Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains and many applications demand a high degree of automation, supported by reliable Conflict Detection and Resolution (CD&R) and Collision Avoidance (CA) systems. At the same time, public mistrust, safety and privacy concerns, the presence of uncooperative airspace users, and rising traffic density are driving a shift toward decentralized concepts such as free flight, in which each actor is responsible for its own safe trajectory. This survey reviews CD&R and CA methods with a particular focus on decentralized automation and encounters with noncooperative intruders. It analyzes classical rule-based approaches and their limitations, then examines Machine Learning (ML)–based techniques that aim to improve adaptability in complex environments. Building on recent regulatory discussions, it further considers how requirements for trust, transparency, explainability, and interpretability evolve with the degree of human oversight and autonomy, addressing gaps left by prior surveys.

Abstract: Building Information Modeling (BIM) represents a paradigmatic transformation in architecture and engineering, facilitating the transition from two-dimensional documentation to integrated multidimensional collaborative environments. This article synthesizes a systematic literature review (2014-2024) combining meta-analyses, case studies, and quantitative-qualitative research on the adoption of BIM in the AEC sector. The results document significant benefits: reductions of 25-30% in design errors, 20% in execution time and 15% in costs. However, adoption reveals geographic fragmentation (US 60%, UK 80%, Latin America <25%) and multidimensional barriers: lack of specialized training, cultural resistance, absence of specific legal frameworks in developing countries, and limited interoperability. The analysis identifies that successful integration requires deep organizational transformation, coordinated public policies, and educational curricular adaptation. Recommendations include micro-regional contextual strategies, contractual standardization (ISO 19650) and applied research in BIM-Facility Management integration and emerging technologies (XR, digital twins). BIM integrates geometric (3D), temporal (4D-schedule), economic (5D-costs) and operational (6D-facility management) information into collaborative parameterized models. Beyond visualization, the methodology calls for clarity on specific Development Levels (LODs) for each phase of the asset lifecycle, from LOD 100 (conceptual) to LOD 500 (as-built). Interoperability using IFC (ISO 16739) and ISO 19650 standards requires robust model validation and accurate definition of model views (MVDs), areas where 74% of projects in developing countries still have critical gaps. This article emphasizes that BIM is not only a software tool, but a comprehensive information management protocol that permeates processes from conceptual design to sustainable operation and demolition.

Abstract: Microwave photonics has recently come to the forefront as a valuable approach to generating, processing, and measuring signals in high-performance domains such as communication, radar, and timing systems. Recent studies have introduced a range of photonics-based phase-noise analyzers (PNAs) that utilize a variety of architectures, including phase detection, frequency discrimination, and hybrid mechanisms that combine optical with electronic processing. This review delves into the microwave photonics methodologies developed with the specific purpose of measuring phase noise, by exploring their fundamental principles, system design frameworks, and performance indicators. Through the integration of insights garnered from recent publications, our objective is to deliver a comprehensive understanding of the strengths and limitations associated with PNAs and to pinpoint new and promising areas for advancing research in the field of oscillator metrology.

Abstract: In complex traffic environments, image degradation caused by haze, low illumination, and occlusion significantly undermines the reliability of vehicle and pedestrian detection. To address these challenges, this paper proposes an aerial vision framework that tightly couples multi-level image enhancement with a lightweight detection architecture. At the image preprocessing stage, a cascaded “dehazing + illumination” module is constructed. Specifically, a learning-based dehazing method, Learning Hazing to Dehazing, is employed to restore long-range details affected by scattering artifacts. Additionally, HVI-CIDNet is introduced to decouple luminance and chrominance in the Horizontal/Vertical Intensity (HVI) color space, thereby simultaneously enhancing structural fidelity in low-light regions and achieving global brightness consistency. On the detection side, a lightweight yet robust detection architecture, termed GDEIM-SF, is designed. It adopts GoldYOLO as the lightweight backbone and integrates D-FINE as an anchor-free decoder. Furthermore, two key modules, CAPR and ASF, are incorporated to enhance high-frequency edge modeling and multi-scale semantic alignment, respectively. Evaluated on the VisDrone dataset, the proposed method achieves improvements of approximately 2.5–2.7 percentage points in core metrics such as mAP@50–90 compared to similar lightweight models (e.g., the DEIM baseline and YOLOv12s), while maintaining low parameter count and computational overhead. This ensures a balanced trade-off among detection accuracy, inference efficiency, and deployment adaptability, providing a practical and efficient solution for UAV-based visual perception tasks under challenging imaging conditions.

Abstract: Sulfate-reducing bacteria (SRB)-induced corrosion presents a considerable challenge to the integrity of shale gas pipelines. Conventional reliance on chemical biocides is limited by the potential for microbial resistance and environmental impact. As an alternative, the bio-competitive exclusion approach, utilizing microbes such as denitrifying bacteria (DNB), offers a promising strategy. This study investigates an integrated control method, combining the biocide glutaraldehyde with DNB to synergistically inhibit SRB activity and corrosion. The efficacy and mechanisms were systematically evaluated through electrochemical measurements, weight-loss analysis, surface characterization, and microbial community profiling. Following synergistic treatment with glutaraldehyde and DNB, the average corrosion rate was reduced by 44.2% and the maximum corrosion depth decreased by 84.3% compared to the SRB-inoculated system. Microbial community analysis revealed a substantial decline in SRB abundance from 62.7% on day 1 to 11.9% by day 14 under the synergistic treatment. The combined approach proves economically and environmentally viable, offering the advantages of reduced chemical dosage and the avoidance of additional corrosion typically associated with DNB. These results provide a novel strategy for developing microbial-influenced corrosion control measures in shale gas infrastructure.

Abstract: Water supply and drainage networks are essential components of urban infrastructure, directly influencing both residents' quality of life and the efficiency of city operations through their safety and stability. Over time, these networks often develop non-structural turbid water conditions, which present challenges for traditional maintenance methods. Leveraging the advantages of spatial visualization, three-dimensional environmental reconstruction technology has emerged as a promising solution to address these issues, while also advancing the use of intelligent maintenance technologies within water supply and drainage systems. This paper focuses on the causes of non-structural turbid water in these networks, and evaluates the optimization, effectiveness, and limitations of turbid water imaging, image feature recognition, and 3D environmental reconstruction technologies. Additionally, it re-views the current technical challenges and outlines potential future research directions, aiming to support the development and application of 3D reconstruction technologies for pipeline networks under non-structural turbid water conditions.

Abstract: The constant growth of IP data traffic, driven by sustained annual increases surpassing 26%, is pushing current optical transport infrastructures towards their capacity limits. Since the deployment of new fiber cables is economically demanding, ultra-wideband transmission is emerging as a promising costly-effective solution, enabled by multi-band amplifiers and transceivers spanning the entire low-loss window of standard single-mode fibers. In this scenario, an accurate modeling of the frequency-dependent fiber parameters is essential to reliably model optical signal propagation. In particular, the combined impact of attenuation slope and inter-channel stimulated Raman scattering (SRS) fundamentally shapes the power evolution of wide wavelength division multiplexing (WDM) combs and directly affects nonlinear interference (NLI) generation. In this work, a set of analytical approximations for the frequency-dependent attenuation and Raman gain coefficient is presented, providing an effective balance between computational efficiency and physical fidelity. Through extensive simulations covering C, C+L, and ultra-wideband U-to-E transmission scenarios, the accuracy in reproducing the behavior of the power evolution and NLI profiles of fully numerical SRS models with the proposed approximations is demonstrated.

Abstract: This study proposes and systematically validates a new analytical wake model that incorporates atmospheric stability effects. By introducing a stability-dependent turbulence expansion term with a square of a cosine function and the stability sign parameter, the model dynamically responds to varying atmospheric conditions, overcoming the reliance of tranditional models on neutral atmospheric assumptions. It achieves physically consistent descriptions of turbulence suppression under stable conditions and convective enhancement under unstable conditions. A newly developed far-field decay function effectively coordinates near-wake and far-wake evolution, maintaining computational efficiency while significantly improving prediction accuracy under complex stability conditions. The Present model has been validated against field measurements from the Scaled Wind Farm Technology (SWiFT) facility and the Alsvik wind farm, demonstrating superior performance in predicting wake velocity distributions on both vertical and horizontal planes. It also exhibits strong adaptability under neutral, stable, and unstable atmospheric conditions. This proposed framework provides a reliable tool for wind turbine layout optimization and power output forecasting under realistic atmospheric stability conditions.

Abstract: The growing global demand for mineral resources is challenging mining operations to maintain productivity while addressing lower-grade ore and increased extraction complexity. Despite the availability of vast datasets across mining stages, much of this information remains underused in decision-making. This study presents an integrated, knowledge-based framework that leverages artificial intelligence (AI) and high-fidelity simulation to model and optimise the full mine to mill process. Using publicly available data from the Barrick Cortez Mine in Nevada, USA, the mining chain from blasting to semi-autogenous grinding (SAG) was modelled using the Integrated Extraction Simulator (IES) from Orica. To mitigate the computational burden of full factorial simulations, three million scenarios were generated to evaluate performance sensitivity. Machine learning models, including linear regression, decision trees, random forests, and XGBoost, were trained and validated. The models achieved an accuracy of more than 90%, underscoring their reliability for predicting process outcomes. SHapley Additive exPlanations (SHAP) were applied to interpret model predictions and quantify feature importance. The findings confirm a strong alignment between simulation and real-world data and highlight key operational parameters that affect downstream process performances. This meta-model approach offers a powerful tool for real-time decision-making, enabling mining operations to improve efficiency, reduce costs, and support sustainable resource management.

Abstract: Driven by the global rise in animal protein demand, poultry farming has evolved into a highly intensive and technically complex sector. According to FAO, animal protein production increased by about 16% in the past decade, with poultry alone expanding 27% and becoming the leading source of animal protein. This intensification requires rapid, complex decisions across multiple aspects of production under uncertainty and strict time constraints. This study presents the development and evaluation of a conversational decision support system (DSS) designed to support decision-making to assist poultry producers in addressing technical queries across five key domains: environmental con-trol, nutrition, health, husbandry, and animal welfare. The system combines a large language model (LLM) with retrieval-based generation (RAG) to ground responses in a curated corpus of scientific and technical literature. Additionally, it adds a reasoning component using Paraconsistent Annotated Evidential Logic Eτ, a non-classical logic designed to handle contradictory and/or incomplete information. Evaluation was conducted by comparing system responses with expert reference answers using semantic similarity (cosine similarity with SBERT embeddings). Results indicate that the system successfully retrieves and composes relevant content, while the paraconsistent inference layer makes results easier to interpret and more reliable in the presence of conflicting or insufficient evidence. These findings suggest that the proposed architecture provides a viable foundation for explainable and reliable decision support in modern poultry production, achieving consistent reasoning under contradictory and/or incomplete information where conventional RAG chatbots would fail.

Abstract: The development of photonic integrated circuits (PICs) for data communication, sensing, and quantum computing is hindered by the high complexity and cost of traditional fabrication methods, which rely on expensive equipment, limiting accessibility for research and prototyping. This study introduces a Direct Laser Writing (DLW) system designed as a low-cost alternative, utilizing an XY platform for precise substrate movement and an optical system comprising a collimator and lens to focus the laser beam. Operating on a single layer, the system employs SU-8 photoresist to fabricate polymer based structures on substrates such as ITO covered glass. Preparation involves thorough cleaning, spin coating with photoresist, and pre and post-baking to ensure material stability. This approach reduces dependence on costly infrastructure, making it suitable for academic settings and enabling rapid prototyping. A user interface and custom slicer process standard .dxf files into executable commands, enhancing operational flexibility. Experimental results demonstrate a resolution of 10 µm, with successful patterning of structures, including diffraction grids, waveguides, and multimode interference devices. This system aims to transform PIC prototype fabrication into a cost-effective, accessible process.

Abstract: Active protection systems have long been employed on military vehicles and installations but have traditionally been too bulky for individual use. To enhance personal safety across military and industrial applications, the Active Neutralization of Celeritous Impacts by Lateral Expulsion (ANCILE) system was developed using open-source, commercially available components. The ANCILE system integrates a semi-modular interception platform designed to detect and deflect fast-moving projectiles and debris, offering an active layer of protection for the wearer. The system employs dual cameras mounted on a wearable, turret-like mechanism that can pneumatically deploy a Kevlar sail to intercept incoming threats. The experimental testing demonstrated reliable detection and interception of objects traveling up to 7.5 m/s, with an average interception probability of 28.6% (± 8.3%) at higher velocities. The Kevlar sail resisted standard ballistic projectiles ranging from .22 Long Rifle to .357 Magnum. Although current detection and response performance remain limited by the hardware, refinement with specialized sensors and actuators could enable higher-speed operation. This preliminary work confirms the feasibility of a low-cost, wearable active protection system with potential applications in construction, manufacturing, aerospace, and defense.

Abstract: This study presents a passive mechanical filter designed to enhance sub-Hertz Venusquake detection by shaping the seismic transfer path with a tunable, high-Q pendulum mounted inside a cylindrical enclosure on a three-ring gimbal. The gimbal provides self-leveling on uneven terrain, while the housing–gimbal assembly remains broadband-stiff (<1–1000 Hz), limiting platform-induced motion and preventing spurious high-frequency amplification. Unlike approaches that rely on broadband digitization followed by digital filtering, which require large dynamic range, high bandwidth, and thermally stable electronics yet not feasible on Venus, the proposed mechanism performs pre-filtering at the mechanical level that can be energy-saving, reducing the required analog-to-digital conversion (ADC) range while amplifying the target band. Response spectrum analysis shows a clear low-pass behavior with peak sensitivity in the 0.5–0.8 Hz range. When tuned to 50-55 mm pendulum length and assumed undamping, the pendulum-mount mechanism improves detectability at best by 10-100 relative to a bare sensor for moderate magnitude (M_s = 3-6) in a 12-h observation window, with signal-to-noise (SNR) ratio of 3, and amplitude spectrum density (ASD) of 10⁻⁸ m/s²/√Hz. Furthermore, we extrapolate that the predicted minimum detectable event rates follow Nmmin∝(SNR)^1.2(ASD)^1.2f_s^0.6, where f_s is the quake wave frequency. A limitation is the quasi-static regime (0.05 Hz or below), where rigid-body motion overrides the benefit. Overall, the passive, power-free architecture offers a robust alternative to existing Venus Lander designs, enabling sub-Hz detection even during short-duration surface operations while adhering to mission constraints.

Abstract: In-circuit testing (ICT) and functional testing (FCT) are diagnostic methods used to assess the operational integrity of discrete electronic components and circuitry on a Printed Cir-cuit Board (PCB). Commercial electrical testing systems are usually modular, consisting of multiple control and processing circuit boards, which makes them highly specialized and expensive. This creates a significant accessibility challenge, especially for small and me-dium-sized industries. In this study, a low-cost device board was developed to perform in-circuit and functional testing based on fundamental principles of electrical testing. The design incorporates relay banks to connect electrical test node probes with the Device Un-der Test (DUT), enabling resistance and capacitance measurements, shorts and open-circuit detection, voltage stimuli, digital multimeter readings, analog signal condi-tioning and amplification, general-purpose input/output, receive/transmit data, among other functions. These operations are managed via RS-232, opto-isolated UART ports, Ethernet TCP/IP, and digital input/output (I/O) control ports. Prototypes were built and tested in automated functional test setups to verify their performance and proper opera-tion. The results show that a single, low-cost PCB can effectively carry out testing tasks typically performed by expensive commercial systems, providing a versatile and econom-ical alternative tool for electrical testing for prototyping, and end-of-line test equipment.

Abstract: This systematic review delves into the revolutionary impact of composite materials on the aerospace industry, emphasizing their role in enabling lightweight and high-performance structures. By synthesizing existing research, the review comprehensively analyzes the application of composite materials in aerospace, highlighting benefits, challenges, and advancements. The review aims to offer valuable insights into the current landscape of composite materials in aerospace applications and to pinpoint potential areas for future research and development. Exploring the transformative impact of composites in aircraft design, performance enhancement, and environmental sustainability, the review underscores the opportunities for innovation and efficiency while addressing challenges such as cost considerations and regulatory compliance. It emphasizes the essential role of research in material development, performance evaluation, and sustainability to drive advancements in aerospace composite technologies. The review advocates for collaborative partnerships and investments in research initiatives as crucial steps towards unlocking the full potential of composites in aerospace, shaping a future characterized by excellence, innovation, and sustainable aerospace solutions.

Abstract: Bone is a hierarchically organized composite material with unique mechanical properties and an intrinsic capacity for regeneration. Conventional repair strategies, including autografts, allografts, xenografts, and metallic or ceramic implants, face limitations such as donor scarcity, immunogenicity, brittleness, and poor long-term integration. Tissue engineering (TE) offers a promising alternative by combining cells, scaffolds, and growth factors to restore bone structure and function. This review outlines the principles and workflow of bone TE, emphasizing scaffold design, and clinical viability. Scaffolds serve as three-dimensional, highly porous templates that support cell adhesion, nutrient diffusion, and extracellular matrix remodeling. Successful bone TE requires osteoconductive scaffolds, osteogenic progenitor cells, and osteoinductive signaling molecules to achieve physiological compatibility and functional integration. Recent advances in biomaterials, scaffold architecture, and fabrication technologies have significantly improved the ability to replicate native bone properties, positioning TE as a transformative strategy for regenerative medicine. Despite persistent challenges in achieving complete integration and mechanical stability under complex loading, ongoing research continues to optimize scaffold performance and cellular approaches, making TE a viable and cost-effective alternative to traditional bone repair techniques.

Abstract:

To address the long-term operational challenges of space environment monitoring buoys under extreme Arctic conditions, this paper proposes an energy management optimization method based on deep reinforcement learning algorithms. By constructing a buoy system model integrating renewable energy and lithium-ion battery power supply units, battery energy storage units, and multi-sensor load units, and incorporating Arctic environmental models with low-temperature battery efficiency degradation patterns, a reward function was designed to minimize unsupplied energy while ensuring functional integrity. Using the Twin Delay Deep Deterministic Policy Gradient (TD3) algorithm on the MATLAB simulation platform, the effectiveness of different energy storage configurations for achieving long-term observation in Arctic environments was compared. Results demonstrate that this method significantly enhances the buoy’s endurance and scheduling intelligence, offering new insights for energy management in intelligent polar observation equipment.

Abstract:

An optimal topographical arrangement of Wind Turbines (WTs) is essential for increasing the total power production of a Wind Farm (WF). This work introduces PSO-GA, a newly formulated algorithm based on the hybrid of Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) method, to provide the best possible and reliable WF Layout (WFL) for enhanced output power. Because GA improves on PSO-found solutions while PSO investigates several regions, PSO-GA can effectively handle issues with multiple local optima. In the first phase of the framework, PSO improves the original variables; in the second phase, variables are changed for improved fitness. The goal function takes into account both the power production of the WF and the total cost of WTs while analyzing wake upshot using the Jenson-Wake model. To evaluate the robustness of this strategy, three case studies are analyzed. The algorithm identifies the best possible position of turbines and strictly complies with industry-standard separation distances to prevent extreme wake interference. The comparative study with the past layout improvement process models demonstrates that the proposed hybrid algorithm has enhanced performance with the power improvement of 0.03-0.04% with the p value< 0.01 and 24-27.3% reduction in the wake loss. The above findings indicate that the proposed PSO-GA can be better than the other innovative methods, especially in the aspects of quality and consistency of the solution.

Abstract: The mechanical behavior of carbon-fiber-reinforced polymer (CFRP) laminates manu-factured using plasma-assisted solvolysis recycled fibers was evaluated experimentally through a comprehensive mechanical testing campaign. The plasma-assisted solvolysis parameters were selected based on an earlier sensitivity analysis that identified the optimal operating conditions for efficient matrix removal and fiber integrity preserva-tion. Prepregs made from both virgin and recycled carbon fibers were fabricated via a hand lay-up process and manually stacked to produce unidirectional laminates of identical nominal thickness. Longitudinal tension tests, longitudinal compression tests, and interlaminar shear strength (ILSS) tests were performed to assess the fundamental mechanical response of the recycled laminates and quantify the retention of mechanical properties relative to the virgin-reference material. Prior to mechanical testing, all laminates underwent ultrasonic C-scan inspection to assess manufacturing quality, detect internal defects, and identify any regions of poor consolidation. While both laminate types exhibited generally satisfactory quality, the recycled-fiber laminates showed a higher density of defects—such as localized voids and small delamination areas. The recycled laminates preserved around 80% of their original tensile strength and maintained an essentially unchanged elastic modulus. Compressive strength was more susceptible to imperfections introduced during remanufacturing, with the recy-cled laminates exhibiting roughly a 14% decrease compared with the virgin material. On the contrary, the compressive modulus was largely retained. The most substantial reduction occurred in ILSS, which dropped by 58%. The observed reduction in me-chanical properties of recycled CFRPs is mainly attributed to deviations in manufac-turing quality and not to the reduced properties of recycled carbon fibers.

Abstract: Metal additive manufacturing (AM) has emerged as a transformative route for producing lightweight, high-precision, and geometrically complex components in aerospace, biomedical, and microelectronic sectors. Among AM technologies, Laser Powder Bed Fusion (LPBF) offers exceptional design freedom; however, its widespread adoption particularly for titanium alloys remains constrained by two persistent challenges: shrinkage-induced dimensional deviation and porosity-related performance loss. In LPBF-processed Ti-6Al-4V, residual linear deviation typically falls within 0.1–0.8% when geometric compensation, preheating, and support strategies are implemented, while raw, uncompensated shrinkage is more commonly reported in the range of 1.2–2.0%, especially for thin-wall or thermally constrained geometries. Volumetric contraction (approximately 2–6%) may remain significant depending on part architecture and localized thermal accumulation. Concurrently, gas-induced and lack-of-fusion pores continue to undermine fatigue resistance and dimensional reliability. Research into process optimization, thermal management, and post-processing such as Hot Isostatic Pressing (HIP), vacuum sintering, and stress-relief annealing has improved density and mechanical integrity, while recent developments in AI-assisted monitoring, physics-informed models, and digital-twin frameworks are redefining defect prediction and control. Drawing on more than 100 peer-reviewed studies, this review synthesizes mechanism-driven insights and outlines a forward-looking roadmap, demonstrating how hybrid processing, real-time sensing, and data-centric control collectively advance the pathway toward defect-minimized, industrial-scale manufacturing of titanium components.