Search Results (226)

Janus nanoparticles (JNPs), named after the bi-faced Roman God has emerged as a hot topic in the present era of nanoscience because of their chemical, structural and physical uniqueness. These particles stand out in the crowd as they are composed of two or more faces with contrasting functional properties in the same molecule. The need for two or more functions in a single molecule that can be used in chemical, biological or physical fields, such as delivering drugs combined with imaging, is the need of the hour and JNPs open gates for addressing this issue as these self-tailored particles have found their application in various in vivo and in vitro domains. However, despite their vivid application, a larger sector of application still needs to be explored. Synthesis methods involve masking, self-assembly and microfluidics, and comparative analysis of these methods, listing their pros and cons, would assist in overcoming the difficulties in the commercialisation of these particles. Moreover, the systematic analysis of differences in their structure with reference to their functionality and characterisation methods would lead us to a better understanding of the subject. This review discusses the various synthesis strategies and their comparison, the anisotropic nature of the JNPs conferring various distinguished properties, their application in emulsion stabilisation, bio-imaging, drug-targeting, drug-delivery, and biosensing domains, the characterisation methods involved, challenges and future aspects. The future aspects section maps a few hypotheses that might be useful in expanding the horizons of usage. The novelty of this review lies in the critical analyses of the synthesis methods and characterisation. Each type of JNP has been analysed for its advantages and limitations, and probable hypotheses to address the existing challenges have been jotted down. Full article

Population-based bio-inspired metaheuristics are the dominant tools for tuning hybrid decomposition–deep-learning forecasters, yet their relative behavior on a common problem is rarely assessed with a leakage-free, physically meaningful protocol. We benchmark eight metaheuristics on the joint nine-dimensional hyperparameter optimization of a discrete-wavelet-transform bidirectional-LSTM (DWT–BiLSTM) architecture for short-term wind speed forecasting, using 409,152 hourly observations from eight meteorological stations. The set comprises six nature-inspired methods (Artificial Bee Colony, ABC; genetic algorithm, GA; Particle Swarm Optimization, PSO; Grey Wolf Optimizer, GWO; Hippopotamus Optimization, HO; and the Raindrop Optimizer) together with two recent metaphor-free or social variants (the Farthest-better Nearest-worse Optimizer, FNO; and the Tuckman Optimization Algorithm, TOA). A multi-stage protocol covers 30 independent runs per algorithm, a joint-versus-sequential comparison, a genuine rolling-origin out-of-sample evaluation, and component ablation. Friedman testing reveals significant differences (χ² = 49.76; p < 10⁻⁸), with the Grey Wolf Optimizer attaining the best mean rank (2.27) and Pareto-dominant run-time; ablation shows the DWT front-end is essential (Cohen’s d = 13.09) and bidirectionality negligible at the one-hour horizon (p = 0.674). Critically, evaluating forecasts in reconstructed physical units reveals that the per-component advantage does not persist: at the one-hour horizon the reconstructed forecast does not exceed a naive persistence baseline (skill ≈ −0.5 in m/s versus +0.44 in normalized component space), a discrepancy independent of decomposition leakage that we report transparently. This work thus contributes a rigorous, leakage-controlled bio-inspired benchmark and a cautionary evaluation methodology. Full article

Predictive maintenance of electric vehicle (EV) fleets requires accurate estimation of Remaining Useful Life (RUL), Time-to-Failure (TTF), and State-of-Health (SOH) from heterogeneous Internet of Things (IoT) telemetry. However, real-world degradation patterns are nonlinear, nonstationary, and highly imbalanced near failure. This study proposes Q-TRACNet, a temporal attention framework that combines causal maintenance-aware preprocessing, adaptive temporal condensation, residual refinement, learnable phase modulation, and hybrid Particle Swarm Optimization–Quantum-Guided Descent parameter tuning. The framework is evaluated on the EV-HLM-RUL dataset and three established prognostics benchmarks: NASA CMAPSS, PHM 2012, and XJTU-SY. Chronological training, validation, and testing partitions are used to preserve temporal causality. On EV-HLM-RUL, Q-TRACNet achieves an MAE of 9.8, an RMSE of 14.7, an $R^2$ of 0.979, and a Critical Degradation Awareness Index (CDAI) of 0.91. It reduces RMSE by 20.11% relative to the strongest competing baseline and achieves an NRMSE of 0.102 and a Kendall correlation of 0.89 ($p<{10}^{-4}$). Cross-dataset experiments demonstrate stable performance for RUL, TTF, and short- and long-horizon SOH prediction. Ablation and sensitivity analyses further confirm the contributions of the temporal and attention components and the stability of degradation-aware evaluation. Q-TRACNet also provides lower training cost and inference latency than competing architectures, supporting practical maintenance planning, inspection prioritization, and resource allocation in connected EV fleets. Full article

Wind turbine gearbox degradation driven by lubricating oil contamination represents one of the most environmentally and economically consequential challenges facing modern wind energy operations. This study proposes a dual-reservoir imperfect preventive maintenance strategy designed to extend gear train service life, reduce the carbon footprint of maintenance operations, and recover renewable energy production losses inherent to conventional intervention practices. The proposed architecture employs two alternating oil reservoirs. While one supplies the active lubrication circuit at full turbine output, the second undergoes filtration, completely decoupling the filtration operation from production continuity. When the concentration of metallic particles resulting from gear wear exceeds a predefined contamination threshold in the lubricating oil, imperfect preventive maintenance (IPM), performed in parallel with an oil change operation, is initiated; this action partially restores the gear train failure rate to an intermediate value between the degraded and as-new states. A mathematical model is derived to jointly optimize the filtration interval TF and the preventive maintenance interval TM, minimizing the average total cost per unit time over a finite operational horizon while explicitly incorporating environmental costs attributable to each filtration cycle. Numerical optimization yields the optimal filtration interval TF and preventive maintenance interval TM that minimize the total average cost per unit time over the operational horizon H. A dedicated environmental performance assessment demonstrates that the proposed strategy substantially recovers lost wind power generation, significantly reduces hazardous lubricating oil waste and lowers total CO₂-equivalent emissions. This confirms the strategy’s meaningful contribution to sustainable wind energy operations. Sensitivity analyses confirm the robustness of the optimal solution across varying operational and economic conditions, providing wind farm operators with an adaptable and environmentally responsible decision-making framework. Full article

High photovoltaic (PV) penetration introduces rapid variability, voltage deviations, and increased real-power losses in distribution networks, necessitating control strategies that remain effective under forecast uncertainty. This paper presents a unified Particle Swarm Optimization-based Receding-Horizon Control (PSO-RHC) framework for optimal coordination of multiple Battery Energy Storage Systems (BESSs) in a PV-rich distribution feeder. The controller employs a receding-horizon structure—using horizon-based forecasts, constraint enforcement, and stepwise decision updates—while PSO serves as the optimization engine that computes BESS power setpoints at each prediction step. Deterministic PV and load forecasts are perturbed with stochastic noise to emulate realistic uncertainty, and each candidate solution is evaluated using a forward–backward sweep load-flow model. Simulation results on the IEEE-69 bus system show that the proposed PSO-RHC scheme reduces total daily energy losses from 1467.50 kWh to 1310.19 kWh (10.72% reduction), improves weakest-bus voltages by 1–4%, and maintains all BESS units within operational limits. The normalized objective components remain small (below 0.5%), indicating balanced operation without excessive cycling. These findings demonstrate the effectiveness and simulation-level effectiveness of PSO-based receding-horizon control for enhancing distribution-network performance under uncertain and dynamic PV conditions. Full article

Solonetz and Solonetzic rangelands are widespread in arid regions of Central Kazakhstan, where pasture degradation is often difficult to assess because surface vegetation patterns do not always reflect subsurface soil constraints. This study aimed to evaluate degradation patterns in Solonetz pasture ecosystems of the Ulytau region by integrating field soil-profile descriptions, laboratory analyses, vegetation observations, forage productivity data and Sentinel-2A-derived MSAVI. Ten monitoring soil profiles were examined for particle-size distribution, soluble salts, ionic composition, exchangeable cations, available N, P and K, vegetation cover and forage yield. USDA textural classification, salt-distribution analysis, Pearson correlation, PCA, RDA and MSAVI-based mapping were used to link soil-profile properties with vegetation and spectral response. The first two PCA axes explained 65.5% of the total variance, while selected soil profile constrains accounted for 58% of the variation in vegetation cover, forage yield and MSAVI in the RDA analyses. The results showed strong profile heterogeneity, with clay enrichment, subsurface salt accumulation, alkalinity and Na- or Mg-related exchange–complex imbalance associated with several degradation pathways. Surface horizons were often weakly saline, whereas deeper layers contained stronger chemical and physical limitations. MSAVI values were low across the monitoring sites and reflected vegetation–soil surface conditions rather than salinity or sodicity directly. MSAVI ranged from 0.0888 to 0.2148, with a mean value of 0.1248. Combining soil-profile diagnostics with Sentinel-2A MSAVI improved the reliability of interpreting spatial degradation patterns and provides a practical framework for monitoring spatially heterogeneous Solonetz rangelands, supporting sustainable rangeland management under arid conditions. Full article

Sandy desert ecosystems of Central Asia are highly vulnerable to climate change, land degradation, and increasing anthropogenic pressure, yet the soil conditions supporting native desert vegetation remain insufficiently characterized. This study investigates soil development and physicochemical properties under the canopy of Calligonum aphyllum across different geomorphological conditions in the southern Balkhash region of Kazakhstan. Field investigations were conducted within the Ili River delta, where nine soil profiles were described across three geomorphological settings. Soil samples were analyzed using standard soil analytical methods to assess particle-size composition, soil organic matter, nutrient availability, carbonate content, salinity, and sodicity indicators. The studied soils were predominantly sandy, with sand fractions ranging from 88 to 96% and very low clay content, resulting in weak horizon differentiation, high permeability, and limited water-retention capacity. Soil organic matter and total nitrogen contents were consistently low across all sites. Available phosphorus decreased with depth, particularly in carbonate-enriched horizons, whereas exchangeable potassium remained comparatively high. Total salinity was low, with chloride–sulfate and calcium–sodium dominance, and no evidence of sodicity was observed based on SAR values. Clear differences among geomorphological settings were identified, including relatively homogeneous sandy substrates, dust-enriched semi-stabilized sands, and actively reworked aeolian ridges. The results indicate that C. aphyllum can persist under nutrient-poor, coarse-textured sandy conditions and is associated with surface root concentration, local substrate stabilization, and early soil-profile differentiation. These findings highlight the ecological importance of C. aphyllum in sandy desert habitats and provide site-specific soil information relevant to vegetation-based restoration and sustainable land management in arid regions of Central Asia. Full article

We propose a lattice structure and space group symmetry, Pmmm (No. 47), for the QCD condensate ground state, whose Wyckoff positions are occupied by the four light quarks and antiquarks $u$, $d$, $\tilde{u}$, $\tilde{d}$. These serve as the fundamental building blocks of both the condensate lattice ground state and the baryonic and leptonic particle excitations embedded within it as topological defects of the nominal Wyckoff occupation, offering a more structured alternative to the QCD instanton liquid picture. Building on Bloch quark wave solutions of a tight-binding Hamiltonian defined on this lattice, we propose a generalization of Einstein’s Equivalence Principle: composite particles embedded in the lattice and propagating by tunnelling cannot distinguish acceleration by gravity, the strong, weak, or electromagnetic forces, or curvature of the lattice itself, arising from local variation in unit cell shape. We derive an eight-by-eight tight-binding Hamiltonian that decouples into two four-by-four blocks separating the quark and antiquark sectors. Electrons, positrons, protons, neutrons, deuterons, and α-particles are embedded in the lattice as defect-induced deviations from the nominal Wyckoff occupation, with their spin and helicity emerging structurally from this picture. We further propose that the lattice’s unit cells carry a small nonzero rest mass, whose collective gravitational effect across a galactic halo may account for the discrepancy between visible mass and rotation curves, identifying the Pmmm condensate as a dark matter candidate. Finally, we outline a mechanism near black hole horizons by which local melting of the condensate lattice followed by quark reactions that conserve the number and flavor of the quarks could yield a new route to baryon asymmetry. We propose a framework that goes several steps beyond the Standard Model by introducing a Pmmm space group unit cell for the QCD condensate ground state, built from the four light quarks and antiquarks $u$, $d$, $\tilde{u}$, $\tilde{d}$. We further propose that topological defects of the Pmmm condensate lattice are the geometric origin of particle excitations. Full article

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments. Full article

We investigate a static and spherically symmetric Schwarzschild–Letelier Black Hole immersed in a King Dark Matter Halo and analyze how the combined effects of the cloud of strings and the dark-matter environment modify the spacetime geometry, particle dynamics, and thermodynamic behavior of the black hole. Particular attention is devoted to the motion of both massless photons and massive test particles in this black hole background. In the geodesic analysis, we derive the effective potential and study the properties of circular photon orbits, the associated black-hole shadow radius, and the innermost stable circular orbit (ISCO), highlighting the role played by the cloud of strings parameter and the King dark-matter halo parameters in shifting the orbital structure relative to the standard Schwarzschild case. To further characterize the spacetime from a topological perspective, we investigate the unstable circular null orbit using a normalized vector field constructed within the framework of Duan’s $\varphi$-Mapping Topological Current Theory. Through this method, we identify the corresponding topological charge and examine the relation between the photon sphere and the underlying topological structure of the black-hole configuration. In addition, we explore the thermodynamic properties of the system by computing the Hawking temperature, entropy, Helmholtz free energy, and heat capacity, thereby analyzing the black hole’s local and global thermodynamic stability. The influence of the surrounding dark-matter halo and cloud of strings on the phase structure and thermal behavior is discussed in detail. We further study the thermodynamic topology of the system via the off-shell free-energy formalism, which provides insight into possible thermodynamic phase transitions and the topological classification of black-hole states. Our analysis demonstrates that the combined effects of the cloud of strings and the King dark-matter halo significantly modify the horizon structure, geodesic dynamics, shadow characteristics, and thermodynamic properties of the black hole when compared with the standard Schwarzschild solution. Full article

For enhancing the level of refinement of short-horizon wind-storage power prediction, this paper introduces an advanced BiLSTM prediction model integrating data preprocessing based on the density-based clustering technique known as DBSCAN, partial least squares regression (PLSR), and particle swarm optimization (PSO). In this paper, “wind-storage power” refers to the net power output of a wind farm integrated with a battery energy storage system (BESS), where the measured data already embed the effects of charge/discharge operations. First, outage and missing data are removed from the historical dataset. DBSCAN is then employed to identify abnormal samples in wind-storage power and meteorological variables, such as wind speed, wind direction, atmospheric pressure, temperature, and humidity, and linear regression is used to correct the detected noise points. Correlation analysis is further conducted to identify the most relevant meteorological inputs, namely wind speed, wind direction, and atmospheric pressure. Next, the PLSR model is applied to generate the preliminary prediction of wind-storage output. On this basis, the BiLSTM network is employed to predict the residual error, which mainly reflects the nonlinear characteristics not captured by the preliminary prediction. Meanwhile, PSO is implemented to determine the most suitable core hyperparameters for the BiLSTM architecture. Ultimately, the preliminary PLSR result is corrected by the predicted residual to obtain the final wind-storage power prediction. The DBSCAN parameters are systematically selected via a k-distance plot (ε = 0.9, MinPts = 2.5), and the PLSR number of components is set to A = 3 based on five-fold cross-validation. Case studies show that, for the 24 h prediction horizon, the proposed method improves prediction accuracy by 2.29%, 11.47%, and 5.54% compared with the BP, Wavelet-LSTM, and standard LSTM models, respectively. Furthermore, statistical significance is confirmed by Diebold–Mariano tests and 10-run confidence intervals. Full article

Salt-bearing foreland fold–thrust belts represent a critical tectonic system for ultra-deep hydrocarbon exploration. In the Kalasu structural belt of the Kuqa Depression—characterized by the “four extremes” of ultra-high temperature, pressure, salinity, and stress—conventional single-detachment models fail to adequately resolve the complex subsalt structures. To address this challenge, this study integrates high-resolution 3D seismic data, field outcrop observations, well logs, balanced cross-sections, and particle image velocimetry (PIV)-monitored physical modeling to propose a ramp–flat multi-detachment model. Our results demonstrate that deformation is governed by four regional detachment horizons: gypsum-salt layers, thick mudstones, coal-bearing strata, and the basement, which vertically partition the basin into six tectonic units: supra-salt, salt, subsalt, supra-coal, coal, and sub-coal basement. The structural architecture is controlled by five key factors: (1) paleo-uplift geometry, (2) distance from the South Tianshan orogenic front, (3) orientation of basin-bounding faults, (4) regional stress regime (pure compression versus transpression), and (5) rheological contrasts among detachment layers. The kinematic evolution follows a progressive sequence: basement-involved thrusting → multi-level ramp–flat detachment folding → cover detachment. Three primary trap levels are identified—subsalt, supra-coal, and sub-coal—hosting six distinct trap styles: pop-up anticlines, imbricate faulted anticlines, structural triangle zones, fault-bend fold anticlines, supra-coal anticlines, and inter-coal/sub-coal anticlines. Notably, under transpressional stress, oblique paleo-uplifts control the formation of enigmatic “fish-scale” arcuate trap belts composed of fault-bend fold anticlines. Full article

Across the Global South, energy inequity disproportionately affects women in off-grid communities. However, existing optimisation models for rural electrification rarely incorporate explicit gender constraints. This study develops and validates a multi-objective optimisation framework for balancing environmental sustainability, economic viability, and gender equity in energy access. The model’s objective functions are environmental impact, unsatisfied energy demand, total system cost, and gender inequality. Optimal values for these objectives were generated based on allocation of energy across solar PV, generators, and firewood sources. The Non-dominated Sorting Genetic Algorithm II (NSGA II), particle swarm optimisation (PSO), and a hybrid NSGA-PSO II approach were used to solve the developed model. A remote Nigerian community (Olooji) with 600 households and a population of 7000, classified as Tier 1 energy consumers, was used as a case study. The hybrid NSGA-PSO II method demonstrated superior performance. It achieved the lowest fitness value (4,461,024) by combining the exploration capabilities of NSGA II with the Pareto-optimal convergence strengths of PSO. Over the 25-year planning horizon, the model projects solar energy share to increase from 19.05% to 47.79%, firewood to decrease from 61.90% to 35.45%, and generator share to increase from 14.3% to 14.7%. The community’s energy demand coverage improves from 95.24% to 97.92%. The community maintains a stable male-to-female energy consumption ratio of approximately 1.18:1, while the energy equity gap decreases from 0.2000 to 0.0800 kWh/person/quarter over the planning period. Results demonstrate that the hybrid NSGA-PSO II effectively manages the complexity of multi-objective energy distribution while promoting energy equity and environmental sustainability in rural electrification. Full article

Neural ordinary differential equations (Neural ODEs) describe the feature evolution of deep networks by continuous-time dynamical systems and enable end-to-end learning through differentiable numerical solvers. Nevertheless, in closed-loop rolling prediction for small-sample time series, conventional Neural ODEs remain vulnerable to error accumulation and numerical instability. To improve the controllability of long-term evolution, this study proposes a neural ordinary differential equation framework based on fractional-order operators. Rather than directly introducing full-history convolution kernels into the governing dynamics, the proposed approach constructs a fractional effective step size from the closed-form expression of the Riemann–Liouville fractional integral of a constant function and consistently embeds it into all sub-steps of a fourth-order Runge–Kutta solver. In this way, the scale of continuous-depth propagation is regulated by a single tunable parameter. Combined with a residual output structure, the method preserves the interpretability of continuous dynamics while effectively suppressing trajectory drift in closed-loop prediction and improving training stability. To investigate the impact of the fractional-order parameter on fitting and extrapolation, particle swarm optimization is employed to search automatically for the optimal order. Experimental evaluations on the linear spiral system and Lorenz continuous dynamical systems and on a small-sample provincial annual electricity-consumption dataset show that the proposed model achieves lower prediction errors across multiple tasks and exhibits superior trajectory preservation and robustness under long-horizon forecasting. Full article

Vegetation strongly influences soil formation, yet its effect on Rare Earth Element (REE) distribution and fractionation across treeline ecotones remains insufficiently constrained. The present study investigated how contrasting plant communities, Vaccinium myrtillus heathlands and Picea abies forests, affect pedogenetic pathways and REE behavior in sandstone-derived soils of the Northern Apennines (Italy). Six soil profiles were characterized for bulk geochemistry, selective Fe–Al extractions, particle-size distribution, and REE concentrations. Principal component analysis and hierarchical clustering identified pedogenetic drivers and horizon groupings. Under Vaccinium myrtillus, thick acidic organic horizons promoted organo-metal complexation and incipient podzolization, whereas Picea abies soils showed thinner organic layers and enhanced mineral weathering, leading to Bw development with higher silt–clay contents and elevated Al/N ratios. These pathways were captured by Fe–Al indicators and the Spodic Index. REE distributions showed vegetation-related differences in surface horizons and Eu–Ce anomalies, but they did not reproduce Fe–Al pedogenetic clusters, reflecting strong parent-material control. The coexistence of podzolic and cambic pathways at the treeline highlights pronounced spatial heterogeneity and vegetation effects. Plant composition may redirect pedogenesis, influencing nutrient cycling and metal mobility. Additionally, these findings emphasize the need to integrate multivariate statistics with established pedogenetic indicators when evaluating geochemical properties in mountain soils. Full article