Miracle-dz/UniFAR-FTUs huggingface dataset:
Bulletin of Electrical Engineering and Informatics

Received Mar 22, 2022 Revised May 28, 2022 Accepted Jun 15, 2022 Prediction using machine learning has evolved due to its impact on providing valuable and intuitive feedback. It has covered a wide range of areas for predicting student’ performance. Instructors can track student’s dropout in a particular course at an early stage and try to improve students’ performance. The problem of students’ future performance prediction using advanced statistics and machine learning is a hard problem due to the imbalanced nature of the student data where the number of students who passed the exam is generally much higher than the number of students who failed the exam. This paper proposes a new type of crossover operator called Even-Odd crossover to generate new instances into the minority class to handle the imbalanced data problem. The experiments are implemented using three machine learning (ML) algorithms: random forest (RF), support vector machines (SVM), and K-Nearest-Neighbor (KNN) to ensure the efficiency of the proposed technique. The performance of the classifiers is evaluated using several performance measures. The efficient ability of the proposed method on solving the imbalance problem is proved by performing the experiments on 22 real-world datasets from different fields and four students’ datasets. The proposed Even-Odd crossover shows superior performance compared to stateof-the-art resampling techniques.
Frontline healthcare workers’ experiences of providing care during the COVID-19 pandemic at a COVID-19 centre in Bulawayo, Zimbabwe: A phenomenological study

Background The coronavirus disease 2019 (COVID-19) pandemic has had a far-reaching, negative impact on healthcare systems worldwide. Healthcare workers play a critical role in the country’s healthcare delivery system, as they facilitate a continuum of care and containment of diseases such as the COVID-19 pandemic. Objectives The aim of this study was to explore and describe the experiences of healthcare workers who provided care to COVID-19 patients at a central hospital in Zimbabwe. Method The researchers used an interpretative phenomenological analysis design. In-depth interviews were conducted virtually with 10 frontline healthcare workers working at a COVID-19 centre in Zimbabwe. Data collection was guided by an interview guide. All audio-recorded interview data were transcribed verbatim into written text. Data analysis was conducted using an interpretative phenomenological analysis framework. An expert in qualitative research acted as an independent co-coder and conducted the open coding of each transcript. Results Findings reveal inadequate preparation and training of healthcare providers before the commencement of duty, resources-related challenges and a lack of support as significant experiences of healthcare providers. Moreover, healthcare providers have been subjected to stigma and discrimination attached to COVID-19, resulting in psychological effects on frontline healthcare providers. Conclusion The COVID-19 pandemic brings unique and challenging experiences for frontline healthcare workers, resulting in a physically and emotionally drained workforce. This study calls for comprehensive support in the form of counselling, reasonable work schedules, training and adequate provision of personal protective equipment.
Translational Value of Tumor-Associated Lymphangiogenesis in Cholangiocarcinoma

The prognosis of cholangiocarcinoma remains poor in spite of the advances in immunotherapy and molecular profiling, which has led to the identification of several targetable genetic alterations. Surgical procedures, including both liver resection and liver transplantation, still represent the treatment with the best curative potential, though the outcomes are significantly compromised by the early development of lymph node metastases. Progression of lymphatic metastasis from the primary tumor to tumor-draining lymph nodes is mediated by tumor-associated lymphangiogenesis, a topic largely overlooked until recently. Recent findings highlight tumor-associated lymphangiogenesis as paradigmatic of the role played by the tumor microenvironment in sustaining cholangiocarcinoma invasiveness and progression. This study reviews the current knowledge about the intercellular signaling and molecular mechanism of tumor-associated lymphangiogenesis in cholangiocarcinoma in the hope of identifying novel therapeutic targets to halt a process that often limits the success of the few available treatments.
Vegetative Growth of Genipa americana L. Accessions

Jagua (Genipa americana L.) is widely distributed throughout the Americas. It has been used since ancient times due to the pigments extracted from its fruit. This study aimed to evaluate the vegetative growth of five Jagua accessions by adjusting non-linear models to dasometric growth variables. The accessions (Porce, Chigorodó, San Carlos, San Luis, and Vigía del Fuerte in Antioquia) were collected in the central-western region of Colombia. The growth, determined through plant height and stem diameter (at 0.10 and 1.30 m), was evaluated for 262 weeks in the municipality of Amalfi. A descriptive analysis of the growth behavior of each accession was performed, and the fit of four nonlinear functional models was evaluated. An influence of the accession factor on growth was found due the different growth rates. The Weibull model was the one with the best fit and predictive capacity, which confirmed the differences between accessions.
Impacts of Changes in Land Use and Land Cover Between 2001 and 2018 on Summertime O3 Formation in North China Plain and Surrounding Areas–A Case Study

Changes in land use and land cover (LULC) influence meteorological fields and biogenic emissions, further affecting the atmospheric chemistry and air quality. Combining the satellite measurements and WRF‐Chem model simulations, we evaluate the impacts of the LULC change between 2001 and 2018 on the summertime ozone (O3) formation in North China Plain and surrounding areas (NCPs). Satellite measurements have revealed that from Taihang to Yanshan Mountain, the fraction of broadleaf and needle forest coverage has increased by 5%–20% and the urban area has increased by up to 25% in the NCP. Additionally, the vegetation density has increased significantly in the NCPs except for urban areas. The LULC change generally enhances biogenic volatile compounds emissions in the NCPs, particularly over Taihang and Yanshan mountain, but the O3 variation is divergent. The maximum daily 8‐ihr average (MDA8) O3 concentrations are reduced by 1%–7% over Taihang and Yanshan Mountain because the raised vegetation density increases O3 dry deposition velocity to accelerate the O3 loss. The raised vegetation density enhances the evapotranspiration to decrease the near‐surface temperature by 0.1°C–1.5°C, which also generates a divergence in the low‐level atmosphere in the NCPs, causing secondary northerly or easterly winds in the NCP. The O3 enhancement along the coastal areas of the NCP is attributed to the perturbation of wind fields and photolysis induced by the LULC change. The divergent variation of the MDA8 O3 concentrations in the NCP is generally caused by the variations of biogenic emissions and photolysis.
On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning

Skin cancer (melanoma and non-melanoma) is one of the most common cancer types and leads to hundreds of thousands of yearly deaths worldwide. It manifests itself through abnormal growth of skin cells. Early diagnosis drastically increases the chances of recovery. Moreover, it may render surgical, radiographic, or chemical therapies unnecessary or lessen their overall usage. Thus, healthcare costs can be reduced. The process of diagnosing skin cancer starts with dermoscopy, which inspects the general shape, size, and color characteristics of skin lesions, and suspected lesions undergo further sampling and lab tests for confirmation. Image-based diagnosis has undergone great advances recently due to the rise of deep learning artificial intelligence. The work in this paper examines the applicability of raw deep transfer learning in classifying images of skin lesions into seven possible categories. Using the HAM1000 dataset of dermoscopy images, a system that accepts these images as input without explicit feature extraction or preprocessing was developed using 13 deep transfer learning models. Extensive evaluation revealed the advantages and shortcomings of such a method. Although some cancer types were correctly classified with high accuracy, the imbalance of the dataset, the small number of images in some categories, and the large number of classes reduced the best overall accuracy to 82.9%.
An evaluation of the Petty Officer Training program of the Republic of Indonesia police using the Kirkpatrick model

This study aims to evaluate the Petty Officer Training Program Noken to determine the program's success based on predetermined character values. The program evaluation uses Kirkpatrick's evaluation model with four levels: reaction, learning, behavior, and result. The data were obtained using a questionnaire, observations, interviews, and documentation and analyzed using quantitative and qualitative descriptive techniques. Before the questionnaire and observation sheets were used, their readability was tested to the Super Team for the National Identity formed by the Republic of Indonesia Police Headquarters and experts in psychology and educational evaluation. The results of the instrument readability test showed excellent results, with a score of 4.8 on a scale of 1-5. However, some revisions were needed, especially for the instruments for policymakers and management. The results of the evaluation of the two groups show that for the assessment at level 1: reaction, the participants' reaction to the organizing committee and resource persons is very high, and there is just a need to pay attention to the availability of facilities. At level 2: learning, the achievement of key competencies is very significant in its assessment. At level 3: behavior, there are behavioral changes including diligence in doing worship, discipline in attending classes, neat and clean clothes, working in groups, communicating well, and accuracy and speed in completing daily tasks. At level 4: result, the level of achievement of character values is very significant. The effect of conditioning and the teaching of materials contributes very significantly to applying national identity in everyday life.
Applications of Smartphone-Based Aptasensor for Diverse Targets Detection

Aptamers are a particular class of functional recognition ligands with high specificity and affinity to their targets. As the candidate recognition layer of biosensors, aptamers can be used to sense biomolecules. Aptasensors, aptamer-based biosensors, have been demonstrated to be specific, sensitive, and cost-effective. Furthermore, smartphone-based devices have shown their advantages in binding to aptasensors for point-of-care testing (POCT), which offers an immediate or spontaneous responding time for biological testing. This review describes smartphone-based aptasensors to detect various targets such as metal ions, nucleic acids, proteins, and cells. Additionally, the focus is also on aptasensors-related technologies and configurations.
Associations of Parenting Style and Resilience With Depression and Anxiety Symptoms in Chinese Middle School Students

Background Parenting style and resilience are independently associated with symptoms of depression and anxiety. However, no study has tested the interaction effects between the patterns of parenting style and resilience on mental health in adolescent populations. Therefore, this study aimed to explore the interaction effects between the patterns of parenting style and resilience on depression/anxiety symptoms among middle school students in China. Methods A sample of 2,179 Chinese middle school students were included in this study. Latent profile analysis (LPA) was used to examine parenting style patterns. Multivariable logistic regression was used to analyze the associations of different parenting patterns and resilience with depression/anxiety symptoms, as well as the interaction effect. Results Latent profile analysis results showed that the most suitable model included three-profile solution, which were labeled as positive parenting, negative parenting, and moderate parenting. Subsequent analyses indicated that students across profiles exhibited significant differences in their depression/anxiety symptoms. Specifically, compared to moderate parenting, negative parenting was positively associated with depression/anxiety symptoms, while positive parenting was negatively associated with these symptoms. Moreover, low levels of resilience were positively associated with depression/anxiety symptoms compared to a high level of resilience. Although the interaction effect was not significant, there were differences in the associations between different parenting patterns and symptoms of depression and anxiety when stratifying resilience. Conclusion The present study identified three-profile solution of parenting styles among Chinese middle school students using LPA as a person-centered approach. Future interventions targeting depression/anxiety symptoms in adolescents may consider the potential influence of patterns of parenting styles, or improved resilience, to achieve better intervention outcomes.
Design and Implementation of ALU Using Graphene Nanoribbon Field-Effect Transistor and Fin Field-Effect Transistor

Arithmetic and logical unit (ALU) are the core operational programmable logic block in microprocessors, microcontrollers, and real-time-integrated circuits. The conventional ALUs were developed using complementary metal oxide semiconductor (CMOS) technology, which resulted in excessive power consumptions, path delays, and number of transistors. Therefore, this article focuses on the design and development of hybrid delay-controlled reconfigurable ALU (DCR-ALU) using field-effect transistor (FinFET) and graphene nanoribbon field-effect transistor (GnrFET) technologies. Initially, a novel carry output predictable full adder (COPFA) and carry input selectable full adders (CISFA) are developed using multiplexer selection logic; then, delay-controlled hybrid adder (DCHA) and delay-controlled hybrid subtractor (DCHS) are developed using these full adders. In addition, a unified delay-controlled hybrid adder and subtractor (DCHAS) is developed by combining these DCHA and DCHS. Further, a delay controller array multiplier (DCAM) also developed using DCHA modules. Finally, DCR-ALU is developed by adopting the DCHAS, DCAM, and logical operations. The obtained simulation results shows that the proposed nanotechnology-based models outperformed the conventional adders and subtractors in terms of area, power, and delay reduction.
Algebraic structures for multi-terminal communication systems

We study a distributed source coding problem with multiple encoders, a central decoder and a joint distortion criterion. The encoders do not communicate with each other. The encoders observe correlated sources which they quantize and communicate noiselessly to a central decoder which is interested in minimizing a joint distortion criterion that depends on the sources and the reconstruction. We are interested in characterizing an inner bound to the optimal rate-distortion region. We first consider a special case where the sources are jointly Gaussian and the decoder wants to reconstruct a linear function of the sources under mean square error distortion. We demonstrate a coding scheme involving nested lattice codes that reconstructs the linear function by encoding in such a fashion that the decoder is able to reconstruct the function directly. For certain source distributions, this approach yields a larger rate-distortion region compared to when the decoder reconstructs lossy versions of the sources first and then estimates the function from them. We then extend this approach to the case of reconstructing a linear function of an arbitrary number of jointly Gaussian sources. Next, we consider the general distributed source coding problem with discrete sources. This formulation includes as a special case many famous distributed source coding problems. We present a new achievable rate-distortion region for this problem based on “good” structured nested random codes built over abelian groups. We demonstrate rate gains for this problem over traditional coding schemes using unstructured random codes. For certain sources and distortion functions, the new rate region is strictly bigger than the Berger-Tung rate region, which has been the best
Creditworthiness and buildings’ energy efficiency in the Italian mortgage market

Energy efficiency represents one of the key planned actions aiming at reducing green-house emissions and the consumption of fossil fuel to mitigate the impact of climate change. In this paper, we investigate the relationship between energy efficiency and the borrower’s solvency risk in the Italian market. Specifically, we analyze a residential mortgage portfolio of four financial institutions which includes about 70,000 loans matched with the energy performance certificate of the associated buildings. Our findings show that there is a negative relationship between a building’s energy efficiency and the owner’s probability of default. Findings survive after we account for dwelling, household, mortgage, market control variables, and regional and year fixed effect. Ad-ditionally, a ROC analysis shows that there is an improvement in the estimation of the mortgage default probability when the energy efficiency characteristic is included as a risk predictor in the model.
Risk Factors Associated with Cartilage Defects after Anterior Cruciate Ligament Rupture in Military Draftees

This study aimed to evaluate the different clinical results and factors associated with cartilage defects in military draftees who underwent different treatments after anterior cruciate ligament (ACL) rupture. Overall, 105 patients who had sustained ACL rupture were military draftees who underwent a conscription examination for physical status assessment from January 2012 to December 2020. Patients were divided into three groups: conservative treatment after ACL rupture, status post-anterior cruciate ligament reconstruction (ACLR), but graft rupture, and status post-ACLR with graft intact. Inter-group comparisons and statistical analyses were performed for age, body mass index (BMI), thigh circumference difference, side-to-side difference in anterior knee translation by KT-2000, meniscus tear, and cartilage defect. Multivariate logistic regression analysis was used to determine the factors associated with cartilage defects. The multivariable regression model showed that BMI (odds ratio OR: 1.303; 95% CI: 1.016–1.672; p = 0.037), thigh circumference difference (OR: 1.403; 95% CI: 1.003–1.084; p = 0.034), tear of lateral meniscus (LM) and medial meniscus (MM) (OR: 13.773; 95% CI: 1.354–140.09; p = 0.027), and graft rupture group (OR: 5.191; 95% CI: 1.388–19.419; p = 0.014) increased the risk of cartilage defects. There was no correlation between cartilage defects and age, KT-2000 difference, tear of LM or MM, or graft intact group. Progression of osteoarthritis was concerned after ACL rupture, and this study identified several factors of post-ACLR graft rupture, greater thigh circumference difference, BMI, and meniscus tear of both LM and MM affecting cartilage defects, which represent early degenerative osteoarthritis changes of the knee. The results of this study should be customized for rehabilitation and military training, especially in military draftees with ACL injuries.
ElasticEdge: An Intelligent Elastic Edge Framework for Live Video Analytics

Cloud computing and edge computing models are popularly applied in emerging applications, such as smart homes, smart parks, and connected autonomous vehicles for large-scale live video analytics. Cloud computing-based models transfer all data to the cloud for video analytics, which burdens network bandwidth and increases the data transmission overhead. Edge computing mode enables video data to be processed at the edge node, thereby reducing the bandwidth overhead. Existing edge computing-based models optimize the performance, but they still have defects in three perspectives: 1) enabling end users to control video content in a real-time format; 2) efficiently locating and transferring the user region of interest (ROI) video data in the video stream; and 3) adapting to various network conditions. To tackle these challenges, we proposed an intelligent elastic edge framework for live video analytics, known as ElasticEdge. ElasticEdge enables the interaction between the end user and the edge node. Elasticity is reflected in two perspectives: 1) the dynamic changes of user requirements and 2) the dynamic changes in network conditions. In addition, ElasticEdge transmits the video stream to the end users based on the tradeoff between the amount of video data and users’ ROI to meet various network conditions. To validate ElasticEdge, we conducted experiments to study its performance in comparison to RTFace. The experimental results show that ElasticEdge has a significant edge over RTFace in terms of data transmission. Using 1/16 reserved images, ElasticEdge saves 75% bandwidth and reduces latency by approximately 10% compared with RTFace. We also find that ElasticEdge adapts to various network conditions when streaming videos, i.e., it can reliably obtain essential information with low latency even when the network condition is poor.
The lncRNA ZNF667-AS1 Inhibits Propagation, Invasion, and Angiogenesis of Gastric Cancer by Silencing the Expression of N-Cadherin and VEGFA

Purpose Gastric cancer is one of the most common malignancies with high mortality worldwide. It is known that long noncoding RNAs (lncRNAs) play important roles in the pathogenesis of gastric cancer. This study investigates the role of lncRNA ZNF667-AS1 in gastric cancer cells. Methods We have applied real-time quantitative PCR (qPCR) to study the levels of ZNF667-AS1 in gastric cancer biopsies and cell lines. The effects of ZNF667-AS1 on the propagation, clonogenicity, metastasis, and angiogenesis of gastric cancer cells were evaluated by calorimetry, colony formation, cell migration, and angiogenesis assays. Western blotting was used to identify the levels of proteins involved in cancer invasion and angiogenesis signaling pathways. Result It was found that lncRNA ZNF667-AS1 was downregulated in gastric cancer biopsies. Overexpression of ZNF667-AS1 reduced the propagation, migration, and angiogenesis of gastric cancer cells. Molecular mechanism studies displayed that the high level of lncRNA ZNF667-AS1 promoted the expression of E-cadherin and inhibited the expression of N-cadherin and VEGFA, leading to the inhibition of the proliferation, migration, and angiogenesis of gastric cancer cells. Conclusion As a tumor suppressor gene, lncRNA ZNF667-AS1 significantly hinders the propagation, metastasis, and angiogenesis of gastric cancer cells by promoting the expression of E-cadherin and inhibiting the expression of N-cadherin and VEGFA. Therefore, lncRNA ZNF667-AS1 could play a synergistic therapeutic role by targeting tumor cells and vascular endothelial cells, which represents a new therapeutic scheme for novel therapeutics of gastric cancer.