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Grasslands are one of the most common land‐cover types, providing important ecosystem services globally, yet few studies have examined grassland critical‐zone functioning throughout hillslopes. This study characterised a coastal grassland over a small hillslope at Point Reyes National Seashore, California, using multidisciplinary techniques, combining remotely‐sensed, geophysical, plant, and soil measurements. Clustering techniques delineated the study area into four landscape zones, up‐, mid‐, and down‐slope, and a bordering riparian ecotone, which had distinct environmental properties that varied spatially across the site, with depth, and time. Soil moisture increased with depth and down slope towards a bordering riparian zone, and co‐varied with soil CO2 flux rates both spatially and temporally. This highlighted three distinct controls of soil moisture on soil respiration: CO2 fluxes were inhibited by high moisture content in the down‐slope during the wet winter months, and converged across landscape positions in the dry summer months, while also displaying post‐rain pulses. The normalised difference vegetation index (NDVI) ranged from 0.32 (September)–0.80 (April) and correlated positively with soil moisture and aboveground biomass, moving down slope. Yet, NDVI, aboveground biomass, and soil moisture were not correlated to soil organic carbon (SOC) content (0.4%–4.5%), which was highest in the mid‐slope. The SOC content may instead be linked to shifts in dominant grassland species and their rhizosphere properties with landscape position. This multidisciplinary characterisation highlighted significant heterogeneity in grassland properties with landscape position, and demonstrated an approach that could be used to characterise other critical‐zone environments on hillslopes. Plain Language Summary Globally, grasslands are both common and important landscapes, but less studies have investigated the influence of hillslope processes on these environments and their properties. This study investigated a coastal grassland on a hillslope at Point Reyes National Seashore, California, by combining data sets from different disciplines, covering satellite, field, and laboratory measurements. The site could be grouped into four environmental zones with different properties along the hillslope. Satellite measurements revealed that plants were more active in the wetter, down‐slope throughout the dry summer months. Soil carbon content was not linked directly to soil moisture. Yet, soil carbon dioxide emissions were related to soil moisture, displaying three different behaviors depending on the moisture level. First, soil carbon dioxide emission was lower in the down‐slope during the wet months (negative relationship), but then behaved similarly at all slope locations during the summer and early fall, and increased when it rained (positive relationship). The clustering analysis showed that our site varied significantly over a small distance (<8 m elevation and 150 m distance) and time (1 yr). Beyond the investigation of this specific site, this study highlights an approach for combining data sets to study ecosystems along hillslopes. Key Points The study used a critical‐zone approach to combine multidisciplinary data sets and characterise a California coastal grassland Heterogeneity was large over a short distance (<150 m) and time (1 yr) and could be clustered by landscape position into four distinct zones Soil CO2 fluxes exhibited contrasting responses to soil moisture, which differed with slope and season at the grassland

Addressing antibiotic‐resistant bacteria requires the efficient development of antibiotic‐free antimicrobial materials. Herein, a high‐throughput parallel chromatic‐zone screening strategy is developed that enables the simultaneous screening of composition and surface structure, thereby optimizing antimicrobial performance. As an example, a MgCuPdGd alloy library with diverse compositions and nanostructures, consisting of 229 samples with continuous compositional gradients and varied nanostructured morphologies is constructed by integrating magnetron co‐sputtering and chemical dealloying. The dealloyed samples exhibit distinct chromatic zones—red, yellow, and green—each associated with unique compositional and microstructural features. Among these regions, the red Cu‐rich region demonstrates the most outstanding antibacterial performance, achieving a 95% reduction of viable Staphylococcus aureus (S. aureus). Comprehensive characterization confirms that the superior antimicrobial efficiency originates from the synergistic contribution of the CuPd alloy and the optimized nanostructure. Furthermore, the observed correlation among surface color, composition, morphology, and antibacterial performance highlights the predictive capability of the chromatic‐zone screening approach, thereby reducing characterization requirements by 90% and enabling morphology and performance estimation across large material libraries. This work not only offers a rapid and cost‐effective strategy for identifying antimicrobial materials but also provides a versatile platform adaptable to the development of functional materials for broader biomedical and environmental applications. A high‐throughput parallel chromatic‐zone screening strategy that enables the simultaneous screening of alloy composition and surface structure. Ultimately linking surface color, composition, and morphology with antimicrobial performance enables the screening and prediction of color zones, thereby reducing characterization requirements by 90% and achieving predictive assessment of morphology and performance.

Rising energy demand for building cooling exacerbates the environmental challenges associated with energy consumption. Incorporating phase change materials (PCMs) into building envelopes, particularly sun‑exposed roofs, can substantially reduce energy use. This study examines the thermal‑storage efficiency of metallic, spherical PCM modules embedded within a reinforced concrete roof, designed for hot‑climate conditions. The roof is divided into four distinct thermal zones: Zone‑1 (conventional concrete), Zone‑2 (empty spherical modules), and Zones 3–4 (modules filled with organic PCMs, organic mixture, 35 °C (OM35) and organic mixture, 37 °C (OM37)). Important thermal performance metrics, such as temperature distribution, heat flux, thermal load, time lag, decrement factor, key response index, and carbon emissions savings, are evaluated. Integrating spherical PCM modules led to significant improvements. These include an average reduction in indoor surface temperature of 10.2 °C, a decrease in cooling load of upto 69%, and a reduced decrement factor. In addition, OM35 showed a higher key response index and enhanced thermal performance than OM37. The findings demonstrate the practical viability of spherical PCM‑integrated roofs as a passive‑cooling strategy for buildings in hot climates.

This article is devoted to the prediction of the service life of selected structural materials under simulated operating conditions. Special attention is paid to the so-called representative volume element, which characterizes the damage behaviour, since it includes a critical number of microdefects. The overall damage prediction is based on the energy approach, and the development of damage comes from the traction separation laws; the shape of the damage varies for different materials. The calculations were performed using the extended finite element method (XFEM), where several minor modifications were made. This method has been successfully used in many areas of engineering sciences for research, simulation, and prediction of the behaviour of structures. XFEM reformulates the continuous boundary and initial value problems into similar variational forms instead of using the classical forms of differential equations. The simulation of fracture and damage phenomena is presented for two different materials: austenitic steel with a pronounced grain structure under creep (viscous) loading conditions and cement pasta reinforced with metal fibres under conditions of predominantly static loading.

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon’s entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years.

Background Elderly patients have different risk profiles and stroke features compared to younger individuals. However, there is a paucity of data on acute ischemic stroke (AIS) in very old subjects, and insufficient data has been published about prognostication in this population. This study aimed to design a nomogram for predicting hospital mortality of AIS patients over 80 years old in intensive care units (ICU) and to establish clear risk strata via the gray zone approach. Methods In this retrospective cohort study, the baseline data and in-hospital prognosis of patients with stroke from the Medical Information Mart for Intensive Care-IV database were retrieved. Patient outcomes were dichotomized into survival and non-survival based on 28-day prognosis. Independent predictors identified through univariate and multivariable logistic regression were used to construct the nomogram. Model performance was evaluated in the validation set using AUC, calibration curves, and decision curve analysis (DCA). The gray zone approach categorized patients into three risk groups. Results Initially, 929 eligible patients with AIS were identified, and 224 (24.1%) achieved the endpoint event. The multivariable analysis revealed that Male, White, APS-III, GCS, glucose and Mechanical ventilation were independent predictors for AIS, which were incorporated into a nomogram. The model demonstrated strong performance in the validation set, with an area under the curve (AUC) of 0.814. The gray zone approach effectively stratified patients into three distinct risk categories with significantly different mortality rates: low-risk (Total points ≤ 105), medium-risk (105 < Total points < 129), and high-risk (Total points ≥ 129). This stratification was further validated by significant Kaplan-Meier survival differences (log-rank p  < 0.001) and a clear dose-response relationship across risk groups (HR = 2.99 per category, p  < 0.001). The current study demonstrated that, within the gray zone, male sex and a higher GCS score were associated with survival. Conclusion In conclusion, the nomogram and its associated risk strata offer a practical tool that directly informs prognosis and clinical decision-making for AIS patients over 80 in the ICU.

Background Ischemic and necrotic damages are complications of digestive diseases and require emergency management. Nevertheless, the decision to surgically manage could be delayed because of no sufficiently preoperative accurate marker of ischemia diagnosis, extension, and prognosis. Methods The aim of this study was to assess the predictive value of serum procalcitonin (PCT) levels for diagnosing intestinal necrotic damages, their extension, and their prognosis in patients with ischemic disease including ischemic colitis and mesenteric infarction by a gray zone approach. Between January 2007 to June 2014, 128 patients with ischemic colitis and mesenteric infarction (codes K55.0 and K51.9) were operated, for whom data on PCT were available. We perform a retrospective, multicenter review of their medical records. Patients were divided into subgroups: ischemia (ID group) versus necrosis (ND group); the extension [focal (FD) vs. extended (ED)] and the vital status [deceased (D) vs. alive (A)]. Results PCT levels were higher in the ND ( n  = 94; p  = 0.009); ED ( n  = 100; p  = 0.02); and D ( n  = 70; p  = 0.0003) groups. With a gray zone approach, the predictive thresholds were (i) for necrosis 2.473 ng/mL, (ii) for extension 3.884 ng/mL, and (iii) for mortality 7.87 ng/mL. Conclusion In our population, PCT could be used as a marker of necrosis; especially in case of extended damages and reflects the patient’s prognosis.

Abstract Background Glomerular filtration rate (GFR) is the most sensitive indicator of initial renal function decline during chronic kidney disease (CKD), but conventional protocols for measuring GFR are labor-intensive and stressful for the dog. Objectives To assess the diagnostic potential for detecting CKD with simplified GFR protocols based on iohexol plasma clearance. Animals Seventeen CKD-positive and 23 CKD-negative dogs of different breeds and sex. Methods Prospective nonrandomized study. Plasma iohexol was measured 5, 15, 60, 90, and 180 minutes after injection. Glomerular filtration rate was calculated using 5 samples (GFR5) or simplified protocols based on 1, 2, or 3 samples. The GFR5 and simplified GFR were compared by Bland-Altmann and concordance correlation coefficient (CCC) analysis, and diagnostic accuracy for CKD by receiver operating characteristic curves. A gray zone for each protocol was bounded by the fourth quartile of the CKD-positive population (lower cutoff) and the first quartile of the CKD-negative population (upper cutoff). Results All simplified protocols gave reliable GFR measurements, comparable to reference GFR5 (CCC >0.92). Simplified protocols which included the 180-minutes sampling granted the best GFR measure (CCC: 0.98), with strong diagnostic potential for CKD (area under the receiver operating characteristic curve ± SE: 0.98 ± 0.01). A double cutoff including a zone of CKD uncertainty guaranteed reliable diagnosis outside the gray area and identified borderline dogs inside it. Conclusions The simplified GFR protocols offer an accurate, hands-on tool for CKD diagnosis in dogs. The gray zone might help decision-making in the management of early kidney dysfunction.

•Develops TADS-MPPM framework for mechanistic risk analysis.•Exposes SSD insufficiency in tunnel approach zones.•Proposes empirically validated DSD-based visual condition for safety margin. In tunnel approach zones (TAZs), drivers must complete a sequence of tasks, including detecting the tunnel, identifying speed limits, and decelerating to enter safely. However, current standards mandate only stopping sight distance (SSD) compliance of TAZs, which may not suffice for all of these complex driving tasks. In this study, we investigated (1) whether SSDs are sufficient for driving tasks in TAZs, (2) the impacts of restricted visibility conditions on cognitive-behavioral processes, and (3) the appropriate visibility condition of TAZs. We selected tunnels with three visibility conditions to conduct both subjective tests of perception and experiments with real vehicles. We propose a research framework called the task analysis of driving scenarios modified predictive processing model (TADS-MPPM). We then construct a multidimensional framework that includes sequences of behaviors and cognitive tasks (with 4 driving behavior nodes and 4 cognitive nodes) for spatiotemporal profiling, as well as active deceleration coefficients (safety and efficacy coefficients) and cognitive-behavioral workload (measured using the extended Jaccard coefficient). Then, we use an MPPM to visualize the evolution of driving predictions, driving behaviors, and sensory inputs during the approach to the tunnel. Finally, we explore the risk mechanisms of TAZs. The results show that SSD designs (1) delay tunnel detection, speed-limit recognition, and deceleration initiation, as well as compressing behavioral-cognitive chains, and (2) degrade safety and compliance due to overloaded operations and cognition. Conversely, ensuring that critical tunnel information is discernible at a longer decision sight distance provides the necessary margin of safety on the road. This creates adequate space and time to perform progressive deceleration to eliminate task compression and restore composed and smooth driving maneuvers.

Nowadays, the influence of negative factors on the fiscal system has been strengthening at both the country and regional levels. In particular, income differentiation and fiscal capacity are increasing, inflation rate exceeds the target, negative trends of growing household debt and enterprise unprofitability are observed. Such factors are not fully considered by existing diagnostic methodologies for analysing fiscal and economic security indicators and threats. Thus, in order to adjust stabilisation decisions and implement relevant fiscal policies, it is necessary to update these approaches, especially in regions. The paper examines key definitions of threats and risks to regional fiscal security and presents the author’s classification of threats according to the incidence. Based on the analysed approaches to threat assessment and a proposed diagnostic mechanism, the research developed a method for diagnosing threats to regional fiscal security by bringing the actual values of indicators to standardised points. In addition, to determine the danger level of threats, indicator values were distributed by security zones. The suggested methodology, which includes 12 indicators, was used to analyse the fiscal security of the Komi Republic. As a result, significant threats to the regional fiscal security were identified in the economic, budget, tax, investment, innovation, price, banking, corporate and social spheres. Baseline data were obtained from Federal State Statistics Service, Federal Customs Service of Russia, Federal Taxation Service of Russia and the Bank of Russia. The proposed methodology is an effective diagnostic tool for analysing threats to fiscal security on the basis of important regional indicators. Federal and local authorities can use this particular method for monitoring regional fiscal systems.