The increasing growth in knowledge about functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technologies development in recent decades, have led to the emergence of a large number of brain-computer interfaces and neuroprosthetics for the regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. Hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for prediction of hippocampal signals in CA1 region based on received biological input in CA3 region. We compare results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. Proposed study can be viewed as a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus.

To understand dynamics in climate change, informing policy decisions and prompting timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of variations in the target. To capture emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to global efforts to address climate change

Scaffold guided breast tissue regeneration (SGBTR) can both transform reconstructive and cosmetic breast surgery. Implant-based surgery is the most common method. However, there are inherent limitations as there is replacement of tissue rather than regeneration. Regenerating autologous soft tissue has the potential to provide a more like-for-like reconstruction with minimal morbidity. Our SGBTR approach regenerates soft tissue by implanting additively manufactured bioresorbable scaffolds filled with autologous fat graft. A pre-clinical large animal study was conducted implanting 100 ml breast scaffolds (n=55) made from medical-grade polycaprolactone in 11 minipigs for 12 months. Various treatment groups were investigated where immediate or delayed autologous fat graft, as well as platelet rich plasma were added to scaffolds. Computed Tomography and Magnetic Resonance Imaging was performed on explanted scaffolds to determine volume and distribution of regenerated tissue. Histological analysis was performed to confirm tissue type. At 12 months we were able to regenerate and sustain a mean soft tissue volume of 60.9 4.5 ml (95% CI) across all treatment groups. There was no evidence of capsule formation. There were no immediate or long-term post-operative complications. In conclusion, we were able to regenerate clinically relevant soft tissue volumes utilizing SGBTR in a pre-clinical large animal model.

The approach to the personal experience and previous ideas about Physical Education of future Primary Education teachers is a starting point of great interest for the teaching of the subject of Didactics of Physical Education. The study aims to investigate these prior beliefs and to verify to what extent this initial perception changes after taking the course on “Didactics of Physical Education”. A concurrent mixed-methods study was conducted, which included two data collection procedures: (1) a pre-experimental design with a single group featuring pre-test and post-test; (2) the analysis of students' autobiographical accounts of their school experience with physical education. The participants were students enrolled in the Bachelor’s degree program in Primary Education at the University of Santiago de Compostela (USC) who undertook the course last year. The results obtained reveal that after taking the subject of Didactics of Physical Education, students give greater value to more positive concepts of learning, socializing, participating, and playing, among others. Similarly, in the post-test, the assessment of concepts such as competitiveness and physical fatigue diminished. In their autobiographical memories, students associate good memories with relationships with classmates and the playful and socializing nature of the subject; and among the bad memories, they highlight the content related to physical performance, competitiveness, and lack of attention to the diversity of students and their individual characteristics.

This work addresses the challenge of EEG visual multiclass classification into 40 classes for Brain-Computer interface applications, by using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage, since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, performing multiclassifiers based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and Minimum-Norm Estimate algorithms are implemented to select discriminant channels and enhance EEG data. Hence, deep EEGNet and Convolutional-recurrent neural networks are implemented separately to classify EEG data of image visualization into 40 labels. By using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures, respectively. Satisfactory results obtained with this method offer a new implementation opportunity for multi-task BCI applications by utilizing a reduced number of channels (<50%), compared to those presented in the related literature where the whole set of channels is used.

Recent studies, typically using patient cerebrospinal fluid (CSF), have suggested that different autoantibodies (Aabs) acting on their respective receptors, may underlie neuropsychiatric disorders. The NR1 subunit of the N-methyl-D-aspartate receptor (NMDAR) has been identified as a target of anti-NMDAR Aabs in a number of central nervous system (CNS) diseases including encephalitis and autoimmune epilepsy. However, the role or the nature of Aabs responsible for effects on neuronal excitability and synaptic plasticity is yet to be established fully. Patient-derived anti-NMDAR Aabs have been shown to bind to specific regions within the NR1 subunit; here, peptide immunisation was used to generate Aabs against selected specific NR1 extracellular sequences. ‘Protein A’ purification was used to obtain total IgG and further peptide purification used to obtain a greater percentage of NMDAR-target specific IgG Aabs. Binding and specificity of these anti-NMDAR Aabs was determined using a range of methodologies including enzyme-linked immunosorbent assay, immunocytochemistry and immunoblotting. Functional effects were determined using different in vitro electrophysiology techniques: two-electrode voltage-clamp in Xenopus oocytes and measures of long-term potentiation (LTP) in ex vivo hippocampal brain slices using multi-electrode arrays. We show that anti-NMDAR Aabs generated from peptide immunisation had specificity for NR1 immunisation peptides as well as target-specific binding to the native protein. Anti-NMDAR Aabs had no clear effect on isolated NMDARs in an oocyte expression system. However, we show that peptide-purified anti-NMDAR Aabs prevented the induction of LTP at Schaffer collateral-CA1 synapses in ex vivo brain slices. Our data demonstrate that anti-NMDAR Aabs cause synaptic NMDAR hypofunction at a network level. This work provides a solid basis to address outstanding questions regarding anti-NMDAR Aabs mechanisms of action and, potentially, development of therapies against CNS diseases.

Newborn screening (NBS) is hailed as a public health success, but little is known about long-term outcomes following a positive newborn screen. There has been difficulty gathering long-term follow-up (LTFU) data consistently, reliably, and with minimal effort. Six programs developed and tested a core set of minimal LTFU data elements. After an iterative data collection process and the development of a data collection tool, the group agreed on minimal LTFU data elements. The denominator captured all infants with an NBS diagnosis, accounting for children who moved or died prior to the follow-up year. They also agreed on three LTFU outcomes: if the child was still alive, had contact with a specialist, and received appropriate care specific to their diagnosis within the year. The six programs representing NBS public health programs, clinical providers, and research programs provided data across multiple NBS disorders. In 2022, 83.8% (563/672) of the children identified by the LTFU programs were alive and living in the jurisdiction; of those, 92.0% (518/563) saw a specialist, and 87.7% (494/563) received appropriate care. The core LTFU data elements can be applied as a foundation to address the impact of early diagnosis by NBS within and across jurisdictions.

Accurate streamflow forecasting is crucial for effectively managing water resources, particularly in countries like Colombia, where hydroelectric power generation significantly contributes to the national energy grid. Although highly interpretable, traditional deterministic, physically-driven models often suffer from complexity and require extensive parameterization. Data-driven models like Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) networks offer simplicity and performance but cannot quantify uncertainty. This work introduces Sparse Variational Gaussian Processes (SVGPs) for forecasting streamflow contributions. The proposed SVGP model reduces computational complexity compared to traditional Gaussian Processes, making it highly scalable for large datasets. The methodology employs optimal hyperparameters and shared inducing points to capture short-term and long-term relationships among reservoirs. Training, validation, and analysis of the proposed approach consider the streamflow dataset from 23 geographically dispersed reservoirs recorded during twelve years in Colombia. Performance assessment reveals that the proposal outperforms baseline Linear Autoregressive (LAR) and Long Short-Term Memory (LSTM) models in three key aspects: adaptability to changing dynamics, provision of informative confidence intervals through Bayesian inference, and enhanced forecasting accuracy. Therefore, the SVGP-based forecasting methodology offers a scalable and interpretable solution for multi-output streamflow forecasting, thereby contributing to more effective water resource management and hydroelectric planning.

Semenov-Tian-Shansky has introduced the modified classical Yang-Baxter equation, which is called the modified $r$-matrix. Relevant studies have been extensive in recent times. This paper is devoted to study cohomology theory of modified Rota-Baxter pre-Lie algebras and its applications. First we introduce the concept and representations of modified Rota-Baxter pre-Lie algebras. We then develop the cohomology of modified Rota-Baxter pre-Lie algebras with coefficients in a suitable representation. As applications, we consider the infinitesimal deformations, abelian extensions and skeletal modified Rota-Baxter pre-Lie 2-algebra in terms of lower degree cohomology groups.

Speech emotion recognition (SER) is a technology that can be applied in distance education to analyze speech patterns and evaluate speakers’ emotional states in real-time. It provides valuable insights and can be used to enhance the learning experience by enabling the assessment of instructors’ emotional stability, a factor that significantly impacts information delivery effectiveness. Students demonstrate different engagement levels during learning activities, and assessing this engagement is an important aspect of controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is the emotional states of their instructors. Accordingly, this research uses deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entails integrating Transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance SER. Feature extraction from audio data used Mel-frequency cepstral coefficients, chroma, Mel spectrogram, zero crossing rate, spectral contrast, centroid, bandwidth, roll-off, and root-mean square, with subsequent optimization processes adding noise to, conducting time stretching, and shifting the audio data. Notably, several Transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The pre-processing and data augmentation methodologies significantly enhanced the precision of the results in that the model achieved accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% on the Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio-Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets. Furthermore, it achieved 83% accuracy on another dataset created for this research—the Saudi Higher Education Instructor Emotions dataset. The results demonstrate this model’s considerable accuracy in detecting emotions in speech data across different languages and datasets.

This paper proposed emotion recognition methods for consecutive facial images, and implements the inference of neural network model on a field-programmable gate array (FPGA). The proposed emotion recognition methods are based on a neural network model architecture that combines convolution neural networks (CNNs), long short-term memory (LSTM), and fully connected neural networks (FCNNs), called CLDNN or ConvLSTM-FCN. This type of neural network mod-el can analyze the local feature sequences obtained through convolution of data, making it suita-ble for processing time-series data such as consecutive facial images. In this paper, sequences of facial images are sampled from videos corresponding to various emotional state of subjects. The sampled images are then pre-processed with the processes includes facial detection, greyscale conversion, resize, and data augmentation if necessary. The 2-D CNN in ConvLSTM-FCN is used for feature extraction for these pre-processed facial images. These sequences of facial im-age features are time sequences with dependent properties between the elements within them. The LSTM is then used to model these time sequences followed by fully connected neural net-works (FCNNs) for classification. The proposed consecutive facial emotion recognition method achieves an average recognition rate of 99.51% on RAVDESS dataset, 87.80% on BAUM-1s dataset and 96.82% on eNTERFACE’05 data set, using 10-fold cross-validation on the PC. Some com-parisons of recognition accuracies between the proposed method and the other existing related works are conducted in this paper. According to the comparisons, the proposed emotion recog-nition methods outperform the existing related researches. The proposed emotion recognition methods are then implemented on an FPGA chip using the neural network model inference algo-rithms in this paper, and the accuracies of the experiments conducted on the FPGA chip are iden-tical to those obtained on the PC. This verifies that the proposed neural network model imple-mented on the FPGA chip performs well.

With the escalation of size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long-Short Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built, that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

The present study analyzed the effect of temperature, pH, retention time, pretreatment and mixing ratio on the anerobic digestion process. The parameters during the anaerobic co digestion of cow manure and food waste were then optimized using the Taguchi experimental design method. ANOVA is carried out to find the significant parameters which influence the biogas production. Experimental tests were carried out at laboratory scale reactors kept at different temperatures (28 °C, 35 °C, and 50 °C). The specific methanogenic Performance (SMP) during anerobic digestion at higher temperatures was characterized with the analysis of acetate, propionate, butyrate, hydrogen, glucose, and formate. The improvement of biogas production with different pre-treatment i.e., ultrasonic, autoclave, and microwave techniques also analysed. The results showed that reactor that maintained at 35 °C showed the highest biogas production. While the reactor that maintained at lower temperature (28 °C) produced the lower volume of biogas. As the retention time increases, the amount of biogas produced increases. Methanogenic activities of microorganisms reduced at higher temperature conditions (65 °C). Biogas production increased by 28.1%, 20.23%, and 13.27 % when the of substrates treated with ultrasonic, autoclave, and microwave, respectively, compared to the untreated substrate. The optimized condition for the highest biogas production during anaerobic co digestion of food waste and cow manure is temperature of 35 °C, pH of 7 and mixing ratio (CM: FW=1.5:0.5). ANOVA showed that temperature is the most important input parameter affecting biogas production, followed by mixing ratio.

The focus of our study was the changes in the composition of semi-natural dry grasslands in Hungary. Since these valuable habitats were created with the help of humans, their preservation also requires regular treatment. Our mowing experiment aimed to investigate the suppression of the native Calamagrostis epigejos L. Roth. In Hungary, in the Cserhát Mountains, eight permanent plots were mowed twice a year. The vegetation was sampled annually between 2001 and 2011. The effects of mowing were investigated with repeated measures analysis of variance (ANOVA). After 10 years, the C. epigejos cover of the mowed plots decreased significantly, from the initial average of 62.38 to 7.50%. Surprisingly, we also experienced a similar decrease in the control plots. Regular treatment also had a positive effect on the number of species and diversity. Species richness increased continuously in both treatment types, which indicates the combined effect of vegetation succession and treatment. The biomass growth of other Poaceae and Fabaceae species, which are important from a grassland management perspective, was also facilitated by mowing. Our results suggest that long-term in situ experiments and continuous botanical studies are needed to establish a reliable utilization of grasslands.

Hypertensive diseases of pregnancy (HDP) represent a global clinical challenge, affecting 5-10% of women and leading to complications for both maternal well-being and fetal development. At the heart of these complications is endothelial dysfunction, with oxidative stress emerging as a pivotal causative factor. The reduction in nitric oxide (NO) bioavailability is a vital indicator of this dysfunction, culminating in blood pressure dysregulation. In the therapeutic context, although antihypertensive medications are commonly used, they come with inherent concerns related to maternal-fetal safety, and a percentage of women do not respond to these therapies. Therefore, alternative strategies that directly address the pathophysiology of HDP are required. This article focuses on the potential of the nitrate-nitrite-NO pathway, abundantly present in dark leafy greens and beetroot, as an alternative approach to treating HDP. The objective of this review is to discuss the prospective antioxidant role of nitrate. We hope our discussion paves the way for using nitrate to improve endothelial dysfunction and control oxidative stress, offering a potential therapy for managing HDP.

Background: Open AI developed ChatGPT, a language model based on the GPT architecture, designed for text-based communication. Trained on diverse internet texts, ChatGPT generates contextually appropriate responses using machine learning. It understands input through analysis and context interpretation, generating coherent and contextually relevant responses. Interaction is possible through messaging platforms. Materials and Methods: In November 2023, we utilized ChatGPT 3.5, the default version at that time, to answer questions from the Italian National Residency Program Admission Tests (SSMs) of 2021, 2022, and 2023. The questions cover clinical, diagnostic, analytical, therapeutic, and epidemiological scenarios, sometimes accompanied by images. The study compared ChatGPT's answers to the official corrections on the Italian Ministry of University and Research (MUR) website. The scoring method used for evaluation was 1 point for correct answers, 0 points for unanswered questions, and -0.25 points for incorrect answers, reflecting the SSM test scoring system. Results: In summary, ChatGPT was tested with a total of 420 questions, 140 for each test. It achieved an overall accuracy of 80.48%, providing correct answers for 338 questions and incorrect answers for 82 questions. When faced with questions containing both text and images, it answered 55% correctly and 45% incorrectly. The model's performance varied over time, with an 82.14% accuracy rate in 2021 and 2022 (115 correct out of 140) and a 77.14% accuracy rate in 2023 (108 correct out of 140). Applying thisscoring method to the SSM test, ChatGPT would have scored 105 points in 2021 and 2022, and 100 points in 2023. Conclusions: ChatGPT has exhibited above-average performance in the last three SSM tests, highlighting its robust capability to interpret clinical scenarios and offer precise diagnostic and therapeutic guidance. Despite this, some limitations persist, notably the software's inability to interpret non-textual information.

The aim of this work was to classify the business incubators in Spain according to the four phases of startups incubation. Taking into account that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis have been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors, finally the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3 called activity monitoring and control and was related to phase 3 or basic incubation variables. Clusters analysis facilitates the grouping of incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small Business Incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factor 1 and 2. Factor 3 although positive is susceptible to improvement. They are the largest incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

Objective: To examine variations in end-of-life care for breast cancer, heart failure, and dementia patients. Data and methods: Data from four Norwegian health registries were linked using a personal identification number. Longitudinal trends over 365 days and the type of care on the final day of life were analyzed using descriptive techniques and logistic regression analysis. Results: Patients with dementia were most commonly placed in nursing homes, while patients with heart failure and breast cancer were more frequently hospitalized. Breast cancer and heart failure patients had a higher likelihood of dying at home. The presence of general practitioners increased the probability of home-based end-of-life care for cancer patients, while non-physician healthcare workers increased the likelihood of home-based care for the other patient groups. Conclusion: Diagnoses, individual characteristics, and service availability are all associated with the place of death in end-of-life care.

Sentiment analysis plays a crucial role in understanding customer feedback, guiding product development, and informing business decisions. This paper introduces term sentiment entropy (TSE), a novel weighting method that leverages the distribution of sentiment labels associated with words to enhance text classification accuracy. TSE complements traditional TF-IDF techniques by capturing the nuances of sentiment variation across different contexts. Experiments across diverse public datasets demonstrate TSE's potential to improve sentiment analysis performance, especially in capturing subtle sentiment shifts and adapting to specific domains. While computational cost and label quality pose challenges, TSE offers a promising avenue for refining sentiment analysis and opening new research frontiers.

The physiological process of iodine uptake in the thyroid is used for 131I treatment of thyroid diseases. Children are more sensitive to radiation compared to adults and may react differently to 131I exposure. The aims of this study were to evaluate the effects on thyroid protein expression in young and adult rats one year after 131I injection, and identify potential biomarkers related to 131I exposure, absorbed dose, and age. Twelve Sprague Dawley Rats (young and adults) were i.v. injected with 50 kBq or 500 kBq 131I and were killed twelve months later. Twelve untreated rats were used as age-matched controls. Quantitative proteomics, statistical analysis and evaluation of biological effects was performed. The effects of irradiation were most prominent in young rats. Protein biomarker candidates were proposed related to age, absorbed dose, thyroid function and cancer and a panel was proposed for 131I exposure. In conclusion, the proteome of rat thyroid was differentially regulated twelve months after low-intermediate dose exposure to 131I in both young and adult rats. Several biomarker candidates are proposed for 131I exposure, age, and many of them are known to be related to thyroid function or thyroid cancer. Further research on human samples are needed for validation.

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

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

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of the estimated wind energy in a desert city in northwestern Mexico. For the statistical modeling, 3 time series of wind speed data were used, corresponding to the years 2017, 2018, and 2019, recorded with a sonic anemometer at a sampling frequency of 10 Hz. From these, the analysis is performed with different stationarity periods (5, 30, 60, and 600 seconds). The estimation of the parameters characterizing the probability density functions was performed using different methods; the statistical models were evaluated by the coefficient of determination and the Nash-Sutcliffe efficiency coefficient, while their accuracy by the measured quadratic error, the mean square error, the mean absolute error, and the mean absolute percentage error. It was found that Weibull with the energy pattern factor method and Gamma using the method of moments were the probability density functions that best describe the statistical behavior of the wind speed and, therefore, better estimate the energy generated, so it is concluded that the proposed methodology will allow having greater confidence, both in the estimation of the wind speed and in the small wind energy available for its use.

Objective: The long-term care insurance (LTCI) system was introduced in 2000 to provide financial support for long-term care services to older adults. What is known on this topic report focuses on a comparative analysis of how the LTCI system has affected older people in Japan. Methods: We obtained data from the literature about the LTCI system, compared and analysed the collected articles, and concluded. Results: The LTCI system has improved access to long-term care services, with more older adults receiving care than before the system’s implementation. This service has improved the quality of life for many elderly individuals who require assistance with daily activities. Furthermore, the LTCI system has alleviated the financial burden of long-term care services since the insurance covers a large portion of the costs. It has made older adults maintain their independence and dignity, as they can afford the necessary care without relying solely on their savings or burdening their families. Conclusions: Japan’s LTCI system has positively impacted older adults by improving access to long-term care services and alleviating financial burdens. However, challenges such as increasing demand and variations in service quality need to be addressed to ensure the system effectively supports older people in Japan.

The objectives of this study were to evaluate fixed risk factors associated with PWM and to estimate genetic parameters for PWM. A total of 927 birth records from a mixed population of purebred and crossbred Boer and Spanish goats born between 2016 and 2023 at the International Goat Research Center (IGRC) were used for this study. Four binary traits were studied: D0-3 (death within 3 days after birth), D4-60 (death between 4 and 60 days), D61-90 (death between 61 and 90 days), and D0-90 (death within 90 days). Logistic regression models were used to evaluate the risk factors associated with PWM traits. Bayesian threshold models and Gibbs sampling were used to estimate the genetic parameters. Birth weight, season, litter size, sex, dam age, breed, and heterosis were found to be significantly associated with at least one of the PWM traits. Heritability estimates were 0.263, 0.124, 0.080, and 0.207, for D0-3, D4-60, D61-90, and D0-90, respectively. The genetic correlations between the studied traits ranged from 0.892 (D0-3 and D0-90) to 0.999 (D0-3 and D61-90). These results suggest that PWM in goats is influenced by both non-genetic and genetic factors and can be reduced by management, genetic selection, and crossbreeding approaches.

Genetic parameters of overreach, term and acuteness in Peruvian Paso horses (PPH) have not been determined to date. It is important to estimate these parameters for application in PPH breeding, therefore, the aim of this study was to estimate the heritability, repeatability, and genetic correlations under field conditions of overreach, term, and acuteness of PPHs. The study included records of up to 137 stallions and mares. All measurements were recorded in MP4 video format with a resolution of 1920 x 1080 megapixels and at 60 frames per second. All traits were measured three times (once per stride), and each trait was analyzed. KINOVEA software version 0.9.5 was used to analyze the measurements. A multivariate repeated measures animal model with sex effect was used to estimate the variance components for each trait using WOMBAT software. The results showed heritability of 0.411, 0.476 and 0.405, for the traits of overreach, term, and acuteness, respectively. Repeatability was high in all traits (> 0.78). Genetic additive correlations ranged from -0.30 to 0.49. It can be concluded that overreach and term have high heritability values, which allows these traits to respond better in a selection process, unlike acuteness which has a moderate heritability value.

The intense motion of a ship can greatly impacts the comfort of crew members and the safety of the vessel. Therefore, accurately estimating and predicting ship attitudes has become an important issue. This paper introduces the latest development in functional deep learning model called DeepOnet. It takes wave height as input and ship motion as output, using a cause-to-result prediction approach. The modeling data used in this study is sourced from publicly available experimental data from the Iowa Institute of Hydraulic Research. Firstly, parameters system tuning was conducted for the neural network's hyperparameters to determine the appropriate combination of parameters. Secondly, the DeepOnet model for wave height and multi-degree-of-freedom motion was established, and the influence of increasing time steps on prediction accuracy was examined. Finally, a comparison was made between the DeepOnet model and the classical time series model LSTM. It was found that the DeepOnet model had a 10-fold improvement in accuracy for roll and heave attitudes. Moreover, as the forecast duration increased, the advantage of DeepOnet showed a trend of strengthening. As a functional prediction model, DeepOnet provides a new promising tool for very short-term prediction of ship motion.

The aim of the study was to examine the long-term prognostic value of changes in cardio-ankle vascular index (CAVI) within a year after coronary artery bypass grafting (CABG). Methods. Patients with coronary artery disease (n = 251) in whom cardio-ankle vascular index (CAVI) was assessed using the VaSera VS-1000 device before and in a year after CABG. Groups with improved CAVI (CAVI decreased from pathological value to normal or the index remained within normal values) or worsened CAVI (persistent pathological index value or an increase in CAVI to pathological values or an increase of 1 unit or more) were identified. The groups were compared for the following events at 10-year follow-up: death from all causes, myocardial infarction, stroke/transient ischemic attack, and the combined endpoint. Results. A year after CABG surgery, 45% of patients showed positive dynamics or stable normal CAVI values. Death from all causes was significantly more common in the group with CAVI progression - 32 (27.6%) than in the group with CAVI improvement - 14 (14.8%; p = 0.029). Patients with CAVI progression were more likely to have MACE (death, MI, stroke/TIA) in 49 (42.2%) cases, compared with patients with CAVI improvement - in 23 (24.5%; p=0.008). Negative dynamics of CAVI (p = 0.024), number of shunts (p = 0.006), and the presence of carotid stenosis more than 30% (p = 0.051) were independent predictors of death from all causes at 10-year follow-up after CABG. The presence of carotid stenosis more than 30% (p = 0.002) and the group with negative dynamics of CAVI after a year (p = 0.008) were independent predictors of the development of the combined endpoint during long-term follow-up. Conclusions. Patients with negative dynamics of CAVI one year after CABG were more likely to experience death from all causes and combined endpoint in the long-term follow-up than patients with normal CAVI. In further studies, it is advisable to evaluate the dynamics of CAVI and, based on this, identify the most effective interventions to improve the prognosis of patients after CABG.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

This paper investigates portfolio optimization methodologies and short-term investment strate-gies in the context of the cryptocurrency market, focusing on ten major cryptocurrencies from January 2020 to November 2023. We employ high frequency data and utilize the Kurtosis Mini-mization methodology, alongside other optimization strategies, to construct and evaluate port-folios under different rebalancing frequencies. The empirical analysis reveals that cryptocurren-cies exhibit significant volatility, skewness, and kurtosis, necessitating sophisticated portfolio management techniques. We find that the Kurtosis Minimization methodology consistently outperforms other optimization strategies, delivering optimal returns to investors, particularly in shorter-term investment horizons. We demonstrate the diversification benefits of integrating cryptocurrencies into multi-asset portfolios and emphasize the importance of regular portfolio rebalancing in the volatile cryptocurrency market. Our findings offer insights for portfolio man-agers and investors seeking to optimize their investment outcomes in the cryptocurrency market, highlighting the importance of risk management, diversification, and dynamic portfolio man-agement strategies.

While providing safe water for the rural poor is considered a basic human right, there are numerous issues associated with existing technologies with shortcomings. Performance issues have continued to fail to meet the needs of impoverished families due to issues including high cost, difficulties with performance, and continuing needs for maintenance. These issues have severely interfered the safe water accessibility. Key aspects of the Guelph Water Filter (GWF) system can avoid/minimize many of these issues. The GWF as described herein enables delivery of low cost, long-term performance at 3Log removal of E.coli and can deliver 1 to 3 L of treated water per hour. The GWF is simple to operate, has an ability to provide sufficient water for a family, maintains longevity of performance, is easy to maintain and has protection against breakage during the cleaning process, is repairable at village level, and operates using a sizable reservoir of water to supply raw water, meaning the technology does not need to be refilled frequently. Hence, the capability of the novel GWF technology is shown to bypass many of the troublesome features of alternative low-tech water treatment technologies. The potential for the GWF to function for 2 days continuously avoids the need for young girls to fetch raw water frequently during a day, thereby enabling them to attend school. Hence, the GWF enhances the potential to result in ‘safe water and full schools’, providing the opportunity for girls to receive education and capture socio-economic benefits for the community.

This research investigates the application of deep learning in sentiment analysis of Canadian maritime case law. It offers a framework for improving maritime law and legal analytics policy-making procedures. The automation of legal document extraction takes center stage, underscoring the vital role sentiment analysis plays at the document level. Therefore, this study introduces a novel strategy for sentiment analysis in Canadian maritime case law, combining sentiment case law approaches with state-of-the-art deep learning techniques. The overarching goal is to systematically unearth hidden biases within case law and investigate their impact on legal outcomes. Employing Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) based models, this research achieves a remarkable accuracy of 98.05% for categorizing instances. In contrast, Conventional Machine Learning techniques such as Support Vector Machines (SVM) yield an accuracy rate of 52.57%, Naive Bayes at 57.44 %, and Logistic Regression at 61.86%. The superior accuracy of the CNN and STM model combination underscores its usefulness in legal sentiment analysis, offering promising future applications in diverse fields like legal analytics and policy design. These findings mark a significant choice for AI-powered legal tools, presenting more sophisticated and sentiment-aware options for the legal profession.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

The mechanical pre-treatment constitutes an unavoidable pre-step prior to biofuels conversion. As the cost of this step is often underestimated, recently researchers have striven to identify and characterize the mechanical pre-treatment effects in terms of post-treatment physical characterization, derived improvement of enzymatic digestibility, and output yields. The status of existing mechanical pre-treatment techniques is reviewed by substrate’s typology for lignocellulose biomass, focusing on comminution techniques. Emphasis has been dedicated to the particle size achieved after treatment, crystallinity breakage and eventual inhibitory phenomena deriving from the application of particle size reduction, which are analysed and discussed.

To handle the challenges and uncertainties of the actual labor market, the concept of employability becomes increasingly relevant, especially for graduates facing the transition from university to work. Graduate employability is a complex concept comprising different dimensions that may trigger effective job search and career management. This study introduces a new multifaceted tool, labeled the Orientamentis Questionnaire, to assess graduate employability from an agentic perspective, testing its psychometric properties. The Orientamentis questionnaire measures 14 dimensions merged into four 4 areas, namely graduate psychological capital, graduate agentic capabilities, graduate career perspective, and graduate proactive strategies. The tool was administered to a sample of 1238 graduate students (66% females) examining its factorial structure and reliability. To test its criterion validity, associations with three occupational outcomes collected one year later were explored on a subsample of 273 participants (66% females). Results attested the factorial structure of the Orientamentis questionnaire and the reliability of all dimensions. Moreover, the four areas and most of the dimensions were differently related to at least one subsequent occupational outcome. The Orientamentis Questionnaire represents a sound and reliable tool providing a multifaceted profile of graduate employability strengths and weaknesses, that could be used in academic placement programs to foster graduates’ self-awareness and proactive job search.

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

The UN Sustainable Development Goal 7 Report 2022 is about ensuring access to clean and affordable energy, which is key to the development of agriculture, business, communications, education, healthcare, and transportation. The lack of access to energy hinders economic and human development. Nevertheless, the current pace of progress is insufficient to achieve Goal 7 by 2030. Huge disparities in access to modern sustainable energy persist. Rising commodity, energy and shipping prices have increased the cost of producing and transporting solar photovoltaics modules, wind turbines and biofuels worldwide, adding uncertainty to a development trajectory that is already far below Goal 7 ambitions. Achieving energy and climate goals will require continued policy support and a massive mobilization of public and private capital for clean and renewable energy such as methane from biomass. An important factor in improving methane yield from biomass waste is pre-treatment of the biomass prior to anaerobic digestion. Size reduction of lignocellulose biomass is important for changing and increasing digestibility thus increasing methane yield. A Hollander beater was used as the mechanical pre-treatment machine throughout this study. Results indicate that methane yield is directly proportional to feedstock/Inoculum (F/I) ratio as higher F/I ratio led to higher methane yield. It was observed at an F/I ratio of 0.5 that 5 minutes pre-treatment time produced highest methane yield, while the lowest methane yield was observed at an F/I ratio of 0.2 with 1 minute of pre-treatment time. Methane yield decreased by 30% from 140 mL gTS-1 at F/I ratio 0.5 with 5 minutes pre-treatment time to 98 mL gTS-1 at F/I 0.2 with 5 minutes pre-treatment time. The optimum methane yield was attained at high F/I ratio allowing sufficient nutrient essential for complete degradation of biomass. Overall, this research shows the Hollander beater is not efficient in optimizing methane yield for cardboard.

This longitudinal study examined the development of executive function and calibration accuracy in pre-adolescents. Two hundred sixty two students(127 females) from grades 4 (n = 91), 5 (n = 89) and 6 (n = 82) took measures of executive function and performance calibration in a sport task, three times within a period of 20 months. A latent growth curve modeling analysis showed a significant relation between the rates of change of executive function and calibration. Further, the results showed a dynamic interplay in the development of executive function and calibration. There were significant inter-individual differences in the estimated population means both in executive function and calibration and in the rate of change of executive function, but not in the rate of change for calibration. The age of the participants had a positive effect only on the estimated population mean of executive function.

Background: Severe eosinophilic asthma (SEA) represents a substantial global health burden. Benralizumab has been shown to restore good control of asthma. Robust data on benralizumab effectiveness over periods longer than 2 years are still scarce. Methods: This retrospective multicentric study was conducted on 108 Italian SEA patients treated with benralizumab for up to 36 months. Clinical outcomes were evaluated at 6, 12, 24 and 36 months. Partial and complete clinical remission (CR) were also assessed. Data were analysed either with descriptive statistics or using linear, logistic, and negative binomial mixed-effect regression models. Results: Benralizumab induced a persistent reduction in exacerbation rates (p

Landsat and Sentinel-2 acquisitions are among the most widely used medium-resolution optical data adopted for terrestrial vegetation applications, such as land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. When combined, both data archives provide over 40 years, and counting, of continuous and consistent observations. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of cloud-, snow-, and shade-free observations at the pixel level is lacking and should be explored individually for each study to correctly parametrize subsequent analyses. However, data exploration is time and resource consuming, thus is rarely performed a-priori. Consequently, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present precomputed data on the daily 1982‑2023 availability of usable Landsat and Sentinel-2 acquisitions across the globe, sampled at a pixel-level in a regular 0.18°-point grid. The dataset comprises separate Landsat- and Sentinel‑2‑specific data records, and is accompanied by a pixel-specific growing season information to facilitate data exploration. The dataset was derived based on freely available 1982-2023 Landsat surface reflectance (Collection 2) and Sentinel-2 top-of-the-atmosphere reflectance (pre‑Collection-1 and Collection-1) scenes from 2015 through 2023, following the methodology developed in the recent study on data availability over Europe [1]. Growing season information was derived based on 2001‑2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6) [1]. As such, the dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much needed a-priori information aiding identification of appropriate methods and challenges for terrestrial vegetation analyses at local to global scale.

This study focuses on enhancing controllable fibrin-based hydrogels for tissue engineering, addressing existing weaknesses. By integrating a novel copolymer, we improve the foundation for cell-based angiogenesis with adaptable structural features. Tissue engineering often faces challenges like waste disposal and nutrient supply beyond the 200 µm diffusion limit. Angiogenesis breaks through this limitation, allowing the construction of larger constructs. Our innovative scaffold combination significantly boosts angiogenesis, resulting in longer branches and more capillary network junctions. The copolymer attached to fibrin fibers enables precise adjustment of hydrogel mechanical-dynamic properties for specific applications. Our material proves effective for angiogenesis, even under suppression factors like suramin. In our study, we prepared fibrin-based hydrogels with and without the copolymer PVP12400-co-GMA10mol%. Using a co-culture system of human umbilical vein endothelial cells (HUVEC) and mesenchymal stem cells (MSC), we analyzed angiogenetic behavior on and within the modified hydrogels. Capillary-like structures were reproducibly formed on different surfaces, demonstrating the general feasibility of three-dimensional endothelial cell networks in fibrin-based hydrogels. This highlights the biomaterial's suitability for in vitro pre-vascularization of biohybrid implants.

We delve into the axion-dilaton model within the supergravity framework, with a specific focus on the intricacies of domain wall construction and stability. Employing holographic vitrification, we unravel the dynamics of domain wall formation in gauge theories featuring periodic vacuum structures. Our model, incorporating a QCD-like axion term and a stabilizing dilaton, undergoes scrutiny for conductivity variations under weak disorder. The investigation reveals the model's resilience, manifesting near-perfect conductivity under mild disorder conditions. However, the rigorous mathematical motivation for the holographic setup demands further elucidation. The scattered nature of our results prompts the necessity for a more systematic interpretation of QCD phenomena and conductivity transitions. This study contributes to the mathematical understanding of the axion-dilaton model's behavior, highlighting the imperative for a refined holographic framework and a more coherent interpretation of observed phenomena.

Transport electrification is essential for reducing CO2 emissions, and technologies such as hybrid and range-extended electric vehicles will play a crucial transitional role. Such vehicles employ an internal combustion engine for on-board chemical energy conversion. The Wankel rotary engine should be an excellent candidate for this purpose, offering high power-to-weight ratio, simplicity, compactness, perfect balance, and low cost. Until recently, however, it has not been in production in the automotive market, due, in part, to relatively low combustion efficiency and high fuel consumption and unburnt hydrocarbon emissions, which can be traced to constraints on flame speed, an elongated combustion chamber, and relatively low compression ratios. This work uses large-eddy simulation to study the in-chamber flow in a peripherally ported 225cc Wankel rotary engine, providing insight into these limitations. Flow structures created during the intake phase play a key role in turbulence production but presence of the pinch point inherent to Wankel engine combustion chambers inhibits flame propagation. Two efficiency-enhancement technologies are introduced as disruptive solutions: (i) pre-chamber jet ignition and (ii) the two-stage rotary engine. These concepts overcome the traditional efficiency limitations and show that Wankel rotary engine design can be further enhanced for its role as a range extender in electrified vehicles.

In this paper, we study the constrained minimization problem for an energy functional which is related to the following Kirchhoff type equation \begin{equation*} -\Big(\eta+b\big(\int_{\R^{3}}|\nabla u|^{2}dx\big)^{s}\Big)\Delta u+V(x)u=\mu u +\lambda|u|^{p}u,\end{equation*} where $b$ is a positive constant, parameters $\eta\geq0, \lambda>0$, exponents $s>0$, $0

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This work addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, this work proposes an efficient deep learning framework to discern stress from facial videos. In contrast to research on wearable devices, this paper investigates the application of *dl for stress detection based on *rppg. The methodology involves selecting suitable *dl models (*lstm, *gru, *1dcnn), optimising hyperparameters, and investigating augmentation techniques. 1D-CNNv1 model achieved the best performance compared to other approaches, particularly with augmentation techniques such as linear interpolation and white noise, achieving a stress classification accuracy of 95.83% while maintaining excellent computational efficiency. The experimental results demonstrate the use of *dl for stress detection based on *rppg, with the potential to make significant contributions to the international standard in the field.

Non-pharmaceutical interventions (NPIs), including social distancing, wearing personal protective equipment, and lockdown measures, have been at the forefront of outbreak control in nursing homes. We used a mixed methodology to assess which NPIs nursing homes in the canton of Geneva, Switzerland, followed for their staff and residents during the first wave of the pandemic, between March 1, 2020 and June 1, 2020. For the qualitative component, we interviewed the attending physicians and/or director of each nursing home. Based on in-vivo codes, NPIs for nursing home workers and residents in each nursing home were thematically classified as: maximally restrictive, moderately restrictive, and minimally restrictive. In the quantitative component, we calculated incident rate ratios (IRR) for infection between the three levels of COVID-19-related measures taken in these nursing homes. We found an equal distribution of maximally (n=4), moderately (n=4), and minimally (n=4) restrictive NPIs. The extent of restriction did not show to be significantly associated with the cumulative incidence of COVID-19 cases among residents (maximally restrictive IRR = 3.90, 95%CI 0.82-45.54, p = 0.184; moderately restrictive IRR = 3.55, 95%CI 0.75-41.42, p = 0.212 ; minimally restrictive IRR = reference). Variabilities in NPIs adopted by nursing homes, and the number of COVID-19 cases appear to be randomly affected.

Evaluation of the patients after the surgical correction of idiopathic scoliosis in long-term follow-up with the clinical neurophysiology methods has not been presented in detail. This study aimed to compare the results of the neurophysiological studies in 45 girls with scoliosis of Lenke 1-3 types performed pre- (T0), postoperatively (T1, one week after surgery), and 6 months after the surgery (T2). Parameters of surface electromyography during the attempt of maximal contraction (mcsEMG) and transcranially evoked motor evoked potentials (MEP) recorded from anterior tibial muscles, as well as the electroneurography (ENG) of peripheral transmission in the peroneal nerve motor fibers were compared. The results indicate that efferent neural conduction function both centrally and peripherally as well as TA muscle function improve immediately after surgical correction of scoliosis (at p=0.05) and further normalization appears after six months in long-term follow-up (at p=0.03). It has been found in sEMG recordings, that the TA muscle motor unit recruitment function after half a year from surgical treatment in IS patients is comparable to the normal condition. ENG recordings results indicated the gradual retreat of the motor fibers injury symptoms mainly of the axonal type in peroneal nerves, the surgeries improved also the lumbar ventral roots’ neural transmission to the functional status considered as normal. MEP amplitude parameters recorded after the surgical scoliosis correction in T1 indicated a slight improvement in the efferent transmission of neural impulses within the fibers of the spinal tracts; in long-lasting T2 observation, they reached values comparable to those recorded in healthy volunteers bilaterally. Preoperatively (T0) the results of all neurophysiological study parameters in IS patients were asymmetrical at p=0.036-0.05 and recorded as worse on the concave side, suggesting the lateralization of neurological motor deficits. One week postoperatively (T1), this asymmetry was recorded as gradually reduced, showing almost no difference between the right and left sides six months later (T2). The presented algorithm of the diagnostic proceeding pre-, early post-, and long-lasting postoperatively using mcsEMG, MEP, and ENG neurophysiological examinations can be significant not only in making the final decision regarding surgical treatment and its personalization but also helps in precise ascertaining its effects as well as in predicting the final result of IS treatment.

Background Few data exist on comparative long-term outcomes of severe aortic stenosis (AS) patients with different flow-gradient patterns undergoing transcatheter aortic valve implantation (TAVI). This study sought to evaluate the impact of pre-TAVI flow-gradient pattern on long-term clinical outcomes after TAVI and assess changes in left ventricular ejection fraction (LVEF) of different subtypes of AS patients following TAVI. Methods Consecutive patients with severe AS undergoing TAVI in our institution were screened and prospectively enrolled. Patients were divided in four subgroups according to pre-TAVI flow/gradient pattern: (i) low flow - low gradient (LF-LG): stroke volume indexed (SVi) ≤ 35ml/m2 and mean gradient (MG) < 40mmHg), (ii) normal flow - low gradient (NF-LG): SVi > 35ml/m2 and MG < 40mmHg, (iii) low flow - high gradient (LF-HG) : SVi ≤ 35ml/m2 and MG ≥ 40mmHg and (iv) normal flow - high gradient (NF-HG): SVi > 35 ml/m2 and MG ≥ 40mmHg. Transthoracic echocardiography was repeated at 1-year follow-up. Clinical follow-up was obtained at 12 months, and yearly thereafter until 5-year follow up was complete for all patients. Results A total of 272 patients with complete echocardiographic and clinical follow-up were included in our analysis. Their mean age was 80±7 years and the majority of patients (N=138, 50.2%) were women. 62 patients (22.8% of the study population) were distributed in LF-LG group, 98 patients (36%) were LF-HG patients, 95 patients (34.9%) were NF-HG, while 17 patients (6.3%) were NF-LG. There was a greater prevalence of comorbidities among LF-LG AS patients. One-year all-cause mortality differed significantly between the four subgroups of AS patients (log rank p: 0.022) and was more prevalent among LF-LG patients (25.8%) compared to LF-HG (11.3%), NF-HG (6.3%) and NF-LG patients (18.8%). At 5-year follow-up, global mortality remained persistently higher among LF-LG patients (64.5%) compared to LF-HG (47.9%), NF-HG (42.9%) and NF-LG patients (58.8%) (log rank p: 0.029). At multivariable Cox hazard regression analysis, baseline SVi (HR: 0.951, 95% C.I.; 0.918 – 0.984), the presence of at least moderate tricuspid regurgitation at baseline (HR: 3.091, 95% C.I: 1.645 - 5.809) and at least moderate paravalvular leak (PVL) post-TAVI (HR: 1.456 , 95% C.I.: 1.106 - 1.792 ) were significant independent predictors of late global mortality. LF-LG patients and LF-HG patients exhibited a significant increase in LVEF at 1-year follow-up. A lower LVEF (p<0.001) and a lower Svi (p<0.001) at baseline were associated with LVEF improvement at 1 year. Conclusions Patients with LF-LG AS have acceptable 1-year outcomes with significant improvement in LVEF at 1-year follow-up, but exhibit an exceedingly high 5-year mortality following TAVI. The presence of low transvalvular flow and at least moderate tricuspid regurgitation at baseline and significant paravalvular leak post-TAVI were associated with poorer long-term outcomes in the entire cohort of AS patients. The presence of a low LVEF or a low SVi predicts LVEF improvement at 1-year.

Thin twin-roll cast strips from a model Al-Cu-Mg-Li-Zr alloy with a small addition of Sc were prepared. A combination of a fast solidification rate and a favorable effect of Sc microalloying impacts the size and distribution of primary phase particles and the dimension of eutectic cells. Small interdendritic spacing in the range of 10-15 um enables substitution of the initial long-term homogenization annealing by a two-step annealing at 300 and 450 °C and a short solution treatment at 530 °C before quenching and the final age-hardening procedure. A dense dispersion of small Al3(Sc,Zr) precipitates intensively inhibiting recrystallization forms during this treatment. Further refining and fragmentation of the structure were performed using constrained groove pressing before the solution treatment. Small pre-straining of quenched specimens and subsequent age-hardening simulating T8 temper were used to promote the formation of a homogeneous distribution of strengthening particles and to suppress preferential heterogeneous precipitation on subgrain and grain boundaries. Microstructural features and microhardness evolution during annealing were critically examined and confronted with microstructural characteristics observed on reference mold cast alloy or alloys without Sc alloying. The proposed treatment represents a model near net shape procedure for manufacturing thicker sheets for the aerospace industry.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

Long-term care (LTC) settings are increasingly used for palliative care in Japan. This study aimed to assess the proportion of LTC settings that provide palliative care services for older clients with advanced dementia in Japan and the trends across the types of settings. A nationwide questionnaire survey was conducted between November 2022 and January 2023, targeting 1,000 geriatric hospitals (GHs), 1,000 geriatric health services facilities (GHSFs), and 1,000 home nursing care stations (HNCSs). The palliative care practices for older patients with advanced dementia and their families in the agencies were assessed. Less than half of the GHs supported family participation or the spiritual needs of their older clients with dementia, while many had access to pain and symptom assessments and analgesic prescriptions by physicians. Less than half the GHSFs used pain assessment tools, assessed and supported family caregivers' pain and distress, and supported family participation in palliative care. More than 80% of the HNCSs practiced most of the listed palliative care. Current palliative care practices for older adults with dementia vary among GHs, GHSFs, and HNCSs. This emphasizes the need for targeted approaches to improve the provision of palliative care for patients with dementia across LTC settings in Japan.

A community-level nutrition intervention was implemented among tribal children (3 to 6 years) in Telangana, India. The one-year intervention involved six nutrient-rich formulations of millet-pulse-groundnut based products, suited to local taste preferences. Anthropometric measurements of height, weight and Mid-Upper-Arm -Circumference (MUAC) along with haemoglobin (Hb) levels were monitored at baseline and endline. The treatment group showed considerable gain in height (3.2 cm), weight (1.68 kg) and MUAC (0.33 cm), over the control group. Paired t-test indicated significant differences (p<.01) between pre- and post-intervention anthropometric measurements. Positive shifts were observed in wasting (WHZ; -1.2 ±1.3 to -0.9± 1), stunting (HAZ; -1.8±1.6 to -0.3±1.3) and underweight (WAZ; -1.9±1.2 to -0.7±1) among treatment group. Hb levels in the treatment group also improved significantly from 9.70 ± 0.14 g/dl (moderately-anaemic) to 11.08 ± 0.13 g/dl (non-anaemic). Post-intervention Focus Group Discussions (FGDs) involving mothers and teachers confirmed the positive impact. Thus, nutritional intervention formulated using climate resilient millets, pulses and groundnut, promotes dietary diversity and improves the nutrition & health status of children.

In-wheel motor vehicles are gaining attention as a new type of electric vehicle due to their efficient power units located inside each wheel hub. However, they are more susceptible to wheel resonance due to the increase in unsprung mass caused by the motor's weight. This can result in decreased both the ride comfort and driving stability. To resolve this issue, this study aims to apply an optimal switching controller with a semi-active n actuator; magnetorheological (MR) damper. For the implementation of the optimal switching controller, a road type classification is also carried out. An acceleration sensor is used for road type classification, and control logics include a ride comfort controller; the linear quadratic regulator (LQR _Paved Road) and a wheel motion controller (LQR_Off Road) for improved driving stability. For paved roads, the LQR_Paved Road control input is applied to the MR damper. However, if a road type prone to wheel resonance is detected, the control logic switches to the LQR_Off Road. During the transition, a weighted average of both LQR_ Paved Road and LQR_Off Road control input is applied to the actuator. Computer simulations are undertaken to evaluate vibration control performance, including the ride comfort and driving stability at various road profiles.

Abstract: (1) Background: Post-COVID syndrome is defined as symptoms that occur simultane-ously with or after COVID-19 infection, last for 12 weeks, and are not due to another diagnosis. Limited data are available on people’s long-term quality of life (QoL) following COVID-19 infection. The aim of this cross-sectional study was to investigate long-term QoL after COVID-19 among employees of the St. Josef Hospital in Paderborn, Germany, and to identify risk factors. (2) Methods: A monocentric, cross-sectional study was conducted using the validated and digitized WHOQOL-BREF questionnaire via Netigate® between 10/2022 and 02/2023. Data on QoL and global health status were collected in the following four domains: physical health, mental health, social relationships, and environment. (3) Results: The response rate was 73.84% (923/1250). Furthermore, 63.45% of hospital staff respondents reported at least one persistent symptom after COVID-19 infection, leading to significant differences in QoL. Pre-existing conditions, persistent symptoms, and disability after COVID-19 infection, as well as high BMI, no partnership, and low educational level were found to significantly contribute to long-term low QoL. (4) Conclusions: Obesity, lack of partnership, and a low level of education were independent risk factors for lower QoL post-COVID-19 infection in this cohort of hospital staff. Further multicenter studies are re-quired to validate the incidence and their suitability as independent risk factors for post-COVID syndrome.

Introduction: Congenital heart defects (CHD) are the most frequent group of major congenital anomalies, accounting for almost 1% of all births. They comprise a very heterogeneous group of birth defects in terms of their severity, clinical management, epidemiology and embryologic origins. Taking this heterogeneity into account is an important imperative to provide reliable prognostic information to patients and their caregivers, as well as to compare results between centers or to assess alternative diagnostic and treatment strategies. The Anatomic and Clinical Classification of CHD (ACC-CHD) aims to facilitate both the CHD coding process and data analysis in clinical and epidemiological studies. Objectives: 1) To describe the long-term childhood survival of newborns with CHD and 2) To develop and validate predictive models of infant mortality based on the ACC-CHD. Materials and Methods: This study is based on data from a population-based, prospective cohort study: Epidemiological Study of Children with Congenital Heart Defects (EPICARD). The final study population comprised 1881 newborns with CHD after excluding cases that were associated with chromosomal and other anomalies. Statistical analysis included non-parametric survival analysis and flexible parametric survival models. The predictive performance of models was assessed by Harrell’s C index and the Royston-Sauerbrei R2 with internal validation by bootstrap. Results: The overall 8-year survival rate for newborns with isolated CHD was 0.96 [0.93- 0.95]. There was a substantial difference between the survival rate of the categories of ACC-CHD. The highest and lowest 8-year survival rates were 0.995 [0.989-0.997] and 0.34 [0.21-0.50] for “interatrial communication abnormalities and ventricular septal defects” and "functionally univentricular heart", respectively. Model discrimination as measured by Harrell’s C was 87% and 89% for the model with ACC-CHD alone and the full model, which included other known predictors of infant mortality, respectively. The predictive performance as measured by R2 was 45% and 50%, for the ACC-CHD alone and the full model. These measures were essentially the same after internal validation by bootstrap. Conclusion: The ACC-CHD classification provided the basis of a highly discriminant survival model with good predictive ability for the 8-year survival of newborns with CHD. Prediction of individual outcomes remains an important clinical and statistical challenge.

We conducted a hybrid-design study on a historical cohort of 359 adult recipients of a liver graft who received transplants between 1996 and 2002. The study aimed to identify predictors of survival and investigate the prevalence of co-morbidities among long-term survivors. The actuarial (95% CI) patient survival of the overall study cohort was 96% (94.6%-98.3%), 69% (64.2%-73.6%), 55% (49.8%-59.9%), 42.8% (37.6%-47.8%), and 34% (29.2%-38.9%) at 1, 5, 10, 15, and 20 years, respectively. The actuarial (95% CI) graft survival was 93.8% (91.6%-97.2%), 67.6% (62.2%-71.5%), 54.3% (49.6%-59.3%), 42.1% (37.3%-47.4%), and 33.8% (29.0%-38.4%) at 1, 5, 10, 15, and 20 years, respectively. The leading cause of death was hepatitis C virus recurrence (24.6%), followed by extrahepatic malignancies (16.9%), infection (14.4%), and hepatocellular carcinoma recurrence (14.4%). The Cox proportional hazards analysis revealed that several independent factors were associated with the survival probability. These factors include younger donor and recipient ages (p=0.001 and 0.004, respectively), female recipient sex (p<0.001), absence of HCV (p<0.01), absence of HCC (p=0.001), and absence of diabetes mellitus at one year (p<0.01). At the latest follow-up, the leading comorbidities in the long-term survivors were hypertension (53.6%), obesity (18.7%), diabetes mellitus (17.1%), hyperlipidemia (14.7%), chronic kidney dysfunction (14.7%), and extrahepatic malignancies (13.8%), with 73.9% of patients having more than one complication. The Kaplan-Meier probability (95% CI) of complication-free survival was 65.4% (60.2%-70.3%), 38.4% (33.4%-43.7%), 17.8% (14.1%-22.2%), 7.2% (4.8%-10.5%), and 1.94% (0.85%-4.15%) at 1, 5, 10, 15, and 20 years, respectively. Aging with a liver graft is associated with an increased risk of complications and requires ongoing care to reduce the long-term attrition rate resulting from chronic immunosuppression.

Urbanization influences food culture, particularly in low- and middle-income countries, where there is an increasing consumption of processed and pre-packaged foods. This shift is contributing to a rise in non-communicable diseases. Food labeling standards are crucial for regulating manufacturing practices and aiding consumers in making healthy food choices. We aimed to assess the compliance of local and imported pre-packaged snacks with Tanzanian and international labeling standards in Dar es Salaam, Tanzania. A cross-sectional study was conducted on 180 snack products. A checklist based on Tanzanian and Codex labeling standards was used to evaluate adherence. We also examined factors influencing adherence, such as product origin, price, category, purchase location, and package size. The majority of the snacks demonstrated partial adherence to labeling standards. Imported products showed significantly better full adherence to both Tanzanian and International standards. Notably, over half of the products used English for labeling, and infrequently used the recommended World Health Organization Front-of-Pack nutrition Labeling. Product category, origin, and package size were significantly associated with higher levels of International standard adherence (p<0.05). The inadequate adherence to mandatory labeling standards, scarce use of Swahili and FoPL highlight the need to strengthen labeling practices and potential challenges faced by consumers in understanding nutritional information. Thus, strengthening and emphasizing good labeling practices is of urgency as we seek to address diet-related Non-communicable diseases.

Nuclear Magnetic Resonance (NMR) and its various forms are extensively utilized in both research and clinical settings to analyse molecules. NMR data pre-processing, while essential to NMR data analysis, can be uniquely complex. Despite the availability of software tools, understanding these processes can be challenging, complicating the selection of appropriate pre-processing steps. In this review, we elucidate pre-processing steps in the time domain from a mathematical and statistical perspective, explaining direct current offset removal, eddy current correction, shift and linear prediction, weighting, zero filling, and domain transformation using plain language and fundamental mathematical formulas. Our objective is to clarify these processes in simple terms and provide general guidance.

The aim of the study was to compare the standard baseline imaging, with extended radiological staging. To assess our hypothesis, we performed a prospective, single-centre study which included 132 participants recruited between October 2015 and March 2020. Descriptive statistics, the Friedman and chi-squared tests were performed and p &lt; 0.05 was considered significant. Patients were grouped into two cohorts, CT scan cohort (n=87) and PET-CT cohort (n=43). In the first group the originally determined disease stage changed in 36.8 % of cases, in second group in 51.2 %. The consistency between assessment of axillary lymph nodes by imaging (cN) and the postoperative pathology report (pN) was evaluated. In most cases, clinical and pathological evaluation of involved nodes was consistent, for CT scan the result was χ2(1) = 18.98; p &lt; 0.001, for PET-CT the result was χ2(1) = 6.41; p = 0.03. Highly customized radiation therapy defined as dose boost within the involved lymph nodes, not covered by surgeon, was applied in nine patients.

The results of numerical experiments with the chemistry-climate model of the Institute of Numerical Mathematics of the Russian Academy of Sciences - Russian State Hydro-Meteorological University (INM-RSHU CCM) in comparison with the MERRA2 reanalysis data are presented. To assess the impact of sea surface temperature (SST) on stratospheric processes and the ozone layer, numerical experiments were carried out for scenarios corresponding to the El Niño and La Niña Southern Oscillation phases. It is shown that the El Niño phase contributes to the enhancement of heat and mass transfer to the polar stratosphere, which strengthen the Brewer-Dobson circulation and the flux of wave activity that affect the zonal wind. As a result, during El Niño, the probability of sudden stratospheric warming increases and the stratospheric polar vortex (SPV) weakens, re-sulting in an increase in total polar stratospheric ozone. The La Niña phase, on the contrary, is accompanied by a weakening of heat and mass transfer from the troposphere to the stratosphere, as a result of which the stratospheric polar vortex strengthens and the total column ozone decreases. It is shown that the effect associated with the Southern Oscillation is more pronounced in the Northern Hemisphere than in the Southern Hemisphere. Numerical experiments were also carried out for scenarios in which the sea surface temperature was set corresponding to the early 1980s, and the results were compared with calculations in which the SST was set corresponding to the late 2010s. This allowed us to assess the influence of the multiyear trend in the SST increase on stra-tospheric processes and the ozone layer. It was shown that the SST increase is most pronounced in the Arctic, which corresponds to the phenomenon of "Arctic amplification", and therefore, the heat and mass transfer to the stratosphere increases in the Northern Hemisphere, which contributes to some weakening of the SPV and increase of the total column ozone. But, compared to the Southern Oscillation, the influence of the SST trend on the SPV and total column ozone is much weaker and is observed mainly in the Northern Hemisphere.

In view of the expected climate changes, a paradigm shift in soil management, through reduced tillage and/or a different use of crop residues, can be a key point in the mitigation of the climate impacts. The transition from a traditional cropping system (i.e., durum wheat in continuous cropping system under conventional tillage and/or straw removal) to those undergoing the conservative practices require to be evaluated in a medium to long time frame to reach an equilibrium and thus to be properly investigated. In this regard, cropping system simulation models are fundamental tools for the in-silico evaluation of the response of crop growth and soil organic matter dynamics to varying cropping scenarios. This paper reports the evaluations on the parameterization and reliability of the ARMOSA crop simulation model calibrated and validated on experimental datasets collected on durum wheat grown in continuous cropping system under several straw and soil management strategies in a Mediterranean environment.

Polycystic ovary syndrome (PCOS) is a multisystem disorder that presents with a variety of phenotypes involving metabolic, endocrine, reproductive, and psychological symptoms and signs. Women with PCOS are at increased risk of pregnancy complications including implantation failure, miscarriage, gestational diabetes, fetal growth restriction, preterm labour, and preeclampsia (PE). This may be attributed to the presence of specific susceptibility features associated with PCOS before and during pregnancy, such as chronic systemic inflammation, insulin resistance (IR), and hyperandrogenism, all of which have been associated with an increased risk of pregnancy complications. Many of the features of PCOS are reversible following lifestyle interventions such as diet and exercise, and pregnant women following a healthy lifestyle have been found to have a lower risk of complications, including PE. This review summarizes the evidence investigating the risk of PE and the role of nutritional factors in women with PCOS. The findings suggest that the beneficial aspects of lifestyle management of PCOS, as recommended in the evidenced-based international guidelines, extend to improved pregnancy outcomes. Identifying high-risk women with PCOS will allow targeted interventions, early pregnancy screening, and increased surveillance for PE. Women with PCOS should be included in risk assessment algorithms for PE.

This study explores the application of the Fully Convolutional Network (FCN) algorithm to the field of meteorology, specifically for the short-term nowcasting of severe convective weather events such as hail, convective wind gust (CG) and thunderstorms, short-term heavy rain(STHR) in Gansu. Training data comes from the European Center for Medium-Range Weather Forecasts (ECMWF) and real-time ground observations, the performance of the proposed FCN model based on 2017 to 2021 training datasets demonstrate a high prediction accuracy, with an overall error rate of 16.6%. Furthermore, the model exhibited an error rate of 18.6% across both severe and non-severe weather conditions when testing against the 2022 dataset. Operational deployment in 2023 has yielded an average Critical Success Index (CSI) of 24.3%, a Probability of Detection (POD) of 62.6%, and a False Alarm Ratio (FAR) of 71.2% for these convective events. It is noteworthy that the predicting performance for STHR was particularly effective with the highest POD and CSI, as well as the lowest FAR. CG and hail predictions had comparable CSI and FAR scores, although the POD for CG surpassed that for hail. The FCN model’s optimal performance for hail prediction occurred at the 4th, 8th, and 10th forecast hours, while for CG, the 6th hour was most accurate, and for STHR, the 2nd and 4th hours were most effective. These findings underscore the FCN model's ideal suitability for short-term forecasting of severe convective weather, presenting extensive prospects for the automation of meteorological operations in the future.

As for their large surface area and pore volume, three-dimensional covalent organic frameworks (3D COFs) have emerged as competitive porous materials. However, due to the structural dynamic behavior particularly for imine-linked 3D COFs, potentially unlock their potential for gas storage application. Herein, we showed a pre-locked linker strategy introduces breaking dynamic behavior in 3D COFs. A predesigned planar linker based 3,8-diamino-6-phenylphenanthridine (DPP) was prepared to produce non-dynamic 3D JUC-595, as benzylideneamine moiety in DPP locked the linker flexibility and restrict the molecular-bond rotation of the imine linkages. Upon solvent inclusion and release, the PXRD profile of JUC-595 remained intake, while JUC-594 with flexible benzidine linker experienced crystal transformation due to framework contraction-expansion. As a result, the activated JUC-595 achieved higher surface areas (754 m² g-1) than that of JUC-594 (548 m² g-1). Furthermore, improved CO2 and CH4 storages were also seen in JUC-595 compared with JUC-594. Impressively, JUC-595 recorded high normalized H2 storage capacity that surpass other reported high surface area 3D COFs. This works shows important insight on manipulating structural properties of 3D COF to tune the gas storage performance.

The mathematical structure underlying a sentence is made of two independent processing units in series. The conditional probability density function of sentence length (measured in words) recordable in the extended short–term memory (E–STM) buffer made of CF cells is Gaussian. The number of sentences that authors of Italian and English Literatures have written is compared to that theoretically available to them. The multiplicity factor α describes this comparison. α&gt;1 is more likely than α&lt;1 and often α≫1, therefore the same sentence pattern is reused many times. These texts are easier to read. Very few novels show α&lt;1. In these cases, there are enough diverse sentence patters to convey meaning, but most of them are not used. Texts are more difficult to read, as measured by a universal readability index GU. A mismatch index IM synthetically describes the process. α, GU. and IM increase with year of publication. Future work will concern other literatures – including ancient ones as Greek and Latin – to confirm what, in our opinion, is general feature of human mind.

X-band marine radar captures signal reflected from the sea surface. Theoretical studies indicate that the initial unfiltered signal contains meaningful information including parameters related to wind waves. Traditional methods for estimating significant wave height (SWH) rely on the physical laws describing signal reflection from rough surfaces. However, recent studies suggest the feasibility of employing artificial neural networks (ANNs) for SWH approximation. Both classical and ANN-based approaches necessitate costly in situ data. In this study, we propose generating synthetic radar images with specified wind wave parameters using Fourier-based approach and Pierson–Moskowitz wave spectrum as a viable alternative. We generate synthetic images and use them for unsupervised learning approach to train a convolutional component of an ANN. After that, we train a regression ANN based on the previous convolutional component to obtain SWH back from synthetic images. Then, we apply preliminary trained weights for the same regression model to train SWH approximation on the dataset of real sea clutter images. In this study, we demonstrate the increase in the accuracy of SWH estimation from radar images with the preliminary training on synthetic data.

This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary ad-vancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address existing models' rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post) with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to as-sess our models' performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their po-tential for both academic and practical applications in the field.

The objective of the present report is the dissemination of information acquired during the application of ISO 6579-1:2017 for the isolation of Salmonella spp. from swine samples. ISO 6579-1:2017 is the officially recommended by the EU protocol for the isolation of Salmonella spp., aiming in the harmonization of effective successful control of Salmonella infection in food producing animals. Successful control of animal salmonellosis is highly dependent on the sensitivity of the biochemical methods reliably detecting the presence of the pathogen in various stages of food production. Application of ISO 6579-1:2017 resulted in the isolation of twelve Salmonella spp. and eighteen other members of the family of Enterobacteriaceae biochemically and antigenically resembling salmonellae. The findings necessitated in the depth evaluation of the culture media xylose-lysine-desoxycholate agar (XLD), Salmonella-Shigella Agar (SS), Brilliant Green Agar (BGA), Salmonella Chromogenic Agar (SCA), Triple Sugar Iron (TSI) and modified semi-solid Rappaport–Vassiliadis (MSRV) agar. The evaluation showed that these culture selective media differed significantly in their performance regarding the isolation of Salmonella from swine samples. It was concluded that the presence of atypical Salmonella strains negatively affects the prevalence of Salmonella, thus the identification of carrier pigs, eventually affecting the efficiency of control programs. Thus, doubtful results require additional biochemical testing for confirming the accuracy of such universally recommended isolation methods.

Natural Fibre Composites (NFC) are eco-friendly, sustainable alternatives to synthetic polymers. However, some intrinsic natural fillers´ properties hinder their widespread implementation as re-inforcement in polymeric matrices and require further investigation. In the scope of this study, the thermal, rheologic, mechanical (tension and flexural modes), morphological properties, water ab-sorption and dimensional stability of the NF polypropylene (PP) based injection moulded compo-sites with the rice husk (rh) and olive pits (op) of 20 wt% and 30% wt%, were investigated. The results suggest that the higher content of the rice husk and olive pits led to a similar reduction of the Melt Flow Index (MFI), independent of the additive type compared to virgin polypropylene (PPv). The melting and crystallization temperatures of the PPrh and PPop composites did not change with statistical significance. The composites are stiffer than the PP matrix by up to 49% and possess higher mechanical strength in what concerns the tension mode at the expense of de-creased ductility. PPrh and PPop have superior flexural modulus in the bending mode, while the flexural strength improvement was accomplished for the PP30%rh. The dimensional stability of the composites was improved as the linear shrinkage in the flow direction was decreased by 49% for PPrh and 30% for PPop, positively correlating with an increase in the filler content and stiff-ness. PPop was less susceptible to water sorption than PPrh due to fibres' composition and larger surface-to-area volume ratios.

The influences of doped hole transporting layer (HTL) on the performances of perovskite solar cells were studied. The influences of electrostatic and surface roughness effect by solvent additive doping were compared. The electrostatic effect of solvent additive doping was compared by addition of 1,8-diiodooctane and 1,8-dichlorooctane to the HTL layer. The surface roughness effect of solvent additive doping was examined using atomic force microscopy. The perovskite crystallinity by doping HTL was studied by X-ray diffraction. The conductivity of HTL was measured using four probe methods and was higher for doped devices. Enhanced conductivity of PEDOT:PSS film was also proved using fluorescence emission quenching. Long-term stability of the device was enhanced by doping HTL. The device with solvent additive exhibited much enhanced stability, retained over 80% of the initial PCE for 400 hours, while PCE of the device without solvent additive decreased to less than 80% after 200 hours. A very weak power microwave treatment was used for post-annealing of the fully fabricated devices. Post-annealing enhanced the lifetime of the device. Comparing different size of cells, the device lifetime was decreased as the cell area increased and as the size of the cell increased, the extent of lifetime enhancement by doping was increased.

Training has a significative effect on physiology of blood coagulation both in humans and in horses. Several hemostatic changes have been reported after exercise in the horse but data available are inconclusive. The aim of this study was to investigate the platelet activation and primary platelet-related hemostasis modifications in young untrained Thoroughbred racehorses in the first 4-month timeframe incremental training period in order to improve knowledge on this topic. Twenty-nine clinically healthy, untrained, 2-year-old Thoroughbred racehorses. Never trained Thoroughbreds were followed during their incremental 4-month sprint exercise schedule. Blood collection was performed once a month (T-30, T0, T30, T60, and T90). Platelet aggregation was measured by light transmission aggregometer in response to various agonists: adenosine diphosphate (ADP), collagen and calcium ionophore A23187. Platelet function was evaluated using the Platelet Function Analyzer (PFA-100®) using collagen/ADP and collagen/adrenaline cartridges. Nitrite-nitrate (NOx) plasma concentrations were measured by a colorimetric assay to assess in vivo nitric oxide bioavailability. Platelet activation was investigated also through gene expression analyses (selectin P-SELP, ectonucleotidase CD39-ENTPD1, prostaglandin I2 synthase-PTGIS, endothelial nitric oxide synthase 3-NOS3). Differences among the time points were analyzed and mean±SEM were calculated. Significant modifications were identified compared with T-30 with increase of platelet aggregation (collagen:32.6±4.8 vs 21.6±4.9%; ADP: 35.5±2.0 vs 24.5±3.1%; A23187: 30±4.7 vs 23.8±4%) and a shorter closure time of C-ADP cartridges (75.6±4.4 vs 87.7±3.4 sec) that tended to return to baseline value at T90. NOx concentrations in plasma significantly increased after 30 days of training program compared with baseline. The first long-term training period induce platelet hyperactivity already after 30 days in untrained Thoroughbreds. Regular physical training blunts the negative effects of acute efforts on platelets activation.

Hundreds of thousands of people living along the Yatsushiro Sea coast have been exposed to methylmercury from the contaminated water of the Chisso factory in Minamata. Methylmercury causes neurological deficits in the cerebrum and cerebellum, with symptoms of ataxia, dysarthria, and visual field constriction in severe cases, but the most common symptom of a milder disorder is somatosensory disturbance due to parietal lobe damage. We investigated the characteristics of the somatosensory disturbances in detail by studying subjective complaints, neurological findings, and quantitative sensory tests in 197 residents (63.2 ± 10.7 years old) from contaminated areas exposed to methylmercury from seafood and 130 residents (63.7 ± 9.3 years old) from control areas. The most commonly affected sensory modalities were superficial peripheral touch and pain in the extremities, followed by two-point discrimination and deep senses, and in the most severe cases, full-body sensory dysfunction and impairment of all sensory submodalities. Although the severity of the sensory submodalities correlated with each other, there was almost no correlation between the severity of these submodalities and indices of peripheral nerve conduction studies, suggesting that the primary cause of the sensory deficits was damage to the central nervous system. The health effects of chronic methylmercury toxicosis can be elucidated by a detailed examination of sensory deficits.

Fungi belong to the species with the highest biodiversity and high potential for use in biotechnology and industry. Therefore, their long-term quality conservation is very important and it is necessary to continuously develop new methods and improve existing methods. Currently, cryopreservation appears to be the best available technique for the preservation of fungi. This study summarises the latest trends and advances in fungal cryopreservation over the last 7 years, i.e. findings from scientific publications from 2017 to 2023 dealing with cryopreservation of both basidiomycetes and non-basidiomycete fungi. The work analyzed the studied organisms and the cryopreservation method used, focused on the use of new procedures and modifications of existing methods; summarized the conditions for successful cryopreservation, such as cryopreservation time and temperature, as well as the substrate and cryoprotectant used.

Background Acute right heart failure (RHF) is associated with high morbidity and mortality. Temporary mechanical circulatory support (tMCS) is now part of the advanced management strategy for refractory cardiogenic shock (CS). Among such support systems, VA-ECMO remains the fastest MCS system to set up. The ProtekDuo cannula can be used as a temporary and percutaneous right ventricular assist device (RVAD) in relay with VA-ECMO in the context of prolonged weaning. Case Summary This case report describes the clinical course of a 69-year-old patient who underwent elective aortic valve replacement surgery complicated by acute RHF due to iatrogenic ligation of the right coronary artery. VA-ECMO was initiated in the context of refractory cardiogenic shock. Faced with prolonged MCS weaning, due to lack of recovery of the right ventricle associated with the absence of any other therapeutic option, a percutaneous RVAD via a ProtekDuo cannula was implanted as a bridge to recovery. The ProtekDuo was gradually weaned while the patient began rehabilitation. The patient was transferred to the medical ward after 5 weeks in the ICU, stayed 5 months in an acute medical setting, went to a rehabilitation center afterward and returned home 6 months after the initial surgery without any neurological sequalae or organ failures. In total, she remained on MCS for 39 days (21 on VA-ECMO and 18 on ProtekDuo). Discussion In cases of refractory RHF, after stabilization and organ perfusion restoration under VA-ECMO, it is feasible to switch to a percutaneous RVAD, allowing early mobilization of patients during prolonged MCS weaning until the right ventricle adapts to new loading conditions.

Intrauterine growth restriction (IUGR) and being born small for gestational age (SGA) are two distinct conditions with different implications for child development. IUGR is associated with greater perinatal morbidity and mortality and can be identified by additional abnormalities (pathological Doppler sonography, oligohydramnios, lack of growth in the interval, estimated weight <3rd percentile). IUGR may also be present in neonates with birth weights above the 10th percentile. Recognizing fetuses being “at risk” of a higher perinatal mortality and morbidity rate in order to monitor them accordingly and deliver them in good time is still challenging. This review article summarizes approaches to increase the diagnostic accuracy of IUGR and presents current concepts of pathophysiology with a focus on oxidative stress and consecutive inflammatory and metabolic changes. Since prenatal influences affect the risk of later diseases, we also discuss the need for interdisciplinary follow-up strategies during childhood and look at future scientific challenges. It should be emphasized that prenatal and postnatal care of IUGR neonates should be regarded as a continuum.

Introduction Infections caused by multidrug-resistant bacteria (MDR) occur more frequently after pancreaticoduodenectomy (PD) compared to other abdominal surgeries, and infective complications are a leading cause of postoperative morbidity following PD. Pre-operative biliary stent (PBS) placement often leads to biliary contamination, which in turn plays a significant role in postoperative infections. The aim of this study is to evaluate the impact of MDR contamination on short-term postoperative outcomes in patients undergoing PD and to examine the relationship between MDR bacteria, PBS, and bile contamination. Methods This is a retrospective study based on a prospectively maintained database comprising 825 consecutive patients who underwent pancreaticoduodenectomy (PD) between January 2010 and December 2020 at our center. All procedures were conducted with a standardized resection by a dedicated group of surgeons in a specialized pancreatic surgery unit, and the patients were managed according to the same perioperative enhanced recovery protocol. Results MDR bacteria were present in 17.5% of bile cultures, exclusively within the stented group. In our multivariate analysis, the development of a major postoperative complication (MPC) was correlated with the presence of MDR bacteria in the bile (OR 1.66, 95% CI: 1.1-2.52; p = 0.02). MDR bacteria were detected early in the surgical drainage in 144 out of 825 patients (12.1%), with 72.2% having a previous biliary stent placement and 27.8% without stents (p < 0.001). In our multivariate analysis, the development of an MPC was associated with the presence of MDR bacteria in the drainage (OR=1.81, 95% CI: 1.21-2.73, p= 0.0042). Conclusions We demonstrated that MDR contamination worsens the short-term outcomes of patients undergoing PD. Specifically, when MDR bacteria are present in both the bile and drainage, there is a statistically significant increase in the incidence of major postoperative complications (MPC). Our data suggest that the majority of MDR surgical site infections stem from biliary contamination resulting from the placement of a pre-operative biliary stent (PBS).

In patients with prior Takotsubo syndrome (TTS), long lasting functional cardiac limitations were described as compared with normal subjects. Emotions-triggered Takotsubo syndrome (E-TTS) has more favorable outcomes than TTS preceded by a physical trigger or by no identifiable fac-tors. The aim of the present study was to assess long-term cardiac functional limitations in a co-hort of asymptomatic E-TTS patients. We enrolled n=39 asymptomatic patients with a diagnosis of E-TTS. Cardiopulmonary exercise tests (CPET) were performed at 30 [12-40] months median fol-low-up from the acute event. A cohort of n=39 individuals matched for age, sex, body mass index and comorbidities served as control. Despite recovery of left ventricular ejection fraction, patients with prior E-TTS had lower peak VO2 and percentage of predicted peak VO2 (17.8 ± 3.6 vs 22.5 ± 6.5; P < 0.001 and 75.2 ± 14.1 % vs 100.6 ± 17.1%, P <0.001), VO2 at anaerobic threshold (AT) (11.1 [10.1-12.9] vs 14.4 [12.5-18.7]; P <0.001), peak O2 pulse (9.7 ± 2.5 vs 13.1 ± 3.5; P <0.001) and higher VE/VCO2 slope (30.4 ± 3.7 vs 27.2 ± 3.5; P <0.001) compared with matched controls. We found no statistically significant differences in heart rate reserve (HRR), respiratory equivalent ratio (RER), mean blood pressure and peak PetCO2 between patients and controls. Despite its favorable out-come, patients with E-TTS in our population were found to have subclinical long-term functional cardiac limitations as compared with a control cohort.

With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.

In order to achieve the target, several activities are being undertaken, including the development of multipurpose reservoir planning for the water supply and sanitation infrastructure, irrigation, and hydropower development for the H project. To undertake this work, the main data given was rainfall data, flow data, catchment area, base population for water supply, and so on. This report presents a specifically preliminary study and design of water supply, irrigation, and hydropower development for the project, which is carried out in accordance with the scope of work given by our professor. The preliminary study and design are therefore to investigate scenarios and alternatives and prepare a document of water supply, irrigation, and hydropower infrastructures for the provision of the assignment. This report, as required in the assignment, contains project background, water sources assessment, selection of suitable hydropower potential, and irrigation sizing, design criteria, descriptions of the proposed scheme, preliminary design of water supply components. The objective of the project is to recommend and design a cost-effective water supply system, irrigation, and hydropower development to supply reliable, sufficient, and safe water from a defined water source to the H project. The specific objectives of the investigation works are: • To assess potential water source alternatives for water supply • Selecting appropriate size for the reservoir which could satisfy all water demand • Assess and select suitable irrigation development and hydropower potential. • Preliminary study and design of all components of the reservoir and appurtenant structures The scope of the project generally focuses on preliminary study and design of water supply, irrigation and hydropower development components from water sources potential and the water balance output.

The present study examines the long-term care service awareness, needs, and usage intention of elderly male homosexuals in Taiwan and their ideal long-term care service model. This study of five elderly male homosexual subjects aged 66 to 73 years was intended as a preliminary exploration. Interviews were used to collect data. The study determined that the five subjects had high awareness of long-term care services, as they had actual experience of long-term care services or even participated in care service staff training to obtain certificates. Some even had experience in applying for home care services and experienced problems during use. The subjects perceived that they were very likely to require long-term care services in the future and tended to use home care services if they required long-term care services. Due to their personal experiences, the subjects had negative awareness of long-term care services and worried that long-term care service staff had poor attitudes toward homosexuals. The subjects considered the most important aspects of long-term care to be basic medical care and lifestyle care. However, they worried that long-term care staff would delay or refuse to provide such services due to the subjects’ sexual orientation or stereotypes, and they were concerned above all about the “friendly attitude” of long-term care staff. They hoped that long-term care staff were friendly toward homosexuals and did not discriminate against them, and they felt that it would be more appropriate for homosexual long-term care staff to provide assistance. In terms of vision, while preferring organizations with homosexual employees, the subjects worried that they would be stigmatized and discriminated against . With regard to ideal long-term care services, while considering institutions with homosexual staff to be ideal, the subjects also worried that these would be labeled as institutions that were dedicated to homosexuals, potentially resulting in discrimination. Therefore, they hoped that the sexuality sensitivity of long-term care staff could be improved and that they would undergo professional continuing education to learn about homosexuals, the situation and care needs of homosexuals, and care techniques for homosexuals.

Accurate short-term solar irradiance forecasting is crucial for the efficient operation of solar energy driven photovoltaic (PV) power plants. In this research, we introduce a novel hybrid ensemble forecasting model that amalgamates the strengths of machine learning tree-based models and deep learning neuron-based models. The hybrid ensemble model integrates the interpretability of tree-based models with the capacity of neuron-based models to capture complex temporal dependencies within solar irradiance data. Furthermore, stacking and voting ensemble strategies are employed to harness the collective strengths of these models, significantly enhancing prediction accuracy. This integrated methodology is enhanced by incorporating pixels from satellite images provided by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). These pixels are converted into structured data arrays and employed as exogenous inputs in the algorithm. The primary objective of this study is to improve the accuracy of short-term solar irradiance predictions, spanning a forecast horizon up to 6 hours ahead. The incorporation of EUMETSAT satellite image pixels data enables the model to extract valuable spatial and temporal information, thus enhancing the overall forecasting precision. This research also includes detailed analysis of the derivation of GHI using satellite images. The study was carried out and the models tested across three distinct locations in Austria. A detailed comparative analysis was carried out for traditional Satellite (SAT) and Numerical Weather Prediction (NWP) models with hybrid models. Our findings demonstrate a higher skill score for all of the approaches compared to smart persistent model and consistently highlight the superiority of the hybrid ensemble model for short-term prediction window of 1 to 6 hours. This research underscores the potential for enhanced accuracy of the hybrid approach to advance short-term solar irradiance forecasting, emphasizing its effectiveness at understanding the intricate interplay of the meteorological variables affecting solar energy generation worldwide. The results of this investigation carry noteworthy implications for advancing solar energy systems, thereby supporting the sustainable integration of renewable energy sources into the electrical grid.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

Cocoa beans of the CCN-51 clone harvested in the Tumaco region (Nariño, Colombia) were fermented following four different treatments: (1) spontaneously (at room temperature for 120 h) in traditional conditions (Control); (2) traditional processing with a pre-drying (sun-dried for 24 h) treatment (PD); (3) with fermentation (for 120 h) after inoculation of a yeast starter culture (YS); and (4) including both treatments, pre-drying and yeast inoculation (PD+YS). Chemical composition, fatty acids, total polyphenol, methylxanthines (theobromine and caffeine) and lactic acid content of cocoa beans were determined. Chemical and fatty acid composition and theobromine content were not affected by the different fermentation treatment applied. When analysing total polyphenol content, YS and PD+YS treatments significantly increased the content of these compounds at the end of the fermentation process, while PD treatment decreased the caffeine content of cocoa beans. Finally, lactic acid content decreased as a consequence of both inoculation of yeast starter and mainly the pre-drying treatment. In conclusion, solar pre-drying in the open air and the inoculation of a yeast starter treatments could improve the final quality of coca beans.

Magnetic Resonance Imaging (MRI) is widely used in clinics and research due to its accurate disease identification and non-invasive nature. MRI is based on Nuclear Magnetic Resonance (NMR), which is also extensively employed in various fields. NMR and its modern versions involve intricate pre-processing steps before data analysis. These steps are initially processed in the time domain and subsequently in the frequency domain. While our previous review focused on time domain pre-processing (https://www.preprints.org/manuscript/202310.2032/v1), this review delves into the mathematical and statistical aspects of frequency domain pre-processing. We discuss essential pre-processing steps like phase error correction, baseline correction, solvent filtering, calibration and alignment, reference deconvolution, binning/bucketing and peak picking, peak fitting/deconvolution and compound identification, integration and quantification, normalization and transformation. Furthermore, we offer practical recommendations for each step.

The Chat Generative Pre-training Transformer (GPT), also known as ChatGPT, is a powerful generative AI model that can simulate human-like dialogues across a variety of domains. However, this popularity has attracted the attention of malicious actors who exploit ChatGPT to launch cyberattacks. This paper examines the tactics that adversaries use to leverage ChatGPT in a variety of cyberattacks. Attackers pose as regular users and manipulate ChatGPT’s vulnerability to malicious interactions, particularly in the context of cyber assault. The paper presents illustrative examples of cyberattacks that are possible with ChatGPT and discusses the realm of ChatGPT-fueled cybersecurity threats. The paper also investigates the extent of user awareness of the relationship between ChatGPT and cyberattacks. A survey of 253 participants was conducted, and their responses were measured on a three-point Likert scale. The results provide a comprehensive understanding of how ChatGPT can be used to improve business processes and identify areas for improvement. Over 80% of the participants agreed that cyber criminals use ChatGPT for malicious purposes. This finding underscores the importance of improving the security of this novel model. Organizations must take steps to protect their computational infrastructure. This analysis also highlights opportunities for streamlining processes, improving service quality, and increasing efficiency. Finally, the paper provides recommendations for using ChatGPT in a secure manner, outlining ways to mitigate potential cyberattacks and strengthen defenses against adversaries.

Arctic sea ice prediction holds significant importance for facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the model's capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction. Additionally, meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin ice region at the edge of the sea ice.

Screening of pollen traits in diploid wild potatoes (Solanum sect. Petota, Solanaceae) is desirable to develop heat-tolerant potato (S. tuberosum) cultivars. To accomplish this goal requires exploring potato genetic resources that are conserved in genebanks. The goal of this study was to assess pollen viability and 2n pollen production of the diploid potato wild relatives under heat stress condition. We assessed pollen viability and size of nine potato accessions conserved at the Embrapa Potato Gene Bank, including S. chacoense (BRA 00167447-2, BRA 00167017-3, BRA 00167023-1, BRA 00167028-0), S. commersonii (BRA00167007-4, BRA00167420-9, BRA00183760-8), and S. malmeanum (BRA 00183755-8), along with a control accession from the cultivated species S. tuberosum (BRA 00167251-8). The plant accessions were cultivated in different growth chambers, simulating both control temperature (ranging from 14 to 27°C) and supraoptimal temperature conditions (ranging from 24 to 34°C). At heat stress, the accessions BRA 00167251-8 did not bloom, and BRA 00167023-1 did not produce pollen. The remaining accessions did not exhibit a significant reduction in pollen viability as the temperature increased. Pollen viability at the control temperature had the lowest value in BRA-00167420-9 (S. commersonii) with 68.5% and the highest in BRA 00183755-8 (S. malmeanum) with 100%. At the supraoptimal temperature the lowest value was in BRA 00167420-9 (S. commersonii) with 54.5% and the highest in BRA 00183755-8 (S. malmeanum). The average of pollen size was 20 μm in all wild potato genotypes, and the increase of temperature did not lead to 2n pollen production. Estimated Genotypic Coefficient of Variation (GCV) was lower than Phenotypic Coefficient of Variation (PCV) for pollen viability. The observed heritability values ranged from 58.82% in BRA00167007-4 to 91.32% in BRA 00183755-8. Our results highlight the genetic variability available in wild potato germplasm concerning pollen viability under heat stress. Furthermore, these first insights offer valuable guidance for ongoing and future endeavors in diploid potato breeding.

Cultural Tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists the tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for Riyadh for peak visitor time. Primarily, the map data and cultural event dataset are processed for location, such as grouping with Kriging Interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes are processed with word embedding. Meanwhile, the Social Network Sites (SNS) data like reviews and news are extracted with an external Application Programming Interface (API). The review data is processed with keyword extraction and word embedding, whereas the news data is processed with score value estimation. Lastly, the data are fused corresponding to a historical site and given to the Multi-Quadratic-Long Short Term Memory (MQ-LSTM) Recommendation System (RS); also, the recommended result with the map is stored in a database. Lastly, the database security is maintained with Locality Sensitive Hashing (LSH); moreover, by attaining higher performance values, the proposed model is experimentally verified.

Based on the teaching material of "Robotics" course, this paper studied the automatic knowledge graph construction, including knowledge points extraction and knowledge point relation extraction. We proposed a new method of extracting the first-level, second-level, and third-level knowledge points as well as their prerequisite relations of knowledge points in the textbook. For the problem of insufficient knowledge of the pre-trained language model in the specific field, methods such as incremental pre-training and optimization of cost functions are employed to integrate subject knowledge into the pre-trained language model, thus improving its effectiveness. To overcome the problem that the traditional method of relationship extraction can not be applied directly to the extraction of teaching materials, a new scheme for knowledge point relationship extraction based on keyword relationship is proposed. The experimental data from textbooks shows that the F1 score of knowledge point extraction reaches 93%, considerably improved compared to the traditional model. Consequently, the knowledge point entity extraction and relationship extraction methods based on the pre-trained model can effectively extract structured information and facilitate the automatic construction of knowledge graphs.

Pure silver as well as silver compounds have been used as antiseptics for centuries. Nowadays, the most frequently applied form of silver is a nanoparticle form. Several studies demonstrate the ability of silver nanoparticles and silver salts to accumulate in the brain of laboratory ani-mals as well as the ability of silver nanoparticles to influence behavioral and cognitive functions of laboratory mammals. For the first time, the influence of a silver salt, in particular, silver citrate on the change dynamics of behavioral and cognitive functions of laboratory mice at the daily oral exposure during 30, 60, 120 and 180 days is investigated in the present research. We observed the anxiety increase after 30 days of the exposure as well as the ability of mice to adapt to the prolonged oral exposure of silver citrate manifested in the anxiety decrease, increase of locomotor activity and the tendency of long-term contextual memory improvement. Increase of locomotor activity and memory improvement might be associated with the sensitivity increase and regarded as compensatory mechanism to anxiety increase. The observed phenomenon is classified as a hormetic effect. A comparison with the analogous study implemented polyvi-nylpyrrolidone coated silver nanoparticles is carried out. It was concluded that the nanoform is more neurotoxic.

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.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

Over the last decade, highly pathogenic avian influenza (HPAI) has severely affected poultry production systems across the globe. In particular, massive pre-emptive depopulation of all poultry within the certain distance has raised concerns regarding animal welfare and food security. Thus, alternative approaches to reducing unnecessary depopulation, such as risk-based depopulation, are highly demanded. This paper proposes a data-driven method to generate a rule table and risk score for each farm to identify preventive measures against HPAI. To evaluate the proposed method, 105 cases of HPAI occurring in a total of 381 farms in Jeollanam-do from 2014 to 2023 were evaluated. The accuracy of preventive measure identification was assessed for each case using both the conventional culling method and the proposed data-driven method. The evaluation showed that the proposed method achieved an accuracy of 84.19%, significantly surpassing the previous 10.37%. The result was attributed to the proposed method reducing the false-positive rate by 83.61% compared with the conventional method, thereby enhancing the reliability of identification. The proposed method is expected to be utilized in selecting farms for monitoring and management of HPAI.

Introduction: Intrauterine fetal death (IFD) is death of fetus after the 20th week of gestation. Despite regular monitoring, the incidence of IFD remains high. This study aims to assess the incidence and risk factors for IFD in term pregnancies for better prevention and reduction of neonatal morbidity and mortality. Materials and Methods: A retrospective cohort study was conducted with term pregnant women diagnosed with IFD. The analysis included the number of deliveries, live births, and stillbirths. Statistical analysis involves descriptive and analytical statistical methods. Results: Average age was 30 years. Most patients had intermediate and high education, and one-third of patients had regular pregnancy monitoring. 53.33% were primiparous, and pregnancies occurred spontaneously. IFD mainly occurred in the 39th week of gestation. The average birth weight was approximately 3000g. 38.3% had one to two associated diseases, 5% more than three, and 56.7% were healthy. Recurrence of IFD was reported by 10% of patients, while 8.33% had a history of spontaneous abortion. Over 80% of placental histopathological findings indicated some pathology (infarction, infections, abruptio placenta). Conclusion: The most significant risk factors were pregnancy-induced hypertensive disorders, obesity, and diabetes, followed by hypothyroidism, maternal tachycardia, and anemia.

BACKGROUND: Pre-marital sex is a sexual interest practiced by people who are yet to get married and has ended up being accepted specifically in western countries. Sex before marriage is sexual behavior engaged in by single people. In the past, having sex before getting married is seen as a moral dilemma that many cultures forbade and that many people regarded to be sin in many religions, although it has gained wider acceptance across the board. OBJECTIVES: This study was carried out to determine the perception, practices, and factors associated with pre-marital sex, courtship, and its influences among Osun State University Undergraduates, Osogbo, Osun State. METHODOLOGY: The study was a descriptive study, and respondents were selected using multi-stage sampling technique. Students of Osun State University, Osogbo main campus form the population of the study and its sample size was calculated using Leslie Kish’s formula. A total of 387 responses were validated from the survey. The instrument for data collection was a self-administered semi-structured questionnaire consisting of four sections. The data obtained were collated, examined for completion, coded, imputed, and analyzed using SPSS v21. RESULTS: The mean age of the respondents was between 19.19±2.17, and the majority were between 18 – 24 years. About 52% of the respondents show a positive perception of premarital sex and courtship. Also, in the practice of courtship majority (88.3%) of the respondents think those that go around together are courting and from the responses obtained and analyzed from the factors leading to pre-marital sex, the findings revealed that peer pressure (90.4%), Internet (86.7%), pornography (83.9%) and drugs (82.9%) are the leading factors which contribute to pre-marital sex among undergraduates. CONCLUSION: The study hereby recommends that Parents should as a matter of responsibility monitor and pays regular visits to their children in the universities and provide them with materials that will make them stay comfortable for them not to fall victim to peer pressure.Pre-marital sex is a practice that should be avoided by undergraduate students, though most students consider courtship as a normal practice, it is still used as a normal factor that leads them to practice pre-marital sex.

Empowering teachers with Critical Thinking and understanding of sustainability is vital for guiding students’ informed decisions and actions in today’s world. Higher education needs innovative, student-centered methods like case-based teaching in order to promote Critical Thinking and the principles of Education for Sustainable Development. The aim of the study was to explore and evaluate pre-service teachers' Critical Thinking when engaged with case studies in a practicum preparation course centered on Education for Sustainable Development. Nine undergraduate students in Teacher Education participated in the study and answered four case studies. The case studies’ responses were analyzed through a bottom-up content analysis revealing eight higher-order categories. While approaching the case studies, student-teachers followed thinking processes similar to the processes proposed by the iterative models of Critical Thinking as perceived by Garrison (1991) and/or Brookfield (1987). Nevertheless, in some cases, pre-service teachers’ disengagement was evident. The rubric assessment highlighted that the responses fell under three profiles, namely the “Deficient Critical Thinker”, the “Emerging Critical Thinker” and the “Competent Critical Thinker”. The findings emphasize the need for targeted and individualized teaching interventions in teacher higher education programs aiming at Education for Sustainable Development complying with the learning needs of student-teachers’ Critical Thinking profiles.

Switched model predictive control (S-MPC) and recurrent neural network with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for energy management in microgrids (MGs). These methods are complementary in terms of constraint satisfaction, computational demand, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method selects optimal models and control strategies dynamically based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) with these powerful control methods improves prediction accuracy and system conditions’ adaptability. This paper compares the two approaches to control and proposes a novel algorithm called Switched Auto-regressive Neural Control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC while enabling continuous learning. Results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. Also, this study evaluated our predictive model’s accuracy: (i) R-squared error is 0.951, indicating strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions with minimal deviations from actual values.

The chemically synthesized polymer polyacrylamide (HPAM) in conventional reservoirs has achieved excellent oil displacement, but its oil displacement is poor in extreme reservoir environments. To develop biopolymer oil flooding agent under extreme reservoir conditions, the viscosity increasing and rheological properties of three biopolymers, diutan gum, xanthan gum, and scleroglucan, were studied under extreme reservoir conditions (high salt, high temperature, strong acid, and alkali), and the effects of temperature, mineralization, pH, and other factors on their viscosities and long-term stability properties were analyzed and compared. The results showed that under the temperature of 90℃ and below, the three kinds of biopolymers had the best viscosity-increasing ability. The viscosity of the three kinds of biopolymers was 80.94, 11.57, and 59.83mPa·s, respectively, when the concentration was 1500mg/L and the salinity was 220g/L. At the shear rate of 250s-1,100℃~140℃, scleroglucan has the best viscosification. At 140℃, the solution viscosity is 19.74mPa·s, and the retention rate can reach 118.27%. The results of the long-term stability study showed that the solution viscosity of scleroglucan with mineralization level of 220 mg/L was 89.54% viscosity retention in 40 days, and the diutan gum could be stabilized for 10 days, and the viscosity was maintained at 90 mPa·s. All three biopolymers are highly acid and alkali-resistant, with viscosity variations of less than 15% in the pH3~10 range. Rheological tests have shown that both of them have better viscoelastic properties than xanthan gum due to the peculiar double-helix structure of xanthan gum and the rigid triple-helix structure of scleroglucan. Therefore, the two biopolymers, diutan gum, and scleroglucan have the potential for extreme reservoir oil displacement applications, and it is recommended to use diutan gum for oil displacement in reservoirs up to 90℃ and scleroglucan for oil displacement in reservoirs between 100℃ and 140℃.

In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction.The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. As a result, there is an increasing requirement to monitor the state of these components in order to avoid such issues from occurring. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (LSTM) model. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the Autoencoder LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. Furthermore, the study used a thorough global metrics evaluation methodology to further test the model’s dependability and efficacy, consequently substantiating the proposed approach’s validity.