Optimal Size Research Articles

Renewable hydrogen production through electrolysis: An analysis of the cost gap for its economic competitiveness in Italy

With global policies targeting greenhouse gas emission reduction, renewable hydrogen produced by water electrolysis powered with renewable electricity appears a promising alternative to fossil fuels. However, still high capital costs, reliance on intermittent renewable electricity sources, and high electricity cost hinder the economic viability of electrolyzers. This work develops a comprehensive analysis for assessing the need of an incentive to make renewable hydrogen economically viable, identifying different archetypes of electrolysis plant representative of a wide range of applications. A techno-economic optimization model is set up that minimize the average hydrogen production cost and compute the cost gap that makes hydrogen economically competitive with the fossil alternative, defining the optimal size and operation of the plant components while satisfying the hydrogen demand. The model is applied to the Italian case study. Sensitivity analyses investigate the impact of economy of scales, renewable electricity availability and introduction of the emission trading system. Cost gaps ranging between 8.1 €/kg and 29.9 €/kg highlights the need of an incentive to allow the deployment of this technology, thus meeting the hydrogen penetration targets.

Quasi-optical design for the cross-polarization scattering diagnostic on the HL-3 tokamak.

As the plasma beta (β) increases in high-performance tokamaks, electromagnetic turbulence becomes more significant, potentially constraining their operational range. To investigate this turbulence, a cross-polarization scattering (CPS) diagnostic system is being developed on the HL-3 tokamak for simultaneous measurements of density and magnetic fluctuations. In this work, a quasi-optical system has been designed and analyzed for the Q-band CPS diagnostic. The system includes a lens group for beam waist size optimization, a rotatable wire-grid polarizer for polarization adjustment, and a reflector group for measurement range regulation and system response enhancement. Laboratory tests demonstrated a beam radius of order 4cm at the target measurement location (near the plasma pedestal), cross-polarization isolation exceeding 30 dB, and poloidal and toroidal angle adjustment ranges of ±40° and ±15°, respectively. These results verify the system's feasibility through laboratory evaluations. The quasi-optical system has been installed on the HL-3 tokamak during the 2023 experimental campaign to support the development of CPS diagnostics.

Biotin-stapled mixed micelles of lapatinib: Unleashing apoptosis power in triple-negative breast cancer

The treatment options currently offered for triple-negative breast cancer (TNBC) encounter numerous obstacles, including disappointing overall survival rates, unavoidable side effects, and the development of drug resistance. Additionally, there is a growing need for nutrients, like vitamins (e.g., biotin), to sustain the unregulated growth of TNBC cells. Hence, targeted therapy (LPT B-MM) for enhancing the treatment of TNBC was explored which encompassed hydrophobized [using α-TOS (α-Tocopheryl succinate] Pluronic-based mixed micelles having inherent sodium-dependent multivitamin transporter (SMVT) targeting (biotin-stapled) ability. LPT B-MM had optimal size (210.72± 25.88 nm), PDI (0.272± 0.023), and enhanced drug loading capacity (∼13.31± 2.12%). Transmission electron microscopy validated the spherical shape of these micelles. In vitro release studies demonstrated drug release in a sustained manner without the risk of hemolysis. Importantly, LPT B-MM displayed heightened cellular uptake, increased cytotoxicity, a lower IC50 value, elevated reactive oxygen species, induced mitochondrial membrane depolarization, and a greater apoptosis index in TNBC cell lines compared to free LPT. This study highlights the considerable potential of LPT B-MM to induce apoptosis, showcasing its promise in enhancing payload capabilities and enabling SMVT specific targeting within the context of TNBC. LPT B-MM demonstrated the synergistic effects of LPT and α-TOS in inducing apoptosis in TNBC cells, leading to enhanced therapeutic efficacy. This novel combination therapy offers a promising strategy for improving treatment outcomes in TNBC patients and represents a significant advancement in the field of TNBC therapy.

TRIPS: Trilinear Point Splatting for Real‐Time Radiance Field Rendering

AbstractPoint‐based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [KKLD23] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [RFS22] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen‐space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole‐free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions.Our evaluation demonstrate that TRIPS surpasses existing state‐of‐the‐art methods in terms of rendering quality while maintaining a real‐time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto‐exposed footage. The project page is located at: https://lfranke.github.io/trips

Advances in nanofiber technology for biomedical application: A review

The application of nanofiber technology in the biomedical field has garnered significant interest due to its potential to revolutionize areas such as tissue engineering, wound healing, and antimicrobial treatments. This paper provides a comprehensive review of the recent advancements in nanofiber technology, particularly focusing on electrospinning and 3D printing methods that enable the fabrication of scaffolds mimicking the native extracellular matrix. These technologies have facilitated the development of nanofibers with high surface-to-volume ratios, adjustable porosity, and enhanced mechanical properties, tailored to meet specific biomedical needs. Despite their promising features, challenges such as the optimization of pore size for effective cell infiltration and the mechanical robustness required for hard tissue regeneration remain. The review also explores the evolution of sustainable polymers from natural resources, highlighting their potential to create biodegradable and biocompatible scaffolding materials. Future directions emphasize the need for cross-disciplinary collaboration to overcome current limitations and scale production from laboratory to industrial levels. The ongoing research and development efforts aim to refine the properties of nanofibers to achieve optimal performance in clinical applications, underlining the dynamic and evolving nature of this field.

Deep-Learning-Based Segmentation of Predefined Chunks in Connected Speech: A Retrospective Analysis

Background and Objectives In institutional settings, manually segmenting connected speech is a time-consuming and labor-intensive process. This study aims to develop a deep-learning model for automating this process, evaluating its accuracy, and determining the minimum dataset size for effective performance.Materials and Method Voice data from 524 individuals with pathological conditions and 502 individuals with normal conditions, totaling 1026 samples, were used. Each voice sample had 17 chunks, including a “summer” sentence (15 chunks) and vowels /α/ and /i/. The deep-learning model employed in this study is based on the multi-layer perceptron-mixer architecture. This study evaluated performance using the Intersection over Union (IoU) metric, commonly employed in artificial intelligence-based image detection for chunk segmentation.Results The accuracy of chunk identification at the frame level was 96.47%. Using IoU metrics, chunk segmentation accuracy was 98.15% at IoU ≥0.6, 96.03% at IoU ≥0.7, and 89.78% at IoU ≥0.8. Optimal dataset size exploration indicated that more than 700 connected speech datasets were needed for successful training, maintaining F1-scores up to 95% at IoU ≥0.7.Conclusion The artificial intelligence model is suitable for the development of an automated system that efficiently divides segments in the institutional collection of voice data. This suggests its potential utility in advancing voice research using connected speech.

Effect of deposition time on the physicochemical properties of co-electrodeposited Cu2SnS3 thin films for photovoltaic applications

This study highlights our optimization of the co-electrodeposition duration of Cu, Sn, and S on the titanium substrate in order to further improve the properties of Cu2SnS3 thin films in photovoltaic applications. After cyclic voltammetry identifying the potential for simultaneous Cu, Sn and S deposit, "Cu–Sn–S″ layers were electrodeposited for deposition durations ranging from 10 → 20 min. Structurally, X-ray-diffraction and Raman-spectroscopy have confirmed the formation of the single Cu2SnS3-phase, crystallizing in the triclinic system after 20 min of electrodeposition. For this duration, the morphological SEM-EDX profile revealed a highly uniform distribution of grains with very optimal sizes for solar cell applications. To reinforce these conclusions, an optical analysis was carried out in the visible wavelength range, highlighting a strong absorption of the developed sample. In addition, the optical gap estimate, indicates that Cu2SnS3 exihibts a direct transition with a bandgap varying from 1.22 to 1.49eV when the deposition time is increased from 10 →20 min.

Coal burst mechanism in large-scale panel under extra-thick key strata

Extra-thick key strata (ETKS) are a key factor causing coal bursts. In this study, field monitoring, physical and numerical modelling, and theoretical analysis were comprehensively adopted to investigate the effect of ETKS on the static and dynamic stress and its impact on coal bursts. Firstly, the coal bursts and the stability of ETKS were investigated. The roof strata structure and the mining-induced stress in a large scale panel under ETKS were analyzed. Then, the coal burst mechanism under ETKS is discussed. The results show that when the panel reaches the 4th and 5th longwall faces, the inferior ETKS and the main ETKS break, respectively, and large-scale cantilever-articulated structures involving the ETKS are formed. This moment, the overburden load is transferred downwards massively, and the coal seam becomes overstressed and accumulates massive elastic energy. Meanwhile, the collaborative collapse of multiple strata in the large cantilever-articulated structures causes the sudden release of elastic energy and high-energy seismicity, which exerts strong dynamic disturbances on the overstressed coal mass and leads to coal burst. Finally, strategies for coal burst control under ETKS including deep-hole pre-splitting blasting and panel size optimization are proposed and validated.

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

Experimental Research of Ultrasonic Cavitation Evolution Mechanism and Model Optimization of RUREMM on Cylindrical Surface

Micro-pits are widely used in the aerospace and tribology sectors on cylindrical surfaces and electrochemical micromachining which are of great significance for the high material removal rate, absence of tool wear, and mechanical stress, while facing significant challenges such as stray corrosion and low machining efficiency. Aiming at the above problems, this paper proposes a comprehensive method called radial ultrasonic rolling electrochemical micromachining (RUREMM) in which an ultrasonic field has been added onto the cylindrical surface. First, a theoretical model was created to gain the rules of the formation and collapse of bubbles in the liquid medium. Second, to analyze the optimal size of the cathode electrode, the COMSOL5.2 simulation software was proposed to research the influence of the electric field on the different dimensions, and the influences of different parameters in RUREMM on material depth/diameter ratio and roughness are explored through processing experiments. Research results found that the cavitation bubble undergoes expansion, compression, collapse and oscillation, where the max deviation is less than 12.5%. The optimized size was chosen as 200 × 200 μm2 and an electrode spacing of 800 μm through a series of electric field model simulation analyses. Relevant experiments show that the minimum pits with a width of 212.4 μm, a depth of 21.8 μm, and a surface roughness (Ra) of 0.253 μm were formed due to the optimized parameters. The research results can offer theoretical references for fabricating micro-pits with enhanced surface quality and processing precision on cylindrical surfaces.

Sustainable energy transition in Bangladeshi academic buildings: A techno-economic analysis of photovoltaic-based net zero energy systems

Bangladesh faces challenges with global carbon emissions and energy security due to its heavy reliance on imported fossil fuels. This paper investigates the techno-economic viability of photovoltaic (PV) systems for achieving net-zero energy in academic buildings as a sustainable alternative. The study evaluates the performance of PV systems based on factors including annual energy production, capacity factor, net-zero energy potential, self-consumption/sufficiency ratio, renewable fraction, levelized cost of energy (LCOE), net present value (NPV), payback period, profitability index (PI), and carbon emission reduction. The results indicate that optimally sized PV systems can generate net positive energy: 6.18 MWh for universities, 4.04 MWh for high schools, and 3.62 MWh for primary schools. Economic viability was assessed under two billing methods (net metering and all energy buy/sell), and all cases showed positive NPV and PI values greater than 1. Decarbonization analysis revealed significant reductions in CO2 emissions: 2026.5 tons for universities, 18.9 tons for high schools, and 9.7 tons for primary schools. Finally, a sensitivity analysis assessed the optimal PV system size for seven other regions in Bangladesh. These findings suggest that the widespread adoption of PV systems in academic buildings can play a crucial role in achieving net-zero energy, contributing significantly to the government and policymakers' pursuit of sustainable development goals.

An automated mineralogy derived criterion for clustering ore samples for mineral liberation studies

Correctly determining the optimal grinding size is crucial for mineral processors, yet it poses several challenges. Still popular among practitioners, the empirical determination of the grinding target involves time-consuming and resource-intensive laboratory routines employed for generating quality, recovery and energy curves from which the target size is set. This approach becomes even more challenging for mines with diverse ore textures, requiring extensive testing on numerous samples to accurately capture the behaviour of the entire ore body. Consequently, a more efficient experimental method is highly desirable. A solution for reducing the laboratory effort is clustering samples according to the mineral liberation characteristics. This paper introduces a novel multivariate criterion for statistical clustering of ore samples for mineral liberation studies, derived from SEM-based automated mineralogy data. The criterion considers three variables: two coefficients (named A and k) derived from the exponential correlation equation between degree of liberation and the top size of the size fraction, and the overall degree of liberation, both easily obtained from the liberation spectrum for a top size of 1 mm. The effectiveness of the proposed criterion has been experimentally demonstrated. It was concluded that the clustering process correctly grouped samples with similar mineral liberation characteristics. The clustering approach can be used to significantly reduce the laboratory effort on liberation studies involving a great number of samples of different types of ore. Although developed using iron ore samples, the criterion's theoretical basis suggests its potential for adaptation to other types of ore.

Optimal sample size division in two-stage seamless designs.

Inferentially seamless 2/3 designs are increasingly popular in clinical trials. It is important to understand their relative advantages compared with separate phase 2 and phase 3 trials, and to understand the consequences of design choices such as the proportion of patients included in the phase 2 portion of the design. Extending previous work in this area, we perform a simulation study across multiple numbers of arms and efficacy response curves. We consider a design space crossing the choice of a separate versus seamless design with the choice of allocating 0%-100% of available patients in phase 2, with the remainder in phase 3. The seamless designs achieve greater power than their separate trial counterparts. Importantly, the optimal seamless design is more robust than the optimal separate program, meaning that one range of values for the proportion of patients used in phase 2 (30%-50% of the total phase 2/3 sample size) is nearly optimal for a wide range of response scenarios. In contrast, a percentage of patients used in phase 2 for separate trials may be optimal for some alternative scenarios but decidedly inferior for other alternative scenarios. When operationally and scientifically viable, seamless trials provide superior performance compared with separate phase 2 and phase 3 trials. The results also provide guidance for the implementation of these trials in practice.

ВЫБОР МОДЕЛИ И АРХИТЕКТУРЫ НЕЙРОННОЙ СЕТИ ДЛЯ СИСТЕМЫ ТЕХНИЧЕСКОГО ЗРЕНИЯ ДЕТЕКЦИИ КОРНЕПЛОДОВ САХАРНОЙ СВЁКЛЫ

The use of a technical vision system, aimed at ensuring greater safety of sugar beet roots during harvesting and storage by analyzing raw materials arriving at factories, is considered among the methods of long-term storage. The process of distribution of trucks at the receiving point of sugar factories is considered by humans in the work. The purpose of the work is to develop a method for detecting sugar beet roots on an image of the surface of a truck embankment at the receiving point of sugar factories. A neural network was chosen as the raw material recognition method due to the extremely diverse nature of the resulting images, which recognition using a threshold transformation cannot cope with. Algorithms (options such as SSD, YOLO, R-CNN) and architectures (options such as AlexNet, VGGNet, GoogleNet, ResNet) of various neural networks are considered. The most suitable neural network for the required tasks has been identified. The optimal size of the database for training has been determined. As a result of training and testing the neural network, the need to create additional filters was identified. Filters have been created and described that clarify the work of the neural network: filtering out “stuck together” objects, objects with low class membership, objects with an impossible area (too small or large), as well as with an aspect ratio of less than 0.2 or more than 5. A neural network capable to find a sufficient number of sugar beet roots in incoming images and to meet the requirements for high-quality functioning of the technical vision system, was trained based on the results of the work performed.

Impact of Design Decisions on Performance of Embarrassingly Parallel .NET Database Application

The implementation of parallel applications is always a challenge. It embraces many distinctive design decisions that are to be taken. The paper presents issues of parallel processing with use of .NET applications and popular Database Management Systems (DBMSes). In the paper, four design dilemmas are addressed: how efficient is the auto-parallelism implemented in the .NET TPL library, how do popular DBMSes differ in serving parallel requests, what is the optimal size of data chunks in the data parallelism scheme, and how the TPL auto-parallelism behaves in the public clouds. They are analyzed in the context of the typical and practical business case originated from IT solutions which are dedicated for the energy market participants. The paper presents the results of experiments conducted in a controlled, on-premises and cloud environments. The experiments allowed to compare the performance of the TPL auto-parallelism with a wide range of manually set numbers of worker threads. They also helped to evaluate four DBMSes: Oracle, MySQL, PostgreSQL, and MSSQL in the scenario of serving parallel queries. Finally, they showed the impact of data chunk sizes on the overall performance.

Size optimization of a stand-alone solar-wind-battery hybrid system for net zero energy buildings: A case study

In this research, a solar-wind-battery hybrid system (PV/WT/BS) is proposed to supply the electricity demand of a stand-alone net zero energy building. for estimating the output power of the hybrid system, wind speed and solar irradiance are measured throughout a year and their uncertainties are estimated by applying the Monte Carlo algorithm. The wind speed Wiebull distribution is estimated, and the solar irradiance distribution is obtained using a novel approach based on the sky cloudiness parameter. Moreover, to simulate the performance of the hybrid system components according to the actual conditions, their availability is calculated using failure and repair rates. The optimal size of the proposed hybrid system is calculated considering, for the first time, the reliability and system costs. The system's size is optimized through the consideration of key decision variables, including photovoltaic panel area, rated power, wind turbine tower height, and battery capacity. Two distinct optimization methods are employed: a single-objective approach utilizing Genetic Algorithm, where the total system cost is minimized with reliability as a constraint, and a double-objective approach using NSGA-II, considering both initial cost and reliability as objectives. The optimal system size is determined through an efficient decision-making process. A practical case study is conducted, monitoring wind speed, solar irradiance, and electricity demand of a residential building at 15-minute intervals over a year. The results reveal the efficacy of the optimization methods, with the single-objective approach yielding a total system cost of €75,495 and reliability of 95.19%, while the double-objective optimization achieves a cost of €73,460 and a reliability of 91.88%.

Numerical Analysis of Non-Fourier Model-Based Bio-Heat Transfer in the Laser-irradiated Axisymmetric Living Tissue.

The current work is related to the numerical investigation of non-Fourier heat transfer inside the short-pulsed laser-irradiated axisymmetric soft tissue phantom. It utilizes the modified discrete ordinate method to solve the transient radiative transfer equation (TRTE) for determining the intensity field. The laser energy absorbed by the soft tissue phantom behaves like a source in the Fourier/non-Fourier heat conduction model based-bio-heat transfer equation (BHTE), which is solved by employing the finite volume method (FVM) to determine the temperature distribution. Despite the prevalent use of non-Fourier BHTE for this purpose, a second law analysis is considered crucial to detect any potential anomalies. Equilibrium entropy production rates (EPR) are initially computed based on classical irreversible thermodynamics (CIT), which may yield negative values, possibly contravening the second law. Consequently, the EPR based on CIT is adjusted using the extended irreversible thermodynamics (EIT) hypothesis to ensure positivity. After that, the current research findings are compared with the results from the literature, and found good agreement between them. Then, the independent study is performed to select the optimum grid size, control angle size, and time step. A comparative analysis of results between the traditional Fourier and non-Fourier models has been performed. The impact of different parameters on the temperature fields and EPRs, is discussed. The effect of the optical properties of the inhomogeneity on the temperature distribution has been investigated. This study may help to enhance the effectiveness of the laser-based photo-thermal therapy.

Determination of Optimal Locations and Parameters of Passive Harmonic Filters in Unbalanced Systems Using the Multiobjective Genetic Algorithm

This paper discusses the problem of optimal placement and sizing of passive harmonic filters to mitigate harmonics in unbalanced distribution systems. The problem is formulated as a nonlinear multiobjective optimisation problem and solved using the multiobjective genetic algorithm. The performance of the proposed algorithm is tested on unbalanced IEEE 13- and 37-bus three-phase systems. The optimal solutions are obtained based on the following objective functions: 1) minimisation of total harmonic distortion in voltage, 2) minimisation of costs of filters, 3) minimisation of voltage unbalances, and 4) a simultaneous minimisation of total harmonic distortion in voltage, costs of filters, and voltage unbalances. Finally, an analysis of the influence of uncertainties of load powers and changes in system frequency and filter parameters on filter efficiency was performed.

Exploring the Micropore Functional Mechanism of N2O Adsorption by the Eucalyptus Bark-based Porous Carbon.

Nitrous oxide (N2O), recognized as a significant greenhouse gas, has received insufficient research attention in the past. In view of their low energy consumption and cost-effectiveness, the application of porous materials in adsorption is increasingly regarded as a potent strategy to reduce N2O pollution. In this study, a series of microporous porous carbons with a preeminent specific surface area (244.54-2018.08 m2 g-1), which are derived from the fast-growing eucalyptus bark, were synthesized by KOH activation at high temperatures. The obtained materials demonstrated a relatively fine N2O capture capability (0.19-0.68 mmol g-1) at normal temperature and pressure. More importantly, the optimal pore size affecting N2O adsorption (0.8 and 1.0 nm) has been detected, which is a meaningful view that has never been put forward in previous studies. The rationality of the N2O adsorption mechanism was also validated by combining the experimental analysis and Grand Canonical Monte Carlo (GCMC) simulation. The calculated results showed that 0.8 and 1.0 nm of the porous carbon were the preferred pore sizes for N2O adsorption, and the interaction force between N2O and the pore wall decreased with the increase of distance. This study provides a significant theoretical basis for the preparation of biomass porous carbon with excellent N2O adsorption performance and practical adsorption application.

Application of Deep Learning for the Detection of Genetic Variations: Its Implementation in Classifying Alzheimer's Disease

Deep learning emerges as a promising technique, utilizing nonlinear transformations for feature extraction from high-dimensional datasets. However, its application encounters challenges in genome-wide association studies (GWAS) dealing with high-dimensional genomic data. This study introduces an innovative three-step method termed SWAT-CNN for the identification of genetic variants. This approach employs deep learning to pinpoint phenotype-related single nucleotide polymorphisms (SNPs), facilitating the development of precise disease classification models. In the first step, the entire genome undergoes division into non overlapping fragments of an optimal size. Subsequently, convolutional neural network (CNN) analysis is conducted on each fragment to identify phenotype-associated segments. The second step, employs a Sliding Window Association Test (SWAT), where CNN is utilized on the selected fragments to compute phenotype influence scores (PIS) and detect phenotype-associated SNPs based on these scores. The third step involves running CNN on all identified SNPs to construct a comprehensive classification model. Validation of the proposed approach utilized GWAS data from the Alzheimer’s disease Neuroimaging Initiative (ADNI), encompassing 981 subjects, including cognitively normal older adults (CN) and individuals with Alzheimer's disease (AD). Notably, the method successfully identified the widely recognized APOE region as the most significant genetic locus for AD. The resulting classification model exhibited an area under the curve (AUC) of 0.82, demonstrating compatibility with traditional machine learning approaches such as random forest and XGBoost. SWAT-CNN, as a groundbreaking deep learning-based genome-wide methodology, not only identified AD-associated SNPs but also presented a robust classification model for Alzheimer's disease, suggesting potential applications across diverse biomedical domains.