bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of internet embeddedness on women’s non-farm employment under the power perception perspective: evidence from rural China
Authors: Yu Song , Yang Lulu , Yang Shimei , Li Lianjun
Published: January 2024

Introduction

Female empowerment can promote gender equality and realize women’s comprehensive development, while the Internet has opened up an effective channel for female empowerment.

Methods

Given the relative lack of women’s rights in rural China and the rapid Internet development, this paper, based on the 2021 China Comprehensive Social Survey data, first analyses the effect of Internet embeddedness on rural women’s non-farm employment using the Probit model. Then, it uses the mediation effect model to explore the role of women’s rights perception in the mechanism between Internet embeddedness and women’s non-farm employment. Finally, based on age and regional differences, we also analyze the impact of Internet embeddedness on female non-farm employment.

Results

The paper draws the following conclusions: (1) Internet embedding can promote female non-farm employment, and the probability of female non-farm employment is 3.71% for each degree of Internet embedding. (2) Internet embedding can enhance women’s perception of their rights and thus promote women’s non-farm employment. (3) Internet embedding can enhance the perception of rights of young rural women, which can promote their nonfarm employment. However, the influence of internet embedding on middle-aged women’s perception of rights is not significant. (4) Internet embedding in the eastern region can enhance women’s perception of rights and realize women’s non-farm employment; on the contrary, in the central and western regions, Internet embedding cannot enhance rural women’s perception of rights.

Discussion

Therefore, this paper proposes to release further the impact of the Internet in promoting rural women’s non-farm employment to improve the deprivation of rural women’s rights and promote gender equality and women’s comprehensive development.
Effects of physical activity participation on cognitive impairment in older adults population with disabilities
Authors: Lim Seung-Taek , Kwak Hyo-Bum , Kang Ju-Hee , Chang Eunwook , Joa Kyung-Lim , Park Hee-Jung , Park Dong-Ho
Published: January 2024

Background

Existing research on the association between cognitive function and physical activity in the older adults population with disabilities is limited. Additionally, there is a need to explore avenues for enhancing the longevity and quality of life among these individuals.

Objective

This study aimed to investigate the independent and joint associations between cognitive function and levels of physical activity in the older adults population with disabilities.

Methods

A total of 315 older adults adults (men = 182, women = 133), identified with disabilities based on medical evaluation, were recruited from the first survey of the Korean Longitudinal Study of Aging (KLoSA). Participants underwent assessments for cognitive function, physical activity (PA), activities of daily living (ADLs), instrumental activities of daily living (IADLs), and grip strength.

Results

ADLs (p < 0.001) and IADLs (p < 0.001) scores were significantly higher in the male normal cognitive group compared to both the male and female cognitive impairment groups. In an unadjusted model, disabled older adults individuals who did not meet the recommended PA guidelines showed an increased odds ratio for cognitive dysfunction (OR = 2.29, 95% CI = 1.32–3.97). Those participating in PA at least 1 day per week also demonstrated an elevated odds ratio (OR = 1.22, 95% CI = 1.08–1.38) for cognitive dysfunction compared to those who engaged in regular PA. A negative correlation was observed between K-MMSE scores and grip strength (r = 0.448, p < 0.001).

Conclusion

This study provides robust evidence that disabled older adults individuals who do not meet the recommended guidelines for PA or who do not participate in PA at least once a week have an increased likelihood of cognitive impairment compared to those who are regularly active.
Perceptions, facilitators and barriers to the implementation of interpersonal group therapy to treat depression among people living with HIV in Senegal: a qualitative study
Authors: Bernard Charlotte , Mané Idrissa , Ziadeh Salaheddine , Tine Judicaël Malick , Diaw Abibatou , Benzekri Noëlle , Ndiaye Ibrahima , Samba Oumar , Font Hélène , Bottai Thierry , Jacquesy Laurent , Verdeli Helen , Ngom Ndeye Fatou , Dabis François , Seydi Moussa , de Rekeneire Nathalie
Published: January 2024

Background

Depression is highly prevalent in people living with HIV (PLWH) but remains under treated in Sub-Saharan Africa. In this context, we conducted the first study of Group Interpersonal Therapy (IPT) to treat depression in PLWH in Senegal. We assessed the perceptions and experiences of patients and group facilitators, as well as barriers to implementation.

Methods

This study was conducted at the Fann National University Hospital Center in Dakar, the urban capital of Senegal. Qualitative data were collected during the implementation phase (February to June 2020 and then from January to February 2021), with a 6-month pause due to the COVID-19 pandemic. Twenty-five patients and three group facilitators were individually interviewed by a socio-anthropologist. Qualitative data were analyzed thematically.

Results

Group IPT was perceived as successful and beneficial by patients and facilitators. Patients reported positive experiences with group IPT and sustained outcomes. Beyond improving depressive symptoms, patients reported improvements in their social and professional lives, and the development of skills to prevent relapse. Group facilitators noted the benefits of therapy for their patients and for their professional skills, reporting greater clinical competence and improved supportive skills. Challenges to intervention implementation included confidentiality and patient privacy concerns, healthcare accessibility issues, and time demands.

Conclusion

In this first qualitative study of group IPT for depression in PLWH in Senegal, participants described both positive experiences with the intervention and challenges to its implementation. Future studies, conducted in suburban and rural communities outside of Dakar, would further inform the implementation of IPT in Senegal.
Promoting clean energy adoption for enhanced food security in Africa
Authors: Li Ning , Agene Deborah , Gu Lihan , Osabohien Romanus , Jaaffar Amar Hisham
Published: January 2024

The research investigated the idea of clean energy and how it affects food security utilizing panel data from selected African nations. The World Development Indicators (WDI) for the years 2005 to 2022, the Food and Agricultural Organization (FAO), and the Country Policy and Institutional Assessment (CPIA) were the sources of data. The study engaged the generalized method of moments (GMM), and the results showed that clean energy indicators have both beneficial and adverse effects on food security. This is crucial for policy toward the actualization of sustainable development goals of no poverty (SDG1), no hunger (SDG2), clean and affordable energy (SDG7), sustainable cities (SDG11), and climate action (SDG13). The findings of this study will benefit policymakers, governments, and organizations working toward promoting clean energy, sustainable agriculture, and food security in Africa. It will also benefit farmers and communities who rely on agriculture for their livelihoods. By implementing the recommendations of the study, these stakeholders can work toward a more sustainable and secure future for Africa. Additionally, the environment and global efforts to mitigate climate change will also benefit from the reduction in greenhouse gas emissions.
Homing and Engraftment of Hematopoietic Stem Cells Following Transplantation: A Pre-Clinical Perspective
Authors: Hasan Tanvir , Pasala Ajay Ratan , Hassan Dhuha , Hanotaux Justine , Allan David S. , Maganti Harinad B.
Published: January 2024

Hematopoietic stem-cell (HSC) transplantation (HSCT) is used to treat various hematologic disorders. Use of genetically modified mouse models of hematopoietic cell transplantation has been critical in our fundamental understanding of HSC biology and in developing approaches for human patients. Pre-clinical studies in animal models provide insight into the journey of transplanted HSCs from infusion to engraftment in bone-marrow (BM) niches. Various signaling molecules and growth factors secreted by HSCs and the niche microenvironment play critical roles in homing and engraftment of the transplanted cells. The sustained equilibrium of these chemical and biologic factors ensures that engrafted HSCs generate healthy and durable hematopoiesis. Transplanted healthy HSCs compete with residual host cells to repopulate stem-cell niches in the marrow. Stem-cell niches, in particular, can be altered by the effects of previous treatments, aging, and the paracrine effects of leukemic cells, which create inhospitable bone-marrow niches that are unfavorable for healthy hematopoiesis. More work to understand how stem-cell niches can be restored to favor normal hematopoiesis may be key to reducing leukemic relapses following transplant.
Preparing for and Not Waiting for Surgery
Authors: Bates Andrew , West Malcolm A. , Jack Sandy , Grocott Michael P. W.
Published: January 2024

Cancer surgery is an essential treatment strategy but can disrupt patients’ physical and psychological health. With worldwide demand for surgery expected to increase, this review aims to raise awareness of this global public health concern, present a stepwise framework for preoperative risk evaluation, and propose the adoption of personalised prehabilitation to mitigate risk. Perioperative medicine is a growing speciality that aims to improve clinical outcome by preparing patients for the stress associated with surgery. Preparation should begin at contemplation of surgery, with universal screening for established risk factors, physical fitness, nutritional status, psychological health, and, where applicable, frailty and cognitive function. Patients at risk should undergo a formal assessment with a qualified healthcare professional which informs meaningful shared decision-making discussion and personalised prehabilitation prescription incorporating, where indicated, exercise, nutrition, psychological support, ‘surgery schools’, and referral to existing local services. The foundational principles of prehabilitation can be adapted to local context, culture, and population. Clinical services should be co-designed with all stakeholders, including patient representatives, and require careful mapping of patient pathways and use of multi-disciplinary professional input. Future research should optimise prehabilitation interventions, adopting standardised outcome measures and robust health economic evaluation.
Non-Equilibrium Wigner Function and Application to Model of Catalyzed Polymerization
Authors: Alvarez-Estrada Ramon F.
Published: January 2024

The quantum Wigner function and non-equilibrium equation for a microscopic particle in one spatial dimension (1D) subject to a potential and a heat bath at thermal equilibrium are considered by non-trivially extending a previous analysis. The non-equilibrium equation yields a general hierarchy for suitable non-equilibrium moments. A new non-trivial solution of the hierarchy combining the continued fractions and infinite series thereof is obtained and analyzed. In a short thermal wavelength regime (keeping quantum features adequate for chemical reactions), the hierarchy is approximated by a three-term one. For long times, in turn, the three-term hierarchy is replaced by a Smoluchovski equation. By extending that 1D analysis, a new model of the growth (polymerization) of a molecular chain (template or te) by binding an individual unit (an atom) and activation by a catalyst is developed in three spatial dimensions (3D). The atom, te, and catalyst move randomly as solutions in a fluid at rest in thermal equilibrium. Classical statistical mechanics describe the te and catalyst approximately. Atoms and bindings are treated quantum-mechanically. A mixed non-equilibrium quantum–classical Wigner–Liouville function and dynamical equations for the atom and for the te and catalyst, respectively, are employed. By integrating over the degrees of freedom of te and with the catalyst assumed to be near equilibrium, an approximate Smoluchowski equation is obtained for the unit. The mean first passage time (MFPT) for the atom to become bound to the te, facilitated by the catalyst, is considered. The resulting MFPT is consistent with the Arrhenius formula for rate constants in chemical reactions.
A Novel Tensor Ring Sparsity Measurement for Image Completion
Authors: Zeng Junhua , Qiu Yuning , Ma Yumeng , Wang Andong , Zhao Qibin
Published: January 2024

As a promising data analysis technique, sparse modeling has gained widespread traction in the field of image processing, particularly for image recovery. The matrix rank, served as a measure of data sparsity, quantifies the sparsity within the Kronecker basis representation of a given piece of data in the matrix format. Nevertheless, in practical scenarios, much of the data are intrinsically multi-dimensional, and thus, using a matrix format for data representation will inevitably yield sub-optimal outcomes. Tensor decomposition (TD), as a high-order generalization of matrix decomposition, has been widely used to analyze multi-dimensional data. In a direct generalization to the matrix rank, low-rank tensor modeling has been developed for multi-dimensional data analysis and achieved great success. Despite its efficacy, the connection between TD rank and the sparsity of the tensor data is not direct. In this work, we introduce a novel tensor ring sparsity measurement (TRSM) for measuring the sparsity of the tensor. This metric relies on the tensor ring (TR) Kronecker basis representation of the tensor, providing a unified interpretation akin to matrix sparsity measurements, wherein the Kronecker basis serves as the foundational representation component. Moreover, TRSM can be efficiently computed by the product of the ranks of the mode-2 unfolded TR-cores. To enhance the practical performance of TRSM, the folded-concave penalty of the minimax concave penalty is introduced as a nonconvex relaxation. Lastly, we extend the TRSM to the tensor completion problem and use the alternating direction method of the multipliers scheme to solve it. Experiments on image and video data completion demonstrate the effectiveness of the proposed method.
Five-Year Survival Analysis and Causes of Late Deaths of Infants Admitted to the Tertiary Newborn Intensive Care in Latvia
Authors: Balmaka Baiba , Skribāne Sandija , Ābele Ildze , Balmaks Reinis
Published: January 2024

Background and Objectives: Studies on long-term survival following admission to neonatal intensive care units (NICUs) are scarce. The aim of this study was to analyse the epidemiology, five-year survival, and causes of late death of infants admitted to the only tertiary NICU in Latvia. Materials and Methods: The study population included all newborns admitted to the Children’s Clinical University Hospital (CCUH) NICU from 1 January 2013 to 31 December 2017. The unique national identity numbers from the infants or their mothers were used to link the CCUH electronic medical records to the Medical Birth Register and the Database of Causes of Death of Inhabitants of Latvia maintained by The Centre for Disease Prevention and Control of Latvia. Results: During the study period, a total of 2022 patients were treated in the tertiary NICU. The average admission rate was 18.9 per 1000 live births per year. One hundred and four patients (5.1%) died in the tertiary NICU before hospital discharge. A total of 131 (6.5%) patients from the study cohort died before 12 months of age and 143 (7.1%) before 5 years of age. Patients with any degree of prematurity had a lower five-year mortality (0.9%, 9 out of 994 discharged alive) than term infants (3.2%, 30 out of 924 discharged alive; p < 0.001). Of the 39 patients who died after discharge from the NICU, the most common causes of death were congenital heart disease 35.9% (n = 14), multiple congenital malformations and chromosomal abnormalities 17.9% (n = 7), cerebral palsy 10.3% (n = 4), and viral infections 7.7% (n = 3). Conclusions: We observed increased mortality up to five years following NICU admission in both premature and term infants. These findings will help to guide the NICU follow-up programme.
Recent Progress on the Synthesis of Bipyridine Derivatives
Authors: Yamanoi Yoshinori
Published: January 2024

Bipyridine and related compounds are starting materials or precursors for a variety of valuable substances such as biologically active molecules, ligands for catalysts, photosensitizers, viologens, and supramolecular architectures. Thus, it is important to classify their synthesis methods and understand their characteristics. Representative examples include methods using homo and heterocoupling of pyridine derivatives in the presence of a catalyst. Because bipyridine compounds strongly coordinate with metal centers, a decrease in catalytic activity and yield is often observed in the reaction system. To address this issue, this review provides insights into advances over the last ~30 years in bipyridine synthesis using metal complexes under both homogeneous and heterogeneous conditions. Moreover, strategies for bipyridine synthesis involving sulfur and phosphorous compounds are examined. These alternative pathways offer promising avenues for overcoming the challenges associated with traditional catalysis methods, providing a more comprehensive understanding of the synthesis landscape.
Enhancing Semi-Supervised Semantic Segmentation of Remote Sensing Images via Feature Perturbation-Based Consistency Regularization Methods
Authors: Xin Yi , Fan Zide , Qi Xiyu , Geng Ying , Li Xinming
Published: January 2024

In the field of remote sensing technology, the semantic segmentation of remote sensing images carries substantial importance. The creation of high-quality models for this task calls for an extensive collection of image data. However, the manual annotation of these images can be both time-consuming and labor-intensive. This has catalyzed the advent of semi-supervised semantic segmentation methodologies. Yet, the complexities inherent within the foreground categories of these remote sensing images present challenges in preserving prediction consistency. Moreover, remote sensing images possess more complex features, and different categories are confused within the feature space, making optimization based on the feature space challenging. To enhance model consistency and to optimize feature-based class categorization, this paper introduces a novel semi-supervised semantic segmentation framework based on Mean Teacher (MT). Unlike the conventional Mean Teacher that only introduces perturbations at the image level, we incorporate perturbations at the feature level. Simultaneously, to maintain consistency after feature perturbation, we employ contrastive learning for feature-level learning. In response to the complex feature space of remote sensing images, we utilize entropy threshold to assist contrastive learning, selecting feature key-values more precisely, thereby enhancing the accuracy of segmentation. Extensive experimental results on the ISPRS Potsdam dataset and the challenging iSAID dataset substantiate the superior performance of our proposed methodology.
Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models
Authors: Liu Shing-Hong , Ting Chi-En , Wang Jia-Jung , Chang Chun-Ju , Chen Wenxi , Sharma Alok Kumar
Published: January 2024

Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.
Fully Automatic Camera for Personalized Highlight Generation in Sporting Events
Authors: Decorte Robbe , De Bock Jelle , Taelman Joachim , Slembrouck Maarten , Verstockt Steven
Published: January 2024

Personally curated content in short-form video formats provides added value for participants and spectators but is often disregarded in lower-level events because it is too labor-intensive to create or is not recorded at all. Our smart sensor-driven tripod focuses on supplying a unified sensor and video solution to capture personalized highlights for participants in various sporting events with low computational and hardware costs. The relevant parts of the video for each participant are automatically determined by using the timestamps of his/her received sensor data. This is achieved through a customizable clipping mechanism that processes and optimizes both video and sensor data. The clipping mechanism is driven by sensing nearby signals of Adaptive Network Topology (ANT+) capable devices worn by the athletes that provide both locality information and identification. The device was deployed and tested in an amateur-level cycling race in which it provided clips with a detection rate of 92.9%. The associated sensor data were used to automatically extract peloton passages and report riders’ positions on the course, as well as which participants were grouped together. Insights derived from sensor signals can be processed and published in real time, and an upload optimization scheme is proposed that can provide video clips for each rider a maximum of 5 min after the passage if video upload is enabled.
Improving Recognition of Defective Epoxy Images in Integrated Circuit Manufacturing by Data Augmentation
Authors: Alam Lamia , Kehtarnavaz Nasser
Published: January 2024

This paper discusses the problem of recognizing defective epoxy drop images for the purpose of performing vision-based die attachment inspection in integrated circuit (IC) manufacturing based on deep neural networks. Two supervised and two unsupervised recognition models are considered. The supervised models examined are an autoencoder (AE) network together with a multi-layer perceptron network (MLP) and a VGG16 network, while the unsupervised models examined are an autoencoder (AE) network together with k-means clustering and a VGG16 network together with k-means clustering. Since in practice very few defective epoxy drop images are available on an actual IC production line, the emphasis in this paper is placed on the impact of data augmentation on the recognition outcome. The data augmentation is achieved by generating synthesized defective epoxy drop images via our previously developed enhanced loss function CycleGAN generative network. The experimental results indicate that when using data augmentation, the supervised and unsupervised models of VGG16 generate perfect or near perfect accuracies for recognition of defective epoxy drop images for the dataset examined. More specifically, for the supervised models of AE+MLP and VGG16, the recognition accuracy is improved by 47% and 1%, respectively, and for the unsupervised models of AE+Kmeans and VGG+Kmeans, the recognition accuracy is improved by 37% and 15%, respectively, due to the data augmentation.
Real-Time Monitoring of Underground Miners’ Status Based on Mine IoT System
Authors: Jiang Yufeng , Chen Wei , Zhang Xue , Zhang Xuejun , Yang Guowei
Published: January 2024

Real-time monitoring and timely risk warnings for the safety, health, and fatigue of underground miners are essential for establishing intelligent mines, enhancing the safety of production, and safeguarding the well-being of miners. This concerns the collection, transmission, and processing of relevant data. To minimize physical strain on miners, data collection functions are consolidated into two wearable terminals: an electronic bracelet equipped with reliable, low-power components for gathering vital sign data and transmitting them via Bluetooth and a miner lamp that integrates multi-gas detection, personnel positioning, and wireless communication capabilities. The gas sensors within the miner lamp undergo regular calibration to maintain accuracy, while the positioning tag supports round-trip polling to ensure a deviation of less than 0.3 m. Data transmission is facilitated through the co-deployment of 5G communication and UWB positioning base stations, with distributed MIMO networking to minimize frequent cell handovers and ensure a low latency of no more than 20 ms. In terms of data processing, a backpropagation mapping model was developed to estimate miners’ fatigue, leveraging the strong correlation between saliva pH and fatigue, with vital signs as the input layer and saliva pH as the output layer. Furthermore, a unified visualization platform was established to facilitate the management of all miners’ states and enable prompt emergency response. Through these optimizations, a monitoring system for underground miners’ status based on mine IoT technology can be constructed, meeting the requirements of practical operations.
Comprehensive Analysis and Evaluation of Anomalous User Activity in Web Server Logs
Authors: Benova Lenka , Hudec Ladislav
Published: January 2024

In this study, we present a novel machine learning framework for web server anomaly detection that uniquely combines the Isolation Forest algorithm with expert evaluation, focusing on individual user activities within NGINX server logs. Our approach addresses the limitations of traditional methods by effectively isolating and analyzing subtle anomalies in vast datasets. Initially, the Isolation Forest algorithm was applied to extensive NGINX server logs, successfully identifying outlier user behaviors that conventional methods often overlook. We then employed DBSCAN for detailed clustering of these anomalies, categorizing them based on user request times and types. A key innovation of our methodology is the incorporation of post-clustering expert analysis. Cybersecurity professionals evaluated the identified clusters, adding a crucial layer of qualitative assessment. This enabled the accurate distinction between benign and potentially harmful activities, leading to targeted responses such as access restrictions or web server configuration adjustments. Our approach demonstrates a significant advancement in network security, offering a more refined understanding of user behavior. By integrating algorithmic precision with expert insights, we provide a comprehensive and nuanced strategy for enhancing cybersecurity measures. This study not only advances anomaly detection techniques but also emphasizes the critical need for a multifaceted approach in protecting web server infrastructures.
Voiceprint Fault Diagnosis of Converter Transformer under Load Influence Based on Multi-Strategy Improved Mel-Frequency Spectrum Coefficient and Temporal Convolutional Network
Authors: Li Hui , Yao Qi , Li Xin
Published: January 2024

In order to address the challenges of low recognition accuracy and the difficulty in effective diagnosis in traditional converter transformer voiceprint fault diagnosis, a novel method is proposed in this article. This approach takes account of the impact of load factors, utilizes a multi-strategy improved Mel-Frequency Spectrum Coefficient (MFCC) for voiceprint signal feature extraction, and combines it with a temporal convolutional network for fault diagnosis. Firstly, it improves the hunter–prey optimizer (HPO) as a parameter optimization algorithm and adopts IHPO combined with variational mode decomposition (VMD) to achieve denoising of voiceprint signals. Secondly, the preprocessed voiceprint signal is combined with Mel filters through the Stockwell transform. To adapt to the stationary characteristics of the voiceprint signal, the processed features undergo further mid-temporal processing, ultimately resulting in the implementation of a multi-strategy improved MFCC for voiceprint signal feature extraction. Simultaneously, load signal segmentation is introduced for the diagnostic intervals, forming a joint feature vector. Finally, by using the Mish activation function to improve the temporal convolutional network, the IHPO-ITCN is proposed to adaptively optimize the size of convolutional kernels and the number of hidden layers and construct a transformer fault diagnosis model. By constructing multiple sets of comparison tests through specific examples and comparing them with the traditional voiceprint diagnostic model, our results show that the model proposed in this paper has a fault recognition accuracy as high as 99%. The recognition accuracy was significantly improved and the training speed also shows superior performance, which can be effectively used in the field of multiple fault diagnosis of converter transformers.
Wearable Sensor-Based Residual Multifeature Fusion Shrinkage Networks for Human Activity Recognition
Authors: Zeng Fancheng , Guo Mian , Tan Long , Guo Fa , Liu Xiushan
Published: January 2024

Human activity recognition (HAR) based on wearable sensors has emerged as a low-cost key-enabling technology for applications such as human–computer interaction and healthcare. In wearable sensor-based HAR, deep learning is desired for extracting human active features. Due to the spatiotemporal dynamic of human activity, a special deep learning network for recognizing the temporal continuous activities of humans is required to improve the recognition accuracy for supporting advanced HAR applications. To this end, a residual multifeature fusion shrinkage network (RMFSN) is proposed. The RMFSN is an improved residual network which consists of a multi-branch framework, a channel attention shrinkage block (CASB), and a classifier network. The special multi-branch framework utilizes a 1D-CNN, a lightweight temporal attention mechanism, and a multi-scale feature extraction method to capture diverse activity features via multiple branches. The CASB is proposed to automatically select key features from the diverse features for each activity, and the classifier network outputs the final recognition results. Experimental results have shown that the accuracy of the proposed RMFSN for the public datasets UCI-HAR, WISDM, and OPPORTUNITY are 98.13%, 98.35%, and 93.89%, respectively. In comparison with existing advanced methods, the proposed RMFSN could achieve higher accuracy while requiring fewer model parameters.
Luminescent Bacteria as Bioindicators in Screening and Selection of Enzymes Detoxifying Various Mycotoxins
Authors: Efremenko Elena , Lyagin Ilya , Stepanov Nikolay , Senko Olga , Maslova Olga , Aslanli Aysel , Ugarova Natalia
Published: January 2024

Interest in enzymes capable of neutralizing various mycotoxins is quite high. The methods used for the screening and selection of enzymes that catalyze the detoxification of mycotoxins should be sensitive and fast. However toxic compounds can be generated under the action of such enzymes. Thus, the assessment of the overall reduction in the toxic properties of reaction media towards bioluminescent bacteria seems to be the most reasonable control method allowing a quick search for the effective enzymatic biocatalysts. The influence of a wide range of mycotoxins and glucanases, which hydrolyze toxins with different chemical structures, on the analytical characteristics of luminescent photobacteria as a biosensing element has been studied. Different glucanases (β-glucosidase and endoglucanase) were initially selected for reactions with 10 mycotoxins based on the results of molecular docking which was performed in silico with 20 mycotoxins. Finally, the biorecognizing luminescent cells were used to estimate the residual toxicity of reaction media with mycotoxins after their interaction with enzymes. The notable non-catalytic decrease in toxicity of media containing deoxynivalenol was revealed with luminous cells for both types of tested glucanases, whereas β-glucosidase provided a significant catalytic detoxification of media with aflatoxin B2 and zearalenone at pH 6.0.
Process Algebraic Approach for Probabilistic Verification of Safety and Security Requirements of Smart IoT (Internet of Things) Systems in Digital Twin
Authors: Song Junsup , Lee Sunghyun , Karagiannis Dimitris , Lee Moonkun
Published: January 2024

Process algebra can be considered one of the most practical formal methods for modeling Smart IoT Systems in Digital Twin, since each IoT device in the systems can be considered as a process. Further, some of the algebras are applied to predict the behavior of the systems. For example, PALOMA (Process Algebra for Located Markovian Agents) and PACSR (Probabilistic Algebra of Communicating Shared Resources) process algebras are designed to predict the behavior of IoT Systems with probability on choice operations. However, there is a lack of analytical methods in the algebras to predict the nondeterministic behavior of the systems. Further, there is no control mechanism to handle undesirable nondeterministic behavior of the systems. In order to overcome these limitations, this paper proposes a new process algebra, called dTP-Calculus, which can be used (1) to specify the nondeterministic behavior of the systems with static probability, (2) verify the safety and security requirements of the nondeterministic behavior with probability requirements, and (3) control undesirable nondeterministic behavior with dynamic probability. To demonstrate the feasibility and practicality of the approach, the SAVE (Specification, Analysis, Verification, Evaluation) tool has been developed on the ADOxx Meta-Modeling Platform and applied to a SEMS (Smart Emergency Medical Service) example. In addition, a miniature digital twin system for the SEMS example was constructed and applied to the SAVE tool as a proof of concept for Digital Twin. It shows that the approach with dTP-Calculus on the tool can be very efficient and effective for Smart IoT Systems in Digital Twin.
Die Relevanz der Studieneingangsphase für die Herausbildung eines Studierendenhabitus. Irritationen, Krisen und Passungsverhältnisse am Beginn des Lehramtsstudiums
Authors: Pallesen Hilke
Published: January 2024

Der Beitrag diskutiert vor dem Hintergrund der im Lehramtsstudium strukturell hinterlegten Professionalisierungs- und Entwicklungserwartungen einerseits die Frage, ob und wie sich ein Schüler:innenhabitus überhaupt in einen Studierendenhabitus ‚umwandelt‘ und andererseits die Frage, ob die klare Perspektivierung der beruflichen Einmündung in die Schule die Entwicklung eines Studierendenhabitus überhaupt ermöglichen kann. Dabei wird insbesondere die Studieneingangsphase als sensible Phase des Einstiegs in das Studium betrachtet, in der vor allem die Relation biografie- und milieuspezifischer Vorerfahrungen und universitärer Anforderungslogiken eine zentrale Rolle für die hochschulische Sozialisation spielt. Denn sowohl bereits in der Schüler:innensozialisation gefestigte Bilder zu Lehrer:innenberuf und Schule als auch fachbezogene Vorerfahrungen treffen auf studienbezogene Herausforderungen, was als spannungsreich, irritierend und mitunter auch krisenhaft empfunden werden kann. Gleichzeitig gibt es Bestrebungen in der Lehrer:innenbildung, Krisen und Irritationen als Anlässe der Reflexion studentischer Denk-, Wahrnehmungs- und Handlungsmuster beispielsweise über hochschuldidaktische Formate der Fallarbeit zu nutzen. Auch wenn dies durchaus Professionalisierungspotenziale anspricht, die vor allem in strukturtheoretischen und biografieorientierten Ansätzen der Professionalisierung plausibel hinterlegt sind, so plädiert der Beitrag dafür, auch diese hochschulischen ‚Prägungsversuche’ kritisch zu betrachten und dafür zu sensibilisieren, dass auch auf diese Weise bereits sozialisatorisch hinterlegte Dispositionen eine harmonische, aber auch eine antagonistische Passung erfahren können. (DIPF/Orig.)
Professionalisierung in der Studieneingangsphase? Eine praxistheoretisch fundierte Untersuchung zur Reichweite dieser konzeptionellen Idee
Authors: Leonhard Tobias
Published: January 2024

Im Beitrag wird das auf den ersten Blick attraktive und erwartungskonforme Programm einer ‚Professionalisierung in der Studieneingangsphase‘ zunächst einer kritischen Betrachtung unterzogen. Ausgehend davon wird eine auf praxis- und subjektivierungsbezogenen Grundlagentheorien basierende Konzeption pädagogischer Professionalisierung als Gegenstandstheorie skizziert, die die Kritik am Konzept der ‚Professionalisierung in der Studieneingangsphase‘ vorrangig hin zu einer Beobachtungsperspektive verschiebt. Die analytische Kraft dieser Perspektive wird an einem Beispiel plausibilisiert. (DIPF/Orig.)
Studium 'in a nutshell'. Adressierungen und Anforderungslogiken am ersten Studientag
Authors: Müller Andrea
Published: January 2024

An einer Pädagogischen Hochschule in der Deutschschweiz werden zu Studienbeginn während zweier Tage zentrale Praktiken des Studiums ‚gezeigt‘ und die Studierenden werden angerufen an diesen Praktiken teilzunehmen. Um als (Neu-)Studierende in diesem als akademisch markierten Setting anerkannt zu werden, müssen sie sich den Anforderungen dieser spezifischen Praktiken und den ihnen inhärenten Normen in gewisser Weise unterwerfen. Anhand eines audiographisch aufgezeichneten Datums der Erstansprache eines Professors am ersten Tag des Studiums werden in einem adressierungsanalytischen Verfahren „Normen der Anerkennbarkeit“ (Ricken, 2009, 91) rekonstruiert, um der Frage nachzugehen, welche normativen Erwartungen und Anforderungen an die Teilnehmenden gestellt werden und wie sie dadurch zu Studierenden gemacht werden und sich durch ihre (aktive) Teilnahme selbst dazu machen. (DIPF/Orig.)
Die Bedeutung von studentischen Nebentätigkeiten als pädagogische (Vor-)Erfahrung für Professionalisierungsprozesse in der Studieneingangsphase
Authors: Walczuch Sarah
Published: January 2024

In diesem Beitrag wird der Bedeutung pädagogischer (Vor-)Erfahrungen für den Professionalisierungsprozess nachgegangen. Dazu wird die für Studierende bedeutsamen Erfahrung der studentischen Nebentätigkeit in schulischen sowie außerschulischen pädagogischen Tätigkeitsfeldern in den Blick genommen. Diese Perspektive ist vor dem Hintergrund, dass „sich junge Menschen selbstbestimmt mit den für sie als attraktiv erlebten Tätigkeiten […]“ (Römer et. al. 2018, 224) auch außerhalb von universitären Veranstaltungen auseinandersetzten, relevant. Eine solche studentische Nebentätigkeit lässt sich aufgrund fehlender personeller Ressourcen im Kontext der Schule leicht umsetzen. PES (kurz für: Projekt Erweiterte Selbstständigkeit) ist beispielsweise ein Konzept der rheinland-pfälzischen Regierung, bei dem Studierende als Vertretungslehrkräfte in Schulen eingesetzt werden. Durch dieses Programm können Studierende u.a. umfassende Erfahrungen im Unterrichten sammeln. Im Jahr 2021 werden dadurch an 905 Schulen in Rheinland-Pfalz Studierende als Vertretungslehrkräfte eingesetzt (Bildungsserver Rheinland-Pfalz). (DIPF/Orig.)
SAW velocity reduction and spurious modes suppression on the layer structure
Authors: Hu Richeng , Wu Zhaohui , Li Xinzhi , Shi Bin , Bao Jingfu , Hashimoto Ken-Ya
Published: January 2024

This paper discusses the use of the layered structure for the SAW velocity reduction. First, it is shown that the insertion of SiO2 is crucial between the piezoelectric layer and the base substrate to keep a large electromechanical coupling factor even when the SAW velocity is reduced. It is also shown that the use of a high-velocity substrate such as Si is also crucial for bringing spurious resonances much higher than the main resonance. The impact of Si orientation is also discussed. Next, suppression of transverse mode resonances and lateral leakage is studied. It is shown that the double busbar structure offers Q enhancement through good lateral energy confinement, and its combination with the piston design also gives good transverse mode suppression. Furthermore, discussions are extended to a strong spurious resonance that appears near the anti-resonance.
NATURAL KOSMETIK BERBAHAN EKSTRAK CAIR PROPOLIS SEBAGAI AGEN TABIR SURYA DALAM SEDIAAN LOTION DENGAN VARIASI ASAM STEARAT SEBAGAI EMULAGTOR
Authors:   Prodi D3 Farmasi, Universitas Sebelas Maret Surakarta, Rohmani Sholichah , Adhe Wahyu Alfhaza Ibnu , Rochmawati Meissy , Maheswari Anindha Hayu , Syiamsih Desi , Nurlita Nenda Devi , Az Zahra Rifanie Putri , Irwanda Salma Ratnadila , Firdaus Tiara Amalia , Maharani Kartini Putri
Published: January 2024

Sunscreen is a preparation used on the surface of the skin that works to absorb, scatter or reflectrays ultraviolet. One of the compounds that have benefits as a sunscreen is propolis. Propolis has antioxidant activity and its components can be used as a sunscreen formulation. The flavonoids contained in propolis have potential as sunscreen because of the presence of a chromophore group (conjugated double bond) which can absorb both UV B and UV A rays. To make a good preparation, a good formula is needed, one of the components in making lotions is acid. stearicate used as emulgator. This study aims to formulate a lotion with variations of stearic acid which functions as anas emulgator well as to test the physical and chemical properties of thepreparations lotion. The lotion is made in three variations of the composition of stearic acid, namely 2.5%: 5%; 7.5%. The lotion obtained was evaluated for the physical and chemical properties of thepreparation lotion including organoleptic test, homogeneity, emulsion type, pH, spreadability, adhesion and viscosity as well asmeasurement of the SPF value in vitro. The results of the analysis showed that variations in the concentration of stearic acid had a significant effect on the physical properties of the lotion including the results of pH, spreadability, adhesion, and viscosity. Stearic acid the higher the viscosity, consistency, and increased adhesion but the spreadability decreases. The SPF test results on theformula were lotion 6.13 (F1), 6.11 (F2), and 6.05 (F3) and the SPF value decreased after thetest freeze thaw.
STUDI EFEKTIVITAS DAN EFEK SAMPING FAVIPIRAVIR PADA PASIEN COVID-19 PNEUMONIA DI RUMAH SAKIT BHAYANGKARA SURABAYA
Authors:   Universitas Surabaya, Yulia Rika , Herawati Fauna ,   Departemen Farmasi Klinis dan Komunitas, Fakultas Farmasi, Universitas Surabaya, Hima Klara Rosjuita ,   Mahasiswa Program Studi Farmasi, Fakultas Farmasi, Universitas Surabaya, Hartono Ruddy ,   Rumah Sakit Bhayangkara Surabaya HS Samsoeri Mertojoso, Hanum Puri Safitri
Published: January 2024

Favipiravir's indication extended for COVID-19 disease. This background consideration is related to its mechanism of action or effectivity in some COVID-19 patients, with or without pneumonia. This research is a retrospective observational study and analysis descriptively. The research material is medical records of patients with COVID-19 pneumonia using favipiravir from December 2020-May 2021. A total of 33 samples obtained met the inclusion and exclusion criteria. Effectiveness was analyzed by calculating the percentage of COVID-19 pneumonia patients who only used favipiravir while they were being treated until the patient was discharged. The side effects were the number of COVID-19 patients who drug injury liver disease (AST and ALT increase more than three times upper limit) and neutrophenia. This study reported that favipiravir was effective in COVID-19 patients with pneumonia, seen from 78.79% of patients who only used favipiravir during treatment. There were no patients who had an increase in AST ALT >3×upper limit of normal, and only seven patients with neutropenia.
Deteksi Penyakit Malaria Menggunakan Klasifikasi Berbasis CNN
Authors: Yusuf Achmad , Azhar Yufis , Sari Zamah
Published: January 2024

Malaria merupakan penyakit yang serius dan berpotensi fatal yang disebabkan oleh parasit protozoa. Penyakit ini umumnya ditularkan oleh nyamuk dan tersebar luas di berbagai wilayah tropis dan subtropis, salah satu metode deteksi malaria berbasis citra digital yang banyak digunakan adalah deteksi malaria berbasis convolutional neural network (CNN). Tujuan penelitian ini adalah untuk mendeteksi penyakit malaria menggunakan klasifikasi berbasis CNN. Penelitian ini menggunakan metode penelitian systematic literature review. Data dikumpulkan melalui pencarian sistematis dalam database akademik dan perpustakaan digital yang relevan seperti Google Schoolar dengan kata kunci penyakit malaria & CNN. Data yang telah terkumpul kemudian dianalisis mencakup perbandingan, kategorisasi, dan penyajian temuan-temuan yang relevan dari studi-studi yang ada sehingga diperoleh 8 penelitian yang digunakan dalam penelitian ini. Hasil systematic literature review adalah menemukan bahwa diagnosis penyakit malaria berbasis CNN efektif dan dapat diandalkan untuk mendeteksi penyakit malaria dengan persentase lebih dari 90%.
Optimizing the Performance of Education Personnel: A Review of Performance Allowance and Motivation Factors at Maranatha Christian University
Authors: Fajarsyah Bintang , Anatan Lina
Published: January 2024

This study aims to examine how performance allowances and motivation affect Maranatha Christian University education staff's performance. The study population consisted of all 292 educational staff at Maranatha Christian University. The sample size was established using the Slovin method, yielding a sample of 75 respondents. Using Full Model Regression, a multiple linear regression equation was obtained: Y = 12.051 + -001 X1 + 0.072 X2. From this equation, it can be deduced that the coefficient of determination (r2) is 0.466, meaning that the X1-X2 variables may account for 46.6% of the model's variance while the remaining variance (53.4%), which is not accounted for by this regression model, is explained by other variables. The calculated F value was 33,249, with a significant F of 0.000 < 5%. This supports the hypothesis that Ho: b1= b2 ≠ 0, meaning that both factors (X) have an impact on Y.