Search Results (700)

The rapid development of smart home and building technologies requires educational methods that facilitate the integration of theoretical knowledge with practical, system-level design skills. Computer-assisted tools play a crucial role in this process by enabling students to experiment with complex Internet of Things (IoT) and building automation ecosystems in a risk-free, iterative environment. This paper proposes a pedagogical framework that integrates simulation-based prototyping with collaborative and spatial design tools, supported by elements of design thinking and blended learning. The approach was implemented in a master’s-level Smart Building Systems course, to engage students in interdisciplinary projects where virtual modeling, digital collaboration, and contextualized spatial design were combined to develop user-oriented smart space concepts. Analysis of project outcomes and student feedback indicated that the use of simulation and visualization platforms may enhance technical competencies, creativity, and engagement. The proposed framework contributes to engineering education by demonstrating how computer-assisted environments can effectively support practice-oriented, user-centered learning. Its modular and scalable structure makes it applicable across IoT- and automation-focused curricula, aligning academic training with the hybrid workflows of contemporary engineering practice. Concurrently, areas for enhancement and modification were identified to optimize support for group and creative student work. Full article

This paper presents the development and implementation of a shopfloor visualization-oriented digitalization framework for heterogeneous industrial equipment, aimed to enhance sustainable performance in manufacturing environments. The proposed solution addresses a critical challenge in modern industry: the integration of legacy and modern equipment into a unified, real-time monitoring and control system. In this paper, a modular and scalable architecture that enables data acquisition from equipment with varying communication protocols and technological maturity was designed and implemented, utilizing Industrial Internet of Things (IIoT) gateways, protocol converters, and Open Platform Communications Unified Architecture (OPC UA). A key contribution of this work is the integration of various data sources into a centralized visualization platform that supports real-time monitoring, anomaly detection, and performance analytics. By visualizing operational parameters—including energy consumption, machine efficiency, and environmental indicators—the system facilitates data-driven decision-making and supports predictive maintenance strategies. The implementation was validated in a real industrial setting, where the solution significantly improved transparency, reduced downtime, and contributed to measurable energy efficiency gains. This research demonstrates that visualization-oriented digitalization not only enables interoperability among heterogeneous assets, but also acts as a catalyst for achieving sustainability goals. The developed methodology and tools provide a replicable model for manufacturing organizations seeking to transition toward Industry 4.0 in a resource-efficient and future-proof manner. Full article

The lack of modularity in building design information within multi-domain building performance analysis environments impedes efficient multidisciplinary analysis during the building design process. This study proposes a Functional Mock-up Interface (FMI)-based data model to facilitate the translation of building design information into a Building Information Modeling (BIM)-integrated building performance analysis framework that can be seamlessly integrated with object-oriented physical models. The proposed data model employs both FMI and BIM to decouple the design information required for physics-based analysis from existing Building Information Models. It then generates a physical BIM-based Functional Mock-up Unit (PBIM-FMU), which encapsulates the necessary building design information and can operate independently within a multi-domain building performance analysis environment. The PBIM-FMU can be readily interfaced with object-oriented physical modeling (OOPM)-based analysis models, as demonstrated in this study through its integration with an OOPM-based thermal analysis model for estimating annual building energy demand. To validate the proposed framework, simulation results from a manually constructed thermal analysis model were compared with those from a model integrated with the PBIM-FMU. The results were consistent, confirming that the data model supports accurate data exchange between BIM and multi-domain building performance simulation platforms. Full article

Against the background of China’s ecological civilization construction and sustainable development strategies, how remote sensing courses adapt to the demands of the artificial intelligence era has become an urgent issue for undergraduate education in relevant disciplines at universities. This study proposed a trinity teaching reform path of “deep learning and remote sensing, and ecological sustainability”, aiming to cultivate interdisciplinary talents with capabilities in intelligent interpretation and practical application. The study established a three-stage curriculum objective system, integrating knowledge, ability, and literacy, designed a five-dimensional linkage teaching method combining case-driven teaching, modular training, and blended learning, and conducted teaching practices using mainstream deep learning frameworks and cloud platforms. Through hierarchical teaching practice cases and multi-dimensional evaluation data, it was shown that the reform effectively enhanced the experiment group students’ abilities in deep learning applications, complex remote sensing data processing, and ecological problem-solving. The achievement values for all five evaluation indicators exceeded 80%, with the highest improvement reaching 28% compared to the control group. The results indicate that this teaching reform not only enhances learning outcomes but also provides a valuable framework and practical pathway for remote sensing education empowered by artificial intelligence and the cultivation of professional talent in future sustainable development fields. Full article

This paper presents ModuLab, a modular, low-cost sensor platform designed to simplify real-time environmental monitoring for laboratory research and educational settings. Centered on the APP-All MCU 2023 development board with an AVR128DA48 microcontroller (Microchip Technology Inc., Taiwan) ModuLab supports plug-and-play integration of multiple sensor types—including temperature, pH, light, and humidity—using a robust I²C communication protocol. The system features configurable sampling rates, built-in signal conditioning, and a Python-based interface for real-time data visualization. As a proof-of-concept, ModuLab was operated continuously for 48 h to evaluate system stability and filtering capabilities. However, due to institutional data ownership and confidentiality policies, the underlying datasets cannot be disclosed in this submission. The architecture and implementation details described herein are intended to guide future users and research groups seeking accessible alternatives to conventional data acquisition solutions. Comprehensive performance validation and open-access data sharing are planned as the next steps in this ongoing project. Full article

This study investigates the operation and management of an advanced Italian liquid waste treatment platform, focusing on its dual-line configuration and the challenges posed by increasingly heterogeneous waste streams. The main objectives are to (i) characterize the technological and operational features of the system, (ii) evaluate strategies for dealing with variable waste compositions and non-compliant inputs, and (iii) propose governance measures to strengthen cooperation between producers and operators. The methodology integrates the analysis of operational data from 2022 to 2024 (waste volumes, European Waste Catalogue Codes, reagent consumption, sludge production, and energy use) with a critical assessment of acceptance procedures and monitoring protocols. Results show a 10% increase in liquid waste treated over the study period, a growing predominance of complex EWC codes, higher oxygen demand in the thermophilic reactor, and seasonal fluctuations in sludge production. At the same time, the plant achieved stable or improved performance indicators, with specific energy consumption decreasing to 2.08 kWh/kg COD removed in 2024. The study concludes that modular, flexible treatment systems, supported by rigorous waste characterization and real-time decision-making, are essential to ensuring efficiency, regulatory compliance, and long-term environmental sustainability in liquid waste management. Full article

Background/Objectives: This study presents Co.Ge. a Cognitive Generative digital platform for cognitive testing. We describe its architecture and report a pilot study. Methods: Co.Ge. is modular and web-based (Laravel-PHP, MySQL). It can be used to administer a variety of validated cognitive tests, facilitating administration and scoring while capturing Reaction Times (RTs), trial-level responses, audio, and other data. Co.Ge. includes a study-management dashboard, Application Programming Interfaces (APIs) for external integration, encryption, and customizable options. In this demonstrative pilot study, clinical and non-clinical participants completed an Auditory Verbal Learning Test (AVLT), which we analyzed using accuracy, number of recalled words, and reaction times as outcomes. We collected ratings of user experience with a standardized rating scale. Analyses included Frequentist and Bayesian Generalized Linear Mixed Models (GLMMs). Results: Mean ratings of user experience were all above 4/5, indicating high acceptability (n = 30). Pilot data from AVLT (n = 123, 60% clinical, 40% healthy) showed that Co.Ge. seamlessly provides standardized clinical ratings, accuracy, and RTs. Analyzing RTs with Bayesian GLMMs and Gamma distribution provided the best fit to data (Leave-One-Out Cross-Validation) and allowed to detect additional associations (e.g., education) otherwise unrecognized using simpler analyses. Conclusions: The prototype of Co.Ge. is technically robust and clinically precise, enabling the extraction of high-resolution behavioral data. Co.Ge. provides traditional clinical-oriented cognitive outcomes but also promotes complex generative models to explore individualized mechanisms of cognition. Thus, it will promote personalized profiling and digital phenotyping for precision psychiatry and rehabilitation. Full article

This study introduces a real-time, non-intrusive monitoring system designed to support elderly care through vision-based pose estimation and hand gesture recognition. The proposed framework integrates convolutional neural networks (CNNs), temporal modeling using LSTM networks, and symmetry-aware keypoint analysis to enhance the accuracy and reliability of behavior detection under varied real-world conditions. By leveraging the bilateral symmetry of human anatomy, the system improves the robustness of posture and gesture classification, even in the presence of partial occlusion or variable lighting. A total of 21 hand landmarks and 33 body pose points are used to recognize predefined actions and communication gestures, enabling seamless interaction without wearable devices. Experimental evaluations across four distinct lighting environments confirm a consistent accuracy above 90%, with real-time alerts triggered via IoT messaging platforms. The system’s modular architecture, interpretability, and adaptability make it a scalable solution for intelligent elderly individual monitoring, offering a novel application of spatial symmetry and optimized deep learning in healthcare technology. Full article

End-stage chronic kidney disease remains a global challenge, with dialysis and transplantation offering only partial or limited solutions. Recent advances in bioengineering have introduced modular strategies that aim to restore kidney function not by replicating the entire organ, but by rebuilding it one segment at a time. Platforms such as kidney organoids, implantable bioartificial kidneys, 3D-bioprinted tissues, and decellularized scaffolds each target specific nephron functions, from filtration to endocrine signaling. This Perspective examines how these technologies can be integrated into interoperable systems that reflect the nephron’s native structure and functional complexity. We assess translational readiness across key benchmarks, including vascular integration, hormonal responsiveness, immune compatibility, and implantability, and discuss the ethical, regulatory, and design considerations that will shape their clinical future. Collectively, these modular strategies offer a pathway toward more personalized, scalable, and physiologically relevant approaches to kidney replacement. Full article

Three-dimensional bioprinting holds great promise for tissue engineering, but struggles with fabricating complex curved geometries such as vascular networks. Though precise, traditional Cartesian bioprinters are constrained by linear layer-by-layer deposition along fixed axes, resulting in limitations such as the stair-step effect. Multi-axis robotic bioprinting addresses these challenges by allowing dynamic nozzle orientation and motion along curvilinear paths, enabling conformal printing on anatomically relevant surfaces. Although robotic arms offer lower mechanical precision than CNC stages, accuracy can be enhanced through methods such as vision-based toolpath correction. This study presents a modular multi-axis robotic embedded bioprinting platform that integrates a six-degrees-of-freedom robotic arm, a pneumatic extrusion system, and a viscoplastic support bath. A streamlined workflow combines CAD modeling, CAM slicing, robotic simulation, and automated execution for efficient fabrication. Two case studies validate the system’s ability to print freeform surfaces and vascular-inspired tubular constructs with high fidelity. The results highlight the platform’s versatility and potential for complex tissue fabrication and future in situ bioprinting applications. Full article

Immunotherapy, particularly approaches that combine tumor-specific vaccines with immune checkpoint modulation, represents a promising strategy for overcoming tumor immune evasion. While most mRNA-based cancer vaccines focus solely on antigen delivery, there is a need for platforms that simultaneously enhance antigen presentation and modulate the tumor microenvironment to increase therapeutic efficacy. This study presents a novel dual-nanolipid exosome (NLE) platform that simultaneously delivers MUC1 mRNA and CTLA-4-targeted siRNA in a single system. These endogenous lipid-based nanoparticles are structurally designed to mimic exosomes and are modified with mannose to enable selective targeting to dendritic cells (DCs) via mannose receptors. The platform was evaluated both in vitro and in vivo in terms of mRNA encapsulation efficiency, nanoparticle stability, and uptake by DCs. The co-delivery platform significantly enhanced antitumor immune responses compared to monotherapies. Flow cytometry revealed a notable increase in tumor-infiltrating CD8⁺ T cells (p < 0.01), and ELISPOT assays showed elevated IFN-γ production upon MUC1-specific stimulation. In vivo CTL assays demonstrated enhanced MUC1-specific cytotoxicity. Combined therapy resulted in immune response enhancement compared to vaccine or CTLA-4 siRNA alone. The NLE platform exhibited favorable biodistribution and low systemic toxicity. By combining targeted delivery of dendritic cells, immune checkpoint gene silencing, and efficient antigen expression in a biomimetic nanoparticle system, this study represents a significant advance over current immunotherapy strategies. The NLE platform shows strong potential as a modular and safe approach for RNA-based cancer immunotherapy. Full article

This paper presents a comparative analysis of ten state-of-the-art robotic de-mining systems, grouped into (i) sensor-centric platforms for high-precision detection and (ii) rapid mechanical-contact vehicles for clearance. Building on these findings, we propose a lightweight tracked platform (~1.9 T) equipped with a four-channel sensing suite-RGB/IR camera, 32-layer LiDAR, pulsed-induction metal detector, and 2.45 GHz microwave thermography—integrated in an adaptive Bayesian “detect → confirm → neutralize” loop. The modular end-effector permits either pinpoint mechanical intervention or deployment of a linear charge. Modelling indicates an expected detection sensitivity ≥ 95% with a false-positive rate ≤ 5% in humanitarian demining mode and a clearance throughput above 1.5 ha·h⁻¹ in breaching mode. Ongoing work includes CFD analysis of the thermal front, fabrication of a prototype, and performance testing in accordance with IMAS 10.20. Full article

Online education has become an important channel for extensive, inclusive and flexible learning experiences. However, significant gaps persist in providing truly accessible, personalized and adaptable e-learning environments, especially for students with disabilities, varied language backgrounds, or limited bandwidth. This paper presents AccessiLearnAI, an AI-driven platform, which converges accessibility-first design, multi-format content delivery, advanced personalization, and Progressive Web App (PWA) offline capabilities. Our solution is compliant with semantic HTML5 and ARIA standards, and incorporates features such as automatic alt-text generation for images using Large Language Models (LLMs), real-time functionality for summarization, translation, and text-to-speech capabilities. The platform, built on top of a modular MVC and microservices-based architecture, also integrates robust security, GDPR-aligned data protection, and a human-in-the-loop to ensure the accuracy and reliability of AI-generated outputs. Early evaluations indicate that AccessiLearnAI improves engagement and learning outcomes across multiple ranges of users, suggesting that responsible AI and universal design can successfully coexist to bring equity through digital education. Full article

Prompt engineering (PE) has emerged as a transformative paradigm in software engineering (SE), leveraging large language models (LLMs) to support a wide range of SE tasks, including code generation, bug detection, and software traceability. This study conducts a systematic literature review (SLR) combined with a co-citation network analysis of 42 peer-reviewed journal articles to map key research themes, commonly applied PE methods, and evaluation metrics in the SE domain. The results reveal four prominent research clusters: manual prompt crafting, retrieval-augmented generation, chain-of-thought prompting, and automated prompt tuning. These approaches demonstrate notable progress, often matching or surpassing traditional fine-tuning methods in terms of adaptability and computational efficiency. Interdisciplinary collaboration among experts in AI, machine learning, and software engineering is identified as a key driver of innovation. However, several research gaps remain, including the absence of standardized evaluation protocols, sensitivity to prompt brittleness, and challenges in scalability across diverse SE applications. To address these issues, a modular prompt engineering framework is proposed, integrating human-in-the-loop design, automated prompt optimization, and version control mechanisms. Additionally, a conceptual pipeline is introduced to support domain adaptation and cross-domain generalization. Finally, a strategic research roadmap is presented, emphasizing future work on interpretability, fairness, and collaborative development platforms. This study offers a comprehensive foundation and practical insights to advance prompt engineering research tailored to the complex and evolving needs of software engineering. Full article

This case study examines the integration of artificial intelligence (AI) into undergraduate architectural education through a 2024–25 core studio teaching experiment at Zhejiang University. A dual-module framework was implemented, comprising a 20 h AI skills training module and in-class ethics discussions, without altering the existing studio structure. The AI skills module introduced deep learning models, LLMs, AIGC image models, LoRA fine-tuning, and ComfyUI, supported by a dedicated technical instructor. Student feedback indicated phase-dependent and tool-sensitive engagement, and students expressed a preference for embedded ethical discussion within the design studio rather than separate formal instruction. The experiment demonstrated that modular AI education is both scalable and practical, highlighting the importance of phase-sensitive guidance, balanced technical and ethical framing, and institutional support such as cloud platforms and research-based AI tools. The integration enhanced students’ digital adaptability and strategic thinking while prompting reflection on issues such as authorship, algorithmic bias, and accountability in human–AI collaboration. These findings offer a replicable model for AI-integrated design pedagogy that balances technical training with critical awareness. Full article