Miracle-dz/UniFAR-FTUs huggingface dataset:
Be Used as Biomarkers of Oocyte Competence Inducible Nitric Oxide Synthase and Heme Oxygenase 1 Are Expressed in Human Cumulus Cells and

The interplay between oocyte and surrounding cumulus cells (CCs) during follicular growth influences oocyte competence to undergo fertilization and sustain embryo development. The expression of many genes and proteins in CCs has been suggested as potential biomarker of oocyte competence in human in vitro fertilization (IVF). In the present study, we analyzed 90 human cumulus–oocyte complexes obtained during IVF procedure: 30 CCs were analyzed using quantitative real-time polymerase chain reaction and 60 CCs using Western blotting analysis to detect gene and protein expression of some enzymes related to oxidative stress, that is, the 3 nitric oxide synthase (NOS) isoforms and heme oxygenase 1 (HO-1). In the group of 60 CCs, we also investigated the expression and phosphorylation of IkBa, a known inhibitor of the nuclear factor kB (NF-kB) pathway, which controls several redox-sensitive genes. The expression of the messenger RNAs (mRNAs) was related to the oocyte morphological analysis performed by polarized light microscopy and to the occurrence of normal fertilization after intracytoplasmic sperm injection. We observed that the amount of iNOS and HO-1 mRNAs and proteins is significantly higher, and that in the meanwhile the NF-kB pathway is activated, in CCs corresponding to oocytes that were not fertilized in comparison to CCs whose corresponding oocyte showed normal fertilization. Instead, no correlation between the fertilization and the oocytes’ morphological data was observed. These results suggest that the increase in iNOS and HO-1 mRNAs expression in CCs is a negative index of oocyte fertilizability and might be an useful tool for oocyte selection.
Gender, Social Change and Urbanisation in Four North Indian Clusters

Urbanisation in India is reshaping established social and economic patterns of behaviour in ways that scholars are struggling to analyse. This article introduces this special issue presenting new empirical research on the interconnections between gender, social change and urbanisation in India. It does so by relying on a unique dataset drawn from nearly 15,000 households across four consequential urban clusters—Dhanbad, Indore, Patna and Varanasi—in North India. The collection of articles in this issue informs new inquiries into women’s employment, women’s agency and the construction and shaping of social attitudes. Specifically, the articles disentangle the practical barriers to women’s economic empowerment, measure how employment and household dynamics shape women’s agency and explore ways in which status hierarchies and variation in access to information colour women’s social attitudes and political preferences. Collectively, they demonstrate the uneven nature of gender empowerment in the shadow of an urbanising, but highly stratified economy and society.
Removal of toxic hydroquinone: Comparative studies on use of iron impregnated granular activated carbon as an adsorbent and catalyst

In this study, iron (Fe) impregnated granular activated carbon (Fe-GAC) has been synthesized and characterized for various properties. Comparative studies have been performed for use of Fe-GAC as an adsorbent as well as a catalyst during catalytic oxidation of hydroquinone (HQ). In the batch adsorption study, effect of process parameter like initial HQ concentration (Co = 25-1,000 mg/L), pH (2-10), contact time (t: 0-24 h), temperature (T: 15-45°C) and adsorbent dose (w: 5-50 g/L) have been studied. Maximum HQ adsorption efficiency of 75% was obtained at optimum parametric condition of: pH = 4, w = 40 g/L and t = 14 h. Pseudo-second order model best-fitted the HQ adsorption kinetics whereas Langmuir model best-represented the isothermal equilibrium behavior. During oxidation studies, effect of various process parameters like initial HQ concentration (Co: 20-100 mg/L), pH (4-8), oxidant dose (CH2O2: 0.4-1.6 mL/L) and catalyst dose (m: 0.5-1.5 g/L) have been optimized using Taguchi experimental design matrix. Maximum HQ removal efficiency of 83.56% was obtained at optimum condition of Co = 100 mg/L, pH = 6, CH2O2 = 0.4 mL/L, and m = 1 g/L. Overall use of Fe-GAC during catalytic oxidation seems to be a better as compared to its use an adsorbent for treatment of HQ bearing wastewater.
Water Area Object Detection based on YOLO-Fusion Network

In order to improve the efficiency of controlling tourists' private launching behavior in dangerous waters such as seashores and reservoirs, a small target detection network in waters optimized based on the You Only Look Once version 3 (YOLO v3) network structure is proposed. On the basis of YOLO v3, a side road network composed of feature fusion modules is designed. The side road network effectively improves the transmission and utilization of feature information between network layers, especially for fusion interaction between the network shallow feature information and deep information. At the same time, the Spatial Pyramid Pooling Module (SPP Module) is also added to perform multi-scale aggregation of the output features of the main network and the side road network to enhance the characterization ability of the feature map. In this paper, the optimized network is called YOLO-Fusion. After testing, the average detection accuracy of YOLO-Fusion is 89.55%, which is 2.45% higher than that of YOLO v3. It is based on test indicators such as precision and recall. There is also a significant improvement.
Mammal road‐type associations in Kruger National Park, South Africa: Common mammals do not avoid tar roads more than dirt roads

Abstract The majority of Africa's parks and conservation areas have a vast road network, facilitating motorized vehicle game viewing. These roads have an influence that is both road type‐ and species‐specific, on the surrounding ecosystem. Due to their higher traffic volumes, we hypothesized that tar roads and their immediate surrounds within the Kruger National Park, South Africa, are avoided to a greater extent by medium‐to‐large mammals than comparable dirt roads in the park. We systematically recorded the presence of medium‐to‐large mammal species from our vehicle, recording data at 401 tar and 369 dirt road stops in the Kruger National Park. In addition to species presence, we also estimated the proximity of animals to the road, as well as herd sizes. Our results indicate an equal likelihood of viewing the commonly recorded medium‐to‐large mammal species from both road types. The likelihood of observing larger herd sizes was also similar between tar and dirt roads for the three most commonly observed species, African elephant (Loxodonta africana), impala (Aepyceros melampus), and plains zebra (Equus quagga), and the likelihood of viewing impala and zebra close to the road also did not differ between tar and dirt roads. However, elephant was observed more often close to tar roads, compared to dirt roads. We interpreted this as the result of potentially increased woody cover associated with more water runoff in close proximity to tar roads compared with dirt roads. Our results not only have ecological significance, supporting the notion that many of the park's species are habituated to human infrastructure, but also management implications, informing park officials about the influence of road traffic and road type on wildlife distributions.
A Reconfigurable Notched Band Monopole Antenna for C-Band Applications

In this paper, a wideband monopole antenna with reconfigurable frequency notch through wireless local area network (WLAN) (5.15-5.35GHz and 5.725-5.825GHz) or future wireless fidelity 6GHz (Wi-Fi-6E) (5.925-7.125GHz) band for C-band applications is presented. The conventional/basic monopole antenna consists of four-leaf clover antenna structure with cascaded feeder and Defected Ground Structure (DGS). The basic antenna is designed and then simulated using Computer Simulation Technology (CST) and High-Frequency Structure Simulator (HFSS) readymade software programs. The antenna covering an operational bandwidth extends from 4.2GHz to 9.2GHz while the gain is around 4.0dBi. Two simple resonator conductors are added near the thin feeder of antenna to realize the notched frequency. The rejected frequency within WLAN or Wi-Fi 6E bands is controlled by the resonator conductor lengths, so Positive-Intrinsic-Negative (PIN) diodes switches are inserted to achieve the required length for each rejected band. Finally, each of the basic antenna and the proposed notched antenna are fabricated and measured. The measurement results are in good agreements with the simulated results of CST and HFSS, providing good antenna performance and sharp notches with good rejection values.
CatBoost for Haptic Modeling of Homogeneous Viscoelastic Deformable Objects

This paper proposes an alternative data-driven haptic modeling method of homogeneous deformable objects based on a CatBoost approach-a variant of gradient boosting machine learning approach. In this approach, decision trees are trained sequentially to learn the required mapping function for modeling the objects. The model is trained on the input feature vectors consisting of position, velocity and filtered velocity samples to estimate the response force. Our approach is validated with a publicly available two-finger grasping dataset. The proposed approach can model unknown interactions with good accuracy (relative root mean squared error, absolute relative error and maximum error less than 0.06, 0.18 and 0.76 N, respectively) when trained on just 20% of the training data. The CatBoost-based method outperforms the existing data-driven methods both in terms of the prediction accuracy and the modeling time when trained on similar size of the training data.
Neural Networks

[Title: Vehicle Counting Accuracy Improvement By Identity Sequences Detection Based on Yolov4 Deep Neural Networks] Models for vehicle detection, classification, and counting based on computer vision and artificial intelligence are constantly evolving. In this study, we present the Yolov4-based vehicle detection, classification, and counting model approach. The number of vehicles was calculated by generating the serial number of the identity of each vehicle. The object is detected and classified, marked by the display of bounding boxes, classes, and confidence scores. The system input is a video dataset that considers the camera position, light intensity, and vehicle traffic density. The method has counted the number of vehicles: cars, motorcycles, buses, and trucks. Evaluation of model performance is based on accuracy, precision, and total recall of the confusion matrix. The results of the dataset test and the calculation of the model performance parameters had obtained the best accuracy, precision. Total recall values when the model testing was carried out during the day where the camera position was at the height of 6 m and the loss of 500 was 83%, 93%, and 94%. Meanwhile, the lowest total accuracy, precision, and recall were obtained when the model was tested at night. The camera position was at the height of 1.5 m, and 900 losses were 68%, 77%, and 78%.
Exploring the interaction effect of poverty concentration and transit service on highway traffic during the COVID-19 lockdown

During COVID-19 lockdowns, transit agencies need to respond to the decline in travel but also maintain the essential mobility of transit-dependent people. However, there are a few lessons that scholars and practitioners can learn from. Using highway traffic data in the Twin Cities, this study applies a generalized additive model to explore the relationships among the share of low-income population, transit service, and highway traffic during the week that occurred right after the 2020 stay-at-home order. Our results substantiate that transportation impacts are spread unevenly across different income groups and low-income people are less able to reduce travel, leading to equity concerns. Moreover, transit supply influences highway traffic differently in areas with different shares of low-income people. Our study suggests that transportation agencies should provide more affordable travel options for areas with concentrated poverty during lockdowns. In addition, transit agencies should manage transit supply strategically depending on the share of low-income people to better meet people’s mobility needs.
Cinnamon and Rosemary Essential Oils Incorporated Into Alginate Coating Improve Chemical and Sensorial Quality of Chicken Meat

The present study was conducted to evaluate the effectiveness of the edible coating of Cinnamon Essential Oil (CEO) and Rosemary Essential Oil (REO) incorporated into alginate coating to maintain chemical and sensorial characteristics of chicken meat under refrigeration conditions. Firstly in vitro antioxidant activity of essential oils was evaluated. Then fresh chicken meats were coated with alginate solution containing CEO, REO alone and in combination and treatments were evaluated for Peroxide value (PV), total carbonyls, ThioBarbituric Acid Reactive Substances BARS), TriMethylAmine Nitrogen (TMAN), Total Volatile Basic Nitrogen (TVBN) and sensory quality tests. Results indicated that there was a significant difference in chemical parameters and sensorial attributes in all treatments when compared to control during storage. Therefore the functional alginate-sodium coating containing CEO and REO extended the shelf life of fresh chicken meat during refrigerated storage and could have a valuable food preserving potential in the food industry.
Aerial Image Analysis Using Deep Learning for Electrical Overhead Line Network Asset Management

Electricity networks are critical infrastructure, delivering vital energy services. Due to the significant number, variety and distribution of electrical network overhead line assets, energy network operators spend millions annually on inspection and maintenance programmes. Currently, inspection involves acquiring and manually analysing aerial images. This is labour intensive and subjective. Along with costs associated with helicopter or drone operations, data analysis represents a significant financial burden to network operators. We propose an approach to automating assessment of the condition of electrical towers. Importantly, we train machine learning tower classifiers without using condition labels for individual components of interest. Instead, learning is supervised using only condition labels for towers in their entirety. This enables us to use a real-world industry dataset without needing costly additional human labelling of thousands of individual components. Our prototype first detects instances of components in multiple images of each tower, using Mask R-CNN or RetinaNet. It then predicts tower condition ratings using one of two approaches: (i) component instance classifiers trained using class labels transferred from towers to each of their detected component instances, or (ii) multiple instance learning classifiers based on bags of detected instances. Instance or bag class predictions are aggregated to obtain tower condition ratings. Evaluation used a dataset with representative tower images and associated condition ratings covering a range of component types, scenes, environmental conditions, and viewpoints. We report experiments investigating classification of towers based on the condition of their multiple insulator and U-bolt components. Insulators and their U-bolts were detected with average precision of 96.7 and 97.9, respectively. Tower classification achieved areas under ROC curves of 0.94 and 0.98 for insulator condition and U-bolt condition ratings, respectively. Thus we demonstrate that tower condition classifiers can be trained effectively without labelling the condition of individual components.
Assessing naive Bayes and support vector machine performance in sentiment classification on a big data platform

Nowadays, mining user reviews becomes a very useful mean for decision making in several areas. Traditionally, machine learning algorithms have been widely and effectively used to analyze user’s opinions on a limited volume of data. In the case of massive data, powerful hardware resources (CPU, memory, and storage) are essential for dealing with the whole data processing phases including, collection, pre-processing, and learning in an optimal time. Several big data technologies have emerged to efficiently process massive data, like Apache Spark, which is a distributed framework for data processing that provides libraries implementing several machine learning algorithms. In order to evaluate the performance of Apache Spark's machine learning library (MLlib) on a large volume of data, classification accuracies and processing time of two machine learning algorithms implemented in spark: naive Bayes and support vector machine (SVM) are compared to the performance achieved by the standard implementation of these two algorithms on large different size datasets built from movie reviews. The results of our experiment show that the performance of classifiers running under spark is higher than traditional ones and reaches F-measure greater than 84%. At the same time, we found that under spark framework, the learning time is relatively low.
Perceived indicators of American Indian identity in everyday interaction: navigating settler-colonial erasure

ABSTRACT A primary process in settler colonial societies is erasure of Indigenous presence. We employ a symbolic interactionist approach, embedded in macro-level critical analysis, to explore the conveyance and interpretation of American Indian identity in everyday interaction in the settler colonial society of the United States. We surveyed 213 White American participants, asking them to write about indicators they utilize to determine American Indian identity, and 104 American Indian participants, asking them to write about experiences associated with conveying and others interpreting their American Indian identities. Our findings reveal similarities and differences between White and American Indian perspectives associated with three types of indicators: perceived phenotypical, perceived cultural, and verbal. We find that racialized phenotypical perceptions of American Indian identity contribute to misidentification and invisibility of American Indian people. Cultural stereotypes also contribute to invisibility. We situate these findings in dominant U.S. culture and structures rooted in settler colonial processes.
An Adaptive Unscented Kalman Filter for Needle Steering with Missing Measurements

In the robot-assisted puncture surgery, the measurements from the ultrasound image may be lost due to the uneven distribution of image grayscale and blurred image which could affect the estimation accuracy of the filter. Furthermore, the probability of the missing measurement cannot be precisely known due to the heterogeneity of biological tissue and the complexity of the surgery environment. Aiming at these problems, an adaptive unscented Kalman filter algorithm based on virtual measurement noise is proposed to estimate the pose of the needle tip in this paper. The missing measurement is converted into a virtual measurement noise which has an indefinite variance. Then the variance of the virtual noise is estimated in real-time to suppress the influence of missing measurements during the puncture process. Furthermore, according to the strong tracking filtering algorithm, an adaptive fading factor is constructed to reduce the sensitivity of the filter to the statistical characteristics of the noise. Finally, the proposed filter is applied to estimate the pose of the puncture needle and the effectiveness of the proposed method is verified via simulation experiments.
Measuring Motivating Potential Score of Diversified Academic Staffs of the Government Primary Schools in Bangladesh

Job Characteristics Model of Hackman and Oldham is an established tool that helps calculate the Motivating Potential Score (MPS) used in understanding the extent to which employees are motivated in their workplace from different perspectives. High MPS indicates positive work outcomes like high motivation, high employee satisfaction, high performance, low absenteeism, low turnover, etc. In human resource management, a motivating potential score (MPS) is used to track the perceived mental state of the workforce towards their jobs. Understanding this score is vital for both HR and line managers in decision-making. So it has become a part of regular action research by business professionals. This study measures the MPS of government primary school teachers while considering different dimensions of workforce diversity like- academic qualification, educational background, level of experience, gender. The established formula for calculating MPS has been used here. A total of 100 school teachers working in different state-run primary schools of Bangladesh have been considered as respondents of this study. The study finds an overall MPS of 69.78 out of a maximum possible score of 125. However, the study does not show any significant change in MPS due to the presence of workforce diversity. Rather, it shows a moderate level of MPS for all. Policy makers and government officials working for workforce improvement of the primary schools can use these findings to take further steps, though a moderate score may indicate that they have already done a lot to develop the index. Similar studies considering a better sample size might provide better insights and help in decision-making. Moreover, since ensuring quality education is one of the pillars of Sustainable Development Goals (SDGs) adopted by the United Nations, this research is expected to be a good contribution in this regard as the ultimate intention of the study is related to ensuring quality education through skilled, motivated, engaged, and dedicated teachers.
Impact of COVID-19 Disruptions on Global BCG Coverage and Paediatric TB Mortality: A Modelling Study

The impact of COVID-19 disruptions on global Bacillus Calmette-Guérin (BCG) coverage and paediatric tuberculosis (TB) mortality is still unknown. To fill this evidence-gap and guide mitigation measures, we estimated the impact of COVID-19 disruptions on global BCG coverage and paediatric TB mortality. First, we used data from multiple sources to estimate COVID-19-disrupted BCG vaccination coverage. Second, using a static mathematical model, we estimated the number of additional paediatric TB deaths in the first 15 years of life due to delayed/missed vaccinations in 14 scenarios—varying in duration of disruption, and magnitude and timing of catch-up. We estimated a 25% reduction in global BCG coverage within the disruption period. The best-case scenario (3-month disruption, 100% catch-up within 3 months) resulted in an additional 886 (0.5%) paediatric TB deaths, and the worst-case scenario (6-month disruption with no catch-up) resulted in an additional 33,074 (17%) deaths. The magnitude of catch-up was found to be the most influential variable in minimising excess paediatric TB mortality. Our results show that ensuring catch-up vaccination of missed children is a critical priority, and delivery of BCG alongside other routine vaccines may be a feasible way to achieve catch-up. Urgent action is required to support countries with recovering vaccination coverages to minimise paediatric deaths.