Saved Queries

The practical adoption of surface-enhanced Raman scattering (SERS) technology is often hampered by the high cost, complex fabrication, and poor reproducibility of conventional substrates, which typically rely on noble metals or inefficient semiconductors. Herein, we address key challenges in the practical commercialization of surface-enhanced Raman scattering (SERS) technology by reporting a facile, scalable, and environmentally benign strategy for fabricating a hybrid SERS substrate. This approach integrates Au nanoparticles (NPs) with hydrothermally synthesized WO₃ nanowires through a green photoreduction process, which is rapid, organic-solvent-free, and amenable to large-scale production. The design of the Au/WO₃ nanocomposite capitalizes on the synergistic effect between electromagnetic (EM) enhancement from Au NPs and chemical mechanism (CM) enhancement via charge transfer involving the WO₃ semiconductor. This synergy empowers the substrate with exceptional SERS activity, enabling the sensitive detection of Rhodamine 6G (R6G) down to 10⁻¹¹ M and yielding an enhancement factor (EF) of 4.09 × 10⁶. More importantly, this EM-CM synergy proves critical for detecting molecules with weak affinity, such as the nerve agent simulant dimethyl methylphosphonate (DMMP), achieving a significant signal enhancement of 10²–10³ times, which is notably challenging for conventional plasmonic substrates. Beyond sensitivity, the substrate exhibits excellent reproducibility and operational stability, which are paramount for real-world applications. This work presents a nanohybrid strategy that successfully balances scalability, stability, and sensitivity, offering a reliable and cost-effective pathway for advancing SERS technologies toward practical implementation. Full article

(This article belongs to the Special Issue State of the Art of Colloid and Interface Science in Asia)

►▼ Show Figures

Figure 1

21 pages, 2648 KB

Open AccessArticle

A Hybrid Reinforcement Learning Framework Combining TD3 and PID Control for Robust Trajectory Tracking of a 5-DOF Robotic Arm

by Zied Ben Hazem, Firas Saidi, Nivine Guler and Ali Husain Altaif

Automation 2025, 6(4), 56; https://doi.org/10.3390/automation6040056 (registering DOI) - 14 Oct 2025

Abstract

This paper presents a hybrid reinforcement learning framework for trajectory tracking control of a 5-degree-of-freedom (DOF) Mitsubishi RV-2AJ robotic arm by integrating model-free deep reinforcement learning (DRL) algorithms with classical control strategies. A novel hybrid PID + TD3 agent is proposed, combining a Proportional–Integral–Derivative (PID) controller with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and is compared against standalone TD3 and PID controllers. In this architecture, the PID controller provides baseline stability and deterministic disturbance rejection, while the TD3 agent learns residual corrections to enhance tracking accuracy, robustness, and control smoothness. The robotic system is modeled in MATLAB/Simulink with Simscape Multibody, and the agents are trained using a reward function inspired by artificial potential fields, promoting energy-efficient and precise motion. Extensive simulations are performed under internal disturbances (e.g., joint friction variations, payload changes) and external disturbances (e.g., unexpected forces, environmental interactions). Results demonstrate that the hybrid PID + TD3 approach outperforms both standalone TD3 and PID controllers in convergence speed, tracking precision, and disturbance rejection. This study highlights the effectiveness of combining reinforcement learning with classical control for intelligent, robust, and resilient robotic manipulation in uncertain environments. Full article

(This article belongs to the Topic New Trends in Robotics: Automation and Autonomous Systems)

►▼ Show Figures

Figure 1

20 pages, 425 KB

Open AccessArticle

Data-Driven Event-Triggering Control of Discrete Time-Delay Systems

by Yifan Gong, Zhicheng Li and Yang Wang

Information 2025, 16(10), 893; https://doi.org/10.3390/info16100893 (registering DOI) - 14 Oct 2025

Abstract

This paper investigates the data-driven event-triggering control of discrete time-delay systems. When there is enough data available, the system parameters can be determined by identified methods, and the model-based controller design can be implemented. However, with little data, this method does not result in an accurate system. The data-driven control method is introduced to address this issue. This paper classifies discrete-time systems with time delays into those with known delays and those with unknown delays. Controllers for systems with known delays and unknown delays are designed based on limited data, and stability is ensured by constructing improved Lyapunov functions. Two analyses are introduced: For the known delay condition, the lifting model method is presented to raise order and change the time-delay system to a delay-free system. Further, the stabilization criterion is presented. For the unknown time-delay system, according to the basic data-driven assumption, the data-driven stabilization criterion is presented. Also, the introduction of a dynamic event-triggering scheme and the discussion in this paper on how its parameters can be chosen can save more computational resources. Based on the two methods, the Lyapunov function is constructed separately, and the controller is derived through Linear Matrix Inequality. Finally, a discrete time-delay system is used as an example to show the effectiveness of these two methods. In addition, the dynamic event-triggering scheme proposed in this paper is compared with other articles to show that the parameter selection method proposed in this paper has better performance. Full article

►▼ Show Figures

Figure 1

12 pages, 1479 KB

Open AccessArticle

Structure-Guided In-Use Stability Assessment of Monoclonal Antibody Tislelizumab

by David Andre Rudd and Ghizal Siddiqui

Pharmaceuticals 2025, 18(10), 1539; https://doi.org/10.3390/ph18101539 (registering DOI) - 13 Oct 2025

Abstract

Background/Objectives: Monoclonal antibody (mAb) stability is critical not only during manufacturing but also at the point of clinical administration. For therapies like tislelizumab (Tevimbra), a programmed death-1 (PD-1) targeting IgG mAb, delays in dosing often result in prepared infusions being discarded, contributing to substantial drug waste despite being engineered for improved stability. Methods: To evaluate the physicochemical in-use stability of tislelizumab in a ready-to-administer format, we mapped degradation pathways, including post-translational modifications (PTMs); peptide alterations; pH and solution characteristics—under 12-month storage (ultra-long), under 1-month storage (0, 7, 14, 21, 28 and 31 days), and under exposure-related forced degradation conditions including room temperature, elevated temperature, pH (acidic/basic), oxidation and UV exposure. Structural analysis was contextualised to the known PD-1 binding site, making stability assessment relevant to tislelizumab’s mechanism-of-action in blocking PD-1. To assess solution stability, a validated size-exclusion chromatography (SEC) assay was applied to all conditions. Results: Aggregation was identified as the primary degradation pathway during ultra-long-term storage. SEC and chemical assessment revealed no measurable changes in protein quantity, aggregation, peptide integrity, or PTM profile over 31 days at 2–8 °C in polyolefin intravenous bags (1.6 mg/mL). Conclusions: These results support the structural and physicochemical stability of tislelizumab under refrigerated conditions. Full article

(This article belongs to the Topic Optimization of Drug Utilization and Medication Adherence)

►▼ Show Figures

Graphical abstract

24 pages, 6669 KB

Open AccessArticle

Development of Novel Offshore Submersible Seaweed Cultivation Infrastructure with Deep-Cycling Capability

by Chenxuan Huang, Chien Ming Wang, Brian von Herzen and Huu-Phu Nguyen

J. Mar. Sci. Eng. 2025, 13(10), 1958; https://doi.org/10.3390/jmse13101958 (registering DOI) - 13 Oct 2025

Abstract

This paper presents a novel submersible seaweed cultivation infrastructure designed to enhance seaweed growth through deep cycling. The system consists of a square grid of ropes for growing seaweed, supported by buoys, mooring lines, and innovative SubTractors—movable buoys that enable controlled submersion. The grid ropes are stabilized by four SubTractors, an array of small buoys, intermediate sinker weights and mooring lines anchored to the seabed. The SubTractors facilitate dynamic positioning, allowing the seaweed rope grid to be submerged below the thermocline—at depths of 100 m or more—where nutrient-rich deep water accelerates seaweed growth in offshore sites with low surface nutrient levels. Small buoys attached to the grid provide buoyancy, keeping the seaweed rope grid planar and near the surface to optimize photosynthesis when not submerged. This paper first describes the seaweed cultivation infrastructure, then develops a hydroelastic model of the proposed cultivation system, followed by a hydroelastic analysis under varying wave and current conditions. The results provide insights into the system’s dynamic behaviour, informing engineering design and structural optimization. Full article

(This article belongs to the Special Issue Infrastructure for Offshore Aquaculture Farms)

►▼ Show Figures

Figure 1

22 pages, 3254 KB

Open AccessArticle

Optimizing Steel Industry and Air Conditioning Clusters Using Coordination-Based Time-Series Fusion Transformer

by Xinyu Luo, Zhaofan Zhou, Bin Li, Yumeng Zhang, Chenle Yi, Kun Shi and Songsong Chen

Processes 2025, 13(10), 3265; https://doi.org/10.3390/pr13103265 (registering DOI) - 13 Oct 2025

Abstract

The steel industry, a typical energy-intensive sector, experiences significant load power fluctuations, particularly during peak periods, posing challenges to power-grid stability. Traditional studies often overlook its unique production characteristics, limiting a comprehensive understanding of power fluctuations. Meanwhile, air conditioning (AC), as a flexible load, offers stable regulation with an aggregation effect. This study explores the potential for coordinated load dispatch between the steel industry and air conditioning clusters to enhance power system flexibility. A power characteristic model for steel loads was developed based on energy consumption patterns, while a physical ETP model aggregated air conditioning loads. To improve forecasting accuracy, a parallel LSTM-Transformer model predicts both steel and air conditioning loads. CEEMDAN-VMD decomposition reduces noise in steel-load data, and the QR algorithm computes confidence intervals for load responses. The study further examines interactions between electric-arc furnace control strategies and air conditioning demand response. Case studies using real-world data demonstrate that the proposed model enhances prediction accuracy, peak suppression, and variance reduction. These findings provide insights into steel industry power fluctuations and large-scale air conditioning load adjustments. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Enhancing Power System Stability and Operational Efficiency)

26 pages, 5444 KB

Open AccessFeature PaperArticle

ADG-YOLO: A Lightweight and Efficient Framework for Real-Time UAV Target Detection and Ranging

by Hongyu Wang, Zheng Dang, Mingzhu Cui, Hanqi Shi, Yifeng Qu, Hongyuan Ye, Jingtao Zhao and Duosheng Wu

Drones 2025, 9(10), 707; https://doi.org/10.3390/drones9100707 (registering DOI) - 13 Oct 2025

Abstract

The rapid evolution of UAV technology has increased the demand for lightweight airborne perception systems. This study introduces ADG-YOLO, an optimized model for real-time target detection and ranging on UAV platforms. Building on YOLOv11n, we integrate C3Ghost modules for efficient feature fusion and ADown layers for detail-preserving downsampling, reducing the model’s parameters to 1.77 M and computation to 5.7 GFLOPs. The Extended Kalman Filter (EKF) tracking improves positional stability in dynamic environments. Monocular ranging is achieved using similarity triangle theory with known target widths. Evaluations on a custom dataset, consisting of 5343 images from three drone types in complex environments, show that ADG-YOLO achieves 98.4% mAP_0.5 and 85.2% mAP_0.5:0.95 at 27 FPS when deployed on Lubancat4 edge devices. Distance measurement tests indicate an average error of 4.18% in the 0.5–5 m range for the DJI NEO model, and an average error of 2.40% in the 2–50 m range for the DJI 3TD model. These results suggest that the proposed model provides a practical trade-off between detection accuracy and computational efficiency for resource-constrained UAV applications. Full article

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

19 pages, 4130 KB

Open AccessArticle

Deep Learning Application of Fruit Planting Classification Based on Multi-Source Remote Sensing Images

by Jiamei Miao, Jian Gao, Lei Wang, Lei Luo and Zhi Pu

Appl. Sci. 2025, 15(20), 10995; https://doi.org/10.3390/app152010995 (registering DOI) - 13 Oct 2025

Abstract

With global climate change, urbanization, and agricultural resource limitations, precision agriculture and crop monitoring are crucial worldwide. Integrating multi-source remote sensing data with deep learning enables accurate crop mapping, but selecting optimal network architectures remains challenging. To improve remote sensing-based fruit planting classification and support orchard management and rural revitalization, this study explored feature selection and network optimization. We proposed an improved CF-EfficientNet model (incorporating FGMF and CGAR modules) for fruit planting classification. Multi-source remote sensing data (Sentinel-1, Sentinel-2, and SRTM) were used to extract spectral, vegetation, polarization, terrain, and texture features, thereby constructing a high-dimensional feature space. Feature selection identified 13 highly discriminative bands, forming an optimal dataset, namely the preferred bands (PBs). At the same time, two classification datasets—multi-spectral bands (MS) and preferred bands (PBs)—were constructed, and five typical deep learning models were introduced to compare performance: (1) EfficientNetB0, (2) AlexNet, (3) VGG16, (4) ResNet18, (5) RepVGG. The experimental results showed that the EfficientNetB0 model based on the preferred band performed best in terms of overall accuracy (87.1%) and Kappa coefficient (0.677). Furthermore, a Fine-Grained Multi-scale Fusion (FGMF) and a Condition-Guided Attention Refinement (CGAR) were incorporated into EfficientNetB0, and the traditional SGD optimizer was replaced with Adam to construct the CF-EfficientNet architecture. The results indicated that the improved CF-EfficientNet model achieved high performance in crop classification, with an overall accuracy of 92.6% and a Kappa coefficient of 0.830. These represent improvements of 5.5 percentage points and 0.153, compared with the baseline model, demonstrating superiority in both classification accuracy and stability. Full article

►▼ Show Figures

Figure 1

20 pages, 652 KB

Open AccessReview

Short Peptides as Excipients in Parenteral Protein Formulations: A Mini Review

by Dorian Migoń, Zbigniew Jaremicz and Wojciech Kamysz

Pharmaceutics 2025, 17(10), 1328; https://doi.org/10.3390/pharmaceutics17101328 (registering DOI) - 13 Oct 2025

Abstract

Biopharmaceutical medicines represent one of the most dynamic sectors of the pharmaceutical industry, with therapeutic proteins forming the largest and most important group. Their structural complexity and inherent sensitivity to chemical and physical stressors, however, continue to pose major challenges for formulation development and long-term stability. Short peptides have emerged as a promising yet underutilized class of excipients for protein-based drug products. Their modular architecture allows for precise tuning of physicochemical properties such as polarity, charge distribution, and hydrogen-bonding potential, thereby offering advantages over single amino acids. Experimental studies indicate that short peptides can serve multiple functions: stabilizers, antioxidants, viscosity-lowering agents, and as lyo/cryoprotectants or bulking agents in lyophilized formulations. Notably, the relatively small and chemically defined space of short peptides—approximately 400 possible dipeptides and 8000 tripeptides—makes them particularly amenable to systematic screening and computational modeling. This enables rational identification of candidates with tailored excipient functions. This review summarizes current knowledge on the use of short peptides as excipients in parenteral protein formulations, with a focus on their functional versatility and potential for rational design in future development. Full article

(This article belongs to the Section Biopharmaceutics)

►▼ Show Figures

Figure 1

19 pages, 3696 KB

Open AccessArticle

Engineering 3D Heterostructured NiCo₂S₄/Co₉S₈-CNFs via Electrospinning and Hydrothermal Strategies for Efficient Bifunctional Energy Conversion

by Dhananjaya Merum, Rama Krishna Chava and Misook Kang

Nanomaterials 2025, 15(20), 1559; https://doi.org/10.3390/nano15201559 (registering DOI) - 13 Oct 2025

Abstract

The rational design of multifunctional electrocatalysts requires synergistic integration of conductive scaffolds with redox-active components. Here, a hierarchical core–shell NiCo₂S₄ grown/anchored on Co₉S₈-loaded carbon nanofibers (NCS/CS/CNFs) was synthesized via an electrospinning and hydrothermal approach and systematically characterized. FESEM/TEM confirmed a core-shell nanofiber structure with a NiCo₂S₄ shell thickness of ~30–70 nm, increasing the fiber diameter to ~290 ± 30 nm, while BET analysis revealed a surface area of 24.84 m² g⁻¹ and pore volume of 0.042 cm³ g⁻¹, surpassing CS/CNFs (6.12 m² g⁻¹) and NCS (4.85 m² g⁻¹). XRD confirmed crystalline NiCo₂S₄ and Co₉S₈ phases, while XPS identified mixed Ni²⁺/Ni³⁺ and Co²⁺/Co³⁺ states with strong Ni-S/Co-S bonding, indicating enhanced electron delocalization. Electrochemical measurements in 1 M KOH demonstrated outstanding OER activity, with NCS/CS/CNFs requiring only 324 mV overpotential at 10 mA cm⁻², a Tafel slope of 125.7 mV dec⁻¹, and low charge-transfer resistance (0.33 Ω cm²). They also achieved a high areal capacitance of 1412.5 μF cm⁻² and maintained a stable current density for >5 h. For methanol oxidation, the composite delivered 150 mA cm⁻² at 0.1 M methanol, ~1.6 times that of CS and 1.3 times that of NCS, while maintaining stability for 18,000 s. This bifunctional activity underscores the synergy between conductive CNFs and hierarchical sulfides, offering a scalable route to durable electrocatalysts for water splitting and direct methanol fuel cells. Full article

(This article belongs to the Special Issue Design and Application of Nanomaterials in Photoenergy Conversions)

►▼ Show Figures

Graphical abstract

25 pages, 3613 KB

Open AccessArticle

Finite-Time Modified Function Projective Synchronization Between Different Fractional-Order Chaotic Systems Based on RBF Neural Network and Its Application to Image Encryption

by Ruihong Li, Huan Wang and Dongmei Huang

Fractal Fract. 2025, 9(10), 659; https://doi.org/10.3390/fractalfract9100659 (registering DOI) - 13 Oct 2025

Abstract

This paper innovatively achieves finite-time modified function projection synchronization (MFPS) for different fractional-order chaotic systems. By leveraging the advantages of radial basis function (RBF) neural networks in nonlinear approximation, this paper proposes a novel fractional-order sliding-mode controller. It is designed to address the issues of system model uncertainty and external disturbances. Based on Lyapunov stability theory, it has been demonstrated that the error trajectory can converge to the equilibrium point along the sliding surface within a finite time. Subsequently, the finite-time MFPS of the fractional-order hyperchaotic Chen system and fractional-order chaotic entanglement system are realized under conditions of periodic and noise disturbances, respectively. The effects of the neural network parameters on the performance of the MFPS are then analyzed in depth. Finally, a color image encryption scheme is presented integrating the above MFPS method and exclusive-or operation, and its effectiveness and security are illustrated through numerical simulation and statistical analysis. In the future, we will further explore the application of fractional-order chaotic system MFPS in other fields, providing new theoretical support for interdisciplinary research. Full article

(This article belongs to the Special Issue Advances in Dynamics and Control of Fractional-Order Systems)

22 pages, 1656 KB

Open AccessArticle

Investigation into the Multiphase Product Distribution and Evolution During Biomass Pyrolysis Using Wheat Straw and Pine Sawdust

by Jishuo Li, Kaili Xu, Xiwen Yao and Xingyu Luo

Energies 2025, 18(20), 5397; https://doi.org/10.3390/en18205397 (registering DOI) - 13 Oct 2025

Abstract

Understanding the formation mechanisms of three-phase products during biomass pyrolysis is essential for optimizing thermochemical conversion and enhancing the efficient utilization of renewable resources. In this study, wheat straw (WS) and pine sawdust (PS) were selected as representative feedstocks to investigate the thermal decomposition behavior and evolution characteristics of gas, liquid (tar), and solid (char) products during pyrolysis. Thermogravimetric analysis and kinetic modeling revealed that PS exhibited higher activation energy (75.44 kJ/mol) than WS (65.63 kJ/mol), indicating greater thermal resistance. Tar yield increased initially and then declined with temperature, peaking at 700 °C (37.79% for PS and 32.82% for WS), while the composition shifted from oxygenated compounds to polycyclic aromatic hydrocarbons as temperature rose. FTIR analysis demonstrated that most functional group transformations in char occurred below 400 °C, with aromatic structures forming above 300 °C and stabilizing beyond 700 °C. Gas product evolution showed that WS produced higher CO and H₂ yields due to its composition, with CH₄ generated in relatively lower amounts. These findings provide insights into biomass pyrolysis mechanisms and offer a theoretical basis for targeted regulation of product distributions in bioenergy applications. Full article

(This article belongs to the Special Issue Advances in Bioenergy and Biofuel Conversion: Designs and Optimization)

18 pages, 2041 KB

Open AccessReview

Chiral Transition Metal Complexes Featuring Limonene-Derived Ligands: Roles in Catalysis and Biology

by Ghaita Chahboun, Mohamed El Hllafi, Eva Royo and Mohamed Amin El Amrani

Inorganics 2025, 13(10), 336; https://doi.org/10.3390/inorganics13100336 (registering DOI) - 13 Oct 2025

Abstract

Chiral coordination compounds are of growing interest due to their structural diversity and wide applicability. Besides chirality, alcohol and especially oxime-functionalized limonene derivatives confer water solubility, stability, and the appropriate reactivity to enable their use in asymmetric catalysis—such as allylic substitution, alkynylation, transfer hydrogenation, and selective C–C bond formation. Biologically, they have shown promising anticancer, antibacterial, and antibiofilm activity. This review presents an integrated overview of the synthesis, properties, and applications of chiral transition metal complexes featuring ligands derived from inexpensive, naturally occurring R- and S-limonene substrates, and explore their roles in catalysis and biological activity. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series in “Featuring Ligands and Their Applications in Coordination Chemistry”, 2nd Edition)

►▼ Show Figures

Figure 1

22 pages, 497 KB

Open AccessArticle

Trauma-Informed and Healing Architecture in Young People’s Correctional Facilities: A Comparative Case Study on Design, Well-Being, and Reintegration

by Nadereh Afzhool and Ayten Özsavaş Akçay

Buildings 2025, 15(20), 3687; https://doi.org/10.3390/buildings15203687 (registering DOI) - 13 Oct 2025

Abstract

This study investigates how trauma-informed and healing-centred architectural design is associated with rehabilitation and reintegration outcomes in young people’s correctional facilities. Drawing on international case studies, the analysis demonstrates that architecture is not a neutral backdrop but a contributing determinant within broader justice ecosystems. Trauma-informed environments are consistently linked to reductions in re-traumatisation and improvements in emotional regulation, while small-scale, community-oriented facilities are associated with enhanced skill development, autonomy, and reintegration potential. Culturally responsive designs that incorporate Indigenous practices and symbolic architecture are observed to support identity, resilience, and community belonging, underscoring the importance of cultural continuity in rehabilitation processes. In parallel, sustainable features such as biophilic design, renewable energy systems, and natural light are correlated with improvements in ecological performance and psychosocial well-being, indicating that sustainability and rehabilitation may be mutually reinforcing goals. Notably, the analysis highlights that supportive environments are also associated with staff well-being and institutional stability, underscoring the broader organisational benefits of healing architecture. The findings suggest that young people’s correctional facilities should not replicate adult prisons but instead provide safe, developmental, and culturally grounded spaces that respond to adolescents’ unique needs. This study contributes a novel conceptual model—the Trauma-Informed Healing Architecture (TIHA) framework—that integrates trauma-informed, cultural, and ecological design strategies within the Sustainable Development Goals (SDGs). The framework defines global standards as universal principles—safety, dignity, cultural responsiveness, and natural light—while remaining adaptable to local resources and justice systems. In this way, it provides internationally relevant yet context-sensitive guidance for young people’s correctional reform. Full article

(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

►▼ Show Figures

Figure 1

14 pages, 3067 KB

Open AccessArticle

The Phenomenon of Temperature Increase in Poland: A Machine Learning Approach to Understanding Patterns and Projections

by Anna Franczyk and Robert Twardosz

Appl. Sci. 2025, 15(20), 10994; https://doi.org/10.3390/app152010994 (registering DOI) - 13 Oct 2025

Abstract

This study presents an analysis of patterns in mean monthly air temperature increases in Poland using the deep learning model Neural Basis Expansion Analysis for Time Series (N-BEATS) algorithm. The dataset comprises mean monthly temperatures recorded between 1951 and 2024 at eight meteorological stations across Poland. The research was conducted in two phases. In the first phase, the 74-year period was divided into two distinct intervals: one characterized by relative temperature stability, and the other by a marked upward trend. In the second phase, the N-BEATS neural network was employed to extract temporal patterns directly from the data and to forecast future temperature values. The results confirm the capacity of machine learning methods to identify persistent climate trends and demonstrate their utility for long-term monitoring and prediction. Full article

(This article belongs to the Section Environmental Sciences)

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 (113,272)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI