The article presents a chip feature extraction approach to create unique and unclonable hardware fingerprints to increase reliability in a hardware security system. A static D flip-flop-based delay counter is used as a delay unit to propose a lightweight PUF in place of the Configurable Ring Oscillators (CROs) in the existing PUF. Initially, the design is simulated and synthesized using three different Complementary Metal-Oxide-Semiconductor (CMOS) technologies to study process variations. Its reliability is ensured through Multi-Mode Multi-Corner (MMMC) analysis. The circuit stands out with its + 0.004ps slack timing and operates stably within a temperature range of 0°C to 125°C. It is quite interesting to observe that it works efficiently within a voltage range of 1.62V to 1.98V. Overall, 155 unique data paths are resolved and carried out by Static Timing Analysis (STA), which ensures the uniqueness of the core design. Furthermore, the results of Machine Learning techniques and the NIST statistical test suite confirm the proposed structure's randomness strength. A comparison of this work with recent findings indicates the specific advantages of the implemented method. The study classifies the proposed Semicustom Delay Variant PUF (SDV PUF) as either strong or weak by adhering to the security requirements.

Semiconductor lasers have been widely employed in chaos-based information processing due to their ability to generate enhanced chaotic bandwidths. In this study, we investigate broadband polarization chaos in optically injected QD spin-VCSELs and their ability to act as high-speed physical entropy sources for random number generation (RNG). We achieve chaotic bandwidths approaching 50 GHz per polarization mode using elliptical injection. With optimized conditions and post-processing, we demonstrate RNG at rates of up to 240 Gb/s. The quality of the generated random sequences is evaluated using multiple statistical metrics, including entropy estimation based on the NIST SP800-90B framework, uniqueness analysis using Hamming distance, and bias assessment through autocorrelation and histogram analysis. In addition, the influence of different polarization injection schemes on randomness is examined using the NIST SP800-22 statistical test suite. These results highlight the potential of QD spin-VCSELs as compact and ultrafast sources for RNG in secure communication systems.

This paper proposes a multi-strategy Secretary Bird Optimization Algorithm (MS-SBOA) for solving global optimization problems and 3D wireless sensor network deployment. While preserving the original two-phase search framework of SBOA, the proposed algorithm achieves a dynamic balance between global exploration and local exploitation through the synergistic integration of multiple enhancement strategies, including a hybrid initialization scheme combining Latin hypercube sampling and quasi-opposition-based learning, a success-history-based adaptive parameter learning mechanism, a finance-inspired market-state trading operator, and an elite-guided population regulation strategy. Experimental results on the IEEE CEC2020 and CEC2022 benchmark test suites demonstrate that MS-SBOA significantly outperforms nine comparative algorithms, including VPPSO, IAGWO, and QHSBOA, under both 10-dimensional and 20-dimensional settings. The proposed algorithm exhibits superior optimization accuracy, faster convergence speed, and stronger robustness. Statistical analyses using the Wilcoxon rank-sum test and the Friedman mean rank test further confirm that the observed performance improvements are statistically significant. Moreover, MS-SBOA is applied to three-dimensional wireless sensor network (3D WSN) deployment optimization problems, where the average coverage rates reach 76.22% and 82.32% for 30-node and 50-node deployment scenarios, respectively. The resulting node distributions are more uniform, and the computational efficiency is improved compared with competing algorithms.

ABSTRACT Balancing convergence, diversity and feasibility remains a fundamental challenge in constrained optimization. This paper proposes MOEA/D‐PS, which introduces the Pascoletti‐Serafini (PS) scalarization framework into constrained single‐objective optimization for the first time. The algorithm reformulates constrained single‐objective problems into a bi‐objective optimization by treating constraint violation as a secondary objective, then integrates two synergistic mechanisms to achieve a satisfactory balance of convergence, diversity and feasibility: (1) direction vectors sampled from a normal distribution to guide the population search around the elite solution while preserving exploration capability; (2) an adaptive truncation parameter that dynamically adjusts the search region based on the current elite solution. Unlike standard multi‐objective transformations that waste resources approximating the entire Pareto front, the PS‐guided mechanism geometrically restricts exploration to the trajectory toward the feasible optimum, achieving a unified balance among convergence, diversity and feasibility. Comprehensive experiments on 24 benchmark functions from the CEC 2006 test suite show that MOEA/D‐PS attains the best overall ranking among seven state‐of‐the‐art algorithms.

Enterprise web development encompasses distributed architectures, cloud-native infrastructures, and multi-channel user interfaces, generating substantial cognitive demands on engineering teams. This article investigates artificial intelligence as a productivity force multiplier within enterprise development contexts through systematic review of primary empirical studies spanning knowledge management, code engineering, quality assurance, and onboarding workflows. Analysis reveals that retrieval-augmented generation achieves superior factuality ratings compared to parametric-only baselines in knowledge retrieval tasks. Developer interaction studies demonstrate that exploration mode dominates over acceleration mode when engaging with AI code generation tools, with professional developers allocating greater proportions to acceleration mode as expertise increases. Cross-project prediction research establishes that transfer models rarely achieve acceptable precision, recall, and accuracy thresholds simultaneously, underscoring the necessity for context-specific AI augmentation. Whole test suite generation approaches achieve superior branch coverage compared to single-target methods across benchmark evaluations. The contribution comprises a structured human-AI collaboration framework distinguishing augmentation from substitution, incorporating governance mechanisms addressing code security vulnerabilities, documentation inaccuracies, and skill erosion risks. The framework synthesizes empirical findings into actionable design principles for enterprise AI adoption, positioning artificial intelligence as an embedded acceleration layer within human-directed workflows.

Solving constrained multiobjective optimization problems (CMOPs) is highly challenging due to the presence of complicated feasible regions, intense conflicts among objectives, and unevenly distributed constraints. As a result, conventional methods relying on a single constraint-handling mechanism frequently fail to maintain a stable equilibrium among solution feasibility, diversity, and convergence. To overcome these bottlenecks, this article introduces AFFCMO, a novel adaptive feasibility-guided framework tailored for constrained multiobjective optimization. At its core, the proposed approach utilizes a coevolutionary dual-population architecture that divides the search process into two distinct tasks. Specifically, an auxiliary population is tasked with global exploration, while a primary population focuses on the intensive exploitation of discovered feasible areas. To achieve this, the primary population leverages a DE/current-to-pbest/1 differential evolution strategy to closely approximate the constrained Pareto front. Simultaneously, the auxiliary population expands the search space using a mutation operator that adapts to the current evolutionary stage. Furthermore, exploration is bolstered by a multicriterion environmental selection scheme designed for the auxiliary group. By combining Euclidean geometric distributions, constraint relaxation, and value modeling inspired by epidemic dynamics, this strategy successfully preserves valuable infeasible solutions that can guide the search. Additionally, a dynamic resource allocation strategy based on historical search feedback and Thompson sampling is incorporated. This mechanism continuously evaluates the recent search contributions of both populations and adaptively adjusts their offspring sizes, thereby reducing the bias introduced by static allocation schemes. This mechanism continuously assesses the actual search contributions of both populations, allowing for the adaptive resizing of offspring generations and thereby eliminating the inherent biases of static allocation methods. Comprehensive empirical evaluations are conducted on 47 benchmark problems from four distinct test suites. The results indicate that AFFCMO significantly outperforms seven contemporary multiobjective evolutionary algorithms in terms of exploring complex feasible regions, preserving solution diversity, and achieving high convergence accuracy.

Repurposing decommissioned wind turbine blades provides a vital pathway to mitigate carbon emissions, yet the escalating volume of large-scale waste poses a severe environmental challenge. Recognizing the limitation that existing research focuses predominantly on small-scale legacy blades, this study addresses this gap by assessing the mechanical properties and microstructure of a 54-m (2.0 MW) blade decommissioned due to repowering after 10 years of service. GFRP samples extracted from the root, mid-span, and tip were investigated using X-ray computed tomography and a comprehensive suite of mechanical tests. The investigation confirmed a low internal porosity (~1.2%) without service-induced macroscopic interfacial cracking, alongside superior residual performance, exemplified by a tensile strength of 849.5 MPa at the root. Statistical analysis employing ANOVA revealed significant spatial variations, supporting a graded reuse strategy: roots with superior tensile strengths for critical members, mid-spans for axial compression, and tips as a reliable property baseline for general reuse, while Weibull analysis verified the statistical reliability required for structural design. Based on these superior residual properties, a raft-type wave energy converter utilizing repurposed blade segments was proposed. A comparative carbon footprint assessment revealed that this blade-repurposed WEC achieved a 71.5% reduction in carbon emissions and a 37.4% reduction in structural mass compared to conventional steel counterparts. These findings substantiate the viability of large-scale DWTBs as high-value resources for decarbonizing marine infrastructure within a circular economy.

Multilevel threshold image segmentation is a key task in image processing, yet it faces challenges such as low search efficiency in high-dimensional spaces, difficulty in balancing segmentation accuracy and stability, and insufficient adaptability to complex scenes. Existing solutions mainly include traditional thresholding methods and metaheuristic optimization-based schemes, but they still face limitations in high-dimensional and complex segmentation tasks. The standard Seagull Optimization Algorithm (SOA) suffers from shortcomings including a single exploration mechanism, weak local exploitation capability, and a tendency for population diversity to deteriorate, making it difficult to meet the demands of high-dimensional optimization. To address these issues, this paper proposes a multi-strategy fused improved Seagull Optimization Algorithm (MFISOA), which integrates three strategies: adaptive cooperative foraging, differential evolution-driven exploitation, and centroid opposition-based boundary control. These strategies jointly construct a collaborative optimization framework with dynamic resource allocation, fine local search, and population diversity maintenance, thereby improving global exploration efficiency, local exploitation accuracy, and population stability. To evaluate the optimization performance of MFISOA, numerical simulation experiments were conducted on the CEC2017 and CEC2022 benchmark test suites, and comparisons were made with nine other mainstream advanced algorithms. The results show that MFISOA outperforms the competing algorithms in terms of optimization accuracy, convergence speed, and operational stability. Its superiority is further verified by the Wilcoxon rank-sum test and the Friedman test, with statistical significance (p < 0.05). In the multilevel threshold image segmentation task, using the Otsu criterion as the objective function, MFISOA was tested on nine benchmark images under 4-, 6-, 8-, and 10-threshold segmentation scenarios. The results indicate that MFISOA achieves better performance on metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Feature Similarity Index (FSIM), enabling more accurate characterization of image grayscale distribution features and producing higher-quality segmentation results. This study provides an efficient and reliable approach for numerical optimization and multilevel threshold image segmentation.

This study proposes two efficient algorithms for generating true random bits (TRBs) in Internet of Things (IoT) devices, utilizing linear and non-linear congruence equations in combination with sensor-derived inputs. The sensor data, including signals from light-dependent resistors, sound, temperature, and image sensors, are harnessed to introduce high-entropy, unpredictable variables into the generation process, thereby enhancing the randomness of the resulting bitstreams. The algorithms leverage commonly available IoT sensors to capture environmental noise, significantly improving security by reducing TRB predictability. Performance and random quality of the generated sequences are rigorously validated using the NIST SP 800-22 Statistical Test Suite, with all essential tests successfully passed. These results demonstrate that the proposed approach provides a cost-effective and resource-efficient solution for secure, robust TRB generation in constrained IoT environments, advancing the practical deployment of cryptographic systems in diverse, sensor-based low-power applications.

Electronic portal imaging devices (EPIDs) enhance linear accelerator (linac) quality assurance (QA) efficiency but are not universally used beyond patient-specific IMRT QA. A multi-institutional Consortium seeks to standardize linac QA utilizing an EPID-based test suite. This study evaluates the sensitivity of a test suite to intentional linac and plan errors and the concordance with linac fluctuations over time. Baseline and error-introduced tests were performed on a Varian TrueBeam and Clinac. Evaluated parameters included enhanced dynamic wedge factors (EDW) (6MV), central axis dose (6MV, 6MV FFF, 16MV), focal spot alignment and beam symmetry (6MV, 16MV), dose rate-gantry speed and multi-leaf collimator (MLC) leaf speed (6MV). Errors included varying EDW angles (10°-60°), output changes, beam steering for focal spot and symmetry inaccuracies, and meterset adjustments of control points for dose rate-gantry speed and MLC leaf speed tests. Measurements were performed with an EPID, analyzed via test suite software, and compared to an ion chamber (IC) or array. Concordance analysis was performed on a single linac over 5 months to assess the test suite's ability to quantify deviations during routine operations. Measurement reproducibility was assessed. Results were reported as differences between EPID and an IC or array. For each EDW, wedge factor differences were <0.5% (TrueBeam) and <1.5% (Clinac). Central axis dose variations stayed within ±0.8%. For focal spot, EPID underestimated the radial direction and overestimated the transverse direction. Symmetry measurements showed strong linearity (r>0.99), though EPID measurements underestimated changes in symmetry relative to baseline. Maximum differences for dose rate-gantry speed and MLC leaf speed were <0.4%. In the concordance study, output differences averaged 0.3%±0.3% (6MV), 0.5%±0.3% (6MV FFF), and 0.3%±0.2% (16MV). Systematic differences in symmetry were observed between EPID and a 2D-array with standard deviations <0.2%. Comparisons with traditional detectors showed the EPID test suite can detect errors and supports commissioning and clinical integration.

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated strong performance in solving low- and medium-dimensional expensive multi-objective optimization problems (EMOPs). However, their scalability to high-dimensional problems remains a critical challenge. To address this issue, several SAEAs based on variable grouping have been proposed. Yet they often fail to deliver satisfactory results under limited real function evaluations. In this paper, we propose a SAEA grouped by significant influence (SAEA-SI), in which variable grouping is adaptively adjusted according to the optimization state. The method is motivated by the observation that different decision variables often contribute unequally to different objectives. SAEA-SI adopts a two-phase optimization framework, where the first phase focuses on convergence, while the second phase enhances diversity through adaptive variable grouping. Specifically, before each optimization process, correlation analysis in the objective space is performed to identify solution combinations for each objective. These combinations are used to estimate the influence of each decision variable, and decision variables exhibiting consistent and significant influence are grouped accordingly. Each group is then optimized independently using surrogate-assisted Differential Evolution. We conduct comparative experiments with the five state-of-the-art SAEAs on multiple test suites with up to 1000 decision variables. The results demonstrate that SAEA-SI is able to strike a good balance between convergence and diversity under limited real function evaluations. Furthermore, experiments on real-world test problems confirm the effectiveness of SAEA-SI in high-dimensional EMOPs.

This paper presents an energy-efficient True Random Number Generator (TRNG) architecture, referred to as the Modified Chaotic Logistic Map with Braided XOR Network (MCLM-BXN). The proposed system exploits the diffusion of noise from two 4-bit tapped Ring Oscillators (ROs), which serve as the entropy sources. These jitter-induced signals are injected into a Modified Chaotic Logistic Map (MCLM) module to enhance the generated randomness. To further improve entropy and reduce statistical correlation, an 8-input, 1-output Braided XOR Network (BXN) module is employed as a post-processing stage. The MCLM-BXN architecture is implemented on the INTEL 5CGXFC9D6F27C7 Cyclone V GT FPGA chip. Supported by the Data Storage and Transmitting Unit-300 (DSTU-300), the TRNG achieves a throughput of 300 Mbps. Experimental evaluations confirm high entropy levels, recording approximately 7.998893 bits/byte using the AIS-31 T8 test and 7.963032 bits/byte according to the NIST SP800-90B test. Furthermore, the proposed TRNG passes the full NIST SP800-22 statistical test suite, validating its effectiveness and suitability for cryptographic applications.

Differential evolution exhibits strong performance in real parameter single objective optimization. Ensemble of similar differential evolution algorithms has been proposed in literature. Building on previous research, we introduce a novel ensemble framework of similar differential evolution algorithms, where the primary algorithm initially manages all individuals. If, over a series of generations, an individual consistently fails to be eliminated, one of the secondary algorithms is chosen based on historical performance to provisionally handle the individual’s position. This ensemble system requires optimization of a single parameter, namely, the upper limit of successive generations without improvement in a position. Based on differential evolution algorithms within the same family, we present two versions of our ensemble. Our experimental analysis utilizes two benchmark test suites and a group of real-world problems. The results reveal that the first version outperforms existing state-of-the-art methods. Furthermore, our ablation experiment on the first version confirms the effectiveness of the proposed ensemble schemes. By observing the performance of the both versions, we establish a relationship between the individual algorithms’ performance and that of the ensemble as a whole.

The field of Dynamic Multi-Objective Optimization (DMOO) has witnessed a surge of interest from both academia and industry, as numerous time-evolving real-world applications can be naturally formulated as Dynamic Multi-Objective Optimization Problems (DMOPs). This growing demand thus necessitates advanced benchmarks to rigorously evaluate optimization algorithms under realistic conditions. This paper introduces a comprehensive and principled framework for constructing highly realistic and challenging DMOO benchmarks. The proposed framework incorporates several novel components, including: a generalized formulation that allows the Pareto-optimal Set (PS) to change on hypersurfaces; a mechanism for creating controlled variable contribution imbalances to generate heterogeneous landscapes; and dynamic rotation matrices for inducing time-varying variable interactions and non-separability. Furthermore, we incorporate a temporal perturbation mechanism to simulate irregular environmental changes and propose a generalized time-linkage mechanism that systematically embeds historical solution quality into future problems, thereby capturing critical real-world phenomena such as error accumulation and time-deception. Extensive experimental results validate the effectiveness of the proposed framework, demonstrating its superiority over conventional benchmarks in terms of realism, complexity, and its capability for discriminating state-of-the-art algorithmic performance. Thus, this work establishes a new standard for dynamic multi-objective optimization benchmarking and provides a powerful tool for the development and evaluation of next-generation algorithms capable of addressing the complexities of real-world dynamic systems. • Proposes a novel, comprehensive framework for generating highly realistic DMOO benchmarks. • Introduces hypersurface-based Pareto Set changes, controlled imbalance, and dynamic rotation. • Incorporates irregular temporal perturbations and a generalized time-linkage mechanism. • Establishes a new DMOO benchmark standard and demonstrates algorithm discrimination effectiveness.

Optimization and control of wastewater treatment using a multi-strategy improved red-billed blue magpie optimizer and hybrid neural network.

Abstract We present pkdgrav3 , a high-performance, fully parallel tree smoothed particle hydrodynamics (SPH) code designed for large-scale hydrodynamic simulations including self-gravity. Building upon the long development history of pkdgrav , the code combines an efficient hierarchical tree algorithm for gravity and neighbor finding with a modern implementation of SPH optimized for massively parallel hybrid CPU/GPU architectures. Its hybrid shared/distributed memory model, combined with an asynchronous communication scheme, allows pkdgrav3 to scale efficiently to thousands of CPU cores and GPUs. We validate the numerical accuracy of pkdgrav3 using a suite of standard tests, demonstrating excellent agreement with analytic or reference solutions. The code was already used in several peer-reviewed publications to model planetary-scale impacts, where SPH’s Lagrangian nature allows for accurate tracking of material origin and thermodynamic evolution. These examples highlight pkdgrav3 ’s robustness and efficiency in simulating highly dynamical, self-gravitating systems. pkdgrav3 thus provides a powerful, flexible, and scalable platform for astrophysical and planetary applications, capable of exploiting the full potential of modern heterogeneous high-performance computing systems.

Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original DMOA: hybrid population initialization, adaptive vocalization parameters, elite-guided learning strategy, and intelligent restart mechanisms. This work proposed the integration of CEDMOA with a novel vision-based threat detection system using YOLO object detection technology, enabling the identification and incorporation of threats into the optimization process. CEDMOA was comprehensively evaluated on the CEC2022 benchmark test suite, demonstrating superior performance compared to other state-of-the-art algorithms in solution quality and convergence stability. The results show the approach successfully generates an optimal collision-free flight trajectory in complex environments in UAV trajectory planning with both static and dynamic threats. Combining metaheuristic optimization with computer vision technology provides a robust framework for autonomous navigation that adapts to changing threat conditions. Experimental results validate the effectiveness of both the enhanced algorithm and the vision-based threat integration approach for practical UAV operations.

Enterprise software organizations face mounting pressure to accelerate deployment cycles while maintaining comprehensive quality assurance standards that protect business operations and customer trust. Traditional sequential testing approaches within continuous integration and continuous deployment pipelines create critical bottlenecks that constrain software delivery velocity and force difficult trade-offs between test coverage breadth and feedback speed. This article examines the implementation of parallel and distributed testing architectures leveraging Selenium Grid and Docker containerization to address these challenges in large-scale enterprise environments. The distributed framework employs hub-node topology coordinating test execution across containerized browser nodes with intelligent load balancing algorithms and dynamic scaling mechanisms. Performance evaluation demonstrates substantial execution time reductions enabling transformation from extended overnight testing cycles to rapid feedback loops compatible with continuous integration practices. Deployment frequency increases directly attributable to reduced feedback cycle duration enable authentic continuous delivery practices where individual features deploy independently upon completion. Cost-benefit analysis reveals optimal parallelization configurations balancing performance improvements against infrastructure expenses and resource utilization efficiency. Scalability measurements confirm sub-linear execution time growth as test suites expand organically, indicating sustainable accommodation of increasing quality coverage requirements. Reliability metrics demonstrate operational stability comparable to serial execution approaches while maintaining high availability essential for production pipeline integration. Strategic implications extend throughout software development lifecycle management, enabling shift-left quality practices and sophisticated release management capabilities including progressive rollouts and rapid experimentation. The documented architectural patterns, implementation guidance, and empirical performance characteristics provide actionable frameworks for organizations seeking to resolve tensions between comprehensive quality assurance and competitive delivery velocity in demanding enterprise contexts.

Effective test case generation is crucial for ensuring software correctness, whereas generating high-coverage test suites efficiently remains a challenge. Graph transformations provide a formal way to specify and analyse software systems by modeling system operations as transformation rules and constructing a state-based representation of system behavior. Model-based testing (MBT) often uses model checking over this representation to discover execution paths that satisfy certain test requirements. However, such approaches suffer from severe scalability issues due to the rapid growth of the state space and the high computational cost of exhaustive exploration. While optimization-based approaches mitigate these issues by exploring a reduced portion of the state space, they still struggle to scale effectively. MBT approaches using graph transformation faces the same scalability and often face additional challenges due to the richer structural complexity of graph-based models. However, apart from the behavioral information derived from state transitions, graph transformation systems also encode explicit structural relationships between states and transformation rules. These structural characteristics can be used to define and evaluate test objectives. To exploit this, we propose a novel approach based on deep reinforcement learning to generate test suites for systems specified through graph transformations. We use the reward/penalty mechanism of reinforcement learning to optimize the selection of moves within the state space, enabling the generation of test cases based on prior decisions. Our goal is to achieve greater coverage of test objectives while minimizing the size of the test cases. The method has been implemented in GROOVE, an open-source toolset for designing and model checking graph transformation systems. Experimental results on well-known case studies demonstrate that our approach achieves higher coverage with reduced computational cost compared to state-of-the-art techniques.

This paper introduces a sophisticated, scalable testing system that integrates observability-driven automation with AI-augmented proactive quality engineering to tackle contemporary software delivery difficulties. The suggested system enhances PreventativeTestPro, an open-source, hybrid testing platform that combines black-box and white-box methodologies, by incorporating an innovative observability-based test orchestration layer. The platform utilizes logs, metrics, events, and traces alongside browser and server-side monitoring to promptly identify anomalies, enhance test case selection, and automate the creation of functional, performance, and security test suites. A distinctive characteristic is the incorporation of large language models (LLMs) to provide root cause insights and autonomously construct new test cases based on production behaviors and identified abnormalities, thus providing adaptive regression coverage and intelligent remediation. The system facilitates concurrent test execution with instantaneous AI-driven log analysis, fostering a continuous feedback loop between operations and testing. It has been validated in several enterprise scenarios, including microservices-based SaaS platforms and SAP BTP ecosystems. Empirical findings from four production deployments and a beta group of 49 engineers indicate a decrease of up to 30% in mean time to resolution, over 95% compliance with SLAs, and substantial improvements in both test coverage and defect traceability. The effortless connection with industry-standard tools illustrates its plug-and-play capability. This research presents a comprehensive, tool-independent, and forward-looking quality engineering methodology consistent with agile and DevOps principles. Future endeavors encompass dynamic anomaly classification through machine learning, extension to mobile and user experience-oriented systems, and augmented large language model capabilities for domain-specific test development and failure forecasting.