This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles, detected before the robot starts to move; it is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; it is realized by means of a velocity control law based on the null space method for redundancy control. A term of the control law takes into account the speed of the obstacles as well as their position. In order to test the algorithms, a set of simulations are presented: the robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system.

In January 2020, the World Health Organization declared a public health emergency and announced a new coronavirus disease (COVID-19), which would later go on to be declared as a pandemic, changing the global sphere and placing the economies of almost all countries under heavy stress. The airline industry, that had just begun recovering after facing crises one after another in the last two decades, from early 2000 due to 9/11, to the global financial crisis later, is now oce again facing an enormous challenge of closed borders and greater lockdowns due to the pandemic. Borders are closed, with very few planes are in the air, while the rest are grounded. The purpose of this paper is to give a conceptual understanding of the current pandemic situation and its consequences on the airline industry. The paper takes a unique perspective of human resource management (HRM) that is rarely used in the airline industry.

The main purpose of this research is to analyze the perception of climate change impacts on human health in Bangladesh through data from nationality representative surveys conducted in some district of Bangladesh. In Bangladesh there have a few research has been conducted on public perceptions about the impact of climate change on human health. A structured questionnaire method was conducted, and data collected from 615 respondents. The findings of this study reveal that out of 615 respondents, 76.0% of the respondents replied positively while remaining 24.0%, almost one-fourth of total respondents, indicated that they have not heard the term climate change before. Knowledgeable in climate change, 92.5% of respondents agreed that climate change has an impact on human health while only 7.5% respondents disagreed with this statement. 90.5% of respondents argued that they are agreed with the opinion that climate change is a serious threat to human health.

Organophosphate pesticides (also known OPPs) have for many years been the choice candidate globally for pest control. OPPs have for over 80 years, been used in gardens, fields and greenhouses as crop protection agents, and even in homes as insecticides and mosquito abatement. Thus, year after year and with the repeated application of organophosphate agents, many problems have appeared as a result of excessive use of pesticides. The adverse effects of pesticides are well documented in human health, environment, pesticide residue in crops, soil and water contaminated by these pesticides. Therefore, it is necessary to shed more light on the risks associated with the irresponsible usage of organophosphate pesticides.

The microbiota of human breast milk (HBM) contributes to infant gut colonization; however, whether bacterial extracellular vesicles (EVs) are present in HBM or might contribute to this process remains unknown. In the present study, we characterized the HBM microbiota of healthy Korean mothers and measured the key bacteria likely affecting infant gut colonization by analyzing both the microbiota and bacterial EVs. A total of 22 HBM samples were collected from lactating mothers. The DNA of bacteria and bacteria-derived EVs was extracted from each sample. Gene analysis was performed using Illumina MiSeq. Firmicutes accounted for the largest portion among the phyla, followed by Proteobacteria, Bacteroides, and Actinobacteria in both bacteria and bacterial EV samples. At the genus level, Streptococcus (25.1%) and Staphylococcus (10.7%) were predominant in bacterial samples, whereas Bacteroides (9.1%), Acinetobacter (6.9%), and Lactobacillaceae(f) (5.5%) were prevalent in bacterial EV samples. Several genera including Bifidobacterium were significantly positively correlated between the two samples. Our findings reveal the diverse bacterial communities in HBM of healthy lactating mothers and suggest the presence of key bacteria with metabolic activity in HBM and that EVs derived from these bacteria may contribute to the vertical transfer of gut microbiota from mother to infant.

Human resources are the major input of health systems therefore the equitable distribution of human resources in health care remain critical in making progress towards the goal of universal health coverage and thus to sustainable development. The purpose of this study is to evaluate the health care human resources distribution across regions of Poland between 2010 and 2017 and estimate the level of equity. This research by applying to Polish conditions will allow to fill in the gap in existing literature. Data was derived from the Knowledge Database Health and Health Care of Statistic Poland and Polish Statistical Yearbook. In purpose to examine the distribution of health resources against population size and geographic size in Poland, the Gini coefficient calculated based on the Lorenz Curve was engaged. This study has several major findings. The amount of most types of human resources – as apart from internist, obstetrics and gynecology, occupational medicine - when expressed as number of them per 10,000 population or 1 square km increased in 2017 compared to 2010. Moreover, for most types of health care human resources the level of access slightly changed during analysied period as Gini coefficients decreased. However, the geographical distribution of all types of human resources is less equitable then in case of population distribution. The most troublesome is relatively lower equity in case of oncologist, family medicine and occupational medicine and cardiologist in both population and geographical distribution. Thus this research provides some implications for policy and practice. As the main reason to establish National Health Fund (which meant the centralization of the system) in Poland was to to eliminate regional differences in access to health care, thus this research confirms that still some more corrective actions, in this field, should be undertaken.

The use of fluoride (F^-) for decreasing the prevalence and incidence of tooth decay was the greatest worldwide preventive public health measure of the 20th century. There have been controversial reports about the benefits of the use of F^-, because in small amounts it helps prevent dental caries, but in high concentrations it can be potentially toxic and harmful to dental and systemic health. Since the mid-20th century, F^- has been studied by toxicologists, looking at its deleterious effects in human populations. During the last decade, the interest on the undesirable effects has resurfaced because of the knowledge that it interacts with the cellular system, even in low doses, with a very small safety margin. Acute ingestion of toxic amounts of fluoride can cause corrosive gastric mucosa injury. Also respiratory effects such as bleeding, pulmonary edema, tracheostomy and shortness of breath, have been observed in individuals who inhale hydrogen fluoride. Some researchers had shown that F^- is an oxidizing agent and a well-known reversible enzymatic inhibitor that interferes with the enzyme activity of at least 80 proteins, can altered the intracellular redox equilibrium, lipid peroxidation, as well as, alteration in the gene expression and apoptosis induction. The primary purpose of this review is to examine findings of the action of inorganic F^-, and an overview of hard and soft tissue disturbances, known as fluorosis. The goal of this review is to enhance understanding of the mechanisms by which F^- affects cells with an emphasis on human tissue-specific events.

This paper sets out to reconsider the Hohfeldian framework of rights in celebration of the centenary anniversary of Hohfeld's death. It begins by conceptualising each of the Hohfeldian incidents or rights before outlining the molecular or complex structure of rights to ‘things’. I adopt a broad use of the term of ‘right’ and apply it to Legal, Moral, Equitable and Human conceptions and constructions. It sets out an argument in favour of a further definitional model - in addition to Hohfeld’s scheme of opposites and correlatives – which focuses on the function of these conceptual rights. Finally, it will consider the broader implications of how rights are held and the relationships which these rights govern. Ultimately, this paper seeks to demonstrate the benefit, and indeed necessity, of the Hohfeldian model in any discussion of rights. Without it ‘rights talk’ is debased and impoverished.

This study investigated the presence of human Norovirus (HuNoV) by genotype in 1,486 groundwater samples, collected from 843 groundwater wells suspected of contamination during 2007-2016, in Republic of Korea. We identified and genotyped 186 HuNoV sequences in 178 HuNoV-positive samples using the RIVM-NoroNet norovirus genotyping tool (NGT) and phylogenetic tree analysis based on RIVM-NoroNet reference sequences. HuNoV GII was more prevalent than GI. The major genotypes detected were HuNoV GII.4 (43.0%), GII.22 (15.6%), GI.5 (10.2%) and GI.1 (8.6%); 14 genotypes accounted for < 5.0%. Increasing HuNoV GII.17 has displayed a worldwide trend, including in Europe and Asia since 2010, and it was the dominant HuNoV genotype during 2013-2014 in Korea. However, HuNoV GII.4 was the major genotype detected in groundwater in Korea in 2015-2016, and it is becoming increasingly prevalent similar to HuNoV GII.17.

OpenAI has managed to turn 100 million heads in two months towards their new Language model tool (LM) ChatGPT. The third generation Generative Pretrained Transformer (GPT-3) has the capacity to tackle simple to complex and sophisticated problems, while providing reasoning behind its generated answers. ChatGPT can be used to increase productivity and improve efficiency and face challenging problems. Programming mobile applications is a challenging task that requires professional software engineers, skills and abilities to be developed. The following paper takes a case study approach to assess how novice app developers can use ChatGPT to generate Java scripts that will be used in Android studio to create a functional application. The results after, many iterations and ongoing conversations with ChatGPT managed to create an application for the anticipated function. Important insights have been drawn from the case study that could set the ground for any novice user seeking to create applications using Java scripting and Android Studio.

Emerging organic contaminants (EOCs) of anthropogenic origins are ubiquitous in environmental compartments, including aquatic systems. Thus, EOCs have attracted considerable research and public attention due to their potential human and ecological health risks. However, compared to other aquatic environments such as wastewater systems, comprehensive reviews focussing on the occurrence and human health risks of EOCs in drinking water systems are still lacking. Therefore, to address this knowledge gap, the current review posits that drinking water systems harbour a cocktail of toxic EOCs, which pose public health risks via multiple exposure routes. In the present review, global evidence is examined to track EOCs along the source-pathway-receptor-impact-mitigation (SPRIM) continuum. Evidence shows that, various groups of EOCs, including pharmaceuticals and personal care products, solvents, plasticizers, endocrine disrupting compounds, gasoline additives, per- and polyfluoroalkyl substances (PFAS), food colourants, artificial sweeteners, and musks and fragrances, have been detected in drinking water systems. The anthropogenic sources of EOCs detected in drinking water systems, including wastewater systems and industrial emissions, are summarized. Further, the behaviour and fate of EOCs in the drinking water systems, including removal processes are discussed. Once in drinking water systems, human exposure to EOCs may occur via ingestion of contaminated drinking water and cooked foods, and possibly dermal contact and inhalation. The high-risk environments, and risk factors and behaviours predisposing humans to EOC exposure are discussed. Evidence on the human health risks of the various EOCs and a critique of the data are presented. Notably, besides inferential data, quantitative epidemiological evidence directly relating the occurrence of EOCs in drinking water systems to specific adverse human health outcomes is still scarce. Lastly, future research directions, including the need for quantitative public health risk assessment, and the application of emerging detection tools are discussed.

Human pluripotent stem cells (hPSCs) have widespread potential biomedical applications. There is a need for large-scale in vitro production of hPSCs, and optimal culture methods are vital in achieving this. Physiological oxygen (2% O2) improves key hPSCs attributes, including genomic integrity, viability, and clonogenicity, however, its impact on hPSC metabolism remains un-clear. Here, Selected Ion Flow Tube-Mass Spectrometry (SIFT-MS) was used to detect and quantify metabolic Volatile Organic Compounds (VOCs) in the headspace of hPSCs and their differentiated progeny. hPSCs were cultured in either 2% O2 or 21% O2. Media was collected from cell cultures and transferred into glass bottles for SIFT-MS measurement. The VOCs acetaldehyde and dimethyl sulfide (DMS)/ethanethiol were significantly increased in undifferentiated hPSCs compared to their differentiating counterparts, and these observations were more apparent in 2% O2. Pluripotent marker expression was consistent across both O2 concentrations tested. Transcript levels of ADH4, ADH5, and CYP2E1, encoding enzymes involved in converting ethanol to acetaldehyde, were upregulated in 2% O2, and chemical inhibition of ADH and CYP2E1 decreased acetaldehyde levels in hPSCs. Acetaldehyde and DMS/ethanethiol may be indicators of altered metabolism pathways in physiological oxygen culture conditions. The identification of non-destructive biomarkers for hPSC characterization has the potential to facilitate large-scale in vitro manufacture for future biomedical application.

Tracking and early identification of suspected cases are essential to control and prevent potential COVID-19 outbreaks. One of the most popular techniques used to track this disease is the use of Infrared cameras to identify individuals with elevated body temperatures. However, they are limited by their inability to be implemented in open public settings such as public parks or even outdoor recreational centers. This limits their ability to effectively track possible COVID-19 patients as open public recreational places such as parks, concert venues and other public venues are hotspots for the spreading of the virus. Other technological solutions such as thermal scanners require an individual to perform the actual testing as they are not individual standalone technologies. This method of testing can potentially cause the transmission of the virus between the tester and the individual getting tested. As can be seen, an alternative solution is essential to solving this issue. In this study, we aim to present the system, design and potential scope of a non-invasive system that can diagnose and identify potential COVID-19 patients using thermal and optical images of the individual using drone technology. The proposed system (COVIDRONE) combines multi-modal machine intelligence, computer vision and real-time monitoring to enable scalable monitoring. The system will also involve the use of machine learning algorithms for better and more accurate diagnosis. We envisage that development of such technologies may help in developing technological solutions to combat infectious disease threats in the future pandemics.

Cloud computing transformed IT organizations with its on-demand model, notably Infrastructure as a Service (IaaS). Cloud providers manage extensive physical devices, consuming substantial energy and bandwidth for data traffic during virtual machine deployment. Balancing energy and bandwidth are critical. This thesis presents BOHGOA integrated with ACO to optimize virtual machine placement in clouds, simultaneously considering bandwidth and energy. It yields Pareto-optimal solutions for this trade-off. Using CloudSim, BOHGOA outperformed GA, ACO, and FFD algorithms, reducing bandwidth consumption by 54.14%, 32.11%, 57.47% (240 VMs) and 38.12%, 22.76%, 47.05% (500 VMs) respectively. Additionally, it decreased physical machine energy usage by 37.70%, 34.01%, 40.14% (240 VMs) and 27.50%, 22.28%, 30.52% (500 VMs) respectively. These results underscore BOHGOA's effectiveness in optimizing cloud VM placement.

Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures and technology used to detect and identify weeds in different crops; 61 articles applying CNN architectures and technology were analyzed in the last five years (2019-2023). The results show the used of different devices to acquire the image for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

This study addresses the challenge of assessing the carcinogenic potential of hazardous chemical mixtures, such as PFAS, which are known to contribute significantly to cancer development. Here, we propose a novel framework called HNNMixCancer that utilizes a Hybrid Neural Network (HNN) integrated into a machine-learning framework. This framework incorporates a mathematical model to simulate chemical mixtures and enables the creation of classification models for binary and multiclass classification and regression. Through extensive experimentation, we demonstrate that our HNN model outperforms other methodologies, including Random Forest, Bootstrap Aggregating, Adaptive Boosting, Support Vector Regressor, Gradient Boosting, Kernel Ridge, Decision Tree with AdaBoost, and KNeighbors, achieving a superior accuracy of 92.7% in binary classification. To address the limited availability of experimental data and enrich the training data, we generate an assumption-based virtual library of chemical mixtures using a known carcinogenic and non-carcinogenic single chemical for all the classification models. Remarkably, in this case, all methods achieve accuracies exceeding 98% for binary classification. In external validation tests, our HNN method achieves the highest accuracy of 80.5%. Furthermore, in multiclass classification, the HNN demonstrates an overall accuracy of 96.3%, outperforming RF, Bagging, and AdaBoost, which achieved 91.4%, 91.7%, and 80.2%, respectively. In regression models, HNN, RF, SVR, GB, KR, DT with AdaBoost, and KN achieve average R2 values of 0.96, 0.90, 0.77, 0.94, 0.96, 0.96, and 0.97, respectively, showcasing their effectiveness in predicting the concentration at which a chemical mixture becomes carcinogenic. Our method exhibits exceptional predictive power in prioritizing carcinogenic chemical mixtures, even when relying on assumption-based mixtures. This capability is particularly valuable for toxicology studies that lack experimental data on the carcinogenicity and toxicity of chemical mixtures. To our knowledge, this study introduces the first method for predicting the carcinogenic potential of chemical mixtures. The HNNMixCancer framework offers a novel alternative for dose-dependent carcinogen prediction. Ongoing efforts involve implementing the HNN method to predict mixture toxicity and expanding the application of HNNMixCancer to include multi-mixtures.

Numerous new technologies have been implemented in image analysis methods that help researchers withdraw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Here is the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. Results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures As expected, machine learning methods applied to digital image analysis can overtake the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their great nutritional qualities and biological plasticity.

Machine Learning (ML) and Artificial Intelligence(AI) have revolutionized almost all fields that are linked to the acquisition of intelligent behavior in the real world. It is an attractive alternative for a researcher of artificial intelligence. Contrary to rule-based programming, ML is an algorithmic approach in which learning comes from existing data. The more data we have these computer systems look at, we say we’re ‘training’ the computer system, and as the computers begin to identify patterns in the data, identify abnormalities in the data from these abnormalities we improve the system architect according to the requirement. This article introduces the use of comprehensive concepts of machine learning, in general, particular, and their potential applications in communications. Furthermore, the current state and futuristic potentials of enabling universal communication with implications of machine learning methods have been explained. In this review paper, we offer a comprehensive talk on distinctive methods/techniques of information analytics, artificial intelligence (AI), and machine learning (ML) moved forward the contact aware communication system.

This study investigates the interpretability of machine learning (ML) models applied to cumulative damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), Permutation and Impurity-based technique. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution in respect with the range of final damage is evaluated. Nonlinear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by Park and Ang damage index (DIPA) and Intensity Measures (IMs), respectively. The study offers a comprehensive review of cutting-edge ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined, with critical predictors for final damage being the initial damage caused by the first shock and the IMs of the subsequent shock: IFVF and SIH. The importance of these predictors is supported by feature importance analysis and local/global explanation methods, enhancing the interpretability and practical utility of the developed model.

Abstract: The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.

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

Human adipose stem cell-derived extracellular vesicles (hASC-EVs) are key mediators of paracrine signaling with promising therapeutic applications. Although hASC-EVs are derived from human cells and are less immunogenic, their immunogenicity cannot be completely excluded. Here, we evaluate the immune responses of ICR and C57BL/6 mice to high doses of hASC-EVs for 10 days after injection. Lymphocyte subpopulations are analyzed using flow cytometry at 0.5, 1, 3 and 24 h post injection. In the spleen and blood of C57BL/6 mice, neutrophils sharply increased at 0.5 h and decreased at 3 h following hASC-EV treatment. We observe increased proportions of monocytes, macrophages, and natural killer cells at 3 h but returned to similar level of vehicle control at 24 h post injection in the spleen and blood of ICR mice. Although the in vivo experiments reveal different immune responses to hASC-EV treatment in C57BL/6 and ICR strains, no major changes occur in human peripheral blood mononuclear cell composition after applying hASC-EVs in vitro. In conclusion, unlike those in mice, immune responses to hASC-EVs in humans are not detectable, indicating a minimal risk of fatal side-effects from hASC-EV-based therapies.

Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, making advanced analytics accessible to a broader range of organizations. By automating complex tasks like data preprocessing, model selection, and hyperparameter tuning, AutoML reduces the time and resources needed to develop and deploy machine learning models. This accelerates decision-making and enables quicker responses to market changes. AutoML empowers businesses to build accurate predictive models using sophisticated algorithms, optimizing model performance for reliable insights and better outcomes. A key advantage of AutoML is its accessibility; even organizations without a dedicated data science team can leverage machine learning, reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success. The systematic review will examine the bibliometric literature on how AutoML can boost business, analyzing 74 academic and scientific documents from the Scopus database.

The aim of this article is to discuss the transformative amalgamation of Internet of Things and Machine Learning technologies within the domain of vegetable hydroponic systems for nutrition management. Hydroponics, being an efficient cultivation method, benefits a great deal from the precision and adaptability that machine learning algorithms can offer, along with the real-time monitoring facilitated by the devices using the Internet of Things. This study summarizes the latest research and underlines how ML and IoT may work together, focusing on nutrient optimization, plant development, and resource efficiency. The use of ML algorithms, the function of IoT devices for real-time monitoring, communication protocols, scalability issues, and implementation are among some of the key subjects of the discussion. The research indicates benefits to crop output, efficiency in using resources, and sustainability based on the case studies and the analysis of results. However, ethical problems and some complications concerning data privacy do call for responsible adoptions. The conclusion of this paper provides directions for further research and calls for more investigation into state-of-the-art machine learning approaches and scalable solutions for the re-silient and sustainable future of hydroponic agriculture. The machine learning and deep learning models introduced in this research were evaluated against contemporary studies, revealing an accuracy enhancement ranging from 1.17% to 5.25%, depending on the dataset and algorithm employed. The present study conducts a comparative analysis involving machine learning algo-rithms, indicating that among all the models, the Decision Tree and Gradient Boosting Classifier achieved an accuracy of 99.42% in the dataset by making stage-wise decisions.

Cricket, the most popular sports in the world to chase by the fans. Many cricket fans are interested in analyzing the elements and activities that affect the outcomes of matches. Machine learning in sports analytics is a relatively new topic in computer science. The purpose of this survey is to anticipate the outcome of a PSL champion team and build a winning strategy. Machine learning (ML) has exhibited favorable outcomes in a variety of sectors for diverse prediction using classification, regression, and so on, and is proven to be accurate. The innovative frameworks based on ML have the ability to learn from previous experiences. The cricket pitch is the most important aspect, alongside home game an edge, coin toss, innings of play, day/night match, physical conditioning, and dynamic plans, among other things. The dataset from previously ESPN Cricifno was used for this purpose. Recent classification approaches including, Random Forest, Gradient Boost, Deep Neural Networks used to conduct a comparative analysis based on their results and performances, as well as team strategies.

Maintaining the judiciary's power requires a high level of budgetary expenditure, but the specifics of this relationship have yet to be fully explored. Although several studies have analyzed the impact of spending in the judiciary through productivity and performance-related measures, none have employed machine learning techniques. This study examines the productivity of the judiciary based on spending and other variables using machine learning techniques, including clustering and neural networks. The final neural network model supports the results of Pearson's parametric correlation test, which found no significant correlation between expenditure and productivity. This study's findings demonstrate the importance of understanding that increased public budgetary expenditure alone is insufficient for improving the judiciary's efficiency. Instead, other administrative and technical measures are necessary to meet the demands of the Brazilian judiciary and improve service delivery rates. These findings offer important theoretical and managerial contributions to the field.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

We study the brain segmentation by dividing the brain into multiple tissues. Given possible brain segmentation by deep, machine learning can be efficiently exploited to expedite the segmentation process in the clinical practice. To accomplish segmentation process, a MRI and tissues transfer using generative adversarial networks is proposed. Given the better result, we propose the transfer model using GAN. For the case of the brain tissues, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) are segmented. Empirical results show that this proposed model significantly improved segmentation results compared to the stat-of-the-art results. Furthermore, a dice coefficient (DC) metric is used to evaluate the model performance.

The present research project entitled "Development of a method for predicting the optimal maturity level of avocado using machine learning" aims to establish an accurate and efficient approach to assess the maturity of avocados using machine learning methodologies. This research specifically focuses on identifying the relevant physical and chemical characteristics of avocados, creating a dataset containing categorized images of maturity levels, and creating machine learning models capable of accurately predicting fruit maturity. The studies reveal that the application of machine learning, in particular convolutional neural networks and multisensor models, has the potential to transform the prediction of avocado maturity and thus improve product quality and customer satisfaction. The findings indicate that the proposed techniques achieve an accuracy rate of over 90%, demonstrating their viability for integration into mobile applications that can benefit growers and suppliers in their decision-making processes.

In order to conduct an accurate classification of the heterogeneous landscape in Jiului Valley, Romania mining basing, four machine learning algorithms (SVMs) and two common algorithms (MLC and MD) have been compared, using a temporal series of Landsat satellite images from the period 1988-2017. By using independent validation, an accuracy assessment was established together with the analysis of the differences between the classification algorithms used. Although all six algorithms used have shown a high overall accuracy (ranging from 80.29% to 93.14%) and Kappa values (from 0.77 to 0.92), SVM-RBF appears to have a higher overall applicability in describing the spatial distribution and the cover density of each land cover category. Results have indicated a large difference in classification accuracy between the SVM-RBF algorithm and commonly used algorithms, the SVM-RBF algorithms have slightly outperformed the MLC with an overall accuracy of 7.14–8.86% and by 0.0833–0.1033 kappa coefficient. On the other hand, the same algorithm have outperformed the MD by and overall accuracy of 9.71–10.86% and by 0.1133–0.1267 kappa coefficient. By using SVM-RBF, certain classified maps have been developed and used for assessing changes by post classification comparison. The results have shown an average growth of 6.5% in mined areas over the studied period.

The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

The combination of new-generation information technology and manufacturing technology has resulted in major and profound impact on future development paradigm of manufacturing. It is challenging for existing methods to conduct a multidimensional trend exploration related to machine tool domain, which is the basis of virtually everything in manufacturing. In this paper, we proposed an integrating approach framework combined topic models, bibliometric, trend analysis and patent analysis to mine insightful information about future development from multi-source data related to machine tool, such as papers, grants, patents and news. Specifically, papers and grants provided two different perspectives to explore the current focuses and future trends in machine tool research. Furthermore, the future technology development of machine tool was investigated through patents analysis. Finally, news related to machine tool industry in recent years was analyzed to analyze future machine tool business mode. The integration of the above various analytical methods and the multi-dimensional mining of literatures enabled the analysis of the future development of machine tool domain systematically from multi-perspectives which include research, technology development and industry. The conclusions obtained in this paper is beneficial to different communities of machine tool in terms of determining the research directions for researchers, identifying industry opportunities for corporations and developing reasonable industry policy for policy makers.

The knowledge of protein function is essential for the study of biological processes, the understanding of disease mechanism and the exploration of novel therapeutic target. Apart from experimental methods, a number of in-silico approaches have been developed and extensively used for protein function prediction. Among these approaches, BLAST predicts functions based on protein sequence similarity, and machine learning predicts functional families from protein sequences irrespective of their similarity, which complements BLAST and other methods in predicting diverse classes of proteins including distantly related proteins and homologous proteins of different functions. However, their identification accuracies and the false discovery rate have not yet been assessed so far, which greatly limits the usage of these prediction algorithms. Herein, a comprehensive comparison of the performances among four popular functional prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these algorithms were systematically assessed by four metrics (sensitivity, specificity, accuracy and Matthews correlation coefficient) based on the independent test datasets generated from 93 protein families defined by UniProtKB Keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model species (homo sapiens, arabidopsis thaliana, saccharomyces cerevisiae and mycobacterium tuberculosis). As a result, the substantially higher sensitivity and stability of BLAST and SVM were observed compared with that of PNN and KNN. But the machine learning algorithms (PNN, KNN and SVM) were found capable of significantly reducing the false discovery rate (SVM < PNN ≈ KNN). In summary, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

Peptide-based inhibitors represent a promising approach for the treatment of HIV-1, offering a range of potential advantages including specificity, low toxicity, and the ability to target various stages of the viral lifecycle. This review outlines the current state of research on peptide-based anti-HIV therapies, highlighting key advancements and identifying future research directions. Over the past few years, there has been significant progress in developing synthetic peptide-based drugs that target various stages of the viral life cycle, including entry and replication. These approaches aim to create effective anti-HIV therapies. Additionally, peptides have proven valuable in the development of anti-HIV vaccines. In the quest for effective HIV vaccines, discovering potent antigens and designing suitable vaccine strategies are crucial for overcoming challenges such as low immunogenicity, safety concerns, and increased viral load. Innovative strategies for vaccine development through peptide research are, therefore, a key focus area for achieving effective HIV prevention. This review aims to explore the strategies for designing peptides with anti-HIV activity and to highlight their role in advancing both therapeutic and preventive measures against HIV.

Wild-grown edible mushrooms are important in world diet, which are also efficient metal accumulators. Yunnan, southwest China is the main producing region with typically high geochemical metals. Environmental factors, bioaccumulation, distribution and human health risk of metals were examined in paired soil and Tricholoma matsutake (n=54). T. matsutake grows on acidified soils (pH=3.95–6.56) and metals showed strong heterogeneity with Fe, Mn, Zn and Cu ranged at 16–201, 0.046–8.58 g kg–1 and 22.6–215, 3.7–155 mg kg–1. High soil Fe content led to its great accumulation in T. matsutake (0.24–18.8 g kg–1). However, though soil Mn content was great higher than Zn and Cu, their concentrations in T. matsutake were comparable (21.1–487 vs. 38.7–329 and 24.9–217 mg kg–1). This suggested that T. matsutake prefers to accumulate Zn and Cu than Mn, and supported by bioaccumulation factor (BAF=0.32–17.1 vs. 0.006–1.69). Fe was mainly stored in stipe, while Mn, Zn and Cu were in cap, with translocation factor (TF) was 0.58 vs. 1.28–1.94. Therefore, stipe Fe showed the highest health risk index (HRI) at 1.28–26.9, followed by cap Cu (1.01–2.33), while 98–100% of Mn and Zn were risk-free. The higher concentration and greater risk of Fe was attributed to the significant effect of soil Fe content (R=0.34) and soil pH (R=–0.57). This study suggested that Fe as an essential mineral may exert toxic effects via T. matsutake consumption from geochemical high background areas.

Activating the cytosolic innate immune sensor cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway via natural and synthetic agonists holds great potential for priming robust antitumor immunity. However, the effectiveness of STING agonists is often impeded by the insufficient tumor accumulation, poor cellular entry, and rapid clearance, thus leading to difficulty of their clinic translation. Herein, we apply the human serum albumin as the single-molecule nanoreactor to encapsulate SR-717 (SH-NPs) for enhanced targeting delivery efficiency and biological potency. SH-NPs with rational size distribution exhibited superior serum stability, considerable cellular uptake and abundant tumor accumulation, thus increasing the STING signaling including phosphorylation of STING-associated proteins (TBK1, IRF3), and cytokines secretion inside tumor region. Subsequently, SH-NPs remarkably reshape the immunosuppressive tumor microenvironment into an immunogenic one that boosts antitumor T-cell immunity and improves the therapeutic efficacy of checkpoint blockade against murine tumors. We also validate SH-NPs in freshly isolated human renal tumor tissues to highlight their clinic translation capability. These findings pave the way towards targeted STING agonist delivery for reinvigorated immunotherapy of intractable cancers.

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of this data present substantial challenges in data modeling and analysis, which have been tamed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of Large Language Models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and generation of human behavior through the lens of wearable sensor data. This survey explores current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensors data, the capabilities and limitations of LLMs to model them and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensors data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensors data and LLMs, offering insights into the current state and future prospects of this emerging field.

Cancer develops from abnormal cell growth in the body, causing significant mortalities every year. To date, potent therapeutic approaches have been developed to eradicate tumor cells, but intolerable toxicity and drug resistance can occur in treated patients, limiting the efficiency of existing treatment strategies. Therefore, searching for novel genes critical for cancer progression and therapeutic response is urgently needed for successful cancer therapy. Recent advances in bioinformatic and proteomic techniques have allowed the identification of a novel category of peptides encoded by non-canonical open reading frames (ncORFs) from historically non-coding genomic regions. Surprisingly, many of the ncORFs express functional microproteins that play a vital role in human cancers. In this review, we provide a comprehensive description of different ncORF types with coding capacity and technological methods in discovering ncORFs among human genomes. We also summarize the diagnostic and prognostic value of these ncORF as well as their carcinogenic role in cancer progression and underestimated potential as therapeutic targets.

Flavonoids are important natural compounds characterized by their extensive biological activities. Citrus flavonoids form a significant segment within the broader flavonoid category. Naringenin, an integral part of this series, is recognized for its powerful anti-inflammatory and antioxidant properties. In addition, considering the lack of existing research on naringenin's potential effectiveness and intracellular mechanisms of action in skin-related applications, especially as a cosmetic ingredient, this study aimed to explore naringenin's role in reducing the fundamental generation of reactive oxygen species. This was achieved by examining its inhibitory effects on the expression levels of NADPH oxidase and iNOS, ultimately leading to a reduction in NO production. This research examined the anti-inflammatory and antioxidant capacities of naringenin by employing a cellular senescence model of LPS-induced HDFs. The evaluation of naringenin's efficacy was validated through several investigative procedures, including the NF-κB luciferase assay, ELISA assay, and qRT-PCR. To verify the anti-inflammatory effectiveness of naringenin, the study measured the responsive elements of NF-κB using a luciferase reporter assay. This assessment revealed that naringenin could decrease the concentration of genes activated by NF-κB. Moreover, the study found that naringenin inhibited the transcriptional expression of known NF-κB regulated inflammatory cytokines, including IL-1β, IL-6, and IL-8. In addition, results from the qRT-PCR analysis indicated a reduction in iNOS expression facilitated by naringenin. Based on the data gathered and analyzed in this study, it can be conclusively inferred that naringenin possesses promising potential as a cosmetic ingredient, offering both anti-inflammatory and antioxidant benefits.

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Un-derstanding vegetation dynamics and its response to climate change and human activities provides essential information for ecological and environmental resource management. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine plat-form and Landsat satellite images, employing trend analysis and other methods to examine spati-otemporal changes in vegetation in the SRB. Additionally, partial correlation and residual analysis were utilized to explore the response of FVC to climate change and human activities. The main results were as follows: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 y–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 y–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 y–1. Road construction from 2004 to 2006 significantly reduced the FVC within 200 meters of the roads. FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that FVC has a strong positive correlation with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. There is a weak negative correlation between FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) Compared to climate factors, human activities have a greater impact on vegetation dynamics in the SRB. The contributions of climate change to vegetation recovery are increasing. (4) The area changes of dif-ferent land types are closely related to climate factors and human activities, reflecting their impact on vegetation. These results provide valuable information for ecosystem restoration and environ-mental protection in the SRB.

Marine industries are dependent on the ocean and often have a direct impact on the marine environment. Although previous studies have talked about pollution from marine industries, it is not known to what extent and to what extent these behaviors affect the ocean. Therefore, this study examines the impacts of major marine industries on the marine environment, using China as an example. The Ministry of Ecology and Environment of the People's Republic of China categorizes the quality of seawater into Class I, Class II, Class III, Class IV, and Inferior Class IV in descending order according to the quality from good to poor. In this study, water quality of Class I and Class II are defined as good quality seawater, Class III as average quality, Class IV and Class IV as poor quality, and the proportion of the area of seawater with different water quality to the total area of the sea area symbolizes the overall quality of the marine environment. We use panel data from 11 provinces and cities along the coast of mainland China from 2006 to 2020, estimated by the full feasible generalized least squares (FGLS) method, to study the impacts of the development of coastal tourism, marine transportation, marine shipbuilding, marine salt, mariculture, and marine fishing on the marine environment. The empirical results show that, in descending order of the degree of influence, the marine industries that cause a decrease in the area of good quality seawater are coastal tourism, mariculture and shipbuilding, and the marine industries that cause an increase in the area of poor quality seawater are marine transportation and shipbuilding, while the marine salt industry has no obvious influence on the marine environment, and the development of the marine fishing industry indirectly contributes to the improvement of the marine environment.

Retiring coal power plants can reduce air pollution and health damages. However, the spatial distribution of those impacts remains unclear due to complex power system adjustments and pollution chemistry and transport. Focusing on coal retirements in Pennsylvania (PA), we analyze six counterfactual scenarios for 2019 that differ in retirement targets (e.g., reducing 50% of coal-based installed capacity vs. generation) and priorities (e.g., closing plants with higher cost, closer to Environmental Justice Areas, or with higher CO₂ emissions). Using a power system model of the PJM Interconnection, we find that coal retirements in PA shift power generation across PA and the Rest of PJM region leading to scenario-varying changes in the plant-level release of several air pollutants. Due to air pollution chemistry and transport and socio-demographics, these changes in turn give rise to a reduction of 10 to 182 PM_2.5-attributable deaths in PJM across the six scenarios, with most reductions occurring in PA. Scenarios that reduce more coal power generation yield greater aggregate health benefits due to air quality improvements in PA and adjacent downwind regions. In addition, vulnerable populations—in both PA and Rest of PJM—benefit most in scenarios that prioritize plant closures in PA near Environmental Justice Areas. These results demonstrate the importance of considering cross-regional linkages and socio-demographics in designing equitable retirement strategies.

Nicotinamide mononucleotide (NMN), a key NAD+ precursor, exhibits distinct metabolic characteristics and potential health benefits. Recent investigations underscore the pivotal role of gut bacteria and specialized uptake mechanisms in NMN metabolism. While human trials demonstrate NMN's safety and promising health outcomes, challenges persist. Variability in results is likely influenced by lifestyle, health conditions, and individual genetic and gut microbiota differences. Understanding how these factors affect NMN metabolism is crucial for personalized approaches and maximizing NMN's potential. Advances in delivery systems show promise for enhancing NMN bioavailability, but further understanding of optimal NAD+ levels and reliable measures of NMN's effects is necessary. This comprehensive approach is key to optimizing NMN's impact on human health and longevity.

A new trend in cow's milk has emerged in the market called type A1 and A2 milk. These products have piqued the interest of both consumers and researchers. Recent studies suggest that A2 milk may have potential health benefits, which is why researchers are investigating this product further. It is interesting to note that the A1 and A2 milk types have area-specific characteristics compared to breed-specific characteristics. Extensive research has focused on milk derivatives obtained from cow’s milk, primarily through in vitro and animal studies. However, few clinical studies have been conducted in humans, and the results have been unsatisfactory. New molecular techniques for identifying A1 and A2 milk may help researchers to develop new studies that can clarify certain controversies surrounding A1 milk. It is essential to exercise extreme caution when interpreting the updated literature. It has the potential to spread panic worldwide and have negative economic implications. Therefore, this study aims to investigate the new aspects of A1 and A2 milk in different research areas and clarify some aspects regarding these two types of milk.

Apoptotic cells possess immunomodulatory effects that can be utilized to treat imbalanced immune conditions. Information on the preclinical safety of such treatment is sparse. In this study, the safety of apoptotic cells (Allocetra-OTS) was assessed in a GLP toxicological study on Sprague Dawley rats. Three doses of Allocetra-OTS or vehicle were administered intravenously (IV) for 3 consecutive days. Animals in the Main study were sacrificed on day 4, while animals from the Recovery groups were kept for 14 or 28 days. Allocetra-OTS was well tolerated, and no adverse effects were observed in body weight, clinical signs, food consumption or ophthalmologic observation. Thus, the No Observed Adverse Effect Level (NOAEL) dose was determined as the highest dose administered. An observed elevation in immune cells was suspected to be due to Allocetra-OTS; similarly to other clinical chemistry parameters, however, it was resolved in the Recovery phases. Splenomegaly and a dose-related Extramedullary Hematopoiesis (EMH) in the red pulp were observed, with no adverse events, and were considered to be a normal and expected reaction following IV administration of cell-based therapies. In conclusion, under the conditions of this study Allocetra-OTS was concluded to be safe, further supporting its potential candidacy for clinical studies

In this study, the effect of NaOCl (10–200 ppm of Cl2) on the inactivation of human norovirus (HuNV) GII.4 and hepatitis A virus (HAV) in groundwater was investigated using PMA/RT­qPCR. Initially, 4.00 log copies/μL of HuNV GII.4 or 5.50 log copies/μL of HAV were artificially inoculated in ground water. The titers of HuNV GII.4 and HAV decreased significantly (p < 0.05) with increasing Cl2 concentrations. Ground water was treated with 10, 30, 50, 100, 150, and 200 ppm of Cl2, and the viable HuNV GII.4 was significantly (p < 0.05) reduced to 3.28 (0.21 log reduction), 3.18 (0.31 log reduction), 3.01 (0.48 log reduction), 2.75 (0.74 log reduction), 2.54 (0.95 log reduction), and 2.34 (1.15 log reduction) log copies/μL, respectively. The viable HAV was also significantly (p < 0.05) reduced to 4.99 (0.23 log reduction), 4.76 (0.46 log reduction), 4.55 (0.67 log reduction), 4.21 (1.01 log reduction), 3.89 (1.33 log reduction), and 3.64 (1.58 log reduction) log copies/μL, respectively. The decimal reduction time (D-value) (1-log reduction) of HuNV GII.4 and HAV infectivity in ground water was predicted as 116.7 and 98.9 ppm of Cl2, respectively using the first-order kinetics model (HuNV GII.4 ; y = - 0.0054x + 3.3585, R2= 0.97, HAV ; y = - 0.0091x + 5.0470, R2= 0.97). The result specifically suggests that 150–200 ppm Cl2 can potentially be used for the inactivation of > 1 log copies/μL HuNV GII.4 and HAV in ground water.

Clusterin (CLU) protein is involved in various pathophysiological processes including carcinogenesis and tumor progression. In recent years, the role of the secretory isoform has been demonstrated in tumor cells, where it inhibits apoptosis and favors the acquisition of resistance to conventional treatments used to treat cancer. To determine the possible therapeutic potential of inhibiting this protein, numerous studies have been carried out in this field. In this paper, we show the existing knowledge on the inhibition of this protein in different types of cancer and discuss the importance it could have in the development of new therapies directed against this pathology.

Information and telecommunications technology (ITT) have expanded into many aspects of modern life and have positively affected human life even in the most remote areas of developing countries. The rapid growth in ITT has led to an improvement in the capacity of computers but simultaneously to a decrease in the product’s lifetime as a result of which increasingly large quantities of e-waste are generated annually. It contains hazardous components that if it is not properly managed, can have adverse environmental and health effects. The management of e-waste poses significant challenges in developing countries. Due to the lack of adequate infrastructure, e-waste is burned or dumped in open areas. Informal and inefficient recycling practices are generally employed which further exacerbate pollution and health risks. So, the implementation of e-waste management, adequate recycling, and waste disposal facilities have become crucial concerns. This article reviews the concept of e-waste generation, challenges, disposal, and its impact on human health & environment in detail.

As the most important city in China, Beijing has experienced an economic soar, large-scale population growth and eco-environment changes in recent 20 years.Evaluating climate and human-induced vegetation changes could reveal the relationship of vegetation-climate-human activities and provide important insights for the coordination of economic growth and environmental protection. Based on a long-term MODIS vegetation index dataset, meteorological data (temperature, precipitation) and impervious surface data, the Theil-Sen regression and the Mann-Kendall method are used to estimate vegetation change trend in this study and the residual analysis is utilized to distinguish the impacts of climate factors and human activities on vegetation restoration and degradation from 2000 to 2019 in Beijing. Our results show that the increasing vegetation areas account for 80.2\% of Beijing. The restoration of vegetation is concentrated in the urban core area and mountainous area, while the degradation of vegetation is mainly concentrated in the suburbs. In recent years, the vegetation in most mountainous areas has changed from restoration to significant restoration, indicating that the growth of mountain vegetation has continued to restore. We also found that in the process of urban expansion, vegetation browning occurred in 53.1\% of the urban built-up area, while vegetation greening occurred in the rest part. We concluded that precipitation is the main climatic factor affecting the growth of vegetation in Beijing mountain areas through correlation analysis. Human activities have significantly promoted the vegetation growing in the northern mountainous area thanks to the establishment of environmental protection areas. The negative correlation between vegetation and the impervious surface tends to gradually expand outwards, which is consistent with the trend of urban expansion. The positive correlation region remains stable, but the positive correlation is gradually enhanced. The response of vegetation to urbanization demonstrated a high degree of spatial heterogeneity. These findings indicated that human activities played an increasingly important role in influencing vegetation changes in Beijing.

On February 28, 2023, the Facebook page of the United Nations Children's Fund (UNICEF) South Africa, announced the beginning of the HPV vaccination campaign for 2023. The aim of this study was to examine the attitudes of Facebook users, who commented on the UNICEF South Africa post, towards HPV vaccination to inform the development and implementation of interventions that can increase HPV vaccine acceptance and uptake. Several comments were questions regarding the eligible criteria for vaccination, side effects, consent form, and vaccination strategies. Many Facebook users were hesitant towards HPV vaccination due to various reasons including concerns around side effects, religious beliefs, vaccine stock outs, and their distrust in the institutions or systems involved in vaccination programmes. Few users accepted HPV vaccination because they believed that it was very important thing to do, beneficial for the children’s health, and they have vaccinated they children before. Our findings of the current study contribute to the promotion of acceptance and uptake of HPV vaccination by developing and implementing interventions tailored to address identified barriers that are associated with hesitant to HPV vaccination.

This paper exposes the initial part of a larger research on the impact of the automotive on human health and the quality of life and environment. After a brief introduction on the need for responsible and sustainable approaches and few questions on the subject, some considerations and statistical data are presented, based on a study of the literature on the impact of road traffic and vehicles noise and vibration on people's comfort and health and their quality of life. The research results show, based on official statistics, the significant harmful impact of noise and vibration from motor vehicles and road transport on people's health and quality of life, especially in urban areas during the day. Also, the significant increase in the number of electric and hybrid vehicles from one year to another, a reality and a necessity nowadays, and the awareness that electric vehicles are not perfectly quiet and comfortable, open new research opportunities and require the development of new standards, materials, tools, equipment and test methods in the field of NVH, in a sustained synergistic approach from all stakeholders, to meet the needs and demands of today's consumers, and to comply with existing regulations and standards on environmental protection and sustainable development.

Besides living as a free-ranging primate in the horn of Africa and the Arabian Peninsula, the hamadryas baboon has an important place in zoos and can be found in biomedical research centers worldwide. To be valuable as a non-human primate laboratory model for man, its anatomy should be portrayed in detail allowing for the correct interpretation and translation of obtained research results. Reviewing the literature on the use of the baboon in biomedical research revealed that very limited anatomical works on this species are available. Anatomical atlases are incomplete, use archaic nomenclature and fail to provide high definition color photographs. Therefore, the skeletons of two male hamadryas baboons were prepared by manually removing as much soft tissues as possible followed by maceration in warm water to which enzyme-containing washing powder was added. The bones were bleached with hydrogen peroxide and degreased by means of methylene chloride. Photographs of the various bones were taken, and the anatomical structures were identified using the latest version of the Nomina Anatomica Veterinaria. As such, the present article shows 31 annotated multipanel figures. The skeleton of the hamadryas baboon generally parallels the human skeleton, but some remarkable differences have been noticed. If these are taken into consideration when evaluating the results of experiments using the hamadryas baboon, justified conclusions can be drawn.

Stem cells can be used in cellular therapy. This happended in order to replace damaged cells or having aim to regenerate organs. The definition of stem cells can be extended. From this point of view, we can mention taking in consideration the idea in which it is known that these cells form the base of the building body. More than, we can mention that stem cells are characterized by two knowing properties namely differentiation and selfrenewal. Based on the observation that differentiation of adult stem cells into specific derivativescan be controlled by laboratory techniques, it is anticipated that adult stem cells may become the basis of therapies for many, and various types of medical conditions. Taking on stem cells, regenerative medicine (RM) it is known as being one of the hottest topics in biotechnology nowadays. From this purpose, stem cells can be used in regenerative medicine (RM). The principles of regeneration are found in different types of cultures, from long time ago, centuries etc.

The non-pharmaceutical interventions implemented to prevent the spread of COVID-19 have affected the epidemiology of other respiratory viruses. In South Korea, Human metapneumovirus (HMPV) typically occurs from winter to the following spring; however, it was not detected for two years during the COVID-19 pandemic and re-emerged in the fall of 2022, which is a non-epidemic season. To examine the molecular genetic characteristics of HMPV before and after the COVID-19 pandemic, we analyzed 427 HMPV-positive samples collected in the Gwangju area from 2018 to 2022. Among these, 24 samples were subjected to whole-genome sequencing. The results showed a shift in the predominant genotype from A2b2 before the COVID-19 pandemic to A2b1 in 2022. Furthermore, a significant increase in HMPV cases was observed in the 6–10-year age group. Since the onset of the COVID-19 pandemic, social distancing have reduced the possibility of HMPV exposure and herd immunity due to non-transmission, resulting in the introduction of new HMPV genotypes in different seasons.

The oncogenic potential of human papillomavirus (HPV) may be used to determine the tissue tropism of each HPV type. Cervical cancer develops in the squamo-columar junction of the cervices, and most such lesions are induced by high-risk (HR) HPV types. This suggests that HR types preferentially infect the cervix, whereas the preferential infection site for low-risk (LR) types is not well defined. Determination of HPV tropism when using cytology samples can be uncertain since it is difficult to avoid contamination of cell samples between the cervix and the vagina. Herein, cell samples were carefully collected by independently scraping the cervix and vagina, after which the HPV types were determined. HPV tissue tropism was determined by considering what HPV types were positive at only one of the sites (the cervix or the vagina) as the viruses that preferentially infected that site. This method revealed that all LR types were only identified in vaginal samples, whereas 87% of HR types were identified in cervical sites. Thus, LR types may preferentially infect the vagina, whereas HR types infect the cervix. These findings suggest that preferential tissue tropism of certain HPV types is a probable factor for malignant progression.

Modern medical needs call for the efficient remedies against bacterial diseases including severe pulmonary infections reported as a secondary SARS-Cov-2 infection. Here, we propose imprinted drug ciprofloxacin (CF) into the polymer based on methyl-β-cyclodextrin (MCD) via template synthesis. The obtained polycarbamide nanoparticles possess CF's sustained drug release. The interaction of human serum albumin (HSA) with CF and CF-MCD carriers was conducted by FRET, FTIR, fluorescence, and circular dichroism spectroscopy. These studies uncovered that MCD decreases CF's binding efficiency by 2 times, whereas CF’s encapsulation in polymer matrix doubles the Ka value. The changes in HSA’s secondary structure indicate no alterations in the main mechanism of complex formation between CF and HSA in the presence of MCD-based carries. The drug delivery system demonstrates prolonged CF release at pH 7.4 in presence of HSA (model conditions of blood plasma). CF-MCD carriers inhibit E. coli and B. subtilis growth, but for MAX systems we observed a MIC’s increase (~2 times). We believe that our findings are important for further development of new efficient drug forms.

Most studies modelling population mobility and the spread of infectious diseases, particularly using meta-population-multi-patched models, tend to focus on theoretical properties and numerical simulations of such models. There is relatively scanty literature published on fit, inference and uncertainty quantification on epidemic models with population mobility. In this research, we have used three estimation techniques to solve an inverse problem and quantify its uncertainty on a human mobility-based multi-patched epidemic model, using mobile phone sensing data and COVID-19 confirmed positive cases in Hermosillo, Mexico. First, we have utilized a Brownian bridge model using mobile phone GPS data to estimate residence and mobility parameters of the epidemic model. In the second step, we have estimated the optimal model epidemiological parameters by deterministically inverting the model using a Darwinian inspired evolutionary algorithm (EA) known as the genetic algorithm (GA). The third part of the analysis involves performing inference and uncertainty quantification on the epidemic model using two Bayesian Monte Carlo sampling methods: t-walk and Hamiltonian Monte Carlo (HMC). The results show that the estimated model parameters and incidence adequately fit the observed daily COVID-19 incidence in Hermosillo. Moreover, the estimated parameters from HMC result into large credible intervals, improving their coverage for the observed and predicted daily incidences. We also observe improved predictions when using multi-patch model with mobility against the single-patch model.

The growing prevalence of social robots in various fields necessitates a deeper understanding of touch in Human-Robot Interaction (HRI). This study investigates how human-initiated touch influences physiological responses during interactions with robots, considering factors such as anthropomorphic framing of robot body parts and attributed gender. Two types of anthropomorphic framings are applied: the use of anatomical body part names and assignment of male or female gender to the robot. Higher physiological arousal is observed when touching less accessible body parts than when touching more accessible body parts in both conditions. Results also indicate that using anatomical names intensifies arousal compared to the control condition. Additionally, touching the male robot resulted in higher arousal in all participants, especially when anatomical body part names were used. This study contributes to the understanding of how anthropomorphic framing and gender impact physiological arousal in touch interactions with social robots, offering valuable insights for social robotics development.

Introduction The increase in survival of extremely preterm infants has led to increased rates of bronchopulmonary dysplasia (BPD). Therefore, a potential role of human milk feeding in protecting against this condition has been suggested. Material and methods Retrospective descriptive study based on data about morbidity in the population of infants born from 22+0 to 26+6 weeks of gestation, included in the Spanish network SEN1500 during the period 2004-2019 and who were alive at discharge. The primary outcome was moderate-severe BPD. In addition, associated conditions were studied, including breastfeeding at discharge. The temporal trends of BPD and breastfeeding rates at discharge were also studied. Results In the study population of 4341 infants who survived to discharge, the rate of moderate-severe BPD was 43,7% and increased over the period to a rate >50% in the last three years. The factors significantly associated with a higher risk of moderate-severe BPD were male sex, high-frequency oscillatory ventilation, inhaled nitric oxide, patent ductus arteriosus, and late-onset sepsis. Exclusive human milk feeding at discharge and any amount of human milk at discharge were associated with a lower incidence of moderate-severe BPD (OR 0.752, 95% CI 0.629-0.901, and OR 0.714, 95% CI 0.602-0.847, respectively). The rates of human milk at discharge in infants with moderate-severe BPD increased over the period. In the last years, more than one-third of extremely preterm infants were discharged on exclusive human milk feeds, and about two-thirds of them on any amount of human milk feeding. Conclusions
The results of the present study strongly support the role of any amount of human milk in preventing BDP in extremely preterm infants.

Parvovirus B19 infection is associated with a wide range of clinical manifestations, from asymp-tomatic to severe neurological disorders. Its major clinical symptoms, fever and rash, are com-mon to multiple viruses, and laboratory tests to detect B19 are frequently not available. Thus, the impact of B19 on public health remains unclear. We report the case of a 38-day old girl ad-mitted to São Paulo Clinical Hospital, Brazil, with an initial diagnosis of bacterial meningitis, seizures, and acute hydrocephalus. Antibiotic therapy was maintained for one week after admis-sion and discontinued after negative laboratory results were obtained. Nine days after symp-toms onset, a cerebral spinal fluid (CSF) sample revealed persistent pleocytosis. The complete B19 complete genome was subsequently identified in her CSF by a metagenomic next-generation sequencing approach. This report highlights the possible involvement of B19 in the occurrence of acute neurological manifestations, and emphasizes that its possible involve-ment might be better revealed by the use of metagenomic technology to detect viral agents in clinical situations of unknown or uncertain etiology.

It is known that people perceive animacy in objects. However, many studies on animacy and emotional expressions are limited in that the investigated motions were created by experimenters themselves. This makes the objective validity unclear. Moreover, it remains unclear what types of movements can express emotions with animacy due to the limited number of investigations examining both animacy and emotional expressions. Therefore, we investigated the motion elements for both animacy perception and emotional expressions using simple objects that lack features of specific living things, such as eyes, ears, tails, and voices in this study. First, we investigated the motion elements for animacy perception and emotional expressions using a robot simulator that enabled participants to create undulatory motions by tuning parameters for speed, height, and randomness. In total, 64 participants created motions in Normal (neutral), Joy, Sad, Relaxed, and Angry conditions. The results showed that the medians of speed and height in Normal, related only to animacy, were 0.5569[Hz] and 3.050cm at the edges/4.575cm at the center. The differences in Joy were 0.4028[Hz] and 3.348cm/5.022cm, in Sad were −0.1652[Hz] and −0.9982cm/−1.497cm, in Relaxed were −0.1979[Hz] and −0.4902cm/−0.7353cm, and in Angry were 0.5212[Hz] and 4.688cm/7.032cm. Second, we investigated whether the motion elements revealed in the first experiment were sufficient to express emotions with animacy, using a robot simulator that reflected the results of the motion element investigation. In total, 44 online participants observed the simulator. The results showed that participants could understand emotional arousal levels at the same time as animacy, but they did not fully understand emotional valence. Our findings provide design guidelines for robots that exhibit emotional expressions and closely interact with humans.

As a fundamental, overall, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities given constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Given the effect of human migration prediction, in combination with artificial intelligence and big data technology, the paper concludes with specific suggestions and countermeasures aimed at enhancing human migration prediction research results to serve economic and social development and national strategy.

Mpox (monkeypox) is a zoonotic disease that has been endemic in African countries for decades, with a recent outbreak in several countries around the world. A 39-year-old male with HIV-HBV coinfection and poor adherence to antiretroviral treatment, who was severely immunocompromised and had a concurrent diagnosis of Mpox infection, presented to our hospital with disseminated dermatosis (over 350 lesions), perianal ulcers, odynophagia, oral intolerance, diarrhea, and soft-tissue bacterial superinfection of the lower extremities. Laboratory results were consistent with HBV infection, with an absolute CD4 cell count of 40 cells/uL and a positive PCR result for Mpox. An abdominopelvic CT scan showed evidence of severe proctitis and perineal soft-tissue infection. After 65 days of Mpox PCR, new lesions in the vesicular stage continued to appear, eventually developing hemodynamic instability and sepsis, resulting in a fatal outcome. Our case highlights the importance of intentionally looking for risk factors such as HIV/HBV coinfection and evaluating immune status (CD4 cell count) in patients with severe Mpox infection because it could be related to higher mortality.

Anatomical airways parameters as length, diameter and angles have a strong effect on the flow dynamics. Aiming to explore the effect of variations of the bifurcation angle (BA) and carina rounding radius (CRR) of lower human airways on the respiratory processes, numerical simulations of airflow during inhalation and exhalation were performed using a synthetic bifurcation models. Geometries for the airways models were parameterized based on a set of different BA's and several CRR's. A range of Reynolds numbers (\Reyn) relevant to the human breathing process were selected to analyzed the airflow behaviour. The numerical results show a significant influence of BA and the CRR on the development of the airflow within the airways, and therefore affecting some relevant features of the flow, namely the deformation of velocity profiles, alterations of pressure drop, the flow patterns, and finally enhance or attenuation of wall shear stresses (WSS) appearing during the regular respiratory process. Numerical results show that increases in the bifurcation angle value are accompanied by pressure increases of about 20\%, especially in the regions close to the bifurcation. Similarly, increases in the BA value lead to a reduction in peak shear stresses of up to 70\%. For the ranges of angles and radii explored, an increase in pressure of about 20\% and a reduction in wall shear stress of more than 400\% were obtained by increasing the carina rounding radius. Analysis of the coherent structures and secondary flow patterns also revealed a direct relationship between the location of the vortical structures, the local maxima of the velocity profiles and the local vorticity minima. This relationship was observed for all branches analysed, for both the inhalation and exhalation processes of the respiratory cycle.

Recent advances in DNA sequencing technology have shown that the human milk microbiota of healthy women varies substantially. The gDNA extraction method may influence the observed variation, biasing the microbiological reconstruction after all. In this study, a genomic DNA extraction method for DNA isolation from human milk samples was standardized and compared with commercial and standard hose make methods. Spectrophotometric measurements, gel electrophoresis, and PCR amplifications were used as criteria for evaluating the quantity, quality, and functionality of the extracted DNA. Furthermore, the standardized method of extracting gDNA from human milk was evaluated for its ability to isolate functional DNA from gram-positive, and gram-negative bacteria and fungi, to improve the reconstruction of microbiological profiles. The novel DNA extraction method increased the quantity and quality of the gDNA extracted compared with commercial and standard house-make protocols. This method even allowed PCR amplification of the V3-V4 regions of the 16S ribosomal gene in all samples, and the ITS-1 region of the fungal 18S ribosomal gene in 95 % of the samples as well. It is concluded that the proposed method provides better performance for the extraction of gDNA from complex samples such as human milk.

Globally,coastal regions are vital areas of human activities and, as such, are centres of population growth and urban and economic development. Long-term human development has had a major impact on the ecological environment of coastal zones. Therefore, exploring the distribution and provenance of marine sediment types in coastal areas heavily influenced by human activities can provide scientific evidence and references for the current and future ecological management of these sensitive environments. For this reason, we have conducted an analysis on the sediment grain size, Endmember, organic matter content and geochemical elements in the Lu’erhuan River‒Malan Island‒Sandun Island area in the eastern part of Qinzhou Bay, a region heavily influenced by human activities. Sediment grain size clearly differs throughout the study site and the material provenances and hydrodynamic conditions are also varied, likely due to the local environmental conditions and the significant impact human activities have had on the area. The finest-grained sediment is imported from either inland or coastal areas via rivers and weak tidal currents, the next finest component is input from coastal areas through weak tidal currents, and the moderately coarse component mainly originates from nearby beaches. The two coarsest-grained sediment components are influenced by the combination of human activities, tidal currents and waves and enter the water via erosion. Organic matter provenance resembles that of the sediment components, exhibiting varied characteristics. This is due to the combination of natural and human activities in the bay. The organic matter in the upper reaches of the Lu’erhuan River originates from the river and coastal paddy fields, with obvious terrigenous characteristics; the organic matter in northern Malan Island mainly comes from external sources related to oyster farming; while organic matter in eastern Sandun Island is mainly produced endogenously by marine plankton. Al, Ti, Fe, Mg, K, Ga and other elements indicate that terrestrial sediments are significantly disturbed by human activities. However，Mn reflects the marine distribution of terrestrial sediments from the Lu’erhuan River to Jishuimen. Ca and Sr, indicators of marine sediments, are distributed in the eastern offshore area of Sandun Island, which is connected to the open waters. Due to the influence of human activities, As and Cd are highly enriched in the study area, while Cu is less affected by human activities.

Human trafficking is the third most lucrative form of trafficking in the world (just behind drugs and counterfeit goods). Multiple outbreaks of unrest between October 2016 and August 2017 in the Rakhine State of Myanmar triggered around 745,000 influxes of Rohingyas crossing into Bangladesh through the border boundaries at Teknaf and Ukhiya sub-districts of Cox’s Bazar. In this regard the media confirmed that over a thousand Rohingya people particularly women and girls were victims of human traffickers. This research aims to explore the underlying cause of human trafficking (HT) during emergency response and seeks how the knowledge and capacity of the refugee, local administration and law enforcement agencies in Bangladesh can be improved in promoting counter trafficking (CT) and safe migration processes. In order to achieve the objectives, this research starts with reviewing acts, rules, policies and action plans of the Government of Bangladesh on the HT, CT and safe migration processes. Then, a case study method has been applied to present ongoing the CT and safe migration programs of a NGO called Young Power in Social Action (YPSA) that received funding and technical support from the International Organization of Migration (IOM) for this purpose, the research in this paper will later evaluate the effectiveness of the program through conducting key informant interviews (KII) and focus group discussion (FGDs) with beneficiary and non-beneficiary participants from refugees, law-enforcing agencies (LEA) and non-governmental organizations’ (NGOs) in Teknaf, and Ukhyia sub-districts in Bangladesh. Thus, this research identified program level strengths and weaknesses in relation to the CT, and safe migration process and provide key directions how they can be improved.

We describe a dataset that contains job description published on a popular online website in the information and technology sector. As the website focus mainly on United Kingdom based jobs, the data have a specific focus on this country. It contains 11.501 job vacancies and 13 related meta data information. The dataset is suitable for HR analysis using machine learning techniques such as natural language processing and neural networks.

Muscle spindles are encapsulated sensory organs found in most of our muscles. Prevalent models of sensorimotor control assume the role of spindles is to reliably encode limb posture and movement. Here, I argue that the traditional view of spindles is outdated. Spindle organs can be tuned by spinal γ motor neurons that receive top-down and peripheral input, including from cutaneous afferents. A new model is presented, viewing γ motor activity as an intermediate coordinate transformation that allows multimodal information to converge on spindles, creating flexible coordinate representations at the level of the peripheral nervous system. That is, I propose that spindles play a unique overarching role in the nervous system: that of a peripheral signal-processing device that flexibly facilitates sensorimotor performance, according to task characteristics. This role is compatible with previous findings and supported by recent studies with naturalistically active humans. Such studies have so far shown that spindle tuning enables the independent preparatory control of reflex muscle stiffness, the selective extraction of information during implicit motor adaptation, and for segmental stretch reflexes to operate in joint space. Incorporation of advanced signal-processing at the periphery may well prove a critical step in the evolution of sensorimotor control theories.

Human Parechoviruses, officially known as Parechovirus A (PeV-A), is associated with mild gastrointestinal and respiratory illness in young children, however, they may also give rise to Central Nervous System (CNS) infections and neonatal sepsis. While studies have delved into the detection of PeV-A in different populations, the detection of PeV-A in Hispanic populations in Latin American countries is not well-known. The aim of this study was to determine the presence of PeV-A in respiratory, gastrointestinal, and neurological clinical samples of pediatric patients in Panama. Two hundred samples of pediatric patients with a negative diagnosis for the main respiratory viruses, rotavirus and neurological viruses such as herpesvirus, enterovirus and cytomegalovirus, collected between 2014 and 2015, were analyzed by real-time RT-PCR. Eight positive PeV-A infections were detected, 2 in respiratory samples, 5 in stool samples and one detected in cerebrospinal fluid. This is the fisrt report of PeV-A in Panamá.

The majority of adults in the world (around 83%) carry antibodies reactive with HCMV and are thought to retain inactive or latent infections lifelong. The virus is transmitted via saliva so infection events are likely to be common. Indeed it is hard to imagine a life without exposure to HCMV. From 45 seronegative individuals (13 renal transplant recipients, 32 healthy adults), we present seven cases who had detectable HCMV DNA in their blood and/or saliva, or a CMV-encoded homologue of IL-10 (vIL-10) in their plasma. One case displayed NK cells characteristic of CMV infection, and HCMV DNA became undetectable. In other cases, the infection may persist with seroconversion blocked by vIL-10. Future research should seek mechanisms that can prevent an individual from seroconverting despite a persistent HCMV infection, as HCMV vaccines may not work well in such people.

Fucosylated carbohydrates and glycoproteins from human breast milk are essential for the development of the gut microbiota in early life because they are selectively metabolized by bifidobacteria. In this regard, α-L-fucosidases play a key role in this successful bifidobacterial colonization allowing the utilization of these substrates. Although a considerable number of α-L-fucosidases from bifidobacteria have been identified by computational analysis, only a few of them have been characterized. Hitherto, α-L-fucosidases are classified into 3 families, GH29, GH95 and GH151 based on their catalytic structure. However, bifidobacterial α-L-fucosidases belonging to a particular family show significant differences in their sequence. Because this fact could underlie distinct phylogenetic evolves, here extensive similarity searches and comparative analyses of the bifidobacterial α-L-fucosidases identified were carried out with the assistance of previous physicochemical studies available. This work reveals 4 and 2 paralogue bifidobacterial fucosidase groups within GH29 and GH95 families, respectively. Moreover, Bifidobacterium logum subsp. infantis species exhibited the greatest number of phylogenetic lineages in their fucosidases clustered in every family GH29, GH95 and GH151. Since α-L-fucosidases phylogenetically descended from other glycosyl hydrolase families, we hypothesized that could exhibit additional glycosidase activities other than fucosidase, raising the possibility about their application to transfucosylate other substrates than lactose in order to synthesis novel prebiotics.

Introduction: The imbalance between technological development and ideological strengthening has resulted in the fading of the Pancasila ideological values of millennials. Technology that accompanies everyday life seems to be the main character in human life. Yet if humans themselves do not use technology wisely, technology can be a threat to both individuals and the life of the nation and state. If technology is increasingly out of control, threats to the Pancasila ideology are very likely to occur, such as in the G 30S PKI incident. Therefore, it is necessary to take action both from the government and the community as individuals to safeguard the development of technology to be used wisely. That way, we can prevent the threat to the Pancasila ideology together. Writing this article aims to analyze how the role and influence of the Pancasila ideology on the millennial generation in the 4.0 industrial revolution and to find solutions so that the values of Pancasila remain attached to the next generation of the Indonesian nation. Method: Writing This article uses a qualitative method by using literature reviews from the results of related research journals that have been published online through websites and other online media. Results: Thirteen journals and one book have met the criteria for the inclusion of a predetermined review. Research is based on the stigma of society regarding technological developments in the Industrial revolution 4.0. Therefore, the government is expected to be more severe in implementing human capital management. Conclusion: By procuring human capital management, it will be possible that Human Resources (HR) in Indonesia will be able to carry out the ideals of a golden Indonesian generation with Pancasila values inherent in the hearts of the people and the nation's future generations.

This paper describes a new generation of computational intelligence founded on the ancient idea of compassion called Artificial Compassion. The creation of Artificial Compassion is the result of two coinciding historical developments. The first is the increasing discoveries of human sciences in new fields like neuroendocrinology and psychoneuroimmunology. This provides the spark for Artificial Compassion. For example, we once thought with certainty that our brain is fixed for life but neuropsychology and a device called the fMRI have shown it is “plastic”. It changes constantly throughout our lives in response to our experiences. Remarkably, we also now know it is changed for the better through positive emotions like compassion, kindness and happiness. So, too, are the immune, endocrine, genetic, cardio and neural systems influenced and changed by our emotional experiences. This new perspective on emotions and plasticity validates much of ancient wisdom in medical systems outside the west. Long held assumptions about emotion are unsuitable for humanity. The second development is ‘machine rub off’. We are in symbiotic relation with our devices today and we are plastic. We are changed by our interactions but many people have computer rage. We need Artificial Compassion to replace computer rage with positive plasticity.

Memory is probably one of the most complex cognitive functions of the human, and in many years, thousands of studies have helped us to better recognize this brain function. One of the reference textbooks in neuroscience, which has also elaborated on the memory function, is written by Prof. Kandel and his colleagues. In this book, I encountered a number of ambiguities when it was explaining the memory system. Here, I am sharing those points, either to find an answer for them, or to let them be a suggestion for our future works. Prof. Kandel has spent most of his meritorious lifetime on studying the memory system; however, the brain is extremely complex, and as a result, we still have many years to comprehensively understand the neural mechanisms of brain functions.

Few-layer graphene oxide (GO) has shown none or very weak cytotoxicity and anti-proliferative effects in a wide range of cell lines such as glyoma cells and human skin HaCaT cells, in concentrations up to 100 µg/mL However, multi-layer GO has been hardly explored in the biomedical field. Thus, multi-layer GO was examined here in human keratinocyte HaCaT cells treated with different concentrations ranging from 0.01 to 150 µg/mL during different periods of times (3, 12 and 24 hours). The results of this study showed a time-concentration dependence with two non-cytotoxic concentrations (0.01 and 0.05 µg/mL) and a median effective concentration value of 4.087 µg/mL at 24 hours of GO exposure. Contrary to what has been reported for few-layer GO, cell proliferation of the HaCaT cells in contact with the multi-layer GO at 0.01 μg/mL showed identical proliferative activity compared to an epidermal growth factor (1.6-fold greater than the control group) after 96 hours. The effects of the multi-layer GO on the expression of 13 genes (SOD1, CAT, MMP1, TGFB1, GPX1, FN1, HAS2, LAMB1, LUM, CDH1, COL4A1, FBN and VCAN) at the non-cytotoxic concentrations of GO in the HaCaT cells were analyzed after 24 hours. Thus, the lowest non-cytotoxic GO concentration was able to up-regulate the CAT, TGFB1, FN1 and CDH1 genes, which confirms the great potential of multi-layer GO in the biomedical field.

In this article, we report how we constructed a dataset that contains the responses to a survey designed for investigating the relationship that humans have with their smart devices. The dataset has been collected in May-July 2020 and is a sample of over 500 respondents of various different ethnicities and backgrounds. These data have been used for modelling the ways people relate to their devices using the notion of agency. However, the data can be used for complementing any study that intends to investigate a tool-mediated communication from the perspective of the users and via a variety of attitudes and expectations the users invest in their devices and in themselves as users. This article presents the survey items as well as some preliminary data insights. The data have been collected in English and answers have been anonymized in order to ensure GDPR compliance. They are stored in a .csv file containing the respondents’ answers to the questions.

Transposable elements (TEs), also known as mobile elements (MEs), are interspersed repeats that constitute a major fraction of the genomes of higher organisms. As one of their important functional impacts on gene function and genome evolution, TEs participate in regulating the expression of genes nearby and even far away at transcriptional and post-transcriptional levels. There are two known principal ways by which TEs regulate expression of genes. First, TEs provide cis-regulatory sequences in the genome with their intrinsic regulatory properties for their own expression making them potential factors for regulating the expression of the host genes. TE-derived cis-regulatory sites are found in promoter and enhancer elements, providing binding sites for a wide range of trans-acting factors. Second, TEs encode for regulatory RNAs with their sequences showed to be present in a substantial fraction of miRNAs and long non-coding RNAs (lncRNAs), indicating the TE origin of these RNAs. Furthermore, TEs sequences were found to be critical for regulatory functions of these RNAs including binding to the target mRNA. TEs thus provide crucial regulatory roles by being part of cis-regulatory and regulatory RNA sequences. Moreover, both TE-derived cis-regulatory sequences and TE-derived regulatory RNAs, have been implicated to provide evolutionary novelty to gene regulation. These TE-derived regulatory mechanisms also tend to function in tissue-specific fashion. In this review, we aim to comprehensively cover the studies regarding these two aspects of TE-mediated gene regulation, mainly focusing on the mechanisms, contribution of different types of TEs, differential roles among tissue types, and lineage specificity, based on data mostly in humans.

Community is a veritable ingredient for social change and development in a society. The potentials of individuals and groups in the community are a great source or resource for promoting unity, development and patriotism. The general objective of this study is to examine the influence of diversity in community composition, on the operation of community policing style in Nigeria. The study adopted qualitative research approach to collect and analyze data. In-depth interview is the instrument of data collection while content analysis is the method of data analysis. The study took place in Kwara State, North central Nigeria. Twenty community leaders and youth groups heads were purposefully selected through snowball sampling method. Where this great resource or human capital is adequately galvanized by the leaders and community heads, the community becomes formidable and capable of solving her social problems together. However, these benefits of community are hampered by other socio-cultural and economic variables in its members. Community on its own cannot achieve much until members are mobilized to support and partner with government in any developmental projects.

Starting from the importance of risk perception for taking certain preventive measures to protect people and their property from disasters, the subject of the research is to examine the factors influencing public perception of mythically-based human behavior in disaster conditions. Using the random sampling method, 250 adult respondents were surveyed in the city of Belgrade, using a specially created and adapted survey questionnaire. The results of the research show that there is no statistically significant influence of gender, age, educational and economic factors on the public perception of human behavior in disaster conditions. The results of the research can be used to improve strategies and campaigns based on risk assessment, aimed at improving the safety of people in disasters.

Aminoacyl-tRNA synthetases (ARSs) are essential enzymes for translating amino acids for protein synthesis. Their function in pathogen-derived infectious diseases has been well established, which has led to development of small molecule therapeutics. The applicability of ARS inhibitors for other human diseases such as fibrosis has recently been explored in the clinical setting. There are active studies to find small molecule therapeutics for cancers. Studies on central nervous system (CNS) disorders are burgeoning as well. In this regard, we present a concise analysis of the recent development of ARS inhibitors based on small molecules from the discovery research stage to clinical studies as well as a recent patent analysis from the medicinal chemistry point of view.

Bacterial internalization is a strategy that non-intracellular microorganisms use to escape the host immune system and survive inside the human body. Among bacteria species, Staphylococcus aureus showed ability to interact and infect osteoblasts causing osteomyelitis as well as bone and joint infection, while also becoming increasingly resistant to antibiotic therapy and a reservoir of bacteria that can make the infection difficult to cure. Despite being a serious issue in orthopedic surgery, little is known about the mechanisms that allow bacteria to enter and survive inside the osteoblasts, also due to the lack of consistent experimental models. In this review, we describe the current knowledge about S. aureus internalization mechanisms and various aspects of the interaction between bacteria and osteoblasts (e.g. best experimental conditions, bacteria-induced damages and immune system response), focusing on studies performed using the MG-63 osteoblastic cell line, so far the best model for the study of this phenomenon.

In recent years, advances in science, technology, and the way in which we view our world have led to anincreasingly broad use of the term “intelligence”. As we learn more about biological systems, we find more and more examples of complex and precise adaptive behavior in animals and plants. Similarly, as we build more complex computational systems, we recognize the emergence of highly sophisticated structures capable of solving increasingly complex problems. These behaviors show characteristics in common with the sort of complex behaviors and learning capabilities we find in humans, and therefore it is common to see them referred to as “intelligent”. These analogies are problematic as the term "intelligence" is inextricably associated with human-like capabilities. While these issues have been discussed by leading researchers of AI and renowned psychologists and biologists highlighting the commonalities and differences between AI and biological intelligence, there have been few rigorous attempts to create an interdisciplinary approach to the modern problem of intelligence. This article proposes a comparative framework to discuss what we call “purposeful behavior”, a characteristic shared by systems capable of gathering and processing information from their surroundings and modifying their actions in order to fulfill a series of implicit or explicit goals. Our aim is twofold: on the one hand, the term purposeful behavior allows us to describe the behavior of these systems without using the term "intelligence", avoiding the comparison with human capabilities. On the other hand, we hope that our framework encourages interdisciplinary discussion to help advance our understanding of the relationships among different systems and their capabilities.

The knowledge of human body proportion and segmental properties of limbs, head and trunk are of fundamental importance in biomechanical research. As many methods are employed, it is important to know which they are currently available, which data on human body masses, lengths, center of mass (COM) location, weights and moment of inertia are available and which methods are most suitable for a specific research purposes. The present review examines the literature concerning human body segments properties for biomechanical purposes. It emerges that data obtained in studies on cadaveric specimens are still the most accurate, whilst technological tools currently available are manifolds, each one with proper advantages and disadvantages. Classical studies were focused mainly on white men, while in recent year the available data of body segments has been extended to children, woman, and other races. Also, data on special population (obese, pregnant women) are starting to appear in the scientific literature.

In central Senegal malaria incidences have declined from 2000 to 2010 in response to scaling-up of control measures and then remained stable, making elimination improbable. Additional control measures are needed to reduce transmission. We simulated chemoprophylaxis interventions targeting malaria hotspots, using a meta-population mathematical model based on differential equation framework and incorporating human mobility. The model was fitted to weekly malaria incidences from 45 villages. Three approaches for selecting intervention targets were compared: a) villages with malaria cases during the low transmission season of the previous year; b) villages with highest incidences during the high transmission season of the previous year; c) villages with highest connectivity with adjacent populations. Our modeling, considering human mobility, showed that the intervention strategies targeting hotspots would be effective in reducing malaria incidence in both targeted and untargeted areas. But whatever the intervention, pre-elimination stage (1-5 cases per 1,000 per year) would not be reached without simultaneously increasing vector control by more than 10%. Targeted interventions allow increasing overall malaria control and elimination potential.

Three decades ago, human arsenic (As) contamination has been recognized in Ron Phibun, a sub-district with tin mining activity in southern Thailand. Since then different government bodies have attempted to mitigate the As-contamination problem by providing safe water in households. The most recent study conducted during 2000-2002 reported only a small fraction of population still had high urinary As level. Less attention has been paid to this issue afterwards. The present study aimed to re-assess the current situation, including human As contamination, water use behavior as well as identify risk factors of elevated As concentration among residents of Ron Phibun. The survey of 560 participants living in Ron Phibun with urinary As assessment was conducted. The median urinary As concentration of study participants was higher than normal. Consumption of shallow well water, a source generally considered as As-contaminated, was higher than a previous survey. A significant association was observed between urinary As concentrations and water sources for drinking and cooking. Gender and educational level were found to be associated with urinary As concentration. Significant associations between urinary As concentration and certain diseases (respiratory diseases, dermatitis, and dyslipidemia) were observed. The findings suggested further investigation of all water sources in the area for As contamination.

As a result of extensive research in the field of mobile robots (rovers) and swarms, a number of algorithms exist to assist them for executing a mission in the three levels of software architecture: strategic (interaction loop level), tactic (planning) and operational (sensing, control and actuation). They allow them to achieve their goals while adapting to their environment through a multitude of methods designed for each situation. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction applied to help humans in hazardous environment such as militarized zone. In this paper, we will present some interesting algorithms for interactive and autonomous mobile robots acting in swarms in an open and crowded environment. A discussion will focus on comparing different algorithms and their advantages and disadvantages.

Aedes aegypti is the primary mosquito vector of several human arboviruses including dengue virus (DENV). Vector control is the principal intervention to decrease the transmission of these viruses. The characterization of molecules involved in the mosquito physiological responses to blood-feeding may help to identify novel targets useful in the design of effective control strategies. In this study, we evaluated the in vivo effect of feeding adult female mosquitoes with human blood containing either heat-inactivated (IB), normal serum (NB), and RNA-seq based transcript expression was compared against sugar-fed (SF) mosquitoes. In the in vitro experiments, we treated Aag2 cells with a recombinant version of the complement proteins (hC3 or hC5a) and compared transcript expression to untreated control cells after 24h. The transcript expression analysis revealed that human complement proteins modulate approximately 2,300 transcripts involved in multiple biological functions, including the immune system. We also found 161 up-regulated and 168 down-regulated transcripts differentially expressed when hC3 and hC5a were compared against the control untreated cells. We conclude that active human complement induces significant changes in the transcriptome of Ae. aegypti mosquitoes, which can influence the infective capacity of pathogens ingested during blood meals.

Background: Human papillomavirus (HPV) infection plays a key role in the development of cervical cancer that ranks as the third most common gynecological cancer in China. We attempt to investigate the age and genotype-specific prevalence of HPV DNA in western Chinese females. Methods: A total of 301,880 woman were recruited from 4 different regions of western China. Routine cervical samples were collected and HPV screening was performed using 21 HPV Genotyping test (Hybribio). The overall prevalence, age-specific prevalence, and genotype distribution were analyzed. Results: The overall prevalence of HPV was 18.01%. The high-risk HPV infection rate was 79.14%, the low-risk HPV infection rate was 12.56%, and the mixed HPV infection rate was 8.30%. The most common 4 HR HPV subtypes were HPV-52, 16, 58 and 53, which accounted for 20.49 %, 19.93 %, 14.54 % and 10.01 %. In LR HPV genotype, HPV-6 ranked the highest (28.17 %), followed by HPV-81 (9.09 %), HPV-11 (3.78 %). HPV genotype subgroup analysis also showed that single-type infections had the highest prevalence rate (77.26%) among HPV positive individuals. Among muti-infection genotype, double infection was most common with frequencies of 76.04%. Conclusion: This large report shows that the overall prevalence of HPV was high in China, whose distribution exhibits different patterns across different particular age and regions. Viral genotypes HPV53, 6 were are frequently detected in this population, which is worth of significant clinical attention.

Drinking water supplies in the developing world often serve as a biosphere for various organisms. Viral gastroenteritis is a neglected area of research in Pakistan, there is no data for the prevalence of enteric viruses in drinking water of the largest city of Karachi. The present study aimed to provide a survey of the existence of enteric viruses: human adenovirus (HAdV), human enteroviruses (hEV), and genotype A rotavirus (GARV) in tap water. Using a simple PCR approach, we detected 20%, 43%, and 23% of HAdV, hEV, and GARV in tap water respectively. We have also shown an overall quality of tap water at the pumping station and consumer tap. Moreover, we assessed the efficiency of small-scale water treatment methods for the removal of viruses.

Social distancing, washing hands and good hygiene are essential and currently the most potent methods available to curb down the unprecedented speed by which the new coronavirus is spreading across the globe. Even under lockdown, which is necessary to significantly reduce the number of people that get infected by an ill person, are there additional measures that each of us can embrace to even further reduce the risk of infection and the severity of the COVID-19 disease? Given the lack of licensed drugs that target SARS-CoV-2 specifically, we have to look into additional non-specific defense mechanisms that animals and humans evolved to protect themselves from pathogen invasions. The goal of this article is to describe how various of our non-specific defense mechanisms work, which actually precede the inflammatory response, and to discuss whether we can exploit the unique features of the coronavirus envelope and the self-cleaning machinery of the human respiratory tract to strengthen our self-defense. The challenge is to actively interfere with supportive measures during the short time window between getting exposed and before an inflammatory response gets initiated.

Specialized, reductionist, and linear approaches are applied in clinical research; they are based on linear logic and used towards therapeutic molecule-based targets. However, those approaches do not consider a systemic vision that describes the remote cause of the pathogenic activation. We propose new theoretical and practical methods for the next drug generation development. Self-organization, network structure, hierarchical organization, non-linearity, feedback circuits, reactions to information, and the view of drugs as information clarify the existing pharmacological methods. We suggest a perspective and hierarchical vision of the human organism based on six levels (mechanic and structural; metabolic; bodily dynamic; emotional, cognitive, spiritual). The therapy should restore the self-organization of every level using the “intelligent” modulation of the network responses. Multi-targeted drugs should act on the remote cause of the pathogenic cascade and be administered based on personal variability and networks. This approach may help the development of individualized, precise, and integrated medicine.

Atopic dermatitis (AD) is a chronic inflammatory skin disease, which can cause skin barrier function damaged. Although co-incubation with docosahexaenoic acid (DHA) exerts a positive effect in deficient skin model, there is no study to investigate the effects of topical treatment with DHA in inflammatory reconstructed human epidermis (RHE) model. The effects of DHA on monolayer normal human epidermal keratinocyte (NHEK) cells were evaluated via CCK-8, qPCR and ELISA. The skin related barrier function was assessed by hematoxylin-eosin (HE) staining, western blot (WB), Immunohistofluorescence (IF) and ELISA in normal and inflammatory RHE models. DHA upregulated filaggrin and loricrin expression at mRNA levels in addition to suppress overexpression of TNF-α，IL-1α and IL-6 stimulated by poly I:C plus LPS (stimulation cocktail) in cultured NHEK cells. After topical treatment with DHA, cocktail induced inflammatory characteristics of skin diseases including barrier morphological, differentiation proteins and TSLP secretion, which were alleviated in RHE models. Supplementation with DHA can improved related barrier function and have anti-inflammation effects in monolayer keratinocytes and RHE models, which indicated that DHA may have a potential value for the treatment of inflammation-associate skin diseases.

Bartonella spp. bacteria can be found around the globe and are the causative agents of multiple human diseases. The most well-known infection is called cat-scratch disease, which causes mild lymphadenopathy and fever. As our knowledge of these bacteria grows, new presentations of disease have been recognized with serious manifestations. Not only has more severe disease been associated with these bacteria, but Bartonella species have also been discovered in a wide range of mammals and the pathogens’ DNA can be found in multiple vectors. This review will focus on some common mammalian reservoirs as well as the suspected vectors in relation to disease transmission and prevalence. Understanding the complex interactions between these bacteria, their vectors, and reservoirs as well as the breadth of infection by Bartonella around the world will help toassess the impact of Bartonellosis on public health.

The potential to affect gene expression via G-quadruplex stabilization has been extended to all domains of life, including viruses. Here, we investigate the polymorphism and structures of G-quadruplexes of the human papillomavirus type 52 with UV, CD and NMR spectroscopy and gel electrophoresis. We show that oligonucleotide with five G-tracts folds into several structures and that naturally occurring single nucleotide polymorphisms (SNPs) have profound effects on the structural polymorphism in the context of G-quadruplex forming propensity, conformational heterogeneity and folding stability. With help of SNP analysis, we were able to select one of the predominant forms, formed by G-rich sequence d(G3TAG3CAG4ACACAG3T). This oligonucleotide termed HPV52(1-4) adopts a three G-quartet snap back (3+1) type scaffold with four syn guanine residues, two edgewise loops spanning the same groove, a no-residue V loop and a propeller type loop. The first guanine residue is incorporated in the central G-quartet and all four-guanine residues from G4 stretch are included in the three quartet G-quadruplex core. Modification studies identified several structural elements that are important for stabilization of the described G-quadruplex fold. Our results expand set of G-rich targets in viral genomes and address the fundamental questions regarding folding of G-rich sequences.

Objective:The application of information technology (IT( is fundamental in the hospitals to stay competitive.In this regard, recognizing the main risks to the implementation of IT in hospitals can provide vastopportunities to improve its efficiency and help to make strategic decisions. This study aimed tosearch for the main risks of implementation of IT projects in the hospitals of Tehran. Methods: This was a practical and cross-sectional study which was conducted in the 18 hospitals of Tehran,Iran, 2018; in which a sample of 65 members were studied. The required data were collected using a questionnaire to examine seven main risks, including market, project management, human resources, technical, organizational, financial, strategic risks. The collected data were analyzed using SPSS 19.0. Additionally, the method used to test the risks in this study was structural equation modeling, which was ran using LISREL 9.30. Results: The results showed that among the seven main risks of to the implementation of IT in hospitals, the highest and lowest means were related to the human resource risks and the market risks, respectively. Also, according to the SEM, human resource risks and market risks had the highest and lowest effects, respectively. Conclusion: Announcing the use of IT in the hospitals, holding conferences about new IT developments with employees, suitable training, encouraging them to use IT tools, providing a motivating atmosphere to use IT tools for employees, are a few effective ways of overcoming the human resource risks.

Indigenous Australians have been particularly affected by type 2 diabetes mellitus due to their genetic susceptibility and a range of environmental risk factors. Recent genetic studies link predisposition to some diseases, including diabetes, to archaic humans, such as Neanderthals and Denisovans, suggesting persistence of ancient alleles in the genomes of modern humans. In this review we discuss the evolutionary role of the negative genetic selection associated with an adopted Western lifestyle as well as DNA variants influencing predisposition to obesity and diabetes in the Australian Indigenous population. We review the contribution of the ancient gene/pathways to the modern human phenotypes including the Neanderthal haplotype-tagging SNPs in NTRK2 gene, which may continue to play a role in obesity in Indigenous Australians.

Gesture-based recognition is one of the most intuitive methods for inputting information and is not subject to cumbersome operations. Recognition is performed on human's consecutive motion without reference to retrial or alternation by user. We propose a gesture recognition model with a mechanism for correcting recognition errors that operates interactively and is practical. We applied the model to a setting involving a manual grading task in order to verify its effectiveness. Our system, named GERMIC, consists of two major modules, namely, handwritten recognition and interactive correction. Recognition is materialized with image feature extraction and convolutional neural network. A mechanism for interactive correction is called on-demand by a user-based trigger. GERMIC monitors, track, and stores information on the user's grading task and generates output based on the recognition information collected. In contrast to conventional grading done manually, GERMIC significantly shortens the total time for completing the task by 24.7% and demonstrates the effectiveness of the model with interactive correction in two real world user environments.

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new smartphone accelerometer dataset designed for activity recognition. The dataset includes 11,771 activities performed by 30 subjects of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with two different classifiers and with different configurations. The best results are achieved with k-NN classifying ADLs only, considering personalization, and with both windows of 51 and 151 samples.

The Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD) indices are used to assess the indoor environment in terms of human thermal comfort-discomfort. In this study, an experimental combined objective and subjective investigation of thermal comfort perception has been performed in students between 16-18 years old, in a non-air-conditioned school building. The objective approach included instrumentation measurements and data processing according to ISO 7730, whereas, the subjective one was based on answers collection following ISO 10551. The study is mainly devoted to the verification of Fanger’s approach in a building, in free running conditions, under a mild (moderate) climate.The comparison between instrumentation data and questionnaire results presented an underestimation of the mean vote, predicting a cooler sensation than the actual one.