Miracle-dz/UniFAR-FTUs huggingface dataset:
Penjadwalan Waktu Istirahat Optimal Untuk Mengurangi Risiko Musculoskeletal Disorders Berdasarkan OCRA Index

Repetitive tasks carried out by workers of packaging department of mineral water company led to some complaints of pain and fatigue on a particular body part of the workers, especially on their upper body part. This study aims to determine risk level of musculoskeletal disorders with OCRA index and provides optimal break schedule to reduce the risk level. Based on calculations using the OCRA index method, it is found that risk of injury to the workers were medium with high level of risk. Despite small workload of the operators, this may be caused by poor distribution of rest periods and lengthy working time for highly repetitive tasks. Improvement proposed in this study is type of work-compliant break time allocation. Optimal scheduling of which main objective is optimal break time is eventually obtained by applying enumeration algorithm with integer linear programming model. Based on OCRA index after improvement, it is clear that the proposal decreases the risk level of the workers.
The Relationship Between Intellectual Capital and Information Technology: Findings Based on a Systematic Review

: The world is experiencing a knowledge-based economy with a revolution in information technology, innovation, and telecommunications. The rise of the "new economy", driven by information and knowledge, has led to an increased interest in intellectual capital theory, which aims to manage intangible assets of organizations. Firms belonging to technology and knowledge-based industries recognize intellectual capital as the key knowledge base that contributes to the creation of a competitive advantage for the firm. This paper aims to answer the question "How are Intellectual Capital (IC) and Information Technology (IT) related? " through a systematic review based on four steps: 1) search conduction; 2) selection of papers based on their titles and abstracts; 3) content analysis of selected papers; 4) evidence mapping and discussions. The analyzed papers were categorized into five themes: "Statistical analysis or case study in IT companies from the Intellectual Capital perspective"; "IT as a tool for Intellectual Capital Management"; "Intellectual Capital or technology knowledge assets influencing innovation and development", "Intellectual Capital assets to evaluate a technology" and "Intellectual Capital theory as a way to understand and share knowledge in IT projects". Our findings evidenced that the Human Capital was the main dimension studied by the authors, followed by Structural Capital and Relational Capital. We believe that this work may help to clarify on Intellectual Capital Management procedures into Information Technology projects, thus opening new topics for future research.
Debiased Balanced Interleaving at Amazon Search

Interleaving is an online evaluation technique that has shown to be orders of magnitude more sensitive than traditional A/B tests. It presents users with a single merged result of the compared rankings and then attributes user actions back to the evaluated rankers. Different interleaving methods in the literature have their advantages and limitations with respect to unbiasedness, sensitivity, preservation of user experience, and implementation and computation complexity. We propose a new interleaving method that utilizes a counterfactual evaluation framework for credit attribution while sticking to the simple ranking merge policy of balanced interleaving, and formally derive an unbiased estimator for comparing rankers with theoretical guarantees. We then confirm the effectiveness of our method with both synthetic and real experiments. We also discuss practical considerations of bringing different interleaving methods from the literature into a large-scale experiment, and show that our method achieves a favorable tradeoff in implementation and computation complexity while preserving statistical power and reliability. We have successfully implemented our method and produced consistent conclusions at the scale of billions of search queries. We report 10 online experiments that apply our method to e-commerce search, and observe a 60x sensitivity gain over A/B tests. We also find high correlations between our proposed estimator and corresponding A/B metrics, which helps interpret interleaving results in the magnitude of A/B measurements.
A Simulation of Neutrino Events in Detectors with Ray Tracing

Neutrinos are the least understood particles of the standard model, and as a result, they have been a central piece of study in particle physics in recent years. Due to its miniscule probability of interacting with material, it is cumbersome to perform any ex-periment with neutrinos. To resolve this issue, experiments require large complex detector systems. While the experiment has many facets of its own, a computational tool is necessary to simulate the neutrino events and the positions of interactions in a detector. In this paper, we develop a tool using ray tracing to determine the interaction points within the simulated detector. Thus far, only simple shapes, such as a sphere, cube, or cylinder are available.
Research Article Risk Factors for Progression of CKD with and without Diabetes

Objective . We aim to identify independent risk factors to predict CKD progression to end stage renal disease (ESRD) in patients with or without diabetes. Methods . In this retrospective study, we enrolled CKD stage 3-4 patients between January 2013 and December 2018 and followed them until December 2020 or the initiation of dialysis. We used Kaplan-Meier to plot the survival curve. Univariate and multivariable Cox proportional hazards model was used to explore risk factors a ﬀ ecting the progression of CKD. The ﬁ nal model was used to construct nomogram for predicting CKD progression. Calibration plots and concordance index (C-index) were used to evaluate the accuracy and discrimination of the risk model. Results . We enrolled 309 CKD patients, including 80 cases in G3a, 98 cases in G3b, and 131 cases in G4. Among them, 141 patients had diabetes and 168 did not. The mean age of patients at enrolled was 57.86 ± 15.10 years, and 67% were male. The median follow-up time was 25.6 months. There were 81 patients (26.2%) that started dialysis in the total CKD cohort, 52 cases (36.9%) in the CKD with diabetes group, and 29 cases (17.3%) in the CKD without diabetes group. Hypoalbuminemia ( HR =2.655, P < 0 : 001 ), proteinuria ( HR =2.592, P = 0 : 042 ), increased LDL ( HR =2.494, P < 0 : 001 ), diabetes ( HR =2.759, P < 0 : 001 ), hypertension ( HR =3.471, P = 0 : 037 ), and CKD stage ( HR =2.001, P = 0 : 046 ) were risk factors for CKD progression to ESRD in the overall population. For those without diabetes, only hypoalbuminemia ( HR =2.938, P = 0 : 030 ) was a risk factor for CKD progression to ESRD. For those with diabetes, both hypoalbuminemia ( HR =2.758, P = 0 : 002 ), the increased level of LDL ( HR =3.982, P < 0 : 001 ), and CKD stage ( HR =3.781, P = 0 : 001 ) were risk factors for CKD progression to ESRD. The C-index of the ﬁ nal nomograms was 0.760 ( P < 0 : 001 ). Conclusions . The results from our risk factor model suggest that CKD disease progression can be predicted and early strategic intervention is necessary for CKD patients to avoid renal function deterioration.
Algorithm for the Symmetric Tridiagonal Eigenvalue/Eigenvector Problem

Computing the eigenvalues and orthogonal eigenvectors of an $n\times n$ symmetric tridiagonal matrix is an important task that arises while solving any symmetric eigenproblem. All practical software requires $O(n\sp3)$ time to compute all the eigenvectors and ensure their orthogonality when eigenvalues are close. In the first part of this thesis we review earlier work and show how some existing implementations of inverse iteration can fail in surprising ways. The main contribution of this thesis is a new $O(n\sp2),$ easily parallelizable algorithm for solving the tridiagonal eigenproblem. Three main advances lead to our new algorithm. A tridiagonal matrix is traditionally represented by its diagonal and off-diagonal elements. Our most important advance is in recognizing that its bidiagonal factors are "better" for computational purposes. The use of bidiagonals enables us to invoke a relative criterion to judge when eigenvalues are "close". The second advance comes by using multiple bidiagonal factorizations in order to compute different eigenvectors independently of each other. Thirdly, we use carefully chosen dqds-like transformations as inner loops to compute eigenpairs that are highly accurate and "faithful" to the various bidiagonal representations. Orthogonality of the eigenvectors is a consequence of this accuracy. Only $O(n)$ work per eigenpair is needed by our new algorithm. Conventional wisdom is that there is usually a trade-off between speed and accuracy in numerical procedures, i.e., higher accuracy can be achieved only at the expense of greater computing time. An interesting aspect of our work is that increased accuracy in the eigenvalues and eigenvectors obviates the need for explicit orthogonalization and leads to greater speed. We present timing and accuracy results comparing a computer implementation of our new algorithm with four existing EISPACK and LAPACK software routines. Our test-bed contains a variety of tridiagonal matrices, some coming from quantum chemistry applications. The numerical results demonstrate the superiority of our new algorithm. For example, on a matrix of order 966 that occurs in the modeling of a biphenyl molecule our method is about 10 times faster than LAPACK's inverse iteration on a serial IBM RS/6000 processor and nearly 100 times faster on a 128 processor IBM SP2 parallel machine.
Long Live The Honey Badger: Robust Asynchronous DPSS and its Applications

Secret sharing is an essential tool for many distributed applications, including distributed key generation and multiparty computation. For many practical applications, we would like to tolerate network churn, meaning participants can dynamically enter and leave the pool of protocol participants as they please. Such protocols, called Dynamic-committee Proactive Secret Sharing (DPSS) have recently been studied; however, existing DPSS protocols do not gracefully handle faults: the presence of even one unexpectedly slow node can often slow down the whole protocol by a factor of O ( n ) . In this work, we explore optimally fault-tolerant asynchronous DPSS that is not slowed down by crash faults and even handles byzantine faults while maintaining the same performance. We first introduce the first high-threshold DPSS, which offers favorable characteristics relative to prior non-synchronous works in the presence of faults while simultaneously supporting higher privacy thresholds. We then batch-amortize this scheme along with a parallel non-high-threshold scheme which achieves optimal bandwidth characteristics. We implement our schemes and demon-strate that they can compete with prior work in best-case performance while outperforming it in non-optimal settings.
A STUDY OF CHALLENGES FACED BY REGULATORY AUTHORITIES FOR IMPLEMENTING HEALTH AND SAFETY COMPLIANCE IN THE GHANA CONSTRUCTION INDUSTRY CONTEXT

In general, the construction industry is one of the world’s occupational settings that has high incidents of fatalities and injury rates. Developing countries, Ghana inclusive are faced with much more challenges to effective management of occupational health and safety due to the increasing rate of industrialization and weak occupational health and safety framework. The study aims to investigate the challenges faced by the regulatory authorities. The study adopted a quantitative approach and collect data using a survey. The findings indicated that Regulatory Institutions (RIs) faced challenges like; a lack of comprehensive national OHS policy, lack of effective supervision and inability to prosecute companies who violate health and safety standards, lack of adequate resources on-site, lack of logistics, and lack of a well-defined framework for regulating health and safety standards. It is recommended that the government adequately resourced Regulatory Institutions (RIs) to effectively carry out their mandate to significantly reduce risk and accidents on construction sites. Keywords: Health and Safety, Compliance, Construction, Contractors, Projects, Ghana.
Noninvasive computed tomography derived fractional flow reserve simulation based on microvascular tree model reconstruction

To establish a novel method for noninvasive computed tomography derived fractional flow reserve (CT‐FFR) simulation based on microvascular tree model reconstruction and to evaluate the feasibility and diagnostic performance of the novel method in coronary artery disease compared with invasive fractional flow reserve (FFR). Twenty patients (20 vessels) who underwent coronary computed tomography angiography (CCTA) and invasive FFR were retrospectively studied. The anatomic epicardial coronary artery model was reconstructed based on CCTA image, and the microvascular tree model was simulated based on patient‐specific anatomical structures and physiological principles. Numerical simulation was subsequently performed using the CFD method with full consideration of the variation of viscosity in microvascular. Two patients with the FFR value of . 80 were selected for adjusting the parameters of the model, while the remaining 18 patients were selected as a validation cohort. After simulation, CT‐FFR was compared with invasive FFR with a threshold of . 80. Eleven (55%) patients had an abnormal FFR that was less than or equal to . 80. Compared with invasive FFR, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CT‐FFR with an optimal threshold of . 80 were 100%, 77.8%, 81.8%, 100%, 88.89%, respectively. There were a good correlation and consistency between CT‐FFR and invasive FFR. Time per patient of CT‐FFR analysis was less than 15 min. CT‐FFR based on microvascular tree model reconstruction is feasible with good diagnostic performance. It requires a short processing time with excellent accuracy. Large multicenter prospective studies are required for further demonstrating the diagnostic performance of this novel model in myocardium ischemia evaluation.
An Efficient Methodology to Estimate Probabilistic Seismic Damage Curves

The incremental dynamic analysis (IDA) is a powerful methodology that can be easily extended for calculating probabilistic seismic damage curves. These curves are metadata to assess the seismic risk of structures. Although this methodology requires a relevant computational effort, it should be the reference to correctly estimate the seismic risk of structures. Nevertheless, it would be of high practical interest to have a simpler methodology, based for instance on the pushover analysis (PA), to obtain similar results to those based on IDA. In this article, PA is used to obtain probabilistic seismic damage curves from the stiffness degradation and the energy of the nonlinear part of the capacity curve. A fully probabilistic methodology is tackled by means of Monte Carlo simulations with the purpose of establishing that the results based on the simplified proposed approach are compatible with those obtained with the IDA. Comparisons between the results of both approaches are included for a low- to midrise reinforced concrete building. The proposed methodology significantly reduces the computational effort when calculating probabilistic seismic damage curves.
Neural Network-Based Real-Time Odometry Using IMU for Crane System and Its Application to Large-Scale 3D Mapping

This paper introduces a method for neural network-based real-time odometry using an IMU (inertial measurement unit) and its application in large-scale 3D mapping using a slowly rotating 2-D LiDAR. In this method, a neural network consisting of a convolutional neural network (CNN) and long short-term memory (LSTM) is employed to estimate the change in pose. Firstly, online pre-filtering using a low-pass filter is implemented on the time windows of IMU measurements before feeding them as the input to the neural network to estimate the change in position and rotation of the sensor. After that, the estimated sensor pose is used to register the scans of 2D-rotating LiDAR to build a large-scale 3D map. The proposed method is tested in a gazebo environment by attaching the sensors to a crane boom. In this study, we also investigate the impact of different time windows of IMU measurements on the accuracy of pose estimation by the neural network.
A portable multisensor system to assess cardiorespiratory interactions through photoplethysmography

Nowadays, the ever-growing interest to health and quality of life of individuals and the advancements in electronic devices technology are pushing the development of portable and wearable biomedical devices able to pursue a minimally invasive monitoring of physiological parameters in daily-life conditions. Such devices can now carry out a real-time assessment of the subjects' overall health status and possibly even detect ongoing diseases. In this context, we have designed and implemented a multisensor portable system able to perform synchronous real-time acquisitions of electrocardiographic (ECG), photoplethysmographic (PPG) and airflow breathing signals. We investigated cardiorespiratory interactions between heart period and respiratory time series, extracted from combined ECG and breathing signals (considered as the reference), or using the PPG signal only, through Granger Causality measures in time and frequency domain. The aim was to assess to what extent the non-invasive and cost-effective PPG technique can be employed alone to assess cardiorespiratory interactions, thus avoiding the simultaneous acquisitions of ECG or breathing signals with more bulky or uncomfortable devices. The analysis was carried out on 6 healthy young subjects, undergoing a two-phase protocol consisting in spontaneous and controlled breathing phases. Our findings show that linear interactions measures behave similarly if ECG or PPG are used for detecting the heart period and sampling the airflow respiratory signal, while the utilization of a respiratory signal extracted through filtering or as the envelope of the PPG waveform could lead to causality underestimates and must be further investigated.
Prediction of Cardiovascular Disease Using Feature Selection Techniques

—Cardiovascular diseases is one of the harmful diseases and many people suffered from this disease across the world. In the field of the Healthcare Industry, on-time and efficient prediction of cardiovascular diseases plays a prominent role in healthcare. Currently, the Medicare industry is “Data-rich" yet “Insight poor”. The aim of this research work is to develop an efficient and accurate system to inspect cardiovascular diseases and the system is based on data mining techniques that can help to remedy such a situation. The system is developed based on classification algorithms like Random Forest, Logistic Regression, Naive Bayes’ and Support Vector Machine while feature selection algorithm has been used like Pearson Correlation and Chi-Square in order to increase the accuracy and reduce the execution time of classification systems. With these results, it is found that Logistic Regression achieved the highest accuracy of 84% as compared to the others.
Impact of network parameters on a U-Net based system for rectal cancer segmentation on MR images

The use of Deep Learning (DL) algorithms in the medical imaging field is increasing in recent years. However, they require the selection of a set of parameters to properly perform. In this study we evaluated the impact of three factors (the construction of the training set, the number of network layers and the loss function) on the performance of a U-Net system in the segmentation of Locally Advanced Rectal Cancer (LARC) on Magnetic Resonance Imaging (MRI). Images from 3 different institutions and 4 different scanners were used to this scope, for a total of 100 patients. All images underwent a pre-processing step to normalize and to highlight the tumoral area. The sequences of two scanners were used to construct the networks while the remaining sequences were employed for validating the best performing systems. From our results, it emerged that Dice Similarity Coefficient is not affected by any of the evaluated factors. Conversely, the choice of loss function could bias the results towards either precision or recall and, thus, it should be properly performed according to the scope of the network. Moreover, a slightly improvement of the performances was observed using a training set based on clustering, maybe due to a better representation of the heterogeneity characterizing medical images.
Research on Management and Efficiency Evaluation of Physical Education Course Selection in Colleges Based on Random Matrix Theory

The administration of bodily schooling instructing in schools and universities is creating toward informatization and digitalization. The scholar path determination device can noticeably limit the workload of instructional administrators, enhance the provider satisfactory of instructing management, realise the standardization and transparency of college students’ direction decision process, and meet quite a number of teachers, college students, and managers needed. At the same time, it has additionally considerably multiplied the work effectivity of instructional administration personnel, decreased a lot of human funding in academic administration management, and flawlessly elevated the modern state of affairs of heavy duties in faculty management. Physical coaching route decision in colleges and universities is a huge volume of statistics computing behavior that occurs intensively. Its typical overall performance requirements are very quintessential and have grown to be the most quintessential element of its availability. The overall performance optimization of the path decision gadget is the focal point of the data work of the instructional administration system. This paper first analyses the overall performance issues of the authentic path resolution system, places ahead a multilevel overall performance optimization scheme to optimize and enhance the enterprise process, and decreases the attention of facts processing in the course of direction selection. The stress check device was once used for overall performance test, and the overall performance index was once considerably extended to meet the diagram requirements. The route desire machine meets the requirements of 5000 clients online all thru the route choice pinnacle period, the response speed is lowered to plenty much less than 5 seconds, and the software program server runs stably, which properly meets the route dedication requirements of whole universities.