Miracle-dz/UniFAR-FTUs huggingface dataset:
A 7-year review of men's and women's ice hockey injuries in the NCAA.

BACKGROUND Ice hockey is a high-speed collision sport with recognized injury potential. Body checking, identified as a primary cause of injury, is allowed in men's hockey but is not allowed at any level for female players. The injury patterns in collegiate hockey should reflect this fundamental difference in how the game is played. In this study, we reviewed the injuries sustained by National Collegiate Athletic Association (NCAA) hockey players over a 7-year period. METHODS We conducted a retrospective database review of injuries and exposures reported to the Injury Surveillance System to determine rates of injury or differences in the pattern of injury between the sexes. RESULTS The rate of injury during games for men (18.69/1000 athlete-exposures [AEs]) and women (12.10/1000 AEs) was significantly higher than the rate of injury during practice. The rate of concussion was 0.72/1000 AEs for men and 0.82/1000AEs for women, and the rate remained stable over the study period. Player contact was the cause of concussions in game situations for 41% of women and 72% of men. CONCLUSION Both men and women had increased rates of practice-related injuries that resulted in time loss during the study period. In addition, there were high rates of concussions from player contact. The concussion rate in women was higher than expected. A more detailed examination with focused data collection may impact these findings.
Improved electric Vehicle Performance Using on line PSO approach

This work focuses on two in-wheel motors propelling an electrical vehicle. Depending on vehicle speed reference and steering angle, two regulation loops are implemented to determine the torque reference of each motor. An improved on line PSO approach is proposed to calculate the weighted gains of each PI regulator. Comparing to classic PI controllers, on line PSO method proves better torque response and vehicle speed stability. Software results show the effectiveness of the suggested PSO approach.
Effect of psychosocial factors on metabolic syndrome in male and female blue-collar workers.

AIM The purposes of this study were to examine the relationship between psychosocial factors and metabolic syndrome among male and female blue-collar workers, and which factors influence their metabolic syndrome by sex. METHODS A cross-sectional study was completed of 154 men and 80 women working at small companies in Korea. The data were collected through a structured questionnaire, blood test, and anthropometric and blood pressure measure. Metabolic syndrome was diagnosed from the results of blood test and the measurements of waist circumference and blood pressure. RESULTS The prevalence of metabolic syndrome among male and female blue-collar workers was 24.0% and 7.5%, respectively. Multiple logistic regression analysis was performed to examine factors of metabolic syndrome associated with sex. After controlling for age, marital status, smoking, alcohol drinking, shift work, overtime work, and physical exercise, job stress (odds ratio [OR] = 3.10, P = 0.005) and risk perception (OR = 1.12, P = 0.016) were influencing factors for men, and low job stress (OR = 0.05, P = 0.04), low social support (OR = 1.51, P = 0.009), and risk perception (OR = 1.27, P = 0.023) for women. CONCLUSION Metabolic syndrome among blue-collar workers is closely related to psychosocial factors, such as job stress, social support, and risk perception, with the effect of job stress a point of difference between men and women. Occupational health nurses should be cognizant of the importance of assessing the effect of psychosocial factors on cardiovascular risk for blue-collar workers.
Partial Shading Detection and Global MPPT Algorithm for PV System

This paper proposes a partial shading detection and model free global maximum power point tracking (GMPPT) algorithm for a photovoltaic array system. The proposed algorithm is decomposed of three parts: searching part, tracking part, detecting and avoiding the dead points in the partially shaded P-V curve, if exists, part. Simulation results with comparisons with the state of art methods are provided to highlight the fruitiness to the proposed GMPPT algorithm.
A Study on Fault Tolerance Technique for AGR-MAC Protocol Based Sensor Networks

Sensors collect data in many ways and provide various services based on that data. Sensors can be distributed or placed anywhere unreachable from human beings. In wireless sensor network environment, it is hard to replace sensor nodes when they fail to operate. In an anchor-based group relay MAC(AGR-MAC), an anchor node can be isolated when any other anchor nodes do not exist in the communication range. And data collected into isolated sensor nodes could be discarded. In order for solving this kind of node isolation phenomenon, we have proposed isolation detection and recovery method for AGR-MAC and prevented the nodes from unwantedly discarding the data.
ZF/MMSE and OSIC Detectors for UpLink OFDM Massive MIMO systems

A Least Squares Channel Estimation (LSCE) method is designated for a Massive MIMO system combined with Orthogonal Frequency Division Multiplexing (OFDM). The Mean Square Error (MSE) of LSCE is computed firstly in a Rayleigh fading channel. Secondly, if channel estimation was available at the receiver (Base Station), the data detection could be made by linear and nonlinear detectors i.e (ZF, MMSE) and (SNR-Based Ordering, OSIC-SINR-Based Ordering) respectively. Simulation results show that by increasing antennas number of Base Station (BS), the nonlinear detectors perform more in terms of Bit Error Rate (BER).
Optimal design of a boost-type DC-DC converter for PV power-supplied wireless sensor networks

Wireless Sensor Networks (WSNs) are widely installed during the last years for monitoring multiple parameters of interest over distributed areas in the environment, buildings and industries. The WSN nodes are frequently installed in geographically remote areas. Thus, they are power-supplied by renewable energy sources and a DC-DC power converter is employed for interfacing the generated energy to a battery bank and the electric load of the WSN node. In this paper, an optimization method is presented for performing circuit-level optimization of a Photovoltaic (PV) power-supplied Boost-type DC-DC power converter, which is employed in a WSN node. Using the proposed technique enables to calculate the optimal switching frequency and values of the components comprising the circuit of the DC-DC converter, such that either the power loss at nominal output power, or the total power loss during the year, or the Levelized Cost Of the Electricity generated (LCOE), are alternatively minimized. The design optimization and experimental results demonstrate the features of the proposed technique and confirm the performance superiority of the DC-DC converters, which are designed using the proposed method, compared to the non-optimized DC-DC converter structures.
Ultra Low-Power Drowsiness Detection System with BioWolf

Drowsiness is a cause of accidents in industrial and mining activities. A considerable amount of effort has been put into the detection of drowsiness, and since then it has been integrated into a large variety of wearable systems. Nevertheless, the technology still suffers from high intrusiveness, short battery life and lack of generality. An opportunity to address these shortcomings arises from the use of physiological and behavioral features for bio-signals like EEG and IMU sensors. In this work, we propose an energy-efficient wearable platform for drowsiness detection. Our platform features a minimally invasive setup, based on dry EEG sensors to acquire neural data, and Mr. Wolf, an 8-core ultra-low-power digital platform. The system has been validated on three test subjects, achieving detection accuracy of 83%, using a Nearest Centroid Classifier, modeled with a semi-supervised algorithm from previously collected data. This work further extends the capabilities of our previous system, providing a more sophisticated classification mechanism that includes real-time and onboard sensor fusion processing while running into a highly efficient and unobtrusive hardware platform, outperforming the current State of the Art (SoA) in terms of wearability and battery lifetime.
Simultaneous Recording and Stimulation Instrumentation for Closed Loop Spinal Cord Stimulation

Chronic neuropathic pain is a debilitating disorder. Many patients receive pain relief when implanted with a Spinal Cord Stimulation (SCS) system. However, there is currently no way to automatically determine the most effective parameters to use in stimulation, so some patients must return to the clinic periodically to have their device re-programmed. Further, postural changes can shift the position of the stimulating electrodes and require programming changes to optimize pain relief. Evoked compound action potentials (ECAPs) could be used as feedback signals to determine optimal patient stimulation parameters. To determine the feasibility of closed-loop feedback in SCS, we have designed a recording system that simultaneously records evoked compound action potentials (ECAPs) during stimulation. Using differential recording and high dynamic range amplifiers, this system is capable of simultaneous stimulation and recording without saturating. Here we show in vitro the capability of this system to record simulated evoked compound action potentials across all channels during stimulation without saturation and with low noise. This data acquisition system provides the ability to add recording to existing clinical stimulation trial systems with minimal changes to standard clinical practice.
OFF Types of Mouse Retinal Ganglion Cells Are Less Sensitive To a Change in Electric Stimulus Charge Than ON Type *

For individuals blinded by retinitis pigmentosa or age-related macular degeneration, retinal prosthetic devices can restore some form of useful vision. Unfortunately however, their performance varies considerably across subjects and the best performance has not yet reached the level of legal blindness. It has been thought that the limitation of retinal prostheses comes from the lack of comprehensive understanding about how various types of retinal ganglion cells (RGCs) respond to an identical electric stimulus as well as about how changes in stimulus parameters alter their response patterns. In particular, it is crucial to better understand spiking patterns of ON and OFF types of RGCs in response to electric stimulation because these two types are known to be critical in forming visual percepts. Our previous studies reported that the two types have different sensitivities to changes in various stimulus parameters such as stimulation rate and stimulus duration, suggesting a possibility of similar difference in response to other changes. In this paper, we explored response changes of ON and OFF types of alpha RGCs to varying stimulus charges. We found that, at a fixed current amplitude, the response magnitudes of OFF types of alpha RGCs are minimally altered by doubled stimulus charge while those of ON RGCs are significantly altered by the same change. This finding may be used for optimizing overall retinal responses to electric stimulation for better artificial vision.
Performance Enhancement in 5G Cellular Networks Using Priorities in Network Slicing

Network Slicing is one of the key features of the new 5G cellular network communication, it proposes the division of one physical network into multiple virtual networks to achieve specific goals such as security, flexibility and control over the network that will provide logical isolation in the devices, services and core networks set up for different characteristics and different types of services. Our proposed work uses end-to-end network slicing concept in 5G networks to solve the key issue of isolating the slices via prioritizing them in order to increase performance and decrease latency for high priority applications. Simulation results using NS-3 network simulator prove our claims and show enhancements in latency and performance
Auditory imagery classification with a non-invasive Brain Computer Interface

Brain Computer Interfaces (BCIs) controlled by motor imagery have been explored during many years mainly because of its ease of imagination, and localization of cortex activity. Auditory imagery, nonetheless, has been a rarely explored approach, but a one that might open new possibilities in musical interfaces, communication and speech synthesis. It can also bring a complementary way to motor imagery for improving accuracy in BCIs.Using a non-invasive BCI, open source software, and auditory imagery of white noise, we tested several classification algorithms to determine the accuracy and usefulness of using non-motor imagery EEG signals.We tested 15 healthy adults with a 6 electrode EEG setup and the open source platform OpenVibe. The signals were analyzed with MultiLayer Perceptron, Linear Discriminant Analysis, and Support Vector Machines classifiers.Our results show that using a Support Vector Machine classifier and our specific experimental setup, we could achieve up to 93% accuracy in white noise imagery in our subjects (Linear Discriminant Analysis and MultiLayer Perceptron setups yielded accuracy of 73-76%). Thus, auditory imagery can be a promising complementary approach to traditional motor-imagery BCIs.
3 Dimensional Point Cloud Filtering Using Differential Evolution Algorithm

The ability to model an object or an environment using 3-dimensional point cloud data is very important for some areas such as photogrammetry, remote sensing, material processing and production, reverse engineering, construction industry, virtual reality and medicine. However, 3-dimensional point cloud data obtained with existing technologies contain some noise due to the nature of the measurement device and man-made errors. Therefore, it has great importance to filter raw point cloud data or surface elements derived from point cloud data to increase the quality of the 3D model. In this study, a filtering method was developed based on plane fitting by differential evolution algorithm to filter noisy point cloud data. The proposed heuristic algorithm-based filtering method is compared with the singular value decomposition method, which is frequently used in literature to obtain the plane parameters. Both visual and numerical results show that the plane fitting method based on the differential evolution algorithm is more successful to remove noise than the classical filtering method based on singular value decomposition.
Prognostic factors for overall survival in patients with metastatic colorectal carcinoma treated with vascular endothelial growth factor-targeting agents.

OBJECTIVE Angiogenesis represents a key element in the pathogenesis of malignancy. There are no robust data on prognostic factors for overall survival (OS) in patients with metastatic colorectal cancer treated with vascular endothelial growth factor (VEGF)-targeted therapy. The present study was conducted to establish a prognostic model for patients using an oxaliplatin-based or irinotecan-based chemotherapy plus bevacizumab in metastatic colorectal cancer. METHODS Baseline characteristics and outcomes on 170 patients treated with FOLFIRI or XELOX plus anti-VEGF therapy-naive metastatic colorectal cancer were collected from three Turkey cancer centers. Cox proportional hazards regression was used to identify independent prognostic factors for OS. RESULTS The median OS for the whole cohort was 19 months (95% CI, 14.3 to 23.6 months). Three of the seven adverse prognostic factors according to the Anatolian Society of Medical Oncology (ASMO) were independent predictors of short survival: serum lactate dehydrogenase (LDH) greater than the upper limit of normal (ULN; p<0.001); neutrophils greater than the ULN (p<0.0014); and progression free survival (PFS) less than 6 months (p =0.001). CONCLUSION Serum LDH and neutrophil levels were the main prognostic factors in predicting survival, followed by PFS. This model validates incorporation of components of the ASMO model into patient care and clinical trials that use VEGF-targeting agents.
Evidence for an alteration of the tonotopic map in the gerbil cochlea during development

We have investigated developmental alterations in the tonotopic projection of the gerbil lateral superior olive. Single neurons were characterized in the frequency domain and the recording site marked with fast green. Transverse tissue sections from the auditory brainstem of each animal were visualized with a video‐equipped microscope, and the image was digitized for subsequent alignment. The three‐dimensional display indicated little variation in the rostrocaudal axis, allowing us to collapse the data into a two‐dimensional tonotopic map. The tonotopic map was found to change with age such that the characterstic frequency of neurons in a given anatomical location became successively higher during development. These results are consistent with the hypothesis that the place code gradually shifts in the developing cochlea.
Raised Cosine Filter Embedded in Stationary Wavelet Packet Transform for Induction Motor Fault Detection

This paper proposes an original combination of Raised Cosine (RC) wavelet filter with the Stationary Wavelet Packet Transform (SWPT) to detect the Broken Rotor Bar (BRB) fault in Induction Motors (IM) through Motor Stator Current Analysis. The combination contributes to the establishment of an efficient and a straightforward approach for extracting the BRB fault feature using wavelet techniques even at low slip. A wise tune of the RC wavelet filter enabled the implementation of a simple SWPT, without the need to its recursivity for fundamental component (i.e. 50Hz) removal. The experimental results show that the proposed method is able to detect faulty conditions at incipient stage with high accuracy even at low slip. A comparative study between the Meyer wavelet based RSWPT and the proposed method shows the superior filtering features of the RC wavelet. The tunable-at-will RC wavelet filter and the reduced sampling frequency and samples size are considered as appreciated features for a hardware implementation in embedded systems.
Big Data Impacts and Challenges: A Review

Data is everywhere, organizations, governments, clicks, web server, business partner, and even our body. Big Data comes to life with insights to improve the technology fashion and lifestyle with calling for readiness to change. Big Data has become a significant factor that could be a material asset for both business and government organizations. As such, it is important to understand the definition of Big Data, their key characteristics, and the challenges surrounding the concept and characteristics. The implementation of Big Data brings new critical challenges that need to be addressed before starting the Big Data journey. The main challenges that are commonly faced by organizations are the implementation of Big Data alongside the critical challenges that involve the challenges related to technology, organization, process, data management, and skills. The challenges are difficult and the growth in terms of Big Data is increasing exponentially which calls for further investigation. This paper reviewed the existing literature on Big Data to achieve three objectives: first, to highlight the definitions and characteristics of Big Data and to summarize the most common definitions of existing works; second, to identify the impacts and the opportunities for the Big Data; third, to present and identify the main critical challenges related to Big Data and categorize these challenges as (People, Technology, Organization, Process, and Data management) challenges.
Optimizing a novel nerve cuff electrode to record bidirectional neural activity*

Neural recordings can provide useful information regarding specific sensory or motor function that can be applied to functional electrical stimulation systems for persons with spinal cord injury or stroke patients. Among myriad neural interfaces, the nerve cuff electrode offers an attractive tool for communicating with peripheral nervous system. The tripolar cuff electrode is the most common nerve cuff design; however, the physical symmetry of this configuration does not allow for the differentiation of action potentials propagating in opposite directions (i.e., afferent vs. efferent activity). The main goal of this study was to test the feasibility of using a tetrapolar configuration to achieve high SNR, directionally-sensitive recording of nerve activity. The objectives for this design were to combine (1) low-noise recordings from two sets of tripolar contacts with (2) the directional information provided by differentially measuring the two tripolar signals.
Diet‐induced insulin resistance promotes amyloidosis in a transgenic mouse model of Alzheimer's disease

Recent epidemiological evidence indicates that insulin resistance, a proximal cause of Type II diabetes [a non‐insulin dependent form of diabetes mellitus (NIDDM)], is associated with an increased relative risk for Alzheimer's disease (AD). In this study we examined the role of dietary conditions leading to NIDDM‐like insulin resistance on amyloidosis in Tg2576 mice, which model AD‐like neuropathology. We found that diet‐induced insulin resistance promoted amyloidogenic β‐amyloid (Aβ) Aβ1–40 and Aβ1–42 peptide generation in the brain that corresponded with increased γ‐secretase activities and decreased insulin degrading enzyme (IDE) activities. Moreover, increased Aβ production also coincided with increased AD‐type amyloid plaque burden in the brain and impaired performance in a spatial water maze task. Further exploration of the apparent interrelationship of insulin resistance to brain amyloidosis revealed a functional decrease in insulin receptor (IR)‐mediated signal transduction in the brain, as suggested by decreased IR β‐subunit (IRβ) Y1162/1163 autophosphorylation and reduced phosphatidylinositol 3 (PI3)‐kinase/pS473‐AKT/Protein kinase (PK)‐B in these same brain regions. This latter finding is of particular interest given the known inhibitory role of AKT/PKB on glycogen synthase kinase (GSK)‐3α activity, which has previously been shown to promote Aβ peptide generation. Most interestingly, we found that decreased pS21‐GSK‐3α and pS9‐GSK‐ 3β phosphorylation, which is an index of GSK activation, positively correlated with the generation of brain C‐terminal fragment (CTF)‐γ cleavage product of amyloid precursor protein, an index of γ‐secretase activity, in the brain of insulin‐resistant relative to normoglycemic Tg2576 mice. Our study is consistent with the hypothesis that insulin resistance may be an underlying mechanism responsible for the observed increased relative risk for AD neuropathology, and presents the first evidence to suggest that IR signaling can influence Aβ production in the brain.