bluuebunny/crossref_metadata_2025 huggingface dataset:
EcoBlock de autoconstrucción, para viviendas sociales.
Authors: Anderson Ibar Federico
Published: June 2023

EcoBlock de autoconstrucción para viviendas sociales del tipo "Hágalo Usted Mismo" (Do-It-Yourself), sin consumo de energía para su cocción son bloques de autoconstrucción ecológicos (material compuesto de cemento + caucho SBR reciclado de neumáticos + huecos de botellas de PET de la basura utilizados para que encastren los ladrillos entre sí), para individuos con mano de obra de baja calificación; pensado para combatir el déficit habitacional de la pobreza estructural y su fabricación "in situ" de las necesidades. Son bloques de autoconstrucción para viviendas sociales de poblaciones de bajos recursos, es económico y ambientalmente sustentable de vital importancia para la economía circular de la basura. Con lo que se pretende lograr un Desarrollo Ecológico, Tecnológico y Social Sustentable combinado con el Desarrollo Económico Sustentable (Economía Circular); lo cual debe conducir a un desarrollo amigable con el medio ambiente y responsable en la reducción de la “Huella de Carbono”. El caucho SBR (estireno-butadieno) triturado a partir de los neumáticos de descartes, en una planta recicladora de neumáticos se mezcla muy bien con el cemento portland para crear un material compuesto (composite) de hormigón liviano de un valor aproximado de 1500 (Kg/m3), siendo que para áridos normales un hormigón es de aproximadamente 2500 (Kg/m3) y para áridos pesados de 3500 (kg/m3) y una resistencia a la compresión de aproximada de 15 (MPa). La resistencia a la compresión del hormigón de caucho puede variar entre 10 y 30 (MPa), dependiendo de la cantidad de caucho utilizado y de la calidad del mismo. La resistencia a la tracción del hormigón de caucho puede ser entre 0.5 y 2.5 (MPa). Módulo de elasticidad: el módulo de elasticidad del hormigón de caucho suele ser más bajo que el del hormigón convencional, estando en el rango de 5 a 10 (GPa). Este proyecto fue desarrollado para el Concurso Nacional INNOVAR 2023 del MINCYT (Ministerio de Ciencia y Tecnología de la Nación) conjuntamente con el INET (Instituto Nacional de Educación Tecnológica), para la Escuela Técnica Nº 2 “Independencia” de Concordia, Provincia de Entre Ríos. Conjuntamente con el apoyo del Proyecto B374 acreditado en la Secretaría de Ciencia y Técnica – FBA - Universidad Nacional de La Plata.
Penerapan Intervensi I-Message pada Pasangan Suami Istri dengan Usia Pernikahan Muda sebagai Upaya Peningkatan Ketahanan Keluarga
Authors: Jafar Eka Sufartianinsih , Patiung Rita , Qur’ani Besse , Ansar Wilda , Irdianti Irdianti
Published: June 2023

Ketahanan keluarga adalah kemampuan anggota keluarga untuk bertahan serta beradaptasi terhadap kondisi yang dinamis sehingga tercipta kesejahteraan dalam keluarga. Tujuan dari penelitian ini adalah agar komunikasi pasangan suami dan istri lebih terbuka dan saling memahami tentang diri dan hubungan yang mereka jalani tanpa adanya pengabaian atau perasaan tidak nyaman. Metode yang digunakan adalah metode kualitatif melalui intervensi I-Message. Data dianalisis menggunakan salah satu model Spradley yaitu analisis dominan. Hasil penelitian menunjukkan bahwa pasangan suami dan istri lebih terbuka dan memahami perasaan, penyebab konflik diantara mereka dan keinginan mereka terhadap pasangannya. Hasil penelitian juga menunjukkan bahwa subjek lebih merasa lega dan senang setelah intervensi dilakukan. Kesimpulan dari penelitian ini adalah bahwa penerapan intervensi I-Message efektif dalam membangun dan mempertahankan keluarga yang tangguh.
Patient-Specific Readmission Prediction and Intervention for Health Care
Authors: Zhang Yan
Published: June 2023

Hospital readmission is often associated with unfavorable patient outcomes and a large cost of resources. Therefore, preventing avoidable re-hospitalizations is imperative. To target this problem, one important metric that researchers and practitioners strive to reduce is the 30-day hospital readmission rate. In this paper, we introduce a general decision support system that utilizes machine learning (ML) based patientspecific prediction to guide the suggestion of patient intervention program assignment, with the objective of minimizing the readmission cost for hospitals. This work has three major contributions. First, the proposed solution is highly scalable by using PySpark. Second, we outline solution architecture components including (1) data injection (both real-time sensor reading and data at rest), processing, and analysis, and (2) ML model building, evaluation, deployment and scoring. Third, we discuss how the ML prediction results can be taken into account in a decision support system by presenting a rich visualization.
Transfer Active Learning Framework to Predict Thermal Comfort
Authors: Natarajan Annamalai , Laftchiev Emil
Published: June 2023

Personal thermal comfort is the feeling that individuals have about how hot, cold or comfortable they are. Studies have hown that thermal comfort is a key component of human performance in the work place and that personalized thermal comfort models can be learned from user labeled data that is collected from wearable devices and room sensors. These personalized thermal comfort models can then be used to optimize the thermal comfort of room occupants to maximize their performance. Unfortunately, personalized thermal comfort models can only be learned after extensive dataset collection and user labeling. This paper addresses this challenge by proposing a transfer active learning framework for thermal comfort prediction that reduces the burdensome task of collecting large labeled datasets for each new user. The framework leverages domain knowledge from prior users and an active learning strategy for new users that reduces the necessary size of the labeled dataset. When tested on a real dataset collected from five users, this framework achieves a 70% reduction in the required size of the labeled dataset as compared to the fully supervised learning  approach. Specifically, the framework achieves a mean error of 0.822±0.05, while the supervised learning approach achieves a mean error of 0.852±0.04.
Long-Term Modeling and Monitoring of Neuromusculoskeletal System Performance Using Tattoo-Like EMG Sensors
Authors: Yang Kaiwen , Nicolini Luke , Kuang Irene , Lu Nanshu , Djurdjanovic Dragan
Published: June 2023

This paper introduces stretchable, long-term wearable, tattoo-like dry surface electrodes for highly repeatable electromyography (EMG). The tattoo-like sensors are hair thin, skin compliant and can be laminated on human skin just like a temporary transfer tattoo, which enables multiday noninvasive but intimate contact with the skin even under severe skin deformation. The new electrodes were used to facilitate a system-based approach to tracking of long-term fatiguing and recovery processes in a human neuromusculoskeletal (NMS) system, which was based on establishing an autoregressive moving average model with exogenous inputs (ARMAX model) relating signatures extracted from the surface electromyogram (sEMG) signals collected using the tattoo-like sensors, and the corresponding hand grip force (HGF) serving as the model output. Performance degradation of the relevant NMS system was evaluated by tracking the evolution of the errors of the ARMAX model established using the data corresponding to the rested (fresh) state of any given subject. Results from several exercise sessions clearly showed repeated patterns of fatiguing and resting, with a notable point that these patterns could now be quantified via dynamic models relating the relevant muscle signatures and NMS outputs.
Design of an Affordable Socially Assistive Robot for Remote Health and Function Monitoring and Prognostication
Authors: Johnson Michelle J , Sobrepera Michael J. , Kina Enri , Mendonca Rochelle
Published: June 2023

To address shortages in rehabilitation clinicians and provide for the growing numbers of elder and disabled patients needing rehabilitation, we have been working towards developing an affordable socially assistive robot for remote therapy and health monitoring. Our system is being designed to initially work via remote control, while addressing some of the challenges of traditional telepresence. To understand how to design a system to meet the needs of elders, we created a mobile therapy robot prototype from two commercial robots and demonstrated this system to clinicians in two types of rehabilitation care settings, a daycare setting and a inpatient rehabilitation setting. We propose to introduce the prototype as a social and therapy agent into clinician-patient interactions with the aim of improving the quality of information transfer between the clinician and the patient. This paper describes an investigative effort to understand how clinicians who work with elders accept this prototype. Clinicians from each setting differed in their needs for the robot. Those in daycare settings preferred a more social robot to encourage and motivate elders to exercise as well as monitor their health. Clinicians in the inpatient rehabilitation setting desired a robot with more therapeutic and treatment capabilities. Both groups wanted a robot with some autonomy that was portable, maintainable, affordable, and durable. We discuss these results in detail along with the ethical implications of increasing the robots autonomy and suggest additional requirements for achieving a smarter robot that can meet the clinicians social, health monitoring and prognostication desires.
Anomaly Detection on Time Series with Wasserstein GAN applied to PHM
Authors: Ducoffe Mélanie , Haloui Ilyass , Gupta Jayant Sen
Published: June 2023

Modern vehicles are more and more connected. For instance, in the aerospace industry, newer aircraft are already equipped with data concentrators and enough wireless connectivity to transmit sensor data collected during the whole flight to the ground, usually when the airplane is at the gate. Moreover, platforms that were not designed with such capability can be retrofitted to install devices that enable wireless data collection,as is done on Airbus A320 family. For military and heavy helicopters, HUMS (Health and Usage Monitoring System) also allows the collection of sensor data. Finally, satellites send continuously to the ground sensor data, called telemetries. Most of the time, fortunately, the platforms behave normally, faults and failures are thus rare. In order to go beyond corrective or preventive maintenance, and anticipate future faults and failures, we have to look for any drift, any change, in systems’ behavior, in data that is normal almost all the time. Moreover, collected sensor data is time series data. The problem is then anomaly detection or novelty detection in time series data. Among machine learning techniques that can be used to analyze data, Deep Learning, especially Convolutional Neural Networks, is very popular since it has surpassed human capacities for image classification and object detection. In this field, Generative Adversarial Networks are a technique to generate data similar to a potentially high dimension original dataset. In our case, generate new data could be useful to enrich the learning dataset with generated abnormal data to make it less unbalanced. Yet we are more interested in the potential of such techniques to perform anomaly detection for high dimensional data, comparing newly observed data with data that could have been generated from a distribution built from normal examples.
Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models
Authors: Chao Manuel Arias , Kulkarni Chetan , Goebel Kai , Fink Olga
Published: June 2023

With the increased availability of condition monitoring data on the one hand and the increased complexity of explicit system physics-based models on the other hand, the application of data-driven approaches for fault detection and isolation has recently grown. While detection accuracy of such approaches is generally very good, their performance on fault isolation often suffers from the fact that fault conditions affect a large portion of the measured signals thereby masking the fault source. To overcome this limitation, we propose a hybrid approach combining physical performance models with deep learning algorithms. Unobserved process variables are inferred with a physics-based performance model to enhance the input space of a data-driven diagnostics model. The resulting increased input space gains representation power enabling more accurate fault detection and isolation. To validate the effectiveness of the proposed method, we generate a condition monitoring dataset of an advanced gas turbine during flight conditions under healthy and four faulty operative conditions based on the Aero-Propulsion System Simulation (C-MAPSS) dynamical model. We evaluate the performance of the proposed hybrid methodology in combination with two different deep learning algorithms: deep feed forward neural networks and Variational Autoencoders, both of which demonstrate a significant improvement when applied within the hybrid fault detection and diagnostics framework. The proposed method is able to outperform pure data-driven solutions, particularly for systems with a high variability of operating conditions. It provides superior results both for fault detection as well as for fault isolation. For the fault isolation task, it overcomes the smearing effect that is commonly observed in pure data-driven approaches and enables a precise isolation of the affected signal. We also demonstrate that deep learning algorithms provide a better performance on the fault detection task compared to the traditional machine learning algorithms.
Scalable Change Analysis and Representation Using Characteristic Function
Authors: Tagawa Takaaki , Tadokoro Yukihiro , Yairi Takehisa
Published: June 2023

In this paper, we propose a novel framework to help human operators- who are domain experts but not necessarily familiar with statistics- analyze a complex system and find unknown changes and causes. Despite the prevalence, researchers have rarely tackled this problem. Our framework focuses on the representation and explanation of changes occurring between two datasets, specifically the normal data and data with the observed changes. We employ two-dimensional scatter plots which can provide comprehensive representation without requiring statistical knowledge. This helps a human operator to intuitively understand the change and the cause. An analysis to find two-attribute pairs whose scatter plots well explain the change does not require high computational complexity owing to the novel characteristic function-based approach. Although a hyper-parameter needs to be determined, our analysis introduces a novel appropriate prior distribution to determine the proper hyper-parameter automatically. The experimental results show that our method presents the change and the cause with the same accuracy as that of the state-of-the-art kernel hypothesis testing approaches, while reducing the computational costs by almost 99% at the maximum for all popular benchmark datasets. The experiment using real vehicle driving data demonstrates the practicality of our framework.
A Physics-informed Neural Network for Wind Turbine Main Bearing Fatigue
Authors: Yucesan Yigit A. , Viana Felipe A. C.
Published: June 2023

Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the component design lives. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling the remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics-informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (i.e., grease degradation).
Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data
Authors: Voronov Sergii , Krysander Mattias , Frisk Erik
Published: June 2023

Predictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles with no driver who can notice first signs of a component problem. The lead-acid battery in a heavy vehicle is mostly used during engine starts, but also for heating and cooling the cockpit, and is an important part of the electrical system that is essential for reliable operation. This paper develops and evaluates two machine-learning based methods for battery prognostics, one based on Long Short-Term Memory (LSTM) neural networks and one on Random Survival Forest (RSF). The objective is to estimate time of battery failure based on sparse and non-equidistant vehicle operational data, obtained from workshop visits or over-the-air readouts. The dataset has three characteristics: 1) no sensor measurements are directly related to battery health, 2) the number of data readouts vary from one vehicle to another, and 3) readouts are collected at different time periods. Missing data is common and is addressed by comparing different imputation techniques. RSF- and LSTM-based models are proposed and evaluated for the case of sparse multiple-readouts. How to measure model performance is discussed and how the amount of vehicle information influences performance.
Remaining Useful Life Estimation Based on Detection of Explosive Changes: Analysis of Bearing Vibration
Authors: Barraza Diana Barraza , Tercero-Gómez Víctor G. , Cordero-Franco A. Eduardo , Beruvides Mario G.
Published: June 2023

The monitoring of condition variables for maintenance purposes is a growing trend amongst researchers and practitioners where decisions are based on degradation levels. The two approaches in Condition-Based Maintenance (CBM) are diagnosing the level of degradation (diagnostics) or predicting when a certain level of degradation will be reached (prognostics). Using diagnostics determines when it is necessary to perform maintenance, but it rarely allows for estimation of future degradation. In the second case, prognostics does allow for degradation and failure prediction, however, its major drawback lies in when to perform the analysis, and exactly what information should be used for predictions. This encumbrance is due to previous studies that have shown that degradation variable could undergo a change that misleads these calculations. This paper addresses the issue of identifying explosive changes in condition variables, using Control Charts, to determine when to perform a new model fitting in order to obtain more accurate Remaining Useful Life (RUL) estimations. The diagnostic-prognostic methodology allows for discarding pre-change observations to avoid contamination in condition prediction. In addition the performance of the integration methodology is compared against adaptive autoregressive (AR) models. Results show that using only the observations acquired after the out-of-control signal produces more accurate RUL estimations.
Cross-domain Transfer of Defect Features in Technical Domains Based on Partial Target Data
Authors: Schlagenhauf Tobias , Scheurenbrand Tim
Published: June 2023

A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions. Following a contrastive learning approach, a CNN encoder is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.
Machine Learning Based Approach for EVAP System Early Anomaly Detection Using Connected Vehicle Data
Authors: Omrani Ala , Kumar Pankaj , Dudar Aed , Casedy Michael , Szwabowski Steven , Dawson Brandon
Published: June 2023

From automobile manufacturers’ perspective, reduction of warranty cost leads to less expenditures, which then yields higher profits. Hence, it is crucial to leverage the different methods and available tools to achieve such outcome. Connected vehicle data is one critical resource that can be a gamechanger, reducing the associated costs and improving the business profitability. This project uses Mode06 (On-Board diagnostics reported tests results) connected vehicle data along with contextual data to early detect EVAP and purge monitors’ anomalies. Early detection allows fixing the issue through software (SW) and/or hardware (HW) upgrades before it turns into a failure (preventive maintenance), yielding then system quality improvement. Root cause analysis, which can be developed based on the anomaly detection outcomes and which is not within the scope of this paper, allows diagnostics of HW and/or SW related issues in a timely manner and eventually be prepared ahead of time for system failures. In this paper, statistics-based early anomaly detection models, based on vehicle data and fleet data, are developed. The proposed solution is a generic tool that does not make assumptions on data distribution and can be adapted to other systems by tweaking mainly the data cleaning process. It also incorporates specific system definitions of abnormal behavior, which makes it more accurate compared to conventional anomaly detection tools, which are mainly affected by the imbalanced data and the EVAP and purge definition of an anomaly. When deployed with field data, the algorithm showed higher performance, compared to popular anomaly detection techniques, and proved that failures can be prevented through detection of the anomalies several weeks/miles before the actual fail.
Anomaly Detection for Early Failure Identification on Automotive Field Data
Authors: Jain Aditya , Tarey Piyush
Published: June 2023

The automotive industry is witnessing its next phase of transformation. The vehicles are getting defined by software, becoming intelligent, connected and more complex to design, develop and analyze. For these complex vehicles, prognostics and proactive maintenance has become ever more critical than before.OEMs and suppliers analyze probable failures that a vehicle component is likely to encounter, define fault codes to identify those failures, and provide procedure or guided steps to resolve them. For smarter vehicles, it is required that vehicles be capable to catch potential problems as soon as the component’s condition starts to deteriorate and becomes a failure. These failures could be known (defined) or new (undefined). Given the vehicle development timelines and increasing complexity, many problems are not analyzed at design stage and remain undetected before production. Hence, no fault code or test case exist for them. Diagnosing such problems become very difficult, postproduction.The aim of this paper is to propose a Machine Learning (ML) based framework which utilizes minimally labelled or unlabeled sensor data generated from a vehicle system at a given frequency. The framework utilizes an ML model to identify any anomalous behavior or aberration, and flag it for further review. This framework can be adopted on large amount of real time or time series data to identify known as well as undefined failures early. These models could be deployed on cloud or on edge (on vehicles) for analyzing real-time sensor data for a given system/component and flag any anomaly. It could further be utilized to create a part specific Predictive Maintenance (PM) model to provide proactive warnings and prevent downtime.
Explainable Models for Multivariate Time-series Defect Classification of Arc Stud Welding
Authors: Naddaf  Shargh Sadra , Naddaf-Sh Mahdi , Dalton Maxim , Ramezani Soodabeh , Kashani Amir R. , Zargarzadeh Hassan
Published: June 2023

Arc Stud Welding (ASW) is widely used in many industries such as automotive and shipbuilding and is employed in building and jointing large-scale structures. While defective or imperfect welds rarely occur in production, even a single low-quality stud weld is the reason for scrapping the entire structure, financial loss and wasting time. Preventive machine learning-based solutions can be leveraged to minimize the loss. However, these approaches only provide predictions rather than demonstrating insights for characterizing defects and root cause analysis. In this work, an investigation on defect detection and classification to diagnose the possible leading causes of low-quality defects is proposed. Moreover, an explainable model to describe network predictions is explored. Initially, a dataset of multi-variate time-series of ASW utilizing measurement sensors in an experimental environment is generated. Next, a set of pre-possessing techniques are assessed. Finally, classification models are optimized by Bayesian black-box optimization methods to maximize their performance. Our best approach reaches an F1-score of 0.84 on the test set. Furthermore, an explainable model is employed to provide interpretations on per class feature attention of the model to extract sensor measurement contribution in detecting defects as well as its time attention.
Lives para pessoas idosas no contexto da Covid-19
Authors: Koeche Cristiane , Oliveira Letycia Parreira de , Sandim Lucíola Silva , Freitas Eduarda Rezende , Carvalho Gustavo de Azevedo , Silva Henrique Salmazo da , Carvalho Luiz Sérgio Fernandes de , Oliveira Maria Liz Cunha de
Published: June 2023

Objetivo: Investigar as lives disponibilizadas nas redes sociais de instituições e conselhos profissionais brasileiros sobre pessoas idosas no contexto de enfrenta-mento da Covid-19, publicadas entre abril e setembro de 2020. Método: Realizou-se um estudo retrospectivo, exploratório, descritivo e infométrico. Resultados: Três instituições relacionadas diretamente a pessoas idosas divulgaram lives vol-tadas para essa população ou para cuidadores e familiares. Entre os profissionais que mais tiveram participações nas lives encontram-se os médicos geriatras. O maior número de acesso (quase oito mil) ocorreu em uma live intitulada “Pande-mia Covid-19”, realizada em junho, por um geriatra. Ela foi divulgada no YouTu-be da Sociedade Brasileira de Geriatria e Gerontologia – instituição que mais di-vulgou lives sobre a temática. A rede social mais utilizada para esse fim foi o Ins-tagram. Conclusão: há uma carência na atenção e no cuidado com a pessoa idosa no contexto da pandemia de Covid-19.
Descritores: Idoso; Covid-19; Lives.
PENGARUH LATIHAN POWER OTOT TUNGKAI TERHADAP PRESTASI RENANG GAYA DADA
Authors: Iwan Suhendra, Maulidin, Dadang Warta Chandra
Published: June 2023

Tujuan dari penelitian ini adalah untuk mengetahui latihan power otot ungkai terhadap kemampuan renang gaya dada pada siswa putra kelas VII MTs Raudlatussibyan NW Belencong . Power (daya ledak) adalah usaha yang dilakukan oleh otot secara maksimal dalam waktu yang sesingkat-singkatnya yang merupakan perpaduan antara kecepatan dan kekuatan, dalam renang gaya dada power otot tungkai merupakan salah satu komponen kondisi fisik yang sangat penting berperan dalam melakukan ayunan kaki secepat-cepatnya. Mengingat hal tersebut maka perlu diberikan suatu bentuk latihan yang bertujuan untuk meningkatkan power otot tungkai, salah satu metode latihan yang dapat meningkatkan power otot tungkai adalah latihan knee tuck jump pengaruh latihan power otot tungkai terhadap kemampuan renang gaya dada pada Siswa Putra  Kelas VII MTs Raudlatussbyan NW Belencong. Sampel yang digunakan dalam penelitian ini  sebanyak 15 orang siswa putra kelas VII. Metode analisis data menggunakan analisa statistik dengan menggunakan rumus t-test . Metode analisis data menggunakan analisa statistik dengan menggunakan rumus t-test. Hasil uji hipotesis menggunakan rumus t-test diperoleh “t-hitung<dari t-tabel”yakni dengan rinciant-hitung =  0.604 < t-tabel= 2.145 pada taraf signifikansi 5%, dengan kata lain “hipotesisnihil (Ho) diterima dan hipotesis alternative (Ha) ditolak”. Dari hasil yang diperoleh dapat disimpulkan bahwa  “Tidak ada pengaruhlatihanpower otot tungkai terhadap kemampuan renang gaya dada pada Siswa Putra Kelas VII MTs Raudlatussibyan NW Belencong.
PENGARUH LATIHAN DECLINE HOP TERHADAP KEMAMPUAN SHOOTING DALAM PERMAINAN SEPAKBOLA PADA ADIK FC DESA APITAIK KECAMATAN PRINGGABAYA KABUPATEN LOMBOK TIMUR
Authors: Rian Kurniawan
Published: June 2023

Penelitian ini bertujuan untuk mendeskrifsikan pengaruh latihan decline hop terhadap kemampuan shooting dalam permainan sepakbola karena masalah yang ada dalam penelitian ini adalah masih rendahnya kemampuan shooting para pemain ADIK FC Desa Apitaik Kecamatan Peringgabaya Kabupaten Lombok Timur, Subyek penelitian ini seluruh pemain ADIK FC yang berjumlah 20 orang, Instrumen dalam penelitian ini adalah bola, stop watch, gawang sepakbola, tali, nomor-nomor. Teknik analisis data yang digunakan adalah analisis statistik dengan rumus t-test Dari analisis data menggunakan rumus t-test didapat hasil t-hitung adalah 0,326 sedangkan t-tabel dengan taraf signifikan 5%, N=20 adalah 2,093, dengan Kenyataan ini menunjukan bahwa t-hitung lebih kecil dari pada t-tabel sehingga dapat dikemukakan Hipotesis nihil (Ho) diterima dan Hipotesis alternatif (Ha) ditolak. Maka kesimpulan analisa dalam penelitian ini adalah “tidak ada pengaruh latihan decline hop terhadap kemampuan shooting dalam permainan sepakbola pada ADIK FC Desa Apitaik Kecamatan Pringgabaya Kabupaten Lombok Timur”.
PENGARUH LATIHAN LADDER DRILL TERHADAP PENINGKATAN KELINCAHAN PADA PEMAIN BULUTANGKIS PB. PENANGGAK GUNUNG SARI
Authors: Muhamad Agus, Intan Primayanti
Published: June 2023

Tujuan jangka panjang dari penelitian ini adalah sebagai sumbangan bagi ilmuan di bidang olahraga, dapat menjadi masukan informasi dan pedoman bagi guru olahraga maupun pelatih. Rancangan yang digunakan dalam penelitian ini adalah “ One Group Pre test-Post test Design” dan bersifat eksperimen (percobaan) lapangan. Analisis data dalam penelitian ini menggunakan analisis yang bersifat kuantitatif (angka) yaitu uji-t atau t-test. Populasi dan sampel dalam penelitian ini adalah seluruh pemain atau anggota PB. Penanggak Gunungsari yang berjumlah 15 orang yang diambil dengan menggunakan penelitian populasi. Berdasarkan hasil analisis uji-t tentang tingkat kelincahan pada pemain Bulutangkis PB. Penanggak Gunungsari dapat diperoleh nilai thitung sebesar 5,058 lebih besar dari ttabel 2,145 dengan taraf signifikansi 5%, berarti ada pengaruh yang signifikan sebelum dan sesudah perlakuan latihan ladder drill. Untuk membuktikan peningkatan kelincahan pada pemain Bulutangkis PB. Penanggak Gunungsari dibuktikan dengan nilai pre-test dan post-test 370,031 (second) dan 357,512 (second), dengan hasil tersebut menunjukkan peningkatan nilai sebesar 12,519 (second) setelah diberikan perlakuan latihan ladder drill pada pemain Bulutangkis PB. Penanggak Gunungsari
Profil Destinasi Wisata Alam Sumber Manis Sebagai Media Promosi Wisata Di Banyuwangi
Authors: Mulyadi Adi , Abdurrahman  , Indari
Published: June 2023

This paper discusses the profile of the Sumber Manis Tourism destination. The destination profiles are used to promote tourism for local, domestic and foreign tourists. The manager of Alam Sumber Tourism is experiencing problems regarding the use of social media, and the potential for tourism to introduce its destinations. So that students and lecturers propose to collaborate with PT and Tourism Destinations in Banyuwangi in creating social media accounts and making profile books for promotion for visiting tourists. The method is carried out by survey, socialization and mentoring for 45 days. The results show that the impact of social media can increase visitors by 10% on Saturdays and Sundays and tourists understand tourism potential with profile books.
Pelatihan Penggunaan Aplikasi Mendeley Bagi Guru Sekolah Dasar (SD) di Kabupaten Jeneponto
Authors: Akhyar Muchtar  , Hilda Ashari  , Sari Dessy Ana , Elfira Makmur  , Muh. Iswal Burhan
Published: June 2023

This Community Partnership Program (PKM) partner is SD Negeri 13 Allu. The problems are: (1) teachers lack knowledge about the concept of citation in scientific work,(2) teachers have doubts about writing reading sources as a reference for research writing, (3) the citation system has so far been done manually, and (4) teachers have not been able to use the reference creation application automatically. The external goal is scientific work that is more systematic and coherent in citing reference sources. The methods used are lectures, demonstrations, discussions, questions and answers, and companion partners. The results achieved are (1) partners know the concept of citing in scientific work, (2) partners have the skills to write reference sources appropriately, (3) partners can use developing technology, especially in writing references automatically, (4) teachers have skills using Mendeley's application in writing scientific papers.
Tindakan Preventif Dampak Pinjaman Online Sebagai Potensi Kegiatan Pengabdian Masyarakat
Authors: Alfiana  , Pri Handini Dharmayanti
Published: June 2023

Online loans (pinjol) are a growing money lending service. This application has a negative impact on the user community. Various research and community service activities have been carried out. This article aims to describe preventive treatments for the impact of online loans as potential community service activities. The description is carried out with a brief review based on the publication of community service activities that have been carried out. The results of the review show that legal and economic aspects are the main topics of activity. Activities are carried out using the socialization method with the target group of the public community and among housewives. The potential targets for the next community service activities in this area are teachers and layoff victims as the two top list of loan victims. The scope of new activities can be directed at the security of digital transactions and the impact of knowledge in minimizing negative effects.
Pacientes Portadores de Necessidades Especiais (PPNE) em Odontopediatria: desafios e abordagens adaptadas
Authors: de Lima Pereira Alexandra , Bezerra Gomes Akila Weslly , Do Vale Araújo Rosana , da Silva Mariano Niziana , Gomes Marinho Francicleide , Firmino dos Santos Daniela , Cordeiro Welliton Arevalo , Mota Maklouf Sâmia , Franco dos Santos Márcia Maria , Moraes Hauache Kássem , De Figueiredo Meira Gabriela
Published: June 2023

Crianças com necessidades especiais abrangem diversas condições que exigem cuidados médicos específicos, intervenções especializadas e o uso de serviços ou programas adaptados. Essa definição também se aplica ao contexto do atendimento odontológico. Este artigo possui como objetivo explorar os desafios enfrentados pelos odontopediatras no atendimento a Pacientes Portadores de Necessidades Especiais (PPNE) e discutir as abordagens adaptadas que podem ser implementadas para superar tais dificuldades. Para a elaboração deste artigo, foi realizado um extenso levantamento bibliográfico utilizando as bases de dados SciVerse Scopus, Scientific Eletronic Library Online (Scielo), U.S. National Library of Medicine (PUBMED) e ScienceDirect. Essas fontes foram exploradas com o auxílio do gerenciador de referências Mendeley, permitindo uma busca abrangente e sistemática pelos artigos relevantes para a revisão da literatura sobre cirurgia parendodôntica. Os pacientes com necessidades especiais em odontopediatria apresentam desafios únicos que exigem abordagens adaptadas e uma compreensão abrangente por parte dos profissionais da área. Através da implementação de estratégias como orientação comportamental, sedação consciente e trabalho em equipe multidisciplinar, é possível proporcionar um atendimento de qualidade, garantindo o bem-estar e a saúde bucal desses pacientes.
Media Leaflet dan Poster Pada Tingkat Pengetahuan Masyarakat Tentang Hipertensi
Authors: Page Muhammad Taufik , Erviana Erviana , Sikin Aryang Gunina
Published: June 2023

Hipertensi disebut sebagai the silent killer karena tidak menunjukan gejala apapun selama sepuluh sampai dua puluh tahun yang akan mendatang dan biasanya diketahui setelah terjadinya komplikasi pada organ tubuh lain. Tekanan yang tinggi dapat membebani arteri dan jantung sehingga pengidap hipertensi bisa menyebabkan serangan jantung, stroke, atau penyakit ginjal. Tujuan penelitian untuk mengetahui faktor-faktor yang mempengaruhi penyebab hipertensi di wilayah kerja Puskesmas Banggae 1. Metode penelitian yang digunakan adalah quasi eksperimen dengan menggunakan rancangan one group pre and post test design. Populasi yaitu penderita hipertensi yang ada diwilayah kerja Puskesmas Banggae I sebanyak 85 orang. Teknik sampling yang digunakan yaitu purposive sampling. Penelitian ini dilakukan di Puskesmas Banggae I. Uji statistik yang digunakan adalah paired t-test. Hasil penelitian ini menunjukkan bahwa terdapat perbedaan rata-rata tingkat pengetahuan sebelum dan sesudah diberikan pendidikan kesehatan melalui media leaflet p-value=0,359 (> 0,05) dan poster dengan p-value = 0,000 (< 0,05), sehingga pendidikan kesehatan dengan media poster lebih efektif dibandingkan dengan media leaflet. Diharapkan agar tenaga Kesehatan yang akan melakukan promosi Kesehatan diharapkan dapat memilih media yang akan digunakan agar pesan yang akan disampaikan bisa sampai dengan baik ke masyarakat.
Tingkat Kepuasan Masyarakat terhadap Kinerja Pelayanan Kesehatan
Authors: Ananda Rusda , Damayanti Risna , Maharja Rizky
Published: June 2023

Rumah sakit sebagai institusi pelayanan kesehatan memiliki tanggung jawab untuk memberikan pelayanan yang berkualitas dan memenuhi kebutuhan kesehatan masyarakat. Pelayanan kesehatan yang berkualitas penting untuk meningkatkan kesehatan dan kesejahteraan masyarakat secara keseluruhan. Penelitian ini bertujuan mendeskripsikan tingkat kepuasan masyarakat terhadap kinerja pelayanan kesehatan di rumah sakit dengan menilai unsur yang meliputi persyaratan pelayanan yang diberikan, sistem, mekanisme, dan prosedur pelayanan, waktu penyelesaian layanan, biaya/tarif yang dikeluarkan, produk spesifikasi jenis pelayanan, kompetensi pelaksana baik medis maupun non medis, perilaku petugas ketika memberikan pelayanan, mekanisme penanganan pengaduan, saran, dan masukan, serta kelengkapan sarana dan prasarana yang tersedia. Hasil penelitian menunjukkan mutu pelayanan rumah sakit berada pada kategori kurang baik khususnya pada unsur perilaku petugas kesehatan. Dalam peningkatan kualitas pelayanan, diprioritaskan kepada unsur yang mempunyai nilai paling rendah untuk lebih dahulu diperbaiki, kemudian unsur yang memiliki nilai yang tinggi minimal harus tetap dipertahankan.
STUDY LITERATURE: FACTORS ASSOCIATED WITH MENSTRUAL PERSONAL HYGIENE IN ADOLESCENTS WITH MENTAL RETARDATION
Authors: Sholihah Amalia Rizqi , Widiasih Restuning , Solehati Tetti
Published: June 2023

Menstruation is experienced by every young woman, both young women with special needs and normal young women. In young women with mental retardation, they have limitations in doing menstrual personal hygiene, they may need help and guidance from parents. This literature review aimed to find out factors related to doing menstrual personal hygiene in adolescent girls with mental retardation. The databases were Google Scholar and Pubmed. Keywords in Indonesian and English included factors, menstrual personal hygiene, and mental retardation. The search results found 175 articles. The inclusion criteria used were full text published articles, in the period 2010 - 2019, the article discussed menstrual personal hygiene in adolescents with mental retardation. Based on the inclusion criteria, 9 articles were found that met the review requirements. The results of the analysis identified that there were two factors related to menstrual personal hygiene in young women, including internal factors: knowledge, classification of mental retardation, and external factors which are parental support, parental education level, and parenting. Internal factors have a significant relationship with adolescent girls when performing menstrual personal hygiene. Further research on the experience of adolescents with mental retardation is needed to see the readiness of adolescents in the face of menstruation.Keywords: Adolescent girls, factors, menstruation, personal hygiene, mental retardation.
THE EFFECT OF ANTENATAL YOGA ON LOW BACK PAIN IN THIRD-TRIMESTER OF PREGNANCY
Authors: Kosayriyah Salva Dwi , Kholisotin Kholisotin , Munir Zainal , FR Handono , Wahid Abdul Hamid
Published: June 2023

Low back pain is pain that occurs in the lumbosacral region and would get worse with increasing the gestational age. The purpose of this study was to determine the decrease of the low back pain before and after doing antenatal yoga. The design of this study was quasy experiment study using unequal control groups, the group divided into experimental and control groups. The study was conducted at BPM Hj. Suharni Cermee Bondowoso, the number of respondent was 50 pregnant women that chosen using a purposive sampling technique. The instrument used an observation sheet. In this study yoga was performed 4 times in 2 weeks with a duration of 1 hour per session. Data analysis using the Paired T-Test. Statistical results showed the value of p = 0,000 <0.05. There is a significant effect of antenatal yoga to the low back pain scale in the third trimester of pregnant women.Keywords: Low back pain, pregnant women, antenatal yoga.
PREGNANT WOMEN'S NUTRITIONAL STATUS
Authors: Nurliawati Enok , Komariah Etty , Astuti Eka Widianing
Published: June 2023

The research was based on pregnant women who experienced malnutrition in the  PHC of Tamansari, Tasikmalaya. Malnutrition on pregnant women causes interference to maternal and fetal health. Malnutrition can be anticipated by conducting screening in pregnant women so that it can be immediately identified and immediately addressed. This study aimed to identify the nutritional status based on upper arm circumference, BMI, and body weight gain based on BMI. The type of research was a quantitative descriptive. The sampling technique used stratified random sampling, the number of samples were 89 people. The study was conducted in the area of Tamansari  PHC  Data collection used weight scales, microtome stature meter, upper arm circumference, and body weight before pregnancy was taken secondary from the MCH book.  The results showed that majority of the respondents based on upper arm circumference were 66  people (74.2%) who did not experience malnutrition, based on BMI  49 people (55.1%) were normal, and based on body weight, 43 respondents (48.3%) were categorized as overweight according to gestational age. This research can be used as a basis for health promotion information regarding the nutritional status and further research needs to be done related to the impact of malnutrition and excess nutrition.Keywords: Pregnancy, malnutrition, nutritional status.
THE RELATIONSHIP BETWEEN SOCIO DEMOGRAPHIC CHARACTERISTICS WITH BREAST SELF EXAMINATION PRACTICES (BSE) IN FACULTY OF NURSING UNPAD STUDENTS
Authors: Anumilah Novarina Ismayani , Rahayuwati Laili , Mirwanti Ristina
Published: June 2023

BSE practice as an effort in detecting breast cancer needs to be emphasized in the fertile age group, especially students. One of the factors that influence BSE practice is socio demographic characteristics. The purpose of this study is to determine the relationship between socio demographic characteristics with BSE practice. The research design in this study was a correlational study. The sample in this study was 225 female students of the Faculty of Nursing UNPAD, with a stratified random sampling method. The instrument used in this research was a questionnaire of socio demographic characteristics and BSE practice. The results showed that only 22% of respondents had practiced BSE correctly. The results of bivariate analysis with Cramer’s V test showed that the year of grade of the respondents had a significant relationship with BSE practice (p = <0.05). While the score of the GPA, family history, ethnicity, religion, and family income were not found to be related (p = >0.05). The conclusion from this study is that BSE respondents' practice has a significant relationship with the respondent's grade years. Based on this, it is hoped that information related to BSE can also be given to freshman students who are just entering college and to further researchers, it is expected to expand the research field so that more variant results can be found.
DESCRIPTION OF BODY IMAGE AMONG TEENAGERS
Authors: Hidayati Nur Oktavia , Nurhalimah Hamidah , Suhendar Iwan , Nurhidayah Ikeu
Published: June 2023

Body image has a big influence on teenagers in seeing themselves positively or negatively. A teenager sees him/herself positively, then he/she will have satisfaction with the body shape they have, so that his/her self-confidence increases. Meanwhile, if they judge themselves negatively there will be dissatisfaction with their body shape, which has an impact on decreasing his/her self-confidence. The purpose of this research was to determine the description of body image in teenagers at SMAN 17 Garut. This research was a descriptive research with quantitative approach. The population in this research were 606 teenagers who were students at SMAN 17 Garut. The sample of the research was 241 teenagers by taking stratified random sampling. Data collection techniques with body image instrument modified by Andiyati from the questionnaire (MBRSQ-AS). The results showed that 125 people (51.9%) were in the negative category and 116 people (48.1%) were in the positive category which meant this research showed that the body image of teenagers at SMAN 17 Garut were in the negative category and the results on aspects of respondent's body image was in the negative category. Based on the results of the research conducted at SMAN 17 Garut, to anticipate the emergence of body image in teenagers can be done by providing direction or motivation to prevent the occurrence of higher body image disorders among teenagers. This can be done through cooperation between teachers and nurses.