Miracle-dz/UniFAR-FTUs huggingface dataset:
A Design and Development of Learning Innovations for Training Courses on Smart Greenhouse Systems

This article aims to design and develop a learning innovation for a training course on a smart greenhouse system for Thung Yai Sarapee Learning and Innovation Center at Songkhla Rajabhat University. There are two objectives of this research. First, to create innovative materials for training courses and second, to assess the quality of the learning innovation package. The process of designing and developing innovative training materials consists of two main parts. The first part is the design of the smart control system. The second part focuses on designing learning content. After developing the innovative media, an expert assessment was conducted. Five experts evaluated the innovative media developed and the overall evaluation results of all topics were appropriate at a high level (mean = 4.45 and S.D. = 0.55). Similarly, the assessment of learning content for all topics was also highly appropriate (mean = 4.45 and S.D.= 0.51). These findings suggest that the learning innovations created in this research have the potential to effectively support individual training in smart greenhouse systems.
Analysis and Study of an Integrated System Based on Eddy Current for the Osteogenesis Process

The study of the impact of electromagnetic fields on the body is gaining ground at a scientific level. The growing interest stems from the observation of the therapeutic benefits gained from electromagnetic stimulation. Experiments are also being conducted on cellular and molecular models to better understand the biological implications behind bioelectromagnetic interactions. This research deals with a new biomedical technology based on the use of induced currents in specific parts of the human body. Consequently, the study focused on the design and implementation of a device that can harness the action of induced currents for therapeutic applications. The aim is to provide further knowledge on a subject of study that is currently little used in the biomedical field. The experimentation stems from the creation of a system capable of improving the healing process and also being particularly simple to use. The research provided outstanding results, particularly when it came to the direct impact of electromagnetic stimulation on osteoblast, chondrocyte, and calmodulin activation as well as bone morphogenetic protein production. The model supported the idea that eddy current distributions accelerate osteogenesis.
Streaming Matrix Transposition on FPGAs Using Distributed Memories

Matrix transposition, the procedure of swapping rows and columns of a matrix, has applications in various signal processing applications, such as massive multiple-input multiple-output (MIMO) communication systems, data compression, and multidimensional fast Fourier transforms – which are used in MIMO radar systems. In low-latency high-throughput streaming applications, specialized circuits for matrix transposition are needed in order to perform transposition in real-time. This is in contrast to "slower" applications, where transposition can be adequately performed by storing a matrix in a shared memory and afterward reading it back in a transposed order. In this paper, a design procedure for streaming matrix transposition on field-programmable gate arrays (FPGAs) using distributed memories is presented. It is shown that significantly fewer FPGA resources are required for small- to medium-sized streaming matrix transpositions compared to recent related works.
Correcting Temporal Inaccuracies in Labeled Training Data for Electromyographic Control Algorithms

Electromyographic (EMG) control relies on supervised-learning algorithms that correlate EMG to motor intent. The quality of the training dataset is critical to the runtime performance of the algorithm, but labeling motor intent is imprecise and imperfect. Traditional EMG training data is collected while participants mimic predetermined movements of a virtual hand with their own hand. This assumes participants are perfectly synchronized with the predetermined movements, which is unlikely due to reaction time and signal-processing delays. Prior work has used cross-correlation to globally shift and re-align kinematic data and EMG. Here, we quantify the impact of this global re-alignment on both classification algorithms and regression algorithms with and without a human in the loop. We also introduce a novel trial-by-trial re-alignment method to re-align EMG with kinematics on a per-movement basis. We show that EMG and kinematic data are inherently misaligned, and that reaction time is inconsistent throughout data collection. Both global and trial-by-trial re-alignment significantly improved offline performance for classification and regression. Our trial-by-trial re-alignment further improved offline classification performance relative to global realignment. However, online performance, with a human actively in the loop, was no different with or without re-alignment. This work highlights inaccuracies in labeled EMG data and has broad implications for EMG-control applications.
Enhancing Measurement Specificity in Biological Samples with Differential Probing Analysis

Developing integrated sensors for unobtrusive in-vivo biomarker analysis is strongly desired. Various sensor designs have been created to detect permittivity variations related with biomedical processes at microwave frequencies. However, achieving measurement specificity remains a challenge due to complex biological tissues and electromagnetic field scattering. To address this challenge, in our previous work, we introduced differential probing analysis (DPA). DPA involves the use of open-ended coaxial probes (OCP) of different sizes and was validated for low-permittivity and low-loss samples. In this work, experimental findings on pork neck chop samples demonstrate the accurate determination of underlying muscle tissue permit-tivity from superficial measurements on fat, thereby proving the feasibility of DPA in the biological domain for enhancing measurement specificity.
Ceramic RFID Tag for Omnidirectional Long-Range Communication

Radio frequency identification (RFID) is a wireless technology that allows contactless readout of data, encoded on time-modulated backscattering from a passive device. While this technology was already found beneficial in many retail applications, its further spread demands developing pathways to reading range extension and immunity to an interrogation direction. Here we demonstrate a new architecture, where a standard metallic RFID tag antenna is replaced by a ceramic resonator. The high permittivity of the structure allows encompassing of several cascaded resonances within a subwavelength volume and exploits the multi-channel operation of the communication link to shrink the footprint of the device. Spectral sharing between several mutually orthogonal dipolar resonances is designed to achieve an omnidirectional operation. As a result, a structure, encompassed in a 15 mm radius sphere, assessable from a 16 $\mathbf{m}$ distance is numerically demonstrated. By now, this is the best reading range in the category of omnidirectional RFID tags. Further efforts on footprint reduction can bring ceramic RFID architectures towards mm-scale layouts, making them beneficial in numerous applications, including the Internet of Small Things.
Assessing the Performance of an Architecture-Aware Optimization Tool for Neural Networks

The important growth in the demand for Neural Network solutions has created an urgent need for efficient implementations across a wide array of environments and platforms. As industries increasingly rely on AI-driven technologies, optimizing the performance and effectiveness of these networks has become crucial. While numerous studies have achieved promising results in this field, the process of fine-tuning and identifying optimal architectures for specific problem domains remains a complex and resource-intensive task. As such, there is a pressing need to explore and evaluate techniques that can improve this optimization process, reducing costs and time-to-deployment while maximizing the overall performance of Neural Networks. This work focuses on evaluating the optimization process of NetAdpat for two neural networks on an Nvidia Jetson device. We observe a performance decay for the larger network when the algorithm tries to meet the latency constraint. Furthermore, we propose potential alternatives to optimize this tool. Particularly, we propose an alternative configuration search procedure that allows us to enhance the optimization process, achieving speedups of up to $\sim 7/\times$.
Understanding Solar Cell Mismatch Losses Through Statistical Evaluation and Simulation

To achieve optimum power output with satellite solar arrays, manufacturers can perform targeted allocations of individual cells to their position in a string and section. By analyzing radiation test data, we show that this process, called matching, does not necessarily provide benefits at End of Life (EOL) because of the poor link between Begin of Life (BOL) and EOL solar cell behavior. Through Monte Carlo simulations of random cell allocations we show that the benefits of matching, even though difficult to quantify, might be overestimated by current loss factors for certain designs, and that consistency in individual solar cells' performances plays an important role.
Influence of Crystallization Conditions on Local Atomic Structure of Austenite in Rapidly Quenched Tinicu Shape Memory Alloys

The local crystalline environment of Ni and Cu atoms in the quasi-binary TiNi-TiCu shape memory alloys containing 30 and 40 at. % Cu has been studied using EXAFS spectroscopy. Amorphous melt-spun alloy ribbons have been crystallized by isothermal annealing or by exposing to a short (10 ms) electric pulse. Analysis of the EXAFS spectra has shown that electropulse crystallization has a more noticeable effect on the local atomic structure of austenite compared to martensite. In particular, it leads to a decrease in the local disorder of atoms in the Cu coordination shells, a decrease in the lengths of Cu-Ni and Cu-Cu bonds, hinders an increase in the lattice parameter of the austenite phase and stabilizes its crystal structure. The observed changes in the local atomic structure of the austenite phase of the alloys contribute to the improvement in the shape memory properties.
Multi-agent Reinforcement Learning for Improving Supply Chain Visibility in Inventory Management

This paper proposes a novel approach to enhance supply chain (SC) visibility, cooperation, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine learning techniques. The SC inventory policies are optimized using multi-agent reinforcement learning (MaRL) and SC network topological information. Furthermore, we conduct a simulation-based evaluation that demonstrates the superior performance of our method compared to alternative optimization approaches. This research effectively addresses the dual objectives of ensuring information security and achieving cost reduction in SC inventory management.
D OES L EADERSHIP M ATTER I N C OLLABORATIVE G OVERNANCE ? C ASES I N B ANYUWANGI A ND K ULON P ROGO R EGENCY , I NDONESIA

In nearly all of the framework models of collaborative governance (CG), leadership is always shown as having a major and strategic role. However, specific studies that discuss leadership tend to be limited. Most of the studies on CG are more concerned with the implementation of CG as carried out through several examples of cases. This study intends to perform a precise analysis of the leadership typologies exhibited by the regents of Kulon Progo and Banyuwangi in mobilizing CG, as well as to discover similarities in the indicators that they display in serving their leadership role. This research uses the quantitative approach by referring to the theory of leadership and leadership typologies as developed by Ricard et al. (2017). Ricard distinguished between 5 leadership typologies in the public sector, encompassing transactional, interpersonal, transformational, entrepreneurial, and network governance typologies as arranged into 23 indicators. Data was obtained by total and purposive sampling through a survey of 39 respondents who are Heads of Agencies who have intensive interaction with the Head of the Region. The purposive sampling technique is due to the fact that the heads of agencies interact with the leader (Head of the Region) directly, so they have a better understanding of the leadership styles and traits of the Regents. The results of this survey show that the leadership of the two Regents have a tendency to display similarities in typologies, dominated by the entrepreneurial, network governance, and transformational typologies. In the case of the two leaders, the transactional and interpersonal typologies have the lowest average value. Meanwhile, the predominant characteristics of the leadership of these two heads of the region include indicators such as taking initiative, being visionaries, showing commitment to colleagues and organizations, and lastly, being problem oriented. The results of this research may serve as a reference for other Heads of the Region in exercising leadership for collaborative governance.
Real-Time 2-D Coherent Multi-Transducer Ultrasound Imaging in Vivo

The field of view and resolution of ultrasound scanners are known to be limited by the probe aperture. Coherent multi-transducer ultrasound (CoMTUS) imaging has been proposed to create an extended aperture using multiple probes. Due to the computational load required to beamform and compound all the received data, the algorithm has been so far limited to offline operation. In this work, we present the first real-time CoMTUS implementation based on two ULA-OP 256 scanners connected to two 144-element linear arrays and a single personal computer. This imaging system was tested on a healthy volunteer at frame rates up to 20 Hz.
Characterizing Flaky Tests in Node.js Applications

Regression testing is an important means of assessing the quality of Node.js applications. However, non-deterministic executions inside Node.js framework could make test cases intermittently pass or fail on the same version of code, which are called flaky tests. Flaky tests can cause unreliable test results, and make developers waste a significant amount of time debugging the bugs that do not belong to the target application. In this paper, we conduct an empirical study on 87 flaky tests from 7 popular Node.js applications, and analyze the non-determinism that causes these flaky tests. Through this study, there is a wide range of non-determinism to cause flaky tests, including non-deterministic event triggering order, non-deterministic function calls, non-deterministic process/thread scheduling order, non-deterministic execution of asynchronous tasks and non-deterministic event triggering data. The result reveals that, existing approaches on event race detection are not sufficient for flaky test detection. In future, researchers can design flaky test detection approaches targeted at different categories of non-determinism.
Using Ensemble Approaches and Different Sampling Techniques to Handle Class Imbalance Challenges in Coronary Heart Disease Prediction

Coronary Heart Disease (CHD) has a significant impact on death rates worldwide; therefore, early and accurate prediction is vital for enhancing timely prevention and improving patient outcomes. However, predicting CHD is challenging because of the imbalanced nature of the dataset. A number of sampling techniques are proposed to overcome this challenge, including random oversampling, undersampling, and synthetic minority oversampling techniques (SMOTE), as well as three ensemble methods, including XGBoost, Random Forest, and AdaBoost. Incorporating these techniques minimizes classifier bias in favor of the majority group and emphasizes the learning of minority concepts appropriately. Undersampling techniques give the highest accuracy in AdaBoost, which is 53%. However, the oversampling approach and SMOTE achieved the highest accuracy of 82%, which is in the Random Forest algorithm. The sensitivity and precision scores are also increased in the proposed model. Therefore, our proposed approach offers a trustworthy and effective solution to the class imbalance issue in CHD prediction, and it has the potential to be used in other healthcare areas that face similar challenges.
Min-Coverage Competitive Facility Location Under Random Utilities

This work concerns a facility location problem in a competitive market where customer demand is modeled and predicted by a random utility maximization model. We study a variant where, instead of maximizing a capture demand objective function as in prior work, we aim to minimize the opening cost of the new facilities while ensuring that the overall demand achieved is at least a certain proportion of the competitor's captured demand. To solve the proposed problem, we leverage some important properties of the capture demand function, such as submodularity and convexity, to propose three solution methods, namely, a poly-time local search heuristic algorithm with guaran-tees, a mixed-integer linear program (MILP) reformulation that allows the problem to be solved exactly by an off-the-shelf solver, and a cutting plane method. Experiments based on generated instances of various sizes show the efficiency of our proposed solution methods, especially the cutting plane approach, in solving large-scale instances.
Analog Electronic Systems for Biomedical Sensor Applications: a Review on Current Mode Approach

In this review work, a study of the scope and importance of electronic systems for biomedical sensor applications is presented. Starting from a quick overview of sensors and basic electronic read-out circuits, the paper presents some interfaces, based on active blocks. Recalling the importance, in the biomedical field, of the devices autonomy, a brief examination on the concept of energy harvesting is also done. Finally, some case studies are described, the most proposed by the authors and related to the development of analog electronic systems employing current-mode blocks (CCII and VCII).
Optimization of Hierarchical-QAM based Downlink Multiple Access with Statistical CSI

This paper considers a downlink multiple access scenario with OFDM transmissions in frequency selective channels. The users are assigned with different bit streams from hierarchical quadrature amplitude modulation (QAM) rather than assigned with different subcarriers as in orthogonal frequency division multiple access (OFDMA). Based on statistical channel state information in the form of the average signal-to-noise ratios of the users, we derive a closed-form solution on the hierarchical constellation design. The numerical results demonstrate the performance advantage of the proposed system over OFDMA where power control is also applied based on the same channel feedback.
Impact of Uplink Traffic on 5G-Based Passive Radar

Several pioneering works have presented experimental results of 5G-based passive radars. In those works, the illumination source is a signal transmitted by the 5G base station (simulated and real-life). Therefore the uplink (UL) signal (transmitted from user equipment to the base station with a time division duplex (TDD)) was considered a source of interference and was cut to achieve successful target detection. This work presents an in-depth analysis of the impact of the uplink signal on the performance of the 5G-based passive radar. The study shows that in most scenarios, the implications of uplink traffic are insignificant, which is a valuable conclusion considering the real-time implementation of the 5G-based passive radar. It is shown that skipping the step of uplink cancelation in the TDD 5G network-based passive radar results in significant computational complexity reduction. The simulations were carried out under various conditions, including the different instantaneous amounts of resources allocated in both directions of wireless network traffic and the distance from the radar receiver to the base station and the user equipment. The experimental validation (using real-life 5G signals) of the conducted research proved the simulation results.
Android Malware Detection Using Incremental Learning Approach

Android is the most popular Mobile operating system covering above 70% of all smartphone users. Due to its popularity, an increasing number of malware applications are targeting the Android platform, stealing sensitive information, accessing banking apps, etc. These malware applications can be detected with the help of machine learning algorithms by extracting features from those applications. However, previous attempts use traditional methods which require high memory overhead and computational power which is not suitable for continuous real-time data inflow. In this paper, we have pro-posed an incremental decision tree approach to detect mobile malware. Our model has achieved an accuracy of 93.33%, precision of 91.27%, recall of 95.83 %, and F1-Score of 93.50%. Our proposed model can help reduce model retraining time when new malware samples appear. It also requires less storage for historical data while retraining the model.
A Source-to-Source NUMA Profiling Approach

The design of HPC processors is driven by the purpose of packaging an increasing number of CPU cores. This trend in the multicore design faces the physical reality of integrating circuits into a single die in addition to the bottleneck of components sharing, thus the advent of Non-Uniform Memory Access (NUMA) with its typical packaging. Cutting-edge supercomputers are made up of such (manycore) compute nodes. In any case, the main issue is scalability. With a NUMA configuration, a memory access can be local (within the same NUMA node) or remote (from a NUMA node to another). The latter is the main concern w.r. t to efficiency because of the associated overhead is much more important. Dealing with this concern explicitly when designing a program is called NUMA-aware implementation. With an existing code, the problem can be addressed by starting with an appropriate profiling. This is the focus of the present work, where we suggest a way to instrument the native code in order to get the type (i.e. local or remote) of each memory access and we provide a tool that supports the profiling process. We then propose a metric that takes these statistics about memory accesses and provides a value indicating the potential associated overhead.
Learning how to model ecosystem trade-offs at the farm scale

The ecosystem service framework provides a forum for scientists from a range of disciplines to communicate and work together alongside other key stakeholders. However to be effective, place-based comparison of the tradeoffs of ecosystem services need further development. These place-based comparisons are vital in agricultural systems due to the increasing global demand for food production, coupled with the realization that this should be achieved with minimal negative impact on the environment. The farm is the logical unit of management in agricultural systems and hence there is a need for ecosystem tradeoff assessments at the farm scale. We have carried out a literature review of the tradeoffs in the delivery of ecosystem services from intensively managed temperate grassland systems. Building on this work, we are now setting up a farm scale experiment to examine the tradeoffs, identified from the refereed literature, as requiring further investigation due to either limited or conflicting evidence. To facilitate an improved understanding of these tradeoffs we need to learn how to model them, based on previous and current modelling frameworks and coupled with improved knowledge of international best practice. Fundamentally, this requires a dialogue between modellers and field scientists.