Miracle-dz/UniFAR-FTUs huggingface dataset:
Pengaruh Person-Job Fit dan Motivasi Berprestasi Terhadap Kinerja Melalui Komitmen Organisasi sebagai Mediasi

Penelitian ini bertujuan untuk menguji pengaruh person-job fit dan motivasi berprestasi terhadap kinerja dengan mediasi komitmen organisasi. Metode pengambilan sampel yang digunakan adalah teknik non probability sampling yakni sampling jenuh yaitu mengambil sampel secara keseluruhan atau semua anggota populasi digunakan sebagai sampel.Responden pada penelitian ini adalah guru tidak tetap pada SMK Negeri 1 Alian berjumlah 38 orang. Penelitian ini meggunakan metode analisis deskriptif dan statistik dilakukan uji validitas, uji reliabilitas, uji mutikolinearitas, uji heteroskedastisitas, uji normalitas, uji korelasi, koefisien determinasi, uji t, analisis jalur, dan perhitungan pengaruh. Pengujian dalam uji t menunjukkan bahwa person-job fit berpengaruh signifikan terhadap komitmen organisasi. Motivasi berprestasi berpengaruh signifikan terhadap komitmen organisasi. Person-job fit tidak berpengaruh terhadap kinerja. Motivasi berprestasi berpengaruh terhadap kinerja guru tidak tetap SMK Negeri 1 Alian. Komitmen organisasi berpengaruh terhadap kinerja guru tidak tetap SMK Negeri 1 Alian. Person-job fit terhadap kinerja melalui komitmen organisasi tidak dapat memediasi antara person-job fit dengan kinerja. Motivasi berprestasi berpengaruh terhadap kinerja melalui komitmen organisasi komitmen organisasi mampu memediasi antara motivasi berprestasi dengan kinerja.
A Reliable Way to Improve Electrochemical Migration (ECM) Resistance of Nanosilver Paste as a Bonding Material

Electrochemical migration (ECM) of sintered nano-Ag could be a serious reliability concern for power devices with high-density packaging. An anti-ECM nano-Ag-SiOx paste was proposed by doping 0.1wt% SiOx nanoparticles rather than previously used expensive noble metals, e.g., palladium. The ECM lifetime of the sintered nano-Ag-SiOx was 1.5 to 3 times longer than that of the sintered nano-Ag, due to the fact that the SiOx could protect the Ag from oxidation. The thermo-mechanical reliability of the sintered nano-Ag-SiOx was also improved by sintering under 5 MPa assisted pressure. The lesser porosity and smaller grain boundaries of the sintered nano-Ag-SiOx could also be beneficial to retard the silver ECM. In the end, a double-sided semiconductor device was demonstrated to validate the better resistance to the ECM using the sintered nano-Ag-SiOx.
266–2133 MHz phase shifter using all-digital delay-locked loop and triangular-modulated phase interpolator for LPDDR4X interface

A 266-2133 MHz phase shifter is proposed for LPDDR4X interface, utilising an all-digital delay-locked loop (DLL) and a triangular-modulated phase interpolator (PI) to improve the jitter and linearity. The DLL consists of two kinds of DLLs: a global DLL to assist fast locking; and a local DLL which uses an adaptive-window phase detector and a folded delay line to reduce jitter and improve linearity. The PI uses a triangular-modulated clock waveform to achieve good linearity over a wide frequency range. The prototype chip is implemented in a 65 nm CMOS process. The measured jitter of the DLL is 3.08 ps rms /19.93 ps pp at 2133 MHz. The measured differential non-linearity of the phase shifter is <;0.91 LSB at 2133 MHz, and the power efficiency is about 2.7 mW/GHz.
Pengaruh Internal Locus of Control dan Komunikasi Organisasi terhadap Motivasi Kerja dengan Kepuasan Kerja sebagai Variabel Pemediasi

Penelitian ini bertujuan untuk menguji pengaruh internal locus of control dan komunikasi organisasi terhadap motivasi kerja dengan kepuasan kerja sebagai variabel pemediasi guru PNS di MTs Negeri 2 Kebumen. Variabel dalam penelitian ini terdiri dari tiga jenis variabel, yaitu variabel bebas yang meliputi internal locus of control dan komunikasi organisasi. Variabel terikat meliputi motivasi kerja dan variabel mediasi yaitu kepuasan kerja. Populasi dalam penelitian ini adalah guru PNS MTs Negeri 2 Kebumen yang berjumlah 48 orang. Teknik pengambilan sampel adalah sampel jenuh karena jumlah responden yang relatif kecil sehingga jumlah seluruh populasi digunakan sebagai sampel. Teknik pengumpulan data menggunakan kuesioner, analisis data yang digunakan adalah uji instrumen validitas dan reliabilitas, uji asumsi klasik, uji hipotesis, analisis korelasi, sobel test, dan analisis jalur. Alat bantu pengelolaan data menggunakan SPSS for windows versi 25. Hasil penelitian menunjukan bahwa internal locus of control berpengaruh positif dan signifikan terhadap kepuasan kerja. Komunikasi organisasi berpengaruh positif dan signifikan terhadap kepuasan kerja. Internal locus of control berpengaruh positif dan signifikan terhadap motivasi kerja. Komunikasi organisasi berpengaruh positif dan signifikan terhadap motivasi kerja. Kepuasan kerja berpengaruh positif dan signifikan terhadap motivasi kerja. Kepuasan kerja dapat memediasi antara internal locus of control dan motivasi kerja serta kepuasan kerja dapat memediasi antara komunikasi organisasi dan motivasi kerja.
Readability of Annual Reports on the Vienna Stock Exchange: A Test of Management Obfuscation Hypothesis

This research examines the relation between annual report readability and company performance in a German-speaking country, Austria. The incomplete revelation hypothesis, management obfuscation hypothesis and agency theory assume that firms with lower performance strategically use readability in their disclosures to obfuscate negative results. For investors, reading, analysing, and interpreting data becomes a costly affair; this weakens the negative effect of such data on a firm’s reputation and share price. We use LIX and Flesch formulas to measure the readability of letters to the shareholders and/or interviews with the board in annual reports. The sample consists of 37 companies that are listed on the Prime Market of the Vienna Stock Exchange and their data from the year 2009 to 2020. Company performance is measured by the change in turnover, profit, and share price. The analysed sections mostly show high to very high levels of difficulty. During the observation period, readability levels do not change significantly. We find that the annual reports of firms with lower performance are not harder to read and, therefore, cannot confirm the management obfuscation hypothesis. A significant influence of change in profit/loss on readability is minutely observed. Possible reasons for this observation could be characteristics of the German language, statistical outliers, the long observation period, more professional investor relations offices, and changing communication channels between companies and stakeholders. The last point, changing communication channels, also puts the obfuscation hypothesis and its application to readability up for discussion again. Implications for Central European audience : Our study shows that also Central European countries are confronted with low levels of readability in annual reports. Nevertheless, we cannot see a clear tendency towards obfuscation in corporate disclosures. our on the
Isolation and Characterization a Novel Catabolic Gene Cluster Involved in Chlorobenzene Degradation in Haloalkaliphilic Alcanivorax sp. HA03

Simple Summary This study has demonstrated for the first time the metabolic pathway of chlorobenzene (CB) in haloalkaliphilic bacteria Alcanivorax sp. HA03 isolated from soda lakes in Wadi E1Natrun-Egypt. The complete chlorobenzene dioxygenase gene cluster (α and β subunits, ferredoxin and ferredoxin reductase) was detected by PCR amplification and confirmed by DNA sequencing and expression. This gene cluster has the capability to ring-hydroxylating CB transformed into 3-chlorocatechol during the first steps of biodegradation; with further chloride release and subsequent paths, HA03 showed capability for CB mineralization. Abstract Chlorobenzene (CB) poses a serious risk to human health and the environment, and because of its low degradation rate by microorganisms, it persists in the environment. Some bacterial strains can use CB as growth substrates and their degradative pathways have evolved; very little is known about these pathways and the enzymes for CB degradation in high pH and salinity environments. Alcanivorax sp. HA03 was isolated from the extremely saline and alkaline site. HA03 has the capability to degrade benzene, toluene and chlorobenzene (CB). CB catabolic genes were isolated from HA03, which have a complete gene cluster comprising α and β subunits, ferredoxin and ferredoxin reductase (CBA1A2A3A4), as well as one gene-encoding enzyme for chlorocatechol 1,2-dioxygenase (CC12DOs). Based on the deduced amino acid sequence homology, the gene cluster was thought to be responsible for the upper and lower catabolic pathways of CB degradation. The CBA1A2A3A4 genes probably encoding a chlorobenzene dioxygenase was confirmed by expression during the growth on CB by RT-PCR. Heterologous expression revealed that CBA1A2A3A4 exhibited activity for CB transformation into 3-chlorocatechol, while CC12DOs catalyze 3-chlorocatechol, transforming it into 2-chloromucounate. SDS-PAGE analysis indicated that the sizes of CbA1 and (CC12DOs) gene products were 51.8, 27.5 kDa, respectively. Thus, Alcanivorax sp. HA03 constitutes the first bacterial strain described in the metabolic pathway of CB degradation under high pH and salinity conditions. This finding may have obvious potential for the bioremediation of CB in both highly saline and alkaline contaminated sites.
Single Winding Type Determination of Dual Winding Three-Phase Motor Considering Overheat Problem in Integrated Electric Braking System of Autonomous Vehicles

This paper introduces the single winding type selection process for a dual winding (DW) three-phase motor. Unlike the conventional DW three-phase motor design, the proposed single winding type selection process simultaneously considers the mechanical length determined by the single winding type and the motor overheating problem caused by faulty operation for each single winding type. The proposed single winding type selection process consists of four steps. First, all single-winding combinations, the PSDW, LSDW, CSDW, for DW three-phase motor are investigated. Second, according to the selected single winding types, the BH required for dual division is designed and compared. Third, the stack length of motor in the point symmetry dual winding (PSDW), the line symmetry dual winding (LSDW), and the cross symmetry dual winding (CSDW) obtained through overheat compensation design to avoid the overheating problem in the faulty mode are investigated and compared. In the overheat compensation design, the thermal map utilizing measurement data from the previously developed motor for an IEB system, a radial basis function, an overheat compensation coefficient, and the IEB brake load condition are utilized. Finally, the single winding type of DW three-phase motor is selected as the CSDW by comparing the total motor size. As a result of the DW three-phase motor design, the proposed CSDW can reduce the motor size by 12.0% and 4.2%, respectively, compared to the PSDW and LSDW.
Quantifying machine learning-induced overdiagnosis in sepsis

The proliferation of early diagnostic technologies, including self-monitoring systems and wearables, coupled with the application of these technologies on large segments of healthy populations may significantly aggravate the problem of overdiagnosis. This can lead to unwanted consequences such as overloading health care systems and overtreatment, with potential harms to healthy individuals. The advent of machine-learning tools to assist diagnosis -- while promising rapid and more personalised patient management and screening -- might contribute to this issue. The identification of overdiagnosis is usually post hoc and demonstrated after long periods (from years to decades) and costly randomised control trials. In this paper, we present an innovative approach that allows us to preemptively detect potential cases of overdiagnosis during predictive model development. This approach is based on the combination of labels obtained from a prediction model and clustered medical trajectories, using sepsis in adults as a test case. This is one of the first attempts to quantify machine-learning induced overdiagnosis and we believe will serves as a platform for further development, leading to guidelines for safe deployment of computational diagnostic tools.
Hierarchical Estimation-Based LiDAR Odometry With Scan-to-Map Matching and Fixed-Lag Smoothing

LiDAR odometry (LO) has gained popularity in recent years due to accurate depth measurement and robustness to illumination. Typically, the solutions based on scan-to-map matching mainly optimize current pose. To further reduce the accumulated error of pose estimation, the fixed-lag smoothing that optimizes fixed-size poses simultaneously by matching corresponding point features of multiple frames becomes necessary. The integration of fixed-lag smoothing with LO still needs further exploration. In this paper, a general fixed-lag smoothing module is proposed, which can be appended to existing LO framework to improve the consistency of trajectory. Also, a fast scan-to-map matching module based on sparse features is developed to guarantee the real-time performance. Besides, the feature-centric feature management strategy is adopted in both scan-to-map matching and fixed-lag smoothing modules, which makes the proposed LO efficient. On this basis, a hierarchical estimation-based LiDAR odometry is presented, where low-level scan-to-map matching estimates pose of each frame by aligning associated features in the frame and corresponding surrounding map with high efficiency, and high-level fixed-lag smoothing further optimizes keyframe poses in a sliding window by matching associated features among multiple frames with high accuracy. As a result, a fast and accurate pose estimation is achieved, which is verified by experiments on the KITTI dataset, Newer College dataset, and an actual outdoor scenario.
Serious Mental Illness in the Nursing Home Literature: A Scoping Review

Nursing homes (NH) and other institutional-based long-term care settings are not considered an appropriate place for the care of those with serious mental illness, absent other medical conditions or functional impairment that warrants skilled care. Despite policy and regulatory efforts intended to curb the unnecessary placement of people with serious mental illness (SMI) in these settings, the number of adults with SMI who receive care in NHs has continued to rise. Through a scoping review, we sought to summarize the available literature describing NH care for adults with SMI from 2000 to 2020. We found that SMI was operationalized and measured using a variety of methods and diagnoses. Most articles focused on a national sample, with the main unit of analysis being at the NH resident-level and based on analysis of secondary data sets. Understanding current evidence about the use of NHs by older adults with SMI is important to policy and practice, especially as we continue to grapple as a nation with how to provide quality care for older adults with SMI.
Simulation of Squat Exercise Effectiveness Utilizing a Passive Resistive Exoskeleton in Zero Gravity

Weightlessness induced bone loss is an unsolved health risk to anyone spending long duration stays in space. It causes returning astronauts to face months of recovery time, and years of heightened risk of fracture. Effective countermeasures must be developed to ensure the safety of future long term missions to an asteroid or Mars. The capability to simulate an exercise countermeasure and analyze its usefulness for bone loss mitigation will greatly advance the ability to design countermeasures for specific missions. This study succeeded assessing the performance of a prototype exoskeleton exercise countermeasure device, and creating a simulation model of it. This model was successfully used to demonstrate the capability to analyze the relative osteogenic performance of an exercise in weightlessness. Evidence for the use of exoskeleton based exercise countermeasures was shown by the results, as well as the improved osteogenic potential of dynamically loaded exercises over statically loaded ones. Several deficiencies in the models and simulations were also found, and areas to improve were outlined. Further research must be done to optimize the musculoskeletal models for resistive exercise, and improve the modeling of interfaces between man and machine.
Soil Organic Carbon Mineralization and Its Temperature Sensitivity under Different Substrate Levels in the Mollisols of Northeast China

Soil organic carbon (SOC) mineralization plays an important role in global climate change. Temperature affects SOC mineralization, and its effect can be limited by the substrate available. However, knowledge of the effects of temperature and substrate quality on SOC mineralization in the Mollisols of Northeast China is still lacking. In this study, based on a spatial transplant experiment, we conducted a 73-day incubation to examine the effects of temperature on SOC mineralization and its temperature sensitivity under different carbon levels. We found that the SOC content, incubation temperature and their interaction had significant effects on SOC mineralization. A higher SOC content and higher incubation temperature resulted in higher SOC mineralization. The temperature sensitivity of SOC mineralization was affected by the substrate quality. The temperature sensitivity of SOC mineralization, showed a downward trend during the incubation period, and the range of variation in the Q10 declined with the increment in the SOC content. The study suggested that there was a higher SOC mineralization in high levels of substrate carbon when the temperature increased. Further, SOC mineralization under higher SOC contents was more sensitive to temperature changes. Our study provides vital information for SOC turnover and the CO2 sequestration capacity under global warming in the Mollisols of Northeast China and other black soil regions of the world.
SWIPT-Based Energy Scheduling for Solar-Powered WSN in Full-Duplex Mode

Solar harvesting, as the most mature environmental energy harvesting technology, can provide a convenient, low-carbon, and off-grid solution for energy-constrained WSN in remote environmental monitoring. However, the stochastic and intermittent nature of solar energy seriously affects network energy efficiency. In this paper, we jointly consider solar harvest deployment and scheduling for clustered WSN collaborating with SWIPT, where the solar-powered cluster (SC) coordinates SWIPT to periodically broadcast energy to a set of wireless charging nodes in full-duplex mode, each member node exhausts the recharged energy for data sensing and forwarding. To maximize network energy efficiency, we formulate a Solar harvesting collaborative SWIPT Energy Scheduling(SS-CoES) optimization problem, which jointly optimizes the deployment of SCs, the energy broadcast power with time allocation, and data sensing size under the constraint of the self-sustaining demand for network energy. Given the combinational nature of the energy deployment with its strong coupling with energy scheduling, we propose a multi-layer iteration decoupling optimization algorithm based on the Dinkelbach method, which decouples the problem into three subproblems and proposes the Hybrid discrete Firefly algorithm, Lagrangian dual subgradient algorithm, and bisection search method to obtain near-optimal feasible solutions. Simulation results show that the proposed SS-CoES can effectively maximize network energy efficiency and eliminate the dependence on the grid and move chargers, achieving rechargeable wireless sensor networks (RWSN) energy self-sustainability in remote areas.
Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction

It is proven that radiomic characteristics extracted from the tumor region are predictive. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and requires a highly trained physician. This process could be automated using computer-aided detection (CAD) tools. Current state-of-the-art methods are trained in a supervised learning setting, which requires a lot of data that are usually not available in the medical imaging field. The challenge is to train one model to segment different types of tumors with only a weak segmentation ground truth. In this work, we propose a prediction framework including a 3D tumor segmentation in positron emission tomography (PET) images, based on a weakly supervised deep learning method, and an outcome prediction based on a 3D-CNN classifier applied to the segmented tumor regions. The key step is to locate the tumor in 3D. We propose to (1) calculate two maximum intensity projection (MIP) images from 3D PET images in two directions, (2) classify the MIP images into different types of cancers, (3) generate the class activation maps through a multitask learning approach with a weak prior knowledge, and (4) segment the 3D tumor region from the two 2D activation maps with a proposed new loss function for the multitask. The proposed approach achieves state-of-the-art prediction results with a small data set and with a weak segmentation ground truth. Our model was tested and validated for treatment response and survival in lung and esophageal cancers on 195 patients, with an area under the receiver operating characteristic curve (AUC) of 67% and 59%, respectively, and a dice coefficient of 73% and 0.77% for tumor segmentation.
An ML Agent using the Policy Gradient Method to win a SoccerTwos Game

: We conducted an investigative study of Policy Gradient methods using Curriculum Learning applied in Video Games, as professors at the Federal University of Goi´as created a customized SoccerTwos environment to evaluate the Machine Learning agents of students in a Reinforcement Learning course. We employed the PPO and SAC as state-of-arts in on-policy and off-policy contexts, respectively. Also, the Curriculum could improve the performance based on it is easier to teach people in a complex gradual order than randomly. So, combining them, we propose our agents win more matches than their adversaries. We measured the results by minimum, maximum, mean rewards, and the mean length per episode in checkpoints. Finally, PPO achieved the best result with Curriculum Learning, modifying players’ (position and rotation) and ball’s (speed and position) settings in time intervals. Also, It used fewer training hours than other experiments.