Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Web GIS for Public Health Education

An interdisciplinary curriculum unit that used Web GIS mapping to investigate malaria disease patterns and spread in relation to the environment for a high school Advanced Placement Environmental Science course was developed. A feasibility study was conducted to investigate the efficacy of the unit to promote geospatial thinking and reasoning skills and content understandings. Results revealed increased content understandings and significant effect sizes for all three geospatial thinking and reasoning subscales inferences, relationships, and reasoning. The findings provide support that Web GIS, with appropriate curriculum design can improve both learning outcomes and geospatial thinking and reasoning skills.
A Five-Port UWB MIMO Slot Antenna Design with High Isolation

This paper designs a high isolation five-port UWB MIMO slot antenna on FR4 substrate. The antenna structure with stepped slots can expand bandwidth to cover the frequency range of 1.7 GHz to 6 GHz. The proposed antenna reduces mutual coupling between antenna components by introducing a decoupling structure composed of symmetrical horizontal and vertical open slots, and the isolation is better than 15.33 dB throughout the entire operating bandwidth range. Meanwhile, the envelope correlation coefficient (ECC) of the antenna is less than 0.058, which is much lower than the specified 0.5, indicating the good performance of the MIMO antenna system.
Monitoring Ionospheric Total Electron Content (TEC) and Irregularities at the Conjugate Point Between Indonesia and China

The ionosphere, a crucial region in the Earth's upper atmosphere, plays a pivotal role in the propagation of Global Navigation Satellite System (GNSS) signals from satellite systems such as GPS, GLONASS, GALILEO, SBAS, COMPASS, and QZSS. Variations in Total Electron Content (TEC) and Rate of TEC Index (ROTI) within the ionosphere can significantly influence the accuracy and reliability of these signals. This research conducts a comprehensive statistical analysis of TEC variations and ROTI between two strategically located stations: INTU in Bandung City, Indonesia (Lat -6.98N, Lon 107.63E), and GXGN in Guilin City, China (Lat 25.35N, Lon 110.35E). These cities, situated in low-latitude regions, were hypothesized to share a magnetic conjugate relationship. Utilizing data from GNSS Ionospheric Monitor during the equinox season of March 2022, the study found that the GXGN station recorded a higher overall mean TEC value of 45.38 TECU compared to INTU's 28.64 TECU. The study reveals notable anomalies in daily ROTI values, INTU exhibiting irregularities on Day 52 and Day 93, while GXGN showed anomalies on Day 77. These findings warrant further investigation into the potential magnetic conjugate relationship between the two stations. The primary contribution of this paper is to investigate the ionospheric conditions and their implications for Global Navigation Satellite Systems (GNSS) signals. Specifically, the study aims to understand the TEC and ROTI variations, their effect on the accuracy and reliability of the GNSS signal, and whether the two stations exhibit a magnetic conjugate relationship.
The Modified hybrid Multi-Objective Genetic Algorithm and Loss Sensitivity Factor for Optimal Siting and Sizing of PV-Based Distributed Generation in Distribution Networks

Many optimization algorithms have been developed for the problem of optimal siting and sizing of Photo-Voltaic (PV) Distributed Generators (DGs) in distribution networks. However, most of them fail to include a clear solar potential assessment at the buses of these networks. In this paper, two well-known algorithms, the Multi-Objective Genetic Algorithm (MOGA) and Loss Sensitivity Factor (LSF) are combined to form the Modified hybrid MOGA-LSF algorithm that extends the hybrid MOGA-LSF formulation to include a solar potential assessment at each bus of the network and jointly minimize active power losses, voltage deviations, and energy costs. The proposed hybrid algorithm is tested on the IEEE-15 bus radial distribution network and compared to the conventional MOGA. The resulting PV DGs allocations show that solar potential is a determinant factor in the optimal siting and sizing of PV DGs in distribution networks.
Digital twin-based energy management for home microgrid: A quantification of redundant supply capacity

Microgrid power autonomy and peer-to-peer trading are used exploratively in some parts of Northern Europe. Thus, a unified mathematical indicator is required to evaluate the power supply capacity of microgrids in the context of peer-to-peer trading of microgrids. This paper develops an evaluation index of redundancy power for home microgrid-available transaction power (ATP). A forward-looking data-driven model is integrated into the physical microgrid asset to forecast the multi-type power variation. The utilization of feedback design and the interactive iterative underpin the interaction between asset-twin systems in the home microgrid. Furthermore, optimal operation parameters of the home microgrid digital twin (HMDT) are leveraged to calculate ATP. The simulation and experimental results verify the effectiveness of the proposed approach.
A Conceptual Framework for Wireless Interconnection of Pole Mount Distribution Transformers Using Directional Beamforming by Phased Array Antenna System

Pole-mount transformers, commonly used in the distribution network of an electrical power system, are prone to faults and failures due to various reasons. Therefore, it is essential to monitor the condition of these transformers to ensure reliable and uninterrupted power supply to consumers. A central monitoring system for these transformers requires a robust communication network to interconnect the components. While wired communication technologies like power line communication and optical fiber have traditionally been popular for their reliability, wireless technologies are now emerging in the power system. However, most wireless communications are still limited to low-range, low-power networks that cover small geographic areas. This paper presents the concept of a futuristic Wide Area Mesh Network (WAMN) that interconnects pole-mount transformers using directional beamforming through a phased array antenna system. This method is advantageous over wired connections due to its lower cost, and it also outperforms other wireless technologies owing to its superior energy efficiency. Although the star topology is considered, a mesh network is preferred due to its unique advantages. The static nature of transformers eliminates mobility issues that mobile communication systems face, and the environment surrounding the pole-mount transformers does not change as rapidly as that of mobile communication networks. These advantages allow the network to rely on simple phased array antennas rather than advanced beamforming technologies. The presented concept can be explored and validated through relevant research studies and experiments.
Concept design and optimization methodology of a wound field synchronous motor for commercial vehicle applications

Due to increased costs and risk of supply chain uncertainty, rare-earth free traction motors are gaining significant interest in the automotive industry. The wound field synchronous motor offers a rare-earth free alternative to the permanent magnet synchronous motor and also shows additional benefits such as high-power factor, lower iron loss at high speeds and improved safety under fault conditions. In this paper a methodology is developed for design and optimization of such a motor for high volume automotive traction applications. The optimization process consists of two steps. The first step employs an analytical model coupled with a high population genetic algorithm to search in a large design space. The second step uses the Surrogate Finite Element (SFE) method for fast design optimization using the output of the first step as the initial population. A detailed cost and efficiency trade-off is used to select the motor for the final design.
Voltage Stability Assessment for Load-shedding Distribution

This paper started off with steady-state voltage stability analysis of IEEE-30 and IEEE-300 bus systems using different methods such as Newton-Raphson, Fast-Decoupled, Runge-Kutta, Iwamoto and Simple Robust method. Investigation on system voltage stability is then done using the Continuation Power Flow (CPF) method and modal analysis method. Simulation results showed that both methods are able to mitigate the shortfalls of conventional load flow method and are useful in determining the sequence and distribution of load-shedding for a system defense or load-shedding scheme.
Comparison and performance evaluation of the mid-point voltage balance modulation schemes for a three-phase 2L-3L DAB

Capacitor voltage balancing is critical for neutral-point-clamped-based DAB converters. This paper focuses on the mid-point voltage balancing of a three-phase two/three-level dual-active-bridge dc–dc converter (2L-3L DAB3). The voltage imbalance leads to overvoltage stress on power devices and can cause saturation of transformers or device failure in extreme cases. Two modulation schemes are proposed in the literature for the DAB3: λ-modulation and -δ-modulation (Duty Cycle adjustment). In this paper, a comparison of the two modulation schemes is provided for the 2L-3L DAB3. The average mid-point current for the two modulation schemes is modeled. Different characteristics, such as polarity inversion, operational limits, and impact on power transferred, are compared for the two modulation schemes. Operating regions are identified where one modulation scheme is shown to be superior. Comprehensive simulation and experimental results on a hardware prototype are provided to support the analysis.
Reduced Computational Burden of Modulated Model-Predictive Control for Synchronous Reluctance Motor Drive Applications

This paper introduces a novel geometric approach to significantly reduce the computational burden of modulated predictive controllers while maintaining the same steady-state performance and satisfactory dynamic behavior. The proposed geometric method leverages the symmetric properties of the active vectors with respect to the zero vectors in two-level inverters. In addition, the structure of the controller is designed to include the integral of error terms, ensuring zero steady-state tracking error. Several operating points are considered and compared with respect to standard modulated model-predictive control approaches showing similar steady-state performance with a reduced computational effort (about 50%). This enables a broader spectrum of power electronic systems and applications that can be used with the same steady-state performance of standard modulated model-predictive control (M2PC). In addition, the latter option allows for the application of M2PC with high switching-frequency devices, given that a higher sampling frequency leads to an increased switching frequency. The effectiveness of the proposed approach is demonstrated with a synchronous reluctance motor drive application.
Impact of Crypto Art Sentiment on Art Valuation

This paper examines the impact of sentiment on the value of Non-Fungible Tokens (NFTs) in the cryptocurrency market. Non-fungible tokens (NFTs) have gained significant attention in recent years as a new form of digital art investment. However, there is limited research available on the relationship between NFT sentiment and art value. In this paper, we sought to address this gap by using OpenSea, the largest NFT marketplace, and VADER (Valence Aware Dictionary and Sentiment Reasoner), a sentiment analysis tool, to gather and analyze NFT sentiments. The OpenSea API was utilized to extract over one million NFT transactions. The data collected included information such as NFT asset name, description, date of transaction, and transaction price. After the data was extracted, it was ingested into a data storage system for analysis. The NFT sentiment was then analyzed using the VADER sentiment analysis tool, which uses a lexicon of words to determine the sentiment polarity of a given text. Before analyzing the sentiment, the data was preprocessed to remove any irrelevant information and to ensure that the data was in a consistent format. This involved cleaning the data to remove any duplicates, outliers, and missing values. The results indicate that most artists tend to use neutral words when describing their art, but the average price of NFTs with negative sentiment is higher than those with positive sentiment. This paper contributes to a better understanding of the factors that influence NFT value and offers a valuable resource for those interested in NFT investment such as digital artists, digital art collectors, blockchain-based companies, or anyone with basic knowledge in blockchain and non-fungible token. The paper approach is unique in terms of topic, problem-solving and the count data used in the experiment. The impact of digital art sentiment on art value is not only examined in this paper, but it opens the way for future research.
A Wide Linear Range Ultra-low Transconductance Amplifier for Biomedical Sensor Applications

In the field of biomedical sensors, active filters with very low cutoff frequencies are required because of the weak electrical signals from the human body. The Gm-C filters are more prominent among the filter designs, which consist of an operational transconductance amplifier (OTA) and a capacitor. The key design issues are transconductance and linearity. In this paper, a new ultra-low transconductance OTA structure is proposed. The basic idea is to extend the linear input range of the OTA using the source degeneration pseudo-resistor technique and the bulk-driven technique. The transconductance is reduced by using series-parallel current mirrors for the OTA. This ultra-low transconductance OTA has an extremely low bias current of 0.1nA and a shunt factor of 100, reducing the OTA transconductance to 0.4pS with 39. 55μVrms noise. It also increases the linear input range from ±50mV to ±2.5V, extending it by a factor of 50.
Massive MIMO Data Detection Using Matrix Inversion Method and Deep Neural Network

As the number of users within a cellular system increase, so does the need for a higher quality of service and performance. Massive multiple-input multiple-output (MIMO) is a staple technology in the implementation of fifth-generation (5G) cellular networks. The technology leverages multiple antennas at the base station and within user devices to increase spectral efficiency, link reliability, and range. With massive MIMO being a significant area of research, several data detection techniques exist. Classical methods leveraging linear detection algorithms and linear approximate matrix inversions exist. However, as of recently, detection algorithms utilizing deep learning (DL) have been proposed. The detection problem requires techniques that are both robust and provide near-optimal performance at the expense of minimal complexity within different channel scenarios. DL utilizes machine learning to train a detection algorithm providing comparable performance to classic techniques with the advantage of lower complexity. This paper offers a hybrid detection algorithm consisting of a linear approximate matrix inversion step followed by a DL algorithm. The approximated message vector calculated in the first step is the initial iterate fed to the DL algorithm, which continuously improves the detection accuracy. Various simulations are conducted, demonstrating the significant superiority of the proposed framework. The study is concluded with an analysis of the complexity of the hybrid algorithm in addition to a discussion of the model's performance.
Revolutionizing Load Management in SDIoT Networks: A Multicriteria Approach for SDN Controller

In response to the burgeoning complexity of Internet of Things (IoT) networks, this study addresses the pressing issue of load management within Software Defined Networking (SDN) controllers. IoT networks, marked by high mobility and the extensive size of nodes, often exhibit load imbalances among SDN controllers, undermining their performance and efficiency. This research introduces a novel load-balancing technique to counter these challenges. Central to this approach is a unique load estimation method, which averages the load across SDN controllers, thereby facilitating effective load distribution. The performance of the proposed technique is evaluated based on the degree of load balancing, network response time, and migration cost. These metrics provide insights into the method's efficacy in maintaining load stability and the overhead associated with the migration process. Through this research, we aim to enhance load distribution management in IoT networks, which is essential for their ongoing evolution and progress. The proposed approach showed that the load imbalance degree of the proposed method decreased by an average of 53.3% and 43.5% compared to EASM and SMS respectively.
Supply Chain Disturbance Management Scheduling Model Based on HPSO Algorithm

— The continuous expansion of business has led to the development of enterprises from vertical integration to horizontal integration, and the interlocking of the supply chain system, but the influence of anti-production behavior factors and the frequent occurrence of disruption events lead to difficulties in supply chain scheduling, which affects the development of enterprises. To address the above problems, the study analyzes the factors influencing counterproductive behavior based on system dynamics, constructs a supply chain disruption management scheduling model on this basis, and solves the supply chain disruption management scheduling model using Hybrid Particle Swarm Optimization algorithm. The findings indicate that the number of non-inferior solutions, uniformity of distribution of non-inferior solutions, dominance ratio of non-inferior solutions, average distance between non-inferior solutions and optimal Pareto, maximum distance, dispersion of non-inferior solutions and coverage of non-inferior solutions of the hybrid particle swarm algorithm are 12.3, 5.283, 0.264, 0.611, 4.474, 4.627, 601.300, respectively in the A condition, 601.300. The number of non-inferior solutions, uniformity of non-inferior solution distribution, dominance ratio of non-inferior solutions, average distance between non-inferior solutions and optimal Pareto, maximum distance, dispersion of non-inferior solutions and coverage of non-inferior solutions for the hybrid particle swarm algorithm under B condition are 12.3, 5.283, 0.264, 0.611, 4.474, In summary, the proposed algorithm has excellent performance and can effectively reduce the impact of interference events, thereby improving the level of supply chain interference management and scheduling, and promoting the sustainable development of this field.
A STRATEGY FOR CONSTRUCTING MULTIPLE GROUNDED THEORIES: THE CONSTRUCTIVIST APPROACH

: Grounded theory methodology is perceived as challenging due to its systematic and rigorous process. However, this is because most people do not realize that the strategies used in the grounded theory data analysis process are used by laypeople and professionals regularly, if not daily. This article aims to help others interested in grounded theory with a background in qualitative data analysis feel comfortable engaging in the methodology and constructing multiple theories in the same study. It shares my latest experience incorporating various data analysis strategies with the constructivist approach: holistic and systems thinking, situational analysis, dramaturgical analysis, perspective taking, and abstraction. The examples remove the mystery from abstraction and abductive reasoning.
Multimedia Streaming via TCP : An Analytic Performance Study

TCP is widely used in commercial media streaming systems, with recent measurement studies indicating that a significant fraction of Internet streaming media is currently delivered over HTTP/TCP. These observations motivate us to develop analytic performance models to systematically investigate the performance of TCP for both live and stored-media streaming. We validate our models via ns simulations and experiments conducted over the Internet. Our models provide guidelines indicating the circumstances under which TCP streaming leads to satisfactory performance, showing, for example, that TCP generally provides good streaming performance when the achievable TCP throughput is roughly twice the media bitrate, with only a few seconds of startup delay.
Impact of typhoons on the ocean in the pacific

Tropical cyclones (TCs) change the ocean by mixing deeper water into the surface layers, by the direct air–sea exchange of moisture and heat from the sea surface, and by inducing currents, surface waves, and waves internal to the ocean. In turn, the changed ocean influences the intensity of the TC, primarily through the action of surface waves and of cooler surface temperatures that modify the air–sea fluxes. The Impact of Typhoons on the Ocean in the Pacific (ITOP) program made detailed measurements of three different TCs (i.e., typhoons) and their interaction with the ocean in the western Pacific. ITOP coordinated meteorological and oceanic observations from aircraft and satellites with deployments of autonomous oceanographic instruments from the aircraft and from ships. These platforms and instruments measured typhoon intensity and structure, the underlying ocean structure, and the long-term recovery of the ocean from the storms' effects with a particular emphasis on the cooling of the ocean beneath th.
High-dimensional regression and classiﬁcation under a class of convex loss functions

The weighted L 1 penalty was used to revise the traditional Lasso in the linear regression model under quadratic loss. We make use of this penalty to investigate the high-dimensional regression and classiﬁcation under a wide class of convex loss functions. We show that for the dimension growing nearly exponentially with the sample size, the penalized estimator possesses the oracle property for suitable weights, and its induced classiﬁer is shown to be consistent to the optimal Bayes rule. Moreover, we propose two meth-ods, called componentwise regression (CR) and penalized componentwise regression (PCR), for estimating weights. Both theories and simulation studies provide supporting evidence for the advantage of PCR over CR in high-dimensional regression and classiﬁcation. The eﬀectiveness of the proposed method is illustrated using real data sets.