Search Results (1,537)

Reducing unsprung mass in high-performance braking systems is critical for vehicle dynamics, but applying topology optimization (TO) to conventional solid discs under combined thermo-mechanical loading often causes “thermal entrapment” and structural failure. This study addresses this limitation by proposing a floating disc architecture and a two-stage computational framework. First, TO via the SIMP algorithm was applied to the Aluminum 7075-T6 inner carrier strictly under mechanical loads, decoupling artificial thermal stresses and yielding a fixed, optimized geometry. Results show the TO achieved a drastic 53% mass reduction (from 0.218 kg to 0.102 kg) specifically for the inner carrier component. Second, a high-fidelity Surrogate Model (Digital Twin) of this new geometry was developed to bypass the immense computational cost of multi-parameter, non-linear thermo-mechanical Finite Element Analysis (FEA). Utilizing Latin Hypercube Sampling (LHS) across 15 design scenarios and the Genetic Aggregation algorithm, the surrogate model was trained to predict real-time responses. Subsequently, the Digital Twin predicted stress and temperature fields with near-perfect accuracy (R² ≈ 0.9998), rapidly identifying the limit braking scenario. Fatigue analysis confirmed the final component safely withstands 10⁶ extreme braking cycles (safety factor 1.65). Full article

Advancing structural sustainability requires a rigorous evaluation of the economic and environmental trade-offs inherent in engineering design and material selection. To address this in sustainable construction, this paper presents a comparative study of the lateral-torsional buckling (LTB) resistance of glulam timber, structural steel, and reinforced concrete beams under fluctuations in material and labor costs and environmental carbon impacts. Two parallel series of multi-parameter cost optimizations are performed using discrete mixed-integer non-linear programming (MINLP) via the outer-approximation/equality-relaxation algorithm to analyze configurations with active LTB controls (laterally unrestrained beams) and without LTB controls (laterally restrained beams) across various discrete spans and imposed loads. The objective functions define the production-dependent material, power, labor, and CO₂ emission costs of the beams. The optimization results show that active LTB constraints force steel and concrete elements into sturdier profiles. Conversely, timber demonstrates a unique geometric behavior reversal under medium-to-high loads, shifting into tall, narrow sections. In the projected near future, when the global warming price CGW increases to a tenfold threshold, the combined self-manufacturing and production-induced CO₂ emission costs of beams on average could increase by 15.2% for timber, 35.8% for steel, and 50.2% for reinforced concrete. In this case, LTB-unrestrained configurations, compared to restrained beams, will cause total production costs to increase by up to nearly one-third for timber, more than double for steel, and by one-half for reinforced concrete. At higher CGW increases, the total costs expand further, reaching up to 38.1% for timber, 144.8% for steel, and 62.5% for reinforced concrete at a hundredfold escalation. Similar cost increases can likely be expected across the construction industry at large. The sector will have to adapt. Full article

Accurate retrieval of leaf area index (LAI), chlorophyll content (Cab), and canopy water content (Cw) is critical for growth monitoring and irrigation scheduling in spring wheat. Yet the quantitative impact of unmanned aerial vehicle (UAV) multispectral spatial resolution on the retrieval accuracy of these key traits remains elusive, and whether a single optimal resolution can support coordinated multi-parameter monitoring is unresolved. To bridge this gap, this study generated 14 spatial resolutions (0.07–3.03 m) by pixel aggregation resampling from the original four-band UAV multispectral imagery with a native spatial resolution of 0.07 m, acquired at the jointing and filling stages. By coupling the PROSAIL radiative transfer model with random forest, we systematically assessed scale-dependent retrieval performance and employed texture entropy to elucidate how spatial structure governs accuracy. Our results revealed that retrieval accuracy of all three parameters exhibited non-monotonic responses to changing spatial resolution. At the jointing stage, optimal resolutions diverged markedly—0.49 m for LAI, 2.03 m for Cab, and 2.80 m for Cw. Following canopy closure at the filling stage, the optimal resolution converged uniformly to 2.03 m across all parameters. Mechanistically, this non-monotonic variation was closely associated with the combined behavior of spectral coefficient of variation and texture entropy, rather than with any single indicator alone. Building on these findings, we propose a growth-stage-adaptive resolution strategy and develop a Heterogeneity-Scale Game Model (HSGM) as an exploratory framework to characterize the formation mechanism of optimal aggregation scales. This study moves beyond empirical scale selection and establishes an exploratory framework for multi-parameter crop monitoring, delivering actionable guidance for selecting appropriate spatial scales in UAV data processing and multi-scale parameter retrieval in precision agriculture. Full article

Tertiary lymphoid structures (TLSs) are ectopic lymphoid aggregates within the intratumoral and peritumoral microenvironment. In hepatocellular carcinoma (HCC), the presence of TLSs was closely associated with immunotherapy response and prognostic outcomes. However, TLSs exhibit pronounced spatial heterogeneity within tumors and biopsy, being invasive and prone to sampling bias, may cause inaccurate assessments. Multi-parametric imaging techniques based on computed tomography (CT)/magnetic resonance imaging (MRI)CT/MRICT/MRI with the aid of artificial intelligence (AI) algorithms could effectively integrate anatomical structures with functional metabolic information, quantifying and visualizing key pathological molecular features of tumor treatment response across multiple dimensions. This review summarizes the biological characteristics and clinical significance of TLSs in hepatocellular carcinoma. Furthermore, it focuses on the latest advances in non-invasive TLS assessment using multi-parametric imaging techniques, integrating these technologies with large-scale artificial intelligence models to explore their application value in prognosis prediction and immunotherapy benefit evaluation. Full article

This paper develops a multidimensional differential-transform-based framework for computing matrix characteristics of complex-valued multiparameter matrix functions. The proposed approach extends differential-transform techniques from one-parameter matrix functions to functions depending on several independent variables and constructs multidimensional D-analogues of the Leverrier and Faddeev methods. In the spectral domain, products of parameter-dependent scalar and matrix functions are replaced by multidimensional convolutions, which makes it possible to compute the spectra of characteristic-polynomial coefficients, determinants, and inverse-matrix entries by recurrence relations. The convergence of the inverse multidimensional transform is discussed in terms of analyticity in a polydisc, and truncation-error estimates and residual-based a posteriori indicators are introduced for controlling the accuracy of reconstructed inverse matrices in locally nonsingular parameter regions. The method is implemented in a Python 3.14-based computational framework that stores and manipulates multidimensional differential spectra. The approach is verified on multiparameter complex matrix examples, including a three-degree-of-freedom damped vibration system and a dense $7 \times 7$, seven-parameter complex matrix. Numerical residuals confirm the consistency of the reconstructed inverse matrices with MATLAB R2025b-based verification. The results show that the proposed recurrence-based method is especially useful for sparse multidimensional spectra, bounded-order local reconstructions, and repeated evaluations of matrix characteristics near a fixed expansion point. Full article

Indoor air quality (IAQ) in healthcare environments is affected by dynamic interactions between occupancy, ventilation, operational activities, and indoor emission sources, which are not always captured through conventional punctual assessments. This study evaluated long-term IAQ dynamics in different hospital microenvironments using continuous low-cost sensor monitoring combined with targeted chemical characterization of volatile organic compounds (VOCs) and specific aldehydes. Continuous monitoring was conducted from June 2023 to December 2025, measuring CO₂, PM_2.5, PM₁₀, formaldehyde (CH₂O), temperature, relative humidity, and total VOCs (TVOC). A total of 1.79 million raw records were processed, generating 1.42 million indoor measurements and 264,311 hourly aggregated observations. Complementary VOC and aldehyde sampling campaigns supported the interpretation of pollutant specific temporal patterns. Results revealed differentiated and recurrent IAQ signatures across hospital areas. CO₂ dynamics were mainly associated with occupancy and ventilation demand, whereas formaldehyde showed more persistent and seasonally dependent sensor patterns, compatible with the influence of indoor emission sources and ventilation heterogeneity. Targeted chemical characterization further identified area specific pollutant profiles associated with cleaning activities, laboratory processes, materials, and operational conditions. Importantly, recurrent periods were identified in which acceptable occupancy related CO₂ conditions coincided with elevated chemical pollutant levels. These findings show how long-term multi-parameter monitoring can distinguish function and pollutant specific IAQ signatures that would remain obscured by aggregated or single parameter assessments, providing an evidence base for area specific monitoring strategies and future adaptive hospital IAQ management. Full article

This paper presents a multi-parameter characterisation of the atmospheric composition at the RADO-Bucharest observatory, a regional WMO-GAW and ACTRIS facility in southeastern Europe, by anchoring recent observations within a multi-annual baseline (2015–2024). This paper utilises data from multi-wavelength active remote sensing, high-resolution near-surface speciation, and modelling to evaluate complex urban and transboundary processes across different aerosol and clouds regimes. The year 2025 was marked by an atmospheric perturbation, which was characterised by a quantifiable departure from the decadal climatology. Using Z-score analysis, this perturbation was identified as a seasonal decoupling: an atypical reversal in the vertical particle-size distribution occurred in the free troposphere between July and October, while a transition toward a high-absorption aerosol regime was recorded near the surface during the winter months. These shifts are quantified by a significant drop in the columnar Single Scattering Albedo and a systemic increase in high-troposphere Lidar ratios exceeding 70 ± 12 sr, indicating the presence of advected combustion products aloft. At the surface level, chemical speciation measurements recorded an overall increase in wintertime particulate mass concentrations alongside elevated levels of More-Oxidized Oxygenated Organic Aerosol (MOOOA) compared to previous years, reflecting an intensified accumulation of aged, processed emissions during the January–February period. Furthermore, this paper documents cloud vertical structure and phase occurrence, highlighting a persistent seasonal stratification. The application of unified inversion frameworks is demonstrated through case studies of smoke and mineral dust, applying the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm to retrieve vertically distributed aerosol microphysics through lidar–photometer integration. Maintained under rigorous quality assurance protocols, these results demonstrate the value of continuous, multi-instrumental profiling to quantify transboundary perturbations and improve regional energy budget representations. Full article

Continuous multi-parameter monitoring is important for characterizing environmental conditions in commercial beef cattle facilities, but sensor performance and system reliability require evaluation under production conditions. This study developed a LoRa-based multi-sensor node for air temperature, relative humidity, CO₂, NH₃, air speed, and illuminance. Field performance was evaluated at one commercial farm by synchronous comparison with commercial instruments using 1-min paired observations and regression- and agreement-based analyses. Nine prototype nodes were subsequently deployed at three sites for 14-day monitoring periods. Temperature and relative humidity showed the strongest linear relationships with the comparison instruments (R² = 0.995 and 0.994), followed by illuminance, air speed, and CO₂ (R² = 0.990, 0.863, and 0.773). NH₃ showed a weaker relationship (R² = 0.604), improving after 10-min aggregation; most application-monitoring estimates below 5 ppm were extrapolated rather than validated absolute concentrations. Overall data availability was 99.66%, and continuous monitoring captured temporal patterns in thermal conditions, air quality, local airflow, and illuminance. The evaluation focused on environmental monitoring performance rather than animal-based outcomes. These findings support parameter-specific environmental monitoring and multi-site deployment in commercial beef cattle facilities, with potential for future anomaly detection and environmental control. Full article

Post-disaster underground mine rescue requires environmental perception and remote operation before personnel can enter hazardous areas. In this article, we describe the system-level integration of a tracked detection robot, a 1432–1442 MHz wireless Mesh link, three deployable relay beacons, a handheld terminal, and multi-parameter sensing. The radio modules use 0.5 W transmit power, a nominal 30 Mbps data rate, and vertically polarized 5 dBi omnidirectional antennas, while the analytical formulation links differential-drive commands, path-loss and link-quality estimates, and a consecutive-sample beacon deployment rule. Physical records from three mine rescue test facilities document manual motion, navigation interface operation, Mesh topology formation, environmental data display, and audio/video return. While these records establish prototype feasibility and functional-chain continuity, they do not provide retained quantitative RSSI, packet loss, latency, trajectory error, stopping distance, endurance, or repeated-trial statistics. Accordingly, the proposed models are presented as design and measurement frameworks rather than validated predictors or evidence of performance superiority. Full article

Winter wheat is a cornerstone of China’s grain production, contributing substantially to national food security and overall cereal output. This study modeled the nonlinear associations between multitemporal remote sensing variables and winter wheat yield. To produce high-spatiotemporal-resolution inputs, we used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to integrate Sentinel-2 normalized difference vegetation index (NDVI) data with MODIS NDVI data, generating NDVI composites at 8-day intervals with a 10-m spatial resolution. The NDVI, actual evapotranspiration (ET), land surface temperature (LST), precipitation (PRE), and soil moisture (SM) were selected as predictors for yield estimation because they are closely associated with winter wheat growth and yield formation during primary growth stages. By integrating the local temporal feature-learning capacity of a one-dimensional convolutional neural network (1-D CNN) with the strength of a bidirectional long short-term memory (BiLSTM) model in capturing temporal dependencies within time series, a BiLSTM–CNN model was constructed for wheat yield estimation and prediction. The BiLSTM–CNN model showed higher estimation accuracy than individual BiLSTM and 1-D CNN models, with an R² of 0.69 and root mean square error (RMSE) of 478.68 kg/hm². The use of all the parameters produced the best estimation performance among all the parameter combinations. Approximately two months before harvest, the model still provided satisfactory yield prediction accuracy. This study provides an important theoretical basis for high-accuracy regional winter wheat yield estimation and pre-harvest forecasting. Full article

Bursting is fundamental to neural information encoding and plays critical roles in rhythm generation, sensory processing, and motor control. However, the dynamical mechanisms by which multiple parameters coordinately regulate bursting properties in mesencephalic trigeminal neurons remain unclear. Here, using fast–slow decomposition we reveal three distinct asymmetries in the regulation of bursting dynamics by four key parameters, g_NaR, g_NaP, I_app, and k_b. First, asymmetric shifts in the SNLC and subH bifurcation curves in the two-parameter plane, rather than symmetric displacements, underlie the expansion or compression of the subH–SNLC window, directly modulating bursting duration (BD) and inter-burst interval (IBI). Second, g_NaP produces a paradoxical asymmetry: although it narrows the subH–SNLC window, it simultaneously reduces the per-spike consumption of the slow variable h_p via sustained depolarization, thereby prolonging BD while shortening IBI, a dissociation that cannot be predicted from the bifurcation window alone. Third, I_app induces an opposing asymmetry: it widens the subH–SNLC window while shortening IBI, revealing that bifurcation expansion and the silent phase duration are not monotonically coupled. All four parameters increase spike count per burst (SCPB) and BD, yet their effects on IBI diverge, with g_NaR and k_b extending it whereas g_NaP and I_app shorten it, establishing that active and silent phases are asymmetrically regulated. Our findings demonstrate that bursting characteristics emerge from the cooperative interplay of bifurcation positions, membrane potential, slow-variable dynamics, and hysteresis. These results provide dynamical insights into the asymmetric regulation of bursting rhythms by ion channel parameters, and may offer theoretical guidance for neuromodulation strategies targeting channel-related disorders. Full article

Fibre-optic Fabry–Pérot (FP) multi-cavity sensors hold significant promise for multi-parameter measurements in extreme environments. However, precisely demodulating signals from each cavity within the composite interference spectrum remains a key bottleneck that limits measurement accuracy. To address this issue, this paper proposes a high-precision demodulation method that combines digital filtering with distortion region suppression. This method first uses digital bandpass filters to separate the interference signals from each sub-cavity from the composite spectrum. It then removes the spectral regions distorted by the filters, retaining only the stable central spectral band for interference order fitting and cavity length calculation. To comprehensively evaluate the method, we systematically compared the distortion characteristics of six finite impulse response (FIR) window-function filters and three infinite impulse response (IIR) filters during spectral separation, along with their impact on demodulation accuracy. Simulation results show that, within the 490–510 μm cavity-length range, FIR filtering combined with distortion suppression reduces the demodulation error to below 0.04 nm, approaching the theoretical limit. In contrast, the best-performing IIR filter still yields an error of 0.58 nm after the same processing. Furthermore, in simulations covering a wide range of 200–800 μm, the maximum demodulation error of this method is less than 0.53 nm, demonstrating its excellent robustness. In dual-cavity temperature sensing experiments, the partial-spectrum demodulation strategy with distortion suppression reduced the standard deviation of cavity-length fluctuations from 0.112 nm to 0.072 nm, and the maximum adjacent point jump from 2.158 nm to 0.464 nm, compared with full-spectrum demodulation. This significantly enhances the continuity and stability of demodulation. This study not only provides a high-precision, highly robust demodulation scheme for multi-cavity FP sensors but also offers clear theoretical and experimental grounds for selecting and optimising digital filters in practical engineering applications. Full article

Sesbania grandiflora (Agathi) is a nutritionally and pharmacologically rich tropical legume tree whose leaves contain substantial quantities of phenolic acids, flavonoids, carotenoids, calcium, and iron. Despite their well-documented bioactivity, comprehensive multi-parameter characterisation of paneer fortified with S. grandiflora leaf powder has not previously been reported, representing the focus of the present study; paneer is the most widely consumed “acid–heat coagulated” dairy product across South Asia. The present study developed and characterised paneer fortified with S. grandiflora leaf powder at 0% (T1, control), 0.5% (T2), 1.0% (T3), and 1.5% (T4) and comprehensively evaluated the effects on proximate composition, pH, instrumental colour (CIE L*, a*, b*), total phenolic content (TPC), flavonoid content, antioxidant activity (phosphomolybdenum and DPPH methods), texture profile analysis (TPA), water activity (aw), HPLC-based phenolic profiling, “SDS PAGE” protein characterisation, microbiological quality, and sensory acceptability. Fortification significantly increased protein (19.00–22.00%), crude fibre (0.40–2.30%), ash (1.20–1.30%), TPC (2.18–5.82 mg GAE/g dw), flavonoid content (0.31–1.56 mg RE/g dw), and antioxidant activity (phosphomolybdenum assay: 0.03–0.16 mg AAE/g; p < 0.05) with increasing leaf powder levels, while fat and moisture declined proportionally. HPLC profiling identified quercetin and kaempferol as the dominant flavonoids in fortified treatments, with total quantified polyphenols reported at 3.35 mg/g dry weight in T4. TPA revealed that T3 achieved comparable hardness to T1 (58.3 vs. 62.1 N), while maintaining superior springiness (0.84) and cohesiveness (0.68). Water activity decreased significantly from 0.973 in T1 to 0.951 in T4, corroborating enhanced microbial stability. “SDS-PAGE” revealed additional protein bands (18–35 kDa) in fortified samples, consistent with a plant-protein contribution, although this does not by itself confirm structural integration into the casein matrix. Microbiological counts were significantly reduced in fortified treatments throughout a 9-day refrigerated storage period, with T3 remaining within acceptable limits (<6.0 log CFU/g TPC) throughout. Sensory evaluation identified T3 (1.0%) as the optimal formulation with the highest overall acceptability score (8.2/9). These findings establish S. grandiflora-fortified paneer as a promising functional dairy formulation with an improved nutritional and antioxidant profile, which may contribute to nutritional enrichment in South and Southeast Asian populations, pending further validation of bioavailability and health outcomes. Full article

The rapid upscaling of offshore wind turbines has made local scour around monopile foundations a critical concern for foundation capacity and long-term service safety. This state-of-the-art review examines local scour geometry and its implications for monopile bearing performance. Drawing predominantly on the literature published between 1990 and 2026, the review systematically evaluates the influence of foundation dimensions, wave–current conditions, and seabed properties on scour development, and critically assesses key geometric parameters—including maximum scour depth, width, side slope angle, volume, and three-dimensional morphology—for their representation of soil loss and use in current engineering practice. The applicability and limitations of physical experiments, empirical methods, numerical simulations, and field monitoring are compared. The mechanistic linkage between scour geometry and bearing response is elucidated through stress-path alterations, soil confinement degradation, and geometric interactions, with effects on lateral capacity, cyclic response, and natural frequency analyzed. The findings demonstrate that maximum scour depth alone is insufficient; reliable assessment requires multi-parameter characterization incorporating width, slope, volume, and 3D morphology. Distinct from existing reviews focused on prediction or monitoring, this review contributes a systematic elucidation of the mechanistic pathways—soil removal, stress unloading with confinement degradation, and stiffness migration—through which geometry affects bearing performance, and proposes standardized reporting protocols. Future research priorities include addressing scale effects for large-diameter monopiles, advancing long-term 3D monitoring, and developing coupled hydrodynamic–geotechnical frameworks. This review offers a structured reference for researchers and practitioners in scour assessment and foundation design. Full article

The rapid development of quantum technology and single-photon LiDAR places ever-increasing demands on detection systems in terms of bandwidth suppression, wavelength stability, and transmission accuracy. Conventional narrowband filters, however, are falling short in sub-nanometer bandwidth control and high-temperature stability, thereby failing to support high-precision measurements. Consequently, ultra-narrowband optical filters have emerged as a key approach to overcoming these performance bottlenecks. This paper focuses on the core technologies involved in the development of such filters. In the design phase, multiparameter optimization based on a Fabry–Perot (F-P) etalon translates application requirements into fabrication parameters. For substrate fabrication, a sequential process of computer numerical control (CNC) milling, lapping, chemical–mechanical polishing (CMP), and ion-beam polishing, followed by atomic layer deposition (ALD) step formation and ion-beam evaporation coating, is employed to achieve a nanometer-level surface figure and roughness. Temperature control is achieved via a dual-tank hybrid circulation system, maintaining stability within ±0.1 °C. Test results show that the fabricated filter exhibits a full width at a half maximum (FWHM) of 30–60 pm, a free spectral range (FSR) of 260 ± 5 pm, a temperature stability of ≤3 pm/°C, and a peak transmittance of ≥80%. These results preliminarily confirm the device’s excellent overall performance. Full article