Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional Regulation and Its Relationship to the Quality of Emotions in the Work Environment for People with Physical Disabilities Working in the Government Sector in the Kingdom of Saudi Arabia

The study identified the level of emotional regulation, the level of quality of emotions in the work environment for people with physical disabilities in the government sector. The descriptive survey method was used to achieve the study objectives. The study sample consisted of (149) male individuals with physical disabilities working in the government sector, and their ages ranged between (25-50) years. Two questionnaires were used to measure the level of emotional regulation and the level of quality of emotions in the work environment. The results showed that the level of emotional regulation as a whole and its domains from the point of view of the study sample were at low levels. In addition, there is a positive (directive) and statistically significant correlation between positive re-evaluation, re-planning and positive focus, emotional regulation as a whole, and quality of emotions in the work environment for people with physical disabilities in the government sector.
Children with developmental coordination disorder have less variable motor unit firing rate characteristics across contractions compared to typically developing children

Introduction Understanding the nuances of neuromuscular control is crucial in unravelling the complexities of developmental coordination disorder (DCD), which has been associated with differences in skeletal muscle activity, implying that children with DCD employ distinct strategies for muscle control. However, force generation and control are dependent on both recruitment of motor units and their firing rates and these fine details of motor function have yet to be studied in DCD. The purpose of this study was therefore to compare motor unit characteristics in a small muscle of the hand during low level, handgrip contractions in typically developing (TD) children and children with DCD. Methods Eighteen children (9 TD vs. 9 DCD) completed a series of manual handgrip contractions at 10 ± 5% of their maximum voluntary contraction. High density surface electromyography was used to record excitation of the first dorsal interosseus muscle. Recorded signals were subsequently decomposed into individual motor unit action potential trains. Motor unit characteristics (firing rate, inter-pulse interval, and action potential amplitude) were analysed for contractions that had a coefficient variation of <10%. Results and Discussion This study found few differences in average motor unit characteristics (number of motor units: TD 20.24 ± 9.73, DCD 27.32 ± 14.00; firing rate: TD 7.74 ± 2.16 p.p.s., DCD 7.86 ± 2.39 p.p.s.; inter-pulse interval: TD 199.72 ± 84.24 ms, DCD 207.12 ± 103 ms) when force steadiness was controlled for, despite the DCD group being significantly older (10.89 ± 0.78 years) than the TD group (9.44 ± 1.67 years). However, differences were found in the variability of motor unit firing statistics, with the children with DCD surprisingly showing less variability across contractions (standard deviation of coefficient of variation of inter-pulse interval: TD 0.38 ± 0.12, DCD 0.28 ± 0.11). This may suggest a more fixed strategy to stabilise force between contractions used by children with DCD. However, as variability of motor unit firing has not been considered in previous studies of children further work is required to better understand the role of variability in motor unit firing during manual grasping tasks, in all children.
A Study Over Determination of Asymptotic Deceleration and Absolute Acceleration Points in Logistic Growth Model

Since growth models has generally upper horizontal asymptote, they do not have a maximum point. We wonder about after which point growth can be considered constant, that is, after which point the curve of the growth function is too close to its asymptote. That point is called maximum deceleration point. After this point the deceleration is very slow and the second derivative of the growth function goes to zero as time tends to infinity. After this point it is considered that the amount of the growth is quite small. Moreover, we wonder about which point is an absolute acceleration point so that before that point acceleration is very slow and after that point actual acceleration starts. So we could say that after this point actual growth starts. In this study, the logistic growth model was used to investigate these points, asymptotic deceleration and absolute acceleration points in addition to the other critical and important points such as inflection point, maximum acceleration point, maximum deceleration point. The graphs of the logistic growth model which show all these points mentioned above are also given by using a data set.
AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review

Abstract Objective Human error estimating food intake is a major source of bias in nutrition research. Artificial intelligence (AI) methods may reduce bias, but the overall accuracy of AI estimates is unknown. This study was a systematic review of peer-reviewed journal articles comparing fully automated AI-based (e.g. deep learning) methods of dietary assessment from digital images to human assessors and ground truth (e.g. doubly labelled water). Materials and Methods Literature was searched through May 2023 in four electronic databases plus reference mining. Eligible articles reported AI estimated volume, energy, or nutrients. Independent investigators screened articles and extracted data. Potential sources of bias were documented in absence of an applicable risk of bias assessment tool. Results Database and hand searches identified 14,059 unique publications; fifty-two papers (studies) published from 2010 to 2023 were retained. For food detection and classification, 79% of papers used a convolutional neural network. Common ground truth sources were calculation using nutrient tables (51%) and weighed food (27%). Included papers varied widely in food image databases and results reported, so meta-analytic synthesis could not be conducted. Relative errors were extracted or calculated from 69% of papers. Average overall relative errors (AI vs. ground truth) ranged from 0.10% to 38.3% for calories and 0.09% to 33% for volume, suggesting similar performance. Ranges of relative error were lower when images had single/simple foods. Conclusions Relative errors for volume and calorie estimations suggest that AI methods align with – and have the potential to exceed – accuracy of human estimations. However, variability in food image databases and results reported prevented meta-analytic synthesis. The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be adequate for training and testing and by reporting accuracy of at least absolute and relative error for volume or calorie estimations.
Human Probiotic Lactobacillus paracasei-Derived Extracellular Vesicles Improve Tumor Necrosis Factor-α-Induced Inflammatory Phenotypes in Human Skin

Lactic acid bacteria (LAB), a probiotic, provide various health benefits. We recently isolated a new Lactobacillus paracasei strain with strong anti-inflammatory effects under lipopolysaccharide-induced conditions and proposed a new mode of action—augmenting the endoplasmic reticulum stress pathway for anti-inflammatory functions in host cells. The beneficial effects of the L. paracasei strains on the skin have been described; however, the effects of L. paracasei-derived extracellular vesicles (LpEVs) on the skin are poorly understood. Herein, we investigated whether LpEVs can improve inflammation-mediated skin phenotypes by determining their effects on primary human skin cells and a three-dimensional (3D) full-thickness human skin equivalent under tumor necrosis factor (TNF)-α-challenged inflammatory conditions. LpEVs were efficiently taken up by the human skin cells and were much less cytotoxic to host cells than bacterial lysates. Furthermore, low LpEV concentrations efficiently restored TNF-α-induced cellular phenotypes, resulting in increased cell proliferation and collagen synthesis, but decreased inflammatory factor levels (matrix metalloproteinase 1, interleukin 6, and interleukin 8) in the human dermal fibroblasts, which was comparable to that of retinoic acid, a representative antiaging compound. The beneficial effects of LpEVs were validated in a 3D full-thickness human skin equivalent model. LpEV treatment remarkably restored the TNF-α-induced epidermal malformation, abnormal proliferation of keratinocytes in the basal layer, and reduction in dermal collagen synthesis. Additionally, LpEVs penetrated and reached the deepest dermal layer within 24 h when overlaid on top of a 3D full-thickness human skin equivalent. Furthermore, they possessed superior antioxidant capacity compared with the human cell-derived EVs. Taken together, the anti-inflammatory probiotic LpEVs can be attractive antiaging and antioxidant substances for improving inflammation-induced skin phenotypes and disorders.
Validation of Claims Data for Absorbing Pads as a Measure for Urinary Incontinence after Radical Prostatectomy, a National Cross-Sectional Analysis

Simple Summary Radical prostatectomy (RP) is a common treatment for prostate cancer but has a risk of side-effects. Urinary incontinence (UI) after RP ranges from 4 to 31%, depending on the method of reporting and definitions used. The aim of this study was to evaluate if healthcare insurance claims data of absorbing pads in the Netherlands provide a valid alternative in the measurement of post-prostatectomy UI (defined as the use of ≥1 pad(s) per day), compared to self-reported pad use. Claims data and self-reported use was available for 416 patients. According to the claims data, patients had a UI rate of 31%, compared to a self-reported UI rate of 45% one year after RP. The agreement between both measures was moderate. Claims data can be considered as a conservative quality indicator for UI after RP. Abstract The use of healthcare insurance claims data for urinary incontinence (UI) pads has the potential to serve as an objective measure for assessing post-radical prostatectomy UI rates, but its validity for this purpose has not been established. The aim of this study is to correlate claims data with Patient Reported Outcome Measures (PROMs) for UI pad use. Patients who underwent RP in the Netherlands between September 2019 and February 2020 were included. Incontinence was defined as the daily use of ≥1 pad(s). Claims data for UI pads at 12–15 months after RP were extracted from a nationwide healthcare insurance database in the Netherlands. Participating hospitals provided PROMS data. In total, 1624 patients underwent RP. Corresponding data of 845 patients was provided by nine participating hospitals, of which 416 patients were matched with complete PROMs data. Claims data and PROMs showed 31% and 45% post-RP UI (≥1 pads). UI according to claims data compared with PROMs had a sensitivity of 62%, specificity of 96%, PPV of 92%, NPV of 75% and accuracy of 81%. The agreement between both methods was moderate (κ = 0.60). Claims data for pads moderately align with PROMs in assessing post-prostatectomy urinary incontinence and could be considered as a conservative quality indicator.
Which Animation API Should I Use Next? A Multimodal Real-Time Animation API Recommendation Model for Android Apps

UI animation is a widely adopted design element in the UI of Android apps. There are many animation APIs available for a variety of purposes, and developers can utilize them to realize the UI animations to avoid reinventing the wheel and thus improve the development efficiency. However, the number of animation APIs is as high as thousands and it is non-trivial for developers to systematically master their use. Facing such a problem, we construct a multi-modal real-time animation API recommendation model called U-A2A in this paper, which can provide the available animation API for developers of Android apps in real-time throughout the animation realization according to the multi-modal information, that is, the information of UI animation task and the animation API context of current program (i.e., the animation API sequence that has been used). The reason for considering the animation API context is that realizing a UI animation requires the use of multiple animation APIs and relevant animation APIs roughly follow a sequence. U-A2A consists of two important parts: feature extractor and predictor. The feature extractor, which is constructed based on 3D CNN and GRU, can gain the combined feature of UI animation task as well as animation API context. The predictor consists of a fully connected layer as well as a softmax layer, and it can predict and recommend the next available animation API according to the result from feature extractor. Furthermore, we use the development experience about animation APIs of existing app products as the basis to adjust the parameters of U-A2A, thereby completing the training work of recommendation model. The experimental result shows that when 1, 3, 5, and 10 animation APIs are considered, U-A2A can achieve 45.13%, 65.72%, 72.97% and 81.85% accuracy respectively, which is much higher than the baseline LUPE.
Human capital readiness and global market orientation to business performance: The mediation role of green innovation

This study aims to empirically examine the mediating effect of green innovation on the influence of human capital readiness and global market orientation on micro, small and medium enterprises (MSMEs) business performance. This study was conducted on MSMEs in East Java, Indonesia, with a total of 326 respondents. The data analysis technique in this study used the partial least square (PLS) method. The results of this study indicate that green innovation partially mediates the effect of human capital readiness on MSME business performance. Moreover, green innovation partially mediates the influence of global market orientation on business performance .
Altered synaptic plasticity at hippocampal CA1–CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models

Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at hippocampal CA1–CA3 synapses. The goal of the present study is to build computational models that incorporate the effect of AD-related peptides on CA1 pyramidal neuron and hippocampal synaptic plasticity under the AD conditions and investigate the potential pharmacological treatments that could normalize hippocampal synaptic plasticity and learning in AD. We employ a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model that includes the separate receptor contributions on long-term potentiation (LTP) and long-term depression (LTD) and embed it into the a detailed compartmental model of CA1 pyramidal neuron. Modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues LTP in AICD and Aβ conditions. The model provides insight into the complex interactions in AD pathophysiology and suggests the conditions under which the synchronous activation of a cluster of synaptic inputs targeting the dendritic tree of CA1 pyramidal neuron leads to restored synaptic plasticity.
Three-Dimensional Magnetic Rehabilitation, Robot-Enhanced Hand-Motor Recovery after Subacute Stroke: A Randomized Controlled Trial

We developed an end-effector-type rehabilitation robot that can uses electro- and permanent magnets to generate a three-way magnetic field to assist hand movements and perform rehabilitation therapy. This study aimed to investigate the therapeutic effect of a rehabilitation program using a three-dimensional (3D) magnetic force-based hand rehabilitation robot on the motor function recovery of the paralyzed hands of patients with stroke. This was a double-blind randomized controlled trial in which 36 patients with subacute stroke were assigned to intervention and control groups of 18 patients each. The intervention group received 30 min of rehabilitation therapy per day for a month using a 3D magnetic force-driven hand rehabilitation robot, whereas the control group received 30 min of conventional occupational therapy to restore upper-limb function. The patients underwent three behavioral assessments at three time points: before starting treatment (T0), after 1 month of treatment (T1), and at the follow-up 1-month after treatment completion (T2). The primary outcome measure was the Wolf Motor Function Test (WMFT), and secondary outcome measures included the Fugl–Meyer Assessment of the Upper Limb (FMA_U), Modified Barthel Index (MBI), and European Quality of Life Five Dimensions (EQ-5D) questionnaire. No participant safety issues were reported during the intervention. Analysis using repeated measures analysis of variance showed significant interaction effects between time and group for both the WMFT score (p = 0.012) and time (p = 0.010). In post hoc analysis, the WMFT scores and time improved significantly more in the patients who received robotic rehabilitation at T1 than in the controls (p = 0.018 and p = 0.012). At T2, we also consistently found improvements in both the WMFT scores and times for the intervention group that were superior to those in the control group (p = 0.024 and p = 0.018, respectively). Similar results were observed for FMA_U, MBI, and EQ-5D. Rehabilitation using the 3D hand-rehabilitation robot effectively restored hand function in the patients with subacute stroke, contributing to improvement in daily independence and quality of life.
Domestic Water Conservation in the Federal Capital Territory of Nigeria: Perceptions and Practices

Many regions in Nigeria currently experience physical or economic water scarcity; a situation that isexpected to worsen because of the nation’s rapid population growth, which has resulted in increased pressure on available freshwater resources. This study, therefore, examines water conservation practices in the Federal Capital Territory (FCT), one of the fastest-growing areas in Nigeria. A questionnaire was administered to 649 respondents, from across the six Area Councils that make up the Territory. The findings revealed that water investments in the FCT have not yielded 100% public water supply coverage for residents. About half of the respondents (48.7%) reduced their water use by minimizing the water used for washing, and a slightly higher proportion (53.5%) minimized water used for bathing. While rain harvesting was the most widespread conservation practice, with 65.3% of the respondents adopting it, the least practised conservation method is wastewater reuse, where only about 41% of the respondents adopted it. Although themajority of the respondents were literates, education did not impact their conservation practice. The age and gender of respondents played no significant role in the adoption of conservation practices. More public awareness and water education are needed to promote greater domestic water conservation among residents.
Domain adaptation by preserving topology

Domain adaptation is highly researched area among machine learning experts. In domain adaptation we use a domain with enough class label (source domain) and try to predict class label of a different data which doesn’t have any class label (target domain) . Both source and target domain share the same features space. Many approaches are there but one popular approach is to reduce the distance between source and target domain data distributions. There are many algorithm that tries to project the source and target data into a latent space so that the distance between data distribution of source and target domain reduces. But when we try to project data into a latent space the shape of data may change . Most domain adaptation algorithm does not try to preserve the shape of data explicitly. Projection of data into a latent space may change the shape of data or precisely the topology of the data. If we can preserve the topology of the data when we are projecting the data into a latent space then we may achieve better accuracy . In this thesis we have developed a method so that we can preserve the topology of the data at the time of projecting it into a latent space . For projection in to a latent space we have used auto-encoder that create encoded version of data at hidden layers. Hidden layer representation of an autoencoder is a projection of data into a latent space . The distance between data distribution of hidden layer representation of source and target data reduces . In an auto-encoder ,the shape of the data may not be preserved i.e auto-encoder is not forced to preserve any topological property of the data. In this thesis we have added a extra constraint in a auto-encoder so that auto-encoder can preserve topological property of the data . Preserving topology of data can enhance the ability of the classifier that is trained on source data to correctly classify target data .
Effect of phosphorus and sulphur application on their dynamics and nodulation in soil under black gram [Vigna mungo (L.) Hepper] crop

The experiment comprising of four levels of phosphorus, i.e., 0, 20, 40 and 60 kg ha-1 and three levels of sulphur, i.e., 0, 15 and 30 kg ha-1, was conducted during summer of 2015-16 to investigate the effect of phosphorus and sulphur application on their dynamics in soil under the crop of black gram cv. Uttara. The treatments were laid out in randomized block design (factorial) and replicated three times. The results reveal that the available and organic phosphorus (kg ha-1) significantly increased with each successive application of phosphorus in soil up to highest level (60 kg ha-1) at 20 days after sowing (DAS) of black gram, while it showed decreasing trend with time intervals of 40 DAS and at maturity of black gram. The application of successive doses of sulphur had no significant effect on available and organic phosphorus at each time interval. Similarly, significant increase has also been recorded in available and organic sulphur (kg ha-1) in soil with each successive application of sulphur up to 30 kg ha-1 at 20 DAS of black gram, thereafter, it showed decreasing trend. The application of successive doses of phosphorus had no significant effect on available and organic sulphur at each time interval. Number of nodules plant-1 also increased significantly with increasing levels of phosphorus and sulphur up to highest level and the optimum values were recorded with combined application of phosphorus 60 kg ha-1 and sulphur 30 kg ha-1.