Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum Symbol Error Probability Discrete Symbol Level Precoding for MU-MIMO Systems With PSK Modulation

This study focuses on the development of low-resolution symbol-level precoding techniques for multiuser MIMO downlink systems with PSK modulation. While for QPSK the established minimum symbol error probability criterion is used, a criterion for PSK modulation, in general, is proposed based on the minimum union-bound symbol-error probability. Based on these criteria different low-resolution precoding approaches are proposed. First, suboptimal solutions are computed via a partial greedy search method. Then the suboptimal solutions are utilized as initialization for a novel branch-and-bound algorithm that can exploit knowledge of the system’s quality-of-service demands. Different than existing branch-and-bound approaches the proposed quality-of-service branch-and-bound method searches for a solution that attains a target symbol-error probability while going in the direction of the global optimal solution. In this sense, the proposed branch-and-bound method allows for tunable complexity performance trade-offs. Numerical results confirm that the proposed quality-of-service branch-and-bound algorithm yields reduced symbol-error probability with significantly smaller computational complexity than other state-of-the-art branch-and-bound designs.
Patch‐wise local projection stabilized finite element methods for convection–diffusion–reaction problems

In this article, we develop patch‐wise local projection‐stabilized conforming and nonconforming finite element methods for the convection–diffusion–reaction problems. It is a composition of the standard Galerkin finite element method, the patch‐wise local projection stabilization, and weakly imposed Dirichlet boundary conditions on the discrete solution. In this paper, a priori error analysis is established with respect to a patch‐wise local projection norm for the conforming and the nonconforming finite element methods. The numerical experiments confirm the efficiency of the proposed stabilization technique and validate the theoretical convergence rates.
A High-Reliability Edge-Side Mobile Terminal Shared Computing Architecture Based on Task Triple-Stage Full-Cycle Monitoring

Edge computing has emerged as a promising paradigm for addressing the challenges of latency, bandwidth, and energy consumption in the era of big data and the intelligent Internet of Things. However, the limited computing resources of edge devices and their vulnerability to failures pose significant challenges to the reliability and availability of edge computing systems. To this end, we propose a novel architecture for reliable edge computing that leverages the collective computing power of mobile edge devices in this article. Our architecture employs a task-oriented triple-stage monitoring mechanism to ensure system reliability. Moreover, we present a shared computing framework that allows edge devices to dynamically share their computing resources based on the current availability and workload. We evaluate the effectiveness of the proposed architecture with several computational tasks, including $\pi $ calculation and video processing. The results show that our architecture achieves high reliability and availability while also improving the performance and energy efficiency of edge devices.
ANALISIS PENGGUNAAN APLIKASI KEHADIRAN PEGAWAI BERBASIS ANDROID MENGGUNAKAN METODE SYSTEM USABILITY SCALE

Aplikasi absensi membantu mencatat dan mengelola kehadiran pegawai dengan lebih efisien dan akurat daripada proses manual. Aplikasi ini juga mengelola data masuk dan pulang serta hitungan jam kerja pegawai. Namun, tidak adanya informasi mengenai tingkat efektifitas, efisiensi, dan kepuasan pengguna membuat sulit untuk menentukan langkah-langkah pengembangan aplikasi yang akan dilakukan di masa yang akan datang. Tujuan penelitian ini adalah untuk mengukur tingkat usability pada Kantor Desa Mekarjaya melalui evaluasi usability. Evaluasi ini akan membantu memahami kemudahan penggunaan aplikasi untuk memberikan keuntungan bagi pengguna. Metode System Usability Scale (SUS) dipilih untuk melakukan usability testingkarena mudah dipahami oleh responden, memiliki hasil yang baik dengan sedikit sampel, dan menentukan kualitas aplikasi. Hasil evaluasi menunjukkan bahwa tingkat efektivitas aplikasi absensi di Kantor Pemerintah Desa Mekarjaya adalah 95%, tingkat efisiensi sebesar 0,393 goals/sec, dan tingkat kepuasan pengguna mencapai 88.75%. Ini menunjukkan bahwa aplikasi absensi efektif, efisien, dan memuaskan penggunanya.
Using a new algorithm in Machine learning Approaches to estimate level-of-service in hourly traffic flow data in vehicular ad hoc networks

The primary goals of transportation agencies and researchers studying traffic operations are to ease traffic and increase road safety through the use of vehicular ad hoc networks. Agencies can't achieve their goals without reliable and consistent data on the current traffic situation. The Level-of-Service (LOS) index is a helpful measure of freeway traffic operations. Conventional fixed-location cameras and sensors are impractical and expensive for gathering reliable traffic density data on every road in large networks. Flow data is a new, low-cost option that has the potential to boost safety and operations. This study proposes an algorithm for hourly LOS assessment by incorporating flow data provided by the MIDAS (Motorway Incident Detection and Automatic Signaling) system. The proposed algorithm uses machine learning techniques to classify LOS data based on the flow of traffic. The input features that are subject to prediction are a group of technical indicators. The real-world LOS was determined by analyzing data from stationary sensors. The outcomes demonstrate that technical indicators can be utilized to enhance the accuracy of LOS estimation (Random Forest= 93.1, k-nearest neighbors = 92.5, and Support Vector Machine = 91.4). The current work introduces a novel approach to the selection of technical indicators and their use as features, which allows for highly accurate short-term prediction of LOS estimation.
A Bayesian hierarchical model for estimating the cost of postponing the cyclo-cross national championships

ABSTRACT In early January 2015, the multi-event national cyclo-cross bicycle races were set to take place in Austin, Texas. Cyclo-cross has a rich history in this country, and throughout the world, attracting huge crowds and competitors. Being primarily a winter sport, these athletes often compete in harsh conditions, which include rain, snow, mud, and revel in the excitement that comes with such elements. Unfortunately, the competition was postponed mid-event when a local arborist group protested to the parks department. The issue: there was too much mud, in an event where many spectators and racers alike hope for such conditions. For many competitors, the postponement generated additional expenses, such as flights, hotels, and car rentals. Although people on opposite sides of the debate may greatly disagree, we instead focus on the competitors themselves. We analyze the financial impact of the disagreement using a hierarchical Bayesian mixed model which accounts for heterogeneity within the costs endured by the event's participants.
Evaluation of electrolarynx controlled by real-time statistical F0 prediction

One of the major speaking methods for laryngectomees is a speaking method using an electrolarynx to generate artificial excitation sounds, instead of vocal fold vibration. Although electrolaryngeal (EL) speech is relatively intelligible, its naturalness is quite low owing to the artificial excitation sounds. To make it possible to produce more naturally sounding EL speech, we have proposed an automatic control method of the fundamental frequency (F0) patterns of the excitation sounds generated from the electrolarynx based on real-time statistical F0 prediction. In this method, a vibration of the electrolarynx to generate the excitation sounds is controlled not according to additional signals consciously provided by the laryngectomees but using only their produced EL speech signals. In the previous report, we have developed a prototype system by implementing our proposed method to the electrolarynx and have evaluated its performance objectively through a simulation. In this paper, we evaluate its performan.
Osteoclast-Derived Exosomal miR-5134-5p Interferes with Alveolar Bone Homeostasis by Targeting the JAK2/STAT3 Axis

Background In chronic periodontitis, exosomes transport various informative substances between osteoclasts and osteoblasts in alveolar bone. Herein, we aimed to investigate the effect of exosomal micro-ribonucleic acid (miRNA/miR)-5134-5p derived from osteoclasts on osteoblastic proliferation and differentiation and the development of periodontitis in vivo and in vitro. Methods The effects of OC-Exos on the proliferation and differentiation of osteoblasts were identified by Real-time quantitative reverse polymerase chain reaction (qRT-PCR), Western blot(WB), alkaline phosphatase(ALP) staining, etc. Exosomal miRNA expression was analyzed by sequencing. The sites of miRNA action were predicted through TargetScan and tested by double luciferase assay. After transfecting miR-5134-5p mimic/inhibitor into osteoblasts, we measured the proliferation and differentiation of osteoblasts by ALP staining and WB, etc. Furthermore, OC-Exos were injected into the gingival sulcus at the ligation site. Inflammation was observed by Hematoxylin-eosin (H&E) staining, the expression of inflammatory factors were detected by qRT-PCR, the resorption of alveolar bone was observed by Micro CT. Results Osteoblastic proliferation and differentiation were negatively regulated by OC-Exos in vitro. miRNA sequencing analysis revealed that miR-5134-5p expression was significantly elevated in OC-Exos, which also increased in osteoblasts following OC-Exo intervention. The dual-luciferase assay revealed that miR-5134-5p and Janus kinase 2 (JAK2) had binding sites. miR-5134-5p-mimics could upregulate miR-5134-5p expression in osteoblasts while downregulating Runt-related transcription factor 2(Runx2), phosphorylated-JAK2 (p-JAK2), and phosphorylated-signal transducer and activator of transcription 3 (p-STAT3) expression and inhibited osteogenic differentiation. However, miR-5134-5p-inhibitor had the opposite effect. In vivo, the OC-Exo group demonstrated morphological disruption of periodontal tissue, massive inflammatory cell infiltration, upregulation of inflammatory factors mRNA expression, a significant decrease in BV/TV, and an increase in the cementoenamel junction and alveolar bone crest distance. Conclusion Osteoclast-derived exosomal miR-5134-5p inhibits osteoblastic proliferation and differentiation via the JAK2/STAT3 pathway. OC-Exos exacerbate periodontal tissue inflammation and accelerate alveolar bone resorption in mice with experimental periodontitis.
Survey on digital dependency, writing by hand, and group learning as learning styles among Japanese medical students: Assessing correlations between various accomplishments

BACKGROUND: Although digital learning devices have become increasingly more common in medical education settings, it remains unclear how they influence medical student learning styles and various outcome measures. This study aimed to assess student learning styles, specifically as they relate to digital dependency, writing habits, and group learning practices among current medical students. MATERIALS AND METHODS: This questionnaire study was approved by the Research Ethics Committee of Osaka Medical and Pharmaceutical University. We conducted a questionnaire survey of 109 medical students who were 5th year students during the 2021 school year. Medical students were asked about their level of digital dependency, writing by hand, and group learning practices. We also analyzed the correlation between student learning styles and their respective outcomes on several summative evaluations. RESULTS: Of the 109 students targeted, we received responses from 62 (response rate, 56.8%). Among the respondents, digital dependency was 83.4 ± 18.6%, while hand writing ratio 39.8 ± 29.9% and group learning ratio 33.5 ± 30.5%. We also assessed correlations between these learning styles and scores on the CBT, OSCE, CC, and CC Integrative Test. Only writing by hand showed a small positive correlation with CC Integrative Test scores. CONCLUSION: Our questionnaire survey assessed the rates of digital dependency, writing by hand, and group learning practices, and analyzed the correlations between these learning styles and respective outcomes. Current medical students exhibited high digital dependency which was not correlated with performance outcomes.
Plastic Optical Fiber Integrated With Smartphone for Gait Monitoring

Correctly identifying gait patterns and measuring characteristics like stride frequency, step count, gait phase, and plantar pressure in gait are essential for disease assessment and effective rehabilitation. This article demonstrates a portable optical sensor for human gait monitoring. The device is based on a smartphone integrated with plastic optical fiber (POF) as a sensor for gait monitoring. The monitoring of multiple sensors by a smartphone is achieved by image segmentation based on Voronoi tessellation. With the sensor, experimental tests following different gait patterns were carried out. In order to obtain valuable information on gait parameters, such as step count, stride frequency, gait stages, and pressure at different plantar regions, the signal is processed using FFT analysis and analyzed in both the time and frequency domains. Compared to real values, the deviation in the estimated steps is found to be around 3.4%, with a distribution of step frequency between 0.6 and 1.4 Hz. Because of its low cost, noninvasiveness, and portability, this innovative sensor can be used in a variety of applications such as motion feedback, geriatric health monitoring, etc., and is of great promotional significance.
Potential Routes Extraction for Urban Customized Bus Based on Vehicle Trajectory Clustering

Customized bus (CB) is a kind of demand-responsive and one-stop transit service for commuters with similar travel demands. In actual applications, the bus companies identify commuter demands including origin-destination (OD) flow and departure time using online surveys, and manually plan the CB routes, which is inefficient and costly. This study aims to identify spatial patterns of travel demands using a clustering algorithm based on vehicle trajectory data, and automatically extract the potential CB route from each cluster. The clustering algorithms are based on similarity measurements. However, the existing measurements cannot be used to synchronously identify the multiple characteristics of vehicle trajectories, such as OD location, direction, and multiple sub-sequences. To address this issue, we propose a comprehensive similarity (CS) to simultaneously evaluate all the characteristics. Subsequently, we utilize the density-based spatial clustering of applications with noise (DBSCAN) to divide the trajectories into several groups, and determine the trade-off parameters in the DBSCAN using vlseKriterijumska optimizacija i kompromisno resenje (VIKOR), which means a multi-criteria optimization and compromise solution. Then, a case study is conducted based on one-week taxi trajectory data collected in Suzhou, China. The extracted results are compared to the existing routes designed by bus companies, providing a coverage rate of 66.7%. Finally, the advantages of the proposed method were discussed from the two perspectives of clustering algorithm and trajectory similarity measurement. The proposed method can help traffic management authorities and bus companies to plan and design new CB routes.
Feature Selection using Random Forest Classifier for Foot Strike Event Detection in Toe Walkers

Automated Gait event identification of Foot Strike (FS) and Foot Off (FO) in pathological gait data, can be time saving in comparison to conventional manual annotations done currently. Identification of FS and FO allows breaking walking trials into gait cycles and hence aids in comparison of gait parameters like joint angles, forces and moments across gait cycles. Automated Gait Event Detection is also useful in development of wearable sensor devices and robotic systems that assist gait. Researchers have proposed several automatic gait event detection algorithms based on kinematic parameters and systematic study of the literature suggests specific parameters to have higher contribution in identification of FS event in all common pathological gait patterns. We used Random Forest Classifier Feature selection technique to identify high contributing features in FS event in toe walking pediatric pathological gait dataset and the results suggest high similarity in selected features by the machine learning technique with those suggested by popular event detection algorithms based on kinematic parameters for pathological gait. Hence we conclude that RFC feature selection is suitable for feature selection in toe walkers gait dataset for event detection purpose. Keyword : Feature selection, foot off, foot strike, pathological gait.
Nonlinear Snell law for grazing incidence along interfaces with discontinuous second-order susceptibilities

Xiaohui Zhao,1,2 Yuanlin Zheng,1,2 Huaijin Ren,3,* Ning An,4 Xuewei Deng,5 and Xianfeng Chen1,2,† 1State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Physics and Astronomy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China 2Key Laboratory for Laser Plasmas (Ministry of Education), Collaborative Innovation Center of IFSA (CICIFSA), Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China 3Institute of Applied Electronics, China Academy of Engineering Physics, Mianyang, Sichuan 621900, China 4Shanghai Institute of Laser Plasma, China Academy of Engineering Physics, Shanghai 201800, China 5Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang, Sichuan 621900, China (Received 13 March 2016; published 26 April 2017)
Approximate algorithms for unrelated machine scheduling to minimize makespan

We study two unrelated machine scheduling problems with machine dependent release dates to minimize the makespan. For the case with fixed processing time where processing job $j$ on machine $i$ requires time $p_{ij}$ and incurs a cost of $c_{ij}$, we derive a $2$-approximation algorithm. For the problem with variable processing times where the cost increases linearly as the processing time decreases, we propose a $(2+\epsilon)$-approximation algorithm.
Radiation Myelopathy: A Case Report and Literature Review

Proton beam therapy is a common type of radiation treatment that delivers a beam of proton particles to treat cancer and minimize damage to nearby healthy tissue. In this paper, we describe a case of a 20-year-old male patient with osteosarcoma of the distal right femur that eventually metastasized to his thoracic cavity. The patient underwent radiation beam therapy treatment that was directed at his left thorax and nine months later presented with clinical and radiographic findings of delayed radiation myelopathy (RM).
Unilateral Abducens Nerve Palsy as the Lone Sign of Cerebral Venous Sinus Thrombosis: A Case Report and Literature Review

Cerebral venous thrombosis (CVT) is a rare condition characterized by the obstruction of cerebral venous sinuses or cortical veins, leading to stroke-like symptoms. This case report presents a case of a 74-year-old male with isolated unilateral abducens nerve palsy as the sole sign of CVT, without accompanying symptoms or focal deficits. Neuroimaging, including CT and magnetic resonance venography, confirmed the diagnosis of CVT with a thrombus in the right transverse sinus. The patient was co-managed with the medical team and initiated on anticoagulation therapy. Follow-up showed resolution of diplopia and improvement in extraocular muscle movements. Unilateral abducens nerve palsy in CVT is rare, with most cases presenting as bilateral palsy. The case report emphasizes the importance of considering CVT in the differential diagnosis of isolated abducens nerve palsy and highlights the role of neuroimaging in early detection. Timely diagnosis and appropriate management are crucial for favorable outcomes in CVT cases. Further research is needed to enhance understanding of the pathophysiology, prognosis, and optimal management of this uncommon presentation.
5DMETEORA FRAMEWORK: MANAGEMENT AND WEB PUBLISHING OF CULTURAL HERITAGE DATA

Abstract. Cultural Heritage (CH) management software represents virtual information in various ways aiming either at usability and long-term preservation or interactivity and immersiveness. A single web-based framework that couples the organization of geospatial, multimedia and relational data with 4D visualization, Virtual Reality (VR) and Augmented Reality (AR) is presented in this paper (https://meteora.topo.auth.gr/5dmeteora.php). It comprises the 5dMeteora platform and the Content Management System (CMS) for uploading, processing, publishing and updating its content. The 5dMeteora platform integrates a responsive 3D viewer of high-resolution models in the basis of 3DHOP (3D Heritage Online Platform) and Nexus.js multi-resolution library. It offers data retrieval and interpretation mechanisms through navigation tools, clickable geometries in the 3D scene, named hotspots, and semantic organization of metadata. Its content and interactive services are differentiated, based on the scientific specialty or the field of interest of the users. To achieve the sense of spatial presence, VR and AR viewports are designed to give a clearer understanding of spatial bounds and context of 3D CH assets. The proposed CMS allows dynamic content management, automation of 3DHOP’s operations regarding 3D data uploading and hotspots defining, real-time preview of the 3D scene as well as extensibility at all levels (e.g., new data types). It is built upon a MySQL Database Management System and developed with PHP scripting, backend JavaScript and Ajax controllers as well as front-end web languages. The database maintains and manages the entities of every type of data supported by the platform, while encryption methods guarantee data confidentiality and integrity. The presented work is the first valid attempt of open-source software that automates the dissemination of 3D and 2D content for customized eXtented Reality (XR) experiences and reaches multiple levels of interactivity for different users (experts, non-experts). It can meet the needs of domain experts that own or manage multimodal heritage data.
Statistical inference for the burr type III distribution under type II censored data

In this study, estimation and prediction problems for the Burr typeIII distribution under type II censored data are considered. Maximum likelihood and maximum product spacing estimation methods are used to estimatemodel parameters. EM algorithm is employed to obtain maximum likelihoodestimates. Unobserved future order statistics are predicted with best unbiasedprediction method. A simulation study is carried out to exhibit performanceof the estimation methods. Further, a numerical example is presented to illustrate the usefulness of the Burr III distribution