Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of an Active Break Intervention on Nonspecific Low Back Pain and Musculoskeletal Discomfort during Prolonged Sitting among Young People—Protocol for a Randomized Controlled Trial

Background: The most recent evidence has shown that the pandemic of COVID-19 caused an increasing problem with spinal pain in the population of teenagers and young adults. This may be explained by prolonged sitting times in flexed positions with electronic devices. Positions maintained for a prolonged time cause overloading of soft tissue and discogenic symptoms. This study aims to evaluate the effectiveness of the active break program in reducing musculoskeletal discomfort and LBP (low back pain) among young people. Methods: This will be a randomized controlled study. The participants will be recruited from Bachelor’s course students of the Physical Education Department aged 18–25 years. The participants will be assigned to an experimental group (with an active break) and a control group. The group with an active break with lumbar and hip extension exercises will be recommended to take a break for every 30 min of sitting. The control group will receive self-care recommendations. The primary outcomes will be pain intensity (Visual Analogue Scale), disability index (Oswestry Disability Index), and perceived musculoskeletal discomfort during prolonged sitting (Borg scale), assessed at baseline and after the intervention, and the Global Perceived Effect, only assessed after the 12-week intervention. The secondary outcome will be a Post-Intervention Questionnaire (a 5-item self-completed questionnaire), only assessed after the 12-week intervention. Results: Our main research outcome—exercise protocols and interventions—will lead to the development of recommendations and protocols for the LBP population. It is important to determine the effect of interventions that are feasible and effective in addressing LBP and perceived musculoskeletal discomfort in young people. Conclusions: This is the first study examining the effect of active breaks with proposed lumbar and hip extension exercises on reducing or decreasing LBP in students based on a search of the literature. Exercises and recommendations will be the basis for developing proprietary preventative and therapeutic programs, which will be implemented in selected educational institutions.
Interpretable and robust hospital readmission predictions from Electronic Health Records

Rates of Hospital Readmission (HR), defined as unplanned readmission within 30 days of discharge, have been increasing over the years, and impose an economic burden on healthcare services worldwide. Despite recent research into predicting HR, few models provide sufficient discriminative ability. Three main drawbacks can be identified in the published literature: (i) imbalance in the target classes (readmitted or not), (ii) not including demographic and lifestyle predictors, and (iii) lack of interpretability of the models. In this work, we address these three points by evaluating class balancing techniques, performing a feature selection process including demographic and lifestyle features, and adding interpretability through a combination of SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE) post hoc methods. Our best classifier for this binary outcome achieves a UAC of 0.849 using a selection of 1296 features, extracted from patients’ Electronic Health Records (EHRs) and from their sociodemographics profiles. Using SHAP and ALE, we have established the importance of age, the number of long-term conditions, and the duration of the first admission as top predictors. In addition, we show through an ablation study that demographic and lifestyle features provide even better predictive capabilities than other features, suggesting their relevance toward HR.
Caffeine and Taurine from Energy Drinks—A Review

The purpose of this paper is to review the specialized literature to highlight the effects produced by energy drinks in terms of skin health. To carry out this review, we consulted previous articles with descriptive cross-sectional designs, case series, and individual case reports published between 2000 and 2023. Therefore, while caffeine acute consumption among adults can have beneficial effects, in children, it can cause health problems like overnight breaks, headaches, and dulled cognition since their organs are still developing and their endocrine system is not yet stable. Despite the antioxidant and neuroprotective effects of caffeine from energy drinks, their excessive consumption among adolescents can cause disorders like high systolic blood pressure, agitation, nausea, anxiety, osteoporosis, heart palpitations, poor sleep quality, and stomach ulcers. Among athletes, taurine supplementation has been proven to improve exercise capacity in cold weather conditions. Also, vigilance, attention, and reaction time were all improved by caffeine consumption. Caffeine administration in low doses caused a risk of cardiovascular disease. It was effective in treating migraines in children, but raised systolic blood pressure, and contributed to skin healing in adolescents. On the other side, taurine prevents obesity among children, causes positive effects on oxidative stress and inflammation in adolescents, helps shield the skin from damaging oxidative stress among students, and impacts exercise capacity in athletes. Significant increases in serum levels of uric acid, creatinine, BUN, ALT, and ALP caused by chronic intake of EDs indicated different degrees of injury to the kidneys and liver. Furthermore, the effects on the cardiovascular system could be worse if taurine and caffeine are combined. Caffeine alone does not significantly decrease sleep as much as a taurine/high caffeine ratio. On the other hand, a low ratio does.
Drift Compensation of Wearable Textile Sensors in Mobile Applications

This paper presents a novel approach to address the challenges associated with the long-term use of conductive textiles as sensors, a critical area of research for the sustainable use of smart textiles. The key advancement over the existing state-of-the-art is the development of simple techniques for compensating for the effects of aging and degradation effects in mobile scenarios on resource-limited embedded systems. Through accelerated aging tests and cyclic stress tests, we demonstrate how integrated measurement circuits can be used to detect and compensate for aging effects. We also provide a software-based solution to complement mechanical protective measures. We were able to show that after the initial calibration and model determination, no further communication with a server structure is required for the adaptation of sensor models or their drift compensation.
Design of a 3D Digital Product Based on DSP Real Time Video Transmission

Image recognition and processing technology is an important application direction of artificial intelligence technology. With the growth of demand for various types of video intelligent analysis, the importance of using multiple models and artificial intelligence technology for image recognition and video analysis has become increasingly prominent. This article delves into the multi object tracking technology in image and video processing, and based on image object detection and recognition algorithms and DSP technology, Real time tracking of products in the production workshop, combined with 3D hybrid modeling technology to create corresponding product models, digitize the produced products, and provide feedback on specific product data. Providing data and graphics support for later analysis of product conformity and understanding of product conditions, improving workshop production efficiency.
Exploring the Antioxidant Potential of Cissus woodrowii (Stapf Ex Cooke) Santapau: A Study on Leaves and Stem

Cancer, diabetes mellitus, cardiovascular disease, neurodegenerative illness, inflammatory disease, and many other pathologies have a common denominator: oxidative stress. The situation arises from the overproduction or ineffective quenching of free oxygen and nitrogen species within the cell. Antioxidant activity, nutritional value and traditionally roots were made into a powder and applied to cut wounds where pus had formed, ethnobotanical/traditional use is as an antitumor in Maharashtra. Owing to its ethnomedicinal importance, proper identification with pharmacognostic and phytochemical details and evaluation is vital for drug development and to prevent adulteration highlights and their role in laying down standardization. The present paper discusses phytochemical and antioxidant study of Cissus woodrowii (Stapf ex Cooke) Santapau. The both extract showed occurrence of glycosides, phenolic compounds, alkaloids, flavonoids also tannin and aqueous and ethanol extract of stem and leaves showed good antioxidant properties by DPPH assay.
Interactive rapid prototyping combining 3D Printing and Augmented Reality

In the development of new products by the industry, a rapid prototyping stage is recommended so that an initial version of the product can be evaluated. In this way, any necessary corrections can be applied while still in the prototyping stage, preventing design errors from reaching the final product. Augmented Reality (AR) and 3D Printing are techniques that have become ubiquitous in recent years due to the reduction of equipment costs. Several works in the area of rapid prototyping have been developed with one of these techniques in isolation; a few works have tried to unite these two tools. In this work, we propose a new functional rapid prototyping process, combining 3D Printing and AR to create functional interactive prototypes. This process is accomplished by projecting the AR onto the 3D-printed prototype. It interprets the user’s gestures on the physical prototype, converting clicks and touches into actions to be executed on the AR virtual prototype, making the prototype functional and interactive. The proposed system is evaluated by means of case studies and the application of the UEQ (User Experience Questionnaire) to users who have tested the system. This way, it is possible to evaluate the relevance of the proposed process.
Controls on Mixing and Non-Mixing Dependent Denitrification in River Hyporheic Zones

Increases in reactive nitrogen from human actions have led to negative impacts on surface water (SW) and groundwater (GW) quality, and it is important to better understand denitrification processes in aquatic systems. The hyporheic zone has unique biogeochemical conditions, and is known to attenuate contaminants originating from SW and traveling through the hyporheic zone, together with necessary reactants. However, the ability of the hyporheic zone to attenuate contaminants from deeper upwelling GW plumes as they exit to SW is less understood. I used MODFLOW and SEAM3D to simulate hyporheic flow cells induced by riverbed dunes and upwelling GW together with mixing dependent denitrification of an upwelling nitrate (NO3 ) plume. My basecase model scenario entailed dissolved organic carbon (DOC) and dissolved oxygen (DO) advecting from SW and DO and NO3 advecting from GW, which is typical of water in agricultural land uses. I conducted a sensitivity analysis to determine controls on mixing dependent denitrification. Mixing dependent denitrification increased with increasing hydraulic conductivity, decreasing lower bottom flux, as well as increasing DOC in SW and NO3 in GW. Non-mixing dependent denitrification also occurred when there was SW NO3 , and I found its magnitude was much greater than mixing dependent denitrification. Nevertheless, potential for hyporheic zones to attenuate upwelling NO3 plumes seems to be substantial, though highly variable depending on biogeochemical reaction rates as well as geomorphic, hydraulic and biogeochemical conditions. Stream and river restoration efforts may be able to increase both mixing and non-mixing dependent reactions by increasing hyporheic zone residence times.
Analisis Kualitas Tes dan Butir Soal Integral pada Evaluasi Formatif Matematika Teknik

Abstrak: Analisis kualitas butir soal pilihan ganda merupakan alat penting untuk mengidentifikasi item yang dapat dipertahankan, direvisi, atau dikeluarkan. Tujuan penelitian adalah menguji kualitas item berdasarkan validitas, reliabilitas, tingkat kesukaran, daya pembeda, dan efek pengecoh. Metode penelitian berfokus pada item analysis dari 35 soal pilihan ganda yang dilakukan untuk 83 mahasiswa Pendidikan Teknik Bangunan. Sampel penelitian adalah total sampling. Analisis statistik menggunakan Microsoft Excel dan IBM SPSS. Teknik pengumpulan data melalui LMS spada.uns. ac.id. Analisis data menggunakan analisis deskriptif kuantitatif. Hasil analisis menghasilkan validitas isi 0,89, konstruk 97,14%, valid. Reliabilitas Kuder-Richardson 0,876 dan Intraclass Correlation Coefficient 0,880, reliabilitas tinggi. Difficulty index (DIF I) 28 (80%) soal memiliki tingkat kesukaran baik, tiga (8,57%) terlalu sulit, empat (11,43%) terlalu mudah, dan Mean ± SD 61,83% ± 16,61%. Discrimination index (DI) 35 (100%) soal memiliki daya pembeda yang dapat diterima hingga sangat baik dengan Mean±SD 53,90% ± 17,26%. Distractor effectiveness (DE) Mean ± SD 92,86% ± 17,75% dengan 92,86% distraktor fungsional secara keseluruhan. Dari keseluruhan hasil analisis disimpulkan butir soal tes integral pada evaluasi formatif Matematika Teknik memiliki kualitas soal yang baik untuk penilaian kognitif mahasiswa. Penelitian selanjutnya dapat dilakukan investigasi tentang korelasi DIF I, DI, dan DE untuk meningkatkan kualitas item pada bank soal.Abstract: The item analysis of multiple choice questions (MCQ) is essential for identifying items that can be retained, revised, or removed. The research analyzes items' quality based on validity, reliability, difficulty index, discrimination index, and distractor effectiveness. The study focuses on item analysis of 35 MCQ administered to 83 Building Engineering Education students. The research sample is total sampling—statistical analysis using Microsoft Excel and IBM SPSS—data analysis quantitative descriptive analysis. The study results had a content validity of 0.89 and a construct of 97.14%, which is valid—Kuder-Richardson reliability of 0.876 and Intreclass Correlation Coefficient of 0.880, high reliability. Difficulty index (DIF I) 28 (80%) items had a good difficulty, three (8.57%) were too difficult, four (11.43%) were too easy, Mean ± SD 61.83% ± 16.61%. Discrimination index (DI) 35 (100%) items are acceptable to excellent with Mean ± SD 53.90% ± 17.26%. Distractor effectiveness (DE) Mean ± SD 92.86% ± 17.75% with 92.86% functional distractors. The study concluded that the integral test in the formative evaluation of Engineering Mathematics had good-quality questions for students' cognitive assessment. Further research investigates the correlation of DIF I, DI, and DE to enhance the quality of items in the question bank.
Holter ECG Monitoring in Assessing Arrhythmias in Patients with Palpitation

BACKGROUND Palpitation is one of the most common indications for Holter study. 24- hour Holter monitoring helps to detect abnormal cardiac rhythm which can be managed earlier if picked up sooner. Spot ECG has less sensitivity to pick episodic abnormal rhythm so longer monitoring time is essential. Various treatment modalities can thus be planned for non-benign and relatively benign arrhythmias like VT and PSVT respectively. METHODS This was a single center prospective observational cross-sectional study conducted at BPKIHS, Dharan for three months between July 2023 to September 2023. The primary objective was to study abnormal cardiac electrical events in patients having palpation. There were 38 patients of palpitations that underwent 24-hour Holter study after fulfilling inclusion criteria. The data was recorded on an excel sheet and was analyzed using software SPSS 20. RESULTS There were 47.4% (18) males and 52.6% (20) females. The mean age was 49.61± 17.63 years. There were 36.8% (14) patients in age group <45 years while 42.1% (16) were in age group 45 to 65 years and 21.1% (8) were in the age group >65 years. The maximum and minimum Heart rate were 180 and 35 beats per minute respectively. There was abnormal Holter ECG in 57.9% (22) and 42.1% (16) had normal ECG. Most common arrhythmia was VPC 17(77.2%) in our study. The association between the age group and Normal vs Abnormal Holter ECG was statistically significant (P Value=0.023) (Table 8). DM was the most common co-morbid illness in 5(13.2%), one participant had DM and HTN along with Dyslipidemia and other findings were smoking (5.3%), dyslipidemia (5.3%). There was no association between comorbid conditions and normal vs abnormal ECG Holter rhythm (P Value = 0.448). CONCLUSION Out of 38 participants VPC was the most common (77.2%) arrhythmia. The association between Age group and Normal vs Abnormal Holter was statistically significant (P Value=0.023). Most of the arrhythmias were benign. Diabetes was the most common risk factor present while 50% of subjects had no co-morbid illness.
DS-CIM: A 40nm Asynchronous Dual-Spike Driven, MRAM Compute-In-Memory Macro for Spiking Neural Network

Compute-in-memory (CIM) based on emerging nonvolatile memory (eNVM) is an effective way to deploy neural networks to low-power edge devices for both storage and computation. NVMs such as ReRAM have been widely used in CIM. Meanwhile, MRAM has higher read and write cycles, lower device and cycle variation and a lower bit error rate, making it equally attractive for storage. However, the high read current and low on/off ratio result in large energy consumption in MRAM read limiting its large-scale application in CIM. The spiking neural network (SNN) represents the information as sparse spike sequences and facilitates hardware to achieve low-power computing by taking advantage of its spatial-temporal sparsity. To further increase the input sparsity of SNN and reduce the read energy consumption, this paper proposes ADC-free, dual-spike (DS) -CIM macro, a spiking MRAM CIM macro driven by asynchronous dual spikes. Compared to the conventional rate coding, our dual-spike coding method uses only 2 spikes to encode the information without losing accuracy. Moreover, the event-driven feature allows the macro to have sub-nW static power consumption. Our DS-CIM macro achieves comparable or higher accuracy while maintaining very low energy consumption. Specifically, it achieves accuracies of 96.99%, 82.87%, 90.00%, and 85.97% for digit classification, image classification, gesture recognition, and action recognition tasks, with energy consumption of only 8.07nJ, 71.26nJ, 729.3nJ, and 369.82nJ, respectively. These results emphasize the significance of DS-CIM and provide ideas for low-power inference on edge devices.
Super-Resolution Reconstruction Algorithm Based on Improved ESRGAN

In response to the current image super-resolution reconstruction algorithm still suffers from insufficient detail processing such as texture and artifacts, this paper proposes an image super-resolution algorithm based on an improved enhanced super-resolution generative adversarial network. The Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) model is improved in terms of both generative network and loss function. First, the shift convolution is introduced in the generative network to effectively extract the image's local structure information while keeping the complexity comparable to that of 1×1 convolution; the multi-scale feature extraction module is introduced in the densely connected module, and then a channel attention mechanism is added after this module to adjust the feature response values of different channels, thus improving the deep information extraction module in the generative network model. Secondly, the loss function of the network is improved by adding a local perceptual loss function to further improve the detail retention capability of the network. Finally, the improved reconstruction module is used to complete the image super-resolution reconstruction. The 2-fold and 4-fold super-resolution reconstructions are tested on datasets such as Set5, and the proposed algorithm is compared with methods such as Bicubic. The results show that the reconstructed images obtained by the proposed algorithm have sharper edges and can provide relatively more details, which greatly improves the visual effect of the images.
Next-Generation Security: Detecting Suspicious Liquids Through Software Defined Radio Frequency Sensing and Machine Learning

Hazardous liquids, such as nitroglycerin, are now preferred over traditional explosives in modern terrorist attacks. Their inconspicuous nature poses an identification challenge, necessitating urgent large-scale security inspections to prevent terrorism in public spaces. However, conventional liquid detection methods face obstacles in terms of cost, accuracy, and scalability, hindering their widespread use. This article introduces a platform that combines radio frequency (RF) sensing based on software-defined radio (SDR) technology and state-of-the-art machine learning (ML) algorithms to detect and classify suspicious and nonsuspicious liquids. The detection method utilizes fine-grained samples of orthogonal frequency division multiplexing (OFDM) to acquire channel state information (CSI) of the liquids present in the environment at operating frequencies 900 MHz and 2.45 GHz. ML algorithms are employed for classification purposes based on liquids’ dielectric properties, and their effectiveness is evaluated based on accuracy, prediction speed, and training time. The analysis of experimental results demonstrated that our method successfully classified over 95% of both suspicious and nonsuspicious liquids. It also identified more than 97% of suspicious liquid types and classified up to 98% of nonsuspicious liquids. These findings confirm the efficacy of the proposed system. The software-defined RF sensing system is versatile, portable, adaptable, and cost-efficient.
Information Systems Resilience: An African Banking Case Study

The banking sector is a prime example of digital transformation initiatives, but instability and system outages have plagued banks in South Africa. A systems resilience view of information systems (IS) in the banking sector will illuminate and contextualize how IS can respond and recover. This research investigates a bank's response to system instability and resilience development. We selected one of the multinational banks in Africa as our case study (Bank A), which can be regarded as representative of large multinational banks on the African continent. Bank A identified nine areas of improvement as part of its Systems Resilience Programme. Bank A's response fits Weber et al.'s (2021) understanding of system resilience well, as it is a mix between a socio-technical response and a time perspective. Very little formal research has been done on system resilience in banking in Africa. This paper is a first step in remedying instability facing IS and offers a new perspective regarding resilience in the IS and developing nation context.