Search Results (630)

In this research project a large linear electromagnetic actuator (LLEA) was designed and manufactured. The electromagnetic performance was published in previous works, but in this paper we focus on the technological challenges related to the manufacturing in particular. This LLEA was based on the magnet-free switched-reluctance principle, having six effective energised stator “teeth” and four passive mover parts (4:6 ratio). Various aspects and challenges encountered during the manufacturing, transport, and assembly are discussed. Thermal expansion of steel contributed to the decision of the modular design, with each module having 1.3 m in length, with a 2 mm longitudinal dilatation gap. The initial prototype was tested with a 10.6 m length, with plans to extend the test track to 60 m, which was fully achievable due to the modular design and required 29 tons of electrical steel to be built. The stator laminations were cut by a bespoke progressive tool with stamping, and other parts by a CO₂ laser. Mounting was based on welding (back of the stator) and clamping plates (through insulated bolts). The linear longitudinal force was on the order of 8 kN, with the main air gap of 7.5–10 mm on either side of the mover. The lateral forces could exceed 40 kN and were supported by appropriate construction steel members bolted to the concrete floor. The overall mechanical tolerances after installation remained below 0.5 mm. The technology used for constructing this prototype demonstrated the cost-effective way for a semi-industrial manufacturing scale. Full article

Helical milling presents a promising alternative to conventional drilling for hole production, offering superior surface quality and improved production efficiency. While this technique has been extensively applied in the aerospace industry, its potential for machining common engineering materials, such as AISI 1045 steel, remains underexplored in the literature. This study addresses this gap by systematically evaluating the influence of key process parameters—cutting speed (V_c), axial depth of cut (a_p), and tool diameter (D_t)—on hole quality attributes, including surface roughness, burr formation, and nominal diameter accuracy. A full factorial experimental design (2³) was employed, coupled with analysis of variance (ANOVA), to quantify the effects and interactions of these parameters. The results reveal that, with a higher V_c, it is possible to reduce surface roughness (R_a) by 30% to 40%, while an increased a_p leads to a 50% increase in R_a. Additionally, D_t emerged as the most critical factor for nominal diameter accuracy, reducing geometrical errors by 1% with a larger D_t. Burr formation was predominantly observed at the lower end of the hole, highlighting challenges specific to this technique. These findings provide valuable insights into optimizing helical milling for low-carbon steels, offering a foundation for broader industrial adoption and further research. Full article

The increasing utilization of hard-to-cut materials in high-performance sectors such as aerospace and defense has pushed manufacturing systems to be flexible in processing large workpieces with a wide range of materials while also delivering high precision. Recent studies have highlighted the potential of integrating industrial robots (IRs) with electric discharge machining (EDM) to create a non-contact, low-force manufacturing platform, particularly suited for the accurate machining of hard-to-cut materials into complex and large-scale monolithic components. In response to this potential, a novel robotic EDM system has been developed. However, the manual programming and control of such a convoluted system present a significant challenge, often leading to inefficiencies and increased error rates, creating a scenario where the EDM process becomes unfeasible. To enhance the industrial applicability of this robotic EDM technology, this study focuses on a novel methodology to develop and validate a digital twin (DT) of the physical robotic EDM system. The digital twin functions as a virtual experimental environment for tool motion, effectively addressing the challenges posed by collisions and kinematic singularities inherent in the physical system, yet with proven 20-micron EDM gap accuracy. Furthermore, it facilitates a CNC-like, user-friendly offline programming framework for robotic EDM cutting path generation. Full article

The rapid evolution of deep learning (DL) has fundamentally transformed the paradigm for detecting special components in agricultural products, addressing critical challenges in food safety, quality control, and precision agriculture. This comprehensive review systematically analyzes many seminal studies to evaluate cutting-edge DL applications across three core domains: contaminant surveillance (heavy metals, pesticides, and mycotoxins), nutritional component quantification (soluble solids, polyphenols, and pigments), and structural/biomarker assessment (disease symptoms, gel properties, and physiological traits). Emerging hybrid architectures—including attention-enhanced convolutional neural networks (CNNs) for lesion localization, wavelet-coupled autoencoders for spectral denoising, and multi-task learning frameworks for joint parameter prediction—demonstrate unprecedented accuracy in decoding complex agricultural matrices. Particularly noteworthy are sensor fusion strategies integrating hyperspectral imaging (HSI), Raman spectroscopy, and microwave detection with deep feature extraction, achieving industrial-grade performance ($RPD$ > 3.0) while reducing detection time by 30–100× versus conventional methods. Nevertheless, persistent barriers in the “black-box” nature of complex models, severe lack of standardized data and protocols, computational inefficiency, and poor field robustness hinder the reliable deployment and adoption of DL for detecting special components in agricultural products. This review provides an essential foundation and roadmap for future research to bridge the gap between laboratory DL models and their effective, trusted application in real-world agricultural settings. Full article

Modern heritage (MH) is a key component of our built environment; however, it currently lacks widespread recognition and a clear, universally accepted definition, placing it in an emerging phase. This category of heritage, understood within the context of modernisation processes and the changes characteristic of the late modern period, remains underrepresented and warrants further study. The objective of this article is to fill the identified research gap, thereby fostering awareness of MH, improving its accessibility and enhancing its visibility and appreciation. It offers a diagnostic analysis of the corpus on MH through the design and development of a concrete methodology, which is transferable to the other heritage categories. This study reveals insights into the present understanding of the term ‘Modern Heritage’ and its relevance within an international framework. This understanding prompts a reflection on the terminology used to describe this concept, which serves not only as a significant result in itself but also as a foundation for future research. Despite the close association of modern heritage with the 20th century, this research identifies a cross-cutting nature that needs to be recognised, encompassing a wide range of periods, themes and typologies in this category. Full article

Foodborne pathogenic bacteria critically threaten public health and food industry sustainability, serving as a predominant trigger of food contamination incidents. To mitigate these risks, the development of rapid, sensitive, and highly specific detection technologies is essential for early warning and effective control of foodborne diseases. In recent years, biosensors have gained prominence as a cutting-edge tool for detecting foodborne pathogens, owing to their operational simplicity, rapid response, high sensitivity, and suitability for on-site applications. This review provides a comprehensive evaluation of critical biorecognition elements, such as antibodies, aptamers, nucleic acids, enzymes, cell receptors, molecularly imprinted polymers (MIPs), and bacteriophages. We highlight their design strategies, recent advancements, and pivotal contributions to improving detection specificity and sensitivity. Additionally, we systematically examine mainstream biosensor-based detection technologies, with a focus on three dominant types: electrochemical biosensors, optical biosensors, and piezoelectric biosensors. For each category, we analyze its fundamental principles, structural features, and practical applications in food safety monitoring. Finally, this review identifies future research priorities, including multiplex target detection, enhanced processing of complex samples, commercialization, and scalable deployment of biosensors. These advancements are expected to bridge the gap between laboratory research and real-world food safety surveillance, fostering more robust and practical solutions. Full article

In recent years, much research has been devoted to the evaluation of physical phenomena and the technological effects of precise and micromilling processes. However, the available current literature lacks synthetic work covering the current state of the art regarding cutting force–tool displacement interactions in precise and micromilling manufacturing systems. Therefore, this literature review aims to fill this research gap and focuses on the critical literature review regarding the current state of the art within the prediction methods of cutting forces and machining system’s displacements/vibrations during precise and micromilling techniques. In the first part, a currently available cutting force, as well as the static and dynamic machining system displacement models applied in precise and micromilling conditions are presented. In the next stage, a relationship between the geometrical elements of cut and generated cutting forces and tool displacements are discussed, based on the recent literature. A subsequent part concerns the formulation of the generalized analytical models for a prediction of cutting forces and vibrations during precise and micromilling conditions. In the last stage, the conclusions and outlook are formulated based on the conducted analysis of the literature. In this context, this paper constitutes a synthetic work presenting current trends in the prediction of precise milling and micromilling mechanics. Full article

Multi-source fusion techniques have emerged as cutting-edge approaches for spatial precipitation estimation, yet they face persistent accuracy limitations, particularly under extreme conditions. Machine learning offers new opportunities to improve the precision of these estimates. To bridge this gap, we propose a hybrid artificial neural network–geographically weighted regression (ANN–GWR) model that synergizes event recognition and quantitative estimation. The ANN module dynamically identifies precipitation events through nonlinear pattern learning, while the GWR module captures location-specific relationships between multi-source data for calibrated rainfall quantification. Validated against 60-year historical data (1960–2020) from China’s Yongding River Basin, the model demonstrates superior performance through multi-criteria evaluation. Key results reveal the following: (1) the ANN-driven event detection achieves 10% higher accuracy than GWR, with a 15% enhancement for heavy precipitation events (>50 mm/day) during summer monsoons; (2) the integrated framework improves overall fusion accuracy by more than 10% compared to conventional GWR. This study advances precipitation estimation by introducing an artificial neural network into the event recognition period. Full article

Antimicrobial resistance (AMR) has emerged as one of the most pressing global health challenges of our time. Alarming projections of increasing mortality from resistant infections highlight the urgent need for innovative solutions. While many candidates have shown promise in preliminary studies, they often encounter challenges in terms of efficacy and safety during clinical translation. This review examines cutting-edge approaches to combat AMR, with a focus on engineered antimicrobial peptides, functionalized nanoparticles, and advanced genomic therapies, including Clustered Regularly Interspaced Short Palindromic Repeats-associated proteins (CRISPR-Cas systems) and phage therapy. Recent advancements in these fields are critically analyzed, with a focus on their mechanisms of action, therapeutic potential, and current limitations. Emphasis is given to strategies targeting biofilm disruption and quorum sensing interference, which address key mechanisms of resistance. By synthesizing current knowledge, this work provides researchers with a comprehensive framework for developing next-generation antimicrobials, highlighting the most promising approaches for overcoming AMR through rational drug design and targeted therapies. Ultimately, this review aims to bridge the gap between experimental innovation and clinical application, providing valuable insights for developing effective and resistance-proof antimicrobial agents. Full article

The integration of Building Information Modeling (BIM) and Digital Twin (DT) technologies offers new opportunities for enhancing reinforcement design and on-site constructability. This study addresses a current gap in DT applications by introducing an intelligent framework that simultaneously automates rebar layout generation and reduces rebar cutting waste (RCW), two challenges often overlooked during the construction execution phase. The system employs heuristic algorithms to generate constructability-aware rebar configurations and leverages Industry Foundation Classes (IFC) schema-based data models for interoperability. The framework is implemented using Autodesk Revit and Dynamo for rebar modeling and layout generation, Microsoft Project for schedule integration, and Autodesk Navisworks for clash detection. Real-time scheduling synchronization is achieved through IFC schema-based BIM models linked to construction timelines, while embedded clash detection and constructability feedback loops allow for iterative refinement and improved installation feasibility. A case study on a high-rise commercial building demonstrates substantial material savings, improved constructability, and reduced layout time, validating the practical advantages of BIM–DT integration for RC construction. Full article

As digital transformation progresses, schools are increasingly confronted with psychosocial challenges such as technostress, digital overload, and unequal participation in digital (learning) environments. This article investigates the conceptual relevance of digital well-being for school development, particularly in relation to social inequality. Despite growing attention, the term remains theoretically underdefined in educational research—a gap addressed through a theory-driven review. Drawing on a systematic search, 25 key studies were analyzed for their conceptual understanding and refinement of digital well-being, with a focus on educational relevance. Findings suggest that digital well-being constitutes a multidimensional state shaped by individual, media-related, and socio-structural factors. It emerges when individuals are able to successfully manage the demands of digital environments and is closely linked to digital inequality—particularly in terms of access, usage practices, and the resulting opportunities for participation and health promotion. Since the institutional role of schools has thus far received limited attention, this article shifts the focus toward schools as key arenas for negotiating digital norms and practices and calls for an equity-sensitive and health-conscious perspective on school development in the context of digitalization. In doing so, digital well-being is repositioned as a pedagogical cross-cutting issue that requires coordinated efforts across all levels of the education system, highlighting that equitable digital transformation in schools depends on a critical reflection of power asymmetries within society and educational institutions. The article concludes by advocating for the systematic integration of digital well-being into school development processes as a way to support inclusive digital participation and to foster a health-oriented digital school culture. Full article

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article

Direct Air Capture (DAC) is emerging as a critical climate change mitigation strategy, offering a pathway to actively remove atmospheric CO₂. This comprehensive review synthesizes advancements in DAC technologies, with a particular emphasis on the pivotal role of nanostructured solid sorbent materials. The work critically evaluates the characteristics, performance, and limitations of key nanomaterial classes, including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), zeolites, amine-functionalized polymers, porous carbons, and layered double hydroxides (LDHs), alongside solid-supported ionic liquids, highlighting their varied CO₂ uptake capacities, regeneration energy requirements, and crucial water sensitivities. Beyond traditional temperature/pressure swing adsorption, the review delves into innovative DAC methodologies such as Moisture Swing Adsorption (MSA), Electro Swing Adsorption (ESA), Passive DAC, and CO₂-Binding Organic Liquids (CO₂ BOLs), detailing their unique mechanisms and potential for reduced energy footprints. Despite significant progress, the widespread deployment of DAC faces formidable challenges, notably high capital and operational costs (currently USD 300–USD 1000/tCO₂), substantial energy demands (1500–2400 kWh/tCO₂), water interference, scalability hurdles, and sorbent degradation. Furthermore, this review comprehensively examines the burgeoning global DAC market, its diverse applications, and the critical socio-economic barriers to adoption, particularly in developing countries. A comparative analysis of DAC within the broader carbon removal landscape (e.g., CCS, BECCS, afforestation) is also provided, alongside an address to the essential, often overlooked, environmental considerations for the sustainable production, regeneration, and disposal of spent nanomaterials, including insights from Life Cycle Assessments. The nuanced techno-economic landscape has been thoroughly summarized, highlighting that commercial viability is a multi-faceted challenge involving material performance, synthesis cost, regeneration energy, scalability, and long-term stability. It has been reiterated that no single ‘best’ material exists, but rather a portfolio of technologies will be necessary, with the ultimate success dependent on system-level integration and the availability of low-carbon energy. The review paper contributes to a holistic understanding of cutting-edge DAC technologies, bridging material science innovations with real-world implementation challenges and opportunities, thereby identifying critical knowledge gaps and pathways toward a net-zero carbon future. Full article

Next to solid wood, laminated particleboard is the most widely used wood-based material in the furniture industry. Ensuring the high quality of the laminate surface after machining is of critical importance for furniture manufacturers, particularly prior to the edge banding process, as this process significantly influences the final aesthetic and functional quality of panel elements. The objective of this review article is to gather and evaluate the current state of knowledge regarding the influence of machining process parameters and the physical and mechanical properties of laminated particleboard on machining quality. Particular emphasis is placed on the occurrence of laminate damage, commonly referred to as delamination, a prevalent defect in the furniture manufacturing sector. Both categories of influencing factors—process-related and material-related—are analyzed within the context of the three primary technological processes employed in the woodworking industry, namely drilling, cutting, and milling. The analysis revealed that a persistent research gap concerns the relationship between machining quality and material parameters, particularly in the case of milling—a process of critical importance in the furniture industry. Full article