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.

Abstract: Image captioning, the task of automatically generating textual descriptions for images, lies at the intersection of computer vision and natural language processing. Architectures combining Convolutional Neural Networks (CNNs) for visual feature extraction and Long Short-Term Memory (LSTM) networks for language generation have become a dominant paradigm. This survey provides a comprehensive overview of fifteen influential papers employing these CNN-LSTM frameworks, summarizing their core contributions, architectural variations (including attention mechanisms and encoder-decoder designs), training strategies, and performance on benchmark datasets. A detailed comparative analysis, presented in tabular format, evaluates these works by detailing their technical approaches, key contributions or advantages, and identified limitations. Based on this analysis, we identify key evolutionary trends in CNN-LSTM models, discuss prevailing challenges such as generating human-like and contextually rich captions, and highlight promising future research directions, including deeper reasoning, improved evaluation, and the integration of newer architectures.

Abstract:

Precise orbit determination for multi-spacecraft deep-space missions faces challenges including long communication delays, sparse tracking, dynamic model uncertainties, and inefficient data fusion. Presenting a hybrid estimation architecture, this study integrates onboard autonomous navigation with ground-based batch processing of delayed measurements. The framework makes three key contributions: (1) a delay-aware fusion paradigm that dynamically weights space- and ground-based observations according to real-time Earth–Mars latency (4–22 min); (2) a model-informed online calibration framework that jointly estimates and compensates dominant dynamic error sources, reducing model uncertainty by 60%; (3) a lightweight hierarchical architecture that balances accuracy and efficiency for resource-constrained “one-master-multiple-slave” formations. Validated through Tianwen-1 mission-data replay and simulated Mars sample-return scenarios, the method achieves absolute and relative orbit determination accuracies of 14.2 cm and 9.8 cm, respectively—an improvement of >50% over traditional centralized filters and a 30% enhancement over existing federated approaches. It maintains 20.3 cm accuracy during 10-minute ground-link outages and shows robustness to initial errors >1000 m and significant model uncertainties. This study presents a robust framework applicable to future multi-agent deep-space missions such as Mars sample return, asteroid reconnaissance, and cislunar navigation constellations.

Abstract: This study examined the impact of supportive institutional environments and ethical concerns on students’ readiness to use ChatGPT and how this readiness predicts their intention to use it. The mediating role of readiness to use was also explored in the relationships between institutional support and its intended use, and between ethical concerns and intended use. We used data from 103 Nigerian university engineering students collected through a cross‐sectional online survey. Results indicate that only student readiness to use ChatGPT significantly predicts intention to use (β = .73, p < .001), while institutional support (β = −.01, p < .97) and ethical concerns (β = −.04, p < .88) do not significantly predict intention to use. Mediation analysis revealed that readiness to use ChatGPT does not significantly mediate the relationship between institutional support and their intended use (β = .30, p < .43), and between ethical concerns and intended use (β = .45, p < .24). These findings suggest that innovative use of AI in education is possible when students’ readiness keeps pace with technological advancement. However, adequate regulation must guide ethical use.

Abstract: An equivalent circuit model (ECM) is a highly practical tool for simulating Li-ion battery behavior. There are many relevant studies which compare different ECM variants or suggest algorithms to extract model parameters from the experimental data. However little attention has been given to the battery tests used for identification of the ECM parameters. Therefore, we systematically studied the influence of experimental test pulse characteristics on the parameterized ECM accuracy. Test pulse duration and amplitude were varied along with the portion of the relaxation phase data used by the parameters fitting algorithm. That resulted in 168 parameter sets, each validated using 9 diverse current profiles including one based on a realistic drive cycle. The validation results prove that the impact of the test pulse choice on the parameterized ECM accuracy is great to the point it can overshadow the use of a higher order Thevenin model. By choosing the optimal parameter set the simulated voltage root mean square error was reduced to as low as 1.2 mV.

Abstract: This study analyzes the frequency response of a common-emitter bipolar junction transistor (BJT) amplifier using Fourier methods. We developed a small-signal model and applied Fourier analysis in MATLAB/Simulink to characterize the amplifier's gain and bandwidth. Key performance metrics, including mid-band gain and lower/upper cut-off frequencies, were extracted. The analysis is based on the concept of a transistor with a copper-enhanced base region, which is known to reduce base resistance and improve high-frequency performance. This work demonstrates the practical application of the Fourier transform for converting time-domain circuit behavior into a frequency-domain transfer function, providing a clear methodology for high-frequency amplifier analysis.