Search Results (13,457)

Vacancy-ordered double-perovskite Cs₂SnI₆ is known to be a good candidate for perovskite photovoltaics, as it is a light harvesting material which has potential both as an individual compound and as a component of a composite material. The compound is interesting due to being free of atom sites in B cationic positions, making the lattice “breathable” and giving it optoelectronic characteristics that vary with dopants. Here, antimony was examined as a possible heterovalent dopant with an ionic radius larger than that of Sn⁴⁺. In practice, it has been found that most of the materials are composites of Cs₂SnI₆ and Cs₃Sb₂I₉ phases. In the CsI–SnI₄–SbI₃ phase triangle, the melt crystallization process produced a layered (111)-oriented microstructure of crystallites with an increasing percentage of antimony. Two-dimensional perovskite materials look more promising in the decomposition of a solid solution to Cs₂SnI₆ and Cs₃Sb₂I₉ phases than in heterophase nucleation. The observed effect of (111)-oriented growth could be translated to other inorganic halides to form new oriented films or single crystals of perovskite materials. Diffuse reflectance spectroscopy showed an additional absorption shoulder in the NIR region for all groups of compounds, most likely induced by point defects in I sublattices of Cs₂SnI₆. Expanding the Cs₂SnI₆ absorption range to the NIR region could lead to new perspectives for its application. Full article

Ambient Assisted Living (AAL) systems have become increasingly relevant as aging populations intensify the demand for technologies that promote autonomy, safety, and quality of life. However, the widespread adoption of audiovisual sensing in smart homes raises critical concerns regarding data protection, privacy, and user trust. Ensuring secure processing while maintaining accurate activity recognition remains a key challenge. This work introduces DistSense, a distributed Peer-to-Peer (P2P) system designed to enhance activity detection in domestic environments through collaborative inference among intelligent audiovisual sensors. DistSense prioritizes privacy by performing local processing, sharing only high-level events, and leveraging distributed ledger mechanisms to ensure data integrity and auditability and support cross-device validation. This collaborative strategy reduces false positives caused by occlusions, illumination variability, and acoustic noise. To assess the system, functional tests were conducted for each module, followed by two use cases evaluated in both simulated and real edge hardware environments. The trained models achieved 88% accuracy for audio and 80% for video, and the system demonstrated effective performance in detecting daily activities and domestic hazards under varying noise conditions. Results indicate that DistSense successfully balances security, user acceptance, and inference robustness, positioning it as a viable solution for privacy-preserving activity monitoring in smart home contexts. Full article

This paper develops a reliability analysis model for a class of computer systems composed of hardware and software in series, considering a repairman taking multiple vacations. The system follows a series failure rule: hardware can be repaired to be as good as new after failure; software undergoes minor repairs to maintain operability after the first $N-1$ failures with an increasing failure rate, and is overhauled to be as good as new with cycle reset after the N-th failure. Based on the principle of probability conservation and the supplementary variable method, the state probability evolution equations of the system are derived. A Banach space is constructed, and a linear operator is defined, whose denseness, dissipativity, and closedness are verified. It has been proven that the operator generates a positive contractive $C_0$-semigroup, thus rigorously establishing the well-posedness of the model and the existence of a unique positive dynamic solution. Further spectral analysis verifies that zero belongs to the continuous spectrum rather than the point spectrum of the system operator.This indicates that the investigated system admits no time-invariant constant steady-state probability distribution,and only presents slowly decaying quasi-stationary dynamic behavior. The results can provide theoretical support for the reliability design and maintenance strategy optimization of hardware–software series repairable systems. Full article

The article presents the calculation of temperature fields for a casting (a cylinder 20 mm in diameter) made of Al–5%wt.Cu alloy, poured into sand (sand–clay) and metal (steel) molds at a temperature of 1123 K (with a metal mold temperature of 523 K). Many existing analytical approaches do not explicitly account for key features such as the time-dependent temperature evolution at the casting surface and center, as well as the variable temperature gradient within the casting. In this paper, the parameters calculated for the sand mold include the surface temperature change over time, as do the dynamics of the solidification front progression, and ultimately, the overall thermal field of the casting. For the metal mold, the process first determines the change in the center temperature over time, followed by the surface temperature dynamics, and finally, the complete thermal field of the casting. Particular attention is paid to determining the position of the mushy zone, namely the zero fluidity and feeding temperatures (the point at which the liquid phase loses mobility upon cooling). These temperatures are critical for casting structure formation and the initiation of shrinkage defects. To perform the calculations, the authors developed original mathematical models and provided solutions to the resulting differential equations. The study demonstrates the differences between the thermal fields in sand and metal molds: the maximum temperature difference is 195 K in the sand mold, compared to 90 K in the metal mold. Therefore, the solidification conditions for this casting in the metal mold are more favorable. The metal mold provides more favorable thermal conditions and a lower analytically predicted tendency toward shrinkage defects, but it does not guarantee their complete absence. Full article

We applied an algebraic approach, developed within the framework of the theory of a commutative monoid of self-dual symmetric polynomials, to the problem of effective isotropic conductivity ${\sigma }_e({\sigma }_1,\dots ,{\sigma }_n)$ in two-dimensional n-phase symmetric composites with partial isotropic conductivities ${\sigma }_j$. The upper $\Omega ({\sigma }_1,\dots ,{\sigma }_n)$ and lower $\omega ({\sigma }_1,\dots ,{\sigma }_n)$ bounds for ${\sigma }_e({\sigma }_1,\dots ,{\sigma }_n)$, found by the algebraic approach for $n=3,4$, are universal (independent of the composite microstructure) and possess all algebraic properties of ${\sigma }_e({\sigma }_1,\dots ,{\sigma }_n)$ that follow from physics: first-order homogeneity, full permutation invariance, Keller’s self-duality, positivity, and monotony. The bounds are compatible with the trivial solution ${\sigma }_e(\sigma ,\dots ,\sigma )=\sigma$ and satisfy Dykhne’s ansatz. Their comparison with previously known numerical calculations, asymptotic analysis, and exact results for the effective isotropic conductivity ${\sigma }_e({\sigma }_1,\dots ,{\sigma }_n)$ of two-dimensional three- and four-phase composites showed complete agreement. The bounds $\Omega ({\sigma }_1,\dots ,{\sigma }_n)$ and $\omega ({\sigma }_1,\dots ,{\sigma }_n)$ in both cases $n=3,4$ are stronger than the currently known variational bounds. Full article

Background: Food security systems are central to nutritional health and Sustainable Development Goal 2 (SDG 2), yet existing assessments have paid limited attention to cross-dimensional coordination within food security systems. This study assessed both system performance and coordination in low- and middle-income countries (LMICs) during 2019–2021. Methods: Based on a multidimensional 25-indicator framework, the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) approach was used to evaluate system performance. Spearman’s rank correlation and Bland–Altman agreement analyses against the SDG 2 Index and the Under-Five Mortality Rate (U5MR) were used to examine the validity. The coupling coordination degree (CCD) model was used to assess coordination across the four dimensions of food security: availability, access, utilization, and stability. Results: Among all included LMICs, composite scores ranged from 0.103 to 0.698. Regionally, Europe and Central Asia showed the strongest overall performance (mean = 0.54), whereas Sub-Saharan Africa exhibited the lowest levels (mean = 0.27). The dimensions of access and stability were identified as the principal global bottlenecks of overall food security system development. The proposed index correlated positively with the SDG 2 Index (R = 0.662, p < 0.001) and inversely with the U5MR (R = −0.769, p < 0.001). The coupling degrees were consistently high but exceeded coordination levels across regions, indicating that strong interdependence among dimensions did not necessarily translate into balanced or synergistic system development. Conclusions: Food security systems in LMICs are constrained by weaknesses in the access and stability dimensions, as well as by insufficient cross-dimensional coordination. Strengthening them requires integrated, cross-sectoral strategies that enhance both system performance and interdimensional coordination. Full article

Precise 3D positioning in GPS-denied environments is a critical enabler of autonomous robotics, industrial automation, and smart logistics within the emerging cyber-physical landscape. This paper presents a distributed and reconfigurable architecture designed to benchmark and provide unified multimodal indoor localization for mobile edge nodes. Unlike rigid commercial solutions, our architecture employs a distributed, reconfigurable framework that allows the rapid interchange of Absolute Localization Methods (UWB, External RGB-D Vision) and Relative Localization Methods (Inertial Odometry, Visual Odometry). We evaluate these modalities individually and in hybrid configurations using a custom low-cost mobile edge node. Experimental results in a controlled environment demonstrate that while all-optical systems offer high precision, a cost-effective fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data provides a robust balance of accuracy and reliability. Conversely, we identify significant limitations in monocular visual odometry within feature-poor indoor spaces. The developed platform serves as a reproducible foundation for researchers to prototype hybrid localization algorithms and assess the trade-offs between hardware cost and operational accuracy within complex cyber-physical ecosystems. Full article

Particle swarm optimization (PSO) is an optimizing method that is based on the theory of swarm intelligence. PSO is an effective algorithm that is used to search in a parallel manner compared to other methods. However, PSO has a tendency towards local optima when tackling complex multimodal optimization problems. It also has the disadvantages of slow convergence process and poor stability in the latter evolutionary period. In view of these demerits, a hybrid PSO method based on chaotic opposition-based initialization and an adaptive learning strategy is presented in this work (abbreviated as ACMPSO). First, the chaos initialization and opposition-based learning (OBL) are employed to produce high-quality initial particles in the feasible region, which is able to improve the quality of the initial solutions. Second, the logistic mapping embedded inertia weight is formulated to better trade off the global and local search process. Third, the global optimal particle is regulated by an exclusive velocity and position updating strategy whereas the rest particles are adjusted by the standard updating mechanism so as to prevent particles from premature convergence. Furthermore, an adaptive position update paradigm is developed to finely regulate the global exploration and local exploitation. Finally, conducted experiments on CEC’13 and CEC’22 reveal that the proposed ACMPSO outperforms several other advanced PSO variants regarding their convergence rate and accuracy. Alternatively, to further illustrate the effect of ACMPSO, we have applied it to two real-world problems, and simulation results ascertain its effectiveness and robustness. Full article

Reliable and fast radiation pattern prediction is critical for large-scale tightly coupled linear antenna arrays. Strong mutual coupling and finite-array edge effects limit the accuracy of conventional array factor methods, while full-wave simulations become computationally prohibitive for large arrays. To address this issue, a fast and efficient radiation pattern prediction method (FERPP) is proposed. For central elements, the far-field response is obtained from a calibrated reference array and extended through position-dependent phase compensation. For edge elements, responses are extracted from independent local full-wave simulations. All element responses are assembled into a global far-field response matrix, enabling direct radiation pattern synthesis using the extended method of maximum power transmission efficiency. Simulation results obtained with a 1024-element linear microstrip patch antenna array operating at 3.5 GHz, with small inter-element spacing, demonstrate close agreement with full-wave simulations. For a broadside single-beam case, the predicted peak gain is 29.10 dBi, compared with 29.02 dBi from full-wave simulation. For a scanned beam at 30°, the predicted peak gain is 28.22 dBi, while the full-wave result is 28.99 dBi. For an equal-weight three-beam configuration at −30°, 0°, and 30°, the proposed method yields a peak gain of 23.87 dBi, compared with 24.21 dBi from full-wave simulation. In terms of computational efficiency, the proposed method requires only about 1.8% of the computational time required for a full-wave simulation. These results demonstrate that the proposed FERPP method provides a practical and efficient solution for radiation pattern prediction and beamforming analysis of large-scale tightly coupled linear antenna arrays. Full article

Pyrolysis liquid (PL) derived from biomass pyrolysis exhibits biopesticidal properties and represents a promising value-added product within the sustainable circular economy framework. However, knowledge about the antimicrobial potential of PLs produced from walnut residue at different pyrolysis temperatures remains limited. We investigated the chemical composition and antimicrobial activity of PLs obtained from agricultural walnut residue (Juglans regia L.) against selected plant pathogenic bacteria and fungi. PLs were produced at four temperature ranges: 200–300 °C (W-1), 300–400 °C (W-2), 400–500 °C (W-3), and 500–600 °C (W-4). Chemical characterization was performed using Gas chromatography–mass spectrometry (GC-MS), High-performance liquid chromatography (HPLC), and Inductively coupled plasma optical emission spectrometry (ICP-OES), with determination of total phenolic and flavonoid contents. Pyrolysis temperature significantly influenced the chemical profile and bioactive compound content of the PLs, with W-4 showing the highest total phenolic and flavonoid levels. Heavy metal analysis indicated minimal contamination in all samples. Antibacterial activity was observed in stock solutions, whereas diluted applications showed limited effects. The W-4 fraction showed the strongest antibacterial activity and exhibited MIC values of 12.50 µL/mL against Clavibacter michiganensis subsp. michiganensis, Xanthomonas euvesicatoria, and Pseudomonas syringae pv. syringae, and 25.00 µL/mL against Erwinia amylovora. Antifungal activity differed markedly across temperature ranges, with W-3 and W-4 displaying superior activity against Fusarium oxysporum and Verticillium dahliae, achieving complete mycelial growth inhibition at 5%, compared to 10% for W-2 and 20% for W-1. Positive controls confirmed assay validity (ciprofloxacin for antibacterial assays and cycloheximide for antifungal assays), whereas negative controls showed no inhibitory effect. Overall, higher pyrolysis temperatures, particularly 400–600 °C, enhanced the antimicrobial potential of walnut residue-derived PLs, supporting their possible use as bio-based antifungal agents for sustainable crop protection. Full article

In the saline–alkaline lake (SAL) systems of the Nhecolândia region, Brazilian Pantanal, soils exhibit complex mineralogical assemblages controlled by sediment inheritance, pedogenesis, and hydrogeochemical gradients. This study investigates the distribution and transformation of 2:1 phyllosilicates along representative SAL toposequences. Soil samples were characterized by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD), supported by granulometry and adjustment of the FTIR spectra. Mineralogical data were integrated with geochemical (Al, K, Mg, Ca, Na) and pH data and examined using principal component analysis (PCA). Greenish loamy horizons act as key morphological controls on hydrogeochemistry, regulating solute retention along mid- to downslope transitions. Illite is more strongly associated with upslope positions, whereas downslope alkaline environments are associated with smectitic phases (e.g., montmorillonite and Mg-rich varieties such as saponite) and mixed-layer minerals structures (e.g., illite–smectite and montmorillonite–vermiculite structures). These assemblages are consistent with non-linear transformation pathways, with illite as a possible transitional phase between micas and expandable structures. The PCA results suggest a primary mineral distribution structured by fine-material content and depth, while pH and alkalinity emerge as key geochemical controls that differentiate mineral stability fields and reinforce the hydrogeochemical compartmentalization of the profiles. Geochemical data show strong associations of Al, Mg, and K with fine-fraction accumulation. The integration of these approaches highlights that a 2:1 phyllosilicate assemblage results from multiple superimposed pedogenetic pathways, offering a conceptual framework for studying complex soil–lake systems. Full article

Physics-based simulation of debris flows over complex terrain is essential for hazard assessment, but repeated numerical integration is costly when many scenarios must be explored. We develop a general deep-learning surrogate modelling framework for two-dimensional (2D) debris-flow propagation, here applied to the Morino–Rendinara area (central Italy) using a three-dimensional (3D) Fourier Neural Operator (FNO) trained on synthetic simulations generated by a validated in-house finite-volume shallow-water solver. The solver reproduces debris-flow propagation over complex terrain and is specifically developed for artificial intelligence (AI) applications. It is based on a depth-averaged 2D formulation using the Harten–Lax–van Leer–Contact (HLLC) approximate Riemann solver, hydrostatic reconstruction, positivity-preserving wet–dry treatment, and Voellmy-type basal friction, and was verified through analytical benchmarks, numerical tests, and back-analyses of real events. The dataset was built from four site-specific release settings derived from real topography, combining different released volumes and bulk densities while preserving local geomorphological and rheological characteristics. Each simulation was stored as a full spatio-temporal tensor and used to train an FNO conditioned on coordinates, topography, friction parameters, bulk density, and initial release thickness. Training used a novel loss to emphasize active-flow areas and improve velocity reconstruction, and was performed using a graphics processing unit (GPU). The surrogate shows effective generalization to within-distribution validation samples, with global relative mean squared errors of 5.49% for flow thickness, 5.34% for velocity component u, and 2.60% for v, and mean $R^2$ values of 0.95, 0.94, and 0.97. For a representative sample, the surrogate predicts the full spatio-temporal solution in $0.52$ s, versus about 47 s for the first-order finite-volume solver, corresponding to a speed-up of about $91\times$, with an even larger gap expected for higher-order solvers, since, whilst the computation time of the solver increases as its complexity increases, the computation time of the FNO remains essentially unchanged. These results indicate that the proposed FNO is a reliable site-specific surrogate for rapid approximation of 2D debris-flow dynamics over real terrain, with potential for uncertainty propagation, Monte Carlo analysis, large-ensemble simulation, and hazard-oriented scenario assessment. Full article

In the field of drone navigation systems, a high-precision positioning solution can be provided by an integrated strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS). But when satellite signals are interfered with or blocked by tall buildings, the errors of SINS will disperse rapidly due to the complex air and mechanical vibrations, leading to a serious degradation of navigation accuracy. To enhance the positioning performance in this situation, this paper proposes a hybrid information fusion method based on attention-augmented temporal convolutional network (TCN) for drone SINS/GNSS navigation system. A feature integration and prediction model is constructed to provide a pseudo-positioning reference for the integrated navigation filter during GNSS-denied periods, in which TCN is used to establish a predictive positioning error correction model based on inertial measurements and SINS data, while a self-attention model is incorporated to extract complex global drone motion features. The performance of the proposed method has been experimentally verified using Global Positioning System (GPS) and SINS data collected from real drone flight test. Comparison results among the proposed model, SINS with TCN, SINS with convergent Kalman filter (KF) prediction section and SINS-only indicate that the proposed method can effectively improve the drone positioning accuracy in specific GNSS-denied environments. Full article

Climate control systems are critical to ensuring the continuous operation of data centers, as they maintain the environmental conditions required by sensitive electronic equipment. In this context, continuous supervision of refrigeration compressors is essential to prevent failures that may compromise thermal stability. This work presents the design, implementation, and experimental validation of a remote monitoring and condition-based maintenance framework built on Industrial Internet of Things (IIoT) technologies for air-conditioning compressors used in data centers. The proposed architecture integrates industrial-grade sensors for temperature, electric current, and vibration, a Siemens LOGO! programmable logic controller (PLC) for signal acquisition and scaling, a Node-RED middleware layer for data flow management, and the ThingSpeak cloud platform for remote storage and analysis. The novel contributions of this work are: (i) a fully integrated low-cost IIoT stack validated on a Copeland ZR144KCE-TF5 scroll compressor under real operating conditions over a continuous 49-day monitoring period; (ii) a hybrid anomaly detection model that combines Z-score statistical baselines with moving-average prediction error to reduce false positives from transient events; and (iii) a condition-based maintenance decision framework that maps the three monitored variables to ISO 10816-3 vibration severity zones and manufacturer-referenced thermal and electrical thresholds, producing recommended maintenance actions. The framework was applied to the acquired dataset, confirming predominantly stable operation (93.4% of samples in ISO 10816-3 Zones A–B) while detecting an emergent mechanical-wear trend (5.64% of samples in Zone C) concentrated in the final days of the monitoring period and demonstrating the feasibility of the proposed architecture as a scalable and replicable solution for condition monitoring and maintenance decision support in critical technological infrastructures. Full article

To address the key limitations of traditional automated landslide detection methods—namely their reliance on large training datasets, insufficient detection accuracy, and high false positive rates—this study proposes an InSAR-based automated landslide detection approach integrating multi-weight factor coupling, referred to as an Improved Hot Spot Analysis (IHSA) method. Built upon InSAR-derived surface deformation data, the proposed method optimizes the hotspot detection model through a spatial weighting matrix that incorporates multi-feature fusion. Morphological processing is further applied to refine landslide boundaries. Validation against manually interpreted ground truth data demonstrates that the proposed method achieves a precision of 90.20%, representing an improvement of 53.61 percentage points over the conventional hotspot analysis method, while maintaining a stable recall rate of 92.00%. The extracted landslide boundaries exhibit high consistency with manual interpretation results, effectively overcoming common issues in traditional approaches such as fragmented outputs and internal voids. This study provides an efficient, training-free solution for large-scale early identification of potential landslides, offering critical methodological support and data foundations for regional landslide detection and hazard mitigation. Full article