Search Results (1,400)

This study investigates the influence of near-surface air temperature on the performance of internal combustion engines during mobile transects conducted in Mexicali, Baja California, Mexico, one of the hottest cities in North America. Field measurements were carried out along a 15 km urban transect on representative days in April, August, and February. Air temperature and relative humidity were recorded simultaneously at two engine air intake heights (0.66 m and 2.5 m), complemented by surface temperature data obtained from both in situ measurements and Landsat 8 thermal imagery. The results indicate that near-surface air temperature exhibits considerable spatial and temporal variability and is closely associated with land surface temperature (LST) patterns derived from satellite observations. The correction factor (Cf), used to quantify the combined effects of air temperature and atmospheric pressure on engine performance, showed that extremely high temperatures (approaching 50 °C) may reduce engine performance by up to 3.35% relative to standard test conditions. Conversely, cooler winter conditions may improve engine performance by approximately 4.6%. These results suggest that vehicle operation under extremely hot climatic conditions may deviate from the standardized assumptions adopted by the Intergovernmental Panel on Climate Change (IPCC) for emission factor estimation. This study contributes to a better understanding of the effects of extreme urban heat on vehicle performance and demonstrates that localized thermal conditions may influence the assumptions commonly used in vehicle emission assessments. The findings provide valuable information for improving greenhouse gas emission inventories and support evidence-based climate adaptation and urban planning strategies in arid cities. Full article

Microbial biostimulants and microbial plant protection products overlap in biological function, creating both R&D opportunities and regulatory challenges. In particular, multi-strain bacterial consortia may simultaneously affect nutrient mobilisation and abiotic stress tolerance, induce resistance, and demonstrate direct antagonism against phytopathogens. This multifunctionality complicates early product development because strain identity alone is sometimes insufficient in predicting product function, efficacy, or the most appropriate regulatory and claims strategy. Here, we used non-targeted LC-MS metabolomics as a hypothesis-generating tool to support formulation decisions for microbial consortia. Three bacterial consortia were compared: a full soil-oriented consortium C1 containing Bacillus spp., Rhodopseudomonas palustris, Nitrosomonas europaea, and Nitrobacter winogradskyi; a Bacillus-only consortium C2 intended for foliar stress-resilience applications; and a Bacillus-only consortium C3 grown with a chitin-related inducer to promote biocontrol-associated metabolism. Metabolomic profiling revealed clear differences between formulations. The full consortium C1 showed higher relative abundances of features putatively associated with biofertilising and growth support, whereas the Bacillus-only consortium C2 contained features putatively associated with biocontrol and induced resistance that were not detected in C1 under the applied criteria. The addition of the chitin-related inducer (C3) did not yield a completely distinct metabolite profile but increased the relative abundance of selected features putatively associated with biocontrol, while decreasing features putatively annotated as auxin-related or associated with abiotic stress responses. These results suggest that non-targeted metabolomics can help differentiate metabolic profiles putatively associated with biostimulant- and plant-protection-oriented formulations and thereby support prioritisation before extensive greenhouse or field testing. By linking formulation, medium composition, and microbial interactions to measurable metabolic signatures, metabolomics provides an evidence-based, hypothesis-generating framework for formulation development and the prioritisation of subsequent efficacy trials. Full article

Sprint performance improves throughout adolescence as a result of both structural and neuromuscular development. In particular, increases in body height, lower limb length, and muscle volume have been consistently linked to improvements in sprint performance. However, allometric scaling of force, speed, and power remain lower in adolescents compared to adults even after structural differences are accounted for, implicating neural factors as independent contributors to performance development. The purpose of this study was to investigate differences in neuromuscular activation patterns of the lower limb muscles during maximal sprinting between youth male athletes across two age groups (U19: 17–19 years; U16: 13–16 years). Eighteen athletes performed a 50 m maximal sprint. Spatiotemporal variables (running speed, step frequency, step length) were measured over 30–50 m using a high-speed camera (240 Hz) and timing gates. Electromyographic (EMG) signals were recorded simultaneously from ten lower limb muscles using wireless EMG sensors (2000 Hz): rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gluteus maximus (Gmax), gluteus medius (Gmed), vastus lateralis (VL), vastus medialis (VM), tibialis anterior (TA), gastrocnemius (GAS), and soleus (SOL). Root mean square (RMS) amplitude was calculated across four gait phases (contact, early-swing, mid-swing, late-swing) and normalised to maximal voluntary Isometric contraction (%MVIC). The U19 group demonstrated significantly greater running speed (U19: 9.49 ± 0.39 vs. U16: 8.67 ± 0.25 m·s⁻¹, p < 0.001), step frequency (U19: 4.49 ± 0.12 vs. U16: 4.35 ± 0.16 Hz, p = 0.004), and step length (U19: 2.12 ± 0.12 vs. U16: 1.99 ± 0.06, p = 0.010) than U16. The overall pattern of lower limb muscle activation across the gait cycle was broadly similar between groups; however, a significant group × phase interaction was observed for RF (p = 0.003, F = 5.257, η² = 0.247), with post hoc analysis revealing greater RF activation during early swing in U19 (p = 0.033). These findings may indicate that sprint-specific training in youth athletes is associated with not only structural but also neuromuscular differences, specifically reflecting enhanced RF recruitment during the phase-critical moment of early swing—a window in which high-threshold motor unit activation is most mechanically decisive. EMG-based assessment of hip flexor activation during maximal sprinting may provide a complementary tool, pending further validation, for talent identification and training prescription in youth track and field. Full article

Active surface shape control is a key engineering technology enabling high-frequency operation and high-performance observations in modern large-aperture radio telescopes. By determining the achievable controllable accuracy of the primary reflector, its performance further constrains the aperture efficiency and long-term stability of telescope sensitivity. As millimeter- and submillimeter-wave astronomy advances toward higher operating frequencies and larger survey scales, key astrophysical questions increasingly demand the simultaneous achievement of high angular resolution, high surface-brightness sensitivity, and high imaging efficiency over wide fields of view. Limited by field-of-view coverage, sensitivity, or spatial-scale uniformity, traditional single-dish or interferometric array systems struggle to simultaneously satisfy these observational requirements. Consequently, large-aperture, wide-field millimeter/submillimeter single-dish telescopes are regarded as an important technological pathway for achieving multi-scale, high-fidelity observational capability. Their performance critically depends on effective control of primary reflector accuracy and system stability under multiple disturbance sources, such as gravity and thermal effects. From a system-level perspective, this paper provides an overview of the overall architecture of active surface control technologies for large-aperture millimeter- and submillimeter-wave single-dish radio telescopes. Focusing on three core components—surface measurement, actuator execution, and surface control strategies—it systematically reviews the underlying technical principles, representative engineering practices, technological evolution, and recent research progress. The characteristics of different technical approaches are summarized and analyzed, providing a reference for the design and further study of active surface control systems for large-aperture radio telescopes. Full article

Magnetorheological elastomers (MREs) exhibit magnetic-field-dependent stiffness and can, therefore, be used to tune the natural frequency of a dynamic vibration absorber through the applied coil current. This study evaluates the extension of a previously developed MRE-based semi-active absorber to two automotive noise, vibration, and harshness transmission paths: road-input-related subframe vibration and engine-induced seat vibration. A standard cylindrical actuator was installed below the subframe of a passenger vehicle and tested on a rough road at 10, 20, and 30 km/h. Vehicle speed was used as the operating-condition feedback variable for current selection, while additional fixed-current measurements were conducted to characterize the current-dependent response. Cabin sound pressure was measured simultaneously. A compact actuator with a lightweight resin housing was also developed for the seat application; engine speed was used as the feedback variable, and the current was selected with reference to the second-order engine excitation. The natural frequencies of both actuators increased with current, although the compact actuator had a smaller tuning range. The actuator-installed conditions produced local, current-dependent reductions in subframe and seat vibration spectra. Changes in cabin sound pressure were smaller, frequency-dependent, and not uniform. The results demonstrate the feasibility of the operating-condition-based tuning of MRE dynamic absorbers for local automotive vibration paths, while also identifying limitations associated with passive installation effects, magnetic-circuit efficiency, packaging, and multi-path cabin acoustics. Full article

Refractory high-entropy and compositionally complex alloys are routinely compared by nominal composition and as-processed phase, yet processing changes the material that is actually tested. This is a critical, mechanism-led narrative review rather than a systematic review; the databases, complete search strings, screening sequence, inclusion and exclusion criteria, and evidence-grading rubric are reported so that coverage and selection bias can be assessed independently. This review synthesizes 186 publications around the NbTaV compositional core and compares alloys through directly measurable features of the processed state: interstitial content, local chemical order, grain-boundary chemistry, defect and grain architecture, phase morphology, compositional gradients and surfaces. Every source is assigned to a compositional tier and graded along four evidence axes: 96 of the 186 sources report Nb–Ta–V-containing states (Tier I), 58 are body-centered cubic refractory comparators (Tier II) and 32 are transferred-mechanism analogues (Tier III), and only 14 Tier I sources supply direct tensile, fracture or tensile-creep measurements. This asymmetry, rather than any disagreement between compositions, is the field’s binding evidence constraint. Direct tensile, fracture, and creep measurements are kept separate from compression, hardness, calculation, and screening evidence. This separation reconciles observations that otherwise appear to conflict: oxygen can strengthen or embrittle; lattice distortion can raise strength while lowering dislocation mobility; local order can harden the alloy, redirect defects or precede decomposition; and second phases help only within morphology- and service-specific compatibility windows. The strongest tensile behavior is obtained when mobile plasticity carriers are preserved, and interstitials, interfaces and phase continuity are simultaneously controlled. High-temperature, environmental, and irradiation performance depend additionally on the transition from the as-manufactured condition to the state that evolves during service. Quantitative matching tolerances for the convergent-state falsification test, service-condition-specific validation hierarchies, ordinal scoring rules for the phase-compatibility map, and a source-level audit of every quantitatively compared value are provided so that the framework can be tested and the synthesis independently checked. Full article

The rapid expansion of high-speed railway networks has increased the operational energy demand and indoor overheating risk of large-scale, high-volume railway station buildings. Natural ventilation is a climate-responsive passive strategy that can improve indoor environmental quality and reduce reliance on mechanical cooling. However, its contribution to the operational sustainability of large transportation buildings remains insufficiently quantified, particularly in hot-summer and cold-winter regions. This study investigates a seasonally adaptive window-opening strategy for Chengdu Station, with particular attention to major functional spaces such as waiting halls and commercial areas. A DesignBuilder model was used to simulate six ventilation scenarios, ranging from doors-only operation to fully open doors and windows. The effects of different window-opening ratios on hourly indoor temperature, relative humidity, adaptive thermal comfort, and annual building energy use were systematically evaluated. The simulation approach was further assessed against field measurements obtained from a comparable large railway station. The results reveal a pronounced nonlinear and seasonal response to the window-opening ratio. In winter, maintaining a very low opening ratio or keeping only the entrance doors open limits unnecessary heat loss. During the transitional seasons, opening ratios of 40–60% are sufficient to remove residual indoor heat while maintaining acceptable thermal conditions. In summer, the marginal improvement in ventilation performance becomes limited when the side-window opening ratio exceeds approximately 80%; therefore, an opening ratio of 80% was selected as a practical operating threshold rather than an absolute thermal optimum. Based on these seasonal characteristics, a month-by-month window-opening strategy was developed. Compared with the doors-only baseline, the proposed strategy reduced the annual high-temperature-hour ratio from 33.4% to 16.36%, corresponding to a decrease of 17.04 percentage points and a relative reduction of approximately 51.0%. Total annual building energy use decreased from 32,238.8 MWh to 25,923.8 MWh, representing an energy saving of 19.6%. These findings demonstrate that seasonally adaptive natural ventilation can simultaneously reduce overheating risk and operational energy demand while maintaining acceptable indoor thermal conditions. The proposed strategy provides a quantitative basis for the sustainable, energy-efficient, and intelligently managed operation of large-space railway stations in hot-summer and cold-winter regions. Full article

Approximately 2.3 billion people still cook over open fires or on basic stoves using polluting fuels, generating indoor air pollution responsible for 3.7 million premature deaths annually. Progress toward real-world health impact has been constrained by a persistent disconnect between computational fluid dynamics (CFD) modeling, standardized experimental evaluation, and sociocultural adoption research. This scoping review maps the current state of evidence across these three domains, analyzing 143 peer-reviewed studies published between 2007 and 2025 using predefined inclusion criteria and bibliometric analysis with VOSviewer v.1.6.20. Thirteen cookstove technologies were characterized by compiling heterogeneous evidence from Water Boiling Tests (WBTs), CFD simulations with k-ε turbulence closure, and CO and PM_2.5 emission protocols. Direct combustion stoves achieve thermal efficiencies of 10–21% under real-world conditions, while TLUD gasifiers and forced-draft systems with densified fuels reach 30–47%. Bibliometric analysis reveals that engineering, epidemiology, and social sciences operate as isolated research communities. None of the technology reviewed simultaneously integrated computational validation, field emissions assessment, and clinical impact evaluation; this a gap remains the central barrier to translating laboratory performance into measurable public health outcomes. These findings point toward integrated research designs connecting fluid dynamic optimization with exposure modeling, clinical follow-up, and the sociocultural needs of communities. Full article

Agrivoltaic (APV) systems enable the simultaneous production of food and renewable electricity. They create spatially heterogeneous environments that influence crop productivity. The quantitative relationships linking cumulative photosynthetically active radiation (PAR) with wheat productivity remain poorly studied. The objective of this study was to quantify the spatial distribution of cumulative PAR beneath a commercial single-axis tracking APV system and determine its relationship with wheat flowering, physiological responses, and grain yield. Wheat was cultivated across a 19-row transect between photovoltaic arrays at the Bar-Ilan University Agrivoltaic Research Farm, Israel. Cumulative PAR was measured separately for flowering (88 days after sowing, DAS) and physiological maturity (158 DAS). Physiological traits (plant height, SPAD chlorophyll index, and leaf nitrogen concentration), flowering, grain yield, and yield loss were quantified along the radiation gradient. Cumulative PAR varied among the 19 rows from 347 to 1917 mol m⁻² at flowering and from 1570 to 4451 mol m⁻² at maturity, while corresponding PAR losses ranged from 82.2% to 1.6% and 65.2% to 1.3%, respectively. Flowering increased from 33% in the most shaded row to 100% in the central rows and exhibited a strong quadratic relationship with cumulative PAR (R² = 0.916; r = 0.913; p < 0.001). Plant height increased with increasing cumulative PAR, whereas SPAD and leaf nitrogen were greatest in the shaded edge rows, indicating physiological acclimation to reduced irradiance. Grain yield ranged from 2.96 to 6.04 t ha⁻¹, corresponding to 46.2% yield loss to a 9.8% yield gain relative to the open-field reference. Grain yield was strongly associated with cumulative PAR (R² = 0.811; r = 0.862; p < 0.001), while grain-yield loss closely followed PAR loss (R² = 0.842; r = −0.883; p < 0.001). The results demonstrate that cumulative seasonal PAR is the principal environmental variable governing wheat development and productivity beneath tracking APV systems. The predictive equations developed here provide a practical framework for designing agrivoltaic systems that maximize crop productivity while maintaining efficient photovoltaic electricity generation. Full article

Multi-trace amplitude variation with angle inversion (MAVAI) is a vital tool for estimating the physical parameters of subsurface media, and it plays an important role in oil and gas exploration. However, the existing MAVAI method relies on the Kronecker product to construct an extremely large-scale inverse problem, and its computational inefficiency limits its widespread application. Furthermore, regarding regularization constraints, the existing MAVAI method only considers the smoothness of the inversion parameters, which leads to ambiguous formation boundaries and hinders accurate identification for complex reservoirs. To address these issues, a compressed MAVAI method with elastic half-norm regularization is proposed. Specifically, we first developed a compressed MAVAI (CMAVAI) framework that uses compressed measurements of seismic data and reference models in a sparse domain to construct the CMAVAI objective function, thereby reducing the scale of the inversion problem and improving inversion efficiency. Subsequently, the elastic half-norm is introduced into the CMAVAI framework as a regularization constraint for reservoir parameter estimation. Since the elastic half-norm can simultaneously characterize both the smoothness and blocky features of the subsurface medium, it effectively improves inversion accuracy compared to the MAVAI method. Finally, the performance of the proposed method is evaluated using a theoretical model and field data. The results demonstrate that, compared with the traditional MAVAI algorithm, the CMAVAI framework can effectively improve inversion efficiency while maintaining inversion accuracy. Moreover, the CMAVAI method regularized by the elastic half-norm can improve the accuracy of inversion parameters while retaining the high prediction efficiency of the CMAVAI framework. Full article

Accurate estimation of forest structural attributes over operational scales remains challenging because unmanned laser scanning (ULS) provides extensive spatial coverage but limited representation of internal stem structure, whereas terrestrial and mobile laser scanning (TLS/MLS) provide detailed stem measurements over relatively small areas. This study investigates a calibration-transfer framework in which small areas of terrestrial or fused LiDAR are used to improve diameter at breast height (DBH) estimation across much larger regions surveyed only by ULS. ULS, TLS, MLS and fused laser scanning (FLS) datasets were analysed for radiata pine and eucalyptus plantations. TreeLS-derived DBH measurements from terrestrial and fused point clouds were used as reference data to evaluate several distribution-aware and voxel-based imputation approaches for correcting regression-derived ULS estimates. Across the study sites, the best-performing imputation methods reduced stand-level mean DBH differences by as much as 95% relative to the uncorrected ULS regression estimates, resulting in substantially improved agreement with field-observed stand means while simultaneously producing DBH distributions that more closely matched the corresponding TreeLS-derived reference distributions. Voxel-based imputation performed particularly well for radiata pine and remained competitive for eucalyptus, while several distribution-based approaches achieved comparable or better performance in particular stands. These findings demonstrate the potential for transferring information from relatively small terrestrial LiDAR calibration areas to larger ULS-only acquisitions, improving stand-level DBH distribution estimates without requiring complete terrestrial coverage. Because validation was performed using stand-level field summary statistics rather than matched individual trees, the reported performance should be interpreted as demonstrating the potential of the approach under the conditions evaluated rather than universal individual-tree accuracy. Full article

Waterfront multi-pit excavation in highly permeable gravel deposits can induce complex pile deformation because excavation unloading, groundwater drawdown, and river-stage disturbance act simultaneously. This problem is particularly important for foundation pits constructed near existing pile-supported buildings, yet the combined effects of excavation sequence, pit spacing and excavation depth under river-connected gravel aquifers remain insufficiently quantified. This study fills the research gap on the seepage–stress-coupled deformation mechanism of adjacent building piles under multi-pit excavation in highly permeable gravel strata, and quantifies the spatial superposition effect of excavation disturbance. In this study, a three-dimensional seepage–stress-coupled finite-element model was established for the Yidu Green Intelligent Shipbuilding Industrial Park project on the right bank of the Yangtze River. The model was validated against field monitoring data from the slipway pit excavation, and comparisons show that the relative errors of pile horizontal displacement and ground settlement between simulation and measurement are both less than 8%, verifying the reliability of the numerical model. The validated model was then used to evaluate single-pit excavation, different multi-pit excavation sequences, pit group spacing, excavation-depth ratio and steel sheet pile parameters. The results show that pile deformation is controlled not only by the excavation of an individual pit, but also by the interaction between pit groups located on opposite sides of the building. Simultaneous excavation reduced the peak horizontal displacement of the adjacent building pile by 45.7% compared with single excavation of the slipway pit and by 31.6% compared with the slipway-first sequence. For pits on the same side of the building, a far-to-near excavation sequence produced the smallest pile displacement and settlement. The inter-pit ground deformation changed from heave-dominated to settlement-dominated when the spacing increased to approximately 90–100 m. The research results can provide reference for deformation control and safety assessment of adjacent buildings during multi-pit excavation in similar highly permeable gravel areas. These findings indicate that coordinated excavation sequence and spacing control can effectively reduce deformation risks in waterfront multi-pit projects, although the proposed thresholds should be verified for different layouts, geological conditions and hydrogeological conditions. Full article

Quantitative precipitation estimation (QPE) is a fundamental task of hydrometeorological applications, ranging from flash-flood detection to water-resource management. While weather radars offer superior spatial coverage compared with rain gauges, traditional estimation based on the empirical $Z-R$ (reflectivity factor vs. rainfall rate) relationship fails to capture spatial variability and tends to underestimate extreme rainfall. The idea pursued here is to learn the radar-to-rainfall mapping by means of a convolutional neural network (CNN) trained on co-located radar and rain-gauge data; thus, once trained, the network can convert radar reflectivity measures into rainfall values, even where gauges are unavailable. Although machine learning techniques are promising for this task, they typically demand large training sets. To operate in a limited-data regime, we introduce a U-Net architecture that separates the analysis of space from that of time: each block first looks at the structure of the reflectivity field and then at how it changes over consecutive radar scans, extracting spatiotemporal features from volumetric data with fewer parameters than a full three-dimensional filter. The model is evaluated on a severe convective event that affected Tuscany, Italy, on 2 November 2023, benchmarking its performance against the classical Joss–Waldvogel $Z-R$ relationship (suitable for convective events), a data-driven log-regression of weather-radar and rain-gauge data, and a baseline CNN architecture. The main advantages are negative bias—i.e., underestimation of rainfall—more than halved and correlation with rain-gauge measures more than doubled, under the same operational conditions. What is noteworthy is the capability of learning the model from radar and rainfall data taken in different times and places, as well as the possibility of converting a reflectivity map into a rainfall map without the need for simultaneous rain-gauge measures. This is an asset of fixed parametric methods; however, they are far less accurate. Full article

The Cryogenic Anticoincidence (CryoAC) detector is a critical subsystem designed to reduce the particle background for the X-ray Integral Field Unit (X-IFU) instrument onboard the NewAthena space observatory, the next ESA X-ray Large mission. Advancing this technology to Technology Readiness Level 5 (TRL5) requires a unified validation spanning both cleanroom microfabrication repeatability and mK low temperature operational performance. This work presents the complete development cycle of the Demonstration Model 1.2 (DM 1.2), which is aimed at completing the TRL5 demonstration path featured by all the critical technologies operating simultaneously. First, single-process verification protocols were established for Iridium pulsed laser deposition, Reactive Ion Etching (RIE), and deep silicon trenching via the Bosch process. Second, three identical single-pixel prototypes were fabricated and subjected to mK characterization. Four-wire resistance measurement results confirmed a 2/3 production yield against strict design targets (T_C ~100 mK). Finally, functional testing at a bath temperature of 50 mK using a VTT FAB4 SQUID readout demonstrated excellent performance, including a low-energy threshold of ~0.6 keV, a pixel power dissipation of 5.15 nW, and an energy resolution ΔE_FWHM = 735 eV at 6 keV. These combined achievements successfully validate the entire manufacturing and operational baseline against all primary space mission requirements. This paper has to be considered as a review of the CryoAC technology path toward the TRL5 achievement; main findings will be reported and discussed. Details are relegated to other papers. Full article

Magnetic field (MF) technologies have been explored in agriculture since the 1930s, with research activity increasing markedly since 2016. However, they have not achieved mainstream adoption, partly because no MF-specific validated methodology exists for evaluating their effects under realistic field conditions. Unmanned Aerial Vehicle (UAV)-based multispectral sensing represents a potential pathway to address this limitation by providing spatially explicit, non-destructive estimates of key canopy physiological variables at field scale, thereby enabling, for the first time, the systematic evaluation and validation of MF treatment responses under open-field conditions. To realise this potential, however, a common evidential basis must first be established by identifying crop physiological variables that are both consistently modulated by MF treatments and reliably detectable by UAV remote sensing. This study addressed this challenge through a dual-stream evidence synthesis of 216 peer-reviewed publications, comprising 102 studies on MF treatments in agricultural crops and 114 studies on UAV-based multispectral monitoring. Evidence from both research domains was synthesised to identify physiological variables that are simultaneously responsive to MF treatments and detectable through UAV remote sensing. Five direct bridge variables were identified: chlorophyll content, nitrogen use efficiency (NUE)/nitrogen assimilation, above-ground biomass (AGB), leaf area index (LAI), and yield. Chlorophyll content emerged as the strongest bridge variable, combining consistent MF responsiveness with UAV estimation accuracies of up to R² = 0.90. Based on these findings, a conceptual framework was developed linking MF treatments, UAV-derived vegetation indices, ground-truth measurements, and machine-learning approaches for field-scale validation. The review revealed a complete absence of integration between the two research domains within the reviewed corpus, despite their strong biological and methodological compatibility. The proposed framework is conceptual and remains to be experimentally validated; it provides the first operational pathway for evaluating MF technologies under realistic farming conditions and may support future research on sustainable and digitally enabled crop production systems. Full article