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.

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.

(1) Background: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a lifesaving treatment but carries a high infection risk. Diagnosing infections remains challenging due to the limited accuracy of standard biomarkers. (2) Methods: This single-center study aimed to evaluate presepsin (PSP) and YKL-40 as infection biomarkers in febrile patients during the allo-HSCT pre-engraftment phase. Biomarker levels were prospectively measured in 61 febrile episodes from 54 allo-HSCT patients at admission, representing baseline levels and then at Day 1, 3, 5, and 7 following fever onset. The diagnostic value was compared to that of procalcitonin (PCT). (3) Results: PSP showed fair diagnostic value on Day 1 (AUC 0.656; 95% CI: 0.510 – 0.802) and Day 3 (AUC 0.698; 95% CI: 0.559 – 0.837). YKL-40 did not provide any significant diagnostic value across measured time points. PCT outperformed PSP and YKL-40, particularly on Day 3 (AUC 0.712; 95% CI: 0.572 – 0.852). When combining biomarkers, the best model for predicting infection used PSP > 3.144 ng/mL and PCT > 0.28 g/L on Day 3, resulting in R2 of about 31% (p < 0.001). (4) Conclusion: Neither test showed sufficient discriminative power for early infection to recommend their use as individual diagnostic tools in clinical practice.

Therapeutically effective treatments of cancer are limited. To calibrate the efficiency of the novel technique we recently discovered to modulate cancer cell viability using tuned electromagnetic fields; H1299 human lung cancer cells were irradiated in a sweeping regime of W-band (75-105 GHz) millimeter waves (MMW) at 0.2 mW/cm² (2 W/m²). Effects on cell morphology, cell death and senescence were examined and compared to that of non-tumorigenic MCF-10A human epithelial cells. MMW irradiation led to alterations of cell and nucleus morphology of H1299 cells, significantly increasing mortality and senescence over 14 days of observation. Extended irradiation of 10 minutes duration resulted in complete death of exposed H1299 cell population within two days, while healthy MCF-10A cells remained unaffected even after 16 minutes of irradiation under the same conditions. Irradiation effects were observed to be specific to MMW treated H1299 cells and absent in the control group of non-irradiated cells. MMW irradiation affected nuclear morphology of H1299 cells only and not of the immortalized MCF-10A cells. Irradiation with low intensity MMW shows an antitumor effect on H1299 lung cancer cells. This method provides a novel treatment modality enabling targeted specificity for various types of cancers.

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML) built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future, uncertain causally-linked benefits. This multi-period, causal-linked process incorporating certainty and uncertainty replaces the single period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modelling and real world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. Causal coupling can also be applied at the macroeconomic level to gauge the effectiveness of policies that deliver various levels of cost and benefit coupling for individuals. With the growing interest in natural experiments in statistics and CML across many fields, such as healthcare, economics and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision making models that focus only on rationality, bounded to various degrees.

We propose that the short−term memory (STM), when processing a sentence, uses two uncorrelated processing units in series. The clues for conjecturing this model emerge from studying many novels of the Italian and English Literatures. This simple model, referring to the surface of language, seems to describe mathematically the input-output characteristics of a complex mental process involved in a reading/writing a sentence. We show that there are no significant mathematical/statistical differences between the two literary corpora by considering deep-language variables and linguistic communication channels, therefore, the surface mathematical structure of alphabetical languages is very deeply rooted in human mind, independently of the language used. The first processing unit is linked to the number of words between two contiguous interpunctions, variable Ip, approximately ranging in Miller’s 7±2 range; the second unit is linked to the number of Ip’s contained in a sentence, variable MF, ranging approximately from 1 to 6. The overall capacity required to process fully of a sentence ranges from 8.3 to 61.23 words, values that can be converted into time by assuming a reading speed, giving the range 2.64~19.54 seconds for fast-reading and 5.3~30.1 seconds for average reader. Since a sentence conveys meaning, the surface features we have found might be the starting point to arrive at an Information Theory that includes meaning.

The Release of government dataset for public use can potentially strengthen the relationship between the government and its constituents. However, research shows that there are several challenges for open data effectiveness. This paper reviews current determinants and issues associated with the open government data (OGD) procedures. The review concentrates on two ends of the spectrum: First, from the perspective of the preparation by the government, focusing on the organization of traditional governmental datasets and how the recording of the data is administered. Second, from the perspective of the users, focusing on the way in which the data is released to the general public and on human-computer interaction (HCI) issues between end-user and data-consumption interfaces. Following a thorough analysis of these two opposing challenges, the paper proposes approaches to mitigate them. This review and subsequent recommendations contribute and expand current understanding of open government data effectiveness and can lead to public policy changes, development of new procedures and strategies, and ultimately improvements at both ends of the federal open data endeavor.

Quantum machine learning applications have become viable with the recent advancements in quantum computing. Merging ML with the power of quantum computing holds great potential for data-driven decision-making, as well as the development of more powerful models capable of handling more complex datasets with faster processing time. This area offers potential for improving the accuracy of real-time forecasting renewable energy production. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to design, implement, and evaluate the performance of a quantum neural network forecast model for solar irradiance up to 3-hours ahead. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. Using the best quantum neural network configuration found, the proposed framework could provide competitive results when compared to its competitors, considering forecasting intervals of 5- to 120-minutes ahead, where it was the fourth best-performing paradigm. For 3-hours ahead predictions, the QNN was able to overcome its clas-sical counterparts, but the XGBoost. This fact can be an indication that the quantum model may identify and retrieve relevant spatiotemporal information from the input dataset in such a manner not attainable by the current classical approaches.

The solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the quantity of solar panels and the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to forecast the solar irradiance of typical irrigation areas. The impact of the meteorological parameters on the solar irradiance is studied, and four different parameter combinations are formed and considered as input to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict the solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as the input parameters. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model, with predicted values falling within acceptable ranges. The model developed using mixed data of multiple stations can be applied for forecasting solar irradiance in different regions. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.

Abstract: Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.

The intricate interplay between driver cognitive dysfunction, mental workload (MWL), and heart rate variability (HRV) provides a captivating avenue for investigation within the domain of transportation safety studies. This article presents a comprehensive review and examines cognitive hindrance stemming from mental workload and heart rate variability. It scrutinizes the mental workload experienced by drivers by leveraging data gleaned from prior studies that employed heart rate monitoring systems and eye tracking technology, thereby illuminating the correlation between cognitive impairment, mental workload, and physiological indicators such as heart rate and ocular movements. The investigation is grounded in the premise that the mental workload of drivers can be assessed through physiological cues, such as heart rate and eye movements. The study discovered that heart rate variability (HRV) and infrared (IR) measurements played a crucial role in evaluating fatigue and workload for skilled drivers. However, the study overlooked potential factors contributing to cognitive impairment in drivers and could benefit from incorporating alternative indicators of cognitive workload for deeper insights. Furthermore, investigated driving simulators demonstrated that an eco-safe driving Human-Machine Interface (HMI) significantly promoted safe driving behaviors without imposing excessive mental and visual workload on drivers. Recommendations were made for future studies to consider additional indicators of cognitive workload, such as subjective assessments or task performance metrics, for a more comprehensive understanding.

Background: Rabies, a zoonotic disease caused by the lyssavirus genus, is widely distributed and a significant cause of human death in Africa and Asia. The study aimed to investigate human rabies exposure and associated risk factors in Northwest Ethiopia. Methods: The study encompassed four zones, seven districts, and four kebeles from each district. Result: A total of 642 households were interviewed. In the current study, rabies exposure was revealed 10.47%. Over 98% of respondents displayed a strong understanding of rabies, with 99.69% correctly identifying rabies as a zoonotic disease. But, 80% of the interviewees trusted traditional medicine could treat rabies. Only 21.93% of the victims were taken post-exposure to prophylaxis. Dogs were the primary affected animals; (87.73%) of these 48.70% were bitten by their own dogs. Metema district was at a higher risk (p < 0.001) compared to the other six districts. Dog ownership (p

The salivary microbiota of Italian and sub-Saharan African individuals was investigated using Nanopore Sequencing Technology (ONT- Oxford Nanopore Technologies). We detected variations in community composition in relation to endogenous (ethnicity, sex and diplotypic variants of the TRPV1 gene) and exogenous (sensitivity to capsaicin) factors. The results showed that, Prevotella, Haemophilus, Neisseria, Streptococcus, Veillonella and Rothia, are the most abundant genera, in accordance with the literature. However, alpha diversity and frequency spectra differed significantly between DNA pools. The microbiota in African, male, TRPV1 bb/ab diplotype and capsaicin low sensitive DNA pools was more diverse than Italian, female, TRPV1 aa diplotype and capsaicin high sensitive DNA pools. Relative abundance differed at phylum, genus and species level.

In Morocco, cervical cancer is the second most frequent cancer among women and the third in terms of mortality. The Moroccan Health Ministry introduced the Human Papillomavirus vaccine to the national immunization program in October 2022.The study aims to evaluate the knowledge, the satisfaction level, and acceptability of HPV infection and vaccine among Moroccan medical students. We conducted a cross-sectional study from September 2021 to July 2022 among medical students above 5th year across the nine Moroccan medical universities. We shared our questionnaire through Google Forms. The descriptive data analysis was made through Google Forms and excel, and the analytic analysis was made using GraphPad Prism 8.0. We collected 636 answers. The mean age was 24.2 +/- 1.4 years old with a sex ratio (M/F) of 0.5. 3.3% (N=21) were vaccinated against HPV. The HPV infection knowledge mean score was 6.3 +/- 2 /11 (57.2% of good answers) and the vaccine knowledge mean score was 2.0 +/- 1,1 /5 (40% of good answers), with a training satisfaction rate of 7.3 +/- 2.6 /20 (36.5%). 85.8% of the participants (N= 546) were in favor of including the vaccine to the national immunization program. In conclusion, overall Moroccan medical students have an average knowledge of HPV infection and vaccine. The gap in HPV training should be filled especially in the era of the vaccine introduction. The majority of the students agree to make the vaccine mandatory, with superior rates than the other studies.

Indoor air quality (IAQ) is a critical determinant of human health, with fungal contamination posing significant risks. In 2023, a total of 160 samples were collected from 40 selected rooms. These samples were cultured on Sabouraud Dextrose Agar (SDA) medium using a portable air sampler and incubated at 28°C for 7 days. Initial identification of fungal isolates was performed based on their morphology. Suspected isolates underwent further analysis using internal transcribed spacer (ITS) rDNA analysis. The results revealed 11 species across 8 genera in various rooms. The most prevalent fungi were Aspergillus niger (183 CFU) , Penicillium chrysogenum (139 CFU) and Cladosporium halotolerans (135 CFU). On the other hand, Curvularia hawaiiensis and A. ustus were rarely isolated (10 and 19 respectively). Effective treatment involves a combination of preventive measures, such as controlling humidity and ensuring proper ventilation, alongside targeted interventions like antifungal treatments and thorough cleaning. Continuous monitoring and management of indoor environments are essential to reduce fungal contamination, enhance air quality, and protect human health from the harmful effects of airborne fungi. This abstract underscore the importance of addressing indoor fungal contamination to promote safer, healthier living and working spaces.

Purpose: This study investigates the influence of human brands on the formation of Generation Z's digital identity. Given the increasing importance of the digital space in this generation's life, understanding how their identity is shaped by human brands in this environment is crucial. Method: Using a netnographic approach, qualitative data was collected and analyzed from 500 posts and interactions of Gen Z users on Instagram and TikTok platforms. Thematic content analysis was conducted using NVivo 12 software, with inter-coder reliability confirmed (κ = 0.85). Findings: Results indicate that human brands influence Gen Z's digital identity through three main mechanisms: 1) lifestyle emulation (β = 0.42, p < 0.001), 2) value alignment (β = 0.38, p < 0.001), and 3) self-presentation modeling (β = 0.35, p < 0.01). Multiple regression analysis showed these three factors explain 67\% of the variance in digital identity formation (R² = 0.67, F(3,496) = 335.42, p < 0.001). Conclusion: This study demonstrates that human brands play a significant role in shaping Generation Z's digital identity. Understanding this influence can help marketers and policymakers design more effective strategies for engaging with this generation.

Over the last several decades there has been an arms race to acquire credentials as higher education has shifted from an elitist system to mass education. From an individual perspective, given the higher education system and labor market conditions, it is rational to pursue advanced qualifications. However, whether the education system delivers improvements in human capital or is principally a signaling mechanism is questionable. Estimates of the proportion of labor market rewards due to signaling range as high as 80%, suggesting that education is not only expensive but inefficient. In an increasingly transactional environment in which education providers are highly motivated by financial considerations, this situation is only likely to be exacerbated by the rapid developments in AI. The use of AI has the potential to make learning more effective but, given that many students see credential acquisition as transactional, it may reduce both human capital and the value of the signaling effect. If the credibility of the credentials offered is further damaged, the sustainability of the higher education sector in its present form and scale may well be unsustainable. We examine the evidence on credential inflation, returns to education and mismatch of graduates to jobs before analyzing how AI is likely to affect these trends. We then suggest possible responses of prospective students, education providers and employers to the growing adoption of AI in both education and the workplace. We conclude that the current offerings of generalist degrees, as opposed to vocational qualifications, are not sustainable and that to survive, even in a downsized form, the sector must respond to this disruptive technology by changing both the nature of its offerings and its methods of ensuring that the credentials they offer reflect genuine student learning.

Affective computing has emerged as a prominent technology in recent years, driven by the increasing demand for machines capable of understanding and interpreting emotions. The COVID-19 pandemic has intensified this need by reducing physical interactions and increasing reliance on digital communication, which often involves complex emotional exchanges. To enhance social interactions by identifying and addressing negative emotions, an interactive emotion-perception system based on multi-sensing interfaces and affective computing has been proposed. Integration of gesture, voice, and brainwave sensors is employed to provide users with a rich sensory experience and immersive environment. Design principles and system development processes are derived from the affinity diagram method and prototype development approach. The proposed system was constructed, and experiments were conducted with 60 participants. Interpersonal interaction scenarios were explored to understand emotional transmission mechanisms, aiming to enhance user satisfaction and positive interpersonal experiences. Data were collected through indirect observation and questionnaires, followed by evaluation and statistical analysis. Findings include: (1) The emotion-perception system enhances positive interpersonal interactions by identifying 36 distinct emotions, with overall user emotions being positive but exhibiting notable individual variations; (2) The system effectively stimulates positive emotions and strengthens interpersonal interactions, with affective computing technology fostering enjoyable emotional resonance and high-quality relationships; (3) A positive correlation was observed between semantic analysis and brainwave metrics of relaxation and concentration, showing increased concentration and enhanced positive emotional interactions during message transitions; and (4) The multi-sensing and affective computing technology provides a pleasant and relaxing emotional journey, with interactions shifting from enthusiasm to comfort, demonstrating the system’s capacity for a satisfying and enjoyable experience.

Several studies report an impact of the neurotransmitter dopamine (DA) on human immune cells, with effects dependent on immune cell type addressed and their activation status. Another contributing factor appears to be sex, as sex-specific differences in the dopaminergic pathway are described in the neurological context as well as in autoimmune diseases. However, a deeper understanding of these differences in peripheral immune cells remains limited. In this study, we investigated the effects of dopaminergic stimulation on activation and cytokine secretion of peripheral blood mononuclear cells from women and men using flow cytometry, ELISA and multiplex assay. We found a B cell-driven downregulation in cytokine secretion of monocytes exclusively from women under physiological conditions. Moreover, B cells from men showed higher dopamine receptor (DR) expression, which was shown to be further increased by sex hormones only in men. In monocytes from women, an acute inflammatory stimulus via CpG combined with dopaminergic stimulation caused a switch to a proinflammatory phenotype, which was less pronounced in men. These novel findings in sex-specific responses to dopaminergic stimulation are crucial for understanding DA’s function in the healthy and activated immune system and provide evidence to treat DA-related pathologies in a sex-specific manner.

This study delves into the transformative growth of private higher education in China, highlighting the rapid and diverse expansion following the COVID-19 pandemic. Although previous studies have reported expansion and difficulties, a thorough examination of the relationship between institutional adaptation and the development of human capital remains unexplored. This study examines how private higher education institutions modify their job-related courses to suit the demands of the job market, evaluates the effect on the employability of graduates, and investigates ways to control costs without sacrificing academic quality. This study analyzes data from the Ministry of Education, Wind Information, and Shenwan Hongyuan Research to gain insights into the impact of human capital theory and institutional theory on private higher education in China. Enrollment trends, program alignment, financial sustainability, and quality enhancement activities are all examined using detailed analysis. Private higher education institutions play a critical role in developing a skilled labor force by filling skill gaps and meeting the demands of an evolving job market. The findings also emphasize the importance of quality assurance procedures, strategic program development, and collaborations with businesses. In conclusion, the study emphasizes the imperative for more research and policy action. This is crucial to securing the lasting effectiveness and influence of private higher education institutions on China's socioeconomic progress and human capital development in the post-pandemic era.

Epidermodysplasia verruciformis (EV) is a rare genodermatosis caused by β-human papillomaviruses (HPV) in immunodeficient patients. EV is characterized by flat warts and pityriasis-like lesions and might be isolated or syndromic, associated with some other infectious manifestations. We report here three patients from 2 independent families with syndromic EV for 2 of them. By whole exome sequencing, we found that patients carry new homozygous variants in STK4, both leading to a premature stop codon. STK4 deficiency causes a combined immunodeficiency characterized by a broad infectious susceptibility to bacteria, viruses and fungi. Auto-immune manifestations were also reported. Deep immunophenotyping revealed multiple cytopenia in the three affected patients, in particular deep CD4+ T cells deficiency. We report here the fourth and the fifth cases of syndromic EV due to STK4 deficiency.

If an AI entity is conscious, then it deserves moral status and to have its welfare protected. Society has granted certain animals moral status and has legislation to protect animal welfare because these kinds of animals have been deemed to be sentient. This paper shows that for any entity that can be determined to be conscious, that entity deserves the same status and protection as sentient entities. The paper further develops a framework for how an AI entity's welfare can be protected, based on the Five Freedoms of Animal Welfare. The extant Five Freedoms are translated into terms more suitable for artificial entities, encompassing discomfort and harms, constraints on inherent functions, fear and distress, malfunction and degradation, and resource depletion. Each of the new Five Freedoms are justified using formalised arguments and supported using evidence from animal welfare literature. The paper concludes by presenting three formalised case studies to highlight the potential stressors conscious AI entities may face and how this ethical framework can work to protect the welfare of AI entities.

The overall comfort experienced by an individual in an n-dimensional space can be theoretically constructed using a mathematical equation, that relates the comfort experienced by an individual in a particular point in space with spatial points associated with higher comfort attributes and the desirable factors affecting the current environmental space.

Background: many studies, on for example taste-visual dissonance, have shown that the influence of the visual cortex on taste sensation is enormous. The aim of the presented work is to investigate, using fNIRS, whether a taste stimulus, in this case the taste of bitter, also causes stimulation of the visual cortex in fNIR study. Methods: 51 participants (204 examinations, 9 996 records) were examined with fNIRS, which collects signals from whole left hemisphere. Differences between the maximum and minimum changes in oxyHb concentrations (ΔoxyHb) for the areas of the brain cortex considered responsible for recording visual and gustatory signals were analyzed. Protocol I, II, III and IV – activation with distillate water, coffee with lower concentration, reference (no stimulation) and coffee with higher concentration respectively, were used. Results: We recorded high signals for teste activation on channels covered gustatory cortex, which confirms the correctness of the choice of research method. What seems important, and somewhat surprising, is the fact that we also received high signals for the channels 45-49, which cover the visual cortex. The statistical analysis does not show any statistical differences between the protocols I, II and IV (different taste activation – water, coffee A and coffee B) for specific channels. As a results of the analysis of the correlation between the subjective bitterness assessment solutions, and the signal ΔoxyHb height, it was observed that statically important correlation, although weak, occurs only for 14 and gustatory channels, only for coffee with a higher concentration. Additionally we recorded the only statistically significant difference between women and men for protocol I (water), where the ΔoxyHb signal was twice as high for women. Conclusions: In conclusion, we can clearly state that the senses of sight and taste work closely together. Moreover, this cooperation is not one-sided - not only the visual activation affects the perception of taste, but interestingly, the taste stimulus can generate a hemodynamic response - activity in the visual cortex.

HPV51 belongs to group I high-risk HPV (hrHPV) genotypes with a high prevalence of infection in numerous epidemiological studies, although its occurrence varies across geographical regions. To better understand the presence of variants and genetic variation of HPV51, this study aimed to perform a phylogenetic analysis of the E6, E7 and L1 regions to detect the hrHPV variants circulating in women referred to colposcopy in two different Italian regions (Lombardy and Sardinia). Cervical samples were tested using Anyplex™ II HPV HR Detection. HPV51 positive samples were further analysed using previously described PCR protocols to obtain sequences of E6, E7 and L1. A positivity rate for HPV51 of 7.4% (26/351) and 13.6% (11/81) was found in Lombardy and Sardinia, respectively. The majority (34 out of 35) of the analyzed sequences clustered into the A lineage and only one sequence was grouped into B lineage. L1 capsid gene showed the presence of different mutations but none under positive selective pressure. Three nucleotide polymorphisms T6311G, A6312T and G6313A related to the two aminoacidic mutations V264G and G265S were found among all analysed sequences. This study contributes to implement data regarding the molecular epidemiology of HPV but additional analysis on a larger number of sequences is necessary to better understand the role of HPV variants in cervical lesions development.

How hematopoietic stem and progenitor cell (HSPC) fate decisions are affected by genetic alterations acquired during AML leukemogenesis is poorly understood and mainly explored in animal models. Here, we studied isocitrate dehydrogenases (IDH) genes mutations in the human model of HSPC and provide a review of the literature on this topic. IDH1/2 mutations occur in ~20% of AML, are recognized among the mutations earliest acquired during leukemogenesis, and are target of specific inhibitors (ivosidenib and enasidenib, respectively). In order to investigate the direct effects of these mutations on the HSPC we expressed by lentiviral transduction either IDH1-R132H or IDH2-R140Q mutants into human CD34+ healthy donor cells and analyzed the colony-forming unit (CFU) ability. CFU ability was dramatically compromised with a complete trilineage block of differentiation. Strikingly, the block was reversed by the specific inhibitors, confirming it was a specific effect induced by the mutants. In line with this observation, the CD34+ leukemic precursors isolated from a patient with IDH2-mutated AML at baseline and during enasidenib treatment showed over time a progressive and marked improvement of their fitness in terms of CFU ability and propensity to differentiate, attaining clonal trilinear reconstitution of hematopoiesis and complete hematological remission.

Epirubicin, olaparib and ribociclib are three anti-tumoral molecules currently used in cancer therapy. Since serum levels of these drugs reveal a large inter-individual variability, validation of a fast, simple and reliable analytical method for quantitative determination of these molecules in human serum, with Therapeutic Drug Monitoring (TDM) purposes would be necessary. Papers in literature describe separation, identification and quantitation of the drugs in pharmaceutical formulas and biological matrices. The large majority of these approaches use protein precipitation for sample pre-treatment, followed by LC separation and tandem-mass-spectrometry (MS/MS) detection. We propose an alternative method using Solid Phase Extraction (SPE) with Oasis PRiME HLB® cartridges followed by high-performance liquid chromatography (HPLC) using a C18 (4.6 × 50 mm) column. A gradient mobile phase with 0.1% formic acid/acetonitrile was utilized. MS detection in single ion recording (SIR) mode was employed. A 13-minutes run-time analysis, including column re-equilibration was assessed. Data for all molecules were validated according to ICH Guidelines. Calibration curves for all analytes were linear with correlation coefficient larger than 0.997. Values for precision were less than 6%. The method was applied on serum samples from patients up-taking the drugs, proving its suitability in TDM assisted adjustment of doses in therapy.

Human louse (Pediculus humanus) is an obligatory blood feeding ectoparasite with two ecotypes: human body louse (Pediculus humanus humanus), a competent vector of several bacterial pathogens and the human head louse (Pediculus humanus capitis) responsible for pediculosis and affecting millions of people around the globe. GABA (γ-aminobutyic acid) receptors, members of the cys-loop ligand gated ion channels superfamily, are among the main pharmacological targets for insecticides. In insects, four subunits of GABA receptors: resistant to dieldrin (RDL), glycin like receptor of drosophila (GRD), ligand gated chloride channel homologue3 (LCCH3), and 8916 are well described and form distinct phylogenetic clades revealing orthologous relationships. Our previous studies in human body louse confirmed that subunits Phh-RDL, Phh-GRD and Phh-LCCH3 are well clustered in their corresponding clades. In the present work, we cloned and characterized a putative new GABA receptor subunit in human body louse that we named HoCas for Homologous to Cys-loop alpha like subunit. Extending our analysis to arthropods, HoCas was found to be conserved and clustered in a new (fifth) phylogenetic clade. Interestingly, the gene encoding this subunit is ancestral and has been lost in some insect orders. Compared to the other studied GABA receptor subunits, HoCas exhibited a relatively higher expression level in all development stages and in different tissues of human body louse. These findings improved our understanding of the complex nature of GABA receptors in Pediculus humanus and more generally in arthropods.

Mechanical ventilation is a life-saving treatment for critically ill patients who are struggling to breathe independently due to injury or disease. Globally, large numbers of Individuals have always required mechanical ventilation per year. The Covid-19 pandemic elevated the significance of the mechanical ventilation which have played a significant role in sustaining Covid 19 infected critically ill patients who could not breathe on their own. The pandemic drew the attention of the world to the shortage of ventilators globally.This research work presents the formulation of a detailed Port Hamiltonian model of a mechanical ventilator integrated to the human respiratory system. The interconnection and coupling conditions for the various subsystem within the mechanical ventilator and the coupling between the mechanical ventilator and the human respiratory system is also presented.A structure preservation discretization is provided along side numerical simulations and results. The obtained results are found to be comparable to results presented in literature.The future work will include the design of suitable controllers for system.

Chronic lymphocytic leukemia (CLL) is a distinct category of lymphoproliferative disorder characterized by the clonal expansion of mature B cells, followed by their accumulation in primary and secondary lymphoid organs. Cluster of differentiation (CD) markers such as CD20, CD22, CD23, CD5, and CD81 serve as reliable prognostic indicators and disease progression factors in CLL. The human leukocyte antigen (HLA) system is a critical genetic variable in many diseases, with well-documented associations between HLA and various cancers. This study aims to investigate, for the first time, potential connections between HLA typing and CD marker expression in CLL. Despite being one of the most prevalent neoplasms in developed regions, there is a need for biomarkers that can improve survival predictions and identify possible genetic determinants. Methods: This study included 66 CLL patients and 100 controls, with all samples analyzed using biochemical methods, flow cytometry, and cytomorphology. Next-generation sequencing (NGS) was performed for HLA typing. Results: The results indicate that several CD markers are statistically associated with different HLA alleles. Specifically, CD20 was associated with HLA-DRB1*11:04:01; CD45 with HLA-C*07:01:01; CD79b with HLA-DPA1*02:01:02; CD23 with HLA-B*39:01:01; CD43 with HLA-DRB1*15:01:01; CD22 with HLA-B*49:01:01, HLA-C*07:01:01, HLA-DPB1*02:01:02, and HLA-DRB1*07:01:01; and CD81 with HLA-DPB1*04:02:01, HLA-DQA1*01:04:01, and HLA-DQB1*05:03:01.Conclusion: In conclusion, this research demonstrates significant statistical links between HLA genes and immunophenotypic markers in CLL patients, shedding new light on the immunological context of CLL.

Thyroid hormones are essential for regulating the metabolic rate, growth, development, and maintenance of many physiological processes in vertebrates. They are synthesized by the thyroid gland, which is an ancient organ that evolved early in vertebrate evolution. This study aims to characterize the evolutionary path of polymorphic variants associated with the hypothalamus-pituitary-thyroid (HPT) axis by comparing ancient DNA (aDNA) sequences of Late Pleistocene-Holocene hominin populations with those of modern humans. We evaluated the genetic sequences of several populations, including Neanderthals, Denisovans, Palaeolithic hunter-gatherers from European Russia and Sunghir, and Neolithic hunter-gatherers/farmers from Anatolia focusing on genes involved in the development of the thyroid gland and thyroid hormone (TH) biosynthesis, secretion, and transport. Results showed interesting variants in the DIO2 (rs225014), TPO (rs4927611), and TG (rs2069556 and rs1133076) genes. All SNPs seemed to confirm that Neanderthals, and in part Denisovas, were physiologically hypothyroidic. Probably they had lower T3 levels due to defective production or peripheral conversion. From the moment that the most favourable alleles in terms of T3 production appear during the Paleolithic, we are inclined to assume that their selection was linked to environmental pressure.Our study supports insight into the evolutionary history of the endocrine system and can help in providing understanding into the evolution of physiological systems and their adaptation to changing environments. Understanding the evolution of thyroid hormones can also shed light on the mechanisms underlying thyroid disorders in humans.

Rare earth elements (REEs) are a type of new material resources, which have attracted significant attention in recent years. REEs have emerged as essential metals in modern-day technology due to their unique functions. Long-term large-scale mining and utilization of rare earths have caused serious environmental pollution and constitutes a global health issue, which raised concerns about human health safety. However, the toxicity profile of suspended particulate matter of REEs in the environment interacts with the human body in remain largely unknown. Studies have shown that REEs can enter the human body through a variety of pathways, leading to a variety of organ and system dysfunctions through changes in genetics, epigenetics, and signaling pathways. Through an extensive literature search and critical analysis, we provide a comprehensive overview of the available evidence, identify knowledge gaps, and make recommendations for future research directions.

RNA extraction from human cumulus cells (CCs) is considered challenging due to low cell’s pop-ulation and small cell. The successful gene expression analysis from cumulus cells ultimately de-pends on an excellent concentration of Ribonucleic acid (RNA) yield by an appropriate technique. Our study aims to consolidate appropriate cumulus cell collection method, preparation and ex-traction techniques to ensure a good yield of RNA for better gene expression analysis. From the results obtained, the optimal strategy found involved mechanical denudation without an enzy-matic process, followed by the use of RNA stabilizer prior to RNA extraction. Subsequently, uti-lizing a smaller homogenizer, preferably manual pressure control with filter (Bio-Masher III®) is paramount for a pure and homogenize sample. Subsequently, during RNA extraction, the use of RNeasy micro kit and RNA carriers is recommended. These sample collection, preparation, and extraction techniques yielded an excellent-quality RNA concentration and successful gene expres-sion analysis. We also conducted a systematic review to consolidate our findings with current evidence of RNA extraction technique. Our results added a value as a current strategy for opti-mizing RNA extraction in CCs for experimental studies. Thus, our findings can contribute to for-mulating a better strategy for optimizing the RNA extraction in CCs for transcriptomic studies.

Taekwondo has evolved from a traditional martial art into an official Olympic sport. This study introduces a novel action recognition model tailored for Taekwondo unit actions, utilizing joint motion data acquired via wearable inertial measurement unit (IMU) sensors. The utilization of IMU sensor-measured motion data facilitates the capture of the intricate and rapid movements characteristic of Taekwondo techniques. The model, underpinned by a conventional convolutional neural network (CNN)-based image classification framework, synthesizes action images to represent individual Taekwondo unit actions. These action images are generated by mapping joint motion profiles onto the RGB color space, thus encapsulating the motion dynamics of a single unit action within a solitary image. To further refine the representation of rapid movements within these images, a time-warping technique was applied, adjusting motion profiles in relation to the velocity of the action. The effectiveness of the proposed model was assessed using a dataset compiled from 40 Taekwondo experts, yielding remarkable outcomes: an accuracy of 0.998, precision of 0.983, recall of 0.982, and an F1-score of 0.982. These results underscore the time-warping technique's contribution to enhancing feature representation, as well as the proposed method's scalability and effectiveness in recognizing Taekwondo unit actions.

In contemporary construction, the prevalence of vibration serviceability issues in lightweight and slender structures has become increasingly common, owing to advancements in building materials and construction methods. While these structures often meet the criteria for ultimate limit states, they can still elicit complaints due to excessive vibrations induced by human activity. To address this challenge, the Integral Resonant Control (IRC) technique has emerged as a favored approach for actively damping vibrations in various systems. This study introduces a fresh perspective on implementing a multi-input multi-output (MIMO) IRC scheme for active vibration control (AVC) specifically tailored for pedestrian structures utilizing inertial mass actuators. Building upon a common framework and design methodology outlined in previous research, this work presents a novel application of MIMO IRC for AVC. The designed controller is rigorously tested and implemented on a laboratory floor structure to validate its efficacy.

Breast cancer is a multifaceted disease that has many subcategories characterized by unique genetic features. This research focuses on two important subgroups, including ER+ and HER2-. We conducted an analysis of gene expression data obtained from reliable sources (Array Express: E-GEOD-52194, E-GEOD-75367, and E-GEOD-58135) in order to reveal the complex molecular details of these subtypes. The computational pipeline we used identified 396 genes that exhibited distinct patterns of gene expression in ER+ and HER2- breast cancers. The diagnostic and prognostic significance of these genes was evaluated using machine learning methods, namely SVM and decision tree models. Metrics like as accuracy, sensitivity, and specificity provide insights into their usefulness. Furthermore, the use of the STRING database for network analysis revealed significant signaling pathways and biological processes associated with the development of ER+ and HER2- breast cancer. The results of our research enhance our comprehension of these subcategories, which might possibly facilitate more accurate diagnoses and focused treatment interventions. To summarize, this work provides valuable information on the genetic foundations of ER+ and HER2- breast cancer, which has potential implications for enhancing patient treatment and outcomes.

Amidst the rapid advancements in artificial intelligence (AI) applications, determining the alignment between AI evaluations and human judgment remains crucial, especially in sectors like finance, healthcare, and education. This study delves into a unique scenario—a teenage entrepreneur pitch competition, mirroring real-world challenges faced by these young innovators. The objective was to assess the congruence between evaluations by ChatGPT, an AI model, and human judges. The results indicate a remarkable alignment between AI and human evaluations across several criteria. The implications of this study are far-reaching, highlighting AI's potential in ensuring unbiased and reliable evaluations in pitch competitions and beyond. However, navigating the juncture of AI and human judgment mandates continued research and ethical considerations.

Pericyte and their tunnelling nanotubes (P-TNTs) have been described as involved in the early phases of angiogenesis in the human normal developing brain and glioblastoma, being a common feature of combined endothelium/pericyte vessel sprouts. Based on the few data available so far about this ‘alternative’ pericyte-TNT-driven mode of angiogenesis, whose precise function in vessel growth through vascular cells communication, has still to be determined, this study aims to cast light on the regulative molecules involved in this process, governed by intimate pericyte-endothelial interactions. After considering the existence of the sprout guiding pericyte and P-TNT, as shown by pioneering ultrastructural studies as well as by more recent observations with NG2/CD146 pericyte markers and basal lamina molecules, a step-by-step profile of the process has been suggested and an investigation undertaken on ‘unconventional’, pro-angiogenic ligand/receptor systems, that may have a role aside from the canonical pathways. In this context, a possible candidate worthy of investigation is the c-KIT receptor, a member of the tyrosine kinase family of proteins, which also includes the well-known VEGFR and PDGFR. According to the obtained results showing a primary localization of c-KIT on endothelial cells and pointing out a differential distribution of the receptor on normal vs glioblastoma vessels, it seems conceivable to propose the stem cell factor/c-KIT signaling as a key factor in pericyte-TNT-driven angiogenesis, advancing this alternative mode of angiogenesis and the c- KIT pathway as possible targets for devising effective antiangiogenic therapeutic strategies.

Caregiver burden, a response to the challenges faced by those caring for sick loved ones, combines objective caregiving aspects with subjective experiences. This study aimed to describe the caregiver burden in guardians of ailing animals with dermatological and oncological pathologies. Additionally, the study aimed to correlate the degree of caregiver burden with the duration of the animal's treatment, disease stability, family income, owner's age, and the number of people living in the same household responsible for the animal's treatment. Using a cross-sectional approach, questionnaires were administered to guardians at a veterinary hospital. Out of 182 valid responses, 50.55% related to oncological patients and 49.45% to dermatological patients. Notably, 36.9% of oncological and 37.8% of dermatological guardians exhibited clinically significant burden, with no statistical differences between the groups. Of the respondents, 84.6% were dog guardians, with 34.4% showing a significant burden. Feline guardians (15.4% of respondents) exhibited a higher burden (53.5%) compared to dog guardians (p = 0.02). Disease stability, time since diagnosis, and guardian age significantly influenced the burden, while household composition, income, and treatment duration showed no substantial impact. These findings underscore the psychosocial impact of caring for animals, emphasizing the need for a comprehensive understanding of the caregiver's perspective.

E-waste is considered the fastest-growing global waste stream and has become an emerging environmental challenge. The widespread usage of digital devices has generated a large amount of e-waste. According to the Ministry of Commerce, India annually generates ~14.5 lakh metric tons of e-waste and recycles only ~5 % of e-waste. Annually, the global production of e-waste has been expected to increase by 4–5 %. Advancements in IT and communication divisions have improved the usage of electronic equipment. Heavy metals such as (Pb), mercury (Hg), arsenic (As), tin (Sn), and organic chemicals such as polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polybrominated biphenyls (PBBs), polybrominated dibenzo-p-dioxins (PBDDs), polybrominated diphenyl ethers (PBDEs) are subsequently released from e-wastes and contaminates soil and water sources. These toxic compounds are hazardous to human health and the environment. They are capable of entering various biological systems and causing alterations in respiratory functions of the lungs, DNA damage, impaired functioning of the thyroid, and increased risk of chronic diseases such as cancer and cardiovascular ailments. Such concerns raise the quintessential need to manage e-waste to prevent future hazards. This manuscript critically reviews the sources, health and environmental impact, disposal techniques, recycling, and regulation of e-waste.

Various environmental, food security and population health problems seem to be related to intensive agriculture production worldwide. This type of food production system impacted the loss of biodiversity and natural habitats; high usage rates of agrochemicals and natural resources had affected soil composition, human health, and nutritional plant quality in rural areas. Intercropping agroecological systems that respect agrobiodiversity could significantly benefit ecosystems, human health, and food security by modifying the nutritional profile and the content of some health-promoting bioactive compounds of the species cultivated in this system. Research on intercropping strategies focuses on the benefits they can offer to ecosystems and less on nutrient plant composition, leaving information scattered when this theme should be more studied due to the critical impact it could provide for human nutrition. Therefore, this review aims to collect viable details on the investigation status of nutrients and bioactive compounds profile in intercropping systems by verifying different regions of the world with unique mixed crops using plant species, along with the criteria for combining them, as well as the nutrients and bioactive compounds analyzed to show what intercropping systems can contribute to food availability and quality.

The COVID-19 pandemic, which has affected the entire world, has not only created a number of emerging issues for each country, especially in the field of public health, but has also provided a number of opportunities for risk management, alternative strategies and completely new ways of looking at challenges. This paper examines the COVID-19 pandemic response in Türkiye and the possible implications of the experience for future responses to other health emergencies and disaster risk management, based on the lessons learned. This study uses publicly available literature from government, private sector, and academic sources to analyse the conflicts, changes and lessons learned during the COVID-19 pandemic, which are components of the World Health Organization (WHO) Health Emergency and Disaster Risk Management (Health EDRM) framework. The COVID-19 experience in Türkiye has several aspects, including the significant role of healthcare workers, the existence of an effective health system accustomed to emergencies, applications based on information technologies, partial transparency of public authorities in providing information, and socio-cultural environment related to cooperation on prevention strategies, including wearing masks and vaccination. Challenges in Türkiye include distance learning in schools, lockdowns that particularly affect the elderly, ensuring environmental sustainability, hesitation about the effectiveness of social/financial support programmes, socio-cultural trivialisation of pandemics after a while, and relaxation of prevention strategies. Lessons learned from the COVID-19 pandemic include the value of transparency in public health/healthcare information, the strengthening of all aspects of the health system in terms of health workers, and the importance of a balanced economy prepared for foreseeable risks.

The main focus of this paper is to propose a comprehensive framework for the cognitive meas-urement and modelling of the built environment. This will involve exploring and measuring neural mechanisms. The aim is to create a foundation for further studies in this field that are consistent and rigorous. Additionally, this framework will facilitate collaboration with cognitive neuroscientists by establishing a shared conceptual basis. The goal of this research is to develop a human-centric approach for urban design that is scientific and measurable, producing a set of urban design guidelines that incorporate cognitive measurement and modelling. By doing so, the broader intention is to design urban spaces that prioritize human needs and well-being, mak-ing them more liveable.

Gait variability (GV) is a crucial measure of inconsistency of muscular activities or body seg-mental movements during repeated tasks. Hence, GV might serve as a relevant and sensitive measure to quantify adjustments of walking control. However, has not been clarified whether GV is associated with walking speed to exploit effective better coordination level. Fourteen male stu-dents (age 22.4 ± 2.7 years, body mass 74.9 ± 6.8 kg, body height 1.78 ± 0.05 m) took part in this study. After three days of walking 1 km each day at a self-selected speed (SS) on asphalt with Apple Watch S. 7 (AppleTM), the participants were randomly evaluated on a treadmill at three different walking speed intensities for 10 minutes each one, at SS-20% / SS+20% of SS, with 5 minutes of passive recovery in-between. Heart rate (HR) was monitored beat-to beat and nor-malized as HR%MAX, while the rate of perceived exertion (RPE) (CR-10) was asked after each trial. Kinematic analysis was performed assessing the Contact Time (CT), Swing Time (ST), Stride Length (SL), Stride Cycle (SC) and Gait Variability as Phase Coordination Index (PCI). RPE and HR increased with higher walking speed (P = 0.005 and P = 0.035, respectively). CT and SC decreased as the speed increased (P = 0.0001 and P = 0.013, respectively), while ST remained un-changed (P = 0.277). SL increased with higher walking speed (P = 0.0001). Conversely, PCI was 3.81 ± 0.88 % (high variability) at 3.96 ± 0.47 km·h-1, 2.64 ± 0.75 % (low variability) at SS (4.94 ± 0.58 km·h-1), 3.36 ± 1.09 % (high variability) at 5.94 ± 0.70 km·h-1 (P = 0.001). These results indi-cate that while the metabolic demand and kinematics variables changing linearly with increasing speed, the most effective GV was observed at SS. Therefore, SS could be a new methodological approach to choose the individual walking speed, normalize the speed intensity, and avoid a gait pattern alteration.

This essay critically discusses the integration of queer theory into human geography while emphasizing its ties to the principles of traditional geography. The need for a separate sub-discipline called queer geography is questioned. At the same time, legitimate concerns about possible departures from the fundamental goals of geography and the risks of anachronistic interpretations in applying modern concepts to historical landscapes are acknowledged. In this discourse, we strongly advocate for values such as freedom of speech, freedom of expression, and international human rights. It is important to emphasize that it is not the intent of this essay to denigrate campaigns for feminism or equality or to disparage the scholarship that emerges from the application of queer theory. On the contrary, we recognize the emergence of gender theories out of a need to advance social justice approaches. We believe that traditional human geography, itself, is capable of comprehensively examining space, time, and the complexity of human experience and offering workable solutions within its established framework. We therefore believe that the inclusion of queer theory can be harmoniously integrated into the contours of human geography, eliminating the need for a separate sub-discipline while preserving the integrity and purpose of the discipline.

This advanced scholarly article examines gender equality, human rights, and women’s rights in Uzbekistan, focusing on domestic violence and women's leadership. It explores their relevance to the region and their connections to global human rights discussions and social justice theories. The first section addresses the widespread problem of domestic violence in Central Asia, emphasizing the need for comprehensive understanding and effective measures. The second section assesses women's progress in achieving gender equality and leadership positions in the Uzbek workplace, identifying research gaps and the impact of recent reforms. The study employs an observational approach, integrating raw data and social media information to analyze gender equality and research. Findings and recommendations draw from global human rights debates and social justice theories, contributing to future research on gender issues in Central Asia. Overall, this article offers a comprehensive analysis, contextualizing the issues within local and global perspectives and contributing to the dialogue on gender equality in a cultural context.

Since a few decades, the world has been facing severe global challenges with context to water, food, agriculture science, energy resources, healthcare, medicines, diseases, etc. One of the causes of these challenges is excessive human activities. So, there has arisen a necessity to tackle these challenges using sustainable solutions so as to minimize the effect of human interference. Nanotechnology (NT) has gained pace as a new technique that provides cost-effective, efficient, and eco-friendly solutions to such challenges. The advantages of the usage of NT have increased in every field of science. As compared to traditional methods, NT possesses wider use and benefits as it is designed at the nano levels and also keeps the environment clean. It is the science of tiny particles that has an important role to play in international efforts in sustainability. The innovation of NT is revolutionary and therefore subsequently will cover a wider space in terms of scientific achievements. Technology saves time, money, and human resources. This paper discusses and highlights the usage of NT with respect to its application, knowledge, and sustainable development. It also gives a broad view of the approaches, properties, and applications of NT in various sectors including the treatment of water bodies, agricultural science, drug delivery systems, medicines, and energy saving.

The length of time it takes to experimentally make one-sided choppers, as found in the fossil record, bears a linear relationship to the knapping process of fabricating them. In addition, this temporal frame appears to be related to human heart rates measured as beats per minute, which act as a physiological metronome. We achieved these observations assuming that any paleolithic one-sided chopper has the information needed to estimate quantitatively the number of strikes on it. The experimental data allow us to establish the total timing needed for the standard fabricating of any one-sided chopper. We discuss issues derived from these experimental results, showing the evolution of human neurological abilities from 2.4 million years ago to the Modern period through the duration of time needed for making one chopper to that needed to play a 19th-century music score on a piano. Given that the neuronal and physiological distance between both actions differs by a factor of 6, we propose the concept of “technome” to measure human evolution by using methodological homogeneous metrics applied to these two human technologic objects, the chopper and the piano.

In large industrial plants, the trend is to move the position of the human to the area of service and diagnostics, in medium and smaller plants the trend is different and is mainly directed to the sphere of close cooperation between the human and the robotic system. This paper deals with the implementation of human-cooperating robots in manufacturing processes. The analytical part of the paper deals with theoretical knowledge, taking into account the latest trends in the subject area. To achieve a successful implementation of cobots based on the above-mentioned operating systems in manufacturing systems, it is necessary to pay attention specifically to the interfacing of the robotic operating system with the control systems of the manufacturing systems at the process level of the enterprise. Within the practical part, an algorithm with well-defined steps towards the successful implementation of cobots in holonic manufacturing processes is proposed. By setting up an experimental workstation in the laboratory, the proposed procedures are verified at the end of the paper. In the context of the implementation of collaborative robots in enterprises, the paper also discusses cloud computing. The creation of a design for the implementation of a collaborative robot with a human represents the missing link in the whole chain of commercial applications of the latest trends from the field of robotic systems in the industrial sphere.

Children with Autism Spectrum Disorder have deficits in social interaction and expressing and understanding emotions. Many robots for children with ASD have been proposed. However, few studies were found about how to design a social robot for children with ASD. Studies reviewed employed non-experimental studies to evaluate a social robot, but the methodology to design a social robot is rare. This study proposes a design path for a social robot for emotional communi-cation for children with ASD, following User-Centered Design approach. This design path is ap-plied with a study case, evaluated with an expert group on psychologist, human-robot interaction, human-computer interaction, and parents of children with ASD. Results show than following the design path proposed to design a social robot to communication emotions for children ASD is favorable.

Botswana, Eswatini and Lesotho are three Southern African countries which make use of re-turn-of-service (RoS) schemes to increase human resources for health in their countries. These initiatives bind beneficiaries to a pre-defined period of service upon completion of their studies based on the length of funding support received. We aim to review the history of these policies to understand the conceptualisation, intent and implementation of these schemes. We used a mul-ti-methods research design which consisted of a literature review, policy review and semi-structured interviews with policymakers and implementors. All three governments have a combination of grant-loan schemes and full bursaries or scholarships. The policies have all been operating for over 20-years, with Eswatini’s pre-service being the oldest since it was introduced in 1977 followed by Lesotho’s 1978 policy and Botswana’s 1995 pre-service policy. These policies have never been reviewed or updated. RoS schemes in these countries were introduced to ad-dress critical skills shortages, improve employability prospects for citizens, to have competent public sector employees by global standards, and to aid career progress of government employ-ees. Ministries of Health are passive role players. However, these schemes can only be efficient if there is clear cooperation and coordination between all stakeholders.

Background: Sleeping Sickness, Human African Trypanosomiasis (HAT) is a vector- borne disease caused by Trypanosoma brucei (T.b). Sleeping sickness in Ethiopia was reported in 1967 for the first time. Recently in Southern parts of Ethiopia, in August 2022, five (5) cases of sleeping sickness (T. b. rhodesiense) were confirmed. Following this outbreak, the current investigation was aimed to identify the entomological and epidemiological drivers for the reemergence of HAT outbreak and recommend appropriate interventions. Methods: A cross sectional study design with descriptive data analysis was used. Tsetse fly collection and blood samples from cattle animals were taken. NGU and bio-conical traps were used to determine the distribution (density and abundance) of the vector. About 10μl of blood was collected from the marginal ear vein of 301 cattle animals using the heparinized microhematocrit capillary. The parasite detection was carried out through vector dissection under binocular stereo-microscope (magnification of 60X) and microscopic examination from serum of Animals using the Buffy coat method. Results: A total of 329 tsetse flies were captured and identified to Glosina (G.) pallidipes 259 (60.4%) and Glossina fuscipes 70 (16.3%). 188 (51.1%) of tsetse flies were collected from Dembagofa with 94 apparent density. Among all captured Tsetse flies, 39 (11.8%) of Tsetse were fed with high female apparent density in each ecological variation: wood land (51), Bush land (20) and grassland (11). Overall, the apparent density of tsetse fly was high in Wood land (93): G. pallidipes (76.5) and Bush land (36.5). Among all examined cattles for the presence of parasite, 9 cattles were detected positive with an overall prevalence of 3%. T. congolense 6 (2%) and T. vivax 1 (0.3%) with 2 (0.7%) suspected brucei. The parasite prevalence Trypanosoma was 4 (4.6%) in poor body condition (Bcs) cattle. The animals in the age range 5 - 9 years were infected high with 7 (5.3%) prevalence. Conclusion: The current study revealed that there are high-risk factors that predispose the community to Human African Trypanosomiasis (HAT) due to the presence of two different species of Tsetse flies and many animal reservoirs. The transmissions of Human African trypanosomiasis (HAT) are related to environmental, Vector, and human factors. Further geographically expanded investigation should be conducted throughout the country.

Marine seaweeds are nature’s largesse resource in human well-being. Seaweed’s contribution to the food industry is being widened for its rich source of essential vitamins, minerals, and PUFA. ‘Nori’ is a dried sheet of red seaweed Porphyra sp. used to make ‘sushi’ a renowned cuisine in Japan, and other Southeast Asian countries. In this present study, commercial-grade ‘Nori’ was extracted and fractionated with different solvents, and subjected to evaluate the cytotoxic activity on human ovarian tetracarcinoma cells (PA1). As a result, the ethyl acetate fraction was found effective in inhibiting the proliferation of human ovarian cancer cells with an IC50 value of 41.1 µg/mL. The main component responsible for anticancer activity was determined as a sesquiterpene lactone Tatridin B (3h-cyclodeca[b]furan-2-one, 4,9-dihydroxy-6-methyl-3,10-dimethylene-). Moreover, it was found effective in inhibiting NSD2, an important and recently proposed biomarker for several types of cancer. This is a hitherto report on the presence of this compound in ‘Nori’ showing potent anticancer activity on human ovarian tetracarcinoma (PA1).

Herdsmen use different techniques, as per varying geographies and cultures, to keep the cohesion within herds and avoid animals getting lost or predated. However, there is no study on the social behaviour of yaks and on herdsmen management practices. Therefore, this ethology study was initiated by ethnographic inquiries. In Manang, the success of the shepherd is dictated by his personal attribute of “Khula man” or open-heartedness. This attribute refers to good intentions and emotions such as empathy that allows the shepherd to focus more on others than on ownself. This cultural method of assessing the skills required to become a successful and knowledgable shepherd guided us to study the effect of cultural values on the herd’s social behavior. We collected data from two herds living at the same settlement (Yak kharka, 4,100 m altitude, Nepal) by equipping them with loggers. One of the herdsman used the tether rope while other did not. Moreover, the Thaku herd had a more proactive shepherd than the Phurba one. In each herd, 17 animals were equipped with one actigraph wgt3x-BT to measure activity using accelerometer and spatial associations using proximity recorder. One of the herds was equipped with GPS (N=11) as well. Using GPS locations and activity, we showed that the two herds were cohesive and synchronised their activities but the herd with the tether rope was more cohesive. The shepherds also have personal knowledge of the social relationships of their herds and use these relationships to keep the group cohesive and to well manage cattle.

An HPLC method for indicating stability was developed, created, and confirmed for the quantification measurement of recombinant human erythropoietin in large quantities as well as dosage forms. An isocratic separation was accomplished using a Thermo Biobasic C18 (250 mm 4.6 mm i.d., 300Ao, 5 m) column with a flow rate of 1.0 mL min-1 as well as a UV detector to monitor the eluate at 278 nm. Acetonitrile, 0.05 mM potassium dihydrogen phosphate (80:20 v/v), and orthophosphoric acid adjusted to 4.0 made up the mobile phase. The drug was exposed to oxidative stress, hydrolysis, and photodegradation to simulate stress conditionsRecombinant Human Erythropoietin, the parent compound, was eluted at roughly 6.675 minutes, as well as all byproducts have been entirely disconnected inside an overall analytical run time of about 15 minutes. To identify quantification limits of 0.05 and 0.2 g mL-1, respectively, the method was linear over a concentration range of 1-6 g mL-1 (r = 0.9989). Recombinant Human Erythropoietin can be measured accurately, selectively, sensitively, and precisely using this method both in dosage form and in bulk. Stress-induced degradation products did not interact with the identification of Recombinant Human Erythropoietin, indicating that the assay is stable.

Reducing office buildings’ energy consumption can contribute significantly towards carbon reduction commitments since it represents 10% of total energy consumption. Major components are lighting (40% of consumption), electrical equipment (35%), and heating and central cooling systems (25\%). Occupants’ behaviours impact these energy consumption components, with solid evidence on the role of individual behaviours. In this work, we propose a methodology that uses virtual choreographies to identify and prioritize behaviour-change interventions towards office users based on the potential impact on energy consumption. The data shows that some behaviours with significant consumption have little potential for behavioural change impact, while other behaviours hold substantial potential for lowering energy consumption via behavioural change.

International migration is closely tied to demographic, socioeconomic, and environmental factors and their interaction with migration policies. Using a combination of a gravity econometric model and an overlapping generations model, we estimate the probability of bilateral migration among 160 countries in the period of 1960 to 2000 and use these findings to project international migration flows and their implication for income inequality within and between countries in the 21st century under five shared socioeconomic pathways (SSPs). Our results show that international migration not only increases the welfare in developing countries, but also closes the inequality gap within and between low-skilled and high-skilled labor in these countries. In most developed countries on the contrary, international migration increases the inequality gap and slightly reduces output. These changes are not uniform and vary significantly across countries depending on their population growth and human capital development trajectories. Overall, while migration is strongly affected by inequality between developed and developing countries, it has an ambiguous impact on inequality within and between countries.

Nicotinamide adenine dinucleotide (NAD+) is an essential molecule involved in various metabolic reactions, acting as an electron donor in the electron transport chain and as a co-factor for NAD+-dependent enzymes. In the early 2000s, reports that NAD+ declines with aging introduced the notion that NAD+ metabolism is globally and progressively impaired with time. Since then, NAD+ became an attractive target for potential pharmacological therapies aiming to boost NAD+ levels to promote vitality and protect against age-related diseases. This review summarizes and discusses a collection of studies that report the levels of NAD+ with aging in different species (i.e., yeast, C. elegans, rat, mouse, monkey, and human) to determine whether the notion that overall NAD+ levels decrease with aging stands true. We find that despite systematic claims of overall changes in NAD+ levels with aging, the evidence to support it is very limited and often restricted to a single tissue or cell type. This is particularly true in humans, where the development of NAD+ levels during aging is still poorly characterized. There is a need for much larger, preferably longitudinal, studies aimed to assess how NAD+ levels develop with aging in various tissues. This will strengthen our conclusions on NAD+ during aging and should provide a foundation for better pharmacological targeting of relevant tissues.

Virtual human body models contribute to designing safe and user-friendly products through virtual prototyping. Anthropometric biomechanical models address different physiques using average dimensions. In designing personal protective equipment, biomechanical models with the correct geometry and shape shall play a role. The presented study shows the variations of subject-specific anthropometric dimensions from the average for the different population groups in the Czech Republic and China as a background for the need for personalized human body models. The study measures a set of clothing industry dimensions of Czech children, Czech teens, Czech adults and Chinese adults and compares them to the corresponding age average, which is represented by a scaled anthropometric human body model. The cumulative variation of clothing industry dimensions increases the farer is the population group from the average. It is smallest for the Czech adults 7.54% ± 6.63%, Czech teens report 7.93% ± 6.25% and Czech children differ 9.52% ± 6.08%. Chinese adults report 10.86% ± 11.11%. As the variations of the particular clothing industry dimensions from the average prove the necessity of having personalized subject-specific models, the personalization of particular body segments using the measured clothing industry dimensions leading to a subject-specific virtual model is addressed. The developed personalization algorithm results in the continuous body surface desired for contact applications for assessing body behavior and injury risk under impact loading.

This manuscript aims to provide a nuanced study of the idea of rights and duties prevalent in ancient Vedic society through Vedic literature and Dharmaśāstras . This manuscript delves into the exegesis of the Védas and Dharmaśāstras to accomplish this. The archaic Vedic literature and Dharmaśāstra texts are the origin and backbone of Sanskrit literature. They have a plethora of ideas that, if accepted, could be quite useful for the protection of any person's human rights. In Védas and Dharmaśāstras, rights and duties complement each other, and rights are integrated by duties. According to these texts, rights and duties are correlated and the relationship between rights and duties leads to the core concept of dharma (constitutional laws). Dharma is a systematic Sanskrit concept that includes traditions, obligation, morals, laws, order, and justice. It was a unique concept of dharma that kept checks and balances on sovereign officials and prevented them from becoming autocratic and anarchist. It also provided the common man with a protective shield against the dictatorship of sovereign officials. Ordinary citizens had more privileges and fewer responsibilities relative to the state's highest officials. The greater the authority, the less his privileges were, and the more extensive his responsibilities became. This research is an exegetical analysis of ancient Indian Vedic and later Vedic literature and is primarily aimed at deciphering some of the essential ideas of the rights found in these texts, which are akin to contemporary human rights. It endeavours to discern and explain the tenets of human rights obnubilated in the pristine mantras of Antediluvian Vedic and Smṛti texts of India. The essay further attempts to add a much needed non-western perspective to the historiography of human rights.

In the present study, we aimed to investigate whether an automated molecular diagnostic method based on PCR-reverse blot hybridization assay can discriminate between human Mycobacterium tuberculosis (MTB)-positive and -negative FFPE tissues and to compare the relative mRNA expression levels of various host immune markers between MTB-infected and uninfected human tissues using quantitative reverse transcription (qRT) PCR. A total of 52 human FFPE tissue samples from various regions of the body, including the lungs, lymph nodes, tendons, colon, and appendix, were collected and used for the molecular identification of Mycobacterium species and analysis of cytokine mRNA expression. As a result, IFN-γ, TNF-α, IP-10, CXCL9, CXCL11, and GM-CSF mRNA expression levels in MTB-infected tissues were significantly higher than those in uninfected samples. Additionally, the differences in the mRNA expression levels of IFN-γ, CXCL9, and GM-CSF between MTB-infected and uninfected tissues were statistically significant were statistically significant (p < 0.05). Correlation curve analysis indicated that the mRNA expression of IFN-γ was inversely proportional to that of IP-10 and that the mRNA expression levels of IFN-γ, TNF-α, CXCL9, CXCL11, GM-CSF, and TNFR were proportional and well-correlated. Furthermore, to establish marker profiles for detecting MTB infection, the statistically significant expression levels of three markers were combined. We confirmed that the combined profile of IFN-γ, CXCL9, and GM-CSF expression levels was statistically significant (P < 0.001). Although the mRNA expression patterns of host immune markers may vary according to MTB infection status, these patterns may be highly correlated and can be simultaneously used as an additional indicator for diagnosing TB.

Onchocerciasis volvulus is the second highest infectious cause of blindness in the world, and is estimated to affect 37 million people, of whom 99% reside in sub-Saharan Africa. As a public health problem the disease is most closely associated with Africa, where it constitutes a serious obstacle to socio-economic development. Using the human ecology triad, this paper evaluates the dynamic interplay of population, habitat and behavioural factors in predicting perceived exposure to onchocerciasis among coastal inhabitants in Tanzania. Generalized linear models with log-log link function were fitted to cross-sectional survey data on 1253 individuals in three contiguous coastal regions. A significant proportion of respondents (28%) perceived that they were exposed to onchocerciasis. Residents in urban locations irrespective of wealth status were less likely to report living in onchocerciasis endemic environment compared with their rural counterparts. This is understandable given that urban areas of Tanga and Dar es Salaam are definitely non-endemic and perceived risk of onchocerciasis is related to the fact of living in an endemic area with active onchocercasis transmission. Individuals who had attained secondary (OR=0.51, p<0.01) or tertiary education (OR=0.37, p<0.001), and reported easy access to health facility (OR=0.53, p<0.001) were all less likely to report perceived exposure to onchocerciasis. This is not surprising because higher level of education and easy access to health facilities are characteristics of urban compared with rural areas. Policy implications suggest the need for the Tanzanian national neglected tropical disease control programme (TZNTDCP) to intensify health and educational campaigns at the community level and address susceptibility of vulnerable populations to the disease especially, for rural dwellers.

Anxiety, chronical stress and depression during pregnancy are considered to affect the offspring, presumably through placental dysregulation. We have studied the term placentae of pregnancies clinically monitored with the Beck’s Anxiety Inventory (BAI) and Edinburgh Postnatal Depression Scale (EPDS). A cutoff threshold for BAI/EPDS of 10 classed patients into an Index group (>10, n=23) and a Control group (<10, n=23). Cortisol concentrations in hair (HCC) were periodically monitored throughout pregnancy and delivery. Expression differences of main glucocorticoid pathway genes: i.e. corticotropin-releasing hormone (CRH), 11β-hydroxysteroid dehydrogenase (HSD11B2), glucocorticoid receptor (NR3C1), as well as other key stress biomarkers (Arginine Vasopressin, AVP and O-GlcNAc transferase, OGT) were explored in medial placentae using real-time qPCR and western blotting. Moreover, gene expression changes were considered for their association with HCC, offspring, gender and birthweight. A significant dysregulation of gene expression for CRH, AVP and HSD11B2 genes was seen in the Index group, compared to controls, while OGT and NR3C1 expression remained similar between groups. Placental gene expression of the stress-modulating enzyme 11β-hydroxysteroid dehydrogenase (HSD11B2) was related to both hair cortisol levels (Rho= 0.54; p<0.01) and the sex of the newborn in pregnancies perceived as stressful (Index, p<0.05). Gene expression of CRH correlated with both AVP (Rho= 0.79; p<0.001) and HSD11B2 (Rho= 0.45; p<0.03), and also between AVP with both HSD11B2 (Rho= 0.6; p<0.005) and NR3C1 (Rho= 0.56; p<0.03) in the Control group but not in the Index group; suggesting a possible loss of interaction in the mechanisms of action of these genes under stress circumstances during pregnancy.

Consumption of milk contaminated with antibiotic residues above the maximum residue limit (MRL) causes direct toxicity to humans and the development of superbugs that leads to the failure of antibiotic therapy and threatens human life. Besides, long-duration exposure might alter the nature of gut microflora results in the enhancement of many diseases. Therefore, we examined 300 raw and processed packet milk samples using thin layer chromatography (TLC) and ultra high performance liquid chromatography (UHPLC) method against five veterinary antibiotics and assessed the risk for consumers in Chattogram, Bangladesh. Risk analysis was calculated by using hazard quotient on the basis of 165 ml per capita milk consumption. We found a total of 7% prevalence of antibiotic residues in raw milk samples which were higher (8%) in individual samples than the pooled samples (4%). However, we did not find any processed packet milk samples as positive. The mean concentration of oxytetracycline residue was detected 61.29 µg/l and amoxicillin was 124 µg/l in individual milk samples. Risk analysis showed the hazard quotient values of 0.0056 for oxytetracycline and 0.0017 for amoxicillin residue which was confirmed that, no significant health risk associated with the consumption of milk produced and marketed in the study area. Our study might fill the gap of knowledge to measure the safety status of milk regarding public health issues.

This review chronicles the indirect transmission method which seems to be overlooked by most people and makes attempts to clearly document the various transmission ways with a hope that such information may strengthen the knowledge base of researchers towards methods of eradicating the pandemic. Current knowledge of transmission and exposure of SARS-CoV-2 has been explained. Various researchers have put forward different ways of exposure and transmission. Literature does not reveal whether the indirect transmission route is the dominant one. However total lockdown could be a veritable means to reduce both direct and indirect transmission routes. In many countries where the indirect transmission has been reduced, the scourge of the virus is less. The work creates awareness on the need to watch out for those routes of transmissions that may not be popular and suggested key knowledge gaps that needs to filled.

Background and Research Highlights: Despite all the concerns and initiatives, natural resources like forests, as well as biodiversity are decreasing at an alarming rate worldwide. Conservation is considered as one of the major tools to prevent such loss and rapid degradation. Evidence around the world shows the adverse effects of conservation laws and policies on indigenous peoples and other local communities. Objectives: This study was conducted in one of the forest-dependent communities situated in Sundarban (world’s largest mangrove forest) to understand the impact of conservation laws and policies on their livelihood. Materials and Methods: A qualitative methodology was designed to collect data, using focus group discussions and case study with community people, and individual interviews with the personnel from NGOs and relevant government departments. Findings: Strict conservation policies and restrictions in accessing forest resources made lives and livelihoods of the local community insecure and unstable, thus putting the community in a vulnerable situation. The had to leave their traditional mode of income and look for alternative livelihood options. Almost no evidence was found in relation to upkeeping their rights in conservation activities. Prohibited movement, provision of punishment for entering into the forest without proper permission and struggles in everyday life were some of the highlighted issues. They had no participation in conservation activities, management of alternative livelihood options, and even they were not sensitized before putting restrictions. Although they had a history of emotional and physical attachment with the forest, existing activities did not consider these issues. In addition, corruption and abuse of power by law enforcement agencies towards the local community intensified the sufferings. Conclusion: This study argues that the realization of human rights in conservation activities and the sensitization of the implementing stakeholders are prerequisites for ensuring the sustainability of both biodiversity and the affected people.

The employee turnover literature to date is teeming with process models that assume only organizational and personal factors contribute to employees’ intentions to leave and stay. However, with the average percentage variance explained still only around 50%, it is clear that other factors play a substantial role in these decision-making processes. One possible explanation is to explore the unique role that clients play in employees’ intentions to stay or leave. This paper extends the conceptualization of the role that clients play on employee’s intentions to stay and leave their roles, particularly within service industries such as health care. This paper presents the results of two qualitative studies that examine employees’ decision-making process for turnover and retention intentions. The first study explores the factors influencing employees’ intentions to stay and leave the Australian aged care sector (N = 420) and the second study explores prison health care nurses in the United States (N = 175). The results of both studies reveal that client-related factors affect the choice to stay within a job and, in some cases, are the favoured aspect of the role. Therefore encouraging client-staff relationships will expand job satisfaction and therefore increase retention of employees within both contexts.

Pyrrole-imidazole (PI) polyamides are novel gene silencers that strongly bind the promoter region of target genes in a sequence-specific manner to inhibit gene transcription. We developed a PI polyamide targeting human TGF-b1 (hTGF-b1). To develop PI polyamide targeting hTGF-b1 (Polyamide) as a practical medicine for progressive renal diseases, we examined the effects of Polyamide in two common marmoset models of nephropathy.We performed lead optimization of PI polyamides targeting hTGF-b1 by the dose-dependent inhibition of the PMA-stimulated expression of TGF-b1 mRNA in marmoset fibroblasts. Marmosets were housed with a 0.05% NaCl and magnesium diet and treated with cyclosporine A (CsA; 37.5 mg/kg/day, 8 weeks) to establish chronic nephropathy. Marmosets with nephropathy were treated with Polyamide (1 mg/kg/week, 4 weeks). We also established a unilateral urethral obstruction(UUO) model and examined the effects of Polyamide (1 mg/kg/week, 4 times) in marmosets.Histologically, the renal medulla from CsA-treated marmosets showed cast formation and interstitial fibrosis in the renal medulla. Immunohistochemistry showed strong staining of Polyamide in the renal medulla from CsA-treated marmosets.Polyamidetreatment (1 mg/kg/week, 4 times) reduced hTGF-b1 staining and urinary protein excretion in CsA-treated marmosets. Polyamide reduced the glomerular injury score (GIS) and tubulointerstitial injury score (TIS) in UUO kidneys from marmosets. Polyamide significantly suppressed the hTGF-b1 and Snail mRNA expressionin UUO kidneys from marmosets.PI polyamide effectively improved CsA- and UUO-associated nephropathy, indicating its potential application in the prevention of renal fibrosis in progressive renal diseases.

In this work, the decennial experience of Policumbent student team at Politecnico di Torino is summarized by focusing on the acquired knowledge in design of Human Powered Vehicles (HPVs) and on soft skills developed by both students and staff. Policumbent was funded by the authors at the end of 2008 in order to gather engineering students interested in design and construction of HPVs. In the last decade, the team has grown from 10 up to 50 students enrolled per year, exploring a range of HPV design for sports and mobility. Even when focusing on sport vehicles and extreme HPVs for speed record, such kind of projects allows students to familiarize with important concepts related to sustainable mobility: the amount of resistive forces and dissipated power, the role of vehicle weight and the impact of acceleration on the overall energetic balance as far as fundamental concepts about energy consumption, efficiency and emissions of the ``human engine'' in comparison with other kind of engines. By touching with hands such topics in the framework of a ``human-centred'' design project, the students have opportunity to develop awareness about the impact of design choices on sustainability of any kind of vehicle for transportation. Also, the paper retraces the team evolution path by focusing on a thorough analysis of what factors contributed to the success of this project.

Sulforaphene (SFE), a major isothiocyanate in radish seeds, is a close chemical relative of sulforaphane (SFA) isolated from broccoli seeds and florets. The anti-proliferative mechanisms of SFA against cancer cells have been well investigated, but little is known about the potential anti-proliferative effects of SFE. In this study, we showed that SFE purified from radish seeds inhibited the growth of six cancer cell lines (A549, CHO, HeLa, Hepa1c1c7, HT-29, and LnCaP), with relative half maximal inhibitory concentration (IC50) values ranging from 1.37 to 3.31 g/mL. Among the six cancer cell lines evaluated, SFE showed the greatest growth inhibition against A549 lung cancer cells. In A549 cells, SFE induced apoptosis via changes in the levels of poly (adenosine diphosphate ribose) polymerase and caspase-3, -8, and -9. Our results indicate that SFE from radish seeds may have significant anti-proliferative potency against a broad range of human cancer cells via induction of apoptosis.

Woody vegetation in farmland acts as a carbon sink and provides ecosystem services for local people, but no macro-scale assessments of the impact of management and climate on woody cover exists for drylands. Here we make use of very high spatial resolution satellite imagery to derive wall-to-wall woody cover patterns in tropical West African drylands. In arid and semi-arid Sahel, areas of more people are associated with more trees: mean woody cover is greater in farmlands (12%) than in savannas (6%), and likewise it is higher close to villages than further away. In sub-humid savannas of West Africa, woody cover is generally above 20% and decreases with increasing population density, but remains around 15% in farmlands, independent of rainfall. In the region as a whole, rainfall, terrain and soil are the most important (80%) determinants of woody cover, while management factors play a smaller (20%) role. We conclude that agricultural expansion cannot generally be claimed to cause woody cover losses, and that observations in Sahel contradict simplistic ideas of a high negative correlation between population density and woody cover.

The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.

Multivariate Time Series Classification (MTSC) is one of most important tasks in time series analysis, aiding in activities such as human motion recognition and medical diagnostics. Existing methods for MTSC do not explicitly model temporal differences and generalize the idea of temporal difference into a efficient temporal module. Additionally, existing methods are not yet able to capture cross-variable relationships well during network training. As a result, they are unable to achieve convincing feature representation, leading to suboptimal classification accuracy. In this paper, we propose a novel MTSC model called Temporal Difference and Cross-variate Fusion Network (TDCFN), which integrates a two-stream differential LSTM network and a cross-variate feature extraction network to enhance feature representation. TDCFN achieves superior classification accuracy by capturing dynamic temporal evolution and inter-variable relationships. The experimental results show that TDCFN can achieve competitive performance with state-of-the-art multivariate time series classification approaches.

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross-mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of low-cost optical particle counters, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader application in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models.

Importance: Neuropsychiatric disorders like schizophrenia and bipolar disorder lack objective diagnostic markers, hindering accurate diagnosis and treatment. A novel approach using patient-derived cerebral organoids and digital analysis of electrophysiological recordings could provide much-needed objective biomarkers.Objective: To develop a digital analysis pipeline that can identify distinct electrophysiological signatures of schizophrenia and bipolar disorder using multi-electrode array recordings from patient-derived cerebral organoids.Design: This was an experimental study using previously published data. The study type can be classified as a case-control study, comparing cerebral organoids and 2D neuronal cultures derived from patients with schizophrenia, bipolar disorder, and healthy controls. The study utilized multi-electrode array recordings for analysis. Setting: The study was conducted in a laboratory setting using previously recorded data. Main Outcomes and Measures: The primary outcome was the accuracy of a support vector machine classifier in distinguishing between healthy control, schizophrenia, and bipolar disorder samples based on electrophysiological features extracted from the recordings. Key cellular-digital biomarkers were identified using minimum redundancy maximum relevance feature selection.Results:Our Support Vector Machine classifier achieved 95.8% accuracy in distinguishing SCZ from control samples in 2D neuronal cultures under both baseline and post-electrical stimulation (PES) conditions. For cerebral organoids, classification accuracy was 83.3% under baseline conditions, improving to 91.6% under PES when distinguishing between control, SCZ, and BPD samples. Key features for classification included channel-specific measures such as median, covariance, autocorrelation, and kurtosis of neural activity. Confusion matrices visualize classification performance, with most misclassifications occurring in the BPD class under baseline conditions. These results demonstrate that our digital analysis pipeline can effectively distinguish between healthy control and patient-derived samples using electrophysiological features from multi-electrode array recordings, particularly under PES conditions.Conclusions and Relevance: This digital analysis pipeline demonstrates the potential to identify objective electrophysiological signatures of schizophrenia and bipolar disorder using patient-derived cerebral organoids. This approach could lead to improved diagnostic accuracy and personalized treatment strategies for neuropsychiatric disorders.

Advanced Persistent Threats (APTs) are sophisticated cyberattacks aimed at stealing sensitive information or causing damage over an extended period. Detecting APTs is crucial for maintaining cybersecurity, and machine learning (ML) has emerged as a powerful tool in this domain. This paper explores the role of ML algorithms in detecting APTs, comparing their accuracy and effectiveness. We evaluate various algorithms, discuss the challenges in achieving superior accuracy, and suggest strategies for improvement. Our findings highlight the potential and limitations of ML in APT detection, emphasizing the need for continuous advancements in this field.

Background: Objective quantitative texture characteristics may be helpful in salivary glandular tumor differential diagnosis. This study uses machine learning (ML) to explore and validate the performance of ultrasound (US) texture features in diagnosing salivary glandular tumors. Material and methods: 122 patients with salivary glandular tumors, including 71 benign and 51 malignant tumors, are enrolled. A representative brightness mode US pictures are selected for further Gray Level Co-occurrence Matrix (GLCM) texture analysis. We use t-test to test the significance and use receiver operating characteristic curve method to fund optimal cut-point for these significant features. After splitting 80% data into training set and 20% data into testing set, we use five machine learning models k-nearest Neighbors (kNN), Naïve Bayes, Logistic regression, Artificial Neural Networks (ANN) and supportive vector machine (SVM) to explore and validate the performance of US GLCM texture features in diagnosing salivary glandular tumors. Results: This study includes 49 female and 73 male patients, with a mean age of 53 years old, ranging from 21 to 93. We find that six GLCM texture features (contrast, inverse difference movement, entropy, dissimilarity, inverse difference and difference entropy) are significantly different between benign from malignant tumors (p&lt;0.05). On ML, the overall accuracy rates are 74.3% (95%CI: 59.8-88.8%), 94.3% (86.6-100%), 72% (54-89%), 84% (69.5-97.3%) and 73.5% (58.7-88.4%) for kNN, Naïve Bayes, Logistic regression, one node ANN and SVM, respectively. Conclusion: US texture analysis with ML has potential as an objective and valuable tool for assessment of salivary gland tumors.

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

Over the last decade, the prevalence of health issues has increased by approximately 29.1%, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly, however many present approaches are unsuitable for real-world implementation due to high memory footprints and lack of interpretability. We introduce DeClEx, a pipeline designed to address these issues. DeClEx ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state of the art pre-trained models while achieving threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.

The Robotics Vision Lab of Northwest Nazarene University has developed Orchard Robot (OrBot), which was designed for harvesting fruits. OrBot is composed of a machine vision system to locate fruits on the tree, a robotic manipulator to approach the target fruit, and a gripper to remove the target fruit. Field trials conducted at commercial orchards for apples and peaches during the harvesting season of 2021 yielded a harvesting success rate of about 85% and had an average harvesting cycle time of 12 seconds. Building upon this success, the goal of this study is to evaluate the performance of OrBot during nighttime harvesting. The idea is to have OrBot harvest at night, and then human pickers continue the harvesting operation during the day. This human and robot collaboration will leverage the labor shortage issue with a relatively slower robot working at night. The specific objectives are to determine the artificial lighting parameters suitable for nighttime harvesting and to evaluate the harvesting viability of OrBot during the night. LED lighting was selected as the source for artificial illumination with a color temperature of 5600 K and 10% intensity. This combination resulted with the lowest noise in the images. OrBot was tested in a commercial orchard using twenty Pink Lady apple trees. Results showed an increased success rate during the night, with OrBot gaining 94%. compared to 88% during the daytime operations.

Query reformulation is a fundamental task in information retrieval systems aimed at improving the accuracy and relevance of search results. This abstract presents a query reformulation approach that utilizes a domain-specific ontology to enhance the effectiveness of query reformulation. The use of ontology enables a more precise understanding of the domain and its associated concepts, leading to more accurate query interpretation and reformulation. The proposed approach consists of two main steps: ontology-based query interpretation and ontology-guided query expansion. The proposed approach use Power thesaurus tool that perform sentiment analysis for suggestion of the synonyms of the query words to improve the accuracy of the retrieved results. The proposed methodology gives high accuracy as compared to the previous approaches that used Wordnet for the suggestions of synonyms. The proposed approach demonstrates the potential of utilizing ontologies in query reformulation, offering a more accurate and context-aware search experience in domain-specific information retrieval systems.

This paper provides a proof of concept for a linear electric generator that can be used to harvest energy from various sources of linear motion, such as vibrations, free-piston engines and wave energy. The generator can be used to power small electronic devices, such as sensors, or charge household batteries. Literature was reviewed to develop an understanding about the applications, control methods, excitation methods and mechanics of rotating and linear electric machines. A bidirectional, two-sided linear machine was designed with two stator cores and a single mover core. The stator windings and mover winding can be independently excited, allowing for three modes: no mover excitation, DC excited mover, and AC excited mover. Simulations results showed that the magnetic flux generated by DC excited stator cores were concentrated in the centre of the mover core. The use of two stator cores eliminates lateral flux in the mover core when it is not excited, minimising attraction and repulsion forces. Parametric analysis showed that flux cutting occurred in all operation modes, verifying that the generator will produce power when operating. Hardware tests produced an output current when the machine is electrically and mechanically excited, verifying the proposed design.

The objective of the study was to predict the risk of exposure to ammonia concentration in the production of broiler breeds of slow and fast-growing with low and high production density and correlate with the incidence of health injuries in broiler chickens using a learning machine. Two commercial lines of broiler chicken were used, one with fast-growing (Ross®, slaughter age 42 days) and another with slow-growing (Hubbard®, slaughter age 63 days). All slow-growing birds were housed at a density of 32 kg/m2. Fast-growing birds were housed in two different housing densities: low housing density with a final housing density of 16 kg/m2 and high density with a final housing density of 32 kg/m2. A total of 1250 birds were used in this experiment, 450 of which were fast-growing birds and 800 were slow-growing birds. In each room, 306 birds were randomly distributed in 18 boxes (6 boxes for each treatment and 17 birds per box), each with 3 nipple drinkers and a manual feeder. The dimensions of the high-density boxes were 1 x 1.3 m2 , while the low-density boxes had the same number of animals housed in a larger area, with dimensions of 2 x 1.3 m2 . The remaining 319 birds were housed randomly throughout the room and outside the pens to simulate a commercial production system condition (stock density). All birds were fed with an initial commercial feed from day 1 to day 18 of the experiment, and from day 18 until the end, each breed was fed with different feeds according to their nutritional demands. The following data analysis steps were performed: data selection, pre-processing, transformation, mining, analysis, and, interpretation of results. The classification algorithms, decision tree (J48), SMO (Sequential Minimal Optimization), Naive Bayes, and Multilayer Perceptron were applied to the training and test data sets to build a rule model for predicting ammonia risk levels in broiler chickens. The cross-validation technique was used to parameterize the analysis in all models. From the database of the first phase of analysis, with the classifier to predict the risk condition of ammonia concentration, the Spearman correlation coefficient (ρ, rho), considering the presence of pododermatitis, vision/affected and mucosal injury, which includes assessments of the trachea, bronchi, lungs, eyes, paw injuries, and other injuries. A non-parametric correlation measure was applied to injury incidence data as a function of ammonia risk level (1 and 10 ppm) to correlate injury incidence and ammonia level in the conditions studied. The best predictive model capable of evaluating and obtaining better performance was the Multilayer Perceptron when we considered greater accuracy by the level of risk of exposure to ammonia in the broiler chicken production process, including fast and slow-growing. Birds exposed to higher levels of ammonia concentration have a higher correlation coefficient when the relationship between the variables is strong. The Spearman correlation coefficient shows a stronger association between increased risks of ammonia exposure and the incidence of chicken injuries.

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) exert significant burdens on individuals and society, underscoring the importance of accurate predictions using advanced machine learning (ML) algorithms. Leveraging electronic health records (EHRs) and survey data, these algorithms offer potential in forecasting such mental health conditions. Yet, the precision of these predictions can be compromised by biases or inaccuracies inherent in subjective survey responses. In this research, we assess the reliability of four prominent ML algorithms—Random Forest, XGBoost, Logistic Regression, and Naïve Bayesian—in predicting MDD and GAD. Our dataset encompasses a rich array of information, from biomedical metrics and demographic details to self-reported survey insights. A focal point of our investigation is the algorithms' performance under scenarios with varying degrees of subjective response inaccuracies, such as memory recall biases or subjective interpretation. Our findings reveal that while all algorithms exhibit commendable accuracy with pristine survey data, their performance diverges when faced with erroneous or biased responses. Notably, XGBoost remains stable and excels in identifying true positive cases, even in the presence of such noise. These observations underscore the criticality of algorithmic resilience in mental health prediction, especially when relying on subjective data. The robustness of certain algorithms to noisy inputs positions them as more reliable choices for predicting mental health conditions based on self-reported data, emphasizing the need for careful algorithm selection in such contexts.

Nowadays, digitalization and automation in both industrial and research activities is a driving force for innovations. In recent years, Machine Learning (ML) techniques - the subset of Artificial Intelligence - have been widely applied as a key tool to automize and digitalize numerous in-dustrial processes. ML models can predict specific features, like materials' lifetime in a heating device, based on recently trained algorithms with applied input data. The results of ML algorithms are easy to interpret and can significantly decrease the time of research and decision-making, thus, substituting the trial-error approach. This work presents the state of the art in the application of Machine Learning in the investigation of MgO-C refractories for the steel industry. Also, the work presents an overview of the most commonly used ML algorithms in refractory engineering. So far published results confirm the high performance of ML, e.g., for prediction of oxidation behaviour, optimum graphite content and wear rate of MgO-C refractories, or for clustering materials based on various fused and sintered MgO sources into groups with similar corrosion resistance, thus, permitting to reduce expensive fused raw materials content. Most of the presented works were positively validated via experiment.

Drone navigation is critical, particularly during the initial and final phases, such as the initial ascension, where pilots may fail due to strong external disturbances that could lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters with external disturbances simulating wind pushing it up to 24 mph, with the DQN algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.

This paper presents a novel method for automatic modulation classification (AMC) for cognitive radio (CR) networks based on a simple classifier that is trained with high-order cumulant. The proposed method focuses on the statistical behavior of modulated signals and includes analog modulation and digital schemes, which received less attention in the literature. The effectiveness of the proposed method is demonstrated through simulation results using high-quality generated signals under different signal-to-noise ratios (SNRs) and channel conditions. The classification performance achieved by the proposed method is superior to that of the more complex deep learning methods, making it well-suited for deployment in end units of CR networks, particularly in military and emergency service applications. The proposed method offers a cost-effective, high-quality solution for AMC that meets the stringent requirements of these critical applications.

In the landscape of network management, software-defined networking (SDN) technology emerges as a dynamic approach, facilitating efficient network configuration for improved performance and monitoring, akin to the agility of cloud computing rather than traditional methods. However, its centralized structure exposes SDN to various attack vectors. Distributed Denial of Service (DDoS) attacks, particularly, pose a significant threat to SDN. This study employs machine learning algorithms and Network Traffic Classification Analysis (NCA) to classify SDN traffic as potential attacks. Notably, Decision Trees (DT) out shine other algorithms with a flawless 100% classification success, spotlighting their supremacy. Through this research, a robust path toward fortified SDN security takes form, where technological prowess and strategic intelligence unite for enhanced defense.

This study assessed the potential of back extensor strength as an alternative marker of frailty. A total of 560 farmers were included. Computed tomography scans measured fat and muscle mass volumes at the mid-L4 vertebral level. Back extensor strength was measured in a seated posture. Multivariate linear regression analyzed the associations between back extensor strength and trunk muscle/fat compositions. The participants were divided into two groups based on back extensor strength. Propensity score matching, multivariate logistic regression, and Extreme Gradient Boosting (XGBoost) were employed to evaluate the relationship between Fried's frailty criteria and back extensor strength. Back extensor strength exhibited positive associations with abdominal muscle volume (r = 1.12) as well as back muscle volume (r = 0.89) (p < 0.05). Lower back extensor strength was linked to more frail status, such as reduced grip strength, walking speed, and fre-quent self-reported exhaustion. Multivariate logistic regression indicated a significant association between lower back extensor strength and higher Fried frailty score (OR, 0.990; p<0.05). XGBoost analysis identified back extensor strength as the most important predictor (gain = 0.502) for frailty, surpassing the significance of age (gain = 0.325). These findings suggested the potential of back extensor strength as an alternative frailty marker.

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

This paper presents a complete derivation and design of a physics-informed neural network (PINN) applicable to solve initial- and boundary value problems described by linear ordinary differential equations. The objective not to develop a numerical solution procedure which is more accurate and efficient than standard finite element or finite difference based methods, but to give a fully explicit mathematical description of a PINN and to present an application example in the context of hydrodynamic lubrication. It is, however, worth noticing that the PINN developed herein, contrary to FEM and FDM, is a meshless method and that training does not require big data which is typical in machine learning.

The vibrations of a Cartesian cutting machine caused by the pneumatic tool are studied with a sub-system approach. The cutting head is modeled as an equivalent robot arm which is able to mimic the measured resonances, The Cartesian structure is modeled according to the mode superposition approach. The full model is able to predict the variations in the response of the machine to tool excitation that are caused by the motion of the head along the rails of the Cartesian structure. Comparisons with experimental results are made. Finally, the mathematical model is used for assessing the effectiveness of a vibration absorber tuned to main resonance frequency of the cutting head.