Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Computing-Enabled Parkinson's Disease Diagnosis and Treatment: Opportunities and Challenges

There is presently no cure for Parkinson's disease (PD), a degenerative and devastating neurological ailment that affects millions of people worldwide. The field of PD could benefit significantly from quantum computing, an emerging technology that has the potential to revolutionize healthcare research. After introducing Parkinson's disease (PD), its symptoms, and current treatments, this paper dives into the role that quantum computing could play in PD diagnosis and treatment. The research explains how quantum machine learning algorithms might enhance the accuracy of PD diagnosis and prediction by analyzing complicated datasets, including genetic data and medical pictures, to uncover biomarkers for early identification. In addition, the feasibility of using quantum computing to expedite drug discovery and back up personalized medicine approaches in PD treatment is investigated. The constraints and ethical concerns of applying quantum computing to healthcare are also discussed. Research is needed to address the limits of quantum computing technology. Still, the study does imply that quantum computing has the potential to dramatically improve healthcare applications, particularly in the field of neurological illnesses.
Smart Irrigation System with GSM Module using PIC16F690

Farming and agriculture account for a large share of the Gross Domestic Product (GDP) in developing and developed countries. Thus, improvising and optimizing the current farming technologies is a necessity. This will not only help in the flourishing sustainable development of mankind, flora, and fauna but will also help in dealing with global crises such as climate change and drought. With better technological advances comes better harvest; thereby preventing situations like starvation and malnutrition. As water is becoming scarce around the world, there is a need to use the water resource wisely to have water in the future for consumption and agricultural use. An irrigation method used before such as furrow irrigation which does not consider the amount of water required by crops has contributed to water wastage. This has also killed crops and caused a loss of revenue which then also contributed to food insecurity. Loss of revenue is a dent in the economy, which means an imminent need for water rationing must come into effect. The study aims to ensure water is used sparingly and wisely while also ensuring that crops develop properly, thus ensuring food security and increasing revenue for business owners. This project is what is needed in most parts of the world that face water crises with cities such as Port Elizabeth and/or countries such as Ethiopia where damns are drying up. This research uses automotive devices and methods to ensure that the process of watering the crops, testing out the conditions of the water requirement, turning ON/OFF irrigation, and water pumps are done through minimal human interaction. In this research, a microcontroller is programmed with a set of instructions as to when to turn ON/OFF the water and Irrigation pumps with signals received from sensors according to the water requirement.
Lending Rationale of Financial Institution and Fintech on the Perception of Borrowers: A Qualitative Study

The development of Financial Institutions including Financial Technology (Fintech) in providing loans to borrowers is increasingly important in the current business financing system. This study aims to determine the perceptions of borrowers by exploring their views on lending practices pro- vided by financial institutions and fintech and identifying the factors that influence the institution’s decision to provide loans to them. This study uses a qualitative research method by asking a number of questions to debtors in one of the Micro and Small and Medium Enterprises (MSMEs) commu- nities in the city of Bandung, Indonesia. The questionnaire form is in the form of open-ended ques- tions and is distributed to 47 MSMEs in the city with various types of business fields. The results show that the factors that influence the success of borrowers in obtaining credit from both financial institutions and fintech are the factors classified as 5 Cs of credit in terms of Character, Capacity, Capital, Collateral, and Conditions. All these factors are summarized as soft information and hard information, which are applied by financial institutions and financial technology institutions.
Efficient Wireless Power Transfer System for Mobile Devices: Asymmetric and Planar Magnetic Resonance Coupling Scheme

This paper presents an asymmetric, doublelayered, and fully planar magnetic resonance coupling-based (MRC) wireless power transfer (WPT) system that operates at 13.56 MHz. The transmitter is designed on a FR-4 substrate measuring 100 mm × 100 mm, while the receiver is developed on a flexible polyimide substrate with a footprint of 50 mm × 50 mm. Complete planarization is achieved by applying printed spiral coils on both the transmitter and receiver. At a separation distance of 60 mm between the transmitting and receiving coils, a maximum measured efficiency of 67% was attained. The thinness of the polyimide substrate allows the receiving unit to be compact, making it easily compatible with various mobile charging devices.
Climate change: impacts and adaptation in England's woodlands.

The changing climate presents a challenge for forest planning and forest management in England because the projected increases in temperature, changes in the seasonality of rainfall, and an increased frequency of extreme events add complexity to species selection and silvicultural practice. By actively adjusting forest management now, to anticipate future changes, we can hope to increase resilience by reducing exposure to risks in forestry and in the goods and services that woodlands provide for society. Tree growth will increase in some areas and decline in others, and the effects will vary with species. Some relatively less known species will become more suitable – including some from other continents and current climates more similar to those projected for England. New approaches to woodland management will be required to address the threats of drought and increased risk of damage from pests, diseases, wind and fire. There are many uncertainties associated with climate change, and the likely impact on trees, silviculture and forest operations. This uncertainty should not prevent adaptation but, instead, should direct woodland managers to implement measures that increase resilience whatever climate change brings, or that are likely to reap the greatest rewards in the future. A key concept in managing risk is diversification: from broadening the choice of genetic material and mixing tree species in different ways, to varying management systems and the timing of operations. Research Note
Undrained behavior of sand under cyclic paths that match storm-wave loading conditions

ABSTRACT In ocean engineering, storm-wave loading imposes irregular cycles in the magnitudes acting on the coastal or seabed cohesionless soils, giving rise to more complex consequences on the their liquefaction susceptibility, compared with those under uniform cyclic loadings that were considered in most routine engineering designs. This paper presents a systematic experimental investigation into the undrained behavior of saturated loose sand subjected to irregular cyclic triaxial paths that match the storm-wave loading conditions, with the presence of static shear stress that mimics the initial condition of sand deposits in the field, along with the conventional uniform cyclic triaxial tests for comparison. The results indicate that both the initial static stress and the cyclic irregularity influence the pore pressure development and cyclic resistance of sand. When the static shear stress presents, the sand samples under storm-wave loading show a monotonic increase in cyclic resistances to liquefaction. In addition, the concept of strain energy is used to delineate the accumulation of pore pressure during cyclic loading. A unique relationship between the normalized pore pressure and the strain energy is observed, irrespective of the static stress level, cyclic stress amplitude, and applied cyclic loading patterns.
GeoMeta: Realidade Virtual e Aumentada no Ensino De Geometria

O GeoMeta é uma aplicação educacional que utiliza Realidade Virtual (VR) e Realidade Aumentada (AR) para criar uma experiência imersiva no ensino de geometria. A motivação por trás do GeoMeta é solucionar os desafios comuns no ensino da geometria, como a abstração dos conceitos e a falta de interesse dos alunos. O objetivo principal deste estudo é apresentar o GeoMeta como uma nova ferramenta de ensino, destacando seu impacto no aprendizado dos alunos por meio de sua implementação em um ambiente de sala de aula virtual e análise estatística dos resultados. Os resultados demonstraram que o GeoMeta resultou em melhorias na compreensão e no progresso dos alunos.
Incidence of central retinal artery occlusion peaks in winter season

Introduction Stroke incidence exhibits seasonal trends, with the highest occurrences observed during winter. This study investigates the incidence of central retinal artery occlusion (CRAO), a stroke equivalent of the retina, and explores its monthly and seasonal variations, as well as potential associations with weather and ambient air pollutants. Methods A retrospective search of medical records spanning 15 years (January 2008–December 2022) was conducted at the University Eye Hospital Tübingen, Germany, focusing on diagnosed cases of CRAO. Incidences were evaluated on a monthly and seasonal basis (winter, spring, summer, fall). Weather data (temperature, precipitation, atmospheric pressure) and concentrations of ambient air pollutants [fine particulate matter (PM2.5), coarse particulate matter (PM10), nitrogen dioxide (NO2), and ozone (O3)], were analyzed for a potential association with CRAO incidence. Results Out of 432 patients diagnosed with CRAO between 2008 and 2022, significantly varying incidences were observed monthly (p = 0.025) and seasonally (p = 0.008). The highest rates were recorded in February and winter, with the lowest rates in June and summer. Concentrations of NO2, PM2.5 and lower ambient air temperature (average, minimum, maximum) showed significant correlations with CRAO incidence. Discussion This comprehensive 15-year analysis reveals a pronounced winter peak in CRAO incidence, with the lowest occurrences in summer. Potential associations between CRAO incidence and ambient air pollutants and temperature underscore the importance of considering seasonal trends and call for further investigations to elucidate contributing factors, potentially leading to targeted preventive strategies and public health interventions.
Real-Time Infrared Small Target Detection Based on 1-D Bidirectional Vector Feature Measure in a Novel SRD Mode for Complex Scenes

Real-time infrared (IR) small target detection is of great significance for practical engineering projects. The existing algorithms mainly focus on single-frame detection (SFD) and multiframe detection (MFD), which have a certain degree of latency. This letter proposes a novel single-row detection (SRD) method based on 1-D bidirectional vector feature measure (1BVFM). A 1-D bidirectional vector (1BV) model is defined, and based on this, a 1BV contrast (1BVC) operator is established to suppress the background. At the same time, a 1BV gradient (1BVG) operator is established to enhance the target. By integrating 1BVC and 1BVG, the background suppression effect is enhanced and the target saliency is improved. The experimental results show that the algorithm proposed in this letter has certain advantages. It is a robust IR small target detection method that can be used for optoelectronic detection equipment.
A Small Highly Sensitive Glucose Sensor Based on a Glucose Oxidase-Modified U-Shaped Microfiber

Diabetes patients need to monitor blood glucose all year round. In this article, a novel scheme is proposed for blood glucose detection. The proposed sensor is based on a U-shaped microfiber prepared using hydrogen-oxygen flame-heating technology, and then 3-aminopropyltriethoxysilane (APTES) and glucose oxidase (GOD) are successively coated on the surface of the U-shaped microfiber via a coating technique. The glucose reacts with the GOD of the sensor surface to produce gluconic acid, which changes the effective refractive index and then shifts the interference wavelength. The structure and morphology of the sensor were characterized via scanning electron microscope (SEM) and confocal laser microscopy (CLM). The experimental results show that the sensitivity of the sensor is as high as 5.73 nm/(mg/mL). Compared with the glucose sensor composed of the same material, the sensitivity of the sensor increased by 329%. The proposed sensor has a broad application prospect in blood glucose detection of diabetic patients due to the advantages of miniaturization, high sensitivity, and good stability.
Framework of Cloud Computing Resource Scheduling for Vehicle Fault Diagnosis

Internet of Vehicles (IoVs) provides communication and computing resources, which makes the on-board diagnosis of vehicle faults possible. However, those resources need to be expanded to support the accurate analysis of the on-board diagnosis. Vehicular Cloud Computing (VCC) can solve the pressure of local vehicle processing but will cause an unavoidable delay. Thus, the accuracy and timeliness of on-board diagnosis cannot be guaranteed. To address the issue, we propose a Mobile Edge Caching based Resource Scheduling (MECRS) mechanism for the on-board diagnosis of vehicle faults. According to the urgency of vehicle fault diagnosis, we first design a cloud scheduling algorithm to meet the computation requirements of both the essential business of IoVs and the fault diagnosis. Subsequently, the priority allocation strategy is made for all four types of requests. Then, the urgent requests can be processed timely. Specifically, a multi-objective optimization method is proposed to allocate communication and computing resources for the above requests. In addition, we present a mobile edge caching algorithm in which the large-scale file with high popularity is offloaded to alleviate the pressure of the cloud. Finally, we carry out comprehensive simulations. The results reveal that the developed mechanism provides a high service rate for on-board diagnosis with limited network resources, while the performances of the other three essential services are not compromised.
Attrition from Face-to-Face Pediatric Outpatient Chronic Pain Interventions: A Narrative Review and Theoretical Model

There is limited understanding of attrition (premature treatment withdrawal and non-completion) from pediatric chronic pain services. This narrative review aimed to summarize attrition prevalence from face-to-face pediatric outpatient chronic pain interventions, identify associated factors and develop a theoretical model to account for attrition in this setting. A comprehensive search of the published literature revealed massive variability (0–100%) in the reported attrition rates from pediatric chronic pain interventions that varied in type and format (individual vs. group, single discipline vs. interdisciplinary, psychological only vs. multiple combined interventions, of different durations). The factors associated with attrition from pediatric chronic pain programs varied between the studies: some have assessed patient sex, psychological and other comorbidities, avoidance strategies, missed schooling, family composition/tensions, caregiver catastrophizing, scheduling, caregiver leave and clinic access. A theoretical model is presented depicting youth, caregiver and service factors that may impact attrition from pediatric chronic pain interventions. Where available, literature is drawn from the pediatric chronic pain context, but also from adult chronic pain and pediatric weight management fields. The implications for research and clinical practice are discussed, including improved reporting, patient screening and targeted supports to promote intervention completion. This review contributes to a better understanding of attrition, which is crucial for optimizing pediatric chronic pain service outcomes.
Report on the 3rd Workshop on the Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023) at RecSys 2023

Evaluation is a central step when developing, optimizing, and deploying recommender systems. The PERSPECTIVES 2023 workshop, held as part of the 17th ACM Conference on Recommender Systems (RecSys 2023), served as a forum where researchers from both academia and industry critically reflected on the evaluation of recommender systems. The goal of the PERSPECTIVES workshop series is to capture the current state of evaluation from different perspectives and discuss the different targets that recommender systems evaluation should strive for. In the third edition of the workshop, we discussed problems and lessons learned, and aimed to move the discourse forward within the community. Date : 19 September 2023. Website : https://perspectives-ws.github. io/2023/.
Building Character

This project is an attempt to design a method of teaching English as a foreign language to Ecuadorian university students by applying Gee's theory of "situated learning”. The researcher posed the following problem: How can global empathy be incorporated into the EFL classroom so as to increase students’ intrinsic motivation to learn the language and help them to learn it better? and suggested the following answer: Through a role-playing game in which students create avatars with specific, realistic characteristics including nationality, religion, socio-economic status and gender, and place them within the context of an ethical dilemma. The game was implemeted with two groups of students, one at the A1 level (Common European Framework of Reference for Languages) and the other at the B2 level. Preliminary results show increased global empathy and are also promising in terms of student motivation and language acquisition.
Efectividad de la metodología de aula inversa en el ámbito universitario. Una revisión sistemática

El aula inversa (AI) es un enfoque metodológico que invierte el modo de enseñanza tradicional poniendo el foco activo en el estudiante. Es una metodología dinámica en la que el profesor facilita el aprendizaje de los estudiantes proporcionando material adecuado para la preparación previa de las clases y acompañando en la profundización de los contenidos y resolución de situaciones o problemas relacionados con el tema de estudio dentro del aula. Por eso, está considerada como una metodología de innovación docente. El objetivo general del presente trabajo es analizar la evidencia científica sobre la efectividad del AI en el ámbito universitario. Siguiendo las recomendaciones PRISMA se realizó una revisión sistemática de la literatura publicada en las bases de datos Web of Science, Scopus y ERIC entre los años 2016 y 2022, se han analizado un total de 27 estudios experimentales o cuasiexperimentales que cumplieron los criterios de selección definidos. Se analizaron variables descriptivas, de diseño y la valoración de la eficacia, la valoración de los estudiantes y los condicionantes que inciden en la eficacia del AI. Los resultados muestran un mayor número de publicaciones en los continentes asiático y americano y en los ámbitos de ciencias y educación. El 87% de los artículos estudian el nivel de grado universitario y más del 81% utilizan grupo control en su diseño de estudio. La evidencia muestra la efectividad de la aplicación del AI en relación con los resultados académicos de los estudiantes en los distintos grados, así como en la adquisición de habilidades consideradas transversales en el ámbito universitario. La satisfacción de los estudiantes respecto a la metodología es buena y las mejoras en su efectividad se relacionan con aspectos que dependen de los mismos estudiantes, los profesores y la universidad.
Emotion Recognition Using Smart Bands

Emotion recognition is an important research area within the field of affective computing, which aims to build systems that can recognize, interpret, and respond to human emotions. The feasibility and costliness of continuous emotion recognition have led to numerous studies. Rather than labeling emotional states continuously, the focus is on modeling time intervals that better represent the presence of affective states. Instead of tracking ongoing affective changes within a specific timeframe, this technique aims to identify the most prominent emotional incidents. This approach offers a practical and natural way of learning in a weakly supervised environment, considering the uncertainty of emotional responses.
Energy Harvesting and Buffering for Cyber-Physical Systems: A Review

Cyber-Physical Systems (CPS) enable data processing in the field. By keeping the processing unit in close proximity to the data-acquisition point, latency can be reduced and the impact of limited communication bandwidth and power availability can be mitigated. To work autonomously for extended periods of time without requiring user support, CPS must harvest energy from sources such as solar radiation, ambient RF, wind, rain, or vibrations. Additionally, the energy harvested by CPS needs to be buffered because of the uncertain availability of harvested sources. The two primary buffering mechanisms are batteries and supercapacitors, with the batteries being further classified into their own sub-categories. This chapter reviews existing energy sources for CPS and compares/contrasts these sources. Based on the characteristics of the different sources, conclusions are drawn as to which sources are apt for what type of environments.
Data Poisoning Attacks over Diabetic Retinopathy Images Classification

Data poisoning represents a set of techniques aimed at perturbing data for training machine learning models, affecting performance. Such intentional attacks are widespread in many applications involving deep learning algorithms and are aimed to provide misclassifications. In this paper, data poisoning on retinal images for the diabetic retinopathy binary classification (health and sick) is presented and evaluated. The presented attacks are almost imperceptible perturbations of the images that nevertheless decrement the metrics of the trained models. Once exposed to the data poisoning distortions on these images, a possible countermeasure to enhance the security from these attacks is shown. In this way, the robustness and vulnerabilities of the network are highlighted and the best result is also analyzed through the use of heatmaps, for the qualitative point of view. The paper aims to focus on the effects of data poisoning in deep learning model testing phase and to discuss possible countermeasures.