Miracle-dz/UniFAR-FTUs huggingface dataset:
DESIGN ISSUES: DUTY CYCLE CONTROL IN WIRELESS SENSOR NETWORKS

Wireless sensor networks are envisioned to be deployed in the physical environment to monitor a wide variety of real-world phenomena. Wireless sensor networks (WSN's) are becoming popular in military and civilian applications such as surveillance, monitoring, disaster recovery, home automation and many others. Almost any sensor network application faces the challenging design constraints of efficient energy consumption. The report presents concept of basic duty cycle control algorithms. Further studies will stress on the shortcoming mentioned in duty cycle control algorithms SIPF under different types of traffic conditions applied to the WSN and how different types traffic structures affect the SIPF algorithm will be examined.
Adaptive Dual Droop Control of MTDC Integrated Offshore Wind Farms for Fast Frequency Support

This paper proposes an adaptive dual droop control (ADDC) scheme, it can provide the disturbed onshore system with fast frequency support from both other undisturbed onshore systems, and voltage source converter-based multi-terminal direct current (VSC-MTDC) integrated offshore wind farms (OWFs). With conventional droop control at onshore converters, the DC voltage and power flow will change once frequency events occur, it will lead to frequency variations in other undisturbed systems. The proposed ADDC scheme firstly detects the disturbed and undisturbed systems, and then makes the undisturbed onshore system provide more frequency support power, while ensuring safe operation by settling the support power limitation and regulating the droop coefficients. Moreover, the offshore stations will estimate the onshore DC voltage as the control signal for fast frequency support. After that, the OWFs will recover their rotor speed with an asymptotic control scheme to reduce the second frequency drop. Case studies are carried out on 3-terminal and 5-terminal test systems, and the Opal-RT real-time simulation platform, respectively. Different control schemes are compared, and the parameter uncertainty and noise disturbance are considered to illustrate the performance and effectiveness of the proposed ADDC scheme.
Some aspects of electromagnetic testing of composites

The high strength-to-weight ratio of composites make them attractive for the manufacture of items ranging from aerospace products such as helicopter rotor blades to sports equipment. Despite their advantages, composites still need to be monitored for quality and safety reasons during the fabrication process and in-service operations. Detection by NDT of flaws in composites remains difficult due to their heterogeneity, anisotropy and multi-layered structure. Each non-destructive testing technique has its own strength and limitations depending on the material tested and the type of flaw being sought. Among these, electromagnetic techniques for composite examination are a low-cost alternative to more conventional techniques such as ultrasound, and are reviewed in this paper.
Design of Adaptive PID Controller for Lower Limb Rehabilitation Robot Based on Particle Swarm Optimization Algorithm.

The proportional-integral-derivative (PID) is still the most common controller and stabilizer used in industry due to its simplicity and ease of implementation. However, in most of the real applications, the controlled system has parameters that slowly vary or are uncertain. Thus, PID gains must be adapted to cope with such changes. In this research, an Adaptive Proportional-Integral-derivative controller (APID) is proposed to control the 2-DOF lower limb rehabilitation robot system. The parameters gains of the proposed controller are optimized using the Particle Swarm Optimization algorithm (PSO). The simulation results show no overshoot and zero steady-state error, but large settling time (ts=3.654 sec. for link1 and ts=2.844 sec. for link2) for linear path, and the actual path tracks the desired path with a large error for the nonlinear path. The results illustrate that the robot's performance is inefficient for linear and nonlinear paths when using the APID controller to control the lower limb rehabilitation robot. Therefore, the controller needs to modify for controlling the robot efficiently.
The impact of tobacco (Nicotiana tabacum) farming on the survival of honeybees (Apis mellifera) in Nyamakate Communal Area, northern Zimbabwe

ABSTRACT The impact of tobacco (Nicotiana tabacum) farming on the survival of honeybees (Apis mellifera) was investigated through the documentation of mortality of honeybees, the number of trees cut and planted, and agrochemicals used in tobacco farming in Nyamakate Communal Area, Hurungwe District, northern Zimbabwe. The study was conducted in the wet season, i.e. between December 2017 and March 2018 using a stratified random sampling design to sample tobacco farmers and apiculturists. Honeybee mortalities across five sampled villages were significantly different (Kruskal–Wallis χ2 test = 74.54, df = 4, p < 0.05). The survey recorded 14 different agrochemicals that tobacco farmers used in tobacco farming, although the local regulator banned five of them in the 2018 and 2019 agriculture season. All five villages recorded an estimated 5,220 indigenous trees that were cut to cure tobacco whereas 483 Eucalyptus trees were planted as part of ongoing reforestation efforts in the district within the study period. It was concluded that tobacco farming negatively impacts honeybees through reduced forage and nesting sites (resulting from deforestation) and increased mortalities from the use of toxic agro-chemicals. There is a need for legislative alignment to ensure effective law enforcement on compliance. Government and partners need to encourage tobacco farmers to plant fast-growing indigenous trees for afforestation and adopt modern technology such as the use of solar-powered tobacco curing barns.
How would foreign entrepreneurs deal with pressures for corruption? A micro-foundational approach

Is corruption good or bad for entrepreneurship? Prior research has presented mixed results when exploring this question. This article takes a micro-foundational approach by focussing on how foreign entrepreneurs cognitively deal with corruption in the host environment. We argue that the entrepreneur’s motivational antecedent to engaging in corruption, or corruption propensity, represents a cognitive belief in the legitimacy and effectiveness of corruption. We find that higher corruption propensity leads to lower firm growth. In addition, the effects of corruption propensity are conditioned by the corruption difference between home and host countries. Finally, we discuss implications for research, practice and public policy.
Ciliary body medulloepithelioma treated with intra-operative brachytherapy after fine needle aspiration biopsy

Medulloepithelioma is the second most common type of pediatric intra-ocular tumors. It commonly arises from ciliary body, and it is generally diagnosed in the first decade of life. Management options for medulloepithelioma include enucleation, resection, or radiotherapy, but further investigation is still needed. Herein, we report a case of a 1-year-old girl with a ciliary body mass highly suggestive of medulloepithelioma, which caused recurrent acute episodes of intense pain. Fine needle aspiration biopsy (FNAB) of ciliary body mass was performed with trans-scleral approach, and treatment with a iodine-125 brachytherapy COMS10 plaque was undertaken during the same interventional procedure. Lesion was treated using a plaque brachytherapy, with total radioactivity of 13.5 mCi distributed in 5 seeds with immediate disappearance of pain episodes and decrease of tumor size. This is the first case of medulloepithelioma treated with brachytherapy plaque after an extemporaneous anatomo-pathological examination in children with favorable response. We consider that intra-operative brachytherapy therapy after FNAB in selected ocular tumors may be safe and effective therapeutic option, but longer follow-up is needed to confirm safety and applicability of this approach in a larger group of patients.
Promoting Self-Regulated Learning among First-Year Accounting-Student Teachers: A Student-Empowerment Pedagogical Framework

The advent of the Covid-19 pandemic significantly diminished opportunities for face-to-face learning. This came at the backdrop of the consistent failure of first-year students to cope with the demands of learning in a complex higher educational system, which prioritises autonomous learning. Such a failure has been attributed to the structured and supportive learning environments in basic education, which have not only produced excessively dependent learners, but also a cohort of first-year students with low levels of resilience, self-efficacy, and motivation. Against this exposition, this quantitative study investigated those teaching approaches that lecturers can use to promote self-regulated learning among first-year accounting-student teachers. Guided by the fundamental precepts of the social-cognitive model of self-regulated learning and informed by interpretivism, quantitative data were collected using a Likert Scale questionnaire. The statistical analysis of the data revealed that meaningful and sustainable self-regulated learning can only be promoted by a purely student-centred approach. With an overall mean above 4.0, problem-based learning was found to frequently promote the self-regulated learning of students. Conversely, given an overall mean of just above 2.0, scaffolding and guided instruction were found to rarely promote self-regulated learning. A low standard deviation of below 1.0 on all three teaching approaches suggests a very small variance in the students’ scores. On the basis of these findings, the study calls for a pragmatic adoption of radical student-centred constructivist teaching approaches. In line with this recommendation, institutions of higher learning need to capacitate lecturers to teach students problem-solving and self-regulated learning skills.
Effort–Reward Imbalance among a Sample of Formal US Solid Waste Workers

Background: Solid waste workers are exposed to a plethora of occupational hazards and may also experience work-related stress. Our study had three specific hypotheses: (1) waste workers experience effort–reward imbalance (ERI) with high self-reported effort but low reward, (2) unionized workers experience greater ERI, and (3) workers with higher income have lower ERI. Methods: Waste workers from three solid waste sites in Michigan participated in this cross-sectional study. We characterized perceived work stress using the short-version ERI questionnaire. Descriptive statistics and linear tests for trend were assessed for each scale. Linear regression models were constructed to examine the relationship between structural factors of work stress and ERI. Gradient-boosted regression trees evaluated which factors of effort or reward best characterize workers’ stress. Results: Among 68 participants, 37% of workers reported high effort and low reward from work (ERI > 1). Constant pressure due to heavy workload was most indicative of ERI among the solid waste workers. Union workers experienced 79% times higher ERI than non-unionized workers, while no significant differences were observed by income, after adjusting for confounders. Conclusions: Organizational-level interventions, such as changes related to workload, consideration of fair compensation, and increased support from supervisors, can decrease work stress.
Toward the Design of an Energy Consumption Feedback System

In this article, we examine two novel issues in user requirements analysis for a feedback system on household energy consumption: (a) microgeneration and (b) the “Must, Should, Could, Won’t have” (MoSCoW) method. We report on a qualitative user requirements analysis using the MoSCoW method for the prioritization of user requirements. Fifteen interviews resulted in three user groups that were abstracted into personas with different user requirements. Finally, we show that changes in cutoff values resulted in MoSCoW prioritization changes for a maximum of only 5.9%. These are promising results for the robustness of the MoSCoW method.
CK1α protects cardiomyocytes in sepsis-induced myocardial depression by repressing the interaction of ATG5 with myD88/NF-kappaB signaling

Purpose: To explore the effects of casein kinase 1α (CK1α) on cardiomyocytes in sepsis-induced myocardial depression.Methods: Colorectal ligation puncture (CLP) surgery was performed for the establishment of the mouse model. Total RNAs of the lungs, kidneys, liver tissues and alveolar macrophages were extracted using TRIzol™ reagent, while gene expression analysis was performed by quantitative polymerase chain reaction (qPCR) following reverse transcription. Western blot was employed to evaluate protein expression, and echocardiography was conducted to assess cardiac function. Immunofluorescent assay was performed to determine the expression of p-FOXO3a in primary cardiomyocytes.Results: Inhibition of CK1α impaired autophagy influx, and significantly increased inflammatory cytokines in H9C2 cells after lipopolysaccharide (LPS) treatment (p < 0.05). Moreover, aberrant activation of the Toll-like receptor 4(TLR4)/myD88/NF-kappaB pathway was observed in the H9C2 cell line after LPS treatment (p < 0.05). Immunoprecipitation analysis revealed an interaction between MyD88 and autophagy-related gene 5 (Atg5), and cardiac dysfunction in mice intravenously injectedwith an adenoviral vector containing shRNA (casein kinase 1 α) CSNK1A1 was suppressed (p < 0.05). In contrast, overexpression of CK1α remarkably improved cardiac systolic function (p < 0.05), the expression of inflammatory cytokines was repressed, and autophagy was enhanced in the hearts of mice with the specific overexpression of CSNK1A1 in cardiomyocytes (p < 0.05). In Atg5-deficient mice pretreated with DC661, the protective effect of adenoviral vector containing CK1α overexpression was eliminated.Conclusion: CK1α protects cardiomyocytes during sepsis after the inhibition of TLR/MyD88/NF-kappaBpathway via interaction of Atg5 with MyD88. The results of the current study may provide new insights into the treatment of sepsis-induced myocardial depression.
A Proposed Fraud Detection Model based on e-Payments Attributes a Case Study in Egyptian e-Payment Gateway

—As per Payfort's 2017 report, titled State of payments in the Arab world; Egypt had a 22% yearly increase in the overall volume of internet payments in 2016, which was assessed at $6.2 billion. e-Payments are the major point of life nowadays in Egypt and the whole world; with tens of e-payments companies in Egypt and more than 5 million transactions done every day and 60 billion EGP volume of payments in 2018. Online and mobile fraud was estimated at $10.7 billion in 2015, as per Juniper Research, and is expected to reach $25.6 billion by the end of the decade. As the whole e-payments business is affected by fraud, e-payments firms and their consumers lose a lot of money. On the other hand, one of the most powerful techniques that could be used for fraud predictive is data mining techniques such as the decision tree. This paper introduces a prediction model for managing the risk of fraud in the Egyptian e-payment market that helps to reduce the loss of money. This model is developed using a real dataset from one of Egypt's top e-payment gateways based on the e-payment transaction attributes importance like transaction time, transaction amount, transaction limit, and transaction customer No. repetition limit. The importance of these attributes was determined using IBM SPSS modeler's decision tree and its predictors' importance. The model significantly assisted in the reduction of fraud cases by a very high rate, with an accuracy of 88.45% and a precision of 93.5% resulting in a savings of 101970.52 EGP out of 131297.83 EGP.
Development of Hausa Acoustic Model for Speech Recognition

—Acoustic modeling is essential for enhancing the accuracy of voice recognition software. To build an automatic speech system and application for any language, building an acoustic model is essential. In this regard, this research is concerned with the development of the Hausa acoustic model for automatic speech recognition. The goal of this work is to design and develop an acoustic model for the Hausa language. This is done by creating a word-level phonemes dataset from the Hausa speech corpus database. Then implement a deep learning algorithm for acoustic modeling. The model was built using Convolutional Neural Network that achieved 83% accuracy. The developed model can be used as a foundation for the development and testing of the Hausa speech recognition system.
Relational Deep Learning Detection with Multi-Sequence Representation for Insider Threats

—Insider threats are typically more challenging to be detected since security protocols struggle to recognize the anomaly behavior of privileged users in the network. Intuitively, an insider threat detection model depends on analyzing the audit data, representing trusted users’ activity streams, on recognizing malicious behaviors. However, the audit data is high dimensional data in that it presents n dependent streams of activities where it establishes a complex feature extraction. In this context, the dependent streams represent user activities where each activity is represented by an ordered set of real variables that pertain to a speciﬁc occurrence, such as log-in records. As a result, multiple actions can be represented simultaneously, with one or more values being recorded at each timestamp. Moreover, the relations between dependent streams are typically neglected while detecting the anomaly behavior. Ideally, relation learning is commonly considered to recognize occurrence patterns in streaming data. Thus, the latent relations are thought to have insight for the accurate detection of anomaly behavior concerning insider threats. This study introduces a novel model to detect insider threats by representing audit data as multivariate time series to explicitly learn the existing inter-relations between activity streams using a Recurrent Neural Network (RNN). The model considers learning the latent relationships to effectively extract features for modeling the behavior proﬁle where anomaly behavior can be detected accurately. The evaluation, using the CERT dataset has shown that the proposed model outperforms the comparator approaches to insider threats detection with AUC of 0 . 99 .
Application of the Fuzzy Delphi Method to Identify and Prioritize the Social-Health Family Disintegration Indicators in Yemen

— Constantly increasing political events and socially related changes have led governments worldwide to adopt strategies to reduce their negative effects on the cohesion of societies, which requires developing assessment frameworks that include realistic, measurable, and useful indicators for analyzing the family disintegration causes, taking into consideration the circumstances surrounding the countries and the development trends that they adopt. Therefore, this study aims to identify and prioritize indicators of a decision-making support framework for evaluation, ranking, and structural comparison of the family disintegration causes resulting from the child marriage phenomenon in Yemen. To achieve this, the Fuzzy Delphi Method was applied. Firstly, a set of related literature and theories were analyzed to extract the expected framework's suitable initial indicators. Then, with the participation of twenty-four local experts, the extracted factors were revised, and the most suitable factors were selected. As a result, one social factor out of nine social-health factors was excluded due to its inappropriateness, and a framework of eight indicators was built. Also, with a rating average of 0.727, it was consistently agreed that the indicator "Increasing divorce rates in marital cases that do not take place according to the common desire of the spouses" is the most important indicator. Also, with high consistent evaluation averages (0.652–0.658), all three health indicators were ranked in the second and third places, while the other four social indicators were ranked in the last three positions (fourth– sixth). Finally, the real applications of the proposed framework were recommended.
Designing a Renewal Approach of Rejuvenating Historic Districts: Taking Nantong Temple Street as an Example

In recent years, people’s opinions have shifted with the continuous development and growth of cities. The category of historical heritage is increasingly being shifted from the city’s star fire to the backdrop color of the entire city. However, some cities are experiencing issues such as aging, regeneration, and stagnation. With the merging of sustainable development ideas with urban renewal techniques, sustainable urban renewal has developed as a new area of research. Data mining and analytics have played a significant role in extracting knowledge and decision-making in the revitalization of historic districts during the last decade. Machine learning, as a computational engine for learning analytics, is a fundamental technique for extracting information, data pattern detection, and prediction. From the perspective of machine learning, this study investigates the historical block update approach, which is utilized in feature space and culture at the same time. Furthermore, it investigates the development of countermeasures for injecting new energy into the historical block by using the functions of space and scale to search for the block transformation and the combination of natural and cultural landscapes. In addition, in order to interact with visitors and residents and realize the renewal of old blocks, in this research work, multifunctional culture is grafted into the historical block and buried underground, so that their life can be awakened. To highlight the efficiency of this work, we have taken the Nantong Temple Street in China as an example which verifies the validity of this study.
A Fair and Rational Data Sharing Strategy Toward Two-Stage Industrial Internet of Things

The easy and pervasive involvement of devices in Industrial Internet of Things has greatly benefited the implementation and adoption of various smart services. One prominent prerequisite of such trends is the extensive and continuous support and sharing of data and resources among devices. However, previous efforts usually treat the data sharing as one-time task among devices, which are incapable when the data are applied for the distributed and iterative training task of machine learning models. Therefore, this article proposes a novel framework for continuous data sharing in Industrial Internet of Things. The system consists of different system owners, each brings devices and participate the distributed training of models. Specifically, system owners hold different scales of devices, data, and resources, while devices own heterogeneous availability in different time periods. In this case, the goal is to properly assign devices for qualified model training process in different rounds, such that no devices will devote unlimited resources and the overall efforts and consumptions among different owners are balanced. Accordingly, three algorithms for device allocation are proposed, based on whether the availability of devices in each training round are known at the beginning of the training procedure. The analysis shows that all algorithms can achieve a rational allocation for devices and balance the performance among system owners. Finally, evaluation results reveal that the proposed solutions outperform baseline methods in providing better data sharing plans.