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The technology of 3D printing has become one of the most effective methods of creating various parts, such as those used for fast prototyping. The most important aspect of 3D printing is the selection and application of the appropriate material, also known as filament. The current review concerns mainly the description of the mechanical and physical properties of the different filaments and the possibilities of improving those properties. The review begins with a short description of the development of 3D printing technology. Next, the basic characteristics of thermoplastics used in the fused filament fabrication (FFF) are discussed, namely polylactic acid (PLA), acrylonitrile butadiene styrene (ABS), and polyethylene terephthalate glycol (PETG). According to modern con-cepts, the printed parts can be reinforced with the use of different kinds of fibers, namely synthetic fibers (carbon, glass, aramid) or natural fibers (wood, flax, hemp, jute). Thus, the impact of such a reinforcement on the performance of FFF composites is also presented. The current review, unlike other works, primarily addresses the problem of the aging of parts made from the thermoplastics above. Environmental conditions, including UV radiation, can drastically reduce the physical and mechanical properties of printed elements. Moreover, the current review contains a detailed discussion about the influence of the different fibers on the final mechanical properties of the printed elements. Generally, the synthetic fibers improve the mechanical performance, with documented increases in tensile modulus reaching, for instance, 700% for carbon-fiber-reinforced ABS or over 15-fold for continuous aramid composites, enabling their use in functional, load-bearing components. In contrast, the natural ones could even decrease the stiffness and strength (e.g., wood–plastic composites), or, as in the case of flax, significantly increase stiffness (by 88–121%) while offering a sustainable, lightweight alternative for non-structural applications. Full article

(This article belongs to the Special Issue Manufacturing Technology: Materials, Innovations and Applications, Second Edition)

24 pages, 4131 KB

Open AccessArticle

Pedestrian-Induced Bridge Vibration Driven by Behavioral Preferences

by Jinbao Yao, Yueyue Chen, Weiwei Yang, Yu Sun and Zhaozhi Wu

Buildings 2025, 15(22), 4114; https://doi.org/10.3390/buildings15224114 - 14 Nov 2025

Abstract

Modern lightweight pedestrian bridges exhibit heightened susceptibility to human-induced vibration due to low natural frequencies and high flexibility. This study integrates behavioral science to explore pedestrian–structure coupling, developing a novel bidirectional biomechanical model capturing vertical/lateral movements. Body dynamics were solved iteratively. Concurrently, an agent-based cellular automata model embedded pedestrian social attributes and mutual exclusion to simulate crowd flow. Coupling these with finite element bridge analysis simulated vibration responses. Experimental validation confirms the model’s validity. This work advances a behavioral science perspective for mechanistically understanding pedestrian-induced vibration in flexible bridges, thereby contributing to strategies for mitigating vibration-induced disasters like structural damage or crowd panic. Full article

(This article belongs to the Section Building Structures)

25 pages, 1746 KB

Open AccessArticle

Enhancing Student Motivation and Competencies via the WWH Teaching Method: A Case Study on the NoSQL Database Course

by Bin Yu, Yihong Liu, Yuhui Fan, Shaohua Liu, Xiaoyan Li and Ruoyu Li

Electronics 2025, 14(22), 4453; https://doi.org/10.3390/electronics14224453 - 14 Nov 2025

Abstract

NoSQL databases are vital for modern big data applications, yet traditional teaching methods struggle with lagging content, insufficient practice, and low student engagement. To address these issues, this paper proposes the WWH-integrated teaching method “Why learn, What learn, How learn” for a NoSQL database course. WWH combines three core approaches: the general–special method, which structures knowledge from foundational concepts to specialized technologies; the comparative method, which contextualizes NoSQL value via real-scenario analysis; and the theory–practice combination method, which links concepts to hands-on tasks, supplemented by the problem-guidance and key-highlighting strategies. A quasi-experiment with two cohorts (80 students each; 2023 cohort as control, 2024 as experimental) validated WWH. Quantitative results showed significant improvements: theoretical exam scores rose by 9.2 points (t(158) = 9.21, p < 0.001) and experimental scores by 10.3 points (t(158) = 7.92, p < 0.001), and classroom discussion rates increased from 45.2% to 82.7% (χ²(1) = 28.90, p < 0.001). Qualitative analysis of student essays and project reports further confirmed deeper conceptual understanding, stronger tradeoff awareness, and enhanced knowledge integration in the experimental cohort. This study provides an evidence-based, student-centered framework for modernizing NoSQL instruction, better preparing students for industry data management needs. Full article

(This article belongs to the Topic Advances in Integrative AI, Machine Learning, and Big Data for Transformative Applications)

21 pages, 9128 KB

Open AccessArticle

Discovery and Mechanistic Elucidation of Glycyrrhizic Acid Composite Gel in Promoting Wound Healing: A Modernized Study Based on Shengji Yuhong Ointment

by Hai-Xin Liu, Min-Yu Wang, Ying-Wei Li, Bin Xu, Zi-Xuan Wang, Xiang-Long Meng, Hui-Fang Li and Shi-Yuan Wen

Pharmaceuticals 2025, 18(11), 1737; https://doi.org/10.3390/ph18111737 - 14 Nov 2025

Abstract

Objectives: Shengji Yuhong Ointment (SJYHO) is a classic Traditional Chinese Medicine prescription used for refractory wounds, yet its systemic pharmacological mechanisms remain unclear. This study aimed to identify its key active compounds and develop a simplified, effective topical formulation. Methods: We employed an integrated approach, combining network pharmacology and machine learning to screen the key constituents and core targets of SJYHO. The lead compound, glycyrrhizic acid, was formulated into a hydrogel (GA-Gel). Its therapeutic efficacy was evaluated in a full-thickness excisional wound model in Sprague-Dawley rats over 21 days, assessing healing kinetics, histology, and pain behavior. The interaction between glycyrrhizic acid and the identified target PPIA, along with its immunomodulatory effects, was validated through molecular docking, molecular dynamics simulation, and RT-qPCR. Results: Our integrated analysis identified PPIA as the core target and glycyrrhizic acid as a key bioactive component of SJYHO. Animal experiments demonstrated that GA-Gel significantly accelerated wound closure, which was driven by its multi-faceted actions: reducing inflammation, promoting collagen deposition, alleviating pain, and modulating late-stage angiogenesis. Mechanistically, we confirmed that glycyrrhizic acid stably binds to PPIA. Furthermore, GA-Gel treatment mediated wound immune infiltration by specifically regulating CD8⁺ T cells, neutrophils, and memory B cells, an effect that was dependent on PPIA targeting. Conclusions: This study demonstrates that glycyrrhizic acid, formulated as GA-Gel, recapitulates the wound-healing benefits of SJYHO by specifically targeting PPIA and modulating the immune microenvironment. Our findings not only elucidate a key mechanistic pathway but also present GA-Gel as a rationally designed, clinically translatable therapy for acute and chronic wounds. Full article

(This article belongs to the Section Pharmaceutical Technology)

41 pages, 2701 KB

Open AccessReview

Quantum Shannon Information Theory—Design of Communication, Ciphers, and Sensors

by Osamu Hirota

Entropy 2025, 27(11), 1158; https://doi.org/10.3390/e27111158 - 14 Nov 2025

Abstract

One of the key aspects of Shannon theory is that it provides guidance for designing the most efficient systems, such as minimizing errors and clarifying the limits of coding. This theory has seen great developments in the 50 years since 1948. It has played a vital role in enabling the development of modern ultra-fast, stable, and highly dependable information and communication systems. Shannon theory is supported by statistical communication theories such as detection and estimation theory. The theory of communication systems that transmit Shannon information using quantum media is called quantum Shannon information theory, and research began in the 1960s. The theoretical formulation comparable to conventional Shannon theory has been completed. Its important role is to suggest that application of quantum effects will surpass existing communication performance. It would be meaningless if performance, efficiency, and utility were to deteriorate due to quantum effects, even if a certain new function is given. This paper suggests that there are various limitations to utilizing quantum Shannon information theory to benefit real-world communication systems and presents a theoretical framework for achieving the ultimate goal. Finally, we present the perfect secure cipher that overcomes the Shannon impossibility theorem without degrading communication performance and sensors as an example. Full article

(This article belongs to the Section Quantum Information)

55 pages, 19831 KB

Open AccessReview

Advances and Future Trends in Electrified Agricultural Machinery for Sustainable Agriculture

by Yue Shen, Feng Yang, Jianbang Wu, Shuai Luo, Zohaib Khan, Lanke Zhang and Hui Liu

Agriculture 2025, 15(22), 2367; https://doi.org/10.3390/agriculture15222367 - 14 Nov 2025

Abstract

The global transition toward sustainable and intelligent farming has positioned Electrified Agricultural Machinery (EAM) as a central focus in modern equipment development. By integrating advanced electrical subsystems, high-efficiency powertrains, and intelligent Energy Management Strategies (EMSs), EAM offers considerable potential to enhance operational efficiency, reduce greenhouse-gas emissions, and improve adaptability across diverse agricultural environments. Nevertheless, widespread deployment remains constrained by harsh operating conditions, complex duty cycles, and limitations in maintenance capacity and economic feasibility. This review provides a comprehensive synthesis of enabling technologies and application trends in EAM. Performance requirements of electrical subsystems are examined with emphasis on advances in power supply, electric drive, and control systems. The technical characteristics and application scenarios of battery, series hybrid, parallel hybrid, and power-split powertrains are compared. Common EMS approaches (rule-based, optimization-based, and learning-based) are evaluated in terms of design complexity, energy efficiency, adaptability, and computational demand. Representative applications across tillage, seeding, crop management, and harvesting are discussed, underscoring the transformative role of electrification in agricultural production. This review identifies the series hybrid electronic powertrain system and rule-based EMSs as the most mature technologies for practical application in EAM. However, challenges remain concerning operational reliability in harsh agricultural environments and the integration of intelligent control systems for adaptive, real-time operations. The review also highlights key technical bottlenecks and emerging development trends, offering insights to guide future research and support the wider adoption of EAM. Full article

(This article belongs to the Section Agricultural Technology)

21 pages, 1626 KB

Open AccessArticle

Eco-Friendly Design and Practice of Integrating Agricultural and Fishery Waste into Modern Architecture

by Xiao-Dong Wang and Shu-Chen Tsai

Buildings 2025, 15(22), 4109; https://doi.org/10.3390/buildings15224109 - 14 Nov 2025

Abstract

This study employs a practice-oriented research method, emphasizing practical application rather than laboratory testing, and was conducted in Pingtung County, Taiwan, from 2017 to 2023. The practical results of the five case studies demonstrate that (1) eco-friendly buildings integrating agricultural and fishery waste overcome the obstacles of obtaining building permits and (2) the carbon emissions of exterior walls made of pozzolana are only 44% of those of reinforced concrete. This study contributes to understanding the contemporary characteristics of sustainable buildings and provides directly applicable insights into and suggestions on how buildings can actively utilize local materials. Full article

(This article belongs to the Special Issue Trends and Prospects in Sustainable Green Building Materials)

32 pages, 1982 KB

Open AccessArticle

Chemical Composition and Biological Activity of Extracts from the Aerial Parts of Epilobium parviflorum Schreb

by Mashenka Dimitrova, Inna Sulikovska, Elina Tsvetanova, Vera Djeliova, Anelia Vasileva and Ivaylo Ivanov

Appl. Sci. 2025, 15(22), 12109; https://doi.org/10.3390/app152212109 - 14 Nov 2025

Abstract

Epilobium parviflorum Schreb. is used in folk and modern medicine for the treatment of prostate diseases. It is also known to alleviate gastrointestinal ailments. The aim of the present study is to define the chemical composition of diverse extracts from the herb, to test their inhibitory properties toward post-proline-specific peptidases and to elucidate the mechanisms of their antitumor activity on colorectal carcinoma cells in vitro. The extractions were performed using mono- or biphasic systems of solvents. Their chemical compositions were defined by LC-HRMS. Inhibitory properties towards prolyloligopeptidase (POP) and fibroblast activation protein (FAP) were studied by kinetic assays on human recombinant enzymes. Antioxidant activity was measured by three methods. Genotoxicity to HT-29 colorectal carcinoma cells was analyzed with the comet assay. FACS analyses and flow cytometry were used to evaluate the extracts effect on the cell cycle and their pro-apoptotic properties on HT-29 cells. The extract derived using 80% ethanol was chosen for the next studies due to its efficient and selective inhibition of POP. It contains mainly oenotein B and myricetin-3-O-rhamnoside. Its antioxidant and moderate genotoxic activities can contribute to the antitumor effect on HT-29 cells. The extract has a small effect on the cell cycle but a pronounced pro-apoptotic action on those cells. In conclusion, the 80% ethanol extract of E. parviflorum concentrates the ellagitannin oenotein B, which is a selective inhibitor of POP. Antitumor activity of the extract towards HT-29 cells may be due to the inhibition of POP, the antioxidant, genotoxic and pro-apoptotic activities. Full article

(This article belongs to the Special Issue Advancements in Natural Compounds: Antimicrobial, Antioxidant, and Therapeutic Potentials)

17 pages, 3178 KB

Open AccessArticle

Laser-Synthesized Plasmono-Fluorescent Si-Au and SiC-Au Nanocomposites for Colorimetric Sensing

by Yury V. Ryabchikov

Crystals 2025, 15(11), 982; https://doi.org/10.3390/cryst15110982 - 14 Nov 2025

Abstract

Sensing represents one of the most rapidly developing areas of modern life sciences, spreading from the detection of pathogenic microorganisms in living systems, food, and beverages to hazardous substances in liquid and gaseous environments. However, the development of efficient and low-cost multimodal sensors with easy-to-read functionality is still very challenging. In this paper, stable aqueous colloidal suspensions (ζ-potential was between −30 and −40 mV) of ultrasmall (~7 nm) plasmonic Si-Au and SiC-Au nanocomposites were formed. Two variants of pulsed laser ablation in liquids (PLAL)—direct ablation and laser co-fragmentation—were used for this purpose. The co-fragmentation approach led to a considerable decrease in hydrodynamic diameter (~78 nm) and bandgap widening to approximately 1.6 eV. All plasmonic nanocomposites exhibited efficient multi-band blue emission peaking at ~430 nm upon Xe lamp excitation. Co-fragmentation route considerably (~1 order of magnitude) increased the PL efficiency of the nanocomposites in comparison with the laser-ablated ones, accompanied by a negligible amount of dangling bonds. These silicon-based nanostructures significantly affected the optical response of rhodamine 6G, depending on the synthesis route. In particular, directly ablated nanoparticles revealed a stronger influence on the optical response of dye molecules. The observed findings suggest using such types of semiconductor-plasmonic nanocomposites for multimodal plasmonic and colorimetric sensing integrated with luminescent detection capability. Full article

(This article belongs to the Special Issue Synthesis, Characterization and Applications of Multi-Component Hybrid and Composite Nanostructures)

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59 pages, 3591 KB

Open AccessReview

Digital Regulatory Governance: The Role of RegTech and SupTech in Transforming Financial Oversight and Administrative Capacity

by Niloufar Bagherifam, Sajjad Naghdi, Vahid Ahmadian, Alireza Fazlzadeh and Milad Baghalzadeh Shishehgarkhaneh

Int. J. Financial Stud. 2025, 13(4), 217; https://doi.org/10.3390/ijfs13040217 - 14 Nov 2025

Abstract

Rapid digitalization is transforming how public and private institutions manage regulation, compliance, and supervision. This paper explores the rise of Regulatory Technology (RegTech) and Supervisory Technology (SupTech) as instruments of digital regulatory governance and examines their implications for administrative efficiency, defined as the optimization of regulatory and supervisory processes through automation and data-driven coordination, institutional capacity, and policy innovation. Using a systematic literature review of 59 peer-reviewed studies published between 2017 and 2025, the study identifies how RegTech enhances compliance management and risk control in financial institutions, while SupTech enables regulators to improve supervisory agility, transparency, and real-time oversight. The findings show that these technologies create significant administrative value by streamlining reporting, enhancing accountability, and strengthening governance networks across the public–private interface. However, adoption is constrained by cybersecurity vulnerabilities, algorithmic opacity, regulatory fragmentation, and organizational resistance. To address these issues, the study proposes an integrated governance framework that maps opportunities and barriers across compliance, risk, technology, and institutional coordination. By synthesizing fragmented evidence, this research contributes to the field of administrative sciences by positioning RegTech and SupTech not only as technical innovations but as transformative tools of digital public administration and regulatory modernization. Full article

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41 pages, 3813 KB

Open AccessArticle

Enhancing Power Quality and Reducing Costs in Hybrid AC/DC Microgrids via Fuzzy EMS

by Danilo Pratticò, Filippo Laganà, Mario Versaci, Dubravko Franković, Alen Jakoplić, Saša Vlahinić and Fabio La Foresta

Energies 2025, 18(22), 5985; https://doi.org/10.3390/en18225985 - 14 Nov 2025

Abstract

The rapid growth of renewable energy integration in modern power systems brings new challenges in terms of stability and quality of electricity supply. Hybrid AC/DC microgrids represent a promising solution to integrate photovoltaic panels (PV), wind turbines, fuel cells, and storage units with flexibility and efficiency. However, maintaining adequate power quality (PQ) under variable conditions of generation, load, and grid connection remains a critical issue. This paper presents the modelling, implementation, and validation of a hybrid AC/DC microgrid equipped with a fuzzy-logic-based energy management system (EMS). The study combines PQ assessment, measurement architecture, and supervisory control for technical compliance and economic efficiency. The microgrid integrates a combination of PV array, wind turbine, proton exchange membrane fuel cell (PEMFC), battery storage system, and heterogeneous AC/DC loads, all modelled in MATLAB/Simulink using a physical-network approach. The fuzzy EMS coordinates distributed energy resources by considering power imbalance, battery state of charge (SOC), and dynamic tariffs. Results demonstrate that the proposed controller maintains PQ indices within IEC/IEEE standards while eliminating short-term continuity events. The proposed EMS prevents harmful deep battery cycles, maintaining SOC within 30–90%, and optimises fuel cell activation, reducing hydrogen consumption by 14%. Economically, daily operating costs decrease by 10–15%, grid imports are reduced by 18%, and renewable self-consumption increases by approximately 16%. These findings confirm that fuzzy logic provides an effective, computationally light, and uncertainty-resilient solution for hybrid AC/DC microgrid EMS, balancing technical reliability with economic optimisation. Future work will extend the framework toward predictive algorithms, reactive power management, and hardware-in-the-loop validation for real-world deployment. Full article

(This article belongs to the Special Issue Applications of Advanced Control and Artificial Intelligence in Smart Grids)

23 pages, 1177 KB

Open AccessReview

A Survey on Privacy Preservation Techniques in IoT Systems

by Rupinder Kaur, Tiago Rodrigues, Nourin Kadir and Rasha Kashef

Sensors 2025, 25(22), 6967; https://doi.org/10.3390/s25226967 - 14 Nov 2025

Abstract

The Internet of Things (IoT) has become deeply embedded in modern society, enabling applications across smart homes, healthcare, industrial automation, and environmental monitoring. However, as billions of interconnected devices continuously collect and exchange sensitive data, privacy and security concerns have escalated. This survey systematically reviews the state-of-the-art privacy-preserving techniques in IoT systems, emphasizing approaches that protect user data during collection, transmission, and storage. Peer-reviewed studies from 2016 to 2025 and technical reports were analyzed to examine applied mechanisms, datasets, and analytical models. Our analysis shows that blockchain and federated learning are the most prevalent decentralized privacy-preserving methods, while homomorphic encryption and differential privacy have recently gained traction for lightweight and edge-based IoT implementations. Despite these advancements, challenges persist, including computational overhead, limited scalability, and real-time performance constraints in resource-constrained devices. Furthermore, gaps remain in cross-domain interoperability, energy-efficient cryptographic designs, and privacy solutions for Unmanned Aerial Vehicle (UAV) and vehicular IoT systems. This survey offers a comprehensive overview of current research trends, identifies critical limitations, and outlines promising future directions to guide the design of secure and privacy-aware IoT architectures. Full article

(This article belongs to the Special Issue Security and Privacy in Wireless Sensor Networks (WSNs))

34 pages, 3428 KB

Open AccessReview

Modern Approaches to Software Vulnerability Detection: A Survey of Machine Learning, Deep Learning, and Large Language Models

by Md. Shazzad Hossain Shaon and Mst Shapna Akter

Electronics 2025, 14(22), 4449; https://doi.org/10.3390/electronics14224449 - 14 Nov 2025

Abstract

Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adaptability, motivating the adoption of data-driven methods. In this survey, we present a comprehensive review of Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) techniques for vulnerability detection. We analyze recent advances in feature representation, fine-tuning strategies, generative approaches, and prompt engineering, while highlighting their ability to capture both syntactic and semantic properties of source code. Furthermore, we examine commonly used evaluation metrics and provide a critical discussion of key challenges, including the lack of large-scale real-world datasets, limited vulnerability coverage, class imbalance, interpretability gaps, hallucination, and high computational costs. To address these issues, we outline promising future research directions, such as neuro-symbolic hybrid methods, parameter-efficient fine-tuning, continual learning, cross-language generalization, and explainable AI for vulnerability detection. Unlike previous studies, the present work explores learning paradigms from ML to LLMs using comprehensive evaluation criteria that highlight analytical capability, feature interpretability, and code-context comprehension. By combining these factors, our study addresses the methodological gap between classic feature-based approaches and current LLM-driven reasoning frameworks, providing beneficial insights to develop robust, scalable, and trustworthy software vulnerability detection systems. Full article

(This article belongs to the Special Issue Artificial Intelligence in Cybersecurity: Practices, Challenges, and Innovations)

32 pages, 3930 KB

Open AccessReview

Recent Advances in Agricultural Sensors: Towards Precision and Sustainable Farming

by Jiaqi Lin and Shuping Wu

Chemosensors 2025, 13(11), 399; https://doi.org/10.3390/chemosensors13110399 - 14 Nov 2025

Abstract

Global population growth, intensifying climate change, and escalating food security demands are mounting. In response, modern agriculture must transcend the limitations of traditional experience-based cultivation models to address issues such as low resource utilization, poor environmental adaptability, and significant yield fluctuations. As the core technical support of smart agriculture, agricultural sensors have become the key to transformation. This review systematically introduces the classification and working principles of current mainstream agricultural sensors: according to the monitoring parameters, they can be divided into humidity sensors, light sensors, gas sensors, pressure sensors, nutrient sensors, etc. At the same time, breakthroughs in emerging technologies such as microneedle sensing, nanosensing, and wireless sensor networks are being explored, which are breaking the application limitations of traditional sensors in complex agricultural environments. Combined with specific cases, the practical value of sensor technology is improving in agricultural drought monitoring, soil detection, and agricultural product quality assessment. Looking ahead, if agricultural sensors can overcome existing limitations through breakthroughs in material innovation, multi-sensor unit integration, and artificial intelligence algorithm fusion, this will provide stronger technological support for the further advancement of smart agriculture. Full article

(This article belongs to the Special Issue Application of Chemical Sensors in Smart Agriculture)

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15 pages, 3161 KB

Open AccessArticle

ChronoSort: Revealing Hidden Dynamics in AlphaFold3 Structure Predictions

by Matthew J. Argyle, William P. Heaps, Corbyn Kubalek, Spencer S. Gardiner, Bradley C. Bundy and Dennis Della Corte

SynBio 2025, 3(4), 18; https://doi.org/10.3390/synbio3040018 - 14 Nov 2025

Abstract

Protein function emerges from dynamic conformational changes, yet structure prediction methods provide only static snapshots. While AlphaFold3 (AF3) predicts protein structures, the potential for extracting dynamic information from its ensemble predictions has remained underexplored. Here, we demonstrate that AF3 structural ensembles contain substantial dynamic information that correlates remarkably well with molecular dynamics simulations (MD). We developed ChronoSort, a novel algorithm that organizes static structure predictions into temporally coherent trajectories by minimizing structural differences between neighboring frames. Through systematic analysis of four diverse protein targets, we show that root-mean-square fluctuations derived from AF3 ensembles can correlate strongly with those from MD (r = 0.53 to 0.84). Principal component analysis reveals that AF3 predictions capture the same collective motion patterns observed in molecular dynamics trajectories, with eigenvector similarities significantly exceeding random distributions. ChronoSort trajectories exhibit structural evolution profiles comparable to MD. These findings suggest that modern AI-based structure prediction tools encode conformational flexibility information that can be systematically extracted without expensive MD. We provide ChronoSort as open-source software to enable broad community adoption. This work offers a novel approach to extracting functional insights from structure prediction tools in minutes, with significant implications for synthetic biology, protein engineering, drug discovery, and structure–function studies. Full article

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