Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiotherapy Rehabilitation in Arthroscopic Rotator Cuff Repair Patient- A Case Report

Rotator cuff pathology is usual, and the ailment's natural history suggests that tears enlarge with age. Rotator cuff tears are a familiar origin of pain and ailment in the shoulder. The research on treatment of rotator cuff tears is mixed. The aim of this review is to evaluate the evidence regarding the effectiveness of physiotherapy in lowering pain and enhance shoulder function in patients with symptomatic rotator cuff tears. Rotator cuff injuries are typically followed by a gradual onset of pain, but they can also be the result of an acute injury. Chronicity, underlying anatomic and physical factors, age, and the presence and severity of tendon tears all influence how rotator cuff conditions are managed. Conservative care, such as physical therapy, is typically used as the first line of defence, but surgical repair may be required later on. Rotator cuff (RC) tears can range from partial to full thickness tears of a single tendon to massive cuff tears. Rotator cuff tearing can be caused by glenohumeral trauma as well as pathological process. As a result, Treatment necessitates a diligent diagnosis as well as a distinguished approach that considers morphological and patient-specific effects. The therapeutic approach is determined by the patient's pathogenicity, tear morphology, clinical symptoms, and functional demands. The current case is about an 51 year old male who was admitted to the hospital with tear injury to right shoulder due to fall from truckmuscle strength and range of motion were main focus of physiotherapy intervention. The prime goal of this case study was to assess the patient’s response to physiotherapy for reinforcement and functional task.
Coding of synthetic aperture radar data

Synthetic Aperture Radar (SAR) is a dedicated high-resolution sensor with imaging capability in all weather and day-night conditions and has been employed in several earth and interplanetary observation applications. A significant characteristic of this system is the generation of a large amount of data that involves major problems related to on-board data storage and downlink transmission. The near future SAR satellite missions planned would be pushing downlink data bandwidths to prohibitive levels, which dictate efficient on-board compression of raw data. Due to the limitation of the on-board resources in the satellite, it is desirable to have a computationally efficient encoder. In this thesis, we address the compression of the complex-valued SAR raw data in the compressed sensing (CS) framework, in which the encoder is simple whereas the decoder is computational expensive. CS is an emerging technique for signal measurement and reconstruction that takes advantage of the fact that many signals are sparse under some basis or frame. The measurement of the signal in the CS framework is obtained by taking a small number of projections of the signal onto an incoherent basis. For the SAR raw data compression here we have considered a simple encoder, with a 2D-FFT followed by a random sampler. The reconstruction of the sparse coefficients of the signal from these projections is then based on the sparsity induced optimization techniques like Orthogonal Matching Pursuit (OMP) and iterative reconstruction methods. We demonstrate empirically that the CS framework for compression of the complex-valued SAR raw data is effective for the cases when the SAR image is sparse in the spatial domain. We also address the limitation of this framework while dealing with
The Potential Roles of Cervical Plexus Abnormalities in Occipital Neuralgia: An Anatomic Variant Explored

Occipital neuralgia (ON) is a condition defined as a headache characterized by paroxysmal burning and stabbing pain located in the distribution of the greater occipital nerve (GON), lesser occipital nerve (LON), or third occipital nerves (TON). This condition can be severely impairing in symptomatic patients and is known to have numerous etiologies deriving from various origins such as trauma, anatomical abnormalities, tumors, infections, and degenerative changes. This study reports four cases of a previously undescribed anatomical variant in which the (spinal) accessory nerve (SAN) fuses with the LON before piercing the sternocleidomastoid (SCM). The fusion of these two nerves and their route through the SCM points to a potential location for nerve compression within the SCM and, in turn, another potential source of ON. This anatomical presentation has clinical significance as it provides clinicians with another possible cause of ON to consider when diagnosing patients who present with complaints of a headache. Additionally, this study explores the prevalence of piercing anatomy of the LON and GAN and discusses their clinical implications.
Adding coordination to the management of high -end storage systems

ZHANG, ZHE. Adding Coordination to the Management of High-End Storage Systems. (Under the direction of Dr. X. Ma). Today’s scientific and commercial applications rely heavily on high-end computing (HEC) facilities, including large scale datacenters, supercomputers, and so forth. In these facilities, the storage subsystems are playing an increasingly important role in the overall computing experience perceived by users. Meanwhile, it is a challenging task to provide high performance and reliability to those high-end storage systems due to their high I/O demands, large scales, and complex architectures. We observe that in addition to the well-recognized lack of I/O resources relative to computing demands in an aggregate perspective, one main challenge faced by highend storage systems lies in the growing scale and complexity of the entire environment. Individually developed system components or algorithms often behave with isolated local optimizations, and handle concurrent user workloads without considering inter-workload relationships. The author’s Ph.D. research focuses on three novel instances of bringing adaptive coordination to the management of commercial and scientific high-end storage systems, at different levels of the HEC storage hierarchy. Firstly, on a single storage server, we present a memory cache allocation mechanism which coordinates multiple concurrent sequential access streams with different request rates. Our work is based on the interesting observation that this problem bears a strong resemblance to situations long studied in the field of supply chain management (SCM), used by large vendors and retailers. Furthermore, in a multilevel storage architecture, we address the problem of information distortion in uncoordinated prefetching operations on different storage caches. We develop a simple information sharing mechanism, as well as a transparent hierarchy-aware optimization component named PreFetching-Coordinator (PFC), which monitors both upperand lower-level caches, and adjusts the aggressiveness of lower-level prefetching. Finally, we improve the data availability in an entire distributed storage system by coordinating it with the HPC job scheduler and remote data sources. We implemented the proposed techniques in real software environments, including a state-of-the-art operating system kernel, a widely used job scheduler and a popular parallel file system, as well as verified simulators. Our experimental results collected from real system experiments and simulations show that our proposed techniques can significantly improve system performance and reliability by coordinating among system components and requests. Adding Coordination to the Management of High-End Storage Systems
Performance Analysis and Impedance Modeling of Rectangular and Circular Split-Ring Resonator Antennas in 2.4/5.2GHz Bands

In this paper, impedance modeling is presented for analyzing the metallic loading effect on the performance of a split ring resonator (SRR) antenna in (2.4–2.5)/(5.1–5.8)GHz frequency bands. Two SRR antennas of rectangular and circular rings have been designed on ANSYS HFSS software, and their return losses are obtained as −16.63/−25.26 dB at 2.7/5.8GHz and −10/−20.09 dB at 2.2/5.2GHz, respectively. Then the metallic loadings are incorporated in both rectangular and circular SRR antennas, which move the peak resonant frequency to 2.5/5.1GHz with simulated return losses of −14.39/−22 dB for rectangular SRR antenna and to 2.6/5.1GHz with −17.64/−11.10 dB, respectively for circular SRR antenna. Then, to analyze the effect of metallic loading on SRR antenna performance, a set of equations are derived from the equivalent circuit of the SRR antenna without and with metallic loading to evaluate the lumped elements values. The circular SRR antenna with metallic loading is fabricated, and its measured return loss is found to be −17.94/−15.76 dB at 2.415/5.23GHz. The lumped component values are calculated from the measured return loss using the derived equations, and these values are compared with those obtained from the simulated return loss for circular SRR antenna. A shift in resonant frequencies towards the desired bands is observed due to the inductive effect of the metallic loading. The axial ratio values higher than 15 dB confirm that the proposed SRR antennas with metallic loadings are linearly polarised. The 2D patterns in E-plane andH-plane, as well as 3D far-field patterns, confirm an omnidirectional radiation pattern for circular SRR antenna, which is useful for WLAN applications.
Is the Mandibular Condyle Involved in Medication-Related Osteonecrosis of the Jaw? Audit of a Single Tertiary Referral Center and Literature Review

RESEARCH ARTICLE Is the Mandibular Condyle Involved in Medication-Related Osteonecrosis of the Jaw? Audit of a Single Tertiary Referral Center and Literature Review Giorgio Lo Giudice, Antonio Troiano, Carmelo Lo Faro, Mario Santagata, Marco Montella, Salvatore D’Amato, Giampaolo Tartaro and Giuseppe Colella Department of Neurosciences, Reproductive and Odontostomatological Sciences, Maxillofacial Surgery Unit, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy Multidisciplinary Department of Medical-Surgical and Dental Specialties, Oral and Maxillofacial Surgery Unit, University of Campania “Luigi Vanvitelli”, Via Luigi De Crecchio, 6, 80138 Naples, Italy Department of Mental and Physical Health and Preventive Medicine, Pathology Unit, University of Campania “Luigi Vanvitelli”, Via Luigi De Crecchio, 6, 80138 Naples, Italy
Artificial Neural Network Context-Aware Reasoning Engine Model for Predicting User-Ranked Activities

The choice of users’ activity in a context-aware environment depends on users’ preferences and background. Users tend to rank concurrent activities and select their preferred activity. Researchers and developers of context-aware applications have sought various mechanisms to implement context reasoning engines. Recent implementations use Artificial Neural Networks (ANN) and other machine learning techniques to develop a context-aware reasoning engine to predict users’ activities. However, the complexities of these mechanisms overwhelm the processing capabilities and storage capacity of mobile devices. The study models a context-aware reasoning engine using a multi-layered perceptron with a gradient descent back-propagation algorithm to predict activity from user-ranked activities using a stochastic learning mode with a constant learning rate. The work deduced that working with specific rules in training a neural network is not always applicable. Training a network without approximation of neuron’s output to the nearest whole number increases the accuracy level of the network at the end of the training.
The Impact of Health Subsidy on Health Care Utilization in Indonesia

Equal opportunity and access to health care services regardless of one’s socio-economic background is important for health equity. To achieve this, the Indonesian government introduced a national health insurance program named Jaminan Kesehatan Nasional in 2014 and provided insurance payment subsidies for poor people. This study evaluates the health subsidy policy’s impact on the utilization of different kind of health care facilities, both public and private. Two waves of Indonesian socio-economic surveys, namely 2013 and 2018, and two analysis methods, namely logistic regression, and difference-in-differences, were applied in this study. The results show that in first-level health care facilities, the health subsidy improves the probability of outpatient visits in public health centers and polyclinics but decreases the probability of outpatient visits in medical practices. Then, in secondary-level health care facilities, the health subsidy was found to increase the likelihood of outpatient visits in public hospitals; however, it lowers the probability of outpatient visits in private hospitals.
Deep Reinforcement Learning, LSTMs and Pointers for Downlink Scheduling in LTE

Downlink scheduling in the LTE system is an open problem for which several heuristic solutions exist. Recently, there has been an increase in interest in applying machine learning to networking problems, including downlink scheduling. Improvements in Physical Layer capabilities have generated new resource-intensive use cases and continuously modifying existing heuristic solutions could result in the development of systems too complex to maintain over time. We propose a LSTM/Pointer Network-based downlink scheduler which aims to improve upon the current models which utilize feed forward neural networks. Our scheduler flexibly handles changing numbers of UEs via the use of a recurrent neural network. We formulate the downlink scheduling problem as a Markov Decision Process that integrates the channel quality indicator and the buffer size of each UE as the observation and solve it using a Deep Reinforcement Learning algorithm. Our experiments demonstrate that our approach results in a scheduler which generalised across changing number of UEs and resource blocks and performed within the range of traditional schedulers.
Traditional beliefs and Practices in Newborn Care among Mothers in Shaanxi Province, China

China has the largest population country in the world with more than 70% of the people living in urban areas more than 34% of children under the age of 5 years are responded to demonstrate moderate or severe growth stunting, [1] Recent childbirth and early infant care strategy and practice in China is not allied with WHO recommendations for several main interventions [2]. A numeral of cultural practices and beliefs held by families, predominantly grandparents, prevent evidence-based practices. Chinese customary birth rituals need a woman to be restricted at home for one full month after giving birth, and to scrutinize a wide set of preventive prescriptions and proscriptions [3]. At current still, inadequate care is causing maternal and child health problems, especially in developing countries. Of the entire deaths which happen in the first five years of life, 36% are newborn deaths. In fact, of the approximately four million global neonatal deaths that occur annually, 98% occur in developing countries. As most births and deaths occur outside any established health care facility, a reduction in neonatal mortality may depend significantly on interventions involving promotion or adaptation of traditional care behaviors practiced at home [4]. Cultural principles, attitudes, beliefs, and behaviors influence lifestyle and health. For that reason, culture is considered a dynamic factor that plays a significant role in health and diseases. Traditional practices follow-on from principles of a particular culture affect children mainly. A family unit, which mirrors values, civilization, customs, and beliefs, i.e. culture of a society to which it belongs, plays an essential role in physical, psychological, and social development and health in children. Customary child care practices influence children’s health [5]. The common newborn’s mortality rate in developing countries is more than eight times that prevailing in developed countries [6]. Irresistible *Corresponding author: Prof. Li Hui MD, PhD, Department of Neonatology, First Affiliated Hospital of Xian Jiaotong University, School of Medicine, 277 Yanta West Road, Xi’an-Shaanxi 710061, People’s Republic of China Abstract
Visual compensation in localization of a robot on a ceiling map

In a visual odometry system, location of a mobile robot is automatically estimated (localized) from video. When the video is captured by an "upward" camera fixed to an indoor mobile robot, a panorama image of the ceiling (ceiling map) is generated by using a visual motion between two adjacent frames in the video. Similarly, location of another robot can be estimated on the ceiling map by using a visual motion between the current frame and the previously generated ceiling map. Under the assumption that the robot goes straight or rotates around a fixed point, there is no problem on the localization as far as the floor is flat. However, when there is debris on the floor, the estimated location contains error. In this paper, we reduce this error by utilizing visual motions in video from the "forward" camera fixed to the robot. This is a visual compensation of motions in the "upward" camera's video with those in the "forward" camera's video. It was experimentally confirmed that the maximum absolute value of the error was reduced to approximately 11%. Key words: Visual odometry, localization, robot vision, motion estimation.
Wireless Charging Efficiency Optimization Method Based on Adaptive Robust Control

This paper designs, analyzes and optimizes the electric vehicle wireless charging system and its control method. Compared with the traditional plug-in conduction charging, wireless charging is more convenient, safe, reliable and has better environmental adaptability. Inductive power transmission technology (IPT) is the main technology for wireless charging of electric vehicles and plug-in hybrid vehicles. Based on the fundamental approximation method, the critical self inductance of the primary or secondary coil is derived in this paper. This critical value reflects the power transmission capacity and control performance of the two chargers when they are interoperable. Compared with the non integrated structure, the magnetically integrated LCC compensated wireless charging system can transmit the same power with smaller compensation inductance. Based on the voltage dependent dynamic state space model, four working modes of magnetically integrated LCC compensated wireless charging system are studied in this paper. Simulation and experimental results show that the voltage dependent equivalent circuit model can be effectively applied to the basic characteristic analysis of wireless charging system. The voltage dependent dynamic state space model is more accurate and realistic. In addition to reflecting the basic characteristics of the wireless charging system, it can also reflect the working mode of the wireless charging system.
An Anti-Collusion Information Sharing Scheme Provides Secure Scheme for Data Privacy in Cloud Computing

: In cloud computing, the cloud service providers provide space for shoppers to network with other information management system by moving native management system into cloud services. This provides a strong and effective use of resources and, also cuts down the expenditure for the providers. There is a limitation for the cloud suppliers in preserving the information more secure and economically within the cloud. They also have to encrypt the information before transferring into the cloud to maintain privacy. The suppliers have found a crypto graphical storage system to share secure and economical information based on the following technique, in which the files are divided and encrypted into groups within a file-block key. These file-block keys were driven to be updated and shared for user revocation. There are complications when alternative schemes used for information sharing with un-trusted servers. The revocation in these schemes measure lengthy with the information from the suppliers and also call back the users. An Anti-Collusion Information Sharing Scheme provides secure private keys to consumers on the addition or revocation of a customer. The new customers can obtain the keys from team manager through certified authorities and secure communication channels. Thus a revoked customer won’t be able to retrieve usual data documents even if they plot with un-trusted cloud.
Classification of daily crop phenology in PhenoCams using deep learning and hidden markov models

Near surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images our workflow incorporates a hidden markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83-0.85, which improved to 0.86-0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local scale dataset of field states and phenological stages for agricultural research.
How reliable are large-scale jobs in parallel clusters?

As large-scale platforms continue to grow in scale and compl exity, the applications running on them are increasingly being prone to errors and failures. A recent st udy by Snir et al. [9] predicts that large parallel applications running on an exascale machine may abort as fre quently as once every 30 minutes. Minimizing the rate at which large-scale jobs fail is especially critical f or long-running applications that can take up to weeks or months to finish executing successfully [8]. Emerging chall enges, such as the increasing complexity of parallel application workflows [5] or the rising silent data corrupti on (SDC) rates in large-scale platforms [6], require substantial improvements in both system-level and applica tion-level resilience. In this technical report we are interested in answering the q u stion of what makes jobs fail in large, parallel clusters. Towards this end, we use workload traces collecte d at real world systems to study the effect of different factors onjob reliability and to identify which ones are predictive of job failures in p arallel applications. We investigate factors such as job configuration parameters (e .g. the degree of parallelism, priority, etc), job resource requests and constraints, job resource utilization (e.g. m emory and CPU usage), and others.
World-class organisations : only a developed country phenomenon?

Not satisfied that good is good enough; world-class organisations benchmark their own levels of optimum performance against best in class in comparable businesses. Although benchmarking in itself cannot be hailed as a panacea to attain world-class performance, the process, correctly applied, enables world-class organisations to constantly reference and adjust their beacons by continuously raising the bar.