Search Results (405)

This paper proposes an optimal-sliding-mode-based adaptive dynamic programming (ADP) master–slave synchronous control strategy for the actuator saturation and performance constraints that AUVs face in dynamic recovery. First, by introducing the sliding-mode function into the value function to optimize the state error and its derivative simultaneously, the convergence speed is significantly improved. Second, by designing the performance constraint function to directly map the sliding-mode function, the evolution trajectory of the sliding-mode function is constrained, ensuring the steady-state and transient characteristics. In addition, the hyperbolic tangent function (tanh) is introduced into the value function to project the control inputs into an unconstrained policy domain, thereby eliminating the phase lag inherent in conventional saturation compensation schemes. Finally, the requirement for initial stability is relaxed by constructing a single-critic network to approximate the optimal control policy. The simulation results show that the proposed method has significant advantages in terms of the position and attitude synchronization error convergence rate, steady-state accuracy, and control signal continuity compared with the conventional ADP method. Full article

Background and Objectives: Early ambulation and timely detection of postoperative complications are cornerstones of colorectal Enhanced Recovery After Surgery (ERAS) programmes, yet the traditional bedside checks performed every 4–8 h may miss clinically relevant deterioration. The consumer wearables boom has spawned a new generation of wrist- or waistband-mounted sensors that stream step count, heart-rate and temperature data continuously, creating an opportunity for data-driven early-warning strategies. No previous systematic review has focused exclusively on colorectal surgery. Methods: Three databases (PubMed, Embase, and Scopus) were searched (inception—1 May 2025) for prospective or retrospective studies that used a consumer-grade or medical-grade wearable to collect objective physical-activity or vital-sign data during the peri-operative period of elective colorectal resection. Primary outcomes were postoperative complication rates, length-of-stay (LOS) and 30-day readmission. Two reviewers screened records, extracted data and performed risk-of-bias appraisals with ROBINS-I or RoB 2. Narrative synthesis was adopted because of the heterogeneity in devices, recording windows and outcome definitions. Results: Nine studies (n = 778 patients) met eligibility: one randomised controlled trial (RCT), seven prospective cohort studies and one retrospective analysis. Five studies relied on step-count metrics alone; four combined step-count with heart-rate or skin-temperature streams. Median wear time was 6 d (range 2–30). Higher day-1 step count (≥1000 steps) was associated with shorter LOS (odds ratio 0.63; 95% CI 0.45–0.84). Smart-band–augmented ERAS pathways shortened protocol-defined LOS by 1.1 d. Pre-operative inactivity (<5000 steps·day⁻¹) and low “return-to-baseline” activity on the day before discharge independently predicted any complication (OR 0.39) and 30-day readmission (OR 0.60 per 10% increment). A prospective 101-patient study that paired pedometer-recorded ambulation with daily lung-ultrasound scores found fewer pulmonary complications when patients walked further (Spearman r = –0.36, p < 0.05). Conclusions: Continuous, patient-worn sensors are feasible and yield clinically meaningful data after colorectal surgery. Early postoperative step-count trajectories and activity-derived recovery indices correlate with LOS, complications and readmission, supporting their incorporation into digital ERAS dashboards. Standardised outcome definitions, open algorithms for signal processing and multicentre validation are now required. Full article

This paper investigates the dispersion of atmospheric pollutants in urban environments using a fractional-order convection–diffusion-reaction model with dynamic line sources associated with vehicle traffic. The model includes Caputo fractional time derivatives and Riesz fractional space derivatives to account for memory effects and non-local transport phenomena characteristic of complex urban air flows. Vehicle trajectories are generated stochastically on the road network graph using Dijkstra’s algorithm, and each moving vehicle acts as a mobile line source of pollutant emissions. To reflect the daily variability of emissions, a time-dependent modulation function determined by unknown parameters is included in the source composition. These parameters are inferred by solving an inverse problem using synthetic concentration measurements from several fixed observation points throughout the area. The study presents two main contributions. Firstly, a detailed numerical analysis of how fractional derivatives affect pollutant dispersion under realistic time-varying mobile source conditions, and secondly, an evaluation of the performance of the proposed parameter estimation method for reconstructing time-varying emission rates. The results show that fractional-order models provide increased flexibility for representing anomalous transport and retention effects, and the proposed method allows for reliable recovery of emission dynamics from sparse measurements. Full article

Driven by both natural and anthropogenic factors, farmland abandonment and recultivation constitute complex and widespread global phenomena that impact the ecological environment and society. In the Inner Mongolia Yellow River Basin (IMYRB), a critical tension lies between agricultural production and ecological conservation, characterized by dynamic bidirectional transitions that hold significant implications for the harmony of human–nature relations and the advancement of ecological civilization. With the development of remote sensing, it has become possible to rapidly and accurately extract farmland changes and monitor its vegetation restoration status. However, mapping abandoned farmland presents significant challenges due to its scattered and heterogeneous distribution across diverse landscapes. Furthermore, subjectivity in questionnaire-based data collection compromises the precision of farmland abandonment monitoring. This study aims to extract crop phenological metrics, map farmland abandonment, and recultivation dynamics in the IMYRB and assess post-transition vegetation changes. We used Landsat time-series data to detect the land-use changes and vegetation responses in the IMYRB. The Farmland Abandonment and Recultivation Extraction Index (FAREI) was developed using crop phenology spectral features. Key crop-specific phenological indicators, including sprout, peak, and wilting stages, were extracted from annual MODIS NDVI data for 2020. Based on these key nodes, the Landsat data from 1999 to 2022 was employed to map farmland abandonment and recultivation. Vegetation recovery trajectories were further analyzed by the Mann–Kendall test and the Theil–Sen estimator. The results showed rewarding accuracy for farmland conversion mapping, with overall precision exceeding 79%. Driven by ecological restoration programs, rural labor migration, and soil salinization, two distinct phases of farmland abandonment were identified, 87.9 kha during 2002–2004 and 105.14 kha during 2016–2019, representing an approximate 19.6% increase. Additionally, the post-2016 surge in farmland recultivation was primarily linked to national food security policies and localized soil amelioration initiatives. Vegetation restoration trends indicate significant greening over the past two decades, with particularly significant increases observed between 2011 and 2022. In the future, more attention should be paid to the trade-off between ecological protection and food security. Overall, this study developed a novel method for monitoring farmland dynamics, offering critical insights to inform adaptive ecosystem management and advance ecological conservation and sustainable development in ecologically fragile semi-arid regions. Full article

Motor impairments significantly affect individuals’ ability to perform activities of daily living, reducing autonomy and quality of life. In response to this, robot-assisted rehabilitation has emerged as an effective and practical solution, enabling controlled limb movements and supporting functional recovery. This study presents the development of an upper-limb exoskeleton designed to assist rehabilitation by integrating neurophysiological signal processing and real-time control strategies. The system incorporates a proportional–derivative (PD) controller to execute cyclic flexion and extension movements based on a sinusoidal reference signal, providing repeatability and precision in motion. The exoskeleton integrates a brain–computer interface (BCI) that utilizes electroencephalographic signals for therapy selection and engagement enabling user-driven interaction. The EEG data extraction was possible by using the UltraCortex Mark IV headset, with electrodes positioned according to the international 10–20 system, targeting alpha-band activity in channels O1, O2, P3, P4, Fp1, and Fp2. These channels correspond to occipital (O1, O2), parietal (P3, P4), and frontal pole (Fp1, Fp2) regions, associated with visual processing, sensorimotor integration, and attention-related activity, respectively. This approach enables a more adaptive and personalized rehabilitation experience by allowing the user to influence therapy mode selection through real-time feedback. Experimental evaluation across five subjects showed an overall mean accuracy of 86.25% in alpha wave detection for EEG-based therapy selection. The PD control strategy achieved smooth trajectory tracking with a mean angular error of approximately 1.70°, confirming both the reliability of intention detection and the mechanical precision of the exoskeleton. Also, our core contributions in this research are compared with similar studies inspired by the rehabilitation needs of stroke patients. In this research, the proposed system demonstrates the potential of integrating robotic systems, control theory, and EEG data processing to improve rehabilitation outcomes for individuals with upper-limb motor deficits, particularly post-stroke patients. By focusing the exoskeleton on a single degree of freedom and employing low-cost manufacturing through 3D printing, the system remains affordable across a wide range of economic contexts. This design choice enables deployment in diverse clinical settings, both public and private. Full article

Rio Grande do Sul accounts for 22% of Brazil’s losses from extreme events, mainly droughts and floods. The state had the second-worst economic performance in the country between 2000 and 2022. This study quantifies the impacts of major events such as droughts, floods, and the COVID-19 pandemic on economic sectors. Three methods were applied: structural breaks, recovery time, and sector-specific loss estimates. The analysis covers 15,365,123 observations of monthly invoice values from January 2017 to April 2025, involving 357,001 companies paying value-added tax on consumption. The results indicate that negative structural breaks occurred in a few sectors, which account for 5% of the state’s economy. The recovery time followed a similar trajectory between droughts and COVID-19. On average, sectors took 12 months to recover from COVID-19, compared with about 6 months for natural hazards. The sectors most impacted were travel, artistic activities, machinery and equipment industry, accommodation, and domestic services. Aggregated loss estimates were highest during the COVID-19 pandemic (−8%), followed by floods (−1%) and droughts (0%). The results indicate remarkable overall short-run economic resilience. Furthermore, sectors such as information technology, consulting, business services, and healthcare performed exceptionally well. Full article

Rapid regional development and intensified human activities increasingly disturb ecosystems, posing substantial threats to the integrity of large-scale ecological zones. As a World Natural Heritage site and a crucial national ecological zone, the Zhangjiajie Scenic Area plays a pivotal role in China’s environmental conservation efforts. To comprehensively assess tourism ecological security in the Scenic Area and strengthen the scientific basis for resource management and policymaking, this study developed a multi-dimensional ecological security evaluation system covering 2010–2024, incorporating dynamic changes in perturbation, reaction, and governance. Using entropy weight–TOPSIS and coupling coordination models, combined with obstacle degree analysis, we examined the temporal trajectory of ecological security and analyzed its underlying driving mechanisms. The study also examined factors influencing the sustainable development of the ecosystem. The results indicate the following: (1) Tourism ecological security in the Scenic Area followed a V-shaped trajectory of “rapid degradation—steady recovery—impact and rebound.” It declined sharply to an unsafe level between 2010 and 2014, steadily recovered from 2015 to 2019, briefly dropped in 2020, and then rebounded, reaching a peak evaluation value of 0.519 in 2024. (2) The co-evolution of perturbation, reaction, and governance subsystems has matured: their coupling coordination degree has increased annually and has remained at the level of “intermediate coordination” since 2020. The reaction subsystem plays a central role, serving as a bridge between perturbation and governance. (3) The driving factors exhibit a phased evolutionary pattern of “elements—facilities—structure—function.” Cultivated land area, total road mileage, and artificial afforestation area constitute the main long-term constraints. This research provides important insights for strengthening ecological security and sustainability in the Scenic Area while advancing regional ecosystem development. It also offers valuable guidance for ecological security management and policymaking in similar nature reserves. Full article

Multi-UAV cooperative missions demand millisecond-level coordination across three key resource dimensions—battery energy, wireless bandwidth, and onboard computing power—where traditional Min or linearly weighted schedulers struggle to balance safety with efficiency. We propose a prediction-enhanced two-stage T-norm–Choquet–OWA resource aggregator. First, an LSTM-EMA model forecasts resource trajectories 3 s ahead; next, a first-stage T-norm (min) pinpoints the bottleneck resource, and a second-stage Choquet–OWA, driven by an adaptive interaction measure $\varphi$, elastically compensates according to instantaneous power usage, achieving a “bottleneck-first, efficiency-recovery” coordination strategy. Theoretical analysis establishes monotonicity, tight bounds, bottleneck prioritization, and Lyapunov stability, with node-level complexity of only $O\left(1\right)$. In joint simulations involving 360 UAVs, the method holds the average round-trip time (RTT) at 55 ms, cutting latency by 5%, 10%, 15%, and 20% relative to Min, DRL-PPO, single-layer OWA, and WSM, respectively. Jitter remains within 11 ms, the packet-loss rate stays below 0.03%, and residual battery increases by about 12% over the best heuristic baseline. These results confirm the low-latency, high-stability benefits of the prediction-based peak-shaving plus two-stage fuzzy aggregation approach for large-scale UAV swarms. Full article

Objectives: Sex-based disparities in COVID-19 outcomes are well-documented, with men experiencing greater acute severity and women showing increased vulnerability to post-viral syndromes. However, longitudinal immunometabolic trajectories in vaccinated individuals remain underexplored. In this study, sex-based differences in long-term metabolic, endocrine, renal, cardiovascular, and inflammatory responses were investigated among vaccinated individuals recovering from SARS-CoV-2 infection. Methods: This retrospective single-center cohort study included 426 adults (199 females, 227 males) with PCR-confirmed symptomatic COVID-19 and at least two vaccine doses. Serial assessments were conducted at baseline, 18-, 24-, and 30-month post-infection. Parameters included fasting glucose, HbA1c, lipid profile, thyroid function, renal markers, CRP, D-dimer, fibrinogen, troponin, and hematologic indices. Statistical analyses assessed longitudinal changes and sex-stratified correlations. Results: Fasting glucose and HbA1c levels significantly declined over time, more prominently in males. Glucose correlated with age and BMI only in females. Lipid levels remained largely unchanged, although males had higher baseline triglycerides. Females showed rising TSH levels and persistently lower free T3; males exhibited higher creatinine, urea, and troponin levels throughout. Inflammatory markers declined significantly in both sexes, with males displaying higher CRP and troponin, and females showing sustained fibrinogen elevation and a temporary lymphocyte surge. D-dimer was elevated in females at the 30-month point. Conclusions: Sex-specific physiological recovery patterns were evident among vaccinated COVID-19 survivors. Males exhibited earlier metabolic and cardiac alterations, while females had more persistent endocrine and inflammatory shifts. These findings underscore the need for sex-tailored long-term monitoring strategies prioritizing early metabolic and cardiac screening in men and prolonged immunoendocrine surveillance in women. Full article

GNSS-Inertial integration brings great potential to detect and mitigate the effect of erroneous (spoofed) GNSS data. When a trajectory of an airplane diverges from (or is inconsistent with) inertial data, the integrated system may detect this erroneous GNSS trajectory and may be able to maintain navigation integrity by rejecting this data. A GNSS-Aided Inertial System can provide both self-contained detection of a GNSS spoofing event as well as mitigation, where mitigation is hard to achieve globally with other commercial aviation systems relying on good ground system coverage. This paper provides an overview of the newly developed Inertial Spoofing Monitor for aviation grade navigation systems, which was designed to detect multiple simultaneous erroneous (spoofed) satellite measurements. The Inertial Spoofing Monitor was then thoroughly tested, and simulations were performed to evaluate and demonstrate the detection, mitigation, and recovery capability of the spoofing monitor. The performance validation followed the process prescribed by Appendix Q of the RTCA DO-384 MOPS (Minimum Operation Performance Standard). The results show great detection, mitigation, and recovery performance of the developed Inertial Spoofing Monitor, but also indicate constraints regarding the assumed sensor error model. Full article

In the framework of global efforts to mitigate climate change and in alignment with the “Dual Carbon” objectives, the construction sector, a fundamental cornerstone of the national economy, has garnered significant attention concerning its development and carbon emissions. This study collected data from the construction sector across 30 Chinese provinces (including autonomous regions and municipalities) to develop an evaluation index system for assessing high-quality development. The random forest algorithm was utilized to assess the levels of high-quality development, whereas the carbon emission factor approach was used to quantify emissions at the provincial level. Subsequently, a coupling coordination model was employed to analyze the interrelationship between development levels and carbon emissions. Key findings indicate the following: (1) China’s construction sector has shown sustained improvement in high-quality development; however, significant regional disparities persist, with eastern provinces (e.g., Beijing, Jiangsu) outperforming their central and western counterparts (e.g., Guangxi, Guizhou). (2) Carbon emissions from the construction sector exhibited an M-shaped fluctuation pattern, characterized by an increase from 2013 to 2014, followed by a decline in 2015, a subsequent recovery from 2016 to 2019, a transient decrease in 2020, and an eventual rebound in 2021 and 2022. Spatially, the developed coastal provinces of Jiangsu and Zhejiang exhibited significantly higher carbon emissions compared to regions such as Hainan and Ningxia. (3) The coupling coordination degree indicated a gradual increase from 0.50 to 0.55 (mean values); however, 78% of provinces remained at the “barely coordinated” level (0.5 ≤ D < 0.6), leading to a notable spatial distribution that is marked by elevated values in the eastern and southern regions, while exhibiting reduced values in the western and northern areas. Regional divergence was observed through four characteristic evolutionary trajectories: eastern China exhibited a U-shaped recovery, western China maintained linear growth, central China experienced inverted V-shaped fluctuations, and northeast China displayed W-shaped oscillations. Full article

Although the chemical and petrochemical (C&P) industry is a cornerstone of the Italian and European economies, it is also an intensive energy consumer and a high emitter of greenhouse gases. Europe’s decarbonisation trajectory is often examined through the lens of individual countries, as key factors such as industry status, structure and resource accessibility may differ across nations. This study specifically examines the Italian C&P industry, with an emphasis on the basic chemicals sector. It reviews the current status of the production processes, technologies, energy consumption and carbon footprint in the sector, along with advancements towards decarbonisation. Key decarbonisation technologies are reviewed, highlighting their current use or research and development status. The primary barriers to the adoption of prospective decarbonisation solutions (e.g., increased costs and need for additional renewable capacity and infrastructure development) are discussed. While the Italian C&P sector has adopted strategies to enhance energy efficiency and waste recovery and utilisation, it is uncertain whether the industry will be able to meet the 2050 carbon emissions targets by relying on these two decarbonisation approaches alone. A combination of additional decarbonisation technologies, including electrification, green hydrogen and carbon capture utilisation and storage, will likely be necessary. However, technical challenges exist due to the maturity level of these technologies and their applicability to highly integrated processes. Appropriate, timely policy support will be crucial to aiding the green transition of the Italian C&P sector while safeguarding its significant role in the Italian economy. Full article

Understanding how main plant functional strategies scale from species to communities is critical for guiding restoration in tropical disturbed areas by unsustainable livestock grazing; yet, the patterns and drivers of functional trait space along successional trajectories remain poorly understood. Here, we investigated functional trait space using principal component analyses (PCAs) based on eight traits related to leaf, stem, and seed morphology across 226 tree species and 33 forest communities along a chronosequence of natural regeneration following cattle ranching abandonment in deforested landscapes of the Colombian Amazon. We identified three species-level functional axes—namely, the ‘Structural–Reproductive Allocation Axis’, the ‘Mechanical Support and Tissue Investment Axis’, and the ‘Leaf Economics Axis’—and two community-level axes: the ‘Colonization–Longevity Axis’ and the ‘Persistence–Acquisition Axis’. These axes aligned with the life-history strategies of short-lived pioneers, long-lived pioneers, and old-growth species, and reflected their relationships with key environmental drivers. Community-level functional composition reflected species-level patterns, but was also shaped by soil properties, microclimate, and tree species richness. Forest age and precipitation promoted conservative strategies, while declining soil fertility suggested a decoupling between above- and belowground recovery. Functional richness and divergence were highest in mid-successional forests dominated by long-lived pioneers. Our findings highlight the role of environmental and successional filters in shaping functional trait space and emphasize the value of functionally diverse communities. Particularly, our results indicate that long-lived pioneers (LLP) such as Astrocaryum chambira Burret and Pouteria campanulata Baehni, with traits like large height, intermediate wood density, and larger seed size, represent ideal candidates for early enrichment strategies due to their facilitation roles in succession supporting restoration efforts in regenerating Amazonian forests. Full article

Globally, the mutations around forest landscapes continue to draw significant scientific interest, despite fragmented evidence on the socio-ecological outcomes linked to this process. This knowledge gap is evident in the Congo Basin—one of the world’s major ecosystems. To contribute towards addressing the knowledge gap, this study analyzed two decades of forest landscape mutations and the socio-ecological transformation-cum-outcomes linked to the process in Cameroon. A mixed-methods approach was employed, combining remote sensing-based land use/land cover (LULC) analysis (using multi-date Landsat imagery at 30 m resolution) with household surveys involving 100 randomly selected forest-dependent households across three forest blocks: Ebo, Ndokbou, and Makombé for ground truthing. Survey data were analyzed using descriptive statistics and combined spatial analysis to reveal the following. Firstly, forest cover has significantly increased within the 20-year period; this involved a 104.01% increase between 2004 and 2014, and an additional 47.27% between 2014 and 2024. In that vein, agricultural land declined by more than 20%, whereas settlement and water bodies increased by 226.4% and 376.2%, respectively. Secondly, forest landscape mutations in the Yabassi Forest Area were primarily driven by a convergence of social (notably population growth at 57% and livelihood diversification), economic (agricultural expansion and timber exploitation), political (tenure ambiguity and development-driven land conversion), and environmental (climate variability at 36% and ecological restoration efforts) forces. These interwoven drivers shaped the land use change process, revealing how the human-environment feedback defines landscape trajectories in complex and non-linear ways. Thirdly, while the ecological outcomes of forest mutations were largely positive—with significant gains in forest cover, the social outcomes were skewed towards the negative. Communities experienced both improvements in livelihoods and infrastructure (66%), but also faced land conflicts (67%), the loss of traditional access (69%), and resource-based insecurity. By applying the socio-ecological systems (SES) framework, this study provides novel insights on how governance, ecological processes, and human behavior co-evolve in forest landscapes. The findings do not only edify the SES framework but also challenge the mainstream position about forest decline by highlighting areas of recovery. The evidence informs adaptive forest governance processes in the Congo Basin and similar contexts. Further research should investigate the institutional and adaptive mechanisms that influence these dynamics across the Congo Basin. Full article

Managed freshwater replenishment is a significant restoration method in the Yellow River Delta. However, their impacts on plankton communities, which are key bioindicators of aquatic ecosystem health and sensitive to the changes in the environment, remain poorly quantified. In this study, we conducted plankton surveys across wetlands subjected to freshwater restoration durations ranging from 5 to 22 years. We assessed shifts in phytoplankton and zooplankton community structure, biomass, diversity, and their relationships with environmental drivers. Results revealed distinct temporal dynamics: phytoplankton biomass and diversity followed a “U-shaped” trajectory (initial decline followed by recovery), while zooplankton biomass decreased but diversity increased with restoration duration. Canonical Correspondence Analysis (CCA) and Partial Least Squares Path Modeling (PLS-PM) identified salinity (Cl⁻, SO₄²⁻) and dissolved nitrate (NO₃⁻) as primary environmental controls for both groups. Cyanobacteria dominated phytoplankton biomass initially but declined with restoration age, while rotifers replaced copepods as the dominant zooplankton taxon over time. These findings demonstrate that freshwater restoration restructures plankton communities through salinity-mediated physiological constraints and altered nutrient availability, with implications for ecosystem function and adaptive management in anthropogenically influenced deltas. Full article