In this study, drying characteristics, color values, and optimum thin layer drying model of celery slices were determined by drying them in a temperature-controlled infrared dryer. Changes in surface area and sphericity of celery slices throughout the drying process were also determined. Present findings revealed that temperature was a significant criterion for drying the celery slices. Decreasing drying times were observed with increasing drying temperatures. Drying operations were carried out at three different temperatures (50, 60, and 70 ºC). The longest drying time (7.25 hours) was observed at 50 ºC and the shortest drying time (4.25 hours) was achieved at 70 ºC drying temperature. The closest fresh color values to the color values of the fresh product were obtained from 60 ºC drying temperature. In terms of chroma values, the closest value to the fresh product was observed again at 60 ºC. The most used Page, Midilli-Küçük, Yagcıoglu, and Jane Das thin-layer drying models were used to generate drying curves of celery slices. Among these models, the Midilli-Küçük model yielded the greatest R2 value, thus it was selected as the best thin-layer drying model for celery slices.
Due to changing climatic conditions, living things leave their habitats and migrate to other geographies. One of these creatures, the pufferfish, has created new habitats by migrating from warm seas to the Mediterranean and Aegean seas in recent years. The pufferfish is a dominant and invasive species, and its population is rapidly increasing wherever it is found because there is no other creature hunting it. Since it is a poisonous species, there is no possibility of biological control and hunting of this creature in fishing activities and controlling its population are encouraged, but this method is not effective. The scope of the study describes the development of a pufferfish trap developed with artificial intelligence models that targets the invasive pufferfish. Different sized trap designs have been suggested for different sized pufferfish species. To detect the pufferfish, 3200 pufferfish images and 3000 images of Mediterranean fish species were trained on the system with a supervised machine learning model to distinguish this species from other species. Addition, the calculation of trap size according to the species of this creature, which has different sizes, was handled with another artificial intelligence model. The developed model detects pufferfish from distances of 35 cm-250 cm underwater and reacts to catch the fish. In this study, studies were carried out on finding the appropriate trap modeling for catching the target pufferfish, developing a suitable model for catching the target fish species, and verifying the target species recognition by using different artificial intelligence methods.
This paper proposes a novel control framework, termed Binary Switching Linear Model Control (BSLMC), for regulating the motion modes of Linear Time-Invariant (LTI) systems. The approach leverages a modal coordinate transformation to decompose an LTI system into a set of decoupled first- and second-order subsystems, enabling selective control of dominant modes without altering the system’s eigenvector structure. By adjusting the natural frequencies and damping ratios of these active modes, the proposed method achieves effective pole reassignment while preserving mode shapes. A frequency-domain analysis demonstrates that the Bode diagram of the original system can be approximated within specific frequency bands by controlling only a subset of modes, thereby reducing computational complexity. Theoretical investigations rigorously analyze the stability of BSLMC using the Lyapunov stability theorem. It is shown that when the switching models form a set of commuting matrices, a common Lyapunov function exists, allowing for arbitrarily small mode-dependent dwell times. For non - commuting cases, a perturbation-based analysis provides conditions for robust stability. The proposed method’s efficacy is validated through numerical simulations on a multi-degree-of-freedom structural system, highlighting its potential for active vibration control and structural dynamics applications.
Seismicity and geological settings of Turkey are very active and complex, where advanced techniques have to be implemented to mitigate seismic risks through techniques such as soil microzonation. The present study critically reviews the soil microzonation practice under the provisions of the Turkish earthquake code (TEC) and the implementation problems, such as inconsistent geotechnical data, insufficient local capacity, and enforcement gaps in regulations. The study highlights ort similarities and differences in the classification of soils, site-specific ground motion analyses, and geotechnical hazards assessments (e.g., liquefaction, landslides) by comparing Turkey against international practices such as Eurocode (EU), Japanese, American (NEHRP/ASCE/IBC), and Chinese (GB). The methodology puts important emphasis on detailed investigations of the site, including drilling boreholes, conducting in situ tests (SPT, CPT), and geophysical survey (MASW) to derive seismic microzonation maps as a reliable means. These maps provide assessments of shear wave velocity, amplification potential, and liquefaction susceptibility—information essential for seismic design and urban planning. Comparative results show that while site-specific assessments and Vs-based soil classification are common to all standards, they differ in their regulatory frameworks and treatment of hazards and in the rigor applied to their implementation. Findings support the ort h of embracing the best practices of the global standards in order to enhance the Turkish soil microzonation, which will lead to safer construction practices and urban development resilient to earthquakes.
Developing technology diversifies and improves the applications of geotechnical engineering as well as all other engineering fields. In the context of this study, an experimental program was conducted to investigate the effect of geotextile, which has been used in remediation works for a long time, and the recently increasingly popular geopolymer coated geotextile elements on the behavior of cohesionless and cohesive soil underloads. Firstly, the optimum geotextile embedment depth for cohesionless and cohesive soil was investigated. Then, geotextile elements coated with geopolymers with different mixture ratios were placed at the optimum depth and were subjected to load with vertical loading tests. Load-displacement curves of the experiments were generated; failure load values were obtained and compared. When the results of experimental studies were evaluated, it was observed that uncoated geotextile and coated geotextile components improved the behavior of both cohesive and non-cohesive weak soil underloads. The obtained increases reached up to 279% compared to the weak soils, however, amounts of increase were variable for both soils. It was predicted that the results obtained from the study can provide inspiration and guidance for the further usage of geopolymer coating applications in field and literature studies.
This study aims to evaluate and compare various models for the classification of Electrooculogram (EOG) signals, which play a significant role in fields such as human-computer interaction and medical diagnostics due to their ability to non-invasively monitor eye movements. Accurate EOG signal classification enables precise detection and interpretation of eye movement patterns, which are crucial for various clinical and assistive applications. A wide range of models was investigated, including traditional machine learning techniques, ensemble methods, and interpretable deep learning approaches. Each model was assessed using accuracy, precision, recall, F1 score, and specificity metrics. The Random Forest model achieved the highest accuracy of 99.997%, while models such as CatBoost, XGBoost, LightGBM, and TabNet also demonstrated strong performance. Additionally, we computed the Roza composite index to jointly capture both the balance and overall strength of metrics across models. Roza values confirmed the ranking observed with standard metrics. The findings provide valuable insights into the effectiveness of different algorithms for EOG signal classification and contribute to the development of more accurate and practical EOG-based systems.
This study investigates the thermodynamic performance of an organic Rankine cycle (ORC) system driven by hybrid solar and geothermal energy sources. The proposed system utilizes a Fresnel solar collector and a geothermal heat exchanger to supply thermal energy to the ORC, which employs dry organic fluids including butane, pentane, toluene, cyclohexane, and hexane. A comprehensive thermodynamic analysis is conducted, evaluating key performance indicators such as net power generation, exergy irreversibility, energy and exergy efficiencies. The EES (Engineering Equation Solver) program is used for hybrid plant's thermodynamic analysis. The results reveal that butane exhibits the highest exergy efficiency (0.2483) but yields the lowest power output (103.9 kW), while toluene achieves the maximum power generation (112.6 kW) with the lowest exergy destruction (523.5 kW), albeit with modest efficiency values. The performance of the working fluids is further analyzed under varying solar radiation, geothermal source temperatures, and geothermal mass flow rates. As solar irradiation raises from 500 to 1000 W/m2, all fluids show enhanced power generation and energy efficiency, but a corresponding increase in exergy destruction and a decline in exergy efficiency. Similarly, higher geothermal temperatures lead to improved power output and exergy efficiency, while increased geothermal flow rates enhance power generation but reduce both energy and exergy efficiencies due to intensified irreversibilities. Among the tested fluids, pentane offers a favorable balance between energy and exergy efficiency, while toluene excels in power generation. These results highlight the critical influence of working fluid selection on the thermodynamic behavior of hybrid solar-geothermal ORC systems and contribute to the identification of suitable fluids for future low- to medium-temperature renewable power applications.
In this study, data obtained from a 360-degree (or 2D) Light Detection and Ranging sensor, which is commonly used for Simultaneous Localization and Mapping, were converted into an image/frame matrix to apply optical flow methods. Key features on the resulting image matrix were identified as corners using the Shi-Tomasi method. The positional changes of these key features during each complete measurement cycle of the LiDAR sensor were calculated using the Lucas-Kanade method, an optical flow technique. These positional changes were analyzed to estimate the vehicle’s displacement within the environment. The developed method was tested in a simulation environment with different object densities, moving linearly at different speeds, both constant and variable.
This paper discusses the case of an off-grid school in Kano, Nigeria. A self-sufficient and environmentally friendly green energy system was designed for the school. The system has been designed to meet the electricity needs of an isolated school with 45 students. The system consists of solar panels and lithium-ion batteries. The average daily energy consumption of the school was calculated to be 4.8 kWh, based on the specific devices used and their operating durations. The study also examined the impact of three critical parameters on energy generation and battery performance: (i) soiling loss, (ii) panel tilt angle, and (iii) battery operating temperature. A total of 27 different scenarios were analyzed using the PVsyst 8 simulation program, with the panel tilt angles varying (10°, 20°, 30°), the battery operating temperatures varying (20°, 25°, 30°), and soiling loss varying (5%, 10%, 15%). A 5% soiling loss, 10° panel tilt and 25° battery temperature achieved the highest energy generation, 1,754.92 kWh. Consequently, the proposed system effectively met the school’s annual energy needs without any energy loss.
Air-entraining vortices at intake structures present serious challenges to the functionality of water intake systems, resulting in reduced efficiency and possible damage to hydraulic equipment. The present study aims to predict the critical submergence, Sc, which is the minimum vertical distance between the free surface and intake pipe to avoid air-entraining vortices, for horizontal intakes under asymmetrical approach flow conditions. Dimensional analysis was utilized to investigate the influences of intake geometry, approach Froude number, intake Froude number, Reynolds number, and Weber number. Two hundred and twenty-five experimental data for asymmetrical approach flow conditions were evaluated to derive more general and accurate empirical equations predicting the dimensionless critical submergence, Sc/Di. The results indicate that the intake Froude number (Fr)i, and the geometric parameter are the most important parameters that affect Sc/Di. Accordingly, the most accurate empirical equations were obtained while considering all the flow and geometric parameters to predict Sc/Di. On the contrary, the accuracy of the empirical equations considering only (Fr)i gives moderate results in predicting Sc/Di.
In this study, rice husk ash (RHA), a waste-based and sustainable adsorbent, was evaluated for its efficiency in removing Co(II) ions from aqueous solutions. The physicochemical characteristics of RHA, including surface morphology, elemental composition, and functional groups, were analyzed using scanning electron microscopy–energy-dispersive X-ray spectroscopy (SEM–EDS) and Fourier transform infrared spectroscopy. The experiments were conducted to investigate how variables such as pH, RHA dosage, initial Co(II) concentration, contact time, and temperature affect adsorption efficiency. Among the isotherm models tested, the Freundlich equation provided the most accurate representation of the equilibrium data (R² = 0.9993 at 20 °C), indicating multilayer adsorption on a non-uniform surface. Kinetic analysis revealed that the pseudo-second-order model best fit the adsorption process (R² = 0.9989 at 20 °C), suggesting chemisorption as the dominant mechanism. Thermodynamic evaluation confirmed that the adsorption was spontaneous (ΔG°
Functionally graded materials are advanced inhomogeneous materials in which element gradation changes continuously and smoothly from one phase to the other. The purpose of this study is to derive a novel and flexible model for metal-metal functionally graded circular beams. By means of this novel model, it is possible to determine the displacements numerically under all kinds of loading as well as to perform both elastic and plastic stress analysis. The mathematical formulation is derived through higher-order shear deformation theory. For this purpose, macro-mechanical model of functionally graded circular beam is constructed initially. Then, kinematic relations of functionally graded circular beam are derived. Based on Hamilton's principle, a system of differential equations governing the static behavior of a functionally graded circular beam is derived. Numerical results are presented after exact solutions are obtained. The advantage of this novel model is that it is possible to define not only linear gradation but also parabolic gradation from center to the surface of the beam.
This study presents a comprehensive comparison of two distinct computational approaches, Fuzzy Inference System (FIS) and The Bees Algorithm (BA) for optimizing the design of balancing holes in centrifugal pump impellers. These holes are essential for reducing axial thrust without compromising hydraulic efficiency. The optimization parameters include hole center angle, hole diameter, radial placement, and the number of holes. By employing computational fluid dynamics (CFD) simulations, a data-driven regression model was derived from 111 configurations of a TKF 125-400 pump model. The Mamdani-type FIS uses triangular and trapezoidal membership functions and linguistic rules, while the BA, inspired by the foraging behavior of honeybees, utilizes a swarm intelligence approach for multi-objective optimization. Comparative performance is evaluated based on axial thrust minimization and efficiency retention. The findings suggest that while BA provides superior optimization accuracy, the FIS offers greater speed, interpretability, and adaptability for real-time applications in pump design.
This study presents a detailed assessment of the economic impacts of battery energy storage system (BESS) integration in active distribution networks, focusing on the influence of varying storage capacities and initial State-of-Charge (SoC) levels. A two-stage stochastic mixed-integer linear programming (MILP) framework is employed to evaluate four BESS capacities 4, 6, 8, and 10 MWh under initial SoC levels of 20%, 40%, 60%, and 80%, with uncertainty represented via Monte Carlo scenario generation and reduction. The model incorporates realistic market prices, load profiles, and photovoltaic generation data to reflect operational conditions encountered in modern distribution systems. Economic performance is assessed through annual operating profit and investment payback period, and the present net value, with negative operating costs consistently interpreted as profit. By analyzing charge–discharge behavior, cost dynamics, and payback characteristics under different storage configurations, the study provides comprehensive insights into the design and operation of economically efficient BESS deployments. The joint evaluation of storage capacity and initial SoC under uncertainty provides a novel perspective on economically efficient deployment strategies and offers guidance relevant to policy frameworks such as carbon pricing, incentive mechanisms, and other regulatory instruments. This study contributes to the growing body of literature by offering a comprehensive techno-economic framework for BESS deployment, bridging critical gaps in the understanding of storage system economics and providing actionable insights for policymakers, investors, and operators navigating the evolving energy landscape.
Maritime surveillance system requires sensor fusion of radar Plan Position Indicator (PPI) imagery with Automatic Identification System (AIS) data. However, systematic azimuth errors (0°–5°) and irregular echo shapes reduce ship localization accuracy significantly. This study compares three geometric enclosure methods (bounding boxes, convex hulls, and concave hulls) using the publicly available DLR Baltic Sea dataset (X-band, ~10 m resolution, 781 synchronized scans of 4 ships). A 4.5° azimuth offset compensation is applied to minimize target position deviation. Target positions are estimated from calculated bounding box centers and hull centroids from radar images. Validation against AIS ground truth data achieved the lowest average localization errors (58–70 m) for concave hull centroids. It demonstrates up to 12.7% improvement over bounding boxes solution and a 2.1% improvement over convex hulls solution. The results show the accuracy of the concave hulls method in target detection and in preserving echo concavities caused by sea clutter, and noise etc. The concave hull method offers a lightweight and robust solution for real-time Vessel Traffic Services (VTS) and autonomous surface vehicles (ASVs).
Despite extensive research on Ni-P, Ni-B, and Ni-W systems, the combined Ni-P-B-W quaternary system has not been systematically examined under identical bath conditions, representing a significant gap in literature. In this study, sodium tungstate (Na2WO4) was added to a ternary Ni-P-B coating to investigate the effect of W on the tribological properties of an electroless ternary Ni-P-B polyalloy coating. All coatings' surface morphology, phase structure, hardness, and tribological properties were examined and compared while keeping the Ni-P-B and Ni-P-B-W bath parameters (pH, temperature, and deposition time) constant. The surface morphology of the Ni-P-B coating was transformed from spherical nodular to clear-shaped with the addition of W. All types of coatings resulted in a combination of amorphous and crystalline phases. The ternary Ni-P-B coating exhibited a higher hardness compared to the quaternary Ni-P-B-W coating, attaining a value of approximately 655±10 HV0.025. Dry reciprocating wear tests carried out against Al2O3 balls demonstrated that the quaternary Ni-P-B-W coating achieved up to a 31 % reduction in the friction coefficient and up to a 91 % reduction in specific wear rate compared to the ternary Ni-P-B coating.
This study presents a fully web-based, interactive simulator designed to facilitate understanding and training in autoclave sterilization processes, which are critical in healthcare and laboratory environments. Given the complexity of traditional theoretical training methods and the high costs and safety risks of using real equipment, digital simulation tools offer a crucial educational alternative. The simulator is a single-file application built with modern web technologies — HTML5, CSS3, and JavaScript — and requires no external installation or an internet connection. The core features of the simulator include real-time phase simulation, parameter adjustment with dynamic graphics, and interactive safety warnings for critical safety conditions. Users can experience all stages of the sterilization cycle (pre-vacuum, heating, sterilization, drying) and instantly observe how parameters such as temperature, time, and load type affect the process. In a pilot usability study conducted with 20 undergraduate students, more than 90% reported that the simulator improved their understanding of autoclave phases and safety protocols, highlighting its pedagogical effectiveness. Preliminary data and pilot feedback indicate that the interface is intuitive, understandable, and educationally beneficial. This digital tool demonstrates significant potential to reinforce theoretical knowledge and to prepare learners for practical applications, thereby addressing a critical gap in education.
Given the multifaceted nature of reality, phenomena can be interpreted not only through singular perspectives but also by bringing together various dimensions. Meaning often emerges from the convergence of diverse perspectives, contexts, and forms of representation. The construction of systems capable of analyzing this multilayered structure requires integrating heterogeneous types of information within a holistic, interactive framework. For this multilayered structure to be processable by artificial intelligence systems, the synthesis of heterogeneous information types from various sources in a holistic structure is mandated. In response to this requirement, multimodal learning is an approach that aims to develop more contextual and generalizable artificial intelligence systems by combining heterogeneous data from different modalities (e.g., text, images, audio, sensor data) within an integrated structure. Based on recent literature, this review examines the conceptual foundations of multimodal learning and its key technical challenges, including representation learning, alignment, fusion, translation, missing modality, and co-learning. This study systematically compares and classifies more than 50 of the most prominent review articles published between 2010 and 2025 in a comprehensive table, summarizing the challenges they address, their application areas, and practical contributions. Attention has been drawn to areas often neglected in the literature, such as co-learning and missing modality, as well as to other critical gaps persisting in the field. Furthermore, the paper presents multimodal applications in healthcare, robotics, autonomous driving, remote sensing, and security, along with common multimodal datasets. By bridging theoretical foundations and real-world applications, this study provides a comprehensive reference for the field of multimodal learning.
The Dining Philosophers problem represents a fundamental challenge in concurrent programming, where resource allocation must balance efficiency with fairness to prevent deadlock and starvation. This study investigates how modern reinforcement learning algorithms compare against classical distributed coordination methods in solving this synchronization problem. We implemented and compared four RL approaches—proximal policy optimization (PPO), deep Q-Network (DQN), advantage actor-critic (A2C), and soft actor-critic (SAC)—along with three classical algorithms—Dijkstra's resource ordering, the centralized waiter protocol, and Chandy-Misra's distributed token-passing—in a multi-agent environment with varying philosopher counts (5, 40, 160). Our experimental results across 20000 training episodes reveal distinct performance characteristics. At a small scale (n=5), PPO achieved the highest cumulative reward (750) with fairness 0.90, matching Chandy-Misra's equity while exceeding its throughput. SAC achieved comparable performance (700 reward, 0.88 fairness) with superior convergence stability. At the medium scale (n=40), PPO maintained strong performance (300 reward, 0.85 fairness), whereas classical algorithms demonstrated consistent deterministic behavior. At large scale (n=160), all methods struggled: PPO and SAC degraded gracefully (-5000, -6000 rewards; 0.65 fairness), classical algorithms maintained predictable performance, whereas DQN failed catastrophically (-10,000+ reward). Multi-dimensional analysis across sample efficiency, scalability, and final performance revealed that classical algorithms provide immediate deployment readiness with zero training cost, whereas RL methods achieve higher performance ceilings after extensive training (1000-2000 episodes). These findings demonstrate that algorithm selection critically depends on deployment constraints: classical methods excel in static environments with deterministic requirements, whereas policy-gradient RL offers adaptive optimization for dynamic systems where coordination rules must evolve, though at a significant computational cost.
The accurate forecasting of energy losses in the interconnected grids of Türkiye assumes crucial importance in facilitating strategic planning, optimizing resource allocation, and fostering infrastructure development within the energy sector. A limited number of studies in the existing literature address the estimation of network losses, with investigations into such losses have predominantly relied on conventional machine learning (ML) methodologies. This study proposes an arithmetic optimization algorithm (AOA) approach to the total network losses (TNL) estimation of Türkiye. Firstly, the models are generated with AOA, IAOA, and LevyAOA methods, and then long-term TNL projections were conducted for three scenarios between 2021 and 2050. TNL is modelled as a linear regression model, and for this model, import, export, population, and gross domestic product (GDP) indicators are used as input parameters, and the TNL indicator is used as the output parameter. In the experiments, the historical data records of Türkiye from 1979 to 2020 are used to create the estimation model. Then, long-term TNL estimations with different scenarios are realized for Türkiye up to 2050. The TNL are expressed in gigawatt-hours (GWh) on an annual basis. According to the experimental results and comparisons, the IAOA method has shown quality and robust performance for estimating the TNL compared to other methods. Compared to the standard AOA, the proposed IAOA reduced the total error by 42.57% and the total relative error by 55.49% on the 1979–2020 dataset.
