Miracle-dz/UniFAR-FTUs huggingface dataset:
Electro-Acupuncture Effects Measured by Functional Magnetic Resonance Imaging—A Systematic Review of Randomized Clinical Trials

Introduction: Electro-acupuncture, an innovative adaptation of traditional acupuncture, combines electrical stimulation with acupuncture needles to enhance therapeutic effects. While acupuncture is widely used, its biological mechanisms remain incompletely understood. Recent research has explored the neurophysiological aspects of acupuncture, particularly through functional magnetic resonance imaging (fMRI) to investigate its effects on brain activity. Methods: In this systematic review, we conducted an extensive search for randomized clinical trials examining electro-acupuncture effects measured by fMRI. We employed strict eligibility criteria, quality assessment, and data extraction. Results: Five studies met our inclusion criteria and were analyzed. The selected studies investigated electro-acupuncture in various medical conditions, including carpal tunnel syndrome, fibromyalgia, Crohn’s disease, irritable bowel syndrome, and obesity. Notably, electro-acupuncture was found to modulate brain activity and connectivity in regions associated with pain perception, emotional regulation, and cognitive processing. These findings align with the holistic approach of traditional Chinese medicine, emphasizing the interconnectedness of body and mind. Discussion: In carpal tunnel syndrome, electro-acupuncture at both local and distal sites showed neurophysiological improvements, suggesting distinct neuroplasticity mechanisms. In fibromyalgia, somatosensory electro-acupuncture correlated with reduced pain severity, enhanced brain connectivity, and increased gamma-aminobutyric acid levels. For Crohn’s disease, electro-acupuncture influenced the homeostatic afferent processing network, potentially mitigating gut inflammation. Electro-acupuncture for irritable bowel syndrome led to decreased activity in the anterior cingulate cortex, offering pain relief, while electro-acupuncture for obesity impacted brain regions associated with dietary inhibition and emotional regulation. Conclusion: This systematic review provides evidence that electro-acupuncture can positively impact a range of medical conditions, possibly by modulating brain activity and connectivity. While the quality of the reviewed studies is generally good, further research with larger sample sizes and longer-term assessments is needed to better understand the mechanisms and optimize electro-acupuncture protocols for specific health conditions. The limited number of studies in this review emphasizes the need for broader investigations in this promising field. The research protocol was registered in PROSPERO (CRD42023465866).
Effect of music on driving performance and physiological and psychological indicators: A systematic review and meta-analysis study

Background: Many studies have assessed the effect of music on driving. However, their results are very scattered and contradictory. Therefore, this systematic review is conducted to determine the effect of music on driving performance and drivers’ physiological and psychological indicators. Methods: Scopus, PubMed, and Web of Science databases were searched until July 2023. A manual search in Google Scholar for gray literature was conducted. The Simulation Research Rubric (SRR) tool was used to assess the reporting quality of the studies. Stata software (StataCorp, version 16) was used to perform a meta-analysis. Results: A total of 2650 records were identified. The findings of 19 studies were analyzed. Most of them were carried out in high-income countries (HICs) using simulators. The most frequently used music style was classic rock. The meta-analysis results indicated that music with high and medium volume increases the average driving speed, and music with low volume decreases it. Although music in every mood reduces the average reaction time, it positively reduces response delay and increases coherence. Music with high volume decreases the heart rate, but music with medium and low volume increases it. Listening to music increases the level of arousal and mental load. Conclusion: It was concluded that, in some indicators, listening to music has adverse effects on driving. However, in many indicators, music has a positive impact on improving driving safety. It is better to choose appropriate music for different driving conditions and to train the drivers about it.
The Practical Application Of 3D Vision in the Field: Measuring Reindeer (Rangifer Tarandus) Antler Growth Velocities

Advances in three‐dimensional (3D) optical imaging have made possible precise and accurate measurements of many scenes, ranging from engineering to architecture to art. However, measurements of some 3D objects are more difficult to obtain than others, particularly if the edges do not feature regular geometry, the colour is dark and variable, and if the object moves haphazardly. Such objects occur regularly in biology, and the present study illustrates some of the challenges of evaluating such objects. The growing antlers of three live reindeer (Rangifer tarandus) is presented as an example of how 3D imaging, specifically time‐of‐flight range imaging, can be used to solve to a reasonable extent a problem that is very difficult to approximate using traditional techniques. Mean antler growth velocities of the order of 7 to 9 mm/day were estimated, using the proposed methodology, from data of these three animals collected over a seven‐week period.
Application of Neuro-Fuzzy for Improving Volume Control in Water Canning Industry

The computation minimization which is associated with neurofuzzy models and controllers may be installed in industrial programmable Logic controllers (PLC). The implementation of thermal sterilization is both model and control in order to validate the pilot plant of water canned industry. Volume control in beverage manufacturing companies requires that a certain measured volume be filled into specific container sizes to conform to already predetermined standard to ensure competitiveness in the industry. However, most industry does not have accurate and reliable monitoring mechanism capable of sensing when the canned bottles are not properly filled. This operational failure can be overcome by designing a model that will monitor and control the filling process thereby improving volume control in water canning industry using feedback Neuro-fuzzy control. MATLAB Software was used to carry out simulations to develop volume control in water canning industry with aims of improving operational mechanism of industry. The result of the research revealed the empirical data collected in Rancor Nig. Ltd., Enugu, Nigeria and feedback Neurofuzzy. This ANN model can then be trained with values generated from an already existing mathematical model to be able to monitor and control the filling of the cans. The result showed that volume control in water canning industry with and without feedback Neuro-fuzzy were 63cl and 50cl respectively. The volume increased by 13cl. With these results, it shows that using feedback Neuro-fuzzy gives a better result in terms of filling to the required volume of the bottle than when feedback Neuro-fuzzy is not used.
Eventos de Precipitação Extrema na Região do Centro de Lançamento de Alcântara

Abstract Daily precipitation data were used to identify the Extreme Precipitation (EP) events in the Alcântara Launch Center( Centro de Lancamento de Alcântara , CLA) region for the period from Sept-1984 to Aug-2014 (30 years). Selecting the99 th percentile (44 mm d -1 ) as the threshold for EP, 104 events were identified in the period. The annual cycle of the EPevents frequency and exceedances was similar to the annual cycle of precipitation, with maxima (minima) in the MAM(SON) quarter. Using the peaks-over-threshold approach, the annual maximum daily precipitation (defining hydrologi-cal year as 12 months from September) could be statistically modeled as a Gumbel distribution. The 1-day precipitationwith return period of 100 years would be 115 mm, which is lower than the values found in the literature for localitieswithintheCLAregion.FortheMarchandAprilbimester,whenthelargestfrequencyofeventsoccurred,theinterannualvariability of the frequency was correlated positively and significantly (
Development of Inexpensive Microcontroller-Based Soil Moisture Measurement Device Implemented Using Time Domain Reflectometry

Soil moisture measurement apparatus based on time domain reflectometry is considered expensive in Indonesian farming activities. To provide an economical time domain reflectometer that performs well for such applications, we develop a microcontroller-based soil moisture time domain reflectometer. Implemented on M4 ARM Cortex microcontroller, equivalent time sampling technique and delta sigma modulation was utilized to achieve a 10 pico-second resolution soil moisture meter working at frequency 1 MHz and expected to have accuracy below 1% of relative humidity.
Comparation Classification of EMG Signals in the Time Domain and Time-Frequency Domain

Electromyography (EMG) has been used extensively in motion recognition for rehabilitation or prosthetic control. One of the challenges in EMG-based motion recognition is the effective and accurate classification of EMG signals. In this study, we compare the performance of EMG signal classification between feature extraction in the time domain and time-frequency domain. EMG signal data was obtained from a public dataset with a total of 36 subjects. The feature extraction method in the time domain will use statistical features such as root mean square (RMS), mean absolute value (MAV), variance (VAR), zero crossing (ZC), and Integrated EMG (IEMG), while the method in the time-frequency domain will use the wavelet transform (WT). EMG signal classification will be carried out using several classifiers including Support vector machines, K-NN (k-Nearest Neighbor), Decision Tree (DT), and Random Forest (RF). The results showed that the method with feature extraction in the time-frequency domain was significantly better than the method in the time domain in classifying EMG signals using a random forest classifier with a higher classification accuracy ratio (99.95% vs. 95.50%). The results of this study can be used as a reference in selecting a more accurate and effective EMG signal classification method in motion recognition during rehabilitation or prosthetic control.
Targeting Farnesoid X Receptor in Tumor and the Tumor Microenvironment: Implication for Therapy

The farnesoid-X receptor (FXR), a member of the nuclear hormone receptor superfamily, can be activated by bile acids (BAs). BAs binding to FXR activates BA signaling which is important for maintaining BA homeostasis. FXR is differentially expressed in human organs and exists in immune cells. The dysregulation of FXR is associated with a wide range of diseases including metabolic disorders, inflammatory diseases, immune disorders, and malignant neoplasm. Recent studies have demonstrated that FXR influences tumor cell progression and development through regulating oncogenic and tumor-suppressive pathways, and, moreover, it affects the tumor microenvironment (TME) by modulating TME components. These characteristics provide a new perspective on the FXR-targeted therapeutic strategy in cancer. In this review, we have summarized the recent research data on the functions of FXR in solid tumors and its influence on the TME, and discussed the mechanisms underlying the distinct function of FXR in various types of tumors. Additionally, the impacts on the TME by other BA receptors such as takeda G protein-coupled receptor 5 (TGR5), sphingosine-1-phosphate receptor 2 (S1PR2), and muscarinic receptors (CHRM2 and CHRM3), have been depicted. Finally, the effects of FXR agonists/antagonists in a combination therapy with PD1/PD-L1 immune checkpoint inhibitors and other anti-cancer drugs have been addressed.
Non Invasive Glucose Detection Using a Triple-Pole CSRR Sensor

In this paper, a novel triple pole Complementary split ring resonator with square shaped (TP-CSRR) has been designed for the non-invasive method of glucose checking. The proposed sensor is placed on top of an FR4 dielectric substrate. The sensor elements are excited via a coupled 50-ohm microstrip line etched on the bottom side of the substrate. In this proposed sensor, the thumb will be placed on the resonator which causes changes in input impedance which is proportional to the change of the blood permittivity on different glucose concentrations. When the thumb is placed on the sensor there will be a shift in the resonance frequency. The sensor was simulated on Ansoft HFSS simulation software. The 3D electromagnetic model of the thumb has also been simulated using a Cole-Cole model. A shift of the resonant frequency is observed as the concentration of glucose gets higher. The size of the sensor is 50x20x0.8 mm3 which makes it suitable for the handheld use.
NRG1 Regulates Proliferation, Migration and Differentiation of Human Limbal Epithelial Stem Cells

Limbal epithelial stem/progenitor cells (LESCs) proliferate, migrate and differentiate into mature corneal epithelium cells (CECs) that cover the ocular surface. LESCs play a crucial role in the maintenance and regeneration of the corneal epithelium, and their dysfunction can lead to various corneal diseases. Neuregulin 1 (NRG1) is a member of the epidermal growth factor family that regulates the growth and differentiation of epithelial tissues. Here, we depicted the dynamic transcriptomic profiles during human CEC differentiation, identifying six gene co-expression modules that were specific to different differentiation stages. We found that the expression of NRG1 was high in human LESCs and decreased dramatically upon differentiation. Knockdown of NRG1 significantly inhibited LESC proliferation and upregulated the expression of the terminal differentiation marker genes KRT3, KRT12 and CLU. In addition, the scratch wound closure assay showed that knockdown of NRG1 attenuated wound closure of LESCs over 24 h. Together, we dissected the transcriptional regulatory dynamics during CEC differentiation and identified NRG1 as a key regulator that promoted LESC proliferation and migration and maintained the undifferentiated state.
A New Virtual Human Model Based on AR-601M Humanoid Robot for a Collaborative HRI Simulation in the Gazebo Environment

Human-robot collaboration (HRC) involves a cooperative and combined work of humans and robots to achieve specific goals. Collaborative robots provide additional support to humans by automating arduous tasks. This enhances efficiency, quality, and optimization of production processes. Prior to conducting real-world experiments, it is crucial to verify a collaboration safety through a simulation. In this paper, we explore a potential of simulating HRC in the Gazebo simulator by using an anthropomorphic robot AR-601M model as a human model and propose key requirements for a human virtual model. Our findings contribute to the development of safe and efficient HRC systems.
DetParWav: Parkinson’s Disease Detection from Wavelet Features with Machine Learning Approach Using PCA and Mutual Information Gain

Parkinson’s Disease (PD) is a kind of neurological illness that primarily affects the nerve cells in the brain and gradually deteriorates the neurons. Promptly and precise identification of PD is essential for its effective management and therapy. Nowadays, researchers have looked into the boundary of speech analysis as a non-invasive approach to differentiate between individuals afflicted by Parkinson’s disease and those who do not have the condition. The main goal of this work is to Detect Parkinson’s Disease using the Wavelet feature (DetParWav) by combining different classification techniques with the feature selection methods known as Principal Component Analysis (PCA) and Mutual Information Gain (MIG). For the experiment, we have used a PD dataset from the UCI (University of California Irvine) repository of 188 PD and 64 HC’s (Healthy Control) voice signals. We have found an accuracy of 91.60% was achieved from the classifier, Support Vector Machine (SVM) with RBF (Radial Basis Function) kernel and 88% accuracy was achieved from the classifier Decision Tree, which shows that these approaches provide reliable computational tools for accurately detecting the presence of PD in the initial phase.
From point cloud to BIM: an integrated workflow for documentation, research and modelling of architectural heritage

Heritage buildings traditionally have been tackled from several points of view: architectonic features, architectural style, archaeology, history, etc. Documents derived from those studies were classified and put together to form the documentation for consultation, taking decisions about its conservation, and restoration. Such a document organisation has some inconveniences: objects composing the building were treated as isolated objects not related to its adjacent objects. Nowadays technology may help to discover the relationship between architectural objects forming heritage buildings. The tool that makes it possible to include functionality in architectural objects is BIM (Building Information Modelling). In this paper the historical evolution of Saint Jeromés Church in Baza will be analysed and stored in a functional model which includes geometry and its current state. We propose a procedure for building the BIM through its historical roots and evolution to be included in each remarkable object modelled from the point cloud surveyed.
Natural language processing for the automated detection of intra-operative elements in lumbar spine surgery

Background The aim of this study was to develop natural language processing (NLP) algorithms to conduct automated identification of incidental durotomy, wound drains, and the use of sutures or skin clips for wound closure, in free text operative notes of patients following lumbar surgery. Methods A single-centre retrospective case series analysis was conducted between January 2015 and June 2022, analysing operative notes of patients aged >18 years who underwent a primary lumbar discectomy and/or decompression at any lumbar level. Extreme gradient-boosting NLP algorithms were developed and assessed on five performance metrics: accuracy, area under receiver-operating curve (AUC), positive predictive value (PPV), specificity, and Brier score. Results A total of 942 patients were used in the training set and 235 patients, in the testing set. The average age of the cohort was 53.900 ± 16.153 years, with a female predominance of 616 patients (52.3%). The models achieved an aggregate accuracy of >91%, a specificity of >91%, a PPV of >84%, an AUC of >0.933, and a Brier score loss of ≤0.082. The decision curve analysis also revealed that these NLP algorithms possessed great clinical net benefit at all possible threshold probabilities. Global and local model interpretation analyses further highlighted relevant clinically useful features (words) important in classifying the presence of each entity appropriately. Conclusions These NLP algorithms can help monitor surgical performance and complications in an automated fashion by identifying and classifying the presence of various intra-operative elements in lumbar spine surgery.
Psychological Status, Influencing Factors and Intervention Countermeasures of Hospital Pharmacists in Extreme Working Environments in the Post-Pandemic Era--An Embedded Mixed Study

Objective The study explores the psychological state and related influences of hospital pharmacists enclosed in extreme work environments in the post-epidemic era, and also explores potential measures to alleviate negative emotions. Methods An embedded mixed research methodology was used. In the qualitative research phase, semi-structured interviews were carried out with 30 pharmacists consistently confined to their work environments. The data were managed and analyzed using NVivo12 software. In the quantitative research phase, 146 pharmacists with experience in extreme work environments were selected, and the data were collected through questionnaires (GAD-7 and CD-RISC-25) and self-administered questions generated during the qualitative phase. The Shapiro–Wilk test was utilized to assess data normality. Spearman correlation was conducted to evaluate correlations among self-designed questions, resilience, and anxiety. Results The results from interviews with 30 pharmacists revealed four factors influencing the psychological state of pharmacists in the post-epidemic era: personal factors, interpersonal relationships, environmental factors, and policy and public opinion. Mitigation measures for negative emotions encompass material security, life adjustment, epidemic prevention policy, public opinion information, and organizational management. The results of a quantitative study of 146 pharmacists showed that only 1% had severe anxiety, but the psychological resilience scores were generally low, and 62% had poor psychological resilience, with scores below 73. Simultaneously, it was observed that, except religious beliefs, factors influencing psychological status in extreme work environments were significantly negatively correlated with personal anxiety levels and significantly positively correlated with psychological resilience. Conclusion Our study holds significance in unraveling the psychological aspects of pharmacists as healthcare workers. It also offers insights into how healthcare organizations respond to the negative emotions experienced by healthcare workers in emergencies or extreme environments.
Compact Metal-Lens-Integrated Horn Antenna

A gain-enhanced horn antenna is proposed. This antenna is integrated with an H-plane metal lens, giving a compact size. It can be fabricated in one go by using 3-D printing technique. The metal lens can make the horn antenna radiate a quasi-uniform wavefront and thus enhance antenna gain. Using this idea, a horn antenna operating at Ku-band is designed. Simulated results show that the proposed antenna can provide a gain of 16.65 dBi with a -10-dB impedance bandwidth (|S11| < – 10 dB) of 37.9% (12.6-18.5 GHz). The maximum gain enhancement is 4 dB at 18.5 GHz compared with the conventional horn antenna. The antenna length is smaller than half of that of optimum horn antennas.
Clinical Diagnosis and Treatment of Chronic Pain

More than 600 million people globally are estimated to be living with chronic pain. It is one of the most common complaints seen in an outpatient setting, with over half of patients complaining of pain during a visit. Failure to properly diagnose and manage chronic pain is associated with substantial morbidity and mortality, especially when opioids are involved. Furthermore, it is a tremendous financial strain on the healthcare system, as over USD 100 billion is spent yearly in the United States on healthcare costs related to pain management and opioids. This exceeds the costs of diabetes, heart disease, and cancer-related care combined. Being able to properly diagnose, manage, and treat chronic pain conditions can substantially lower morbidity, mortality, and healthcare costs in the United States. This review will outline the current definitions, biopsychosocial model, subclassifications, somatosensory assessments, imaging, clinical prediction models, and treatment modalities associated with chronic pain.