Search Results (6,236)

Given the depletion of conventional oil and gas resources, oil shale represents a promising alternative source of hydrocarbons that can be recovered through pyrolysis. This study examines the thermal decomposition of raw oil shale from the Tarfaya deposit and its decarbonized concentrate, studied by thermogravimetric analysis at different heating rates (5, 10, 20 and 40 °C/min). Pretreatment with acetic acid enabled the selective removal of calcite, confirmed by elemental, XRF, and XRD analyses, which revealed a relative enrichment in silica and dolomite in the oil shale concentrate. Pyrolysis of the raw shale occurs primarily between 300 and 500 °C, with a conversion rate of approximately 30%. In contrast, for the oil shale concentrate, the pyrolysis process begins at a relatively low temperature, within a wider temperature range (260–520 °C). Kinetic analysis based on Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) methods shows that at a conversion rate of 60%, the activation energy achieves 14.09 kJ/mol and 10.78 kJ/mol, respectively. The results indicate that the selective removal of calcite by acetic acid treatment facilitates kerogen pyrolysis by reducing mineral–organic interactions. Indeed, calcite dilutes the reactive organic fraction and can act as a physical barrier limiting heat and mass transfer within the oil shale. Its removal improves, on the one hand, the accessibility of kerogen to thermal cracking and promotes its decomposition, and on the other hand, reduces the amount of residue after pyrolysis. In addition, the kinetic analysis based on Criado master curves reveals changes in the reaction mechanism after decarbonation treatment depending on the heating rate (β). A shift from a two-dimensional Avrami–Erofeev model (A2) to a three-dimensional model (A3) was observed at a low heating rate (β = 5 °C/min), suggesting a change in nucleation and growth dynamics during kerogen decomposition. At high heating rates (10, 20 and 40 °C/min), the thermal decomposition of kerogen combines several reaction mechanisms depending on the temperature range considered. Full article

The high-rate recycling of scrap steel introduces persistent residual copper (Cu), which accumulates at prior austenite grain boundaries at the surface, during high-temperature reheating, leading to Cu-induced sensitization and deleterious “hot shortness”. To address this, a predictive analytical framework was derived using Fick’s Second Law and the Sekerka, Jeanfils, and Heckel (SJH) approach to model the dissolution of Cu-rich films as a 1D planar moving boundary problem. The validity of this analytical framework was first established through experimentation on controlled Cu-coated steel wire rods, where theoretical concentration profiles showed strong agreement with empirical depth profiles. When applied to a 0.21 wt.% Cu steel at 1000 °C, the model predicted a critical extinction time (t*) of approximately 8.57 min for the complete dissolution of a 20 nm sensitized film. Experimental trials on sensitized wire rods confirmed this prediction, demonstrating an 89% reduction in the frequency of detectable sensitized zones and a significant decrease in zone width following a 10 min thermal dwell. The approach provides a standardized, scalable, and composition-adaptable methodology, grounded in a 1D planar approximation, for optimizing desensitization heat treatments across a range of Cu contents, offering a practical strategy to increase scrap steel utilization while mitigating hot shortness. Full article

The valorisation of lignocellulosic residues into bio-based feedstocks is a key strategy for advancing circular bioeconomy models. In this study, chestnut wood residues, including virgin wood (VW) and detannized wood (DT) from the tannin industry, were evaluated as substrates for polyhydroxyalkanoate (PHA) production using Cupriavidus necator. Biomass was subjected to thermo-acid hydrolysis followed by ion-exchange detoxification, yielding hydrolysates rich in organic acids (levulinic, acetic, and formic acids) and residual inhibitory compounds. Both substrates supported microbial growth and PHA accumulation, although clear differences in performance were observed. The maximum biomass concentration reached 1.26 ± 0.01 g L⁻¹ in VW hydrolysate and 0.40 ± 0.03 g L⁻¹ in DT hydrolysate. PHA production was higher in VW hydrolysate, reaching 68.51 mg L⁻¹ with 5.44% (w/w) accumulation, while DT hydrolysate yielded 0.21 mg L⁻¹ with 6.01% (w/w). The reduced biomass formation in DT hydrolysate was associated with the greater persistence of inhibitory compounds generated during thermo-acid treatment. Although the obtained PHA yields are lower than those reported for optimized lignocellulosic systems, this study demonstrates for the first time the feasibility of producing PHA from chestnut wood residues, including industrial detannized byproducts, without nutrient supplementation. These findings highlight the potential of tannin-industry waste streams as alternative feedstocks for biopolymer production, while indicating that optimization of hydrolysis conditions, detoxification efficiency, and fermentation strategy is required to improve process performance. Full article

Background/Objectives: Dental metallic implants cause severe streaking artifacts in kilovoltage CT (kVCT), compromising dose calculation in radiotherapy (RT) treatment planning. The purpose of this study is to assess the dosimetric agreement of synthetic MVCT (sMVCT) images generated from artifact-affected kVCT using a deep learning network with respect to true MVCT (tMVCT) acquired at the treatment machine. Methods: Nineteen head and neck cancer patients with dental metallic implants treated with RT were included. Planning kVCT images were converted to sMVCT using Metal Artifact Reduction through Domain Transformation Network (MAR-DTN), a UNet-inspired deep learning network. The sMVCT images were rigidly registered to true MVCT (tMVCT) acquired on the Hi-Art II Tomotherapy system. Mean Hounsfield Unit (HU) values were compared across seven structures (thyroid, bilateral parotids, brainstem, spinal cord, GTV, PTV70) using pairwise Wilcoxon tests and Two One-Sided Tests (TOST) for statistical equivalence within a pre-specified margin of ±20 HU (corresponding to a 2% deviation in physical density). Dose distributions were recalculated on sMVCT using the AAA algorithm and compared to reference tMVCT-based plans via dose–volume histogram (DVH) metrics, evaluated for equivalence by TOST within a margin of ±2% of the prescribed dose (±142 cGy of 70.95 Gy), and via 3D gamma index, evaluated by one-sided non-inferiority test against the clinically accepted thresholds of 90% (2 mm/2%) and 95% (3 mm/3%). A pre-specified sensitivity analysis was performed by repeating all comparisons on the strictly independent sub-cohort (n = 16) excluding three patients drawn from the MAR-DTN training set. Results: All seven anatomical structures showed statistical equivalence between sMVCT and tMVCT under the ±20 HU margin (TOST p < 0.05; mean HU differences in the range −1.1 to +8.4 HU; all Wilcoxon p > 0.05). All nine DVH metrics achieved formal dosimetric equivalence within ±2% of the prescribed dose (TOST p < 0.05). Mean 3D gamma pass rates were 94.3% (95% CI: 89.3–97.1) for the 2 mm/2% criterion and 97.6% (95% CI: 94.8–99.0) for the 3 mm/3% criterion, both formally non-inferior to the respective clinical thresholds (p < 0.0001). Residual gamma failures were concentrated at the patient surface, consistent with inter-session repositioning uncertainty rather than errors in synthetic image generation. Sensitivity analysis on the n = 16 sub-cohort confirmed all conclusions, with mean HU and DVH differences smaller than in the full cohort for the structures showing the largest mean differences, and comparable for the remaining structures, with all TOST equivalence and gamma non-inferiority tests confirmed in both cohorts. Conclusions: sMVCT images generated via MAR-DTN show dosimetric agreement with physically acquired tMVCT in head and neck patients with dental implants, formally demonstrated by TOST equivalence within ±2% of prescribed dose for all DVH metrics. The combined HU and gamma index framework presented here represents a promising quality assurance approach for AI-based synthetic imaging tools in radiotherapy, pending validation in larger prospective multicentre cohorts. Full article

Intensive pesticide application sustains global agriculture but poses poorly characterized risks to complex soil ecosystems. Here, we quantitatively evaluated pesticide residues and utilized high-resolution environmental DNA (eDNA) metagenomics to decode multi-trophic community responses across a typical major grain-producing region located in China. Among 39 targeted pesticides, 26 were detected with total concentrations ranging from 27.9 to 478.8 ng/g. While herbicides and fungicides dominated the residual mass, insecticides posed the most severe ecological threat. Notably, the neonicotinoid imidacloprid exhibited high-risk levels (RQ = 1.78 ± 1.49) at >61.1% of the sampling sites. eDNA profiling and Procrustes analyses revealed a clear trophic-dependent sensitivity gradient (p < 0.01). Lower-trophic microbial communities were significantly altered in composition; pesticide stress was strongly associated with profound non-target suppression on keystone plant-beneficial bacteria (e.g., Nocardioides). Concurrently, the fungal eDNA profiles indicated that the soil mycobiome harbored an alarming 34.7% of potential phytopathogenic fungi (e.g., Aspergillus and Colletotrichum), intrinsically driving the massive fungicide reliance. In contrast, higher-trophic soil metazoa (Rotifera, 40.4%) and weed communities (e.g., Digitaria sanguinalis) exhibited significant spatial stability, reflecting robust environmental buffering and herbicide-driven ecological escapes. Furthermore, co-occurrence networks decoupled target from non-target toxicities, uniquely revealing that persistent herbicide metabolites (desethylatrazine) induce prolonged legacy toxicities on specific soil fauna. Collectively, this study unveils the deep, cross-kingdom ecological disruptions caused by current pesticide regimes, underscoring the urgency of integrating eDNA biomonitoring to guide precision pest management and safeguard soil health in vital agricultural hubs. Full article

The physicochemical mechanisms and numerical characterization of amine-ether gemini surfactant emulsion flooding remain insufficient, limiting its field application in low-permeability reservoirs. This study developed a refined numerical simulation method that integrates full-process emulsion kinetics, including generation, coalescence, dispersion-assisted oil displacement, and demulsification, with graded emulsion characterization using the differentiated inaccessible pore volume (IPV) and residual resistance factor (RRF). Core-flooding validation demonstrated that the model accurately reproduced the key dynamic responses of water cut reduction and oil production increase, with a relative error of about 3.0%. Mechanistic analysis showed that the enhanced oil recovery performance arose from the combined effects of ultralow interfacial tension and emulsion-induced profile control. Relative to conventional surfactant flooding, emulsion flooding increased oil recovery by an additional 4.8–5.0% and lowered water cut by about 12 percentage points. For the Shengli Oilfield pilot block, the optimized injection design involved a surfactant concentration of 1.2 wt.%, an injection rate of 60 m³/d, a slug size of 0.01 PV, an injection–production ratio of 0.95, and a stepwise concentration-decline strategy. The field pilot further confirmed the applicability of the method: daily oil production of the well group increased by 46.5%, while comprehensive water cut decreased by 8.6 percentage points. These results demonstrate the value of the proposed method for both mechanistic characterization and field design of amine-ether gemini surfactant emulsion flooding in heterogeneous low-permeability reservoirs. Full article

To investigate the micro-scale combustion characteristics of composite propellants, a two-dimensional BDP micro-scale model for ammonium perchlorate/hydroxyl-terminated polybutadiene (AP/HTPB) was developed. Numerical simulations were conducted to evaluate how AP particle size and mass fraction influence the diffusion behavior, flame structure, and gas-phase temperature distribution. The results indicate that increasing the AP particle size significantly enhances the overall diffusion characteristics, which gradually become the dominant factor in the combustion process. Specifically, the flame exhibits more pronounced diffusion features, and the temperature distribution near the burning surface becomes increasingly non-uniform. Furthermore, as AP particle size increases, each gaseous component demonstrates greater diffusion tendencies, requiring a thicker mixing layer to achieve complete homogenization. Regarding the AP mass fraction, its increase strengthens the thermal feedback from the gas phase to the solid phase, leading to a slight rise in the burning surface temperature. While the diffusion characteristics of AP-derived macromolecules remain relatively stable, the concentration of residual oxygen increases with higher AP mass fractions. Full article

Background: Certain metabolites of the tryptophan-kynurenine (Trp-KYN) pathway, which are primarily cleared via tubular transport, have been linked to end-stage kidney disease (ESKD). Uromodulin—a protein expressed exclusively in the kidneys—is a key regulator of renal structure and function, as well as a direct marker of tubular health. This preliminary study explores the hypothesis that serum uromodulin correlates with Trp-KYN metabolites, potentially revealing new pathophysiological pathways in patients undergoing kidney replacement therapy (KRT). Given the link between serum uromodulin, Trp-KYN metabolites, and tubular function, we examined their correlation in KRT patients. Furthermore, we assessed how various clinical and dialysis parameters influence serum uromodulin levels. Methods: A total of 64 stable patients from a single dialysis center receiving hemodialysis (HD) or hemodiafiltration (HDF) were enrolled. Pre- and post-dialysis concentrations of uromodulin, Trp, KYN, kynurenic acid (KYNA), 3-hydroxykynurenine (3-OHKYN), and their reduction ratios (RRs) were established. High-performance liquid chromatography (HPLC) was used to estimate the KYN pathway metabolite levels, whereas uromodulin concentration was measured using an immunoenzymatic assay. Results: Detectable serum uromodulin was found in only 30 patients. This group was predominantly male (p < 0.001) and characterized by shorter dialysis vintage (p < 0.001), a higher prevalence of residual kidney function (RKF) (p = 0.001) and diabetes mellitus (p = 0.028), higher pre-dialysis serum phosphorus levels (p = 0.015), and more frequent use of loop diuretics (p = 0.004). Furthermore, univariate analysis revealed significantly higher pre-dialysis (p = 0.004) and post-dialysis (p = 0.025) serum Trp concentrations in the uromodulin-positive group. Pre-dialysis serum uromodulin concentration correlated positively with pre-dialysis Trp level (p < 0.001) and negatively with the pre-dialysis KYN/Trp ratio (p = 0.008), but not with other metabolites that are also subject to tubular transport mechanisms. Post-dialysis uromodulin levels correlated positively only with post-dialysis Trp level (p = 0.005). Patients treated with HDF had significantly higher RR for uromodulin than those treated with HD (p = 0.01). Conclusions: The presented data indicate that serum uromodulin levels are correlated with RKF. Additionally, the presence of detectable serum uromodulin may indicate reduced immunological activation, leading to diminished activity within the Trp-KYN pathway. Full article

Litter decomposition regulates the quantity and quality of plant-derived carbon (C) and nitrogen (N) inputs to soil and is closely associated with microbial community structure. However, how elevated ozone (O₃) and nitrogen (N) addition interactively affect residual litter inputs and their associations with soil microbial communities remains poorly understood, especially in agroforestry systems. Here, we conducted a 12-month in situ litter decomposition experiment using two poplar clones (107 and 546) under ambient or elevated O₃ with or without N addition (60 kg N ha⁻¹ yr⁻¹) at an O₃-FACE platform in northern China. Litter mass and chemical traits were measured during decomposition, and endpoint soil microbial community structure was characterized using phospholipid fatty acid (PLFA) profiling. Treatment effects and litter–microbe associations were evaluated using linear mixed-effects models, correlation analysis, and redundancy analysis (RDA). Endpoint litter mass remaining was significantly affected by O₃, clone identity, and their interactions with N addition, while endpoint litter chemical traits showed trait-specific responses. PLFA-derived microbial community indices also showed treatment- and clone-dependent responses, particularly in bacterial groups, AM fungi, and the fungal-to-bacterial ratio. Endpoint litter mass remaining showed the strongest statistical association with PLFA-derived microbial community structure, whereas individual nutrient concentrations showed weaker independent effects. These findings suggest that O₃- and N-induced changes in residual litter quantity and quality are associated with shifts in PLFA-derived microbial community structure. Because PLFA characterizes microbial community structure rather than process rates, these findings should be interpreted as evidence of structural microbial reorganization associated with altered residual litter inputs, rather than direct evidence of changes in C or N cycling rates. Full article

Residual grit particles introduced during grit blasting are important process-induced defects that can significantly affect the interfacial damage evolution of thermal barrier coatings (TBCs) under thermal cycling; however, the coupled effects of thermally grown oxide (TGO) amplitude, grit size, and grit position on crack propagation in the bond coat (BC) remain insufficiently understood. In this work, a two-dimensional finite element model containing residual alumina grit particles was established to investigate the influence of these three factors on the radial stress distribution and crack growth behavior in the BC, and their individual contributions and interaction effects were further quantified using response surface methodology. The results showed that TGO morphology and interfacial grit defects jointly controlled the stress concentration and crack propagation behavior in the BC. Increasing the TGO amplitude intensified the radial tensile stress concentration in the BC and gradually shifted the critical stress region during thermal cycling. Larger grit particles further aggravated the local stress concentration near the grit tips, while the movement of grit particles toward the TGO peak led to a more pronounced increase in stress concentration and crack propagation tendency. The crack growth behavior was found to be consistent with the corresponding stress evolution characteristics. Response surface analysis further revealed that grit size and grit position had much stronger effects on crack propagation than TGO amplitude, and their interaction was the most significant among all factor combinations. The minimum crack length in the BC layer was obtained at a TGO amplitude of 0.01 mm, a grit size of 20 μm, and a position parameter of 0.752, and the predicted value agreed well with the finite element result. This study provides a comparative basis for interfacial damage assessment and grit-blasting parameter optimization in TBCs containing residual grit defects. Full article

Bivalves are widely used in aquatic monitoring, but the potential of their shells to provide comparative metal signatures remains insufficiently explored in the Caspian Sea. This study quantified major and trace elements in empty shells of five bivalve genera (Cerastoderma, Didacna, Dreissena, Hypanis, and Mytilaster) collected from a single shell accumulation site on the southern Caspian coast. The aim was to assess intergeneric variability and provide a preliminary comparative framework for shell-based metal profiling. Element concentrations were measured by inductively coupled plasma mass spectrometry (ICP-MS), and median concentrations, internal residual enrichment factors, relative concentration factors, a relative multielement loading index, and centered log-ratio principal component analysis were applied as within-dataset comparative tools. Significant differences among genera were found for most elements. Dreissena and Cerastoderma showed the highest relative multielement loading, with comparatively elevated values of Cr, Fe, and Hg, whereas Hypanis showed marked element-specific deviations, particularly for Cu and Co. In contrast, Mytilaster and Didacna generally showed lower overall relative loading, although Mytilaster also displayed a strong Co-specific pattern. PCA-clr analysis showed structured genus-related separation based on multielement shell composition, with contrasting associations among Al, As, Cr, Fe, Co, Cu, Hg, and carbonate- or shell-associated elements such as Ba, Mg, Sr, Ti, and Zn. Overall, the results indicate that Caspian bivalve shells display distinct genus-related multielement profiles. Within the limitations of a single-site shell-accumulation dataset and the absence of paired environmental, tissue, mineralogical, and microstructural data, these findings should be interpreted as a preliminary comparative assessment rather than as evidence of environmental contamination or validated bioindicator performance. The results identify genera and elements that may deserve priority in future shell-based biomonitoring studies after validation with living populations, broader spatial replication, and paired sediment, water, particulate, and soft-tissue data. Full article

Accurate prediction of solvent-bitumen viscosity is essential for the design and optimization of extended-solvent steam-assisted gravity drainage (ES-SAGD) processes, where viscosity reduction governs fluid mobility and recovery efficiency. Due to the highly nonlinear dependence of viscosity on temperature, pressure, solvent concentration, and fluid properties, conventional empirical and thermodynamic models often show limited generality across different operating conditions. In this study, data-driven machine learning techniques were employed to develop predictive models for solvent-bitumen viscosity using an extensive experimental database compiled from literature. The dataset was subjected to systematic preprocessing, including data cleaning, feature standardization, and 80/20 train-test splitting. Two optimized tree-based ensemble algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were trained using hyperparameter tuning. Model performance was evaluated using R², RMSE, and MAE metrics, along with cross-plots, residual analysis, and feature importance evaluation. Results demonstrate that both models successfully capture the strong nonlinear relationships governing viscosity behavior, with XGBoost providing the highest prediction accuracy and the best generalization capability. The R², RMSE, and MAE values for the test dataset are 0.932791, 811.882, and 111.091 for XGBoost, and those values for RF are 0.854601, 1194.149, and 150.703, respectively. Feature importance analysis confirms that temperature and solvent mole fraction are the dominant variables influencing viscosity reduction. The developed model offers a rapid, reliable, and data-driven alternative to experimental and thermodynamic approaches and can be integrated into ES-SAGD simulation and optimization workflows. Full article

An issue of common concern in lemon production is finding a safe and efficient alternative to 2,4-dichlorophenoxyacetic acid (2,4-D). In this study, ‘Eureka’ lemon fruits were treated with three concentrations (1, 2 and 3) of fluroxypyr-meptyl (FME), a combination of fluroxypyr-meptyl and fluroxypyr (FLFM), 2,4-dichlorophenoxypropionic acid (2,4-DP), and 2-methyl-4-chlorophenoxyacetic acid (MCPA). Water and 2,4-D served as controls. We measured the storage performance indicators of fruit, such as weight loss rate and decay rate, and shelf-life quality parameters, such as juice yield, flavor compounds and pesticide residues. During storage, weight loss was significantly higher in water than under all other treatments. Weight loss rates under FME2 and 2,4-DP3 were significantly lower than under 2,4-D. Decay rates in FLFM1, 2,4-DP2, and the FME series were significantly lower than in 2,4-D and water, whereas those in 2,4-DP1 and the MCPA series were significantly higher than in 2,4-D at 200 days after treatment. Regarding shelf-life performance, juice yield in water (65.14%) and 2,4-D (68.26%) was significantly lower than under most other treatments. The highest juice yield was observed in FME2 (77.84%). Treatments 2,4-DP1, 2,4-DP2, and FME2 were superior to 2,4-D and water in maintaining total soluble solids, titratable acid, and vitamin C contents, while other treatments showed no negative effects on internal quality. Fruits under MCPA2, 2,4-DP3, 2,4-DP2, and FME2 maintained better flavor compound profiles than those in water. Notably, MCPA2 resulted in significantly higher levels of terpenes (e.g., D-limonene) and aldehydes (e.g., citral); FME2 effectively maintained linalool, geraniol, and α-terpineol; and 2,4-DP3 performed well in maintaining D-limonene, sesquiterpenes, and alcohols compared with other treatments. All treated fruits complied with Chinese National Food Safety Standard Maximum Residue Limits for Pesticides in Food GB 2763-2026 and meet the EU standard limits for citrus. Overall, FME2 treatment resulted in the best storage performance and quality, followed by 2,4-DP3, indicating that these treatments may serve as effective postharvest alternatives for lemon preservation. Full article

Brewer’s spent grain (BSG) is an abundant by-product rich in proteins and polysaccharides. This study evaluated ultrasound (US) to enhance alkaline protein extraction in 110 mM NaOH and to obtain a polysaccharide-enriched residue, with mechanical agitation (AG) as the control. First, 40 min extraction curves were evaluated at 25 and 50 °C and fitted to the Weibull model. At 25 °C, US increased the 40 min protein yield (40.8 ± 0.1 g/100 g initial protein) by 2.5-fold compared with AG; heating increased AG yield, whereas US showed negligible temperature sensitivity between 25 and 50 °C. Subsequently, extractions were performed at 25 °C in one or two 20 min cycles using fresh solvent. Extracts were recovered by pH-shift precipitation and freeze-dried. US maximised protein recovery (47.6 ± 0.1 g/100 g initial protein after two cycles) but co-extracted other compounds, reducing purity. AG extracts showed higher emulsifying activity (up to 9.6 ± 0.1 m²·g⁻¹), while US increased emulsion stability (up to 46 min). US residues showed up to ~35% lower glucose content than BSG (42 ± 2 g/100 g dry matter for BSG) and higher arabinoxylans (up to 23.5 ± 0.6 g/100 g dry matter; ~2.4-fold BSG), supporting a dual valorisation route based on the production of a protein-rich extract and an arabinoxylans-rich concentrate. Full article

Land productivity assessments used in SDG 15.3.1 commonly rely on NDVI trends, which may be strongly influenced by precipitation variability and can therefore misrepresent actual land condition change, particularly in dryland environments where vegetation productivity responds rapidly to rainfall fluctuations. To address this issue, this study presents a land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow integrating high-resolution 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core methodological contribution is a precipitation-conditioned hybrid productivity framework that dynamically selects among NDVI trends, Rain-Use Efficiency (RUE), and Residual Trends (RESTREND) according to local rainfall dynamics. By adapting productivity metrics to precipitation conditions, the framework reduces precipitation-driven misinterpretation of vegetation trends, operationalizes a more climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1, and enables systematic comparison of productivity metrics under contrasting rainfall regimes. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that $18\%$ of land showed declining productivity, $75\%$ remained stable, and $6\%$ showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions. Full article