Miracle-dz/UniFAR-FTUs huggingface dataset:
Controlling BLDC Motor with Bidirectional Converters with Cyber-Physical Systems

Brushed DC motor has simple speed control techniques. In brushed DC motor, commutation is done by the rotating part which is placed on the rotor and brushes known as commutator. The amount of losses in brushed DC motors is high because of the presence of mechanical parts. But the commutation in brushless DC motors (BLDC) is achieved with the aid of solidstate switches (Electronic commutation). As there is reduction in the losses, the motor’s performance is improved. Hence, brushed motors are being replaced by BLDC motors as they are used in many applications due to its high mechanical and electrical properties. The advantages of BLDC motors are low maintenance, electronic control and high speed therefore finds application in computer peripherals, portable power tools and vehicles such as model planes to automotive. The regulation of a BLDC motor using a bidirectional DC-DC converter is demonstrated in this research work. The output of the DC-DC converter is supplied into the three-phase voltage source inverter (VSI), that powers the motor. PI and Fuzzy controllers are used to regulate the speed of a BLDC motor. The simulation is performed with help of MATLAB R2019b version software and the results are verified. Cyber Physical Systems (CPS) for controlling BLDC motors with bidirectional converters offers several benefits, including improved accuracy, speed, and reliability of motor control, as well as reduced energy consumption and maintenance costs
Second-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation

Unsupervised feature selection aims to select a subset from the original features that are most useful for the downstream tasks without external guidance information. While most unsupervised feature selection methods focus on ranking features based on the intrinsic properties of data, most of them do not pay much attention to the relationships between features, which often leads to redundancy among the selected features. In this paper, we propose a two-stage Second-Order unsupervised Feature selection via knowledge contrastive disTillation (SOFT) model that incorporates the second-order covariance matrix with the first-order data matrix for unsupervised feature selection. In the first stage, we learn a sparse attention matrix that can represent second-order relations between features by contrastively distilling the intrinsic structure. In the second stage, we build a relational graph based on the learned attention matrix and perform graph segmentation. To this end, we conduct feature selection by only selecting one feature from each cluster to decrease the feature redundancy. Experimental results on 12 public datasets show that SOFT outperforms classical and recent state-of-the-art methods, which demonstrates the effectiveness of our proposed method. Moreover, we also provide rich in-depth experiments to further explore several key factors of SOFT.
Design Of Low Area Interconnect Architecture for CPU-GPU Network-On-Chips (NoCs)

This paper’s major objective is to suggest a creative strategy for improving CPU performance in real-time operating systems by developing a fresh way for the round-robin CPU scheduling algorithm. Round-robin and priority scheduling methods are combined in the proposed method to create the Priority based Round-Robin CPU Scheduling algorithm. This algorithm addresses the starvation prevention issue present in round-robin scheduling while including the benefits of priority ordering. By giving different priorities to processes, it also adds the idea of ageing. Due to its flaws, such as high context transition rates, prolonged wait times, lengthy responses, prolonged turnaround times, and limited throughput, the current round-robin CPU scheduling algorithms are not suited for real-time operating systems. These restrictions are removed by the suggested approach, which also fixes the round-robin algorithm’s problems. The study compares the proposed algorithm with the existing round-robin scheduling method based on a number of variables, including time quantum, average waiting time, average turnaround time, and number of context shifts, in order to assess its efficacy. Although the semiconductor industry has a lot of interest in CPU-GPU multi-core research, the problems raised in the literature have not yet been solved. This project focuses on the creation of a novel heterogeneous crossbar style network-on-chip (NoC) that connects heterogeneous CPU-GPU processors in order to overcome these issues.
Species Sensitivity Distributions in Ecotoxicology

General Introduction to Species Sensitivity Distributions, L. Posthuma, T.P. Traas, and G.W. Suter II North American History of Species Sensitivity Distributions, G. W. Suter II European History of Species Sensitivity Distributions, N. M. van Straalen, K. J. van Leeuwen Theory of Ecological Risk Assessment Based on Species Sensitivity Distributions, Nico M. van Straalen Normal Species Sensitivity Distributions and Probabilistic Ecological Risk Assessment, T. Aldenberg, J. S. Jaworska, and T. P. Traas Extrapolation Factors for Tiny Toxicity Data Sets from Species Sensitivity Distributions with Known Standard Deviation, T. Aldenberg and R. Luttik Species Sensitivity Distributions in Ecological Risk Assessment: Distributional Assumptions, Alternate Bootstrap Techniques, and Estimation of Adequate Number of Species, M. C. Newman, D. R. Ownby, L. C.A. Mezin, D. C. Powell, T. R.L. Christensen, S. B. Lerberg, B. -A. Anderson, and T. V. Padma Observed Regularities in SSDs for Aquatic Species, D. de Zwart The Value of the Species Sensitivity Distribution Concept for Predicting Field Effects: (Non-)Confirmation of the Concept Using Semi-Field Experiments, P. J. Van den Brink, T. C.M. Brock and L. Posthuma Effects Assessment of Fabric Softeners: The DHTDMAC-Case, K. J. van Leeuwen, J. Jaworska Use of Species Sensitivity Distributions in the Derivation of Water Quality Criteria for Aquatic life by the U.S. Environmental Protection Agency, C. E. Stephan Environmental Risk Limits in the Netherlands, D. T.H.M. Sijm, A. P. van Wezel, and T. Crommentuijn A Rank-Based Approach to Deriving Canadian Soil and Sediment Quality Guidelines, C. Gaudet, D. Bright, K. Adare, and K. Potter Ecotoxicological Soil Quality Criteria in Denmark, J. J. Scott-Fordsmand, and J. Jensen Probabilistic Risk Assessment Using Species Sensitivity Distributions, K.R. Solomon and P. Takacs The Potentially Affected Fraction as a Measure of Ecological Risk, T. P. Traas, D. Van de Meent, L. Posthuma, T. H.M. Hamers, B. J. Kater, D. de Zwart, and T. Aldenberg Methodology for Aquatic Ecological Risk Assessment, W. J. Warren-Hicks, B. R. Parkhurst, and J. B. Butcher Toxicity-Based Assessment of Water Quality, D. de Zwart and A. Sterkenburg Mapping Risks of Heavy Metals to Birds and Mammals Using Species Sensitivity Distributions, T. P. Traas, R. Luttik, and H. Mensink Ecotoxicological Impacts in Life Cycle Assessment, M. Huijbregts, D. van de Meent, M. Goedkoop, and R. Spriensma Issues and Practices in the Derivation and Use of Species Sensitivity Distributions, G. W. Suter II, T. P. Traas, and L. Posthuma Conceptual and Technical Outlook on Species Sensitivity Distributions, L. Posthuma, T. P. Traas, D. de Zwart, and G. W. Suter II Appendix A: The Interactive Poster Session "Use of Species Sensitivity Distributions in Ecotoxicology", T. H.M. Hamers, T. P. Traas, and L. Posthuma Appendix B: List of programs References Glossary Acronyms Subject Index
Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach

Simple Summary This paper presents a cervical cancer detection approach where the KNN Imputer techniques is used to fill the missing values and after that SMOTE upsampled features are utilized to train a multi-model ensemble learning approach. Results demonstrate that use of KNN Imputed SMOTE features yields better results than the original features to classify cancerous and normal patients. Abstract Objective: Cervical cancer ranks among the top causes of death among females in developing countries. The most important procedures that should be followed to guarantee the minimizing of cervical cancer’s aftereffects are early identification and treatment under the finest medical guidance. One of the best methods to find this sort of malignancy is by looking at a Pap smear image. For automated detection of cervical cancer, the available datasets often have missing values, which can significantly affect the performance of machine learning models. Methods: To address these challenges, this study proposes an automated system for predicting cervical cancer that efficiently handles missing values with SMOTE features to achieve high accuracy. The proposed system employs a stacked ensemble voting classifier model that combines three machine learning models, along with KNN Imputer and SMOTE up-sampled features for handling missing values. Results: The proposed model achieves 99.99% accuracy, 99.99% precision, 99.99% recall, and 99.99% F1 score when using KNN imputed SMOTE features. The study compares the performance of the proposed model with multiple other machine learning algorithms under four scenarios: with missing values removed, with KNN imputation, with SMOTE features, and with KNN imputed SMOTE features. The study validates the efficacy of the proposed model against existing state-of-the-art approaches. Conclusions: This study investigates the issue of missing values and class imbalance in the data collected for cervical cancer detection and might aid medical practitioners in timely detection and providing cervical cancer patients with better care.
Dynamics of magnetization under spin-lock pulse with T1 relaxation

To overcome temporal limitation of functional MRI, researchers have investigated novel principles independent of hemodynamics to measure brain function. Among them, we focused on spin-lock sequences to measure tiny neural magnetic fields using magnetic resonance. Although several spin-lock sequences are proposed, the difference depends on visualized component of magnetization. Thus, it is important to investigate the dynamics of magnetization during spin-lock pulses. In this paper, we modified our previous analytical solution of the Bloch equation by distinguishing rotating frames to derive homogeneous and particular solution. Then, we compared the analytical solutions to numerical simulation using 4th-order Runge-Kutta method and observed decrease of errors on vertical magnetization components to spin-lock axis.
Debottlenecking The Complexities of Stakeholder Management in Indonesia Offshore Platform Decommissioning

Indonesia’s Offshore platform decommissioning is a complex activity involving lots of stakeholders with different interest and power. To date only one (1) platform that has been removed from its origin place offshore and sank as rig-to-reef near Kalimantan, Indonesia. The whole business process took 7 (seven) years from the first discussion in 2017 until the execution in 2022, which is not desirable for future decommissioning projects. This research collected data from the key stakeholders including the Production Sharing Contract (PSC) Contractor and several institutions involved in the project to examine the complexities of stakeholder alignment as study case for future decommissioning project. The qualitative analysis utilizes Current Reality Tree to find the root cause and Future Reality Tree to identify required injection to improve the stakeholder alignment. The results show three main challenges: unavailability of Abandonment and Site Restoration (ASR) Fund, decision of end-of-state of the facilities, and process approval for Asset Write-Off. The root causes are weak implementation of existing decommissioning regulations and lack of understanding of stakeholder’s needs. Injection into the future reality tree include formalizing funding mechanism for the PSC with no ASR fund, introduction of umbrella regulation to cover different business process in different ministries and establishment of ASR strategy by taking into account the PSC organizational capability.
Cardiac Arrhythmia Detection using Ensemble Machine Learning Techniques

Arrhythmia is a sudden, irregular, slow, or rapid heartbeat. An arrhythmia is a serious medical disease that requires immediate care. In order to record the abnormal heart beats, an electrocardiogram or monitor is often required for its diagnosis. Electrocardiogram (ECG) results can assist determine whether arrhythmia is to blame for cardiac arrest. In this research we will focus on passing the ECG dataset into different machine learning algorithms like Logistic regression, Decision Tree, KNN, Random Forest classifier, Support Vector Machine and Gradient Boosting classifier. The best classifier’s hyperparameters will be fine-tuned via randomised search cross validation. All the performance measures will be recorded and the top five classifiers i.e. Decision tree and hyperparameter tuned Random Forest, Gradient boosting classifier, KNN and SVM classifier will be passed through ensemble classifier method to get a more accurate result i.e. an accuracy of 98.49%.
Assessment of the Feasibility of Employing Chaos Synchronization in Two Cascaded Microresonators for Secure Data Transmission

As the limits of computational power continue to increase, concern has arisen regarding insufficient security along with a demand for improvements in encryption methods for data transmission, which might be achieved by chaos synchronization [1], [2]. Using modified Lugiato-Levefer equations (LLEs), we numerically explore two cascaded microresonator frequency combs [3]. To obtain a chaotic state, both cavities are pumped by continuous-wave lasers until modulation instability (MI) combs are generated. By injecting the output of the first resonator into the second resonator, both chaotic fields in the cavities are synchronized. In this work, we investigate to what extent the chaos is synchronized and how this phenomenon may be implemented in existing data transmission systems over a chaotic channel to enable secure communication.
Multiresolutional texture analysis based on morphological techniques

The authors show the power of mathematical morphology as a tool for analysis of grey-level images, and especially texture. Textures are investigated by extracting a local fractal signature for a range of scales. The variation with changing resolution then characterizes the texture. Some results of a practical application of this technique to a brushed leather surface, are presented. >
Antiferromagnetic Droplet Soliton in a Nano-Contact Spin-Transfer Torque Oscillator

We demonstrate that a spin current flowing through a nano-contact into a uniaxial antiferromagnet with first- and second-order anisotropy can excite a self-localized dynamic magnetic soliton, known as a spin-wave droplet in ferromagnets. In this case, the frequency of the Néel vector precession lies below the antiferromagnetic resonance and decreases with the applied current. Above a certain threshold, the soliton mode disappears, and the oscillator instead emits spin waves propagating in the antiferromagnetic layer.
Impact of Pulse Amplitude on Voltage-Driven Precessional Switching Dynamics Using Macrospin Modeling

In Voltage-Controlled Magnetic Anisotropy (VCMA) switching of magnetic random-access memory (MRAM), the precessional dynamics of the magnetization is behind the writing mechanism. This work aims to give a fundamental understanding of VCMA-induced precessional switching dynamics through macrospin modeling. We start investigating the effects of different voltage pulse amplitude and pulse shapes on the precessional dynamics of the magnetization. We show that high voltage does not necessarily result in a faster and a more consistent switching and that adding a few hundreds of picoseconds ramp times will have an important impact on the switching dynamics and its final state. Moreover, we perform the analysis considering two damping parameters, an unrealistically high Gilbert damping parameter for CoFeB thin films (α = 0.2) and a realistic parameter for CoFeB (α = 0.01), showing how they impact the precession dynamics.
Coverage, Placement and Impact of Quality Control and Assurance on Quality of Products in Zimbabwean Polytechnic Clothing and Textile Production Units

This study sought to establish the coverage, placement and impact of Quality Control and Assurance on Quality of Products in Zimbabwean Polytechnic Clothing and Textile Production Units, using the qualitative approach. The population comprised of 103 Clothing and Textiles National Diploma students and 27 lecturers from three polytechnics. Stratified sampling was used to select 32 ND students and 6 lecturers who participated in the study. The study used a questionnaire, FGD and interviews as sources of data. Data was analyzed through the thematic approach. The study established that the component of Quality Control and Assurance contained very important aspects of quality management issues. The quality control component had a positive impact on the quality of products produced in the polytechnic CTPUs as it equipped participants with skills and competences required to produce high quality products that satisfy customers. One of recommendations is that the Ministry of Higher and Tertiary Education, Innovation Science and Technology through the Higher Education Examination Council should reconsider the course structure and placement of Quality Control and Assurance component in the Clothing and Textile curriculum. The fundamentals of quality control in garment industry should be taught at National Certificate level as it can be a terminal course for those who do not wish to advance to National Diploma level.
The Primary Prevention of Atopy: Does Early Exposure to Cats and Dogs Prevent the Development of Allergy and Asthma in Children? A Comprehensive Analysis of the Literature

The current literature shows mixed results relating to the significance of environmental exposure, such as owning a pet, and the development of atopy in children. Our review aimed to collect the most recent evidence on the association between early-life cat and dog ownership and the development of allergy and asthma. A comprehensive literature review was performed using PubMed and Scopus. The search included the main keywords of our PICO: (((early exposure) AND (children) AND (allergy)) OR (asthma)) AND (dog) OR (cat). Randomized controlled trials (RCTs), case–control studies, clinical trials, meta-analyses, and systematic reviews of children within the last five years (2018–2023) were searched and included. During the search process, 52 articles from PubMed and 43 from Scopus were found. A total of 17 articles were deemed to be suitable and included. Fairly consistent results regarding early exposure to pets, in particular dogs, and the prevention of food allergies have been described. Furthermore, there seems to be a protective effect against allergy and asthma in relation to the number of pets owned. The likelihood of a child developing allergy and asthma seems to be influenced by various factors, including the child’s genetic background and early exposure to different environmental factors, including allergens that may interact with the gut microbiota and immune system.
Reviving grain quality in wheat through non‐destructive phenotyping techniques like hyperspectral imaging

Abstract A long‐term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time‐consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non‐destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high‐quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.
Atypical teratoid/rhabdoid tumour with CDK6 amplification in a child: a case report and literature review

Atypical teratoid/rhabdoid tumours (AT/RTs) are rare central nervous system neoplasms that frequently occur in infants and children and have a very poor prognosis. In recent years, molecular analysis of AT/RTs has shown that biallelic inactivation of SMARCB1 (INI1, SNF5, BAF47) or SMARCA4 (BRG1) frequently occurs. Here, we present a case of basal ganglia AT/RT with SMARCB1 gene deficiency and CDK6 gene amplification in a 5-year-old child. A 5-year-old boy was hospitalized due to a 1-week history of frontal and parietal headache. Magnetic resonance imaging (MRI) demonstrated a 3 cm × 2 cm × 1.5 cm heterogeneous enhanced mass located at the right basal ganglia that partially protruded into the right lateral ventricle. The lesion was successfully resected under electrophysiological monitoring and neuronavigation. The postoperative pathological examination implied an AT/RT diagnosis, with loss of SMARCB1 protein, SMARCB1 gene deficiency and CDK6 gene amplification. Unfortunately, the patient died due to respiratory and circulatory failure at 5 weeks after the operation. To date, standard regimens have not yet been established due to the lack of large-scale prospective studies for AT/RT. The p16-RB signalling pathway should be considered as a potential target for AT/RT treatment modalities. Apart from traditional regimens, targeted therapies, especially CDK4/6 inhibitors, are likely a promising therapeutic option for AT/RT treatment.
Influences of Level 2 Automated Driving on Driver Behaviors: A Comparison With Manual Driving

It is becoming a common scene to observe the usages of level 2 automated driving in our daily life. To ensure driving safety while using the level 2 automated driving systems in various traffic conditions, it is an essential issue to clarify the influences of the level 2 automated driving on driver behaviors. Previous studies normally focused on drivers’ reactions to emergency events while using level 2 automated driving. However, it is still unclear that how will drivers interact with the level 2 automated driving systems during the periods when no emergency event occurs. Therefore, a driving simulator experiment was performed, and the differences in driver behaviors, especially eye-gaze behaviors, under level 2 automated driving and manual driving were analyzed. Meanwhile, to simulate non-driving related tasks that may occur in real driving environment, a visual task, Surrogate Reference Task (SuRT), was applied in the experiment. It was observed that the percentage of gaze that fell within the road center area and speedometer significantly decreased, and the gaze time to the left and right mirrors, and the SuRT display significantly increased during level 2 automated driving. Meanwhile, the eyelid closure time were significant longer and the subjective evaluation scores of attention to front and surroundings were significant lower while applying level 2 automated driving. The results indicated that drivers’ attention levels, especially for the front areas, might be significantly reduced during level 2 automated driving, compared to that of manual driving.