Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality Assessment of Apple and Grape Juices from Serbian and German Markets by Planar Chromatography—Chemometrics

The high consumption of plant-based foods on a global scale has increased the number of adulterations in the food industry. Along with this, analytical approaches to fraud detection need to be further developed. A nontargeted effect-directed profiling by high-performance thin-layer chromatography hyphenated with five effect-directed assays (free radical scavenging assay, Aliivibrio fischeri bioassay, and acetylcholinesterase, butyrylcholinesterase, and tyrosinase inhibition assays) and multi-imaging provided additional information on the antioxidative, antimicrobial, and enzyme inhibition activities for 18 apple and 18 grape juices from markets in Serbia and Germany. Bioactive zones of interest were eluted using an elution head-based interface and further characterized by electrospray ionization high-resolution mass spectrometry. The different profiles were evaluated chemometrically, and several compounds, which were characteristic of samples from different markets located in Serbia and Germany, were identified in apple juice (such as chlorogenic acid, phloridzin, epicatechin, and caffeic acid) and grape juice (such as chlorogenic acid, epicatechin, and quercetin). The developed rapid and simple method for the quality assessment of fruit juices coming from different (geographic) markets showed clear quality differences. Thus, it could be used to learn more about quality differences, to detect fraud in fruit juice production, and to verify the authenticity of the origin.
Under Pressure: OHS of Vulnerable Workers in the Construction Industry

The New Zealand construction industry provides a good illustration of the changing nature of work and the impact this has had on the occupational health and safety (OHS) of sub-contracted construction workers. In particular, we examine the vulnerability of workers in the context of the construction industry post-2010 Canterbury earthquakes. In doing so, we apply Quinlan and Bohle's (2004; 2009) 'Pressures, Disorganization and Regulatory Failure' (PDR) model to frame the changing nature and organisation of work and the impact this has had on the OHS of sub-contracted construction workers. Finally, we discuss what can be done going forward in terms of creating a more effective regulatory regime and a safer and healthier industry.
Association of Functional Characteristics and Physiotherapy with COVID-19 Mortality in Intensive Care Unit in Inpatients with Cardiovascular Diseases

Background and Objectives: To estimate the association between admission functional outcomes and exposure to physiotherapy interventions with mortality rate in intensive care unit (ICU) inpatients with cardiovascular diseases and new coronavirus disease (COVID-19). Materials and Methods: Retrospective cohort including 100 ICU inpatients (mean (standard deviation), age 75 (16) years) split into COVID-19+ or COVID-19−. The association of in-ICU death with admission functional outcomes and physiotherapy interventions was investigated using univariable and multivariable regression models. Results: In total, 42 (42%) patients tested positive for COVID-19. In-ICU mortality rate was 37%, being higher for the COVID-19+ group (odds ratio, OR (95% CI): 3.15 (1.37–7.47), p = 0.008). In-ICU death was associated with lower admission ICU Mobility Scale score (0.81 (0.71–0.91), p = 0.001). Restricted mobility (24.90 (6.77–161.94), p < 0.001) and passive kinesiotherapy (30.67 (9.49–139.52), p < 0.001) were associated with in-ICU death, whereas active kinesiotherapy (0.13 (0.05–0.32), p < 0.001), standing (0.12 (0.05–0.30), p < 0.001), or walking (0.10 (0.03–0.27), p < 0.001) were associated with in-ICU discharge. Conclusions: In-ICU mortality was higher for inpatients with cardiovascular diseases who had COVID-19+, were exposed to invasive mechanical ventilation, or presented with low admission mobility scores. Restricted mobility or passive kinesiotherapy were associated with in-ICU death, whereas active mobilizations (kinesiotherapy, standing, or walking) were associated with in-ICU discharge in this population.
Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation

In this study, we established an explainable and personalized risk prediction model for in-hospital mortality after continuous renal replacement therapy (CRRT) initiation. This retrospective cohort study was conducted at Changhua Christian Hospital (CCH). A total of 2932 consecutive intensive care unit patients receiving CRRT between 1 January 2010, and 30 April 2021, were identified from the CCH Clinical Research Database and were included in this study. The recursive feature elimination method with 10-fold cross-validation was used and repeated five times to select the optimal subset of features for the development of machine learning (ML) models to predict in-hospital mortality after CRRT initiation. An explainable approach based on ML and the SHapley Additive exPlanation (SHAP) and a local explanation method were used to evaluate the risk of in-hospital mortality and help clinicians understand the results of ML models. The extreme gradient boosting and gradient boosting machine models exhibited a higher discrimination ability (area under curve [AUC] = 0.806, 95% CI = 0.770–0.843 and AUC = 0.823, 95% CI = 0.788–0.858, respectively). The SHAP model revealed that the Acute Physiology and Chronic Health Evaluation II score, albumin level, and the timing of CRRT initiation were the most crucial features, followed by age, potassium and creatinine levels, SPO2, mean arterial pressure, international normalized ratio, and vasopressor support use. ML models combined with SHAP and local interpretation can provide the visual interpretation of individual risk predictions, which can help clinicians understand the effect of critical features and make informed decisions for preventing in-hospital deaths.
Low-Temperature Hydrothermal Growth of ZnO Nanowires on AZO Substrates for FACsPb(IBr)3 Perovskite Solar Cells

Electron and hole transport layers (ETL and HTL) play an essential role in shaping the photovoltaic performance of perovskite solar cells. While compact metal oxide ETL have been largely explored in planar n-i-p device architectures, aligned nanowires or nanorods remain highly relevant for efficient charge extraction and directional transport. In this study, we have systematically grown ZnO nanowires (ZnO NWs) over aluminum-doped zinc oxide (AZO) substrates using a low-temperature method, hydrothermal growth (HTG). The main growth parameters were varied, such as hydrothermal precursors concentrations (zinc nitrate hexahydrate, hexamethylenetetramine, polyethylenimine) and growing time, in order to finely control NW properties (length, diameter, density, and void fraction). The results show that ZnO NWs grown on AZO substrates offer highly dense, well-aligned nanowires of high crystallinity compared to conventional substrates such as FTO, while demonstrating efficient FACsPb(IBr)3 perovskite device performance, without the requirement of conventional compact hole blocking layers. The device performances are discussed based on NW properties, including void fraction and aspect ratio (NW length over diameter). Finally, AZO/ZnO NW-based devices were fabricated with a recent HTL material based on a carbazole moiety (Cz–Pyr) and compared to the spiro-OMeTAD reference. Our study shows that the Cz–Pyr-based device provides similar performance to that of spiro-OMeTAD while demonstrating a promising stability in ambient conditions and under continuous illumination, as revealed by a preliminary aging test.
Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network

To date, for the diagnosis of dentofacial dysmorphosis, we have relied almost entirely on reference points, planes, and angles. This is time consuming, and it is also greatly influenced by the skill level of the practitioner. To solve this problem, we wanted to know if deep neural networks could predict postoperative results of orthognathic surgery without relying on reference points, planes, and angles. We use three-dimensional point cloud data of the skull of 269 patients. The proposed method has two main stages for prediction. In step 1, the skull is divided into six parts through the segmentation network. In step 2, three-dimensional transformation parameters are predicted through the alignment network. The ground truth values of transformation parameters are calculated through the iterative closest points (ICP), which align the preoperative part of skull to the corresponding postoperative part of skull. We compare pointnet, pointnet++ and pointconv for the feature extractor of the alignment network. Moreover, we design a new loss function, which considers the distance error of transformed points for a better accuracy. The accuracy, mean intersection over union (mIoU), and dice coefficient (DC) of the first segmentation network, which divides the upper and lower part of skull, are 0.9998, 0.9994, and 0.9998, respectively. For the second segmentation network, which divides the lower part of skull into 5 parts, they were 0.9949, 0.9900, 0.9949, respectively. The mean absolute error of transverse, anterior–posterior, and vertical distance of part 2 (maxilla) are 0.765 mm, 1.455 mm, and 1.392 mm, respectively. For part 3 (mandible), they were 1.069 mm, 1.831 mm, and 1.375 mm, respectively, and for part 4 (chin), they were 1.913 mm, 2.340 mm, and 1.257 mm, respectively. From this study, postoperative results can now be easily predicted by simply entering the point cloud data of computed tomography.
Developing a Speech Recognition System for Recognizing Tonal Speech Signals Using a Convolutional Neural Network

Deep learning-based machine learning models have shown significant results in speech recognition and numerous vision-related tasks. The performance of the present speech-to-text model relies upon the hyperparameters used in this research work. In this research work, it is shown that convolutional neural networks (CNNs) can model raw and tonal speech signals. Their performance is on par with existing recognition systems. This study extends the role of the CNN-based approach to robust and uncommon speech signals (tonal) using its own designed database for target research. The main objective of this research work was to develop a speech-to-text recognition system to recognize the tonal speech signals of Gurbani hymns using a CNN. Further, the CNN model, with six layers of 2DConv, 2DMax Pooling, and 256 dense layer units (Google’s TensorFlow service) was also used in this work, as well as Praat for speech segmentation. Feature extraction was enforced using the MFCC feature extraction technique, which extracts standard speech features and features of background music as well. Our study reveals that the CNN-based method for identifying tonal speech sentences and adding instrumental knowledge performs better than the existing and conventional approaches. The experimental results demonstrate the significant performance of the present CNN architecture by providing an 89.15% accuracy rate and a 10.56% WER for continuous and extensive vocabulary sentences of speech signals with different tones.
Body Composition-Specific Asthma Phenotypes: Clinical Implications

Background: Previous studies have indicated the limitations of body mass index for defining disease phenotypes. The description of asthma phenotypes based on body composition (BC) has not been largely reported. Objective: To identify and characterize phenotypes based on BC parameters in patients with asthma. Methods: A study with two prospective observational cohorts analyzing adult patients with stable asthma (n = 541 for training and n = 179 for validation) was conducted. A body composition analysis was performed for the included patients. A cluster analysis was conducted by applying a 2-step process with stepwise discriminant analysis. Logistic regression models were used to evaluate the association between identified phenotypes and asthma exacerbations (AEs). The same algorithm for cluster analysis in the independent validation set was used to perform an external validation. Results: Three clusters had significantly different characteristics associated with asthma outcomes. An external validation identified the similarity of the participants in training and the validation set. In the training set, cluster Training (T) 1 (29.4%) was “patients with undernutrition”, cluster T2 (18.9%) was “intermediate level of nutrition with psychological dysfunction”, and cluster T3 (51.8%) was “patients with good nutrition”. Cluster T3 had a decreased risk of moderate-to-severe and severe AEs in the following year compared with the other two clusters. The most important BC-specific factors contributing to being accurately assigned to one of these three clusters were skeletal muscle mass and visceral fat area. Conclusion: We defined three distinct clusters of asthma patients, which had distinct clinical features and asthma outcomes. Our data reinforced the importance of evaluating BC to determining nutritional status in clinical practice.
LGMSU-Net: Local Features, Global Features, and Multi-Scale Features Fused the U-Shaped Network for Brain Tumor Segmentation

Brain tumors are one of the deadliest cancers in the world. Researchers have conducted a lot of research work on brain tumor segmentation with good performance due to the rapid development of deep learning for assisting doctors in diagnosis and treatment. However, most of these methods cannot fully combine multiple feature information and their performances need to be improved. This study developed a novel network fusing local features representing detailed information, global features representing global information, and multi-scale features enhancing the model’s robustness to fully extract the features of brain tumors and proposed a novel axial-deformable attention module for modeling global information to improve the performance of brain tumor segmentation to assist clinicians in the automatic segmentation of brain tumors. Moreover, positional embeddings were used to make the network training faster and improve the method’s performance. Six metrics were used to evaluate the proposed method on the BraTS2018 dataset. Outstanding performance was obtained with Dice score, mean Intersection over Union, precision, recall, params, and inference time of 0.8735, 0.7756, 0.9477, 0.8769, 69.02 M, and 15.66 millisecond, respectively, for the whole tumor. Extensive experiments demonstrated that the proposed network obtained excellent performance and was helpful in providing supplementary advice to the clinicians.
Ecofriendly Usability of Mushroom Cultivation Substrate as a Ruminant Feed: Anaerobic Digestion Using Gas Production Techniques

Simple Summary Ruminants might use process and agricultural by-products to meet their maintenance, growth, and production needs. Generally, feed is the major cost for an animal farm, yet utilization of agro-industrial waste (such as mushroom cultivation substrate) not only reduces these expenses, but could also help with the issue of environmental pollution. Accordingly, mushroom cultivation waste might be used for animal feeding after harvesting, because of its good-quality substrate, which is beneficial for ruminants. Therefore, the objective of this study was to evaluate the mushroom-cultivated substrate microscopic surface and its fermentability. Mushroom cultivation led to lower ether extract, acid detergent fiber, and crude fiber level of substrate. Our results show that biological treatment could reduce fiber components while increasing feed digestibility, providing new insight into the use of biological pretreatment to produce ruminant feed. According to the results, biological processing of mushroom cultivation substrate might increase in vitro fermentation. In conclusion, mushroom-cultivated substrate might be used as feed in ruminant diets. Abstract The current study was carried out to evaluate the nutritive value of mushroom-uncultivated and -cultivated substrates, and their in vitro gas and methane production. The experiment was conducted in a completely randomized design, and analyzed with GLM using SAS 9.4. Analysis of the structural morphology of mushroom-cultivated substrate was performed using a scanning electron microscope. Mushroom cultivation led to lower ether extract, acid detergent fiber, and crude fiber level of substrate (p < 0.05). Mushroom-cultivated substrate showed higher in vitro cumulative gas production (p < 0.05). Moreover, mushroom cultivation led to a higher sample surface, and improved the microorganisms’ access to feed materials, thus stimulating rumen fermentation and increasing methane production (p < 0.05). The organic matter digestibility, metabolizable energy, and net energy lactation values were higher for mushroom-cultivated substrate than uncultivated substrate. The results demonstrate that mushroom-cultivation not only increases the contact surface of cellulose, leading to higher ruminal microorganisms’ access to feedstuff, but could also had higher nutritive value; this material might be used in ruminant ration formulation, to reduce environmental pollution and feed costs.
Distinguishing the Effect of Time Spent at Home during COVID-19 Pandemic on the Mental Health of Urban and Suburban College Students Using Cell Phone Geolocation

The aim of this study was to assess the correlation of depression and anxiety with time spent at home among students at two universities—one urban and the other suburban—during the COVID-19 pandemic. Methods: Geolocation data from the smartphones of 124 participants were collected between February 2021 and May 2021. The level of depression was estimated by the PHQ-9 and PHQ-2 screening tools, and anxiety scores were estimated by the GAD-2 and GAD-7 screening tools. Results: 51% of participants in the PHQ-9 surveys indicated mild to severe depression. Participants spent on average 75% of their time at home during COVID. Time spent at home had a positive correlation with the mental health of urban students but a negative correlation with suburban students. The relation between the time at home with mental health was stronger among female participants than among male participants. Correlations between female depression, anxiety, and time at home were significant. Conclusions: Lockdown and distance learning contributed to the high levels of depression in university students. This research highlights the importance of time spent at home for mental health being during the pandemic and the importance of distinguishing between urban and suburban settings when formulating public health recommendations. Quality of time spent at home versus time spent outside differentiated the mental well-being of students located in different environments. Staying at home may be recommended for students without access to safe outdoor places as it is associated with lower levels of depression.
Power Distribution Grid Reliability Assessment Considering Protection and Control Devices’ Optimal Post-Fault Operation

Article history: Received 23 November 2021 Received in revised form 05 February 2022 Accepted 24 February 2022 The reliability level of the distribution network is a judgment tool of the grid and protection design quality, the effectiveness of the fault management unit, and customers’ satisfaction. In this paper, a new approach is presented to evaluate common reliability indices namely ENS, SAIDI, SAIFI, MAIFIe, etc., while reliability improvement via optimal post-fault restoration describes the coordinated operation of various protection and control devices in temporary and permanent fault event conditions. Customers’ outage times are calculated considering different switching operation times to capture manual operation issues, e.g., traffic level, geographical issues, fuse replacements, etc. The optimal service restoration scheme being formulated in a mixed-integer linear programming (MILP) fashion is constrained to network technical limitations, e.g., line thermal capacity, load points voltage level, DG units’ parameters, and island operation. The performance of the proposed framework is verified in IEEE 33-bus test system.
Optimal Solutions for Performance Improvement -- A Case Study of IT Center at Jazan University

Since the inception of Jazan University in the southern part of Saudi Arabia in 2006, Jazan University has been striving hard these years to facilitate the improvement of state-of-the-art services to its students. The IT Centre established in 2007 on campus is an important strategic decision in this direction. It is intended to offer various online services round the clock for both students and staff. Some of these services include e-learning, testing management, program scheduling, academic advising etc. As the university is expanding in its length and breadth, the demand for such services is also growing rapidly and the problems are in increase at the same time. There is an urgent need to identify the major management issues regarding to the service provided and find out solutions through analysis of potential root cause of these problems. This study is based on the application of Pareto Principle and Cause & Effect Diagram for trouble-shooting and suggest optimal solutions to reduce problems at the IT Centre. The research findings through this empirical approach will help to better formalize the operating strategy of the IT Centre and thus improve its service quality and the satisfaction level among the stakeholders.
High Prevalence of Astigmatism in Children after School Suspension during the COVID-19 Pandemic Is Associated with Axial Elongation

During the COVID-19 pandemic, the Hong Kong Government enforced a “school from home” policy between February and September 2020. This cross-sectional epidemiological study was conducted to investigate the prevalence of astigmatism and visual habits after the home confinement period. Vision screenings were conducted at three local government-funded primary schools in Hong Kong from October 2020 to December 2020. A total of 418 ethnically Chinese primary school children completed the eye examination and returned questionnaires concerning demographic information and visual habits. It was found that 46.5% (95% CI, 41.7–61.4%) of the children aged 8 to 11 years had astigmatism ≥ 0.75 D, which was predominately With-The-Rule astigmatism. The prevalence of astigmatism reported in these children is generally higher than that of studies conducted before COVID. Compared to their non-astigmatic peers, astigmatic children had a longer axial length (p < 0.001) and engaged in fewer outdoor activities (p = 0.04). Multiple linear regression analyses also revealed significant relationships between axial length and both cylindrical error and J0 astigmatism. Due to the high astigmatism prevalence, there is a pressing need for further studies on the long-term impact of the pandemic on children’s vision.