Search Results (679)

Wettability-functional surfaces are crucial in both theoretical investigation and engineering applications. Compared to traditional micro/nano fabrication methods (such as ion etching, sol–gel, chemical vapor deposition, template techniques, and self-assembly), femtosecond laser processing has unique advantages, such as unmatched precision, flexible controllability, and material adaptability, widely used for the fabrication of wettability-functional surfaces. This paper systematically discusses the principle and advancement of femtosecond laser micro/nano processing in regulating surface wettability and analyzes the laser modulation mechanisms and structural design strategies for wettability-functional surfaces on various materials. Additionally, this paper reviews the practical applications of femtosecond laser-based wettability-functional surfaces in environmental engineering, aerospace, and biomedical fields, while highlighting the challenges and future directions for femtosecond laser processing of wettability-functional surfaces. Full article

This study presents a hybrid modeling framework synergizing process-based crop modeling with evolutionary optimization to reconcile yield sustainability with nitrogen management in arid cotton systems. Building upon the DSSAT-CROPGRO model’s demonstrated superiority over pure machine learning approaches in simulating nitrogen–crop interactions (calibrated with multi-year phenological datasets), we develop a genetic algorithm-embedded decision system that simultaneously optimizes nitrogen use efficiency (NUE) and economic returns. Field validations across contrasting growing seasons demonstrate the framework’s capacity to reduce nitrogen inputs by 15–20% while increasing profitability by 8–12% compared to conventional practices, without compromising yield stability. The tight coupling of mechanistic understanding with multi-objective optimization advances precision agriculture through two key innovations: (1) dynamic adaptation of fertilization strategies to both biophysical processes and economic constraints and (2) closed-loop integration of crop physiology simulations with evolutionary computation. This paradigm-shifting methodology establishes a new template for developing environmentally intelligent decision-support systems in water-limited agroecosystems. Full article

Nanostructured materials have garnered significant attention for their unique properties, such as the high surface area and enhanced reactivity, making them ideal for electrocatalysis. Among these, perovskite oxides, with compositional and structural flexibility, stand out for their remarkable catalytic performance in energy conversion and storage technologies. Their diverse composition and tunable electronic structures make them promising candidates for key electrochemical reactions, including the oxygen evolution reaction (OER), hydrogen evolution reaction (HER), and carbon dioxide reduction (CO₂RR). Nanostructured perovskites offer advantages such as high intrinsic activity and enhanced mass/charge transport, which are crucial for improving electrocatalytic performance. In view of the rapid development of nanostructured perovskites over past few decades, this review aims to provide a detailed evaluation of their synthesis methods, including the templating (soft, hard, colloidal), hydrothermal treatments, electrospinning, and deposition approaches. In addition, in-depth evaluations of the fundamentals, synthetic strategies, and applications of nanostructured perovskite oxides for OER, HER, and CO₂RR are highlighted. While progress has been made, further research is needed to expand the synthetic methods to create more complex perovskite structures and improve the mass-specific activity and stability. This review offers insights into the potential of nanostructured perovskite oxides in electrocatalysis and provides potential perspectives for the ongoing research endeavor on the nanostructural engineering of perovskites. Full article

Human–robot skill transfer enables robots to learn skills from humans and adapt to new task-constrained scenarios. During task execution, robots are expected to react in real-time to unforeseen dynamic obstacles. This paper proposes an integrated human–robot skill transfer strategy with offline task-constrained optimization and real-time whole-body adaptation. Specifically, we develop the via-point trajectory generalization method to learn from only one human demonstration. To incrementally incorporate multiple human skill variations, we encode initial distributions for each skill with Joint Probabilistic Movement Primitives (ProMPs) by generalizing the template trajectory with discrete via-points and deriving corresponding inverse kinematics (IK) solutions. Given initial Joint ProMPs, we develop an effective constrained optimization method to incorporate task constraints in Joint and Cartesian space analytically to a unified probabilistic framework. A double-loop gradient descent-ascent algorithm is performed with the optimized ProMPs directly utilized for task execution. During task execution, we propose an improved real-time adaptive control method for robot whole-body movement adaptation. We develop the Dynamical System Modulation (DSM) method to modulate the robot end-effector through iterations in real-time and improve the real-time null space velocity control method to ensure collision-free joint configurations for the robot non-end-effector. We validate the proposed strategy with a 7-DoF Xarm robot on a series of offline and real-time movement adaptation experiments. Full article

Low-energy ion bombardment (IB) has emerged as a promising, maskless nanofabrication tool for quasi-periodic nanoripples, marked by a high throughput and low cost. As templates, these IB-induced, self-organized surface nanoripples have shown potential for applications in diverse fields. However, the challenge of tailoring the ordering of these ripple patterns is preventing the widespread application of IB. Moreover, the enhancement of the ordering of these self-organized nanostructures involves the fundamental academic questions of nanoripple coupling (or superimposition) and guided self-organization. This review first focuses on the experimental progress made in developing representative strategies for the ordering enhancement of IB-induced nanoripples in terms of ion beams and targets. Second, we present our understanding of these developments from the perspectives of ripple superposition and guided self-organization. In particular, the basic conditions for ripple superposition under the non-conservation of mass are deduced based on the common features of the results from rocking bombardments of a single material and the bombardment of bilayer systems, providing insight into the mechanisms at play and deepening our understanding of these experimental observations. Finally, areas for future research are given, with the aim of improving ripple ordering from the viewpoints of ripple superimposition and guided self-organization. All this may re-stimulate interest in this field and will be of importance in advancing the academic research and practical applications of IB-induced nanopatterns. Full article

Wetlands are vital ecosystems that provide diverse ecosystem services. However, their degradation poses an environmental threat globally, impacting human society. Recognizing their economic importance amidst escalating degradation emphasizes the urgent need for wetland conservation. Wetland restoration emerges as a crucial strategy to recover lost acreage and ecosystem functions. Despite substantial investments in restoration, the success of these efforts remains uncertain. We addressed this gap by investigating temporal changes in biological communities in restored wetlands to assess restoration effectiveness on biodiversity conservation. Utilizing aquatic funnel traps and plot surveys, we monitored changes in community structure, species richness, and abundance of amphibians and reptiles. The results revealed nuanced responses in herpetofaunal communities to restoration age and habitat characteristics, including contrasting responses between taxa with the reptile diversity increasing while the amphibian diversity decreased with restoration age. Adult herpetofaunal richness and abundance were higher at the five-year post-restoration sites compared to one-year post-restoration, while larval amphibian richness and abundance were greater at the latter. Species–habitat associations were significantly pronounced among the adult herpetofauna, highlighting the complex interplay between environmental factors and biological communities. Restoration age and habitat types also exhibited significant influences on adult herpetofaunal community structure, whereas larval amphibian communities showed limited temporal turnover. Our findings challenge widely held notions, suggesting that prolonged post-restoration trajectories may not always lead to greater amphibian abundance and diversity. The temporal shifts in the reptile community structure we documented are indicative of habitat template development and ecological succession upon wetland restoration. We underscore the importance of considering both temporal and spatial heterogeneity in restoration planning to promote biodiversity and ecosystem function. Our contributions will help decode biological responses to wetland restoration efforts and guide future restoration efforts. We underscore the importance of long-term monitoring and adaptive management strategies to ensure restoration success in conserving biodiversity and ecosystem services. Full article

Layered sodium trititanate (Na₂Ti₃O₇) is a promising anode material for sodium-ion batteries due to its suitable charge/discharge plateaus, cost-effectiveness, and eco-friendliness. However, its slow Na⁺ diffusion kinetics, poor electron conductivity, and instability during cycling pose significant challenges for practical applications. To address these issues, we developed a template-free method to synthesize Na₂Ti₃O₇-C hollow microspheres. The synthesis began with polymerization-induced colloid aggregation to form a TiO₂–urea–formaldehyde (TiO₂-UF) precursor, which was then subjected to heat treatment to induce inward crystallization, creating hollow cavities within the microspheres. The hollow structure, combined with a conductive carbon matrix, significantly enhanced the cycling performance and rate capability of the material. When used as an anode, the Na₂Ti₃O₇-C hollow microspheres exhibited a high reversible capacity of 188 mAh g⁻¹ at 0.2C and retained 169 mAh g⁻¹ after 500 cycles. Additionally, the material demonstrated excellent rate performance with capacities of 157, 133, 105, 77, 62, and 45 mAh g⁻¹ at current densities of 0.5, 1, 2, 5, 10, and 20C, respectively. This innovative approach provides a new strategy for developing high-performance sodium-ion battery anodes and has the potential to significantly advance the field of energy storage. Full article

Hollow flower-like multi-metallic nanocrystals have attracted significant research attention due to their exceptional catalytic properties, which stem from their high surface area-to-volume ratio and abundant active sites. Nevertheless, conventional synthesis methods for noble metal nanocrystals typically involve complex procedures or require harsh reaction conditions. In this work, we developed a facile and environmentally benign strategy for fabricating hollow flower-shaped trimetallic nanocrystals at ambient temperature. Our approach employs AgCl nanocubes, derived from AgNO₃ and HAuCl₄, as self-sacrificing templates. Through ascorbic acid-mediated reduction of metal precursors, we successfully synthesized three distinct types of hollow flower-like nanocrystals: AuAgCu, AuAgPt, and AuAgPd. Comprehensive characterization confirmed the well-defined morphology and precise composition control of the as-prepared nanocrystals. The catalytic performance was systematically evaluated through in situ UV–vis spectroscopy monitoring of 4-nitrophenylthiophenol reduction, revealing the following activity trend: AuAgCu > AuAgPt > AuAgPd. This study not only provides a versatile platform for constructing sophisticated multi-metallic nanostructures but also offers valuable insights into the structure–activity relationship of complex catalysts. Full article

Hard carbon (HC) is considered to be a highly promising anode material for sodium-ion batteries. However, the synthesis conditions and pore structure regulation are still challenging for high-capacity sodium-ion storage. In this study, HCs using polyethylene glycol terephthalate (PET) as a carbon resource and ZnO as a nanopore template were synthesized and systematically investigated. By optimizing the additive amount of zinc gluconate, the starting material for ZnO, PET-derived HCs with a proper mesoporous structure were obtained. The as-prepared hard carbon demonstrated a high reversible capacity of 389.42 mAh·g⁻¹ at 20 mA·g⁻¹, with the plateau capacity accounting for 68%. After 75 cycles, the discharge capacity stabilized at 367.73 mAh·g⁻¹ with a retention ratio of 89.4%. The rate performance test indicated that a proper mesopore structure helped to improve the sodium-ion diffusion coefficient, effectively enhancing the charge–storage kinetics. This work provides a promising strategy for converting PET into valuable carbon materials for application in the field of renewable energy technology. Full article

As robotic systems become more integrated into our daily lives, there is growing concern about cybersecurity. Robots used in areas such as autonomous driving, surveillance, surgery, home assistance, and industrial automation can be vulnerable to cyber-attacks, which could have serious real-world consequences. Modern robotic systems face a unique set of threats due to their evolving characteristics. This paper outlines the SESAME project’s methodology for the automated security analysis of multi-robot systems (MRS) and the production of Executable Digital Dependability Identities (EDDIs). Addressing security challenges in MRS involves overcoming complex factors such as increased connectivity, human–robot interactions, and a lack of risk awareness. The proposed methodology encompasses a detailed process, starting from system description and vulnerability identification and moving to the generation of attack trees and security EDDIs. The SESAME security methodology leverages structured repositories like Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Attack Pattern Enumeration and Classification (CAPEC) to identify potential vulnerabilities and associated attacks. The introduction of Template Attack Trees facilitates modeling potential attacks, helping security experts develop effective mitigation strategies. This approach not only identifies, but also connects, specific vulnerabilities to possible exploits, thereby generating comprehensive security assessments. By merging safety and security assessments, this methodology ensures the overall dependability of MRS, providing a robust framework to mitigate cyber–physical threats. Full article

Currently, there is no publicly available dataset for the classification of potato pest and disease-related queries. Moreover, traditional query classification models generally adopt a single maximum-pooling strategy when performing down-sampling operations. This mechanism only extracts the extreme value responses within the local receptive field, which leads to the degradation of fine-grained feature representation and significantly amplifies text noise. To address these issues, a dataset construction method based on prompt engineering is proposed, along with a question classification method utilizing a gated fusion–convolutional neural network (GF-CNN). By interacting with large language models, prompt words are used to generate potato disease and pest question templates and efficiently construct the Potato Pest and Disease Question Classification Dataset (PDPQCD) by batch importing named entities. The GF-CNN combines outputs from convolutional kernels of varying sizes, and after processing with max-pooling and average-pooling, a gating mechanism is employed to regulate the flow of information, thereby optimizing the text feature extraction process. Experiments using GF-CNN on the PDPQCD, Subj, and THUCNews datasets show F1 scores of 100.00%, 96.70%, and 93.55%, respectively, outperforming other models. The prompt engineering-based method provides a new paradigm for constructing question classification datasets, and the GF-CNN can also be extended for application in other domains. Full article

Molecularly imprinted electrochemical sensors (MI-ECSs) are a significant advancement in analytical techniques, especially for water quality monitoring (WQM). These sensors utilize molecular imprinting to create polymer matrices that exhibit high specificity and affinity for target analytes. MI-ECSs integrate molecularly imprinted polymers (MIPs) with electrochemical transducers (ECTs), enabling the selective recognition and quantification of contaminants. Their design features template-shaped cavities in the polymer that mimic the functional groups, shapes, and sizes of target analytes, resulting in enhanced binding interactions and improved sensor performance in complex water environments. The fabrication of MI-ECSs involves selecting suitable monomeric units (monomers) and crosslinkers, using a target analyte as a template, polymerizing, and then removing the template to expose the imprinted sites. Advanced methodologies, such as electropolymerization and surface imprinting, are used to enhance their sensitivity and reproducibility. MI-ECSs offer considerable benefits, including high selectivity, low detection limits, rapid response times, and the potential for miniaturization and portability. They effectively assess and detect contaminants, like (toxic) heavy metals (HMs), pesticides, pharmaceuticals, and pathogens, in water systems. Their ability for real-time monitoring makes them essential for ensuring water safety and adhering to regulations. This paper reviews the architecture, principles, and fabrication processes of MI-ECSs as innovative strategies in WQM and their application in detecting emerging contaminants and toxicants (ECs and Ts) across various matrices. These ECs and Ts include organic, inorganic, and biological contaminants, which are mainly anthropogenic in origin and have the potential to pollute water systems. Regarding this, ongoing advancements in MI-ECS technology are expected to further enhance the analytical capabilities and performances of MI-ECSs to broaden their applications in real-time WQM and environmental monitoring. Full article

The prerequisite for the fusion processing of heterogeneous SAR images lies in high-precision image matching, which can be widely applied in areas such as geometric localization, scene matching navigation, and target recognition. This study proposes a method for high-precision matching of heterogeneous SAR images based on the combination of the single-scale ratio of an exponentially weighted averages (ROEWA) operator and angle-weighted gradient (RAWG). The method consists of the following three main steps: feature point extraction, feature description, and feature matching. The algorithm utilizes the block-based SAR-Harris operator to extract feature points from the reference SAR image, effectively combating the interference of coherent speckle noise and improving the uniformity of feature point distribution. By employing the single-scale ROEWA operator in conjunction with angle-weighted gradient projection, the construction of a 3D dense feature descriptor is achieved, enhancing the consistency of gradient features in heterogeneous SAR images and smoothing the search surface. Through the optimal feature construction strategy and frequency domain SSD algorithm, fast template matching is realized. Experimental comparisons with other mainstream matching methods demonstrate that the Root Mean Square Error (RMSE) of our method is reduced by 47.5% compared with CFOG, and compared with HOPES, the error is reduced by 15.4% and the matching time is reduced by 34.3%. The proposed approach effectively addresses the nonlinear intensity differences, geometric disparities, and interference of coherent speckle noise in heterogeneous SAR images. It exhibits robustness, high precision, and efficiency as its prominent advantages. Full article

In today’s business landscape, artificial intelligence (AI) plays a pivotal role in shopping processes and customization. As the demand for customization grows, virtual reality (VR) emerges as an innovative solution to improve users’ perception and decision making in virtual shopping experiences (VSEs). Despite its potential, limited research has explored the integration of contextual information and emotions in VR to deliver effective product recommendations. This paper presents CORES (context-aware emotion-driven recommendation system), a novel approach designed to enrich users’ experiences and to support decision making in VR. CORES combines advanced large language models (LLMs) and embedding-based context-aware recommendation strategies to provide customized products. Therefore, emotions are collected from social platforms, and relevant contextual information is matched to enable effective recommendation. Additionally, CORES leverages transformers and retrieval-augmented generation (RAG) capabilities to explain recommended items, facilitate VR visualization, and generate insights using various prompt templates. CORES is applied to a VR shop of different items. An empirical study validates the efficiency and accuracy of this approach, achieving a significant average accuracy of 97% and an acceptable response time of 0.3267s in dynamic shopping scenarios. Full article

Background: Mentorship has benefits for students and faculty, helping to support their professional development, connectedness, and career endeavors. While the value of mentorship programs is well documented in the literature, there is less practical guidance and few compiled resources to start a program. This paper reviews different mentorship practices in pharmacy education and provides a list of strategies to develop high-functioning mentorship programs or groups. Methods: A review of the literature was conducted through PubMed and other databases. If the titles and abstracts met the initial criteria for relevance to the topic, the complete article was reviewed in the context of the inclusion and exclusion criteria. Included articles focused on mentorship, mentorship programs, mentorship development, mentoring faculty or students, or mentoring in the workplace. Results: Twenty-three studies were included in the final review. Summaries and key points from the studies were reviewed and discussed. The advantages of mentorship programs include increased social connection, goal setting, and professional development. Challenges include increased time commitments and difficulty in determining objective markers of success. Critical components have been extracted from the literature, and key resources and templates have been provided to aid in mentorship program development. Conclusions: This review summarizes the pharmacy mentorship literature and provides user-friendly tables to quickly locate resources to build a mentorship program in pharmacy education. Full article