bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental mental health understanding program for high school students
Authors: Le Son Quang , Bui Dieu Thi Thanh , Hoang Hai The , Le Duyen Thi , Lu Huyen Thi Thu
Published: July 2024

Mental health literacy among youth is crucial for the early detection and intervention in mental health disorders. Despite growing awareness, many young individuals lack the knowledge to recognize and respond to mental health concerns effectively. Educational interventions, such as targeted practice sessions, offer promise in addressing this gap. This study investigates the effectiveness of targeted practice sessions in enhancing students' ability to identify mental disorders. Situational questions were employed to assess participants' comprehension of various psychological disorders, including depression, post-traumatic stress, stress, and social phobia. A total of 80 high school students from Hoang Hoa 2 High School, Thanh Hoa, Vietnam, participated, with 40 assigned to the control group and 40 to the experimental group. The Paired-Samples T-test was used for data analysis, with a significance level of p < 0.05. Results revealed significant improvements in recognition abilities among students in the experimental group, highlighting the efficacy of targeted interventions in promoting mental health literacy. The study underscores the importance of addressing mental health literacy within educational settings and contributes valuable insights into evidence-based strategies for enhancing students' understanding of psychological disorders. Targeted practice sessions represent a promising approach to promoting mental health awareness and support among youth, with implications for improving mental health outcomes and reducing stigma surrounding mental illness.
Assessing the use of ICT in Indonesian law firms
Authors: Romdoni Muhamad , Assed Lussak  , Ramadhan Rizaldy
Published: July 2024

The study was conducted in Jakarta, Indonesia's capital, with the goal of gaining a better understanding of lawyers' perspectives on information and communication technology (ICT) and determining whether these perspectives influence the extent to which they have adopted and used it. The study's objectives are to identify the various types of information and communication technology (ICT) employed in law firms, as well as their scope and applications. This study also looked into the elements that influence people's decisions whether or not to use information and communication technologies. The study used a survey as its research method, with thirty attorneys from five different law offices in Jakarta participating. Qualitative and quantitative data were collected using semi-structured questionnaires and interview schedules. According to the study's findings, ICT compatibility, trialability, and observability all have a favorable impact on environmental pressures and ICT use. However, it appears that environmental pressures are negatively impacting the use of information and communication technologies.
Stability of substructure Malapedho Bridge in Ngada Regency, East Nusa Tenggara Province
Authors: Suku Yohanes Laka , Radja Veronika Miana
Published: July 2024

Bridge failure is often caused by problems with the substructure, such as shearing, overturning and foundation subsidence which can cause the bridge structure to tilt and make it unfit for use, and even have the potential to cause the bridge to collapse. Research on the structural stability of the Malapedho Bridge's substructure aims to ensure the strength and stability of the bridge against sliding, overturning, soil bearing capacity and settlement, considering that the bridge had previously collapsed due to flooding. The bridge data used is existing data according to the planning drawings, for bridge loading refers to SNI 1725:2016, and earthquake loading refers to SNI 2833:2016. The safety factor value as a requirement for the strength and stability of the substructure is based on SNI 8460:2017. From the results of the analysis of shear stability, overturning, soil bearing capacity, and settlement of the lower building structure, it was found that the safety factor value exceeds the requirements set by the SNI 8460:2017 standard. Therefore, it can be concluded that the bridge substructure has adequate strength and stability. The research results also indicate that earthquake forces have a significant impact on the shear and overturning stability of the bridge substructure, resulting in a decrease in the strength of the structure.
Brain tumor segmentation using U-Net++ with dense connection
Authors: Makwana Chintan , Nayak Nikunjkumar , Sagar Maheshwari
Published: July 2024

One of the most crucial tasks in the diagnosis and treatment planning of patients with brain cancer is the segmentation of brain tumor regions from MRIs. Conventional manual segmentation techniques take a lot of time and are prone to mistakes by people, leading to inconsistencies and inefficiencies in clinical practice. A convolutional neural network model identifies and classifies tumor regions within the brain using MRI scans. A more sophisticated variant of the U-Net design known as U-Net++ has layered and densely linked skip routes that enhance the network's capacity to record contextual and fine-grained information. Dense connections, which mitigate the vanishing gradient problem and encourage feature reuse, are incorporated into U-Net++ to further improve gradient flow and feature propagation. This architecture is made up of a sequence of blocks that are both encoders and decoders. The encoder route blocks extract progressively abstract information, while the matching decoder path blocks rebuild the input image's spatial resolution.The study utilizes the MICCAI BraTS2020 dataset for training and evaluation, which contains a collection of labeled brain tumor MRI scans. Various data augmentation techniques are employed to enhance the model's performance. The models' average training accuracy for whole tumor (WT) values is as follows: Simple U-Net, U-Net with Dropout, U-Net with Batch Normalization, U-Net with Res Path, and U-Net++ with Dense Connection are 66%, 50%, 99%, 100%, and 100%, respectively. Similarly, the average validation accuracy for whole tumor (WT) values is 65%, 50%, 98%, 99%, and 99%, respectively. Furthermore, the average DICE scores for whole tumor (WT) values for the models are 0.60, 0.70, 0.84, 0.90, and 0.93, respectively. Finally, the average Intersection Over Union (IOU) for whole tumor (WT) values for the models are 0.43, 0.55, 0.72, 0.82, and 0.86, respectively, on the brain tumor segmentation dataset BraTS2020.
The exploration of science teacher’s acceptance towards virtual laboratory (VL)
Authors: Sukmawati Fatma , Santosa Eka Budhi , Juwita Ratna , Cahyono Budi Tri , Trisnaningsih Sari , Ridhani Jovita
Published: July 2024

Teacher acceptance of technology is very important to increase the effectiveness of the learning process in the digital era.This research aimed to explore the acceptance of virtual laboratories among science teachers, comparing the perspectives of inexperienced and experienced teachers. Utilising a quantitative approach, the research adopted the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. An analysis was conducted to examine the impact of performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), and habit (HA) on behavioural intention (BI) and use behaviour (UB). Data were collected through surveys with a sample of science teachers from secondary school levels in Surakarta city, Indonesia. The findings indicate a generally positive acceptance of virtual laboratories, with differences between the two groups of teachers. The results show that perceived ease of use (PE) and expectancy of enjoyment (EE) have a significant influence on behavioural intention (BI) and usage behaviour (UB) in both groups, with a greater influence of habit (HA) on experienced teachers.
Enhancing child safety: Evaluating a toolkit for sexual abuse prevention education in primary schools
Authors: Nguyen My Thi Diem , Huynh Son Van , Do Thien Tat
Published: July 2024

This study aims to evaluate the effectiveness of a newly developed toolkit designed to aid primary school teachers in teaching child sexual abuse prevention skills in Binh Duong Province, Vietnam. A survey was conducted among 534 primary school teachers, who were randomly assigned to either an experimental group (128 teachers) or a control group (129 teachers). Both groups assessed the difficulties and feasibility of using supporting materials before and after the experiment. Initially, both groups reported comparable levels of difficulty with the materials. After the experiment, the experimental group had a significant reduction in difficulty (p < 0.05), showing the intervention was beneficial. Teachers in the experimental group assessed the materials as highly feasible for teaching sexual abuse prevention techniques. Notably, a large proportion of Di An City and Thuan An City teachers assessed them as "Very feasible" or "Feasible" (40.7% and 59.3%, respectively), with no negative feedback. Comprehensive training and well-supported resources significantly enhance teachers' confidence and ability to teach sexual abuse prevention. Effective program implementation requires targeted professional development, interactive teaching techniques, and community involvement to create a supportive learning environment. Prioritizing these factors can improve program effectiveness, contributing to the safety and well-being of children.
Pemenuhan Hak Pekerja (Upah Lembur) Berdasarkan Perjanjian Kerja di CV. X
Authors: Firensya Nickita Michaela , Adhitya Widya Kartika
Published: July 2024

Perusahaan berkewajiban untuk memenuhi hak upah lembur karyawan ketika memperkerjakan dengan melebihi waktu yang telah ditentukan, yang mana hal tersebut harus dituangkan dalam perjanjian kerja antara kedua belah pihak. Ditemui bahwa CV. X dalam perjanjian kerja dengan karyawannya tidak menuangkan mengenai hak upah lembur sekaligus dalam pelaksanaannya juga tidak memberikan upah lembur. Tujuan penelitian ini untuk menggali perlindungan hukum dalam pemenuhan hak upah lembur berdasarkan perjanjian kerja di CV. X. Penelitian ini dilakukan berdasarkan metode penelitian tipe normatif, dengan pendekatan terhadap peraturan perundang-undangan dan konseptual. Hasil penelitian didapati bahwa adanya pengusaha/pemberi kerja yang mempekerjakan pekerjanya di luar jam yang berada dalam perjanjian dan tidak memberikan upah lembur ialah bertentangan dengan peraturan perundang-undangan, sehingga dengan demikian menunjukkan bahwa perlindungan hukum belum diwujudkan sebagaimana mestinya.
Environmental and Social Risk Factors in the Event of Dengue Hemorrhagic Fever (DHF) in the Work Area of Jiwan Health Center, Madiun Regency
Authors: Sari Mardiana Indah , Rahardjo Mursid , Astorina Yunita Dewanti Nikie
Published: July 2024

In the past three years, the Jiwan Health Centre's area has had the highest dengue fever incidence in Madiun Regency. This area still has many water puddles that serve as mosquito breeding grounds. The Jiwan Health Center reported 40 cases and 2 deaths in 2019, 6 cases in 2020, and 16 cases in 2021. This study aimed to identify environmental and social risk factors linked to dengue fever cases in this region. We used an analytical observation method, including a case-control study, and analyzed data with the chi-square test, using probability sampling. A study that looked at just one variable found that air temperature (36.3%), humidity (83.3%), house index (68.8%), container index (41.3%), the lack of mosquito larvae observer cadres (87.5%), fogging activity (55%), and DHF PSN activity (27.5%) were all dangerous. Bivariate test results showed a correlation between the container index (p = 0.000) and fogging activities (p = 0.042) with DHF incidence. There was no significant connection between dengue incidence, air temperature (p = 0.642), humidity (p = 1.000), house index (p = 0.335), jumantic cadre activities (p = 0.737), or PSN DHF activities (p = 0.453). Environmental and social factors, such as positive larvae presence and fogging activities, contribute to the dengue fever incidence in the Jiwan Health Center's area. For the past three years, this area has consistently had the highest dengue fever cases in Madiun Regency, primarily due to persistent mosquito breeding sites.
Noise Levels and Comfort Levels of Populations Living Around Mistar Cokrokusumo Bangkal District
Authors: Sapwandi Rully , Junaidi Junaidi , Ali As Zulfikar , Pahruddin Muhammad
Published: July 2024

Today, many houses are located on the edge of highways, potentially exposing these homes to additional noise from the traffic flow. This study aims to determine the relationship between noise levels and the comfort levels of residential homes in the Bangkal sub-district. This observational study, which has a cross-sectional design, includes 12 sample points for noise measurement and 77 houses directly adjacent to the road for comfort level sampling. Noise levels were measured using a sound level meter, while traffic density was measured using a compass and counter. The relationship between the two variables was analyzed using the Spearman correlation test. Results showed that the volume of traffic flow ranged from 334 pcu/hour to 402 pcu/hour. The highest noise level was recorded at TS-7 (65.7 dBA) and the lowest at TS-9 (61.7 dBA). According to the South Kalimantan governor's regulation number 53 of 2007, all 12 sample points of noise measurement exceed the quality standard. The average correlation between noise level and comfort level indicated that 88% of households reported being less comfortable, 12% felt uncomfortable, and none felt comfortable. Houses can reduce road traffic noise by installing thick curtains on their windows and doors, planting trees and ornamental plants in pots, and constructing artificial walls.
The Relationship of Teachers' Role and Students' Attitudes Towards Clean and Healthy Living Behavior In Islamic Boarding Schools
Authors: Putri Syarifah Dewi , Nurul Fadillah Hilda , Mahameru Kaffah Lentera , Surury Istianah
Published: July 2024

Clean and Healthy Living Behavior (PHBS) is a practice carried out by students, teachers, and school members as part of learning to be proactive in preventing and maintaining health and playing an active role in forming a healthy environment. Children of school age are very susceptible to disease. PHBS is often associated with the number of diseases that occur in school-aged children. The method used in this study is quantitative, with a cross-sectional study design. The sample in this study was 136 respondents using the two-stage cluster technique. Data were taken using a questionnaire from the students of SMP Pondok Pesantren Mumtaz Ibadurrahman provided data using a questionnaire. According to the research findings, factors related to PHBS are the teacher's role. The teacher's role and attitude are factors that influence PHBS. Therefore, teachers should increase their support and education to students, emphasizing the importance of implementing PHBS through the learning process and encouraging cooperation activities to clean the classroom environment. Ion activities to clean the pesantren environment.
Factors Related to Dengue Hemorrhagic Fever (DHF) in Bontang Kuala Sub- district, North Bontang District
Authors: Shandy Adelia Fitria , Badrah Sitti , Sedionoto Blego
Published: July 2024

Dengue Hemorrhagic Fever is one of the leading public health problems in Indonesia. The number of DHF pain in Bontang Kuala Sub-district has increased over the past three years, namely 5 cases in 2019, 19 cases in 2020, and 33 cases in 2021. This study aims to determine several factors related to dengue disease in Bontang Kuala Sub-district, North Bontang District. This type of research used a cross-sectional study design. The population in this study was 1,791 households and a sample of 91 respondents. Samples were taken using the proportional random sampling method. The data analysis technique used a chi-square test with a degree of meaningfulness (p = 0.05). The results of this study showed that 68.1% of respondents had poor used goods management with (p = 0.000), 65.9% had good waste disposal facilities with (p = 0.164), 64.8% had a habit of hanging clothes less well with (p = 0.001) and 72.5% had a habit of using anti- mosquito lotion less well with (p = 0.002). This research advises people to make more frequent efforts to eradicate mosquito nests (PSN) coupled with avoiding the habit of hanging clothes and using mosquito-repellent lotions so that the transmission of DHF disease can be reduced.
Фактори та механізми формування продуктивних вторинних колекторів у глибокозалягаючих нафтогазоносних комплексах. Стаття 2.
Authors: Lukin O. Yu. , Gafych I. P. , Lukin Ya. V.
Published: July 2024

Аутигенні карбонатні мінерали разом з глинистою речовиною відіграють провідну роль у цементації теригенних фанероморфних (зернистих) порід. Вивчення закономірностей карбонатного аутигенезу має першочергове значення для нафтогазової геології. Через різку зміну тектонічного режиму на катагенетичну зональність регіонально накладається дислокаційний епігенез. Як і катагенез, він має регіональний характер, але інтенсивність його, як у розрізі, так i латерально, варіює у широких межах, визначаючись насамперед зсувною тектонікою. Найбільш яскравим індикатором цієї кардинальної зміни є заміщення анкериту сидеритом. Найважливішою особливістю сидериту є наявність у різному ступені вираженого штрихування, що властиве зсувним пластичним (мікро)деформаціям. Це, з одного боку, дозволяє розглядати пізньоепігенетичний сидерит як тектонофізичний індикатор, а з іншого – свідчить про кардинальну заміну тектонічного режиму, завдяки чому катагенез замінюється дислокаційним епігенезом. Це дозволяє зробити виключно важливі висновки для з’ясування механізмів формування покладів нафти і газу взагалі та в глибокозалягаючих комплексах зокрема.
Digital Industry transformation strategy analysis and decision-based on intelligent decision technology
Authors: Liu Yishu , Hou Jia
Published: July 2024

Expanding and being competitive in the current economic environment requires companies to embrace digital transformation. In the framework of Industry 4.0, the network of interconnected machines, sensors, and software known as the IIoT plays a crucial role in transforming conventional manufacturing facilities into smart factories, notably in monitoring and optimising the manufacturing process. The issues about enormous record storage and how they react challenge conventional automated methods in the IIoT. Cognitive systems optimally modify production settings based on managing uncertainty and sensory inputs. This work uses the Internet of Things-based decision support system with cognitive automation (IoT-DSS-CA) for industrial informatics across the board, including data collection, transmission, processing, and storage. Incorporating the elements frequently neglected during digital transformation, the suggested method uses the business process management (BPM) paradigm to give a systematic approach that industrial organizations may employ to aid their path towards Industry 4.0. The proposed mechanism is thoroughly investigated and evaluated compared to an original solution using several sensing and decision-making features in industrial parameter settings determined by Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP).
Arbitrary style transformation algorithm based on multi-scale fusion and compressed attention in art and design
Authors: Wu Yunan , Zhang Haitao
Published: July 2024

In art and design, style conversion algorithms can fuse the content of one image with the style of another image, thereby generating images with new artistic styles. However, traditional style conversion algorithms suffer from high computational complexity and loss of details during row image conversion. Therefore, this study introduces VGG16 multi-scale fusion feature extraction in any style transition algorithm and introduces a compressed attention mechanism to improve its computational complexity. Then it designs an arbitrary style transformation algorithm on the ground of multi-scale fusion and compressed attention. The results showed that the designed algorithm took 0.014 s and 0.021 s to process tasks on the COCO Stuff dataset and WikiArt dataset, respectively, proving its high computational efficiency. The loss values of the designed algorithm are 0.046 and 0.052, respectively, indicating strong fitting performance and good generalization ability. The IS score and FID score of the design algorithm are 2.36 and 91.67, respectively, proving that the generated images have high diversity and quality. The above results demonstrate the effectiveness and practicality of design algorithms in art and design. It has important theoretical and practical value in promoting the development of style conversion technology, enhancing the creativity and expressiveness of art and design.
Context propagation based influence maximization model for dynamic link prediction
Authors: Shelke Vishakha , Jadhav Ashish
Published: July 2024

Influence maximization (IM) in dynamic social networks is an optimization problem to analyze the changes in social networks for different periods. However, the existing IM methods ignore the context propagation of interaction behaviors among users. Hence, context-based IM in multiplex networks is proposed here. Initially, multiplex networks along with their contextual data are taken as input. Community detection is performed for the network using the Wilcoxon Hypothesized K-Means (WH-KMA) algorithm. From the detected communities, the homogeneous network is used for extracting network topological features, and the heterogeneous networks are used for influence path analysis based on which the node connections are weighted. Then, the influence-path-based features along with contextual features are extracted. These extracted features are given for the link prediction model using the Parametric Probability Theory-based Long Short-Term Memory (PPT-LSTM) model. Finally, from the network graph, the most influencing nodes are identified using the Linear Scaling based Clique (LS-Clique) detection algorithm. The experimental outcomes reveal that the proposed model achieves an enhanced performance.
Chicken swarm algorithm with deep convolutional neural network based tongue image analysis for gastric cancer classification
Authors: Tiwari Ajay , Katiyar Alok
Published: July 2024

Tongue images (the size, shape, and colour of tongue and the thickness, colour, and moisture content of tongue coating), reflecting the medical condition of entire body based on the model of traditional Chinese medicine (TCM) are extremely utilized in China for millions of years. Gastric cancer (GC) is great lethal kind of cancer in countries and societies. The screening and analysis of GC yet depend on gastroscopy, however its application was significantly restricted due to its invasive, maximum rate and the requirement for expert endoscopists. Early recognition in GC patients and direct treatment contribute significantly to safety for health. Consequently, this study introduces a Chicken Swarm Algorithm with Deep learningbased Tongue Image Analysis for Gastric Cancer Classification (CSADL-TIAGCC) system. The projected CSADL-TIAGCC approach studies the input tongue images for the identification and classification of GC. To accomplish this, the CSADL-TIAGCC system uses improved U-Net segmentation approach. Besides, residual network (ResNet-34) model-based feature extractor is used. Furthermore, long short term memory (LSTM) approach was exploited for GC classification and its hyperparameters are selected by the CSA. The simulation outcome of the CSADL-TIAGCC algorithm was examined under tongue image database. The experimental outcomes illustrate the enhanced results of the CSADL-TIAGCC technique with respect of different evaluation measures.
Towards Standardizing Nomenclature in Huntington’s Disease Research
Authors: DiFiglia Marian ,   Huntington’s Disease Nomenclature Working Group: BatesGillian P.University College London, Elena Cattaneo, University of Milan ChaJang-HoLatus Bio FaullRichardUniversity of Aukland FeiginAndrewNew York University GantmanEmily C.CHDI Management, Inc. GusellaJames F.Harvard University HaydenMichaelUniversity of British Columbia HousmanDavidMassachusetts Institute of Technology HowlandDavidCHDI Management, Inc. LangbehnDougUniversity of Iowa LandwehrmeyerBernhard G.ULM University LongJeffrey D.University of I, Leavitt Blair R. , Macdonald Douglas , Thompson Leslie M.
Published: July 2024

The field of Huntington’s disease research covers many different scientific disciplines, from molecular biology all the way through to clinical practice, and as our understanding of the disease has progressed over the decades, a great deal of different terminology has accrued. The field is also renowned for its collaborative spirit and use of standardized reagents, assays, datasets, models, and clinical measures, so the use of standardized terms is especially important. We have set out to determine, through a consensus exercise involving basic and clinical scientists working in the field, the most appropriate language to use across disciplines. Nominally, this article will serve as the style guide for the Journal of Huntington’s Disease (JHD), the only journal devoted exclusively to HD, and we lay out the preferred and standardized terminology and nomenclature for use in JHD publications. However, we hope that this article will also serve as a useful resource to the HD research community at large and that these recommended naming conventions will be adopted widely.
An application of the Bayesian network model based on the EN-ESL-GA algorithm: Exploring the predictors of heart disease in middle-aged and elderly people in China
Authors: Gao Wenlong , Zeng Zhimei , Ma Xiaojie , Ke Yongsong , Zhi Minqian
Published: July 2024

BACKGROUND: The morbidity and mortality of heart disease are increasing in middle-aged and elderly people in China. It is necessary to explore relationships and interactive associations between heart disease and its risk factors in order to prevent heart disease. OBJECTIVE: To establish a Bayesian network model of heart disease and its influencing factors in middle-aged and elderly people in China, and explore the applicability of the elite-based structure learner using genetic algorithm based on ensemble learning (EN-ESL-GA) algorithm in etiology analysis and disease prediction. METHODS: Based on the 2013 national tracking survey data from China Health and Retirement Longitudinal Study (CHARLS) database, EN-ESL-GA algorithm was used to learn the Bayesian network structure. Then we input the data and the learned network structure into the Netica software for parameter learning and inference analysis. RESULTS: The Bayesian network model based on the EN-ESL-GAalgorithm can effectively excavate the complex network relationships and interactive associations between heart disease and its risk factors in middle-aged and elderly people in China. CONCLUSIONS: The Bayesian network model based on the EN-ESL-GA algorithm has good applicability and application prospect in the prediction of diseases prevalence risk.
Effects of wearable low-intensity continuous ultrasound on muscle biomechanical properties during delayed onset muscle soreness
Authors: Oh Sungyeon , Ko Chang-Yong , Lee Jaewon , Nekar Daekook M. , Kang Hyeyoon , Kim Hansung , Yu Jaeho
Published: July 2024

BACKGROUND: Delayed onset muscle soreness (DOMS) is one of the most prevalent musculoskeletal symptoms in individuals engaged in strenuous exercise programs. OBJECTIVE: This study investigated the effects of wearable low-intensity continuous ultrasound on muscle biomechanical properties during DOMS. METHODS: Twenty volunteers were distributed into a wearable ultrasound stimulation group (WUG) (n= 10) and medical ultrasound stimulation group (MUG) (n= 10). All subjects performed wrist extensor muscle strength exercises to induce DOMS. At the site of pain, ultrasound of frequency 3 MHz was applied for 1 h or 5 min in each subject of the WUG or MUG, respectively. Before and after ultrasound stimulation, muscle biomechanical properties (tone, stiffness, elasticity, stress relaxation time, and creep) and body temperature were measured, and pain was evaluated. RESULTS: A significant decrease was found in the tone, stiffness, stress relaxation time, and creep in both groups after ultrasound stimulation (all p< 0.05). A significant decrease in the pain and increases in temperature were observed in both groups (all p< 0.05). No significant differences were observed between the groups in most evaluations. CONCLUSION: The stiffness and pain caused by DOMS were alleviated using a wearable ultrasound stimulator. Furthermore, the effects of the wearable ultrasound stimulator were like those of a medical ultrasound stimulator.
Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis
Authors: Zhang Hongru , Wang Chen , Yang Ning
Published: July 2024

BACKGROUND: Early identification of sepsis has been shown to significantly improve patient prognosis. OBJECTIVE: Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction. METHODS: Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy. RESULTS: The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70–0.90; P< 0.001; I2= 99.7%), the specificity was 0.91 (95% CI = 0.86–0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91–0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68–0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90–0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92–0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75–0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75–0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91–0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed. CONCLUSION: Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.
TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm
Authors: Rajeshkumar C. , Soundar K. Ruba
Published: July 2024

BACKGROUND: Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. OBJECTIVE: Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. METHODS: The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. RESULTS: According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. CONCLUSION: From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.
Pencegahan Paham Radikalisme Lewat Penguatan Moderasi Beragama Melalui Ekstrakurikuler Rohani Islam
Authors: Aulia Mizar
Published: July 2024

Penguatan moderasi beragama dinilai penting untuk dibangun dalam mewujudkan sikap beragama yang damai dan penuh toleransi. Upaya penguatan moderasi beragama tentu diperlukan kepada generasi muda khususnya siswa sekolah dengan memanfaatkan organisasi kesiswaan seperti Rohani Islam. Tujuan penelitian ini adalah untuk menguraikan fungsi Rohani Islam dalam penguatan moderasi beragama, menguraikan implementasi penguatan moderasi beragama melalui Rohani Islam dan mendeskripsikan implikasi dari penguatan moderasi beragama melalui rohani Islam di SMA Negeri 1 Binjai. Metode penelitian yang digunakan adalah kualitatif deskriptif, proses pengumpulan data dilakukan melalui observasi secara langsung, wawancara dan studi dokumentasi. Hasil penelitian menemukan bahwa Rohani Islam berfungsi strategis dalam penguatan moderasi beragama di SMA Negeri 1 Binjai. Implementasi penguatan moderasi beragama dilakukan melalui sebuah kegiatan kelompok bersama yang mencakup seluruh siswa dengan agama berbeda dan melalui kampanye moderasi beragama lewat youtube dan media sosial. Implikasi penguatan moderasi beragama terlihat pada peningkatan sikap moderasi beragama siswa di lingkungan sekolah yang tercermin dalam sikap berteman siswa yang tidak membeda-bedakan ras, suku, budaya dan agama. Dengan demikian, melalui penelitian ini dapat disimpulkan bahwa penguatan moderasi beragama melalui Rohani Islam dapat meningkatkan sikap moderasi beragama siswa di SMA Negeri 1 Binjai.
Religious Moderation in the Perspective of Maqasid Syariah Jasser Auda
Authors: Tohari Achmad
Published: July 2024

Kajian maqashid syari'ah sering mendapat perhatian dalam berbagai kajian di dunia akademik tidak hanya di ranah hukum, tetapi juga di ranah sosial. Namun belakangan ini, keberagaman tersebut mulai tergerus dengan semakin banyaknya perselisihan sosial berakar agama yang masih terus berlanjut hingga saat ini. Mulai dari insiden ujaran kebencian, penistaan agama dan diskriminasi satu komunitas dengan komunitas lainnya. Tulisan ini bertujuan untuk membahas pemikiran Jasser Auda tentang Maqasid Syariah dan relevansinya dengan moderasi beragama di Indonesia. Penelitian ini merupakan penelitian kepustakaan (library research), adapun data primer yang digunakan adalah karya Jasser Auda yang berjudul "Maqasid al-Shariah as Philosophy of Islamic Law:  A System Approach", sedangkan data sekunder berupa karya-karya yang relevan dengan penelitian ini. Hasil dari penelitian ini menunjukkan bahwa metode Al-Maqasid yang digagas oleh Jasser Auda sangat relevan untuk digunakan dalam mengatasi masalah-masalah sosial di Indonesia, khususnya yang berkaitan dengan moderasi beragama.
Informed Traffic Signal Preemption for Emergency Vehicles
Authors: Silaghi Ioana , Alssadi Zobaida , Silaghi Marius
Published: July 2024

Emergency vehicle traffic light preemption systems are extended by using more detailed predicted trajectories to provide favorable changes of signals. The location of the emergency vehicle is tracked and traffic signals on the requested travel route are contacted and switched to become green in time, before the emergency vehicle reaches the selected intersection. The resulting time to destination obtained by our simulation is compared to a control simulation without preemption. In simulations, when an emergency vehicle reaches a red light, a delay of five seconds is considered to happen naturally to the vehicle’s journey due to preventive driving. The switching of colors requires an eight second green light warning, also for reasons of safety. According to the improvements shown by our simulations, given the number of seconds gained from the knowledge of trajectory details and information passing with the traffic lights, from the 356,000 out-of-hospital cardiac arrests in the United States per year, approximately 3,000 lives would be saved. This gain can also significantly decrease the anxiety and improve the mental health of surrounding involved participants including family, health workers, and other bystanders thereby improving the quality of services that health workers can provide. The change of lights exploits knowledge of compatibility between paths allowed by current default light combination and the path of the emergency vehicle to save essential security buffer seconds.
Pulmonary Disease Classification on Electrocardiograms Using Machine Learning
Authors: Abdoulaye Soumana Aboubacar , Lamichhane Prajwol , Shabbir Mehlam , Liu Xudong , Nasseri Mona , Helgeson Scott
Published: July 2024

Pulmonary diseases, such as chronic obstructive pulmonary
disease (COPD) and asthma are among the leading causes of
death in the US. These lung diseases often are diagnosed by
pulmonologists using physical exam (e.g., lung auscultation)
and objective measurement of lung function with pulmonary
function testing (PFT). These extensive tests, however, can
be inaccessible to many patients due to limited resources and
availability. In this paper, we explore the use of the easily accessible
electrocardiograms (ECGs) to train machine learning
models to classify pulmonary diseases. To this end, we developed
and experimented with two approaches: deep neural
networkmodels trained (e.g., Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs)) on ECG
signals directly, and non-neural models (e.g., support vector
machines (SVMs) and logistic regression) trained on derived
features from ECGs. In the task of classifying whether a patient
has obstructive lung disease, our results show that deep
neural network models outperformed the non-neural models,
though the difference is within 3% on accuracy and F1-score
metrics.
Dynamic PageRank with Decay: A Modified Approach for Node Anomaly Detection in Evolving Graph Streams
Authors: Ekle Ocheme Anthony , Eberle William
Published: July 2024

Given a large graph stream with dynamically changing structures over a given timestep, it is important to detect the sudden appearance of anomalous patterns, such as sudden spikes in IP-network attacks or unexpected surges in social media followers. In addition, it is important that we promptly identify these abrupt changes in the network by considering swift and short-term responses within the network structure. To design a model capable of adapting to dynamic changes, we introduce an approach that utilizes a modified dynamic "PageRank-with-Decay" as a node scoring function. This method enables the detection of sudden dynamic graph changes based on node importance scores, leveraging the temporal evolution of graph structures at each timestep. This approach provides a refined anomaly detection mechanism for tracking rapid structural changes in the network. Through experiments conducted on a real-world dataset, our model demonstrates faster and more accurate results (in terms of precision and recall) compared to state-of-the-art methods.
Knowledge-infused and Explainable Malware Forensics
Authors: Kumar Neha Mohan , Islam Sheikh Rabiul
Published: July 2024

Despite considerable progress in malicious software forensics, the challenge of accurate attribution, formulation of appropriate response and mitigation strategies, and ensuring the interpretability of deep learning methods persists. While being less flexible and robust to noise compared to deep learning models, Knowledge Graphs are natively developed to be explainable and are a promising solution for exploring new features and relations, and enhancing understandability of decisions. In this work, we aim to develop an explainable malware classifier which can classify PE executable as malign or benign, by infusing external knowledge using Knowledge Graph (KG). We enrich our Knowledge Graph using MITRE Attack ontology (i.e., domain knowledge) and EMBER dataset and utilize Graph2Vec algorithm to embed KG knowledge into our classifier. We found that our classifier yields satisfactory results while maintaining a high level of explainability.
Growth Mindset Emojifier Multimodal App
Authors: Kumar Yulia , Manikandan Anjana , Morreale Patricia , Li J. Jenny
Published: July 2024

The study introduces the Growth Mindset Emojifier Multi-modal App (GMEMA), a novel application designed to improve educational feedback by including Growth Mindset and emojis. Using OpenAI’s AI models, GMEMA provides emotionally nuanced feedback to boost student engagement and foster growth mindsets. The application's capabilities extend to the automated creation and assessment of Java quizzes, delivering personalized feedback that blends motivational phrases and chosen emojis to match students’ achievements. Preliminary observations indicate that incorporating emojis into educators' feedback significantly heightens students' emotional engagement and comprehension, illustrating the profound impact of emotional AI on educational practices.
An Introduction to Probabilistic Graphical Models
Authors: Portinale Luigi
Published: July 2024

This tutorial covers an introduction to Probabilistic Graphical Models (PGM), such as Bayesian Networks and Markov Random Fields, for reasoning under uncertainty in intelligent systems. Basic terminology, formal concepts, representational and inference issues will be discussed, starting from basic notions about probability theory, in such a way that the novice and the less skilled in the field will be able to follow the details.Further reading and software packages and frameworks will also be discussed.
Bridging the Gap: A Comprehensive Study on Named Entity Recognition in Electronic Domain using Hybrid Statistical and Deep Knowledge Transfer
Authors: Dekhili Ghaith , Le Tan Ngoc , Sadat Fatiha
Published: July 2024

Training deep neural network models in NLP applications with a small amount of annotated data does not usually achieve high performances. To address this issue, transfer learning, which consists of transferring knowledge from a domain with a large amount of annotated data to a specific domain which lacks annotated data, could be a solution. In this paper, we present a study case on named entity recognition for the electronic domain, that relies on several approaches based. on statistics, deep learning, and transfer learning. Our
evaluations showed a significant improvement in overall performance, with the best results using transfer learning, up to +15% compared to other approaches.
As Transformers-based models have shown their effectiveness in many NLP tasks in the last years, in this study, we compare our models performance to some Transformers-based models.
Enhancing Image Classification through Exploitation of Hue Cyclicity in Convolutional Neural Networks
Authors: Kuang Jiatao , Cha Teryn , Cha Sung-Hyuk
Published: July 2024

This study introduces innovative methodologies for image classification employing Convolutional Neural Networks (CNNs) by leveraging the cyclical attributes of hue within the HSV color space. Two distinct kernels are explored to linearize the circular values of hue. The first kernel converts the angular values to three modulo distance values corresponding to three color hue points. The second kernel utilizes trigonometry to convert angles into sine and cosine linear values. Experimental evaluations demonstrate that linearizing hue values leads to a notable enhancement in classification accuracy. This research provides insights into optimizing CNN-based image classification by integrating hue cyclicity, thereby advancing the capabilities of computer vision systems.
Learning Cohesive Behaviors Across Scales for Semi-Cooperative Agents
Authors: Sawtell Reid , Kitchen Sarah , Aris Timothy , McGroarty Christopher
Published: July 2024

The development of automated opponents in video games has been part of game development since the very beginning of the field. The advent of modern AI approaches such as reinforcement learning has opened the door to a wide variety of flexible and adaptive AI opponents. However, challenges in producing realistic opponents persist, namely scalability and generalizability. Scalability is of particular importance when many individual opponents are required to act cohesively over long distances, but this makes learning more difficult. This paper presents a novel architecture applying graph convolutional layers in a U-net with custom pooling operators in order to achieve learning across scales. League play reinforcement learning was used to train competitive agents in a navigation mesh environment.
Teknik Cerita dalam Pembelajaran Pendidikan Agama Islam Berbasis Tafsir Isra’iliyyat
Authors: Rahmat Rahmat , Khoiriyah Maulidatul , Syauqillah Muhammad
Published: July 2024

Artikel ini memuat tips implementasi teknik cerita dalam pembelajaran Pendidikan Agama Islam (PAI), dengan mengambil sampel materi terkait Kisah Keteladanan Ashabul Kahfi yang diajarkan di Kelas 6 SD/MI. Secara garis besar, ada lima tahap implementasi teknik cerita. Pertama, guru dan siswa mengawali pembelajaran dengan membaca ayat Al-Qur’an, terutama Surat al-Fatihah. Kedua, guru meminta beberapa siswa untuk membaca ayat Al-Qur’an dan terjemahnya yang relevan dan siginifikan terkait Ashabul Kahfi. Ketiga, guru bercerita tentang Ashabul Kahfi dengan diperkaya Tafsir Isra’iliyyat yang merujuk pada tafsir terpercaya, seperti Tafsir al-Munir karya Wahbah al-Zuhaili. Keempat, guru menayangkan video pendek terkait gua yang dipercaya sebagai gua Ashabul Kahfi dan/atau film pendek terkait kisah Ashabul Kahfi. Sebagai alternatif media video, guru dapat menggunakan media gambar yang dicetak dan ditempelkan di papan tulis. Kelima, sebagai evaluasi pembelajaran, guru memberi tugas kepada siswa agar mencatat hikmah-hikmah yang dapat dipetik oleh siswa untuk diterapkan dalam kehidupan sehari-hari.
Pasar Sehat Perspektif Hukum Islam dan Relevansinya bagi Pengembangan Pasar Sehat di Indonesia
Authors: Anwar Mustofa , Fauzi Moh. Mansur
Published: July 2024

Keberadaan pasar tradisional di Indonesia memiliki peranan penting bagi pertumbuhan ekonomi nasional, khususnya bagi kalangan menengah ke bawah. Lebih dari 50 juta orang atau setara 25% jumlah penduduk Indonesia mengais rezeki di pasar tradisional. Di sisi lain, keberadaan pasar tradisional semakin hari semakin memprihatinkan. Banyak faktor yang mempengaruhi kondisi tersebut, antara lain kondisi pasar yang kumuh dan kecurangan pedagang di pasar tradisional. Atas dasar itu, artikel ini mengajukan gagasan terkait konsep Pasar Sehat yang selaras dengan aturan pemerintah selaku Ulil Amri, serta sesuai dengan ketentuan hukum Islam (Fikih). Wujudnya adalah Pasar Sehat yang memenuhi standar kebersihan (bersih dan suci), keamanan, kenyamanan dan kesehatan.