Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound Imaging in Predicting the Autograft Size in Anterior Cruciate Ligament Reconstruction: A Systematic Review and Meta-Analysis

Anterior cruciate ligament (ACL) reconstruction is widely used to restore knee stability after injury, but the risk of revision surgery increases when the autograft size is inadequate. Ultrasound (US) measurements of preoperative target tendons have been applied to predict the intraoperative autograft size, with various outcomes across different studies. This systematic review and meta-analysis aimed to summarize the evidence and investigate the usefulness of US in predicting autograft size. Electronic databases were searched for relevant studies from inception to 19 January 2022. The primary outcome was the correlation between the preoperative US measurements of donor tendons and intraoperative autograft size. The secondary outcomes encompassed the predictive performance of US for autograft size and the comparison between US and magnetic resonance imaging (MRI) for preoperative tendon measurements. Nine studies, comprising 249 patients, were enrolled. The preoperative US measurements of the donor tendons demonstrated a significant positive correlation with their intraoperative autograft diameter, with a pooled correlation coefficient of 0.443 (95% confidence interval [CI], 0.266–0.591, p < 0.001) for the gracilis and semitendinosus autograft, 0.525 (95% CI, 0.114–0.783, p = 0.015) for the semitendinosus autograft, and 0.475 (95% CI, 0.187–0.687, p = 0.002) for the gracilis autograft. The pooled sensitivity and specificity of US imaging in predicting the autograft diameter were 0.83 (95% CI 0.57–0.95) and 0.70 (95% CI, 0.36–0.91), respectively. Moreover, no significant differences were observed between US and MRI measurements in predicting the sizes of the gracilis and semitendinosus autografts. Preoperative US measurements of the target tendons were moderately correlated with the intraoperative autograft size. US imaging has a discriminative performance similar to that of MRI in predicting the autograft size. A standardized US scanning protocol is needed for future studies to minimize the variations in tendon measurements across different investigators and increase the comparability of US imaging with intraoperative findings.
Value of Cerebroplacental Ratio and Uterine Artery Doppler as Predictors of Adverse Perinatal Outcome in Very Small for Gestational Age at Term Fetuses

The aim of this study was to evaluate the association between cerebroplacental ratio (CPR), mean uterine artery (mUtA) Doppler and adverse perinatal outcome (APO) and their predictive performance in fetuses with birth weight (BW) <3rd centile (very small for gestational age, VSGA) in comparison with fetuses with BW 3rd–10th centile (small for gestational age, SGA). This was a retrospective cohort study including singleton pregnancies delivered at term (37 + 0–41 + 6) in a single tertiary referral center over a six-year period. APO was defined as a composite of cesarean section for intrapartum fetal compromise (IFC), umbilical artery pH < 7.20, and admission to the neonatal intensive care unit for >24 h. The characteristics of the study population according to BW (VSGA and SGA) as well as the presence of composite APO were assessed. The prognostic performance of CPR and mUtA-PI was evaluated using receiver operating characteristic (ROC) analysis. In total, 203 pregnancies were included. Of these, 55 (27%) had CPR <10th centile, 25 (12%) mUtA-PI >95th centile, 65 (32%) VSGA fetuses, and 93 (46%) composite APO. VSGA showed a non-significantly higher rate of composite APO in comparison to SGA (52% vs. 43%; p = 0.202). The composite APO rate was significantly higher in SGA with CPR <10th centile (36% vs. 13%; p = 0.001), while in VSGA with CPR <10th centile was not (38% vs. 35%; p = 0.818). The composite APO rate was non-significantly higher both in VSGA (26% vs. 10%; p = 0.081) and SGA (14% vs. 6%; p = 0.742) with mUtA-PI >95th centile. The ROC analysis showed a significantly predictive value of CPR for composite APO in SGA only (AUC 0.612; p = 0.025). A low CPR was associated with composite APO in SGA fetuses. VSGA fetuses were more frequently affected by composite APO regardless of Doppler values. The predictive performance of CPR and uterine artery Doppler was poor.
EAGL: Enhancement Algorithm based on Gamma Correction for Low Visibility Images

Under poor light conditions or improper acquisition settings, the image degrades due to low contrast, poor brightness and suffer poor visual quality of the picture. An enhancement is required to manipulate the scale of pixel intensity for significant improvement in the image. This paper proposed the method of gamma correction with a self-adaptive value in accordance with the intensity scale of the image. After transformation to HSI (hue, saturation and intensity) channel, a multi-scale wavelet transform is implemented on the intensity component of the image. The gamma scale is computed from the combination of reformed scale constant of logarithm function and Minkowski distance measure. Lastly, wavelet based denoising technique is applied to suppress high noise coefficients to improve quality of the image. The proposed method is evaluated in terms of visual appearance, measure of information content, signal to noise ratio, and universal image quality index. It demonstrated that the proposed method showed its efficacy in terms of quality and improved visibility. Keywords—Low scale intensity images; discrete wavelet decomposition; gamma correction; quality metrics
The Relationship Between Absolute Poverty Income Inequality in Rural and Urban Areas in Indonesia: The Granger Causality Approach

This study aims to determine the relationship between poverty and inequality in rural and urban areas by using the Granger causality test approach. This research method uses the Granger causality test analysis tool with rural and urban areas as objects in all provinces in Indonesia with use data from thirty four province and period year from 2014 until 2020. The results of the Granger causality test for rural areas show that there is a realtionship unidirectional relationship between poverty and inequality, the relationship shows significant inequality that causes poverty in rural areas, that meaning in rural area absolute poverty happen because inequality income distribution, while the results of causality tests for urban areas show that there is no relationship at all between poverty and inequality in the urban areas. that meaning In urban areas no causality between absolute poverty and inequality income distribution, this result shows that poverty and inequality in urban areas do not contain a direct one way or two-way relationship.
Probabilistic Modelling of Electric Vehicle Charging Demand based on Charging Station Data

The number of electric vehicles is expected to increase significantly, which will affect the operation of future power systems. Yet the availability of EV charging station data is very low, and hence, it is challenging to perform the EV impact studies with real (or randomly generated) EV data that accurately represents the spatio-temporal uncertainty of the electric vehicle (EV) charging demand. This study analyzes electric vehicle (EV) charging station data to derive appropriate probability distributions for EV charging stations so that EV data can be randomly generated to perform probabilistic EV impact studies. This study has collected aggregated residential, commercial and public EV charging station data for one-year duration. Seven probability distributions have been tested to identify the appropriate probability distributions for aggregated residential, slow commercial and fast public charging stations. It is observed from the simulation results that aggregated residential, slow commercial, and fast public EV charging station data follow extreme value, exponential and Gaussian probability distributions, respectively. Further, power flow simulations have been performed with EV data generated using a variety of probability distributions and voltage violation scenarios have been recorded in a test distribution network. The number of voltage violations again verified the above selection of probability distributions for aggregated residential, slow commercial and fast public charging stations.
Towards Fully Sparse Training: Information Restoration with Spatial Similarity

The 2:4 structured sparsity pattern released by NVIDIA Ampere architecture, requiring four consecutive values containing at least two zeros, enables doubling math throughput for matrix multiplications. Recent works mainly focus on inference speedup via 2:4 sparsity while training acceleration has been largely overwhelmed where backpropagation consumes around 70% of the training time. However, unlike inference, training speedup with structured pruning is nontrivial due to the need to maintain the fidelity of gradients and reduce the additional overhead of performing 2:4 sparsity online. For the first time, this article proposes fully sparse training (FST) where `fully' indicates that ALL matrix multiplications in forward/backward propagation are structurally pruned while maintaining accuracy. To this end, we begin with saliency analysis, investigating the sensitivity of different sparse objects to structured pruning. Based on the observation of spatial similarity among activations, we propose pruning activations with fixed 2:4 masks. Moreover, an Information Restoration block is proposed to retrieve the lost information, which can be implemented by efficient gradient-shift operation. Evaluation of accuracy and efficiency shows that we can achieve 2× training acceleration with negligible accuracy degradation on challenging large-scale classification and detection tasks.
Optimum parameters extraction of proton exchange membrane fuel cells using Fractional‐Order Whale Optimization Algorithm

This study proposes a novel metaheuristic‐based technique for optimum parameters' estimation of the proton exchange membrane fuel cells (PEMFCs). To provide better results with more reliability and accuracy, a Fractional Order‐based design of the Whale Optimizer Algorithm (FO‐WOA) is designed. A validation test showed that the proposed method provides a good trade‐off between accuracy and convergence speed. Performing the algorithm for multiple independent runs also shows that the proposed method delivers reliable results toward some other comparative metaheuristic algorithms. This algorithm is then used for the minimization of the sum of square deviation between the experimental voltage‐current polarization and the optimal achieved results by the model based on FO‐WOA. The method is validated by considering two practical case studies, which are the Nexa PEMFC and 250 W PEM system, and its achievements are put in comparison with some approaches to indicate the higher effectiveness of the proposed method toward the others. The experimental results on the Nexa PEMFC indicate that the proposed FO‐WOA‐based method with 12 sum square errors (SSE) provided the minimum error toward the other. Also, the experiments on the 250 W PEM Stack indicated that for 250 W PEM Stack with 3/5 bar and 80°C, the proposed method with 0.01 SSE provides the fittest profile with the experimental data, and finally, the experiments on the same stack with 3/5 bar and 80°C showed the higher accuracy of the proposed method with the least SSE value (0.16) toward the others.
Magnitude of Preoperative Anxiety and Associated Factors Among Adult Surgical Patients in Debre Berhan Comprehensive Specialized Hospital

Background Anxiety is an un-pleasurable emotional state associated with psychophysiological changes in response to an intra-psychic conflict. The perioperative period is one of the worrying events for most surgical patients. Despite preoperative anxiety having many negative consequences on post-operative physical and mental health, no adequate information on the degree to which the preoperative period exposed clients to preoperative anxiety and its associated factors. Objective To assess the prevalence of preoperative anxiety and associated factors among adult surgical patients in Debre Berhan Comprehensive Specialized Hospital, Ethiopia. Methods Institution-based cross-sectional study was conducted from June 1, 2020, to August 30, 2020. State-Trait Anxiety Inventory Scale (S-STAI) was used to measure the level of pre-operative anxiety. A systematic random sampling technique was employed to collect the data. The data were entered to Epi-Data version 4.6 and exported to SPSS version 25 for data analysis. A statistically significant association was declared at a p-value less than 0.05. Results A total of 330 patients were enrolled in the study with a response rate of 93.2%. The prevalence of a high level of preoperative anxiety was 53.6% (95% CI (49.6–58.7)). Being female 3.2 (2.19, 3.71) illiterate 4.1 (2.01, 15.39), fear of death 2.12 (1.30, 3.44), results of operation 2.60 (1.75, 4.18), postoperative pain 2.35 (1.37, 4.02), and surgical complication 1.9 (1.03, 12.13) were statistically significant factors. Conclusion Preoperative anxiety affects more than half of surgical patients which is a high and serious health burden. Additionally, this study revealed that being female, illiterate, fear of death, having a history of surgical complications and fear of complications, and fear of postoperative pain were significantly associated with preoperative anxiety. Assessing during the preoperative period helps to detect and ameliorate the problem. Preoperative counseling and informed consent taken as a prerequisite for surgery will help in reducing preoperative anxiety.
An Integrated System for Drivers’ Drowsiness Detection Using Deep Learning Frameworks

Drowsiness or sleepiness is a transition stage between alertness and sleep. Drowsy driving causes collisions, injuries and deaths. This paper presents an integrated system for drivers’ drowsiness detection based on deep learning frameworks. The integrated system consists of three parts, i.e., eye region detection, eye state detection and classification, and an alert system generation based on drowsiness level. Faster region based convolutional neural network (f-RCNN) has been employed for eye region detection to extract eye regions from facial images of driver with complicated background. In the next stage, image containing only eye region is fed into a CNN for necessary identification. Finally, an alert system has been generated based on the drowsiness level of drivers’ eye states. The proposed module has been implemented using an Atmega328p microcontroller to achieve a real time evaluation.
Smart Hospitals and IoT Sensors: Why Is QoS Essential Here?

Background: the increasing adoption of smart and wearable sensors in the healthcare domain empowers the development of cutting-edge medical applications. Smart hospitals can employ sensors and applications for critical decision-making based on real-time monitoring of patients and equipment. In this context, quality of service (QoS) is essential to ensure the reliability of application data. Methods: we developed a QoS-aware sensor middleware for healthcare 4.0 that orchestrates data from several sensors in a hybrid operating room. We deployed depth imaging sensors and real-time location tags to monitor surgeries in real-time, providing data to medical applications. Results: an experimental evaluation in an actual hybrid operating room demonstrates that the solution can reduce the jitter of sensor samples up to 90.3%. Conclusions: the main contribution of this article relies on the QoS Service Elasticity strategy that aims to provide QoS for applications. The development and installation were demonstrated to be complex, but possible to achieve.
Habitat use of the white‐headed langurs in limestone forest of Southwest Guangxi, China: Seasonality and group size effects

Abstract Understanding how animals cope with habitat‐specific environmental factors can assist in species conservation management. We studied the habitat use of four groups (two large and two small groups) of white‐headed langurs (Trachypithecus leucocephalus) living in the forest of southwest Guangxi, China between September 2016 and February 2017 via instantaneous scan sampling. Our results showed that the langurs primarily used hillsides (55.91% ± 6.47%), followed by cliffs (29.70% ± 5.48%), hilltops (7.26% ± 3.55%), flat zones (6.99% ± 6.58%), and farmlands (0.14% ± 0.28%). The langurs moved most frequently on hillsides (49.35% ± 6.97%) and cliffs (35.60% ± 9.17%). The hillsides were more frequently used (66.94% ± 7.86%) during feeding, and the langurs increased the use of hilltops during the rainy season, and the use of cliffs in the dry season. The langurs frequently rested on hillsides (49.75% ± 8.16%) and cliffs (38.93% ± 8.02%). The larger langur group used cliffs more frequently when moving and resting, whereas the small langur group used hillsides more frequently while resting. Langurs in all groups avoided the flat zones for feeding. Their use of habitat reflected the balancing of foraging needs, thermoregulation, and predator avoidance. We conclude that the ecological factors are determinants of habitat use for white‐headed langurs. Our findings suggest that conservation efforts should focus on protecting the vegetation on the hillsides and restoring the vegetation on the flat zones.
Marked crosswalks in US transit-oriented station areas, 2007–2020: A computer vision approach using street view imagery

Improving the built environment to support walking is a popular strategy to increase urban sustainability and walkability. In the past decade alone, many US cities have implemented crosswalk visibility enhancement programs as part of road safety improvements and active transportation plans. However, there are no systematic ways of measuring and monitoring the presence of key built environment attributes that influence the safety and walkability of an area, such as marked crosswalks. Furthermore, little is known about how these attributes change over time at a national scale. In this paper, we introduce an innovative approach using a deep learning-based computer vision model on Street View images to identify changes in intersection-level marked crosswalks around more than 4,000 US transit stations over a 14-year period. We found an increase in the overall number of marked crosswalks at intersections. Furthermore, high-visibility crosswalks became more common, as they replaced existing parallel-line crosswalks. We further examine crosswalks around transit stations in New York City and San Francisco to illustrate geographic variations and compare associations with other characteristics of the built environment as reported in the Smart Location Database. Areas with increases in high-visibility crosswalks focused on high density residential areas and areas with a higher percent of zero-vehicle households. However, geographic variations exist. For example, in San Francisco, transit station areas outside downtown or major corridors (South and Southwest of the city) had the lower prevalence of marked crosswalks. This analysis confirms important gaps in crosswalk visibility that call for safety enhancements and opens the door for additional research involving these data. We conclude by discussing the limitations and future research opportunities using computer vision to automatically detect large-scale transportation infrastructure changes at a relatively low cost.
Inductive Power Transfer Based on Variable Compensation Capacitance to Achieve an EV Charging Profile With Constant Optimum Load

Wireless charging must be highly efficient throughout the entire battery charging profile to compete in the electric vehicle (EV) industry. Thus, optimum load matching is commonly used: it operates at the equivalent load that maximizes the efficiency, which depends on the coil’s alignment. In this article, the optimum load is made independent of the coils’ position by changing the system’s resonant frequency through switch-controlled capacitors (SCCs). This eliminates the need for load-side voltage control. The output current follows the battery voltage rise during the battery charging cycle to always match the optimum load, which can be achieved by regulating the input voltage via the power factor correction (PFC) converter. This method is called here constant optimum load (COL). Two SCC topologies have been implemented in a 3.7-kW hardware demonstrator. The one implementing the half-wave modulation achieves higher efficiency than the one employing full-wave modulation, with 96.30% at 3.2 kW and aligned coils. When misalignment occurs, the half-wave modulation technique results in higher efficiency than the conventional-fixed compensation, where the efficiency is lower by up to 0.68% at partial load. Based on these results, the proposed COL method is proven suitable for 3.7-kW EV-static wireless charging achieving one of the highest peak efficiencies listed in today’s literature for the same power class.
Can we predict learners' personalities through their behavioural patterns? A pilot study using Behaviour Analytics-Moodle plugin

Given the importance of personality in affecting learners' behaviours, it becomes necessary to take it into consideration when designing online learning environments. However, the traditional method to identify personality, namely questionnaires, could be inaccurate as learners might try to look in a fashionable way when they are assessed by others. Therefore, this study presents a new developed Behavioural Analytics (BA) MoodIe plug-in which can help in identifying learners' personalities based on their learning behaviour patterns. In particular, this plug-in applies the k-means machine learning algorithm to cluster the learners based on their common learning behaviours in a specific course. To evaluate the accuracy of the personality identification based on the clustering results, a pilot was conducted at a public Tunisian university with 23 learners enrolled in an online course. The obtained results highlighted that the BA plug-in clustering has a good accuracy rate which is 65% in predicting extraversion personality. The obtained findings can help to understand the behavioural patterns of learners according to their personalities, hence designing personalized learning environments accordingly.
Quality-Aware Bag of Modulation Spectrum Features for Robust Speech Emotion Recognition

Automatic speech emotion recognition (SER) has gained popularity over the last decade and numerous Challenges have emerged. While the latest Challenges have shown that deep neural networks achieve the best results, existing input features are still a bottleneck and cause severe performance degradation in realistic “in-the-wild” scenarios. In this paper, we propose two innovations to tackle this issue. First, we propose to combine the bag-of-audio-words methodology with modulation spectrum features for environmental robustness. Second, we take advantage of the inherent quality-awareness properties of modulation spectrum and propose the use of a quality feature as an additional feature to be used by the speech emotion recognizer. Experiments are conducted with three multi-lingual speech datasets used in recent SER Challenges degraded by different noise sources and levels, and room reverberation. Experimental results show the proposed features i) consistently outperforming benchmark systems, ii) providing complementary information to classical features, hence improving performance with feature fusion, and iii) showing robustness against environment and language mismatch. Moreover, we show that when the proposed system is provided with quality information, further improvements are obtained. Overall, the proposed bag of modulation spectrum features are shown to be a promising candidate for “in-the-wild” SER.