Miracle-dz/UniFAR-FTUs huggingface dataset:
A Methodology Based on Quantum Evolutionary Algorithm for Topology Optimization of Electromagnetic Devices

Stochastic algorithms are powerful global optimizers in topology optimizations (TOs). However, the low convergence speed and high computational cost, due to the large dimensionality of a TO problem, of such an approach are still two open issues to be addressed. To address these two issues, a TO methodology based on a modified quantum evolutionary algorithm is proposed. In the proposed methodology, an adaptive updating mechanism for the rotation gate is introduced to balance the exploration and exploitation searches; and a redistribution mechanism of design variables is developed to reduce the computational cost of the algorithm. The numerical results as reported validate the effectiveness and feasibility of the proposed methodology and demonstrate its advantages over a traditional ON/OFF method and its ancestor.
Robust Feature Extraction Using Temporal Context Averaging for Speaker Identification in Diverse Acoustic Environments

Speaker identification in challenging acoustic environments, influenced by noise, reverberation, and emotional fluctuations, requires improved feature extraction techniques. Although existing methods effectively extract distinct acoustic features, they show limitations in these adverse settings. To overcome these limitations, we propose the Temporal Context-Enhanced Features (TCEF) approach, which provides a consistent audio representation for better performance under various acoustic conditions. TCEF leverages a context window to average features in adjacent frames, effectively reducing short-term variations caused by noise, reverberation, fluctuations in emotional speech, and those in neutral recordings. This approach improves the distinctive features of a speaker voice, improving speaker identification in challenging and neutral acoustic environments. To evaluate the performance of TCEF against conventional features, One-Dimensional Convolutional Neural Network (1D-CNN) was used for a detailed frame-level analysis and Long Short-Term Memory (LSTM) for a comprehensive sequence-level analysis.We used four datasets to assess the effectiveness of the TCEF approach. The GRID and RAVDESS datasets represent neutral and emotional speech, respectively. To test the robustness of our system under adverse acoustic conditions, we created two additional datasets: GRID-NR and RAVDESS-NR. These are modified versions of the original GRID and RAVDESS, incorporating added noise and reverberation. Performance evaluation results showed that TCEF significantly outperformed existing feature extraction methods in identifying speakers in diverse acoustic environments.
Effect of Obstacle Training in Frailty among Geriatric Population

Background: This study was developed to determine the effectiveness of obstacle training in frailty amonggeriatric population.Purpose: The purpose of the study is to evaluate the effectiveness of obstacle training in frailty among geriatricpopulation and to analyze the balance and gait analysis and number of falls.Method: A quasi experimental study was done in geriatric old age home with 30 samples by convenient samplingtechnique, allotted in a single group. In phase 1 the prevalence of frailty among Geriatric population will beexamined using the Edmonton frail scale. In phase 2 the Tinetti scale and morse fall scale were used as outcomemeasures.Results: According to the statistical analysis there is a clinically significant in groups, p value >o.oo1. Conclusion: We concluded that obstacle training has an improvement in reducing risk of frailty among geriatricage group population.
Genomic analysis of Salmonella enterica from Metropolitan Manila abattoirs and markets reveals insights into circulating virulence and antimicrobial resistance genotypes

The integration of next-generation sequencing into the identification and characterization of resistant and virulent strains as well as the routine surveillance of foodborne pathogens such as Salmonella enterica have not yet been accomplished in the Philippines. This study investigated the antimicrobial profiles, virulence, and susceptibility of the 105 S. enterica isolates from swine and chicken samples obtained from slaughterhouses and public wet markets in Metropolitan Manila using whole-genome sequence analysis. Four predominant serovars were identified in genotypic serotyping, namely, Infantis (26.7%), Anatum (19.1%), Rissen (18.1%), and London (13.3%). Phenotypic antimicrobial resistance (AMR) profiling revealed that 65% of the isolates were resistant to at least one antibiotic, 37% were multidrug resistant (MDR), and 57% were extended-spectrum β-lactamase producers. Bioinformatic analysis revealed that isolates had resistance genes and plasmids belonging to the Col and Inc plasmid families that confer resistance against tetracycline (64%), sulfonamide (56%), and streptomycin (56%). Further analyses revealed the presence of 155 virulence genes, 42 of which were serovar-specific. The virulence genes primarily code for host immune system modulators, iron acquisition enzyme complexes, host cell invasion proteins, as well as proteins that allow intracellular and intramacrophage survival. This study showed that virulent MDR S. enterica and several phenotypic and genotypic AMR patterns were present in the food chain. It serves as a foundation to understand the current AMR status in the Philippines food chain and to prompt the creation of preventative measures and efficient treatments against foodborne pathogens.
Developing a Large-Scale Language Model to Unveil and Alleviate Gender and Age Biases in Australian Job Ads

This study explores the use of large-scale language models to detect and diminish gender and age biases in job ads. After training and testing various models, including RoBERTa, ALBERT, and GPT-2, RoBERTa performed the best in identifying such biases. Analysis revealed significant male bias in industries such as information and communication technology, manufacturing, transportation and logistics, and services. Age bias was also observed, favoring younger applicants over older candidates. Additionally, the study explores the use of ChatGPT’s natural language generation to address gender bias. Two versions of job ads were generated: one using gender-neutral language and the other intentionally incorporating feminizing language. User research showed that both versions significantly improved the attraction of female candidates and reduced gender bias. Overall, the study provides a comprehensive analysis of gender and age biases in Australian job ads, develops bias detectors with large-scale language models, and explores natural language generation to mitigate gender bias. By addressing these biases, the study aims to create a more inclusive and fair job market.
Dim and Small Target Detection Based on Local Feature Prior and Tensor Train Nuclear Norm

When faced with complex scenes containing strong edge contours and noise, there are still more background residuals in the detection results of traditional algorithms, leading to a high false alarm rate. To solve the above problems, we propose an infrared dim and small target detection method that combines local feature prior and tensor train nuclear norm (TTNN). To suppress the strong edge contours, we first establish a background edge contour suppression function based on the structure tensor. Secondly, we propose a multi-frame density peak search algorithm to obtain local feature information by combining the features associated with multiple contiguous frames of the target. Then we use the local feature information and reweighting strategy to constrain the sparse components of the target signal, and describe the background low-rank components by the tensor train nuclear norm. Finally, we separate the target image from the background image using the alternating direction multiplier method. As compared with eight advanced algorithms, the method in this paper has a better background strong edge contour and a stronger noise suppression.
Study of Seedling Growth of Radish Seed Using Dielectric Barrier Discharge at Atmospheric Pressure

This work deals with the evaluation of effects of period of plasma treatment on radish seeds. The parameters such as germination rate (2nd to 9th day), shoot length (5th to 25th day), root length (8th to 12th day), and leaf area (measured for a duration of 12 days) were observed and analyzed. It is revealed that the untreated seeds exhibited the lowest values for all growth parameters whereas there was a significant improvement in germination rate, shoot length, root length, and leaf area after the increment of treatment duration. Experimentally, it is observed that those parameters such as germination rate, shoot length, root length, and leaf area become optimum at a treatment duration of 3 minutes and 30 seconds. This indicates that the plasma treatment using the DBD method has a positive impact on the seedling growth of radish plants, enhancing their overall development and vigor. This work could be useful in agriculture farming for the enhancement of the yielding growth.
A Deep Learning Approach for Climate Parameter Estimations and Renewable Energy Sources

This paper introduces a novel deep learning approach for predicting global solar radiation and temperature. We propose an architecture based on Gated Recurrent Unit (GRU) neural networks able to refine weather predictions returned by the MM5 Regional Climate Model. Measured values from a weather station and outputs from the MM5 system are used to train and validate the model. The forecasting capability is assessed for three-day estimations. The results demonstrate that the model, by correcting MM5’s periodic tendencies to underestimate or overestimate the outputs, leads to a more accurate forecasting of the weather variables.These more precise predictions are then adopted for calculating the electrical energy production of a photovoltaic cell. Also in this case, the proposed model allows better results than the traditional MM5 system, enabling adaptive adjustments in intelligent energy systems.
The potential of polarization measurements from space at mm and sub-mm wavelengths for determining cirrus cloud parameters

Abstract. The millimeter and sub-millimeter waves have been attracting a lot of attention recently in the cloud remote sensing community. This is largely because of their potential use in measuring cirrus cloud parameters with airborne or space-borne radiometers. In this study, we examine the possibility of using polarization measurements in this frequency range to get information on the microphysical properties of cirrus clouds. By using a simple radiative transfer model, we calculated the brightness temperature differences at the vertical and horizontal polarization channels for the following seven frequencies: 90, 157, 220, 340, 463, 683, and 874 GHz. The ice crystals in cirrus clouds are modeled with nearly spherical particles, circular cylinder, and circular plate, as well as with mixtures of these types. We found that the polarization difference signal shows a unique "resonance'' feature with the change of ice particle characteristic size: it has a strong response only in a certain range of ice particle size, beyond that range it approaches zero. The size range where this resonance happens depends to a large extent on particle shape and aspect ratio, but to a much less extent on particle orientation. This resonance feature appears even when ice clouds are composed of a mixture of ice crystals in different shapes, although the magnitude and the position of the resonance peak may change, depending on how the mixture is made. Oriented particles generally show larger polarization difference than randomly oriented ones, and plates have larger polarization difference than cylinders. However, the state of particle orientation has a significantly stronger effect on the polarization difference than the particle shape (cylinder or plate). This makes it difficult to distinguish particle shapes using millimeter and sub-millimeter radiometric measurements, if there is no information available on particle orientations. However, if the state of particle shape mixture can be predetermined by other approaches, polarization measurements can help to determine ice particle characteristic size and orientation. This information, in turn, will benefit our retrieval of the ice water path of cirrus clouds.
Toward sustainable development: Green economy with economic growth and carbon emission in Vietnam

Transitioning to a green economy is a global concern, considered a pathway to sustainable development. This paper aims to investigate the effect of the transition into a green economy on Vietnam’s sustainable development and its two economic and environmental dimensions, with consideration of several essential issues including renewable energy, technological innovation, natural resource rents (oils, forest, and minerals), foreign direct investment, and trade. This paper utilizes data from 1996 to 2020 and then applies the autoregressive distributed lag (ARDL) method for analysis. The results conclude that renewable energy is a driving key to reducing environmental degradation, but it hampers economic growth, while the contrast occurs with technology. Our results emphasize the dependence on non-renewable energy, whereas the innovation of technology does not show a green orientation in Vietnam. Furthermore, there is a lack of sustainability in the effect of natural resource rents, foreign direct investment, and trade. Overall, the transition into a green economy in Vietnam does not illustrate the sustainable orientation. The findings of this research provide empirical evidence to clarify the relationship between this transition and its driving factor, with sustainable development and the two economic environment dimensions. In addition, this study will bring worthwhile implications for the policymakers and scholars on whether the transition to a green economy fulfills the orientation towards sustainability, then enhancing the economy's efficiency to achieve green growth, following the pathway to sustainable development.
Lung Nodule Segmentation in LDCT: Modified 3D nnUNet with Unified Focal Loss

Cancer ranks first among the top ten causes of death in Taiwan, and lung cancer has the highest mortality rate among all cancers. Pulmonary nodules are early signs of lung cancer. The growth rate, shape, location, and density of pulmonary nodules are all crucial information for evaluating the degree of malignancy. To calculate these features, accurate segmentation of pulmonary nodules is a necessary base. This paper contributes to the improvement of two existing problems: (1) applying Unified Focal Loss to greatly improve the segmentation accuracy of ground glass opacifications (GGO), and (2) improving the existing nnUNet model, using Res2Net Block combined with Dilated Convolution to strengthen the semantic communication between the encoding layer and the decoding layer and add multi-scale information to improve the segmentation performance of the Model. The model training uses the public data set of LIDC-IDRI (Lung Image Database Consortium Collection and Image Database Resource Initiative) and the pathological and health examination data provided by National Cheng Kung University Hospital. Our improved nnUNet can achieve an average Dice score of 83.4% on the public dataset LIDC-IDRI for 5-Fold Validation. Experiments show that our results have very competitive results in terms of stability and segmentation accuracy.
Fuzzy Logic Control Based Higher Order Dual Output Buck Converter

This paper presents the design of a dual-output buck converter. The baseline converter is a fourth-order buck converter with an input C-L filter. A dual output is generated with an additional inductor and two switching transistors. The converter is operated in continuous conduction mode (CCM) with three switching modes. The two fuzzy voltage-mode control loops are deployed to regulate the dual output voltages. A logic circuit is inserted to map the closed loop duty cycle commands to the three switching transistors of the proposed converter. The operation of the converter is verified through simulations performed in the MATLAB Sim-Power-Systems toolbox, where the converter’s performance is verified using both the load and line regulation tests.
Performance Analysis of Deep Learning based Hybrid Precoder/Combiner in Millimeter Wave Massive MIMO Architecture

This paper presents a performance analysis of a Deep Learning based Hybrid Precoder/Combiner for Millimeter Wave (mmWave) Massive Multiple Input and Multiple Output (MIMO) Architecture. Unlike traditional hybrid precoder techniques, which suffer from high energy consumption and underutilization of spectrum, this approach leverages deep learning to address these challenges in mmWave Massive MIMO systems. Specifically, a deep learning model is developed to optimize a cascaded hybrid precoder/combiner using an improved Convolutional Neural Network (CNN). The model predicts the analog precoder/combiner according to channel vector, and then the digital precoder/combiner vector is predicted using the Approximate Message Passing (AMP) algorithm to improve a spectral efficiency of the mmWave Massive MIMO Architecture. The proposed Deep Learning (DL)-based hybrid precoder/combiner using AMP algorithm is provide 15% improvement in achievable rate for the given channel measurement Mt =Mr =8 value compared to the existing Algorithm.
New Aggregators for Global Reputation on Bi-Lattice Based Trust Model

Within large and growing human communities where interactions occur, trust is a key factor to consider. Computational trust models have then been widely studied since the 2000s targeting items ratings (e.g. in e-commerce) or M2M (e.g. in IoT network). Among these models, EigenTrust is today one of the most popular and studied ones. It provides a global reputation calculation and is efficient in distributed networks, but not fully satisfactory for human interactions. On the opposite, the Bi-lattice model is well suited for human networks interactions such as solidarity networks and/or human services exchange networks but is limited to local trust results. In this paper, we propose a new aggregator that extends the Bi-lattice model to enable a global reputation calculation. This new aggregator discovers the trust links from the member whose score is to be evaluated to every other members he is connected to on the trust network. It then computes the global reputation of this member based on these trust links. Furthermore, it enables a lightweight approach, as it is able to compute a global reputation based only on a partial knowledge of the trust network. Throughout the paper, the proposed aggregator is presented, evaluated and compared to Eigentrust to show its effectiveness.
Forest Fire Detection using WSOA Based Geodesic Distance Encoding Segmentation Model in Smart IoT

Forest fires have become a significant worldwide hazard as a result of both global warming and an increase in the number of people. Other negative effects include the greenhouse effect and climate changes. Surprisingly, a disproportionate amount of forest fires have been started by human activity. The secret to managing an unforeseen incident is quick detection with high precision. Conversely, newly proposed CNN-based communicating division algorithms are limited by low talk on photos of forest fires, while traditional communicating division approaches need a large digit of concentration planes. This study offers a innovative deep-rooted knowledge-based segmentation method that achieves excellent results and can identify forest fires using publically accessible data. The research specifically uses a limited number of extra user clicks to process the original segmentation in order to produce an enhanced segmentation that incorporates inner boundary points using the suggested geodesic distance encoding approach. The White Shark Optimisation Algorithm (WSOA) then chooses the weight in the best possible way. The suggested segmentation model outperformed previous methods in terms of accuracy for successfully identifying the forest fire.
Laparoscopic cholecystectomy in cirrhotic patients

The aim of this study was to value in a cohort of cirrhotic patients undergoing cholecystectomy, the safety of laparoscopic approach. In the period 2002-2012 we treated 65 patients with cirrhosis who underwent a cholecystectomy: 28 males, 37 females; mean age 58.4 years (range 27-83 y). we evaluated a in this cirrhotic group the results between laparoscopic, converted to open and open approach. The choice of the approach was made on the basis of clinical evaluation of each patient. We also compared the results with a control group of non cirrhotic patients. Statistical analysis was completed using the Student t test as appropriate. Sixty-five procedure were performed. Subdivision according Child-Pugh score was: Class A 66.2%; Class B 29.2%; Class C 4.6%. Six patients in the cirrhotic cohort underwent OC, the remainder LC with 12 converted to OC (conversion rate 20.3%). Conversion rate in non-cirrhotic group was 3.7%. No mortality in our series. In cirrhotic group in all were 17 (26.15%) blood or blood products transfusion; none in non cirrhotic group. Postoperative morbidity was 10.8% in cirrhotics, whereas was 2.46% in non cirrhotic group. The changes of LFTs between pre and postoperative control was not statistically significant in cirrhotics. The mean hospital stay with laparoscopic approach was 4.8 days vs 9.1 days in cirrhotic series. Cirrhotic patients with symptomatic cholelithiasis have increased hospital stay, operative time and postoperative morbidity with open cholecystectomy versus laparoscopic approach; therefore minimally invasive surgery should be the preferred initial choice in cirrhotics.
Electrostatically Doped Junctionless Graphene Nanoribbon Tunnel Field-Effect Transistor for High-Performance Gas Sensing Applications: Leveraging Doping Gates for Multi-Gas Detection

In this paper, a new junctionless graphene nanoribbon tunnel field-effect transistor (JLGNR TFET) is proposed as a multi-gas nanosensor. The nanosensor has been computationally assessed using a quantum simulation based on the self-consistent solutions of the mode space non-equilibrium Green’s function (NEGF) formalism coupled with the Poisson’s equation considering ballistic transport conditions. The proposed multi-gas nanosensor is endowed with two top gates ensuring both reservoirs’ doping and multi-gas sensing. The investigations have included the IDS-VGS transfer characteristics, the gas-induced electrostatic modulations, subthreshold swing, and sensitivity. The order of change in drain current has been considered as a sensitivity metric. The underlying physics of the proposed JLGNR TFET-based multi-gas nanosensor has also been studied through the analysis of the band diagrams behavior and the energy-position-resolved current spectrum. It has been found that the gas-induced work function modulation of the source (drain) gate affects the n-type (p-type) conduction branch by modulating the band-to-band tunneling (BTBT) while the p-type (n-type) conduction branch still unaffected forming a kind of high selectivity from operating regime point of view. The high sensitivity has been recorded in subthermionic subthreshold swing (SS < 60 mV/dec) regime considering small gas-induced gate work function modulation. In addition, advanced simulations have been performed for the detection of two different types of gases separately and simultaneously, where high-performance has been recorded in terms of sensitivity, selectivity, and electrical behavior. The proposed detection approach, which is viable, innovative, simple, and efficient, can be applied using other types of junctionless tunneling field-effect transistors with emerging channel nanomaterials such as the transition metal dichalcogenides materials. The proposed JLGNRTFET-based multi-gas nanosensor is not limited to two specific gases but can also detect other gases by employing appropriate gate materials in terms of selectivity.