Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Heart Disease Prediction System using Multilayer Perceptron

Stroke, Heart Failure, Arrhythmia, and myocardial infarction are the most common cardiovascular diseases which record high mortality rates around the world. Heart defects are not detected in the early stages due to the impractical costs of the tests available. Thus, a fast, real-time, and reliable system that predicts the chances of a patient having heart disease in an optimized manner is required.In this research, a Neural Networks model using a Multilayer Perceptron (MLP) is proposed for the prediction system. Experimental analysis resulted in an accuracy of 85.71% for UCI Heart Disease dataset and 87.30% for Cardiovascular Disease dataset. When compared to previous research the increase in accuracy was approximately 12-13%. A simple web application tool is also developed using python programming to test the prediction system. This research works towards making a comprehensible tool for medical professionals as well as common people.
Metadata model for supporting hierarchical Edge Device arrangements in an IoT deployment

An increasing number of "Smart Devices" are coming online every year. These Smart Devices have the ability to communicate with other Smart Devices across the web, exchange information and make intelligent decisions. Measures to combat the added load brought on by these new connections have inadvertently resulted in increased network complexity. This paper considers why understanding that complexity is necessary, especially on the server side. Existing architectures are thereafter explored to gain insight into a possible solution. A metadata-based model is then proposed that would provide a central server with an overview of the network arrangement. The novelty of this model lies in its simplicity, scalability and technology agnosticism. An implementation of this model is then tested in a mix of realworld and simulated environments and evaluated. The results obtained indicate improvements in benchmarks along with performance gains with regards to network scaling.
Design and Performance Evaluation of a Planar Bi-Quad Antenna Operating at 2.4GHz

this paper presents the design and fabrication of a Planar Bi-Quad Antenna operating at 2. 45GHz. The modeling and simulation of the antenna is done using the CST Studio Suite (Student Edition) and the testing of the fabricated antenna is done using the Antenna Measurement System Kit (AMS Kit) developed by Akademica. This antenna shows improved bandwidth and characteristics over conventional wired Bi-quad antennas. Various antenna parameters such as Radiation Pattern, Directivity, VSWR, S11, etc. are calculated. A close agreement is found between the simulation and the experimental results.
Transition to Semi-Virtual Classrooms

Educational institutions worldwide continue to improve their teaching and learning activities in almost all the facets of the profession. Upon the declaration of COVID 19 as a pandemic, learning activities were abruptly shifted from the in-person to virtual classroom activities. The transition required changes in the strategies and methods/styles of teaching. Despite the success in most of the activities, classroom interactions and assessments of students’ work emerged as areas that deserve more attention. The objective of this study is to develop a pilot project aimed at maintaining acceptable and secured levels of interactions and assessments. The setting for this study includes virtual classroom interactions, peer level tutoring sessions, and formative assessments. To improve the interactions, efficacious assessment methods deploying incremental levels of thinking were used. This requires a third-party program for embedding questions on prerecorded lectures. The questions were then randomized and assigned to the subjects. The use of hierarchical randomization of multiple-choice questions for the short quizzes resulted in reduction of cheating. The performance of the students in the four sections of a calculus course used for this project improved by 17%. For complete eradication of cheating, the midterm was returned from online back to the traditional classrooms. Such move will include the final exam in mid-December.
Early Prediction of Electronics Engineering Licensure Examination Performance using Random Forest

Graduate's success on licensure examinations has a significant impact on various facets of a higher educational institution. Using a comprehensive data mining process, this research compared the accuracy of multiple classification algorithms to determine predictors of students' professional certification performance. The Random Forest model achieved the best cross-validated accuracy score of 92.70% based on the evaluation data. A model inspection method of permutation feature importance was used to uncover information from 500 graduates of Southern Luzon State University's electronics engineering program from 2014 to 2019. Among the 33 variables examined, the verbal reasoning or reading comprehension ability of students unveils a clear attribution with their licensure test results along with ratings from different courses in mathematics, professional, and electrical circuits. Thus, the data-driven information can be used to develop programs, initiatives, and techniques to improve success on the electronics engineering licensure examinations.
Solving the Pattern Formation by Mobile Robots With Chirality

Among fundamental problems in the context of distributed computing by mobile robots, the <italic>Pattern Formation</italic> (PF) is certainly the most representative. Given a multi-set <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> of points in the Euclidean plane and a set <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> of robots such that <inline-formula> <tex-math notation="LaTeX">$|R|=|F|$ </tex-math></inline-formula>, PF asks for a distributed algorithm that moves robots so as to reach a configuration similar to <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>. Similarity means that robots must be disposed as <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> regardless of translations, rotations, reflections, uniform scalings. In the literature, PF has been approached by assuming asynchronous robots endowed with chirality, i.e. a common handedness. The proposed algorithm along with its correctness proof turned out to be flawed. In this paper, we propose a new algorithm on the basis of a recent methodology studied for approaching problems in the context of distributed computing by mobile robots. According to this methodology, the correctness proof results to be well-structured and less prone to faulty arguments. We then ultimately characterize PF when chirality is assumed.
Cramér-Rao Bound for DOA Estimation Exploiting Multiple Frequency Pairs

The Cramér-Rao bound (CRB) for direction of arrival (DOA) estimation exploiting both auto-correlation and cross-correlation information within multiple frequencies of the received array signals is derived. It provides a tighter bound than the existing CRB for the dual-frequency scenario. For the multiple frequencies, it is much lower than its dual-frequency counterpart, and also exists for a greater number of sources, thereby validating that exploiting multiple frequency pairs can improve both estimation accuracy and target resolvability.
Adaptive Two-Sided Beam Alignment in mmWave via Posterior Matching

Millimeter wave band communication is an enabler technology for multi Gbps data rates, though with associated technical challenges. Among them are the very high pathloss and the crucial importance of smart signal processing to overcome it. This paper addresses the problem of identifying the location of the strongest scatterers of the channel in the angle domain. The base station in our system operates agnostically with respect to the users, who estimate the strongest path locally and communicate it back just at the end of the process, resulting in a scalable system. The users decide adaptively the directions to test based on a posterior matching approach. Numerical results confirm the validity of our scheme, especially when the time available for beam alignment is limited.
Sensorless Human-Robot Collaborative Assembly Considering Load and Friction Compensation

The collaboration of humans and robots can combine human flexibility with the efficiency of robots, so that robots can accomplish a variety of complex tasks, such as handling and assembly. However, most robots have poor collaboration capabilities and rely on expensive sensors to obtain the force interaction information of limited space range. To solve this problem, a human-robot collaborative assembly scheme without using additional force/torque sensors is proposed in this letter. First, a load identification and compensation method that does not require the load model is proposed to realize the decoupling of load force and human-robot interaction force. Next, to realize a compliant dragging effect of robots, an improved LuGre friction model is proposed based on the experimental discovery that the motion of the load has a non-negligible effect on the friction torque of robot joints. Finally, the kinesthetic teaching experiments of compliant assembly using a universal 6-DOF collaborative robot have been completed by designing a task-determined adaptive impedance control algorithm. The experimental results prove that the proposed scheme can effectively improve the safety, compliance, and flexibility of the loaded robots compared with the existing position-encoded robots, indicating its great potential for industry application.
Improvement of β-Ga2O3 MIS-SBD Interface Using Al-Reacted Interfacial Layer

In this article, a <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> metal-interlayer-semiconductor Schottky barrier diode (MIS-SBD) using Al-reacted aluminum oxide as the interlayer is demonstrated for the first time and compared with conventional metal-semiconductor (MS) Schottky barrier diode (SBD). The aluminum oxide is formed by sputtering a thin Al layer on Ga<sub>2</sub>O<sub>3</sub> substrate and then annealed in O<sub>2</sub> at 300 °C. With the insertion of Al-reacted interlayer, the SBD subthreshold swing (SS) is significantly improved to 61 mV/dec with an average current range of >6 orders. Example of atomic layer deposited (ALD) Al<sub>2</sub>O<sub>3</sub> as the interlayer is also fabricated and characterized. <inline-formula> <tex-math notation="LaTeX">${J}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> study corrected by Gaussian distribution model shows that all the samples statistically exhibit similar mean barrier heights (BHs). This indicates that the interlayer hardly affects the electrostatic field and band bending as experienced by carrier injections. <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> study provides different BH results in different sample setups. The result proves that Al-reacted interfacial layer helps eliminate interface degradation as compared with ALD Al<sub>2</sub>O<sub>3</sub>. Overall, MIS-SBD by Al-reaction method exhibits improved SS, reduced reverse leakage current (<inline-formula> <tex-math notation="LaTeX">${I}_{0}$ </tex-math></inline-formula>), low ideality factor, good ON-OFF ratio (<inline-formula> <tex-math notation="LaTeX">$> 10^{9}$ </tex-math></inline-formula>), and minimized interface charges as compared with its respective counterparts. The result of this article serves as a promising interface engineering technique for Ga<sub>2</sub>O<sub>3</sub>-based SBD designs.
Performance Analysis of Single Operational Transresistance Amplifier Based Bandpass Filter and Bandreject Filter

The authors have presented an operational transresistance amplifier (OTRA) based realization of bandpass filter (BPF) and bandreject filter (BRF) along with their different performance analysis like temperature analysis, worst case analysis and noise analysis. All the proposed filters are realized with a single OTRA and few passive components. All the simulation works are performed in SPICE with 180 nm CMOS model of OTRA. The proposed circuit has also been experimentally verified with AD844AN IC. The simulation and experimental results matches very well, establishing the validity of the work.
Automatic Identification Algorithm of the Rice Tiller Period Based on PCA and SVM

The tillering period of rice is the crucial phenological period for the cultivation of high-quality and high-yield rice. Currently, human inspection is mainly used for identification, but it is time-consuming, laborious, and prone to mistakes. To efficiently and accurately identify the start date of rice tillering in the monitoring area, this paper proposed a new algorithm called rice tiller period recognition combining principal component analysis (PCA) and a support vector machine (SVM) (RTR-CPS). This algorithm characterizes the problem of identifying the rice tiller stage as a binary classification problem of rice either entering or not entering the tiller stage. To improve the image segmentation quality of traditional visual segmentation methods, the algorithm was designed to extract five image feature values to describe the rice tiller stage in a multi-featured way, reducing the impact of single feature value bias on the identification of the rice tiller stage. To improve the performance of the rice tiller stage recognition model, the algorithm selects ideal principal features in limited sample data by the PCA algorithm and optimizes SVM classification model hyper-parameters by combining 5-fold cross-validation. The experimental results showed that the accuracy of the algorithm for identifying the tillering date of potted rice was as high as 97.76%, which is significantly higher than other competitive methods, and the maximum error between the detection results and human inspection of potted rice tillering period was no more than 2 days. The rice tillering stage recognition model was applied to the field, and the images of field rice planted by two different methods were tested, which verified that the algorithm proposed in this paper is generalizable. These results fully demonstrate the feasibility and superiority of the algorithm in this paper.
Bistable-Triplet STDP circuit without external memory for Integrating with Silicon Neurons

Spiking Neural Network can adapt to the environment if it has the capacity to learn based on spike timing-dependent plasticity (STDP) by which the synaptic weight gets modified based on time difference between pre and postsynaptic spikes. The classical pair-based STDP model which considers only a pair of pre and post spikes has failed to account for synaptic activity when driven by a series of spikes. Whereas, Triplet based STDP model provides best fit for the experimental data as well as maps on to the Bienenstock-Cooper-Munro (BCM) learning rule. Implementation of plasticity rules at circuit level is necessary for realizing efficient computational very large scale integration (VLSI) systems which incorporates learning and memory functions. The analog VLSI implementation of TSTDP available in literature so far requires external circuitry to identify precise timing between two immediate successive pre and post spikes. The TSTDP circuit proposed in this paper is capable of identifying precise time difference between any two spikes, provides potentiation or depression based on sign and strength of the time difference, and also inherits the BCM rule when driven with Poisson spike trains. The circuit has been simulated in LTspice-XVII with the “TSMC 180nm” technology library.
Local Structural Aware Heterogeneous Information Network Embedding Based on Relational Self-Attention Graph Neural Network

Heterogeneous Information Networks (HINs) are ubiquitous in our daily life, as they can describe complex interactions among various types of objects. Heterogeneous information network embedding aims to project the network elements of HINs into low-dimensional node representation vectors, which can facilitate effective analyze of HINs. To learn appropriate HIN embeddings, most existing methods usually adopt meta-paths to capture the heterogeneous semantic information, which require domain knowledge and trial-and-error to find suitable meta-paths. Besides, most existing methods fail to preserve high-order local structural information, and pay little attention to the implicit semantics in HINs. In this paper, we propose a local structural aware heterogeneous information network embedding model named LSA-HNE. Specifically, we first design a relational self-attention graph neural network model to aggregate heterogeneous information and automatically extract semantic similarity without using meta-paths. In addition, we employ a biased random walk based sampling method to extract the local structural information and preserve the implicit semantics in HINs. The experiments conducted on four real-world datasets show that our proposed model is effective compared with the state-of-the-art methods.
Suitability of the Static Converters For the Power Factor Correction (PFC)

In this paper, suitability of the static converter topology along with standard structures and applications are proposed to assess the power factor (PF). Various usual issues of static converters are framed out by gross evaluation on the power factor correction (PFC) and improvements. The power quality judgments and indices are co-related by the high power factor improvements to boost the efficiency and reliability of the power distributions as suitability & objective of the paper
Closed-Form Preconditioner Design for Linear Predictive Control (cid:63)

: Model Predictive Control (MPC) with linear models and constraints is extensively being utilized in many applications, many of which have low power requirements and limited computational resources. In these resource-constrained environments, many designers choose to utilize simple iterative ﬁrst-order optimization solvers, such as the Fast Gradient Method. Unfortunately, the convergence rate of these solvers is aﬀected by the conditioning of the problem data, with ill-conditioned problems requiring a large number of iterations to solve. In order to reduce the number of solver iterations required, we present a simple closed-form method for computing an optimal preconditioning matrix for the Hessian of the condensed primal problem. To accomplish this, we also derive spectral bounds for the Hessian in terms of the transfer function of the predicted system. This preconditioner is based on the Toeplitz structure of the Hessian and has equivalent performance to a state-of-the-art optimal preconditioner, without having to solve a semideﬁnite program during the design phase.
TEACHER FEEDBACK AND STUDENT FEEDBACK ON WRITING TASKS: A COMPARATIVE STUDY

This research aims to investigate if peer feedback is any different from teacher feedback. The study was conducted in a non-profit foundation university with 40 A1 level preparatory school students. Students were given a writing task and afterward, they were asked to give feedback on their peers’ papers by using error codes provided by the teacher. At the same time, teachers gave feedback to the same papers by using the same error correction codes. Data were collected by means of a writing task which was supported by written feedback from students and analyzed through a comparative approach. By the end of these sessions, the focus of students and teachers were investigated to find out if they gave importance to the same issues. The results revealed that teachers used more correction codes than the learners and students mostly corrected surface level mistakes and this includes codes regarding language use.
On the lifecycle of a shallow cumulus cloud: Is it a bubble or plume, active or forced?

A cloud’s lifecycle determines how its mass flux translates into cloud cover, thereby setting Earth’s albedo. Here, an attempt is made to quantify the most basic aspects of the lifecycle of a shallow cumulus cloud: the degree to which it is a bubble or plume, and active or forced. Quantitative measures are proposed for these properties, which are then applied to hundreds of shallow cumulus clouds in Oklahoma using data from stereo cameras, a Doppler lidar, and large-eddy simulations. The observed clouds are intermediate between active and forced, but behave more like bubbles than plumes. The simulated clouds, on the other hand, are more active and plume-like, suggesting room for improvement in the modeling of shallow cumulus.
How to Reduce Uncertainty in Supply Chains? The Role of the Interactive Control Lever

In the management control literature, several researchers have expressed the need to pay more attention to the integration of inter-organizational issues in the study of management control systems since the emergence of new forms of organizations. referred to as hybrids, networks, or even supply chains. In this article, we study supply chain management control through the prism of the interactive lever. The research adopts a qualitative analysis based on a theoretical framework based on the concept of the interactive lever of control developed by Simons (1995). The objective is to answer the following research question: how does the interactive dimension translate into supply chain management control and what is its contribution to the intra and inter-organizational management of the supply chain?
Estimating the Effect of Personality on Male-Female Earnings

Estimating the Effect of Personality on Male-Female Earnings This paper uses the Five-Factor Model of personality structure as an organizing framework to explore the effects of personality on earnings. Using data from a longitudinal survey of American high school graduates, we find that extroversion, agreeableness, conscientiousness, neuroticism and openness to experience are rewarded/penalized significantly and differentially across genders. Antagonistic, emotionally stable and open men enjoy substantial earnings advantages over otherwise similar individuals. In case of women, the labor market appears to value conscientiousness and openness to experience. The positive returns to openness are very similar across genders, suggesting that being creative, unconventional and artistic is equally important for men and women working in all types of occupations. Moreover, we find significant gender differences in personality characteristics. Decomposition of personality-based earnings differentials into trait and parameter effects suggests that gender-atypical traits reduce the earnings advantage that individuals would otherwise enjoy under their own-sex wage structure. Overall, we find that the impact of personality on earnings is significant but not large – not trivial either – and comparable to the impact of differences in cognitive ability. JEL Classification: J16, J31
Hemp Waste Valorization Through Enzymatic Hydrolysis for Biofuels and Biochemicals Production

Enzymatic hydrolysis is an essential step in the lignocellulosic biomass treatment for the conversion of cellulose and hemicellulose into fermentable sugars. In the present study, a physical steam explosion (200°C) and chemical alkaline pre-hydrolysis (10% v/v) were applied to hemp (Cannabis sativa L.) biomass, to verify the effectiveness of different pretreatment processes, in order to increase enzymatic hydrolysis and the final sugar yield. The enzymatic degradations were carried out also utilizing extremophilic microorganisms, exploited as a source of (hemi)cellulases to be used as components of the enzymatic cocktail applied in the bioconversion into fermentable sugars for production of second generation ethanol and high value products. Alkaline pretreatment showed to be superior over the physical method with respect to the rate of enzymatic hydrolysis, with the obtainment of an hydrolysis yields of 90% xylose and 40% glucose, respectively.