Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Student Engagement, Confidence, and Usefulness for Female Students in CS Class at High School Using Machine Learning

Females remain underrepresented in computer science (CS), despite numerous studies investigating the causes using different data types and analysis techniques. In recent years, machine learning (ML) has been increasingly used in education, particularly for analysing student engagement using some engagement dimensions. However, no study has yet used ML algorithms to analyse student behavioural, cognitive, emotional, and social engagement survey data. In this paper, we present a study investigating whether these four dimensions of engagement are related to female high school students' beliefs in the usefulness of learning computer science and their confidence in taking CS classes in Saudi Arabia. We also employ ML techniques to identify important indicators that can predict students' confidence and beliefs in the usefulness of learning CS. Additionally, we compare three supervised ML techniques, Random Forest (RF), Decision Tree (DT), and LightGBM (LGBM), to determine which algorithms better predict confidence and usefulness in learning CS. Our sample consisted of 284 participants from four schools who completed the multidimensional survey, and we evaluated the ML algorithms using Mean squared error (MSE), Mean absolute error (MAE), and Determination coefficient (R2). Our findings show that each dimension of student engagement positively correlates with the confidence and usefulness of learning computer science, and it is possible to predict them from student engagement indicators. The RF model outperformed DT and LGBM, identifying 'enjoyment in learning new things' and 'interest in topics in CS class' out of 28 indicators as the most important features to predict usefulness. Additionally, from 28 features, 'looking forward to CS class' and 'enjoyment in learning new things' are the most important indicators influencing confidence. Our findings contribute to a broader understanding of student engagement in CS education and highlight various indicators used to measure student engagement. These findings shed light on factors that may motivate and interest female students toward CS learning. Future work will use a larger sample of participants from schools and higher education in different countries.
Chronic Obstructive Lung Disease: Treatment Guidelines and Recommendations for Referral and Multidisciplinary Continuity of Care

Chronic Obstructive Pulmonary Disease (COPD) constitutes a major public health problem, and it is anticipated that its prevalence will continue to increase in the future. Its progressive nature requires a continuous and well-coordinated care approach. The follow-up for COPD should primarily focus on disease screening and control, which includes monitoring of pulmonary function, prevention of exacerbations, identification of aggravating factors and comorbidities, as well as ensuring treatment adequacy and adherence. However, existing clinical practice guidelines and consensus documents offer limited recommendations for the follow-up. In this context, we undertake a review of COPD treatment and the continuity of care recommendations endorsed by several scientific societies. Moreover, we underscore the importance of the involvement of nursing and community pharmacy in this process, as well as the utilization of quality indicators in the provision of care for the disease.
Nilotinib

One recurrent feature of drug development for hematologic malignancies is that once we have a therapy that works, soon even better agents come along. Examples include hairy cell leukemia, where IFN-a was followed by pentostatin and then 2chlorodeoxyadenosine or acute promyelocytic leukemia, where all-trans retinoic acid has been joined by arsenic trioxide as a second extremely active agent. In chronic myeloid leukemia (CML), the major breakthrough has occurred with the introduction of imatinib (Gleevec), a small-molecule inhibitor of BCR-ABL, the constitutively active tyrosine kinase that causes the disease. Imatinib has completely changed our therapeutic approach to CML. Now, 6 years after its first approval, the therapeutic space is being populated by second-line BCR-ABL inhibitors with the potential to make even better what is already very good. The newest of these agents is nilotinib (Tasigna; ref. 1).
Electromagnetic Design of High-Speed Interior Permanent Magnet Motor

In this paper, the electromagnetic design of a high-speed interior permanent magnet (IPM) is proposed. During the design process, not only the electromagnetic and loss characteristics of the high-speed IPM motor, but also the maximum stress of the rotor should be considered. First, the influence of rotor design parameters, including magnetic bridge thickness, rib width and stiffener thickness, on no-load back-EMF and rotor stress is analyzed. Then, the influence of the thickness and width of the PM on the motor rated-load performance and the maximum rotor stress is analyzed, based on the rotor structure of the PM in three segments. Finally, the electromagnetic properties and rotor stresses of the motor are verified by the finite element model.
Safety and Efficacy upon Infection in Sheep with Rift Valley Fever Virus ZH548-rA2, a Triple Mutant Rescued Virus

The introduction of three single nucleotide mutations into the genome of the virulent RVFV ZH548 strain allows for the rescue of a fully attenuated virus in mice (ZH548-rA2). These mutations are located in the viral genes encoding the RdRp and the non-structural protein NSs. This paper shows the results obtained after the subcutaneous inoculation of ZH548-rA2 in adult sheep and the subsequent challenge with the parental virus (ZH548-rC1). Inoculation with the ZH548-rA2 virus caused no detectable clinical or pathological effect in sheep, whereas inoculation of the parental rC1 virus caused lesions compatible with viral infection characterised by the presence of scattered hepatic necrosis. Viral infection was confirmed via immunohistochemistry, with hepatocytes within the necrotic foci appearing as the main cells immunolabelled against viral antigen. Furthermore, the inoculation of sheep with the rA2 virus prevented the liver damage expected after rC1 virus inoculation, suggesting a protective efficacy in sheep which correlated with the induction of both humoral and cell-mediated immune responses.
Risk factors for symptomatic rotator cuff tears: a retrospective case–control study

Background The incidence and diagnostic rate of rotator cuff tears (RCTs) have increased significantly. The purpose of this study was to investigate and analyze the risk factors for symptomatic RCTs to provide a basis for their prevention and treatment. Methods We retrospectively analyzed the relevant clinical indicators of 193 patients with RCTs and 161 patients without RCTs hospitalized with shoulder pain as the main complaint from January 1, 2017, to August 31, 2021. Univariate analysis and multivariate logistic regression analysis were used to analyze the differences in potential risk factors between the two groups. Results Univariate analysis revealed that age (p < 0.001), body mass index (BMI) (p = 0.036), hypertension (p < 0.001), coronary heart disease (p = 0.028), history of shoulder trauma (p < 0.001), hyperlipidemia (p = 0.025), type III acromion (p = 0.012) and critical shoulder angle (CSA) (p < 0.001) increased the risk of RCTs. Multivariate logistic regression analysis revealed that age ≥ 60 years (OR = 2.61, 95% CI = 1.23 to 5.12), CSA ≥ 35° (OR = 4.24, 95% CI = 1.60 to 11.22), hypertension (OR = 2.34, 95% CI = 1.33 to 4.11) and history of shoulder trauma (OR = 5.20, 95% CI = 2.87 to 9.45) were independent risk factors for symptomatic RCTs. Conclusion The results of this study showed that age ≥ 60 years, CSA ≥35°, hypertension and history of shoulder trauma are independent risk factors for symptomatic RCTs and can provide directions for further development of prevention and treatment strategies. Future studies need to clarify the mechanism underlying the association between these risk factors and symptomatic RCTs.
Assessing the Importance of Video Visualization Timings in a Flipped Classroom Context

This Research Work-in-Progress focuses on how students make use of supporting videos provided in a flipped classroom context. More specifically, this study analyzes the academic implications of the timing of video accesses: late (during the few days preceding an exam) or early. The experiment was performed in a first-year engineering course. The course is divided into 6 splits, ranging from 2 to 4 weeks each. Every split introduces new videos and ends with an exam. Data from 90 first-year students were collected. Two main analysis tasks were undertaken: assessing the correlation between the number of videos accessed early or late and exam scores, and student clustering depending on the general timing of video accesses. Significant correlation between early accesses and exam scores was only observed on the first split. Also in this first split, 3 student groups were identified via clustering: low activity, mostly late and mostly early. Interestingly, there were significant differences in long-term performance between these groups. This is a valuable result towards the development of an early warning system, as it helps identify students at risk at a early stage.
Knee Up: an Exercise Game for Standing Knee Raises by Motion Capture with RGB-D Sensor

In this work, we present the design and the implementation of Knee Up, an exergame that promotes knee health via standing knee raises exercises. It allows users to exercise at home and perform the exercise in a gaming environment. By motion capture using RGB-D data, estimated positions of the user’s skeletal joints are acquired and processed in real-time. For the game, 3D virtual environments have been created with gamiﬁcation elements so that users can enjoy more interactive and engaging exercise sessions. The game facilitates to evaluate the quality of performed standing knee raises exercise through a rule-based recognition algorithm and, in return, to provide timely feedback. A formal user study was conducted in order to assess the usability and gamiﬁcation aspects of Knee Up. Study results demonstrate that Knee Up is generally well-received in terms of usability, engagement, ease of learning-to-play, and exercise sustainability; and validate that the rule-based recognition algorithm works satisfactorily well.
Common Comorbid Condition of Patients With Autism Spectrum Disorder and Pharmacotherapy for Patients With Autism Spectrum Disorder

This article describes common comorbid condtion of the autism spectrum disorder (ASD) and recommends treatment guidelines of pharmacotherapy for patients with ASD. More than 95% of people with ASD have at least one additional disorder and guidelines how to evaluate and treat comorbid conditions in patients with ASD and 7 recommendations for treatment with medication for ASD.
Established and Evolving Roles of the Multifunctional Non-POU Domain-Containing Octamer-Binding Protein (NonO) and Splicing Factor Proline- and Glutamine-Rich (SFPQ)

It has been more than three decades since the discovery of multifunctional factors, the Non-POU-Domain-Containing Octamer-Binding Protein, NonO, and the Splicing Factor Proline- and Glutamine-Rich, SFPQ. Some of their functions, including their participation in transcriptional and posttranscriptional regulation as well as their contribution to paraspeckle subnuclear body organization, have been well documented. In this review, we focus on several other established roles of NonO and SFPQ, including their participation in the cell cycle, nonhomologous end-joining (NHEJ), homologous recombination (HR), telomere stability, childhood birth defects and cancer. In each of these contexts, the absence or malfunction of either or both NonO and SFPQ leads to either genome instability, tumor development or mental impairment.
Reliable SDN Controller Architecture for Integrated Satellite-Terrestrial Network Convergence: A Review

The current 5G era has met expectations for high data rates and low latency, yet falls short in delivering resilient and ubiquitous connectivity. The terrestrial 5G technology, while successful in some aspects, faces challenges due to congestion and limited capacity in emerging applications, as well as geographical limitations and poor ROI. To combat these issues, integrating satellite technology into terrestrial networks has emerged as a solution. However, the cooperative utilisation of both networks remains a challenge. Diverse research and standardisation efforts propose solutions, notably leveraging Software-Defined Networking (SDN). This paper reviews existing work in this area and presents conceptual solutions. The proposed concept targets reliable convergence, achieving access network selection and traffic steering for end-to-end data transmission.
On-Device Smishing Classifier Resistant to Text Evasion Attack

Smishing (SMS phishing) is a cybercrime in which criminals send fraudulent messages, including malicious links, to steal the victims’ private data or cause financial losses. The damage caused by smishing has become more severe, particularly with the proliferation of mobile devices. In smishing, a major difficulty faced by victims is discrimination between normal and smishing messages. To resolve this problem, we present an on-device smishing classifier based on a deep-learning model. In real-world scenarios, access to a substantial, authentic dataset is crucial. We trained and evaluated the classifier using real SMS datasets containing approximately 250,000 smishing messages and 950,000 normal messages obtained from victims in Korea. To ensure privacy, the classifier operates solely on mobile devices without externally transmitting any data. It utilizes a lightweight method that does not require significant computing power on mobile devices. We explored several models to determine a suitable model for mobile devices and optimized it using real datasets. Furthermore, our statistical analysis of actual smishing messages revealed that 98% of smishing messages are variants of previously sent messages. To address the prevalence of variant smishing messages, we propose a text evasion attack tool called EVA that is capable of generating pseudo-variant messages from a given message using an adversarial attack approach. We used this tool to evaluate and enhance the robustness of our classifier against various messages. Our classifier exhibited exceptional classification accuracy (0.99) while being lightweight (at 127 kB) and robust against variant smishing messages (attack success rate of 0.41).
Knowledge on Nutrition and Dietary Diversity Among Community School Adolescents in Kathmandu Valley

Nutritional knowledge and practices among adolescents are critical to combat health problems that can continue into adulthood. The study determines the nutritional knowledge and dietary diversity and its associated factors among school adolescents of Kathmandu Valley. The study adopted a cross-sectional design of 487 school adolescents using multistage cluster sampling. Information on socio-demographic characteristics, Health Belief Model constructs, nutrition knowledge and dietary diversity were collected. Bivariate and multivariate logistic regression models were used to identify the factors associated with nutrition knowledge and dietary diversity. Nutrition knowledge and dietary diversity were found inadequate; 47.4% had adequate dietary diversity and 42.5% had adequate nutrition knowledge Variables that were significantly associated with adequate nutrition knowledge (versus inadequate) in the presence of other variables were compared to reference group included age (adjusted odds ratio [aOR]= 0.83; 95% CI = 0.71-0.96), being from ethnic groups of Janajatis (aOR=2.79; 95% CI: 1.47-5.28) and Brahmin/Chhetri (aOR=1.9; 95% CI: 1.06-3.4), belonging to nuclear family (aOR=1.75; 95% CI: 1.18-2.6), literate father (aOR=2.16; 95% CI: 0.99-4.73), perceived severity (aOR= 1.19; 95% CI: 1.06-1.33) and perceived barrier (aOR= 1.02 ; 95% CI: 1-1.05). Variables that were significantly associated with consuming adequate dietary diversity (versus inadequate) in the presence of other variables include being male (aOR=1.52;95% CI: 1.01-2.29), having literate mother (aOR=1.7;95% CI: 1.02-2.83), consuming unhealthy diet daily (aOR=4.23;95% CI: 1.85-9.67), having adequate knowledge (aOR=2.4;95% CI: 1.59-3.63) and self-efficacy (aOR= 1.15;95% CI: 1.06-1.24). This study highlights the need for specific theories and model-based targeted interventions to encourage adolescents' healthy behavior and healthy eating.
Social Impact Assessment in Urban Security Management Projects: A Case Study from Pakistan

Public safety and security management projects are devised to reduce crime, fear and calamities by prevailing law and order to reduce the harm in society. In a certain context, social impact assessment is a novel way to reveal the extent of effectiveness for these projects. This case study presents an Expert System based methodology regarding social impact assessment for two urban security management projects in Pakistan. An Expert System, DoctuS is employed as a tool to build a rule-based model tool using social impact attributes (as variables) from literature and expert knowledge (Author’s own insight as being involved in projects). Case-based reasoning (CBR) method is employed for the measurement of the impact of urban security management KPIs (Key Performance Indicators) in the two case projects. The empirical findings in this case study approaches the impact assessment by quantitative analysis of crime rates in JMP statistical package and Qualitative Expert knowledge-based model in DoctuS. Thereby, the study evaluates the pre and post ante project situations in order to assess the project’s effectiveness and impact on improved urban safety and security. In the first step, the projects are briefly described, followed by the crime rate analysis under the project jurisdictions and rules between attributes are defined. The second step defines the qualitative rule-based model along with the two cases described, and validated in DoctuS tool. Some attributes and rules have changed, and a new concise model is presented. The Expert System compares the project cases and evaluates the social impact assessment based on the defined KPIs. The Expert system validation presents a novel model for social impact assessment of subject urban security management projects. We propose this methodology and model as useful in investigating social impact assessment of projects specifically dealing with urban safety and security management. However, the generalizability of the findings of this case-study based articles need more sophisticated tests as recommendation for the extension to this study. Received: 31 May 2023 / Accepted: 19 November 2023 / Published: 5 January 2024
Federated Medical Learning Framework Based on Blockchain and Homomorphic Encryption

Federated learning-based medical data privacy sharing can promote the development of medical industry intelligence, but limited by its own security and privacy deficiencies, federated learning still suffers from a single point of failure and privacy leakage of intermediate parameters. To address these problems, this paper proposes a privacy protection framework for medical data based on blockchain and cross-silo federated learning, using cross-silo federated learning to establish a collaborative training platform for multiple medical institutions to enhance the privacy of medical data, introducing blockchain and smart contracts to realize decentralized federated learning to enhance trust between distrustful medical institutions and solve the problem of a single point of failure. In addition, a secure aggregation scheme is designed using threshold homomorphic encryption to prevent the privacy leakage problem during parameter transmission. The experimental and analytical results show that the accuracy of this paper’s scheme is consistent with the original federated learning scheme, effectively deals with the problems of single-point failure and inference attacks of federated learning, improves system robustness, and is suitable for medical scenarios with more stringent requirements on security and accuracy.
Seven seasons of learning and engagingsmallholder farmers in the drought-prone areas of sub-Saharan Africa and South Asia throughTropical Legumes, 2007–2014

Tropical Legumes II (TL II) is a Bill & Melinda Gates Foundation sponsored project implemented by three International Agricultural Research Centers – International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), International Centre for Tropical Agriculture (CIAT) and International Institute for Tropical Agriculture (IITA). The project aims to improve the livelihoods of smallholder farmers in the drought-prone areas of Sub-Saharan Africa (SSA) and South Asia (SA) through improved productivity and production of six major grain legumes – chickpea, common bean, cowpea, groundnut, pigeonpea and soybean. The project activities were implemented in Burkina Faso, Ghana, Mali, Niger, Nigeria, Senegal, Ethiopia, Kenya, Malawi, Mozambique, Tanzania, Uganda and Zimbabwe in SSA, and India and Bangladesh in SA. The project has been running for the past two phases: Phase I (2007 – 2011) and Phase II (2012 – 2014). The project is designed to help the smallholder farmers to overcome constraints, such as drought, pests, diseases and lack of improved seed varieties. TL II is expected to enhance the productivity by at least 20% through increased adoption covering 30% of legume area, strengthening national breeding programs and generating at least $1.3 billion in added value. This has resulted in significant achievements. The active breeding programs are now in place in all 15 countries. New seed varieties (163) have been released and are fast replacing the old ruling seed varieties. Thirty seven national partners were trained at MSc and PhD levels. As a result of the enhanced skills and knowledge of seed value chain actors, seed production significantly increased by 221% (from 139,048 to 446,359 tons) over the project period. The program adopted an inclusive approach for the poor, especially women, through promotion of various innovative approaches, such as small seed packs, seed loans and decentralized production schemes. Since 2007, dissemination of improved varieties has been adopted on at least 2 million hectares and more than $448 million has been generated from the project funding and nearly $976 million from the project and investment partners. Even when using the adoption rates data from adoption and expert opinion surveys, the aggregate gross benefits from TL II-related modern legume varieties is estimated at about $978 million, which is still far above the total TL II investment grossly compounded at $48 million. In effect, for each dollar invested, the project generated $9 with direct project investment or $20 with partnership’s investment and again $20 when using adoption rate based estimate. These successes and associated challenges will be discussed in detail in subsequent chapters of this book.