The urban heat island (UHI) becomes more and more serious with the acceleration of urbanization. Many researchers have shown interest in studying the UHI by using remote sensing data. But these studies rarely examine the mountainous cities. The studies on UHI in mountainous cities often used empirical parameters to estimate the land surface temperature (LST), and lacked satellite-ground synchronous experiment to test the accuracy. This paper revised the parameters in mono-window algorithm used to retrieve the LST according to the characteristics of mountainous cities. This study examined the spatial and temporal patterns of the UHI intensity in Chongqing, a typical mountainous city, and its relationship with land cover from 2007 to 2011 based on the Landsat TM data and the improved method. The accuracy of the LST derivation increased by about 1°C compared to the traditional method. The high LST areas increased and extended from the downtown to suburban area each year, but the rate of change decreased. The UHI is dramatically impacted by the rivers. There is a good relationship between the urban sprawl and the UHI. The LST was reduced by about 1°C within a 300m distance from large urban fringe green spaces. The urban landscape parks had a strong effect relieving the UHI at a 100m distance. The LST was reduced by about 0.5°C. The study greatly improves the accuracy of LST derivation, and provides a reliable parameters for the UHI researched in mountainous city.

The City of Pasadena is located in southern California; a region which has a Mediterranean climate and where the vast majority of rainfall occurs between October and April with the period between January and March being the most intense. A significant amount of the local water supply comes from regional rainfall, therefore any changes in precipitation patterns in the area has considerable significance. HYPOTHESIS: Local climate change has been occurring in the Pasadena area over the last 100 years resulting in changes in air temperature and rainfall. AIR TEMPERATURES: Between 1886 and 2016 the air temperature in Pasadena, California has increased significantly, from a minimum of 23.8°C in the daytime and 8.1°C at night between 1911 and 1920 to 27.2°C and 13.3°C between 2011 and 2016. The increase in nighttime temperature was uniform throughout the year, however daytime temperatures showed more seasonal variation. There was little change in the daytime temperatures May through July but more change the rest of the year. For example, the median daytime temperature for June between 1911 and 1920 was 27.9°C but was 28.7°C between 2011 and 2016, a difference of 0.8°C. In contrast, for October for the same periods the median daytime temperatures were 25.6°C and 28.9°C, a difference of 3.3°C. RAINFALL: There has been a change in local rainfall pattern over the same period. In comparing rainfall between 1883 – 1949 and 1950 – 2016, there appeared to be less rainfall in the months of October, December, and April while other months seemed to show no change in rainfall. For example, between the two periods mentioned above, the median rainfall in October was 12.4 mm and 8.9 mm respectively while for December they were 68.6 mm and 40.4 mm. There was comparatively a smaller change in the median volume of rainfall in April (18.8 mm vs. 17.5 mm). However, between 1883 and 2016 there were 13 with less than 1 mm of rain, 12 of which occurred after 1961. In the same line of logic, no measureable amount of rain occurred for 23 Octobers, 15 of those occurred after 1961. CONCLUSION: As air temperatures increased over the last 100 years in the Pasadena area, rainfall may have decreased in October, December, and April.

Security has become a critical issue in today’s highly distributed and networked systems. Network intrusion detection systems (NIDSs), especially signature-based NIDSs, are being widely deployed in a distributed network environment with the purpose of defending against a variety of network attacks. Most of the commercially available NIDSs are software based and rely on pattern matching to extract the threat from network traffic. The increase in network speed and traffic may make existing algorithms to become a performance bottleneck. Therefore it is very necessary to develop faster and more efficient pattern matching algorithm in order to overcome the troubles on performance of NIDSs. Therefore, we propose a multi fusion pattern matching algorithm for Network Intrusion Detection Systems. The results obtained in percentages from the proposed fusion algorithm given better values in terms processing time in milliseconds than the existing algorithms when data English text are applied to evaluate the fusion performances.

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.

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.

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.

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.

Purpose: To examine the effectiveness of patterned laser trabeculoplasty (PSLT) as an additional treatment for patients of open-angle glaucoma (OAG) receiving maximized ocular hypotensive medications (OHM). Methods: Forty eyes of 33 patients (average age 72.7±10.7 years) who had not previously undergone open glaucoma surgery or laser trabeculoplasty and were treated with maximized OHM between June 2018 and March 2022 were included. A 360-degree PSLT was conducted and postoperative intraocular pressure (IOP) and survival curves at 1, 3, 6, 9, and 12 months were evaluated. Results: According to the Kaplan-Meier survival analysis, the average survival time was 8.1 months with death defined as postoperative IOP reduction of less than 10% or requiring additional treatment. The average survival time was 4.9 months with death defined as postoperative IOP reduction of less than 20% or requiring additional treatment. In the 31 eyes which received no additional treatment after PSLT, the mean preoperative IOP was 18.5±3.9 mmHg, which reduced to 15.3±4.1 mmHg (p=1.62 x 10-6)), 15.5±3.4 mmHg (p=1.51 x 10-5), 15.7±4.0 mmHg (p=1.75 x 10-5), 14.7±4.38 (p=2.89 x 10-6), and 15.0±4.0 mmHg (p=5.74 x 10-9) at 1, 3, 6, 9 and 12 months after PSLT, respectively. The IOP reduction rate one year after PSLT was 18.7%. Thirteen of the 31 eyes (42%) achieved a 20% reduction in IOP compared to the baseline. Conclusion: Adjunctive treatment with PSLT in OAG patients receiving maximized OHM may be effective over 12-months of follow-up.

The article presents an innovative method for synthesizing radiation patterns efficiently by 1 combining the Taguchi method and neural networks, while validating the results on a 10-element 2 antenna array. The Taguchi method aims to minimize product and process variability, while neural 3 networks are used to model the relationship between antenna design parameters and radiation 4 pattern characteristics. This approach utilizes Taguchi parameters as inputs for the neural network, 5 which is then trained on a dataset generated by the Taguchi method. After training, the network is 6 validated using a real 10-element antenna array. Analytical results demonstrate that this method 7 enables efficient synthesis of radiation patterns with a significant reduction in computation time 8 compared to traditional approaches. Furthermore, validation on the antenna array confirms the 9 accuracy and robustness of the approach, showing a high correlation between predicted performances 10 by the neural network model and actual measurements on the antenna array.In summary, our article 11 highlights that the combined use of the Taguchi method and neural networks, with validation on a 12 real antenna array, offers a promising approach for efficient synthesis of antenna radiation patterns. 13 This approach combines speed, accuracy, and reliability in antenna system design.

Oncolytic virotherapy is a promising immunotherapy approach for cancer treatment, utilizing viruses to preferentially infect and eliminate cancer cells while stimulating immune responses. In this review we synthesize current literature on the molecular circuits of immune sensing and response to oncolytic virotherapy; focusing on viral DNA or RNA sensing by infected cells, cytokine and danger-associated signal sensing by neighboring cells, and subsequent downstream activation of immune pathways. The sequential sense-and-response mechanisms involve the triggering of molecular sensors by viruses or infected cells to activate transcription factors and related genes for a breadth of immune responses. We describe how molecular signals induced in the tumor upon virotherapy can trigger diverse immune signaling pathways, activating both antigen-presenting cell-based innate and T cell-based adaptive immune responses. Insights into these complex mechanisms provide valuable knowledge for enhancing oncolytic virotherapy strategies.

Recanalization poses challenge in coil embolization for cerebral aneurysms. Establishing predictive models for post-embolization recanalization is important for clinical decision-making. However, conventional statistical and machine learning (ML) models may overlook critical parameters during the initial selection process. In this study, we automated the identification of significant hemodynamic parameters using a PointNet-based deep neural network (DNN), leveraging their inherent 3D features. Further feature analysis was conducted utilizing saliency mapping, an explainable artificial intelligence (XAI) technique. The study encompassed the analysis of velocity, pressure, and wall shear stress (WSS) in both pre- and post-coiling models derived from computational fluid dynamic (CFD) simulations for 59 aneurysms. Velocity was identified as the most pivotal parameter, supported by the lowest P-value from statistical analysis and the highest area under the curves (AUROC)/precision-recall curves (AUPRC) values from DNN model. Moreover, visual XAI analysis revealed robust injection flow zones with notable impingement points in pre-coiling models, as well as pronounced interplay between flow dynamics and the coiling plane were important 3D features in identifying the recanalized aneurysms. The combination of DNN and XAI was found to be an accurate and explainable approach not only at predicting post-embolization recanalization but also at discovering unknown features in the future.

Since there are no morphological clues capable of making a pathologist suspect a possible mammary origin of a metastatic lesion without adequate clinical information, the histologic diagnosis of brain metastasis from BC is still based on the immunohistochemical expression of mammary gland markers such as GATA-3, ERs, PgRs and HER-2. The present retrospective study aimed to select purely morphological features capable to suggest the mammary origin of a metastatic carcinoma to the brain. The following histological features were collected on a series of 30 cases of brain metastases from breast cancer:(i) solid growth pattern; (ii) presence of comedonecrosis; (iii) glandular differentiation. Our results showed that most cases histologically exhibited solid growth pattern with at least focal comedonecrosis, producing an overall morphology closely reminiscent of mammary high-grade ductal carcinoma in situ. Although the above-mentioned morphological parameters are not strictly specific to a mammary origin, they may have an important diagnostic utility in leading pathologists to suspect a possible breast primary tumor and to include GATA-3, ERs, PgRs and HER-2 to the immunohistochemical panel.

The popularity of online retail is followed by decreasing transactions at shopping centres. Leisure and companionship trips to restaurants and cafés are replacing the frequency of intra-urban shopping trips. Several studies demonstrated a notable trend in urban commercial areas, wherein functionality has shifted from shopping centres to primarily focused on restaurants and cafes. Leisure and companionship places in the urban south have greater diversity than formal restaurants and cafés. This article employs a comprehensive framework to examine the categorization of restaurants and cafés in the urban south, referred to as food services. This study aims to investigate the spatial pattern of food service in Surabaya and ascertain whether there is a concentration of all food service variants around existing shopping centres. This research employs the POI of food service units gathered through web scraping from Google Maps. Applying the k-means cluster analysis demonstrates that the spatial pattern of food services is categorized into several distinct clusters. This article discloses five types of urban spaces characterized by varying compositions of food service variants within them. These spaces include the city centre, sub-city centre, areas along main roads, residential neighbourhoods, and peri-urban areas.

In the investment casting process, the pattern made of wax is obtained in a die for further formation of shell mold. This work investigates commercial pattern waxes fluidity with a newly developed injection fluidity test. A good correlation between wax fluidity and its viscosity is observed, which is different from metallic alloys, where the solidification behavior is more critical. The difference in investigated filled waxes fluidity can be associated with the type and amount of filler.

The time when a patient has mild cognitive impairment (MCI) is an important period for delaying the onset of dementia and has been the main target of dementia prevention research in recent years. The purpose of this study was to investigate the dietary intake and dietary patterns of MCI patients in Taiwan. In total, 40 subjects were enrolled in this cross-sectional study that was conducted from July 2019 to September 2021 at Linkou Chang Gung Memorial Hospital. Results of the clinical dementia rating (CDR) and mini-mental state examination (MMSE) were obtained from medical records. Participants were divided into two groups: a healthy group (MMSE ≥ 26 points, CDR=0) and an MCI group (MMSE ≥ 26 points, CDR=0.5). Results indicated that the MCI group had significantly higher copper and lower low-fat meat intake compared to the healthy group. Furthermore, the high MIND (Mediterranean-dietary approaches to stop hypertension intervention for neurodegenerative delay) diet score represented a lower risk of MCI. After adjusting for age and gender in the multivariate regression analysis, calcium and fruit intake levels were positively associated with the MMSE. In conclusion, the prevalence of MCI was closely associated with dietary intake, such as copper and calcium, as well as dietary patterns, particularly the MIND diet.

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predict eventual BCR with an AUC of 0.97 and classifying cancer at an accuracy of 89.9%. This study showcases the application for a radiomics-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

With the development of free trade port in Hainan Island, the construction of tourist roads around the island is also in full swing. Under the weather conditions of strong typhoon and rainstorm in Hainan, the highway cutting slope built on the coastal weak sandy terraces has strong sand loss and is easy to be scour by rainfall. MICP green spray irrigation solidification technology is used to strengthen the sandy cutting, and pore water pressure monitoring is carried out on the slope model during MICP solidification and rainfall scour. Combined with the model pore water pressure and flow slip failure pattern, dynamic analysis was carried out. The results show that MICP sprinkler irrigation technology can solidified the surface of the slope model in a short time, and the cementation depth of the model reaches 4cm after three sets of rotation reinforcement. The surface reinforcement effect is good, and the sand samples are closely connected. Under the erosion effect of simulated rainfall intensity, the sand loss of the slope is weakened, and no sand binding damage occurs, and the integrity is enhanced. Due to the cementation between sand grains, most of the rainfall was converted into runoff, and the slope slid after 150s. When the slope began to slip, the leading edge of the slope model lost sand and unloaded, and the failure mode was graded creep slip failure. Finally, the slope was divided into several blocks due to the continuous expansion of cracks after the slope failure. The erosion stability of the sandy slope under heavy rains is optimized and the sand loss is prevented effectively. In this study, a new method of MICP remediation techniques is proposed, which provides a new test basis for the application of MICP technology in practical engineering.

Healthy eating behaviour of women is critical not only for their health but also for their children's health and well-being. The present study examined the dietary pattern, nutrition knowledge and attitudes of working women in Western Province, Sri Lanka. In addition, identify the factors associated with Dietary Diversity Score (DDS). A cross-sectional study was conducted among 300 working women, aged 20-60 years, in Western Province, Sri Lanka. The data on so-cio-demography, dietary patterns, and nutrition-related knowledge and attitudes were collected. Overall, 38% of the women were overweight and 13% were obese. The median frequency of intake of chicken, fish, eggs, milk and milk products, green leafy vegetables and fruits were 2, 5, 2, 9, 5 and 10 respectively, per week. A large majority of the women (70%) had tea/coffee with sugar and snacks (60%) at least four times a week. Only a third of the women met the minimum DDS, while more than half of the women had good nutrition-related knowledge and attitudes. Multiple regression analysis showed that age and household income were significantly independently related to DDS, while attitudes were negatively associated. While there was a trend, the association of nutrition-related knowledge with DDS was not statistically significant (p=0.057). The overall F ratio (8.46) was highly significant (p=0.000) and the adjusted R2 was 0.093. The results demonstrated that a significant proportion of working women have good basic nutrition-related knowledge and attitudes, while two-thirds of them do not meet the minimum DDS. Furthermore, age, family income and knowledge were positively associated with DDS, while attitudes were negatively associated. Before designing any intervention, further research is needed using a qualitative approach to understand how nutrition knowledge and eating behaviour are related in this population group.

Background: Streptococcus agalactiae or Group B Streptococcal colonization of the gastrointestinal and genital tracts of pregnant women usually remains asymptomatic; even if it is the critical determinant of infection in neonates and young infants. It causes early and late onset of invasive Group B Streptococcus (GBS) disease manifesting as septicemia, meningitis and pneumonia. Now it is recognized as an important cause of maternal and neonatal morbidity and mortality in many parts of the world including Ethiopia where the magnitude of the problem has been little studied. The aim of this study was to assess the prevalence of GBS colonization, to identify associated risk factors and antimicrobial susceptibility pattern among pregnant women at selected health facilities of Wolaita Sodo Town, Southern Ethiopia. Methodology: A health facility based cross-sectional study design was conducted at WSUCSH & Wolaita Sodo Health Center from June to August, 2022. A total of 279 pregnant women who were in ANC follow up with at 35-37 weeks of gestation were included. For GBS isolation, recto-vaginal swabs were inoculated in 1ml Todd-Hewitt broth medium supplemented with 10μg/ml colistin and 15μg/ml nalidixic acid and followed by identification of isolates based on colonial morphology, gram stains, catalase reaction and CAMP tests. Antimicrobial susceptibility testing was performed using modified Kirby–Bauer disk diffusion method. All collected data were organized in Epi info 4.6.0.2, then transfer tabulated using SPSS version 20. Logistic regression analysis was used to see the association between variables. Finally, the p-value < 0.05 were considered statistically significant. Results: In present study, 279 pregnant mothers included and their age was between 15 to 38 years with a mean of 26.5 ± 4.5 years. Of all participants, the highest 120 (43.01%) were housewives. The overall carriage rate of GBS was 67(24.0%). GBS colonization showed a statistically significant association with college and above levels of maternal education [AOR= 6.610, 95% CI (1.724 - 25.349), P=0.01]. High susceptibility of GBS isolate was seen to Penicillin G & Chloramphenicol (92.5%) for each, Ampicillin, and Ceftriaxone (89.6%) each, following Vancomycin (74.62%), and Erythromycin (77%). Relatively, GBS showed high resistance to Tetracycline (88 %). Conclusion: In this study, the overall prevalence of GBS colonization was 24.0%. College and above educational level was statistically significant with GBS colonization. This study used to give attention to the management of pregnant women by making GBS culture one of the routine diagnoses during ANC follow-up and to prevent infection by early detection.

Ground penetrating radar (georadar, GPR), widely used in geology, archeology and road construction, is an efficient tool for searching and studying subsurface objects and structures. When planning GPR missions, it is necessary to predict the resolution of the device and the achievable probing depth. The article discusses the methods and results of assessing these characteristics of the Loza-V and Loza-N radars obtained in the course of archaeological and geographical expedition works.

The agricultural sector is undergoing a revolution that requires sustainable solutions to the challenges that arise from traditional farming methods. To address these challenges, technical and sustainable support is needed to develop projects that improve crop performance. This study focuses on the onion crop and the challenges presented throughout its phenological cycle. Aerial monitoring using unmanned aerial vehicles (UAV) and digital image processing were used to identify patterns in the onion crop, including humid areas, weed growth, vegetation deficits, and decreased harvest performance. An algorithm was developed to identify the patterns that most affected crop growth, as the average local production reported was 40.166 ton/ha, but only 25.00 ton/ha was reached due to blight caused by constant humidity and limited sunlight. This resulted in the death of leaves and poor development of bulbs, with 50% of the production being of medium size. It is estimated that approximately 20% of the production was lost due to blight and unfavorable weather conditions.

Considering the current needs of industry 4.0, especially in the condition of systems linked to artificial intelligence, this multidisciplinary concept work and difficult to execute, proposes the main idea of recognizing patterns using mathematical concepts of simple access and logical resolution of great potential. The objective of this work is to demonstrate a process for pattern recognition by applying the Fibonacci concept to vibration signals of hybrid dynamical systems. For this to be possible, it was necessary to use the concepts of preprocessing in a vector for pattern recognition, together with the concepts of the golden rule of Fibonacci, finally, all applied in the theorem of Jeffcott Rotors. The general result, from a technical point of view, demonstrates positivity and relationship of patterns, because, considering the aspect of the location between the values of the experimental and numerical result, both signs have similarities.

(1) Background: Foot strike pattern and cadence are two important variables that are related to sport performance and injury risks; the objective of this study is to analyse the foot strike pattern and cadence of the male elite athlete that participated in the Tokyo Olympic Games marathon. (2) Methods: Two independent researchers analysed the foot strike pattern of the first 51 participants in the 5km, and the cadence of the finalists in three different segments of the race. Descriptive statistics were presented for the main variables of this study. A repeated measures ANOVA was used to study the differences in cadence between different sections of the race (p&lt;0.05). (3) Results: Average of the cadence of the eight finalists was 185.5 steps per minute (SD ± 5.1). No differences between sections of the race were found. The more most common foot strike pattern was midfoot strike pattern, followed by rearfoot strike pattern, and forefoot strike pattern. The results of cadence are in line with previous studies that analysed elite athletes, with the values being higher than other research involving recreational runners. (4) Conclusions: elite marathon runners mostly run with a running non-rearfoot strike pattern and a cadence of over 185 steps per minute.

As Christopher Alexander discovered, all space or matter – either organic or inorganic – has some degree of order in it according to its structure and arrangement. The order refers to a kind of structural character, called living structure, which is defined as a mathematical structure that consists of numerous substructures with an inherent hierarchy. Across the hierarchy, there are far more small substructures than large ones, while on each level of the hierarchy the substructures are more or less similar in size. In this paper we develop a new approach to representing geographic space as a hierarchy of recursively defined subspaces for computing the degree of order. A geographic space is first represented as a hierarchy of recursively defined subspaces, and all the subspaces are then topologically represented as a network for computing the degree of order of the geographic space, as well as that of its subspaces. Unlike conventional geographic representations, which are mechanical in nature, this new geographic representation is organic, conceived, and developed under the third view of space; that is, space is neither lifeless nor neutral, but a living structure capable of being more living or less living. Thus, the order can also be referred to as life, beauty, coherence, or harmony. We applied the new representation to three urban environments, 253 patterns, and 35 black-white strips to verify it and to demonstrate advantages of the new approach and the new kind of order. We further discuss the implications of the approach and the order on geographic information science and sustainable urban planning.

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating this data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited to painless future expansion. Availability:https://glyconnect.expasy.org/glystreem/wiki

Universe created with the fundamental laws of science. Nature is lazy and needs to form with the least possible to be perfect. A natural pattern, such as pinecones, sunflowers, pineapples, and cacti, has a double spiral structure. Once we look at these plants' centers, we will see the seeds line up in spirals shape. The number of spirals whirling in each direction will give us the Fibonacci numbers. We can give more examples representing these natural patterns; however, one example is unique and remarkable. The similarities between spiral galaxies- Milky Way and hurricanes. Are they similar in every property or just in shape and rotational movements? What are the similarities between them? This short review article will try to find these questions' answers by reviewing some literature articles. The first part of this article gave some information about hurricanes and galaxies. The second of this article focused on the comparison between hurricanes and galaxies. Finally, we will conclude the article with our remarks.

As in glaucoma and other optic neuropathies cellular dysfunction often precedes cell death, sensitive assessment of retinal ganglion cell (RGC) function represents a key outcome measure for neuroprotective strategies aimed at targeting distressed but still viable cells. Here we offer a conceptual framework to identify progressive stages of RGC dysfunction leading to cell death in mouse models of glaucoma and other optic neuropathies based on non-invasive pattern electroretinogram (PERG), to differentiate phenotypic and altered RGC response dynamics, to assess susceptibility to stressors and to assess reversible dysfunction.

Objectives The most severe type of peripheral arterial disease (PAD) is critical limb ischaemia (CLI). In CLI, calcification of the vessel wall plays an important role in symptoms, amputation rate and mortality. However, calcified arteries are also found in asymptomatic persons (non-PAD patients). We investigated whether the calcification pattern in CLI patients and non- PAD patients are different and could possibly explain the symptoms in CLI patients. Materials and Methods 130 CLI and 204 non-PAD patients underwent a CT of the lower extremities. This resulted in 118 CLI patients (mean age 72±12, 70.3% male) that were age-matched with 118 non-PAD patients (mean age 71±11, 51.7% male). The characteristics severity, annularity, thickness and continuity were assessed in the femoral and crural arteries and analysed by binary multiple logistic regression. Results Nearly all CLI patients have calcifications and these are equally frequent in the femoropopliteal (98.3%) and crural arteries (97.5%), while the non-PAD patients had in just 67% any calcifications with more calcifications in the femoropopliteal (70.3%) than in the crural arteries (55.9%, p<0.005). The crural arteries of the CLI patients had significantly more complete annular calcifications (OR 2.92, p=0.001.) while in the non-PAD patients dot-like calcifications dominated. In CLI patients, the femoropopliteal arteries had more severe, irregular / patchy and thick calcifications (OR 2.40, 3.27, 1.81, p≤0.05, respectively) while in non-PAD patients, thin continuous calcifications prevailed. Conclusions Compared with non-PAD patients CLI patients are more frequently and extensively calcified. Annular calcifications were found in the crural arteries of CLI patients while dot-like calcifications were mostly present in the non-PAD patients. These different patterns of calcifications in CLI point at different etiology and can have prognostic and eventually therapeutic consequences.

Motivated by the way animals react in nature, we introduce a fully-connected neural-network model, which incorporates the external pattern presented to the system as a fundamental tool of the recognition process. In this new scenario, in the absence of this external field, memories are not attractors inside basins of attraction, as in usual attractor neural networks, although basins may be created according to the external pattern, thus allowing storing a much larger number of memories. The key point consists in calibrating the influence of the external pattern such as to cancel the noise generated by those memories not correlated with the external pattern.We illustrate how this proposal works by including this new contribution in the standard Hopfield model, showing a significant increase in its recognition capacity (typically by a factor $10^{2}$). As an additional feature of this model, we show its ability to react promptly to changes in the external environment, a crucial attribute of living beings. This procedure can be applied to a wide variety of neural-network models to increase considerably their recognition and reaction capabilities.

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the identification of characters, compound characters, and word. This study proposes the use of the deep convolutional neural network with dropout to recognize the Brahmi words. This study also proposed a DCNN for Brahmi word recognition and a series of experiments are performed on standard Brahmi dataset. The practical operation of this method was systematically tested on accessible Brahmi image database, achieving 92.47% recognition rate by CNN with dropout respectively which is among the best while comparing with the ones reported in the literature for the same task.

The prevalence and case fatality rates of Pediatric Lassa fever disease (LFD) are not well documented. This study was aimed at determining the prevalence, pattern and outcome of Pediatric LFD. It was a prospective observational study. A total of 183 subjects that met the criteria for LFD suspects were recruited consecutively and subjected to Lassa virus PCR test. Structured questionnaire was used to collect information. Of the 183 children recruited, 24 tested positive to Lassa virus PCR, giving a positivity rate of 13.1%. Mean duration of illness at presentation was 8.54 ± 3.83 days. Fever, abdominal pain and vomiting were the three highest presenting complaints. Seven out of 24 children died giving a case fatality rate (CFR) of 29.2%. Subjects with bleeding, poor urine output, convulsions and unconsciousness were more likely to die of LFD. Positivity and CFR of LFD are high. Improved case finding and prompt treatment is advocated.

Traditionally, neutron scattering is an essential method for the analysis of spin structures and spin excitations in bulk materials. Over the last 30 years, polarized neutron scattering in terms of reflectometry has also contributed largely to the analysis of magnetic thin films and magnetic multilayers. More recently it has been shown that polarized neutron reflectivity is, in addition, a suitable tool for the study of thin films laterally patterned with magnetic stripes or islands. We provide a brief overview of the fundamental properties of polarized neutron reflectivity, considering different domain states, domain fluctuations, and different domain sizes with respect to the neutron coherence volume. The discussion is exemplified by a set of simulated reflectivities assuming either complete polarization and polarization analysis, or a reduced form of polarized neutron reflectivity without polarization analysis. Furthermore, we emphasize the importance of the neutron coherence volume for the interpretation of specular and off-specular intensity maps, in particular when studying laterally non-homogeneous magnetic films. Finally, experimental results, fits, and simulations are shown for specular and off-specular scattering from a magnetic film that has been lithographically patterned into a periodic stripe array. These experiments demonstrate the different and mutually complementary information that can be gained when orienting the stripe array parallel or perpendicular to the scattering plane.

Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data harvesting and analysis for numerous plants, which is automatable using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Models of sections, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections. F1 measure in automated spike classification into three types is 0.78 using logistic regression (one projection) and 0.85 using random forest method (four projections). The proposed method is implemented in Java; examples of images and user guide are available at http://wheatdb.org/werecognizer.

The acoustic emission (AE) technique was applied to monitor the pitting corrosion of carbon steel in NaHCO₃ + NaCl solutions. The open circuit potential (OCP) measurement and the corrosion morphology in-situ capturing using optical microscope were conducted during AE monitoring. The corrosion micromorphology was characterized with scanning electron microscope (SEM). The propagation behavior and AE features of natural pitting on carbon steel were investigated. After the performing of signal processing including pre-treatment, shape preserving interpolation and denoising for raw AE waveforms, three types of AE signals can be classified in the correlation diagrams of new waveform parameters. Finally, a 2D pattern recognition method was established to calculate the similarity of different continuous AE graphics, which is quite effective to distinguish the localized corrosion from uniform corrosion.

In this paper we present an efficient technique for the synthesis of very large sparse arrays, with arbitrary circularly symmetrical upper bounds for the pattern specifications. The algorithm, that is based on iterative smooth re-weighted L1 minimizations is very flexible, and is capable of achieving very good performances with respect to competitive algorithms. Furthermore, thanks to its efficiency, planar arrays of hundreds of wavelengths can be synthesized with limited computational effort.

Dynamic laser speckle is applied as a reliable sensor of activity in all sort of material. Traditional applications are based on a time rate that is usually higher than 10 frames-per-second (FPS). Even in drying processes, where there is a high activity in the first moments after the painting and a slow activity after some minutes or hours, the process is based on the acquisition of images in a time rate that is the same in both moments of high and low activity. In this work, we present an alternative approach to follow the drying of paint and the other processes related to restauration of paintings that takes long-term to reduce the activity. We illuminated, using three different wavelength lasers, an accelerator (Cyclododecane) and a varnish used in restauration of paintings and monitor them at long-term drying using an alternative fps, comparing the results to the traditional method. The work also presents a way to do the monitoring using a portable equipment. The results present the feasibility to use the portable device and show the improvement in the sensitivity of the dynamic laser speckle to sense long-term process regarding the drying of Cyclododecane and Varnish used in restauration.

A pump station is a very important flood control facility for mitigating inundation of urban lowland areas. It is not easy to secure a site to increase the capacity of a pump station in an urban area because of various limitations or to maximize the discharge capacity of the pump sump. Adding a facility to improve the pump capacity of a pump station may affect the flow characteristics, such as unexpected increases in the flow velocity and vorticity, and cause severe problems with operating the pump station. To solve those problems, anti-vortex devices (AVDs) have been developed and adopted for appropriate design standards. The Korean design criteria for AVDs are based on experience and foreign standards because not enough data on the AVD are available. In this study, the flow in the sump was numerically simulated at various AVD heights to collect information on improving the design and efficiency of a pump station. Consequently, an appropriate height for the AVD and changes in the flow pattern and vortex in the pump sump were determined and compared with 12 cases of inflow conditions with respect to the vertical location.

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 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.

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.

Recent climate change and water pollution has affected algal diversity resulting in emergence of more tolerant species. They can be the indicators of water quality, pollution and seasonal changes. Present study revealed the green algal diversity and distribution pattern according to water parameters and season in the Lucknow City, India. The water sample were collected from seven sites within Lucknow and outskirts. The occurrence of a total of 36 taxa of green algae belonging to 11 genera are recorded. It has been observed that less polluted water is dominated by Spirogyra; where conductivity, TDS and salinity of water were low. Whereas the more polluted water was dominated by desmids. Dominance index was found highest in site 4 (0.61), where only three taxa were identified; whereas the lowest (0.13) was measured in site 1, where nine taxa were identified. This study also recorded several algal taxa for the first time from Lucknow.

With recent advancements in network technology and spread of Internet, use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges occur over time. Additionally, owing to the need for efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time.

: Human settlements have historically thrived near rivers for navigation, trade, and availability of water supply and resources. Night light data, representing economic activities provide a novel approach to studying the interactions between human activity and rivers over time. Here, we use the Defense Meteorological Satellite Program (DMSP) stable night light data from 2000-2013 as a proxy for human presence and activities to quantify the statistical relationships between night light presence and intensity in the Indus Basin, Asia. We test how these data are affected by proximity to trunk channels and by channel type (single/multi-thread) in the study area. We find that night light presence is enhanced by 26% within a 0 to 5 km proximity range of the Indus River and its tribu-taries, relative to the basin as a whole. We interpret this to represent increased human presence and activity within this zone. However, the mean intensity is lower near the river and higher away from the river signifying denser settlements, such as towns and cities which are preferentially located away from the Indus and its tributaries. Moreover, the enhancement of lit pixels signifying human presence and activities is increased by 18% near single-thread sections of the Indus River, compared to segments of the Indus displaying multi-thread morphologies. We suggest that this is due to the enhanced stability of single-threaded channels, relative to mobile multi-threaded channel reaches. This study demonstrates how night lights are an important tool to constrain the relationship between human presence and river dynamics in large catchments such as the Indus, and we suggest this data will have an important role in assessing differential flood spatial and social vulnerability at a regional scale.

Mountain ecosystems are crucial for global biodiversity conservation. However, their landscape features are constantly changing owing to urban expansion. Understanding the relationships between biotic communities and landscape features is essential for biodiversity conservation. This study aimed to examine the effect of land-cover type on avian communities in Lishui, a mountainous urban area in eastern China. Avian surveys were conducted using a total of 168 line transects across different land-cover types once per season from December 2019 to January 2021. We assessed the diversity of bird communities by calculating various metrics at both taxonomic and functional levels. Among the land-cover types measured, woodland, built-up land, cultivated land, and water bodies significantly influenced bird community diversity and composition. Species richness, species abundance, and functional richness were negatively correlated with the proportion of woodland but positively correlated with the proportion of non-natural land cover, such as built-up and cultivated land. In contrast, functional evenness was positively correlated with the proportion of woodland and grassland but negatively correlated with the proportion of non-natural land cover. Land-cover type also exhibited significant correlations with avian functional characteristics such as diet, foraging strata, and body mass, thereby influencing the overall community structure. Our results indicated that mountainous landscape patterns substantially affect avian communities. Different land-cover types possess varying resource endowments that affect the distribution of avian species. Therefore, urban landscape planning in mountainous areas should carefully consider the various functions provided to organisms by different types of land cover to promote biodiversity.

Background: Poisoning is a challenging medical condition facing health care providers, as it usually results in serious outcomes in terms of morbidities or mortalities. Poisoning is a challenging medical condition facing health care providers, as it usually results in serious outcomes in terms of morbidities or mortalities. The lack of or inadequate information about the circumstances, substances and populations at risk are considered barriers to the actual prevention of poisoning and effective intervention strategies in Saudi Arabia. The aim of the current study was to describe the pattern and determinants of chemical and drug overdose poisoning in Makkah.Subjects and methods: All reports of chemical and drug overdose poisoning delivered to the Environmental Health and Occupational Safety Department in Makkah over two years (2018-2019) were reviewed.Results: The total number of cases reported in 2018 was 209 cases, while it reached 42 cases in 2019. The overwhelming majority of the cases were Saudi (91.6%), with a slight predominance of males (57.4%) over females (42.6%). Generally, almost two-thirds of the cases occurred in children below 13 years (61.4%), most of the poisoning cases resulted from drug overdose (63.3%), while 34.7% were chemical poisoning. The most common drug poisoning was attributed to analgesics followed by antiepileptics, antihypertensive drugs, antipsychotics and antimicrobials, while cleaning and detergent agents were the most commonly poisoned chemicals. The majority of these agents were taken by the oral route. Children's chemical poisoning was significantly higher than that in older age groups (p<0.05). All intentional poisoning occurred in adults; the great majority of them (88.9%) used drug overdosing rather than chemical substances.Conclusion and recommendations: The current study results are consistent with most of the previous studies carried out in different regions of Saudi Arabia. Further in-depth studies are needed to elaborate other social and demographic factors associated with chemical and drug overdose poisoning. Public health plans, policies and legislations should be implemented to reduce these factors. Intensive supervision of children is needed. Suicidal poisoning patients should seek psychiatric help to limit the possibility of attempting suicide again in the future. Furthermore, efforts are needed to plan and implement health education programs about the factors proven in the current study to be significantly related to chemical and drug overdose poisoning.

A drinking strategy aiming to replace a given percentage of the sweat losses incurred during exercise should result in reproducible fluid intake volume and, hence, fluid balance from one exercise session to the other performed under similar scenarios. Whether this may also be the case with ad libitum drinking during exercise is unclear. We characterized the repeatability of ad libitum fluid intake during repeated 1 h exercise sessions and examine its effect over time on fluid balance and selected physiological functions and perceptual sensations. Twelve (3 women) healthy individuals participated in this study. At weekly intervals, they completed 4, 2 x 30 min walking/jogging exercise bouts (55% VO2max, 40°C, 20-30% relative humidity) interspersed by a 3 min recovery period. During exercise, participants replaced water (20°C) ad libitum. There were no significant differences among the 4 exercise sessions for absolute fluid intake volume (~ 1000 mL · h-1), percent body mass loss (~ 0.4%), sweat rate (~ 1300 mL · h-1) and percent of sweat loss replaced by fluid intake (~ 80%). Heart rate, rectal temperature and perceived thirst and heat stress did not differ significantly between the first and fourth exercise session. Perceived exertion was significantly lower during the fourth vs. the first exercise session, but the difference was trivial, &lt; 1 arbitrary unit. In conclusion, ad libitum fluid intake during 4 successive identical 1 h walking/jogging sessions conducted in the heat will result in similar fluid intake volumes and perturbations in fluid balance, heart rate, rectal temperature and perceived thirst, heat stress and exertion.

The change of habitat pattern is one of the key factors affecting the survival of moose population. The study of habitat landscape pattern is the key to protect Chinese cold temperate forest moose population and monitor the global distribution of moose. By means of MaxEnt model, landscape index calculation and ecological risk assessment model, combined with field survey and infrared camera monitoring data from April 2014 to January 2023, the author evaluated the habitat suitability of moose population in Nanwenghe National Nature Reserve of the Great Khingan Mountains, and divided the range of moose habitat based on the logical threshold of the model. The landscape pattern index of moose habitat was calculated by Fragstats software and a landscape ecological risk assessment model was established to analyze the landscape pattern and ecological risk dynamic changes of moose habitat in 2015 and 2020. The results showed that under the premise of global warming, the habitat landscape contagion index decreased by 4.53 and the split index increased by 4.86 from 2015 to 2020. In terms of ecological risk: the area of low ecological risk areas increased by 0.88%; the area of medium ecological risk areas decreased by 1.11%; and the area of higher ecological risk areas increased by 0.23%. The fragmentation risk of landscape pattern of moose habitat tends to increase, the preferred patch type is dispersed, the degree of aggregation is low, and the risk of patch type transformation increases. And the middle and high ecological risk areas are mainly concentrated in the river area and its nearby forests, showing a fine and scattered distribution. Under the interference of global warming and human activities, the fragmenta-tion trend of moose habitat in the study area is increasing, and the habitat quality is declining, which is likely to cause moose population migration. For this reason, the author believes that the whole cold temperate forest is likely to face the risk of increasing the transformation trend of dominant patch types in the cold temperate coniferous forest region mainly caused by global warming, resulting in an in-crease in the risk of habitat fragmentation. While the distribution range of moose is reduced, it has a significant impact on the diversity and ecological integrity of the whole cold temperate forest ecosystem. This study is helpful for human beings to strengthen their awareness of forest and river protection, avoid further intervention in more human activities, and formulate a reasonable plan for forest protec-tion and sustainable development in cold temperate zones. to provide theoretical reference for effective monitoring and protection of cold temperate forest and moose population dynamics.

In offshore drilling, accidents such as gas invasion, overflow, and kicks are inevitable, and they can escalate into blowouts and other catastrophic events that result in casualties and substantial economic losses. Therefore, maintaining drilling safety requires accurate monitoring of gas invasion and overflow. The majority of overflow monitoring methods currently utilized at drilling sites are threshold-based. However, monitoring parameters acquired during actual drilling operations frequently contain noise signals, making it difficult for threshold-based methods to strike a balance between improving accuracy and minimizing false positives. In this paper, Pattern Recognition-based Kick Detection (PRKD) is proposed as a novel method for diagnosing overflow in offshore drilling. This method utilizes the overflow evolution process by integrating multi-phase flow calculations, data filtering theory, pattern recognition theory, the Bayesian framework, and other theoretical models. The PRKD effectively detects and monitors gas intrusion and overflow based on single parameters by analyzing the shape and wave characteristics of the curves. The case analysis demonstrates that the proposed method for monitoring drilling overflow achieves high precision while maintaining a low false positive rate. By combining advanced computational techniques with pattern recognition algorithms, the PRKD improves the accuracy and reliability of kick detection, enabling proactive responses to potential risks, protecting the environment and human lives, and optimizing drilling operations.

Most research findings in Ghana and all over the globe lay much emphasis on the technical knowledge of plastics; especially giving preference to the biodegradable over the synthetic/Single-Use plastics. Questionnaires were administered to about 200 respondents in the Ho Municipality out of which 163 responses were filled and returned. Results showed that of all the variables tested only Legislative Framework has a meaningful impact on the behaviour of Ho indigenes. Awareness level: (Synthetic Plastics and Biodegradable Plastics) as well as Attitudinal Change do not influence people’s behaviour when using plastics. Findings from this study are further discussed and recommendations were proffered to instil eco-friendly behaviour among people.

In this work, we consider two SIR patterns with demography: the classical pattern and a modified pattern with a linear transmission coefficient of the infection. By reformulating of each first order differential systems as a system with two second-order differential equations, we investigate the nonlinear dynamics of the system from the Jacobi stability point of view by using the KCC geometric theory. We will study the intrinsic geometric properties of the systems by determining the geometric associated objects: the zero-connection curvature tensor, the nonlinear connection, the Berwald connection, and the five KCC invariants: the first invariant - the external force εi, the second invariant - the deviation curvature tensor Pji, the third invariant - the torsion tensor Pjki, the fourth invariant - the Riemann-Christoffel curvature tensor Pjkli, and the fifth invariant - the Douglas tensor Djkli. In order to obtain necessary and sufficient conditions for the Jacobi stability near the equilibrium points, the deviation curvature tensor will be determined at each equilibrium points. Furthermore, we will compare the Jacobi stability with the classical linear stability, inclusive by diagrams related to the values of parameters of the system.

Background: The AWaRe tool was set up by the World Health Organization (WHO) to promote the rational use of antimicrobials. Indeed, this tool classifies antibiotics into four groups, Access, Watch, Reserve and not-recommended antibiotics. In Republic Democratic of Congo, data on antibiotic dispensing (prescribing) by health professionals according to the AWaRe classification are scarce. In this research work, we aimed to explore antibiotic dispensing pattern from health professionals according to the WHO AWaRe classification to strengthen the national antimicrobial resistance plan. Methods: For this purpose, a survey was conducted from July to December 2022 in the district of Tshangu in Kinshasa. From randomly selected drugstores, drug-sellers were interviewed and randomly selected customers attending those drugstores were included in the study for medical prescriptions collection. The prescribed antibiotics were classified into the Access, Watch, Reserve and not recommended antibiotics group and by antibiotics number by prescription among pharmacies surveyed. Results: Of 400 medical prescriptions collected, 301 (75.25%) contained antibiotics. Of 301 prescriptions containing antibiotics, 164 (164/301; 54.5%) contained one antibiotic, 117 (117/301; 38.9%) two antibiotics, 15 (15/301; 5%) three antibiotics and 5 (5/301; 1.6%) 4 antibiotics. Out of the total of 463 antibiotics prescribed, 169 (169/463; 36.5%) were from the Access group, 200 (200/463; 43.2%) from the Watch group and 94 (94/463; 20.3%) from not recommended antibiotics group, respectively. Based on the anatomical, therapeutic and chemical (ATC) classification, it can be seen that third generation cephalosporins contained 34.33% of the prescribed antibiotics, followed by penicillins 17.17%, macrolides 7.63%, aminoglycosides 7. 36% and Imidazoles 7.36%, thus accounting approximately for 74% of the classes of antibiotics prescribed. Of the 463 antibiotics prescribed, the most frequently prescribed antibiotics were Ceftriaxone (21.38%), Amoxicillin (11.01%), Gentamycin (5.61%), Amoxicillin-clavulanic acid (5 .61%), Azithromycin (4.97%) and Metronidazole (4.75%), thus accounting for approximately 54% of all antibiotics prescribed. Conclusion: These results highlight the importance of strict implementation of the national plan to combat antimicrobial resistance and the need to train health workers in the correct application of the WHO AWaRe classification.

Many studies have focused on the impacts of rainfall duration and intensity while overlooking the role of rainfall patterns on intensive tillage erosion in hilly agricultural landscapes. The objective of this study was to determine the combined effects of rainfall patterns and intensive tillage erosion on surface runoff and soil loss on sloping farmland in the purple-soil area of China. Five simulated rainfall patterns (constant, rising, falling, rising-falling, and falling-rising) with the same total precipitation were designed, and the intensive tillage erosion treatment (IT) and no-tillage treatment (NT) were subjected to simulated rainfall using rectangular steel tanks (2 m × 5 m) with a slope of 15°. To analyse the differences in the hydrodynamic characteristics induced by tillage erosion, we calculated the flow velocity (V), Reynolds number (Re), Froude number (Fr), and Darcy-Weisbach resistance coefficient (f). The results indicate that significant differences in surface runoff and soil loss were found among different rainfall patterns and stages (P < 0.05). The falling pattern and falling-rising pattern had faster runoff-initiating times and larger sediment yields than those of the other rainfall patterns. f varied from 0.30 to 9.05 for the IT and 0.48 to 11.57 for the NT and exhibited an approximately inverse trend to V and Fr over the course of the rainfall events. Compared with the NT, the mean sediment yield rates from the IT increased the dynamic range of 8.34%–16.21% among different rainfall patterns. The net contributions of the IT ranged from 2.77% to 46.39% on surface runoff and 10.14%–78.95% on soil loss on sloping farmland. Surface runoff and soil loss were positively correlated with rainfall intensity, V, and Fr but negatively correlated with f irrespective of tillage intensive (P < 0.05). For varying-intensity rainfall patterns, soil and water loss fluctuated during rainfall events, suggesting that the changes in rainfall intensity and tillage intensity would result in drastic variations in soil hydrological characteristics and sediment transport mechanisms. The time sequences of rainfall intensity in each rainfall pattern significantly affected surface runoff, soil erosion, and their contribution rates to total soil and water loss. Moreover, tillage erosion effects on soil and water loss were closely related to rainfall patterns in hilly agricultural landscapes. Our study not only sheds light on the mechanism of tillage erosion and rainfall erosion but also provides useful insights for developing tillage-water erosion prediction models to evaluate soil and water loss on cultivated hillslopes.

Josephson junctions (JJs) in the presence of a magnetic field exhibit qualitatively different interference patterns depending on the spatial distribution of the supercurrent through the junction. In JJs based on two-dimensional topological insulators (2DTIs), the electrons/holes forming a Cooper pair (CP) can either propagate along the same edge or be split into the two edges. The former leads to a SQUID-like interference pattern, with the superconducting flux quantum $\phi_0$ (where $\phi_0=h/2e$) as a fundamental period. If CPs’ splitting is additionally included, the resultant periodicity doubles. Since the edge states are typically considered as being strongly localized, the critical current does not decay as a function of the magnetic field. Here we go beyond this approach and inspect a topological JJ in the tunneling regime featuring extended edge states. We consider the possibility that the two electrons of a CP propagate and explore the junction independently over length scales comparable with the superconducting coherence length. As a consequence of the spatial extension, we obtain a decaying pattern with different possible periods. In particular, we show that, if crossed Andreev reflections (CARs) are dominant and the edge states overlap, the resulting interference pattern features oscillations whose periodicity approaches $2\phi_0$.

The emerging advancements in separation and classification of various biological matters (e.g., living cells and proteins) using magnetic levitation (MagLev) technology have proven to be effective for improving disease diagnostics. MagLev technique has the capacity to detect and separate useful diagnostic biomarkers from biocomplex environments (e.g., blood and plasma), minimizing the unpleasant daunting task of sample preparations and labeling procedures. Here, we demonstrate the capability of this technique combined with image analysis and machine learning approaches for discriminating the various types of multiple sclerosis (MS) as an important model disease. To arrive at a systematic expert system, we combined robust statistical analysis with machine learning to (1) detect and remove outliers from the raw MagLev image datasets; then (2) process the images and output a low dimensional representation of massive data without losing the main statistical features; and finally (3) predict the MS clinical disease type (Relapsing-Remitting, Primary–Progressive, or Secondary–Progressive) using a classifier. This is expected to improve MS diagnostics since the current practices rely solely on clinical observation and central nervous system imaging, making management approaches are often reactional and inefficient. Thus, there is a need to identify the disease type early on. MagLev is expected to improve MS diagnostics, thereby aiding in prognosis and guiding adequate treatment choices before the patient exhibits signs of permanent neurological deficits.

The prevalence of obesity in Korean adults was 56.1% in 2019. This study aimed to find obesity phenotypes from the KNHANES(2015–2019) and determine the risk factors associated with each type. In 15,179 participants, three obesity phenotypes, high BMI (short heavy obesity, n=1,685), high waist circumference (WC) (tall abdominal obesity, n=549), and BMI/WC combination (heavy abdominal obesity, n=3,538), were compared to non-OB (n=9,425). Marital status, lower income and education levels were highly associated with abdominal obesity. Insufficient exercise or skipping breakfast were associated with the WC or BMI/WC obesity type. The BMI/WC risk was 3.25 times higher in males with high TG and 4.6–4.8 times higher in females with high FAS or TG, and the risk increased to 7-8 times higher in three combinations (HYP+HDLc+FBS) compared to the control. The K-Score to test balanced diets was lower in BMI/WC than in others. Low intakes of FI-4(fruits) in men and FI-2(meat), FI-3 (vegetables), and FI-5 (milk/milk products) in women with BMI/WC and WC types, but BMI type was not associated with diets. Compared to BMI, the BMI/WC or WC obesity type was an indicator of metabolically unhealthy obesity associated with obesogenic environments including food/diets pattern and unhealthy behaviors.

Landslides are serious geological hazards that become a disaster worldwide, causing a large number of casualties and economic losses every year. There are many factors affecting landslide susceptibility, such as rainfall, soil and slope. Each of them has an important role in the process of slope losing stability. In this paper, the effects of rainfall intensity, rainfall pattern, slope gradient and soil type on landslide susceptibility are studied. In the process of rainfall-induced landslide, the relevant physical quantities of soil changes continuously. Their values and processes are closely related to the time of landslide occurrence. Hence, the variation of soil volumetric water content, matrix suction, pore water pressure and total stress throughout the rainfall are measured. As the results, soil type, slope gradient and rainfall intensity have a large influence on landslide susceptibility. The occurrence of landslides has a prerequisite that the slope is greater than or equal to 15°. The rainfall intensity needs to be not less than 80 mm/h. The difference of rainfall pattern also affects the landslide susceptibility. The rainfall pattern with rainfall intensity peak at the later stage is more likely to induce landslide. Coarser soils with gravels are prone to landslides when other conditions are the same. Steeper slopes, stronger rainfall, and coarser soils can all increase the amount of sediment yield.

Glycine is an important neurotransmitter in vertebrates, performing both excitatory and inhibitory actions. Synaptic levels of glycine are tightly controlled by the action of two glycine transporters, GlyT1 and GlyT2, located on the surface of glial cells and glycinergic or glutamatergic neurons, respectively. Glycinergic neurotransmission in invertebrates has so far only been investigated in a very limited number of species, and, although it was suggested that its functions are to some extent conserved with vertebrates, the evolution of glycinergic neurotransmission remains very poorly understood. Here, by combining phylogenetic and gene expression analyses, we characterized the glycine transporter complement of amphioxus, an important invertebrate model for studying the evolution of chordates. We show that amphioxus possesses three glycine transporter genes, two of which (GlyT2.1 and GlyT2.2) are closely related to GlyT2 of vertebrates, while the other (GlyT) is a member of an ancestral clade of deuterostome glycine transporters. While expression of GlyT2.2 is predominantly non-neural, GlyT and GlyT2.1 are widely expressed in the amphioxus nervous system and are characterized by differential expression in neurons and glia, respectively. However, in vertebrates, glycinergic neurons express GlyT2 and glia GlyT1, suggesting that the evolution of the chordate glycinergic system was accompanied by complex genetic remodeling leading to the paralog-specific inversion of gene expression. Albeit this genetic divergence between amphioxus and vertebrates, we found strong evidence for a general conservation of the role of glycinergic neurotransmission during larval swimming, allowing us to hypothesize that the neural networks controlling the rhythmic movement of chordate bodies are homologous.

With the exponential growth in today’s technology and its expanding areas of application it has become vital to incorporate it in education. One such application is Knowledge Discovery in Databases (KDD) which is a subset of data mining. KDD deals with extracting useful information and meaningful patterns from the database that were not known before. This study is a detailed application of KDD and focuses on analyzing why a particular set of students performed better than others and what factors influenced the results. The study is conducted on a dataset of 480 students and across 16 different features. The authors implemented 4 major classification techniques namely Logistic Regression, Decision Tree, Random Forest and XGB classifier. Obtaining the key features from the top performing ML algorithms that have a major impact on the performance of the student, the study takes these features as a baseline for further analysis. Further data analysis highlights patterns in the data. The study concludes that there are a lot of non-academic factors that influence the overall performance of a student and should be taken into consideration by universities and other relevant bodies.

The Mediterranean diet represents one of the most studied dietary patterns, however, there is no single tool for measuring the grade of adherence and no single criteria to adapting these indices to pregnant women. We characterized the adherence to the Mediterranean diet (MDA) of pregnant women participating in the NELA cohort and identified the sociodemographic determinants and lifestyle habits associated with a higher risk of a low MDA. Maternal diet during gestation was assessed by a validated Food Frequency Questionnaire (FFQ) (n=665). We estimated Relative Mediterranean Diet score (rMED), Alternative Mediterranean Diet score (aMED) and Alternate Healthy Index-2010 (AHEI-2010). Multivariate regression models were performed to identify the sociodemographic and lifestyle factors associated to each index. Mothers with lower age and more previous deliveries had a greater probability of low MDA (P &lt;0.05). Only, for aMED index, mothers with university education and / or who practiced 2 or more hours per week sport activities had a lower probability of a low MDA (P &lt;0.01). These results may be useful in order to design intervention strategies and dietary recommendation for pregnant women.

：Sleep stage on the whole night is not steady. Sleepers generally pass through three to five cycles. In each cycle, there are occur four typical sleep stages, such as wake stage (WS), light stage (LS), deep sleep (DS), rapid eye movement sleep stage (REM). According to the natural routine, in this paper, we investigate the stage transition and analyze the feature of stage transition using the local cluster Algorithm (LCA). Two-cycle sleep model (TCSM) is proposed to automatically classify sleep stages using over-night continuous heart rate variability (HRV) data. The generated model is based on the characteristics of the nested cycle's sleep stage distribution and the transition probabilities of sleep stages. Experiments were conducted using a public data set including 400 healthy subjects (female 239, male 161) and the model’s classification accuracy was evaluated for four sleep stages: WS, LS, DS, REM. The experimental results showed that based on the TCSM model, the segmentation classification of pure sleep is 5.2% higher than that of the traditional method, and the accuracy of segmentation classification is 11.2% higher than the traditional sleep staging accuracy. The experimental performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art methods. The study contributes to improve the quality of sleep monitoring in daily life using easy-to-wear HRV sensors.

Cortical actomyosin flows, among other mechanisms, scale up spontaneous symmetry breaking and thus play pivotal roles in cell differentiation, division, and motility. According to many model systems, myosin motor-induced local contractions of initially isotropic actomyosin cortices are nucleation points for generating cortical flows. However, the positive feedback mechanisms by which spontaneous contractions can be amplified towards large-scale directed flows remain mostly speculative. To investigate such a process on spherical surfaces, we reconstituted and confined initially isotropic minimal actomyosin cortices to the interfaces of emulsion droplets. The presence of ATP leads to myosin-induced local contractions that self-organize and amplify into directed, large-scale actomyosin flows. By combining our experiments with theory, we found that the feedback mechanism leading to a coordinated, directional motion of actomyosin clusters can be described as asymmetric cluster vibrations, caused by intrinsic non-isotropic ATP consumption, in conjunction with spatial confinement. By tracking individual actomyosin clusters, we identified fingerprints of vibrational states as the basis of directed motions. These vibrations may represent a generic key driver of directed actomyosin flows under spatial confinement in vitro and in living systems.

We propose a novel method for salient object detection in different images. Our method integrates spatial features for efficient and robust representation to capture meaningful information about the salient objects. We then train a conditional random field (CRF) using the integrated features. The trained CRF model is then used to detect salient objects during the online testing stage. We perform experiments on two standard datasets and compare the performance of our method with different reference methods. Our experiments show that our method outperforms the compared methods in terms of precision, recall, and F-Measure.

Populaces are prominent indicators of livability in creating ‘street life’ for cities and public spaces. Evaluating people’s experiences is significant in creating a city liveliness and urban heritage trail. The exploration of design strategies that could allow them to integrated and connected with other spaces in the context of city and heritage as well as creating a social life among them. The measuring variables are people’s experience on the spaces; thus, create the pattern of behavior, which cause spaces liveliness, using Kuala Lumpur Heritage Trail as an example of pedestrian priority streets, city environment and heritage value. The aim of this research is to evaluate people’s experiences and analyze their behavior towards the urban street design within cities environment and Malaysia heritage values. The analyzed review comprising on the livability of the street in the context of interrelation between behavioral patter of people and the preferences of urban heritage. Hence, this paper will give an understanding of how people experiences the presence of urban streets and heritage values, thus, bringing liveliness to the streets.

Identifying group movement patterns of crowds and understanding group behaviors is valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

Gait abnormalities are one of the distinguishing symptoms of patients with Parkinson's disease (PD) that contribute to fall risk. Our study compares the gait parameters of people with PD when they walk through a predefined course without assistance, with a conventional walker, and with a motorized walker under different speed cues. Six PD subjects were recruited at the New York Institute of Technology College of Osteopathic Medicine to participate in this study. Spatial posture and gait data of the test subjects were collected via a VICON motion capture system. We developed a framework to process and extract gait features and applied statistical analysis on these features to examine the significance of the findings. The results showed that motorized walkers with haptic cues significantly improved gait symmetry of PD subjects. Specifically, the asymmetry index of the gait cycle time was reduced from 6.7% when walking without assistance to 0.56% and below when using a walker. Furthermore, the double support time of a gait cycle was reduced by 4.88% compared to walking without assistance.

The Inverted F Antenna (IFA) with the parasitic element on a finite conducting plane is proposed in the range frequency of 0.1 to 5.5 GHz and its characteristics are analyzed numerically. In this research, the parasitic element and the main IFA are investigated to obtain the resonance frequency for multiband operation purposes. The parasitic element is placed beneath adjusted to the main IFA to derive more frequency bands. The distance between the parasitic element and main horizontal element extremely affects the performance of the IFA. It is found that when the parasitic element is located closer to the conducting plane, this element is coupled by the current on the conducting plane. Consequently, the return loss bandwidth becomes narrower. Therefore, the gain of the proposed IFA becomes a bit higher. The antenna gain is about 8.21 dB at band #3 (λ_1.747), 7.43 dB at band #5 (λ_2.967) and 8.82 dB at band #6 (λ_4.023). This occurs when the calculation condition is antenna height h1 = 23 mm, h2 = 21 mm, horizontal antenna elements L = 173.2 mm, L1 = 140.9 mm and Lp = 152 mm, shorted element Ls = 30.7 mm, the distance between parasitic element and shorted element pyl = 5 mm. While the size of conducting plane is considered pxp+pxm by pyp+pym as 57.5+57.5 mm by 200+50 mm. In the numerical analysis, the electromagnetic simulator WIPL-D based on Method of Moment is used. The results show that the proposed IFA has UHF and SHF channel receiver which are suitable for advanced wireless service (band #3), mobile phones, Bluetooth, maritime service, radiolocation service (band #5) and radars, mobile phones, commercial wireless LAN (band #6).

The geographical environment, food culture, and dietary habits are substantially different between the southern and northern regions in China. We investigated the associations with dietary patterns and metabolic syndrome between Chinese adult from the southern and northern regions (North: 1249; South: 1849) using data from the Chinese Health and Nutrition 2009 survey. Respectively, four dietary patterns were identified by factor analysis in the two regions. Each dietary pattern of factor score was calculated for three groups by tertile (T1<T2<T3). In the northern region, the association between the Alcohol and Western pattern and the risk of abdominal obesity (OR: 1.31; 95%:1.01, 1.68), hypertriglyceridemia (OR: 1.35; 95%:1.05, 1.74), high fasting blood glucose (OR: 1.37; 95%: 1.05, 1.80), and hypertension (OR: 1.55; 95%: 1.45, 1.99) was increased. In the southern region, the Convenience Food pattern was positively associated with hypertriglyceridemia (OR: 1.53; 95%: 1.03, 2.26), low HDL-cholesterol (OR: 1.96; 95%: 1.12, 3.43), and metabolic syndrome (OR: 1.79; 95%: 1.03, 3.11). The Alcohol dietary pattern was positively associated with high fasting blood glucose (OR: 1.83, 95%: 1.13, 2.97). There are some dietary pattern differences in the two regions. It is necessary to consider the factors of food culture and food intake habits in order to provide nutrition education to Chinese individuals from different regions in the future.

The variety of risks due to climate change with a potential impact on Cultural Heritage CH requires a multidisciplinary approach and a preliminary overview of the existing methods for risk analysis and assessment in different fields. Thus, the starting point of the research conducted by the Sapienza PNRR working group consists in a systematic literature review of risk assessment approaches, methods and indicators. As a first outcome, the research has designed a multidisciplinary comprehensive framework consisting of the definition of risk categorization, the framework represented by a measurable index and the main target, according to the classification of Cultural Heritage based on the harmonization of European vocabularies definitions and protocols. The risk assessment framework sets the methodological core of a future multi criteria matrix and digital platform for decision focused on the historic built environment and it is functional to the definition of specific operational guidelines connected to each risk for elaborating descriptive/predictive Architectural Heritage’s transformation scenarios to support the development of mitigation and adaptation strategies.

Industrial Control Systems (ICS) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing its ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data.

This paper proposes an optimal trading algorithm based on a novel application of the conventional Control Engineering (CE). We consider a fundamental CE concept, namely, the feedback control and apply it to the Algorithmic Trading (AT). The concrete feedback control strategy is designed in a form of the celebrated Proportional-Integral-Derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realisation of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above makes it possible the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in the financial engineering, namely, the "stock market energy" concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python based prototype software. We finally apply the corresponding prototype software to a dataset from the Binance BTC / USDT stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.

Wave buoy measurements were carried out near the north-eastern Black Sea coast at the natural reserve Utrish in 2020-2021. In total, about 11 months of data records were collected during two stages of the experiment at 600 and 1500 meters offshore and depths of 18 and 42 meters. The measured waves propagate almost exclusively from the seaward directions. Generally, the waves do not follow the local wind directions, thus, implying a mixed sea state. Nevertheless, dimensionless wave heights and periods appears to be quite close to the previously established empirical laws for the wind-driven seas. The results of the wave turbulence theory are applied for estimates of spectral energy fluxes and their correspondence to the energy flux from the turbulent wind pulsations. These estimates are consistent with today's understanding of wind-wave interaction. It is shown that the main fraction of the wind energy flux goes to the direct Kolmogorov-Zakharov cascade to high wave frequencies and then to dissipation in small scales. Less than 1% of the wind energy flux is directed to low frequency band (the so-called inverse Kolmogorov-Zakharov cascade), thus, providing wave energy growth.

For the emerging autonomous swarm technology, from the perspective of systems science and systems engineering, there must be some novel elements and methods to aggregate multiple systems into a group, which distinguish with the general components with specific functions, and we expect to provide the presentation of their existence in the swarm development processes. The inspiration of our methodology origins from the integration of swarm ontology, multi-paradigm modeling, multi-agent system, cyber-physical system, etc. Therefore, we choose the model-driven technology as a framework to acquire an approach of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism is defined and formed in parallel with ConOps analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by the nonlinear causation among individual and whole behaviors. This approach highlights to use the MBSE processes and their artifacts to embed the meso mechanism to integrate the operational and the functional, and which means to connect the macro and micro aspects in formalism and so to become a whole with its expected goals, and then to verify and validate within a L-V-C simulation environment.

We consider a class of density-driven flow problems. We are particularly interested in the problem of the salinization of coastal aquifers. We consider the Henry saltwater intrusion problem with uncertain porosity, permeability, and recharge parameters as a test case. The reason for the presence of uncertainties is the lack of knowledge, inaccurate measurements, and inability to measure parameters at each spatial or time location. This problem is nonlinear and time-dependent. The solution is the salt mass fraction, which is uncertain and changes in time. Uncertainties in porosity, permeability, recharge, and mass fraction are modeled using random fields. This work investigates the applicability of the well-known multilevel Monte Carlo (MLMC) method for such problems. The MLMC method can reduce the total computational and storage costs. Moreover, the MLMC method runs multiple scenarios on different spatial and time meshes and then estimates the mean value of the mass fraction. The parallelization is performed in both the physical space and stochastic space. To solve every deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion. We use the solution obtained from the quasi-Monte Carlo method as a reference solution.

With the rise of powerful computational technologies, healthcare systems are going through a paradigm shift to the era of healthcare 5.0, also known as" smart healthcare". This new-age healthcare system not only enhances the life of patients but also aims to reduce healthcare costs significantly. People's health data collected through various means, including various Internet of Things (IoT) devices, is too sensitive to be regulated by particular organisations owing to increasing privacy and security threats. The sheer size of data makes it difficult for centralised bodies to regulate, due to which third-party involvement is introduced, which again poses threats to the privacy of the data holder. Motivated by the abovementioned gaps, we propose a decentralised approach using Ethereum blockchain technology and Oasis protocol. The patient-driven solution approach effectively solves the privacy problem by providing complete control of data to the owner. Along with this, byproducts such as fine-grained access control and on-chain data processing capabilities are also obtained.

Although Cable driven rehabilitation devices (CDRD) have advantages over traditional link-driven devices, including lightweight, ease of reconfiguration, remote actuation, etc, the majority of existing lower-limb CDRD is limited to rehabilitation in the sagittal plane. In this work, we extend our previous sagittal plane model (2DOF) to accommodate hip motion in the frontal plane (abduction/adduction) toward studying the feasibility of tracking bi-planar motion (combined frontal and sagittal plane motion) via intra-planar cable routing. Two optimization problems have been formulated to estimate an optimal location of the hip cuffs, first to estimate the optimal cuff location at each time step to identify a single ‘averaged’ optimal cuff location and second to calculate a single optimal cuff location for the entire gait cycle. The optimization solutions identified for the 3DOF model revealed that optimization of the location of cuffs on the anterior and posterior side of the hip joint for 4 cable configuration is not sufficient to generate the desired bi-planar motion. For simultaneous tracking of the bi-planar motion, 2 additional cables have been added at hip joints and are routed in an intra-planar manner. The simulation result with the 3DOF model confirmed successful bi-planar trajectory tracking. The various number of cables and cable routings for tracking bi-planar motion will be studied in future work.

Economic expedience of waste heat recovery systems (WHRS), especially for low temperature difference applications, is often questionable due to high capital investments and long pay-back periods. By its simple design isobaric expansion (IE) machines could provide a viable pathway to utilize otherwise unprofitable waste heat streams for power generation and particularly for pumping liquids and compression of gases. Different engine configurations are presented and discussed. A new method of modelling and calculation of the IE process and efficiency is used on IE cycles with various pure and mixtures as a working fluid. Some interesting cases are presented. It is shown in this paper, that the simplest non-regenerative IE engines are efficient at low temperature differences between a heat source and heat sink. Efficiency of non-regenerative IE process with pure working fluid can be very high approaching Carnot efficiency at low pressure and heat source/heat sink temperature differences. Regeneration permits to increase efficiency of the IE-cycle to some extent. Application of mixed working fluids in combination with regeneration permits to significantly increase the range of high efficiencies to much larger temperature and pressure differences.

Ethnobotany has been, for too long, a descriptive discipline. However, ethnobotanists are increasingly calling for a paradigm shift towards the formulation of unifying theories and hypothesis-driven research in ethnobotany. Here, we formulated a theory, termed time-since-introduction theory, to explain the integration of alien plants into local pharmacopoeias in their recipient environment. This theory suggests that the factor time is paramount in determining which alien plants are more likely to be included in the medicinal flora of the areas they are introduced in. The theory relies on three hypotheses, the availability and versatility hypotheses alongside the residence time hypothesis newly proposed in the present study. We tested this theory by fitting a structural equation model to ethnobotanical data collected on South Africa’s alien woody flora. Although residence time is a direct predictor of medicinal status of alien plants, it is a better predictor when mediated through plant versatility. These findings are in support of the theory, and we consequently proposed a framework with which to understand different paths linking all three hypotheses. Collectively, our study shows the value of time in the development of ethnobotanical knowledge and fully responds to the pressing call for paradigm shift in ethnobotany.

Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes.Furthermore, all models were quality-controlled using Mᴇᴍᴏᴛᴇ, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.

Offshore wind farms are increasingly built in the North Sea and the number of HVDC systems transmitting the wind power to shore increases as well. To connect offshore wind farms to adjacent AC transmission systems, onshore and offshore modular multilevel converters transform the transmitted power from AC to DC and vice versa. Besides, modern wind farms mainly use wind turbines connected to the offshore point of common coupling via voltage source converters. However, converters and their control systems can cause unwanted interactions, referred to as converter-driven stability problems. The resulting instabilities can be predicted by applying an impedance-based analysis in the frequency domain. Considering that the converter models and system data are often confidential and cannot be exchanged in real systems, this paper proposes an enhanced impedance measurement method suitable for black-box applications to investigate the interactions. The proposed method is applied to assess an offshore HVDC system’s converter-driven stability, using impedance measurements of laboratory converters and a wind turbine converter controller replica. The results show that the onshore modular multilevel converter’s interactions with AC grids of moderate short-circuit ratios can lead to instabilities. The offshore system analysis reveals that considering the offshore grid topology is crucial for assessing interactions between the wind turbine controllers and the offshore modular multilevel converter. It is shown that different stability margins result from varying offshore grid layouts.

The unsteady Ekman problem involves finding the response of the near-surface currents to wind stress forcing under linearized dynamics. Its solution can be conveniently framed in the frequency domain in terms of a quantity that is known as the transfer function, the Fourier transform of the impulse response function. In this paper, a theoretical investigation of a fairly general transfer function form is undertaken with the goal of paving the way for future observational studies. Building on earlier work, we consider in detail the transfer function arising from a linearly-varying profile of the vertical eddy viscosity, subject to a no-slip lower boundary condition at a finite depth. The linearized horizontal momentum equations are shown to transform to a modified Bessel’s equation for the transfer function. Two self-similarities, or rescalings that each effectively eliminate one independent variable, are identified, enabling the dependence of the transfer function on its parameters to be more readily assessed. A systematic investigation of asymptotic behaviors of the transfer function is then undertaken, yielding expressions appropriate for eighteen different regimes, and unifying the results from numerous earlier studies. A solution to a numerical overflow problem that arises in the computation of the transfer function is also found. All numerical code associated with this paper is distributed freely for use by the community.

Multiferroics, showing both ferroelectric and magnetic order, are promising candidates for future electronic devices. Especially, the fundamental understanding of ferroelectric switching is of key relevance for further improvements, which however is rarely reported in literature. On a prime example for a spin-driven multiferroic, LiCuVO₄, we present an extensive study of the ferroelectric order and the switching behavior as function of external electric and magnetic fields. From frequency-dependent polarization switching and using the Ishibashi-Orihara theory, we deduce the existence of ferroelectric domains and domain-walls. These have to be related to counterclockwise and clockwise spin-spirals leading to the formation of multiferroic domains. A novel measurement – multiferroic hysteresis loop – is established to analyze the electrical polarization simultaneously as a function of electrical and magnetic fields. This technique allows characterizing the complex coupling between ferroelectric and magnetic order in multiferroic LiCuVO₄.

A three-dimensional, unstructured grid, hydrodynamic and suspended-sediment transport model (i.e., SELFE-SED) was developed to simulate temporal and spatial variations of suspended sediment and was applied to the subtropical subalpine Tsuei-Feng Lake (TFL) of Taiwan. The model was validated with measured water level and suspended‑sediment concentration in 2009, 2010, and 2011. The overall model simulation results are in quantitative agreement with the observational data. The validated model was then applied to explore the most important parameter that affects the suspended-sediment concentration and to investigate the effect of wind stress on the mean current and suspended‑sediment distribution in this shallow lake. Modeling results of sensitivity analysis reveal that the settling velocity is a crucial parameter and erosion rate is less important in the suspended-sediment transport model. Remarkable lake circulation was found based on the strength of wind speed and wind direction. Strong wind would result in higher mean current in the top layer and suspended-sediment distribution in the top and bottom layers. This study demonstrated that the wind stress played a significant influence on mean circulation and suspended-sediment transport in a shallow lake.

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated error in multi-floor scene. The proposed method involves two steps: motion pattern recognition and position matching calibration. The motion pattern recognition aims to detect different motion states, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion states and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR , achieving accurate 3D positioning . The average error is reduced from 6.60 m to 1.83 m.

In general sound waves propagate radially outwards from a point source. These waves will continue in the same direction, decreasing in intensity, unless a boundary condition is met. To arrive at a universal understanding of the relation between frequency and wave propagation within spatial boundaries, an analysis and comparison is made between the results of Chladni plate experiments and Chladni plate simulations. We consider a finite range of frequencies and plate geometries and present a model that has been shown to successfully recreate the nodal line patterns to a first approximation. We discuss the benefits of such a model and the future work necessary to develop the model to its full predictive capacity.

The field of nephrology has recently directed a considerable amount of attention towards the stimulator of interferon genes (STING) molecule since it appears to be a potent driver of chronic kidney disease (CKD). STING and its activator, the cyclic GMP-AMP synthase (cGAS) are among the most relevant inducers that promote the expression of type I interferons (IFN-Is). These cytokines have been long recognized as part of the mechanism used by the innate immune system to battle viral infections; however, their involvement in sterile inflammation remains unclear. Mounting evidence pointing to the involvement of the IFN-I pathway in sterile kidney inflammation provides potential insights into the complex interplay between the innate immune system and damage to the most sensitive segment of the nephron, the glomerulus. The STING pathway is often cited as the cause of renal disease originating from non-infection related caused by the induction of IFN-I expression. However, other receptors have been implicated in this process, mainly by recognizing host-derived nucleic acids. This review explores the main endogenous inducers of IFN-I in glomerular cells, discusses what exposure to the main autocrine and paracrine cytokines has on these cells, and identifies the pathways that are implicated in the development of glomerular damage.

Forest-grass intercropping patterns of protected cultivation has been widely used to increase productivity and sustainability in modern agriculture. However, there have been few studies of wolfberry (L. barbarum) intercropping and it is cultivated mostly by clean tillage. This paper takes 10 kinds of wolfberry-forage intercropping patterns, compares the effects of different planting modes on the growth, yield, photosynthetic characteristics, nutritional quality, and the degree of occurrence of diseases and pests, which aims to make clear the effects of wolfberry-forage intercropping patterns on wolfberry. The results showed that different intercropping combinations had different effects on wolfberry. The overall effect of Wolfberry-forage intercropping patterns on the growth of wolfberry were promote the photosynthetic characteristics. Ryegrass could increase the branch number, Lvyuan 5 and mangel increase branch length; Mangel, clover, sweet sorghum, ryegrass and stipas could reduce the damage of powdery mildew by 69%, and alfalfa, stipa and ryegrass could reduce the occurrence of aphids by 67% (P<0.05). Intercropping could increase carotenoids, flavonoids and ascorbic acid contents to improve wolfberry quality by 21%, 53% and 127%, respectively (P<0.05). Among all the performances, wolfberry-mangel intercropping has absolute advantages in promoting wolfberry yield and quality increase. Therefore the best patterns of wolfberry-mangel, wolfberry-ryegrass, wolfberry-alfalfa and wolfberry-clover were chosen.

This work introduces an innovative approach that unites a PIDND2N2 controller and the balanced arithmetic optimization algorithm (b-AOA) to enhance the stability of an automatic voltage regulator (AVR) system. The PIDND2N2 controller, tailored for precision, stability, and responsiveness, mitigates the limitations of conventional methods. The b-AOA optimizer is obtained through integration of pattern search and elite opposition-based learning strategies into the arithmetic optimization algorithm. This integration optimizes the controller parameters and the AVR system's response, harmonizing exploration and exploitation. Extensive assessments, including evaluations on 23 classical benchmark functions, demonstrate the efficacy of the b-AOA. It consistently achieves accurate solutions, exhibits robustness in addressing a wide range of optimization problems, and stands out as a promising choice for various applications. In terms of AVR system, comparative analyses highlight the superiority of the proposed approach in transient response characteristics, with the shortest rise and settling times and zero overshoot. Additionally, the b-AOA approach excels in frequency response, ensuring robust stability and a broader bandwidth. Furthermore, the proposed approach is compared with various state-of-the-art control methods for the AVR system, showcasing impressive performance. These important results underscore the significance of this work, setting a new benchmark for AVR control by advancing stability, responsiveness, and reliability in power systems.

Sensors and actuators are fundamental units in Cyber-Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), that are patterns that describe abstract conceptual entities. For concreteness, we study them here in the context of autonomous cars. An autonomous car is a complex system because, in ad-dition to its own complex design, it interacts with other vehicles and with the surrounding in-frastructure. To handle these functions, it must incorporate various technologies from different sources. An autonomous car is an example of a Cyber-Physical System, where some of its func-tions are performed by Internet of Things units. Sensors are extensively used in autonomous cars to measure physical quantities; actuators are commanded by controllers to perform appro-priate physical actions. From AEPs we can derive concrete patterns, a structure combining re-lated AEPs is an Entity Solution Frame (ESF). Both sensors and actuators are susceptible to mali-cious attacks due to the large attack surface of the system where they are used. Our work is in-tended to make autonomous cars more secure, which also increases their safety. Our final objec-tive is to build a Security Solution Frame for sensors and actuators of autonomous cars that will facilitate their secure design. A Security Solution Frame is a solution structure that groups to-gether and organizes related security patterns. This article is the first stage of a secure unit that can be used to design not only secure autonomous cars but also any system where sensors and actuators are used. This paper concentrates on AEPs and ESFs for sensors and actuators; that is, on the functional aspects of these devices.

In every agricultural setting, weed seeds can be found in every cubic centimeter of soil. Weed seeds, as a valuable trait underlying the fate of weed populations, exhibit differing levels of seed dormancy, ensuring their survival under uncertain conditions. Seed dormancy is considered as an innate mechanism that constrains germination under suitable conditions that would otherwise stimulate germination of non-dormant seeds. This work provides new insight into changes in germination patterns along the dormant to nondormancy continuum in seeds with physiological dormancy. Notable findings are: (1) germination synchrony can act as a new parameter that quantitatively describes dormancy patterns and subsequently weed population dynamics, (2) germination synchrony is dynamic, suggesting that the more dormancy decreases, the more synchrony is obtainable, (3) after-ripening and stratification can function as a synchronizing agent that regulates germination behavior. Brassica napus showed the most synchronous germination with the value of 3.14, while lower level of germination asynchrony was for Sinapis arvensis, with the asynchrony value of 2.25. After-ripening and stratification can act as a synchronizing factor through decreasing asynchrony level and increasing synchrony. Weed establish a firm relationship between dormancy cycling and germination synchrony patterns, ensuring their survival and reproductive strategies. By germinating in synchrony, which is accompanied by cycling mechanisms, weeds have more opportunities to persist. The synchrony model used in the present study predicts germination behavior and synchrony along the dormant to nondormancy continuum in weed seeds with physiological dormancy, suggesting a useful method for quantification of germination strategies and weed population dynamics.