Miracle-dz/UniFAR-FTUs huggingface dataset:
Folic acid supplements and perinatal mortality in China

Introduction Periconceptional use of multivitamins containing folic acid prevents external major birth defects, especially neural tube defects. We aimed to explore the effects of maternal folic acid supplementation alone on perinatal mortality with or without external major birth defects plus neural tube defects. Methods From the China-US Collaborative Project for Neural Tube Defects Prevention, we identified 222, 303 singleton pregnancies with detailed information on periconceptional folic acid use, defined as folic acid supplementary before the last menstrual date until to the end of the first trimester. Perinatal mortality included stillbirths after 20 weeks’ gestation and early neonatal deaths within 7 days of delivery. Results Among the fetuses or infants of women who did not take folic acid, the rate of perinatal mortality was 2.99% and 1.62% at least 20 weeks’ gestation in the northern and southern regions. Among the fetuses or infants of the women with periconceptional use of folic acid, the rates were 1.85% and 1.39% in the northern and southern region. The estimated relative risk for perinatal mortality [adjusted risk ratio (RR), 0.72; 95% confidence interval (CI), 0.61- 0.85], stillbirth (adjusted RR, 0.78; 95% CI, 0.64-0.96), early neonatal mortality (adjusted RR, 0.61; 95% CI, 0.45-0.82), and neonatal death (adjusted RR, 0.64; 95% CI, 0.49-0.83) in northern China was significantly decreased in association with periconceptional folic acid supplementation. Compared with northern, there was a lesser effect in southern China. Conclusion Periconceptional intake of 400μg folic acid daily reduces the overall risk perinatal mortality, as well as the risk from external major birth defects and neural tube defects, especially in northern China.
Research on nonlinear infectious disease models influenced by media factors and optimal control

In this article, a mathematical model was developed to describe disease control by media factors. The Lambert W function was used to convert the system definition by implicit functions into explicit functions. We analyzed the dynamics of the defined piecewise smooth system and verified the correctness of the theoretical analysis through numerical simulation. Research revealed that media factors can delay the peak of an epidemic and reduce the scale of the epidemic. It is worth noting that adopting different control measures has a certain impact on the scale of the epidemic; the analysis results indicate that implementing dual-control is the most effective way to limit the spread of diseases and this strategy may provide clues for disease control.
The Use of Sharia Fintech Among Gen Z: Does Sharia Financial Literacy Play a Role?

The objective of this research is to explore and analyze the moderating role of sharia financial literacy in enhancing the relationship between perceived convenience, perceived benefits, and perceived security on the desire to use sharia financial technology (fintech) services among Gen Z in Banda Aceh City, Indonesia. From a total of 37,892 Gen Z individuals in the city, 150 respondents were selected as the research sample using a purposive sampling technique. The primary data were collected by distributing the questionnaires to the respondents and analyzed using the Moderated Regression Analysis (MRA) method with the support of SEMPLS software. The study recorded that perceived convenience, perceived benefits, and perceived security have a positive impact on interest in using sharia fintech services. The study found an insignificant role of the sharia financial literacy in moderating the effects of perceived convenience, perceived benefits, and perceived security on interest in using sharia fintech services among Gen Z in Banda Aceh City. However, sharia financial literacy positively influenced interest of using sharia fintech services. Thus, to increase interest of Gen Z in using sharia fintech services, it is important to increase sharia financial literacy, perceived convenience, perceived benefits, and perceived security among Gen Z.
Assessment of Prevalence of Communicable and Non-Communicable Diseases in a Rural Community and its Neighborhood in Enugu State, Nigeria: A Cross-Sectional Epidemiological Survey from a Health Outreach

Background: Africa faces a double burden of communicable and chronic non-communicable diseases which have longterm public health impact. This study is to ascertain the disease prevalence, the degree, and the predisposing risk factors of communicable and non-communicable disease in Uvuru, in Uzo-Uwani Enugu State. Materials and Methods: This is a descriptive cross-sectional survey using convenience sampling technique conducted on residents in Uvuru Agada community in Uzo-Uwani Local Government Area of Enugu State using a two-part pro forma. The data was analysed using the Statistical Product and Service Solution (SPSS version 23.0) and results were presented as frequencies and percentages. Chi-square and Pearson correlation coefficient were used to determine statistical relationship existing among variables. Significance difference was accepted at 95% precision (P < 0.05). Results: Majority 495(70.7 %) of the respondents were females while 656 (93.7 %) were adults (> 19 years). Higher percentage 429(61.3 %) had a normal body mass index (BMI). Very few 8 (1.1 %) had respiratory diseases while 469(67.0 %) had malaria. Majority 682(97.4%) of the respondents had at least one non-communicable disease while only 91(13.0%) had at least one non-communicable disease. Conclusion: The prevalence of non- communicable diseases is high. Age, Gender and BMI are risk factors.
Deep learning architectures for diagnosing the severity of apple frog-eye leaf spot disease in complex backgrounds

Introduction In precision agriculture, accurately diagnosing apple frog-eye leaf spot disease is critical for effective disease management. Traditional methods, predominantly relying on labor-intensive and subjective visual evaluations, are often inefficient and unreliable. Methods To tackle these challenges in complex orchard environments, we develop a specialized deep learning architecture. This architecture consists of a two-stage multi-network model. The first stage features an enhanced Pyramid Scene Parsing Network (L-DPNet) with deformable convolutions for improved apple leaf segmentation. The second stage utilizes an improved U-Net (D-UNet), optimized with bilinear upsampling and batch normalization, for precise disease spot segmentation. Results Our model sets new benchmarks in performance, achieving a mean Intersection over Union (mIoU) of 91.27% for segmentation of both apple leaves and disease spots, and a mean Pixel Accuracy (mPA) of 94.32%. It also excels in classifying disease severity across five levels, achieving an overall precision of 94.81%. Discussion This approach represents a significant advancement in automated disease quantification, enhancing disease management in precision agriculture through data-driven decision-making.
Acoustic Data Communication Over a Stainless Metallic Rod

This article describes the use of a stainless steel rod as a communication medium for a low-rate system. The setup has been evaluated as a proof of concept using a two transducers configuration. Acoustic data is transmitted axially, along the longer dimension of the rod. Experimental setup reaches up to 400 kbit/s for a BER of 1 error out of 1 million bits.
The Impact of Clinical Pharmacist Diabetes Education on Medication Adherence in Patients with Type 2 Diabetes Mellitus: An Interventional Study from Khartoum, Sudan

Background: Continuous therapeutic care with good medication adherence is the cornerstone of management of all chronic diseases including diabetes. This study aimed to evaluate the impact of clinical pharmacist intervention on the medication adherence in individuals with type 2 diabetes (T2DM). Methods: This was a randomized, double-blind, controlled trial conducted at a diabetes clinic located at Omdurman Military Hospital, Sudan. Individuals with T2DM attending the diabetes clinic within 1 year were selected. The sample size was 364 participants (182 control and 182 interventional group). We used a pre-structured standardized questionnaire and checklist to collect the data. Data were analyzed by using the Statistical Package for the Social Sciences (SPSS) (version 28). Results: Majority, 76.4% (n = 278) were females, and they consisted of 80.8% (n = 147) of the interventional group and 72% of the controls. The mean age of the interventional group was 54.5 (±10) years; 31.9% (n = 58) of the interventional group had diabetes for 6–10 years, compared with 26.4% (n = 48) of the control group. Among the control group, the mean adherence score was 6.8 (±1.7) at baseline and it was 6.7 (±1.6) at the end of the study (p < 0.001), while in the interventional group, the mean adherence score was 6.8 (±1.7) at baseline and it was 7.4 (±1.5) at the end of the study (p < 0.001). Conclusion: Adherence score among the intervention group was increased significantly from baseline to the end of the study when compared to the control group.
A Subpixel Concrete Crack Measurement Method Based on the Partial Area Effect

To improve the accuracy of concrete crack measurement with a machine vision method in structural health monitoring and in technical status evaluation, a subpixel crack measurement method based on the partial area effect was proposed. (1) First, a pixelwise crack image segmentation method was established through a multi-step process of multi-threshold fusion and morphology operation, and a novel pixel degree crack width calculation method was developed with the extraction of the middle points, the center line and its normal, and the intersection of the center line normal and crack edges. (2) Then, a subpixel algorithm based on the partial area effect was introduced to locate vertical, horizontal, and oblique cracks in subpixel crack edges, and the subpixel crack width could be calculated along the crack center line pixelwise. (3) Finally, the proposed method was verified by indoor concrete beam crack measurement tests with a digital microscope, and the results show that the maximum relative errors of the subpixel width of the horizontal, vertical, and oblique straight cracks measured by the proposed method were 3.06%, 8.97%, and 5.16%, respectively. The absolute error of the crack length was less than 0.30 mm, and the measurement accuracy could reach 0.01 pixels. The subpixel crack measurement method provides a novel possible solution for structural health monitoring.
A Robust InSAR Phase Noise Reduction Method Based on Sliding Window Tensor Model

Interferometric synthetic aperture radar (InSAR) images are severely corrupted by noise in both magnitude and phase, and as a powerful tool for extracting geophysical parameters, it is of significantly important to recover the interferometric phase during InSAR signal processing. In this paper, a novel algorithm of robust sliding window decomposition for InSAR phase denoising is proposed with the achievement of improved noise reduction. In the scheme, a sliding window tensor model of interferogram is constructed by stacking the image patches. Due to the locally scattering mechanism similarity principle, a superior low-rank property of the tensor data can be achieved, which is also confirmed by using the numerical experimental analysis. Then, a robust tensor decomposition method is used for noise reduction and an alternating direction method of multipliers (ADMM) solution is applied to robustly and accurately restore the noise-free interferometric phase. As a result, the proposed algorithm performs well in effectively restoring noise-free interferometric phase. Last but not least, the experimental analysis using measured InSAR data has been demonstrated, and excellent results are obtained.
Breed-Specific Skull Morphology Reveals Insights into Canine Optic Chiasm Positioning and Orbital Structure through 3D CT Scan Analysis

Simple Summary Short-headed dogs exhibit shallow orbits and forward-facing eyes, while their medium- and long-headed counterparts have deep orbits with relatively laterally oriented eyes; these traits are classified by skull index (SI) value. In this study, we employed landmark-based morphometric analysis based on computed tomography scan data of 50 adult dogs to investigate the correlation between the SI and optic chiasm, and orbital shape. We found a consistent placement of the optic chiasm at the anterior neurocranial margin across all breeds. However, short-headed breeds exhibit a wider angle between the bilateral optic canals, and the anterior margin of the zygomatic bone-forming orbit was wider in the anterior direction compared to medium- and long-headed breeds. Breed-specific orbital differences were determined by the zygomatic bone, which connects the face to the neurocranium. The orbital margin of the zygomatic bone projects outward and forward, correlating with the degree of facial shortening. Taken together, our findings suggest that the zygomatic bone influences breed-specific orbital formation, especially in cases of facial shortening. Abstract This study’s CT scan-based morphometric analysis of 50 adult dogs explored the relationship between skull shape variations (determined by the skull index, SI), optic chiasm, optic canals, and orbital shape. Dogs were classified as brachycephalic (SI ≥ 59), mesocephalic (SI ≥ 51 but <59), and dolichocephalic (SI < 51). No significant age or weight differences were observed. Skull lengths (brachycephalic: 11.39 ± 1.76 cm, mesocephalic: 15.00 ± 2.96 cm, dolichocephalic: 17.96 ± 3.44 cm) and facial lengths (brachycephalic: 3.63 ± 1.00 cm, mesocephalic: 6.46 ± 1.55 cm, dolichocephalic: 8.23 ± 1.03 cm) varied significantly, with shorter orbital depths (brachycephalic: 2.58 ± 0.42 cm, mesocephalic: 3.19 ± 0.65 cm, dolichocephalic: 3.61 ± 0.77 cm) in brachycephalic dogs. The optic chiasm-to-inion horizontal length ratio to cranial horizontal length positively correlated with the SI (r = 0.883, p < 0.001), while the ratio to neurocranial length showed no SI correlation (range: 55.5–75.0). Brachycephalic breeds had a significantly wider optic canal angle (93.74 ± 16.00°), along with broader lacrimal-zygomatic and zygomatic frontal process angles. These findings highlight the zygomatic bone’s role in influencing breed-specific orbital variations by connecting the face to the neurocranium, projecting the orbital rim outward and forward with facial shortening.
Speech Emotion Recognition using AdaMax and Weighted Adam Optimizers

Machine learning and speech emotion detection are two technologies that are being used to create more natural and engaging ways for humans to interact with computers. These technologies are still in their early stages of development, but they have the potential to revolutionize the way we interact with technology. This work proposes the use of weighted Adam (AdamW) and AdaMax (Adamax) Optimizers to improve results on the current schemes. Weighted Adam is an extension of Adam that assigns different learning rates to different parameters. It can help to improve convergence and avoid overfitting. Adamax is a variant of Adam that uses a more robust learning rate schedule. It can be more stable than Adam, especially in high-dimensional problems. We combine three well known speech emotion datasets - RAVDESS, SAVEE and TESS. SVM Classifiers and Convolutional Neural Networks were used for the prediction process and a wide range of features were extracted from the wav file in the preprocessing phase. We achieved a testing accuracy of 84.79%, 82.73% and 88.53% with Adam, AdamW and AdaMax respectively.
Evaluation Study of Linear Combination Technique for SVM related Time Series Forecasting

Time series forecasting and SVM are widely used in many domains, for example, smart city and digital services. Focusing on SVM related time series forecasting model, in this paper we empirical investigate the performance of eight linear combination techniques by using M3 competition dataset which includes 3003 time series. The results reveals that the “forecast combination puzzle” is not exist for combining SVM related forecasting model as the simple average is almost the worst combination technique.
Motion-Aware Correlation Filter-Based Object Tracking in Satellite Videos

Object tracking in satellite videos poses a significant challenge for existing trackers due to the typical involvement of small objects, multiple similar disruptors, and occlusions. To improve the performance of remote sensing tracking, a novel motion-aware correlation filter (MACF) algorithm is developed in this study. The proposed approach provides the following improvements: 1) a motion estimation module based on the historical trajectory of the target is embedded into an enhanced spatial–temporal regularized correlation filter (CF)-based tracking framework, to suppress distractions caused by similar objects (SOs); and 2) a failure correction module is employed to deal with the occlusion problem, thereby further enhancing the tracking robustness. Extensive experiments are conducted on the publicly available VISO and SatSOT datasets, with the experimental results demonstrating that the proposed MACF algorithm achieves superior accuracy in comparison to state-of-the-art trackers. Particularly, the present approach has offered the first-place solution for the single object tracking task given in the ICPR 2022 challenge, on moving object detection and tracking in satellite videos (SatVideoDT).
Piezoelectric Nanocomposite‐Based Multifunctional Wearable Bioelectronics for Mental Stress Analysis Utilizing Physiological Signals

This study multifunctional wearable bioelectronics (MWBs) integrated with a piezoelectric sensor and a micropatterned temperature sensor for accurate detection of human physiological signals. Piezoelectric nanocomposite uniformity, stretchability, and adhesion is improved through the functionalization of nanoparticles with glycidyl silane and nonionic surfactants dispersion in the organic polymer matrix. Additionally, crack‐free nanometal layers are deposited on the nanocomposite interface with high adhesion by thiol silane functionalization and oxygen plasma treatment. The MWBs exhibit a temperature sensitivity of 7.1 Ω °C−1 (R2 of 0.999) between 30 °C to 40 °C, a pressure sensitivity of 72 mV kPa−1 (R2 of 0.998) within 0.5 kPa to 10 kPa, and a stable mechanical durability. MWBs have demonstrated to detect subtle pulse waveforms at an arm‐mimicking phantom and human skin, respectively. Furthermore, MWBs are applied to analyze physiological signal characteristics caused by human stress, demonstrating its potential for use in healthcare electronics in various medical applications.
Excessive sodium supplementation but not fluid load is correlated with overall morbidity in extremely low birth weight infants

Abstract Objectives Sodium homeostasis in extremely low birth weight (ELBW) infants is critical. While a lack of sodium delays growth, excessive supplementation increases morbidity. Methods We performed a single‐center retrospective study on sodium and fluid management during the first 2 weeks of live including all ELBW infants born between June 1, 2017 and May 31, 2019. Results Forty‐seven patients (median GA 26 + 6 weeks, median BW 845 g) were included. Mean sodium intake was above the ESPGHAN recommendation, 4.58 mmol/kg/day during the first 2 days and 1.99 mmol/kg/day during the following period. Incidence of PDA, IVH, and ROP was directly associated with sodium intake (OR 1.6, 1.3, and 1.4, respectively), but not with fluid supplementation. No association to BPD was found. The most important source for inadvertent sodium intake were 0.9% saline given by arterial lines. Sodium supplementation did not correlate directly with serum sodium levels, but a linear regression model combining sodium intake and fluid supplementation was able to predict serum sodium changes 24–48 h in advance (correlation coefficient of 0.294, p < 0.05). Conclusions Sodium application substantially exceeded ESPGHAN recommendations in ELBW infants. An excess in sodium was associated with an overall increased morbidity, justifying increased efforts to identify inadvertent sodium sources in these patients with the aim to decrease sodium excess.
Dietary intervention improves metabolic levels in patients with type 2 diabetes through the gut microbiota: a systematic review and meta-analysis

Background Poor dietary structure plays a pivotal role in the development and progression of type 2 diabetes and is closely associated with dysbiosis of the gut microbiota. Thus, the objective of this systematic review was to assess the impact of dietary interventions on improving gut microbiota and metabolic levels in patients with type 2 diabetes. Methods We conducted a systematic review and meta-analysis following the PRISMA 2020 guidelines. Results Twelve studies met the inclusion criteria. In comparison to baseline measurements, the high-fiber diet produced substantial reductions in FBG (mean difference −1.15 mmol/L; 95% CI, −2.24 to −0.05; I2 = 94%; P = 0.04), HbA1c (mean difference −0.99%; 95% CI, −1.93 to −0.03; I2 = 89%; P = 0.04), and total cholesterol (mean difference −0.95 mmol/L; 95% CI, −1.57 to −0.33; I2 = 77%; P = 0.003); the high–fat and low–carbohydrate diet led to a significant reduction in HbA1c (mean difference −0.98; 95% CI, −1.50 to −0.46; I2 = 0%; P = 0.0002). Within the experimental group (intervention diets), total cholesterol (mean difference −0.69 mmol/L; 95% CI, −1.27 to −0.10; I2 = 52%; P = 0.02) and LDL-C (mean difference −0.45 mmol/L; 95% CI, −0.68 to −0.22; I2 = 0%; P < 0.0001) experienced significant reductions in comparison to the control group (recommended diets for type 2 diabetes). However, no statistically significant differences emerged in the case of FBG, HbA1c, HOMA-IR, and HDL-C between the experimental and control groups. The high dietary fiber diet triggered an augmented presence of short-chain fatty acid-producing bacteria in the intestines of individuals with T2DM. In addition, the high-fat and low-carbohydrate diet resulted in a notable decrease in Bacteroides abundance while simultaneously increasing the relative abundance of Eubacterium. Compared to a specific dietary pattern, personalized diets appear to result in the production of a greater variety of beneficial bacteria in the gut, leading to more effective blood glucose control in T2D patients. Conclusion Dietary interventions hold promise for enhancing metabolic profiles in individuals with T2D through modulation of the gut microbiota. Tailored dietary regimens appear to be more effective than standard diets in improving glucose metabolism. However, given the limited and highly heterogeneous nature of the current sample size, further well-designed and controlled intervention studies are warranted in the future.
Analyzing the Impact of Body Shaming on Twitter: A Study Using Naive Bayes Classifier and Machine Learning

Twitter is one type of social media that is still widely used today. However, it happens frequently for Twitter users to post remarks that tend to criticize other Twitter users. Twitter users routinely make nasty remarks regarding body shaming, which has detrimental impacts on the victims such as diminishing self-esteem, leading to depressive illnesses, and, more seriously, raising the chance of suicide. Body shaming is the practice of criticizing someone's physical attributes, such as being slim, overweight, or having a flat nose. This study will use the Naive Bayes Classifier approach to do sentiment analysis based on the actions of body shaming on Twitter. Based on the performance testing results of Accuracy, Precision, and Recall using Machine Learning Rapid Miner with an initial dataset of 1000 body shaming tweets and a test dataset of 329 tweets, the following results were obtained: Accuracy of 80.55%, positive Precision of 100%, negative Precision of 80.43%, positive Recall of 3.03%, and negative Recall of 100%. In the preprocessing stage, tokenization resulted in a word cloud with the top 5 words being "overweight" at 51%, "body shaming" at 20%, "thin" at 11%, "people" at 10%, and "eating" at 8%.