Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Average Neutral Point Current in 3-level NPC Converter under Generalized Unbalanced AC Input Condition

This paper presents a neutral point deviation compensating control algorithm applied to a 3-level NPC converter under generalized unbalanced ac input conditions. The neutral point deviation is analyzed with a focus on the current flowing out of or into the neutral point of the dc-link in 3-level NPC converter. The model of neutral point deviation and neutral current are also constructed. The positive and negative sequence components of the pole voltages and ac input currents are employed to accurately explain the behavior of 3-level NPC converter and its impact on neutral point deviation. This paper includes the harmonic characteristic of neutral point current under various imbalance AC operating conditions.
Casiopeinas® as SARS-CoV-2 main protease (Mpro) inhibitors. A combined DFT, molecular docking and ONIOM approach

Casiopeinas® are well-known planar copper compounds with potent anticancer activity; their general formulae are [Cu(N-N)(L-L)]n+ (n= 1,2), where N-N= 4,7-dimethyl-1,10-phenanthroline or 4,4’-dimethyl-2,2’-bipyridine and L-L= different bidentate chelates. The CasIII-ia: [Cu(4,7-dimethyl-1,10-phenanthroline)(acacetylacetonate)]NO3.
Comparing the Transcriptomes of Two Different Tissues in Helicoverpa assulta (Guenée)

Helicoverpa assulta (Guenée), a moth species belonging to the Noctuidae (Lepidoptera) family, is a destructive agricultural pest that infests multiple cash crops. To assess differences in the gene expression profiles of different tissues in H. assulta, we analyzed the transcriptomes of two tissue types (midgut and hemocytes) using the Illumina Hiseq 2000 platform, on the basis of which we obtained 52076750 and 53404200 high-quality clean reads, respectively. De novo assembly yielded 46146 and 33707 unigenes from the midgut and hemocytes, respectively. After screening, we identified 23726 unigenes differentially expressed between the midgut and hemocytes. Taking the midgut as the control, we detected 7448 and 16278 unigenes that were up- and downregulated in hemocytes, respectively. Gene Ontology functional annotation divided the differentially expressed unigenes (DEUs) into three categories (biological process, cellular component, and molecular function) and 51 branches, whereas the Kyoto Encyclopedia of Genes and Genomes metabolic pathway annotation assigned the DEUs to six categories, mapping these to 258 pathways. In addition, we detected 224918 single-nucleotide polymorphic sites. Our findings based on transcriptome sequencing, data assembly, and functional gene annotation of two different tissues in H. assulta will provide a valuable reference for further excavation and study of functional genes in H. assulta.
FAULT DETECTION TECHNIQUES IN WIRLESS SENSOR NETWORK

Overloading of electrical system is one of the factors that affect the satisfactory operation of electricity supply of a housing estate. Overloading is a fundamental error at design stage of electricity supply which can be averted if a right approach is used to estimate the electrical needs prior to the specification and installation of electrical equipment. In this paper, the electrical requirement of a housing estate is calculated, then the relevant regulations of Institute of electrical and electronics engineers (IEEE) for safety and economic reasons are applied and the sizes of electrical equipments are specified. The main objective is to prepare a computer program in object oriented programming language to be able to estimate the electrical load for any housing estate with a click of few buttons on computer interface. The developed program was tested with the data of Palmarium Estate at Karudu Commercial Layout Amac, Abuja. Also, it can be easily modified to accommodate changes if the need arises.
Galvanizing Grief: Black Maternal Politics, Respectability, and the Pursuit of Elected Office

ABSTRACT In the aftermath of public outcry of multiple high-profile killings of Black teens at the hands of police or under the auspices of stand-your-ground laws, several bereaved mothers became symbols of resistance to white supremacist policing and surveillance. Known as the “Mothers of the Movement,” this paper traces three of these women (Sybrina Fulton, Lucy McBath, and Lezley McSpadden) from the national stage at the Democratic National Convention in 2016 to the transition to candidates themselves. Their engagement in maternal politics is part of a long lineage of Black maternal activists that have sought justice for their fallen children through public advocacy. While McSpadden, Fulton, and McBath all lobby against white supremacist violence, they are most politically successful when they make their case from a rhetoric that affirms a respectability politics that measures the value of Black citizens in their ability to comport themselves within middle-class cultural standards.
Assess of Combinations of Non-Pharmacological Interventions for the Reduction of Irritability in Patients with Dementia and their Caregivers: A Cross-Over RCT

Introduction: Dementia is a very common disorder that affects people over 65 years old all over the world. Apart from the cognitive decline, Behavioral and Psychological Symptoms of Dementia (BPSD) are a crucial matter in dementia, because they affect up to 90% of the patients during the course of their illness. Irritability has been found to be a common BPSD and one of the most distressing behaviors for the caregivers. The aim of the current study was to explore the efficacy of a combination of non-pharmacological interventions to treat irritability. Methods: Sixty patients with different types and stages of dementia with irritability were participated in a cross-over RCT. Three non-pharmacological interventions were used; (a) Validation Therapy (VT)/Psycho-educational program, (b) Aromatherapy/massage and (c) Music Therapy (MT). The study assessed the three non-pharmacological interventions in order to find the most effective combination of the interventions. This study did not compare pharmacological and non-pharmacological treatments. The interventions lasted for five days. There was no drop-out rate. All patients were assessed at baseline using Mini Mental State of Examination (MMSE), Addenbrooke’s Cognitive Examination Revised (ACE-R), Geriatric Depression Scale (GDS), Functional Rating Scale for symptoms in dementia (FRSSD), and Neuropsychiatric Inventory (NPI) (sub questions for irritability). Only NPI used for the assessment after each intervention. The analyses used categorical variables, Wilcoxon signed-rank test, Chi-square test and z value score. Results: The most effective combination of non-pharmacological interventions was Aromatherapy/massage (p = 0.003)-VT plus Psycho-educational program (p = 0.014) plus MT (p = 0.018). The same combination was the most effective for the caregivers’ burden, too (p = 0.026). Conclusions: The above combination of non-pharmacological interventions can reduce irritability in patients with dementia and caregivers’ burden.
Comparative Study on Students’ Engagement and Academic Outcomes in Live Online Learning at University

Many studies have assessed students’ engagement with learning and academic outcomes and studied the influence of the engagement level on academic outcomes. Nevertheless, there are few studies that demonstrate the results of live online learning and reveal whether gender or/and education level mediates the relationships between students’ engagement and academic outcomes. A total of 301 under- and postgraduate students from the Humanity Institute of Peter the Great Polytechnic University voluntarily participated in this study. We conducted online surveys to reveal the level of students’ engagement and academic outcomes in the process of live online learning and analyzed the associations between these variables. Results displayed higher levels of engagement and academic outcomes for females, and the difference between the results was significant, particularly for behavioral and emotional engagement (p < 0.001). Significant differences of the measurement factors including behavioral engagement (p < 0.01), cognitive engagement (p < 0.01), and academic outcomes (p < 0.05) were revealed between undergraduate (UG) and postgraduate (PG) students. The findings obtained by using the linear regression analysis technique indicated that engagement scores significantly predict the students’ academic outcomes for both females and males, while UG and PG students demonstrated different results. It was detected that cognitive engagement has a greater effect on academic outcomes for PG students, while behavioral engagement has a greater effect on academic outcomes for UG students.
Identification of Robust and Key Differentially Expressed Genes during C2C12 Cell Myogenesis Based on Multiomics Data

Myogenesis is a central step in prenatal myofiber formation, postnatal myofiber hypertrophy, and muscle damage repair in adulthood. RNA-Seq technology has greatly helped reveal the molecular mechanism of myogenesis, but batch effects in different experiments inevitably lead to misinterpretation of differentially expressed genes (DEGs). We previously applied the robust rank aggregation (RRA) method to effectively circumvent batch effects across multiple RNA-Seq datasets from 3T3-L1 cells. Here, we also used the RRA method to integrate nine RNA-Seq datasets from C2C12 cells and obtained 3140 robust DEGs between myoblasts and myotubes, which were then validated with array expression profiles and H3K27ac signals. The upregulated robust DEGs were highly enriched in gene ontology (GO) terms related to muscle cell differentiation and development. Considering that the cooperative binding of transcription factors (TFs) to enhancers to regulate downstream gene expression is a classical epigenetic mechanism, differentially expressed TFs (DETFs) were screened, and potential novel myogenic factors (MAF, BCL6, and ESR1) with high connection degree in protein–protein interaction (PPI) network were presented. Moreover, KLF5 cooperatively binds with the three key myogenic factors (MYOD, MYOG, and MEF2D) in C2C12 cells. Motif analysis speculates that the binding of MYOD and MYOG is KLF5-independent, while MEF2D is KLF5-dependent. It was revealed that KLF5-binding sites could be exploited to filter redundant MYOD-, MYOG-, and MEF2D-binding sites to focus on key enhancers for myogenesis. Further functional annotation of KLF5-binding sites suggested that KLF5 may regulate myogenesis through the PI3K-AKt signaling pathway, Rap1 signaling pathway, and the Hippo signaling pathway. In general, our study provides a wealth of untapped candidate targets for myogenesis and contributes new insights into the core regulatory mechanisms of myogenesis relying on KLF5-binding signal.
A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces

This paper presents a shallow architecture based on Convolutional Neural Networks (CNN) for detecting Micro-cracks in Photovoltaic (PV) cells within the manufacturing environment. Based on Electro Luminescence (EL) imaging principles, this research presents a mechanism for determining the number of filters within the convolutional blocks, gradient guided filter tuning (GGFT). Observing the similarity between the original EL images and the filter output images obtained via GGFT, the research further introduces a mechanism for generating PV cell images based on EL Modelling, termed Filter Fused Data Scaling (FFDS). The effectiveness of both techniques is presented by benchmarking our developed architecture against ‘off the shelf’ augmentations and State-of-the-Art (SOTA) networks. The performance criteria was widened to include accuracy, computational, architectural, and post-deployment metrics. The high performance of our architecture in an intensive and wide-scoped evaluation demonstrates the high efficacy of our proposed mechanisms for developing PV-specific architectures and addressing the issue of data scarcity, particularly the difficulty in the procurement of quality EL images from the manufacturing site.
Investigation on Natural Infection of Covert Mortality Nodavirus in Farmed Giant Freshwater Prawn (Macrobrachium rosenbergii)

Simple Summary Covert mortality nodavirus (CMNV) is a newly discovered aquatic animal virus in recent years. Here, we detected CMNV positive in farmed giant freshwater prawn (Macrobrachium rosenbergii) from Jiangsu, China by TaqMan RT-qPCR. Meanwhile, in situ hybridization and histological analysis indicated that the intestine, gill, hepatopancreas and ovary of giant freshwater prawn were the target organs of CMNV. In addition, a large number of CMNV-like particles were observed in the hepatopancreas and gill tissues under transmission electron microscopy. Overall, our study confirms that giant freshwater prawn is a susceptible host of CMNV, further expands the known host range of CMNV, and provided a new direction for further investigation and exploration of multiple pathogenic factors of giant freshwater prawn disease. Abstract Covert mortality nodavirus (CMNV), from the Nodaviridae family, is characterized by its unique cross-species transmission and wide epidemic distribution features. In this study, Macrobrachium rosenbergii was proved to be infected naturally by CMNV, which further expand the known host range of CMNV. Here, 61.9% (70/113) of the M. rosenbergii samples collected from Jiangsu Province were CMNV positive in the TaqMan RT-qPCR assay, which indicated the high prevalence of CMNV in M. rosenbergii. Meanwhile, the sequences of CMNV RdRp gene cloned from M. rosenbergii were highly identical to that of the original CMNV isolate from Penaeus vannamei. In situ hybridization (ISH) and histology analysis indicated that the intestine, gill, hepatopancreas and ovary were the targeted organs of CMNV infection in M. rosenbergii, and obvious histopathological damage including vacuolation and karyopyknosis were occurred in the above organs. Notably, the presence of CMNV in gonad alerted its potential risk of vertical transmission in M. rosenbergii. Additionally, numerous CMNV-like particles could be observed in tissues of hepatopancreas and gill under transmission electron microscopy. Collectively, our results call for concern of the potential negative impact of the spread and prevalence of CMNV in M. rosenbergii on its aquaculture, as well as providing a renewed orientation for further investigation and exploration of the diverse pathogenic factors causing M. rosenbergii diseases.
Cooperative-Game-Based Day-Ahead Scheduling of Local Integrated Energy Systems With Shared Energy Storage

In the context of the current sharing economy, the application of shared energy storage (SES) among local integrated energy systems (LIESs) is underexplored. There is an urgent need for developing appropriate modeling and solution methods so as to facilitate the application of SES among LIESs. To this end, this paper proposes a cooperative-game-based day-ahead scheduling model of LIESs with SES. The proposed model can be transformed into a sequential two-step optimization problem for alleviating the computational complexity. More specifically, the subproblem of cooperative alliance profit maximization is established in the first step to obtain the optimal scheduling strategies of each LIES, while the subproblem of SES bargaining is constructed in the second step to obtain the optimal pricing information. Besides, a distributed solution algorithm based on the alternating direction method of multipliers with a warm start and dual update accelerated iteration strategies (ADMM-W-D) is proposed to effectively preserve the privacy of each LIES and improve the convergence performance of traditional ADMM. Case studies show that the proposed model can achieve lower operating costs and higher renewable energy consumption levels of LIESs after considering SES. It is also demonstrated that the proposed algorithm based on ADMM-W-D has stronger convergence and better scalability than traditional algorithms.
The Future of Brand Awareness and Brand Loyalty on Travel Agencies

Tourism has emerged as one of the great activities of the 21st century; however, due to globalization and the development of the Internet, travel agencies see some of their business principles being changed by new technologies. Furthermore, the impacts of the pandemic crisis (SARS-COV2) have been highly negative. In the field of tourism, travel agencies have been one of the parts that have undergone the most changes. Countless people have gone to a travel agency to book their trips in the past decade. However, this process is not always carried out in the same way. With the appearance and constant development of new technologies, the first online travel agencies emerged. In addition, the consumer becomes increasingly independent in their travel purchases, and the travel agent's work is limited. Online travel agencies advertise more, offering more attractive prices and other conditions that attract customers to buy their trips without going to a travel agency in a physical space. The relationship between agency and tourist is essentially online. Thus, it is essential to analyze the role of travel agents who have their physical spaces open and intend to increase their business by strengthening customer relationships. It is necessary to focus on consumer trust and loyalty while creating and establishing brand or company value. Thus, the results obtained in this study, through a self-administered questionnaire, from a sample of 512 observations, demonstrate that the perceived quality of the relationship influences the brand's awareness and customer loyalty: consumer satisfaction and trust. In this context, this study highlights the importance of the human relationship in selling tourist products and the tourist's previous experience in loyalty to the travel agency.
Morphological parameters of ovarian masses and accuracy of the risk of malignancy index in diagnosing ovarian malignancy

Introduction To detect the morphological parameters of ovarian masses and the accuracy of the risk of mali-gnancy index (RMI) in diagnosing ovarian malignancy. Material and methods 264 women in 3 groups (reproductive, premenopausal, and postmenopausal) presented with ovarian masses and scheduled for surgery were included in this study. The participants’ preoperative RMI was compared to the postoperative histology (gold standard) to detect the accuracy of RMI in diagnosing ovarian malignancy. Results The incidence of malignant and benign ovarian tumours in the reproductive group was 9.1% and 90.9%, respectively, while it was 35.2% and 64.8%, respectively, in the premenopausal group, and 35.2%, and 64.8%, respectively, in the postmenopausal group. The incidence of malignant ovarian tumours was significantly higher in the premenopausal (35.2%) and postmenopausal (35.2%) groups compared to the reproductive group (9.1%), (p = 0.0008, and p = 0.0008, respectively). The receiver operating characteristic curve showed that RMI at cut-off value > 247.5 had 82.9% sensitivity, 100% specificity, 100% positive predictive value (PPV), and 98.1% negative predictive value (NPV) in diagnosing ovarian malignancy in the 3 studied groups (AUC 0.955, p < 0.001). There was significant positive correlation between the participants’ age, and RMI (p = 0.001), and between participants’ cancer antigen-125 (CA-125) and RMI (p < 0.0001) in the ovarian malignancy group. Conclusions The multimodal RMI is an effective tool for primary evaluation of suspected ovarian masses. Risk malignancy index at cut-off value > 247.5 had the best performance (82.9% sensitivity, 100% specificity, 100% PPV, and 98.1% NPV) in diagnosing ovarian malignancy in the 3 studied groups. There was significant positive correlation between participants’ age, and RMI, and between participants’ CA-125 and RMI, in the studied malignant ovarian tumours.
Using game theory and machine learning to provide white cell automation support in the adjudication of war games

White Cell adjudication evaluates impact of player moves to create randomness representing realistic scenarios. Players are limited to the view of the game state based on their technologies’ capabilities and intel sources, while the White Cell knows game state truth. The Automated White Cell (TAWC) is developed to provide adjudication guidance as to how much and what types of testing are sufficient to determine acceptable levels of randomness to evaluate AI/Autonomous platforms, including manned-unmanned teaming scenarios. TAWC is based on multi-model data integration, using AI/ML/Game Theory solutions, enhancing the predictive fidelity of Test and Evaluation, Verification and Validation (TEVV) /Live Virtual Construct (LVC) facilities in assessing AI/Autonomous technology strengths and weaknesses.
Automatic modulation classification of NLFM radar signal in multipath conditions

The information about intrapulse modulation is commonly used to augment data in electronic intelligence systems. However, increasingly popular nonlinear frequency modulated (NLFM) waveform is hardly ever distinguished as a separate waveform class by electronic support systems. In this paper a method of recognizing NLFM radar signal is presented. The algorithm is based on two novel features extracted from fractional Fourier transform (FrFT) and instantaneous frequency estimated via quasi-maximum-likelihood (QML) method. Moreover, the algorithm is designed to be resistant to the multipath effects, which are inherent to land-based intercept systems.
A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.