Search Results (5,787)

The combined effects of soil properties, zinc (Zn), and chloride ion (Cl⁻) concentrations on Zn distribution across soil fractions are poorly understood, even though zinc chloride (ZnCl₂) contamination in industrial soils is a major source of mobile Zn and poses significant environmental risks. This study aimed to (1) assess how the soil type, physicochemical properties, and Zn concentration affect Zn distribution in Community Bureau of Reference (BCR)-extracted fractions; (2) evaluate the impact of Cl⁻ on Zn mobility; and (3) develop predictive models for mobile and stable Zn fractions based on soil characteristics. Zn mobility was analyzed in 18 soils differing in Zn and Cl⁻, pH, specific surface area (SSA), organic matter (OM), and texture (sand, silt, clay (CLY)), using a modified BCR method. Zn fractions were measured by atomic absorption spectroscopy (AAS). Analysis of Covariance was used to assess Zn distribution across soil types, while Zn fractions were modeled using non-linear regression (NLR). The results showed that mobile Zn increased with the total Zn, and that the soil type and Zn levels influenced Zn distribution in soils contaminated with ZnCl₂ (Zn 304–2136 mg·kg⁻¹ d.m.; Cl⁻ 567–2552 mg·kg⁻¹; pH 3.5–7.5; CLY 11–22%; SSA 96–196 m²·g⁻¹; OM 0–4.8%). Although Cl⁻ enhanced Zn mobility, its effect was weaker than that of Zn. Predictive models based on the total Zn, SSA, and CLY accurately estimated Zn in mobile and stable fractions (R > 0.92), whereas the effects of the pH and OM, although noticeable, were not statistically significant. Full article

The rapid growth of wireless communication demands has led to heightened competition for limited spectrum resources, with traditional allocation methods proving insufficient to meet evolving needs. In response, DSA has emerged as a promising strategy, allowing secondary users to access underutilised portions of the spectrum, particularly in bands primarily allocated for satellite communication, such as the 3.4–4.2 GHz range. DSA offers a flexible solution by enabling the secondary use of the underutilised spectrum while protecting primary users like terrestrial FSS/SS. This paper surveys state-of-the-art DSA techniques and introduces the concept of DSUE to quantify real-time spectrum reuse effectiveness under coexistence constraints. Emphasis is placed on integrating FSS ground station parameters—such as location, antenna orientation, and sensitivity—into intelligent spectrum management frameworks. The review also evaluates ML/AI-driven resource allocation and interference mitigation approaches that enhance coexistence performance. By structuring a DSUE-aware environment, this study provides technical direction for harmonising terrestrial wireless mobile broadband and satellite systems, enabling more efficient, adaptive, and interference-aware spectrum sharing. Full article

Smart mobility has emerged as a transformative enabler for achieving the United Nations Sustainable Development Goals (SDGs), offering technological and systemic solutions to pressing urban challenges such as congestion, environmental degradation, accessibility, and economic inclusion. Realizing this potential, however, depends not only on technological maturity but also on robust education and capacity-building frameworks. This paper addresses two critical gaps: the absence of a systematic review of structured academic curricula, vocational training programs, and professional development pathways dedicated to smart mobility, and the lack of a formal approach to demonstrate how structured, research-oriented education can effectively bridge theory and practice. The review examines a wide spectrum of initiatives, including academic programs, industry training, challenge-based competitions, and community-driven platforms. The analysis shows significant progress in Europe and North America but also reveals important gaps, particularly the limited availability of structured initiatives in the Global South, the underrepresentation of accessibility and inclusivity, and the insufficient integration of governance, ethical AI, policy, and cybersecurity. A case study of the AI for Smart Mobility course, developed using a design science methodology, illustrates how research-oriented education can be operationalized in practice. Since 2020, the course has engaged hundreds of students and professionals, with project dissemination through the AI4SM Medium hub attracting more than 20,000 views and 11,000 reads worldwide. The findings highlight both the progress made and the persistent gaps in smart mobility education, underscoring the need for wider geographic reach, stronger emphasis on inclusivity and governance, and structured approaches that effectively link theory with practice. Full article

Human exposure to micro- and nanoplastics (MNPs) in the environment and their potential health effects are of growing public interest. Regarding water, that interest grows because multiple studies found MNPs in different matrices including tap and bottled water. We intended to (i) understand how MNPs enter freshwater systems and drinkable water, (ii) assess the evidence of human exposure to MNPs in water, and (iii) identify data gaps to support the determination of potential health effects. We searched the literature and selected studies via rigorous inclusion criteria, analyzed the data assessing the reliability of findings, and identified data gaps associated with human exposure to MNPs in water. The lack of standard sampling and analytical methods for testing MNPs in water constitutes a barrier to make accurate comparisons. The diverse analytical methods to fully characterize MNPs led to different findings in samples of similar matrices. Current drinking and wastewater treatment systems are not designed to remove MNPs. However, efforts to enhance the precision and accuracy of MNPs’ characterization and their removal by treatment systems are promising. Therefore, addressing data gaps could produce reliable data for conducting exposure and risk assessments, protect our communities, and control the mobility of MNPs to minimize exposures. Full article

Rapid urbanization in developing countries has widened the gap between urban and rural development, due to inefficient land markets and weak institutional systems in rural areas. China’s innovative “Urban–rural Construction Land Linkage” policy was designed to address this imbalance by encouraging rural land consolidation and creating a transferable development rights mechanism. While this approach has shown potential in improving the utilization efficiency of existing construction land and continuously supplying urban development space, concerns remain about its actual benefits to villagers and rural development, with some arguing it disrupts traditional livelihoods and favors government interests over rural needs. To respond to this debate, this study investigates two core questions: first, does China’s transferable land development rights (TDR) program genuinely improve rural welfare as intended; second, why does the theoretically preferred self-organized governance model sometimes fail in practice? To address these research questions, this paper develops a new analytical framework combining the IAD framework of Ostrom with the hierarchical institutional framework of Williamson to examine three implementation approaches in China’s TDR implementation: government-dominated, market-invested, and self-organized models. Based on case studies, surveys, and interviews across multiple regions, this study reveals distinct strengths and weaknesses in each approach in improving villagers’ lives. Government-dominated projects demonstrate strong resource mobilization but limited community participation. Market-based models show efficiency gains but often compromise equity. While self-organized initiatives promise greater local empowerment, they frequently face practical challenges including limited management capacity and institutional barriers. Furthermore, this study identifies the preconditional institutional environment necessary for successful self-organized implementation, including clear land property rights, financial support, and technical assistance. These findings advance global understanding of how to combine efficiency with fair outcomes for all stakeholders in land governance, which is particularly relevant for developing countries seeking to manage urban expansion while protecting rural interests. Full article

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° ($\sigma =0.8°$, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, $\sigma =12$ ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics. Full article

With the advancement of mobile communications, technologies based on high-element-count antenna systems—such as massive Multiple Input Multiple Output (massive MIMO)—are playing an increasingly important role in enhancing network capacity. However, they introduce new challenges in the measurement and evaluation of electromagnetic field (EMF) exposure. This study presents a detailed, laboratory-based methodology for assessing EMF exposure in cellular systems using Single Input Single Output (SISO) and MIMO technologies. To address the limitations of traditional exposure assessment techniques—particularly under the conditions introduced by 5G and active antenna systems—a shielded test environment with directional antennas was developed and applied across lowband and midband frequency ranges (700–2100 MHz). Downlink electromagnetic power density was measured under standardized modulation, coding, and bandwidth settings for both SISO and MIMO configurations. The results show that MIMO technology does not lead to a significant increase in EMF exposure compared to SISO, with average differences remaining below 1 dB. Moreover, in lower-frequency bands, massive MIMO systems can ensure the required user capacity at significantly lower transmission power, resulting in more than 15 dB reductions in EMF exposure. These findings confirm the potential of massive MIMO to enhance network performance while reducing the level of electromagnetic exposure. Full article

In this paper, we introduce an Unmanned Aerial Vehicle (UAV) deployment design with a connectivity constraint for UAV-assisted communication networks. In such networks, multiple UAVs are collaboratively deployed in the air to form a network that realizes efficient relay communications from ground mobile clients to the base station. We consider a scenario where ground clients are widely distributed in a target area, with their population significantly outnumbering available UAVs. The goal is to enable UAVs to collect and relay all client data to the base station by continuously moving while preserving end-to-end connectivity with the base station. To achieve this, we propose two dynamic UAV deployment methods: genetic algorithm-based and modified $\epsilon$-greedy algorithm-based methods. These methods are designed to efficiently collect data from mobile clients while maintaining UAV connectivity, based solely on local information about nearby client positions. Through numerical experiments, we demonstrate that the proposed methods dynamically form UAV-assisted networks to efficiently and rapidly collect client data transmitted to the base station. Full article

The widespread application of multi-agent robotic systems in domains such as agricultural collaboration and automation has accentuated the challenges faced in seeking to achieve rapid synchronization and sustain high-performance control under conditions where velocity states remain unmeasurable. To relieve these challenges, a synchronization control framework is proposed for multi-agent systems, employing non-uniform sampling communication protocols. Initially, a state-variable transformation is applied to construct a composite Lyapunov function that integrates a sampling term. An explicit relation is then derived between the communication interval and the global exponential synchronization rate, thereby establishing a theoretical foundation for the design of non-periodic sampling-based control strategies. Second, a linear-state feedback controller is introduced, which balances convergence speed with the limited frequency of information updates, ensuring asymptotic stability even under prolonged sampling intervals. Third, a velocity observer was designed based on Immersion and Invariance ($I\&I$) theory to solve the problem of unmeasurable velocity states, ensuring the exponential convergence of the estimation error. Finally, the simulation results demonstrate that, with sampling intervals of $h\in [0.03,0.08]$ s, the position errors $\parallel q_i-q_{d,i}\parallel$ of all six robots converge to below ${10}^{-2}$ within 7 s; meanwhile, the velocity estimation errors decay to nearly zero within 7 s, confirming the effectiveness of the proposed method. The main contributions of this work can be summarized as follows: (1) a new $I\&I$ velocity observer is tailored for discrete-time communication; (2) rigorous proof of global exponential convergence is provided via a composite Lyapunov energy function; (3) a reproducible MATLAB simulation framework is presented that enhances both the verifiability and applicability of the proposed approach. Full article

Urban growth and rising vehicle usage have intensified congestion, accidents, and environmental impact, exposing the limitations of traditional traffic management systems. This study introduces a dual-incident simulation framework to investigate the potential of Vehicle-to-Everything (V2X) technologies in enhancing urban mobility. Using the Eclipse MOSAIC platform integrated with SUMO, a realistic network in Iași, Romania, was modeled under single- and dual-incident scenarios with three V2X penetration levels: 0%, 50%, and 100%. Unlike prior works that focus on single-incident cases or assume full penetration, our approach evaluates cascading disruptions under partial adoption, providing a more realistic transition path for mid-sized European cities. Key performance indicators, i.e., average speed, vehicle density, time loss, and waiting time, were calculated using mathematically defined formulas and validated across multiple simulation runs. Results demonstrate that full V2X deployment reduces average time loss by 18% and peak density by more than 70% compared to baseline conditions, while partial adoption delivers measurable yet limited benefits. The dual-incident scenario shows that V2X-enabled rerouting significantly mitigates cascading congestion effects. These contributions advance the state of the art by bridging microscopic vehicle dynamics with network-level communication modeling, offering quantitative insights for phased V2X implementation and the design of resilient, sustainable intelligent transportation systems. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article

Background and Objectives: Aging is associated with heterogeneous declines in cognitive and physical functions, yet little is known about how baseline functional levels influence the effectiveness of intervention programs. This study analyzed the effects of a psychomotor intervention program on cognitive and physical functions in community-dwelling older adults, considering their baseline functional levels. Materials and Methods: Fifty-one participants (75.4 ± 5.6 years) were divided into an experimental group, which underwent the intervention, and the control group. The experimental group was further divided into lower-functioning (LFG) and higher-functioning (HFG) subgroups based on baseline assessments. Participants were assessed at baseline, 24-week post-intervention, and after a 12-week follow-up. Results: Significant improvements were observed in both experimental subgroups, particularly LFG, in processing speed, executive functions, reaction time, attention, lower-body strength, balance, and mobility (p < 0.05). Cognitive gains persisted post-follow-up, while physical gains were reversed, especially in LFG (p < 0.05). Effect sizes ranged from medium to large in both lower- and higher-functioning groups. Discussion: The intervention improved cognitive and physical functions in both lower- and higher-functioning groups. Although older and less educated, the lower-functioning group showed greater gains but also more decline after follow-up. These findings emphasize that older adults with diverse baseline functional levels can improve substantially, highlighting the need for tailored psychomotor interventions to maximize benefits and address individual variability. The study was registered at ClinicalTrials.gov (NCT03446352). Full article

Le Lectier pears (hereafter, Pears) are graded based on appearance, requiring farmers to inspect tens of thousands in a short time before shipment. To assist in this process, a grading support system was developed. The existing cloud-based system used mobile devices to capture images and analyzed them with Convolutional Neural Networks (CNNs) and texture-based algorithms. However, communication delays and algorithm inefficiencies resulted in a 30 s execution time, posing a problem. This paper proposes an edge computing-based system using Mask R-CNN for appearance deterioration detection. Processing on edge servers reduces execution time to 5–10 s, and 39 out of 51 Pears are accurately detected. Full article

Introduction: The change in environmental and social context during the COVID-19 pandemic affected daily activities of people with spinal cord injury (SCI), their interactions within the community, and, consequently, their social participation during the first wave of the pandemic. However, there is little information about the changes in social participation as the pandemic evolved in Canada. Objective: Our aim was to explore the change in the social participation of adults with SCI after the first two years of the COVID-19 pandemic in Canada. Methods: A follow-up from a previous study exploring the social participation of adults with SCI living during the first wave was conducted eight months later (second wave). Social participation was measured using the Assessment of Life Habits (LIFE-H 4.0) and Measure of Quality of the Environment (MQE) among 18 adults with SCI. Results: Participants reported increases between both waves of COVID-19 in some life habit categories, including mobility, personal care and health, nutrition, and recreation. New environmental factors were identified as facilitators, including the increased availability of businesses in the community. Conclusion: These findings indicate that people with SCI experienced greater realization and satisfaction with certain life habits. Although most barriers and facilitators showed little or no change between the two waves, the reduction in environmental barriers and the increase in facilitators may have contributed to improved social participation as the pandemic progressed. Full article