bluuebunny/crossref_metadata_2025_split huggingface dataset:
Re-Evaluasi Proses Penyelesaian Pelanggaran Hak Asasi Manusia
Authors: Arief Muhammad 
Published: February 2021

Tujuan Penelitian menganalisis proses penyelesaian pelanggaran Hak Asasi Manusia (HAM) berat pada Persitiwa Trisaksi oelh Kejaksanaan Agung Republik Indonesia. Metode Penelitian yang digunakan adalah metode penelitian hukum. Normative dengan pendekatan kualitatif yang diuraikan secara deskriptif. Hasil peneltian menunjukan bahwa pelanggaran HAM berat pada peristiwa penanganan demonstrasi di Kampus Universitas Trisakti pada tahun 1998, oleh aparat keamanan; sehingga menyebabkan tewasnya empat orang mahasiswa dan ratusan lainnya luka. Komnas HAM telah melakukan (investigasi) penyelidikan terhadap peristiwa tersebut, dan laporan Komnas HAM menyimpulkan telah terjadi pelanggaran HAM berat atas insiden tersebut. Hasil penyelidikan tersebut telah dilaporankn Komnas HAM, dan berkas laporan tersebut telah diserahkan kepada Jaksa Agung; tetapi sampai saat ini (±18 tahun) Jaksa Agung belum memproses ke tingkat peneyidikan dan penuntutan. Alasan Jaksa Agung, berkaitan dengan hal-hal teknis yuridis, yang oleh publik dianggap tidak relevan dengan substansi kasus/masalah. Tetapi intinya aspek politis dan non yudisial lebih mengitari kasus-kasus tersebut.
The purpose of this research is to analyze the process of resolving gross violations of human rights (HAM) at the Trisaksi event by the Supreme Court of the Republic of Indonesia. The research method used is legal research methods. Normative with a qualitative approach described descriptively. The research results show that gross human rights violations during the handling of demonstrations at the Trisakti University Campus in 1998, by security forces; causing the death of four students and hundreds of others injured. Komnas HAM has conducted (investigated) investigations into the incident, and the Komnas HAM report concludes that gross human rights violations have occurred in the incident. The results of the investigation have been reported by Komnas HAM, and the report files have been submitted to the Attorney General; but until now (± 18 years) the Attorney General has not yet proceeded to the level of investigation and prosecution. The Attorney General's reasons relate to juridical technical matters which the public deems irrelevant to the substance of the case / problem. But in essence, the political and non-judicial aspects surround these cases.
Penegakan Hukum Melalui Pendekatan Restorative Justice Penyelesaian Perkara Tindak Pidana
Authors: Hambali Azwad Rachmat 
Published: February 2021

Penelitian ini bertujuan untuk menganalisi pendekatan hukum melalui pendekatan restorative justice penyelesaian perkara tindak pidana. Penelitian ini menggunakan metode penelitian kualitatif. Sumber data yang diperoleh melalui penelitian lapangan, maupun studi dokumen. Hasil penelitian menemukan bahwa sebagai suatu filosofi pemidanaan, restorative justice dapat membingkai berbagai kebijakan, gagasan program dan strategi penanganan perkara pidana sehingga diharapkan hasil proses tersebut dapat menciptakan keadilan yang dirasakan oleh pelaku, korban dan masyarakat serta menjawab berbagai permasalahan yang dihadapi oleh sistem peradilan pidana saat ini.
This study aims to analyze legal issues through reforming restorative justice to request criminal cases. This study used qualitative research methods. Sources of data obtained through field research, or document studies. The results of the research find facts as a philosophy of criminalization, restorative justice can frame various policies, programs and case planning strategies requiring results that can be made related to disputes, victims and the community related to solving the problems raised by the justice system.
Interpretable Unsupervised Feature Extraction and Learning of Abnormal System State Transitions in Aircraft Sensor Data
Authors: Sundareswara Rashmi , Betz Franz David , Lu Tsai-Ching 
Published: February 2021

Commercial aircrafts generate a huge amount of data during each flight by sampling hundreds of variables at different resolutions during all phases of flight. While having this enormous source of data is useful for learning of faulty system behavior, its huge dimensionality and size can be an impeding factor to such analysis.
To address this problem, we have devised a data-driven process that automatically extracts persistent, underlying latent states that can succinctly describe the data and thereby reduce its dimensionality, while preserving the most salient aspects important for fault or potential fault analysis. By analyzing how these latent states transition in time by computing a transition matrix for every leg, which we use as features, we can classify certain precursors which are indicative of a potential fault. Specifically, this is achieved by supervised and unsupervised learning of hundreds of latent state transitions for a given subsystem. Analysis of temporal dynamics of state transitions allows us to pinpoint at what time the sensor variables were behaving atypically in a flight leg, thus allowing airline maintainers to fix the faulty component quicker and avoid flight delays due to unplanned maintenance. We demonstrate our method of supervised and unsupervised classification over temporal dynamics of system state transition on two subsystems, the Fan Air Modulating Valve (FAMV) and the Flow Control Valve (FCV), and have obtained 100% true positive rate (for both systems) and a false positive rate of 0.05-0.08%.
Feature Selecting Hierarchical Neural Network for Industrial System Health Monitoring
Authors: Michau Gabriel , Arias Chao Manuel , Fink Olga 
Published: February 2021

Industrial System Health Monitoring relies usually on the monitoring of well-designed features. This requires both, the engineering of reliable features and a good methodology for their analysis. If traditionally, features were engineered based on the physics of the system, recent advances in machine learning demonstrated that features could be automatically learned and monitored. In particular, using Hierarchical Extreme Learning Machines (HELM), based on random features, very good results have already been achieved for health monitoring with training on healthy data only.
Yet, although very useful and mathematically sound, random features have little popularity as they contradict the intuition and seem to rely on luck. This tends to increase the “blackbox” effect often associated with Machine Learning. To mitigate this, in this paper, we propose to modify the traditional HELM architecture such that, while still relying on random features, only the most useful features among a large population will be selected.
Traditional HELM are made of stacked contractive autoencoders with `1- or `2-regularisation and of a classifier as last layer. To achieve our objective, we propose to opt for expanding auto-encoders instead, but trained with a strong Group-LASSO regularization. This Group-LASSO regularisation fosters the selection of as few features as possible, making the auto-encoder in reality (or in testing condition) contractive. This deterministic selection provides useful features for health monitoring, without the need of learning or manually engineering them.
The proposed approach demonstrates a better performance for fault detection and isolation on case studies developed for HELM evaluation.
Lifetime Model Development for Integration in Power Management of HEVs By Terms of Minimizing Fuel Consumption and Battery Degradation
Authors: Beganovic Nejra , Moulik Bedatri , Ali Ahmed Mohamed , S¨offker Dirk 
Published: February 2021

Along with increasingly frequent use of electric and hybrid electric vehicles, the constraints and demands placed on the them become stricter. The most noticeable challenge considering Hybrid Electric Vehicles (HEVs) is to provide an optimalpower flow between multiple electric sources alongside provided as less as possible aging of energy storage components. To provide efficient battery usage with respect to batteries lifetime, it becomes unavoidable to develop battery lifetime models, which do not only reflect the State-of-Heath (SoH) but also allow battery lifetime prediction. The lifetimeoriented battery models have to be integrated in power management. To be used efficiently and to provide optimal power split ensuring mitigation of battery degradation without sacrificing desired power consumption, accurate modeling of battery degradation is of utmost importance. This implies that gradual battery degradation, which is directly affected by applied loading profiles, has to be monitored and used as additional control input. Moreover, the lifetime model developed in this case has to provide model outputs also in the timeframe of power management. In this contribution, a machine state-based lifetime model for electric battery source is developed. In this particular case, different degradation states as well as machine state transitions are identified in accordance to current operating conditions. Here, the change in charging/ discharging rate (C-rate), overcharging/undercharging of the battery (depth-of-discharge), and the temperature are taken in consideration to define machine model states. The End-of-Lifetime (EoL) is defined as deviation between nominal and current ampere-hour (Ah)-throughput. The proposed machine state-based lifetime model is verified based on existing battery lifetime models using simulation setup. The developed lifetime model in this way serve as a prerequisite forits integration into power management with an aim to provide the trade-off between aforementioned conflicting objectives; fuel consumption and battery degradation.
Data-driven Approach to Equipment Taxonomy Classification
Authors: Saetia Kittipong , Lukens Sarah , Pijcke Erik , Hu Xiaohui 
Published: February 2021

A standardized taxonomy enables asset-intensive industrial organizations to systematically measure and track efficiency and performance of assets at different levels in an asset hierarchy. Having a well-structured taxonomy also allows companies to take advantage of emerging data-driven technologies such as PHM through enabling straightforward mapping of assets to analytical content specific to equipment commonalities, e.g., failure modes. However, the complexity and use of equipment taxonomy and coding structures in maintenance management systems vary widely for different organizations. This paper describes a data-driven approach for identifying equipment taxonomy from equipment records in maintenance management systems. The approach combines machine learning-based and rule-based methods into a hybrid man-in-the-loop workflow, which enables rapid and consistent mapping of equipment into a standard taxonomy. A case study is presented to demonstrate the performance and challenges of the proposed approach on equipment taxonomy classification.
Prognostics Framework for Power Semiconductor IGBT Modules through Monitoring of the On-State Voltage
Authors: Degrenne Nicolas , Kawahara Chihiro , Mollov Stefan 
Published: February 2021

This paper presents a literature review and an overview of original contributions on diagnostic and prognostic technologies for IGBT power modules based on the monitoring of the on-state voltage (Von).
First, different kinds of Von sensing circuits are discussed in terms of specifications, implementation, performance and cost. A low-cost and practical circuit is experimentally demonstrated. Then, a method is presented and evaluated for estimating wire-bond degradation. Wire-bond lift-off is the most common wear-out mechanism for industrial power semiconductor IGBT modules. It is effectively detected by measuring Von at the Zero Temperature Coefficient (ZTC) current value. Next, a method is presented to estimate the die temperature. Knowing the die temperature allows estimating the thermo-mechanical stress imposed to the wire-bonds. It is demonstrated to be feasible with ±3°C accuracy/precision using after careful calibration of Von as a Temperature Sensitive Electrical Parameter (TSEP). Finally, an algorithm is presented to process the information generated from the monitoring of Von and estimate the Remaining Useful Life (RUL). It considers Von evolution prior the first wire-bond lift-off and it combines both condition-based and damage accumulation based predictions.
The major contribution of this paper is to present, for the first time, a complete framework for diagnostics and prognostics of power semi-conductor IGBT modules.
Inter-Turn Short-Circuit Failure of PMSM Indicator based on Kalman Filtering in Operational Behavior
Authors: Mansouri Badr , Janati Idrissi Hicham , Venon Audrey 
Published: February 2021

In the next twenty years traffic aircraft will be doubled. Thus, avionic devices will become more and more electric and the aircrafts become lighter in order to save more fuel. Thus, the more electric aircraft will face a great challenge that of the predictive maintenance of its electrical equipments. A key component of these devices is the Permanent Magnet Synchronous Motor (PMSM). In this article we are interested in one of the most recurrent failure of electric motor, that of the inter-turn short circuit failure. The purpose of this study, therefore, is to develop an interturn short-circuit sensitive indicator. It’s based on a linear Kalman filter for a healthy model to estimate residual voltage drops in the rotor reference (d,q). The proposed study shows a high sensitive indicator to the inter-turn short-circuit fault even under external disturbances. As well, several features can result from it, especially the signal energy, spectral and statistical information, etc. These features can highlight aging laws that will be used as patterns for Prognosis and Health Management (PHM) of inter-turn short-circuit failure.
Tingkat Pengetahuan Akseptor KB Tentang Metode Kontrasepsi Jangka Panjang dengan Keikutsertaan Terhadap Metode Kontrasepsi Jangka Panjang
Authors: ASWITAMI PRAMITA , Purnamayanthi Pande Putu Indah , Udayani Ni Putu Mirah Yunita , Prameswari I Gst Ayu Agung 
Published: February 2021

Keluarga Berencana (KB) menurut World Health Organisation (WHO) adalah tindakan yang membantu individu atau pasangan suami istri untuk menghindari kelahiran yang tidak diinginkan, mendapatkan kelahiran yang diinginkan, mengatur interval kelahiran, mengontrol waktu saat kelahiran dalam hubungan dengan umur suami dan istri, menentukan jumlah anak dalam keluarga. Salah satu faktor yang memengaruhi keikutsertaan sebagai akseptor metode kontrasepsi jangka panjang adalah pengetahuan suami istri mengenai kontrasepsi tersebut. Tujuan penelitian ini adalah untuk mengetahui hubungan tingkat pengetahuan akseptor KB tentang metode kontrasepsi jangka panjang (MKJP) dengan keikutsertaan MKJP di banjar Air Kuning Kecamatan Jembrana. Penelitian ini menggunakan rancangan non eksperimen dengan pendekatan kuantitatif. Desain penelitian yang digunakan adalah penelitian analitik dengan pendekatan cross sectional yaitu melakukan pengumpulan data dalam satu waktu dan tiap subyek penelitian diobservasi sekali saja. Hasil uji statistik chi square (X2) didapatkan hasil hitung untuk tingkat pengetahuan akseptor KB tentang metode kontrasepsi jangka panjang (MKJP) sebesar 61,267 dengan tingkat kemaknaan 95% dengan nilai p<0.05 yang artinya terdapat hubungan yang signifikan antara tingkat pengetahuan akseptor KB tentang metode kontrasepsi jangka panjang (MKJP) dengan keikutsertaan terhadap MKJP. Disarankan kepada petugas kesehatan untuk memberikan edukasi lebih banyak tentang kontrasepsi jangka panjang sehingga akseptor KB MKJP akan bertambah untuk mengontrol kehamilan dengan baik
COMPARISON OF FUNCTIONAL OUTCOME OF DYNAMIC CONDYLAR SCREW AND CONDYLAR BLADE PLATE IN TYPE A FRACTURES OF DISTAL FEMUR IN SKELETALLY MATURUE PATIENTS
Authors: Dilshad Gill  , Muhammad Javaid Iqbal Awan  , Osma Bin Saeed  , Basharat Manzoor  
Published: February 2021

ABSTRACT
BACKGROUND & OBJECTIVE: Distal femur fractures are increasingly common injuries in today’s orthopedic practice. Early treatment with suitable implant brings in promising functional outcomes. There is a growing debate over the type of implant used in these injuries. Therefore, we have compared two commonly used implants to determine the functional outcomes in search of an implant that has less post op complications, less technically demanding and produces better results. To compare the functional outcome of dynamic condylar screw with condylar blade plate in treatment of distal femur fractures.
METHODOLOGY: A total of 372 skeletally mature patients aging 18-60 years of both gender with fracture distal femur (Type A, according to OTA classification) presented within 3 weeks of fracture time were included. Patients meeting inclusion criteria were stratified into two groups having 186 patients each. Group A and Group B. Group A received dynamic condylar screw fixation and in group B, condylar blade plate fixation was preferred. Patients were followed for 3 years and their Functional outcome was evaluated with the help of Lysholm Knee Score.
RESULTS: Lysholm knee score after 3 years in group A (Dynamic condylar screw) was excellent (95-100) in 173 patients (76.9%), Good in 33 (17.7 %), Fair in 8 (4.3%), poor in 2 (1.1%).However, group B had Excellent in 57(30.6%), Good in 59(31.7%), Fair in 38 (20.4%), and poor in 32 (17.2%).
CONCLUSION: Dynamic condylar screw can be used as an effective treatment with better functional outcome as compared to condylar blade plate in type A fractures of distal femur.
EFFECTIVENESS AND SAFETY OF SOFOSBUVIR AND VELPATASVIR IN HEMODIALYSIS PATIENTS SUFFERING WITH HEPATITIS C INFECTION
Authors: Mehmood Yasir , Muhammad Imran Ashraf  , Khadija Mastoor  , Adnan Afzal  , Zafar Latif Awan  
Published: February 2021

BACKGROUND & OBJECTIVE: There is scantiness of evidence-based knowledge of anti-viral therapy with sofosbuvir (SOF) and velpatasvir (VLP) in patients on maintenance haemodialysis. This report is an attempt to rationalise the safety and effectiveness of SOF and VLP in haemodialysis patients in Pakistan.
METHODOLOGY: Twenty treatment-naïve patients were incorporated in this study. Patients on maintenance haemodialysis are being administered SOF and VLP. Before initiation of treatment, all necessary investigations such as viral load, liver fibro scan, genotyping and upper GI endoscopy were made. Patients received 400 mg/day and 100 mg/day dose of SOF and VLP, respectively.
RESULTS: Mean age was 25 to 53 years; 30% were male and 70% were female as categorized. No one of these has clinical affirmation of progression of cirrhosis. The most prevalent genotype was genotype 4 that was found in 45% of cases and the second most evident was genotype 1 in 25% of cases and genotype 5 in 30% of cases. Time span of antiviral therapy was 12 weeks.
CONCLUSION: SOF and VLP based direct antiviral agents were effectual, puissant and were aptly tolerated in patients on MHD. This drug combination was well tolerated in haemodialysis patients. Not a single patient discontinued this combination therapy due to severe complications.
THE ASSESSMENT OF EMOTIONAL INTELLIGENCE IN MEDICAL FACULTY WORKING AT POONCH MEDICAL COLLEGE (PMC), AJK
Authors: Akram Zareena , Ahsan Sethi  , Aabish Mehreen  , Marium Sohail  
Published: February 2021

ABSTRACT:
BACKGROUND & OBJECTIVE: There has been limited understanding of role of emotional intelligence in education and also limited understanding of emotional intelligence levels in teaching and clinical practice. The aim of the current study is to investigate the level of emotional intelligence in medical teachers and see whether emotional intelligence differs in male/female and senior/junior faculty.
METHODOLOGY: A cross sectional survey was conducted at Poonch Medical College (PMC), Rawalakot, AJK for 2 months i.e. July to August 2018. Faculty (n=117) from basic pay scale-17 to 21, of basic and clinical sciences departments were included in study after informed consent. Data were collected using “Leadership Toolkit Emotional Intelligence Questionnaire.” Descriptive and inferential statistics were calculated to measure the level of emotional intelligence in faculty members and to quantify variations across gender and position.
RESULTS: The response rate in this study was (92.3%) 108/117. There were 56 (51.9%) female and 52 (48.1%) male respondents. Majority of the faculty members 69 (63.9%) were junior level teachers whereas 39 (36.1%) were senior level faculty. The overall mean emotional intelligence was 178.4 ± 21.4, which did not vary across male, and females, or level positions. Self-awareness, was found higher in females (39.9 versus 38.2), however this difference was statistically insignificant (p-value, 0.12). The mean score in managing emotions were also slightly higher in females (33.1 versus 32.5).
CONCLUSION: The medical faculty at PMC, AJK has a reasonably high level of emotional intelligence. There was no difference in the overall emotional intelligence levels across gender and position.
Classifying High-cadence Microlensing Light Curves. I. Defining Features
Authors: Khakpash Somayeh , Pepper Joshua , Penny Matthew , Gaudi B. Scott , Street R. A. 
Published: February 2021

Abstract
Microlensing is a powerful tool for discovering cold exoplanets, and the Roman Space Telescope microlensing survey will discover over 1000 such planets. Rapid, automated classification of Roman’s microlensing events can be used to prioritize follow-up observations of the most interesting events. Machine learning is now often used for classification problems in astronomy, but the success of such algorithms can rely on the definition of appropriate features that capture essential elements of the observations that can map to parameters of interest. In this paper, we introduce tools that we have developed to capture features in simulated Roman light curves of different types of microlensing events, and we evaluate their effectiveness in classifying microlensing light curves. These features are quantified as parameters that can be used to decide the likelihood that a given light curve is due to a specific type of microlensing event. This method leaves us with a list of parameters that describe features like the smoothness of the peak, symmetry, the number of peaks, and the width and height of small deviations from the main peak. This will allow us to quickly analyze a set of microlensing light curves and later use the resulting parameters as input to machine learning algorithms to classify the events.