Miracle-dz/UniFAR-FTUs huggingface dataset:
Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class.
Antihyperglycemic and Antilipidemic Properties of a Tea Infusion of the Leaves from Annona cherimola Miller on Streptozocin-Induced Type 2 Diabetic Mice

The antihyperglycemic and antilipidemic effects of the tea infusion extracts of leaves from Annona cherimola Miller (IELAc-0.5, IELAc-1.5, and IELAc-3.0) were evaluated on normoglycemic (NG) and streptozocin-induced diabetic (STID) mice. In the acute test, IELAc-1.5 at 300 mg/kg bodyweight (bw) exhibited antihyperglycemic activity on STID mice since the first hour of treatment. Then, its antidiabetic potential was analyzed in a subchronic evaluation. IELAc-1.5 was able to reduce the blood glucose level, glycated hemoglobin (HbA1c), cholesterol (CHO), and triglycerides (TG); high-density lipoprotein (HDL) showed an increase at the end of treatment. IELAc-1.5 did not modify the urine profile at the end of the evaluation, and neither toxicity nor macroscopic organ damage were observed in acute and subchronic assays. In addition, a major flavonol glycoside present in the tea infusion extracts was identified using high-performance liquid chromatography with diode array detection (HPLC-DAD). The analysis of the tea infusion extracts by HPLC revealed that rutin was the major component. This study supports the use of tea infusions from Annona cherimola for the treatment of diabetes and suggests that rutin could be responsible, at least in part, for their antidiabetic properties.
Use of Cement Suspension as an Alternative Matrix Material for Textile-Reinforced Concrete

Textile-reinforced concrete (TRC) is a material consisting of high-performance concrete (HPC) and tensile reinforcement comprised of carbon roving with epoxy resin matrix. However, the problem of low epoxy resin resistance at higher temperatures persists. In this work, an alternative to the epoxy resin matrix, a non-combustible cement suspension (cement milk) which has proven stability at elevated temperatures, was evaluated. In the first part of the work, microscopic research was carried out to determine the distribution of particle sizes in the cement suspension. Subsequently, five series of plate samples differing in the type of cement and the method of textile reinforcement saturation were designed and prepared. Mechanical experiments (four-point bending tests) were carried out to verify the properties of each sample type. It was found that the highest efficiency of carbon roving saturation was achieved by using finer ground cement (CEM 52.5) and the pressure saturation method. Moreover, this solution also exhibited the best results in the four-point bending test. Finally, the use of CEM 52.5 in the cement matrix appears to be a feasible variant for TRC constructions that could overcome problems with its low temperature resistance.
Spatio-Thermal Variability and Behaviour as Bio-Thermal Indicators of Heat Stress in Dairy Cows in a Compost Barn: A Case Study

Simple Summary The thermal distribution inside a compost-bedded pack barn and the behavioural aspects plays an important role in terms of welfare and sustainability for dairy cows. Through a spatial variability assessment of thermal conditions in a compost barn, we found different regions with comfortable or stressful conditions based on air and bed temperature, as well as wind speed. Regarding the behaviour of cows with different number of lactations, we observed a higher probability of water intake in primiparous cows and increased walking behaviour in multiparous cows during the hottest periods. We suggest that special attention must be given to environmental control in a compost barn, mainly during hot seasons, to avoid different hot spots inside the facility. Additionally, with unbalanced environmental resources, the hierarchy of multiparous over primiparous cows might predominate the alleviation of the herd’s thermal stress. Abstract The spatial variability and behavioural aspects of compost-bedded pack barns remain unknown in subtropical regions. In this study, we investigated whether spatial variability occurs in the thermal environment of a compost barn and how the behaviour of dairy cows with different numbers of lactations differs in this system. The spatial sampling design comprised a grid of 108 locations inside the facility. At each location, we measured air temperature, relative humidity, wind speed, and bed temperature at 9:00, 12:00, and 15:00. We performed 24-h behavioural observations. Regarding spatial variability, the north face showed high air temperature values, and the distribution of relative humidity varied from the north to the south face. Kriging maps revealed a high bedding temperature trend, indicating heterogeneous ventilation management. Primiparous cows visited the water trough during the hottest hours of the day, whereas multiparous cows displayed a higher probability of walking during these periods. In conclusion, we observed a heterogeneous management of ventilation through the spatial distribution of the thermal environment inside the compost-bedded pack barn, with multiparous cows exhibiting dominance over primiparous cows seeking environmental resources.
Mindfulness-Based Stress Reduction (MBSR) and Self Compassion (SC) Training for Parents of Children with Autism Spectrum Disorders: A Pilot Trial in Community Services in Spain

This study aims to develop a clinical trial to test the efficacy of a mindfulness-based stress reduction (MBSR) and self-compassion (SC) program on self-reported values of anxiety, depression, and stress in parents of children with autism spectrum disorder (ASD) in primary school, in order to assess their integration into the framework of community intervention programs in Spain. Methods: A brief 8-week training program using mindfulness-based intervention (MBSR) and self-compassion (SC) has been applied to twelve Valencian ASD parents, ten of whom completed the program. Participants were assigned to two groups; both groups received the same treatment but at two different measurement moments. Depression, anxiety, stress, satisfaction with life and mindful attention awareness measurements were performed, in all participants, in three testing stages. Results: Analysis of variance results suggested that MBSR and SC training reduces stress and anxiety and increases mindful attention awareness. No significant changes were found in life satisfaction measurements. Conclusions: The small number of participants prevents us from generalizing the results found. More MBSR and SC clinical trials are needed in parents of ASD with results on anxiety, depression and stress in order to demonstrate the relevance of the inclusion of these programs in community-based early intervention services.
Advanced Biomarkers: Therapeutic and Diagnostic Targets in Urticaria

Urticaria is a type of skin disease characterized by rapid onset of hives (superficial dermis edema, erythema, pruritus, or burning sensation). According to whether the natural course exceeds 6 weeks, urticaria can be divided into acute and chronic urticaria (CU). At present, the evaluation of CU activity mainly depends on the Urticaria Activity Score (UAS), but the evaluation indicators are relatively single, and we need more reliable experimental data for evaluation. We typically summarize advanced biomarkers and several related pathogenic pathways discovered in recent years on urticaria, including the cell adhesion/chemotaxis pathway, interleukin (IL)-6/Janus tyrosine kinase/STAT pathway, IL-17/IL-23 pathway, basophil- and mast cell-related pathway, coagulation/fibrinolysis-related pathways, single-nucleotide polymorphism, and some other pathways. This review aims to find appropriate biomarkers so that we can evaluate disease activity, discover novel therapeutic targets, and predict the patients’ response more accurately to therapeutic agents.
Lifestyle changes associated with COVID-19 quarantine among young Saudi women: A prospective study

Background Negative lifestyle behaviors are associated with an increased risk of adverse outcomes from coronavirus disease (COVID-19). This study aimed to assess lifestyle changes affecting weight, sleep, mental health, physical activity, and dietary habits prospectively from before COVID-19 to during lockdown. Methods A total of 297 Saudi women, aged 19–30 years (mean age, 20.7 ± 1.4 years), were interviewed at two time points, before and during the quarantine. The data collected included anthropometrics, sociodemographic data, clinical history, food frequency questionnaire responses, Pittsburgh Sleep Quality Index scores, Global Physical Activity Questionnaire (GPAQ) responses, and Perceived Stress Scale measures. In addition, during quarantine, COVID-19 and nutrition-related information and Generalized Anxiety Disorder-7 and Patient Health Questionnaire-9 scores were collected. Multivariate multinomial logistic regression analysis was used to examine the indicators of weight gain and loss from before COVID-19 (baseline) until during lockdown. Results Although approximately half of the participants did not report a weight change, 30% revealed weight loss and 18%, weight gain. The variables associated with increased weight gain were self-quarantine since COVID-19 started (OR: 5.17, 95% CI: 1.57–17.01, p = 0.007), age (OR: 1.53, 1.03–2.28, p = 0.04), and stress at baseline and during lockdown (OR: 1.15, 1.03–1.29, p = 0.01; OR: 1.10, 1.01–1.19, p = 0.03, respectively). The variables associated with a reduced risk of weight gain were the GPAQ score during lockdown (OR: 0.16, 0.04–0.66, p = 0.01), coffee consumption (OR: 0.36, 0.19–0.67, p = 0.01), and total sleep time (OR: 0.70, 0.51–0.97, p = 0.03). Conclusion While most young Saudi women experienced no weight change during the COVID-19 lockdown, one-third lost weight and a significant proportion gained weight. Factors associated with weight, such as stress, sleep hours, physical activity, and coffee consumption, highlight the need to carefully consider those at risk during future circumstances that may require lockdowns. These factors could also aid in implementing policies for future lockdowns and support those most at risk of gaining weight.
Seismic Applications of Downhole DAS

Distributed Acoustic Sensing (DAS) is gaining vast popularity in the industrial and academic sectors for a variety of studies. Its spatial and temporal resolution is ever helpful, but one of the primary benefits of DAS is the ability to install fibers in boreholes and record seismic signals in depth. With minimal operational disruption, a continuous sampling along the trajectory of the borehole is made possible. Such resolution is highly challenging to obtain with conventional downhole tools. This review article summarizes different seismic uses, passive and active, of downhole DAS. We emphasize current DAS limitations and potential ways to overcome them.
Resistant Sources and Genetic Control of Resistance to ToLCNDV in Cucumber

Tomato leaf curl New Delhi virus (ToLCNDV) is a severe threat for cucurbit production worldwide. Resistance has been reported in several crops, but at present, there are no described accessions with resistance to ToLCNDV in cucumber (Cucumis sativus). C. sativus var. sativus accessions were mechanically inoculated with ToLCNDV and screened for resistance, by scoring symptom severity, tissue printing, and PCR (conventional and quantitative). Severe symptoms and high load of viral DNA were found in plants of a nuclear collection of Spanish landraces and in accessions of C. sativus from different geographical origins. Three Indian accessions (CGN23089, CGN23423, and CGN23633) were highly resistant to the mechanical inoculation, as well as all plants of their progenies obtained by selfing. To study the inheritance of the resistance to ToLCNDV, plants of the CGN23089 accession were crossed with the susceptible accession BGV011742, and F1 hybrids were used to construct segregating populations (F2 and backcrosses), which were mechanically inoculated and evaluated for symptom development and viral load by qPCR. The analysis of the genetic control fit with a recessive monogenic inheritance model, and after genotyping with SNPs distributed along the C. sativus genome, a QTL associated with ToLCNDV resistance was identified in chromosome 2 of cucumber.
Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation

Monitoring continuous BP signal is an important issue, because blood pressure (BP) varies over days, minutes, or even seconds for short-term cases. Most of photoplethysmography (PPG)-based BP estimation methods are susceptible to noise and only provides systolic blood pressure (SBP) and diastolic blood pressure (DBP) prediction. Here, instead of estimating a discrete value, we focus on different perspectives to estimate the whole waveform of BP. We propose a novel deep learning model to learn how to perform signal-to-signal translation from PPG to arterial blood pressure (ABP). Furthermore, using a raw PPG signal only as the input, the output of the proposed model is a continuous ABP signal. Based on the translated ABP signal, we extract the SBP and DBP values accordingly to ease the comparative evaluation. Our prediction results achieve average absolute error under 5 mmHg, with 70% confidence for SBP and 95% confidence for DBP without complex feature engineering. These results fulfill the standard from Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) with grade A. From the results, we believe that our model is applicable and potentially boosts the accuracy of an effective signal-to-signal continuous blood pressure estimation.
A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.
PHYSICO-CHEMICAL CHARACTERIZATION OF CITY SEWAGE DISCHARGED INTO RIVER ARPA AT BILASPUR, INDIA

The Bilaspur city sewage discharged into river Arpa at various sites. In the present investigation five sewage sampling sites were selected before they mixed with river water for analysis of physico- chemical properties like temperature, turbidity, conductivity, pH, total alkalinity, free carbon dioxide, total acidity, dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, chloride, total hardness, nitrate nitrogen, nitrite nitrogen and total phosphate. The average value of various parameters observed as temperature range 23.54 to 25°c and the pH value ranged between 7.72 to 7.78. The others physicochemical parameters namely turbidity 20-36.17N. T.U. , conductivity 0.64-1.08 mhos.cm¯¹,total alkalinity 594.75969. 41mg.1¯¹,free CO 37.35-60.42mg.1¯¹, total acidity42.45-64.91mg. 1¯¹, dissolve oxygen 0.33-1.85mg. 1¯¹, BOD 58.77112. 42mg.1¯¹, COD 420.62-547.25mg. 1¯¹, chloride 36.97-104.60mg. 1¯¹, total hardness 259.43-384mg.1¯¹, nitrate nitrogen 0.1110. 184mg.1¯¹,nitrite nitrogen 0.017-0.02mg. 1¯¹,and total phosphate within 0.85-1.03mg. 1¯¹range.
Extracellular Vesicles as Biomarkers and Therapeutic Tools: From Pre-Clinical to Clinical Applications

Simple Summary Extracellular vesicles (EVs) are membrane-bound vesicles released by all cell types, differing in biogenesis, physical characteristics, and contents. Due to their central role in intercellular communication and their variable cargo, EVs are involved in several biological processes. The possibility of isolating them from different biofluids makes EVs valuable biomarkers to be analyzed for the diagnosis or prognosis of several conditions. Moreover, these natural nanoparticles have been investigated as therapeutic tools in many pathological conditions. In this context, EVs have shown innate immunosuppressive and anti-inflammatory properties when isolated from stem/progenitor cells and have also been considered vehicles to be edited for drug delivery. The aim of the review is to report some of the pre-clinical and clinical studies distinguishing those in which EVs have been examined as biomarkers from those in which they have been used as therapeutics. Abstract Extracellular vesicles (EVs) are ubiquitous masters of intercellular communication, being detectable in tissues, circulation, and body fluids. Their complex cargo reflects the (patho)physiologic status of the cells from which they originate. Due to these properties, the potential of EVs, and in particular exosomes, to serve as biomarkers or therapeutics has grown exponentially over the past decade. On one side, numerous studies have demonstrated that EV-associated nucleic acids and proteins are implicated in cancer progression, as well as neurodegenerative, infectious, and autoimmune disorders. On the other, the therapeutic use of EVs secreted by various cell types, and in particular stem/progenitor cells, present significant advantages in comparison to the corresponding parental cells, such as the less complex production and storage conditions. In this review, we examine some of the major pre-clinical studies dealing with EVs and exosomes, that led to the development of numerous completed clinical trials.
Evaluation of the Diagnostic Accuracy of a New Biosensors-Based Rapid Diagnostic Test for the Point-Of-Care Diagnosis of Previous and Recent Dengue Infections in Malaysia

Dengue is a major threat to public health globally. While point-of-care diagnosis of acute/recent dengue is available to reduce its mortality, a lack of rapid and accurate testing for the detection of previous dengue remains a hurdle in expanding dengue seroepidemiological surveys to inform its prevention, especially vaccination, to reduce dengue morbidity. This study evaluated ViroTrack Dengue Serostate, a biosensors-based semi-quantitative anti-dengue IgG (immunoglobulin G) immuno-magnetic agglutination assay for the diagnosis of previous and recent dengue in a single test. Blood samples were obtained from 484 healthy participants recruited randomly from two communities in Petaling district, Selangor, Malaysia. The reference tests were Panbio Dengue IgG indirect and capture enzyme-linked immunosorbent assays, in-house hemagglutination inhibition assay, and focus reduction neutralization test. Dengue Serostate had a sensitivity and specificity of 91.1% (95%CI 87.8–93.8) and 91.1% (95%CI 83.8–95.8) for the diagnosis of previous dengue, and 90.2% (95%CI 76.9–97.3) and 93.2% (95%CI 90.5–95.4) for the diagnosis of recent dengue, respectively. Its positive predictive value of 97.5% (95%CI 95.3–98.8) would prevent most dengue-naïve individuals from being vaccinated. ViroTrack Dengue Serostate’s good point-of-care diagnostic accuracy can ease the conduct of dengue serosurveys to inform dengue vaccination strategy and facilitate pre-vaccination screening to ensure safety.
A Scoping Review of Cognitive Training in Neurodegenerative Diseases via Computerized and Virtual Reality Tools: What We Know So Far

Most prevalent neurodegenerative diseases such as Alzheimer’s disease, frontotemporal dementia, Parkinson’s disease and multiple sclerosis are heterogeneous in their clinical profiles and underlying pathophysiology, although they typically share the presence of cognitive impairment that worsens significantly during the course of the disease. Viable pharmacological options for cognitive symptoms in these clinical conditions are currently lacking. In recent years, several studies have started to apply Computerized Cognitive Training (CCT) and Virtual Reality (VR) tools to try and contrast patients’ cognitive decay over time. However, no in-depth literature review of the contribution of these promising therapeutic options across main neurodegenerative diseases has been conducted yet. The present paper reports the state-of-the-art of CCT and VR studies targeting cognitive impairment in most common neurodegenerative conditions. Our twofold aim is to point out the scientific evidence available so far and to support health professionals to consider these promising therapeutic tools when planning rehabilitative interventions, especially when the access to regular and frequent hospital consultations is not easy to be provided.