The capacitor-mismatch among diverse defects caused by the manufacturing process variations significantly affects the linearity of capacitor-array used to implement the capacitive digital-to-analog converter (CDAC) in the successive-approximation-register (SAR) analog-to-digital converter (ADC). Accordingly, the linearity of the SAR ADC is limited by that of capacitor-array, resulting in serious yield loss. This paper proposes an efficient foreground self-calibration technique to enhance the linearity of the SAR ADCs by mitigating the capacitor-mismatch based on the split ADC architecture along with variable capacitors. In this work, two ADC channels (i.e., ADC1 and ADC2) for the split ADC architecture include their capacitive DACs (CDACs) whose binary-weighted capacitor-arrays consist of variable capacitors. A charge-sharing SAR ADC is used for each ADC channel. In the normal operation mode, their digital outputs are averaged to be the final ADC output, as in a conventional split ADC. In the calibration mode, every single binary-weighted capacitor for the two ADCs is sequentially calibrated by making parallel or/and antiparallel connection among two or thee capacitors from the two channels. For instance, because the capacitors of the CDACs ideally exhibit the binary-weighted relation as Cn=2×Cn−1, variable capacitor Cn of ADC1 can be updated to be closest to the sum of Cn−1 of ADC1 and Cn−1 of ADC2 for the calibration. For the process, the two capacitor-arrays of the two ADCs can be reconfigured to be connected to each other, so that Cn of ADC1 can be connected with two of Cn−1 of ADC1 and ADC2 in antiparallel. The two voltages at the top and the bottom plates of the CDAC are compared by a comparator of ADC1, and the comparison results are used to update Cn. This process is iterated, until Cn is in agreement with the sum of two of Cn−1. Finally, all the capacitors can be calibrated in this way to have the binary-weighted relation. The simulation results based on the proposed work with a split SAR ADC model verified that the proposed technique can be practically used, by showing that the total-harmonic-distortion and the signal-to-noise-and-distortion ratio were enhanced by 21.8-dB and 6.4-dB, respectively.

This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e. feature engineering, model selection, etc), principles (i.e. bias-variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, output interpretability, etc), models (i.e. neural networks, regression models, etc), methods (i.e. Cross-Validation, data augmentation, etc), metrics (i.e. Mean Squared Error, F1-score, etc) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community over complete and rigorous machine learning pipelines and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers due to brevity, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are not AI experts.

This brief report detailed the investigation of secondary school students' future intention to use Computer-Based Testing (CBT) in Lagos, Nigeria. A sample of 531 students, which is made-up of new and returning users of CBT, responded to a questionnaire based on a four-point Likert scale. The study aimed to explore whether ease of use, gender, computer self-efficacy, and prior experience influence the future intention to use CBT. Findings show that students who find CBT easier to use have a significantly higher intention to use it again. This association remains significant even when controlling for gender, computer self-efficacy, and experience. New users demonstrated a slightly higher mean intention to use CBT than returning users, possibly due to the novelty effect. This report recommends improving CBT's ease of use to accommodate varying levels of computer literacy and self-efficacy. Limitations of the investigation include the nominal nature of the data and the self-reported responses, which could impact the accuracy of the analysis. Future studies should adopt more robust methodologies, such as Structural Equation Modeling, to better explore these relationships.

The work aims to develop an electromagnetic shielding measurement methodology and design an electromagnetic shielding effectiveness test setup for thin sheet material testing. In brief, multiple shielding effectiveness standards are described and analyzed in the thin sheet material testing framework. Two types of the developed shielding effectiveness test probe in high and low-frequency ranges are validated comprehensively. Extensive descriptions and data of developed testing methodology are also reported.

A self-propelled electric tea seed-planter is designed to address the lack of tea seed seeding equipment, low seeding efficiency, and poor seeding uniformity. Based on the physical characteristics of the tea seeds and the agronomic requirements of tea planting, the key structure of the planter is designed, and the structural parameters are determined. The motor and battery configurations are determined by calculating the power consumption of the entire machine. In a field experiment, the driving gear (A), ditching depth (B), and planting gear position (C) are used as test factors, and the acceptance index (AI), replay index (RI) and leakage index (LI) are used as test indexes. The test results showed that the driving speed and planting gear position significantly affect the acceptance index, replay, and leakage index, whereas the ditching depth has little effect on the performance indexes. When the driving speed was medium, ditching depth was 30 mm, and planting gear position was in the third gear, the corresponding acceptance index was 87.22%, the replay index was 8.89%, and the leakage index was 3.89%. The test prototype has a good working quality and can provide theoretical guidance for production practices.

Hair cosmetics are in fashion. Numerous products are commonly used in combination. Frequent allergens are present in hair cosmetics, which, added to almost daily use, increase the risk of sensitization. Familiarity with the clinical distribution of the eczematous rash is necessary to seriously consider patch testing. There are haptens on the market, although not all of them. It is necessary to know how to patch your own products. When avoidance of the allergen cannot be possible, new treatments are being evaluated to combat allergic contact dermatitis, mainly of the occupational type. In this manuscript, a complete and practical review of the main allergens and contact sources of hair cosmetic origin has been carried out.

Acceleration parameters in sit-to-stand (STS) movements are useful for measuring lower-limb function in older adults. The purpose of this study was to examine the relationship between acceleration in STS movements and physical function and the test-retest reliability of acceleration parameters in older adults. We performed cross-sectional analyses on 244 older adults including 107 men (mean age:77.4±4.7) and 137 women (mean age: 75.6±5.3). Four acceleration parameters were measured in STS movements: maximum acceleration (MA), maximum velocity (MV), maximum power (MP), and stand-up time (ST). Good intraclass correlation coefficients (ICC>0.70) were observed for all parameters. For the acceleration parameters, MA, MV, and MP were relatively strongly associated with the 5-time STS test (men: r=-0.36~-0.47; women: r=-0.37~-0.45) and the Timed up and Go test (men: r=-0.39~0.47, women: r=-0.43~-0.51): MP was also strongly associated with grip strength (men: r=0.48, women: r=0.43). All acceleration parameters were poorer in participants reporting mobility limitations than in those reporting no mobility limitations. These findings support the usefulness of sensor-based STS measurement. The system is expected to be useful in various settings where care prevention is addressed.

Salmonella is a foodborne pathogen associated with localized outbreaks that potentially pose a huge risk to the public health in some countries such as Africa and India, especially in the regions lacking health care measures. The focus of the present study is the molecular detection method of salmonella. The study was conducted to identify Salmonella strains by PCR targeting flagellin specific gene from suspected patient’s clinical blood samples. Serologically, Widal test results showed that all serum samples except one patient (ID 7) were positive for O antigen. The serum of three patients (ID 4, 5 and 10) were found to be positive for H and O antigens while one patient (ID 9) showed agglutination for antigen H. The PCR approach used in this study was successful for fast and precise detection of clinically related Salmonella Typhi. Seven samples out of 10 were positive for Salmonella Typhi as fragments of 458-497bp were observed on the gel-agarose 0.8% corresponds to flagellin gene of S. Typhi. Relatively, smaller amplicons of 366-343bp were observed utilizing nested PCR as well, which seems more sensitive than the conventional PCR. Therefore, we recommend the PCR approach performed in this study to be used as a fast, cost-effective, and time-consuming tool for diagnostic purposes of Salmonella.

Introduction:Few robust studies have analyzed association between cognitive tests and the marital status of Mild Cognitive Impairment (MCI) group with ADNI dataset. To test the hypothesis that Rey Auditory Verbal Learning test (RAVLT) cognition performance with marital status is associated with greater odds of MCI group than either RAVLT independently, we used TADPOLE data to evaluate cross-sectional associations between RAVLT performance in immediate response, learning, forgetting, and perception of forgetting with marital status and MCI. Methods: Subjects with MCI and Normal Cognition were included. Logistic regression models indicate associations between four RAVLT subgroups (low and high performance of immediate response, immediate response with learning, performance of immediate response with learning and forgetting, performance of immediate response with learning, forgetting and perception of forgetting) and MCI group. Models adjust for age, sex, race, marital status, ethnicity, education, APOE4 genotype, hippocampus, whole-brain, ventricles and ICV. Results: The sample (n=6560) had a mean age of 77 / 67 years, 44% were female, 58% in MCI group. Only all RAVLT subgroups test with age 61 to 70(OR 0.26, 95% CI 0.15-0.45), age 71 or older(OR 0.07, 95% CI,0.04-0.12), race:black/african american(OR 0.13, 95% CI 0.03-0.52)race:more than one(OR 0.05, 95% CI 0.01-0.24), marital status:never married(OR 0.2, 95% CI 0.12-0.34). Conclusion: Studies are needed to evaluate other cognitive test with missing data within TADPOLE dataset as modifiable risk factor for MCI.

The main objective of this study focuses on designing and testing body protection systems using advanced materials based on aramid fibers, for high impact speeds of up to 410...430 m/s. The investigation of the failure mechanisms identifies issues of protective materials, major challenges and technological problems for efficient development of these systems. The authors presents an investigation on the failure processes and destructive stages of a ballistic package made of succesive layers of LFT SB1plus, taking into account the particular test conditions from NIJ Standard-0101.06 Ballistic Resistance of Body Armor. The main parameter of interest was the backface signature (BFS), but also details of projectile arrest and SEM investigaton could offer arguments in using this material for individual protection.

In this study a method for predicting the preferred pleasantness induced by different forest environments, represented by virtual photographs, was proposed and evaluated using a novel Anti-Environmental Forest Experience Scale psychometric test. The evaluation questionnaire contained twenty-one items divided into four different subscales. The factor structure was assessed in two separate samples collected online (sample 1: N = 254, sample 2: N = 280). The internal validity of the four subscales was confirmed using an exploratory factor analysis. Discriminant validity was tested and confirmed using the Amoebic Self Scale (Spatial-Symbolic domain). Concurrent validity was confirmed using the Connectedness to Nature Scale. Predictive validity was based on assessment of pleasantness induced by nine different photographs (control – urban landscapes, forest landscapes, dense forest landscapes), with subscales differently correlated with the level of pleasantness assessed for each photograph. This evaluation instrument is appropriate for predicting preferred pleasantness induced by different forest environments.

Typhoid fever is a communicable disease transmitted by the bacteria Salmonella typhi, related to serotype paratyphi A, B and C. The disease is of a significant health concern in most developing countries especially Cameroon. Objectives: The study aimed at determining the prevalence and associated risk factors of typhoid fever in children ( 0-18 years) attending the “Deo Gratias” hospital in Douala. Method: A hospital base cross sectional study from August to September 2018 was carried out in patients’ age 0-18 years suffering from typhoid fever at the Deo gratias Catholic hospital. Widal slide agglutination test was the diagnostic test used. Positive tested patients were administered questionnaires to evaluate the level of knowledge, attitude and practice toward the disease, as well as their self-management abilities. Data obtained from respondents was analysed by descriptive statistics. One-way ANOVA and means comparison using Tukey’s test (α = 0.05) was performed to check whether the population of respondents differed significantly across risk factor practices. Results were finally presented on bar charts, tables and pie chart. Results: Typhoid fever was more prevalent in females (52.3%) than in males (47.7%), with a high proportion in the ages 5-9 years (38.6%). A significant difference was observed in population of respondents across risk factor practices. Conclusion: Water quality have a great impact on the burden of typhoid fever among children. The identification of risk factors associated to the disease is of great importance in the development of rational control strategies of the disease.

This paper is the continuation of a previous study, which highlighted some inconsistencies in the RILEM guidelines for the interpretation of the diagonal compression test. Although improved compared to the ASTM guidelines, in fact, the RILEM guidelines underestimate the state of hydrostatic stress induced by the diagonal compression test at the center of the wallette. The new interpretation of the diagonal compression test proposed in this article shows that the RILEM guidelines actually underestimate both the hydrostatic and the deviatoric stress states at the center of the wallette. The new formulation complies with the linear elastic theory and allows us to use the the diagonal compression test to identify the three elastic coefficients of masonry. In particular, it allows the identification of the Poisson ratio, which instead takes on a conventional value in the RILEM and ASTM guidelines. The difference of one order of magnitude between the conventional and proposed Poisson’s ratio is in agreement with the experimental results on another brittle material, namely concrete. Finally, the new proposal fills the gap between the results provided by the two tests usually performed to identify the shear behavior of masonry: the diagonal compression test and the shear-compression test.

Cannabidiol (CBD) is a non-intoxicating compound extracted from Cannabis sativa, showing antidepressant-like effects in different rodent models. However, inconsistent results have been described depending on the species and the strain used to assess depressive-like behaviour. Moreover, only a few studies have investigated the effect of CBD in female rodents. Therefore, we aimed to i) investigate the effects of CBD in two different strains of mice (Swiss and C57BL/6) and in a rat model of depression based on selective breeding (Flinders Sensitive and Resistant Lines, FSL and FRL) subjected to tests predictive of antidepressant-like effects; and ii) investigate the influence of sex in the effects of CBD in both mice and rats. CBD induced an antidepressant-like effect in male Swiss but not in female Swiss or C57BL/6 mice in the tail suspension test (TST). In male FSL rats, CBD produced an antidepressant-like effect one-hour post-injection. However, in female FSL, CBD induced a bimodal effect, increasing the immobility time at one hour and decreasing it at two hours. Ketamine produced an antidepressant-like effect in male and female FSL rats at different doses. In conclusion, strain, sex, and administration time affect CBD's behavioural response to rodents exposed to tests predictive of antidepressant effects.

The increasing complexity of IP network infrastructures necessitates efficient and adaptive configuration management solutions. This paper presents an AI-driven framework that integrates machine learning and reinforcement learning techniques to enhance the management of configurations for IP network devices. The proposed approach leverages a comprehensive dataset comprising configuration parameters and performance metrics to train predictive models. Experimental results demonstrate that the framework achieves an accuracy of 88% in predicting optimal configurations, significantly outperforming traditional methods. Additionally, the framework exhibits an average response time of 150 milliseconds for applying configuration changes, underscoring its efficiency. A reinforcement learning agent is implemented to adapt to dynamic network conditions, yielding improved decision- making over time. The user interface designed for the framework facilitates real-time monitoring and visualization of network configurations. The findings suggest that the AI- driven framework not only streamlines configuration management processes but also empowers network administrators to proactively address network challenges. Future work will focus on scalability, integration with emerging technologies, and user feedback mechanisms to further enhance the framework's effectiveness.

In the realm of Lithium-ion batteries, issues like material aging and capacity decline contribute to performance degradation or potential safety hazards. Predicting Remaining Useful Life (RUL) serves as a crucial method to assess the health of batteries, thereby enhancing reliability and safety. Currently hybrid approaches for batteries RUL estimation are prevalence and gained fruitful development. In this paper, a hybrid voting ensembles combining Gradient Boosting, Random Forest and K-Nearest Neighbors is proposed to predict the capacity fade trend and knee point. Finally, extensive experiments are conducted using CALCE CS2 datasets. According to the experimental results, the proposed approach supersedes the single deep learning approach for RUL prediction and the knee point is predicted accurately. Besides prediction purpose, this innovated approach can be also proposed to be integrated into the real-world application for wider usage.

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rain water. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima (2019). The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increases approximately 17%~18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions.

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in IDA ICE software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. Findings indicate that by analyzing measured data and adjusting configurable parameters, building energy consumption can be significantly reduced. Close monitoring of indoor parameters by building operators and corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, the paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

Collaboration among individuals with diverse skills and personalities is crucial in producing high-quality software. The success of any software project depends on the team’s cohesive functionality and mutual complementation. This study introduces a data-centric methodology for forming Software Engineering (SE) teams centred around personality traits. Our study analyzed data from an SE course where 157 students in 31 teams worked through four project phases and were evaluated based on deliverables and instructor feedback. Using the Five Factor Model (FFM) and a variety of statistical tests, we determined that teams with higher levels of extraversion and conscientiousness and lower neuroticism consistently performed better. We examined team member interactions and developed a predictive model using extreme gradient boosting. The model achieved a 74% accuracy rate in predicting inter-member satisfaction rankings. Through graphical explainability, it underscored incompatibilities among members, notably those with differing levels of extraversion. Based on our findings, we introduce a team formation algorithm using Simulated Annealing (SA), built upon the insights from our predictive model and additional heuristics.

Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning methods including Laplacian eigenmaps, locally linear embedding, and Hessian eigenmaps have been utilized in data-driven fault detection. However, only the partial local structure information is extracted from the aforementioned methods. In this paper, these typical manifold learning methods are synthesized to find the underlying manifold structure from different angles. A more comprehensive local structure is discovered under a unified framework by constructing an objection optimization function for dimension reduction of the process data. The proposed method takes advantage of different manifold learning methods. Based on the proposed dimension reduction method, a new data-driven fault detection method is developed. Hotelling’s T2 and Q statistics are established for the purpose of fault detection. Experiments on an industrial benchmark Tennessee Eastman process and a real blast furnace ironmaking process are carried out to demonstrate the superiority and effectiveness of the proposed method.

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis through the integration of blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. By combining blockchain with intelligent power management systems, the efficient utilization of hydrogen resources becomes feasible. The utilization of smart contracts and distributed ledger technology facilitates secure data analysis, real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus contributing to the advancement of clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines potential future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, with a specific focus on secure hydrogen data analysis and intelligent power management systems.

This study delves into the dynamic intersection of artificial intelligence (AI) and English Language Teaching (ELT) within the post-pandemic educational landscape. Grounded in Constructivism, Connectivism, and Technological Pedagogical Content Knowledge (TPACK), this research explores how AI integration impacts language instruction efficiency, benefits and challenges perceived by St. Michael's College students, differences in learning outcomes, and its potential to address the digital divide. Employing a qualitative approach, the study employs a rigorous method that includes intensive in-depth reviews of literature, maximum purposeful sampling, informal interviews, observation, and thematic analysis. Through these methods, data was collected from 30 participants, leading to the identification of five key themes. Findings suggest that AI enhances instructional efficiency, offers personalized learning experiences, and bridges gaps in remote settings. However, challenges and the importance of pedagogical balance emerged. The study underscores the need for adaptive pedagogical strategies to fully harness AI's potential. Future directions and limitations are discussed, emphasizing the study's implications for shaping the future of ELT.

Water quality assessments are crucial for human health and environmental safeguards. The utilization of a subset of artificial intelligence such as Machine Learning (ML) presents significant impacts to enhance the prediction and classification of water quality. In this research, a set of diverse ML algorithms was evaluated to handle a comprehensive dataset of water quality measurements over an extended period. The aim was to develop a robust approach for accurately forecasting water quality. This approach employed machine learning classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), Gaussian Process Classification (GPC), Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree (DT), XGBoost, and Multilayer Perceptron (MLP). The water quality parameters assessed for pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes and turbidity. The XGBoost model exhibited the highest accuracy of 89.47% among the classifiers and Stacked Ensemble Classifiers (SEC) improved the prediction further to 92.98%. The findings suggest that XGBoost and the SEC hold promise as reliable approaches for water quality assessments in contrast of artificial intelligence.

Amidst the dynamic landscape of education, this paper embarks on a transformative journey that explores the integration of Artificial Intelligence (AI) in literature and language education. Through a qualitative exploratory approach, purposeful and maximum variation sampling method, the researcher delves into the experiences of educators and learners, unraveling the multifaceted dimensions of AI-driven pedagogy in SMC. Drawing upon a theoretical framework encompassing Constructivism, Connectivism, and Socio-Cultural Theory, the study examines AI's role in reshaping pedagogical landscapes, fostering critical thinking, and expanding access to education. The results resonate with participants' narratives, unveiling AI's potential in fostering personalized learning pathways, enhancing collaborative engagement, and bridging digital divides. Ethical considerations in AI education are explored, leading to the proposition of responsible guidelines. These findings underline the transformative power of AI in education, aligned with the institution's commitment to quality education and innovation. Overall, the study calls for ongoing exploration, emphasizing the qualitative research method's depth and richness, as well as the theoretical framework's guidance in realizing the study's objectives. As AI continues to shape education's frontiers, this research underscores the significance of adaptability, ethical awareness, and holistic engagement for school administrators, educators, learners, and researchers alike.

In econometrics and finance, volatility modelling has long been a specialised field for addressing a variety of issues pertaining to the risk and uncertainties of an asset. This study presents a robust framework, through a step-by-step design, that is relevant for effective Monte Carlo simulation (MCS) with empirical verifications to estimate volatility using the Generalized Autoregressive Score (GAS) model. The framework describes an organised approach to the MCS experiment that includes "background (optional), defining the aim, research questions, method of implementation, and summarised conclusion". The method of implementation is a workflow that consists of writing the code, setting the seed, setting the true parameter a priori, data generation process, and performance assessment through meta-statistics. Among the findings, it is experimentally demonstrated in the study that the GAS model with a lower unconditional shape parameter value can generate a dataset that adequately reflects the behaviour of financial time series data, relevant for volatility estimation. This dynamic framework is intended to help interested users on MCS experiments utilising the GAS model for reliable volatility calculations in finance and other areas.

Modeling is essential to understand better the generative mechanisms responsible for experimental observations gathered from complex systems. In this work, we are using such an approach to analyze the electrocardiogram (ECG). We present a systematic framework to decompose ECG signals into sums of overlapping lognormal components. We used reinforcement learning to train a deep neural network to estimate the modeling parameters from ECG recorded in babies of 1 to 24 months of age. We demonstrate this model-driven approach by showing how the extracted parameters vary with age. After correction for multiple tests, 10 of 24 modeling parameters showed statistical significance below the 0.01 threshold, with absolute Kendall rank correlation coefficients in the [0.27, 0.51] range. We presented a model-driven approach to the analysis of ECG. The impact of this framework on fundamental science and clinical applications is likely to be increased by further refining the modeling of the physiological mechanisms generating the ECG. By improving the physiological interpretability, this approach can provide a window into latent variables important for understanding the heart-beating process and its control by the autonomous nervous system.

River flow prediction is a pivotal task in the field of water resource management during the era of rapid climate change. The highly dynamic and evolving nature of the climatic variables e.g., precipitation has a significant impact on the temporal distribution of the river discharge in recent days making the discharge forecasting even more complicated for diversified water-related issues e.g., flood prediction and irrigation planning. To predict the discharge, various physics-based numerical models are used using numerous hydrologic parameters. Extensive lab-based investigation and calibration are required to reduce the uncertainty involved in those parameters. However, in the age of data-driven predictions, several deep learning algorithms showed satisfactory performance in dealing with sequential data. In this research, Long Short-term Memory (LSTM) neural network regression model is trained using over 80 years of daily data to forecast the discharge time series up to 3 days ahead of time. The performance of the model is found satisfactory through the comparison of the predicted data with the observed data, visualization of the distribution of the errors and Root Mean Squared Error (RMSE) value of 0.09. Higher performance is achieved through the increase in the number of epochs and hyper parameter tuning. This model can be transferred to other locations with proper feature engineering and optimization to perform univariate predictive analysis and potentially be used to perform real-time river discharge prediction.

Health-related data originating from diverse sources are commonly stored in manifold databases and formats, making it difficult to find, access and gather data for research purposes. In addition, so-called secondary use scenarios for health data are usually hindered by local data codes, missing dictionaries and the lack of metadata and context descriptions. Following the FAIR principles (Findable, Accessible, Interoperable and Reusable), we developed a decentralized infrastructure to overcome these hurdles and enable collaborative research by making the meaning of health-related data understandable to both, humans and machines. This infrastructure is currently being implemented in the realm of the Swiss Personalized Health Network (SPHN), a research infrastructure initiative for enabling the use and exchange of health-related data for research in Switzerland. The SPHN ecosystem for FAIR data consists of the SPHN Dataset (semantic definitions), the SPHN RDF Schema (linkage and transport of the semantics in a machine-readable format), a project RDF template, extensive guidelines and conventions on how to generate SPHN RDF schema, a Terminology Service (converter of clinical terminologies in RDF), and a Quality Assurance Framework (automated data validation with SHACLs and SPARQLs). The SPHN ecosystem has been built in a way that it can easily be adapted and extended by any SPHN project to fit individual needs. By providing such a national ecosystem, SPHN supports researchers in generating, processing and sharing FAIR data.

The report details out programming approach for automating warehouse using Answer Set Programming.

The road traffic injuries risk factors such as driving offenses and average speed are concerns for health organizations to reduce the number of injuries. Without any comprehensive view of each road, one cannot decide about the effective policy. In this manner, the data-driven policy will help to improve and assess the decisions. The count data near the road of two airports is surveyed for investigating the time-varying speed zones. The descriptive statistics, ANOVA, and functional data analysis were used. The hourly data of traffic counts for four different locations at the entrance of the two airports, international and domestics, were collected for one the year 2018 to 2019.The hourly pattern of driving offenses for each road was assessed and the to and from airport roads had different peaks (<0.05). The hour, weekdays, type of airport, direction and their interactions were statistically significant (<0.05) for the chance of driving offenses. The speed average during the day was statistically different (<0.5) by the number of different types of vehicles. The traffic count data is a great resource for decision making in safe driving subjects such as driving offenses. With functional data analysis, we can analyze them to get the most of the characteristics of this data. The airports are public places with high traffic demand in all countries that yields the different pattern of traffic transportation, therefore we extract the factors that affect the driving offenses. Finally, we conclude that conducting a time-varying speed zone near the airports seems vital.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

In the last decades synthetic jet actuators have gained much interest among the flow control techniques due to their short response time, high jet velocity and absence of traditional piping, that matches the requirements of reduced size and low weight. A synthetic jet is generated by the diaphragm oscillation (generally driven by a piezoelectric element) in a relatively small cavity, producing periodic cavity pressure variations associated with cavity volume changes. The pressured air exhausts through an orifice, converting diaphragm electrodynamic energy into jet kinetic energy. This review paper faces the development of various lumped-element models (LEMs) as practical tools to design and manufacture the actuators. LEMs can quickly predict device performances such as the frequency response in terms of diaphragm displacement, cavity pressure and jet velocity, as well as the efficiency of energy conversion of input Joule power into useful kinetic power of air jet. The actuator performance is analyzed also by varying typical geometric parameters such as cavity height and orifice diameter and length, through a suited dimensionless form of the governing equations. A comprehensive and detailed physical modeling aimed to evaluate the device efficiency is introduced, shedding light on the different stages involved in the process. Overall, the influence of the coupling degree of the two oscillators, the diaphragm and the Helmholtz’s one, on the device performance is discussed throughout the paper.

Depression and cognitive impairment are recognized complications of COVID-19. This study aimed to assess cognitive performance in clinically diagnosed post-COVID depression (PCD) patients using neuropsychological testing. The study involved 71 post-COVID patients, with matched control groups: recovered COVID-19 individuals without complications (n=18) and individuals without prior COVID-19 history (n=19). A post-COVID depression group (PCD, n=25) was identified based on psychiatric diagnosis, a comparison group (noPCD, n=46) included participants with neurological COVID-19 complications, excluding clinical depression. The PCD patients showed significantly less scores in the MoCA test, decreased immediate memory recall in the Word Memory Test, decreased processing speed and higher accuracy in the Trail Making Test, and near to significant worse executive control and processing speed in the Stroop task compared to controls and the noPCD patients. The number of post-COVID symptoms negatively correlated with immediate word memory recall and processing speed among all post-COVID patients. In PCD patients, negative correlations between number of post-COVID symptoms and delayed recall, between time after recovery and immediate recall, and positive correlation between the number of acute symptoms and processing speed in the incongruent condition of the Stroop task were found. No differences between groups in Sniffin’s stick olfactory test was found. Overall, our study revealed cognitive impairment in PCD patients similar to those in major depressive disorder.

This study investigated the effects of a ten-visit individual coordinative locomotor training (CLT) program, including both a hands-on and a hands-off approach, on static and dynamic trunk stability in healthy adults with sedentary occupations. During the ten-visit program, the performance of eight subjects was monitored using a modified one-leg stance test in the sprinter and skater position, the Y-balance test, and the scapula and ischial tuberosity control test in the four-legged position. The data were analyzed using parametric and nonparametric statistics at an overall level of significance α=0.05. In the one-leg stance test, all subjects achieved a maximum value of 60 s on all ten visits. The improvement over time in the Y-balance test results was statistically significant for all directions (p<0.001). In addition, a statistically significant improvement in the Y-balance test composite score was observed between the first and the last visit (p=0.015-0.041). Subject-specific patterns were observed in the results of the Y-balance test. Significant improvements were observed over time in the four-legged position test score (p=0.008-0.010). Overall, the CLT program led to a significant improvement in trunk stability and leg extension in the anterior and posterolateral directions, as well as a reduction in instability as determined by the composite score of the Y-balance test.

As time continues to advance, the need for robust security threat mitigation has become increasingly vital in software. However, ensuring early effective security threat mitigation requires optimal test data and consistent test case design. It is a constant struggle to maximize test coverage through test data optimization. We conducted explanatory research to maximize test coverage of security requirements as modeled in Structured Misuse Case Description (SMCD) i.e., structured specification of misuse case, so as to improve consistency in optimal test data generation. We specified constraints upon Mal activity in Object Constraint Language (OCL) in order to minimize human dependency and improve consistency in optimal test data generation. It was evident through results that MC/DC generated optimal test data of security threats through SMCD in comparison to the Decision Coverage method thus resulting in designing a significantly lower number of test cases and yet maximizing test coverage of security threats. MC/DC generated test data with n+1, while Decision Coverage generated test data with〖 2〗^n, we, therefore, conclude that MC/DC maximizes test coverage through optimal test data from SMCD in comparison to Decision Coverage.

Staphylococcus aureus is a major food-borne pathogen. The ability of S. aureus to produce biofilm is a significant virulence factor triggering its persistence in hostile environments. In this study, we screened a total of 420 different food samples and human hand swabs to detect S. aureus and to determine their biofilm formation ability. Samples analyzed were meat, milk, egg, fish, fast foods, and hand swabs. S. aureus were detected by culturing, staining, biochemical, and PCR. Biofilm formation ability was determined by Congo Red Agar (CRA) plate and Crystal Violet Microtiter Plate (CVMP) tests. The icaA, icaB, icaC, icaD, and bap genes involved in the synthesis of bio-film-forming intracellular adhesion compounds were detected by PCR. About 23.81% (100/420; 95% CI: 14.17-29.98%) samples harbored S. aureus as revealed by detection of the nuc gene. CRA plate revealed 20% of S. aureus isolates as strong biofilm producers, while 69% and 11% as in-termediate and non-biofilm producers, respectively. By the CVMP staining method, 20%, 77%, and 3% of the isolates were found strong, intermediate, and non-biofilm producers. Furthermore, 21% of S. aureus isolates carried at least one biofilm-forming gene, where icaA, icaB, icaC, icaD, and bap genes were detected in 15%, 20%, 7%, 20%, and 10% of the S. aureus isolates, respectively. Bivariate analysis showed high significant correlations (p<0.001) between any of the two adhesion genes of S. aureus isolates. To the best of our knowledge, this is the first study in Bangladesh describing the detection of biofilm-forming S. aureus from foods and hand swabs with molecular-based evidence. Our findings suggest that food samples should be deemed a potential reservoir of biofilm-forming S. aureus that indicates a potential public health significance.

This research investigated high-temperature corrosion (500 °C) of Cr-Mo steel processed using water jet peening or multifunction cavitation (MFC), and the suitability of such steel for high-temperature boilers and reaction vessels. High-temperature corrosion was induced using an embedment test and a coating test using sulfide-type K2SO4-Na2SO4 powder. To measure the relaxation of the residual stress due to the decrease in work hardening caused by an increase in specimen temperature and the difference in thermal shrinkage between the surface and interior of the specimen, a thermal cycling test was conducted. For the MFC-processed specimen, the oxide film that formed on the surface suppressed mass loss, prevented crack formation, and reduced the compressive residual stress caused by high-temperature corrosion. MFC-processed Cr-Mo steel is thus suitable for a high-temperature corrosion environment.

This study investigated the impact of six high-intensity interval training (HIIT) running sessions at 1% or 10% slope on various physiological and performance parameters in 25 men. The partic-ipants underwent assessments of VO2max, time to exhaustion at 1% slope (TLim1%), and time to exhaustion at 10% slope (TLim10%) in the initial three visits. They were then randomly assigned to control (CON), HIIT at 1% slope (GT1%), or HIIT at 10% slope (GT10%) groups. Over three weeks, participants performed six HIIT sessions with equalized workload based on their indi-vidual maximal oxygen uptake (vVO2max). The sessions comprised 50% of TLim, with a 1:1 ratio of exercise to recovery at 50% vVO2max. Results indicated significant improvements in VO2max and peak velocity (VPeak) after HIIT at both slopes. Heart rate (HR) behavior differed between sessions for GT1%, while no significant differences were observed for GT10%. Rating of perceived exertion (RPE) significantly reduced for GT1% after the third session, with a similar trend for GT10%. In summary, six sessions of 1% or 10% slope HIIT effectively enhanced VO2max and VPeak, but there was no improvement in TLim performance, suggesting no adaptive transfer between training groups.

Introduction: Pulmonary hypertension (PH) in systemic sclerosis (SSc) can be caused by different pathophysiological mechanisms, including precapillary PH (pulmonary arterial hypertension), post-capillary PH (secondary to left heart diastolic dysfunction), or combined pre- and post-capillary PH. To unmask occult post-capillary PH, fluid loading with saline during right heart catheterization (RHC) is recommended. In the present study, we investigated the response to fluid challenge test (FCT) in patients with SSc who were referred for RHC due to their high likelihood of PH on screening echocardiography. Methods: Demographic data, clinical characteristics, echocardiographic and RHC data, and cardiac magnetic resonance imaging (CMR) results were obtained from the patients' hospital records or by contacting their rheumatologist. A positive FCT was defined as an increase in pulmonary capillary wedge pressure (PCWP) to more than18 mmHg with rapid saline infusion. Patients were followed for a median (IQR) of 15 (7-20) months. Results: Of the total 98 scleroderma patients 34 patients (29 female) were included. The mean age (SD) was 50.6(8.2) years. Baseline RHC data showed that 20 patients had no PH and 14 patients had mild precapillary PH (mPAH). Ten patients (50%) of the patients with no PH and 10 patients (71.4%) with mPAH had a significant increase in PCWP after a fluid loading. There was no statistically significant difference between the patients with positive and negative FCT in terms of the presence of LGE/inflammation on CMR examination. During follow-up, 3 patients died. Mortality was due to SSc-related complications in two patients. Conclusion: This study provides preliminary evidence that FCT may be a useful adjunct to baseline RHC testing to identify SSc patients with latent postcapillary PH. Further studies are needed to confirm these results and determine the optimal role of the fluid challenge test in the clinical management of SSc patients with suspected PH.

The fracture strength was compared in a scratch test of coatings based on the ZrN system with the introduction of Ti, Nb and Hf, which were deposited on a titanium alloy substrate. The coatings were deposited using Controlled Accelerated Arc (CAA-PVD) technology. The comparison was carried out according to two parameters: adhesion strength to the substrate and overall coating strength. The (Zr,Hf)N coating showed better resistance to destruction, but had worse adhesion to the substrate. As a result, although the coating is retained directly in the scribing groove, a large area of delamination and destruction is formed around the groove. The (Ti,Zr,Nb)N coating, with its somewhat lower strength, has high adhesion to the substrate; no noticeable delamination is observed along the groove boundary.

Using numerical simulation method, combined with the pre-branch material characteristics of the measurement results, based on the LS-DYNA software to simulate the branch crushing process, the analysis found that not only the existence of the cutter cutting in the branch destruction process, there is also the cutting of the opposing surface of the bending fracture, with the increase in the speed of the knife rollers, the cutter cutting resistance increases, the cutting resistance reaches 2690 N when the cutter at 2500 r/min, the cutter cutting resistance is the smallest in the cutting simulation at the blade angle of 55°, the blade angle is more suitable for the cutting of the branch. In the cutting simulation under different cutting edge angles, the cutting resistance of the tool is the smallest when the edge angle is 55°, which is 1860 N. This edge angle is more suitable for branch crushing and cutting. Using Fluent software to analyse the airflow characteristics of the crushing device, it was found that with the increase of the knife roller speed, the inlet flow and negative pressure of the crushing chamber are increasing, the speed of 2500 r/min, the inlet flow and negative pressure were 1.92 kg/s and 37.16 Pa, which will be conducive to the feeding of branches, but the speed is too high and it will also lead to the enhancement of the vortex of part of the area in the crushing device, which will affect the feeding of branches and the throwing of the crushed material. affect the feeding of branches and the throwing out of crushed materials, and finally determined the speed range of the crushing knife roller is 1800 ~ 2220 r/min. Based on ANSYS/Model module modal analysis of the crushing knife roller, the knife roller of the first six orders of the intrinsic frequency and vibration pattern, crushing knife roller of the lowest order of the modal intrinsic frequency of 137.42 Hz, much larger than the crushing knife roller operating frequency of 37 Hz, the machine will not resonate during operation.

Cerebral ischemic stroke poses a significant global cause of death, with ischemia-reperfusion injury contributing to neuronal cell death and tissue damage. However, significant therapeutic approaches with meaningful treatment effects for patients with ischemic stroke in actual clinical practice are lacking. This study investigated the potential neuroprotective effects of sildenafil, a phosphodiesterase-5 inhibitor, in a global cerebral ischemia model. We explored the impact of sildenafil on GFAP and AQP-4 expression, which are markers associated with astrocyte activation and water homeostasis, respectively. The immunofluorescence analysis revealed that the number of cells co-expressing the markers, which increased in the ischemia-induced group, was significantly reduced in sildenafil-treated groups, suggesting a potential mitigating effect on ischemia-induced astrocyte activation. Additionally, we conducted diverse behavioral tests, including the open-field test, novel object recognition, Barnes maze, Y-maze, and passive avoidance tests, to assess the effect of sildenafil on the cognitive function impaired by ischemia. Cognitive improvements were observed following sildenafil administration (20mg/kg) compared to the untreated group. Taken together, the results presented here suggest sildenafil's potential as a neuroprotective agent for alleviating delayed neuronal cell death and cognitive function impaired by ischemia.

Background: Diet is a risk factor for gestational diabetes mellitus (GDM). There are few studies on women´s diet and glucose tolerance test (GTT) results. Objective: To evaluate the relationship between previous diet and the number of abnormal values on the diagnostic GTT in women with GDM. We hypothesized there would be an inverse relation between antioxidant micronutrient consumption and the number of abnormal GTT values. Methods: This cross-sectional study included 60 women diagnosed with GDM (2-hours, 75g-GTT). Shortly after diagnosis, participants answered a validated food frequency questionnaire to assess food consumption in the last 6 months. Mann-Whitney´s test was used to compare the dietary intake of participants with one (group 1) versus two-three (group 2) abnormal GTT values. Results: Participant characteristics were similar. There were no significant differences between groups in median intake of total calories, carbohydrates, lipids and proteins. Participants in group 1 had significantly higher intakes of fiber (11.9 vs. 11.0 g/day p= 0.049), vitamin D (40.6 vs. 40.4 mcg/day p= 0.049) and vitamin C (180.0 vs. 151.0 mg/day p= 0.008) than those in group 2. Conclusion: Results suggest a possible association between consumption of fiber and some antioxidant micronutrients and GDM severity at the time of diagnosis.

This study compared the efficacy of alfaxalone and propofol to induce anesthesia in bitches undergoing an ovariohysterectomy or cesarean sections. 34 healthy and pyometra-affected females were induced with IV propofol administration, 35 healthy and pyometra-affected bitches were induced with the IV administration of alfaxalone. For the cesarean sections, females were induced with propofol (n=14) or alfaxalone (n=14). Maternal physiological parameters (heart rate, blood pressure, temperature) were recorded throughout surgeries; the viability and a modified Apgar score was recorded in the neonates at 5, 60 and 120 minutes after delivery. The use of propofol and alfaxalone did not show relevant differences over parameters assessed, when ovariohysterectomy was performed in healthy bitches or females with pyometra, as well as when cesarean sections were compared; only, a transient tachycardia appeared after alfaxalone administration. However, a higher percentage of bitches needed extra-doses of propofol. Neonatal mortality was similar between both groups, but when alfaxalone was used a higher neonatal viability was observed. Propofol and alfaxalone showed similar efficacy to perform the ovariohysterectomy in all bitches. Both protocols were optimal for cesarean sections, but alfaxalone was associated with a higher neonatal viability and a higher neonatal survival rate.

The utilization of vegetal fabric reinforced cementitious matrix (FRCM) represents an innovative approach to composite materials, offering distinct sustainable advantages when compared to traditional steel-reinforced concrete and conventional FRCM composites employing synthetic fibers. This article introduces a design for sandwich solutions based on a core of extruded polystyrene and composite skins combining mortar as a matrix and diverse vegetal fabrics as fabrics such as hemp and sisal. The structural behavior of the resulting sandwich panel is predominantly driven by the interaction between materials (mortar and polyurethane) and the influence of shear connectors penetrating the insulation layer. This study encompasses an experimental campaign involving double-shear tests, accompanied by heuristic bond-slip models for potential design of sandwich solutions. The analysis extends to the examination of various connector types, including hemp, sisal, and steel, and their impact on the shear performance of the sandwich specimens. The results obtained emphasize the competitiveness of vegetal fabrics in achieving effective composite strength comparable to other synthetic fabrics like glass fiber. Nevertheless, the study reveals that the stiffness of steel connectors outperforms vegetal connectors, contributing to an enhanced improvement in both stiffness and shear strength of the sandwich solutions

The massive use of electronic equipment has raised concerns about electromagnetic (EM) pollution, leading to increased attention to EM shielding in both civil and military applications. EM absorption materials play a crucial role in reducing the energy of microwaves projected onto their surface. In this work, we propose a novel OTA (over-the-air) antenna test method to evaluate and analyze the functionality of EM absorption materials. This method offers a new and practical way to assess the effectiveness of these materials. Measured results indicate that the presence of the EM absorption material led to a decrease in antenna gain of approximately 2-3 dB, a reduction in efficiency of more than 10-15%, and a smaller radiation pattern during the actual testing process. These findings highlight the impact of EM absorption materials on antenna performance. Compared to the traditional S-parameter testing method, the gain comparison testing method exhibits the advantages of higher accuracy and simpler operation in the lower frequency experiment.

Comminution is the most power-demanding stage, and the lack of geometallurgical testing, often for financial reasons, may result in an inefficient operation. The Geopyörä Rock Breakage test was developed with the objective of making mineral variability data more accessible by providing both standard comminution parameters and rock mechanical properties at low cost, and with a modest sample size, allowing a larger number of samples to be tested to reduce uncertainties and assure productivity. The objective of this work is to present the results of an extensive validation of this new rock breakage method against two of the main tests currently in use, namely the SMC® and Bond ball mill work index tests. More than 100 samples have been tested and the results compared, showing that the new method can accurately estimate the parameters of the traditional tests. This confirms that the new test is a reliable tool for comminution and geometallurgical tests.

The effect of Mg particles on PLA's thermal, chemical, physical, and mechanical properties has been studied. The thermal and physical properties have been studied by differential scanning calorimetry, analysing the stability of the α and α’-crystals of the PLA. A colloidal route was used to introduce Mg particles inside the PLA matrix, ensuring a good dispersion of the particles. Materials with Mg contents from 0 to 10 wt.% have been prepared, with additions of polyethyleneimine (PEI) and polyethylene glycol (PEG). Fourier Transform Infrared Spectroscopy has been used to confirm the influence of Mg, PEI, and PEG on PLA properties. The mechanical properties have been measured with a universal tensile test machine on printed filaments via Fused Filament Fabrication (FFF), which were naturally aged to stable conditions. Filaments with and without a notch were studied to obtain the materials' tensile strength, elastic modulus, and fracture toughness. Different analytical models to explain the results of the PLA-Mg were studied, in which the interface strength of the PLA-Mg composites was calculated.

Despite their enormous potential the use of Indoor Localization Systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders’ trust in the systems’ performance as a consequence of insufficient use of Test and Evaluation (T&E) methodologies. The heterogeneous nature of ILS, their influences, and their applications pose various challenges for the design of a methodology that provides meaningful results. Methodologies for building-wide testing exist, but their use is mostly limited to associated indoor localization competitions. In this work, the T&E 4iLoc Framework is proposed - a methodology for T&E of indoor localization systems in semi-controlled environments based on a system-level and black-box approach. In contrast to building-wide testing, T&E in semi-controlled environments, such as test halls is characterized by lower costs, higher reproducibility, and better comparability of the results. The limitation of low transferability to real world applications is addressed by an application-driven design approach. The empirical validation of the T&E 4iLoc Framework, based on the examination of a contour-based Light Detection and Ranging ILS, an Ultra Wideband ILS, and a camera-based ILS for the application of Automated Guided Vehicles in warehouse operation, demonstrates the benefits of T&E with the T&E 4iLoc Framework.

2-(Morpholin-4-yl)-4,5-bis(2’’,2’’,2’’-trinitroethoxy)-1,3,5-triazine having QSAR-predicted anti-tumor activity was tested for the cytotoxicity using MTT and LDH cell viability tests. The experiments were conducted using human fibroblasts, peripheral blood mononuclear cells and breast cancer cells and allowed to identify effective cytotoxic concentration ant therapeutic range of this compound. The data obtained suggest the feasibility of the further studies of the test compound as a potential anti-cancer agent.

This investigation was conducted to determine the chemical composition and nutritional value of five plant species commonly used as ruminant feeds namely: Artemisia herba-alba, Acer monspessulanum, Amygdalus lycoides, Amygdalus scoparia, and Atriplex leucoclada. After the collecting samples, the chemical compositions of plants included dry matter (DM), organic matter (OM), crude protein (CP), ether extract (EE), ash (CA), cell wall (NDF), and non-lignin cell wall (ADF) were determined according to standard methods. For degradation testing, the nylon bag technique was applied using three native Sistani fistula calves. Organic matter digestibility (OMD) and metabolisable energy (ME) were determined through the gas production technique. The CP value ranged from 5.30 (Amygdalus scoparia) to 11.72% (Atriplex leucoclada) while NDF value ranged from 52.62 (Amygdalus lycoides) to 69.05% (Amygdalus scoparia). The range of OMD, DOMD, and ME was from 36.67 to 53.27%, 34.67 to 49.11%, and 5.57 to 8.08 (MJ/kg), respectively. The results showed a positive correlation between cell wall composition and dry matter digestibility in plant species. The nutritional value of Amygdalus lycoides, Atriplex leucoclada, Acer monspessulanum, and Artemisia herba-alba was acceptable composition and digestibility.

Background: The 6-minute walk test (6MWT) is a simple and low-cost method that allows assessment of functional capacity in patients with heart failure (HF). However, the prognostic role of 6MWT in HF remains uncertain. Objectives: We aimed to evaluate the 6MWT as a predictor of mid-term adverse outcomes in patients with HF with mid-range and reduced ejection fraction. Methods: Prospective single-center cohort study that included patients with HF with an ejection fraction under 50% at a specialized outpatient HF service. Patients underwent the 6MWT on admission and were compared according to the distance walked: Group I walked ≥350 meters and group II <350 meters. The primary outcome was a composite of death from any cause or hospitalization for HF decompensation in one-year follow-up. Secondary outcomes were the components of the primary outcome in an isolated analysis. Results: Sixty patients were included, 43.3% male, with a mean age of 61.1 ± 12.9 years and ejection fraction 34.3 ± 10.1%. 52 patients (86.7%) were on guideline-directed triple therapy for HF. The average distance walked in the 6MWT was 395.1 ± 98.8 meters, with 40 patients (66.7%) in group I and 20 (33.3%) in group II. The primary outcome in groups I and II were, respectively, 15,0% and 35,0% (p=0.05). One-year mortality was 5.0% vs 15.0% (p=0.18) and the hospitalization rate was 10.0% vs 20.0% (p=0.28). Conclusions: There was no association of distance <350 meters in the 6MWT with the primary outcome in patients with HF. Despite the higher occurrence of outcomes in group II, the difference was not statistically significant in this analysis. On a selective basis, the 6MWT may be a useful tool for prognostic stratification in HF, if combined with other methods.

In only a few months after initial discovery in Wuhan, China, SARS-CoV-2 and the associated COVID-19 disease has become a global pandemic causing significant mortality and morbidity. In the absence of vaccines and effective therapeutics, reliable serological testing can be a key element of public health policy to control further spread of the disease and gradually ease quarantine measures. However, prior to launch of large-scale seroprevalence studies to assess herd immunity, it is critical to understand the limits and potential of current SARS-CoV-2 serological tests on the market. In this study, we provide an overview of serological testing and conduct a systematic review of independent evaluations of SARS-CoV-2 serological tests performance. Our findings show significant variability in the accuracy of marketed tests and highlight several lab-based and point-of-care rapid diagnostic tests with high performance level in detecting SRAS-CoV-2 specific antibodies. The findings of this review highlight the need for ongoing independent evaluations of commercialized COVID-19 diagnostic tests.

The assessment of positive emotional states in animals has been advanced considerably through the use of judgement bias testing. JBT methods have now been reported in a range of species. Generally, these tests show good validity as ascertained through use of corroborating methods of affective state determination. However, published reports of judgement bias task findings can be counter-intuitive and show high inter-individual variability. It is proposed that these outcomes may arise as a result of inherent inter- and intra-individual differences as a result of biology. This review discusses the potential impact of sex and reproductive cycles, social status, genetics, early life experience and personality on judgement bias test outcomes. We also discuss some aspects of test design that may interact with these factors to further confound test interpretation. There is some evidence that a range of biological factors affect judgement bias test outcomes, but in many cases this evidence is limited and needs further characterisation to reproduce the findings and confirm directions of effect. It is our proposition that researchers should consider dedicated study on these factors and their impact on judgement biasing. This is needed to confirm effect and investigate mechanisms. Alternately, consideration and reporting of these factors in JBT studies through incorporation in statistical analyses will provide much needed additional data on their impact. These actions will enhance the validity and practical applicability of the JBT for welfare assessment.

We report the production of metal oxide (TiFe₂O₄, ZnFe₂O₄) nanoparticles by pulsed laser ablation technique in liquid environment. We used nano second Nd: YAG laser systems working at 532 nm and 1064 nm of wavelength, the energy of the laser beam was kept constant at 80 mJ. Absorbance spectra, surface plasmon resonance, optical band-gap and nanoparticle morphology were investigated using ultraviolet-visible (UV-Vis) spectroscopy, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Changing the wavelength of the laser for growth, nanoparticles shown shift between the absorbance and surface plasmon resonance peaks in their UV-Vis spectra, this implies that the optical properties of the colloid nanoparticles depends on laser parameters, this was confirmed with the variation of the band gap energy. Furthermore, red shift for the absorbance peak was observed for samples as-growth at 532 nm around the 150 nm as function of time preparation. Whereas, for the samples as-growth at 1064 nm there is no shift in the absorbance spectra, this can be due to agglomeration and formation of larger particles. The characterization results shown appropriate plasmonic photo-catalysts properties of the particles, hence the photo activation of the nanoparticles was examined on antibacterial effect using colonies of Staphylococcus Aureus and Escherichia coli.

In the European Union, the demand for polyurethane is continually growing. In 2017, the estimated production value of polyurethane was 700,400T, of which 27.3% is taken to landfill, which causes an environmental problem. In this paper the behaviour of various polyurethane foams from the waste of different types of industries will be analysed with the aim of assessing their potential use in construction materials. In order to this, the wastes were chemically tested by means of CHNS, TGA, and leaching tests. They were tested microstructurally by means of SEM. The processing parameters of the waste was calculated after finding out its granulometry and its physical properties i.e. density and water absorption capacity. In addition, the possibility of incorporating these wastes in plaster matrices was studied by determining its rendering in an operational context, finding out its mechanical resistance to flexion and compression at 7 days, its reaction to fire as well as its weight per unit of area and its thermal behaviour. The results show that in all cases, the waste is inert and does not undergo leaching. The generation process of the waste determines the foam’s microstructure in addition to its physical-chemical properties that directly affect building materials in which they are included, thus offering different ways in which they can be applied.

Alumina dispersion-strengthened copper, Glidcop, is used widely in high-heat-load ultra-high-vacuum components for synchrotron light sources (absorbers), accelerator components (beam intercepting devices) and in nuclear power plants. Glidcop has similar thermal and electrical properties to OFE (oxygen free electrical) copper, but has superior mechanical properties, thus making it a feasible structural material; its yield and ultimate strength are equivalent to those of mild-carbon steel. The purpose of this work has been to develop a brazing technique to join Glidcop to Mo, using a commercial Cu-based alloy. The effects of the excessive diffusion of the braze along the grain boundaries on the interfacial chemistry and joint microstructure, as well as on the mechanical performance of the brazed joints, has been investigated. In order to prevent the diffusion of the braze into the Glidcop alloy, a copper barrier layer has been deposited on Glidcop by means of RF-sputtering.

4-(thiazol-2-yldiazenyl)phenol (L₁) and 2-((4-hydroxyphenyl) diazenyl)-5-nitrophenol (L₂) based on azo phenol were synthesised and used as selective colorimetric sensor for CN^- and AcO⁻ ion in DMSO/H₂O-HEPES (v/v; 1:1, pH–7.3 ± 0.2) and showed good sensitivity with large red shifts and nanomolar detection limit for CN^- and AcO^- ion. The stoichiometry of L₁ with CN⁻/AcO⁻ ion was found to be 1:1 and L₂ with CN⁻/AcO⁻ ion was found to be 1:2. Binding constant for L₁+ CN⁻, L₁ + AcO⁻, L₂ + CN⁻ and L₂ + AcO⁻ were calculated by B-H plot as 1.6 × 10³, 8.0 × 10², 8.4 × 10³ and 1.7 × 10² respectively. L₂ showed high selectivity towards CN⁻ ion with low detection limit of 81 nM and large binding constant. In addition, ¹H NMR titration and DFT studies also supported the deprotonation mechanism of receptors in the presence of selective anions.

Thermal stresses belong to the leading factors that influence low-temperature cracking behavior of asphalt pavements. During winter, when temperature drops to significantly low values, tensile thermal stresses develop as a result of pavement contraction. Creep test methods can be suitable for the assessment of low-temperature properties of asphalt mixtures. To evaluate the influence of creep test methods on the obtained low-temperature properties of asphalt mixtures, three point bending and uniaxial tensile creep tests were applied and the master curves of stiffness modulus were analyzed. On the basis of creep test results, rheological parameters describing elastic and viscous properties of the asphalt mixtures were determined. Thermal stresses were calculated and compared to tensile strength of the material to obtain the failure temperature of the analyzed asphalt mixtures. It was noted that lower strain values of creep curves were obtained for the Tensile Creep Test (TCT) than for the Bending Beam Creep Test (BBCT), especially at lower temperatures. Results of thermal stress calculations indicated that higher reliability was obtained for the viscoelastic Monismith method based on the TCT results than for the simple quasi-elastic solution of Hills and Brien. The highest agreement with the TSRST results was also obtained for the Monismith method based on the TCT results. No clear relationships were noted between the predicted failure temperature and different methods of thermal stress calculations.

The integration of advanced nanotechnology solutions in healthcare presents unparalleled opportunities for transformative care and cost savings. However, market penetration remains hindered by regulatory, technological, and adoption barriers. This study explores business analytics strategies facilitating successful market integration of nanotechnology innovations in healthcare. A mixed-methods approach combining qualitative expert interviews, quantitative survey analysis, and data-driven modeling is employed.Key findings highlight the critical role of data-driven decision-making, collaborative partnerships, and tailored market segmentation in overcoming adoption hurdles. Specifically, predictive analytics and machine learning algorithms are identified as essential tools for optimizing nanotechnology product development, pricing strategies, and supply chain efficiency. Furthermore, strategic partnerships with healthcare stakeholders and tailored market segmentation enable targeted commercialization and accelerated diffusion.This research contributes to the existing literature by providing actionable insights for nanotechnology firms, policymakers, and healthcare administrators seeking to harness the transformative potential of nanotechnology in healthcare. The proposed business analytics framework offers a valuable roadmap for navigating the complex healthcare landscape and ensuring the sustainable market integration of advanced nanotechnology solutions.

The use of digital twin and shadow concepts for industrial material processes have promoted new notions to bridge the gap between physical and cyber manufacturing processes. Hence, many multi-disciplinary areas like advanced sensor technologies, material science, data analytics, and machine learning algorithms are employed to create such hybrid systems. On the other hand, new additive manufacturing (AM) processes for metals and polymers based on novel emerging technologies have already shown a promising horizon for manufacturing of sophisticated parts with complex geometries. These processes are going through a major transformation with the emergence of digital technology, hybrid physical-data driven modelling and fast reduced models. This paper presents a fresh look at the hybrid physical-data driven and reduced order modelling (ROM) techniques for digitalization of AM processes within a digital-twin concept. The main contribution is to show the benefits of ROM and machine learning (ML) technology for process data handling, optimization\controlling and their integration into the real-time assessment of AM processes. Hence, a novel combination of efficient data solver technology and architecturally designed neural network (NN) module has been developed for the transient manufacturing processes with high heating\cooling rates. Furthermore, a real-world case study has been presented where the combination of hybrid modelling along with ROM and ML schemes are used for an industrial wire arc AM (WAAM) process.

In modern agriculture, decision-making is deeply intertwined with data, yet essential agricultural knowledge remains encased within dense scientific texts such as journals, manuals, and free-text reports. This necessitates specialized search systems capable of distilling and delivering precise information in response to specific queries from agricultural users. This study introduces AgriQuery, a sophisticated agent designed to field natural language questions from the agricultural sector by exploring vast troves of scientific documents. A comprehensive review and analysis of farmers' information needs have been conducted, leading to the creation of an information retrieval test collection that includes actual queries, a vast array of scientific documents segmented into passages, and a set of ground truth relevance assessments that map these passages to corresponding queries. We explore and benchmark several information retrieval models, highlighting the efficacy of advanced neural ranking models in this context. AgriQuery's proposed deployment architecture features a client interface on the Telegram platform and a backend model on standard hardware. This test collection aims to catalyze further research into matching natural language queries with scientific text answers, with implications extending beyond agriculture.

The integration of Artificial Intelligence (AI) in predicting and managing risks for coastal port developments significantly enhances planning and operational efficiency. This paper presents a theoretical framework utilizing AI to predict future scenarios based on geological, hydrodynamic, and oceanographic factors. Focusing on the effective implementation of AI in ports, with a specific emphasis on Bulgarian Black Sea coastal areas such as Nesebur, Sarafovo, Sozopol, St. Anastasia Island, and the region of Karantinata in Varna, the study explores AI applications for site selection, risk assessment, and operational optimization to ensure resilient, efficient, and sustainable port infrastructure.

Retinal vein cannulation involves puncturing an occluded vessel on the micron scale. Even single millinewton force can cause permanent damage. An ophthalmic robot with a piezo-driven injector is precious enough to perform this delicate procedure, but the uncertain viscoelastic characteristics of the vessel makes it difficult to achieve the desired contact force without harming the retina. The paper utilizes a viscoelastic contact model to explain the mechanical characteristics of retinal blood vessels in order to tackle this issue. The uncertainty in the viscoelastic properties is considered an internal disturbance of the contact model, and an active disturbance rejection controller is then proposed to precisely control the contact force. The experimental results show that this method can precisely adjust the contact force at the millinewton level even when the viscoelastic parameters vary significantly (up to 403.8%). The root mean square (RMS) and maximum value of steady-state error are 0.32 mN and 0.41 mN. The response time below 2.51 s with no obvious overshoot.

This paper presents a theoretical framework aimed at improving IoT network cybersecurity by AI-driven anomaly detection in the context of rapid urbanization and the development of smart cities. The framework draws upon the Complex Adaptive Systems Theory, Theory of Planned Behavior, the Technology Acceptance Model, and the Socio-Technical Systems Theory, and intri-cately examines how AI might be integrated into the intricate ecosystem of smart cities to detect and mitigate cyber threats. It emphasizes AI-driven anomaly detection methods as pivotal in-struments that impact cybersecurity enhancement in smart city infrastructures. The proposed framework also highlights the crucial role that human-related factors, including user behavior and adoption, play in determining how effective AI applications are in enhancing cybersecurity in smart cities. It also recognizes the important moderating roles played by outside factors like legislative frameworks, environmental contexts, and technological breakthroughs in the relationship between AI-enabled anomaly detection and enhanced cybersecurity. The goal of this research is to shed light on the interactions between technology and socio-technical dynamics by offering a thorough theoretical understanding of AI's potential in smart city cybersecurity. It highlights the significance of taking into account a variety of contextual aspects when employing AI to strengthen the digital security of urban areas, and provides a strategic direction for future research and practical implementation.

The Genji firefly is a key symbol of the conservation and management of rural environments. The species lives underwater as larvae, in the soil when they pupate, and emerges on the ground as adults. Because of the large differences in habitat conditions between larvae and adults, the habitat requirements of the Genji firefly must be clarified over a wide range, from the instream to the surrounding riparian environment. In this study, we built a habitat suitability model for the Genji firefly by relating local landscape features to the number of individuals surveyed in a small river in an agricultural watershed. For habitat suitability modelling, tree-based machine learning methods, namely Random Forests (RF) and classification and regression trees (CART), are used to model species distributions and extract quantitative information on their ecological characteristics. Because the model performance on the test dataset was poor, we removed noisy absences using model outputs from the RF computation using the entire dataset. As a result, we observed improved model performance, in which there seems to be an optimal level for removing noisy absences. Variable importance and partial dependence plots were then used to visualize the relationship between the local landscape features and habitat suitability of the adult firefly. Detailed information on landscape features extracted using RF and CART is useful for a deeper understanding of the ecology of the Genji fireflies.

This paper argues that popular misrepresentation of the nature of AI has important consequences concerning how it should be regulated. Viewing AI as something that exists in itself, rather than as a set of cognitive technologies whose characteristics – physical, cognitive, and systemic – are quite different from ours (and at times from each other) leads to inefficient approaches to regulation. It limits our ability to anticipate the consequences of its foreseeable developments and social diffusion. It undermines attempts to protect ourselves from the social and political dangers it presents. After a short introduction, section 2 retraces rapidly the history of the idea that intelligence is essentially a quality, one that AI shares with human intelligence and that this resemblance trumps the differences that exist between artificial and natural cognitive systems. Section 3 reviews two approaches to the dangers of AI that reflect the illusion that AI exists in itself. Section 4 turns to the proper understanding of regulations and what is their main goal and purpose. Section 5 analyses three central characteristics of artificial cognitive systems which section six compares with corresponding characteristics of natural cognitive systems. Finally, section 7 draws some conclusions regarding how we should regulate artificial intelligence.

This study presents the manufacturing process driven development of an interlocking metasurface mechanism (ILM) for Fused Filament Fabrication (FFF) with a focus on open-source accessibility. The presented ILM is designed to enable strong contact between to planar surfaces. The mechanism consists of spring elements and locking pins which snap together when forced into contact. The mechanism is designed to optimize mechanical properties, functionality and printability with common FFF printers. The mechanisms is printed from thermoplastic polyurethane (TPU) filament which was selected for its flexibility, crucial for spring element performance and tolerances of the fabrication method. To characterize the designed mechanism a tensile test is carried out to assess the holding force of the ILM. The force-displacement profiles are analyzed and categorized into distinct phases, highlighting the interplay between spring deformation, sliding, and disengagement. The results show variations in holding forces attributed to geometric and material-related factors. The testing results are compared and discussed to a numerical simulation carried out with a frictionless approach with a nonlinear Neo–Hookean material law. The study underscores the importance of meticulous parameter control in 3D printing for consistent and reliable performance of interlocking metasurface mechanisms. The investigation leads to a scalable model of a ILM element pair with a distinct three-phase snapping characteristics ensuring reliable holding capabilities.

In the context of the emergence and development of varieties of English around the globe, this paper focuses on Kuwait, where the use of English has greatly increased since the mid-20th century. Drawing on the principles of nativisation and indigenisation, I investigate substrate- and superstrate-derived discourse-pragmatic markers (DPMs) in Kuwaiti English: the Arabic loanwords alhamdulillah and ya'ni and their English equivalents (e.g. I mean and thankfully). Data are drawn from the Kuwaiti English National Corpus, a spoken corpus of almost 800,000 words containing interviews, samples of situated language use, and mediated data (podcasts, TV, radio and YouTube). Keyness and concordance analyses were performed to identify the frequency and functions of the DPMs in Kuwaiti English compared to reference corpora of British and American English. The analysis also takes into account contextual and sociolinguistic information such as the speakers’ social identity. Results show that the Arabic-derived DPMs are abundant in both formal and informal settings, indicating that they have become an integral part of this variety of English. Their English equivalents perform similar roles, but are vastly less common (with the exception of I mean for ya'ni). Both the Arabic- and English-based markers show somewhat narrowed discourse-pragmatic functions and are more likely to be placed in clause-initial position than in ‘native’ contexts. Their use appears to be linked to the social identity of the speakers.

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 dof delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data is available. Due to the sequential nature of the data, non-linearity of the system, and correlations between parameter time series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method is capable of calculating RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

The segmentation and de-noising are basic operations, required in every signal processing and classification system. The classical segmentation and de-noising approaches are time-invariant. Consequently, it results in the post processing of an unnecessary information and causes an increase in the system processing activity and power consumption. In this context, an efficient event-driven segmentation and de-noising technique is proposed. It is founded on the principles of level crossing and activity selection. Therefore, it can adapt its sampling frequency, segmentation window length and position along with the filter order by analyzing the input signal local characteristics. As a result, the computational complexity and the power consumption of the proposed system is reduced compared to the counter ones. The suggested system performance is compared with the classical one. It is done for the case of a multi-channel Electroencephalogram (EEG) signals. Results show a noticeable compression gain with an effective adaptation of the de-noising filters order. It aptitudes a significant computational gain, transmission data rate reduction and power consumption reduction of the proposed technique, compared to the counter ones. It shows that the proposed solution is an attractive candidate to embed in the new generation EEG wearables.

Wide bandgap (WBG) power semiconductor devices are increasingly replacing silicon IGBTs in high-power and high-voltage power electronics applications. However, there is a significant gap in the literature regarding efficient testing methodologies for high-power and high-voltage converters under constrained laboratory resources. This paper addresses this gap by presenting comprehensive, hardware-focused testing methodologies for high-power and high-voltage WBG power semiconductor based converters, preceding the control evaluation phase. The proposed methods enable thorough evaluation and evaluation of converter hardware, including device switching characteristics, driving circuit functionality, thermal management performance, insulation integrity, and sustained operation at full power. We utilize the double pulse test (DPT) to characterize switching performance in a two-level phase leg configuration, extract circuit parasitics, and validate magnetic components. The DPT is further applied to optimize gate driving circuits, validate overcurrent protection mechanisms, and measure device on-resistance. Additionally, a multicycle test is introduced to rapidly assess steady-state converter performance and estimate efficiency. Recognizing the critical role of thermal management in high-power converters, our methodologies extend to the experimental extraction of key thermal parameters—such as junction-to-ambient thermal resistance and thermal capacitance—via a heat loss injection method. A correlation method between temperature sensor measurements and junction temperature is presented to enhance the accuracy of device temperature monitoring during tests. To ensure reliability and safety, dielectric withstand tests and partial discharge measurements are conducted at both component and converter levels under conventional 60 Hz sinusoidal and high-frequency PWM waveforms. Finally, we highlight the importance of testing converters under full voltage, current, and thermal conditions through power circulating tests with minimal power consumption, applicable to both non-isolated and isolated high-power converters. Practical examples are provided to demonstrate the effectiveness and applicability of these hardware testing methodologies.

In this paper, the Shannon entropy measure was used to assess changes in precipitation and temperature conditions. Due to the short, low-volume sequences of precipitation and temperature data analysed, a bootstrap method was used in the procedure for calculating Shannon entropy. The analysis used minimum and maximum values of monthly precipitation totals and monthly mean temperatures for 377 catchments distributed across the globe. A 110-year data series from 1901 to 2010 was analysed. Entropy values for the estimated parameters of the generalised extreme value distribution (GEV) were calculated for the adopted data. Entropy value calculations were performed for the left-hand constraint, based on minimum values, and for the right-hand constraint, based on maximum values. The applicability of Shannon's entropy measure in the analysis of climate change was demonstrated by allowing the degree of disorder and complexity of the distributions describing climate variables in the form of precipitation and temperature to be measured. This made it possible to obtain information on the directions of changes occurring with regard to minimum and maximum values in the field of monthly precipitation and mean temperatures in the analysed catchments. The study demonstrated the existence of Shannon entropy trends. The evaluation of entropy trends for precipitation and temperature sequences was performed using non-parametric tests. Mann -Kendall tests at the 5% significance level were used for trend analyses. The Pettitt test was performed to determine the point of change in trend for the rainfall and temperature data. The performed analysis was supported by graphical presentations.

This article presents an analysis of monthly precipitation totals based on the Global Precipitation Climatology Centre database and monthly mean temperatures based on National Oceanic and Atmospheric Administration data for 377 catchments located around the world. Data sequences covering 110 years from 1901 to 2010 were analysed. The long-term sequences of precipitation and temperature were used to assess the variability of climate extremes, referred to here as polarisation. Measures of polarisation used in the natural sciences are discussed. A simple measure of polarisation was presented and applied to long-term sequences of monthly precipitation totals and monthly mean temperatures. Due to the nature of the proposed polarisation measure, other characteristics of precipitation and temperature sequences are also presented as a background for the discussion of the polarisation index. The study showed the existence of trends related to the polarisation of temperature and precipitation phenomena. As a result of the analysis, trends of polarisation factors in the area of precipitation and temperature were identified in 11 catchments out of the 377 analysed catchments. The trend analysis used Mann-Kendall tests at a level of significance of 5%. The Pettitt test was used to determine the point of trend change for precipitation and temperature data. The whole investigation is supported by rich graphical analyses, and the results are presented in tabular form.

The article presents an analysis of monthly precipitation totals based on the GPCC database, and monthly mean temperatures (NOAA data) for 377 catchments distributed across the globe. The analysis included 110-year data sequences from 1901 to 2010 calculated from grid data with a spatial resolution of 0.5°x 0.5° longitude and latitude. Long-term sequences of precipitation and temperature were used to assess the polarization of climatic phenomena. The noticeable impact of polarization in the area of extreme changes in temperature and precipitation is related to anthropogenic factors such as greenhouse gas emissions, deforestation and pollution, which affect ecosystem functioning, biodiversity, water resources and economies. The paper demonstrates the existence of trends related to the polarization of temperature and precipitation phenomena. The measures of polarization used in science are discussed. A simple measure of polarization was proposed and applied to both long-term sequences of monthly precipitation totals and monthly mean temperature. Due to the nature of the proposed polarization measure, other characteristics of the precipitation and temperature sequences are also presented as background for the discussion of the polarization index. The paper presents, for a selection of several hundred catchments from around the world, analyses of the assessment of precipitation and temperature trends using non-parametric tests. The trend analyses use Mann -Kendall tests at the 5% significance level. A Pettitt test was used to determine the trend change point for precipitation and temperature data. The whole is supported by rich graphical analyses and the results are presented tabularly.

In this paper a robust version of the Wald test statistic for composite likelihood is considered by using the composite minimum density power divergence estimator instead of the composite maximum likelihood estimator. This new family of test statistics will be called Wald-type test statistics. The problem of testing a simple and a composite null hypothesis is considered and the robustness is studied on the basis of a simulation study. Previously, the composite minimum density power divergence estimator is introduced and its asymptotic properties are studied.

AISI 1037 and AISI 304 steels are widely used across various industrial sectors, including manufacturing, transportation, petrochemical, and automotive industries. In this study, the effect of preheating on the toughness of dissimilar welded joints between AISI 1037 and AISI 304, joined by Shielded Metal Arc Welding (SMAW) using E309-16 electrodes, was investigated. The preheating process was performed prior to welding, and its influence on the joint's toughness was assessed through impact testing. Additionally, the hardness profile across the welded joint was evaluated using hardness testing. Macrostructure and microstructure examinations were conducted using an optical microscope and scanning electron microscopy (SEM) to analyse the fracture surfaces and microstructural evolution of the welded joints. The experimental results revealed that the impact strength increased as the preheating temperature was elevated. The highest hardness value, recorded in the fusion zone, reached 313 VHN. Compared to joints welded without preheating, the impact strength of the preheated welded joints significantly improved, with a notable transition in fracture mode from brittle to ductile behaviour. This indicates that the preheating process plays a critical role in enhancing the toughness and mechanical properties of dissimilar welded joints.

We investigated the effect of lepidocrocite-type layered titanate, which we have been using to reduce brake-particle emissions in an environmentally friendly manner. The effect of titanate on reduction was evaluated using a small-scale inertia brake-material-friction-test dynamometer. We suggested that brake-particle emissions are related to the microphysical structure of the pad surface, such as the uniformity of friction film and secondary-plateau formation, and that friction materials containing titanate contribute significantly to the reduction in both particle mass (PM) and particle number (PN) emissions of brake particles for both non-asbestos organic (NAO) and low-steel (LS) pads. In our experiments, the PM- and PN-emission levels of brake particles from LS pads with the addition of titanate were comparable to those of NAO pads.

The current study examined the relationship between creative potential, estimated with tests of divergent thinking (DT) and emotional intelligence (EI). Two DT tests were used, one commonly used to estimate creative potential (Titles Game) and the other focusing on social problems (Social Games). The youth version of the Bar-On Emotional Quotient Inventory ( EQ-i: YV) was used to measure EI. The sample comprised 244 male and female gifted (N = 125) and nongifted (N = 119) high school students in Saudi Arabia. The first objective was to examine whether the EI-DT relationship differs based on the nature of the task of the two DT tests used in the current study (Social Games vs Titles Game). The second objective was to test whether the Interpersonal and Intrapersonal subscales of EI are more associated with DT than the Adaptability and Stress Management EI subscales. Canonical correlation analysis showed that the relationship between the Social Games test and EI was stronger than the relationship between the Titles Game test and EI. Two path analyses were run: one for the total sample and the second for the gifted sample. The likelihood ratio test showed that the Social Games test was more strongly associated with EQ-i subscales than the Titles Game test for both samples. As expected, the Inter and the Intrapersonal subscales of the EQ-i were highly related to Social Games fluency and originality scores compared with Stress Management and Adaptability subscales. Limitations and future directions are discussed.

This work presents the study of a reproducible acoustic emission method based on the launching of a metallic sphere and low-cost piezoelectric diaphragm. For this purpose, tests were first conducted on a carbon fiber-reinforced polymer structure, and then on an aluminum structure for comparative analysis. The Pencil Lead Break (PLB) tests were also conducted for comparisons with the proposed method. Different launching heights and elastic deformations of the structures were investigated. The results show higher repeatability for the sphere impact method, as the PLB is more affected by human inaccuracy, and it was also effective in damage detection.

Hydrodynamic resistance is a critical parameter in microfluidics, affecting device functionality and performance. However, quantifying hydrodynamic resistance in microfluidics is a challenge due to many influencing factors and the difficulties associated with the precise measurements of low flow rates (< 10 L/min) and pressure drops (< 5 kPa). This article presents a simple experimental test method for assessing hydrodynamic resistance, correlating with theoretical and numerical calculations. The results demonstrate good agreement between benchtop and theoretical data, suggesting a potential standardized method for assessing hydrodynamic resistance in microfluidic devices.

One aspect of reproducibility in preclinical research that is frequently overlooked is the physical condition in which physiological, pharmacological, or behavioural recordings are conducted. In this study, the physical conditions of mice were altered through the attachments of wireless electrophysiological recording devices (Neural Activity Tracker-1, NAT-1). NAT-1 devices are miniaturised multichannel devices with on-board memory for direct high-resolution recording of brain activity for >48 hrs. Such devices may limit the mobility of animals and affect their behavioural performance due to the added weight (total weight of approximately 3.4 g). Mice were additionally treated with saline (control), N-methyl-D-aspartate (NMDA) receptor antagonist MK801 (0.85 mg/kg), or the muscarinic acetylcholine receptor blocker scopolamine (0.65 mg/kg) to allow exploration of the effect of NAT-1 attachments in pharmacologically treated mice. We found only minimal differences in behavioural outcomes with NAT-1 attachments in standard parameters of locomotor activity widely reported for the open field test between drug-treatments. Hypoactivity was globally observed as a consistent outcome in MK801-treated subjects and hyperactivity in scopolamine groups regardless of NAT-1 attachments. These data collectively confirm the reproducibility for combined behavioural, pharmacological and physiological endpoints even in the presence of lightweight wireless data loggers. The NAT-1 therefore constitutes a pertinent tool for investigating brain activity in e.g. drug discovery, models of neuropsychiatric and/or neurodegenerative diseases with minimal effects on pharmacological and behavioural outcomes.

Oak woodlands are a keystone ecosystem that may not be sustainable under ongoing fire management regimes and climate change. For example, in 2014, sassafras saplings started to emerge in degraded woodlands at the 1200 ha Drury Conservation Area as part of research at the Bull Shoals Field Station in Taney County, Missouri where burning was initiated in 1999. After Spring (March) 2021 burnt, we examined trend in the potential community and ecosystem effects, we assessed sapling density, height, and specific leave weigh (SLW) of white oak and sassafras (<1.0 m) from fall 2021 through fall 2022. In fall 2021, sassafras density was 560/0.1ha with mean height of 25.42cm and SLW of 0.00436g/cm² while white Oak density was 171/0.1ha with mean height of 10.2cm and SLW of 0.00680g/cm. However, in fall 2022, Sassafras density was 582/0.1ha with mean height of 71.689cm and SLW of 0.00366 g/cm² meanwhile, white Oak density was 213/0.1ha with mean height of 50.544cm and SLW of 0.00563 g/cm². We analyzed mass loss for sassafras and white oak after buried for 198 days. The weight loss for sassafras was 0.857g while oak was 0.790g for 61 days of buried. Subsequent sassafras weight loss was 1.942g, 3.343g, 4.222g and 4.82g while that of white Oak was 1.877g, 2.862g, 3.344g and 4.28g for day 105, 153 and 198 respectively. The nutrients composition and carbon sequestration of sassafras and white oak species need to be determined to better understand the effect of their dominance in fire management regime of degraded oak woodland in Southwest Missouri Ozarks.

The process of testing software is examining the artifacts and behavior of the software under test by validating and verifying it. Testing can provide valuable information about the software. Given an input to the system, there is a challenge of distinguishing correct from potentially incorrect behavior, which is the test oracle problem. Although the combinatorial testing approach can effectively reduce the number of test cases, the manual inspection of the outputs is still required. In this paper, we propose an approach to automatically classify the return pages with conducting combinatorial testing based on page similarities and hierarchical clustering, by which potential output anomalies can be identified. In the experiment, we apply our approach to a real-world e-commerce website, an anomaly can be found with the visualization of the clustering hierarchy, which suggests the potential of the applicability of the approach.

Several factors, such as internal algorithm, sensor technology, and obsolescence might skew outcomes when comparing different gas analysers measurements during CPET. Besides aerosol borne infectious disease are a source of contamination during cardiopulmonary exercise tests (CPET). Thus, the main aim of this study was to determine the agreement degree between the Quark RMR and the Jaeger Oxycon Pro, using an antibacterial filter with the latter. Forty-two participants (19-54 years old) performed two non-consecutive maximal graded exercise tests on a cycle-ergometer on two counterbalance and different days using the Oxycon Pro and the Quark RMR in a randomized order. Differences between devices were tested using a generalized linear model adjusted by Bonferroni, and correlation and agreement was assessed using Pearson’s correlation coefficient (R), intraclass coefficient correlation (ICC), Lin’s concordance correlation coefficient (CCC) and Bland-Altman plots. No significant differences were found between devices in any of metabolic or ventilatory parameters for cardiorespiratory fitness assessment (VO_2max: 3131.3±882.1 vs. 3189.8±894.8, p=0.071, VCO_2max: 3436.3±936.0 vs. 3550.3±1043.2, p=0.071, for Quark RMR and Oxycon Pro, respectively). However, Bland-Altman plots showed a trivial tendency towards Oxycon Pro overestimation relative to Quark RMR as air flow volume increases. Both devices showed strong correlation and high level of agreement during maximal and submaximal exercise intensities (Pearson’s R: 0.974 & 0.977; ICC: 0.985 & 0.987; Lin’s CCC: 0.971 & 0.974, for VO₂ and VCO₂, respectively). Therefore, providing consistency to CPET data comparison between both devices, and insight into whether the use of these metabolic carts could be interchangeable or combined when a single device cannot be used for CPET assessment.

This study adapted and assessed a shortened version of the IAT (Internet Addiction Test) scale completed by young people aged 12 years and older regarding their online behaviors and risk of online addiction. The psychometric qualities of the reduced version - Screening IAT - youth are presented in order to validate its use in the early detection of online addiction. The total sample is composed of 3021 participants being 55.9% female and 44.1% male, with a mean age of x ̅=15 years (σ=3.028), attending junior high school (56.2%), high school (37.8%) and college (5.9%). The procedure included a factorial analysis, in which the total sample was randomly divided into three samples. An exploratory factor analysis was performed with one part of the sample, and a confirmatory factor analysis was performed with the others parts, assessing internal consistency, construct reliability, and discriminant validity. The results indicate that this reduced version of the IAT for young people has good psychometric qualities and can be applied in research and clinical settings. With this version and the parent-teacher version there is a direct contribution to a tripartite assessment of internet addiction risk.

Stock bubbles are characterized by unpredictable price surges and subsequent declines, causing significant losses for investors. The study aimed to detect mildly explosive patterns in S&P 500-listed stocks in real-time using the GSADF test. This study focused on the length, the normality of the stock bubble episode and compared the effectiveness of SADF and GSADF tests in bubble detection, consistently finding GSADF's superiority. The duration of breakouts exhibited non-normality, suggesting unusually long bubble durations.

High prevalence of allergic disorders in Indian pediatric patients demands early diagnosis of allergens so that appropriate allergen avoidance methods, pharmacotherapy or immunotherapy can be provided in time to avoid the worsening of symptoms due to triggers. In this context, identification of the most prevalent aeroallergens in a given area is critical for diagnosis and treatment of various allergic reactions especially in case of rhino-conjunctivitis and asthma. The specific diagnosis of IgE mediated allergy is usually based on the correlation between the clinical history and signs and symptoms endorsed by the diagnostic tests. Skin prick tests are the gold standard however has some limitation. In view of this, the aim of this study was to assess the significance of ImmunoCAP assay for assessing serum specific IgE levels, available in India, against skin prick test by standardized allergens, for the diagnosis of allergic sensitization. A single-center observational study was conducted at tertiary care corporate hospitals in North India from 2017-2020. Patients with self-reported allergic symptoms or whose symptoms were suspected for allergies were tested for the type of sensitization using skin prick test (SPT) after their consents followed by ImmunoCAP based quantification of serum specific IgE levels. A total of 256 (95.9%) pediatric patients were analyzed in the study and were analysed. The patients were most commonly (30% patients) sensitized to D. farinae (173 [67.6%]), D. pteronyssinus (174 [68.0%]), Acarus siro (93 [36.3%]), Lepidoglyphus destructor (96 [37.5%]), Plantago lanceolata (Engl.). The ROC curve of Bermuda grass shows that the AUC of serum-specific IgE is larger than that of SPT (1.000 / 0.881). The receiver operating characteristic (ROC) analysis of serum-specific IgE reference method resulted in the largest AUC of all the allergens taken into consideration (0.711 / 1.000) compared to those of SPT (0.589 / 1.000). These results revealed that serum-specific IgE is more reliable method over prick test for accessing the sensitivity of patient for various allergens. This study may be helpful in performing early diagnosis of allergies as well as in taking appropriate preventive measures for managing life threatening allergic reactions

Background: Androgenetic alopecia (AGA) and alopecia areata (AA) are most common types of non-cicatricial alopecia. Both diseases have limited effective therapeutic options and affect patients' quality of life. Pharmacogenetic tests can help predict the most appropriate treatment option by evaluating the single nucleotide polymorphisms (SNPs) corresponding to genes related to alopecia. The objective of the study was to evaluate and compare selected SNPs and genes in AA and AGA patients from Romania and Brazil. Materials and methods: We investigated associations between AA and AGA and 45 tag SNPs of 13 genes in 287 Romanian and 882 Brazilian patients. The DNA samples were extracted from saliva using qPCR technique. Results: GR-alpha gene, GPR44-2 gene, SULT1A1 gene and CRABP2 gene were statistically significantly different in Brazil compared to Romania. Minoxidil may be recommended in half of the cases of AGA and AA. Patients with AGA and high expression of SRD5A1 or PTGFR-2 may benefit from Dutasteride, respectively Latanoprost treatment. Most of the studied genes showed no differences between the two population. Conclusion: The DNA analysis of the patients with alopecia may contribute to a successful treatment.

The magnetic heating effect for the superconducting quarter-wave resonator (QWR) cavities is investigated, and the Q slopes of the superconducting cavities are measured with an increasing accelerating field. Physical phenomena for zero-temperature limit are introduced. Bardeen–Cooper–Schrieffer (BCS) resistance and Casimir force are calculated for the zero-temperature limit. The vertical test is shown for the performance test of the quarter-wave resonator (QWR) cavities. The parameters for the quarter-wave resonator (QWR) cavity are presented. The Q slopes are measured as a function of an accelerating electric field at 4.2 K. The surface resistance of the superconducting cavity increases with an increasing peak magnetic field. The magnetic defects cause the degradation for the quality factor. From the magnetic degradation, we can find the magnetic moments of the superconducting cavities. All the quarter-wave resonator (QWR) cryomodules are installed in the tunnel, and beam commissioning is performed successfully.

Nowadays, the presence of pathogen-specific antibodies in the blood is widely controlled by a serodiagnostic technique based on the lateral flow immunoassay (LFIA). However, its common one-stage format with an antigen immobilized in the binding zone of a test strip and a nanodis-persed label conjugated with immunoglobulin-binding proteins is associated with risks of very low analytical signals. It is caused by the presence of non-specific immunoglobulins in very large excess to the target antibodies in the tested samples thus decreasing their binding with the detected labels. In this study, the first stage of the immunochromatographic serodiagnosis was carried out in its traditional format using a conjugate of gold nanoparticles with staphylococcal immunoglobulin-binding protein A and an antigen immobilized on a working membrane. At the second stage, a labeled immunoglobulin-binding protein was added, which enhanced the coloration of the bound immune complexes. The use of two separated steps, binding of specific antibodies, and further coloration of the formed complexes allowed a significant reducing the influence of non-specific immunoglobulins on the assay results. The proposed approach was ap-plied for the serodiagnosis using a recombinant RBD protein of SARS-CoV-2. As a result, an in-crease in the intensity of test zone coloration by more than two orders of magnitude was demonstrated, which enabled to significantly reduce false-negative results. When testing a panel of 16 positive and 8 negative serum samples, the diagnostic sensitivity of the LFIA was 62.5% for the common format and 100% for the enhanced format; the diagnostic specificity of both variants was 100%.

An accurate understanding of jointed rock mass behavior is important in many applications ranging from deep geological disposal of nuclear waste to deep mining to urban geoengineering projects. The roughness of rock fractures and the matching of the fracture surfaces are the key contributors to the shear strength of rock fractures. In this research, push shear tests with three normal stress levels of 3.6, 6.0, and 8.5 kPa were conducted with two granite samples with artificially induced well-matching tensile fractures with sizes of 500 mm × 250 mm and 1000 mm × 500 mm. The large sample reached on average a -60 % weaker peak shear stress than the medium-sized sample, and a strong negative scale effect was observed in the peak shear strength. The roughness of the surfaces was measured using a profilometer and photogrammetry. The scale-corrected profilometer-based method (JRC) underestimates the peak friction angle for the medium-sized slabs by -27 % for the medium sample and -9 % for the large sample. The photogrammetry-based (Z’2) method produces an estimate with -7% (medium) and +12 % (large) errors. The photogrammetry-based Z’2 is an objective method that consistently produces usable estimates for the JRC and peak friction angle.

Polymethyl methacrylate (PMMA) is a transparent thermoplastic with excellent optical properties, transparent surface, low moisture absorption, tensile and electrical resistance. In this study, the alloy was prepared through PMMA and cycloolefin copolymer (COC) due to some similar properties. The mechanical test showed that properties such as impact resistance, elongation, tensile, and flexural strength decreased by adding COC by up to 20% due to less incompatibility and miscibility, but mentioned properties improved by adding COC 40% due to sub-phase generation. The DSC and DMTA tests showed improvement in the thermal properties of alloys by adding 40% COC. SEM micrographs exhibited a softer surface and more phase elongation of the alloy. Finally, the sample was selected as the optimal sample in terms of mechanical properties irradiated by electron beam, and amplification results showed that a dose of 50 KGY increased the mechanical and thermal properties relatively.

Most diagnostic tests for tuberculosis (TB) rely on sputum samples, which are difficult to obtain and have low sensitivity in immunocompromised patients, patients with disseminated TB, and children, delaying treatment initiation. The World Health Organization (WHO) calls for the development of a rapid, biomarker-based, non-sputum test capable of detecting all forms of TB at the point-of-care to enable immediate treatment initiation. Lipoarabinomannan (LAM) is the only WHO-endorsed TB biomarker which can be detected in urine, an easily collected sample. This review discusses the characteristics of LAM as a biomarker, describes the performance of first-generation urine LAM tests and reasons for slow uptake, and presents considerations for developing the next-generation of more sensitive and impactful tests. Next-generation urine LAM tests have the potential to reach adult and pediatric patients regardless of HIV status or site of infection and facilitate global TB control. Implementation and scale-up of existing LAM tests and development of next-generation assays should be prioritized.

Managing blood cholesterol levels is important for the treatment and prevention of diabetes, cardiovascular disease, and obesity. An easy-to-use, portable cholesterol blood test will accelerate more frequent testing by patients and at-risk populations. We aim to evaluate the performance of smartphone-based point-of-care cholesterol blood tests as compared to that of hospital-grade laboratory tests. We used smartphone systems that are already familiar to many people. Because smartphone systems can be carried around everywhere, blood can be measured easily and frequently. We compared the results of cholesterol tests with those of existing clinical diagnostic laboratory methods. We found that smartphone-based point-of-care lipid blood tests are as accurate as hospital-grade laboratory tests (N=116, R>0.97, P<0.001 for all 3 cholesterol blood tests: total cholesterol, high density lipoprotein, and triglyceride). Our system will be useful for those who need to manage blood cholesterol levels to motivate them to track and control their behavior.

This paper analyses the evolution of the internal damage in two types of steel that show different fracture behaviours, with one of them being the initial material used for manufacturing prestressing steel wires, which shows a flat fracture surface perpendicular to the loading direction, and the other one being a standard steel used in reinforced concrete structures, which shows the typical cup-cone surface. 3mm-diameter cylindrical specimens are tested with a tensile test carried out in several loading stages and, after each of them, unloaded and analysed with X-ray tomography, which allows detection of internal damage throughout the tensile test. In the steel used for reinforcement, damage is developed progressively in the whole specimen, as predicted by Gurson-type models, while in the steel used for manufacturing prestressing steel-wire damage is developed only in the very last part of the test. In addition to the experimental study, a numerical analysis is carried out by means of the finite element method by using a Gurson model to reproduce the material behaviour.

Background: The maximal one-repetition test (1-RM) is widely used in scientific research; however, there are conflict results regarding its reproducibility in elderly population. The present study aimed to analyze the reproducibility the test both before and after a 12 week training period by using the bench press and leg press 45° 1-RM tests in the elderly taking into consideration the training experience and strength level of the women. Methods: Elderly women (n = 376; age, 68.5 ± 14.1 years; height, 162.7 ± 5.5 cm; body mass, 71.2 ± 16.0 kg) who underwent ≥3 months of resistance training performed an initial week of familiarization and a second week of testing and retest, with a 48–72 hour interval. Results: The results showed maximal and relative load strength of 39.3 kg and 0.59 kg/body mass for lower limbs and 20.9 kg and 0.31 kg/body mass for upper limbs. The Kappa indices were 0.93 and 0.95, and the intraclass correlation coefficients were 0.99 and 0.99 for the lower and upper limbs, respectively. Conclusion: Therefore, the present study confirms that the 1-RM test has high reliability and reproducibility in the elderly, for both upper and lower limbs.

The aim of this study was to clarify effects of 3-week work-matched high-intensity intermittent cycling training (HIICT) with different cadences on VO2max in university athletes. Eighteen university athletes performed HIICT with either 60 rpm (n = 9) or 120 rpm (n = 9). HIICT consisted of eight sets of 20-s exercise with a 10-s passive rest between each sets. The initial training intensity was set at 135% of VO2 max and was decreased by 5% every two sets. Athletes in both groups performed 9 sessions of HIICT during 3-week. The total work-load and achievement rate of the work load calculated before experiments in each group were used for analysis. VO2max was measured pre and post-training. After 3-week of training, no significant differences in the total work-load and achievement rate of the work load were found between the two groups. VO2max similarly increased in both groups from pre to post training (p = 0.016), with no significant differences between the groups (p = 0.680). These results suggest that cadence during HIICT is not training variable affecting effect of VO2max.

Oriented strand board (OSB) panels are widely marketed for several applications, from building to packaging. The manufacturing of poplar OSB started recently in Northern Italy representing a relevant innovation in the sector since this product is usually made of coniferous wood. The thermal treatment is widely used for reducing the hygroscopicity of wood-based products, nonetheless it influences the mechanical behavior and degrades the main components of wood, which can affect their finishing. Therefore, it is important to know the properties of the treated surfaces. To this purpose, in this study a lot of OSB panels, made of poplar wood, 15 mm thick and with a density of 590 kg/m³, were thermally treated under vacuum conditions at 190 °C for 2 h and then subjected to accelerated weathering. The changes in color and in wettability due to treatment and accelerated weathering were studied. The thermal treatment determined a significant darkening of the color; the accelerated weathering darkened the untreated surfaces and, on the opposite, lightened the thermo-treated surfaces. The wettability decreased after thermal treatment and increased after weathering, more evidently in treated panels. Overall, this study improves the knowledge about the behavior of the surface of thermo-treated poplar OSB, which is relevant for the industrial coating of this product.

The antioxidant properties and polyphenol content of some selected aromatic plants grown in Greece were studied. Plants were refluxed with 60% methanol after acid hydrolysis. The phenolic substances were quantified by HPLC-DAD. The antioxidant capacity of the extracts was determined with the Rancimat test using sunflower oil as substrate. Free radical scavenging activity was measured using the stable free radical 1, 1-diphenyl-2-picrylhydrazyl (DPPH). Results were compared with standard BHT and ascorbic acid. Total phenol concentration of the extracts was estimated with Folin-Ciocalteu reagent using gallic acid as standard.