Journals
2026 EN
Baraklı Burhan · Yüzkollar Can · Taşçı Tuğrul
+1 more
Salient object detection (SOD) models struggle to simultaneously preserve global structure, maintain sharp object boundaries, and sustain computational efficiency in complex scenes. In this study, we propose SPSALNet, a task-driven two-stage (macro–micro) architecture that restructures the SOD process around superpixel representations. In the proposed approach, a “split-and-enhance” principle, introduced to our knowledge for the first time in the SOD literature, hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions. At the macro stage, the image is partitioned into content-adaptive superpixel regions, and each superpixel is represented by a high-dimensional region-level feature vector. These representations define a regional decomposition problem in which superpixels are assigned to three classes: background, object interior, and transition regions. Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context. At the micro stage, the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions. The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway. Subsequently, channel-aware fusion blocks adaptively combine information from these two sources, producing sharper and more stable object boundaries. Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods. On the PASCAL-S and DUT-OMRON datasets, SPSALNet exhibits a clear performance advantage across all key metrics, and it ranks first on accuracy-oriented measures on HKU-IS. On the challenging DUT-OMRON benchmark, SPSALNet reaches a MAE of 0.034. Across all datasets, it preserves object boundaries and regional structure in a stable and competitive manner.
Journals
2026 EN
Almalaq Abdulaziz · Harrison Ambe · Alsaleh Ibrahim
+2 more
We present a computer-modeling framework for photovoltaic (PV) source emulation that preserves the exact single-diode physics while enabling iteration-free, real-time evaluation. We derive two closed-form explicit solvers based on the Lambert W function: a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator. Unlike Taylor-linearized explicit models, our proposed formulation retains the exponential nonlinearity of the PV equations. It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding, all while maintaining limited computational costs and a small memory footprint. The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator, which generates current references directly from sensed operating points, thus supporting hardware-constrained implementation. Comprehensive numerical experiments conducted on six commercial modules from various technologies (mono, poly, and multicrystalline) demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion: the V-Lambert solver reduces the ±10% Vmpp band error by up to 61 times compared to an explicit-model baseline. Dynamic simulations under varying irradiance, temperature, and load conditions achieve millisecond-scale settling with accurate trajectory tracking. Additionally, processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results. By unifying exact analytical modeling with embedded realization, this work advances computer modeling for PV emulation, MPPT benchmarking, and controller verification in integrated renewable energy systems.
Journals
2026 EN
Zhang Shengkang · Jin Yong · Yap Soon Poh
+5 more
Concrete-filled steel tubes (CFST) are widely utilized in civil engineering due to their superior load-bearing capacity, ductility, and seismic resistance. However, existing design codes, such as AISC and Eurocode 4, tend to be excessively conservative as they fail to account for the composite action between the steel tube and the concrete core. To address this limitation, this study proposes a hybrid model that integrates XGBoost with the Pied Kingfisher Optimizer (PKO), a nature-inspired algorithm, to enhance the accuracy of shear strength prediction for CFST columns. Additionally, quantile regression is employed to construct prediction intervals for the ultimate shear force, while the Asymmetric Squared Error Loss (ASEL) function is incorporated to mitigate overestimation errors. The computational results demonstrate that the PKO-XGBoost model delivers superior predictive accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 4.431% and R 2 of 0.9925 on the test set. Furthermore, the ASEL-PKO-XGBoost model substantially reduces overestimation errors to 28.26%, with negligible impact on predictive performance. Additionally, based on the Genetic Algorithm (GA) and existing equation models, a strength equation model is developed, achieving markedly higher accuracy than existing models (R 2 = 0.934). Lastly, web-based Graphical User Interfaces (GUIs) were developed to enable real-time prediction.
Journals
2026 EN
Rahaman Md. Faishal · Li Xueyuan · Amjad Muhammad
+3 more
Researchers are increasingly focused on enabling groups of multiple unmanned vehicles to operate cohesively in complex, real-world environments, where coordinated formation control and obstacle avoidance are essential for executing sophisticated collective tasks. This paper presents a Distributed Formation Control and Obstacle Avoidance (DFCOA) framework for multi-unmanned ground vehicles (UGV). DFCOA integrates a virtual leader structure for global guidance, an improved A* path planning algorithm with an advanced cost function for efficient path planning, and a repulsive-force- based improved vector field histogram star(VFH*) technique for collision avoidance. The virtual leader generates a reference trajectory while enabling distributed execution; the improved A* algorithm reduces planning time and number of nodes to determine the shortest path from the starting position to the goal; and the improved VFH* uses 2D LiDAR data with inter-agent repulsive force to simultaneously avoid collision with obstacles and maintain safe inter-vehicle distances. The formation stability of the proposed DFCOA reaches 95.8% and 94.6% in two scenarios, with root mean square(RMS) centroid errors of 0.9516 and 1.0008 m, respectively. Velocity tracking is precise (velocity centroid error RMS of 0.2699 and 0.1700 m/s), and linear velocities closely match the desired 0.3 m/s. Safety metrics showed average collision risks of 0.7773 and 0.5143, with minimum inter-vehicle distances of 0.4702 and 0.8763 m, confirming collision-free navigation of four UGVs. DFCOA outperforms conventional methods in formation stability, path efficiency, and scalability, proving its suitability for decentralized multi-UGV applications.
Journals
2026 EN
Akroot Abdulrazzak · Ameen Kayser Aziz · Ibrahim Haitham M.
+2 more
Improving energy efficiency and lowering negative environmental impact through waste heat recovery (WHR) is a critical step toward sustainable cement manufacturing. This study analyzes advanced cogeneration systems for recovering waste heat from the Fallujah White Cement Plant in Iraq. The novelty of this work lies in its direct application and comparative thermodynamic analysis of three distinct cogeneration cycles—the Organic Rankine Cycle, the Single-Flash Steam Cycle, and the Dual-Pressure Steam Cycle—within the Iraqi cement industry, a context that has not been widely studied. The main objective is to evaluate and compare these models to determine the most effective approach for enhancing energy and exergy efficiencies. The methodology involved detailed thermodynamic and exergy analyses of each system, supported by mathematical modelling and simulation using data from plant operations. The results reveal that the Dual-Pressure Steam Cycle emerged as the most effective system, delivering 13.76 MW of net power with a thermal efficiency of 32.8% and an exergy efficiency of 51%. This significantly outperformed the baseline Organic Rankine Cycle (8.18 MW, 18.8% thermal efficiency, 30.7% exergy efficiency). These findings confirm that multi-pressure steam cycles offer a robust and practical solution for the Fallujah plant. This application provides a clear, high-impact pathway to enhance national industrial energy efficiency, significantly reduce CO 2 emissions, and promote clean energy sustainability in Iraq. Future work should consider economic feasibility and potential integration with renewable energy sources to further enhance sustainability.
Journals
2026 EN
Singer Alexander · Schmolke Amelie · Becher Matthias A.
+13 more
The wood mouse, Apodemus sylvaticus , is a small mammal species that occurs across large parts of Europe. The species uses various habitats, has an omnivorous diet and can use pesticide-treated agricultural fields for foraging. Thus, it is one of the focal species in risk assessments of plant protection products ( PPPs ) in the European Union. However, exposures and effects of PPPs on populations in the field are challenging to assess across different regions and landscapes. Population modelling is suggested as a tool in higher-tier risk assessments, complementing and extending data from field studies. Population models can predict population-level effects from modelled life-cycle processes and individual-level exposure and effects. We present the development of a conceptual model for APODEMUS, a POpulation Dynamical spatially Explicit Model of the wood mOUSe. We used best practices for model development recommended in recent guidance documents by the European Food Safety Authority ( EFSA ). Thereby, we focus on the ecological model without the explicit representation of exposures and effects of PPPs as the first part of the model development. We conducted a comprehensive literature search and review on wood mouse ecology. The resulting data compilation was used as the basis for the development of the conceptual model. Decisions about processes included in or excluded from the model were taken in collaboration with stakeholders including small mammal experts, risk assessors and regulators, as well as ecological modellers. Beyond the presentation of the conceptual model for APODEMUS, we discuss the importance of stakeholder involvement in the model development process. In addition, we suggest additions to the comprehensive approach to model development for ecological risk assessment (Pop-GUIDE), particularly with respect to risk assessments of small mammals in the European Union. Our study shows how the systematic development of a population model with continued stakeholder support can lead to a transparent tool for higher-tier risk assessments.
Journals
2026 EN
Karamollaoğlu Hamdullah · Yücedağ İbrahim · Doğru İbrahim Alper
+2 more
The rapid expansion of IoT devices has transformed industries while simultaneously introducing critical security vulnerabilities, particularly Distributed Denial-of-Service (DDoS) attacks that exploit the constrained resources of IoT systems. To address this challenge, a novel intrusion detection system (CBM-IDS) is proposed for the effective identification and mitigation of DDoS attacks in IoT environments. A hybrid deep learning framework is employed, integrating Convolutional Neural Networks (CNN) for spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM) for temporal dependency analysis, and a Multi-Head Attention Mechanism (MHAM) to prioritize critical network traffic patterns. Model robustness is enhanced through Adaptive Synthetic Sampling (ADASYN) and One-Sided Selection (OSS) for class imbalance mitigation, along with dimensionality reduction using an Autoencoder combined with ANOVA F-test-based feature selection. The proposed system is evaluated on the CICDDoS2019 benchmark dataset, achieving a detection accuracy of 99.93%, which demonstrates its efficacy in real-world IoT security applications.
Journal of Universal Computer Science
Journals
2026 EN
Çakırlar İbrahim · Emek Sevcan · Bora Şebnem
+1 more
This study introduces a novel methodology and framework for the verification, validation, and testing of agent-based simulation models: RatKit. Building on repeatable automated testing in ABMS, the present contribution significantly extends the foundation by proposing an integrated metamodel and systematic development methodology that embeds these activities throughout the simulation lifecycle. The RatKit methodology is both general, in that it applies to a wide range of agent-based simulation models using a well-defined metamodel, and comprehensive, in that it addresses the macro-level (societal), the meso-level (interaction) and the micro-level (agent) aspects of simulations. It also provides a generic infrastructure to be able to support various VV&T techniques. RatKit is designed as a general VV&T framework for all ABM frameworks. The methodology comes with a dedicated implemented framework. It is implemented by selecting the Repast ABM development framework. RatKit is demonstrated through a detailed case study of the Boids model, where the dynamics of alignment, cohesion, and separation are examined. Results from the case study show that a test-driven approach can enhance model reliability and ensure that individual agent behaviors coalesce into realistic emergent phenomena. Experiences and feedback obtained during the case studies show that developing ABM with a test-driven method based on VV&T facilitates the creation of desired models.
Journal of Universal Computer Science
Resource
2026 EN
Birk Sebastian · Anzaldua Gerardo · Baattrup-Pedersen Annette
+23 more
Freshwater ecosystems are vital for biodiversity and human well-being, but remain amongst the most degraded globally. Nature-based Solutions ( NbS ) offer a promising pathway to restoration, yet implementation remains fragmented and often limited in scale. This paper synthesises insights from 18 demonstration cases across Europe, carried out under the EU Horizon 2020 MERLIN project, to identify key factors enabling the systemic upscaling of freshwater restoration through NbS . Drawing on practical experiences, five interdependent “building blocks” are proposed: (1) comprehensive status review; (2) narratives of the future; (3) evidence-informed approach; (4) resource management and (5) stakeholder engagement. These dimensions reflect cross-cutting challenges and capacities — such as context-sensitive planning, adaptive learning, financing strategies and inclusive governance. While grounded in diverse local contexts, the framework offers a strategic orientation for scientists, practitioners and policy-makers working to align restoration efforts with the ambitions of the European Green Deal and Nature Restoration Regulation. Rather than prescribing uniform solutions, the paper provides practice-informed guidance for embedding restoration in complex social–ecological systems.HighlightsSynthesises lessons from 18 diverse freshwater restoration cases across Europe under the MERLIN project;Proposes five strategic building blocks for scaling Nature-based Solutions: system understanding, shared vision, evidence use, resource management and stakeholder engagement;Emphasises the interdependence of ecological, institutional and societal dimensions in upscaling restoration;Demonstrates the value of transdisciplinary collaboration, adaptive planning and embedded implementation;Offers practice-orientated insights aligned with the European Green Deal and Nature Restoration Regulation.
Journals
2026 EN
Zaim Saihuddin · Bahar Muh. Akbar · Muzakkir Abdul Rakhmat
+4 more
Chronic kidney disease ( CKD ) poses an increasing public health challenge in Indonesia, yet limited data exist on public knowledge, content preferences, and perceptions of existing CKD information. This study aimed to assess the public’s knowledge regarding CKD , identify key areas of interest and preferred delivery formats, and evaluate perceptions toward current CKD educational materials among the Indonesian population. A cross-sectional survey-based study was conducted from November 2024 to January 2025. Among 651 respondents, 24.0% demonstrated good CKD knowledge, while 33.2% and 42.9% had moderate and poor knowledge, respectively. Female gender, single marital status, higher education, and employment in the health sector were significant factors that influenced knowledge levels (p < 0.0001). Public awareness of CKD in Indonesia remains inadequate, with knowledge levels strongly influenced by education, gender, and professional background. Tailoring educational content and delivery to align with public preferences may enhance engagement and support national efforts in CKD prevention.
Bulgarian Pharmaceutical Scientific Society