In this work, a novel approach is presented for the multivariate prediction of hydrological variables, employing a specialized type of recurrent neural network (RNN) known as the long-term memory network (LSTM).The central objective is to develop a flood prediction model using data collected from two hydrological stations situated along the Atrato River in the Choc´o Department, Colombia. The interconnected variables of water level, water flow, and water precipitation are analyzed in this model to enhance flood prediction accuracy. The LSTM model, integrated into an RNN structure, is designed to capture the complex dynamics and cross-correlations inherent in these hydrological variables. Validation involves a comparison with linear and nonlinear Nonlinear AutoRegressive with eXogenous input (NARX) models, considering factors such as estimation error and computational time. Additionally, the study conducts further analysis for water level prediction under two scenarios: utilizing only outflow and inflow measurements or predicting outflow alongside measuring inflow, with the objective of utilizing the proposed approach as a flood early warning system. † This article is a revised and expanded version of a paper entitled Multivariable NARX based Neural Networks Models for Short-term Water Level Forecasting, which was presented at International Conference on Time Series and Forecasting (ITISE-2023), Gran Canaria, Spain, 12th-14th July, 2023.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

This research delves into the integration of green roofs with Building Information Modeling (BIM), a pivotal advancement in sustainable urban construction. Aligning with the United Nations Sustainable Development Goals, particularly Goal 11, the study explores how this integration can address global challenges like climate change and resource depletion. Using the Dynamo Visual Programming Language within Autodesk Revit, the research develops a computational modeling approach for green roofs, focusing on their thermal and structural characteristics under varied environmental conditions. Key findings demonstrate the significant influence of substrate and drainage material combinations on green roofs’ thermal performance, highlighting the need for tailored designs based on climatic conditions. The study also emphasizes the importance of considering structural performance in both dry and saturated conditions for overall building integrity. The research identifies gaps in current practices, such as limited focus on specific green roof materials and reliance on certain software tools, suggesting the need for broader material selection and software adaptability. Future research directions include expanding material selections, exploring diverse environmental conditions, and integrating green roofs with various BIM software platforms. The study's implications extend to stakeholders in the construction industry, offering a framework for architects, urban planners, and policymakers to design and implement green roofs aligned with environmental goals. This work contributes to the field by proposing a novel approach to sustainable construction, integrating ecological design with digital technology, and setting a new benchmark in the integration of green roofs with BIM. By addressing these aspects, the research paves the way for future developments in sustainable urban construction, enhancing the efficiency, sustainability, and resilience of urban communities in line with global sustainability objectives.

This research looks into the evolving dynamics of ransomware, shifting from conventional encryption-based attacks to sophisticated data exfiltration strategies. Employing the Bidirectional Encoder Representations from Transformers (BERT) model, the study analyzes network traffic patterns to detect ransomware activities, offering new insights into their covert operations. The findings emphasize the need for advanced AI tools in cybersecurity, highlighting the significance of adapting and innovating defense strategies to counter the changing landscape of ransomware threats. The study contributes to a deeper understanding of ransomware evolution and underscores the importance of integrating AI in cybersecurity practices.

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

With the view toward enlivening the Chinese automobile market, this work seeks to develop a framework to reveal the tastes of Chinese car users from online user-generated content (UGC) and identify future product improvement orientation. We construct an importance-satisfaction gap analysis (ISGA) model based on sentiment analysis technique. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users' satisfaction with different attributes based on the rules, and weigh the importance of attributes using TF-IDF method, which lay the statistic foundation for our ISGA model. We also take time into account and further analyze the ISGA model, aiming to provide new insight into the changing tastes of Chinese car users so as to suggest correct design positioning and resource allocation. The final evaluation results show that the method proposed in this paper, which integrates Chinese word-boundary-identified algorithm and sentiment lexicon matching rules, is well supported.

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions on treatment strategies or the use of palliative care for patients; (2) Methods: The gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected the RNA-seq data of breast cancer patients, a total of 1187 RNA-seq data (median age 58 years), in FPKM format from the TCGA database. Among them, 144 RNA-seq data with date of death information was selected to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to transcripts per million (TPM) to build survival prediction model SaBrcada. After normalization and dimension raising, the differential gene expression data were used for testing eight different deep learning architectures. Among them, GoogLeNet performed the best. Considering the effect of age on prognosis, we examined all ages between the lower and upper quartiles of patient age for a stratified random sampling test; (3) Results: Stratifying by age based on a cut-off of 61 years of age improved the accuracy of SaBrcada compared to previous findings, resulting in an accuracy of 0.798. We also built a free website tool to provide 5 kinds of predicted survival period information for clinician reference; (4) Conclusions: We established a breast cancer survival analysis prediction model, SaBrcada, and a website tool with the same name. Through this highly reliable survival analysis model and website tool, information on survival intervals will be provided for clinicians as part of precision medicine.

InSAR and associated analytic methods enable relative surface deformation measurements from low Earth orbit with a potential accuracy of centimeters to millimeters. However, assessing the actual accuracy can be quite difficult. The analytic methods are complicated enough that naive analytic error propagation is infeasible, and, in many settings, InSAR practitioners lack sufficient ground truth to assess results. Phase noise due to partial decorrelation from changes in the scattering properties of the ground is a prominent source of accuracy loss. In this paper we present a method to assess the loss of precision due to this component of phase noise. The proposed method consists of generating synthetic data stacks whose statistical properties match those of the actual input SAR data stacks, and then using the synthetic data for an ensemble calculation. The spread of the results of the ensemble calculation indicates the loss of precision. We show examples of the ensemble analysis at a mining operation in South Africa, and demonstrate the ability to estimate the precision of two InSAR deformation retrieval methods on a point-by-point and epoch-by-epoch basis.

COVID-19 is a well-known respiratory disease spreading worldwide since January 2020, causing many deaths and massive pressure on health systems. This pandemic’s appearance compromised the health services’ sustainability and quality as many procedures were postponed or even canceled out, with an expected increase of adverse events like nosocomial infections, in-hospital deaths, and the worsening of the patients’ clinical status. Therefore, assessing whether the healthcare providers’ performance has changed is paramount. In this case, we evaluated the public hospitals in Portugal as the leading secondary health care providers in the country, which in turn has been highly affected by the pandemic. To this end, we developed a network Data Envelopment Analysis model serially relating efficiency and effectiveness. Using 2,795 observations from public hospitals and 18 variables, we observed consistent drops in efficiency when the pandemic started, followed by a recovery to levels above the pre-pandemic ones. Regarding effectiveness, we observed a positive trend during the evaluated period. In short, these results allow us to conclude that, apart from the great resilience of public healthcare providers, the Portuguese State was not prepared for a pandemic like COVID-19, but still the actions taken (including massive vaccination) were beneficial.

Ransomware is one of the most dangerous types of malware, which is frequently intended to spread through a network to damage the designated client by encrypting the client’s vulnerable data. Conventional signature-based ransomware detection technique falls behind because it can only detect known anomalies. When it comes to new and non-familiar ransomware traditional system unveils huge shortcomings. For detecting unknown patterns and sorts of new ransomware families,behavior-based anomaly detection approaches are likely to be the most efficient approach. In the wake of this alarming condition, this paper presents an ensemble classification model consisting of three widely used machine learning techniques that include Decision Tree (DT), RandomForest (RF), and K-nearest neighbor (KNN). To achieve the best outcome ensemble soft voting and hard voting techniques are used while classifying ransomware families based on attack attributes. Performance analysis is done by comparing our proposed ensemble models with standalone models on behavioral attributes based ransomware dataset..

The COVID-19 crisis has emphasized the importance of digital transformation of SMEs and has served as an accelerator. Digital transformation empowers SMEs to rethink the way they make decisions and apply technology in meaningful and sustainable ways. The purpose of this study is to explore the role of digital transformation, fostered by COVID-19, and its potential contribution to the sustainability of SMEs. This paper examines a new conceptual framework designed on resource-based theory perspectives by using a survey data of 246 SMEs in Latvia and employing mediation analysis. The main findings of the study are that digital orientation and digital capability have direct positive effects on digital transformation; higher levels of digital orientation and capability, mediated by digital transformation, lead to higher revenue; as well as a more sophisticated business model in the case of digital orientation. Even if we cannot confirm that higher digital capability, mediated by digital transformation, leads to a more sophisticated business model, we found statistically significant direct effects from digital capability to digital transformation and from digital transformation to business model. These findings can be useful for policymakers, managers and practitioners to clarify how digital orientation, digital capability intermediated through digital transformation affect sustainability of SMEs.

This paper presents an algorithm for the fully automatic mesh generation for the finite element analysis of ships and offshore structures. The quality requirements on the mesh generator are imposed by the acceptance criteria of the classification societies as well as the need to avoid shear locking when using low degree shell elements. The meshing algorithm will be generating quadrilateral dominated meshes (consisting of quads and triangles) and the mesh quality requirements mandate that quadrilaterals with internal angles close to $90\degree$ are to be preferred. The geometry is described by a dictionary containing points, rods, surfaces and openings. The first part of the proposed method consist of an algorithm to automatically clean the geometry. The corrected geometry is then meshed by the frontal Delaunay mesh generator as implemented in the gmsh package. We present a heuristic method to precondition the cross field of the frontal quadrilateral mesher. Also the influence of the order in which the plates are meshed will be explored as a preconditioning step.

The objective of this article: analysis of research trends on the topic "Materials Science for Energy Engineering" for the period 2012-2021. Materials and methods: bibliometric analysis of data from the Scopus abstract database.Results. It is shown that this topic is developing faster than the general energy topics.The key feature of the research trends is a significant increase in work on the development of materials for renewable energy and a decrease in the number of publications related to nuclear power.The primary goal of many studies is to increase the efficiency of renewable energy production through the use of new materials.The importance of the topic is evidenced by the emergence of new journals that quickly entered the first quartile of the abstract databases Scopus and Web of Science, for example: ACS Applied Energy Materials.There has been a significant increase of interest in perovskite solar cells, layered semiconductors, triboelectric nanogenerators, cobalt compounds, sulfur compounds, and oxygen and hydrogen evolution reaction during Electrolysis.Publications on electric batteries are well represented in all time intervals. The focus is on electrode materials and battery efficiency.

An in-silico WES approach using the Galaxy platform was adopted in the current study to predict the genetic basis of Premature Ovarian Failure (POF), where three affected patients in a Saudi Arabian family of seven, found associated with X-linked recessive mutations. The current analysis discovered 518,054 variants using FreeBayes variant caller that had 1,461,864 effects on variable sites in the genome revealed by SnpEff software. The causal genetic mutations were filtered and annotated with the ClinVar database using the GEMINI tool. This tool retained 369 pathogenic mutations harboring 130 genes. Among the total, 268 variants positioned on 69 genes are shared with three affected individuals, 61 variants on 23 genes are shared by any two of the affected individuals, and 40 of the variants on 38 genes are present in any one of the affected sample. Two mutations in one of the already POF-associated, POF1B gene were also observed e.g. (i) g.84563135T>A; p.M349L and (ii) g.84563194C>T; p.R329Q in the two affected individuals i.e. IV-I-C & IV-6 in the current data. This gene consists of 17 exons that span the region of >100 kb. The putative function of this gene in regulating the actin cytoskeleton due to homology with myosin tail and maintains a number of oocytes during fetal ovary development. In a nutshell, this Galaxy pipeline facilitates all-in-one to pinpoint not only the known pathogenic gene mutations for this disorder but few other novel genetic variants as well, whose gene-disease association may be validated by further experimental studies.

Studies have found critical software malfunctions responsible for some of the worst accidents in recent times. These malfunctions are often only minor defects that snowball into large problems; a few lines of code is all it takes. Complexity, safety, quality, and resilience are among the key attributes defining a software’s operational success. There are many leading factors for complexity, such as increases in the product size, the rate of requirement changes, and the number and type of stakeholders, and failure to manage these issues efficiently always has the same consequence, i.e., massive failure and sometimes technological catastrophe. This work analyzes some of the architecture, design, and implementation guidelines used as detection and mitigation techniques. It also discusses the safety considerations, as considering how the steam industry has handled safety issues could offer some guidance for ensuring safety. Complexity in such systems also causes some of the worst side effects from the quality auditor's perspective. While failures in the software are hard to predict, one of the most significant ways of showing preparedness is practicing software resilience. New mitigation areas, such as the fragility spectrum and failure obviation, and their usage for building a safer system are analyzed. Also discussed are various architecture styles in practice and the dramatic effect human factors can have on the success of the software being developed.

This paper introduces a method for tunnel point cloud and BIM model integration and cross-section monitoring, providing information to analyse tunnel cross-sections and surrounding rock deformation, and support for tunnel maintenance and reconstruction. Three types of data are processed for the integration: laser scanning point cloud, BIM tunnel model and terrain model from oblique photogrammetry. An adaptive BIM modelling scheme is proposed for tunnels with alien structures. Precise spatial registration of the data sets is conducted by applying singular value decomposition (SVD) algorithm to calculate transformation parameters from the point cloud model to the BIM model. Since the tunnel central line has high-order derivability, a cross-section calculation method based on tangent vector is proposed to obtain the cross-sectional profile of tunnels at any mileage. The proposed method has been verified by applying it to a tunnel reconstruction project. The experiment results show that the tunnel point cloud and the BIM model were highly coincident after the integration. The developed program can effectively get the cross-section of the tunnel at any mileage, and correctly express the spatial relationship between the BIM tunnel, the point cloud of tunnel and the external mountainous terrain.

In many developed countries, mental health disorders have become problematic, and the economic loss due to treatment costs and interference with work is immeasurable. Therefore, we developed a method to assess individuals’ mental health using emotional components contained in their voice. We propose two indices of mental health: vitality, a short-term index, and mental activity, a long-term index capturing the trends in vitality. To evaluate our method, we used the voices of healthy individuals (n = 14) and patients with major depression (n = 30). The patients were also assessed by specialists using the Hamilton Rating Scale for Depression (HAM-D). A significant negative correlation existed between the vitality extracted from the voices and HAM-D scores (r = -0.33, p < .05). We could discriminate the voice data of healthy individuals and patients having depression with a high accuracy using the vitality (p = .0085, area under the curve = 0.76). Further, we developed a method to estimate stress through emotion instead of analyzing stress directly from voice data. By daily monitoring of vitality using smartphones, we can encourage hospital visits for people before they become depressed or during the early stages of depression, to prevent adverse consequences of depression.

The rapid development of China's economy has led to a rapid increase in energy production and use. Among them, the excessive consumption of coal in fossil energy consumption is the leading cause of air pollution in China. This paper incorporates renewable energy innovation, fossil energy consumption and air pollution into a unified analysis framework, and uses spatial measurement models to investigate the spatial effects of renewable energy green innovation and fossil energy consumption on air pollution in China, and decomposes the total impact into direct and indirect effects. influences. The empirical results show that China's air pollution, renewable energy green innovation and fossil energy consumption are extremely uneven in geographical space, generally showing the characteristics of high in the east and low in the west, and showing a strong spatial aggregation phenomenon. Fossil energy consumption will lead to increased air pollution, and the replacement of fossil fuels with clean and renewable energy is an important means of controlling pollution emissions. The direct and indirect effects of renewable energy green innovation on air pollution are significantly negative, indicating that renewable energy green innovation not only suppresses local air pollution, but also suppresses air pollution in neighboring areas. The consumption of fossil energy will significantly increase the local air pollution, and the impact on the SO2 and Dust&Smoke pollution in the adjacent area is not very obvious. It is recommended to strengthen investment in renewable energy green innovation, reduce the proportion of traditional fossil energy consumption, and pay attention to the spatial connection and spillover of renewable energy green innovation.

Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18\% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

Here we present 1. a model for amplicon sequencing 2. a definition of the best assignment of a read to a set of reference sequences 3. strategies and structures for indexing reference sequences and computing the best assign- ments of a set of reads efficiently, based on (ultra)metric spaces and their geometry The models, techniques, and ideas are particularly relevant to scenarios akin to 16S taxonomic profiling, where both the number of reference sequences and the read diversity is considerable.

Crowd density estimation is an important task for crowd monitoring. Many efforts have been done to automate the process of estimating crowd density from images and videos. Despite series of efforts, it remains a challenging task. In this paper, we proposes a new texture feature-based approach for the estimation of crowd density based on Completed Local Binary Pattern (CLBP). We first divide the image into blocks and then re-divide the blocks into cells. For each cell, we compute CLBP and then concatenate them to describe the texture of the corresponding block. We then train a multi-class Support Vector Machine (SVM) classifier, which classifies each block of image into one of four categories, i.e. Very Low, Low, Medium, and High. We evaluate our technique on the PETS 2009 dataset, and from the experiments, we show to achieve 95% accuracy for the proposed descriptor. We also compare other state-of-the-art texture descriptors and from the experimental results, we show that our proposed method outperforms other state-of-the-art methods.

The following series of Articles about a contextual system-theory, with focus on biological systems, shall give a guideline of how to explore complex systems. It is written not only for scientists, and therefore I will try to describe part of the way of understanding as an intuitive approach. This approach will also help scientists to get a deeper understanding of complexity. Some definitions and rules may seem at first simple and obvious. If practiced in a multidimensional complex system, one will see the difficulties. Even without mathematization or computer modeling the multidimensional sub space, which we try to describe phenomenologically for approaching the understanding of this Subsystem, will need a lot of training. Easily we get lost in the multidimensional world. Some of the chapters here and in the following publications of this series are written for specialists, but it will always come back to the essence of a phenomenological description. Concepts like Symbolic programming, Object oriented modeling, simulation and optimizing as well as experimental and mathematical approaches are the described tools to unpuzzle a complex scenario.

Technology development changes society and society demands new and innovative technology development. We analyze technology to understand society and technology itself. Many researches have been introduced in various fields. Most of them were about patent analysis. This is because detailed and accurate results of research and development are patented. In this paper, we study on new patent analysis method based on count data model and Bayesian regression analysis. Using count data model, we analyze the technological keywords extracted from the collected patent documents. We use the posterior distribution of Bayesian statistics to reflect the experience and knowledge of the relevant technological experts in the analysis model. Moreover, we apply the proposed model to finding sustainable technologies. Finding and developing sustainable technologies is an important activity for companies and research institutes to maintain their technological competitiveness. To illustrate how our modeling could be applied to real domain, we carry out a case study using the patent documents related to artificial intelligence.

Proteins play a major role in biosensors in which they provide catalytic activity and specificity in molecular recognition. The immobilization process is however far from straightforward as it often affects the protein functionality. An extensive interaction of the protein with the surface or a significant surface crowding can lead to changes in the mobility and conformation of the protein structure. This review will provide an insight of how the analysis of the physico-chemical features of the protein surface features before the immobilization process can help to identify the optimal immobilization approach to preserve the functionality of the protein when on the surface of the biosensor.

Dictionary Learning (DL) and Sparse Representation (SR) based Classifier have impacted greatly on the classification performance and has had good recognition rate on image data. In Video Semantic Analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called Sparsity based Locality-Sensitive Discriminative Dictionary Learning(SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experiment results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

This paper investigates, experimentally and numerically, the buckling behavior of innovative sustainable Low-Cost Bamboo Composite (LCBC) structural columns under compressive loading. The LCBC columns are manufactured from bamboo culms in combination with bio-based resins to form composite structural columns. Different LCBC cross-sectional configurations are investigated in this study including the Russian doll (RD), Big Russian doll (BRD), Hawser (HAW), and Scrimber (SCR). Extra-large, large, medium, and small sizes of bamboo are employed to form the proposed configurations. Two bio-based resins including one bio-epoxy and one furan-based resin, in addition to a soft bio-based filler and a synthetic epoxy resin are applied. The bamboo species used as the cast-in-place giant bamboo for all configurations include Moso, Guadua, and Tali. In addition to experimental testing of the slender LCBC short columns, finite element analysis (FEA) software ABAQUS is applied to model the composite columns and predict their behavior. A buckling analysis using static Riks method is carried out to determine the LBCB’s response to axial compressive loading. A model is calibrated using test results with various imperfections, accounting for the variable behavior of each bamboo specimen, to accurately predict the behavior of LCBC columns with different resin contents. Slender LCBC columns showed maximum stress at buckling up to 60 MPa, highlighting the potential of bio-based resins for structural applications. The study found that the samples with bio-epoxy resin (BE2) exhibited enhanced material stiffness when compared to with synthetic epoxy (EPX) and furan-based resin (PF1), while PF1 specimens demonstrated increased ductility. The BE2 samples showed strain hardening in their stress-strain behavior, and the PF1 samples exhibited a long plateau phase before reaching the maximum load. Among the specimens with Moso bamboo and BE2 resin, those with SCR and HAW configurations achieved the highest compressive strengths.

When the formation flying consists of satellites that are independent from each other and forms a virtual synthetic aperture telescope that replaces a single large telescope with all of its optical elements it is required mutual coordination between the satellites with high accuracy in order to achieve good optical observations. In this paper we consider the problem of developing a control system for a tetrahedral satellite formation flying representing a synthetic aperture telescope in the geostationary orbit for high resolution monitoring of fire in the forest area in infrared spectrum. Synthesizing the image with good quality requires to keep the configuration of formation with µm-class accuracy. Study of dynamics of passive uncontrolled motion of formation flying showed a significant deviation of the configuration from the required shape with a high frequency due to the action of gravitational forces. To keep the configuration in the required form with µm accuracy the analysis of efficiency of various controllers was carried out in the process of numerical simulation. The simulation results made it possible to highlight the features of using various approaches to the development of control system for the satellite formation flying representing a synthetic aperture telescope.

. Accurate coastal wave direction and speed forecasts are crucial in coastal and marine engineering, marine energy, maritime transport, fisheries, naval navigation, environmental research, and risk management. Approximately 269 km of Brunei Darussalam's coastline can generate between 15 and 126 GW of wave energy. As part of the preliminary feasibility study of wave energy harvesting in Brunei Darussalam and net zero commitment, in this study, we used two numerical methods, namely, finite difference and spectral element methods, for modeling and simulation of wave speed and direction. The mean error between numerical and analytical solutions was calculated in each simulation. Explanatory data analysis was used to provide insight into the study data. We then proposed wave direction and speed forecasting models using Long Short-Term Memory (LSTM) stacking on the data computed from the Acoustic Doppler Current Profiler (ADCP) sensor data. A univariate time series forecasting approach was adopted for this research. KerasTuner hyperparameter tuning API was used for tuning and optimizing hyperparameters, leading us to build models with the least training and test errors. Seven separate prediction experiments were conducted for wave speed and direction in degree and radian units for the next 1, 3, 6, 8, 10, 12, and 24 hours, respectively. Mean squared error (MSE) was used as a metric for both training and testing. The experimental results show that wave speed forecast has the lowest MSEs compared to direction, regardless of the unit of measure, but has a longer runtime. Moreover, the forecast of direction in the degree unit has the least errors compared to the radian unit; the running time of the latter is higher than that of the former. In the future, we intend to use advanced multivariate time series techniques to forecast wave speed and direction.

The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorised according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.

This paper presents an approach to the Kirchhoff-Love plate theory (KLPT) using the finite difference method (FDM). The KLPT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally in the case of KLPT, the finite difference approximations are derived based on the fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection surface. The FOPH is made for the fourth and third derivative of the deflection surface while the SOPH is made for its second and first derivative; this leads to a 13-point stencil for the governing equation. In addition, the boundary conditions and not the governing equations are applied at the plate edges. In this paper, the FOPH was made for all of the derivatives of the deflection surface; this led to a 25-point stencil for the governing equation. Furthermore, additional nodes were introduced at plate edges and at positions of discontinuity (continuous supports/hinges, incorporated beams, stiffeners, brutal change of stiffness, etc.), the number of additional nodes corresponding to the number of boundary conditions at the node of interest. The introduction of additional nodes allowed us to apply the governing equations at the plate edges and to satisfy the boundary and continuity conditions. First-order analysis, second-order analysis, buckling analysis, and vibration analysis of plates were conducted with this model. Moreover, plates of varying thickness and plates with stiffeners were analyzed. Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. In first-order, second-order, buckling, and vibration analyses of rectangular plates, the results obtained in this paper were in good agreement with those of well-established methods, and the accuracy was increased through a grid refinement.

Climate change is expected to result in rising temperatures, leading to increased occurrences of extreme weather events like heat waves and cold spells. Urban planning responses are crucial for improving the adaptive capacity of cities and communities in dealing with significant temperature variations across seasons. This study aims to investigate the relationship between urban temperature fluctuations and urban morphology throughout the four seasons. Through quadrant and statistical analyses, the study identifies built-environment factors that contribute to moderate seasonal land surface temperatures (LST). The research focuses on Seoul, South Korea as a case study and calculates seasonal LST values at both the grid level (100m×100m) and street-block level, incorporating factors such as vegetation density, land use patterns, albedo, two- and three-dimensional building forms, and gravity indices for natural reserves. The quadrant analysis reveals spatial segregation between areas demonstrating high LST adaptability (cooler summers and warmer winters) and those displaying LST vulnerability (hotter summers and colder winters), with significant differences in vegetation and building forms. The spatial regression analysis demonstrates that higher vegetation density and proximity to water bodies play key roles in moderating LST, leading to cooler summers and warmer winters. Building characteristics have an invariant impact on LST across all seasons, where horizontal expansion contributes to higher LST, while vertical expansion reduces LST. These findings are consistent for both grid- and block-level analyses. The study emphasizes the flexible role of the natural environment in moderating temperatures.

In this study, the partial least squares regression (PLSR) models were developed using no pre-processing, traditional preprocessing, multi-preprocessing 5 range, multi-preprocessing 3 range, genetic algorithm (GA), and successive projection algorithm (SPA) to assess the higher heating value (HHV) and ultimate analysis of grounded biomass for energy usage employing near-infrared (NIR) spectroscopy. A novel approach was utilized based on the assumption that using multiple pretreatment methods across different sections in the entire NIR wavenumber range would enhance the performance of the model. The performance of the model obtained from 200 biomass samples for HHV and 120 samples for ultimate analysis was compared, and the best model was selected based on the coefficient of determination of validation set, root mean square error of prediction, and the ratio of prediction to deviation values. Based on model performance results, the proposed HHV model from GA-PLSR, and the N and O models from the mul-ti-preprocessing PLSR 5 range method could be used for most applications, including research, whereas the C and H models from GA-PLSR performance is fair and applicable only for rough screening. The overall findings highlight that the multi-preprocessing 5-range method, which was attempted as a novel approach in this study to develop the PLSR model, demonstrated better accuracy for HHV, C, N, and O, improving by 4.1839%, 8.1842%, 3.7587%, and 35.9404%, respec-tively. Therefore, it can be considered a reliable and non-destructive alternative method for rap-idly assessing biomass properties for energy usage and can also be used effectively in biomass trading. However, due to the smaller number of samples used in the model development, more samples are needed to update the model for a robust application.

Mexico has a great diversity of cacti, however, many of their fruits have not been studied in greater depth. Several bioactive compounds available in cacti juices extract have demonstrated nutraceutical properties. Two cactus species are interesting for their biologically active pigments, which are chico (Pachycereus weberi (J. M.Coult.) Backeb)) and jiotilla (Escontria chiotilla (Weber) Rose)). Hence, the goal of this work was to evaluate the bioactive entities, i.e., betalains, total phenolic, vitamin C, antioxidant activity, and mineral content in the extract of the above-mentioned P. weberi and E. chiotilla. Then, clarified extracts were evaluated for their antioxidant activity and cytotoxicity (cancer cell lines) potentialities. Based on the obtained results, Chico fruit extract was found to be a good source of vitamin C (27.19±1.95 mg L-Ascorbic acid/100g fresh sample). Moreover, chico extract resulted in a high concentration of micronutrients, i.e., potassium (517.75±16.78 mg/100 g) and zinc (2.46±0.65 mg/100 g). On the other hand, Jiotilla has a high content of biologically active pigment, i.e., betaxanthins (4.17±0.35 mg/g dry sample). The antioxidant activities of clarified extracts of chico and jiotilla were 80.01±5.10 and 280.88±7.62 (DPPH method), respectively. From the cytotoxicity perspective against cancer cell lines, i.e., CaCo-2, MCF-7, HepG2, and PC-3, the clarified extracts of chico showed cytotoxicity in CaCo-2 (49.7±0.01) and MCF-7 (45.56±0.05). Normal fibroblast cell line (NIH/3T3) was used as a control for comparison purposes. While, jiotilla extract had cytotoxicity against HepG2 (47.31±0.03) and PC-3 (53.65±0.04). These results demonstrated that Chico and jiotilla are good resources of biologically active constituents with nutraceuticals potentialities.

AbstractThe emergence of AI-powered deepfakes, manipulatively realistic synthetic media, raises unprecedented challenges for human information processing and decision-making. This paper explores the cognitive and emotional consequences of exposure to deepfakes, delving into how these digitally fabricated experiences can reshape perception, trust, and social interactions. By reviewing existing research and outlining potential methodological approaches, this paper aims to establish a framework for investigating the multifaceted impact of deepfakes on the human brain.BackgroundThe digital age has ushered in an era of unprecedented information abundance and accessibility. Yet, this abundance breeds vulnerability, particularly with the emergence of AI-powered deepfakes. These hyper-realistic synthetic media creations present a potent threat to the fundamental trust and integrity of information sources. Deepfakes have infiltrated various domains, from entertainment and social media to politics and journalism, blurring the lines between truth and fabrication. Consequently, understanding the cognitive and emotional consequences of encountering deepfakes becomes crucial for navigating this increasingly deceptive landscape.ObjectivesThis research aims to: Investigate how exposure to deepfakes impacts various cognitive functions, including perception, attention, memory, and decision-making. Elucidate the emotional responses elicited by deepfakes and their underlying neural mechanisms. Evaluate the role of individual differences, such as susceptibility to deception and emotional sensitivity, in mediating the impact of deepfakes. Develop a comprehensive framework for understanding the multifaceted influence of deepfakes on the human brain. Results (anticipated)This research is expected to yield insights into: The specific cognitive and emotional processes altered by deepfakes, providing evidence for potential mechanisms of deception and manipulation. The neural circuitry involved in detecting and responding to deepfakes, leading to the development of more effective detection methods. The influence of individual differences (e.g., age, cognitive style) on susceptibility to deepfakes, informing targeted interventions and educational efforts.ConclusionBy deciphering the impact of deepfakes on the human brain, this research will offer valuable tools for mitigating the harmful consequences of misinformation and promoting responsible use of this powerful technology. Ultimately, understanding how deepfakes manipulate our cognitive and emotional landscape is not merely a scientific pursuit, but a critical step towards ensuring a future where truth prevails in the digital world.

The plant graft healing process is an intricate development influenced by numerous endogenous and environmental factors. This process involves the histological changes, physiological and biochemical reactions, signal transduction, and hormone exchanges in the grafting junction. Studies have shown that applying exogenous plant growth regulators can effectively promote the graft healing process and improve the quality of grafted plantlets. However, the physiological and molecular mechanism of graft healing formation remains unclear. In our present study, transcriptome changes in the melon and cucurbita genomes were analyzed between control and NAA treatment, and we provided the first view of complex networks to regulate graft healing under exogenous NAA application. The results showed that the exogenous NAA application could accelerate the graft healing process of oriental melon scion grafted onto squash rootstock through histological observation, increase the SOD, POD, PAL, and PPO activities during graft union development and enhance the contents of IAA, GA3, and ZR except for the IL stage. The DEGs were identified in the plant hormone signal-transduction, phenylpropanoid biosynthesis, and phenylalanine metabolism through transcriptome analysis of CK vs. NAA at the IL, CA, and VB stage by KEGG pathway enrichment analysis. Moreover, the exogenous NAA application significantly promoted the expression of genes involved in the hormone signal-transduction pathway, ROS scavenging system, and vascular bundle formation.

Combining wind and solar photovoltaic (PV) generation can provide complementary renewable power production, but depends on correlated resources. This study analyzed 10 years of wind data from Naama, Algeria to evaluate the potential for evening wind generation to offset the loss of solar at sunset. Average wind speeds showed a distinct increase during evening hours, coinciding with the decrease in solar irradiance. Wind turbine simulations using a 1.5 MW turbine and the wind data showed sufficient resources for profitable power production after sunset. Statistical analyses confirmed significantly higher wind speeds and simulated power output in evening vs daylight periods (p<0.05). The Pearson correlation coefficient between evening wind speeds and decreasing solar irradiance was 0.63, supporting a strong positive relationship. These findings indicate Naama has adequate wind resources to deploy economically viable wind power capacity that can complement existing solar infrastructure and provide renewable electricity after dark , .

This work communicates line strength data and associated scripts for computation and spectroscopic fitting of selected transitions of the diatomic molecules AlO, C2, CN, OH, N2+, NO, and TiO. For ease of use, the scripts for data analysis are designed for inclusion in various software packages or program languages. The accuracy of the data is of the order of less than one picometer, suitable for analysis of laser-induced fluorescence and laser-plasma spectra. Selected results demonstrate the applicability of the program for data analysis in laser-induced optical breakdown spectroscopy primarily at The University of Tennessee Space Institute, Center for Laser Applications. Representative spectra are calculated and referenced to measured data records.

Drug-induced pruritus triggers a desire to scratch, thereby diminishing one’s quality of life. Certain instances of this phenomenon follow complex mechanisms of action that diverge from histamine-mediated pathways, known contributors to pruritus. However, investigations into the relationship between drugs and pruritus are limited. In this study, data mining techniques were employed to comprehensively analyze the characteristics of drugs linked to pruritus, using the FDA Adverse Event Reporting System(FAERS) Data maintained in the United States. Reports linked to pruritus demonstrated noteworthy differences in gender, age, and weight when compared with non-pruritus cases. Among the leading candidates for drugs prompting pruritus were ophthalmic drugs, systemic antibacterials, contrast media, dermatological antifungals, and dermatological preparations. Principal component analysis showed that the second principal component served as an indicator for distinguishing between onsets at mucous membranes or the skin surface. Additionally, the third principal component functioned as an indicator for categorizing administration methods as either invasive or noninvasive. Furthermore, a hierarchical cluster analysis conducted on these obtained principal components revealed the potential for classifying drugs based on the site of pruritus onset and the method of drug administration. These findings contribute to the development of targeted prevention and treatment strategies for avoiding pruritus in clinical practice.

The motivations for the move to electrified vehicles are discussed with reference to their improved energy efficiency, their potential for lower CO2 emissions (if the electricity system is decarbonized), their lower (or zero) NOx/particulate matter (PM) tailpipe emissions, and the lower overall costs for owners. Some of the assumptions made in life-cycle CO2 emissions calculations are discussed and the effect of these assumptions on the CO2 benefits of electric vehicles are made clear. A number of new tribological challenges have emerged, particularly for hybrid vehicles that have both a conventional internal combustion engine and a battery, such as the need to protect against the much greater number of stop-starts that the engine will have during its lifetime. In addition, new lubricants are required for electric vehicle transmissions systems. Although full battery electric vehicles (BEVs) will not require engine oils (as there is no engine) they will require a system to cool the batteries – alternative cooling systems are discussed, and where these are fluid based, the specific fluid requirements are outlined.

Soil-transmitted helminthiasis (STH), a neglected tropical disease (NTD) remains a major health problem all over the world including Ghana, which has STH prevalence of 25.4%. To control the disease, the government of Ghana currently concentrates on implementing mass drug administration (MDA) efforts focusing only among school-aged children. However, various studies have suggested that focusing on only a specific group for MDA may not be cost-effective. Moreover, some adults such as teachers and school-workers spend large fraction of their time with children, who shed more parasite in environment due to unhygienic behavior, and thus have a higher risk of getting infected as compared to other adults. In this study we use a mathematical model to evaluate age-structured and risk-based policies for implementing MDA while capturing transmission dynamics of STH in Ghana. A cost model was developed that included various costs related to MDA to study cost-effectiveness of current policies of MDA in Ghana against novel policies to control STH in Ghana. We carry out analysis for five different scenarios— I: no MDA (baseline), II: current MDA policy (focusing children) in Ghana, III: MDA for different age groups (adults and children groups) for unlimited budget, IV: MDA for different age groups with limitations of number of individuals treated, and, V: MDA for different groups based on their risk of getting infected (adults school workers (high-risk group), adults non-school workers and children groups). Our results suggest that it might be more cost-effective to allocate treatment through MDA to at least some proportion of adults along with children. In case of unlimited budget, the best strategy in Scenario IV would be to treat approximately 22% of adults and approximately 45% of children. The most cost-effective among the 5 scenarios is suggested through scenario V, where high-risk adults group and children are provided MDA at higher level than low-risk adults. In conclusion, age-structured and risk-based allocation of treatment and resources is crucial to reducing STH load in developing countries.

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Singapore Swimming Club. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots. Key results include: 1. The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. 2. QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. 3. Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). 4. A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players' shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics.

The SWOT analysis, pivotal in the competitive healthcare sector, aids dentists in identifying and analyzing internal strengths and weaknesses, as well as external opportunities and threats. This study focuses on senior dental students, aiming to scrutinize their use of SWOT analysis and evaluate its efficacy as an educational tool for career decision-making in dental entrepreneurship. The research sample comprises 116 senior dental students in the final stages of their dental education, with data collection accomplished through the administration of an e-questionnaire. The data extracted from the SWOT analysis encompass internal and external factors, gender distinctions, and outcomes derived from Stepwise Binary Logistic Regression concerning predictor markers. The results from the SWOT analysis of 114 questionnaires, revealed that participants identified communication skills (50%) and organization skills (49.10%) as their primary strengths, followed by favorable personal traits contributing to goal success (36%). Weaknesses predominantly centered around emotional and personal traits like anxiety (41.20%) and other characteristics, alongside practical challenges such as lack of initial capital (24.60%). Main opportunities included collaboration with experienced dentists (33.30%) and access to training programs (27.20%), while economic instability in Greece (77.20%) and the saturated dentist profession (26.30%) were perceived as significant threats. Gender differences were notable, with female dentists more likely to report organization skills as a strength and anxiety as a weakness. Values such as industriousness, persistence, and ethics were commonly shared, with actions focusing on training programs (57.9%) and gaining experience with experienced dentists (29.8%). Cluster analysis identified two subgroups, with one emphasizing on utilizing all available options (n=49) and the other prioritizing on gaining professional skills and experience (n=65). Logistic regression indicated that participants valuing industriousness were less likely to explore all available options, while those recognizing personal traits were more likely to do so. The study's outcomes highlight key predictor factors linked to a proactive orientation in career decision-making among senior dental students. These insights offer valuable implications for educational institutions and career counselors.

Flux-switching permanent magnet (FSPM) motors have gained increasing attention in the electric vehicles (EVs) applications due to the advantages of high power density, high efficiency. However, the heat sources of both permanent magnet (PM) and armature winding are located on the limited stator space in the FSPM motors, which may result in the PM overheated and irreversible demagnetization caused by temperature rise and it is often ignored in the conventional thermal analysis. In this paper, a new electrical-thermal two-way coupling design method is proposed to analyze the electromagnetic performances, where the change of PM material characteristics under different temperatures is taken into consideration. Firstly, the motor topology and design equations are introduced. Secondly, the demagnetization curves of PM materials under different temperatures are modeled due to PM materials are sensitive to the temperature. And based on the electrical-thermal two-way coupling method, the motor performances are evaluated in details, such as the load PM flux linkage and output torque. Then, the motor is optimized, and the electromagnetic performances between initial and improved motors are compared. Finally, a prototype motor is manufactured, and the results are validated by experimental measurements.

Cyanobacteria, microorganisms performing oxygenic photosynthesis, must adapt their metabolic processes to environmental challenges such as day and night changes. PipX, a unique regulatory protein from cyanobacteria provides a mechanistic link between signalling protein PII, a widely conserved (in bacteria and plants) transducer of carbon/nitrogen/energy richness, and the transcriptional regulator NtcA, which controls a large regulon involved in nitrogen assimilation. PipX is also involved in translational regulation by interaction with the ribosome-assembly GTPase EngA. However, increases of the PipX/PII ratio are toxic, presumably by abnormally increased binding of PipX to other partner(s). Here we present mutational and structural analyses of reported PipX-PII and PipX-NtcA complexes, leading to identification of single amino acid changes that decrease or abolish PipX toxicity. Four out of 11 mutations decreasing toxicity did not decrease PipX levels, suggesting that the targeted residues (F12, D23, L36, R54) provide toxicity determinants. In addition, one of those four mutations (D23A) argued against the over-activation of NtcA as the cause of PipX toxicity. Since most mutations at residues contacting PII decreased PipX levels, PipX stability would depend on its ability to bind to PII, a conclusion supported by the light-induced decrease in PipX levels observed in Synechococcus elongatus PCC7942.

The adoption of digital health technologies accelerated during Covid-19, with concerns over the equity of access due to digital exclusion. Using data from a text-based online mental health service for children and young people we explore the impact of the pandemic on service access and presenting concerns and whether differences were observed by sociodemographic characteristics in terms of access (gender, ethnicity and deprivation). We used interrupted time-series models to assess whether there was a change in the level and rate of service use during the Covid-19 pandemic (April 2020-April 2021) compared to pre-pandemic trends (June 2019-March 2020). Routinely collected data from 61221 service users were extracted for observation, those represented half of the service population as only those with consent to share their data were used. The majority of users identified as female (74%) and White (80%), with an age range between 13 and 20 years of age,. There was evidence of a sudden increase (13%) in service access at the start of the pandemic (RR 1.13 95% CI 1.02, 1.25), followed by a reduced rate (from 25% to 21%) of engagement during the pandemic compared to pre-pandemic trends (RR 0.97 95% CI 0.95,0.98). There was a sudden increase in almost all presenting issues apart from physical complaints. There was evidence of a step increase in the number of contacts for Black/African/Caribbean/Black British (38% increase; 95% CI: 1%-90%) and White ethnic groups (14% increase; 95% CI: 2%-27%) ), the sudden increase in service use at the start of the pandemic for the most (58% increase; 95% CI: 1%-247%) and least (47% increase; 95% CI: 6%-204%) deprived areas. During the pandemic, contact rates decreased, and referral sources change at the start. Findings on access and service activity align with other studies observing reduced service utilisation. The lack of differences in deprivation levels and ethnicity at lockdown suggests exploring equity of access to the anonymous service. The study provides unique insights into changes in digital mental health use during Covid-19 in the UK.

The increasing complexity of the design and operation evaluation process of multi-energy grids (MEGs) requires tools for the coupled simulation of power, gas and district heating grids. Most tools analyzed in this paper either do not allow coupling of infrastructures, simplify the grid model or are not publicly available. We introduce the open source piping grid simulation tool pandapipes that – in interaction with pandapower - fulfills three crucial criteria: clear data structure, adaptable MEG model setup and performance. In an introduction to pandapipes we illustrate how it fulfills these criteria through its internal structure and demonstrate how it performs in comparison to STANET®. Then we show two case studies that have been performed with pandapipes already. The first case study demonstrates a peak shaving strategy as interaction of a local electricity and district heating grid in a small settlement. The second case study analyzes the potential of a power-to-gas device to serve as flexibility in a power grid under consideration of gas grid constraints. They both show the importance of a clear database, a simple simulation setup and good performance to set up different large and complex studies on grid infrastructure design and operation.

Background—In 2011, Miller and Goldman published a study in Human and Experimental Toxicology that found a counterintuitive, positive correlation, r = 0.70 (r² = 0.49, p < .0001), demonstrating that as nations require more vaccine doses for their infants, infant mortality rates (IMRs) tend to increase (worsen). The dataset (n = 30) included the United States, a nation that required the most vaccines for their infants, and all nations with better IMRs than the United States. Dr. E. Bailey, a professor at BYU, and her students, recently read the Miller-Goldman study and found it "troublesome that this manuscript is in the top 5% of all research outputs" and falsely claimed that its findings were due to "inappropriate data exclusion," i.e., failure to analyze the "full dataset" of all 185 nations. The "Bailey reanalysis," titled Infant vaccination does not predict increased infant mortality rate: correcting past misinformation, was posted to the medRxiv preprint server on September 10, 2021 (version 1) and October 5, 2021 (version 3) and Europe PMC preprint server on September 10, 2021. Objective—This present study examines the various claims postulated by the Bailey reanalysis and assesses the robustness of their methodology, analyses, and reported results and conclusions. Methods—Data discussed in this paper are based on the previously mentioned study by Miller and Goldman and the Bailey reanalysis. Results—Linear regression analysis of IMR and the number of vaccine doses for each country yield a statistically significant positive correlation of r = 0.70 (p < .0001) for the top nations (n = 30) chosen by Miller-Goldman and r = 0.16 (p < .04) for the "entire dataset" chosen by Bailey et al (n = 185). Bailey also conducted linear regression analyses (for the year 2019) of IMRs as a function of vaccination rates for each of eight different vaccines and reported statistically significant inverse correlations for 7 of 8 vaccines over the entire range of vaccination rates. However, Miller and Goldman reanalyzed the Bailey analyses for nations with vaccination rates below 60% and found no statistically significant correlation for six vaccines (DPT, Hib, hepatitis B, polio, rotavirus, and measles) and statistically significant positive correlations for tuberculosis (r = 0.8, p < .005) and pneumococcal (r = 0.6 p < .023) vaccines. Conclusions—Bailey’s reanalysis corroborates a statistically significant positive correlation originally reported by Miller and Goldman. However, Bailey’s reported correlation (r = +0.16, p < .04) is small, likely due to poor methodology (failing to account for covariates, i.e., disparities among numerous socioeconomic factors that add uncertainty to their conclusion). The r-value reported by the Bailey reanalysis demonstrates an effect size that is about one-fourth (0.16/0.70) that reported by Miller-Goldman—underscoring how critically important it is for Bailey's reanalysis to eliminate confounding variables. Moreover, Bailey’s linear regression analyses of IMR as a function of vaccination rates for each of eight different vaccines demonstrate that some countries with low vaccination rates have low IMRs, while other countries with high vaccination rates have high IMRs. Rather than supporting a strong inverse correlation, the Bailey reanalysis demonstrates high vaccination rates are neither necessary nor sufficient to cause low IMR.

Caribbean sponges of the genus Smenospongia are a prolific source of chlorinated secondary metabolites. The use of molecular networking as a powerful dereplication tool revealed the presence in the metabolome of S. aurea of two new members of the smenamide family, namely smenamide F (1) and G (2). The structure of smenamide F (1) and G (2) was determined by spectroscopic analysis (NMR, MS, ECD). The relative and the absolute configuration at C-13, C-15, and C-16 was determined on the basis of the conformational rigidity of a 1,3-disubstituted alkyl chain system (i.e. the C-12/C-18 segment of compound 1). Smenamide F (1) and G (2) were shown to exert a selective moderate antiproliferative activity against cancer cell lines MCF-7 and MDA-MB-231, while being inactive against MG-63.

Adeno-associated viruses (AAVs, genus dependoparvovirus) are promising gene therapy vectors. In strains AAV1-12, the capsid gene VP1 encodes a recently discovered protein, MAAP, in an overlapping frame. MAAP binds the cell membrane by an unknown mechanism. We discovered that MAAP is also encoded in bovine AAV and in porcine AAVs (which have shown promise for gene transfer into muscle tissues), in which it is probably translated from a non-canonical start codon. MAAP is predicted to be mostly disordered except for a predicted C-terminal, membrane-binding amphipathic α-helix. MAAP has a highly unusual composition. In particular, it lacks internal methionines, and is devoid of tyrosines in most strains. Unexpectedly, we discovered that the N-terminus of VP1 also lacks several amino acids. In all AAVs that encode MAAP, the first 200 aas of VP1 are devoid of internal methionines, probably owing to a selection against ATG codons that could prevent translation of MAAP and of capsid isoforms (VP2, VP3). The N-terminus of VP1 also lacks cysteines, likely to avoid the formation of disulfide bridges when it becomes exposed outside of the capsid during post-endocytic trafficking. Finally, the region common to VP1 and VP2 lacks tyrosine in the vast majority of AAVs that encode MAAP. Avoiding these "forbidden" aas in MAAP and VP1 when creating recombinant AAV capsids might increase the efficiency of capsid design. Conversely, the presence of "forbidden" aas in some rare strains probably indicates that they have unusual properties that could help us understand the viral cycle.

Objective: To evaluate the existing meta-analytic evidence of associations between exposure to ultra-processed foods, as defined by the Nova food classification system, and adverse health outcomes. Design: Systematic umbrella review of existing meta-analyses. Data Sources: MEDLINE, PsycINFO, Embase, and the Cochrane Database of Systematic Reviews, as well as manually searching reference lists from 2009 to June 2023. Inclusion Criteria: Systematic reviews and meta-analyses of cohort, case-control, and/or cross-sectional study designs that evaluated the associations between exposure to ultra-processed foods and adverse health outcomes in humans across the lifespan. Results: The search identified 45 unique pooled analyses, including 13 dose-response and 32 non-dose-response associations (n=9,888,373). Overall, direct associations were found between exposure to ultra-processed foods and 32 (71%) health outcome domains spanning cardiometabolic, gastrointestinal, and respiratory conditions, cancer, mental health, and mortality. Based on a pre-specified evidence classification criteria, convincing evidence (Class I) supported direct associations between greater ultra-processed food exposure and higher risks of incident cardiovascular disease-related mortality (RR: 1.50; 95%CIs: 1.37 to 1.63), type two diabetes (dose-response RR: 1.12; 95%CIs: 1.11 to 1.13), and depressive outcomes (HR: 1.21; 95%CIs: 1.16 to 1.28), as well as higher risks of prevalent anxiety outcomes (OR: 1.48; 95%CIs: 1.37 to 1.59) and combined common mental disorder outcomes (OR: 1.53; 95%CIs: 1.43 to 1.63). Highly suggestive (Class II) evidence indicated that greater exposure to ultra-processed foods was directly associated with higher risks of incident all-cause mortality (RR: 1.21; 95%CIs: 1.15 to 1.27), heart disease-related mortality (dose-response HR: 1.66; 95%CIs: 1.51 to 1.84), and type two diabetes (OR: 1.40; 95%CIs: 1.23 to 1.59), together with higher risks of prevalent obesity (OR: 1.55; 95%CIs: 1.36 to 1.77), adverse sleep-related outcomes (OR: 1.41; 95%CIs: 1.24 to 1.60), and wheezing (RR: 1.40; 95%CIs: 1.27 to 1.55). Out of the remaining 34 pooled analyses, 21 were graded as suggestive or weak strength (Class III-IV) and 13 were graded as no evidence (Class V). Using the GRADE framework, 22 pooled analyses were rated as "Low" quality, with 19 rated as "Very Low” and four rated as "Moderate" quality. Conclusions: Higher exposure to ultra-processed food was associated with a greater risk of adverse health outcomes, especially cardiometabolic, common mental disorder, and mortality outcomes. These findings provide a rationale to evaluate the effectiveness of using population-based measures to target and reduce dietary exposure to ultra-processed foods for improved human health. Systematic Review Registration: PROSPERO CRD42023412732.

In the Formula Student Racing Car, the anti-deformation ability of the frame will affect the four-wheel positioning parameters of the racing car, and then affect the handling and stability performance of the racing car. The quality of the frame directly determines the power and economy of the racing car, so the performance of the frame is very important to the performance of the race. In order to improve the performance of the frame and reduce the total weight of the frame, the research on the lightweight design of the frame is particularly important. In this paper, based on the variable density method, the global topology optimization of the frame is carried out to obtain the distribution of the frame material, realize the efficient use of the material, and improve the torsional stiffness of the frame. Based on the adjoint variable method, the sensitivity analysis of the frame is carried out. According to the sensitivity analysis results, the size of the frame is optimized. The total mass of the frame is reduced by 12.8 %, and the performance of the maximum displacement and maximum stress is improved. The lightweight design of the frame is realized as a whole.

It is known that the class of p-normed spaces (0 < p ≤ 1) is an important generalization of usual normed spaces with rich topological and geometrical structure, but the most of tools and general principles in nature with nonlinearity have not been developed yet, thus the main goal of this paper is to develop tools for nonlinear analysis under the framework of p-vector spaces. In particular, we first develop the general fixed point theorems which provide solutions to answer Schauder conjecture since 1930’s in the affirmative for p-vector spaces when p = 1 (which is just general topological vector spaces); then the one best approximation result for upper semi-continuous mappings is given, which is used as a powerful tool to establish fixed points for non-self set-valued mappings with either inward or outward set conditions; and finally we establish comprehensive existence results of solutions for Birkhoff-Kellogg Problems, and the general principle of nonlinear alternative by including Leray-Schauder alternative and related results. The results given in this paper not only include the corresponding results in the existing literature as special cases, but also expected to be useful tools for the study of nonlinear problems arising from theory to practice.

This manuscript employs a surface stress-driven nonlocal model to explore the combined effects of long-range interaction and surface energy on higher vibration modes of functionally graded nanobeams. The nanobeam theory, based on Bernoulli-Euler kinematics, incorporates surface effects such as surface elasticity, surface residual stresses, surface density, and rotary inertia. Hamilton's principle is applied to derive the governing equation with size-dependent considerations. The main outcomes of a parametric investigation, considering four different kinematic boundary conditions (Cantilever, Simply-Supported, Clamped-Pinned, and Doubly-Clamped) while varying the nonlocal parameter and material gradient index, are presented and discussed. Additionally, the normalized natural frequencies for the second, third, fourth and fifth modes of vibrations are provided and analyzed for each case under study. The results underscore the model's effectiveness in capturing surface energy effects on the overall dynamic behavior of functionally graded Bernoulli-Euler nanobeams, offering a cost-effective approach for designing and optimizing nano-scaled structures.

This article aims to assess, discuss and analyze the disturbances caused by electromagnetic field (EMF) noise of medical devices used nearby living tissues, as well as the corresponding functional control via the electromagnetic compatibility (EMC) of these devices. These are minimally invasive and non-ionizing devices allowing various healthcare actions involving monitoring, assistance, diagnosis and image-guided medical interventions. Following to an introduction of the main items of the paper, the different imaging methodologies are conferred, accounting for their nature, functioning, employment condition and patient comfort and safety. Then the magnetic resonance imaging (MRI) components and their fields, the consequential MRI-compatibility concept and possible image artifacts are detailed and analyzed. Next, the MRI-assisted robotic treatments, the possible robotic external matter introductions in the imager, the features of MRI-compatible materials and the conformity control of MRI-compatibility, are analyzed and conferred. Afterward, the embedded, wearable and detachable medical devices, their EMF perturbation control and their necessary shielding protection technologies are presented and analyzed. Then, the EMC control procedure, the EMF governing equations, the body numerical virtual models, are conferred and reviewed. A qualitative methodology, case study, simple example illustrating the mentioned methodology is presented. The last Section of the paper discussed potential details and expansions of the different notions conferred in the paper, in the perspective of monitoring the disturbances due to EMF noise of medical devices working near living tissues.

Road-embedded inductive power transfer (IPT) systems have the potential to accelerate the electrification of the transportation sector. For these systems to be economically viable however, they need to have a similar durability and lifespan compared to that of asphalt roads. One area that has lacked investigation is thermally induced stresses in a primary IPT pad, which is caused by the increase in temperature of the pad when it is energized and the differing thermal expansion of the materials within. This paper presents an experimental and a finite-element based methodology for investigating the thermal-mechanical behaviour of a ¼-scale double-D pad, which was energized while suspended in air, as well as energized when embedded in pavement. A focus was placed on the measurement and prediction of strains in the magnetic ferrite cores because of their brittleness. Ferrite strains were measured using a combination of resistive strain gauges and non-metallic Fibre Bragg Grating (FBG) sensors. Coupled electromagnetic-thermal-structural simulations were conducted to predict temperature and strains in the system, with temperature-dependent properties obtained through physical testing. At an ambient temperature of 50°C, the temperature in the middle of the copper litz wire coil was predicted to be 100°C in both the suspended and embedded case. There was excellent correlation to experimental results, with a difference of less than 10% for most temperature measurements. When energized, the pad was predicted to experience an upward bow due to its temperature rise, resulting bending strains in the ferrite cores. At an ambient temperature of 50°C, maximum tensile strain in the ferrites of the embedded pad was measured to be 62 microstrain (με), with a root-mean square error that was 18 με across three sensors. The experimental and validated numerical methodology can be applied to full-scale operational IPT pads to analyse and improve their thermal-mechanical performance.

Neat polyimide films are known to be dense and rigid. They are therefore not suitable for use in membranes, sensors and sustainable energy storage applications. In this study a novel technique has been used to simulta-neously improve the rigidity, damping ability and impact resistance of polyimide membranes. It is demonstrated that dispersion of a small amount of polyaniline copolymer-modified clay of about 0.25-0.5 wt.% into the pol-yimide matrix, resulted in enhanced storage modulus while maintaining high damping ability and glass transition temperature, Tg. Novel polyimide/substituted polyaniline-copolymer-clay nanocomposite membranes containing poly(N-ethyl-aniline-co-aniline-2-sulfonic-acid-modified-clay (SPNEAC) was successfully prepared and in-corporated into polyimide matrix to form modified clay/polyimide nanocomposites. UV-Vis analysis of the nanocomposite films show that the optical transparency of the SPNEAC-PI nanocomposite membranes de-creased with increasing SPNEAC concentration due to the high UV-Vis absorption of SPNEAC. Transmittance of about 3 % was observed in the nanocomposite membrane containing 5wt.% modified clay at 500 nm wave-length, which is significantly lower than that for the neat PI membrane of about 36 %. The dispersion of SPNEAC containing high concentration of clay (≥ 40 wt.% clay), SPNEAC2 in polyimide matrix, resulted in attainment of a higher degree of imidization than was possible for the neat organoclay/polyimide nanocomposite. This behavior is believed to be due to the synergistic interaction between PI and polyaniline copolymer modified clay. The viscoelastic property of polyimide and the nanocomposite membranes was measured by using the dynamic mechanical spectroscopy, DMS. It was shown that the glass transition temperature, Tg of the nano-composites decreased, while the damping ability and impact energy increased with increasing weight fraction of fillers.

Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, marker-based optical motion capture, but at much lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientation in which the sensor is attached to the body segments. This knowledge is commonly obtained via an anatomical calibration procedure based on precisely defined poses or motions, which is time-consuming and error-prone. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.

Tefluthrin (Tef) is categorized as a type-I pyrethroid insecticide, telmisartan (Tel) functions as an angiotensin II receptor blocker, and KB-R7943 was identified as an inhibitor of Na+-Ca2+ exchange process. However, the influence of these compounds on the amplitude and gating properties of voltage-gated Na+ current (INa) in neurons associated with pain signaling remains unclear. In cultured dorsal root ganglion (DRG) neurons, whole-cell current recordings revealed that Tef or Tel increased the peak amplitude of INa, concomitant with an elevation in the time constant of INa inactivation, particularly in the slow component. Conversely, exposure to KB-R7943 resulted in a depression in INa, coupled with a decrease in the slow component of the inactivation time constant of INa. Theoretical simulations and bifurcation analyses were performed on a modeled interneuron in the spinal dorsal horn. The occurrence of INa inactivation accentuated the subthreshold oscillations (SO) in the membrane potential. With an increase in applied current, SO became more pronounced, accompanied by the emergence of high-frequency spiking (HF) with a frequency of approximately 150 Hz. Moreover, an elevation in INa conductance further intensified both SO and HF. Consequently, through experimental and in silico studies, this work reflects that Tef, Tel, or KB-R7943 significantly impacts the magnitude and gating properties of INa in neurons associated with pain signaling. The alterations in INa magnitude and gating in these neurons suggest a close relationship with pain transmission.

The degradation of Proton-exchange membrane fuel cell (PEMFC) gasket materials is crucial in electric vehicles as it can cause hazardous hydrogen fuel leaks, which are usually due to high temperatures, pressures, and hydrogen fuel exposure. Degradation of gasket materials in PEMFC presents a critical concern for electric vehicle safety due to potential hydrogen fuel leaks. This study utilizes finite element analysis (FEA) to assess the suitability of gasket materials for PEMFC applications, focusing on aging and tensile conditions. The dual degradation framework, incorporating contact pressure and von Mises stress, is employed to evaluate Liquid Silicon Rubber (LSR) and Ethylene Propylene Diene Monomer (EPDM) materials. Under aging techniques, the Yeoh model exhibits the least Mean Absolute Percentage Error (MAPE) and computational cost of 0.27 seconds, while the Ogden model records the highest computational cost of 0.89 seconds. In evaluating MAE, Root Mean Square Error (RMSE), and R-squared metrics, LSR and EPDM materials demonstrate respective averages of 0.25%, 0.275%, 0.945%, and 0.815%, 0.685%, 0.77%. Tensile testing (Uniaxial) reveals RMSE and MAE values of 0.30%, 0.40%, and 0.50%, 0.40%, respectively. FEA proves instrumental in identifying suitable gasket materials for PEMFC applications. LSR emerges as the superior choice, demonstrating enhanced FEA modelling performance under aging and tensile conditions. These findings contribute valuable insights to the design and development of improved gasket materials, bolstering the safety and reliability of electric vehicles.

Blood gas analysis plays a central role in modern medicine. Advances in technology have expanded the range of available parameters and increased the complexity of their interpretation. By applying user-centered design principles, it is possible to reduce the cognitive load associated with interpreting blood gas analysis. In this international, multicenter study, we explored anesthesiologists' perspectives on Visual Blood, a novel visualization technique for presenting blood gas analysis results. We conducted interviews with participants following two computer-based simulation studies: the first utilizing Virtual Reality (VR) (50 participants) and the second without VR (70 participants). Employing the template approach, we identified key themes in the interview responses and formulated six statements, which were rated using Likert scales: from 1 (strongly disagree) to 5 (strongly agree) in an online questionnaire. The most frequently mentioned theme was the positive usability features of Visual Blood. The online survey revealed that participants found Visual Blood to be an intuitive method for interpreting blood gas analysis (median 4, IQR 4-4, p<0.001). Participants noted that minimal training was required to effectively learn how to interpret Visual Blood (median 4, IQR 4-4, p<0.001). However, adjustments are necessary to reduce visual overload (median 4, IQR 2-4, p<0.001). Overall, Visual Blood received a favorable response. The strengths and weaknesses derived from these data will help optimize future versions of Visual Blood to improve the presentation of blood gas analysis results.

Considering citizens' perceptions of their living environment is very helpful in making the right decisions for city planners who intend to build a sustainable society. Mental map analyses are widely used in understanding the level of perception of individuals regarding the surrounding environment. The present study introduces Aram Mental Map Analyzer (AMMA), an open-source program, which allows researchers to use special features and new analytical methods to receive outputs in numerical data and analytical maps with greater accuracy and speed. AMMA performance is contingent upon two principles of accuracy and complexity, the accuracy of the program is measured by Accuracy Placed Landmarks (APL) and General Orientation (GO), which respectively analyses the landmark placement accuracy and the main route mapping accuracy. Also, the complexity section is examined through two analyses Cell Percentage (CP) and General Structure (GS), which calculates the complexity of citizens’ perception of space based on the criteria derived from previous studies. AMMA examines all the dimensions and features of the graphic maps, and its outputs have a wide range of valid and differentiated information, which is tailored to the research and information subject matter that is required.

Affective computing in general and human activity and intention analysis in particular, is a rapidly growing field of research. Head pose and emotion changes, present serious challenges when applied to player’s training and ludology experience in serious games or analysis of customer satisfaction regarding broadcast and web services or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need of generating comprehensible visual representations from different sets of data, in this paper we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor).

The successful vaccination implies the induction of effective specific immune responses. We intend to find biomarkers among various immune cell subpopulations, cytokines and antibodies which could be used to predict the levels of specific antibody- and cell-mediated responses after measles-mumps-rubella vaccination. We measured 59 baseline immune status parameters (frequencies of 42 immune cell subsets, levels of 13 cytokines, immunoglobulins) before vaccination and 13 response variables (specific IgA and IgG, antigen-induced IFN-γ production, CD107a expression on CD8+ T lymphocytes, and cellular proliferation levels by CFSE dilution) 6 weeks after vaccination for 19 individuals. Statistically significant Spearman correlations between some baseline parameters and response variables were found for each response variable (p&lt;0.05). Due to the low number of observations relative to the number of baseline parameters and missing data for some observations, we utilized three feature selection strategies to select potential predictors of the post-vaccination responses among baseline variables: (a) screening of the variables based on the correlation analysis, (b) supervised screening based on the information of changes of baseline variables at day 7, (c) implicit feature selection is performed using regularization-based sparse regression. We identified optimal multivariate liner regression models for predicting the effectiveness of vaccination against measles-mumps-rubella using the baseline immune status parameters. It turned out that the sufficient number of predictor variables ranges from one to five depending on the response variable of interest.

The effectiveness of immunotherapy for cervical adenocarcinoma (CA) has not been demonstrated yet. It may be possible for us to use programmed cell death 1 (PD-1), programmed cell death-ligand 1 (PD-L1), and CD8 as biomarkers of response to immune therapy in CA patients. In the present study, we aimed to investigate whether the expression levels of PD-1, PD-L1, and CD8 can predict the prognosis of CA patients and their response to ICI therapy. The levels of the PD-1, PD-L1, and CD8 proteins were analyzed by immunohistochemical analysis from formalin-fixed, paraffin-embedded tumor samples. The correlation between the expression levels and patient prognosis was analyzed by the Kaplan–Meier method and univariate and multivariate Cox proportional hazard regression model. We observed a significant inverse-correlation between the PD-1 and CD8 expression (p=0.001, chi square test). We also found a significant inverse-correlation between the PD-L1 and CD8 expression (p=0.027). The overall survival was significantly worse in patients with positive PD-1 expression (p=0.027). Similarly, the progression-free survival was also worse (p=0.087). Our results demonstrate that a high level of PD-1 expression is associated with a poor prognosis in CA patients. Further research is necessary to identify the molecular mechanisms that mediate this association.

The aim of this work was to determine if the development of low-cost or no-cost finite element analysis (FEA) software has advanced to the point where it can be used in place of trusted commercial FEA packages for linear static structural analyses using isotropic material models. Nonlinear structural analysis will be covered in a separate paper. Several suitable packages were identified, these underwent a process of systematic elimination when they were unable to meet the minimum imposed qualitative criteria. Three packages were chosen to be subjected to performance benchmarking, namely: Code_Aster/Salome Meca; Mecway and Z88 Aurora. SimScale, a browser-based analysis package was included as well because it met all the baseline criteria and has the potential to offer a completely cloud-based approach to computer aided engineering, potentially reshaping the way an engineering business views its operational capabilities. This paper presents the test cases and simulation results for packages that fall under the linear static structural analysis type.

The first detection of gypsum (CaSO4.2H2O) by the Mars Science Laboratory (MSL) rover Curiosity in the Gale Crater, Mars created a profound impact on planetary science and exploration. The unique capability of plasma spectroscopy involving in situ elemental analysis in extraterrestrial environments, suggesting the presence of water in the red planet based on phase characterization and providing a clue to Martian paleoclimate. The key to gypsum as an ideal paleoclimate proxy lies in its textural variants, and in this study terrestrial gypsum samples from varied locations and textural types have been analyzed by Laser Induced Breakdown Spectroscopy (LIBS) technique. Petrographic, sub-microscopic and powder X-ray diffraction characterizations confirm the presence of gypsum (hydrated calcium sulphate; CaSO4.2H2O), bassanite (semi-hydrated calcium sulphate; CaSO4.1/2H2O) and anhydrite (anhydrous calcium sulphate; CaSO4) along with accessory phases (quartz and jarosite). The principal component analysis of LIBS spectra from texturally varied gypsums can be differentiated from one another because of the chemical variability in their elemental concentrations. The concentration of gypsum is determined from the partial least-square regressions model. Rapid characterization of gypsum samples with LIBS is expected to work well in extraterrestrial environments.

Fly ash cement has rarely been used in Japan, mainly because its strength development is slower than ordinary Portland cement. In this research, the effect of the new fly ash cement with both high alite (C3S) cement and fly ash modified by electrostatic belt separation method on cracking resistance of precast concrete prepared by steam curing was studied. The mechanical and shrinkage properties of the proposed fly ash concrete were compared with those of concrete made using OPC cement without fly ash. In order to study the cracking tendency of precast concrete with the proposed fly ash cement, thermal stress analysis was conducted taking into consideration of the experimental data of concrete properties with the different concrete mix proportions. A standard precast concrete box culvert model was used in this 3D FEM analysis and the distribution of temperature and relative humidity in cross section and induced restraint stress during and after steam curing were discussed. Steam-cured concrete with fly ash and high alite cement developed higher compressive strength on the first day of age than concrete with OPC. The proposed fly ash concrete developed high cracking resistance in the early days. On the other hand, the results showed that the drying shrinkage at later ages was the main cause of cracking.

Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Also, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.

In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients' health using clustering method, ensemble of binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient's health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients' gene expression profiles: the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps: in beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using SOTA clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters were these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was selection of the optimal cluster at each of the hierarchical levels which corresponded to minimum value of the quality criterion. At the next step, we have implemented classification procedure of the examined objects using four well known binary classifiers: logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC analysis using criteria included as the components the errors of both the first and the second kinds. The final decision concerning extraction of the most informative subset of gene expression profiles was taken based on the use of fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and output is the final solution concerning state of the patient's health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient’s health.

The links between rural and urban have often been the subject of study in Portugal. Nevertheless, the asymmetries between rural and urban have not ceased to increase over the last decades, contributing to an imbalance of economic, social, and territorial dimensions between regions. In order to counter this trend, policies to attract the population and tourism have been endorsed but have not been sufficient to promote territorial cohesion. This makes it necessary to rethink the capacity and the potential of rural territories in terms of mobilising their resources and, through them, promoting new and/or renewed relations with urban territories. This article aims to explore and map the links between urban and rural areas by analysing the routes taken by agri-food products, from production to marketing, with the support of a spatial analysis, using Geographic Information Systems (GIS). For that, we analysed a set of specialised and/or gourmet shops in Oorto and Lisbon, and their relations with the territories from which the commercialized agri-food products come. Most of these products originate in the inland rural regions of the country, and there is a clear geographical connection between the shops analysed and specific territories. Thus, it was possible to verify the increase in interest and consumption of agri-food products of rural origin and the spontaneous increase of this type of shop in the cities analysed, as well as identify the target audience of the shops and the spatial influence they have in the respective cities. These shops, in addition to selling agro-food products, are also promoters of rural territories in an urban environment.

This paper reviews the multiple roles of machine learning in improving the resilience of power grids, especially in applying new energy storage technologies. Energy storage technologies, such as compressed air energy storage, flywheel energy storage, and superconducting coil energy storage, significantly improve the power grid's ability to respond to load fluctuations and emergencies through intelligent control and optimisation of machine learning algorithms. This not only helps to optimise energy dispatch and improve the operational efficiency and flexibility of the grid but also significantly improves the stability and reliability of the grid so that it is better able to meet future challenges and needs.

Interoperable multilayer networks are pivotal and transformative in public digital health. They not only enhance the delivery of healthcare services but also contribute significantly to improving public health outcomes. Through better accessibility, efficiency, and data-driven decision-making, these networks are revolutionizing how we approach public health. The seamless data exchange and integration they facilitate across different healthcare systems and providers can lead to substantial cost savings for public healthcare systems. These networks pave the way for a more efficient and effective healthcare system by reducing redundancies, optimizing resource allocation, and minimizing unnecessary procedures. This study combines the pressing issues of public digital health with the innovative interpretation of multilayer network theory, proposing practical solutions that inspire academics and practitioners alike.Public digital health is not just about improving care access and patient engagement. It is also about ensuring the financial sustainability of our healthcare systems. Innovative multilayer network analysis is critical in this game, improving interoperability and reducing spatial and temporal constraints. These networks streamline processes and improve efficiency by easing complex interactions within healthcare systems. This process, in turn, allows healthcare providers to deliver high-quality care at a lower cost, transforming, whenever possible, in-patients and out-patients into home-patients. Networks are a cornerstone and a beacon of hope for transforming public digital health. They are increasing the Value for Money and enhancing a patient-centric approach, making us optimistic about the future of healthcare.This study shows that revolutionizing digital healthcare methodologies has a profound impact on crucial elements like resilience, interoperability, and patient well-being within public health. The enhancement of interoperability facilitates the smooth flow of data exchange across various platforms and healthcare providers, fostering improved collaboration and decision-making processes and elevating the safety of patients.

Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features causing limited code representation. Addressing this gap, our paper introduces the Statement-grained Hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets. SHT is distinctively designed with two encoders to learn both hierarchical and sequential features of code. One relational attention encoder processes the statement-grained hierarchical graph, producing hierarchical embeddings. Subsequently, another sequence encoder integrates these hierarchical structures with token sequences. The resulting enriched representation is then fed into a vanilla Transformer decoder, which effectively generates concise and informative summarizations. Our extensive experiments demonstrate that SHT significantly outperforms state-of-the-art approaches on two widely used Java benchmarks. This underscores the effectiveness of incorporating global hierarchical information in enhancing the quality of code summarizations.

This study investigates the suitability of the timestamp approach algorithm for predicting blood pressure (BP) using wearable and comfortable devices, showcasing its accuracy even with low-frequency sampled signals. Developed within the SINTEC European project, the algorithm utilizes Electrocardiogram (ECG) and Photoplethysmogram (PPG) sensors, demonstrating promising results in both laboratory and hospital settings. The research emphasizes the algorithm’s potential to transform continuous and non-invasive BP monitoring through wearable technology, offering frequent measurements with minimal discomfort. While the study shows promise, further exploration is needed, focusing on optimizing machine learning coefficients for long-term monitoring applications and addressing potential changes over time. Technological improvements, including the implementation of edge computing algorithms, hold the key to enhancing BP accuracy, denoising techniques, and integrating alarm systems for future wearable solutions. This study emphasizes the adaptability of the algorithm for diverse healthcare environments, paving the way for broader applications in convenient and reliable BP monitoring.

In this paper, we delve into the realm of aspect-level sentiment analysis, a sophisticated analytical task focused on pinpointing and interpreting the sentiment directed towards specific elements within a sentence. Traditional methods in this domain, primarily based on neural networks, have often overlooked the critical role of syntactic structures in sentences. To bridge this gap, we have developed the Syntax-Enhanced Sentiment Graph Network (SentiSyn). This pioneering model represents a significant step forward in aspect-level sentiment analysis, bringing to the forefront the utilization of word dependency relationships to enrich sentiment analysis. SentiSyn stands out by its innovative use of a dependency graph, a tool that meticulously maps out the intricate web of syntactic relationships surrounding a target aspect in a sentence. This approach allows SentiSyn to effectively capture and channel sentiment-related characteristics that are deeply rooted in the syntactic context of the aspect target. By doing so, SentiSyn unlocks a deeper understanding of sentiment dynamics in textual content, enabling a more nuanced and accurate sentiment analysis. Our comprehensive experimental evaluation of SentiSyn showcases its remarkable capabilities. When combined with advanced embedding techniques like GloVe and BERT, SentiSyn demonstrates a superior performance edge over several existing sentiment analysis methods. This performance leap is not just incremental; it represents a significant enhancement in the field of sentiment analysis, underscoring the importance of syntactic context in understanding sentiments. Furthermore, our analysis delves into how SentiSyn effectively leverages these embeddings to gain a more profound and contextually rich insight into sentiment dynamics. The results from our tests indicate that SentiSyn, with its unique approach to integrating syntactic structures and advanced embeddings, sets a new benchmark in aspect-level sentiment analysis, offering both enhanced accuracy and deeper sentiment understanding.

High-throughput research on bimetallic nanoparticles brought a vast overview of their characteristics and catalytic activities.¹ However, traditional bimetallic nano-allloy quantification methods serving catalyst preparations are not suitable for the fast and robust data output of high-throughput experiments. Hence, this work designed and validated a more fast, more neat, and reliable quantitative analysis using X-ray fluorescence spectroscopy for bimetallic nanoparticles synthesized by high-throughput setups. The results demonstrated this method to be accurate with high precision. Furthermore, the agreement to qualitative results from an energy-dispersive X-ray spectrometer equiped on a scanning electron microscope provided an option to quantify a bimetallic nano-alloy by just analyzing one of the two composing elements. Thus, this method is proved to be highly compatible with high-throughput experiments.

Ransomware attacks have risen alarmingly, with encryption techniques becoming more complex. This paper introduces a novel detection model tailored for ransomware's distinctive characteristics. The Intel PIN tool extracts Windows API invocation sequences related to file operations. These sequences are used to construct n-grams, forming feature vectors enhanced by a new Class Feature Weighting (CFW) metric to improve malware detection. Preliminary results demonstrate elevated accuracy and precision versus existing methods. The major contributions are: (1) Introducing an innovative deep learning model for few-shot ransomware classification using entropy features and transfer learning. (2) Achieving high weighted F1-score in classifying ransomware variants into families with limited training data. (3) Demonstrating the potential of entropy-based features to capture intricacies lost in image-based approaches, improving detection of new strains.

Audio recording in classrooms is a common practice in educational research, with applications ranging from detecting classroom activities to analysing student behaviour. Previous research has employed neural networks for classroom activity detection and speaker role identification. However, these recordings are often affected by background noise that can hinder further analysis, and the literature has only sought to identify noise with general filters and not specifically designed for classrooms. Although the use of high-end microphones and environmental monitoring can mitigate this problem, these solutions can be costly and potentially disruptive to the natural classroom environment. In this context, we propose the development of a neural network model that can specifically detect and filter out background noise in classroom recordings. This model would allow the use of lower quality recordings without compromising analysis capability, thus facilitating data collection in natural educational environments and reducing the costs associated with high-end recording equipment.

An important indicator of human-related pollution in watersheds is dissolved oxygen (DO). The DO is highly dependent on both space and time characteristics of the watershed and is directly linked to eutrophication, which impairs the development of both the aquatic fauna and flora, also negatively impacting the water quality. Aspiring to reach a more accurate and precise forecasting approach to predict levels of DO, the present work proposes new graph-based and transformer-based deep learning models. The models were trained and validated for the Credit River Watershed, and the results were compared with both benchmarking and literature-found approaches. The proposed Graph Neural Network Sample and Aggregate (GNN-SAGE) model was the best-performing approach, reaching coefficient of determination (R2) and Root Mean Squared Error (RMSE) values of 97% and 0.34 ppm, respectively. The findings from the Shapley additive explanations (SHAP) indicated that the GNN-SAGE benefited from spatiotemporal information from the surrounding stations, improving the model’s results, and that temperature is a major input attribute for determining future DO levels. The results established that the proposed GNN-SAGE model stands as a state-of-the-art solution for DO forecasting, with potential for real-time water quality applications.

The natural units of measure lauded by Max Planck more than 100 years ago are rarely used today. But many physical constants, including Planck's constant, the gravitational constant, the speed of light, vacuum permittivity, and vacuum permeability, consist of natural units in their unit dimensions. The natural units are present in all formulas containing these constants. The defining characteristic of the natural units is an alignment of unit values at the Planck scale. This alignment gives a computational basis of proportionality from which the correlated properties and dynamics of elementary particles, including wavelength, period, mass, momentum, and energy, manifest in equal or inversely proportional ratios of the Planck scale. These correlations explain many of the defining equations of quantum mechanics, classical gravity, and electromagnetism.

Although tourism seasonality significantly influences the livelihood activities of rural tourism households, limited research has explored how they adapt their livelihood strategies to achieve optimal outcomes. Employing an exploratory sequential mixed-methods design that combines thematic analysis and fuzzy-set Qualitative Comparative Analysis (fsQCA), we investigated the relationship between the livelihood strategies and outcomes of rural tourism households under the influence of tourism seasonality at the Guilin Karst World Heritage Site. The results indicate that livelihood strategies adopted by rural tourism households include "Tourism Persistence,” "Seasonal Employment,” and “Seasonal Farming” during the off-season, and “Extending Working Hours,” “Increasing Staffing Input,” and “Increasing Capital Input” during the peak season. Furthermore, these strategies form flexible combinations to realize livelihood outcomes, with “Extending Working Hours” being a necessary condition. The findings also revealed that the four configurations of seasonal livelihood strategies in the two patterns significantly contributed to high livelihood outcomes. One is named “Peak-Season Driven Pattern,” where “Extending Working Hours” and “Increasing Staffing Input” are the core conditions; the other is named “Peak-Off Blend-Driven Pattern,” where “Extending Working Hours,” “Increasing Capital Input,” and the absence of “Tourism Persistence” are the core conditions. These findings provide theoretical and practical insights for sustainable livelihood research.

Social media platforms are increasingly enabling the propagation of content from groups related to drug use, thus posing risks for the wider population and, in particular, individuals who are amenable to drug use and drug addiction. The detection of drug use content on social media platforms is a priority for governments, technology companies, and drug law enforcement organizations. To counter this issue, various techniques have been developed to identify and promptly remove drug use content, while also blocking its creators from network access. In this paper, we introduce a manually annotated Twitter dataset, comprising 156,521 tweets published between 2008 and 2022, specifically compiled for the purpose of drug use detection. The dataset underwent annotation by several group of expert annotators who classified the tweets as either drug use or non-drug use. Exploratory data analysis was conducted to comprehend the dataset's characteristics. Various classification algorithms, including SVM, XGBoost, RF, NB, LSTM, and BERT were employed using the dataset. Among the traditional machine learning models, SVM utilizing term frequency-inverse document frequency features achieved the highest F1-Score (0.9017). However, BERT with textual features concatenated with numerical and categorical features in ensemble method surpassed the performance of traditional models, attaining F1-Score of 0.9112. To facilitate future research and enhance English online drug use classification accuracy, the dataset will be made publicly available.

Micromobility has gained attention from policymakers, industry stakeholders, and academia; however, a comprehensive conceptualization of micromobility is still missing. Existing definitions are largely vehicle-centric: either listing modes or detailing vehicle characteristics. This paper addresses this gap by developing a ‘beyond vehicles’, multi-dimensional conceptualization of micromobility, accompanied by a novel socio-technical definition. Through a review of related concepts, combined with an analysis of the use and definitions of the term micromobility in publications, this study establishes a new conceptualization of micromobility. It incorporates human, social, and cultural dimensions, considers environmental, economic, infrastructure, vehicle technology, regulatory and policy aspects, and considerations for public health. Our definition of micromobility encompasses a wide range of mobility options typically used for shorter trips and manoeuvrable by an individual without motor assistance, at least for short distances. These modes are characterized by their ‘micro’ attributes, including low energy demand, environmental impact, and road space use relative to automobility. The conceptualization incorporates a range of micromobility modes, including fully human-powered (including walking), partially motor-assisted, and fully powered options. These modes typically operate at speeds not exceeding 25 to 32 kilometres per hour (or 45 km/h for faster options), weigh (typically substantially) below 350 kilograms and often yield significant (public) health benefits. Trip length is generally less than 15 kilometres, and daily distances under 80 kilometres. Importantly, our definition includes the practices, policies, cultures, and infrastructures that emerge around the use of micromobility options and shape their uptake. This proposed conceptualization significantly broadens the prevailing vehicle-focus in micromobility debates towards a socio-technical perspective. Embracing a widely accepted conceptualisation of micromobility would offer several advantages, including robust design standards, legislation, and evaluation metrics and methods. Additionally, this paper highlights the pivotal role micromobilities can play in transcending the limitations of automobility, towards more sustainable and equitable mobility futures.

This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

The abundance of information in academic articles, reports, and studies can make it challenging for researchers to gain insights from the existing literature. To address this issue, there is a growing demand for tools that can help researchers effectively parse and analyze large volumes of data. One such tool is DataDiscoveryLab, a software system that utilizes computer vision algorithms and NLP techniques to parse academic articles into text and figures, creating three separate databases. These databases allow researchers to quickly identify articles that may be relevant to their research questions, gain a deeper understanding of the research presented, and analyze visual data. The integration of article mining and computer vision in the DataDiscoveryLab software system provides researchers with a powerful tool for navigating the vast amount of scientific literature available today. Yet, as we will discuss in the latter papers these databases’ purpose is to create a bridge between researchers’ data and practically unlimited scientific publications. Yet, in this article, we will discuss how we plan to do that, and our efforts on integrating deep learning modes. After all, unlike already existing AI models, DataDiscoveryLab can be their combination and the first Generative AI in academia that can encompass every part of the natural sciences.

This paper presents the development and implementation of a machine learning model to estimate the future price of commodities in the Brazilian market from technical analysis indicators. For this, two databases were obtained regarding the commodities sugar, cotton, corn, soybean and wheat, which were submitted to the steps of data cleaning, pre-processing and subdivision. From the pre-processed data, recurrent neural networks of the long short-term memory type were used to perform the prediction of data in the interval of 1 and 3 days ahead. These models were evaluated using mean squared error, obtaining an accuracy between 0.00010 and 0.00037 on the test data for 1 day ahead and 0.00015 to 0.00041 for 3 days ahead. However, based on the results obtained, it can be stated that the developed model obtained a good prediction performance for all commodities evaluated.

The use of inertial measurement units (IMUs) to compute gait outputs such as the 3D lower limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences of amplitude. The mean RMSE and centered RMSE were respectively 7.5° and 4.0° for IMU-kinematics, and 7.9° and 3.8° for cluster-kinematics. Very good CCs were found in the sagittal plane for both IMUs and cluster-based kinematics at the hip, knee and ankle levels (CCs&gt;0.85). The overall mean amplitude difference was about 7°. These results reflected good accordance of our system with the reference, especially in the sagittal plane, but the presence of offsets requires caution for clinical use.

In this study, the dependence between Bitcoin (BTC) and economic policy uncertainty (EPU) of USA and China is estimated by applying latest methodology of quantile cross-spectral dependence. The findings indicate a positive return interdependence between BTC and EPU is high in short-term, and this dependence decreases as investment horizons increase from weekly to yearly. The information on above interdependence is also extracted by applying wavelet coherence analysis and the estimation results suggest that correlation between BTC and EPU is positive during short-term investment horizon. Furthermore, more diversification benefits of BTC can be obtained during USA-EPU as compared to China-EPU.

Functional Data Analysis (FDA) is a relatively new field of statistics dealing with data expressed in the form of functions. FDA methodologies can be easily extended to the study of imaging data, an application proposed in Wang et al. (2020), where the authors settle the mathematical groundwork and properties of the proposed estimators. This methodology allows for the estimation of mean functions and simultaneous confidence corridors (SCC), also known as simultaneous confidence bands, for imaging data and for the difference between two groups of images. This is especially relevant for the field of medical imaging, as one of the most extended research setups consists on the comparison between two groups of images, a pathological set against a control set. FDA applied to medical imaging presents at least two advantages compared to previous methodologies: it avoids loss of information in complex data structures and avoids the multiple comparison problem arising from traditional pixel-to-pixel comparisons. Nonetheless, computing times for this technique have only been explored in reduced and simulated setups (Arias-López et al., 2021). In the present article, we apply this procedure to a practical case with data extracted from open neuroimaging databases and then measure computing times for the construction of Delaunay triangulations, and for the computation of mean function and SCC for one-group and two-group approaches. The results suggest that previous researcher has been too conservative in its parameter selection and that computing times for this methodology are reasonable, confirming that this method should be further studied and applied to the field of medical imaging.

Biodiversity conservation questions human practices towards biodiversity and, therefore, largely conflicts with ordinary societal aspirations. Decisions on the location of protected areas, one of the most convincing conservation tools, reflect such a competitive endeavor. Operations Research (OR) brings a set of analytical models and tools capable of resolving the conflicting interests between ecology and economy. Recent technological advances have boosted the size and variety of data available to planners, thus challenging conventional approaches bounded on optimized solutions. New models and methods are requested to use such a massive amount of data in integrative schemes addressing a large variety of concerns. Here, we provide an overview on the past, present and future challenges that characterize spatial conservation models supported by OR. By enlarging the spatial, temporal, taxonomic and societal horizons of biodiversity conservation planners navigate around multiple bio-socioeconomic equilibria and are able to decide on cost-effective strategies to improve biodiversity persistence.

A signal analysis of the genoma sequenced of coronavirus variants: B.1.1.7, B.1.135, B.1.429-B.1.427, B.1.525 and P1 is presented. We deal with a certain type of finite alternating sum series having independently distributed terms associated with binary (0,1) indicators for the nucleotide bases A,C,G,T. This method provides additional information to conventional Similarity comparisons and Power Spectrum approaches. It leads to uncover distinctive patterns regarding the intrinsic data organization of genomic sequences according to its progression along the nucleotide bases position. Hence, the method could be useful for survelliance of genoma variants.

Growing energy demand in the distributed energy system (DES) besides new charging loads of electric vehicles (EVs) make DES infrastructure unavailable earlier than normal projections. DES lines need to be upgraded to carry the modern demand of customers. DES line upgrade (LU) cost minimized and deferred to the next years by using energy storage system (ESS). ESS offers the opportunity to use energy flexibly in the distribution grid. It is possible to use ESS to shift the inevitable upgrade costs of the distribution grid elements and increase the power quality of the distribution grid. In this study, a method is proposed to extend the lifespan of DES with ESS and reduce line upgrade and investment costs in the distribution system. For this purpose, the IEEE 33 bus test grid is used, and the proposed method is tested and analyzed with different case studies. According to the results, the proposed method can reduce the investment cost by up to 80%. Besides, ESS usage considering load increase and line conditions of the distribution grid, upgrade costs of distribution grid are shifted, resulting in an optimal dimensioning and positioning while extending the lifespan of the distribution grid elements and, at the same time, offering significant improvements in energy quality. Additionally, it is seen that the power losses of the distribution grid are reduced up to 26%, and the voltage profile of buses is improved with the usage of ESS.

This paper presents results from numerical and experimental investigation on Charpy tests in order to point out failure mechanisms and to evaluate new polymeric blends PP+PA6+EPDM. Charpy tests were done for initial velocity of the impactor of 0.96 m/s and its mass of 3.219 kg and these data were also introduced in the finite element model. The proposed model take into account the system of four balls, including support and the ring of fixing the three balls and it has a finer discretization of the impact area to highlight the mechanisms of failure and their development in time. The constitutive models for four materials (polypropylene with 1% Kritilen, two blends PP+PA6+EPDM and a blend PA6+EPDM) were derived from tensile tests. Running simulations for each constitutive model of material makes possible to differentiate the destruction mechanisms according to the material introduced in the simulation, including the initiation and the development of the crack(s), based on equivalent plastic strain at break (EPS) for each material. The validation of the model and the simulation results was done qualitatively, analysing the shape of broken surfaces and comparing them to SEM images and quantitatively by comparing the impact duration, energy absorbed by the sample, the value of maximum force during impact. The duration of the destruction of the specimen is longer than the actual one, explainable by the fact that the material model does not take into account the influence of the material deformation speed in Charpy test, the model being designed with the help of tests done at 0.016 m/s (1000 mm/min) (maximum strain rate for the tensile tests). Experimental results are encouraging for recommending the blends 20% PP+42% PA6+28% EPDM and 60% PA6+ 40%EPDM as materials for impact protection at low velocity (1m/s). Simulation results are closer to the experimental ones for the more brittle tested materials (with less content of PA6 and EPDM) and more distanced for the more ductile materials (with higher content of PA6 and EPDM).

Whether there is any inclination between structures and functions of antimicrobial peptides (AMPs) is a mystery yet to be unraveled. AMPs have various structures associated with many different antimicrobial functions, including antibacterial, anticancer, antifungal, antiparasitic and antiviral activities. However, none has yet reported any antimicrobial functional tendency within a specific category of protein/peptide structures nor any structural tendency of a specific antimicrobial function with respect to AMPs. Here we examine the relationships between structures categorized by three structural classification methods (CATH, SCOP and TM) and seven antimicrobial functions with respect to AMPs using an enrichment analysis. The results show that antifungal activities of AMPs were tightly related to two-layer sandwich structure of CATH, knottin fold of SCOP, and the first structural cluster of TM. The associations with knottin and TM cluster 1 even sustained through the AMPs with a low sequence identity. Besides, another significant mutual enrichment was observed between the third cluster of TM and anti-gram-positive-bacterial/anti-gram-negative-bacterial activities. The findings of the structure-function inclination further our understanding of AMPs and could help us design or discover new therapeutic potential AMPs.

Background: Periodontitis is a disease that leads to serious functional and aesthetic dysfunctions resulting in impairment of life quality; it exists in different forms and its etiology is related with multiple component causes. Angiogenesis, together with inflammatory infiltrate, are two key processes in the evolution of inflammatory processes involving gingival tissues. The aim of this study was to evaluate their alterations in relation to some important periodontal diseases-associated factors (i.e. smoke, sex, age, plaque, pus, probing pocket depth) in patients with diagnosis of Generalized Aggressive Periodontitis (GAP). Methods: Eighteen subjects with GAP were enrolled for this study. Percentage of inflammatory cells and vascular area were measured and evaluated in relation with each periodontal diseases-associated factor. Results: The results showed a significant decrease of the percentage of vascular area in association with almost all the periodontal diseases-related factors (smoke, age, PPD, plaque) and not significant differences in the percentage of inflammatory cells. Conclusions: Data suggest that, even if these factors are often commonly related to worsening of periodontal status, they did not affect inflammatory and vascular pattern, as we can assume. For the future, additional studies, aimed to evaluated each singular factor in a wider sample size, should be planned.

The sustainability of agriculture depends as much on the natural resources required for production as it does on the stakeholders that manage those resources. It is thus essential to understand the variables that influence the decision-making process of agricultural stakeholders to design educational programs, interventions, and policies geared towards their specific needs, a required step to enhance agricultural sustainability. We examined the perceptions, experiences, and priorities that influence management decisions of five major groups of agricultural stakeholders (conventional small grain producers, organic small grain producers, organic vegetable producers, extension agents and agro-industry crop consultants, and researchers) across the Montana, United States. Results revealed that while stakeholder groups have distinct perceptions, experiences, and priorities, there were similarities across groups. Specifically, organic vegetable and organic small grain producers showed similar responses that were, in turn, divergent of conventional producers, researchers, and crop consultants. Conventional small grain producers and researchers showed overlapping response patterns while crop consultants formed an isolated group. Our results reinforce the need for agricultural education and programs that address unique and shared experiences, priorities, and concerns of multiple stakeholder groups. This study endorses the call for a paradigm shift from the traditional top-down agricultural extension model to one that accounts for participants’ socio-ecological contexts to facilitate the adoption of sustainable agricultural systems that support environmental and human wellbeing.

There are now a wide variety of global metrics. To find the degree of overlap between these different measures, we employ a principal components analysis (PCA) to 15 indices across 145 countries. Our results demonstrate that the most important underlying dimension highlights that economic development and social progress go hand in hand with state stability. The results are used to produce categorical divisions of the world. The threefold division identifies a world composed of what we describe and map as Rich, Poor and Middle countries. A five-group classification provided a more nuanced categorization described as; The Very Rich, Free and Stable, Affluent and Free, Upper Middle, Lower Middle, and Poor and Not Free.

Does the choice of proxy for stock market development matter? This paper suggests that the growth effect of stock market development is sensitive to the choice of proxy and using alternative financial development indicators have practically no influence on the results. We found that using either the stock market capitalization to GDP ratio or the stock market returns; have a positive and significant effect on growth. However, we cannot make same conclusion when one uses either the ratio of total value of trades on the major stock exchanges to GDP or stock market turnover ratio to proxy for stock market development as the coefficient on these variables were found to be statistically insignificant. The indexes extracted from principal component analysis confirm the sensitivity of the effect to the choice of proxy. This finding suggest that stock market development is a conceptual terms, thus, representing it with single indicators make it impossible to identify which stock market development indicators have a significant positive growth effects.

Oscillometric blood pressure (BP) devices currently estimate a single point but do not identify fluctuations in BP or distinguish them from variations in response to physiological properties. In this paper, to analyze BP normality based on oscillometric measurements, we use statistical approaches including kurtosis, skewness, Kolmogorov-Smirnov, and correlation tests. Then, to mitigate uncertainties, we use a deep neural network (DNN) to determine the confidence limits (CLs) of BP measurements based on their normality. The proposed DNN regression model decreases the standard deviation of error (SDE) of the mean error (ME) and the mean absolute error (MAE) and reduces the uncertainty of the CLs and SDEs of the proposed technique. We validate the normality of the distribution of the BP estimation distribution which fits the Gaussian distribution very well. We use a rank test in the DNN regression model to demonstrate the independence of the artificial SBP and DBP estimations. First, we perform statistical tests to verify the normality of the BP measurements for individual subjects. The proposed methodology provides accurate BP estimations and reduces the uncertainties associated with the CLs and SDEs based on the DNN regression estimator.

In this study, we construct a new Atangana-Baleanu fractional model for whooping cough disease to predict future dynamics of the disease, as well as to suggest strategies to eliminate the disease in an optimal way. We prove that the proposed model has a unique solution which is positive and bounded. To measure contagiousness of the disease, we determine the reproduction number R0 and use it to examine the local and global stability at equilibrium points that have symmetry. Through sensitivity analysis, we determine parameters of the model that are most sensitive to R0. The ultimate aim of this research is to analyze different disease prevention approaches in order to find the most suitable one. For this, we include the vaccination and quarantine compartments in the proposed model and formulate an optimal control problem to assess the effect of vaccination and quarantine rates on disease control in three distinct scenarios. Firstly, we study the impact of vaccination strategy and conclude findings with presentation of graphical results. Secondly, we examine the impact of quarantine strategy on whooping cough infection with possible elimination from the society. Lastly, we implement vaccination and quarantine strategies together to visualize their combine effect on infection control. In addition to the study of an optimal control problem, we examine the effect of fractional order on disease dynamics as well as the impact of constant vaccination and quarantine rates on disease transmission and control. We determine that the optimal control strategy with the three controls is more effective in reducing the spread of whooping cough infection. Implementation of Toufik-Atangana type numercial scheme both for the state and adjoint equations is another contribution of this article.