We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the coverage of UAV-mounted 6G mobile base stations. The number and placement optimization of UAV-mounted 6G mobile base stations, deployed to support terrestrial base stations during periods of increased population density in a given area, are addressed using a symbolic limited optimal discrete controller synthesis technique. Within the scope of this study, the UAVs’ altitude and attitude behaviors are optimized to ensure the most efficient trajectory toward the designated base station coordinates. Additionally, at their new locations, these behaviors are adjusted to facilitate accurate coverage estimation from the base stations they serve. In the deployment optimization of UAVs, the placement of base stations is determined using received signal strength data obtained through the ray-tracing-based channel modeling technique. The channel model considered critical parameters such as path loss, received power, weather loss, and foliage loss. Final average path loss values of 102.3 dB, 111.7 dB, and 127.4 dB were obtained at the carrier frequencies of 7 GHz, 26 GHz, and 140 GHz, respectively. These findings were confirmed with MATLAB-based ray tracing simulations. Our proposed approach is validated through experimental evaluations, demonstrating superior performance compared to existing methods reported in the literature.

Task scheduling for multiple humanoid robot manipulators in industrial and collaborative settings remains a significant challenge due to the complexity of coordination, resource sharing, and real-time decision-making. In this study, we propose a framework for modeling task scheduling for multiple humanoid robot manipulators by using the symbolic discrete controller synthesis technique. We encode the task scheduling problem as discrete events using parallel synchronous dataflow equations and apply our synthesis algorithms to manage the task scheduling of multiple humanoid robots via the resulting controller. The control objectives encompass the fundamental behaviors of the system, strict rules, and mutual exclusions over shared resources, categorized as the safety property, whereas the optimization objectives are directed toward maximizing the throughput of robot-processed products with optimal efficiency. The humanoid robots considered in this study consist of two pairs of six-degree-of-freedom (6-DOF) robot manipulators, and the inverse kinematics problem of the 6-DOF arms is addressed using metaheuristic approaches inspired by biomimetic principles. Our approach is experimentally validated, and the results demonstrate high accuracy and performance compared to other approaches reported in the literature. Our approach achieved an average efficiency improvement of 40% in 70-robot systems, 20% in 30-robot systems, and 10% in 10-robot systems in terms of production throughput compared to systems without a controller.

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization.

Bankacılık, telekomünikasyon (5G/6G), savunma sanayii ve nesnelerin interneti gibi kritik altyapılar büyük ölçüde yazılıma bağımlı hâle gelmiştir; bu yazılımların güvenilirliği ise bellek güvenliği ve eşzamanlılık doğruluğu gibi düşük seviyeli garantilerle doğrudan ilişkilidir. Bu çalışma, ağ iletişimi ve siber güvenlik bağlamında kullanılan programlama dillerini bellek güvenliği modeli, performans, ekosistem olgunluğu ve geliştirme ekonomisi açısından karşılaştırmaktadır. Çalışma üç bağımsız veri kaynağına dayanmaktadır: on bir dilde yürütülen kontrollü mikro-ölçüm deneyi, büyük ölçekli bir geliştirici anketinin (n = 49.191) ham mikro-verisi ve kurumsal geçiş literatürü. Ölçümler, Rust'ın sahiplik modelinin tahsis yoğun iş yüklerinde Go'nun çöp toplayıcısından yaklaşık 1,8 kat daha hızlı olduğunu, makine koduna derlenen dillerin soğuk başlatmada JVM'e kıyasla yaklaşık 16 kat daha hızlı olduğunu ve ağ katmanında Rust'ın hem gecikme hem verim bakımından C ile aynı düzeyde kaldığını göstermiştir. Anket analizi, Rust'ın %72,5 ile en yüksek sadakat oranına sahip olmasına karşın fiili kullanımının %9,6'da kaldığını ortaya koymuştur. Bulgular, tek bir dilin tüm senaryolar için en uygun seçenek olmadığını, bileşen sorumluluğuna göre seçilen karma (polyglot) mimarinin en dengeli sonucu verdiğini göstermektedir. Türkiye alt örnekleminde C'nin kullanım oranı küresel ortalamanın 1,65 katı iken Rust'ın oranı küresel düzeyde kalmaktadır. Çalışma, Türkiye için kademeli bir geçiş yol haritası önermektedir.

Machine learning has become a widely adopted approach for URL-based phishing detection, with many studies reporting F1 scores exceeding 0.95 on benchmark datasets. However, recent adversarial machine learning research has questioned the robustness of these models, suggesting that small input perturbations can severely degrade detection performance. In this study, we argue that a substantial part of this reported vulnerability stems from the way adversarial attacks are evaluated. Specifically, many existing studies assess attacks in the feature space, where feature values are modified directly without ensuring that the resulting samples correspond to valid, functional URLs. To investigate this issue, we conduct a two-stage empirical study using both a benchmark feature dataset and a dataset of real phishing URLs. Crucially, to avoid confounding the attack space with dataset differences, we additionally evaluate both feature-space and problem-space attacks on the same real-URL dataset, using an identical model and manipulable-feature budget. Our experiments reveal a striking contrast between these evaluation settings. While feature-space attacks reduce the detection rate of a Random Forest classifier on the benchmark dataset from 0.96 to 0.36, analogous manipulations performed on real URLs have almost no effect on detection performance, as the most informative signals originate from host-related attributes that are difficult for attackers to manipulate. Building on this observation, we propose a set of robust features that capture stable domain characteristics, including lexical word validity, homoglyph disguises, brand impersonation, subdomain depth, character entropy, and transport-related signals. Incorporating these features substantially improves robustness under adversarial conditions, maintaining phishing detection rates between 0.24 and 0.76 where the lexical-only baseline deteriorates to zero under a non-adaptive attacker, while also increasing the clean-data F1 score from 0.985 to 0.994. We further evaluate an adaptive attacker that explicitly targets the proposed features; although the proposed representation raises the attacker’s cost and helps under moderate attacks, host-derived features remain the only strictly attack-invariant component, so we position the proposed features as a complement to host-based signals rather than a standalone defense. Additional analyses, including model comparison, hyperparameter sensitivity analysis, feature ablation, SHAP-based interpretation, multi-seed confidence intervals, a domain-disjoint evaluation, and host-only evaluation, consistently support the proposed approach. The findings demonstrate that problem-space evaluation provides a more realistic assessment of adversarial robustness than conventional feature-space testing and show that robust feature engineering offers a practical strategy for developing phishing detection systems that remain effective under realistic adversarial conditions.