Saved Queries

Geopolymers, as a novel class of low-carbon and eco-friendly cementitious material, exhibit outstanding durability and promote the resource utilization of industrial solid wastes. However, as a promising alternative to ordinary Portland cement, its susceptibility to carbonation-induced degradation may limit its widespread application. To address this challenge, this study systematically examined the effects of magnesium oxide (MgO) content and the metakaolin-to-fly ash ratio on the carbonation performance, mechanical properties, pH value, and microstructures of metakaolin–fly ash-based (MF-based) geopolymer pastes. The findings revealed that an increase in the fly ash ratio correlated with a decline in the compressive strength of MF-based geopolymer pastes. Conversely, the incorporation of MgO significantly enhanced the compressive strength, with higher fly ash ratios leading to more substantial improvements in strength. Furthermore, the addition of MgO and fly ash effectively mitigated the penetration of carbonation and the associated decrease in the pH value of the MF-based geopolymer pastes. Specifically, compared to the control group without MgO (M8F2-0%), MF-based geopolymer pastes with 4% and 8% MgO additions exhibited reductions in carbonation depth of 69.4% and 80.4%, respectively, after 28 days of carbonation, while pH values were observed to be 1.22 and 1.15 units higher, respectively. Additionally, microscopic structural analysis revealed that the inclusion of MgO resulted in a reduction in pore size, porosity, and mean pore diameter within the geopolymer pastes. This improvement was mainly attributed to the promotion of hydration processes by MgO, leading to the formation of fine Mg(OH)₂ crystals within the high-alkalinity pore solution, which enhances microstructural densification. In conclusion, the incorporation of MgO significantly improves the carbonation resistance and mechanical performance of MF-based geopolymers. It is recommended that future studies explore the long-term performance under combined environmental actions and evaluate the economic and environmental benefits of MgO-modified geopolymers for large-scale applications. Full article

(This article belongs to the Special Issue Composite Materials for Civil Engineering Applications)

►▼ Show Figures

Figure 1

34 pages, 2041 KB

Open AccessArticle

Model for Innovation Project Selection Supported by Multi-Criteria Methods Considering Sustainability Parameters

by Jamile Eleutério Delesposte, Luís Alberto Duncan Rangel, Marcelo Jasmim Meiriño, Carlos Manuel dos Santos Ferreira, Rui Jorge Ferreira Soares Borges Lopes and Ramon Baptista Narcizo

Systems 2025, 13(10), 876; https://doi.org/10.3390/systems13100876 (registering DOI) - 7 Oct 2025

Abstract

Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation projects using sustainability-related factors can support more consistent decision-making. Although several models for project selection exist in the literature, few provide a comprehensive approach that incorporates sustainability criteria. This study proposes a model for selecting innovation projects by explicitly considering sustainability aspects, supported by multi-criteria decision support methods. The methodological approach followed the Design Cycle method, grounded in Design Science Research. The main result is a novel, customizable model for evaluating, ranking, and managing innovation projects within a sustainability-oriented context. The model was validated through application in two high-performance organizations recognized for their innovation and sustainability practices. Additionally, this research offered reflections on how sustainability-driven innovation can be implemented in practice. Overall, the findings demonstrated that the proposed model is adaptable to different organizational realities, sectors, and sizes, enhancing the capacity to assess and understand the role of sustainability in innovation projects more effectively. Full article

(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)

27 pages, 1513 KB

Open AccessArticle

Accurate Fault Classification in Wind Turbines Based on Reduced Feature Learning and RVFLN

by Mehmet Yıldırım and Bilal Gümüş

Electronics 2025, 14(19), 3948; https://doi.org/10.3390/electronics14193948 (registering DOI) - 7 Oct 2025

Abstract

This paper presents a robust and computationally efficient fault classification framework for wind energy conversion systems (WECS), built upon a Robust Random Vector Functional Link Network (Robust-RVFLN) and validated through real-time simulations on a Real-Time Digital Simulator (RTDS). Unlike existing studies that depend on high-dimensional feature extraction or purely data-driven deep learning models, our approach leverages a compact set of five statistically significant and physically interpretable features derived from rotor torque, phase current, DC-link voltage, and dq-axis current components. This reduced feature set ensures both high discriminative power and low computational overhead, enabling effective deployment in resource-constrained edge devices and large-scale wind farms. A synthesized dataset representing seven representative fault scenarios—including converter, generator, gearbox, and grid faults—was employed to evaluate the model. Comparative analysis shows that the Robust-RVFLN consistently outperforms conventional classifiers (SVM, ELM) and deep models (CNN, LSTM), delivering accuracy rates of up to 99.85% for grid-side line-to-ground faults and 99.81% for generator faults. Beyond accuracy, evaluation metrics such as precision, recall, and F1-score further validate its robustness under transient operating conditions. By uniting interpretability, scalability, and real-time performance, the proposed framework addresses critical challenges in condition monitoring and predictive maintenance, offering a practical and transferable solution for next-generation renewable energy infrastructures. Full article

(This article belongs to the Special Issue Microgrids: Innovations in Fault Detection, Security, and Addressing Challenges)

►▼ Show Figures

Figure 1

22 pages, 1741 KB

Open AccessArticle

Profit Optimization in Multi-Unit Construction Projects Under Variable Weather Conditions: A Wind Farm Case Study

by Michał Podolski, Jerzy Rosłon and Bartłomiej Sroka

Appl. Sci. 2025, 15(19), 10769; https://doi.org/10.3390/app151910769 (registering DOI) - 7 Oct 2025

Abstract

This paper introduces a novel scheduling model that integrates weather-based productivity coefficients into multi-unit construction projects, aiming to enhance profit and reduce delays. The method is suitable especially for renewable energy, open-area projects. The authors propose a flow-shop optimization framework that considers key aspects of construction contracts, e.g., contractual penalties, downtime losses, and cash flow constraints. A proprietary Tabu Search (TS) metaheuristic algorithm variant is used to solve the resulting NP-hard problem. Numerical experiments on multiple test sets indicate that the TS algorithm consistently outperforms other methods in finding higher-profit schedules. A real-world wind farm case study further demonstrates substantial improvements, transforming an initially loss-making operation into a profitable venture. By explicitly accounting for weather disruptions within a formalized scheduling model, this work advances the understanding of reliable project planning under uncertain environmental conditions. The solution framework offers contractors an effective tool for mitigating scheduling risks and optimizing resource usage. The integration of weather data and cash flow management increases the likelihood of on-time and on-budget project delivery. Full article

►▼ Show Figures

Figure 1

13 pages, 3078 KB

Open AccessFeature PaperArticle

Firefly Species and Nocturnal Activity Patterns of Adults in Peri-Urban Forests of Dongguan

by Qingqing Huang, Haicong Zeng, Chaodong Yan, Ting Liu, Songsong Liu, Zhenkai Sun, Chang Zhang, Zhenye Chen, Feng Peng, Niansheng Li and Cheng Wang

Forests 2025, 16(10), 1545; https://doi.org/10.3390/f16101545 (registering DOI) - 7 Oct 2025

Abstract

Against the backdrop of advancing ecological civilization and increasing public interest in reconnecting with nature, this study examines fireflies—iconic insects cherished for their natural charm—as valuable landscape resources. This study was conducted in Dalingshan Forest Park, Dongguan (Pearl River Delta), using the Forest Science Trail as a sampling site. Surveys combining line transect and point count methods were employed to analyze firefly species composition, adult activity patterns, and flight characteristics. Key findings include: (1) Four species were identified—Asymmetricata circumdata, Pygoluciola qingyu, Aquatica analis, and Luciola satoi—three of which were observed along the trail; (2) Adults appeared sporadically after 19:00, with peak activity occurring between 19:30 and 20:00, showing minor interspecific variation; (3) Although flight height varied slightly among species, most activities concentrated within 0–1.5 m, corresponding to herbaceous and shrub layers; (4) Distinct flight patterns were observed: A. circumdata displayed prolonged intermittent flights, while P. qingyu and L. satoi exhibited shorter perching-based flights. These results provide a scientific basis for firefly habitat conservation, biodiversity promotion, and the sustainable integration of firefly landscapes into nature education and ecotourism. Full article

(This article belongs to the Special Issue Sustainable Urban Forests and Green Environments in a Changing World)

►▼ Show Figures

Figure 1

30 pages, 4890 KB

Open AccessArticle

Distributed Active Support from Photovoltaics via State–Disturbance Observation and Dynamic Surface Consensus for Dynamic Frequency Stability Under Source–Load Asymmetry

by Yichen Zhou, Yihe Gao, Yujia Tang, Yifei Liu, Liang Tu, Yifei Zhang, Yuyan Liu, Xiaoqin Zhang, Jiawei Yu and Rui Cao

Symmetry 2025, 17(10), 1672; https://doi.org/10.3390/sym17101672 (registering DOI) - 7 Oct 2025

Abstract

The power system’s dynamic frequency stability is affected by common-mode ultra-low-frequency oscillation and differential-mode low-frequency oscillation. Traditional frequency control based on generators is facing the problem of capacity reduction. It is urgent to explore new regulation resources such as photovoltaics. To address this issue, this paper proposes a distributed active support method based on photovoltaic systems via state–disturbance observation and dynamic surface consensus control. A three-layer distributed control framework is constructed to suppress low-frequency oscillations and ultra-low-frequency oscillations. To solve the high-order problem of the regional grid model and to obtain its unmeasurable variables, a regional observer estimating both system states and external disturbances is designed. Furthermore, a distributed dynamic frequency stability control method is proposed for wide-area photovoltaic clusters based on the dynamic surface control theory. In addition, the stability of the proposed distributed active support method has been proven. Moreover, a parameter tuning algorithm is proposed based on improved chaos game theory. Finally, simulation results demonstrate that, even under a 0–2.5 s time-varying communication delay, the proposed method can restrict the frequency deviation and the inter-area frequency difference index to 0.17 Hz and 0.014, respectively. Moreover, under weak communication conditions, the controller can also maintain dynamic frequency stability. Compared with centralized control and decentralized control, the proposed method reduces the frequency deviation by 26.1% and 17.1%, respectively, and shortens the settling time by 76.3% and 42.9%, respectively. The proposed method can effectively maintain dynamic frequency stability using photovoltaics, demonstrating excellent application potential in renewable-rich power systems. Full article

(This article belongs to the Special Issue Symmetry/Asymmetry Studies in Modern Power Systems)

►▼ Show Figures

Figure 1

12 pages, 1494 KB

Open AccessArticle

Research on the Synthesis and Conductivity of Titanium Oxycarbide

by Shaolong Li, Fan Yang, Peizhu Mao, Tianzhu Mu, Fuxing Zhu and Shengwei Li

Materials 2025, 18(19), 4621; https://doi.org/10.3390/ma18194621 (registering DOI) - 6 Oct 2025

Abstract

In this study, TiC_xO_y was produced by sintering in an argon atmosphere using carbon–thermal reduction with TiO₂ and graphite powder as the initial materials. The sintered TiC_xO_y was analyzed using X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. As the oxygen content increased, the grain color of the sintered TiC_xO_y gradually shifted from gray to reddish-brown. The structure of TiC_xO_y resembles that of a coral, with a uniform distribution of Ti, C, and O throughout the sample. Analysis using X-ray photoelectron spectroscopy reveals the presence of bivalent, trivalent, and tetravalent titanium. Utilizing General Structure Analysis System software(GSAS-II), the X-ray Diffraction data obtained were refined, revealing a gradual decrease in lattice parameters as the oxygen atom content increased. Furthermore, the conductivity and density of the single phase, determined through the four-probe method and the Archimedes method, respectively, exhibited an increase in tandem with the rise in C content. Full article

(This article belongs to the Section Advanced Materials Characterization)

17 pages, 1071 KB

Open AccessFeature PaperArticle

Morphostructural Characterization of Hunting Dog Packs (Rehalas) Using Multivariate Methodology

by Carlos Poderoso Martínez, Ana González-Martínez, Manuel Luque Cuesta and Evangelina Rodero Serrano

Animals 2025, 15(19), 2908; https://doi.org/10.3390/ani15192908 - 6 Oct 2025

Abstract

On the south–central Iberian Peninsula, big game hunting is traditionally carried out using big-game hunting under the “Montería” modality, with dog packs. Breeders of these dogs value their versatility in both chasing and capturing prey. In this context, the most popular breed is the Large-sized Podenco Andaluz, colloquially known as Podenco Campanero. In this study, we aimed to morphologically characterize the hounds of the Sierra Morena in Córdoba and evaluate their possible relationships with other Spanish hunting dog breeds. For this purpose, 255 dogs were measured to obtain sixteen morphometric measurements and eleven indices. To assess morphostructural differentiation, we applied multivariate methodologies. The Podenco Campanero exhibited pronounced sexual dimorphism, with males being significantly (p < 0.001) longer, taller, wider, and deeper than females. The morphostructural model of this breed demonstrated considerable homogeneity and harmony, and the population exhibited distinct morphostructural characteristics, with body size and regional width varying between individuals. The morphometric characteristics of the breeds used in Monterías on the central and southern Iberian Peninsula highlight that the diversity of these local genetic resources is shaped by genetic relationships and selective breeding models chosen by dog pack breeders, which depend on the hunting modality and the terrain characteristics where it is practiced. Full article

(This article belongs to the Section Wildlife)

22 pages, 1327 KB

Open AccessReview

Unequal Horizons: Global North–South Disparities in Archaeological Earth Observation (2000–2025)

by Athos Agapiou

Remote Sens. 2025, 17(19), 3371; https://doi.org/10.3390/rs17193371 - 6 Oct 2025

Abstract

This systematic review analyzes 4359 archaeologically relevant publications spanning 25 years to examine global disparities in archaeological remote sensing research between Global North and Global South participation. This study reveals deep inequalities among these regions, with 72.1% of research output originating from Global North-only institutions, despite these regions hosting less than half of UNESCO World Heritage Sites. The temporal analysis demonstrates exponential growth, with 47.2% of all research published in the last five years, indicating rapid technological advancement concentrated in well-resourced institutions. Sub-Saharan Africa produces only 0.6% of research output while hosting 9.4% of World Heritage Sites, highlighting a technology gap in heritage protection. The findings suggest an urgent need for coordinated interventions to address structural inequalities and promote technological fairness in global heritage preservation. The research employed bibliometric analysis of Scopus database records from four complementary search strategies, revealing that just three countries—Italy (20.3%), the United States (16.7%), and the United Kingdom (10.0%)—account for nearly half of all archaeological remote sensing research and applications worldwide. This study documents patterns that have profound implications for cultural heritage preservation and sustainable development in an increasingly digital world where advanced Earth observation technologies have become essential for effective heritage protection and archaeological research. Full article

19 pages, 916 KB

Open AccessReview

The Mechanisms of Sphagneticola trilobata Invasion as One of the Most Aggressive Invasive Plant Species

by Hisashi Kato-Noguchi and Midori Kato

Diversity 2025, 17(10), 698; https://doi.org/10.3390/d17100698 - 6 Oct 2025

Abstract

Sphagneticola trilobata (L.) Pruski has been introduced to many countries due to its ornamental and economic value. However, it has been listed in the world’s 100 worst alien invasive species due to its invasive nature. This species easily escapes cultivation and forms dense ground covers. It reproduces asexually through ramet formation from stem fragments. It also produces a large number of viable seeds that establish extensive seed banks. The movement of stem fragments and the dispersal of seeds, coupled with human activity, contribute to its short- and long-distance distribution. S. trilobata grows rapidly due to its high nutrient absorption and photosynthetic abilities. It exhibits high genetic and epigenetic variation. It can adapt to the different habitats and tolerate various adverse environmental conditions, including cold and high temperatures, low and high light irradiation, low nutrient levels, waterlogging, drought, salinity and global warming. S. trilobata has powerful defense systems against herbivory and pathogen infection. These systems activate the jasmonic acid signaling pathway, producing several defensive compounds. This species may also acquire more resources through allelopathy, which suppresses the germination and growth of neighboring plants. These life history traits and defensive abilities likely contribute to its invasive nature. This is the first review to focus on the mechanisms of its invasiveness in terms of growth, and reproduction, as well as its ability to adapt to different environmental conditions and defend itself. Full article

(This article belongs to the Special Issue Ecology, Distribution, Impacts, and Management of Invasive Plants)

38 pages, 3764 KB

Open AccessReview

AI-Enabled IoT Intrusion Detection: Unified Conceptual Framework and Research Roadmap

by Antonio Villafranca, Kyaw Min Thant, Igor Tasic and Maria-Dolores Cano

Mach. Learn. Knowl. Extr. 2025, 7(4), 115; https://doi.org/10.3390/make7040115 - 6 Oct 2025

Abstract

The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems. Full article

►▼ Show Figures

Figure 1

16 pages, 1725 KB

Open AccessArticle

A DAG-Based Offloading Strategy with Dynamic Parallel Factor Adjustment for Edge Computing in IoV

by Wenyang Guan, Qi Zheng, Xiaoqin Lian and Chao Gao

Sensors 2025, 25(19), 6198; https://doi.org/10.3390/s25196198 - 6 Oct 2025

Abstract

With the rapid development of Internet of Vehicles (IoV) technology, massive data are continuously integrated into intelligent transportation systems, making efficient computing resource allocation a critical challenge for enhancing network performance. Due to the dynamic and real-time characteristics of IoV tasks, existing static offloading strategies fail to effectively cope with the complexity caused by network fluctuations and vehicle mobility. To address this issue, this paper proposes a task offloading algorithm based on the dynamic adjustment of the parallel factor in directed acyclic graphs (DAG), referred to as Dynamic adjustment of Parallel Factor (DPF). By leveraging edge computing, the proposed algorithm adaptively adjusts the parallel factor according to the dependency relationships among subtasks in the DAG, thereby optimizing resource utilization and reducing task completion time. In addition, the algorithm continuously monitors network conditions and vehicle states to dynamically schedule and offload tasks according to real-time system requirements. Compared with traditional static strategies, the proposed method not only significantly reduces task delay but also improves task success rates and overall system efficiency. Extensive simulation experiments conducted under three different task load conditions demonstrate the superior performance of the proposed algorithm. In particular, under high-load scenarios, the DPF algorithm achieves markedly better task completion times and resource utilization compared to existing methods. Full article

(This article belongs to the Section Internet of Things)

18 pages, 257 KB

Open AccessArticle

Understanding Rehabilitation Providers: Knowledge, Attitudes, and Practices Toward Older Adults with Substance Use Disorders

by Marybeth Johnson, Michelle L. Cathorall, Tina M. K. Newsham and Elizabeth Fugate-Whitlock

J. Ageing Longev. 2025, 5(4), 41; https://doi.org/10.3390/jal5040041 - 6 Oct 2025

Abstract

Objective: The purpose of this study was to investigate the knowledge, attitudes, and practices (KAPs), including ageism, of rehabilitation service providers regarding older adults with substance use disorders to examine the association between KAPs and ageism on the knowledge of rehabilitation providers and confidence in treating this population. Methods: An online survey was developed to assess providers’ familiarity with geriatric substance use disorders, attitudes towards aging, and perceived barriers to treatment. The survey included the Expectations Regarding Aging (ERA-12) tool to measure ageist attitudes. Data was collected from 25 rehabilitation healthcare providers across rehabilitation centers in North Carolina. Descriptive statistics and ERA-12 scoring were used to analyze the results. Results: Most (52.0%) respondents reported slight or moderate familiarity with specific risk factors for substance use disorders associated with older adults, and participants most commonly expressed ambivalence (48.0% indicated they were neither satisfied nor dissatisfied) with their training on this demographic. Barriers included a lack of specialized training, limited availability of age-appropriate treatment programs, and resistance to change. Negative attitudes towards aging and substance use disorders were prevalent among respondents. Providers indicated a need for enhanced education, clinical guidelines, and access to geriatric-trained professionals. Discussion: The findings highlight a critical need for specialized training for rehabilitation providers to improve care for older adults with substance use disorders. Addressing ageism, increasing awareness, and enhancing provider education are essential to improving treatment outcomes. Implementing targeted training programs and specialized resources could significantly enhance the quality of care for this underserved population. Full article

(This article belongs to the Special Issue Quality of Life among Older Adults: Lifestyle Differentiation and Associated Factors)

41 pages, 33044 KB

Open AccessArticle

An Improved DOA for Global Optimization and Cloud Task Scheduling

by Shinan Xu and Wentao Zhang

Symmetry 2025, 17(10), 1670; https://doi.org/10.3390/sym17101670 - 6 Oct 2025

Abstract

Symmetry is an essential characteristic in both solution spaces and cloud task scheduling loads, as it reflects a structural balance that can be exploited to enhance algorithmic efficiency and robustness. In recent years, with the rapid development of 6G networks, the number of tasks requiring computation in the cloud has surged, prompting an increasing number of researchers to focus on how to efficiently schedule these tasks to idle computing nodes at low cost to enhance system resource utilization. However, developing reliable and cost-effective scheduling schemes for cloud computing tasks in real-world environments remains a significant challenge. This paper proposes a method for cloud computing task scheduling in real-world environments using an improved dhole optimization algorithm (IDOA). First, we enhance the quality of the initial population by employing a uniform distribution initialization method based on the Sobol sequence. Subsequently, we further improve the algorithm’s search capabilities using a sine elite population search method based on adaptive factors, enabling it to more effectively explore promising solution spaces. Additionally, we propose a random mirror perturbation boundary control method to better address individual boundary violations and enhance the algorithm’s robustness. By explicitly leveraging symmetry characteristics, the proposed algorithm maintains balanced exploration and exploitation, thereby improving convergence stability and scheduling fairness. To evaluate the effectiveness of the proposed algorithm, we compare it with nine other algorithms using the IEEE CEC2017 test set and assess the differences through statistical analysis. Experimental results demonstrate that the IDOA exhibits significant advantages. Finally, to verify its applicability in real-world scenarios, we applied IDOA to cloud computing task scheduling problems in actual environments, achieving excellent results and successfully completing cloud computing task scheduling planning. Full article

(This article belongs to the Special Issue Symmetry and Metaheuristic Algorithms)

►▼ Show Figures

Figure 1

22 pages, 1538 KB

Open AccessArticle

Research on Key Technologies and Integrated Solutions for Intelligent Mine Ventilation Systems

by Deyun Zhong, Lixue Wen, Yulong Liu, Zhaohao Wu, Liguan Wang and Xianwei Ji

Technologies 2025, 13(10), 451; https://doi.org/10.3390/technologies13100451 - 6 Oct 2025

Abstract

Intelligent ventilation systems can optimize airflow regulation to enhance mining safety and reduce energy consumption, driving green development in mineral resource extraction. This paper systematically elaborates on the overall architecture, cutting-edge advances, and core technologies of current intelligent mining ventilation. Building upon this foundation, a comprehensive intelligent mine ventilation solution encompassing the entire process of ventilation design, optimization, and operation is constructed based on a five-layer architecture, integrating key technologies such as intelligent sensing, real-time solving, airflow regulation, and remote control, providing an overarching framework for smart mine ventilation development. To address the computational efficiency bottleneck of traditional methods, an improved loop-solving method based on minimal independent closed loops is realized, achieving near real-time analysis of ventilation networks. Furthermore, a multi-level airflow regulation strategy is realized, including the methods of optimization control based on mixed integer linear programming and equipment-driven demand-based regulation, effectively resolving the challenges of calculating nonlinear programming models. Case studies indicate that the intelligent ventilation system significantly enhances mine safety and efficiency, leading to approximately 10–20% energy saving, a 40–60% quicker emergency response, and an average increase of about 20% in the utilization of fresh air at working faces through its remote and real-time control capabilities. Full article

Show export options Show export options

Select all

Export citation of selected articles as:

Error

Oops... you haven't selected anything for export.

Displaying article 1-50 on page 1 of 2000.

Go to page 1 2 3 4 5

Search Results (155,863)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI