Engineering Proceedings doi: 10.3390/engproc2026152009

Authors: Sardorjon Samiev Shokhabbos Doliev Farrukh Juraev Sunnatjon Farxodov Ramziddin Toshtemirov

Today, the mechanical processing and treatment of metals are of great importance in the development of industrial sectors. Therefore, in the process of mechanical metalworking, a need arises to create new modern equipment or improve existing devices. In industrial sectors, there are several problems with devices and machine tools (such as the 1K62 lathe-screw cutting machine) used in the mechanical processing of metals, including the turning of cylindrical and conical parts, machining their external and internal surfaces, thread cutting, and surface polishing. Specifically, it is difficult to precisely control the spindle speed during the metal-cutting process; energy consumption is high due to the high starting current (5–7 times the nominal value) of asynchronous motors during machine operation; the vibration level is high during operation; and there is no torque control. These problems can be eliminated by using scalar (simple) and vector control methods of frequency converters. In this case, the vector control method was used in the research because it has several advantages as a solution to these problems. As a result, it was demonstrated during the research process that the spindle speed can be reduced to 12.5 rpm. This made it possible to precisely control the spindle speed, maintain torque at low speeds, increase energy efficiency, reduce the load on mechanical transmissions, and ensure the stability of the cutting process. In addition, by operating the asynchronous electric motor through a frequency converter, it is possible to increase the efficiency and service life of such machine tools. This is of significant importance in the process of optimizing the electrical power supply system of the machine tool.

Engineering Proceedings doi: 10.3390/engproc2026154075

Authors: Rositsa Velichkova Violeta Ivanova Iskra Simova Neven Krastev Angel Aleksandrov

The current paper presents an experimental study of a hydrokinetic water turbine designed to harness kinetic energy from waves. Six turbine configurations, with different numbers of blades and wing profiles, were tested at three flow velocities (0.5, 0.8, and 1.0 m/s) on a laboratory test rig. The results demonstrate that both the number of blades and the airfoil profile significantly affect turbine performance. Turbines with smaller blade profiles achieve higher rotational speeds, while the optimal number of blades depends on the specific geometry. The study highlights the potential of wave energy as a renewable resource and provides insights for improving turbine design.

Engineering Proceedings doi: 10.3390/engproc2026154071

Authors: Valeri Bakardzhiev Konstantin Chukalov Sabi Sabev Plamen Kasabov Agop Izmirliyan

This article presents a comparative study of the geometric accuracy of spur gears manufactured by additive manufacturing (3D printing) and by injection molding. Gears were produced from two engineering plastics—POM (polyoxymethylene) for injection molding and carbon fiber-reinforced polyamide for 3D printing. The study evaluates three key parameters: deviation from the involute profile, single-pitch deviation, and surface roughness (Ra). Gears with identical geometric parameters (module 3 mm) were produced using both methods, allowing direct comparison between the resulting products under identical design conditions. Profile measurements were performed using a coordinate measuring machine (CMM) according to ISO 1328-1:2013. The surface roughness of gear teeth is a key parameter that determines operability, efficiency, and wear resistance. Smoother surfaces reduce friction and wear, leading to lower operational losses, higher efficiency, and longer service life. High roughness may lead to local stress points, increased noise levels, and vibrations during operation, which are especially critical for high-speed and heavily loaded gears. The obtained data serve as a basis for an objective evaluation of the applicability of additive manufacturing and injection molding for producing gears with different requirements for geometric accuracy, as well as for formulating guidelines for selecting an appropriate manufacturing technology depending on the specific application.

Engineering Proceedings doi: 10.3390/engproc2026154072

Authors: Aitolkyn Rysbek Yeldos Altay Ivaylo Stoyanov

This article presents the results of the classification of measurement errors of electromyography signals caused by interference and noise. It is shown that interference and measurement noise mainly occur during the registration of electromyography signals and significantly reduce the measurement accuracy. To classify measurement errors, the article analyzes and identifies the features of interference and measurement noise. This analysis is based on the mechanism of occurrence and the physical nature of interference and noise, as well as their spectral, frequency, and statistical characteristics. Initially, to analyze and identify the features of interference and noise, the article examines the characteristics of electromyography signals recorded during the movement of a human limb. It is shown that the informative components of the motor units of electromyography signals during limb movement are variable. The analysis results revealed that measurement noise and interference are slowly variable and have a Gaussian distribution. The frequency composition of low- and high-frequency interference and measurement noise is distinct from that of the measured electromyography signals. To systematize the results of the analysis, a classification scheme for measuring errors in electromyography signals is presented. It is recommended to use the measurement error classification scheme when solving the problem of signal filtering, where it is necessary to identify informative components for measuring and evaluating the movement of human limbs using electromyography signals. Furthermore, a comparative evaluation of band-pass filters of different orders was conducted to eliminate interference. A lower-order band-pass filter, even when implemented unidirectionally, improves the signal-to-noise ratio and highlights informative components.

Engineering Proceedings doi: 10.3390/engproc2026154074

Authors: Bryan Morocho Alejandro Piñeiros William Oñate

This study contributes to the design of a bidirectional communication architecture based on the ISA-95 standard, which is integrated with an IoT gateway network to digitize plant-floor data, a manufacturing execution system (MES) with local backup and automatic inventory updates using YOLOv5-based computer vision, and a cloud instance for product order management. According to interoperability metrics, the results demonstrate the feasibility of developing digital scalability in an architecture composed of heterogeneous devices, maintaining interconnectivity throughout the entire value chain.

Engineering Proceedings doi: 10.3390/engproc2026141022

Authors: Hsien-Cheng Chou Yu-Hsien Sun Yu-Shun Liu

Generative AI’s application in hands-on engineering systems has gained substantial attention, particularly in complex hardware–software configurations that involve physical operational risks. Traditional configuration tools often lack real-time contextual awareness, limiting the diagnostic capabilities of novice users. To overcome such limitations, we developed an AI-driven system configuration and diagnostics engine by integrating a deterministic rule-validation layer with a probabilistic generative large language model, balancing factual reliability with adaptive conversational debugging. Using task–technology fit (TTF) theory, the engine’s functional alignment was evaluated through an experimental study involving 100 participants. Structural equation modeling results showed that TTF significantly enhances user learning satisfaction and overall diagnostic learning outcomes. Accounting for 86.3% of the variance in diagnostic performance (R2 = 0.863), the developed engine presents high effectiveness in supporting real-time, interactive engineering diagnostics.

Engineering Proceedings doi: 10.3390/engproc2026154069

Authors: Emilian Bekov Victoria Toncheva-Staneva Angel Marinov

A low-cost phantom for ultrasound diagnostics is proposed for training applications involving small parts imaging. The phantom is constructed from plexiglass and includes openings that enable evaluation of the spatial resolution, measurement accuracy, and grayscale representation. Different filler materials are used to study contrast and shadowing effects in B-mode imaging. Experimental studies using a linear transducer show good agreement between measured and actual dimensions, with acceptable deviations. Clear grayscale differences are observed depending on acoustic impedance. The proposed phantom is inexpensive, easy to fabricate, and suitable for educational and training purposes in ultrasound diagnostics.

Engineering Proceedings doi: 10.3390/engproc2026154068

Authors: Maksat Kurmangazy Ussen Shylmyrza Kassymbek Ozhikenov Aidos Sultan Yerkebulan Nurgizat Dauren Bizhanov Gani Sergazin Nursultan Zhetenbayev

Human motion monitoring plays an important role in biomechanics and rehabilitation, where accurate measurement of limb movement is required. This study presents the design and experimental validation of a wearable system for ankle motion monitoring based on an inertial measurement unit (IMU). The proposed system integrates an IMU sensor, an Arduino Nano microcontroller, a microSD data logging module, and a battery-powered supply within a compact wearable device. Experimental tests were conducted to analyze three oснoвных movements of the ankle joint, including dorsiflexion–plantarflexion, inversion–eversion, and abduction–adduction. The IMU sensor was used to record angular velocity, linear acceleration, and orientation data in real time. The results demonstrate that the system is capable of capturing characteristic motion patterns and distinguishing between different types of ankle movements. The measured orientation angles (Roll, Pitch, and Yaw) correspond well to the expected kinematic behavior of the ankle joint. The obtained ranges are consistent with typical physiological values, confirming the reliability of the proposed approach. Overall, the developed wearable system provides an effective and practical solution for motion monitoring. It can be applied in rehabilitation and biomechanical analysis, while future work will focus on integrating additional sensors to enable a full multisensor platform.

Engineering Proceedings doi: 10.3390/engproc2026154067

Authors: Lyailya Cherikbayeva Zarina Melis Arman Yeleussinov Aisulu Ataniyazova Aktoty Cherikbayeva

Accurate and automated segmentation of ischemic stroke lesions from magnetic resonance imaging (MRI) data is crucial for rapid clinical diagnosis, treatment planning, and patient prognosis. This study presents a comprehensive evaluation of various deep learning architectures for segmenting acute and subacute ischemic lesions using the multicenter ISLES 2022 dataset. We compare traditional 3D U-Net models, attention mechanisms, and modern hybrid architectures, specifically Swin-UNETR (Swin Transformer-based UNet TRansformer). Our experiments utilize multimodal MRI data, including diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps. Results demonstrate that the Swin-UNETR architecture, combined with a specialized pad or crop preprocessing pipeline, advanced data augmentation using the TorchIO library, and a combined Dice–Cross Entropy (DiceCE) loss function, provides competitive performance. The best Swin-UNETR model achieved a Dice of 0.717, precision of 0.793, recall of 0.708, and HD95 of 12.24 mm on the validation set, with a loss of 0.26. The baseline 3D U-Net achieved a comparable Dice of 0.726, while Swin-UNETR showed higher precision, recall, and lower HD95. This study highlights the effectiveness of integrating hierarchical transformers with convolutional decoders for extracting both local and global contextual information in complex medical imaging tasks.

Engineering Proceedings doi: 10.3390/engproc2026154070

Authors: Petar Todorov Teodora Hristova Grigor Mihaylov

This article proposes a methodology for rapid measurement of corrosion growth in drill rods. Oil and gas production in real field conditions is associated with frequent interruptions, measurements, failures, and, above all, high risk. Usually, methods used to determine corrosion growth are time-consuming and expensive. In field conditions, it is useful to immediately determine the rod condition. This will ensure accident-free operation over an extended period and will provide security to personnel and facilities. In addition, increasing security will improve technical, economic, and social positive effects. Therefore, this is done to achieve sustainable development criteria. Applying on-site measurement methods avoids continuous tracking of temperature, pressure, and other factors related to corrosion rate. The proposed methodology supports high-efficiency oil and natural gas production and environmental protection, in accordance with sustainable development criteria. It provides a good basis for future development not only for the workers to implement new methods of work tracking but also for the company to achieve increased efficiency in mining. Future work includes validating the methodology with real measurements and building a rod wear model.

Engineering Proceedings doi: 10.3390/engproc2026154064

Authors: Zahari Parunev Goran Goranov

A software tool for realistic simulation of DDS output phase noise under non-ideal conditions is presented. Unlike approaches that rely solely on ideal clock scaling or require detailed device-specific characterization, the proposed method combines an ideal scaled contribution with an optional quantization floor and a compact phenomenological residual-noise model that captures output-stage limitations. The tool accepts sparse clock phase-noise data and DDS parameters, reconstructs the output spectrum, and exports ADIsimPLL-compatible models. Validation against AD9912 (50 MHz, 150 MHz) and AD9915 (123 MHz, 978 MHz) datasheet curves shows agreement within 2–3 dB for most offsets, with deviations up to 5 dB only at 10 Hz for the 978 MHz case. The approach enables phase-noise reconstruction from limited published data and supports hybrid DDS-driven PLL synthesizer studies.

Engineering Proceedings doi: 10.3390/engproc2026154065

Authors: Konstantin Chukalov Sabi Sabev Valeri Bakardzhiev Plamen Kasabov Agop Izmirliyan Georgi Hristozov

In modern mechanical engineering, the requirements for surface roughness, geometric and dimensional accuracy are constantly increasing, as these characteristics are critical for the reliability, durability and functional application of products. The parameters of 3D printing significantly affect the quality, accuracy and physico-mechanical properties of the manufactured parts. This, in turn, necessitates a deeper understanding of the influence of printing modes on the accuracy and physico-mechanical properties of the printed component. The present study aims to determine the effect of the main technological parameters of the FFF process—printing speed and layer height—on the geometric accuracy and surface roughness of a functional machine element. For this purpose, a central composite design of experiments was developed to optimize both the process and the product. PETG was selected as the experimental material. The obtained results show that the layer height is the dominant factor influencing the roughness of the printed surface, while the printing speed exhibits a nonlinear effect within the considered range. The developed regression model allows determining an optimal range of printing parameters that achieves minimal surface roughness and high geometric accuracy of the part.

Engineering Proceedings doi: 10.3390/engproc2026154063

Authors: Gergana Spasova Iliyan Boychev Ayshe Shaban

The main goal of this work is to develop an algorithm for visual presentation of information through light in three-dimensional space. The problem of controlling a large number of digital outputs at the same time using a very small number of control signals is considered. A single-chip microcontroller Arduino Atmega 328 is used as a processor in a control unit (CU). The control unit is implemented based on shift registers. A physical structure of LEDs is also implemented as an executive device (LED matrix). Remote control is done using a Bluetooth module, and software (for controlling the Arduino chip) is added to control the LED matrix. The matrix consists of a total of 125 LEDs, forming a structure of 5 × 5 × 5 LEDs representing a three-dimensional “screen” through which information is presented. The software environment for developing applications specifically for Arduino is used for the software, namely Arduino IDE. The proposed implementation achieves a low-cost 3D display architecture controlled by a voice-controlled volumetric interface. Based on the shift registers used, effective control over the entire 3D visualization system is achieved.

Engineering Proceedings doi: 10.3390/engproc2026154062

Authors: Delyan Genkov Miroslav Slavov

Software-defined networks are widely deployed in modern networking world. They allow centralized monitoring and control of all network devices through a software application—the controller. Many controllers exist; some of them are open source, and others are commercial. Currently, most networking vendors propose their own solutions, using different controllers, but they are often incompatible with each other. There is no universal solution that can interact with most of the available vendors and platforms for software-defined networking. This article presents a topology-learning, visualization, and statistics module, designed to interact with one of the most popular controllers—OpenDaylight.

Engineering Proceedings doi: 10.3390/engproc2026154058

Authors: Hristo Hristov Vanya Ivanova Ivan Ivanov Kristina Yasenova Georgi Dimov

This article presents the development of a web application for automated translation of TXT, DOCX, and PDF documents, built with Next.js/React and integrated with OpenAI LLM models. The study investigates the influence of the sampling parameters temperature and Top-P (nucleus sampling) on translation quality between Bulgarian and English. Translation experiments were conducted on a bilingual corpus of popular-science and biographical texts, analysing effects on verb tense, lexical choice, word order, and output stability. Proper tuning of these parameters allows control over style and lexical richness without compromising reliability, and the trends remain relevant in newer model versions.

Engineering Proceedings doi: 10.3390/engproc2026143069

Authors: Yolly Fabito Errol Foja Lou Foja Tomas Faminial Sherryll Mindo Fetalvero Savahna Guilene Merano

Educational institutions face pressure to strengthen audit readiness and modernize financial processes amid digital transformation. Romblon State University Catering Services supports campus operations and student training while generating income, yet sustainability depends on transparent, timely, and auditable controls. This study evaluated internal control practices and key transaction processes using the COSO Framework and Hall’s Transaction Processing Cycles. A descriptive qualitative case study employed process walkthroughs from March to June 2024. Results indicate centralized, undocumented procedures, limited audit trails, and weak segregation of duties in purchasing, sales, and billing, alongside absent cost accumulation and allocation, constraining cost visibility and process accountability.

Engineering Proceedings doi: 10.3390/engproc2026154061

Authors: Marwah Kamil Hussein Noor Mohammed Almoosawi Haider M. Al-Mashhadi

In this research, a computer vision model was developed that enables object detection, shape identification, and orientation prediction using image segmentation. The GrabCut algorithm was used to segment images. GrabCut was initialized in two ways: first, by initializing the input image with a bounding box (a box surrounding the object’s position in the image), or second, by initializing the input image with a suggested approximation mask (a mask resembling the segmentation). Three steps are performed iteratively: first, the color distribution of the foreground and background is estimated using a mixed Gaussian model (GMM). Second, a random Markov field is generated to manipulate the pixels (foreground/background). Finally, a histogram optimization algorithm is applied to achieve the desired final segmentation. Although the iterative steps may require time and effort to work within the algorithm, GrabCut consistently delivers high-quality results in segmenting the input image and producing an output image that closely resembles the original.

Engineering Proceedings doi: 10.3390/engproc2026154059

Authors: Tsvetislava Lavchieva Delyan Genkov

This paper presents a prototype system for real-time camera-based monitoring of product presence and change events. The proposed approach combines object detection, class-based counting, and lightweight event logic to determine whether products appear, disappear, or change in quantity over time. The system is intended for live camera input, while the current implementation has also been tested in a controlled manner using prerecorded video sequences. A modular software structure is used to support future extensions, including experiments with additional detection models and product-specific training data. The current results show that the developed prototype can serve as a practical basis for intelligent visual monitoring in product observation scenarios.

Engineering Proceedings doi: 10.3390/engproc2026154047

Authors: Dimitar Stoyanov Vanya Georgieva

This work sets out a complete systems engineering approach to lessen the world’s plastic waste problem—as nearly 91% of original plastic waste is not recycled. The study deals with the financial difficulties of made-to-order production by offering a local production scheme using Distributed Recycling by way of Additive Manufacturing—DRAM. A unique mechanical recycling process was developed, at the heart of which is a straight “bottle-to-strip” supply method. This new idea allows post-use PET waste to become custom figures by not using the energy-heavy filament-pushing step. The technical build joins a three-level e-trade stage with a changed Fused Deposition Modeling—FDM—making line. Findings from tests show the put-in-place system lowers the changeable cost of 3D print material from about $20 a kilo for new stuff to $1–2 a kilo for recycled PET strips.

Engineering Proceedings doi: 10.3390/engproc2026154066

Authors: Ivan Dimchev

Airflow control is a key factor determining the operation of air-handling units (AHUs) with integrated exhaust air heat pumps, as it directly affects both the energy performance of the unit and the operating conditions of the heat pump. The effects of constant air volume (CAV) and demand-controlled ventilation (DCV) strategies were evaluated for two building types with different occupancy profiles: a university lecture hall and an open office. The results show that DCV improves system efficiency in both cases, with the annual coefficient of performance of the AHU increasing from 3.95 to 5.80 in the office scenario (+47%) and from 4.98 to 5.97 in the classroom (+20%). In the classroom case, the DCV-1300–1800 strategy reduces total energy consumption from 3445 kWh to 3102 kWh (−10%), while increasing thermal output from 17,147 kWh to 17,774 kWh (+4%), demonstrating the existence of an optimal airflow range. Further reduction in the airflow decreases energy consumption but also reduces heating energy, indicating a compromise between fan energy savings and heat pump performance. In all cases, the supplied energy exceeds the demand for ventilation heating by up to 71%, highlighting a mismatch between energy production and actual demand.

Engineering Proceedings doi: 10.3390/engproc2026154060

Authors: Penko Mitev Kiril Mitev

This paper presents the design and implementation of an automated quality inspection system based on rotary indexing and inline dimensional measurement. The proposed system integrates a rotary table for precise part positioning with a contact displacement sensor for high-resolution measurement, enabling real-time evaluation of critical geometric parameters during the production cycle. A PLC-based control architecture ensures synchronized operation between mechanical motion and measurement acquisition, utilizing industrial communication over PROFINET for reliable data exchange. A dedicated decision algorithm is implemented to classify parts as acceptable or defective based on predefined tolerance thresholds, allowing immediate feedback and process control. Experimental validation demonstrates high repeatability and measurement stability under continuous operation, confirming the suitability of the system for industrial applications requiring fast and reliable quality control. The results highlight the effectiveness of combining rotary indexing mechanisms with inline measurement techniques for improving production efficiency and reducing inspection time.

Engineering Proceedings doi: 10.3390/engproc2026154052

Authors: Sayat Akhmejanov Zhanar Bigaliyeva Abu Alim Ayazbay Aidos Sultan Yerkebulan Nurgizat Arman Uzbekbayev Kassymbek Ozhikenov Gani Sergazin Nursultan Zhetenbayev

This paper presents the kinematic design and experimental validation of a two-degree-of-freedom ankle mechanism based on stepper motor actuation and ball-screw transmission. The proposed system employs a hybrid architecture, combining actively controlled motion in the sagittal plane with passively compliant motion in the frontal plane. Experimental evaluation under no-load laboratory conditions demonstrated a strong linear relationship between motor steps and joint angle within an operating range of approximately ±22°, with coefficients of determination exceeding 0.97. The results confirm the predictability and repeatability of the kinematic transformation while revealing minor hysteresis effects associated with mechanical transmission. The proposed mechanism is intended as a validation platform for studying motion transformation in multi-DoF (degree-of-freedom) ankle systems, providing a basis for future work on load analysis, torque modeling, and closed-loop control.

Engineering Proceedings doi: 10.3390/engproc2026142020

Authors: Ahmed A. Hakim Mahmoud Ibrahim Abdelsalam Mostafa I. Marei H.E.A. Ibrahim

The dual active bridge (DAB) converter is one of the most common types of isolated bidirectional power converters in modern spacecraft EPS owing to its galvanic isolation, bidirectionality, soft switching, and good controllability. However, the goal of power transfer maximization often clashes with real-world spacecraft EPS constraints, namely thermal compliance, reliability, and the accuracy of simplified models used for analysis. This paper proposes a physics-informed methodology to derive the practical operating range under single phase shift (SPS) control based on a rigorous piecewise time-domain representation. From this model, the steady-state initial condition, general closed-form RMS current expression, ZVS boundary condition, and ZVS-aware loss model linked to the junction-temperature estimate are derived. The validity domain of the fundamental harmonic approximation (FHA) is evaluated against the exact model across the full (φ, k) space, and a two-dimensional operating map superposing power contours, the ZVS limit, and the thermal limit is presented. For the baseline case study at k = 1.0, the thermal constraint limits the nominal feasible upper phase shift to approximately 35°, while the broader 15–45° range remains useful for design assessment and operation toward 45° requires lower effective resistance and/or improved thermal management. The normalized SPS power-transfer curve retains the same shape under variations in L and fs, but RMS current, losses, and thermal feasibility must be reassessed for each converter design. The resulting closed-form framework provides a steady-state feasibility-evaluation tool for spacecraft EPS design and offers a computational basis for future supervisory constraint evaluation under varying voltage, load, and thermal conditions.

Engineering Proceedings doi: 10.3390/engproc2026154056

Authors: Sarah Al-Azzawi Ghaida A. Al-Suhail

Brain tumor classification from magnetic resonance imaging is an important task in computer-aided diagnosis because tumor appearance varies substantially across cases. This study proposes a hybrid deep learning model—namely, BrTxNet—for binary brain tumor classification based on EfficientNetB0 and VGG16. Single-backbone and hybrid configurations were evaluated using a group-aware balanced dataset. Two hybrid fusion strategies were examined: direct concatenation and feature-level learnable fusion. The proposed model projects and adaptively weights features from both branches before classification. Experimental results showed that the learnable fusion model achieved the best performance, reaching 98.79% accuracy and 99.93% ROC-AUC with strong sensitivity and specificity. Furthermore, Grad-CAM and t-SNE analyses support the interpretability and discriminative power of the learned fused representations.

Engineering Proceedings doi: 10.3390/engproc2026154055

Authors: Daniela Petrova

This paper presents a web-based platform for analyzing the impact of hyperparameters on machine learning models for text classification. The system integrates a PHP-based web interface with Python-based machine learning modules, enabling users to experiment with different algorithms and parameter configurations in an interactive environment. The platform supports multiple classification models and allows dynamic adjustment of parameters such as n-gram range, regularization strength, and feature extraction techniques. Experimental results demonstrate that hyperparameter tuning has a significant influence on classification performance. The proposed system provides an intuitive and practical tool for both educational purposes and applied research in natural language processing.

Engineering Proceedings doi: 10.3390/engproc2026154054

Authors: Lazzat Kurmangalieva Assem Kabdoldina Beibit Shingissov Nurgul Smailova Nursultan Kuldeev Baglan Bekbossynova Perizat Rakhmetova

Vision-based perception is essential for robotic manipulator grasping because the accuracy of object localization directly affects grasp planning and motion execution. This paper presents a comparative analysis of computer vision algorithms for object detection and localization in a structured robotic pick-and-place scenario. Classical edge detection methods, including Canny, Sobel, Prewitt, and Roberts, were applied to the same RGB image of a manipulator workspace containing box-shaped objects. The algorithms were evaluated using detection accuracy, F1-score, centroid localization error, contour stability, and processing time. The results showed that Canny provided the most reliable performance, detecting all 12 target objects with the highest localization stability. The study confirms that classical edge detection can support lightweight vision-based robotic grasping.

Engineering Proceedings doi: 10.3390/engproc2026145016

Authors: Svetlana Boshnakova

We investigated the possibility of developing super austenitic stainless steel Avesta SMO 254 X1NiCrMoCuN20-18-7 (EN 10088-4) and austenitic stainless steel X15CrNiSi25-21 (EN 10095) coated with SiC, resulting in the obtainment of a Metal Matrix Composite (MMC) by additive manufacturing (AM) for the upgrade of nozzles for sulfur recovery thermal reactors. One layer of the MMC targets the outer surface of the part that is in constant contact with the flame and the area is subjected to high friction erosion. The Directed Energy Deposition Laser (DED-LB) method has made it possible to produce a high strength-to-weight ratio. The aim is to engage lower-cost material with similar thermal stability and durability in extreme conditions. The robotic unit used for the application allowed for computer control of the positioning, feeding of the SiC particles inside the shielding gas and deposition in the molten pool. After the solidification process, visual testing (VT) and ultrasonic testing (UT) were applied for non-destructive evaluation, checking for disbonding and subsurface imperfections. Then, samples were tested with microhardness measurements, bond strength, microcracking detection, porosity, interface zone assessments and microstructural analysis. The process achieved 0.4 to 0.7 KJ mm−1 heat input with no defects and the intended nozzle surface passed UT and VT. Controlled parameters provided strong metallurgical bonding.

Engineering Proceedings doi: 10.3390/engproc2026154051

Authors: Kadir Kesgin Selahattin Kosunalp Desislava Atanasova

Large university campuses need timely, privacy-conscious information about how indoor spaces are used in order to improve space management, energy efficiency, and operational responsiveness. Yet many indoor positioning solutions still depend on additional hardware such as Bluetooth Low Energy beacons, ultra-wideband anchors, or camera-based sensing, which increases deployment cost and maintenance complexity. This paper presents a lightweight campus occupancy analysis and indoor localization framework that reuses an existing Cisco Wireless LAN Controller (WLC) infrastructure as a sensing layer. The system retrieves received signal strength indicator (RSSI) observations from access points over secure SSH sessions, converts these observations into approximate distance estimates through a calibrated log-distance path loss model, and computes user positions using weighted non-linear least-squares multilateration (Mlat). In addition to point localization, the framework generates occupancy heatmaps, cumulative reliability curves, and access-point-density sensitivity analyses based on a 50 m × 50 m evaluation scenario with 500 randomized samples. To support privacy-preserving deployment, the service layer exposes only zone-level occupancy information and omits personally identifiable network identifiers. The results indicate that Wi-Fi-based localization can provide cost-effective and scalable occupancy intelligence with sufficient accuracy for campus-wide density monitoring and smart building applications.×

Engineering Proceedings doi: 10.3390/engproc2026152007

Authors: Adriana Silion Liliana Rusu

In the context of accelerating global warming and the European Union’s objective of achieving climate neutrality by 2050, offshore wind energy serves as a vital strategy in the transition to a low-carbon energy system. One of the main objectives of this paper is to analyze the expected dynamics of wind power in the Black Sea. Further, an evaluation of the technical feasibility and performance of offshore wind turbines under future climate projections up to 2050 is performed. For this study, wind data were obtained from the CORDEX regional climate database, under the RCP 4.5 scenario, for a 25-year period (2026–2050) at nine locations in the Black Sea, situated at water depths ranging from 40 m to 120 m. The assessment examined six different commercial offshore wind turbines, with rated capacities between 2.3 MW and 6.3 MW evaluated at a common hub height of 100 m, based on the characterized wind potential identified at the selected sites. The proposed methodology integrates regional climate projections with turbine performance to provide a forward-looking assessment of offshore wind potential in the Romanian Black Sea area. The results focus on the comparative performance of the selected wind turbines in the nine examined locations, highlighting the differences in the energy field and efficiency depending on the wind conditions and the water depths.

Engineering Proceedings doi: 10.3390/engproc2026154050

Authors: Cengiz Mert Türkmen Fevzi Doğaner Caner Baybaş Hatice Akavioğlu

The goal of this project is to identify ways to improve the performance of the bushing–flange–circlip connection on the V-arm suspension components of heavy-duty commercial vehicles. Failures related to circlip ejection and flange loosening identified from customer relationship management (CRM) data motivated the development of this computational optimization framework. This study involved generating a parametric dataset, using Latin Hypercube Sampling (LHS) with synthetic data, for the development of the artificial neural networks (ANNs). The networks use the surrogate model to optimize solutions through a combination of Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms. The ANN provided an overall test set of R2 = 0.968 across the three objective functions. The hybrid optimization method produced 11 surrogate-predicted Pareto-optimal candidate designs, simultaneously minimizing the micro-displacement and stiffness loss while maximizing the fatigue life. The present results are based on analytically derived synthetic training data. Simcenter 3D Version 2506 Finite Element Analysis (FEA) integration and prototype validation constitute the planned next phase. The methods described here can be configured for other components of the suspension and are expected to be compatible with similar applications depending on future enhancements.

Engineering Proceedings doi: 10.3390/engproc2026154053

Authors: Abay Dostiyarov Iliya Iliev Yerdaulet Baigozha Madina Kumargazina Nurasyl Tolembay Hristo Beloev Ivan Beloev

The transition to a low-carbon energy paradigm requires reducing nitrogen oxide NOx and carbon monoxide CO emissions to 5–9 ppm. This study investigates the impact of the primary zone excess air ratio α and mixing quality on pollutant yields, addressing the “seesaw” trade-off mechanism between NOx and products of incomplete combustion. Analysis of Lean Premixed and Micromix technologies demonstrates that achieving NOx levels below 5 ppm requires local α fluctuations to remain within a root-mean-square deviation of 3–4%. An original burner design with an intelligent emission control system is presented, enabling dynamic adjustment of local αin to maintain combustion within a narrow stability window. Experimental results confirm that minimum toxicity, with NOx concentrations below 20 ppm, is achieved at αin = 1.7–1.8. The implementation of this technology ensures stable operation across transient and part-load regimes while mitigating thermal NOx formation and thermoacoustic instabilities.

Engineering Proceedings doi: 10.3390/engproc2026154049

Authors: Ioannis Mozakis Emmanuel Karapidakis

Autonomous island power systems face inherent vulnerability to severe contingency events, yet current N-1 frameworks and thermal-only criteria systematically underestimate operational risk in grids with high renewable penetration. This paper presents an integrated 24 h N-2 contingency assessment explicitly combining transmission line outages with generator failures in a 6-bus island system, using PowerWorld Simulator (GOS Education 21) to evaluate 24,192 contingency cases across four load–RES operating scenarios. Results reveal a critical dual-channel risk structure. Thermal violations and network islanding represent mechanistically distinct failure modes that do not co-occur predictably. Critically, relying solely on thermal criteria misses adequacy impacts equivalent to 638 MWh/day in worst-case conditions. The time-dependent nature of vulnerability underscores the inadequacy of static snapshot analyses. These findings establish that N-2 contingency frameworks for autonomous grids must treat thermal security and islanding outcomes as co-equal metrics, fundamentally challenging conventional deterministic planning approaches.

Engineering Proceedings doi: 10.3390/engproc2026154046

Authors: Ivaylo Belovski Blagovesta Midyurova Kaloyan Ivanov Aleksandar Mandadzhiev Todor Mihalev

This study investigates the electrochemical properties of ceramic composite membranes for electricity generation. The system utilizes seawater or other saline solutions as a natural electrolyte in conjunction with copper and zinc electrodes, addressing the pressing need for carbon-emission-free energy sources. A significant challenge lies in identifying clays whose components can be integrated into the composition of low- to medium-power electromotive force generators. Their combination with zeolite, known for its electronegative potential, enables the synthesis of solid-state ceramic membranes. These membranes can subsequently be transformed into galvanic cells following the addition of metal electrodes and an electrolyte. The research focuses on emission-free electricity generation from natural sources, an increasingly relevant scientific objective. Naturally occurring raw materials offer an economically viable and sustainable solution, demonstrating favorable properties for integration as core components in electrochemical cells that utilize saltwater.

Engineering Proceedings doi: 10.3390/engproc2026154045

Authors: Rakesh N. Tiwari M. S. Vasundhara Prabhakar Singh Pradeep Kumar Galeti Vandana Battala Sai Sivani

The design and analysis of a 1 × 4 mm-wave MIMO antenna in a lateral configuration are presented. The antenna utilizes a square slotted patch structure and demonstrates triple-band operation at 28/38/43 GHz. The first band offers antenna bandwidth from 27.79 to 28.35 GHz (0.56 GHz), while the second band spans 37.68 to 38.43 GHz (0.75 GHz) and the third band covers 42.56–43.55 GHz (0.99 GHz), respectively. The antenna is fabricated on Rogers RO3003 with material permittivity of 3.0, a loss tangent of 0.001, and a substrate height of 0.25 mm. For both bands, the antenna demonstrates the isolation > 20 dB. It also provides peak gain values of 6.54 dBi at 28 GHz, 7.10 dBi at 38 GHz, and 6.11 dBi at 43 GHz with an average radiation efficiency of 82%. Key diversity metrices including ECC, MEG, and CCL were evaluated. The ECC remains below 0.022, and the CCL of the antenna is below 0.32 bits/s/Hz, which is favorable for MIMO systems. These results indicate that the reported MIMO antenna is suitable for future 6G communication systems.

Engineering Proceedings doi: 10.3390/engproc2026154044

Authors: Miroslav Tomov Michail Malamatoudis Stanimir Sadinov Plamen Tihov Gancho Tanev

Among the critically important conditions for optimal mobile device power source efficiency, ensuring reliable and long-term operation, is the matching of the output impedance of the radio transmitter with that of the antenna used. A step forward in the development of technology for the implementation of high-quality radio frequency radiation with minimal energy losses in telecommunications equipment could be devices that automatically match the output impedance of radio transmitters designed for high frequencies such as VHF and UHF. What has been achieved to date and implemented in the serial production of telecommunications equipment and in the majority of explorations is relatively optimized impedance matching between the output of the final amplifier stage and the transmitting antenna for a relatively wider frequency bandwidth. This leads to a significant deterioration in the transmission efficiency of some frequencies of the digital signal spectrum and a decrease in the data transfer rate. This publication presents the development and simulation of suitable elements for the implementation of automated impedance matching circuits.

Engineering Proceedings doi: 10.3390/engproc2026154043

Authors: Mirkan Zyuhtyu Georgi Krastev

This paper presents the development of a simulation game that models traffic interactions with a focus on autonomous parking using machine learning techniques. The simulator is built in the Unity environment and employs the Unity ML-Agents toolkit to train a virtual vehicle capable of performing parking maneuvers autonomously. The training process is based on reinforcement learning, where the agent learns through interaction with the environment using virtual sensors that detect distances and surrounding objects. A custom reward system guides the learning process by encouraging safe, accurate, and efficient parking while penalizing collisions and incorrect maneuvers. The developed simulator demonstrates the potential of interactive environments for research and education in autonomous driving and artificial intelligence.

Engineering Proceedings doi: 10.3390/engproc2026154036

Authors: Anna-Maria Lazarova Stiliyan Nikolov Reneta Dimitrova

The article analyzes the design of molds for the production of nonmetallic parts by injection molding. Based on the analysis on an existing mold, a mold with a modular design was developed in environment of the SolidWorks Version 2022 CAD system. The developed design of the matrix serves for the production of small-sized nonmetallic parts, with the possibility of readjustment when changing the parts being produced. The main steps necessary for readjusting the developed mold when changing the manufactured parts are defined. The proposed modular mold design was manufactured and tested during injection molding of two parts, “Handle” and “Fork”. The results obtained during the testing show the operability of the proposed design. The proposed modular mold design reduces the time and costs required to transition production from one part to another.

Engineering Proceedings doi: 10.3390/engproc2026154048

Authors: Kenzhebek Myrzabekov Kuanysh Alipbayev Akylbek Bapyshev Teodor Iliev Alina Fazylova

This article discusses the development of a control system for an electric propellant pump for a light-lift launch vehicle. The relevance of this work stems from the need to create compact and energy-efficient propellant supply systems capable of ensuring stable operation within the weight, size, and energy resource constraints of onboard equipment. The research object is presented as a single electromechanical-hydraulic system comprising an electric drive, a pump unit, a hydraulic circuit, and a digital control system. A mathematical model of the electric pump unit is developed, its numerical parameterization is performed, and a cascade control structure with a feedforward channel is proposed. A series of computational experiments are conducted, including an analysis of transient processes during pressure target changes, a study of the system’s response to hydraulic disturbances, and a robustness assessment under parameter uncertainty. It is established that the proposed control system provides higher response speed, a smaller integral control error, and better resistance to external disturbances compared to a classical cascade PI structure. The obtained results confirm the potential of the proposed approach for electric pump units for fuel supply of ultra-light launch vehicles.

Engineering Proceedings doi: 10.3390/engproc2026154041

Authors: Plamen Stanchev Nikolay Hinov Reni Kabakchieva

This paper presents a comparative analysis of scalar V/F and field-oriented control (FOC) control strategies for induction motor drives, including classical and AI-enhanced implementations. AI-based optimization is used to improve dynamic performance, energy efficiency, and robustness under various load conditions. The evaluation combines time-domain simulations with multi-objective Pareto analysis, taking into account speed-tracking accuracy, current voltage, control effort, and energy consumption. The results show that AI-enhanced controllers achieve superior trade-offs compared to conventional approaches. AI-enhanced V/F control also shows significant improvements over classical scalar control, offering a low-complexity alternative. The study demonstrates that AI effectively complements classical control structures, enabling efficient and reliable operation of induction motor drives.

Engineering Proceedings doi: 10.3390/engproc2026154038

Authors: Alina Fazylova Kuanysh Alipbayev Kenzhebek Myrzabekov Fariza Oraz Bagdat Sabyruly

Stable motion of differential-drive mobile robots on uneven surfaces remains a challenging control problem because local variations in rolling resistance and wheel–terrain interaction generate asymmetric traction forces, trajectory deviation, and heading instability. This study investigates a dual-motor mobile robot in which the left and right driving wheels are actuated independently and coordinated through a coupled control structure that simultaneously regulates linear speed and suppresses yaw motion. A control-oriented nonlinear dynamic model is developed by combining the longitudinal and yaw dynamics of the platform with first-order actuator models and resistance terms that represent uneven-terrain effects through side-dependent rolling losses. Based on this model, a coordinated control law is formulated to redistribute the control effort between the two drives in response to both speed and angular-velocity errors. Numerical experiments are carried out for nominal motion, localized asymmetric resistance, prolonged uneven-surface excitation, robustness-boundary analysis, and actuator-dynamics sensitivity. The results show that the proposed strategy improves directional stability under uneven-surface disturbances, limits the degradation of forward motion, and enlarges the admissible operating region compared with decoupled drive action. The study demonstrates that coordinated inter-wheel control provides a physically interpretable and computationally efficient solution for enhancing the stability of wheeled mobile robots operating on irregular terrain.

Engineering Proceedings doi: 10.3390/engproc2026154040

Authors: Duisenova Sholpan Zhunussova Aiza Amirkulov Bekzat Demessova Saule Yerzhigitov Yerkin

The article examines a “variable-frequency drive–induction motor” system of a pneumatic conveying device from the perspective of improving energy efficiency and enhancing the dynamic characteristics of the electric drive. A structural diagram of the frequency-controlled induction electric drive is presented, transfer functions of the system elements are derived, and its stability is analyzed. The study of the electric drive dynamics is carried out based on mathematical modeling in the MATLAB environment using both symbolic and numerical methods. It is established that the voltage stabilization system of the frequency converter ensures a stable operating mode of the induction motor for various values of the controller parameters. The work also includes the synthesis of control system parameters aimed at minimizing energy consumption. An objective function of energy costs is obtained, and optimal parameters of the frequency converter voltage controller are determined. Numerical modeling demonstrated a reduction in power consumption from 4 kW to 2.89 kW, confirming the effectiveness of the proposed approach. The results of the study indicate that the use of a frequency-controlled electric drive in pneumatic conveying systems makes it possible to increase energy efficiency, improve transient processes, and ensure stable system operation under varying loads.

Engineering Proceedings doi: 10.3390/engproc2026154037

Authors: Ufuk Celik Adem Korkmaz Georgi Krastev

This paper presents a software engineering case study on designing a reusable architecture for interactive 2D card games using Godot 4 and GDScript. Rather than focusing only on a specific game implementation, the study investigates generalizable engineering patterns for managing interactive objects, visual feedback, user interaction, and decision-making systems. A unified Item/Pool architecture is introduced to represent cards and game containers through reusable abstractions, reducing duplicated logic among different scene types. The proposed design separates input handling, object management, and rule enforcement through a signal-driven architecture. A canvas-item shader pipeline demonstrates how perspective tilt and dynamic shadow effects can be achieved in a 2D environment without requiring a 3D rendering pipeline. Furthermore, a transform-aware drop detection method based on affine inverse transformation is presented to overcome interaction errors caused by rotated interfaces. For opponent behavior, a deterministic rule-based artificial intelligence model is developed using weighted suit evaluation, legal-card filtering, and context-dependent card selection strategies. The results show that Godot 4’s scene instancing, typed GDScript, shader materials, and signal system provide suitable mechanisms for building maintainable interactive applications. The discussed solutions and engineering lessons can be applied to a broader class of UI-driven 2D games beyond card games.

Engineering Proceedings doi: 10.3390/engproc2026154039

Authors: Peter Tsonkov Fatima Sapundzhi Slavi Georgiev Ivan Georgiev

IoT devices and robotic technologies can support sensor-based feedback, contextual monitoring, and adaptive control in distributed cyber–physical environments. This article compares three logic design strategies for distributed IoT systems with robotic support: embedded firmware logic, external workflow-based logic, and framework-assisted firmware design. A hybrid architecture is proposed to combine low-latency local control with flexible external orchestration, monitoring, and robotic feedback. To complement the conceptual comparison, a compact validation scenario is introduced, based on sensor reading, local validation, MQTT communication, Node-RED workflow processing, and actuator command execution. The indicative results show that embedded edge logic achieves the shortest response time, with an average value of 1.08 ms, while the hybrid workflow-based path introduces an additional delay, reaching 8.08 ms, but provides greater flexibility, scalability, and multi-device coordination. The findings indicate that hybrid architectures are suitable for inclusive IoT environments when immediate local reactions are combined with configurable external logic and reliable monitoring.

Engineering Proceedings doi: 10.3390/engproc2026154031

Authors: Enis Mustafa Serdzhan Murad Aleksandar Kolev Elena Tolstosheeva

Mental stress has become a significant health issue because it can disrupt autonomic balance and alter breathing patterns. Breathing techniques, especially when monitored by the HeartMath coherence device, are popular for promoting balanced heart–mind interaction. In this project, we developed an open-source Arduino-based photoplethysmography (PPG) system capable of recording heart pulse signals and displaying them on a specialized Python interface. The heart pulse amplitudes are displayed to allow observation of breathing modulation as an envelope of the PPG amplitude train. Simultaneously, the increase in heart rate during inhalation and the decrease during exhalation are displayed. The Python interface enables real-time monitoring of how 0.1 Hz rhythmic breathing exercises synchronize the heart-rate waveform with the PPG amplitude envelope. The proposed platform can serve as a basis for future studies on breathing techniques to foster a harmonious state, enhancing productivity, creativity, and inner peace.

Engineering Proceedings doi: 10.3390/engproc2026154035

Authors: Hristina Stoycheva Georgi Mihalev

The problem of recovering an information sequence in the signal decoding process under the application of a chaotic switching scheme via synchronization is examined in the article. A classical chaotic switching scheme based on the bifurcation parameter of the Lorenz system is used, in which the two logical levels are encoded through fundamentally different system dynamics. Decoding is performed by means of chaotic synchronization, whereby the encoding system with one value of the bifurcation parameter, located in the transmitter, is synchronized with another system located in the receiver. Through the use of a pretrained nonlinear autoregressive artificial neural network (NARX), dynamic nonlinear signal reconstruction through input–output alignment (DNRS-IOA) is implemented for the recovery of the information sequence. Experiments are performed in the MATLAB environment, and graphical and numerical results are presented, showing low error levels (MSE = 0.002) and high correlation levels (R-value = 0.9998) between the output of the neural network and the reference signal.

Engineering Proceedings doi: 10.3390/engproc2026139006

Authors: Wai Yie Leong

Robotics serves as a key enabler in disability support by offering new pathways to augment social work practice and enhance autonomy, safety, and inclusion for persons with disabilities (PWDs). In this study, an interdisciplinary team executed a socio-technical investigation that integrated robotics engineering, artificial intelligence, rehabilitation sciences, and social work. It was examined how assistive and socially interactive robots, including mobility robots, cognitive-assistive systems, exoskeletons, telepresence units, and socially assistive humanoids, must be embedded within disability services to improve functional independence, strengthen care continuity, and address increasing workforce demands. A comprehensive research design was adopted by combining a systematic literature review and technical benchmarking of robot capabilities with qualitative inputs gathered from co-codesign workshops involving PWDs, caregivers, and social workers. To test these applications, the team evaluated three pilot domains: home-based independent living support, community-based rehabilitation, and social-work-led remote engagement utilizing telepresence robotics. The results demonstrate that these robotic interventions improved independent task completion by 22–41% and reduced caregiver burden by 18–34%. Furthermore, the data revealed significant gains in communication and emotional engagement for individuals with cognitive or speech impairments. While robotics cannot replace social workers, these technologies meaningfully complement care delivery when practitioners develop them through ethical, participatory, and contextually sensitive frameworks. Ultimately, this paper highlights clear pathways toward scalable, inclusive robotic support systems that align engineering innovation with person-centered social work values.

Engineering Proceedings doi: 10.3390/engproc2026154034

Authors: Aleksandar Mandadzhiev Ivaylo Belovski Kaloyan Ivanov

With the growing power demand of small electronic devices worldwide, piezoelectric energy harvesting has become a promising solution for low-power energy generation. The primary objective of this study is to experimentally assess the energy harvesting capabilities of a commercially available piezoelectric sensor. The output performance of the piezoelectric harvester is characterized, with particular focus on the generated output voltage and output power, under different resistive loads and various operating conditions, including acceleration amplitude and frequency of mechanical excitation.

Engineering Proceedings doi: 10.3390/engproc2026154033

Authors: Rakesh N. Tiwari M. Jyoshna Prabhakar Singh Pradeep Kumar B. Harshitha K. Iswarya

This paper presents the design and analysis of a graphene-based patch antenna for terahertz (THz) communication applications. The proposed antenna has a compact footprint of 60 μm × 60 μm and employs an inset-fed modified graphene patch to achieve dual-band operation. The antenna exhibits resonances at 0.58 THz and 1.01 THz, with |S11| below −30 dB, indicating excellent impedance matching. The tunability of graphene is investigated by varying the chemical potential and relaxation time, and the antenna performance is optimized accordingly. It is observed that higher values of chemical potential and relaxation time enhance the surface conductivity of graphene, resulting in improved impedance matching and radiation efficiency. A detailed parametric study is also carried out by varying the dimensions of the slot etched on the patch. The results demonstrate that slot dimensions significantly influence both impedance matching and resonance frequency tuning. The antenna achieves a gain > 2.53 dBi and total efficiency exceeding 60% across both operating bands. The radiation patterns in both E- and H-planes are nearly omnidirectional at 0.58 THz and 1.01 THz, making the design suitable for near-field THz applications. The electromagnetic performance of the proposed graphene patch antenna is analyzed using CST Microwave Studio.

Engineering Proceedings doi: 10.3390/engproc2026154030

Authors: Baurzhan Abzhanov Yernazar Ishanov Malike Kazhimanova

This paper presents a scalable edge-based multilingual speech processing system designed for secure communication environments with limited or intermittent network access. The proposed architecture integrates speech enhancement, language identification, streaming automatic speech recognition, confidence-aware re-ranking, and secure local output delivery within a unified on-premise framework. Experimental evaluation was conducted using a multilingual corpus containing clean speech, command phrases, and degraded radio-channel recordings in Kazakh, Russian, English, and Turkish. Results of the experiments indicate that practical deployment performance depends not only on recognition accuracy, but also on latency stability, thermal resilience, queue management, and robustness to mixed-language noisy speech.

Engineering Proceedings doi: 10.3390/engproc2026154032

Authors: Filip Tsvetanov Ivan Ivanov

Unmanned aerial vehicles (UAVs) are increasingly used for wildfire and disaster monitoring, enabling rapid data collection in hazardous, inaccessible areas. Secure and reliable communication is a major challenge in wildfire monitoring, as heat, smoke, terrain obstacles, and electromagnetic interference can degrade or interrupt data transmission. This paper analyzes communication failures in drone-based wildfire-monitoring systems, examining their causes, evolution, and operational implications. A multi-layered analytical framework integrating physical, technical, network, and security aspects is proposed. The study highlights cascading failure processes and supports the design of more resilient UAV communication architectures for dynamic wildfire environments.

Engineering Proceedings doi: 10.3390/engproc2026154029

Authors: Selma Bulut Beyza Buzol Mülayim Bozhana Stoycheva

This study compares three AI platforms, Google NotebookLM (Google Labs, May 2026 release/access), ChatGPT-4o, and Gemini 1.5 Pro, for systematic literature review (SLR) tasks in sustainable textile and fashion research. A corpus of 40 peer-reviewed articles (2019–2025) was queried using 12 standardized prompts across four dimensions: source traceability, thematic synthesis, research gap identification, and output utility. NotebookLM achieved the highest weighted score (8.63/10) due to its document-grounded, structurally constrained architecture, which produced no out-of-corpus citations under the conditions of this study. ChatGPT-4o (8.50/10) excelled in narrative synthesis; Gemini 1.5 Pro (7.78/10) produced superior structured tables. Five corpus-grounded and two inferred gaps, including XAI, Federated Learning, and Edge AI, were identified. A four-step hybrid AI workflow is proposed.

Engineering Proceedings doi: 10.3390/engproc2026154028

Authors: Demet Büyükkaya Sami Açık Hamid Yılmaz Selahattin Koşunalp Mehmet Barış Tabakcıoğlu

In wireless sensor networks, sensor nodes (SNs) powered by small-capacity batteries or supercapacitors are used to monitor an environment. These nodes are typically deployed in areas with harsh environmental conditions, such as volcanic craters and forests. They rapidly deplete their energy by continuously consuming power during data generation, transmission, reception, and idle states. In this study, a Mixed Integer Linear Programming (MILP) model aimed at maximizing the network lifetime of sensor-node-based systems is briefly introduced, and the effects of varying the transmission distances of nodes on routing and network lifetime are discussed. The MILP model is implemented on a 10-node scenario (1 data-generating node, 1 data-collecting node, and 8 transceiver nodes), and its impact on routing and network lifetime is analyzed. For each scenario, 100 simulations were conducted and validated on real hardware.

Engineering Proceedings doi: 10.3390/engproc2026154057

Authors: Dimitrina Koeva Georgi Bankov Metodi Dimitrov

This study aims to demonstrate that, in the context of the energy transition and with solar generation playing a dominant role in the energy mix, key technical parameters of energy infrastructure should be re-evaluated and taken into account, as changes in these parameters affect operational processes and energy-loss calculation mechanisms. Data from newly installed battery energy storage systems (BESSs) connected to the power grid and photovoltaic power plants (PVPPs) in Bulgaria are used to establish a portfolio-level baseline for installed capacity, storage duration, geographic distribution, and preliminary economic indicators. By analyzing annual consumption and generation for 2024, 2025, and the first three months of 2026, the seasonal and cyclical nature of these variables and their dynamics of change are observed. The monthly data distribution allows for an in-depth study of load dynamics and the identification of key factors. The analysis identifies three typical seasonal cycles—winter, summer, and transitional—each of them differing in terms of specific consumption and generation profiles; the operation of PVPPs, BESS, and power transformers exhibits specific characteristics. The results show how changes in power quality indicators, specific technical parameters, and operational values directly or indirectly affect losses during power generation and distribution, and consequently also impact operating, maintenance, and repair costs.

Engineering Proceedings doi: 10.3390/engproc2026146025

Authors: Shiyu He Zhu Xue

Against the backdrop of global efforts to address climate change and China’s “dual carbon” strategic goals, transportation buildings, as a key sector for energy consumption and carbon emissions, are crucial for green and low-carbon transformation. Taking Chengdu Tianfu Station as the research object, this paper systematically analyzes its low-carbon strategies and technical paths throughout the entire process from design and construction to operation. Firstly, the paper describes how Tianfu Station takes the three-star green railway passenger station as its goal and deeply analyzes the integration of its key technologies from three dimensions: low-carbon energy systems, low-carbon resource recycling, and low-carbon environmental regulation. Finally, the paper prospectively discusses Tianfu Station’s carbon management strategies for future operations. Through performance-oriented integrated design and the systematic application of advanced technologies, large-scale transportation hubs can effectively reduce carbon emissions throughout their life cycle while ensuring efficient operation and a comfortable experience, providing a reference for the green and low-carbon construction of similar buildings.

Engineering Proceedings doi: 10.3390/engproc2026154042

Authors: Yusuf Eymen Sezen Muhammad Jamil Adnan Kavak Sema Bayraktar Günay Aslan

This study introduces a hybrid framework that combines Multi-Agent Reinforcement Learning (MARL) based on Mean Field Theory (CSMF) with Large Language Models (LLMs) to provide optimized and explainable charging station recommendations for EV users in Turkey. By integrating real-time data from 11,702 stations and 30,941 sockets via the Energy Market Regulatory Authority of Türkiye (EMRA) API, the system captures dynamic information on station availability, socket types, charging power, and pricing, enabling context-aware, user-centric guidance. The proposed architecture follows a Centralized Training and Decentralized Execution (CTDE) framework, in which multiple agents make local decisions while a centralized critic evaluates global states to improve policy learning and reduce congestion. CSMF reduces computational complexity and lowers the average waiting time by 71.8%, while the LLM component translates technical optimization outcomes into human-readable explanations, enhancing trust and transparency. A React Native mobile application demonstrates end-to-end usability, allowing users to filter stations by distance, cost, socket type, and green energy usage, with real-time availability updates. Experimental results show that the hybrid CSMF + LLM approach achieves a 100% Available Facility Rate (AFR) while maintaining low response time (180 ms), outperforming conventional nearest-station, random, and basic MARL methods. By combining technical efficiency with explainable recommendations and real-world deployment, this study presents a scalable, practical, and trustworthy solution for EV charging infrastructure management, offering both academic contributions and a ready-to-use application for drivers.

Engineering Proceedings doi: 10.3390/engproc2026154026

Authors: Antoaneta P. Ivanova-Bares

Static forecast models used by gas distribution operators in regulatory submissions provide point estimates but cannot quantify the probability that approved targets will be met. This paper proposes an Extended Kalman Filter–Monte Carlo (EKF–MC) framework that (i) jointly estimates the residential client state and the parameters of the logistic S-curve growth model in a sequential Bayesian setting, and (ii) propagates the posterior parameter uncertainty through 100,000 Monte Carlo draws to construct calibrated probability distributions for the 2026–2027 regulatory forecast horizon. Applied to five years of regulatory submission data (2021–2025) for a licensed gas distribution operator in Sofia Province, Bulgaria, the EKF refines the saturation ceiling to M = 1995 ± 30 and the growth rate to r = 0.561 ± 0.083. The Monte Carlo analysis yields 90% forecast intervals of [1930–2002] for 2026 and [1939–2015] for 2027. Both intervals lie entirely below the regulator-approved targets (2039 and 2150), demonstrating a structural over-forecasting tendency in the regulatory approval process. The framework provides operators with a computationally efficient, auditable tool for quantifying forecast uncertainty in rate-case submissions and capital investment planning.

Engineering Proceedings doi: 10.3390/engproc2026154027

Authors: Dimitrina Koeva Ivaylo Ivanov Dimitar Slavov Georgi Bankov Metodi Dimitrov

The energy sector is considered one of the most critical sectors because disruptions to the power supply, problems with systems for monitoring operational parameters, and issues with power management processes have a direct impact on the economy, public order, and national security. On the one hand, the technological maturity of energy facilities is changing, and the share of grid-inverter-connected renewable energy sources (GICRES) is expanding. In this article, the authors discuss the basic concepts and classifications of stability-related subsystems. The new trends and challenges facing the resilience of the electrical energy system (EES) in the context of dynamic processes arising from the deployment of renewable energy sources (RES) are analyzed. This paper aims to present a more comprehensive concept of the static and dynamic stability of the EES, considering the impact of grid-inverter-connected renewable energy sources (GICRES).

Engineering Proceedings doi: 10.3390/engproc2026143068

Authors: Biju Thomas Rohini Venkat Sivakumar Ramasamy Sudhakar Tharuman

Information Technology (IT) projects have historically exhibited low success rates when evaluated narrowly through the “Iron Triangle” of time, cost, and scope. Recent studies emphasize the importance of soft factors—human, psychological, and behavioral dimensions—that logically complement technical and managerial practices. The increasing complexity of digital transformation initiatives has created a demand for intelligent project management systems capable of integrating technical, organizational, and behavioral information into unified decision-support environments. Existing project management frameworks primarily emphasize planning, scheduling, cost management, and resource allocation, while offering limited capability for modeling dynamic human and organizational factors that influence project outcomes. This paper proposes an Adaptive Project Intelligence Framework (APIF), a socio-technical systems architecture that models behavioral signals, organizational conditions, and project performance indicators as interconnected system components within a unified project success ecosystem. This paper proposes a conceptual model for IT project success that integrates mindfulness, creativity, and innovation behaviors. Drawing on the interdisciplinary literature in project management, psychology, and organizational studies, the model highlights how mindfulness fosters resilience and stewardship, creativity enhances decision-making and problem-solving, and innovation behaviors translate ideas into sustainable project outcomes. The paper concludes with implications for practice, limitations, and directions for future research.

Engineering Proceedings doi: 10.3390/engproc2026154021

Authors: Penko Mitev Mariyan Milev Atanasi Tashev Yordan Stoyanov Kiril Mitev

This paper investigates the position drift in a stepper motor used within a part feeding system for the orientation of cylindrical components. The system employs a bowl feeder, orientation sensors, and an indexing mechanism with eight precise positions at 45° intervals. During commissioning, a gradual deviation between the theoretical and actual angular positions of the stepper mechanism was observed, leading to operational interruptions. Experimental tests were conducted to analyze the source of the drift by comparing theoretical and encoder-based angular positions over a large number of repetitions. The results revealed a small but cumulative positioning error caused by the recursive generation of new setpoints from encoder feedback. To eliminate this effect, an improved control strategy was implemented, where each new setpoint is calculated from the previous theoretical value. This modification effectively eliminates the accumulation of measurement variations and prevents long-term drift. Experimental validation demonstrated a significant improvement in long-term positioning accuracy, reducing the total deviation to approximately 0.1–0.2° and enabling stable system operation over extended periods. The proposed solution is simple to implement and enhances the reliability and precision of automated part feeding systems.

Engineering Proceedings doi: 10.3390/engproc2026154025

Authors: Veneta Aleksieva Hristo Valchanov

In today’s digital infrastructure, organizations and enterprises increasingly rely on distributed and remote IT environments. These environments may include multiple network sites, remote branch offices, employee home networks, or servers hosted in geographically distant locations. Monitoring and managing these networks presents a critical challenge, especially when administrators are expected to maintain security, performance, and uptime across all nodes. This paper presents an architecture for a cross-platform, multi-tenant network monitoring and management system that enables centralized visibility and control over a distributed infrastructure.

Engineering Proceedings doi: 10.3390/engproc2026152004

Authors: Nermin Özcan Imam Barket Ghiloubi

Accurate parameter estimation of photovoltaic (PV) models is essential for performance evaluation, efficiency enhancement, and reliable energy forecasting in solar energy systems. However, the nonlinear, multimodal, and implicit nature of the current–voltage (I–V) relationship makes this task challenging for conventional optimization methods, which often suffer from premature convergence and sensitivity to initial conditions. In this study, a modified variant of the Artificial Circulatory System Algorithm, termed Dynamic Elite Cooperative ACSA (DEC-ACSA), is proposed for estimating the unknown parameters of the Single-Diode Model (SDM). The proposed approach extends the original ACSA by incorporating dynamic population grouping, elite-guided cooperative interaction, and directional elite refinement, thereby aiming to improve convergence stability and the utilization of high-quality population information. The objective is to minimize the residual root mean square error (RMSE) of the implicit SDM equation using measured I–V data from four established benchmarks: the RTC France solar cell and the PWP201, STM6-40/36, and STP6-120/36 PV modules. The performance of DEC-ACSA is evaluated against the original ACSA, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Henry Gas Solubility Optimization (HGSO) over 30 independent runs under an equal budget of 50,100 function evaluations. The DEC-ACSA configuration selected on RTC France was retained unchanged for the three additional module benchmarks. DEC-ACSA achieved mean residual RMSE values of 1.2514 × 10−3, 2.656 × 10−3, 2.647 × 10−3, and 1.8108 × 10−2 for RTC France, PWP201, STM6-40/36, and STP6-120/36, respectively, while consistently reducing run-to-run variability relative to ACSA. Holm-corrected tests showed no significant difference from PSO on RTC France and PWP201, whereas significant differences from all comparison algorithms were observed on STM6-40/36 and STP6-120/36. I–V reconstruction further confirmed close agreement with the measured data across all four PV systems.

Engineering Proceedings doi: 10.3390/engproc2026154023

Authors: Antonina Ivanova Georgi Kolev Fatima Sapundzhi Teodora Bakardjieva Slavi Georgiev

Large Language Models (LLMs) are increasingly incorporated into software systems. Their non-deterministic behavior, external hosting, response latency, and operational cost create challenges for established design approaches such as Clean Architecture. This paper examines the integration of an LLM component into a layered software system and compares three possible placements within Clean Architecture: Domain, Application, and Infrastructure. The evaluation considers dependency management, testability, separation of concerns, and implementation complexity. The study proposes an approach in which the LLM is implemented in the Infrastructure layer and accessed through an interface defined in the Application layer. This approach is combined with the Command and Query Responsibility Segregation pattern to isolate LLM interaction within dedicated query handlers. The proposed pattern is demonstrated through the implementation of Budget, a personal finance tracking system that uses GPT-4.1 to convert free-form natural language input into structured transaction records. The results show that placement in the infrastructure layer provides the clearest separation between business logic and external AI services and avoids the introduction of non-deterministic behavior into the core application logic.

Engineering Proceedings doi: 10.3390/engproc2026147019

Authors: Mana Dastoum

Despite significant advances in computational daylight simulation, translating performance metrics into practical architectural decisions remains a major challenge in façade design. This paper proposes a Performance-Informed Design Framework (PIDF) that bridges this gap by systematically linking design context, daylight performance objectives, environmental evaluation, and tessellated façade design variables. Developed through literature synthesis and conceptual modelling, the framework introduces a Performance-to-Design Translation Matrix that converts quantitative daylight indicators into actionable architectural strategies. The proposed methodology supports evidence-based, context-responsive façade design and promotes a shift from performance evaluation toward performance-informed architectural thinking.

Engineering Proceedings doi: 10.3390/engproc2026146021

Authors: Fang Liu Jianing Wang Miaocheng Weng Kaiwen Ling

The complex structure of inclined T-shaped tunnels significantly increases the difficulty of smoke exhaust and rescue operations. This study aimed to investigate the effects of fire source location, bifurcation angle, and opposite jet velocity on smoke control in the opposite jet side. Meanwhile, the smoke flow characteristics were examined during synergistic operation. The results show that the smoke spread length on the opposite jet side approaches a minimum when the opposite jet velocity reaches 1.5 m/s. A smaller bifurcation angle results in a longer smoke spread length. The smoke propagation length downstream of the bifurcation zone is independent of the fire source location. Under synergistic smoke control, optimal extraction performance is achieved when exhaust vent is positioned near the bifurcation zone of the branch tunnel. A prediction model is proposed between the branch tunnel ceiling temperature decay patterns and the exhaust volume during synergistic smoke control in inclined T-shaped tunnels, ensuring both economic efficiency and safety in real-world applications.

Engineering Proceedings doi: 10.3390/engproc2026154022

Authors: Aizhan Oshmanova Valentina Grichshenko Ivaylo Stoyanov

This paper presents a comparative reliability analysis of spacecraft onboard control systems based on their structural representation using directed graph models. A modified approach to reliability assessment is proposed, grounded in representing onboard control systems as directed functional graphs and applied to the analysis of spacecraft architectures in terms of structural fault tolerance. The initial functional schemes are simplified by identifying key system elements and the relationships between them. Based on the resulting representations, graph models are constructed, reflecting the structure of control signal propagation. Structural reliability and fault tolerance assessment are performed using an analysis of graph topological characteristics, including connectivity, graph centrality metrics, the presence of alternative paths, and identification of critical nodes. For systems with a comparable number of elements, differences in structural connectivity significantly affect system resilience to failures. It is shown that a higher degree of connectivity and the presence of redundant control pathways enhance the fault tolerance of the onboard control system. The obtained results confirm the effectiveness of graph-based models for structural reliability and robustness analysis of complex technical systems and can be applied in the design of spacecraft onboard control systems. The proposed approach focuses on the structural and topological properties of the system architecture and does not include probabilistic failure modeling.

Engineering Proceedings doi: 10.3390/engproc2026154024

Authors: Penka Zlateva Angel Terziev Krastin Yordanov Nevena Mileva

This study presents a system-level and subsystem-level evaluation of energy performance in an industrial polyethylene film production system based on real operational data. The analysis focuses on specific energy consumption (SEC) as a key performance indicator, considering both total system behavior and the contribution of energy-intensive subsystems, namely extrusion and converting processes. The initial system exhibits SEC values ranging from 1.267 to 1.688 kWh/kg, indicating relatively high energy intensity compared to established industrial benchmarks. Following technological modernization, SEC is reduced to 0.43 kWh/kg for extrusion and 0.09 kWh/kg for converting, corresponding to improvements exceeding 60%. The total annual energy saving potential is estimated at 906 MWh (39%), accompanied by a proportional reduction in CO2 emissions. The results demonstrate that energy performance is strongly influenced by production load, material losses, and process stability. The novelty of the study lies in the subsystem-level quantification of energy performance using real industrial data and in the identification of the interaction between production efficiency, waste generation, and energy consumption. The findings provide a practical framework for energy optimization in polymer processing systems and allow comparison with European best practices.

Engineering Proceedings doi: 10.3390/engproc2026146024

Authors: Shaohong Zhu

Calcium silicate hydrate (C-S-H) serves as the primary hydration product of cement and constitutes the fundamental strength contributor in concrete matrices. This study presents a novel approach for synthesizing an early-strength admixture for hydrated calcium silicate through the sol–gel methodology. A comprehensive experimental investigation was conducted to evaluate its performance characteristics. The research findings demonstrate that the synthesized admixture, prepared using high-speed mechanical agitation in conjunction with a proprietary dispersant solution, exhibits optimal particle size distribution and dispersion stability at ambient temperature conditions (25 °C). When incorporated into cementitious systems, this innovative additive significantly reduces the energy barrier for nucleation processes, thereby accelerating cement hydration kinetics. The resulting concrete specimens show marked reductions in initial setting times and substantial improvements in early-age compressive strength development.

Engineering Proceedings doi: 10.3390/engproc2026154020

Authors: Tarık Talan Serkan Gökkaya Adem Korkmaz Ivan Beloev

This study aims to autonomously detect and classify Unmanned Aerial Vehicles (UAVs), which are becoming increasingly widespread, based on their emitted radio frequency (RF) signals in highly noisy and interference-prone RF environments where traditional optical and radar-based methods prove insufficient. The dataset used in this study is the “Noisy Drone RF Signal Classification” dataset, which consists of seven classes in total—six different drone models and one noise class—and is balanced across classes. As the methodological approach, a multimodal hybrid deep learning (DL) architecture was developed, integrating raw time-domain IQ signals (1D-CNN) with frequency-domain spectrogram representations (2D-CNN). To enhance model performance and generalization capability, techniques such as Batch Normalization, Dropout, Label Smoothing, and Gradient Clipping were incorporated into the architecture. The model was trained using the AdamW optimization algorithm alongside a cosine annealing learning rate scheduler. In order to efficiently process large-scale datasets, memory management optimizations including memory-mapped files (mmap) and lazy loading were implemented. Experimental results demonstrate that the proposed multimodal model achieves an accuracy of 87.42% and an F1-score of 0.87, indicating strong detection performance. SNR-based analyses reveal that the model achieves accuracy levels ranging between 64.52% and 82.40% under negative-SNR conditions (−20 to 0 dB), while exceeding 89% accuracy under positive-SNR conditions. The findings indicate that multimodal DL approaches can achieve high performance in UAV detection and classification even in noisy RF environments.

Engineering Proceedings doi: 10.3390/engproc2026152003

Authors: Mirinchige B. D. K. Siriwardena Abdul R. Nihmiya Udara S. P. R. Arachchige

Alkaline water electrolysis (AWE) is a promising technology for sustainable hydrogen production, although its performance is limited by electrode overpotential, interfacial charge-transfer resistance, and limited electrochemically accessible surface area. In this study, a monolayer ternary carbon nanomaterial (CNM) composite comprising reduced graphene oxide (rGO), carbon nanotubes (CNTs), and Vulcan XC-72 was fabricated on stainless steel (SS) using a hybrid polyvinyl alcohol–polytetrafluoroethylene (PVA–PTFE) binder. Thermal treatment generated a porous conductive network that enhanced electrolyte accessibility and electron transport. Electrochemical characterization in 0.12 M NaOH showed that the CNM-modified electrode exhibited substantially higher current response and CV-derived double-layer capacitance (Cdl) of 62.61–78.51 mF/cm2, compared with 3.43–3.74 mF/cm2 for bare SS. Electrochemical fitting further showed markedly higher exchange-current density (i0) parameters for the modified electrode, along with a reduced solution resistance (Rs) of ~2.1–2.2 Ω·cm2 and a lower Rct. The oxyhydrogen (HHO) production rate reached 0.304 mL/min at 3.8 V, compared with 0.262 mL/min for bare SS at 4.0 V. Repeated HHO measurements showed ~2% variation (n = 3), indicating good reproducibility of the gas-production response. These results demonstrate that the rGO/CNT/XC-72 composite provides an effective and reproducible surface-engineering approach for enhancing electrochemical performance and HHO production in alkaline electrolysis systems.

Engineering Proceedings doi: 10.3390/engproc2026150137

Authors: Mariyana Kovacheva Vera Katrandzhieva

Governments have a pivotal role in the realm of AI. In order to achieve success within the context of the AI revolution, it is essential to possess robust political intentions. The capacity to exert influence over these entities is predicated on the formulation of policies, rules, and incentives. In the global competition to develop AI and data-driven businesses, the EU’s investments in public computing infrastructure are a key part of encouraging AI innovation across Europe. In this aspect, we look for the internal and external factors under which the countries of the European Union, and specifically AI factories, are clustered and differentiated from one another. The object of the present study is the current artificial intelligence ecosystem in Europe’s efforts to be recognized as a global leader in supercomputing technologies. Contribution: Our strategic points in this study are to indicate the geographical diversification between the two leaders, France and Germany, and the other member states of the European Union.

Engineering Proceedings doi: 10.3390/engproc2026146022

Authors: Ziwei Ma Haiyang Li Xiaoting Wang Tao Wang

This paper proposes a convolutional neural network (CNN)-based image recognition method for crack detection in masonry walls to improve accuracy and efficiency. A sample database is constructed using laboratory-captured images of cracked masonry walls, augmented through rotation, mirroring, gamma correction, contrast adjustment, and brightness modification. A dual-scale CNN architecture is designed by integrating the characteristics of VGG-16 and GoogleNet, enabling multi-scale feature extraction for load-bearing masonry wall images. The network adopts two input scales: a modified VGG-16 branch with 100 × 100 inputs and an optimized GoogleNet branch with 50 × 50 inputs. Both branches undergo feature extraction and classifier training. Leveraging transfer learning, the model is pre-trained on the large-scale ImageNet dataset and fine-tuned using our self-built masonry wall database. Multi-scale features from dual branches are weighted and fused to generate final crack recognition results. Experimental results demonstrate a training accuracy of 94.33%, precision of 94.78%, and F1-score of 94.68%, outperforming traditional CNN models. The proposed method achieves high recognition accuracy for masonry load-bearing wall cracks and provides reliable technical support for practical engineering applications.

Engineering Proceedings doi: 10.3390/engproc2026141021

Authors: Po-Yen Lai Shih-Chieh Chen Shih-Feng Hsu

We integrated a Karakuri mechanism into a low-latency (14.2 ms) event-driven software decision support system by combining the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS). Expert matrices (C.R.=0.0729) prioritized labor saving with machine idle-time reduction (global weight = 0.1119) as the most critical requirement. TOPSIS results showed that the cam mechanism was the optimal configuration, followed by pulley and roller mechanisms. These top-ranked mechanical solutions serve as a low-power, zero-electricity baseline that enhances shop-floor management efficiency, providing a stable foundation for advanced sensor integration. By filtering mechanical noise and stabilizing structural motion, the developed system enables seamless incorporation of micro-sensors and telemetry systems, effectively bridging mechanical automation with high-precision digital sensing. This systematic approach reduces reliance on trial-and-error design, supports lean and Jidoka principles, and offers a scalable pathway for sustainable manufacturing transformation.

Engineering Proceedings doi: 10.3390/engproc2026152006

Authors: Panha Soth Sokna San Socheat Yay Heng Tang Chivon Choeung

Grid-following inverters play a vital role in integrating renewable energy systems into modern power grids. However, their performance deteriorates under grid voltage unbalance and uncertain grid impedance, resulting in current distortion and double-frequency active-power oscillations. This paper proposes a robust dual-sequence current control strategy for a three-phase grid-following inverter operating under these adverse conditions. The inverter model explicitly incorporates grid impedance uncertainty and is represented using a polytopic uncertainty model. Positive- and negative-sequence current controllers are independently designed based on a state-feedback integral structure, where the control gains are synthesized using linear matrix inequality (LMI) optimization to guarantee robust stability over the entire uncertainty range. In addition, sequence-based reference-current generation is employed to suppress the negative-sequence current and eliminate double-frequency power oscillations under unbalanced grid voltages. The effectiveness of the proposed approach is evaluated through simulation studies and compared with a previous robust power controller. The results suggest that the proposed controller significantly suppresses the 120 Hz active-power oscillation despite the presence of grid voltage unbalance and impedance uncertainty. Compared with a previous controller, the magnitude of the 120 Hz component is reduced by approximately 91%, demonstrating improved power-quality performance under grid voltage unbalance and impedance uncertainty.

Engineering Proceedings doi: 10.3390/engproc2026152005

Authors: Imam Barket Ghiloubi Nermin Özcan

Mobile robots are increasingly deployed in autonomous navigation tasks where both tracking performance and reduction in the cumulative control-effort index are critical. This work proposes a control-effort index-aware tuning framework for a nonlinear backstepping controller applied to a two-wheeled mobile robot. The gains are optimized using the Cuckoo Search Algorithm (CSA) under three tuning strategies: accuracy-oriented optimization, weighted control-effort-performance optimization, and an adaptive switching approach. Comprehensive simulations on circular and lemniscate trajectories demonstrate that the proposed adaptive switching strategy achieves an effective compromise between tracking accuracy and control-effort index, reducing control-effort index by up to 12% on complex trajectories while maintaining satisfactory tracking performance.

Engineering Proceedings doi: 10.3390/engproc2026154019

Authors: Darena Mihaylova Velizar Vassilev

This study aims to develop a neural network for the early detection and prevention of insider threats in civil aviation. The approach is based on 23 behavioral indicators represented as binary input features. A multilayer neural network with two hidden layers is designed to model nonlinear relationships between the indicators. Due to the lack of real data, a synthetic dataset is generated based on expert-defined rules. The model is trained using a supervised learning approach and evaluated using standard metrics. The results show stable training and good generalization capability, achieving 89.36% accuracy on the test dataset, confirming the applicability of the proposed approach.

Engineering Proceedings doi: 10.3390/engproc2026154018

Authors: Stefan Tenev Aleksandrina Bankova Anastas Yangyozov Asparuh Atanasov

Particular attention is devoted to the characteristics of elastic couplings as essential connecting elements within machine drive systems. The operational characteristics of such couplings are determined under both static and dynamic loading conditions, while also taking into consideration the nature of the working environment and the specific type of driven machinery, such as crushing machines, mills, and related industrial equipment. The relationship between the transmitted torque load and the deformation of the coupling’s rubber working elements constitutes a significant factor contributing to the compensation of vibrations and shaft misalignments. Furthermore, the rubber elements are characterized by inherent damping properties and residual elastic deformation. The parameters governing damping behavior and deformation exhibit variations over time, which is of particular importance in deformation analysis and measurement. A series of experimental investigations was carried out to evaluate the influence of measurement time on the deformation state of the rubber element in a “SEGME”-type coupling. Four time intervals—5, 10, 20, and 30 s—are considered for recording the instantaneous deformation of the working element. Based on the obtained experimental data, mathematical relationships describing the energy dissipation and damping capacity of the rubber working elements are established as functions of the deformation measurement time. In addition, a coefficient characterizing the influence of deformation measurement duration was determined.

Engineering Proceedings doi: 10.3390/engproc2026146023

Authors: Kunxian Lin Jindong Liu Kefu Nie Bo Pang Jian Yang

Verifying the as-built conformity of reinforcing stirrups is challenging due to occlusion and noise inherent in 3D laser scans. Addressing the limitations of manual inspection and the data dependency of deep learning, this paper proposes an unsupervised, design-driven workflow for automated quality inspection. We first introduce the pose adjustment step to refine orientation, followed by utilizing the slice-based projection to decompose the complex point cloud into candidate clusters. Subsequently, a greedy detect-and-remove strategy is applied to iteratively extract individual stirrup instances. To evaluate alignment reliability, we develop a soft similarity metric based on probabilistic volumetric overlap. This measure robustly quantifies the geometric consistency between CAD templates and noisy fragments, accommodating partial data loss where traditional methods fail. Comprehensive experiments on real-world and synthetic datasets demonstrate that the proposed method significantly outperforms unsupervised baselines. The framework maintains high robustness even with only 25% of data retained, providing a precise, efficient solution for the digital quality control of precast components.

Engineering Proceedings doi: 10.3390/engproc2026154015

Authors: Mariyana Sestrimska Nikolay Komitov

Modeling an artificial neural network to predict the behavior of a heating and domestic hot water system in a residential building is the basis of this report. The heating system includes three types of energy sources: biofuel, solar, and electricity. During transitional seasons, such as spring and autumn, it is possible to use all three sources periodically or simultaneously, which leads to increased consumption and inefficiency in the system. In order to study the dynamics of the process and optimize the control, a monitoring system based on Raspberry Pi Pico W was developed. Data on the operating parameters of the boiler, tank, and radiator is transmitted in real-time to the ThingSpeak cloud platform, using the free access option that requires only registration. Due to the reinforced concrete structure of the building, the local Wi-Fi connection drops at times, so an additional router was added. Although individual values from the data are missing, they can be used to obtain graphs of the transient process. The data received in the platform does not arrive evenly due to the time required for transmission. The artificial neural network was created in MATLAB (v2025, MathWorks, Natick, MA, USA) and uses the raw data to predict changes in the processes. The missing fragments of the data have no impact, and a good match of the actual values with the predicted values was obtained for the tank and radiator, while for the boiler, the match was satisfactory.

Engineering Proceedings doi: 10.3390/engproc2026154017

Authors: Gökhan Uçkan Engin Oğuzay

Emotion analysis through facial recognition has significant potential for enhancing security within organizational and industrial contexts by enabling early risk detection, reducing human error, and supporting real-time decision-making. However, existing systems often demand high computational resources and advanced imaging equipment, limiting their adoption in small and medium-sized facilities. This study presents a cost-effective and practical emotion analysis application based on facial recognition, designed for seamless integration into diverse environments and IoT platforms. The system dynamically adapts its operation to the emotional state of the authorized user, mitigating potential errors when psychological resilience is insufficient. A Convolutional Neural Network (CNN) model was developed for emotion analysis using Conv2D, MaxPooling2D, and Dropout layers. Additionaly, several face recognition algorithms were tested to ensure reliable identification under different conditions. The proposed emotion analysis model achieved 98% overall accuracy and an 89% weighted F1 score. The system was trained and tested on a dataset with 58,179 training images and 28,706 test images, reaching the highest validation accuracy of 98.71% at the 25th epoch. By using a face recognition library, the system provided both high accuracy and fast processing. The developed face recognition-based folder security system offers a more user-friendly and innovative alternative to traditional password-based security methods.

Engineering Proceedings doi: 10.3390/engproc2026154014

Authors: Muazzam Artikova Dilshodbek Jamoliddinov

This paper presents an automated computational workflow for the acquisition, instrument-response correction and three-dimensional visualization of multi-component seismic records obtained from the IRIS Federation of Digital Seismograph Networks (FDSNs) using the open-source ObsPy (v1.5.0) package. The workflow targets engineering applications and consists of four stages: (i) selection of three-component (3C) broadband stations, (ii) bandpass filtering and spectral deconvolution of the instrument response to obtain ground displacement in physical units, (iii) calculation of theoretical P- and S-wave arrival times with the Tau-P kinematic algorithm based on the IASP91 reference Earth velocity model, and (iv) construction of an interactive 3D particle motion visualization in which segments associated with the P-wave, S-wave and background are color-coded. The pipeline is demonstrated on three seismic events recorded in February 2023 by the broadband station KO.BNN, including the destructive Mw 7.8 Kahramanmaraş earthquake. The workflow yields the absolute three-dimensional displacement vector and produces interactive visualizations that are intended for use by structural engineers as a complement to traditional one-dimensional acceleration records.

Engineering Proceedings doi: 10.3390/engproc2026154016

Authors: Krasimir Ivanov Georgi Velev Ivaylo Lazarov

Lightning strikes with large current amplitudes and a steep current wave front in cases of multiple lightning strikes can cause significant overvoltages. The number of overvoltage impulses depends significantly on the lightning parameters, the overhead line parameters and the tower grounding impedance. While the probability for back flashovers from towers to phase conductors (power line trips) depends on lightning current amplitude, impulse steepness and tower grounding resistance, the magnitudes of overvoltages reaching the nearby substation, Ground Potential Rise (GPR) and step and touch voltage distribution profiles depend exclusively on substation grounding impedance. In order to study and take into account such a large number of parameters influencing the probability for overvoltages to occur, several models in the ATP simulation program (v. 7.6) have been implemented. This allows us to study the influence of tower and substation grounding impedances on the magnitude and the form of atmospheric overvoltages and their impact on the safety of personnel in the substation area in cases of direct lightning strikes on nearby power line towers or lightning protection cables. A differentiation has been made between grounding resistance and grounding impulse impedance as a prerequisite for more accurate modeling and more precise calculations.

Engineering Proceedings doi: 10.3390/engproc2026136012

Authors: Yen-Hong A. Chen Yu-Chu M. Li Kai-Jan Lin

We examined the performance of a novel early streamer emission air terminal (ESEAT), which is used as a lightning protection device. ESEAT was tested under a laboratory-simulated lightning strike scenario and compared with a classical air terminal (CAT). The results demonstrate that ESEAT is more effective at capturing artificial lightning in the laboratory setting than CAT. Data on failed lightning captures indicate that the failure rate of the ESEAT is approximately one-tenth that of CAT. These findings validate the effectiveness of the new ESEAT in a controlled laboratory environment.

Engineering Proceedings doi: 10.3390/engproc2026131051

Authors: Umberto Galietti Gabriele Arcidiacono Luigi Bruno Davide Castagnetti Cristiana Delprete Mario Guagliano Vigilio Fontanari Aurelio Somà Nicola Bonora

The AIAS (Italian Scientific Society of Mechanical Engineering Design) was founded in 1971, originally as an association focused on stress analysis, bringing together researchers from academia, research centers and industry to discuss, share and develop scientific knowledge on the subject [...]

Engineering Proceedings doi: 10.3390/engproc2026154013

Authors: Ivan Ivanov Filip Tsvetanov

Secure delivery of packages by drones requires reliable management of cryptographic keys, despite limited on-board resources, multiple users, and the risk of interception or manipulation. This paper proposes a hierarchical model of a Key Distribution Center that manages the generation, distribution, storage, control, and destruction of cryptographic keys. The model uses public-key mechanisms for secure transmission of session keys and lightweight symmetric encryption for communication between the drone and the recipient. A session key lifecycle is defined, including verification, encrypted storage, validity control, and secure deletion. The operational workflow for the secure delivery of packages is also described. The proposed approach improves confidentiality, scalability, and access control.

Engineering Proceedings doi: 10.3390/engproc2026147018

Authors: Kaouther Kerboua Nour El Imene Brahmi Abderrahmane Selmani Nour Hane Merabet

The design of efficient small-scale hydrogen energy storage systems requires balancing hydrogen production rate, electrical efficiency, and system simplicity. This study experimentally investigates the energetic compromise imposed by separator material and electrolyzer architecture through a comparative analysis of finite-gap alkaline, finite-gap acidic, and zero-gap proton exchange membrane (PEM) electrolyzers. Zirfon® Pearl 500 (Agfa, Mortsel, Belgium) diaphragms were employed in alkaline electrolysis using 25 wt.% KOH, whereas Nafion™ 117 (Chemours, Wilmington, DE, USA) membranes were used in both finite-gap acidic electrolysis (2.55 M H2SO4) and a commercial five-cell zero-gap PEM electrolyzer supplied with deionized water. Electrochemical performance was evaluated in terms of polarization behavior, apparent resistance, hydrogen production rate, Faradaic efficiency, and energy conversion efficiency. The zero-gap PEM architecture exhibited the best electrochemical performance, with an apparent resistance of only 0.138 Ω per cell, corresponding to reductions of approximately 43-, 51-, and 64-fold compared with the finite-gap PEM, stainless steel/Zirfon alkaline, and nickel/Zirfon alkaline configurations, respectively. The zero-gap electrolyzer delivered currents from 1.53 to 10.0 A while operating below 2.8 V, demonstrating the benefit of minimizing the ionic transport path. In contrast, the finite-gap acidic configuration achieved higher hydrogen production rates than the alkaline system owing to the superior proton conductivity of Nafion™ 117, whereas the alkaline Ni/Zirfon configuration reached the highest Faradaic efficiency (≈98%) and energy conversion efficiency (≈36%) because of improved gas separation and reduced hydrogen crossover. Electrochemical impedance spectroscopy further revealed that the normalized ohmic resistance of the zero-gap PEM cell was only 0.029 Ω, with charge-transfer processes accounting for approximately 96.2% of the total impedance. These results demonstrate that separator properties and cell architecture govern the trade-off between reaction kinetics and energy efficiency, providing practical guidelines for selecting electrolyzer configurations dedicated to decentralized and small-scale hydrogen energy storage.

Engineering Proceedings doi: 10.3390/engproc2026150136

Authors: Tasho Tashev Radostina Tasheva

The Generalized Nets (GN) apparatus is applied here to describe the Longest_Port_First (LPF)-algorithm for computing a conflict-free schedule for a crossbar switch. The results for throughput (TH) on a switch node with the LPF-algorithm using computer simulations with patterns of uniform load traffic are presented. Using the numerical procedure it is shown that the upper bound of the TH of the LPF-algorithm tends to 100%, with the accuracy increasing three-fold as the buffer value increases approximately twice. The necessary computations were executed on the supercomputer AVITOHOL, which is located in IICT-BAS, Sofia, Bulgaria.

Engineering Proceedings doi: 10.3390/engproc2026150135

Authors: Petr Salač Miroslav Brzezina Jana Příhonská

The contribution deals with the numerical results for the identification problem for heat-transfer coefficient between the outward surface of the mould and the environment, and for the flux density of the modified mass of the glass coefficients between the glass and the mould, and the glass and the plunger. The continuous problem was formulated in detail at AMEE’49. The numerical results were received by the software FreeFem++ version 4.2.1 for the sample of the real vase from the experiment carried out at the Technical University of Liberec in 1999, which was used as a data source for the identification problem. Identification is realized with respect to the measured temperature values at 10 points of the mould and the plunger. The distribution of the searched coefficients along the respective boundaries in the initial, the 10th, the 20th, the 60th and the 100th iterations together with the distribution of temperature throughout the system in the 100th iteration are presented.

Engineering Proceedings doi: 10.3390/engproc2026154012

Authors: Theodora-Maria Maroulaki Despoina Grigoriou Yannis Orphanos Nektarios A. Papadogiannis Vasilis Dimitriou Evaggelos Kaselouris

This study investigates the vibro-acoustic behavior of a thin square aluminum plate with a central clamped hole, inspired by splash cymbal configurations. The structural dynamics are analyzed using finite element method (FEM) modal and frequency response function (FRF) simulations, while experimental measurements validate the modal characteristics. A semi-analytical Rayleigh–Ritz model is also employed to estimate the natural frequencies and provide an additional reference for comparison. Excellent agreement is observed between the predicted and measured resonance frequencies and overall dynamic response. Acoustic radiation is evaluated through coupled structural–acoustic analyses using a Rayleigh integral formulation and an indirect variational boundary element method (BEM). The sound pressure radiated to a field point above the plate is compared with the FEM-derived FRF results. The results demonstrate a correlation between structural vibration and acoustic response, with variations in radiation efficiency influenced by the central clamp and higher-frequency modal behavior. The Rayleigh approach shows good agreement but exhibits numerical noise and overprediction of modal contributions. In contrast, the BEM provides smoother responses and more physically consistent results. These findings are relevant for noise control, vibration reduction, and sound synthesis applications in mechanical systems.

Engineering Proceedings doi: 10.3390/engproc2026154006

Authors: Raid Anis Kerkatou Hacene Belhadef Aicha Eutamene Svetlana Stefanova

Quantum Machine Learning (QML) has recently emerged as a promising approach for cybersecurity applications due to its ability to encode data into high-dimensional Hilbert spaces. However, most QML-based intrusion detection systems rely on a single quantum embedding strategy, limiting representation diversity. This paper proposes a Multi-Embedding Quantum Ensemble (ME-QE) framework that combines Angle Embedding and IQP Embedding using concatenation and weighted fusion strategies. The proposed approach is evaluated on the NSL-KDD dataset for binary intrusion detection using stratified 5-fold cross-validation. Experimental results show that the best fusion configuration achieves 94.75% accuracy, while the classical Random Forest baseline reaches 97.50%. The analysis further demonstrates that Angle Embedding contributes more effectively to classification performance than IQP Embedding in this context. In addition, the study highlights important scalability limitations of quantum kernel methods due to the computational cost of pairwise kernel evaluation. The proposed framework provides insights into hybrid quantum–classical intrusion detection and establishes a foundation for future scalable quantum cybersecurity architectures.

Engineering Proceedings doi: 10.3390/engproc2026131050

Authors: Umberto Galietti Gabriele Arcidiacono Luigi Bruno Davide Castagnetti Cristiana Delprete Mario Guagliano Vigilio Fontanari Aurelio Somà Nicola Bonora

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors strictly following the policies announced on the conference website [...]

Engineering Proceedings doi: 10.3390/engproc2026154011

Authors: Ana-Maria Grigoras Oanca Mihaela Andrei Daniela Tarniceriu

This paper presents a polar-coded anti-jamming communication system designed to improve reliability and security in channels affected by Gaussian jamming and impulsive α-stable noise. The proposed configuration combines polar decoding with an Anti-Jamming (AJ) mechanism based on Processing Gain (PG). Several configurations, including standalone polar decoding, anti-jamming only, and their combination, are evaluated using Bit Error Rate (BER) versus Signal-to-Jamming Ratio (SJR). Simulation results show that the combined Successive Cancellation List (SCL) decoder with anti-jamming achieves the best performance, offering about a 3 dB gain over standalone SCL decoding. The study also demonstrates improved physical-layer security through enhanced secrecy capacity.

Engineering Proceedings doi: 10.3390/engproc2026154008

Authors: Dimitrina Koeva Dimitar Slavov

The transition toward decarbonized power systems has led to the large-scale integration of inverter-connected renewable energy sources (ICRES) and the decommissioning of traditional synchronous generators. This shift significantly reduces system inertia, increasing the rate of change of frequency (RoCoF) and vulnerability during power imbalances. This paper investigates a hybrid system combining a thermal power plant (TPP) with a battery energy storage system (BESS) in grid-forming mode to enhance stability. Using MATLAB/Simulink and technical parameters from an existing turbogenerator, three scenarios with 0%, 30% and 50% renewable penetration were simulated. Results show that as the inertia constant Hsys decreases from 2.32 s to a critical 1.43 s, the frequency during a 20% deficit falls below the 49 Hz under-frequency load shedding (UFLS) threshold. Implementing virtual inertia control allows the BESS to successfully limit RoCoF and maintain frequency above safety limits. These findings justify BESS grid-forming technology as a vital solution for grid stability under low-inertia conditions.

Engineering Proceedings doi: 10.3390/engproc2026154007

Authors: Viktoria Ivanova Delyan Genkov

Due to the large amount of network traffic, manual network security management has become increasingly difficult. This paper proposes an open-source network anomaly detection system prototype that operates with the Security Onion 2.4 platform. The system relies on a custom Python engine that uses the machine learning (ML) algorithm Isolation Forest to detect anomalies in multiple Zeek datasets. The prototype was developed as a virtual machine (VM), which is hosted on a server running the software for virtualization VMware ESXi 6.0.0. For the experimental tests, live network telemetry from a university network was used. The results show that the system achieves a constant anomaly detection rate of 5.4% after a volumetric threshold of 5000 logs is reached within operating periods ranging from fifteen to thirty minutes. During testing, it was discovered that the detection logic has a minimal resource impact (less than 1 GB) on the running system beyond the defined baseline of average Random Access Memory (RAM) usage. This proposed solution is an open-source and zero-cost alternative to other paid network security solutions, demonstrating that abnormal behavior detection in network traffic can be effectively integrated on existing computer configurations without the need for expensive licenses.

Engineering Proceedings doi: 10.3390/engproc2026154010

Authors: Hristina Stoycheva Stanimir Sadinov Georgi Mihalev Nikolay Manchev Boyan Karapenev

River floods are considered a serious threat to human life and infrastructure; therefore, effective monitoring and early warning solutions are required. In this paper, an intelligent system based on a distributed network of ESP32 sensor nodes is proposed for real-time monitoring of river water levels. The sensor nodes are deployed at multiple locations along the river, where hydrological data are continuously collected and transmitted to a central unit. The acquired time-series data are analyzed by means of a nonlinear autoregressive model with exogenous inputs (NARX) and artificial intelligence techniques, so that complex hydrological dynamics can be captured and future water-level variations can be predicted. The proposed system enables the early detection of potential flood events and supports timely decision-making. The solution is implemented as a low-cost Wireless Sensor Network (WSN), which makes it suitable for deployment in remote and resource-constrained areas and contributes to improved flood risk management. The architectures of the applied systems are presented, and an approach for implementing a NARX-based model for river water-level prediction is proposed.

Engineering Proceedings doi: 10.3390/engproc2026154009

Authors: Ismail Mehmedov Darina Dobreva Zoya Tsoneva Momchil Tachev

The study focuses on the influence of airflow on the separation of the three primary materials under investigation: sand, sawdust, and ash. These materials were selected to facilitate a comparative analysis between two distinct airflows. The primary objective of the research is to increase the separation yield while maintaining the original design of the 2A mechanical cyclone model. The model utilized in this study was designed using SolidWorks 2025 and fabricated via a Raise3D Pro2 3D printer (Raise3D Technologies, Inc. 2019, Nantong, China, Е2). The purpose of the experiment is to determine how variations in flow affect the separation of fine particulate matter under 3 μm.

Engineering Proceedings doi: 10.3390/engproc2026148048

Authors: Vasugi Ramdass Dhilip Kumar Venkatesan Oana Geman Roxana Toderean

Counting bacterial colonies by hand is one of the most common tasks in microbiology, but it is slow and often yields different results depending on who does the counting. This paper presents an automated system for detecting and classifying bacterial colonies from Petri plate images. We tested four models: Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and a hybrid CNN+Vision Transformer (CNN+ViT). The system uses OpenCV for image preprocessing and extracts features such as area, perimeter, and circularity. Colonies are then classified by size and health condition. Confusion matrix results show that SVM and ANN achieved the highest overall accuracy (95.6%), while CNN+ViT eliminated false positives entirely. A Streamlit-based web interface allows real-time colony analysis directly in laboratory environments.

Engineering Proceedings doi: 10.3390/engproc2026148047

Authors: Olga Adriana Caliman-Sturdza Roxana Filip

The problem of antimicrobial resistance (AMR) is a significant worldwide health risk, which is why experts are interested in using artificial intelligence (AI) or machine learning (ML) to employ antibiotics more wisely. Diagnostic, empiric antibiotic choices, dosing, and surveillance in stewardship programs are AI-driven promises to make the process straightforward. This narrative review is a synthesis of the recent evidence (within the last 10 years) on the use of AI in antibiotic stewardship. It analyzed the literature on AI-based diagnostic systems, prescribing decision-support systems, resistance prediction models, real-time surveillance systems, and dose algorithms. The use of AI tools by clinicians and stewardship programs must be approached with discretion and human supervision and assessment.

Engineering Proceedings doi: 10.3390/engproc2026150134

Authors: Petr Salač Miroslav Brzezina Jana Příhonská

The article deals with finding a steady heat source that can replace a periodical non-steady heat source representing the repeated cooling of pressed glass products in a system consisting of a mould, glass piece, plunger and plunger cavity during the glass forming process. The stationary heat source is designed to supply the same amount of energy during the cooling period as would be extracted from the glass piece if its surface temperature was reduced linearly to the desired value.

Engineering Proceedings doi: 10.3390/engproc2026154004

Authors: Stanimir Yordanov Hristina Stoycheva Georgi Mihalev Stefan Ivanov

This paper presents a secure data transmission framework for intelligent IoT sensor networks, specifically designed for an electronic nose system used in food quality analysis. The proposed architecture integrates a heterogeneous sensor array with an ESP32-based processing unit and GSM/GPRS communication module. To ensure data confidentiality over public mobile networks, a hybrid security scheme combining cryptographic encryption (ChaCha20) and chaotic modulation (Brusselator + DCSK) is introduced. The data packet is first encrypted using the ChaCha20 stream cipher, then converted into a bipolar bit stream. The chaotic system used to generate a chaotic signal is a nonlinear Brusselator model. The system was implemented on a LILYGO TTGO T-Call V1.4 (ESP32 + SIM800L) and tested for discrimination of olive oil and sunflower oil mixtures. Experimental results demonstrate 98.7% classification accuracy using a neural network, 96% first-try transmission success rate over GPRS with 2.3 s average packet transmission time, and 99% overall reliability under RSSI above −85 dBm.

Engineering Proceedings doi: 10.3390/engproc2026150133

Authors: Georgi Todorov Konstantin Kamberov Konstantin Dimitrov

This study explores a simple free-falling problem, focusing on the determination of the coefficient of restitution for a steel ball through virtual and physical experiment evaluation. Different effects influencing the dynamic behavior and accuracy of the experiment are explored through variation in the physical objects’ diameters. Physical testing results were obtained through high-speed capturing and visual-tracking software in a series of physical experiments. This allowed us to correctly determine the main dynamic parameters of the system and to compare them to the results obtained by numerical simulation of the virtual model. Detected differences and the level of correspondence of the virtual model with the physical one are reviewed and discussed. Further recommendations for virtual prototyping of drop tests are presented.