Engineering Proceedings

1 pages, 138 KiB

Open AccessEditorial

Statement of Peer Review

by Nikolay Kakanakov and Sevil Ahmed-Shieva

Eng. Proc. 2025, 100(1), 67; https://doi.org/10.3390/engproc2025100067 - 15 Aug 2025

In submitting conference proceedings to Engineering Proceedings, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...] Full article

(This article belongs to the Proceedings of The 14th International Scientific Conference TechSys 2025—Engineering, Technologies and Systems)

12 pages, 846 KiB

Open AccessProceeding Paper

Evaluation of Cultural and Creative Products of Jinshan Farmer Painting Using Fuzzy Analytic Hierarchy Process

by Chen Liu, Hong-Mei Dai, Yuan Shen and Yu-Xuan Liu

Eng. Proc. 2025, 98(1), 46; https://doi.org/10.3390/engproc2025098046 (registering DOI) - 15 Aug 2025

Abstract

We evaluated the cultural and creative products of Jinshan Farmer Painting in Shanghai, utilizing the fuzzy analytic hierarchy process (FAHP) to determine the key evaluation indicators. Through a literature review, we constructed a hierarchical framework of evaluation indicators. A questionnaire survey was then [...] Read more.

We evaluated the cultural and creative products of Jinshan Farmer Painting in Shanghai, utilizing the fuzzy analytic hierarchy process (FAHP) to determine the key evaluation indicators. Through a literature review, we constructed a hierarchical framework of evaluation indicators. A questionnaire survey was then conducted to collect expert opinions, followed by FAHP weight calculation and analysis. Finally, the consistency of the evaluation results was verified. The results revealed that market demand, design innovation, and traditional cultural inheritance are the key indicators influencing the success of Jinshan Farmer Painting cultural products. Among these, market demand and design innovation have the highest weights in the overall evaluation, highlighting the critical role of market acceptance and product innovation in the success of cultural products. Additionally, the emphasis on traditional cultural inheritance and cultural symbolism in cultural value underscores the importance of cultural content and artistic expression in a product’s success. These results provide practical information for the development of Jinshan Farmer Painting cultural products and offer a theoretical basis for future research. Full article

(This article belongs to the Proceedings of 2024 4th International Conference on Social Sciences and Intelligence Management (SSIM 2024))

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7 pages, 1684 KiB

Open AccessProceeding Paper

Investigation on Transverse Loading of Auxetic Beams Using Finite Element Methods

by Navneeth Sanjeev and M. P. Hariprasad

Eng. Proc. 2025, 93(1), 24; https://doi.org/10.3390/engproc2025093024 (registering DOI) - 15 Aug 2025

Abstract

Structures that possess negative Poisson’s ratio are termed “Auxetic” structures. They elongate laterally on longitudinal–tensile loading and compress laterally on longitudinal–compressive loading. Auxetic structures are a composition of unit cells that are available in various geometries, which include triangular, hexa-triangular, re-entrant, chiral, star, [...] Read more.

Structures that possess negative Poisson’s ratio are termed “Auxetic” structures. They elongate laterally on longitudinal–tensile loading and compress laterally on longitudinal–compressive loading. Auxetic structures are a composition of unit cells that are available in various geometries, which include triangular, hexa-triangular, re-entrant, chiral, star, arrowhead, etc. Due to their unique shape, these structures possess remarkably good mechanical properties such as shear resistance, indentation resistance, fracture resistance, synclastic behavior, energy absorption capacity, etc. However, they have poor load-bearing capacity. To improve the load bearing strength of these structures, this paper presents a numerical analysis of oriented re-entrant structured (ORS) beams with auxetic clusters aligned at various angles (0°, 45° and 90°), using Finite Element Methods. Oriented re-entrant unit cell clusters enclosed by a bounded frame were modeled and a three-point bending test was conducted to perform a comparison study on deformation mechanisms of the different oriented re-entrant honeycomb structures with honeycomb beams. The computational analysis of ORS beams revealed that the directional deformation and normal strain along the x-axis were the lowest in ORS45, followed by ORS90, ORS0, and honeycomb. Among all the beams, ORS45 displayed the best load-bearing capacity with comparably low mass density. Full article

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12 pages, 1838 KiB

Open AccessProceeding Paper

Edge IoT-Enabled Cyber–Physical Systems with Paper-Based Biosensors and Temporal Convolutional Networks for Real-Time Water Contamination Monitoring

by Jothi Akshya, Munusamy Sundarrajan and Rajesh Kumar Dhanaraj

Eng. Proc. 2025, 106(1), 3; https://doi.org/10.3390/engproc2025106003 (registering DOI) - 15 Aug 2025

Abstract

Water pollution poses serious threats to public health and the environment, therefore requiring efficient and scalable monitoring solutions. This paper presents a cyber–physical system (CPS) that integrates paper-based biosensors with an edge IoT architecture and long-range wide area network (LoRaWAN) for real-time assessment [...] Read more.

Water pollution poses serious threats to public health and the environment, therefore requiring efficient and scalable monitoring solutions. This paper presents a cyber–physical system (CPS) that integrates paper-based biosensors with an edge IoT architecture and long-range wide area network (LoRaWAN) for real-time assessment of water quality. The biosensors detect pollutants such as arsenic, lead, and nitrates with a detection limit of 0.5 ppb. The system proposed was compared with existing LSTM systems based on two performance metrics: detection accuracy and latency. Paper-based biosensors were fabricated using silver nanoparticle-functionalized substrates to show high sensitivity and low-cost pollutant detection. TCN algorithm deployment at the edge allows for real-time processing for time-series data analysis due to its high accuracy and low latency properties compared with LSTM models, which were mainly chosen due to their usage in most applications dealing with time-series-based analysis. Experimentation was carried out by deploying the developed CPS in controlled environments, simulating pollutants at different levels, and executing the models to test their accuracy in detecting pollutants and the latency of data processing. The TCN framework achieved a detection accuracy of 98.7%, which surpassed LSTM by 92.4%. In addition, TCN reduced latency in processing by 38% to enable fast data analysis and decision making. LoRaWAN allowed for perfect packet transmission of up to 15 km, while the loss rate stayed as low as 2.1%. These results establish the proposed CPS as reliable, efficient, and scalable for real-time water contamination monitoring. Thus, this research introduces the integration of paper-based biosensors with advanced computational frameworks. Full article

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8 pages, 205 KiB

Open AccessProceeding Paper

Large Language Model-Assisted Course Search: Parsing Structured Parameters from Natural Language Queries

by Max Upravitelev, Naomi Schoppa, Christopher Krauss, Truong-Sinh An, Bach Do and Aziz Md Abdul

Eng. Proc. 2025, 103(1), 18; https://doi.org/10.3390/engproc2025103018 (registering DOI) - 14 Aug 2025

Abstract

We propose a method to address the challenge of course discovery on search platforms by employing large language models (LLMs) to parse extended search parameters from natural language queries. We developed a set of algorithms that augment a course search platform prototype by [...] Read more.

We propose a method to address the challenge of course discovery on search platforms by employing large language models (LLMs) to parse extended search parameters from natural language queries. We developed a set of algorithms that augment a course search platform prototype by integrating an LLM-based assistant to facilitate 55,000 vocational training sessions. The developed method supports natural language queries and parses optional search parameters. For parameter optionality and to evaluate the feasibility of parameter parsing, we introduce a relevance check mechanism based on cosine similarity. The parsing process was conducted by using a guided generation strategy with grammar-based restrictions to limit the generation possibilities. The developed method enhanced the precision and pertinence of course searches. Full article

12 pages, 1331 KiB

Open AccessProceeding Paper

Tailoring the Optical and Sensing Properties of Sol–Gel Niobia Coatings via Doping with Silica and Noble Metal Nanoparticles

by Tsvetanka Babeva, Venelin Pavlov, Georgi Zlatinov, Biliana Georgieva, Penka Terziyska, Gergana Alexieva, Katerina Lazarova and Rosen Georgiev

Eng. Proc. 2025, 105(1), 4; https://doi.org/10.3390/engproc2025105004 - 14 Aug 2025

Abstract

Nb₂O₅ (niobia) coatings were prepared by spin coating of niobium sol, synthesized using niobium chloride as the precursor and ethanol and water as solvents, followed by high-temperature annealing. Doping of the films was achieved by incorporating commercially available SiO₂ [...] Read more.

Nb₂O₅ (niobia) coatings were prepared by spin coating of niobium sol, synthesized using niobium chloride as the precursor and ethanol and water as solvents, followed by high-temperature annealing. Doping of the films was achieved by incorporating commercially available SiO₂ (Ludox) and noble metal nanoparticles (NPs) into the sol prior to its deposition. Various sizes of Pt (5 and 30 nm), Ag (10, 20, and 40 nm), and Au (5, 10, and 20 nm) NPs were used to enhance sensing behavior of coatings. After annealing, films were subjected to chemical etching to remove the silica phase. This process generated porosity within the films, which in turn enabled the tailoring of both their optical and sensing properties. It was demonstrated that both the type and size of the incorporated nanoparticles significantly influenced the sensing behavior. The most effective enhancement was observed with the addition of 10 nm AuNPs. Optical characterization indicated that 10 nm AuNPs had a minimal effect on the optical properties. In contrast, doping with 20 nm AuNPs led to a reduction in the refractive index and an increase in Urbach energy. No significant alteration in the optical band gap due to doping was observed. Full article

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8 pages, 1422 KiB

Open AccessProceeding Paper

Designing for Diversity: Creating Inclusive Digital Learning Environments for Global Classrooms

by Wai Yie Leong

Eng. Proc. 2025, 103(1), 17; https://doi.org/10.3390/engproc2025103017 - 13 Aug 2025

Abstract

In an increasingly interconnected world, educational systems must meet the needs of diverse learners from varying cultural, linguistic, and socioeconomic backgrounds. This study aims to explore the principles and practices of designing inclusive digital learning environments tailored to global classrooms, where diversity in [...] Read more.

In an increasingly interconnected world, educational systems must meet the needs of diverse learners from varying cultural, linguistic, and socioeconomic backgrounds. This study aims to explore the principles and practices of designing inclusive digital learning environments tailored to global classrooms, where diversity in language, learning styles, accessibility, and technological resources presents unique challenges and opportunities. This study also explores how leveraging digital tools, artificial intelligence, and adaptive learning technologies can create environments that are responsive to individual learners’ needs and sensitive to cultural nuances. Research on inclusive instructional design was compiled, highlighting methods such as localized content adaptation, multi-language support, and flexible learning pathways. Furthermore, the role of collaborative learning platforms was assessed to foster a sense of community across geographic and cultural boundaries. Case studies were conducted from diverse educational perspectives to propose effective strategies for inclusive digital design, highlighting successful approaches and areas for improvement. Ultimately, a roadmap was constructed for educators, designers, and policymakers to create accessible and culturally aware digital learning spaces to support the academic and social development of all learners, regardless of their background. The results of this study underscore the importance of inclusivity in digital education, contributing to a more equitable and connected global learning landscape. Full article

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15 pages, 551 KiB

Open AccessProceeding Paper

Multimedia-Based Assessment of Scientific Inquiry Skills: Evaluating High School Students’ Scientific Inquiry Abilities Using Cloud Classroom Software

by Shih-Chao Yeh, Chun-Yen Chang and Van T. Hoang Ngo

Eng. Proc. 2025, 103(1), 16; https://doi.org/10.3390/engproc2025103016 - 13 Aug 2025

Abstract

We developed and validated an animation-based assessment (ABA) method for evaluating high school students’ inquiry competencies in Taiwan’s 12-Year Curriculum. Contextualized in atmospheric chemistry involving methane and hydroxyl radicals, ABA integrated dynamic simulations, tiered multiple-choice and open-ended tasks, and process tracking on the [...] Read more.

We developed and validated an animation-based assessment (ABA) method for evaluating high school students’ inquiry competencies in Taiwan’s 12-Year Curriculum. Contextualized in atmospheric chemistry involving methane and hydroxyl radicals, ABA integrated dynamic simulations, tiered multiple-choice and open-ended tasks, and process tracking on the CloudClassRoom platform, the assessment focused on measuring two inquiry skills: causal reasoning and critical thinking. The results of 26,823 students revealed that the ABA effectively differentiated student performance across ability levels and academic disciplines, with open-ended items sensitive to higher-order reasoning. Gender difference was not observed, indicating the gender-free design of the developed ABA. While the ABA supports diagnostic insights, limitations need to be addressed, including the underassessment of modeling and creative experimentation skills. Therefore, it is necessary to include open modeling tasks and AI-powered semantic scoring. The developed ABA contributes a scalable, competency-aligned framework for inquiry-based science assessments. Full article

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10 pages, 909 KiB

Open AccessProceeding Paper

Incorporating Animation Films into Moral Education for College Students: A Case Study of the Chinese Animated Film Three Monks

by Hongguang Zhao, Xin Kang, Xiaochen Guo and Xin-Zhu Li

Eng. Proc. 2025, 103(1), 15; https://doi.org/10.3390/engproc2025103015 - 13 Aug 2025

Abstract

This study aims to explore the values of character education in the Chinese animated film Three Monks. This film serves as a teaching tool, not only imparting animation principles to university students majoring in animation but also showcasing Chinese cultural philosophy and [...] Read more.

This study aims to explore the values of character education in the Chinese animated film Three Monks. This film serves as a teaching tool, not only imparting animation principles to university students majoring in animation but also showcasing Chinese cultural philosophy and educational values in implicit, exaggerated, and humorous action design. We employed a descriptive qualitative method. A total of 73 college students majoring in animation watched the film without any prior explanation of animation principles and moral education and then listened to detailed explanations of the character education and animation principles integrated into the film. Through repeated viewing, analysis, and summarization of the storyline, character behaviors, and action design in Three Monks, the values of character education, such as religion, kindness, diligence, independence, responsibility, tolerance, self-reflection, unity and cooperation, and courage to innovate, were embodied. These values are manifested through the film’s storyline, conflicts, character actions, animated performances, and background music. We compared the students’ pre- and post-viewing attitudes based on their discussions, reflections, and course evaluations. The results revealed that conveying moral values through animated films internalized and transmitted character education among university students, shaping cultural identity and social norms. This approach enhanced students’ learning engagement and improved their learning efficiency. Full article

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10 pages, 5133 KiB

Open AccessProceeding Paper

Fuel Species Classification and Biomass Estimation for Fire Behavior Modeling Based on UAV Photogrammetric Point Clouds

by Luis Ángel Ruiz, Juan Pedro Carbonell-Rivera, Pablo Crespo-Peremarch, Marina Simó-Martí and Jesús Torralba

Eng. Proc. 2025, 94(1), 17; https://doi.org/10.3390/engproc2025094017 - 12 Aug 2025

Abstract

In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behavior models, such as Fire Dynamics Simulator (FDS), can represent [...] Read more.

In the Mediterranean basin, wildfires burn an average of 600,000 ha per year, causing severe ecological, economic, and social impacts. Fire behavior modeling is essential for wildfire prevention and control. Three-dimensional physics-based fire behavior models, such as Fire Dynamics Simulator (FDS), can represent heterogeneous fuels and simulate fire behavior processes with greater detail than conventional models. However, they require accurate information about species composition and 3D distribution of fuel mass and bulk density at the voxel level. Working in a Mediterranean ecosystem study area we developed a methodology based on the use of geometric and spectral features from UAS-based digital aerial photogrammetric point clouds for (i) species segmentation and classification using machine learning algorithms, (ii) generation of biomass prediction models at individual plant level, and (iii) creation of 3D fuel scenarios and modeling wildfire behavior. Field measurements were conducted on 22 circular plots with a radius of 5 m. Data from the field measurements, combined with species-specific allometric equations, were used for the evaluation of classification and prediction models. Fire behavior variables such as rate of spread, heat release rate, and mass loss rate were monitored and assessed as outputs from 20 different scenarios using FDS. The overall species classification accuracy was 80.3%, and the biomass regression R² values obtained by cross-validation were 0.77 for Pinus halepensis and 0.83 for Anthyllis cytisoides. These results are encouraging further improvement based on the integration of sensors onboard UAS, and the characterization of fuels for fire behavior modeling. These high-resolution fuel representations can be coupled with standard risk assessment tools, enabling fire managers to prioritize treatment areas and plan for resource deployment. Full article

(This article belongs to the Proceedings of The 1st International Conference on Advanced Remote Sensing – Shaping Sustainable Global Landscapes (ICARS 2025))

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8 pages, 277 KiB

Open AccessProceeding Paper

Optimizing Short-Term Electrical Demand Forecasting with Deep Learning and External Influences

by Leonardo Santos Amaral, Gustavo Medeiros de Araújo and Ricardo Moraes

Eng. Proc. 2025, 101(1), 16; https://doi.org/10.3390/engproc2025101016 - 12 Aug 2025

Abstract

Short-term electrical demand forecasting is crucial for the efficient operation of modern power grids. Traditional methods often fail by neglecting system nonlinearities and external factors that influence electricity consumption. In this study, we propose an enhanced deep learning-based forecasting model that integrates external [...] Read more.

Short-term electrical demand forecasting is crucial for the efficient operation of modern power grids. Traditional methods often fail by neglecting system nonlinearities and external factors that influence electricity consumption. In this study, we propose an enhanced deep learning-based forecasting model that integrates external factors such as meteorological data and economic indicators to improve prediction accuracy. Using an ISO NE (Independent System Operator New England) dataset from 2017 to 2019, we analyze 23 independent variables to assess their impact on model performance. Our findings demonstrate that careful variable selection reduces dimensionality while maintaining forecasting accuracy, enabling the effective application of deep learning models. The CNN plus LSTM composite model achieved the lowest prediction error of 0.15%, outperforming standalone CNN (0.8%) and LSTM (1.44%) approaches. The combination of CNN’s feature extraction capabilities with LSTM’s strength in handling time series data was instrumental in achieving superior performance. Our results highlight the importance of incorporating external influences in electricity demand forecasting and suggest future directions for developing more precise and efficient models. Full article

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7 pages, 188 KiB

Open AccessProceeding Paper

Lightweight Post-Quantum Cryptography: Applications and Countermeasures in Internet of Things, Blockchain, and E-Learning

by Chin-Ling Chen, Kuang-Wei Zeng, Wei-Ying Li, Chin-Feng Lee, Ling-Chun Liu and Yong-Yuan Deng

Eng. Proc. 2025, 103(1), 14; https://doi.org/10.3390/engproc2025103014 - 12 Aug 2025

Abstract

With the rapid advancement of quantum computing technology, traditional encryption methods are encountering unprecedented challenges in the Internet of Things (IoT), blockchain systems, and digital learning (e-learning) platforms. Therefore, we systematically reviewed the applications and countermeasures of lightweight post-quantum cryptographic techniques, focusing on [...] Read more.

With the rapid advancement of quantum computing technology, traditional encryption methods are encountering unprecedented challenges in the Internet of Things (IoT), blockchain systems, and digital learning (e-learning) platforms. Therefore, we systematically reviewed the applications and countermeasures of lightweight post-quantum cryptographic techniques, focusing on the requirements of resource-constrained IoT devices and decentralized systems. We compared the encryption methods based on ring learning with errors (Ring-LWE), Binary Ring-LWE, ring-ExpLWE, the collaborative critical generation framework Q-SECURE, and hardware accelerators for the CRYSTALS-dilithium digital signature scheme. According to the high security and efficiency demands for data transmission and user interaction in e-learning platforms, we developed lightweight encryption schemes. By reviewing existing research achievements, we analyzed the application challenges in IoT, blockchain, and e-learning scenarios and explored strategies for optimizing post-quantum encryption schemes for effective deployment. Full article

7 pages, 1496 KiB

Open AccessProceeding Paper

Multimodal Learning Resources: A Way to Engage Students’ Senses

by Mima Trifonova and Gabriela Kiryakova

Eng. Proc. 2025, 103(1), 13; https://doi.org/10.3390/engproc2025103013 - 12 Aug 2025

Abstract

In the modern digital age, traditional teaching methods are increasingly giving way to innovative approaches that actively engage all students’ senses. Integrating diverse media formats into learning stimulates students’ visual, auditory, and practical perception through active interaction with learning content. Information technology teachers [...] Read more.

In the modern digital age, traditional teaching methods are increasingly giving way to innovative approaches that actively engage all students’ senses. Integrating diverse media formats into learning stimulates students’ visual, auditory, and practical perception through active interaction with learning content. Information technology teachers must possess competencies for creating quality digital resources using artificial intelligence (AI) tools, key skills for understanding the principles of multimodal learning to critically evaluate generated content, and adapt it to specific educational goals. The present study presents the importance of multimodal learning resources as an effective tool for increasing learning motivation and achieving better educational outcomes. Specific examples of key requests to AI systems for creating educational resources, including text, images, animations, and interactive content, are presented. In addition, practical guidelines for formulating effective requests to AI tools, critical evaluation of the generated resources, and possible approaches for their improvement are proposed. Full article

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9 pages, 1214 KiB

Open AccessProceeding Paper

Multi-Criteria Evaluation Model for Campus Disaster Resilience Under Extreme Climate Conditions

by Yue Sun, Xiaohe Bai and Yifei Ouyang

Eng. Proc. 2025, 103(1), 12; https://doi.org/10.3390/engproc2025103012 - 12 Aug 2025

Abstract

As global climate disasters become frequent, colleges and universities in disaster-prone areas are facing problems in disaster response and post-disaster recovery. Based on the theory of urban resilience, we case-studied nine universities in Conghua District, Guangzhou City, China, using the Delphi method and [...] Read more.

As global climate disasters become frequent, colleges and universities in disaster-prone areas are facing problems in disaster response and post-disaster recovery. Based on the theory of urban resilience, we case-studied nine universities in Conghua District, Guangzhou City, China, using the Delphi method and the analytic hierarchy process (AHP). We constructed a multi-criteria evaluation model for campus disaster prevention resilience under extreme climate conditions. By identifying 4 facets and 16 criteria, 9 colleges were ranked. The distance of the college from the city center, the terrain and natural environment of the college, the level of the college, and the ownership of the college affected their ranking The results of this study help campus managers and planners integrate campus resilience plans into campus planning, institutional regulations, campus site selection, and campus construction in the future. Full article

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12 pages, 3691 KiB

Open AccessProceeding Paper

A High-Sensitivity Electrochemical Sensor Based on Polyaniline/Sodium Alginate Composite for Pb and Cd Detection

by Ratiba Wali, Nouha Ghorbel, Ramzi Maalej and Mourad Arous

Eng. Proc. 2025, 106(1), 2; https://doi.org/10.3390/engproc2025106002 - 12 Aug 2025

Abstract

Water pollution remains one of the most pressing global environmental challenges, posing significant threats to ecosystems and human health. Among the various pollutants, heavy metal contamination is particularly concerning, even at trace concentrations, due to its bioaccumulative and toxic effects. The Efficient detection [...] Read more.

Water pollution remains one of the most pressing global environmental challenges, posing significant threats to ecosystems and human health. Among the various pollutants, heavy metal contamination is particularly concerning, even at trace concentrations, due to its bioaccumulative and toxic effects. The Efficient detection of heavy metals is therefore essential for effective environmental monitoring and public health protection. In this study, we present the development of an advanced electrochemical sensor based on polyaniline (PANI) incorporated into a sodium alginate (SA) matrix. The PANI/SA composite was synthesized via in-situ polymerization, improving both the material’s electrical conductivity and mechanical stability. The Scanning Electron microscopy (SEM) analysis confirmed a porous, interconnected structure favorable for electrochemical activity. Excellent sensitivity, stability, selectivity and rapid response times for Pb²⁺ and Cd²⁺ detection were demonstrated by the sensor that was created by fusing the high conductivity of PANI with the biocompatibility and gel-like qualities of SA. Notably, the sensor modified with 10 µL of PANI/SA suspension achieved a sensitivity of 3.183 µA µM⁻¹ cm⁻² for Cd²⁺ detection, representing an eightfold increase compared to the sensor using 5 µL (0.394 µA µM⁻¹ cm⁻²). These results highlight the potential of the PANI/SA-based sensor for real-time and low-level heavy metal ion monitoring in environmental applications. Full article

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13 pages, 1285 KiB

Open AccessProceeding Paper

Wearable Biosensors for Glucose Monitoring in Sweat: A Patent Analysis

by Massimo Barbieri and Giuseppe Andreoni

Eng. Proc. 2025, 106(1), 1; https://doi.org/10.3390/engproc2025106001 - 12 Aug 2025

Abstract

Metabolic diseases are increasing in relevance both in health and the economy in most countries. In this direction, if gold-standard technologies are based on blood analysis, non-invasive glucose monitoring is a relevant and great challenge that has not yet been fully resolved. Sweat [...] Read more.

Metabolic diseases are increasing in relevance both in health and the economy in most countries. In this direction, if gold-standard technologies are based on blood analysis, non-invasive glucose monitoring is a relevant and great challenge that has not yet been fully resolved. Sweat represents a more suitable medium for the non-invasive sensing and monitoring of glucose than other bodily fluids, such as saliva, tears, or urine. However, the measurement of glucose levels requires the use of highly precise and sensitive sensors, given the low glucose concentration in sweat. This paper provides an overview of the patent landscape related to wearable biosensors for the monitoring of glucose levels in sweat. Full article

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22 pages, 1614 KiB

Open AccessProceeding Paper

Integrated Blockchain, IoT, and Green Hydrogen Approach for Sustainable and Connected Supply Chain—Application Case in Morocco

by Abdellah Tetouani, Achraf Taouil, Naoufal Rouky and Mouhsene Fri

Eng. Proc. 2025, 97(1), 55; https://doi.org/10.3390/engproc2025097055 - 11 Aug 2025

Abstract

The global energy transition and digitalization are reshaping traditional production and consumption paradigms. Green hydrogen is emerging as a key element for decarbonizing sectors like industry and transportation, offering a viable alternative to fossil fuels and a pathway toward mitigating climate change. However, [...] Read more.

The global energy transition and digitalization are reshaping traditional production and consumption paradigms. Green hydrogen is emerging as a key element for decarbonizing sectors like industry and transportation, offering a viable alternative to fossil fuels and a pathway toward mitigating climate change. However, implementing green hydrogen supply chains presents challenges related to traceability, operational efficiency, and process certification. This paper explores how blockchain and the Internet of Things can address these challenges and transform the green hydrogen supply chain. Using Morocco as a case study—a country with abundant renewable resources and a strategic focus on green hydrogen—this article proposes innovative technological solutions to support a sustainable energy transition and contribute to a more secure and energy-efficient future. We analyze the current state of research on blockchain, IoT, and green hydrogen, identify key areas for advancement, and present a proposed framework for integrating these technologies. Full article

(This article belongs to the Proceedings of The 1st International Conference on Smart Management in Industrial and Logistics Engineering (SMILE 2025))

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1 pages, 125 KiB

Open AccessEditorial

Statement of Peer Review

by Sehyun Tak

Eng. Proc. 2025, 102(1), 13; https://doi.org/10.3390/engproc2025102013 - 11 Aug 2025

Abstract

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by [...] Read more.

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors strictly following the policies [...] Full article

(This article belongs to the Proceedings of The 2025 Suwon ITS Asia Pacific Forum)

8 pages, 1944 KiB

Open AccessProceeding Paper

Fabrication of Thin-Film Composite Nanofiltration Membrane Employing Polyelectrolyte and Metal–Organic Framework (MOF) via Spin-Spray-Assisted Layer-by-Layer Assembly

by Farid Fadhillah

Eng. Proc. 2025, 105(1), 3; https://doi.org/10.3390/engproc2025105003 - 11 Aug 2025

Abstract

Spin-spray-assisted layer-by-layer (LbL) assembly is an innovative method for producing nanostructured thin films due to its rapid assembly and extensive coverage of substrates. In this study, a nanofiltration (NF) membrane consisting of multilayers of polyethyleneimine (PEI) and poly(sodium-4-styrene sulfonate) (PSS) was fabricated on [...] Read more.

Spin-spray-assisted layer-by-layer (LbL) assembly is an innovative method for producing nanostructured thin films due to its rapid assembly and extensive coverage of substrates. In this study, a nanofiltration (NF) membrane consisting of multilayers of polyethyleneimine (PEI) and poly(sodium-4-styrene sulfonate) (PSS) was fabricated on a polysulfone (PSF) support. The resulting membrane was further coated with a metal–organic framework (MOF303). The resulting (PEI/PSS)₅-MOF303 showed a rejection rate of 18.94 ± 1.58% and a permeability of 0.91 ± 0.13 L/(h·bar·m²)while also showing enhanced antifouling properties. This work explores the possibility of spin-spray-assisted LbL assembly as a promising method for fabricating membranes. Full article

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Journal Description

Engineering Proceedings

Vol. 102

ITS APF 2025

Vol. 100

TechSys 2025

Vol. 99

EEM 2025

Vol. 98

SSIM 2024

Vol. 97

SMILE 2025

Vol. 96

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