DSpace community: 工學院

Double Exponential Smoothing Slime Mould Algorithm For Disease Detection In Iot Healthcare System

Thu, 02 Jul 2026 04:05:25 GMT

title: Double Exponential Smoothing Slime Mould Algorithm For Disease Detection In Iot Healthcare System abstract: This paper presents an algorithm, called the double exponential smoothing slime mould algorithm (DeSSMA), which is formulated to train deep learning models for the precise detection of diseases in patients. The DeSSMA is designed by integrating the principles of double exponential smoothing with the slime mould algorithm. The parameters, including energy depletion, link lifetime (LLT), and distance, are considered by the proposed DeSSMA as objectives aimed at optimizing data routing efficiency. In the base station, a deep residual network (DRN) is trained using the proposed DeSSMA algorithm, which is utilized for disease detection following the processes of data preprocessing, augmentation, and feature selection. Finally, performance evaluation of the DeSSMA-DRN framework is conducted using metrics such as energy consumption, LLT, accuracy, sensitivity, specificity, and receiver operating characteristic. The findings reveal that the proposed framework achieved a minimal energy depletion rate of 0.412 (J), an LLT rate of 0.318, an increased accuracy rate of 0.959, a high sensitivity rate of 0.967, and a specificity rate of 0.931.

Role of Corporate Social Responsibility in the Financial Sustainability of Sports Organizations

Thu, 02 Jul 2026 04:05:23 GMT

title: Role of Corporate Social Responsibility in the Financial Sustainability of Sports Organizations abstract: Corporate social responsibility (CSR) has gradually more important for sports organizations as they seek to improve their financial sustainability. A growing number of sports organizations are recognizing the strategic value and potential benefits of CSR and are implementing CSR initiatives into their operations. The study explores the impact of CSR on the financial sustainability of sports organizations. Through a complete assessment of the literature and analysis of relevant data, the study examines the impact of various CSR initiatives, such as sponsorship and funding, charities through partnerships, organizational commitment, and stakeholder satisfaction on the financial sustainability of sports organizations. Furthermore, the study analyzes the impact of external factors, such as global economic recession and proper fund utilization on the financial sustainability of sports organizations. The findings reveal that proper implementation of CSR initiatives can positively impact the financial sustainability of sports organizations. CSR partnerships with charitable organizations can provide an effective way for sports organizations to improve their social profile, while also helping to generate revenue and financial stability. The study also highlights the significance of stakeholder satisfaction and organizational commitment towards CSR activities in improving financial sustainability. The outcome of this study underscores the importance of CSR in the financial sustainability of sports organizations and provides insights into how sports organizations can leverage CSR to improve their long-term financial sustainability while also benefiting society at large. The findings show that factors, such as sponsorship and funding, proper fund utilization, global economic recession, charities through CSR partnerships, organizational commitment to CSR activities, and stakeholder satisfaction have a significant impact on the financial improvement of sports organizations. The factor “improving social profile” doesn’t have an impact on the financial improvement of sports organizations.

探索細胞自動機在屋頂三維活動路徑系統建構之研究

Tue, 23 Jun 2026 04:06:19 GMT

title: 探索細胞自動機在屋頂三維活動路徑系統建構之研究

整合AI預測交通與街道活動之研究

Tue, 23 Jun 2026 04:06:17 GMT

title: 整合AI預測交通與街道活動之研究

Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial

Tue, 23 Jun 2026 04:06:11 GMT

title: Web-Based Personalized Machine Learning Recommendations to Enhance Shared Decision-Making in Prostate-Specific Antigen Screening: Randomized Controlled Trial abstract: Background: Prostate‑specific antigen (PSA) screening involves complex trade‑offs between early detection and the risks of overdiagnosis. For older adults (aged ≥50 years), shared decision‑making (SDM) is often hindered by limited health literacy, sensory or cognitive impairments, and multimorbidity, which complicate risk comprehension. Traditional decision aids provide foundational knowledge but are often nonpersonalized. Machine learning (ML) may offer individualized recommendations, yet the psychological and behavioral effects of ML‑assisted SDM in geriatric populations remain poorly characterized. Objective: This study aimed to develop and evaluate a web‑based, ML‑driven decision aid integrated into an SDM workflow to provide personalized PSA screening recommendations and to assess its effects on decisional conflict (primary outcome), state anxiety, and decision satisfaction among middle‑aged and older men. Methods: The study followed a 2‑stage design. First, a model establishment group (n=507) was used to train and evaluate 6 ML algorithms based on clinical and values‑clarification data. A random forest model was selected for its superior performance (mean area under the curve 0.933, SD 0.350; 95% CI 0.902-0.963). Second, a randomized controlled trial was conducted with 367 participants (mean age 64.34, SD 10.30 years) randomly assigned 1:1 to the ML suggestion group (MLSG; n=185) or the control group (CG; n=182). Both groups received video‑based education, counseling, and values clarification; only the MLSG received an ML‑generated "second opinion" recommendation. Primary and secondary outcomes were assessed using the Decisional Conflict Scale (DCS), Spielberger State‑Trait Anxiety Inventory (STAI), and Satisfaction with Decision scale. Results: In the randomized controlled trial (n=367), the MLSG reported significantly lower decisional conflict than the CG (total DCS score: mean difference [MD] -3.77, 95% CI -5.55 to -1.99; Cohen d=-0.44; P<.001). The MLSG reported greater perceived support (DCS7: adjusted P=.03), more adequate advice (DCS9: adjusted P<.001), and higher decision confidence (DCS10: adjusted P=.03; DCS11: adjusted P<.001). Regarding psychological well‑being, although total anxiety scores did not differ, the MLSG reported reduced worry (STAI item 6: MD -0.98, 95% CI -1.20 to -0.76; d=-0.89; adjusted P<.001) and increased calmness (STAI item 1: MD 0.30, 95% CI 0.06-0.54; d=0.25; adjusted P=.01). Decision satisfaction was higher in the MLSG across all items (total Satisfaction with Decision score: MD -7.38, 95% CI -8.54 to -6.18; P<.001). Behavioral choices were strongly influenced by the ML recommendation: participants in the MLSG who received an "accept" recommendation were more likely to select "accept" (34/67, 50.7%) than those in the CG (44/182, 24.2%; P<.001). When the system suggested "not now," only 17.8% (21/118) chose "accept," which was lower than in the CG. Conclusions: Integrating personalized ML recommendations into SDM workflows provides emotional scaffolding for older men, reducing decisional distress and enhancing confidence without undermining autonomy. By addressing geriatric‑specific vulnerabilities through a facilitated digital interface, this ML‑driven approach complements traditional clinical consultations. These findings support the scalable integration of artificial intelligence-assisted decision support to foster patient‑centered care in aging populations.

Sustainable beneficiation of high-ash Indian coal: phytochemical-facilitated demineralization and energy enhancement

Tue, 23 Jun 2026 04:06:09 GMT

title: Sustainable beneficiation of high-ash Indian coal: phytochemical-facilitated demineralization and energy enhancement abstract: Improving the quality of high-ash Indian coal remains a significant challenge for enhancing energy efficiency and reducing environmental impact. This study introduces a sustainable beneficiation approach utilizing aqueous stem extracts of Myristica malabarica as a green, plant-derived alternative to conventional chemical leaching agents. Phytochemical characterization confirmed the presence of polyphenols, flavonoids, and chalcones, compounds recognized for their potent chelating and surface-active properties. Bio-extract treatment effectively reduced coal ash content from 35.12% to 31.05%, representing a relative reduction of 11.6%. This demineralization led to significant enrichment in fixed carbon and a substantial increase in gross calorific value (GCV) from 4403 to 4958 kcal/kg. Structural analyses via XRD and FTIR indicated the partial degradation of key crystalline mineral phases, including quartz and kaolinite. SEM-EDS observations corroborated these findings, revealing notable surface decontamination and a significant decrease in the elemental concentrations of silicon and aluminum. These improvements are attributed to the synergistic effects of metal chelation and redox-mediated interactions between the phytochemical constituents and the coal mineral matrix.

An integrated iron electrolysis-electrooxidation for phosphorus recovery as ferric phosphate

Tue, 23 Jun 2026 04:06:07 GMT

title: An integrated iron electrolysis-electrooxidation for phosphorus recovery as ferric phosphate abstract: This study develops an integrated electrodissolution-electrooxidation (ED-EO) process for recovering phosphorus (P) as high-purity ferric phosphate (FePO4) from synthetic acidic phosphate-containing wastewater representative of industrial effluents from semiconductor manufacturing, fertilizer production, and lithium-ion battery-related industries. A sacrificial iron anode releases ferrous (Fe2+) via electro-dissolution (ED), while a Ti/RuO2-IrO2 anode generates in-situ reactive chlorine species (RCS) in the sodium chloride electrolyte to oxidize Fe2+ to ferric (Fe3+), enabling stoichiometric FePO4 precipitation without external oxidants. Process optimization demonstrated that controlled acidic conditions (pH 1.6–2.0), ED current densities of 2.55–7.60 mA/cm2, and Cl−:P molar ratio ≥ 1:1 achieved >99% phosphorus removal at Fe:P ≈ 1:1, with superior settleability (sludge volume index of 111 mL/g P). Iron generation aligned with Faraday's law initially, while prolonged operation at elevated current densities resulted in experimental Fe concentrations slightly exceeding theoretical predictions, suggesting the presence of parallel non-faradaic chemical dissolution pathways under acidic chloride conditions. Multi-technique characterization (XRD, SEM-EDS, and XPS) confirmed the formation of amorphous hydrated FePO4 that transformed into a crystalline, a high-purity FePO4 precursor material for potential lithium-ion battery applications upon calcination at 600 °C. Operational costs were primarily energy-driven and comparable to those of electrochemical vivianite production, while the higher-value FePO4 product (∼3.8 USD/kg) was produced. The reagent-free, single-compartment configuration enhances process controllability and scalability, offering a viable strategy for sustainable phosphorus recovery.

A Transformed Time Conformable-Type Slug Test Solution for Finite-Diameter Wells in Confined Aquifers: Verification, Identifiability, and Field Diagnostics

Tue, 23 Jun 2026 04:06:05 GMT

title: A Transformed Time Conformable-Type Slug Test Solution for Finite-Diameter Wells in Confined Aquifers: Verification, Identifiability, and Field Diagnostics abstract: Slug test interpretation can fail when measured recovery follows a time scale that differs from the classical Cooper–Bredehoeft–Papadopulos (CBP) finite-diameter well solution. This study derives a conformable slug test formulation by showing that a local weighted derivative converts the governing problem into the classical solution evaluated in transformed time. The formulation therefore does not introduce a nonlocal memory kernel; instead, it provides a reproducible diagnostic with one fitted exponent for testing power law time scaling while retaining the finite-diameter wellbore storage boundary condition. The solution is evaluated using double-precision Stehfest numerical inversion with 12 terms and is verified by the exact classical limit and by sensitivity tests on the number of inversion terms. Type curves, Morris sensitivity indices, objective function slices, synthetic benchmarks, and measured slug test data from the Minnelusa and Madison aquifer system near Spearfish, South Dakota, are used to evaluate the added exponent. A benchmark with an exponent above one recovered fitted exponents of 1.397 without noise and 1.417 under Gaussian noise with a standard deviation of 0.01. Field fitting over exponents from 0.5 to 2.0 reduces root mean square error and information criteria relative to the classical model for the analyzed datasets, especially the LA-88B pressure tests. However, exponents above one are interpreted only as accelerated transformed time behavior, not as conventional fractional orders or unique physical mechanisms. Comparison with a published semi-analytical slug test model that represents near-well formation damage and non-Darcy flow for the same field dataset supports using the conformable exponent as a diagnostic indicator of time-scale mismatch alongside mechanistic slug test models.

Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks

Tue, 23 Jun 2026 04:05:47 GMT

title: Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks abstract: In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green’s function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc.

Microwave Imaging for Half-Space Conductors Using the Whale Optimization Algorithm and the Spotted Hyena Optimizer

Tue, 23 Jun 2026 04:05:44 GMT

title: Microwave Imaging for Half-Space Conductors Using the Whale Optimization Algorithm and the Spotted Hyena Optimizer abstract: This research implements the whale optimization algorithm (WOA) and spotted hyena optimizer (SHO) in inverse scattering to regenerate the conductor shape concealed in the half-space. TM waves are irradiated from the other half-space to a perfect conductor with an unknown shape buried in one half-space. The scattered field measured outside the conductor surface with the boundary condition is used to reconstruct the object using the WOA and SHO algorithms. Several scenarios of reconstruction accuracy were compared for the WOA and SHO. The numerical simulations prove that the WOA has a better reconstruction capability.

Comparison of U-Net and OASRN Neural Network for Microwave Imaging

Tue, 23 Jun 2026 04:05:42 GMT

title: Comparison of U-Net and OASRN Neural Network for Microwave Imaging abstract: U-Net and Object-Attentional Super-Resolution Network (OASRN) neural network for electromagnetic imaging are compared and investigated in this paper. The outcome shows that though under limited training data, the regeneration capability is still highly reliable. We first transmit the electromagnetic waves to the scatterer and use the received scattered field information to calculate the estimated permittivity distribution by Green’s function, subspace method and Dominant Current Scheme (DCS). The estimation technique can effectively reduce the training process of the neural network modules. Next, we train the U-Net and OASRN modules for real-time images. Lastly, we used Root Mean Square Error (RMSE) and Structural Similarity Index Measure (SSIM) to compare and analyze the reconstructed images of the two neural networks. Numerical results show that the reconstructed image by OASRN is better than that by U-net with 5% or 20% Gaussian noise for different dielectric constant distributions.

Electromagnetic imaging of Uniaxial objects by Artificial Intelligence Technology

Tue, 23 Jun 2026 04:05:40 GMT

title: Electromagnetic imaging of Uniaxial objects by Artificial Intelligence Technology abstract: The electromagnetic (EM) imaging of uniaxial objects by the artificial intelligence (AI) technology is presented in this article. We study the 2-D inverse scattering problem from uniaxial objects illuminated by the transverse magnetic (TM) and transverse electric (TE) polarized incident waves. As the uniaxial objects have different components of permittivity along different transverse directions, the problem of TE polarization will be more severe than that of TM polarization. We use the dominant current scheme (DCS) and backpropagation scheme (BPS) to calculate the preliminary permittivity distribution. By combining with deep learning and neural networks, the permittivity distribution of those uniaxial objects can be reconstructed more accurately. U-Net is used to reconstruct the permittivity distribution because U-Net has shared the weights and biases, which can effectively reduce the network complexity and is very suitable for solving image processing problems. In the numerical results, we added different noises to compare the reconstruction results of the DCS and BPS initial estimations through the U-Net. Numerical results show that the reconstruction permittivity for the DCS initial estimation is better than that for the BPS initial estimation. Our diversity is that we have reconstructed the uniaxial objects by neural network successfully with less time-consuming effort and real-time imaging.

Wi-Fi 6E Antenna Design for All Metal Housing of Notebook

Tue, 23 Jun 2026 04:05:38 GMT

title: Wi-Fi 6E Antenna Design for All Metal Housing of Notebook abstract: In this paper, we present two antenna structures with the Wi-Fi 6E band, namely, dual-slot and single-slot antennas. The presented antennas can be applied for the all-metal housing on notebook computers and also meet the requirements of notebook computer antenna design for industry. First, we introduce the design of the dual-slot antenna at the top of the metal case. The size of the dual-slot antenna was 53 × 6 × 0.6 mm3. To meet the specifications of commercially available 13-inch laptops, we chose a 305 × 205 × 1 mm3 metal case in the simulation environment. To make our proposed antenna design meet the requirement that the reflection coefficient of the Wi-Fi 6E frequency band is lower than -10 dB, we adjusted the grounded parasitic element and antenna structure to achieve the coupling effect of the slot. This antenna achieved a high screen-to-body ratio and narrow bezels and conformed with current notebook design trends. Next, because some laptops have IR cameras that require a smaller antenna, we introduce the single-slot antenna for use above the metal case. The size of the simulated metal case was also 305 × 205 × 1 mm3, and a monopole antenna with dimensions of 30 × 4.5 × 0.6 mm3 was used. By bending the geometry of the slot antenna, it can be modified to change the coupling effect. The antenna not only achieved a high screen-to-body ratio and narrow bezels, but was also smaller than the dual-slot antenna. The two proposed antenna architectures have the advantage of compactness, with the need to open only one or two slots on the metal case of a notebook computer for the Wi-Fi 6E band.

Beamforming Relay for Millimeter-wave SWIPT System

Tue, 23 Jun 2026 04:05:36 GMT

title: Beamforming Relay for Millimeter-wave SWIPT System abstract: This research aims to develop an ultra-wideband millimeter-wave system with Simultaneous Wireless Information and Power Transfer (SWIPT), and Wireless Power Transmission (WPT) for the relays. As a large attenuation of a millimeter-wave path requires beamforming technology and relay for transmission, high- and low-resolution phase adjustments are used for optimizing beamforming. And a multi-objective function with the preset highest Bit Error Rate (BER) for SWIPT is presented. We discovered that the optimization of the beamforming by applying the adaptive differential algorithm has increased the harvesting power. To do so, we optimize the radiation pattern to meet the BER constraint for SWIPT and increase the harvested power ratio for the system simultaneously. In other words, our algorithms focus on increasing the harvesting power as soon as the information criteria is achieved. With WPT and SWIPT, the ratio of the total energy harvesting for the high-resolution array antennas is two times larger than that for the low-resolution ones. Numerical results also show that the harvesting power for the relay pointing to multiple targeted antennas simultaneously is about two times larger than that of pointing to each antenna by the time division techniques.

Optimization for Indoor 6G Simultaneous Wireless Information and Power Transfer System

Tue, 23 Jun 2026 04:05:34 GMT

title: Optimization for Indoor 6G Simultaneous Wireless Information and Power Transfer System abstract: Antenna beamforming for Simultaneous Wireless Information and Power Transfer (SWIPT) and Wireless Power Transfer (WPT) in an indoor 6G communication system is presented in this paper. The objective function is to maximize the total harvesting power for the SWIPT and WPT nodes with the constraints of the bit error rate and minimum harvesting power. In the study, the power-splitting ratio between harvesting power and decoding information can be adjusted for the SWIPT node. Due to the non-convex problem, we use Self-Adaptive Dynamic Differential Evolution (SADDE) to optimize the designed multi-objective function. We use a symmetric antenna array to study three situations of distance—closer, farther, and similar—between the transmitting antenna and the individual SWIPT and WPT nodes in this paper. Experimental results show that the overall harvesting efficiency is improved, especially in the case of SWIPT nodes closer to the transmitter. The total harvesting power can be improved by 86.7% in the total short-distance case, and by 7.87% in the total long-distance case.

Different Object Functions for SWIPT Optimization by SADDE and APSO

Tue, 23 Jun 2026 04:05:31 GMT

title: Different Object Functions for SWIPT Optimization by SADDE and APSO abstract: Multiple objective function with beamforming techniques by algorithms have been studied for the Simultaneous Wireless Information and Power Transfer (SWIPT) technology at millimeter wave. Using the feed length to adjust the phase for different objects of SWIPT with Bit Error Rate (BER) and Harvesting Power (HP) are investigated in the broadband communication. Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Self-Adaptive Dynamic Differential Evolution (SADDE) and Asynchronous Particle Swarm Optimization (APSO) are used to optimize the feed length of the antenna array. Two different object functions are proposed in the paper. The first one is the weighting factor multiplying the constraint BER and HP plus HP. The second one is the constraint BER multiplying HP. Simulations show that the first object function is capable of optimizing the total harvesting power under the BER constraint and APSO can quickly converges quicker than SADDE. However, the weighting for the final object function requires a pretest in advance, whereas the second object function does not need to set the weighting case by case and the searching is more efficient than the first one. From the numerical results, the proposed criterion can achieve the SWIPT requirement. Thus, we can use the novel proposed criterion (the second criterion) to optimize the SWIPT problem without testing the weighting case by case.

UWB非接觸式偵測高齡者久坐行為之理念實踐

Tue, 26 May 2026 04:05:20 GMT

title: UWB非接觸式偵測高齡者久坐行為之理念實踐

Phase Optimization Scheme of RIS-assisted MC-OTFS Wireless Communication Systems

Tue, 26 May 2026 04:05:15 GMT

title: Phase Optimization Scheme of RIS-assisted MC-OTFS Wireless Communication Systems

Chloride-assisted electro-oxidation for phosphorus recovery from acidic wastewater via ferric phosphate precipitation

Tue, 26 May 2026 04:05:09 GMT

title: Chloride-assisted electro-oxidation for phosphorus recovery from acidic wastewater via ferric phosphate precipitation abstract: Recovering phosphorus from acidic wastewater remains challenging because the highly soluble phosphate is complex to precipitate. This study presents an electro-oxidation precipitation route that converts ferrous (Fe(II)) to ferric (Fe(III)) using in-situ generated reactive chlorine species (RCS), enabling efficient ferric phosphate formation at low pH. The roles of Fe(II) sources, pH, current density, and Fe(II):P ratios were systematically evaluated to clarify the governing mechanism and optimize process performance. In chloride-containing systems, RCS accelerated Fe(II) conversion and enhanced phosphorus removal, achieving up to 98% recovery with a clear correlation between oxidation-reduction potential (ORP) and Fe(II) conversion. pH control (1.7–1.9) improved process stability, minimized competing reactions, and significantly enhanced sludge settleability. Higher current densities shortened the reaction time for complete Fe(II) oxidation, while increased Fe(II):P ratios compensated for Fe loss at the cathode, maintaining the stoichiometric 1:1 Fe:P precipitation behavior. The process costs more than electrochemical crystallization process that produces vivianite (E-Vivianite) due to high energy and iron expenses, but optimization and ferric phosphate's higher value yield economic benefits for acidic wastewater and batteries. Characterization of the recovered solids confirmed the transformation of amorphous FePO4 into a highly crystalline, thermally stable form of FePO4 after calcination, making it suitable for energy-storing applications. Overall, this work demonstrates a robust strategy for phosphorus removal from highly acidic wastewater while producing value-added ferric phosphate materials.

Edge-Aware, Data-Efficient Fine-Tuning of Progressive GANs for Multiband Antennas

Thu, 21 May 2026 04:05:10 GMT

title: Edge-Aware, Data-Efficient Fine-Tuning of Progressive GANs for Multiband Antennas abstract: This study proposes a data-efficient fine-tuning strategy for multi-band antenna synthesis using a Wasserstein Auxiliary-Guided Progressive Growing GAN (WAG-PGGAN). Starting from a pretrained 512 × 512 dual-band PIFA-like generator trained on 4180 samples at 2.45/5.2 GHz, we introduce three 3.5-GHz wideband seeds augmented to 836 images (new:legacy ≈ 1:5) and fine-tune only the highest-resolution stage on the combined 5016-image corpus. A Hough-transform-based edge-enhancement module with an edge-aware loss preserves conductor boundaries and strengthens frequency–geometry correlation. Across n = 8 fabricated prototypes, all achieve |S11| < −10 dB and collectively span 1.86–5.83 GHz; measured total efficiencies are 52–87% (e.g., 73.6% @ 2.68 GHz, 66.7% @ 3.56 GHz, 69.0% @ 5.83 GHz), with radiation patterns consistent with simulation. The method retains prior 2.45/5.2 GHz performance while adding 3.5-GHz wideband behavior using ≤ 17% new data (836/5016), demonstrating effective transfer from small datasets. On an RTX 3060 Ti, inference is ≈ 3 s/design after ~192 h of training. Simulation–measurement agreement confirms that fine-tuned WAG-PGGAN yields high-resolution, physically valid multi-band antennas with reduced data and computational cost.

應用PGGAN和增強特徵映射的機器學習於雙頻天線設計

Thu, 21 May 2026 04:05:08 GMT

title: 應用PGGAN和增強特徵映射的機器學習於雙頻天線設計 abstract: This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual-band PIFA-like antenna structures. The process involves using data augmentation methods to generate 4180 antenna samples. In the latent space, the authors employ Latin Hypercube Sampling to maintain diversity and combine it with the Hough Transform to enhance the edge features of the antennas while providing labelling functionality. This labelling method strengthens the relationship between antenna frequency and wavelength characteristics. The paper clearly outlines the design process, starting from the simulation of two types of single-frequency PIFA-like antennas (2.45 and 5.2 GHz, respectively) to the completion of PGGAN's generation task, resulting in a novel dual-band Wi-Fi PIFA-like antenna structure. The measurement results of the dual-band antennas exhibit excellent consistency with the simulation results.

Automatic BIM-Based Formwork Quantification System

Tue, 19 May 2026 04:05:13 GMT

title: Automatic BIM-Based Formwork Quantification System abstract: Building information modeling (BIM) has significantly enhanced the efficiency of quantity takeoff in construction projects. However, existing BIM-based tools for formwork quantification often lack critical functionalities particularly in handling element intersections, irregular geometries, and practical construction considerations. In addition, many current systems rely on manual reprocessing or require multiple software platforms to reflect updates to the BIM model, which limits their effectiveness in dynamic project environments characterized by frequent design revisions. This study presents the development of a fully automated BIM-based formwork quantification system (BFQS), created in collaboration with a general contractor and validated through real-world implementation. The system focuses specifically on quantifying formwork areas. BFQS operates within a single, integrated Revit-based platform and enables automatic, real-time updates of formwork quantities in both two-dimensional (2D) and three-dimensional (3D) environments. It incorporates advanced modeling logic to accommodate diverse on-site conditions, including stair formwork, intersection deductions, small openings, soft intersections, irregular geometries, and the exclusion of nonformwork elements such as lightweight partitions. By maintaining continuous synchronization with the BIM model, the system effectively facilitates project workflows where frequent design revisions occur, thereby enhancing accuracy and responsiveness throughout the design and construction phases. BFQS addresses key limitations of previous tools by incorporating practical parameter settings that reflect real-world construction conditions, contributing meaningfully to the advancement of BIM-based automation in formwork estimation.

Integration of AR and deep learning-based image classification using CNN for construction project monitoring.

Tue, 19 May 2026 04:05:10 GMT

title: Integration of AR and deep learning-based image classification using CNN for construction project monitoring. abstract: With manual update, progress monitoring of a construction project is notably challenging due to its labor-intensive and time-consuming nature. The issue of manual update has been proposed to be alleviated by automatic update, but most existing AI-based progress tracking approaches are based on counting the elements finished. As a result, they cannot accurately reflect actual progress status to the project management. This research aims to develop a system that updates the progress of the construction project by effectively identifying both the construction category and operational stage. It is achieved by systematically integrating the techniques of Augmented Reality (AR) and deep learning–based image classification with convolutional neural networks (CNNs). The proposed scheme enables real-time data capture, facilitating comprehensive progress evaluation across multiple project locations. Its effectiveness is demonstrated using a case study of an interior finishing project for a building in Tamkang University, Taiwan. It reveals that the proposed system accurately identifies the construction category and operational stage based on the materials applied, enabling precise tracking of the progress. The results confirm that the proposed scheme significantly enhances the accuracy in construction progress monitoring, avoiding manual inspection and minimizing discrepancies between the planned and actual progress.

Energy-dependent carrier masses of zinc blende semiconductors: Solved using the Kane approximation

Fri, 15 May 2026 04:05:21 GMT

title: Energy-dependent carrier masses of zinc blende semiconductors: Solved using the Kane approximation abstract: The mathematical expressions for the effective carrier masses of zinc blende semiconductors are derived, with particular emphasis on their dependence on carrier energy. In this work, Hamiltonian matrix diagonalization is employed to obtain the band energies, followed by the Kane approximation to derive analytical expressions for energy-dependent effective masses. Unlike conventional approaches that assume constant effective mass near the band edge, the present formulation explicitly accounts for band non-parabolicity. InAs and GaAs are used as representative examples to verify consistency between the derived expressions and calculated band structures. The results provide a physically transparent and analytically tractable framework for modeling carrier transport in high-speed electronic and optoelectronic devices

Toward deployment-oriented long legal document classification: a segmentation-based framework for distributed legal evidence integration

Thu, 14 May 2026 04:05:13 GMT

title: Toward deployment-oriented long legal document classification: a segmentation-based framework for distributed legal evidence integration

Recovery of Fluoride from Fluorine Industrial Wastewater: Recent Technologies, Challenges and Perspectives

Fri, 01 May 2026 04:05:37 GMT

title: Recovery of Fluoride from Fluorine Industrial Wastewater: Recent Technologies, Challenges and Perspectives abstract: Industrial wastewater is a significant environmental concern due to its diverse range of pollutants, of which fluoride is notably prevalent due to its widespread use in various industries (e.g., aluminum production, semiconductor manufacturing, glass etching, etc.). This chapter provides a comprehensive review of recent advancements in the recovery and removal of fluoride. We introduced the primary sources of industrial wastewater, emphasizing the industries that contribute the most to fluoride contamination. The core of the chapter delves into the technologies developed for fluoride recovery and removal. These technologies are crucial not only for mitigating environmental pollution but also for addressing the economic need to recover fluoride for reuse. The discussion covers various methods, chemical coagulation, ion exchange, membrane filtration, electrodialysis, electrocoagulation, crystallization, and hybrid processes evaluating each technique’s efficiency, cost-effectiveness, and environmental impact. Particular attention is given to innovative approaches and emerging technologies that promise enhanced performance and sustainability. We also highlight the necessity of balancing these factors to select the most appropriate method for specific industrial applications. Looking ahead, the chapter offers perspectives on future research directions. This chapter thus serves as an essential resource to comprehend and tackle the intricacies of fluoride extraction and recovery in industrial effluents.

Development of a structural health monitoring platform for a building structure

Fri, 01 May 2026 04:05:31 GMT

title: Development of a structural health monitoring platform for a building structure abstract: Frequent earthquakes in Taiwan highlight the urgent need for reliable post-earthquake damage assessment. Advances in lightweight sensors, fast data collection, and wireless transmission make structural health monitoring (SHM) an effective tool for building safety. This study develops an SHM platform for a multipurpose public housing building, integrating seismic sensing, system identification, and building information modelling (BIM). A frequency-domain recursive hybrid genetic algorithm was applied to identify structural parameters for damage diagnosis. Seismic records were analyzed to capture time-varying dynamics, generate diagnostic reports, and compare results from different events. On the other hand, a rapid screening method using interstory drift angles provided preliminary assessments and supported warning signals. The platform includes three modules: (1) BIM visualization of structure and sensor data, (2) real-time monitoring with quick damage screening, and (3) automated reporting with stiffness-based indicators for repair or retrofit decisions. The framework shows the potential of combining SHM and BIM to strengthen seismic resilience and the life-cycle management of buildings.

Decoupled Detection and Category-Level 6D Pose Estimation for Robot Grasping

Thu, 30 Apr 2026 04:05:39 GMT

title: Decoupled Detection and Category-Level 6D Pose Estimation for Robot Grasping abstract: 6D object pose estimation is an essential component for robotic grasping. Most existing deep learning-based approaches focus on instance-level pose estimation, which requires prior object models and consequently limits their applicability on unseen objects in real-world scenarios. In contrast, category-level 6D pose estimation adopts Normalized Object Coordinate Space (NOCS) maps to represent intra-class object geometry, enabling pose prediction without relying on predefined object models and thus improving generalization to unseen instances. However, the original NOCS-based category-level framework typically trains NOCS prediction and object classification in a joint manner, which introduces NOCS regression error among inter-class instances with similar appearances, thereby degrading pose estimation accuracy. To address this issue, we integrate the YOLOv8 object detection with SegFormer and propose a novel Category-Level SegFormer for 6D Object Pose Estimation (CLSF-6DPE). By decoupling object classification from NOCS regression through independent learning branches, the proposed framework significantly improves pose estimation performance. Furthermore, we validate the practical feasibility of CLSF-6DPE by integrating it with a robotic gripper via the Robot Operating System (ROS) in a Real-World grasping setup. Experimental results on the CAMERA and Real-World datasets demonstrate that the proposed method achieves mAP scores of 93.8% and 81.1%, respectively. Overall, the proposed method provides a modular and effective solution for category-level pose estimation in real-world robotic grasping applications.

Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios

Tue, 28 Apr 2026 04:06:07 GMT

title: Integrating deep learning and groundwater dynamics for drought vulnerability assessment under climate scenarios abstract: Drought increasingly threatens agricultural sustainability, particularly in groundwater-dependent regions where irrigation and aquifer recharge are closely linked. Taiwan's Zhuoshui River alluvial fan exemplifies this risk: long-term intensive pumping and rising climate extremes have amplified drought vulnerability. Yet most existing drought indices treat groundwater implicitly, and many AI studies focus on groundwater prediction without translating results into integrated vulnerability metrics. This study develops an AI-driven framework to assess future drought risk from climate, groundwater, and socio-environmental drivers. Groundwater level was predicted using a hybrid Convolutional Neural Network–Backpropagation model (CNN-BP) calibrated with 22 years of basin-wide gridded precipitation, temperature, and SPI data, together with groundwater levels from 18 monitoring wells. CNN-BP outperforms a BPNN benchmark, improving the correlation coefficient by 35.85% and reducing MAE by 19.51%, enabling robust projections for 2021–2100. These groundwater forecasts are then integrated with climatic (SPI), physiographic (soil, land use, elevation, slope, distance to river) and socio-economic (population) drivers to construct the Deep Learning-based Comprehensive Drought Vulnerability Indicator (DCDVI) under SSP1-2.6 and SSP5-8.5. Scenario results indicate consistent intensification of drought vulnerability relative to the historical baseline. SSP1-2.6 yields milder drought conditions and slower groundwater decline, while SSP5-8.5 leads to stronger drying and higher vulnerability. Under SSP5-8.5, highly vulnerable areas increase from 27.31% to 41.26% by 2081–2100. Overall, DCDVI provides a scalable, climate-responsive indicator that converts AI-based groundwater forecasts into actionable vulnerability maps. The framework provides a transferable decision-support tool for drought-prone, groundwater-reliant farming systems under climate change.

A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins

Tue, 28 Apr 2026 04:06:05 GMT

title: A CNN-transformer framework for air quality forecasting to support aeolian dust management in river basins abstract: Accurately forecasting riverbed aeolian dust emissions (PM10) in complex watershed environments is a critical engineering challenge, shaped by the intricate interdependencies among hydrometeorological factors, land surface dynamics, and anthropogenic pollution sources. Traditional models often struggle to capture these nonlinear interactions, limiting their utility for real-time environmental decision-making. This study presents a novel hybrid deep learning framework—combining a 3D Convolutional Neural Network (CNN), dual 1D CNNs, and a Transformer architecture—to enhance the predictive accuracy and interpretability of PM1110 forecasts in Taiwan’s Jhuoshuei River Basin. The model harnesses the spatial feature extraction of the 3D CNN, temporal pattern recognition of the 1D CNNs, and long-range dependency modeling of the Transformer to learn complex, multiscale relationships across diverse environmental variables. Extensive quantitative and qualitative evaluations demonstrate the model’s superior performance over conventional approaches, particularly in capturing seasonal variability and the mitigating effects of water infrastructure (e.g., Jiji Weir discharge) on dust emissions. The model effectively anticipates pollution peaks, offering critical lead time for the implementation of targeted interventions such as reservoir releases or dust suppression. Beyond technical innovation, this research provides actionable insights into the dynamic coupling of atmospheric, hydrological, and operational factors. The model’s scalability and generalizability position it as a robust decision-support tool for engineers, environmental managers, and policymakers. By bridging AI-driven modeling with practical engineering applications, this study advances the field of environmental informatics and supports the development of adaptive, knowledge-based systems for sustainable air quality and watershed management.

A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management

Tue, 28 Apr 2026 04:06:00 GMT

title: A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management abstract: Climate-induced extreme hydrological events threaten irrigation water resources and crop production. Groundwater serves as a vital source of irrigation during periods of surface water scarcity; however, excessive and unsustainable abstraction has resulted in land subsidence. While reducing groundwater over-extraction can alleviate this issue, it may also compromise agricultural productivity, particularly during drought conditions. To address this, a reliable assessment tool is needed to balance sustainable groundwater extraction and agricultural productivity. This study develops a groundwater level prediction model using the extreme gradient boosting (XGB) algorithm, employing power consumption, precipitation, and groundwater level data as input features. Bayesian optimization was used to determine the best-fit hyperparameters, resulting in RMSE, MAE, and R² values ranging from 0.923 to 2.497 m, 0.709–2.132 m, and 0.057–0.914, respectively, during model validation. Model testing from January 2022 to June 2023 showed a strong correlation between monitored and predicted levels, indicating effective trend capture, despite slight overestimations during the dry seasons. Scenario predictions showed that a 50 % reduction in power consumption for double-crop rice led to groundwater level increases of 0.41–2.31 m in the wet season and 0.54–2.52 m in the dry season, maintaining safe thresholds. However, current fallowing subsidies recover only a fraction of the economic profit from rice production, limiting policy adoption. To improve long-term effectiveness, this study recommends institutionalizing adaptive fallowing policies, such as seasonally adjusted quotas based on real-time groundwater and rainfall indicators, and tiered subsidy schemes according to groundwater risk levels. Embedding these tools within broader agricultural governance frameworks can enhance policy responsiveness and sustainability. The proposed model supports both short-term decision-making and long-term climate-informed groundwater management by balancing environmental protection with food security and economic viability.

Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan's largest alluvial fan

Tue, 28 Apr 2026 04:05:58 GMT

title: Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan's largest alluvial fan abstract: Groundwater is crucial for food security and economic development, yet it faces growing threats from over-extraction and extreme weather events. The Zhuoshui River alluvial fan, Taiwan’s largest, has long served as a key water source. However, recent climate change and industrial expansion have significantly affected groundwater recharge and quality, contributing to land subsidence. Accurate forecasting of groundwater levels is essential to ensuring environmental sustainability in the region. This study presents a novel hybrid deep learning model, CNN-BP, which integrates Convolutional Neural Networks (CNN) with Backpropagation Neural Networks (BPNN) to forecast groundwater levels three days in advance at 25 monitoring stations across the Zhuoshui River alluvial fan. The CNN-BP model was benchmarked against a standalone BPNN model. Both models were trained on a dataset of 7,291 daily hydro-geo-meteorological records from 2000 to 2019, including groundwater levels, rainfall, streamflow, temperature, evaporation, and lithology. The study emphasizes comprehensive input selection, feature extraction, and hyperparameter tuning, with Random Forest utilized to filter input factors from 20 rainfall stations, thereby improving forecast accuracy and reliability. The CNN-BP model significantly outperformed the BPNN model, achieving R2 values between 0.94 and 0.98 across various stations and effectively mitigating time-delay issues. The study also explored the relationship between forecast errors and the fan’s lithological characteristics, providing valuable insights for land-use planning and groundwater management. Validation during Typhoons Haitang and Maria further demonstrated the model’s capability to predict groundwater recharge under intense rainfall conditions. By integrating environmental and social factors such as drought frequency, population density, and recharge potential, this study underscores the need for targeted water management strategies. The findings offer critical insights for future regional approaches to groundwater management, promoting sustainable practices across watersheds. Ultimately, this study serves as a valuable resource for informed decision-making in land-use planning and water resource management, advancing the sustainable utilization of groundwater in the Zhuoshui River alluvial fan.

Intelligent Urban Flood Management Using Real-Time Forecasting, Multi-Objective Optimization, and Adaptive Pump Operation

Tue, 28 Apr 2026 04:05:56 GMT

title: Intelligent Urban Flood Management Using Real-Time Forecasting, Multi-Objective Optimization, and Adaptive Pump Operation abstract: Climate-induced extreme rainfall events are increasing the intensity and frequency of flash floods, highlighting the urgent need for advanced flood management systems in climate-resilient cities. This study introduces an Intelligent Flood Control Decision Support System (IFCDSS), a novel AI-driven solution for real-time flood forecasting and automated pump operations. The IFCDSS integrates multiple advanced tools: machine learning for rapid short-term water level forecasting, NSGA-III for multi-objective optimization, the TOPSIS for robust multi-criteria decision-making, and the ANFIS for real-time pump control. Implemented in the flood-prone Zhongshan Pumping Station catchment in Taipei, the IFCDSS leveraged real-time sensor data to deliver accurate water level forecasts within five seconds for the next 10–30 min, enabling proactive and informed operational responses. Performance evaluations confirm the system’s scientific soundness and practical utility. Specifically, the ANFIS achieved strong accuracy (R2 = 0.81), with most of the prediction errors being limited to a single pump unit. While the conventional manual operations slightly outperformed the IFCDSS in minimizing flood peaks—due to their singular focus—the IFCDSS excelled in balancing multiple objectives: flood mitigation, energy efficiency, and operational reliability. By simultaneously addressing these dimensions, the IFCDSS provides a robust and adaptable framework for urban environments. This study highlights the transformative potential of intelligent flood control to enhance urban resilience and promote sustainable, climate-adaptive development.

AI-driven weather downscaling for smart agriculture using autoencoders and transformers

Tue, 28 Apr 2026 04:05:53 GMT

title: AI-driven weather downscaling for smart agriculture using autoencoders and transformers abstract: Artificial Intelligence (AI) is reshaping agriculture by driving smarter, data-driven practices that enhance regional weather forecasting and support proactive, informed decision-making. Advances in Big Data, IoT, Remote Sensing, and Machine Learning are accelerating this transformation, with Transformer architectures increasingly pivotal in refining agricultural management strategies, especially in Taiwan. In this study, we develop a hybrid Convolutional Autoencoder and LSTM-based Transformer Network (CAE-LSTMT) to downscale six-hour simulation data into precise hourly forecasts, validated using 55,538 temperature and relative humidity records (2020–2023) from Taiwan’s Jhuoshuei River basin, provided by the Central Weather Administration (CWA). The model was trained (70 %), validated (10 %), and tested (20 %) to optimize its configuration and performance. This CAE-LSTMT model substantially enhances spatiotemporal weather forecast resolution, transforming six-hour regional data into hourly forecasts with improved accuracy. It yields temperature forecast gains of 5.66 % to 20.39 % and relative humidity improvements of 8.05 % to 12.76 %, with reduced forecast biases compared to traditional LSTM models. The model demonstrates exceptional accuracy in vapor pressure deficit (VPD) predictions, achieving mean absolute errors (MAE) between 0.15 to 0.21 kPa across regions and 0.16 to 0.20 kPa seasonally, significantly outperforming the CWA model. Accurate VPD forecasts allow farmers to manage irrigation and minimize crop stress, directly supporting plant health and yield optimization. For heat index classification, the model achieves up to 96 % ACCURACY, with mean absolute percentage errors (MAPE) of 4 % to 23 %, significantly exceeding the CWA model’s ACCURACY range of 35 % to 79 % and MAPE of 29 % to 70 %. This high precision in heat index forecasting empowers farmers to protect crops and livestock against heat stress. By extracting critical features from high-dimensional data, the CAE-LSTMT model advances environmental downscaling for multi-site, multi-horizon weather data, showing significant promise for Smart Agriculture and Health Advisory Systems. This approach offers precise, actionable forecasts, optimizing agricultural practices and reducing climate-related risks, underscoring its impact on sustainable agricultural and environmental management.

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system

Tue, 28 Apr 2026 04:05:50 GMT

title: Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system abstract: The escalating impacts of climate change have intensified extreme rainfall events, placing urban drainage systems under unprecedented pressure and increasing flood risks. Addressing these challenges requires advanced flood mitigation strategies, optimized sewer operations, and responsive disaster management. This study leverages knowledge graphs to integrate diverse data sources, providing a comprehensive perspective on flood dynamics, and applies deep learning models within a Real-Time Urban Drainage Early Warning System to enhance flood management at Taipei City's Zhongshan Pumping Station in Taiwan. We proposed deep learning models, specifically Convolutional Neural Networks combined with Back Propagation Neural Networks (CNN-BP), to make multi-input multi-output multi-step (MIMOMS) forecasts on sewer water levels at intervals from 10 to 40 min (T+1 to T+4) and MIMO forecasts on the pumping station's internal (forebay) and external (river) water levels at intervals from 10 to 60 min (T+1 to T+6). The CNN-BP model exhibited superior forecast accuracy, reaching an R2 (RMSE) of 0.97 (0.08m) at T+1 for sewer water levels and an R2 (RMSE) of 0.99 (0.06m) at T+1 for both internal and external water levels. These results highlight CNN-BP's capability to accurately capture water level trends, ensuring reliable real-time responsiveness, especially during intense and sudden rainfall events. The CNN-BP's high predictive accuracy enables enhanced pump operations, strengthens early warning systems, and fosters intelligent flood control practices crucial for effective environmental management.

Chloride-assisted electro-oxidation for phosphorus recovery from acidic wastewater via ferric phosphate precipitation

Tue, 28 Apr 2026 04:05:46 GMT

Improving Subseasonal Typhoon Forecasts Using Global Ensemble Models and a Probabilistic Formation Index from Deep Learning

Mon, 20 Apr 2026 04:05:40 GMT

title: Improving Subseasonal Typhoon Forecasts Using Global Ensemble Models and a Probabilistic Formation Index from Deep Learning

Application of AI Techniques to Develop a Probabilistic and Region-Specific Week-2 Typhoon Formation Index Based on Large-Scale Environmental Factors

Mon, 20 Apr 2026 04:05:39 GMT

title: Application of AI Techniques to Develop a Probabilistic and Region-Specific Week-2 Typhoon Formation Index Based on Large-Scale Environmental Factors

Improving Situation-Dependent Uncertainty Estimation in Tropical Cyclone Track Forecasts Using Encoder-Decoder Neural Networks.

Mon, 20 Apr 2026 04:05:36 GMT

title: Improving Situation-Dependent Uncertainty Estimation in Tropical Cyclone Track Forecasts Using Encoder-Decoder Neural Networks.

Verifications of Week-1 to Week-4 Tropical Cyclone Forecasts in the Western North Pacific from the ECMWF 46-Day Ensemble.

Mon, 20 Apr 2026 04:05:30 GMT

title: Verifications of Week-1 to Week-4 Tropical Cyclone Forecasts in the Western North Pacific from the ECMWF 46-Day Ensemble.