Miracle-dz/UniFAR-FTUs huggingface dataset:
Hernia reduction following laparotomy using small stitch abdominal wall closure with and without mesh augmentation (the HULC trial, DRKS00017517): Study protocol for a randomized controlled trial

Background Incisional hernias are among the most frequent complications following abdominal and Despite improvements in abdominal wall closure techniques incisional hernia rates are reported to be between 10-30% following midline laparotomies. There have been two recent innovations with promising results to reduce hernia rates, namely the small stitches technique and the placement of a prophylactic mesh. So far, these two techniques have not been evaluated in combination. Methods The HULC trial is a multicentre randomized controlled, observer and patient blinded surgical effectiveness trial with two parallel study groups. A total of 812 patients scheduled for elective abdominal surgery via a midline laparotomy will be randomized in 12 centres after informed consent. Patients will be randomly assigned to the control group receiving closure of the midline incision with a slowly absorbable monofilament suture in small stitches technique or to the intervention group that will receive a small stitches closure followed by augmentation with a light-weight polypropylen mesh in onlay technique. The primary endpoint will be the occurrence of incisional hernias as defined by the European Hernia Society within 24 months after surgery. Further perioperative parameters, as well as patient-reported outcomes, will be analysed as secondary outcomes.
Doświadczanie bólu a aktywność fizyczna u osób z chorobą dyskową odcinka krzyżowo – lędźwiowego kręgosłupa/ Experience of pain and physical activity in people suffering from the disc disease of the lumbosacral spine

Introduction: Disc disease of the lumbosacral spine is a frequent ailment from which younger and younger people suffer. The main symptom of this disease is severe pain. The disease impairs not only physical condition of patients but also their social and psychological life. It is a chronic illness, therefore it requires changing of one ’ s unhealthy lifestyle to healthy living on a permanent and regular basis. One of manifestations of a health-promoting attitude in the disc disease is the performance of regular exercise chosen for the ailment in order to reinforce perivertebral muscles. Physical activity is both treatment and prevention in the disc disease of the lumbosacral spine. As there is an opinion that pain experienced by the ill people is the main motivating factor for treating the disc disease of the lumbosacral spine, the authors decided to investigate whether there is a relation between pain felt and physical activity undertaken in people with the disc disease of the lumbosacral spine and whether the relation is modiﬁed by sociodemographic variables. Material and methods: The study was a longitudinal type of research (two measurement points at half yearly interval) and it covered 92 people who suffer from disc disease of the lumbosacral spine. The diagnosis was found by MRI scan. The study used an interview (the sociodemographic data), the VAS scale, and a questionnaire on the indicator of health-promoting attitude in the disc disease specially prepared for the purpose of this project. Results: For the group of people with the disc disease of the lumbosacral spine, no relation was found between the pain felt and the physical activity undertaken. A positive relation between the pain felt and the physical activity was found only for the group of younger patients. Conclusions: The pain felt may be the motivating factor for undertaking regular physical exercise only for the group of the youngest patients examined.
Salivary proteome profile of women during fertile phase of menstrual cycle as characterized by mass spectrometry

Background Human saliva contains several biomolecules, especially proteins, some of which have been found to serve as biomarkers of different physiological statuses and/or pathological conditions. Saliva is a much superior biological material for investigation over the other body fluids. Several salivary proteins are present in minute quantities whereupon there is possibility to express the hormone receptors, stress proteins or antimicrobial peptides at subtle to major variations which can reveal the physiological and/or pathological statuses. Ovulation is such a critical physiological process that its non-invasive prediction has several advantages. It has been postulated that saliva, with one or more of biomolecules in it such as proteins, amino acids, antioxidants, antimicrobial peptides, and such others would effectively serve as non-invasive predictor(s)/detector(s) of ovulation.Methods Samples were collected from women volunteers and the procedure adopted was approved by the Institutional Ethical Committee (DM/2014/101/38), Bharathidasan University. The saliva samples were collected between 8.00 to 9.00 AM from 30 healthy female volunteers (age, mean = 24, range = 19 - 30), with prior written consent. The protein expression pattern during different phases of menstrual cycle was analyzed using HR-LC-MS/MS and MALD TOF/TOF.Results The functional annotation of salivary proteins identified therein revealed that the proteins get assigned to the class of “extracellular proteins” which are concerned with regulatory functions. As many as 530 proteins showed up in the saliva during ovulation phase whereas there were only 251 proteins during post-ovulation phase. The protein expression was also analyzed using 2D gel electrophoresis and it was found that the salivary proteins focused at pH range 4-7 and molecular weight around 97-14 kDa. The unique/differentially expressed protein spots appeared during ovulation phase was identified by MALDI-TOF analysis.Conclusions According to spot analysis, Cystatin-S, Prolactin-inducible protein, Cystatin-A, Cystatin-SN, BPI fold-containing family A member 2, Alpha-tubulin N-acetyltransferase 1, and Carbonic anhydrase 6 were highly expressed during ovulation phase. These proteins could serve as an indicator(s) for ovulation after extensive validation studies.
Neurophysiologic Intraoperative Monitoring During Percutaneous Thoracolumbar Kyphoplasty: A Multimodality Approach

ABSTRACT. We report techniques to monitor the functional integrity of the spinal cord and physiologic status of the spinal nerve roots during percutaneous throracolumbar kyphoplasty for treatment of vertebral compression fractures. Monitoring of the functional integrity of the anterior and posterior spinal cord can be accomplished with the use of electric trcinscranial motor evoked potentials (tceMEPs) and somatosensory evoked potentials (SSEPs), respectively. The physiologic status of the spinal nerve roots can be monitored continuously with spontaneous electromyography (EMG) monitoring. Lower extremity SSEPs are obtained using stimulation of the posterior tibial nerve (PTN) at the ankle; upper extremity SSEPs are obtained using stimulation of the median nerve or ulnar nerve. TceMEPs are obtained with stimulation of the motor cortex (precentral gyrus) at the scalp and recording compound muscle action potentials (CMAPs) in the hands and legs. Intramuscular electromyographic activity is referentially recorded in all extremities and specific muscle groups innervated by spinal nerves at the level of the procedure. SSEPs, tceMEPs, and EMG can be effectively obtained and monitored during percutaneous thoracolumbar kyphoplasty. This technique of multimodality neurophysiologic monitoring provides invaluable intraoperative data for the surgeon regarding the functional integrity of the patient's spinal cord and physiologic status of the spinal nerve roots, thereby decreasing the likelihood iatrogenic neurologic injury and post-operative neurologic deficits.
cme: Involuntary Weight Loss in Elderly People

Causes of weight loss in elderly people The causes of weight loss are manifold, as shown in the list in box 1 (1, 2). In elderly people, special circumstances and illnesses need to be considered that may result in a reduced food intake, among others: Gaps in dentition or decaying teeth Badly fitting dentures Impaired sense of smell or taste (dysgeusia) Side effects of medications (table 1, box 2). Oropharyngeal causes include difficulty in swallowing after cerebral insults or Parkinson's disease, myasthenia gravis, or muscular disorders. Causes of esophageal dysphagia include tumors, strictures, achalasia, stenoses from the outside of the esophagus – for example, because of aortic dilatation or lymph node enlargement. Colonization of the small intestine with bacteria and malassimilation associated disorders can cause diarrhea in elderly people. These include non-tropical sprue, chronic pancreatitis,
Streptococcus agalactiae en embarazadas: Prevalencia en el Hospital Nacional Alejandro Posadas

RESUMEN Streptococcus agalactiae (estreptococo grupo B de Lancefield) es el microorganismo mas frecuentemente involucrado en infeccion neonatal por transmision vertical madre-feto. Tambien es responsable de infecciones en mujeres embarazadas y adultos con enfermedades de base. El objetivo de este trabajo fue conocer la prevalencia de portacion de S. agalactiae en mujeres embarazadas que concurrieron al Hospital Posadas y determinar la sensibilidad a penicilina, eritromicina y clindamicina de los aislamientos. De las 1203 gestantes estudiadas, se aislo S. agalactiae en 113 muestras, con una prevalencia de 9,39%. Se realizaron las pruebas de sensibilidad a 87 de los 113 aislamientos. Todos ellos resultaron sensibles a penicilina y unicamente 2 cepas fueron resistentes a eritromicina y clindamicina (mecanismo MLS constitutivo). Resaltamos la importancia de conocer la resistencia a estos dos ultimos agentes, en el caso de mujeres alergicas a los antibioticos beta-lactamicos. Palabras clave: Streptococcus agalactiae, tamizaje neonatal, portacion de S. agalactiae, sensibilidad antimicrobiana SUMMARY Streptococcus agalactiae in pregnant women. Prevalence at the Posadas Hospital. Streptococcus agalactiae (Lancefield group B Streptococcus), is the most frequently microorganism involved in neonatal infections through the mother-fetus vertical transmission. It is also responsible for infections in pregnant women, and adults with underlying diseases. The objective of this work was to know the S. agalactiae carrier prevalence in pregnant women who attended to Posadas Hospital, and to study the susceptibility pattern of the isolates to penicillin, erythromycin and clindamycin. From 1203 pregnant women studied, S. agalactiae was recovered in 113, which means a prevalence of 9.39%. Antimicrobial susceptibility was tested to 87 isolates. All of them were susceptible to penicillin, and only 2 isolates were resistant to erythromycin and clindamycin (constitutive MLS mechanism). We emphasize the importance of knowing these last resistance, in the case of beta-lactam antibiotics allergic women.
Identification of Suitable Locations for HVDC Links within Meshed AC Networks

This paper presents a method for the suitable placement of HVDC links in meshed AC networks. A mixed integer linear programming (MILP) optimization problem is formulated to identify the HVDC links to be installed and to inherently determine their optimal operating point. The optimization aims to reduce congestion situations in the grid. Investment costs are integrated into the optimization problem using the $\varepsilon$-constraint method. The effect of the operating point on the AC grid is calculated via linear sensitivity factors, namely power transfer distribution factors (PTDF) and line outage distribution factors (LODF). An adaption of the approach is proposed to reduce the problem complexity for the application to real-scale grid structures with multiple grid snapshots. The validation is done using the IEEE 39 Bus New England Power System and a synthetic network with 120 stations as well as multiple grid snapshots. The simulation results show the suitability of the adapted approach as well as its applicability to real-scale transmission expansion planning problems. The installation of a single HVDC link increases the level of system security within the synthetic test system by more than 85%. Additional HVDC links enable further reductions of overloads.
An Asynchronous Self-Stabilizing Maximal Independent Set Algorithm in Wireless Sensor Networks Using Two-Hop Information

Maximal independent set (MIS) has significantly important in practical applications for wireless sensor networks (WSNs). A distributed self-stabilizing system can initially start at any illegal state and takes back a legal state as long as there is no external intervention. We propose a novel distributed self-stabilizing MIS algorithm using two-hop information. It stabilizes an unstable system at most n-1 moves under an unfair distributed scheduler where n is the number of nodes in the graph. We use the message passing model as the communication model where this model is very appropriate for WSNs. The communication consumes the most energy in WSNs. So, move count is at least as important as round count where reducing the move count prolongs the lifetime of the network. We analyzed theoretically and tested it on SimPy discrete event simulator on randomly generated connected simple undirected graphs to compare with its counterparts in terms of transmitted bit count and move count against various node degrees and node counts.
Representative Day Selection using Clustering for Distributed Energy Resource Planning

The proliferation of sensors and measurement devices has provided information for the planning and operation of distributed energy resources (DERs). Year-round profiles of electricity demand and renewable energy sources show the variation of these parameters. Clustering can be used to identify representative days to capture this variability as applied to finding the optimal sizes of the DERs. In this work, groups of representative days were generated out of the combinations of options during the pre-and post-processing of the clustering algorithm. An additional option is presented such that the comparison of the cumulative distribution functions (CDFs) of the representative days and all days serves as a criterion in selecting the representative days. Comparison is implemented through the Kolmogorov-Smirnov test. To evaluate this additional option, the groups of representative days were used to generate DER designs through a mixed integer linear programming (MILP)-based microgrid model. Simulation on these design using all days of load and meteorological data instead of the representative days computed for the respective net present cost and the load-notserved. The clustering options that effectively capture variability were determined by comparing these techno-economics to those defined in the MILP-based model. Results show that the inclusion of the CDF as a criterion improves the load-not-served. The optimal design that has the lowest net present cost was also found through this criterion.
Abnormal Gait Recognition Algorithm Based on LSTM-CNN Fusion Network

This paper presents a novel approach to human gait analysis with a sensor-based technique involving a wearable inertial measurement unit (IMU). The proposed system emphasizes the detection of certain abnormal gait patterns, including hemiplegic, tiptoe, and cross-threshold gait. First, we use the dynamic step conjugate gradient algorithm to calculate the attitude of the gait data, and we then use the gait feature information location algorithm to segment the attitude data. The segmented attitude data are used as input in the classification model. In this paper, we propose an algorithm based on a long short-term memory network and convolutional neural network (LCWSnet) for diagnosis and classification of abnormal gait patterns using the leg Euler angle information, and parameters related to features can be adjusted adaptively according to the feedback of objectives and optimization functions. We optimize the convergence layer of the LSTM-CNN model and improve the classification accuracy of abnormal gait. The experimental results demonstrate that the proposed LCWSnet-based technique is able to detect gait abnormality in the data. The proposed personalized gait classification approach is accurate and reliable and can be implemented for the abnormal gait.
Ford Vehicle Identification based on gray-level co-occurrence matrix and genetic neural network

Side-view of car plays an important role in car brand recognition. We proposed a new Ford car identification system. We first collected side-view of 200 cars, including the Ford brand and the non-Ford brand. Second, we used Gray-level co-occurrence matrix(GLCM) to extract image texture features. Third, we employed neural network(NN) as the classifier. Finally, we employed genetic algorithms(GA) optimize neural networks (NN). In the experiment, we used 10 × 10-ford cross validation. The results of the study show that in the same kind of research, the method we used has the highest accuracy.
Angle-Domain NOMA over Multicell Massive MIMO Systems

In this paper, we propose an angle-domain NOMA transmission scheme over multicell massive MIMO systems, where multiple users’ signal can be superposed to be served by the same spatially angle-domain beams. Then, we carefully consider the performance degradation resulting from the severe inter-cell interference, and formulate an optimization problem in terms of jointly optimizing precoders and decoders (JOPD) to seek an optimal transmission policy with quality of service (QoS) requirements of both cell-edge users and cell-center users, which consequently is a maximization of a nonconvex function with nonconvex constraints. To solve this challenging problem, we invoke the constrained concave convex procedure (CCCP) method to optimize precoders with fixed decoders, while the decoders can be readily optimized with obtained precoders. Consequently, we propose an alternating optimization algorithm based on CCCP (AoCCCP) to jointly optimize precoders and decoders and then obtain a suboptimal solution of the prime problem. Simulation results verify that the proposed scheme exhibits significant performance gain in terms of sum rate as well as QoS guarantee.
A radar with 3D imaging capability that uses synthetic aperture in azimuth and compressive sensing MIMO in elevation

A novel Ground-based Synthetic Aperture Radar (GBSAR) system with 3D imaging capability is proposed. It acquires through the horizontal scanning of a compressive sensing (CS) multiple input multiple output (MIMO) radar head. Resolution in the horizontal plane (azimuth) is obtained by mechanical scanning, while resolution in the vertical axis (elevation) is obtained through the vertical disposition of the phase centres of a CS-MIMO. The paper reports the working principle and an experimental test of the equipment.
Price Estimation by Random Walk Mean Reversion in Day-ahead Electricity Market

The fluctuations in the Day-ahead electricity market (DAM) prices generally follow the periodic demand variance. However, the observations of historical data also reveal large jumps or spikes that might be defined as the randomness of the price formation. In this study, the Turkish DAM price is treated as a discrete signal to follow a methodology of utilizing the frequency-domain of the signal and a Fourier Transform analysis is employed for the separation of the random electricity price variations from the periodic ones. The volatility of the random electricity price variation is then used for the estimation of DAM price for a future day based on a random walk with a mean reversion method. The promising results are then compared with a multiple linear regression method from the literature to quantify the effectiveness of the proposed model for price forecasting for a Day-ahead electricity market.
Refining satellite imagery by using UAV imagery for vineyard environment: A CNN Based approach

Frequent vegetation monitoring of agriculture crops during their phenological cycle helps farmers/agronomist to perform their activities in efficient way to gain maximum yield and reduce the environmental noise caused by excessive use of chemicals. Various remote sensing platforms equipped with optical multispectral sensors such as satellite, airborne and unmanned air vehicles (UAVs) are being used in vegetation monitoring. Satellites equipped with multispectral sensors are popular due to their large coverage and temporal resolution. On the other hand, UAVs are preferred where more detailed imagery is needed while its expensive and time consuming if more frequent campaigns have to be performed. In this study, vineyard site is considered to assess the reliability of using satellite images for vegetation monitoring. Indeed, satellite imagery with decametric spatial resolution cannot describe the vegetation status at vine rows level due to the mixed nature of pixel, representing the cumulative effect of inter row terrain and vine rows. Therefore, a pixel refinement is needed to minimize this effect. In this work, a convolutional neural network (CNN) based approach is proposed to gain benefits from high resolution UAV images in order to refine the frequent moderate resolution satellite images over a vineyard.
Body Balancing Control for EILERO Quadruped Robot while Walking on Slope

A robot is a tool that has been developed rapidly today. One type of robot is a Quadruped robot. The problem that often arises in this 4-legged robot is when carrying out an action to be able to walk in the inclined plane or slope on the rule that has been set in the Indonesian Fire Extinguisher Robot Contest referring to the Fire, Fighting Robot Contest Trinity, USA. Kinematic inverses and normal gait patterns such as crawl gait can be used to make the robot balance the body when on a certain slope. But this method alone will result in robot movements less flexible and can accelerate the damage to each servo motor due to unbalanced robot load and causes too much time to act. This paper implements PID control that uses its current attitude as the feedback in balancing the robot on the slope. IMU (Measurement Unit Inertia) is used as the robot attitude sensor which provide the roll, pitch, and yaw Euler angle data of the robot. Then the pitch data from this sensor will be processed to adjust the change on the z axis for each front leg and the rear leg and thus the robot can adapt to run on a slope.
Utilization of Spectrum holes for image transmission using LabVIEW on NI USRP

The human brain perceives, processes and interprets visual information faster and more effectively than any other kind of information. Visual information processing in the human brain alone occupies one-third of the cortical area. Images therefore are the best means of conveying information. Digital image processing and transmission have been made feasible due to technological advancements in recent times. This paper deals with the transmission of grayscale images using one such technology: Software Defined Radios (SDR) using USRP 2930. Transmission over wireless channel is a challenge. Selection of a transmission frequency, suitable modulation techniques, et cetera are exclusively dealt with in digital communication. Several frequency bands go unused during transmission called spectral holes. Tapping into these spectral holes provide an economic means of transmitting data such as images. SDR implementation is fairly recent and rapidly developing. This paper provides an insight about its feasibility as a highly re-programmable basis for image transmission. The objective here is to transmit a single grayscale image using USRP 2930 along a spectral-hole frequency using different modulation schemes.