advantages and risks of using FS for the elderly who are polymedicated and/or malnourished, focusing on the good practices needed to support healthy ageing. In this regard, the paper intends to help health professionals better deal with the issue of polypharmacy and the use of multiple FS to overcome the malnutrition problem and improve the health and well-being of older adults.

In this paper, a seamless connection between sensor technology and machine learning (ML) has been presented which has wide applicability in rehabilitation and physical medicine. The intent is to identify an ML algorithm which has a better performance, easy to deploy, and has less training time and consumes less computational resources. In short, we propose an algorithm which maximizes the synergy between a sensor and ML by maximizing classification performance and easy to do implementation, which is robust in computational power and lower in resource consumption. The premise of the work is to show the important considerations for evaluating ML algorithms for Upper-limb activity recognition for Physical medicine and rehabilitation field.

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic applications. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable products without unnecessary heavy and uncomfortable electronic devices.

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

In this paper it will be considered three deterministic models for the study of the interaction between the human immune system and a virus: the logistic model, the Gompertz model, and the generalized logistic model (or Richards model). A qualitative analysis of this models based on dynamical systems theory will be performed, by studying the local behavior of the equilibria and obtaining local dynamics properties from the linear stability point of view. Also, we will compare this models in order to understanding which is more appropriate to models the interaction between the human immune system and a virus. It will be obtained some natural medical interpretations which are available for all three models and can be useful to the medical community.

Abstract: The VSI represents a significant advancement in the assessment of gastrointestinal-specific anxiety among patients with Irritable Bowel Syndrome (IBS) and other Chronic Inflammatory Bowel Diseases (IBD). However, an Italian version of the instrument is not yet available for the reference population. This study utilized a national sample of 500 individuals, including healthy controls and patients with Ulcerative Colitis and Crohn's Disease, to test the validity and reliability of the Italian VSI. Using back-translation methodology to ensure translation fidelity, the research applied a questionnaire and the VSI through an online format to 500 participants. Data analysis revealed that the Italian VSI had excellent psychometric properties, demonstrating high internal consistency (Cronbach’s α = 0.95) and construct validity. The scale proved sensitive in detecting significant differences in visceral sensitivity among groups, highlighting its utility as a clinical and research assessment tool. Specifically, the Italian VSI exhibited a unidimensional factorial structure and maintained a strong correlation with other measures of anxiety and the severity of gastrointestinal symptoms, confirming its role in enhancing the understanding and management of IBS and IBD in Italy.

In this paper, a novel permanent magnet-assisted synchronous reluctance machine (PMASynRM) with rare-earth PMs and ferrite magnets is proposed. The performance of PMASynRM is discussed with respected to the different magnet ratio of rare-earth PMs and ferrite magnets. Some characteristics including the flux density, output torque, cogging torque, output power, power factor, torque ripple, loss, efficiency, and demagnetization are calculated by 2-D finite element analysis (FEA). The analysis results show that the excellent performance can be obtained by using hybrid magnet of rare-earth PMs and ferrite magnets with the suitable magnet ratio, and provide some desirable cost-performance trade-off.

Glycosphingolipids (GSL) represent a highly heterogeneous class of lipids with many cellular functions, implicated in a wide spectrum of human diseases. Their isolation, detection, and comprehensive structural analysis is a challenging task due to the structural diversity of GSL molecules. In this work, GSL subclasses are isolated from human plasma using an optimized monophasic ethanol–water solvent system capable to recover a broad range of GSL species. Obtained deproteinized plasma is subsequently purified and concentrated by C18-based solid-phase extraction (SPE). The hydrophilic interaction liquid chromatography (HILIC) coupled to electrospray ionization linear ion trap tandem mass spectrometry (ESI-LIT-MS/MS) is used for GSL analysis in the human plasma extract. Our results provide an in-depth profiling and structural characterization of glycosphingolipid and some phospholipid subclasses identified in the human plasma based on their retention times and the interpretation of tandem mass spectra. The structural composition of particular lipid species is readily characterized based on the detailed interpretation of MS and MS/MS spectra and further confirmed by specific fragmentation behavior following predictable patterns, which yields to the unambiguous identification of 154 GSL species within 7 lipid subclasses and 77 phospholipids representing the highest number of GSL species ever reported in the human plasma. The developed HILIC-ESI-MS/MS method can be used for further clinical and biological research of GSL in the human blood or other biological samples.

The dynamic behaviour of a machine tool (MT) directly influences the machining performances. The adoption of lightweight structures may reduce the effects of undesired vibrations and increasing the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidate to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to damp mechanical vibrations. In this way, starting by the kinematic model of a milling machine that highlights the main critical factors, this study paper evaluates a number of prototypes made of Al Foam sandwiches (AFS), Al Corrugated sandwiches (ACS) and materials reinforced by carbon fibres (CFRP). A set of prototypes has been fabricated, represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been evaluated by using both FE simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performances. In particular, the AFS prototype highlighted a damping coefficient that is 20 times greater than a conventional ram (e.g. steel). The CFRP structure is able to satisfy the machining requirements with a reduced weight of 48.5%, while the ACS prototype showed a good trade-off between stiffness and damping.

The effects of CO₂-water-rock interactions on the mechanical properties of shale are essential for estimating the possibility of sequestrating CO₂ in shale reservoirs. In this study, uniaxial compressive strength (UCS) tests together with an acoustic emission (AE) system and SEM & EDS analysis were performed to investigate the mechanical properties and microstructural changes of black shales with different saturation times (10 days, 20 days and 30 days) in water dissoluted with sub-/super-critical CO₂. According to the experimental results, the values of UCS, Young’s modulus and brittleness index decrease gradually with increasing saturation time in water with sub-/super-critical CO₂. Compared to intact samples, 30-days’ saturation causes reductions of 56.43% in UCS and 54.21% in Young’s modulus for sub-critical saturated samples, and 66.05% in UCS and 56.32% in Young’s modulus for super-critical saturated samples, respectively. The brittleness index also decreases drastically from 84.3% for intact samples to 50.9% for samples saturated in water with sub-critical CO₂, to 47.9% for samples saturated in water with super-critical carbon dioxide (SC-CO₂). SC-CO₂ causes a greater reduction of shale’s mechanical properties. The crack propagation results obtained from the AE system show that longer saturation time produces higher peak cumulative AE energy. SEM images show that many pores occur when shale samples are saturated in water with sub-/super-critical CO₂. The EDS results show that CO₂-water-rock interactions increase the percentages of C and Fe and decrease the percentages of Al and K on the surface of saturated samples when compared to intact samples.

The well-being and sociability of individuals have always been part of modernity. The development of new technologies that meet these aspirations is receiving increasing attention. Thus, strengthening the desired objectives of these tools and minimizing their undesirable side effects is the subject of a growing commitment. The present contribution aims, in this context, to evaluate and analyze the desired and undesirable effects of the interaction of electromagnetic fields with living tissues in general. This involves routines reinforcing expected functions as well as those controlling and protecting against undesirable effects. First, the interactions of electromagnetic fields with tissues are analyzed, involving their thermal biological effects of desired and undesired exposures. The roles of blood and sap fluids in bio-affected tissues are then analyzed. Secondly, the governing electromagnetic, bio-heat equations, and their coupled solution are studied. Third, the thermal behavior of tissues and the exposure adverse effects are examined. Next, monitoring and defending of the effects of exposures are discussed. This contribution illustrates and discusses mathematical modeling routines based on a generalized interaction of electromagnetic fields with living tissues.

Much research has been conducted to determine how hair regeneration is regulated, as this could provide therapeutic, cosmetic, and even psychological interventions for hair loss. The current study focused on the hair growth effect and effective utilization of fatty oil obtained from Bryde's whales through high-throughput DNA microarray approach in conjunction with immunohistochemical observations. The research also examined the mechanisms and factors involved in hair growth. In an experiment using female C57BL/6J mice, the vehicle control group (VC: propylene glycol: ethanol: water), the positive control group (MXD: 3% minoxidil), and the experimental group (WO: 20% Whale Oil) were topically applied to the back of the mouse. The results showed that 3% MXD and 20% WO were more effective than VC in promoting hair growth, especially 20% WO. Furthermore, in hematoxylin and eosin-stained skin tissue, an increase in the number of hair follicles and subcutaneous tissue thickness was observed with 20% WO. Whole-genome transcriptomic analysis also showed that 20% WO may have lower stress- and inflammation-related responses than 3% MXD. Therefore, whale oil can be expected to be used as a safe hair growth agent.

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

Maternal type 2 diabetes mellitus (T2DM) has been shown to result in fetal programming of the hypothalamus-pituitary-adrenal (HPA) axis, leading to adverse fetal outcomes. T2DM is preceded by prediabetes and shares similar pathophysiological complications. However, no studies have investigated the effects of maternal prediabetes on fetal HPA axis function and postnatal offspring development, hence this study. Pre-diabetic (PD) and non-pre-diabetic (NPD) female Sprague Dawley rats were mated with non-prediabetic males. After gestation, male pups born from the PD and NPD groups were collected. Markers of HPA axis function, adrenocorticotropin hormone (ACTH) and corticosterone were measured in all dams and pups. Glucose tolerance and expression of mineralocorticoid (MR) and glucocorticoid (GR) receptors were further measured in all pups at birth and their developmental milestones. The results demonstrated increased basal concentrations of ACTH and corticosterone in the dams from the PD group by comparison to NPD. Furthermore, the results show an increase in pups ACTH and corticosterone concentrations, impaired glucose tolerance and dysregulated MR and GR expression in the PD group at all developmental milestones. These observations reveal that pregestational prediabetes is associated with maternal dysregulation of the HPA axis, impacting offspring HPA axis development along with impaired glucose handling.

Huperzine A is an herbal reversible inhibitor of Acetylcholinesterase (AChE). A molecular docking analysis on Huperzine A molecule has been carried out to understand its structure, conformational flexibility, intermolecular interaction and the binding affinity in the active site of AChE enzyme. Further, the charge density distribution of huperzine A molecule (lifted from the active site of AChE) was determined from the high level quantum chemical calculations coupled with charge density analysis. The binding affinity of Huperzine A towards AChE was calculated from the molecular docking; the lowest docked energy is -8.46 kcal/mol. In the active site, huperzine A molecule interacts with acyl binding pocket-Phe330 of AChE, that is, the bicyclo ring group of huperzine A forms an intermolecular interaction with the oxygen atom of main chain of the amino acid residue Phe330 at the distances 3.02 and 3.25 Å respectively. On the other hand, a gas phase study on huperzine A molecule also performed using HF and DFT (B3LYP) methods with the basis set 6-311G**. The molecular structure, conformation, and the charge density distribution of huperzine A molecule in the gas phase have determined using quantum chemical calculations and the charge density analysis. The comparative studies between the gas phase and the active site forms of huperzine A molecule, explicitly reveals the degree of conformational modification and the charge density redistribution of huperzine A when present in the active site. The dipole moment of the molecule in the active site is 6.85 D, which is slightly higher than its gas phase value (5.91 D). The electrostatic potential (ESP) surface of active site molecule clearly shows the strong electronegative and positive ESP regions of the molecule, which are the expected strong reactive locations of the molecule.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

This study investigates the relationship between Corporate Social Responsibility (CSR) dimensions and employees' satisfaction and retention for sustainability in banks. Carroll (1991) proposed a theoretical approach that consists of four components (economic, legal, ethical, and philanthropic) to analyze CSR activities and their effects on employee’s satisfaction and retention in the company. Simple random sampling was used to get the information from 221 participants. The entire form of the dataset is utilized to execute regression and correlation analysis using SPSS. In order to find out the relationship between economic, legal, ethical, and philanthropic factors and employee's satisfaction and retention, regression beta coefficient and correlation coefficients were used to analyze. This study also examines the relationship between job satisfaction and intentions to retain with an organization. The findings demonstrate that the CSR aspects of ethical and philanthropic have a considerable and favorable influence on employee's satisfaction. The outcome also demonstrates a good and prominent influence of legal CSR on the satisfaction of employee's to retain with the firm. Moreover, this study demonstrates that economic aspect of CSR has no significant impact on employee's retention and satisfaction. Correlation analysis depicts that economic CSR is positively and significantly connected with employee's retention and satisfaction. This research came to the conclusion that enhancing employees view regarding CSR activities such as economic, legal, ethical, and philanthropic will increase employee's satisfaction. Therefore, executives and managers in the banks should take steps to influence how employees see CSR areas in order to raise employee's satisfaction and retention in the banks for sustainability.

Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

Background: The study aimed to investigate the association between environmental tobacco smoke exposure and the risk of wheezing among rural and urban preschool children in Mpumalanga province, South Africa, an area associated with poor air quality. Methods: In this study, parents/caregivers of preschool children (n=3145) completed a modified International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. Data were analysed using multiple logistic regression models. Results: The overall prevalence of Wheeze ever was 15.14%, with a higher prevalence in urban pre-schoolers than rural pre-schoolers (20.71% vs 13.30 %, P<0.000). Moreover, the total prevalence of asthma ever was 2.34%. The prevalence was greater in urban pre-schoolers than in rural pre-schoolers (3.92% vs 1.81%, P<0.001). In the final adjusted model, both urban and rural area children who lived with one or more people who smoked in the same household (WE: OR 1.44, 95% CI 1.11-1.86) (CW: OR 2.09, 95% CI 1.38-3.16) and (AE: OR 2.49, 95% CI 1.12-5.54) were found to have an increased likelihood of having Wheeze Ever, Current Wheeze and Asthma Ever as compared to those who lived with non-smokers. Conclusions: The implementation of smoking limits and prohibition is crucial in areas that are frequented or utilized by children. Hence, it is imperative for healthcare providers to actively champion the rights of those who do not smoke within the society, while also endorsing legislative measures aimed at curtailing the extent of tobacco smoke exposure.

The aim of this study was to examine the oxidation of human serum albumin (HSA) caused by oxidative stress after an exhaustive exercise such as ultra-marathon race. Thus, blood samples from 12 adult runners who underwent a 103 km mountain ultra-marathon race were collected pre- and 24, 48 and 72 h post race. HSA was partially purified using affinity chromatography and then was subjected to Western blot analysis for disulfide dimers determination, indicating oxidation. The results were correlated with those from a previous study in which the same samples were analyzed using different oxidative stress markers and a good correlation with protein carbonyls (PC) at all time points was observed. Moreover there was a significant correlation with static oxidation-reduction potential (sORP) at 24 h, and a negative correlation with capacity oxidation-reduction potential (cORP) at 24 and 48 h. In addition, an individual analysis of albumin dimers exhibited great inter-individual differences. This inter-individual variability in the oxidation of HSA may suggest different interventions (e.g. through diet) in order to confront the effects on athletes’ organism after a strenuous exercise. In conclusion, this study supported the importance of the assessment of albumin dimers as a predictive marker for exercise-induced oxidative stress.

The incidence of liver diseases is on the leading rise worldwide, but there is a lack of exact research models mimic liver diseases. Using the normothermic machine perfusion (NMP) platform to study diseased livers recovered from the liver transplantation (LT) recipients seems promising. Therefore, the aim of this study was to establish the first machine perfusion system called Life-XDL to cultivate diseased liver. Four diseased livers recovered from LT recipients within different diagnoses were collected. The diseased livers were connected to the Life-XDL machine perfusion system that circulated Life-XDL perfusate providing oxygen and nutrients. The pressure and flow of the device was recorded and blood gas analysis and laboratory test of perfusate and bile was examined to analyze the function of the diseased livers. Liver tissues after perfusion were collected for histological analysis. Experiments showed that Life-XDL kept the diseased livers at the physiological state with providing stable hemodynamics and suitable partial pressure of oxygen and carbon dioxide. The results of blood gas analysis and laboratory test results demonstrated restoration and maintenance of metabolism and less damage. Moreover, the bile production of case 1, 3 and 4 diseased livers represented its vivid functionality. The pathological characteristics were kept stable before and after NMP. Overall, we successfully establish the Life-XDL machine perfusion system which specially for diseased human whole liver and develop a new diseased human whole organ model in vitro through normothermic machine perfusion that recapitulate the main features of the human liver disease, which showing great promise as disease modeling platforms.

Continual learning (CL), a.k.a lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such systems is to extend the activity model to dynamically adapt to changes in people’s everyday behavior. Current research in CL applied to HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems. To push this field forward, we build on recent advances in the area of continual learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. Online learning is further facilitated using contrastive loss to enforce inter-class separation. LAPNet-HAR is evaluated on 5 publicly available activity datasets in terms of its ability to acquire new information while preserving previous knowledge. Our extensive empirical results demonstrate the effectiveness of LAPNet-HAR in task-free CL and uncover useful insights for future challenges.

To make wind power more competitive, it is necessary to reduce turbine downtime and reduce costs associated with wind turbine Operation and Maintenance (O&amp;M). Incorporating machine learning in developing condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Base Density for the Support Vector Machine (BDSVM) and the evolutionary Fourier spectra of vibrations. This method allows smart monitoring of the function evolution of the turbine. A complex optimal function (FO) for 5-degree order has been developed that will be the boundary function of the BDSVM to timely determine the magnitude, frequency, and place of the failure occurring in wind turbine drive.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

Schistosomiasis, acquired by skin-penetrating cercariae of dioecious digenean schistosomes during freshwater contact, afflicts nearly 260 and 440 million people with active infections and residual morbidity, respectively; about 10 million women at reproductive age contract schistosomiasis during gestation every year. Acute schistosomiasis is characterized by pre-patent pro-inflammatory CD4+ T-helper 1 or CD4+ Th1/T-helper 17 reactivity against immature schistosomulae. Chronic schistosomiasis is dominated by post-patent anti-inflammatory CD4+ T-helper 2 reactivity against ova epitopes; flukes co-exist in immunocompetent definitive hosts as they are capable of evading their defense mechanisms. Preventive measures should be complemented by vaccination, inducing long-term protection against transmission, infection, and disease recurrence, given the latest advancements in schistosomal vaccines. Transmission models incorporating vaccination available in PubMed, Embase and Web of Science up to December 31, 2023 are presented. Besides conceptual model differences, predictions meant to guide decision- and policymaking reveal continued worm harboring facilitating transmission besides residual infections, and increased susceptibility to re-infection and rebound morbidity, both shifted to later life stages following the intervention. Consequently, a vaccination schedule is pivotal considering the optimal age for initial immunization, i.e., pre-schoolchildren or schoolchildren, in a cohort-based or population-based manner while incorporating potential non-adherers promoting ongoing transmission; longevity over magnitude of vaccine protection to antigenic schistosomal moieties is crucial including accounting for existing pre-acquired immunity from natural exposure, in utero priming besides herd immunity, and induced by chemotherapy. Combining as one multi-component approach long-term effects of vaccination with short-term effects of chemotherapy as regular repeated vaccine-linked therapy in contrast to a single-component intervention seems most promising for achieving WHO’s endpoints of transmission elimination and morbidity control.

Hemangioblasts give rise to endothelial progenitor cells (EPCs), which also express the cell surface markers CD133 and C-kit. They may differentiate into the outgrowth endothelial cells (OECs) that control neovascularization in the developing embryo. Reduced levels of EPC in circulation have been linked to human cardiovascular disorders, according to numerous studies. Furthermore, preeclampsia and senescence have been linked to levels of EPCs produced from cord blood. Uncertainties surround how preeclampsia affects the way EPCs function. It is reasonable to speculate that preeclampsia may have an impact on the function of fetal EPCs during the in utero period; however, given the present literature's suggestion that maternal vasculopathies, including preeclampsia, damage fetal circulation. Additionally, the differentiation potential and general activity of EPCs may serve as an indicator of the health of the fetal vascular system since they promote neovascularization and repair during pregnancy. Thus, the purpose of this review is to compare, through assessment of their quantity, differentiation potency, angiogenic activity, and senescence, the angiogenic function of fetal EPCs obtained from cord blood for normal and pregnancy problems. This will shed light on the relationship between the angiogenic function of fetal EPCs and pregnancy complications, which could have an effect on the management of long-term health issues like metabolic and cardiovascular disorders in offspring with abnormal vasculature development.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

Human Activity Recognition (HAR) technology enables continuous behavior monitoring, particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR, were employed for classification. Results demonstrate the ear sensor's efficacy, with deep learning models achieving 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital signs monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.