Search Results (1,855)

Against the backdrop of a technological revolution centered on green and low-carbon development, the deep integration of digitalization and greening has become a core engine for high-quality progress. Moving beyond linear perspectives of environmental governance, this study constructs tripartite evolutionary game models to dissect the strategic interactions among government, enterprises, and consumers. Focusing on the institutional context of e-commerce, we examine how platform-enabled transparency mechanisms (e.g., blockchain traceability and carbon labeling) shape these interactions through key parameters: greenwashing detection (θ), premium loss coefficient (η), and information screening cost (C_D). The analysis reveals that the long-term trajectory is fundamentally determined by the intrinsic economic viability of corporate transformation. Government intervention acts as an equilibrium selector, influencing the speed of convergence, while product value (consumer utility and premium) and platform transparency determine the sustainability of the equilibrium. Critically, the tripartite model shows that the optimal outcome—full enterprise transformation and consumer adoption—can be achieved without sustained government intervention when product fundamentals are sufficiently attractive. This demonstrates the potential for market self-regulation to sustain digital–green synergy. The study makes three contributions: it captures the full tripartite feedback loop, reveals the saturation effect of policy intensity, and embeds platform transparency mechanisms into an evolutionary framework. The findings reframe the government’s role as a temporary enabler and position e-commerce platforms as key governance intermediaries, offering a theoretical basis for adaptive governance strategies in digital commerce. Full article

The large-scale generation of industrial waste salts (IWSs) across sectors such as coal chemical, pesticide, pharmaceutical, and dye manufacturing has raised increasing environmental and regulatory concerns. These IWSs often exhibit complex physicochemical profiles—featuring high concentrations of inorganic salts, persistent organic pollutants, and trace heavy metals—that pose significant challenges for both safe disposal and resource recovery. This review provides a comprehensive and pollutant-oriented overview of industrial waste salts, focusing on their sector-specific characteristics, dominant contaminant types, and tailored treatment strategies. Removal pathways for organic matter (e.g., thermal decomposition, advanced oxidation) and inorganic impurities (e.g., precipitation, ion exchange) are systematically analyzed, followed by technical pathways for salt separation based on crystallization and membrane processes. Resource utilization routes for major salt components, particularly NaCl and Na₂SO₄, are critically assessed in terms of technical feasibility, impurity tolerance, and end-use compatibility. The emergence of reclaimed salt quality standards and sector-specific impurity thresholds reflects a paradigm shift from purity-based to performance-based reuse evaluation. Finally, the review highlights future priorities including adaptive impurity control, downstream-specific salt grading, and enforceable regulatory frameworks to ensure the safe, scalable, and circular deployment of reclaimed salts in industrial systems. This study supports the coordinated advancement of control technologies and reuse standards, enabling the transformation of waste salts from environmental liabilities to secondary resources. Full article

Advanced Air Mobility (AAM) includes innovative aviation technologies and services that could alter how people and goods are transported. However, future AAM growth and potential regional integration are uncertain and influenced by a range of factors. In this paper, we report findings from expert interviews (n = 35) and a scenario planning workshop (n = 32 stakeholders), conducted between August 2024 and July 2025, to explore potential alternative futures for AAM at the San Francisco International Airport (SFO) and the greater San Francisco Bay Area. We applied a two-axis framework: regulatory environment (supportive vs. restrictive) and economic conditions (vibrant vs. stagnant). Building on this, we developed four plausible scenarios for the 2025 to 2030 and post-2030 time horizons. We apply the SPELT (social, political, economic, legal, technological) framework to assess cross-cutting drivers, tensions, and indicators across the four scenarios based on two timeframes, i.e., 2025 to 2030 and post-2030. Our analysis of the scenarios reveals that regulatory clarity and macroeconomic conditions are key influencers that define the pace and scale of AAM growth, while community impacts (e.g., noise), public acceptance, and infrastructure availability are constraints. These factors largely determine whether technical readiness can translate into scaled deployment. Cross-cutting themes across all of the scenarios consistently shape the outcomes: (1) equity and community acceptance strongly influence political feasibility; (2) SFO and other airports can serve dual roles as conveners and practical enablers but face risks of stranded assets; and (3) flexible, modular infrastructure and incremental investment strategies reduce uncertainty for SFO and other Bay Area airports and public agencies. Together, the findings suggest that while the future of AAM is uncertain, policy and planning responses can assist airports, local governments, and other public agencies in preparing for potential developments. Full article

Vehicle-to-grid (V2G) technology has emerged as a key enabler for coupling large-scale electric vehicle (EV) deployment with the operation of smart energy systems. By allowing bidirectional power and information exchange between EVs and the grid, V2G transforms EVs from passive loads into distributed energy resources capable of supporting grid flexibility, reliability, and renewable energy integration. However, the practical realization of V2G remains challenged by technical complexity, system coordination, user participation, and regulatory constraints. This paper presents a comprehensive review of V2G integration from a system-level perspective. Rather than focusing solely on individual technologies, the review examines how V2G is embedded within smart energy systems, emphasizing the interactions among EVs, aggregators, grid operators, energy markets, and end users. Key enabling technologies, including bidirectional charging, aggregation mechanisms, communication frameworks, and data-driven control strategies, are discussed in relation to their system-level roles and limitations. The impacts of V2G on grid operation, energy management, and market participation are analyzed, with particular attention to reliability, battery lifetime, and user trust. Furthermore, this review identifies critical open issues that hinder large-scale deployment, spanning infrastructure readiness, standardization, economic incentives, and cybersecurity. Emerging application scenarios, such as building-integrated V2G, fleet-based services, and artificial intelligence (AI) supported coordination, are also discussed to illustrate potential evolution pathways. By synthesizing technological developments with system-level impacts and unresolved challenges, this paper aims to provide a structured reference for researchers, system planners, and policymakers seeking to advance the integration of V2G into future smart energy systems. Full article

Cultivated lettuce (Lactuca sativa L.) is a major leafy crop and an emerging model for functional genomics within the Asteraceae family, supported by high-quality reference genomes and efficient transformation systems. Although CRISPR/Cas technology offers powerful opportunities for crop improvement, editing efficiency depends on optimized construct architecture and reliable guide RNA (gRNA) validation. However, a rapid platform for evaluating CRISPR reagents in lettuce is still lacking. Here, we developed an efficient hairyroot-based system to accelerate CRISPR/Cas genome editing optimization in L. sativa. Four Agrobacterium rhizogenes strains were compared for hairy root induction in two cultivars, ‘Saladin’ and ‘Osiride’, identifying strain ATCC15834 as the most effective based on transformation frequency and root production. Using this platform, we evaluated multiple CRISPR construct configurations, including alternative promoters for nuclease and gRNA expression. A plant-derived promoter combined with At-pU6-26 variant significantly improved editing efficiency. As a proof of concept, we targeted LsHB2, the putative ortholog of Arabidopsis thaliana ATHB2, a key regulator of the shade avoidance response using SpCas9, SaCas9, and LbCas12a nucleases. The system enabled rapid genotyping and quantitative indel profiling. Overall, this workflow provides a robust framework for efficient guide selection and construct optimization in lettuce genome editing. Full article

Telerehabilitation—the remote delivery of rehabilitation services—is undergoing a paradigm shift with the convergence of immersive virtual reality (VR) and wearable biosensor technologies. This perspective article outlines a vision for home-based motor and cognitive rehabilitation that is engaging, personalized, and data-driven. We describe how immersive VR environments (for example, simulations of home settings or supermarkets) coupled with wearable sensors can address current challenges in rehabilitation by increasing patient motivation, enabling real-time biofeedback, and supporting remote clinician supervision. Gamification mechanisms and rich sensory feedback in VR are highlighted as key strategies to enhance user engagement and adherence to therapy. We discuss conceptual innovations such as multi-sensor data integration, dynamic difficulty adaptation, and AI-driven personalization of exercises, derived from recent research and our development experience, and consider their potential benefits for patients with neuro-cognitive-motor impairments (e.g., stroke, Parkinson’s disease, and multiple sclerosis). Implementation scenarios for home-based therapy are presented, emphasizing scalability, standardized digital metrics for monitoring progress, and seamless involvement of clinicians via telehealth platforms. We also critically examine the current limitations of VR and telehealth rehabilitation and how an integrative model could overcome these barriers. More specifically, this perspective defines the engineering requirements of a closed-loop VR-based telerehabilitation framework, including multimodal data synchronization, calibration, signal-quality management, interpretable adaptive control, digital biomarker validation, and practical strategies to improve accessibility, privacy, and scalability in home-based neurological rehabilitation. Full article

This systematic review analyzes how the introduction and progressive deployment of 5G networks have influenced the evolution of telemedicine between 2014 and 2024, focusing on their impact on performance, accessibility, and the feasibility of advanced clinical applications across the pre-COVID-19, COVID-19, and post-COVID-19 periods. The review was conducted in accordance with PRISMA guidelines and included publications retrieved from SCOPUS, PubMed, and Web of Science using a PICO-based search strategy. Studies were selected based on predefined inclusion and exclusion criteria, and extracted data included clinical parameters, network characteristics such as bandwidth and latency, geographic setting, and type of telemedicine service. A total of 45 studies met the inclusion criteria, with most published between 2020 and 2024. The most frequently reported applications were telediagnosis, particularly robotic ultrasound, followed by telesurgery and teleconsultation. The low latency enabled by 5G networks supported complex telesurgical procedures over distances exceeding 5000 km, while in ultra-remote areas, hybrid solutions combining 5G and fiber-optic networks were often adopted to ensure stable connections. The integration of robotic platforms and AI-based tools further enhanced the precision and reliability of remote procedures. Overall, 5G technology has significantly advanced telemedicine by enabling real-time, high-quality care over long distances, improving access to specialist services and supporting more equitable and efficient digital healthcare delivery, particularly in underserved regions. Full article

The deep integration of the Internet of Things (IoT) and blockchain technology enables emerging applications in multi-party collaboration and trusted data sharing. However, the scalability constraints of blockchain networks remain a major bottleneck when handling high-frequency interactions in IoT–blockchain systems. Sharding addresses this challenge by partitioning the blockchain network into parallel sub-networks. Nevertheless, it introduces significant coordination overhead for cross-shard transactions. Among mitigation strategies, Broker-based mechanisms (e.g., BrokerChain) have attracted increasing attention for their efficiency in handling cross-shard communication by reducing verification overhead and communication latency. Despite these advantages, existing research typically treats the Broker group as a fixed configuration, neglecting the impact of Broker selection on system performance. To bridge this gap, this paper proposes the Accumulative Activity–Temporal Liveness Broker Selection Strategy (AT-BSS) to optimize cross-shard transaction processing in sharded IoT–blockchains. Specifically, we formally characterize the Accumulative Activity and Temporal Liveness of accounts in the account–transaction network and use these two metrics to identify accounts that maximize transaction-aggregation efficiency. We implement AT-BSS on the BlockEmulator platform and evaluate it against two baselines, namely, ABChain and BrokerChain. Under different settings of the number of Brokers (BrokerNum), number of shards (ShardNum), transaction arrival rate (InjectSpeed), and maximum block size (MaxBlockSize), AT-BSS consistently outperforms both baselines in terms of Transactions Per Second (TPS), Transaction Confirmation Latency (TCL), and Cross-shard Transaction Ratio (CTX). Compared with ABChain, AT-BSS achieves up to 15.5% higher TPS and reduces TCL and CTX by up to 80.2% and 28.7%, respectively. AT-BSS yields more pronounced results over BrokerChain, with TPS improvements of up to 229% and reductions of up to 97.7% in TCL and 80.5% in CTX. Full article

The diversification of craft beer styles has stimulated interest in innovative yeast-driven strategies to enhance sensory complexity while maintaining process robustness and stylistic integrity. In this context, non-Saccharomyces yeasts represent promising biotechnological tools for modulating fermentation performance and flavor development in brewing systems. This study evaluated the application of Lachancea thermotolerans and Torulaspora delbrueckii in the production of a Porter-style beer using sequential inoculation with Saccharomyces cerevisiae. All fermentations were conducted in triplicate from a wort with an original gravity of 1042. The final alcohol content ranged from 4.82 to 4.99% (v/v), and apparent attenuation varied between 84.1 and 88.9%, with no significant differences among treatments (p > 0.05). Color (92–94 European Brewery Convention (EBC) and bitterness (~18 International Bitterness Units (IBU) remained within Porter-style parameters across all fermentations. Total acidity ranged from 0.19 to 0.21% (lactic acid equivalents), while volatile acidity was significantly higher in the L. thermotolerans treatment (0.55 g L⁻¹) compared with the control (0.22 g L⁻¹) (p < 0.05). Sequential inoculation influenced early fermentation kinetics and modulated selected sensory attributes. Quantitative Descriptive Analysis (n = 18 panelists) indicated higher aroma intensity and foam quantity in beers produced with L. thermotolerans, whereas T. delbrueckii was associated with moderate increases in foam persistence. The roasted character and overall stylistic perception remained stable across treatments. These findings indicate that sequential inoculation with selected non-Saccharomyces yeasts enables measurable sensory differentiation in dark beer matrices without compromising fermentative performance or stylistic integrity. The results support their controlled integration as technological tools for sensory innovation in Porter-style beers. Full article

Alcoholic fermentation in winemaking is a complex bioprocess governed by physicochemical parameters such as temperature, density, pH, CO₂ and redox potential, which critically affect yeast metabolism and wine quality. This review provides an integrated analysis of fermentative dynamics and emerging sensorization technologies, highlighting how their combined implementation enables real-time monitoring and advanced control in precision enology. Advances in conventional physicochemical sensors, spectroscopic techniques (NIR/MIR/UV-Vis) and non-conventional devices (e-noses, electronic tongues) integrated into IoT platforms enable continuous data acquisition, overcoming traditional manual sampling limitations. Predictive modeling, including kinetic models, machine learning approaches (e.g., Random Forest, XGBoost) and model predictive control (MPC/NMPC), supports anomaly detection, optimization of enological interventions and energy-efficient thermal management, while virtual sensors based on Kalman filters improve the estimation of non-measurable states (e.g., biomass, ethanol kinetics). Despite current challenges in calibration and interoperability, these innovations foster sustainable and reproducible winemaking under climate variability and pave the way for digital twins and semi-autonomous fermentation systems. Full article

The rapid advancement of digital and mobile technologies has reshaped the educational landscape, fostering the adoption of interactive and learner-centered methodologies. Among these, immersive technologies such as Augmented Reality (AR), when coupled with next-generation wireless communication systems, hold the potential to revolutionize knowledge acquisition and student engagement. In this paper, we present the design and development of an AR-based educational tool specifically oriented to teaching concepts of fifth-generation (5G) mobile networks. The tool provides a real-time interactive visualization of 3D network components on mobile devices, enabling learners to explore 5G NSA/SA architectures in an accessible manner with real-world environments through mobile devices and their integrated cameras. The application was developed using Blender for 3D modeling and Unity as the rendering engine, incorporating the Vuforia SDK for marker-based AR tracking, and it was deployed on the Android operating system. Unlike traditional static approaches, the proposed solution enables learners to explore complex network architectures and key functionalities of 5G in an interactive and accessible manner. To assess its perceived effectiveness, quantitative surveys were conducted with both university and high school students, focusing on usability, engagement, and perceived learning outcomes. Results indicate that the tool is user-friendly, enhances motivation, and supports conceptual understanding as perceived by participants of 5G technologies. These findings highlight the potential of AR, supported by advanced wireless networks, as a pedagogical strategy to improve STEM education and foster technological literacy in the era of digital transformation. Full article

(1) Background: Robotics and advanced control architectures are increasingly central to the development of precision agriculture (PA), supporting automated, efficient, and data-driven farm management. This review offers a comprehensive analysis of scientific literature on robotic control systems applied to PA, focusing on technological progress, methodological approaches, and emerging research trends. (2) Methods: A systematic review was conducted according to PRISMA guidelines, combined with a bibliometric analysis using VOSviewer to examine term co-occurrences, thematic clusters, and topic evolution over time. Publications indexed in Scopus between 1976 and 2025 were analyzed. (3) Results: Results reveal a sharp growth in publications after 2010, with a strong acceleration from 2015 onward, reflecting advances in autonomous systems and the integration of artificial intelligence, sensor technologies, and distributed software frameworks. Three principal clusters emerged: algorithmic and control methods (e.g., neural networks, path tracking, simulation); sensing and infrastructure technologies (e.g., LiDAR, SLAM, IMU, ROS, deep learning-based perception); and agronomic applications, including crop monitoring, irrigation, yield estimation, and farm management. Citation trends indicate a shift from foundational control theory to AI-driven solutions. (4) Conclusions: Overall, control architectures are evolving toward modular, scalable, and interoperable systems enabling autonomous decision-making in complex agricultural environments. Full article

After major disruptive events, particularly natural and human-made disasters, community leaders face the challenge of rebuilding societal infrastructure and managing the allocation of funds, which can affect the duration of recovery periods. Decision-makers must quickly determine how to allocate financial resources while minimizing population distress. Conventional methods of assessing damage and evaluating relief requirements fall short of meeting the urgent recovery needs after a disaster, potentially leading to negative effects on communities, such as involuntary relocation and neighborhood gentrification. The study evaluates current methods and technologies to propose a new approach that leverages low-cost consumer drones and modern image analysis techniques to support initial damage assessments and track recovery progress, thereby promoting the dynamic allocation of limited resources. Using low-cost drone imagery enables rapid, cost-effective data collection and dynamic analysis through iterative reviews during the disaster response and recovery phases that can adjust baseline disaster funding allocations. The study investigates the potential of temporary blue tarp roofs (“blue roofs”) as a metric for recovery progress during the 2020 tornado in Middle Tennessee and conducts an R-squared and error analysis. The goal of this research is to evaluate an affordable and efficient data analysis method (e.g., modern image analysis; artificial intelligence; low-cost drones) that can improve post-disaster resource allocation and inform decision-making for governmental and planning officials. Full article

Remote sensing has become a cornerstone of modern agricultural monitoring, addressing the dual challenges of increasing production while ensuring environmental sustainability. Based on a conceptual framework developed over the past decade, key application areas include yield estimation, phenology, stress assessment (e.g., drought), crop mapping, and land-use change detection. In Central Europe, regionally specific conditions such as fragmented land ownership, small and irregular plots, and high climate variability shape these applications. Annual field crops, such as cereals, oilseeds, maize, and forage crops dominate production and represent the primary focus of monitoring efforts. Optical data from Sentinel-2 are effective for mapping crop types and analyzing phenology, especially when dense time series are available. However, persistent cloud cover during critical growth phases limits the effectiveness of optical approaches, prompting the integration of radar data from Sentinel-1. Multi-sensor strategies increase the robustness of classification and temporal continuity, supporting monitoring under adverse conditions. Reliable reference data from systems such as the Land Parcel Identification System enables parcel-level validation and facilitates object-oriented analyses in line with management needs. Future developments will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data. As climate change intensifies drought frequency and yield variability, remote sensing will play a pivotal role in enabling near-real-time monitoring and decision support within the evolving landscape of digital agriculture ecosystems. The aim of this review article is to provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments. Full article

Reconfigurable intelligent surface (RIS) technology is a promising enabler for 6G communication systems due to its ability to reconfigure wireless propagation environments. However, as a passive device, RIS requires significant pilot overhead for accurate channel estimation. Moreover, the integration of RIS with multiple-input multiple-output (MIMO) systems further exacerbates power consumption and hardware costs. To address these challenges, this paper investigates RIS-assisted millimeter-wave (mmWave) MIMO systems with low-resolution analog-to-digital converters (ADCs). Exploiting the inherent sparsity of mmWave channels and considering the distortion introduced by low-resolution quantization, we propose a compressive sensing (CS)-based channel estimation scheme. Furthermore, to mitigate the effects of angular leakage, we introduce an energy capture orthogonal matching pursuit (ECOMP) algorithm. Simulation results demonstrate that the proposed scheme not only improves channel estimation accuracy but also reduces pilot overhead and power consumption, while maintaining enhanced stability in high signal-to-noise ratio (SNR) regimes. Full article