Aintelia Science Notes Journal (ASNJ); uluslararası, açık erişimli, çift-kör hakemli bir mühendislik dergisidir. Yılda iki kez (Haziran ve Aralık), ücretsiz yayımlanır.
BAUDER – Association for Scientific Research and Applications (Bilimsel Araştırmalar ve Uygulamalar Derneği)
Makale gönderen yazarlar, Aintelia Science Notes Journal'da (ASNJ) yayınlanmak üzere kabul edildikten sonra çalışmalarının telif haklarını saklı tutarlar. Göndererek yazarlar şunları kabul ederler: Telif Hakkının Saklı Tutulması: Yayınlanan makalenin telif hakkı yazar(lar)da kalır. BAUDER Press'e, çalışmayı yayınlama, çoğaltma, dağıtma ve arşivleme konusunda münhasır olmayan bir lisans verilir. Lisanslama: Makale, CC BY-NC 4.0 lisansı altında yayınlanır ve üçüncü tarafların, yazar(lar)a ve dergiye uygun şekilde atıfta bulunulması koşuluyla, çalışmayı ticari olmayan amaçlarla paylaşmasına ve uyarlamasına izin verir. Yazar Hakları: Yazarlar, derginin orijinal yayın yeri olarak belirtilmesi koşuluyla, makalelerini herhangi bir ortamda (depolar, tezler, konferanslar, öğretim materyalleri) kısıtlama olmaksızın depolama, dağıtma ve yeniden kullanma özgürlüğüne sahiptir.
Niasinamid, Sodyum Hiyalüronat ve Hidrolize Kolajenin Multifonksiyonel Etkileri
Cilt yaşlanması; nem kaybı, kolajen azalması ve hücresel yenilenmenin yavaşlaması ile karakterizedir. Bu süreçte epidermis, dermis ve hipodermis katmanlarında yapısal ve fonksiyonel değişimler meydana gelir. Bu çalışmada, hiyalüronik asit, niasinamid ve hidrolize kolajen içeren çok fonksiyonlu bir aktif kompleks geliştirilmiş ve %5 oranında yüz serumuna entegre edilmiştir. Formülasyonun etkinliği, 25–39 yaş aralığındaki 20 kadın gönüllü üzerinde 4 hafta boyunca değerlendirilmiştir. Günde iki kez uygulanan serumun kullanım sonucunda, cilt neminde ve pürüzsüzlüğünde artış, kuruluk ve ince kırışıklık görünümünde ise azalma gözlemlenmiştir. Bulgular, geliştirilen aktif kompleksin cilt yüzeyinde aydınlanma sağladığını ve yaşlanma belirtilerinin azaltılmasında etkili olduğunu göstermektedir.
Telif Hakkı (c) 2026 Tuba Güneş (Author) https://creativecommons.org/licenses/by-nc/4.0
Katyonik Kondisyoner Sistemlerin Saçta Taranabilirlik ve Duyusal Performans ÜzerineEtkilerinin İncelenmesi
Bu çalışmada, katyonik yüzey aktif maddelere dayalı kondisyoner sistemlerin saç lifi üzerindeki yüzey etkileşimleri ve buna bağlı performans çıktıları kapsamlı olarak değerlendirilmiştir. Saç lifinin özellikle yıkama sonrasında negatif yüklü bir yüzey karakteri göstermesi, katyonik ajanların elektrostatik çekim yoluyla saç yüzeyine adsorbe olmasını mümkün kılmaktadır. Bu adsorpsiyon mekanizması sonucunda saç yüzeyinde düşük sürtünmeli bir film tabakası oluşmakta, lifler arası etkileşim kuvvetleri azalmakta ve taranabilirlik performansı iyileşmektedir. Çalışma kapsamında, katyonik kondisyoner sistemler içeren formülasyonlar referans sistemler ile karşılaştırmalı olarak incelenmiş; tüm sistemler eşdeğer aktif madde konsantrasyonu (%2) esas alınarak standardize edilmiştir. Islak ve kuru tarama performansı, tekstür analiz cihazı kullanılarak tarama kuvveti (combing force) ölçümleri üzerinden değerlendirilmiştir. Duyusal analizler, eğitimli panelistler tarafından kör test protokolü çerçevesinde gerçekleştirilmiş ve çok parametreli bir yaklaşım ile skorlanmıştır. Elde edilen bulgular, katyonik sistemlerin saç yüzeyinde homojen ve stabil bir film tabakası oluşturarak sürtünme katsayısını anlamlı düzeyde azalttığını ve hem ıslak hem kuru taranabilirliği belirgin şekilde iyileştirdiğini ortaya koymuştur. Ayrıca duyusal değerlendirmeler ile enstrümantal ölçümler arasında yüksek düzeyde korelasyon saptanmıştır. Sonuç olarak, katyonik yüzey aktif maddelere dayalı kondisyoner sistemlerin, saç bakım formülasyonlarında yüksek performanslı ve duyusal açıdan optimize edilmiş çözümler sunduğu ortaya konmuştur.
Telif Hakkı (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.
Telif Hakkı (c) 2026 Muhammed Şara, Ahmet Ak, Ayhan Boyacıoğlu, Yunus Taştutan (Author) https://creativecommons.org/licenses/by-nc/4.0
Ticari Panelvan Araçlar İçin Havalandırma Performansının CAE Tabanlı Analizi ve Tasarım Optimizasyonu
Bu çalışmada, 15 m³ panelvan ve minibüs araçlar için iç trim sistemine entegre edilen havalandırma bileşenlerinin tasarım optimizasyonu ele alınmıştır. Analiz sürecinde bilgisayar destekli mühendislik (CAE) yöntemleri kullanılmış ve farklı tasarım parametrelerinin hava akış performansına etkisi sayısal olarak incelenmiştir. Araç içi hava sirkülasyonunun iyileştirilmesi amacıyla, havalandırma boru çapları ve menfez geometrileri değerlendirilmiştir. Bu doğrultuda, boru çapı parametresi 50 mm ve 70 mm olarak belirlenmiş; menfez geometrileri ise dairesel ve dikdörtgen kesitli olacak şekilde modellenmiştir. Gerçekleştirilen akış analizleri sonucunda, 70 mm boru çapı ve 68 mm çapında dairesel menfez kullanılan tasarımın, hava akış dağılımı, basınç kaybı ve homojenlik açısından en iyi performansı sağladığı tespit edilmiştir. Elde edilen bulgular, iç trim tasarımında geometrik parametrelerin hava kalitesi ve yolcu konforu üzerindeki doğrudan etkisini ortaya koymaktadır. Optimize edilen bu tasarım yaklaşımı, servis araçlarında daha verimli havalandırma, artırılmış yolcu konforu ve potansiyel enerji tasarrufu sağlama açısından önemli katkılar sunmaktadır. Çalışmanın, ticari araç iç trim tasarım süreçlerine ve gelecekte yapılacak optimizasyon çalışmalarına rehberlik etmesi beklenmektedir.
Telif Hakkı (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.
Telif Hakkı (c) 2026 Ecem Turhan Er, Samet Onur Emir (Author) https://creativecommons.org/licenses/by-nc/4.0
Algoritmaların Hasadı: Veriden Verimliliğe Yeni Bir Yol
Modern tarım ekosisteminde dijital dönüşüm, artık basit bir otomasyon sürecinin ötesine geçerek yüksek hassasiyetli bir mühendislik disiplini hâline gelmiştir. Bu çalışma, gelişmiş otomatik yönlendirme sistemlerini, geleneksel mekanik yaklaşımlar ile otonom tarım çağı arasında belirleyici bir köprü olarak ele almakta ve analiz etmektedir. Yüksek torklu aktüasyon yapıları ile akıllı konumlandırma algoritmalarının entegrasyonu sayesinde bu sistemler; çoklu uydu takımyıldızları ve gerçek zamanlı düzeltme sinyallerinden faydalanarak, operasyonel sapmaların santimetre mertebesinde sınırlandığı bir hassasiyet düzeyine ulaşmaktadır. Yüksek hızlı CAN‑Bus iletişimi ve gerçek zamanlı veri füzyonu ile senkronize edilen bu teknolojik altyapı, ekim ve hassas gübreleme gibi kritik tarla operasyonları için karmaşık hareket yollarını optimize etmektedir. Gelişmiş kontrol üniteleri ile sezgisel insan‑makine arayüzleri arasındaki sinerji, operatör kaynaklı hata payını minimuma indirirken; yakıt tüketimi ve mekanik yorgunlukta kayda değer bir azalma sağlamaktadır. Temel yönlendirme işlevlerinin ötesinde, dinamik arazi telafisi ve otomatik başak (headland) dönüş planlaması gibi özellikler, farklı topografik koşullar altında dahi tutarlı konumsal hassasiyet sunmaktadır. Bu araştırma, algoritmaların gerçek "hasadının"; manuel kontrolden veriye dayalı operasyonel zekâya geçişte ortaya çıktığını ileri sürmektedir. Tam otonom tarımsal ekosistemlere geçiş yolunda kritik bir kilometre taşı niteliği taşıyan bu gelişmiş konumlandırma çözümleri, ekonomik optimizasyon ve çevresel sürdürülebilirlik için sağlam bir temel sunarak akıllı mekanizasyonun bir sonraki sınırını tanımlamaktadır.
Telif Hakkı (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.
Telif Hakkı (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.
Telif Hakkı (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.
Telif Hakkı (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.
Parçacık sürüsü optimizasyon algoritmasının bibliyometrik analizi
Günümüzde büyük boyutlardaki çalışma ortamlarında, verimli arama işlemlerini kullanarak çözüm uzayındaki optimum çözüme daha hızlı ulaşıp optimizasyon problemlerine çözüm bulmak amacıyla metasezgisel algoritmalar kullanılmaktadır. Bu algoritmalardan biri olan Parçacık Sürüsü Optimizasyon algoritmasının, kodlamasının kolay olması ve yüksek performansı ile çeşitli optimizasyon problemlerinde, özellikle karmaşık ve çok boyutlu problemlerde, başarılı sonuçlar elde etmesi nedeniyle son yıllarda popüler olarak tercih edildiği görülmektedir. Bu çalışma kapsamında, literatürde son beş yılda (2020-2024) Parçacık Sürüsü Optimizasyon algoritması kullanılarak yapılan araştırmaların bibliyometrik analizi yapılmıştır. Yapılan çalışmada Web of Science veri tabanında “Particle”, “Swarm” ve “Optimization” anahtar kelimeleri aratılarak 29867 yayına ulaşılmıştır. Web of Science veri tabanından edilen veriler VOSviewer programında literatür analizi, ağ analizi ve görsel veri keşfi yöntemleri kullanılarak analiz edilmiştir. Yapılan analizler sonucunda kategori dağılımı, yayınların yıllar içindeki dağılımı, yayınların döküman tipleri, yayınların dizin dağılımı, yayınların ülkelere göre dağılımı gibi çeşitli kriterler ile ilgili çalışmalar tüm detayları ile niceliksel olarak verilmiştir.
Telif Hakkı (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.
Telif Hakkı (c) 2023 AINTELIA Science Notes Journal https://creativecommons.org/licenses/by-nc/4.0
Müşteri Segmentasyonu için K-Means, Bulanık C-Means ve Bulanık Mantık Tabanlı Kümeleme Algoritmalarının Karşılaştırılması
Müşteri segmentasyonu, işletmelerin pazarlama stratejilerini optimize etmeleri ve müşteri sadakatini artırmaları için kritik bir araçtır. Bu çalışma, K-Means, Bulanık C-Means (FCM) ve Bulanık Mantık tabanlı kümeleme algoritmalarını kullanarak, müşterilerin satın alma davranışlarına dayalı olarak segmentlere ayrılmasını amaçlamaktadır. K-Means algoritması, verileri belirli sayıda kümeye ayırarak her küme için bir merkez belirlerken, FCM algoritması, veri noktalarının birden fazla kümeye değişen üyelik dereceleri ile ait olmasına izin vermektedir. Bulanık Mantık tabanlı kümeleme ise, her özellik için tanımlanan bulanık üyelik fonksiyonları ve kurallar sistemi kullanarak daha nüanslı bir segmentasyon sağlar. Çalışmada, Online Retail veri seti kullanılarak her üç algoritmanın performansı Silhouette skoru, Calinski-Harabasz skoru ve Davies-Bouldin skoru ile değerlendirilmiştir. Sonuçlar, K-Means'in genellikle daha yüksek Silhouette skorları ve Calinski-Harabasz indeksi değerleri sunduğunu, FCM'nin kümeler arası benzerlikleri yönetme esnekliği sağladığını ve Bulanık Mantık tabanlı yaklaşımın özellikler arasındaki karmaşık ilişkileri daha iyi yakaladığını göstermektedir. Bulgular, müşteri segmentasyonu ve pazarlama stratejilerinin optimizasyonu için değerli içgörüler sunmaktadır.
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.
Traktör Lastiği Diş Yapısının Sonlu Elemanlar Yöntemi ile Optimizasyonu
Bu çalışmada, arazi için kullanılan traktör lastiğinin Görsel Ortamlarda sonlu eleman analizi (FEA) tekniği kullanılarak modellenmiş ve oluşturulan bu modelin ANSYS yazılımı kullanılarak lastik ömrü tahmin edilmeye çalışılmıştır. Lastik malzemesi olarak, elastikiyeti yüksek olan bir malzeme olan aynı zamanda mekaniğe ve lastiklerde kullanılan kauçuğun davranışına uygun davranışlar sergilemesi nedenleriyle Mooney-Rivlin seçilmiştir. Tekerlek modeli Solid186 elemanlarından oluşacak şekilde katı olarak modellenmiştir.
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.