Aintelia Science Notes Journal (ASNJ) is an international, open-access, double-anonymous peer-reviewed engineering journal, published biannually (June and December), free of charge.
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Authors submitting a manuscript retain copyright of their work upon acceptance for publication in Aintelia Science Notes Journal (ASNJ). By submitting, authors agree that: Copyright Retention: Copyright of the published article remains with the author(s). BAUDER Press is granted a non-exclusive license to publish, reproduce, distribute, and archive the work. Licensing: The article is published under CC BY-NC 4.0, allowing third parties to share and adapt the work for non-commercial purposes, provided appropriate credit is given to the author(s) and the journal. Author Rights: Authors are free to deposit, distribute, and reuse their article in any medium (repositories, theses, conferences, teaching materials) without restriction, provided the journal is cited as the original publication venue.
The Multifunctional Effects ofNiacinamide, Sodium Hyaluronate,and Hydrolyzed Collagen
Skin aging is characterized by moisture loss, a decrease in collagen, and a slowing of cellular renewal. During this process, structural and functional changes occur in the epidermis, dermis, and hypodermis layers. In this study, a multifunctional active complex containing hyaluronic acid, niacinamide, and hydrolyzed collagen was developed and incorporated into a facial serum at a 5% concentration. The formulation's efficacy was evaluated over a 4-week period on 20 female volunteers aged 25–39. Following the application of the serum twice daily, an increase in skin hydration and smoothness, as well as a reduction in dryness and the appearance of fine lines, was observed. The findings indicate that the developed active complex provides skin brightening and is effective in reducing signs of aging.
Copyright (c) 2026 Tuba Güneş (Author) https://creativecommons.org/licenses/by-nc/4.0
An Investigation of the Effects ofCationic Conditioning Systems onHair Combability and SensoryPerformance
In this study, the surface interactions of cationic surfactant-based conditioning systems on hair fibers and the resulting performance outcomes were comprehensively evaluated. The negatively charged surface characteristics of hair fibers, particularly after washing, facilitate the adsorption of cationic agents onto the hair surface via electrostatic attraction. As a result of this adsorption mechanism, a low-friction film layer is formed on the hair surface, reducing inter-fiber interaction forces and improving combability performance. Within the scope of the study, formulations containing cationic conditioning systems were comparatively evaluated against reference systems, and all formulations were standardized based on an equivalent active matter concentration of 2%. Wet and dry combing performance was assessed using a texture analyzer by measuring combing force values. Sensory evaluations were conducted by trained panelists under a blind test protocol and scored using a multi-parameter approach. The findings demonstrated that cationic systems form a homogeneous and stable film layer on the hair surface, significantly reducing the coefficient of friction and markedly improving both wet and dry combability. Furthermore, a strong correlation was observed between sensory evaluation results and instrumental measurements. In conclusion, cationic surfactant-based conditioning systems provide high-performance and sensorially optimized solutions in modern hair care formulations.
Copyright (c) 2026 Aslı Eren Yüksel (Author) https://creativecommons.org/licenses/by-nc/4.0
From Unstructured Documents toIntelligent Insights: An End-to-End AIPipeline for Global Tax Compliance
Over 100 countries now mandate electronic invoicing, with Turkey leading as an early adopter generating over two billion documents annually. For multinational enterprises, managing tax compliance across heterogeneous formats presents severe operational challenges. This study introduces an end-to-end AI pipeline that transforms unstructured invoices (PDFs, images, CSVs) into compliant data within a unified architecture. The pipeline comprises three integrated components: First, the Smart Input Module leverages an ensemble of multimodal models (Gemini Flash, Nova Lite, GPT-4.1), achieving 98% accuracy on legally critical fields. Second, the Cloud Processing Layer utilizes a domain-specific XSLT deduplication technique to yield a 63% storage footprint reduction while preserving digital signatures, extending infrastructure runway to over five years. Supported by RabbitMQ and Kubernetes, this layer scales peak throughput from 32 to 200 transactions per second. Finally, the Sovi Intelligence Engine employs an agentic, retrieval-augmented generation (RAG) architecture to enable natural language querying across five languages, achieving a 94% valid SQL generation rate. Currently in production, this system demonstrates the viability of fully automated, AI-driven tax compliance.
Copyright (c) 2026 Muhammed Şara, Ahmet Ak, Ayhan Boyacıoğlu, Yunus Taştutan (Author) https://creativecommons.org/licenses/by-nc/4.0
CAE-Based Analysis and Design Optimization of Ventilation Performance for Commercial Panel Vans
In this study, the design optimization of ventilation components integrated into the interior trim system of 15 m³ panel vans and minibus are investigated. During the analysis process, computer-aided engineering (CAE) methods were employed, and the effects of different design parameters on airflow performance were numerically examined. In order to improve in-cabin air circulation, ventilation duct diameters and vent geometries were evaluated. Accordingly, duct diameters were defined as 50 mm and 70 mm, while vent geometries were modeled as circular and rectangular cross-sections. The results of the flow analyses indicated that the design with a 70 mm duct diameter and a circular vent with a diameter of 68 mm provided the best performance in terms of airflow distribution, pressure loss, and flow uniformity. The findings demonstrate the direct impact of geometric parameters in interior trim design on air quality and passenger comfort. The optimized design approach offers significant contributions in terms of more efficient ventilation, improved passenger comfort, and potential energy savings in service vehicles. It is expected that this study will guide future optimization efforts and interior trim design processes in commercial vehicles.
Mechanical, Materials, and Manufacturing Engineering
Copyright (c) 2026 Songül KILINÇ, Ümmet AYYILDIZ, Kadir Güyen (Author) https://creativecommons.org/licenses/by-nc/4.0
Comparative Investigation of Engine Compartment Insulation Materials on ECE R51 Pass-By Noise Performance of M3 Category Buses
This study presents a comparative investigation of two insulation materials commonly used in commercial vehicle engine compartments: closed-cell polyurethane foam and polyester nonwoven felt. A comprehensive review of commercially available insulation materials was initially conducted considering both acoustic performance and regulatory requirements. Based on this evaluation, two representative insulation solutions were selected for full-scale experimental validation. Two equivalent M3 category city buses equipped with identical 220 kW rear-mounted Mercedes diesel powertrains were tested under standardized ECE R51 pass-by noise conditions. The first configuration employed closed-cell polyurethane foam, while the second utilized polyester nonwoven felt. Noise measurements were performed according to ECE R51 requirements under homologation-level test conditions. Results demonstrated that the polyester nonwoven insulation achieved a lower average pass-by noise level of 74.9 dB(A) compared to 79.5 dB(A) for the polyurethane foam configuration, corresponding to an overall improvement of approximately 4.6 dB. The improved performance was attributed to the superior sound absorption characteristics of the porous fibrous structure, particularly within the dominant engine noise frequency range of 1500–2000 Hz. The study provides practical guidance for acoustic insulation material selection in commercial vehicle applications and demonstrates the importance of considering both acoustic performance and regulatory compliance during engine compartment insulation design.
Copyright (c) 2026 Ecem Turhan Er, Samet Onur Emir (Author) https://creativecommons.org/licenses/by-nc/4.0
The Harvest of Algorithms: A New Path from Data to Efficiency
In the modern agricultural landscape, digital transformation has evolved into a high-precision engineering discipline. This study analyzes advanced automated guidance systems as a decisive bridge between conventional mechanics and the era of autonomous farming. By integrating high-torque actuation with intelligent positioning logic, these systems utilize multi-constellation satellite networks and real-time correction streams to achieve a precision level where operational deviations are confined to the centimeter scale. This technological framework, synchronized via high-speed CAN-Bus communication and real-time data fusion, optimizes complex field trajectories for critical tasks such as seeding and precision fertilization. The synergy between advanced control units and intuitive human-machine interfaces minimizes the margin of human-induced error, leading to a significant reduction in fuel consumption and mechanical fatigue. Beyond basic navigation, features such as dynamic terrain compensation and automated headland path planning ensure consistent accuracy across diverse topographies. This research posits that the true "harvest" of these algorithms lies in the transition from manual control to data-driven operational intelligence. As a critical milestone toward fully unmanned agricultural ecosystems, these advanced positioning solutions provide a robust foundation for economic optimization and environmental stewardship, defining the next frontier of smart mechanization.
Copyright (c) 2026 Ahmet Mermer (Author) https://creativecommons.org/licenses/by-nc/4.0
Distinguishing Physical Stress Concentration from Numerical Singularity in Large-Scale Structural FEM Analyses
In large-scale structural finite element analyses (FEA), extreme stress peaks frequently occur at geometric discontinuities, boundary conditions, and perfectly bonded interfaces. These values are often directly compared with the material yield strength, which may lead to overly conservative or misleading safety assessments. However, such stress peaks may originate from mathematical singularities inherent to linear elastic formulations rather than physically admissible stress states. This study investigates the distinction between physical stress concentration and numerical singularity in an industrial-scale structural steel model subjected to end loading. A three-dimensional finite element model containing approximately one million elements was analyzed under service loading conditions. The simulation results indicated that the maximum von Mises stresses reached nearly three times the yield strength of S355JR steel. Through theoretical evaluation of asymptotic stress behavior, plastic deformation theory, mesh characteristics, and contact modeling effects, it is demonstrated that these stress peaks represent numerical artifacts rather than actual structural failure conditions. A systematic engineering interpretation framework is proposed to prevent the misclassification of singular stresses in industrial FEM applications.
Copyright (c) 2026 Samet Dönerkaya, Samet Büken (Author) https://creativecommons.org/licenses/by-nc/4.0
Real-Time Forest Fire Detection and Spread Prediction UsingTransformer-Based Vision Models on Edge Computing Nodes: A CaseStudy of the Aegean Pine Forest Zone, Greece
Increasing forest fire frequency in Eastern Mediterranean Pinus brutia ecosystems demands rapid automated detection and real-time spread prediction. This paper presents EgeoFireNet, an end-to-end edge-computing system deployed on NVIDIA Jetson Orin NX nodes within a 43-camera dual-channel monitoring network in Chios, Greece. The system integrates a fine-tuned Swin Transformer detection model with a physics-informed cellular automaton spread prediction module. Optimized via TensorRT INT8, the detection model processes 22.4 FPS, achieving a 0.889 mAP@0.5 and a 0.912 F1-score for fire and smoke. Initialized by real-time segmentation masks and sensor data, the prediction module generates 15-minute georeferenced forecasts in 4.2 seconds with a 0.76 Sorensen-Dice coefficient. EgeoFireNet reduces median end-to-end alert latency to 87 seconds a 12-to-24-fold improvement over conventional helicopter patrols while maintaining an operational false positive rate of 0.023 alerts/camera/hour.
Copyright (c) 2025 BAUDER Press https://creativecommons.org/licenses/by-nc/4.0
Life Cycle Assessment and Carbon Footprint Optimisation of AgrivoltaicSystems Combining Bifacial Solar Panels with Drip-Irrigated VegetableProduction in Semi-Arid Mediterranean Climates
Agrivoltaic (AV) systems co-locate solar photovoltaics and agriculture, offering a high-impact strategy for the food-energy-water nexus in semi-arid Mediterranean regions. This paper presents a 25-year cradle-to-gate Life Cycle Assessment (LCA) of a 2 ha AV system in Beja, Tunisia, combining elevated bifacial PERC panels with drip-irrigated tomato and pepper production. Using ReCiPe 2016 (midpoint H) and ecoinvent 3.9, three configurations are compared: conventional open-field cropping (C1), fixed-rate drip AV (C2), and precision drip AV with soil sensors (C3). The C3 configuration achieves a Global Warming Potential (GWP) of 42.3 g CO₂-eq/kWh, which is 92% lower than the Tunisian grid (527 g CO₂-eq/kWh) and 18% below standard ground-mount PV. Driven by shading-induced evapotranspiration reduction and sensor scheduling, C3 reduces seasonal water consumption by 33.4% compared to C1. Its Land Equivalent Ratio (LER) of 1.42 confirms a 42% land-use efficiency gain over separate monocultures. Additionally, the elevated geometry enables an 8.3% bifacial rear-face irradiance gain, boosting energy yield by 4.1%. These findings provide a quantitative evidence base for Mediterranean AV policy.
Grid-Forming Inverter Control for Black-Start and Islanded Operation inRenewable-Dominated Rural Microgrids: Application to Off-GridCommunities in Sub-Saharan Africa
Sub-Saharan Africa (SSA) hosts approximately 600 million people without electricity access, and solar PV battery mini-grids are the most cost-effective pathway for rural electrification in low-density communities beyond economic reach of grid extension. However, the overwhelmingly inverter-based generation composition of PV-battery mini-grids creates power system stability challenges: conventional gridfollowing (GFL) inverters require an external voltage and frequency reference that is absent during grid-off conditions, precluding black-start and stable islanded operation. Grid-forming (GFM) inverter control architectures which independently establish and regulate voltage and frequency are essential for reliable standalone mini-grid operation. This paper presents a droop-based GFM inverter control implementation with black-start capability, virtual impedance, and load-sharing coordination for a 48 kWp PV/100 kWh lithium-ion battery mini-grid supplying an off-grid community of 320 households in Tambacounda, Senegal. MATLAB/Simulink simulation and hardware-in-the-loop (HIL) validation on a CHIL test bench confirm: voltage magnitude regulation within ±1.5% (1σ), frequency deviation below ±0.3 Hz during cold-load pickup, black-start sequence completion in 380 ms, and reactive load sharing error below 8% between parallel inverters. The architecture provides a replicable technical baseline for GFM mini-grid deployments across SSA.
Federated Learning for Privacy-Preserving Predictive Maintenance inMulti-Site Industrial IoT Networks: Architecture, Convergence Analysis,and Benchmark Evaluation
Predictive maintenance (PdM) in multi-site industrial settings requires training machine learning models on condition monitoring data that is distributed across facilities operated by competing organisations, making centralised data aggregation infeasible due to privacy, intellectual property, and regulatory constraints. Federated learning (FL) offers a paradigm-shift solution by enabling collaborative model training without raw data exchange: each site trains locally and shares only model gradient updates with a central aggregation server. This paper presents FedPdM, a federated learning architecture tailored for industrial IoT predictive maintenance, incorporating: (i) FedAvg-based global aggregation with adaptive client weighting proportional to local dataset quality; (ii) a non-IID robustness mechanism using Scaffold variance reduction to address heterogeneous failure mode distributions across sites; and (iii) differential privacy noise injection at the gradient level to provide formal ε-DP guarantees. FedPdM is evaluated on the CMAPSS turbofan engine degradation benchmark and a proprietary 12 site industrial compressor dataset, achieving Remaining Useful Life prediction RMSE of 14.3 cycles (CMAPSS FD001 subset) comparable to centralised baseline RMSE of 13.8 cycles while providing (ε=2.1, δ=10⁻⁵) differential privacy at a ommunication overhead of 2.3 MB per round. Convergence is achieved in 38 rounds across 12 heterogeneous clients, confirming practical viability for real industrial multi-site deployment.
Copyright (c) 2026 BAUDER Press https://creativecommons.org/licenses/by-nc/4.0
Microplastic Contamination Assessment in Urban Stormwater Runoff UsingHyperspectral Imaging and Machine Learning: A Case Study of the İzmirMetropolitan Drainage Network, Turkey
Microplastic (MP) pollution in urban stormwater systems represents a critical but undercharacterised environmental pathway, with stormwater runoff constituting a primary vector transporting terrestrially originating MPs to receiving water bodies. This paper presents a hyperspectral imaging and machine learning framework for rapid, non-destructive quantification and polymer-type classification of microplastics in stormwater samples collected from the İzmir Metropolitan Municipality drainage network, Turkey. A shortwave infrared (SWIR, 900 - 1700 nm) hyperspectral camera coupled to a conveyor-belt scanning platform acquires spectral data cubes from filtered stormwater membrane samples. A random forest (RF) classifier trained on spectral signatures of seven reference polymer types achieves a mean classification accuracy of 91.3% and a mean F1-score of 0.893 on held-out test samples. Gradient-boosted tree regression models trained on environmental and catchment-scale predictor variables including impervious surface fraction, traffic density, and antecedent dry period explain 76% of variance in MP particle concentration across 38 drainage sub-catchments (R² = 0.76, RMSE = 412 particles/L). SHAP analysis identifies impervious surface fraction and commercial land-use proportion as the dominant drivers of MP concentration. The framework enables basin-scale MP load mapping at operational throughput (80 samples/day) and provides decision-support outputs for targeted source-control intervention.
Mitigation of Voltage Sag and Harmonic Distortion in Weak Industrial Microgrids Using Adaptive STATCOM: A Case Study of the Gafsa Phosphate Mining Complex, Tunisia
Here is the English version, condensed to under 200 words while retaining all technical data, metrics, and case-study details: Industrial microgrids in remote mining environments often operate under weak-grid conditions (<span class="math-inline" data-math="SCR < 5" data-index-in-node="94">$SCR < 5$), amplifying voltage sags during motor starting and causing variable-frequency drive (VFD) harmonics to exceed the IEEE 519-2022 $THD_V$ limit of 8% at the point of common coupling (PCC). This paper presents an adaptive Static Synchronous Compensator (STATCOM) designed for the 30 kV microgrid at the Compagnie des Phosphates de Gafsa (CPG) mining complex in Tunisia. The system utilizes a three-level neutral-point-clamped (NPC) converter regulated by an adaptive proportional-integral (API) scheme. Controlled by a Lyapunov-stability-based adaptation law, it eliminates the need for offline tuning. MATLAB/Simulink simulations, calibrated with CPG field data, demonstrate that during the direct-on-line starting of a 2.4 MW crusher motor, voltage sags recover from 0.72 p.u. to 0.98 p.u. within 18 ms. Furthermore, $THD_V$ drops from 11.3% to 2.8%, achieving IEEE 519-2022 compliance. Compared to fixed-gain PI STATCOMs, active and reactive power transient overshoot is reduced by 31%, validating the architecture's performance under weak-grid conditions.
Optimizing Variable-Rate Linear Move Irrigation Systems Using Edge-AI and Hydrological Modeling: A Case Study of the Danube-Tisa-Danube Canal Basin in Vojvodina
Vojvodina, Serbia's principal agricultural region, faces escalating irrigation demand driven by climate change-induced summer droughts. The 929 km Danube-Tisa-Danube (DTD) canal network, supplying up to 510,000 ha of arable land, is under pressure to improve water allocation precision. This paper evaluates an integrated optimization framework for variable-rate irrigation (VRI) linear move systems supplied by the DTD network, combining three components: (i)A SWAT+ hydrological model of the DTD basin calibrated on 2011–2022 discharge and water-level records. (ii) A field-scale, LSTM-based soil moisture and reference evapotranspiration ($ET_0$) prediction module driven by IoT sensors and near-real-time weather data. (iii)An Edge-AI prescription engine deployed on an NVIDIA Jetson Nano unit mounted on the irrigation machine. In validation, the SWAT+ model achieved a Nash-Sutcliffe Efficiency (NSE) of 0.81 and $R^2$ of 0.87. The LSTM module predicted 48-hour soil moisture with an RMSE of 0.028 $m^3/m^3$. Field trials on maize (Zea mays L.) and sunflower (Helianthus annuus L.) across four sites in Bačka demonstrated a 22.4% reduction in seasonal water application compared to uniform-rate management. Meanwhile, grain yields improved (maize +3.1%, sunflower +1.8%), confirming the operational viability of data-driven VRI optimization.
Development of an Autonomous Weed-Weeding Robot Utilizing Edge-AI and Multispectral Computer Vision for Row-Crop Farming
Weeds represent one of the most economically damaging biotic stresses in row-crop production, yet current chemical control strategies face mounting regulatory pressure and resistance evolution. This paper presents the design, integration, and preliminary field evaluation of an autonomous ground robot for in-season mechanical weed control in row-crop systems, combining Edge-AI inference with a five-band multispectral vision pipeline. The system architecture comprises a four-wheel differential-drive platform, a MicaSense-class multispectral imaging module, an NVIDIA Jetson Orin edge-computing unit, and a servo-actuated inter-row/intra-row tine mechanism. A YOLOv8-nano model, trained on a field-collected and synthetically augmented dataset of 14,200 annotated multispectral frames covering four principal row-crop weed species (Chenopodium album, Amaranthus retroflexus, Galium aparine, Convolvulus arvensis), is deployed for real-time per-frame weed localisation at 18 frames per second on the embedded platform. Vegetation index fusion combining NDVI and NDRE channels provides a secondary spectral discriminator that reduces false-positive detections on soil and crop residue by 34% relative to RGB-only inference. RTK-GNSS-guided row-following navigation achieves lateral deviation of ≤1.8 cm (1σ) at 0.6 m/s travel speed. Field trials on maize and soybean demonstrate a weed removal efficacy of 82–89% with a crop damage rate below 1.2%, at a treatment throughput of 0.45 ha/h.
Optimizing Gallium Nitride (GaN) Based Inverters for Next-Generation Electric Vehicle Fast-Charging Stations
Gallium nitride (GaN) high-electron-mobility transistors (HEMTs) are rapidly emerging as the enabling technology for next-generation electric vehicle (EV) fast-charging stations, offering superior switching performance over conventional silicon-based devices. This paper investigates the optimisation of GaN-based two-stage inverter architectures — comprising a totem-pole bridgeless power factor correction (PFC) front-end and an LLC resonant DC-DC stage — for high-power EV fast-charging applications. Key optimisation parameters including switching frequency selection, dead-time minimisation, zero-voltage switching (ZVS) range extension, and thermal management strategies are systematically analysed. The comparative performance of GaN against silicon carbide (SiC) and silicon (Si) devices is evaluated across efficiency, power density, and electromagnetic interference (EMI) dimensions. Results indicate that GaN-based inverters can achieve power conversion efficiencies exceeding 98% at switching frequencies of 100-500 kHz, with passive component volume reductions of 30-60% relative to Si baselines. Implementation challenges including dynamic on-resistance degradation, gate-driver design complexity, and high-frequency EMI compliance are critically examined. The paper concludes with a topology-selection framework and a research roadmap for GaN deployment in 400 V and 800 V EV charging architectures.
Digital Twin Framework for Real-Time Structural Health Monitoring and Flood Resilience in Urban Smart Infrastructure
Urban infrastructure systems face compounding threats from structural deterioration and climate-driven flood events. This paper proposes an integrated Digital Twin (DT) framework that couples real-time Structural Health Monitoring (SHM) with urban flood resilience management for smart city environments. The framework leverages a heterogeneous Internet of Things (IoT) sensor network — comprising accelerometers, strain gauges, piezometers, and water-level sensors — feeding continuously into a cloud-hosted digital replica of urban built assets. A hybrid machine learning pipeline combining Long Short-Term Memory (LSTM) autoencoders for anomaly detection with ensemble Kalman filtering for real-time model state updating is presented. The flood resilience module integrates hydrodynamic simulation with sensor-driven early-warning logic, enabling proactive emergency response. Architecture components, data flows, computational requirements, and implementation challenges are systematically described. The proposed framework provides a replicable, scalable foundation for data-driven urban resilience, directly applicable to bridges, drainage networks, and critical urban assets. Key contributions include a unified ontology linking structural and hydrological digital twin layers, a latency-optimised edge-cloud processing pipeline, and a multi-criteria decision support module for infrastructure managers.
Bibliometric analysis of particle swarm optimization algorithm.
Nowadays, metaheuristic algorithms are used in large-scale work environments in order to reach the optimum solution in the solution space faster by using efficient search operations and to find solutions to optimization problems. One of these algorithms, the Particle Swarm Optimization algorithm, has been popularly preferred in recent years due to its ease of coding and high performance, and its successful results in various optimization problems, especially in complex and multi-dimensional problems. Within the scope of this study, a bibliometric analysis of the researches conducted in the literature in the last five years (2020-2024) using the Particle Swarm Optimization algorithm was conducted. In the study, 29867 publications were reached by searching the keywords “Particle”, “Swarm” and “Optimization” in the Web of Science database. The data obtained from the Web of Science database was analyzed using literature analysis, network analysis and visual data discovery methods in the VOSviewer program. As a result of the analyzes, studies related to various criteria such as category distribution, distribution of publications over the years, document types of publications, index distribution of publications, distribution of publications by country were given quantitatively with all their details.
Copyright (c) 2024 AINTELIA Science Notes Journal https://creativecommons.org/licenses/by-nc/4.0
A A Review of EEG-Based Face Recognition: Methodologies, Feature Extraction Techniques, and Classification Methods
This review explores EEG-based face recognition, focusing on methodologies, feature extraction techniques, and classification methods. Recognizing familiar and unfamiliar faces is crucial for social interaction, with EEG capturing brain activity to provide objective insights. Critical neural responses like N170, P300, and P600 are discussed in terms of their role in face recognition. The review highlights traditional machine learning approaches, alongside advanced deep learning techniques. Despite advancements, challenges such as signal noise, individual variability, and limited dataset sizes persist. Future research directions include improved noise reduction, personalized models, and data augmentation, aiming to enhance the accuracy and applicability of EEG-based face recognition systems.
Wireless Communication Techniques in Agricultural Mobile Robots: An Expert Review of Architectures and Performance Trade-offs
The integration of autonomous mobile robots (Agri-Robots) is critical for addressing global food security challenges and increasing efficiency in precision agriculture (PA). The operational efficacy of these robots, which perform tasks from planting to harvesting, hinges entirely on robust, high-speed, and low-latency wireless communication. This review analyzes the performance trade-offs among short-range (Wi-Fi, Bluetooth, Zigbee) and wide-area (LoRaWAN, 4G, 5G-SA) technologies, classifying their suitability based on key metrics: Massive Machine-Type Communications (mMTC) for sensing, Ultra-Reliable Low-Latency Communication (URLLC) for control, and Enhanced Mobile Broadband (eMBB) for data offload. Findings indicate that a single standard is insufficient; LoRaWAN excels in long-range, low-power sensing, while private 5G-SA is mandatory for safety-critical, low-latency control. Operational success is inhibited by environmental factors, such as vegetation-induced signal attenuation and systemic issues, including market fragmentation and proprietary data ecosystems. The critical pathway forward involves adopting hybrid network architectures that intelligently combine Low-Earth Orbit (LEO) satellite services for ubiquitous geographic coverage with terrestrial 5G/Wi-Fi for high-throughput edge computing. Further advancements depend on the development of AI-driven autonomous network management and the adoption of lightweight application protocols like CoAP.
Predicting Vehicle Fuel Efficiency: A Comparative Analysis of Machine Learning Models on the Auto MPG Dataset
This study explores the application of various machine learning models to predict vehicle fuel consumption using the Auto MPG dataset. It examines the effectiveness of algorithms such as Decision Tree Regressors, Random Forests, Support Vector Regressors, and neural network-based models like LSTM and GRU. The study aims to enhance fuel efficiency prediction by analyzing factors like engine specifications, driving habits, and vehicle design. The models' performance is evaluated using metrics such as R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) to ensure accuracy and minimize error.
Copyright (c) 2023 AINTELIA Science Notes Journal https://creativecommons.org/licenses/by-nc/4.0
Comparison of K-Means, Fuzzy C-Means and Fuzzy Logic Based Clustering Algorithms for Customer Segmentation
Customer segmentation is a critical tool for businesses to optimize their marketing strategies and increase customer loyalty. This study aims to segment customers based on their buying behavior using K-Means, Fuzzy C-Means (FCM) and Fuzzy Logic based clustering algorithms. While the K-Means algorithm divides the data into a certain number of clusters and determines a center for each cluster, the FCM algorithm allows data points to belong to more than one cluster with varying degrees of membership. Fuzzy Logic based clustering provides a more nuanced segmentation by using a system of fuzzy membership functions and rules defined for each feature. In this study, the performance of all three algorithms is evaluated with Silhouette score, Calinski-Harabasz score and Davies-Bouldin score using Online Retail dataset. The results show that K-Means generally delivers higher Silhouette scores and Calinski-Harabasz index values, FCM provides flexibility to manage similarities across clusters, and the Fuzzy Logic-based approach better captures complex relationships between features. The findings provide valuable insights for customer segmentation and optimization of marketing strategies.
Enhancing Bank Marketing Strategies: The Impact of Feature Reduction Techniques on Machine Learning Model Performance
This research investigates the application of machine learning models in the banking sector, specifically focusing on the classification of bank marketing datasets. We explore the use of feature reduction techniques, including Principal Component Analysis (PCA) and SelectKBest, to enhance the performance of various models such as Multi-Layer Neural Networks (MLNN), K-Nearest Neighbors (KNN), Random Forest(RF), and Linear Discriminant Analysis (LDA). Our study reveals the pivotal role of these reduction techniques in addressing the challenges posed by high-dimensional data and imbalanced distributions in customer behavior prediction. Through comparative analysis, we demonstrate how PCA and SelectKBest, through F-Value and Chi-Squared methods, influence model accuracy and efficiency, providing insights into the effectiveness of these models in strategic bank marketing efforts.
Utilizing Machine Learning for Enhanced Diagnosis and Management of Pediatric Appendicitis: A Multilayer Neural Network Approach
This study focuses on pediatric appendicitis, a leading cause of hospital admissions due to abdominal pain in children, characterized by a substantial risk of perforation, especially in younger patients. Traditional diagnostic methods, while effective, often lack specificity and are sup plemented by varying laboratory and imaging techniques. This research introduces a novel application of machine learning (ML), specifically a multi multi-output neural network model, to address the complexities of diagnosing appendicitis, determining its severit y, and guiding management strategies in pediatric cases. The model, with its unique architecture, has been trained and tested on a comprehensive dataset from Children’s Hospital St. Hedwig in Regensburg, Germany, which includes a wide array of clinical dat a and ultrasound images. The results demonstrate remarkable accuracy in classifying management approaches, severity levels, and diagnosis, highlighting the model's potential in supporting clinical decisiondecision-making. While not a replacement for clinical judgm ent, this model serves as a promising tool in the ongoing efforts to improve pediatric appendicitis care, offering a glimpse into the future of AI AI-enhanced medical diagnostics and treatment planning.
Effect of core material on induced voltage in electromagnetic harvester
With the increasing demand for energy today, diversification of resources and meeting this demand has become crucial. Environmentally friendly alternative solutions have been the subject of research. Especially investments in renewable energy sources have increased in this regard. The power required by low-power sensor technologies can be met by harvesters. For this purpose, the design and Finite Element Analysis (FEA) of the toroid core electromagnetic harvester have been carried out in the study. The material of the toroidal core is determined by selecting Nickel-steel, M19, and nanocrystalline, and the effect of induced voltage and mutual inductance on the core material has been identified. In addition, the line current was determined as a variable and its effect on both flux density and voltage was determined.
PSIM simulation and analysis of LCC series-parallel resonant converter
In the control of DC-DC converters, pulse width modulation (PWM) is commonly used. In converters operating with PWM control, the switches operate under hard-switching conditions, resulting in increased switching losses, current and voltage stresses on the switching elements, and electromagnetic interference (EMI) noise as the operating frequency increases. Achieving higher power density and faster transient response in DC-DC converters is possible only by increasing the switching frequency. Therefore, reducing these losses and noises while increasing the switching frequency is only possible with soft switching techniques. Particularly in cases of variable loads, such as batteries, and when a smoother output voltage is required, the series-parallel LCC-type circuit topology from resonant converter topologies is employed. In this study, a simple algorithm has been developed to rapidly control the output voltage and switching frequency of the Full-Bridge Isolated LCC Series-Parallel Resonance circuit based on the current drawn by the changing load over time. The circuit has been successfully operated in the range of 100-130 kHz.
Conscious Artificial Intelligence: Can Current Artificial Intelligence Achieve Consciousness?
Consciousness is said to be associated with neuronal activity in the brain, but how this relationship works is still unknown. There are different theories and opinions on whether consciousness is a biological phenomenon or a physical process. The question of whether consciousness can be created in artificial intelligence systems has also been raised. In this article, current artificial neural network models that attempt to mimic the workings of the brain have been discussed and evaluated for their adequacy in addressing the issue of consciousness.
The Effect of Rotor Slot Number on the Performance of Asynchronous Motors in Light Electric Vehicle
Light electric vehicles provide significant advantages in reducing environmental pollution and increasing efficiency in urban transportation. The performance of the motor directly affects the overall performance of electric vehicles. This study investigates the performance analysis of asynchronous motors designed for light electric vehicles used in urban transportation. The initial design parameters of the motor are specified, and various motor structures designed with changes in the rotor slot number are presented. Additionally, the effects of the slot number on the motor's magnetic circuit and performance are discussed. Finite Element Analysis (FEA) is used to investigate the motor's performance. Performance parameters such as efficiency, magnetic flux density, weight, starting torque, and power factor are obtained for different slot numbers. The results indicate that the rotor slot number is a critical design parameter affecting motor performance. In particular, it is stated that maximum efficiency can be achieved with a certain slot number, but an excessive increase in the slot number may have adverse effects on performance.
Optimization of Tractor Tire Tread Structure Using Finite Element Method
In this study, the tractor tire used for the field was modeled using the Visual Media Finite Element Analysis (FEA) technique and the tire life of this model was tried to be estimated by using the ANSYS software. Mooney-Rivlin was chosen as the tire material because it is a material with high elasticity and behaves in accordance with the mechanics and behavior of the rubber used in tires. The wheel model is modeled as solid, consisting of Solid186 elements.
Power system optimization by using combined Fast-Decoupled power flow algorithm and UPFC
Electric power systems are structures that should operate stable in all scenarios. Unified power flow controller (UPFC), a flexible alternating current transmission system (FACTS) device, is one of the main assistants to achieve this necessity. In this study a control strategy for power flow optimization that based on the combined Fast-Decoupled (FD) method and UPFC is proposed. A detailed model was designed under MATLAB/Simulink platform in association with MATLAB editor. The IEEE-30 bus system was used to validate the model, and the results were addressed in terms of power loss values and optimization. According to the obtained results, it is observed that proposed model has ability to regulate system parameters for various conditions.