Search Results (1,836)

In densely vegetated and topographically complex mountainous areas, the applicability of SAR data for potential landslide hazard identification depends not only on whether slopes are visible to the radar, but also on whether stable interferometric coherence can be preserved under vegetation and terrain constraints. To clarify the applicability differences between L-band Lutan-1 and C-band Sentinel-1 in such environments, this study focused on Hanyuan County, Sichuan Province, China. Ascending and descending SAR images acquired by the two satellite systems from 2024 to 2025 were processed using stacking-based Interferometric Synthetic Aperture Radar (Stacking-InSAR) and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to extract regional deformation anomalies and time-series deformation characteristics of representative landslides. DEM, LiDAR, optical imagery, fractional vegetation cover (FVC) derived from Sentinel-2, and field investigation data were further integrated to establish a comparative framework linking geometric visibility, interferometric coherence, and landslide identification results. The results show that both Lutan-1 and Sentinel-1 provided favorable geometric observation conditions after combining ascending and descending tracks, with joint visibility proportions of 98.48% and 97.76%, respectively, indicating limited differences in geometric coverage within the study area. However, at a unified grid scale, the mean coherence and valid grid-cell proportion of Lutan-1 reached 0.564 and 72.49%, respectively, substantially higher than those of Sentinel-1, which were 0.320 and 24.26%. As FVC increased, coherence decreased for both datasets, but Lutan-1 maintained higher coherence in densely vegetated areas, suggesting stronger adaptability to vegetation-induced decorrelation. Based on integrated interpretation of multi-source remote sensing data, 77 potential landslide hazards were identified in the study area, including 74 detected by Lutan-1, 17 detected by Sentinel-1, and 14 jointly detected by both datasets. Comparisons of representative landslides further show that Lutan-1 provided a higher density of valid deformation points in densely vegetated and small-scale landslides, with deformation patterns corresponding well to slope geomorphic boundaries and local deformation zones. Sentinel-1, with its higher temporal sampling density, can provide complementary information for time-series verification and multi-source cross-validation of key landslides. These results indicate that Lutan-1 is more suitable for spatial identification of potential landslide hazards in densely vegetated, topographically complex mountainous areas, while the joint use of Lutan-1 and Sentinel-1 can better balance landslide identification detail and time-series monitoring continuity. Full article

Accurate and smooth path tracking is important for autonomous tracked agricultural robots operating in greenhouse-like environments. Existing adaptive look-ahead pure-pursuit methods mainly adjust the look-ahead distance according to vehicle speed or path geometry, while the influence of time-varying localization reliability has not been sufficiently considered. This study proposes a noise-aware adaptive pure-pursuit controller that combines Extended Kalman Filter (EKF) estimation with causal Savitzky–Golay (SG) endpoint smoothing. A bounded look-ahead law is designed by jointly considering normalized vehicle speed, lateral error, path curvature, and an innovation-derived localization-noise indicator. Numerical simulations were conducted on straight, circular, S-shaped, and U-shaped reference paths under prescribed localization disturbances. Under the 0.5 m positional-noise condition, the proposed method achieved an root mean square error (RMSE) of 0.087 m and an angular-velocity root mean square (RMS) of 0.28 rad/s, compared with 0.112 m and 0.36 rad/s, respectively, for conventional fixed-look-ahead pure pursuit. Compared with proportional-integral-derivative (PID), Stanley, model predictive control (MPC), and conventional pure-pursuit controllers, the proposed method provides a favorable balance between tracking accuracy and control smoothness. It also has better computational efficiency than MPC while retaining the low-computational-burden advantage of geometric control. In the sensitivity analysis, the relative RMSE increase from 0.1 to 0.8 m was 36.5% for the proposed method and 103.4% for conventional pure pursuit. These results indicate that the proposed lightweight noise-aware control strategy can improve tracking accuracy, control smoothness, and tolerance to localization disturbances under the specified numerical conditions, providing a practical design reference for low-speed greenhouse agricultural robots. Full article

Carbon dioxide (CO₂) enrichment is an important means of increasing crop productivity in protected cultivation. However, local canopy CO₂ concentration in Chinese solar greenhouses is jointly affected by gas release, pipeline transport, and ventilation disturbances, making fixed-parameter proportional–integral–derivative (PID) control unable to simultaneously achieve rapid tracking, low overshoot, and fast disturbance recovery. This study developed a CO₂ enrichment system comprising controlled thermal decomposition of ammonium bicarbonate, condensation and water scrubbing, near-canopy delivery, and programmable logic controller (PLC)-based closed-loop control, and proposed a dynamic-target fuzzy PID (DT-FPID) strategy. Step-response tests were used to establish a first-order-plus-dead-time model linking heater duty cycle to local canopy CO₂ concentration, followed by fixed-target tracking, rule-based dynamic-target execution, and short-term ventilation-disturbance recovery tests in a local validation zone of a Chinese solar greenhouse. Relative to fixed-parameter PID, DT-FPID showed approximately 68–79% lower maximum overshoot and approximately 35–70% shorter ±20 ppm precision settling time (T₂₀) in simulation. At 600 ppm, the ±5% settling time was approximately 71% shorter, whereas at 800 and 1000 ppm it was broadly comparable to PID. In the greenhouse experiments, each controller–target combination included three independent runs. Based on descriptive comparisons of group means, DT-FPID showed approximately 47–49% lower mean maximum overshoot, approximately 36–40% shorter mean settling time, and approximately 77–80% shorter mean ventilation-disturbance recovery time; its mean maximum overshoot and settling time were also lower than those of conventional fuzzy PID. All three dynamic-target field runs completed the prescribed switches among the 600, 800, and 1000 ppm target levels. These results support control performance only under the short-term local validation conditions of this study; they are not used to determine physiologically or economically optimal CO₂ concentrations or to extrapolate whole-greenhouse uniformity or long-term production effects. Full article

Sustainable marine economic development depends on the balanced integration of economic, social, and environmental dimensions. However, empirical evidence on the current degree of coordination among these dimensions remains limited, particularly in maritime countries. This study assesses the Coupling Coordination Degree (CCD) among these three dimensions of sustainable marine economic development in 15 Indonesian coastal provinces selected based on a proportion of coastal villages exceeding the national average and availability of complete 2024 data. Using subsystem performance scores derived from a previously published Grey Relational Analysis (GRA) index, the study evaluates how balanced the current coordination state is across provinces. The findings show that higher coordination is associated not only with higher subsystem performance but also with the relative balance among dimensions. Provinces with relatively balanced development tend to achieve higher coordination, whereas strong performance in a single dimension does not necessarily lead to a well-coordinated development system. Conversely, high coordination may also arise from uniformly low performance across all dimensions, indicating that coordination and development performance represent distinct characteristics of sustainability rather than interchangeable concepts. These findings demonstrate that CCD complements conventional performance-based assessments by capturing the degree of integration among multiple development dimensions. The study contributes to the sustainable marine development literature by providing a more comprehensive framework for evaluating multidimensional sustainability and offers insights for designing integrated, place-based blue economy policies that better align economic growth, social inclusion, and environmental conservation. Full article

The reuse of post-mining infrastructure for pumped-storage hydropower may reduce new underground construction while supporting the repurposing of decommissioned mines. This study presents a site-specific preliminary technical and pumping-energy assessment of an underground pumped-storage system using Budryk Shaft II as the lower reservoir. The assessment integrated shaft geometry, hydraulic conditions, turbine–generator selection, pressure-pipeline configuration, structural adaptation, hydraulic isolation, and staged water return. A working water volume of 12,000 m³ was adopted. The proposed generation unit comprises a vertical Pelton turbine operating at a gross design head of 900 m, a rated net head of 837.81 m, and a discharge of 0.71 m³/s. The rated turbine output is 5287 kW, and the turbine is coupled to a 6.3 kV synchronous generator. The three-stage pumping calculation yielded energy demands of 7.037, 19.600, and 37.371 MWh, giving a total of 64.008 MWh. These values are calculation-based estimates derived from the listed nominal pump capacities and powers using a simplified proportional power–flow assumption. At the rated turbine output, the calculated generation time of 4.695 h corresponds to 24.822 MWh of mechanical energy at the turbine shaft. Because verified generator-efficiency data are unavailable, the generated electrical energy and electrical round-trip efficiency cannot be determined exactly. The ratio of turbine-shaft energy to the calculated pumping-energy demand gives an upper-bound energy-return indicator of approximately 38.8%. Further work must verify pump operating points, generator performance, hydraulic transients, structural integrity, shaft sealing, auxiliary-energy demand, and the complete hydraulic connection to the upper reservoir. Full article

Lower-limb rehabilitation exoskeletons have emerged as a promising complementary technology to conventional therapy, enabling repetitive, intensive, and personalized gait training. However, achieving accurate trajectory tracking while maintaining robustness against parametric uncertainties, external disturbances, and unpredictable human–robot interaction remains a significant control challenge due to the highly nonlinear dynamics of coupled human–exoskeleton systems. This paper presents an experimental performance comparison of five control strategies for gait rehabilitation exoskeletons, including a classical proportional–integral–derivative (PID) controller, a model-based proportional–derivative controller with gravity compensation (PD+G), a computed torque sliding mode controller (CT-SMC), a computed torque–super-twisting sliding mode controller (CT–ST-SMC) and a hybrid backstepping–super-twisting sliding mode controller (BS–ST-SMC). All the controllers were implemented on the same lower-limb rehabilitation exoskeleton under identical operating conditions. The experimental results demonstrate that the proposed BS–ST-SMC architecture outperforms classical and traditional robust approaches, particularly in mitigating chattering and managing human–robot interaction uncertainties. Specifically, the BS–ST-SMC achieved the highest tracking precision with a mean squared position error (MSEp) of $1.32\times {10}^{-3}{\mathrm{rad}}^2$ and effectively synchronized with the user by reducing the phase lag to just 4.22° at the knee joint. Their overall performance was evaluated using the following metrics: mean squared position error (MSEP), mean squared velocity error (MSEv), peak error, phase lag, jerk index, peak torque, and peak power. Full article

Coumarins are heterocyclic compounds with diverse biological activities and potential applications as alternative acaricidal agents. In this study, nine coumarin derivatives were synthesized through a solvent-free Pechmann reaction using Preyssler heteropolyacid as a reusable catalyst under green chemistry conditions. The synthesized compounds were evaluated against adult females of Rhipicephalus (Boophilus) microplus using the adult immersion test. Survival analysis was performed using the Cox proportional hazard model, while molecular docking studies were carried out on triosephosphate isomerase (TIM). In addition, post hoc analysis of the structures predicted to be most active by the QSAR model suggested an association between higher predicted activity, the presence of hydroxyl groups, and the polarity of C7 side chains, although the topological descriptor identified by the model does not admit a direct physicochemical interpretation. The results showed that hydroxylated coumarins, particularly those substituted at C5 and C7, exhibited the highest acaricidal activity, with IC₅₀ values between 7.24 and 8.78 mg/mL. Docking analysis suggested plausible interactions with residues located at the TIM interface cavity, mainly Lys-112, Asn-65, and Glu-77. The QSAR model indicated that hydroxyl substitution and side-chain polarity contributed positively to activity. This study integrates Preyssler heteropolyacid-catalyzed green synthesis of coumarins with Cox proportional hazard survival modeling, TIM molecular docking, and QSAR analysis within a single acaricidal evaluation against R. (B.) microplus, providing a multi-pronged framework for prioritizing structural modifications in future coumarin-based acaricide development. Full article

Accurate actuator-space tracking is an important engineering requirement for repeatable motion delivery by parallel rehabilitation mechanisms, but controller performance should also be assessed under configuration-dependent dynamics and modeling uncertainty. This study evaluates a bounded fuzzy-supervised proportional–integral–derivative (FSPID) controller for a three-chain 3STC+S parallel rehabilitation mechanism. CAD-derived inverse kinematics generated the three prismatic-joint reference trajectories, while closed-loop behavior was simulated using a Simscape Multibody model including rigid-body mass and inertia properties, gravity, closed-loop constraints, and external force/moment loading. Three fixed-gain PID loops formed the baseline. The FSPID supervisor used zero-order Sugeno inference to adjust proportional and derivative gains within prescribed bounds, while the integral action remained fixed and was implemented with a leaky integrator. Under the nominal 1° trajectory at 0.2 Hz, FSPID reduced mean root-mean-square error (RMSE), maximum absolute error, and integral absolute error (IAE) by 0.25%, 0.37%, and 0.21%, respectively. Under sustained external loading, maximum-error reductions reached 9.33–10.20%, with mean actuator-wise peak-force changes within approximately ±2%. Additional simulations examined trajectory amplitude, synthetic measurement noise, viscous damping, and combined nonidealities. The results support FSPID as a conservative simulation-level refinement of fixed-gain PID; experimental validation remains necessary. Full article

This paper proposes a supervisory soft disagreement framework for adaptive uncertainty regulation in Interval Type-2 (IT2) fuzzy servo control and validates its performance through embedded implementation on an industrial servo platform. The proposed framework introduces a supervisory learning layer that combines supervised classification and unsupervised fuzzy clustering to characterize servo operating conditions using experimentally extracted performance indicators, including rise time, settling time, overshoot, steady-state error, Integral Absolute Error (IAE), control-effort energy, tracking-error standard deviation, and maximum control effort. Operating condition confidence is quantified by measuring the soft disagreement between the posterior class probabilities of a Support Vector Machine (SVM) classifier and the normalized membership degrees of a Fuzzy C-Means (FCM) clustering algorithm using the Bhattacharyya coefficient. The resulting disagreement index adaptively regulates the Footprint of Uncertainty (FOU) of the antecedent membership functions in IT2 fuzzy controller. A closed-form Uncertainty Avoider Defuzzification (UAD) strategy enables computationally efficient uncertainty-aware type reduction for real-time embedded implementation without iterative procedures. The framework was trained using experimental data collected from a Delta ASDA-B2 400 W industrial servo drive under diverse operating conditions. The complete controller was implemented on a Raspberry Pi and experimentally compared with conventional Proportional–Integral–Derivative (PID), Type-1, and fixed-FOU IT2 fuzzy controllers. Experimental results show that the proposed controller achieved an average IAE of 1.08, representing improvements of 55.6% and 27.5% over the PID and fixed-FOU IT2 controllers, respectively. Overshoot was reduced to 2.2% and settling time to 0.24 s, while the supervisory computation required only 4.55 ms, confirming real-time feasibility. The scientific significance of this work lies in introducing a new disagreement-driven supervisory paradigm that links probabilistic machine learning confidence with adaptive fuzzy uncertainty regulation. By establishing a principled connection among supervised learning, unsupervised learning, and Interval Type-2 fuzzy control, the proposed framework provides a general foundation for confidence-aware adaptive uncertainty management in intelligent control systems operating under uncertain and time-varying conditions. Full article

Reducing ordinary Portland cement (OPC) content through waste-derived binders can improve the resource efficiency of mortar and concrete. This study explores the effect of single and combined replacement of ordinary Portland cement (OPC) with ceramic waste powder (CWP) and calcium carbide residue (CCR), with the original contribution being the systematic mixture-design evaluation of a ternary OPC–CWP–CCR binder using mixture design and an integrated assessment of mechanical and durability-related performance. Ten mixes with varying mixture proportions were designed using the mixture design of experiments (DoE). Fresh and hardened properties were evaluated, including flowability, compressive strength, bulk resistivity, and water absorption tests up to 56 days, while chemical durability was assessed by exposure to 5% sulfuric acid. OPC replacement with CCR and CWP reduced early-age strength due to dilution; however, CWP-dominant blends exhibited late-age strength gain. The mixture containing 26.7% CWP and 6.7% CCR reached 44.7 MPa at 90 days and 406.7 Ω·m at 56 days and exhibited the lowest relative strength loss after sulfuric acid exposure (12.8%, versus 16.0% for the control). High CCR contents adversely affected workability, water absorption, strength, and acid resistance. CWP-dominant blends provided the best overall balance of performance and OPC reduction. Full article

Territorial spatial function layout optimization can alleviate the conflict between low-carbon development and economic growth by regulating the quantitative composition and spatial allocation of production, living, and ecological functions. However, existing spatial optimization models remain limited in their ability to simultaneously address quantitative uncertainty, spatial functional conflicts, and the coordination among spatial layouts, quantitative structures, and optimization objectives. This study develops a cellular automata (CA)-based Territorial Spatial Function Layout Optimization (TSFLO) model. The model integrates multi-objective fuzzy linear programming to optimize the target-year quantitative structure, a non-homogeneous Markov process to characterize functional transitions, and a game-theoretic approach to coordinate spatial conflicts. Furthermore, the model inversely derives the optimal quantitative structure of the base year through the Markov state transition equation, adjusts the base-year layout according to coordinated suitability to construct the CA initial state, and compares the TSFLO model with the PLUS model using Qionglai City, China, as a case study. Relative to the 2020 baseline layout, the proportions of areas without functional transitions under the TSFLO scenario were 81.40% and 73.60% in 2025 and 2030, respectively, which were 3.39 and 0.28 percentage points higher than those of the PLUS model. The maximum absolute relative errors between the TSFLO-optimized results and the target quantitative structure were 0.07% and 0.05%, respectively, representing reductions of 99.28% and 98.52% compared with the PLUS model. The minimum matching degrees between the seven territorial spatial functions and their corresponding high-coordinated-suitability areas were 71.86% and 69.51% in 2025 and 2030, respectively. From 2020 to 2030, the decoupling index of ecological regulation service functions was −2.8642, compared with −1.9128 for PLUS, indicating that the TSFLO scenario improved the decoupling intensity by 49.7%. The results demonstrate that TSFLO can integrate quantity optimization, spatial conflict coordination, and initial state construction within a unified CA framework, thereby improving quantitative allocation accuracy and spatial suitability matching. This provides methodological support for coordinating low-carbon development and economic growth through territorial spatial function layout optimization. Full article

The environmental impact of cement production has become a growing global concern due to its substantial contribution to CO₂ emissions. Sugarcane bagasse ash (SCBA), as a supplementary cementitious material, offers a sustainable alternative by partially replacing cement and reducing the carbon footprint of concrete. However, determining optimal mix proportions that balance mechanical strength, cost efficiency, and environmental benefits remains a complex challenge. In this study, a multi-objective optimization framework was developed by integrating automated machine learning (Auto-ML) with the NSGA-III algorithm. A surrogate model for predicting compressive strength was constructed using the TPOT-based Auto-ML tool, achieving high predictive accuracy with R² values of 0.993 and 0.908 for the training and test datasets, respectively. NSGA-III was then employed to derive Pareto-optimal mix designs, enabling simultaneous optimization of strength, cost, and CO₂ emissions. To validate the proposed multi-objective optimization framework, SCBA concrete specimens were prepared using the optimized mix proportions and tested under uniaxial compression. The experimental results exhibited good agreement with the predicted values, with deviations within an acceptable margin, thereby confirming the accuracy and reliability of the framework. This study provides a practical approach for the intelligent design of low-carbon SCBA concrete, contributing to the advancement of sustainable construction practices. Full article

Portfolio optimization models are highly sensitive to estimation errors in expected returns and covariance matrices, often resulting in unstable allocations. Robust optimization mitigates parameter uncertainty by optimizing against worst-case realizations within a specified uncertainty set, whose construction critically determines the effectiveness of the approach. This paper proposes a data-driven framework for constructing polyhedral uncertainty sets that integrates Gaussian mixture models (GMMs) to identify heterogeneous distributional components and ARIMA-GARCH models to capture time-varying volatility dynamics. The construction proceeds in three stages. First, ARIMA-GARCH filters remove serial dependence and volatility clustering. Second, GMM clustering applied to the standardized residuals identifies latent market regimes. Third, convex hulls of observations lying within a Mahalanobis distance threshold form component-wise polyhedral sets, which are aggregated into a global convex uncertainty set. The robust counterpart is derived via linear programming duality, transforming the robust constraint into a tractable quadratic program that preserves convexity and polyhedrality. We prove that the constructed sets are convex and polyhedral, establish probabilistic coverage guarantees under mild regularity conditions, and analyze the computational complexity of the framework. Empirical analysis of Indonesian equity data confirms heavy tails and volatility clustering, with GMM identifying three distinct regimes of approximately equal proportions. Controlled synthetic experiments show that uncertainty set geometry fundamentally influences portfolio outcomes: overlapping clusters yield stable allocations across regimes, whereas well-separated clusters reveal that convex hull aggregation introduces conservatism that masks regime distinctions. Rolling window backtests demonstrate that the proposed approach produces economically higher Sharpe ratios than standard uncertainty set formulations in the reported out-of-sample period, although statistical significance is limited by the small number of independent rebalancing periods (18 quarterly events). The practical advantage should therefore be interpreted as conditional on moderate transaction costs and manageable turnover. These findings provide a statistically grounded, geometrically faithful, and computationally tractable methodology for practical implementation, contributing to financial resilience and sustainable economic growth with broader implications for stable capital markets. Full article

Fish processing and aquaculture wastes are protein- and lipid-rich by-products that can be used to produce renewable energy. However, evidence on their anaerobic valorization is limited. This bibliometric and systematic review maps the field and synthesizes evidence on the anaerobic digestion and dark fermentation of fish-derived waste. Scopus records from 2000 to 2025 were screened according to the PRISMA 2020 guidelines. A total of 164 articles comprised the bibliometric corpus and 120 research articles informed the qualitative synthesis. The annual publication growth rate was 13.29%, with 65% of publications occurring between 2019 and 2025. Most described experiments employed laboratory-scale batch assays, mesophilic conditions and co-digestion. Methane yields from fish offal, silage and recirculating aquaculture system sludge ranged from 48 to 1174 mL CH₄/g VS. These differences reflect variations in feedstock composition and preparation, proportion of fish waste, selection of co-substrates and operating conditions. The process performance was mainly constrained by ammonia, volatile fatty acids and long-chain fatty acids. The modified Gompertz model predominated, whereas multi-step dynamic modeling remained rare. Microbial studies, primarily 16S rRNA gene surveys conducted in a batch-based manner, linked fish waste digestion to bacteria that degrade proteins and lipids, as well as hydrogenotrophic methanogens. However, community responses depended on the composition of the feedstock, inoculum and loading rate. Only three dark fermentation studies were identified, two of which used fish-derived substrates. Overall, progress toward industrial implementation requires fraction-specific characterization, validation in continuous systems, integration of hydrogen and methane production, dynamic modeling, multi-omics, digestate-safety assessment and integrated techno-economic and life cycle assessment based on pilot- and industrial-scale data. To assess potential database coverage bias, the search was repeated in Scopus and an equivalent search was run in Web of Science. Five additional eligible studies were identified. Full article

Reliable soil sealing monitoring supports sustainable land management by identifying persistent soil degradation and the loss of soil functions. This study evaluated CLC+ Backbone (CLC+ BB), Imperviousness Density (IMD), and an object-based proxy derived from the Estonian Topographic Database (ETD). National estimates were compared across available reference years, and a stratified sample of 1500 orthophoto-interpreted 10 × 10 m units was assessed for 2021. A separate probability-based yard assessment evaluated omissions from the ETD-derived proxy and their sensitivity to the operational surface definition. The datasets yielded markedly different area estimates and temporal patterns: for 2021, direct national estimates ranged from 330.1 km² for IMD to 715.0 km² for CLC+ BB, while the broader reference-based estimate was 732.3 km² (95% CI: 643.6–882.4 km²). Similarity between the CLC+ BB and reference totals concealed both omission and commission errors. Under the broader operational definition, estimated ETD-proxy omission was 2.6% (95% CI: 2.0–3.2%) in private yards and 26.5% (18.9–34.7%) in production yards. Narrowing the definition reduced the estimates by similar proportions in both yard types but produced a much larger absolute reduction in production yards. The results support integrated monitoring combining European products, national data, and reference-based validation for sustainable land management and soil protection. Full article