Basic Features Research Articles

Architecture of new generation research institutions under reconstruction conditions

In the article, the authors focus on key methods and approaches that ensure optimal organization of space for scientific and research work. This contributes to achieving versatility, flexibility and the ability to transform the internal environment. Modern compositional techniques for the architectural transformation of objects of the type under consideration and methods of their application at various levels of architectural formation and development have been studied. The main criteria for the architectural and spatial formation of new generation research institutions have been identified, thanks to which an architectural ensemble is formed, the concept of spatial organization, the creation of a unified compositional structure, the organization of recognizable architecture. The basic principles and features that form the nature of sustainability, visual perception, and create a spatial structure of perception have been identified.

Does the type of aortic pathology affect periprocedural outcomes in patients undergoing thoracic endovascular aortic repair?

Aim: To examine whether the type of aortic pathology affects periprocedural outcomes in patients undergoing thoracic endovascular aortic repair (TEVAR). Material and Methods: This retrospective observational cohort study included 47 TEVAR patients in total. Based on the kind of aortic pathology, the patients were categorized into three groups: Group 1 (n=23) included patients with type B aortic dissection (TBAD), Group 2 (n=14) included patients with descending thoracic aortic aneurysm (DTAA), and Group 3 (n=10) included patients with thoracic aortic mural thrombus (TAMT). Preprocedural basic clinical features, procedural data, and postprocedural outcomes and complications were compared between the groups. Results: The study population consisted of 36 males and 11 females, with a mean age of 62.48±14.2 years. Most of the patients in Groups 1 and 2 were male (82.6% and 92.8%), while 40% of the patients in Group 3 were male, and this difference was statistically significant. Compared to patients in other groups, individuals in Group 2 were significantly older and exhibited a higher incidence of chronic obstructive pulmonary disease and coronary artery disease. Group 3 required thromboembolectomy more frequently during the postprocedural period. In terms of other postprocedural outcomes, complications and mortality, there were no significant differences between the groups. Conclusion: Our study demonstrated that the type of aortic pathology did not significantly influence periprocedural outcomes in patients undergoing thoracic endovascular aortic repair (TEVAR). The TEVAR procedure can be effectively performed in suitable patients with various pathologies of the descending thoracic aorta.

Survey of Citizens' Preferences for Combined Contact Tracing App Features During a Pandemic: Conjoint Analysis.

During the COVID-19 pandemic, an increased need for novel solutions such as digital contact tracing apps to mitigate virus spread became apparent. These apps have the potential to enhance public health initiatives through timely contact tracing and infection rate reduction. However, public and academic scrutiny has emerged around the adoption and use of these apps due to privacy concerns. This study aims to investigate public attitudes and preferences for contact tracing apps, specifically in Japan, using conjoint analysis to examine what specifications the public values most in such apps. By offering a nuanced understanding of the values that citizens prioritize, this study can help balance public health benefits and data privacy standards when designing contact tracing apps and serve as reference data for discussions on legal development and social consensus formation in the future. A cross-sectional, web-based questionnaire survey was conducted to determine how various factors related to the development and integration of infectious disease apps affect the public's intention to use such apps. Individuals were recruited anonymously by a survey company. All respondents were asked to indicate their preferences for a combination of basic attributes and infectious disease app features for conjoint analysis. The respondents were randomly divided into 2 groups: one responded to a scenario where the government was assumed to be the entity dealing with infectious disease apps (ie, the government cluster), and the other responded to a scenario where a commercial company was assumed to be this entity (ie, the business cluster). Samples of 500 respondents from each randomly selected group were used as target data. For the government cluster, the most important attribute in scenario A was distributor rights (42.557), followed by public benefits (29.458), personal health benefits (22.725), and profit sharing (5.260). For the business cluster, the most important attribute was distributor rights (45.870), followed by public benefits (32.896), personal health benefits (13.994), and profit sharing (7.240). Hence, personal health benefits tend to be more important in encouraging active app use than personal financial benefits. However, the factor that increased motivation for app use the most was the public health benefits of cutting infections by half. Further, concern about the use of personal data collected by the app for any secondary purpose was a negative incentive, which was more significant toward app use compared to the other 3 factors. The findings suggest that potential app users are positively motivated not only by personal health benefits but also by contributing to public health. Thus, a combined approach can be taken to increase app use.

Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic Mass-Forming Cholangiocarcinoma

Objectives The aim of the study is to investigate the ability of preoperative CT (Computed Tomography)-based radiomics signature to predict microvascular invasion (MVI) of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Materials and Methods Preoperative clinical data, basic CT features, and radiomics features of 121 IMCC patients (44 with MVI and 77 without MVI) were retrospectively reviewed. The loading and display of CT images, delineation of the volume of interest, and feature extraction were performed using 3D Slicer. Radiomics features were selected by the LASSO logistic regression model. Multivariate logistic regression analysis was used to establish the radiomics model, radiologic model, and combined model in the training set (n = 85) to predict the MVI of IMCC, and then verified in the validation set (n = 36). Results Among the 3948 radiomics features extracted from multiphase dynamic enhanced CT imaging, 16 most stable features were selected. The AUC of the radiomics model for predicting MVI in the training set and validation set were 0.935 and 0.749, respectively. The AUC of the radiologic model for predicting MVI in the training set and validation set were 0.827 and 0.796, respectively. When radiomics and radiologic models are combined, the predictive performance of the combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) is optimal, with an AUC of 0.958 in the training set and 0.829 in the test set for predicting MVI. Conclusions CT radiomics signature is a powerful predictor for predicting MVI. The preoperative combined model (constructed with shape, intratumoral vessels, portal venous phase tumor-liver CT ratio, and radscore) performed well in predicting the MVI.

A hybrid local-global neural network for visual classification using raw EEG signals

EEG-based brain-computer interfaces (BCIs) have the potential to decode visual information. Recently, artificial neural networks (ANNs) have been used to classify EEG signals evoked by visual stimuli. However, methods using ANNs to extract features from raw signals still perform lower than traditional frequency-domain features, and the methods are typically evaluated on small-scale datasets at a low sample rate, which can hinder the capabilities of deep-learning models. To overcome these limitations, we propose a hybrid local-global neural network, which can be trained end-to-end from raw signals without handcrafted features. Specifically, we first propose a reweight module to learn channel weights adaptively. Then, a local feature extraction module is designed to capture basic EEG features. Next, a spatial integration module fuses information from each electrode, and a global feature extraction module integrates overall time-domain characteristics. Additionally, a feature fusion module is proposed to extract efficient features in high sampling rate settings. The proposed model achieves state-of-the-art results on two commonly used small-scale datasets and outperforms baseline methods on three under-studied large-scale datasets. Ablation experimental results demonstrate that the proposed modules have a stable performance improvement ability on multiple datasets across different sample rates, providing a robust end-to-end learning framework.

The Social Contract in Epicureanism

Abstract Epicurus held that justice came into being when individuals made compacts with one another to secure the benefit that comes from not harming one another. He also distinguished just laws from those that are not just; and he recognized a virtue of justice. This much is well supported by our evidence. There is also much that is controversial. At the very basis, there is disagreement on his conception of justice. There are also basic questions on how compacts are related to the justice of laws, as well as to the virtue of justice. Plato and Aristotle severed compacts from virtue; and some modern thinkers consider the justice of institutions prior to that of individuals. What was Epicurus’ position? This paper will sketch an outline of Epicurus’ theory of justice as a path to the goal of a pleasant life. As Phillip Mitsis (1988) suggested, Epicurus sought to harmonize a contractual view of justice with the virtue of justice. My aim is to investigate further the coherence of this endeavor. As I will argue, Epicurus presents a unitary theory of justice, composed of a number of steps that develop out of each other in a consistent way. As a developmental path, it admits of much variation. By tracing its basic features, I seek to show how Epicurus puts individuals in charge of attaining their goal, pleasure.

Population Distribution Forecasting Based on the Fusion of Spatiotemporal Basic and External Features: A Case Study of Lujiazui Financial District

Predicting the distribution of people in the time window approaching a disaster is crucial for post-disaster assistance activities and can be useful for evacuation route selection and shelter planning. However, two major limitations have not yet been addressed: (1) Most spatiotemporal prediction models incorporate spatiotemporal features either directly or indirectly, which results in high information redundancy in the parameters of the prediction model and low computational efficiency. (2) These models usually incorporate certain basic and external features, and they can neither change spatiotemporal addressed features according to spatiotemporal features nor change them in real-time according to spatiotemporal features. The spatiotemporal feature embedding methods for these models are inflexible and difficult to interpret. To overcome these problems, a lightweight population density distribution prediction framework that considers both basic and external spatiotemporal features is proposed. In the study, an autoencoder is used to extract spatiotemporal coded information to form a spatiotemporal attention mechanism, and basic and external spatiotemporal feature attention is fused by a fusion framework with learnable weights. The fused spatiotemporal attention is fused with Resnet as the prediction backbone network to predict the people distribution. Comparison and ablation experimental results show that the computational efficiency and interpretability of the prediction framework are improved by maximizing the scalability of the spatiotemporal features of the model by unleashing the scalability of the spatiotemporal features of the model while enhancing the interpretability of the spatiotemporal information as compared to the classical and popular spatiotemporal prediction frameworks. This study has a multiplier effect and provides a reference solution for predicting population distributions in similar regions around the globe.

What the visual system can learn from the non-dominant hand: The effect of graphomotor engagement on visual discrimination.

Previous studies have demonstrated that engaging in graphomotor activity for creating graphemes can enhance their subsequent visual discrimination. This suggests a positive influence of the motor system on visual learning. However, existing studies have emphasized the dominant hand, which is superiorly dexterous in fine-motor movements. This near-exclusive focus prompts the inquiry of whether the observed perceptual facilitation is a general characteristic of the motor system, or specific to pathways controlling the skilled over-trained dominant hand. Furthermore, the mechanistic underpinning of visual facilitation from graphomotor training (i.e., the individual contribution of motor activity, temporal evolution of the visual trace, variability of visual output) remain unclear. To address these questions, we assessed visual discrimination capabilities of healthy right-handed participants (N = 60) before and after graphomotor or visual training. Contrary to our initial expectation, graphomotor engagement with the non-dominant hand did not yield additional benefits to visual learning beyond those attainable through visual training alone. Moreover, graphomotor training with the non-dominant hand resulted in visual discrimination improvements comparable to those of dominant hand training, despite the inherent differences between hands in motor performance and in the amount of improvement in shape tracing throughout training. We conclude that the motor components of graphomotor activity may not be critical for visual learning of shapes through tracing activity. Instead, our results are in agreement with the symbolic theoretical account, suggesting that basic shape features required for discrimination can be acquired through visual inspection alone, providing a perspective on the improvements observed in prior studies.

SEMANTICS AND MEANING OF TERMS WITH THE COMPONENT ’RED’

The article provides an analysis of the results of a study on the semantics and meanings of terms with the component red in modern Bulgarian. The language material on which basis the study was carried out is an electronic corpus of Bulgarian language texts compiled from publicly available internet sources. Statistical approaches to search for concordances and collocations available through the Sketch Engine lexicographic software were used to discover the terms in the corpus, and the order in the search procedures (algorithm) was previously tested in other cited studies. The theoretical approach of the study involves tracing the semantics and meanings of terms for the color red and terms with the component red using both traditional approaches such as the theory of nomination (by detection of the main semantic component), and modern theories, such as the theory of prototypes (by evaluation of color’s prototypes) in the analysis of the semantics of these terms. Results and examples of high-frequency contextual corpus usages of red are provided to analyze the terms’ semantics and meanings both as a color term and terms with other (non-color) meanings (prototypes and terms of the basic features of the prototypes), and related conclusions on semantics and meanings of these terms are provided. The color component of the terms at issue is interpreted in the context of a broader understanding of color (such as macro-color), in its transition to other colors, and it is also noted that those terms relate to similar terms with a color component, for example white, establishing a semantic relation through the macro-color component.

Latent cognitive phenotypes in juvenile myoclonic epilepsy: Clinical, sociodemographic, and neuroimaging associations.

Application of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation. A total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K-means clustering of the resultant factor scores. Identified cognitive phenotypes were characterized and related to clinical, family, sociodemographic, and cortical and subcortical imaging features. Factor analysis revealed three underlying cognitive dimensions (general ability, speed/response inhibition, and learning/memory), with JME participants performing worse than unrelated controls across all factor scores, and unaffected siblings performing worse than unrelated controls on the general mental ability and learning/memory factors, with no JME vs sibling differences. K-means clustering of the factor scores revealed three latent groups including above average (31.4% of participants), average (52.1%), and abnormal performance (16.4%). Participant groups differed in their distributions across the latent groups (p < 0.001), with 23% JME, 22% siblings, and 2% unrelated controls in the abnormal performance group; and 18% JME, 21% siblings, and 59% unrelated controls in the above average group. Clinical epilepsy variables were unassociated with cluster membership, whereas family factors (lower parental education) and abnormally increased thickness and/or volume in the frontal, parietal, and temporal-occipital regions were associated with the abnormal cognition group. Distinct cognitive phenotypes characterize the spectrum of neuropsychological performance of patients with JME for which there is familial (sibling) aggregation. Phenotypic membership was associated with parental (education) and imaging characteristics (increased cortical thickness and volume) but not basic clinical seizure features.

Molecular Interactions and Structural Transformations in Water Complexes with Azoles in Solid Films: IR and DFT Study

AbstractAzoles are widely used as bioactive substances, herbicides, catalysts, corrosion inhibitors, polymeric compositors, organometallic ligands, and optical elements’ components. The interaction with water plays a crucial role in many applications of azoles. The objective of this study is to observe the molecular interaction and structural transformations of 1,2‐ and 1,3‐diazoles’, 1,2,3‐ and 1,2,4‐triazoles’ complexes with water molecules. We have selected the most valuable for application and theoretical interest azoles with different molecular structures, which have allowed us to consider the basic features of their aqueous complexes. We used the IR study method allowing to fix water intermediates in the solid phase. We have also employed the hybrid B3LYP functional DFT method with GD3BJ dispersion correction. At the cluster formation, the hydrogen atom transfer arises, providing a labile spatial arrangement of the water system. The formation of azoles’ complexes with a hydronium ion having relatively low binding energy allows the reshaping of the complexes’ structure at the hydrogen atom cluster‐to‐cluster transition. The complex formation is accompanied by the hydrogen atom transfer with the participation of both water and azole hydrogen atoms. This transfer provides the mobile spatial arrangement of clusters and hydrogen atom transition from donor to acceptor centers.

Transport of particles in a model of 2D Rayleigh-Benard convection that conserves energy and vorticity

In contrast with the Lorenz model for 2-dimensional Rayleigh - Bénard system, the model proposed by Howard - Krishnamurti (HK) allows the possibility of a large scale horizontally shear flow making feasible the study of particles transport. However, this model lacks conservation of energy and vorticity in the absence of forcing and dissipation. A model that incorporates conservation of energy and vorticity was proposed by A. Gluhovsky et al (GTA). We perform the linear stability analysis of this later model and study the transport process on this velocity field background and make a comparison with the features of the former model. We found that the basic bifurcation structure is retained by the GTA model and discuss the differences that they indeed have. In regard to the transport processes, the basic features found using the HK velocity field background are also kept by the GTA model. We determine the size of the rather small gap in the Rayleigh number where the transport process shift from being shear flow dominated to a Brownian diffusion process.

Parameterization of the Vertical Mixing for the Luzon Undercurrent in the Northern Western Pacific Ocean

AbstractThe Luzon Undercurrent (LUC) is one of the most significant western boundary undercurrents in the northern western Pacific Ocean (WPO), essential for subsurface water transport and connecting subtropical–equatorial circulations. Over the last three decades, abundant observational progress has been made in revealing the basic features of the LUC. However, the dynamics and thus successful modeling of the LUC remain unresolved. In this work, we conducted a high‐resolution (3 km, 60 levels) numerical investigation of the WPO circulation using the China sea multi‐scale ocean modeling system. We paid particular attention to the vertical mixing, aiming to reasonably resolve the LUC by modifying the vertical mixing parameterization. Based on physics reasoning and experiments of physically based modeling, we designed an adaptive mixing scheme (AMS), which used a Munk‐like function in the thermocline for enhanced vertical mixing in the areas of small Richardson numbers. Using the AMS, we reproduced the two‐layer WPO circulations well and captured the inshore component of the LUC consistent with observations. Furthermore, we studied the dynamics of the LUC by analyzing the momentum balance and found that the LUC is primarily maintained by baroclinic pressure gradient force due to strong thermocline tilting near the western boundary. Enhanced vertical mixing in this highly sheared region crucially provides sufficient geostrophic support to sustain the LUC. Also, nonlinearity and submesoscale motions contribute positively to the LUC. This work advances physical understanding of the current‐undercurrent system and improves numerical capability in capturing the LUC and WPO circulations.

A review of recent clinical trials to evaluate disease-modifying therapies in the treatment of cardiac amyloidosis.

Cardiac amyloidosis (CA) is a serious condition that results in infiltrative cardiomyopathy and heart failure with preserved ejection fraction (HFpEF) that is caused by the extracellular deposition of amyloid fibrils within heart tissue. While many important features of CA have been known for years, its prevalence in elderly patients with HF is increasingly being recognized. Plasma cells produce monoclonal immunoglobulin light chains which results in the formation and aggregation of amyloid fibrils that are responsible for AL amyloidosis. CA is classified as originating from either transthyretin (ATTR) or light chain (AL) amyloidosis. ATTR CA may result from a genetic mutation in the TTR gene, which is inherited (ATTRv), or from age-related deposition from wild-type ATTR (ATTRwt). Cardiac involvement in AL amyloidosis is attributed to either of two mechanisms: the extracellular deposition of amyloid fibril in the myocardium, or direct cardiotoxicity from the fibril aggregates. Typing of amyloid fibrils, a critical determinant of therapy, has also improved with wider availability of laser capture and mass spectrometry of histologic specimens. Specific and accurate evaluation of CA is now possible using cardiac magnetic resonance imaging and bone scintigraphy tracers. Survival in CA has improved markedly as novel chemotherapy agents have become available, but challenges remain in advanced disease. Broadening the amyloid-specific therapeutic landscape to include RNA inhibitors, fibril formation stabilizers and inhibitors, and immunotherapeutic targeting of amyloid deposits holds promise and may improve outcomes in systemic and cardiac amyloidoses. Treatment strategies for CA has recently undergone transformative changes, leading to some progress in outcomes for certain patients. Here, we discuss the basic features of CA as well as the emergence of novel, disease-modifying strategies that have been recently evaluated in clinical trials for the treatment of CA.

A survey on feature extraction and learning techniques for link prediction in homogeneous and heterogeneous complex networks

Complex networks are commonly observed in several real-world areas, such as social, biological, and technical systems, where they exhibit complicated patterns of connectedness and organised clusters. These networks have intricate topological characteristics that frequently elude conventional characterization. Link prediction in complex networks, like data flow in telecommunications networks, protein interactions in biological systems, and social media interactions on platforms like Facebook, etc., is an essential element of network analytics and presents fresh research challenges. Consequently, there is a growing emphasis in research on creating new link prediction methods for different network applications. This survey investigates several strategies related to link prediction, ranging from feature extraction based to feature learning based techniques, with a specific focus on their utilisation in dynamic and developing network topologies. Furthermore, this paper emphasises on a wide variety of feature learning techniques that go beyond basic feature extraction and matrix factorization. It includes advanced learning-based algorithms and neural network techniques specifically designed for link prediction. The study also presents evaluation results of different link prediction techniques on homogeneous and heterogeneous network datasets, and provides a thorough examination of existing methods and potential areas for further investigation.

Clinical characteristics and prognostic analysis of adult patients hospitalized with acute myocarditis

Objective: To analyze the clinical characteristics of patients hospitalized with acute myocarditis and explore the relevant factors affecting the prognosis. Methods: This study was a retrospective cohort study. Patients with acute myocarditis hospitalized in the Second Affiliated Hospital of Harbin Medical University from March 2010 to October 2022 were included and divided into severe myocarditis group and non-severe myocarditis group according to clinical diagnosis results, retrospectively. Basic clinical features were collected and followed up in both groups. The primary endpoint was cardiac death and the secondary endpoint was cardiac re-hospitalization. According to the clinical outcome, Kaplan-Meier survival curve was drawn and assessed via log-rank P test. Cox proportional risk model was established to analyze the related factors affecting the prognosis of patients with acute myocarditis. Results: A total of 235 patients with acute myocarditis were included, including 138 males (58.7%), aged (34±16) years, 102 patients (43.4%) in the severe myocarditis group and 133 patients (56.6%) in the non-severe myocarditis group. Compared with the non-severe myocarditis group, the severe myocarditis group was older and had more clinical symptoms of dyspnea, edema, and syncope. At a follow-up of 3.8 (2.8, 4.8) years, cumulative cardiac mortality and cardiac readmissions in patients with acute myocarditis were 4.2% and 9.6%, respectively. Among them, patients with severe myocarditis had a higher risk of cardiac death and cardiac readmission than those without severe myocarditis (9.3% vs. 1.0%, log-rank P=0.004; 13.4% vs. 4.2%, log-rank P=0.021). Multivariate Cox analysis showed that the independent risk factor for cardiac death in patients with acute myocarditis was elevated serum creatinine level at baseline (HR=1.016, 95%CI: 1.003-1.028, P=0.013). Independent risk factors for cardiogenic readmission were increased left ventricular end-diastolic diameter on admission (HR=1.108, 95%CI: 1.035-1.187, P=0.003) and ventricular tachycardia or fibrillation during hospitalization (HR=6.563, 95%CI: 2.138-20.142, P=0.001). Conclusions: The patients with acute myocarditis had more clinical symptoms of dyspnea, edema, and syncope. Elevated serum creatinine level on admission was an independent risk factor for cardiac death in patients with acute myocarditis; increased end-left ventricular diameter on admission and ventricular tachycardia or fibrillation during hospitalization were independently associated with cardiac rehospitalization.

Progressive familial intrahepatic cholestasis: a brief review of the literature and our own clinical observation

Progressive familial intrahepatic cholestasis is one of the causes of direct hyperbilirubinemia in young children and the need for its diagnosis arises after the exclusion of such more common causes of cholestasis as biliary atresia, Alajille syndrome, as well as infectious and medicinal hepatopathies. The basic diagnostic feature that allows us to suspect the pathology in question is the absence of an increase in the level of γ-glutamine trans peptidase. To date, 12 types of progressive familial intrahepatic cholestasis have been described. For most of them, both severe cases with neonatal manifestation and rapid progression to cirrhosis, as well as variants with late development and a relatively benign course, are described. The article presents brief genetic and phenotypic characteristics of all 12 types of pathology. The author presents his own observation of a patient whose clinical and laboratory picture corresponded to Byler’s syndrome, but the identified genetic characteristics did not correspond to any of the known types.

On the Definition of Velocity in Discrete-Time, Stochastic Langevin Simulations

We systematically develop beneficial and practical velocity measures for accurate and efficient statistical simulations of the Langevin equation with direct applications to computational statistical mechanics and molecular dynamics sampling. Recognizing that the existing velocity measures for the most statistically accurate discrete-time Verlet-type algorithms are inconsistent with the simulated configurational coordinate, we seek to create and analyze new velocity companions that both improve existing methods as well as offer practical options for implementation in existing computer codes. The work is based on the set of GJ methods that, of all methods, for any time step within the stability criteria correctly reproduces the most basic statistical features of a Langevin system; namely correct Boltzmann distribution for harmonic potentials and correct transport in the form of drift and diffusion for linear potentials. Several new and improved velocities exhibiting correct drift are identified, and we expand on an earlier conclusion that, generally, only half-step velocities can exhibit correct, time-step independent Maxwell–Boltzmann distributions. Specific practical and efficient algorithms are given in familiar forms, and these are used to numerically validate the analytically derived expectations. One especially simple algorithm is highlighted, and the ability of one of the new on-site velocities to produce statistically correct averages for a particular damping value is specified.

An IoT Healthcare System With Deep Learning Functionality for Patient Monitoring

ABSTRACTCurrently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality.

Non-intrusive identification of building loads using EDCA-ShuffleNetV2 with fused feature visualization

Abstract Non-intrusive load monitoring (NILM) enables monitoring of appliance operation status and energy consumption without additional meters, providing innovative data support for energy management. However, current NILM systems still face challenges such as insufficient load feature information and low load identification accuracy. To utilize the load feature information and improve the accuracy of load identification more effectively, this paper first selects loads with high energy consumption and high usage frequency as the research object, makes full use of the load feature information through feature fusion, and converts the fused typical load data into an image through the Gramian summation angular field, and further carries out the identification of loads that still have similarity after feature fusion. Secondly, this paper improves the ShuffleNetV2 model structure by embedding an efficient dual-channel attention (EDCA) module in the basic feature extraction module of ShuffleNetV2 to enhance it to extract adequate load feature information. A residual structure is also introduced to mitigate the information loss and gradient disappearance issues of the model. Finally, the superiority and effectiveness of the fused load features and the proposed model in load identification and typical scenario applications are validated using the iAWE and AMPDS datasets.&amp;#xD;