Search Results (1,471)

Sapphire crystals, owing to their outstanding mechanical and optical properties, which are widely used in advanced optics, microelectronic devices, and medical instruments. The manufacturing precision of sapphire optical components critically affects the performance of advanced optical systems. However, the extremely high hardness and low fracture toughness of sapphire make it a typical hard-to-machine material, prone to brittle surface fractures and subsurface damage during material removal. Improving the machinability of sapphire remains a pressing challenge in advanced manufacturing. In this study, surface modification and enhanced ductility of C-plane sapphire were achieved via ion implantation, and the machinability of the modified sapphire was further improved through laser-assisted diamond machining (LADM). Monte Carlo simulations were employed to investigate the interaction mechanisms between incident ions and the target material. Based on the simulation results, phosphorus ion implantation experiments were conducted, and transmission electron microscopy observation was used to characterize the microstructural evolution of the modified layer, while the optical properties of the samples before and after modification were analyzed. Finally, groove cutting experiments verified the enhancement in ductile machinability of the modified sapphire under LADM. At a laser power of 16 W, the ductile–brittle transition depth of the modified sapphire increased to 450.67 nm, representing a 51.57% improvement over conventional cutting. The findings of this study provide valuable insights for improving the ductile machining performance of hard and brittle materials. Full article

Background/Objectives: Postnatal depression imposes a substantial burden on wellbeing as well as costs estimated to exceed $7 billion for every one-year cohort of births in Australia. Despite this, most cases go untreated, a major barrier being the poor rate of treatment uptake. We developed and pilot tested an integrated screening and clinical decision support system (PIRIMID) to assist maternal and child health nurses (MCHNs) to create individualised management plans, with specific referral pathways, for women depressed postnatally. We assessed the feasibility of PIRIMID by examining acceptability for both nurses and women, ease of implementation, and referral rates, and we monitored treatment uptake and depression. Methods: An extensive co-design and consultation process was used to develop PIRIMID. A pilot cluster randomised controlled trial (RCT) was conducted comparing PIRIMID to Routine care, with partial crossover (PIRIMID followed by crossover to Routine care and Routine care followed by continued Routine care). A state-wide survey of MCHNs in Victoria, Australia, explored perceived benefits and barriers of PIRIMID from a consumer perspective. Results: Twelve MCHNs (PIRIMID: n = 6; Routine care: n = 6) and 229 women (conditions: PIRIMID, n = 52; Crossover Routine care, n = 42; Routine care, n = 57; Continued Routine care, n = 78) were recruited to the RCT. Median scores for depression, anxiety and stress symptoms were low and similar at all timepoints and conditions. PIRIMID was acceptable and helpful to MCHNs and women, and most MCHNs rated integration into their existing clinical systems as easy. There were trends in favour of higher referral rates by PIRIMID MCHNs (18%, 95% CI: 5–40) compared with other conditions (10–15%, 95% CIs: 6–29, 2–27, 6–26), but treatment uptake appeared similar across conditions. The statewide survey (n = 292) revealed that 84% of MCHNs would use PIRIMID, and the main potential barriers to use would be time constraints and technical issues. Conclusions: This pilot work indicates that PIRIMID shows promise as a feasible and acceptable tool to assist MCHNs to develop management plans for women depressed postnatally. Further research with adequate statistical power is needed to explore effects on treatment uptake with larger samples of postnatally depressed women. Full article

Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its bias varies as an even-harmonic function of the pattern angle, which leads to difficulty in estimating and compensating the bias based on the MSRG in the process of attitude measurement. In this paper, an attitude measurement method based on virtual rotation self-calibration and rotary modulation is proposed for the MSRG–RINS to address this problem. The method utilizes the characteristics of the two operating modes of the MSRG, the force-rebalanced mode and whole-angle mode, to perform virtual rotation self-calibration, thereby eliminating the characteristic bias of the MSRG. In addition, the reciprocating rotary modulation method is used to suppress the residual bias of the MSRG. Furthermore, the magnetometer-aided initial alignment of the MSRG–RINS is carried out and the state-transformation extended Kalman filter is adopted to solve the large misalignment-angle problem under magnetometer assistance so as to enhance the rapidity and accuracy of initial attitude acquisition. Results from real-world experiments substantiated that the proposed method can effectively suppress the influence of MSRG’s bias on attitude measurement, thereby achieving high-precision autonomous navigation in GNSS-denied environments. In the 1 h, 3.7 km, long-range in-vehicle autonomous navigation experiments, the MSRG–RINS, integrated with a Laser Doppler Velocimetry (LDV), attained a heading accuracy of 0.35° (RMS), a horizontal positioning error of 4.9 m (RMS), and a distance-traveled accuracy of 0.24% D. Full article

Thanks to the inherent advantages, including being green, broadband, and high security, visible light communication (VLC), as one powerful enabling technology for 6G and beyond the Internet of Things (IoT), has received ever-increasing discussion and attention. In order to improve the quality of VLC links and extend their coverage, various intelligent reflecting surfaces (IRSs) have been massively discussed and optimized into the VLC field. Apparently, the current research works are merely limited to the investigation of well-known Lambertian source-based, IRS-assisted VLC. Consequently, there is a lack of targeted analysis and evaluation of the diversity of beam configurations for light-emitting diodes (LEDs) and the potential non-Lambertian IRS-assisted VLC links. To fill the above research gap of this VLC branch, this article focuses on introducing the innovative LED non-Lambertian beams into typical IRS-assisted VLC systems to construct novel IRS-assisted non-Lambertian VLC links. The investigation results indicate that compared to the baseline Lambertian IRS-assisted VLC scheme, the proposed representative non-Lambertian IRS-assisted VLC schemes could provide up to 22.22 dB and 14.08 dB signal-to-noise ratio gains for side and corner receiver positions, respectively. Moreover, this article quantitatively evaluates the impact of the initial azimuth angle (i.e., beam azimuth orientation) of asymmetric non-Lambertian optical beams on the performance of IRS-assisted VLC and the relevant fundamental characteristics. Full article

Aluminum alloy surface microstructures possess functional characteristics such as hydrophilicity/hydrophobicity and anti-icing and have important applications in fields such as aerospace and power systems. In order to improve the filling quality of the microstructure and verify the anti-icing property of the microstructure, this work develops a scheme for achieving large-area hot embossing of anti-icing functional microstructures based on a multi-arc ion-plating mold. Compared with conventional steel, the hardness of the PVD-coated steel increases by 44.7%, the friction coefficient decreases by 66.2%, and the wear resistance is significantly enhanced. The PVD-coated punch-assisted embossing could significantly improve filling properties. While the embossing temperature is 300 °C, the PVD-coated punch-assisted embossing can ensure the complete filling of the micro-array channels. In contrast, under-filling defects occur in conventional hot embossing. Then, a large-area micro-channel specimen of 100 cm² was precisely formed without warping, and the average surface roughness Ra was better than 0.8 µm. The maximum freezing fraction of the micro-array channel was reduced by about 53.2% compared with the planar, and the complete freezing time was delayed by 193.3%. The main reason is that the air layer trapped by the hydrophobic structures hinders heat loss at the solid–liquid interface. Full article

Methane pyrolysis produces hydrogen (H₂) with solid carbon black as a co-product, eliminating direct CO₂ emissions and enabling a low-carbon supply when combined with renewable or low-carbon heat sources. In this study, we propose a hybrid geothermal pyrolysis configuration in which an enhanced geothermal system (EGS) provides base-load preheating and isothermal holding, while either electrical or solar–thermal input supplies the final temperature rise to the catalytic set-point. The work addresses four main objectives: (i) integrating field-scale geothermal operating envelopes to define heat-integration targets and duty splits; (ii) assessing scalability through high-pressure reactor design, thermal management, and carbon separation strategies that preserve co-product value; (iii) developing a techno-economic analysis (TEA) framework that lists CAPEX and OPEX, incorporates carbon pricing and credits, and evaluates dual-product economics for hydrogen and carbon black; and (iv) reorganizing state-of-the-art advances chronologically, linking molten media demonstrations, catalyst development, and integration studies. The process synthesis shows that allocating geothermal heat to the largest heat-capacity streams (feed, recycle, and melt/salt hold) reduces electric top-up demand and stabilizes reactor operation, thereby mitigating coking, sintering, and broad particle size distributions. High-pressure operation improves the hydrogen yield and equipment compactness, but it also requires corrosion-resistant materials and careful thermal-stress management. The TEA indicates that the levelized cost of hydrogen is primarily influenced by two factors: (a) electric duty and the carbon intensity of power, and (b) the achievable price and specifications of the carbon co-product. Secondary drivers include the methane price, geothermal capacity factor, and overall conversion and selectivity. Overall, geothermal-assisted methane pyrolysis emerges as a practical pathway to turquoise hydrogen, if the carbon quality is maintained and heat integration is optimized. The study offers design principles and reporting guidelines intended to accelerate pilot-scale deployment. Full article

Stable adhesion on non-magnetic, steep, and irregular underwater surfaces (e.g., concrete dams with cracks or biofilms) remains a challenge for inspection robots. This study develops a novel adsorption mechanism based on the synergistic operation of a Venturi-ejector and a composite suction cup. The mechanism utilizes the Venturi effect to generate stable negative pressure via hydrodynamic entrainment and innovatively adopts a composite suction cup—comprising a rigid base and a dual-layer EPDM sponge (closed-cell + open-cell)—to achieve adaptive sealing, thereby reliably applying the efficient negative-pressure generation capability to rough underwater surfaces. Theoretical modeling established the quantitative relationship between adsorption force (F) and key parameters (nozzle/throat diameters, suction cup radius). CFD simulations revealed optimal adsorption at a nozzle diameter of 4.4 mm and throat diameter of 5.8 mm, achieving a peak simulated F of 520 N. Experiments demonstrated a maximum F of 417.9 N at 88.9 W power. The composite seal significantly reduced leakage on high-roughness surfaces (Ra ≥ 6 mm) compared to single-layer designs. Integrated into an inspection robot, the system provided stable adhesion (>600 N per single adsorption device) on vertical walls and reliable operation under real-world conditions at Balnetan Dam, enabling mechanical-arm-assisted maintenance. Full article

With the rapid expansion of offshore wind power, efficient installation methods for 10 MW offshore wind turbines (OWTs) are increasingly being required. Conventional approaches using installation vessels, heavy-lift barges, and mooring systems incur high costs, long schedules, and weather-related constraints, particularly in harsh seas such as the West Sea and Jeju. This study investigates an anchor-free installation method for jack-up-type OWTs employing tugboats instead of specialized vessels. Environmental loads were estimated with MOSES and AQWA, and frequency-domain analyses were performed to evaluate wave responses and towline tensions. Results showed that maximum tensions remained below both the Safe Working Load of towlines and the Effective Bollard Pull of tugboats during all spudcan lowering stages. Even under conservative OPLIM conditions, feasibility was confirmed. The findings indicate that the proposed tug-assisted method ensures adequate station-keeping capability while reducing cost, construction time, and weather dependency, presenting a practical alternative for large-scale OWT installation. Full article

The integration of renewable energy sources has become a strategic necessity for sustainable energy management and supply security. This study evaluates the performance of eight metaheuristic optimization algorithms in scheduling a renewable-based smart grid system that integrates solar, wind, and battery storage for a factory in İzmir, Türkiye. The algorithms considered include classical approaches—Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), the Whale Optimization Algorithm (WOA), Krill Herd Optimization (KOA), and the Ivy Algorithm (IVY)—alongside hybrid methods, namely KOA–WOA, WOA–PSO, and Gradient-Assisted PSO (GD-PSO). The optimization objectives were minimizing operational energy cost, maximizing renewable utilization, and reducing dependence on grid power, evaluated over a 7-day dataset in MATLAB. The results showed that hybrid algorithms, particularly GD-PSO and WOA–PSO, consistently achieved the lowest average costs with strong stability, while classical methods such as ACO and IVY exhibited higher costs and variability. Statistical analyses confirmed the robustness of these findings, highlighting the effectiveness of hybridization in improving smart grid energy optimization. Full article

Trust plays a pivotal role in individuals’ interactions with technological systems, and those incorporating artificial intelligence present significantly greater challenges than traditional systems. The current landscape of higher education is increasingly shaped by the integration of AI assistants into students’ classroom experiences. Their appropriate use is closely tied to the level of trust placed in these tools, as well as the strategies adopted to critically assess the accuracy of AI-generated content. However, scholarly attention to this dimension remains limited. To explore these dynamics, this study applied the POTDAI evaluation framework to a sample of 132 engineering and social sciences students at Camilo José Cela University in Madrid, Spain. The findings reveal a general lack of trust in AI assistants despite their extensive use, common reliance on inadequate verification methods, and a notable skepticism regarding professors’ ability to detect AI-related errors. Additionally, students demonstrated a concerning misperception of the capabilities of different AI models, often favoring less advanced or less appropriate tools. These results underscore the urgent need to establish a reliable verification protocol accessible to both students and faculty, and to further investigate the reasons why students opt for limited tools over the more powerful alternatives made available to them. Full article

Cybersecurity management plays a key role in preserving the operational security of nuclear power plants (NPPs), ensuring service continuity and system resilience. The growing number of sophisticated cyber-attacks against NPPs requires cybersecurity experts to detect, analyze, and defend systems and data from cyber threats in near real time. However, managing a large numbers of attacks in a timely manner is impossible without the support of Artificial Intelligence (AI). This study recognizes the need for a structured and in-depth analysis of the literature in the context of NPPs, referring to the role of AI technology in supporting cyber risk assessment processes. For this reason, a systematic literature review (SLR) is adopted to address the following areas of analysis: (i) critical assets to be preserved from cyber-attacks through AI, (ii) security vulnerabilities and cyber threats managed using AI, (iii) cyber risks and business impacts that can be assessed by AI, and (iv) AI-based security countermeasures to mitigate cyber risks. The SLR procedure follows a macro-step approach that includes review planning, search execution and document selection, and document analysis and results reporting, with the aim of providing an overview of the key dimensions of AI-based cybersecurity in NPPs. The structured analysis of the literature allows for the creation of an original tabular outline of emerging evidence (in the fields of critical assets, security vulnerabilities and cyber threats, cyber risks and business impacts, and AI-based security countermeasures) that can help guide AI-based cybersecurity management in NPPs and future research directions. From an academic perspective, this study lays the foundation for understanding and consciously addressing cybersecurity challenges through the support of AI; from a practical perspective, it aims to assist managers, practitioners, and policymakers in making more informed decisions to improve the resilience of digital infrastructure. Full article

Language learners increasingly rely on intelligent digital tools to supplement their learning experiences, yet existing chatbots often provide limited support, lacking adaptability, personalization, or domain-specific intelligence. This study introduces a novel AI-powered multi-agent chatbot architecture designed to support English–Arabic translation and language learning. Developed through a three-phase methodology, offline preparation, real-time deployment, and evaluation, the system employs both retrieval-based and generative AI models, with specialized agents managing tasks such as translation, example retrieval, user translation review, and learning feedback. The chatbot was developed using a hybrid architecture incorporating fine-tuned Generative Pre-trained Transformer (GPT) model, sentence embedding techniques, and similarity evaluation metrics. A user study involving 40 undergraduate students and 4 faculty members evaluated the system across usability, effectiveness, and pedagogical value. Results show that the multi-agent chatbot significantly enhanced learner engagement, provided accurate and contextually appropriate language support, and was positively received by both students and instructors. These findings demonstrate the value of multi-agent design in language learning applications and highlight the potential of AI-driven chatbots as intelligent educational assistants. Full article

At present, artificial intelligence (AI) has shown significant potential in digestive endoscopy image analysis, serving as a powerful auxiliary tool for the accurate diagnosis and treatment of gastrointestinal diseases. However, mainstream models represented by deep learning are often characterized as complex “black boxes,” with decision-making processes that are difficult for humans to interpret. The lack of interpretability undermines physicians’ trust in model results and hinders the broader use of models in clinical practice. To address this core challenge, Explainable AI (XAI) has emerged to enhance the transparency of decision-making, thereby establishing a foundation of trust for human–machine collaboration. The review systematically reviews 34 articles (7 articles in esophagogastroduodenoscopy, 13 articles in colonoscopy, 9 articles in endoscopic ultrasonography, and 5 articles in wireless capsule endoscopy), focusing on the research progress and applications of XAI in the field of digestive endoscopic image analysis, with particular emphasis on the visual explanation-based methods. We first clarify the definition and mainstream classification of XAI, then introduce the principles and characteristics of key XAI methods based on visual explanation. Subsequently, we review the applications of these methods in digestive endoscopy image analysis. Lastly, we explore the obstacles presently faced in this domain and the future directions. This study provides a theoretical basis for constructing a trustworthy and transparent AI-assisted digestive endoscopy diagnosis and treatment system and promotes the implementation and application of XAI in clinical practice. Full article

Wind power is integral to the transformation of energy systems towards sustainability. However, the increasing number of wind turbines approaching the end of their service life presents significant challenges in terms of waste management and environmental sustainability. Rotor blades, typically composed of thermoset polymer composites reinforced with glass or carbon fibres, are particularly problematic due to their low recyclability and complex material structure. The aim of this article is to provide a system-level review of current end-of-life strategies for wind turbine components, with particular emphasis on blade recycling and decision-oriented comparison, and its integration into circular economy frameworks. The paper explores three main pathways: operational life extension through predictive maintenance and design optimisation; upcycling and second-life applications; and advanced recycling techniques, including mechanical, thermal, and chemical methods, and reports qualitative/quantitative indicators together with an indicative Technology Readiness Level (TRL). Recent innovations, such as solvolysis, microwave-assisted pyrolysis, and supercritical fluid treatment, offer promising recovery rates but face technological and economic as well as environmental compliance limitations. In parallel, the review considers deployment maturity and economics, including an indicative mapping of cost and deployment status to support decision-making. Simultaneously, reuse applications in the construction and infrastructure sectors—such as concrete additives or repurposed structural elements—demonstrate viable low-energy alternatives to full material recovery, although regulatory barriers remain. The study also highlights the importance of systemic approaches, including Extended Producer Responsibility (EPR), Digital Product Passports and EU-aligned policy/finance instruments, and cross-sectoral collaboration. These instruments are essential for enhancing material traceability and fostering industrial symbiosis. In conclusion, there is no universal solution for wind turbine blade recycling. Effective integration of circular principles will require tailored strategies, interdisciplinary research, and bankable policy support. Addressing these challenges is crucial for minimising the environmental footprint of the wind energy sector. Full article

Visual impairment affects millions worldwide, creating significant barriers to environmental interaction and independence. Existing assistive technologies often rely on cloud-based processing, raising privacy concerns and limiting accessibility in resource-constrained environments. This paper explores the integration and potential of open-source AI models in developing a fully offline assistive system that can be locally set up and operated to support visually impaired individuals. Built on a Raspberry Pi 5, the system combines real-time object detection (YOLOv8), optical character recognition (Tesseract), face recognition with voice-guided registration, and offline voice command control (VOSK), delivering hands-free multimodal interaction without dependence on cloud infrastructure. Audio feedback is generated using Piper for real-time environmental awareness. Designed to prioritize user privacy, low latency, and affordability, the platform demonstrates that effective assistive functionality can be achieved using only open-source tools on low-power edge hardware. Evaluation results in controlled conditions show 75–90% detection and recognition accuracies, with sub-second response times, confirming the feasibility of deploying such systems in privacy-sensitive or resource-constrained environments. Full article