Calcium oxalate (CaOx) nephrolithiasis shows wide variation in stone size and density that is not fully explained by urinary risk factors. Plasma oxalate (POx) reflects systemic oxalate balance, but its relationship with imaging-defined stone characteristics has not been studied. We examined whether POx is associated with computed tomography (CT)–derived stone density and size in patients with CaOx stones. In this cross-sectional study, 80 adults with confirmed CaOx nephrolithiasis underwent clinical evaluation, laboratory testing, and non-contrast CT imaging. Stone density was measured in Hounsfield units (HU), and maximum stone diameter was recorded. Associations between POx, stone density, and stone size were evaluated using multivariable linear regression with HC3 robust standard errors adjusted for age, sex, estimated glomerular filtration rate (eGFR), body mass index, diabetes mellitus, disease duration, and the reciprocal stone characteristic. Higher log-transformed POx levels were associated with greater stone density (B = 196.2 HU, 95% CI 96.2–296.1; β = 0.400; p < 0.001). The association remained consistent after removal of stone size from the model (B = 186.9 HU, 95% CI 77.5–296.3; p = 0.001) and in analyses limited to participants with preserved kidney function (B = 218.0 HU, 95% CI 59.7–376.3; p = 0.008) and to patients with a single stone (B = 249.7 HU, 95% CI 97.7–401.7; p = 0.001). In patients with CaOx nephrolithiasis, higher POx levels were associated with greater CT-derived stone density. Prospective studies are required to determine the temporal and clinical implications of this association.
Biomarkers that predict survival and therapeutic response to atezolizumab plus bevacizumab (Atezo/Bev) in unresectable hepatocellular carcinoma (HCC) have yet to be established. We aimed to investigate the association between serum full-length glypican-3 (FL-GPC3) concentration and treatment outcomes in patients with unresectable HCC receiving Atezo/Bev. We included 102 patients treated with Atezo/Bev therapy for unresectable HCC at our institute during 2020–2025. We investigated the association between baseline serum FL-GPC3 concentrations and the therapeutic effectiveness and survival outcomes. The baseline serum FL-GPC3 concentration was lower in patients in whom disease control was achieved (10.8 vs. 26.7 pg/mL, p = 0.002). An FL-GPC3 concentration ≥ 21.7 pg/mL was an independent negative predictor of disease control (odds ratio, 0.377; 95% confidence interval [CI], 0.148–0.963; p = 0.042). The FL-GPC3-high group had a shorter median progression-free survival (PFS; 3.0 vs. 5.6 months, p = 0.031) and overall survival (OS; 13.7 vs. 25.8 months, p = 0.006) than the FL-GPC3-low group. A high FL-GPC3 concentration was an independent negative prognostic factor for PFS (hazard ratio [HR], 1.567; 95% CI, 1.025–2.396; p = 0.038) and OS (HR, 1.694; 95% CI, 1.003–2.862; p = 0.049). In conclusion, baseline serum FL-GPC3 concentration was associated with treatment outcomes in patients with unresectable HCC receiving Atezo/Bev and may provide complementary prognostic information.
Root system development strongly influences nutrient acquisition, yield formation, and these processes are largely influenced by crop management strategies. Meagre information is available on functional traits of roots that regulate nutrient acquisition and productivity of pigeonpea under organic and natural farming systems in semi-arid regions. This study aimed to quantify the spatial and temporal dynamics of root morphological traits, nutrient acquisition and grain yield of pigeonpea under specific crop management strategies. A field experiment was initiated during 2021-22 under a pigeonpea-wheat system with seven treatments viz.; conventional farming (CF), natural farming (NF) and five compost rates (0, 6, 12, 18, and 24 t ha− 1). Root samples were collected at 45, 90, and 135 days after sowing (DAS) from two soil depths (0–15 and 15–30 cm) to assess the impact of different treatments on root functional traits, nutrient acquisition, and seed yield of pigeonpea in the year, 2024. The root growth and nutrient acquisition peaked at the flowering stage (90 DAS), subsequently declined during the pod filling stage (135 DAS). Compared with natural farming and conventional farming, compost (12 t ha− 1) increased root length density by 23.3 & 8.0%, root surface area by 23.0 & 17.6%, root volume by 22.7 & 20.9%, root fresh weight (RFW) by 22.7 & 15.0%, respectively, in the 0–15 cm soil layer at the flowering stage. The higher nutrient content (phosphorus and potassium) was noticed with the same treatment (0.35 and 0.98%, respectively), followed by compost (6 t ha− 1). The order of treatments in terms of nutrient contents were compost (12 t ha− 1)> compost (6 t ha− 1) > CF> compost (18 t ha− 1) > NF > compost (24 t ha− 1); in contrast, higher nitrogen content was observed in NF (3.96%) followed by compost (12 t ha− 1). Root morphological traits positively correlated (p < 0.001) with nutrient uptake, particularly for phosphorus and potassium, suggesting that improved root development enhanced nutrient acquisition from the soil and played a key role in yield formation. Seed yield ranged from 10.1 to 13.4 q ha⁻¹ and crop supplied with compost @ 12 t ha− 1 produced 16.5 and 19.2% higher yield than CF and NF, respectively. Regression analysis showed a linear relationship between grain yield and nutrients uptake across all crop growth stages. The seed yield was positively and strongly correlated to P uptake at 45 DAS (R² = 0.85, p < 0.001), followed by the flowering stage (R² = 0.83) and pod filling stage (R² = 0.75). Spatio-temporal variation in root traits strongly regulated nutrient acquisition and yield formation in pigeonpea. Optimized compost application (12 t ha− 1) enhanced root development and nutrient uptake, with root phosphorus accumulation emerged as the key driver of grain yield, underscoring its importance for sustainable pigeonpea production.
Combining probiotics and antimicrobial peptides (AMPs) may enable targeted pathogen suppression while preserving beneficial bacteria, but the nonselective antimicrobial activity of AMPs can suppress both populations. We developed an experimentally informed synthetic ordinary differential equation (ODE) benchmark and an inverse-design workflow to allow threshold-triggered AMP control. Preliminary growth and AMP-response measurements from Lactococcus lactis and Escherichia coli were used to define plausible simulation ranges rather than to fit a complete multi-strain model. Through 352,000 closed-loop simulations spanning 2,000 biological-profile groups and 176 candidate controllers per group, we implemented and evaluated Tree-Adaptive Regression (TAR), a target-wise stack of single-tree experts trained with group-aware out-of-fold predictions to predict five strain-specific triggering thresholds. Additionally, AMP release amplitude $$U_{\max }$$ was selected separately by post-prediction ODE reinsertion. Among the tree-based models in the final benchmark, TAR had the highest mean target-wise $$R^2$$ and the lowest original-scale RMSE and showed the highest mean ODE-back reference-fidelity $$R^2$$ among the evaluated tree-based models. However, post-prediction $$U_{\max }$$ selection exposed trade-offs among pathogen suppression, probiotic preservation, and AMP exposure. No jointly feasible candidate was identified in the post-prediction $$U_{\max }$$ evaluation, so the selected amplitudes represented minimum-penalty trade-offs rather than universally feasible controllers. The framework is a reproducible computational testbed for prioritizing controller candidates. Its predictions are simulation-derived and require prospective experimental validation.
Retinal fundus images transmitted through teleophthalmology networks carry protected health information subject to HIPAA and equivalent regulations, yet existing chaos-based encryption schemes apply uniform computational effort regardless of the clinical significance of image regions. This paper presents ROI-Aware 4D Chaos-Based Encryption (ROHE-4D), a privacy-preserving framework that allocates computationally stronger encryption operations to a selected anatomical ROI. A lightweight deterministic morphological segmentation procedure identifies the selected anatomical ROI, comprising the optic disc and macular region, without covering approximately 16.6% of image area on average, without model training. ROI pixels undergo key-dependent affine byte substitution with XOR diffusion with cipher feedback, whereas background pixels receive single-pass XOR diffusion, an architectural design choice. For each image, the keystream seed is derived from a 256-bit secret key, the SHA-256 image fingerprint, and a unique 128-bit nonce using a two-stage SHA-256 key-derivation construction. The uniformly generated secret key provides a 256-bit key space. The 4D system has one positive Lyapunov exponent (λ1 ≈ 0.69) and the system is classified as chaotic. Morphological ROI segmentation achieves optic disc IoU of 0.752 ± 0.157 on synthetic images with circular ground-truth masks. Experiments on APTOS 2019 (3662 images, 512 × 512) and IDRiD (516 images, 1024 × 1024) demonstrate the following results: ciphertext entropy 7.9970 and 7.9989 bits/byte respectively. NPCR 99.62% and 99.61%, UACI 33.48% and 33.47%, adjacent-pixel ciphertext horizontal correlation 0.0186 (APTOS) and 0.0113 (IDRiD), key sensitivity ≈ 99.59% per 1-bit key perturbation, encryption latency 9.8 ms on CPU and 47.1 ms on Jetson AGX Orin. An analytical peak-memory estimate of approximately 2.5 MB based on principal array allocations at 512 × 512 and bit-exact lossless recovery across all 4178 test images.
Plant-mediated ‘green’ synthesis of nanoparticles (NPs) is widely reported, but the exact functional role of the retained phytochemical capping layer versus the core metal remains contested. Furthermore, the impact of thermal calcination—a common post-synthesis purification step—on the bio-functional and ecological profile of these NPs is poorly understood. We synthesized four distinct biogenic NPs—Ag and Fe using Salvinia molesta extract, and Cu and Zn using Mimosa pigra extract. While the Cu, Zn, and Fe NPs were evaluated in both non-calcined (as-synthesized) and thermally calcined states, the Ag NPs were evaluated exclusively in their highly active, non-calcined state. We evaluated their physicochemical properties, in vitro antioxidant capacity (with Ag NPs showing 42.08 mg TE/g), and antibacterial efficacy against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. Additionally, the ecotoxicological impact was evaluated via a one-month soil microbial respiration assay for the calcined metal oxides (Cu, Zn, Fe) and the non-calcined Ag NPs. Characterization confirmed that calcination successfully formed highly crystalline metal oxides but stripped the Cu, Zn, and Fe NPs of their organic phytochemical corona. Consequently, the non-calcined NPs exhibited significant antioxidant activity, which was substantially abolished in the metal oxides following calcination. Antibacterial assays revealed a strict metal-dependency; Fe, Cu, and Zn NPs showed no significant antibacterial action even at high screening concentrations, regardless of calcination. In contrast, the non-calcined Ag NPs exhibited potent antimicrobial efficacy, with a minimum inhibitory concentration (MIC) of 7.8 ppm. Crucially, 4-week soil respiration assays (at doses up to 1000 ppm) demonstrated that neither the calcined metal-oxides (Fe, Cu, Zn) nor the highly reactive non-calcined Ag NPs exerted long-term toxic effects on the soil microbiome. Our findings demonstrate that the ‘green’ bioactivity (antioxidant potential) of biogenic NPs is primarily mediated by the uncalcined phytochemical corona, whereas cytotoxicity (antibacterial action) is governed by the core metal identity. The absence of significant suppression of CO₂ respiration suggests minimal acute metabolic disruption in soil microbiomes, providing preliminary evidence for short-term microbial tolerance. These findings warrant further investigation into the potential agricultural applications of these biogenic nanomaterials.
Adolescents are increasingly exposed to ultra-processed foods (UPFs) as food systems change in low- and middle-income countries, yet evidence from sub-Saharan Africa remains limited. This study assessed UPF consumption, associated factors, and overall diet quality among secondary-school adolescents in Adama City, Ethiopia. Baseline cross-sectional data were obtained from a quasi-experimental study conducted in October-November 2024 among 713 students in grades 9–12. Dietary intake was assessed using the Diet Quality Questionnaire and a food frequency questionnaire, with foods classified according to the NOVA system. UPF consumption was defined as intake of at least one UPF item in the previous 24 h, and diet quality was measured using Global Dietary Recommendation (GDR) scores. Overall, 41.9% (95% CI 38.3–45.6%) of adolescents reported UPF consumption in the past 24 h, and 94% reported weekly intake. Fried snacks, cakes, and sugar-sweetened beverages were most commonly consumed. The mean (± SD) GDR-total score was 11.6 (± 1.4) on a 0–18 scale, with higher scores indicating greater adherence to global dietary recommendations. UPF consumption was higher among Grade 12 students and physically active adolescents, and lower among those with greater nutrition knowledge. Adolescents from middle-income households had higher GDR-total score; however, the diet-quality model explained only a small proportion of the variation in GDR scores (R² = 0.027). These findings highlight widespread UPF exposure and socioeconomic and behavioral influences on adolescent diets in urban Ethiopia.
Nile tilapia (Oreochromis niloticus) is a commercially important freshwater fish species worldwide. The present study aims to compare the gut microbial communities of Nile tilapia O. niloticus across seven reservoirs in Sri Lanka, identify the core microbiota, and examine the environmental influences on microbial composition. Notably, the gut microbiota of O. niloticus varied significantly (p ≤ 0.05) by gender and exhibited a distinct composition across the seven reservoirs namely, Padaviya, Yanoya, Minneriya, Kaudulla, Rathkinda, Ulhitiya, and Udawalawe. The dominant bacterial genera included Cyanobium, Clostridium sensu stricto 1, Cetobacterium, and LD29. The richness and diversity of the intestinal microbiota were higher in the Rathkinda and Padaviya reservoirs. Genus Paracoccus and Roseomonas exhibited negative correlations with most other genera, while Romboutsia and Bacillus showed positive correlations with almost all other genera. A series of probiotic bacteria, including Bacillus and Exiguobacterium species, were isolated and are crucial for aquaculture. Some opportunistic pathogenic bacteria such as Plesiomonas, Aeromonas, Rothia and Staphylococcus were isolated from the gut of O. niloticus. The toxin-producing Cyanobium exhibited significant (p ≤ 0.05) negative correlations with multiple water quality parameters including chloride, total hardness, calcium, total alkalinity and total dissolved solids in the studied reservoirs. Our results could provide valuable insights into the assembly of gut microbiomes and sustainable development of the O. niloticus industry in various geographical locations.
The provision of health information that can be easily understood by the general public can help improve health behaviors. However, there is a lack of tools to assess the quality of health education videos in the Chinese context. These tools are essential for the popularization of health education videos. There are various existing tools for evaluating health education video quality in the English context. Among them, the Patient Education Materials Assessment Tool for Audiovisual Materials (PEMAT-A/V) is one of the most commonly used tools. However, this scale has not been adjusted for the Chinese context and verified for use in the Chinese population. The Brislin translation model was applied to achieve Chinese translation and cultural adaptation of the PEMAT-A/V, resulting in the C-PEMAT-A/V. Videos depicting health information related to periodontosis on TikTok, MicroBlog, and Bilibili were identified using a convenient sampling method. Items in the C-PEMAT-A/V scale were analyzed by the critical ratio method and correlation coefficient method. The content validity of the scale was tested by the expert evaluation method. The concurrent validity of the scale was tested using the modified DISCERN. The internal consistency of the scale was tested by Cronbach's α, and the inter-rater reliability was tested by Cohen’s Kappa coefficient, the average percentage agreement, and Gwet’s AC1. After literal translation, back translation, and cultural adjustment, the C-PEMAT-A/V scale was reduced from 17 to 13 items. The item content validity index (I-CVI) of the scale was 0.83–1.00 and the average content validity index (S-CVI) was 0.96. The correlation coefficient of concurrent validity was r = 0.647 (p < 0.01). The Cronbach's α coefficient of the C-PEMAT-A/V scale was 0.703, the Cohen’s Kappa coefficient was 0.86, the average percentage agreement was 87.7%, and Gwet’s AC1 was 0.873. The C-PEMAT-A/V scale has good reliability and validity. It can be used to evaluate the understandability of health science videos in the Chinese context.
This work assesses the seismological reliability of a physics-based ground-motion simulation approach that couples an extended-source representation of the seismic rupture with Discrete Wavenumber (DWN) wave propagation in a horizontally layered anelastic medium. The validation is performed through a multi-intensity-measure procedure in which simulated intensity measures are compared with empirical GMM predictions derived from recorded earthquake datasets by means of standardized residual analysis and residual decomposition. The procedure is applied to a purpose-built database of simulated ground-motion signals, generated by systematically varying the main source, path and site parameters controlling ground-motion generation, including focal mechanism, moment magnitude, rupture variability, crustal structure, local site conditions, source-to-site distance and source-to-receiver azimuth. The results indicate that, within the investigated magnitude–distance–site domain and with respect to the adopted empirical reference models and to the simulation methodology assumptions, the simulated motions capture the main median trends and variability components of the GMMs, with standardized residuals generally within the empirical uncertainty range. The residual decomposition highlights interpretable trends related to VS30-dependent site effects, distance-dependent deviations in less constrained ranges, and offsets associated with the adopted deep crustal models. This work represents a seismological validation step toward the future use of simulated accelerograms in performance-based seismic assessment workflows, subject to dedicated engineering validation.
Efficient concurrency control is key on permissioned blockchain platforms like Corda, where transaction volumes are high and contention is rife. In some hotspot situations, conventional concurrency control mechanisms such as Two-Phase Locking, Optimistic Concurrency Control, Multi-Version Concurrency Control, and Timestamp-Based Concurrency Control have high transaction abort rates, inefficient replay operations, and poor resource utilization. This paper presents an Adaptive Timestamp-Based Concurrency Control framework (ATBCC-Corda), which is based on four key components: Adaptive Conflict Prediction Engine (ACPE), Adaptive Hotspot-Aware Micro-Batching (AHMB), Adaptive Replay Selection Policy (ARSP), and Incremental Deterministic Replay (IDR), all of which are introduced to predict conflicts proactively, optimize the transaction scheduling, select the failed transactions for recovery selectively, and continuously adapt to the workload dynamics through an Adaptive Decision Manager (ADM). Further, a novel Blockchain Resource Efficiency Index (BREI) is proposed to jointly consider throughput, bandwidth consumption, and transaction abort rate. Experimental evaluation with BLOCKBENCH-inspired workloads shows that ATBCC-Corda consistently outcompetes Native Corda, Two-Phase Locking, Optimistic Concurrency Control, Multi-Version Concurrency Control, and conventional Timestamp-Based Concurrency Control in terms of throughput, abort rates, and width usage, and resource efficiency, making it a viable solution for scalable enterprise permissioned blockchain systems.
Anticipatory grief is a major psychological challenge for family caregivers of patients with advanced lung cancer. Understanding how caregivers make sense of this experience is essential for providing targeted support to this population. To explore the cognitive experiences of anticipatory grief among primary family caregivers of patients with advanced lung cancer in China. An interpretive phenomenological design was used. From July 2024 to September 2024, 19 primary family caregivers of patients with advanced lung cancer (age M = 63.42 years, SD = 12.25) were recruited and interviewed in two regional hospitals in Zhejiang, China. Transcripts were analysed inductively, with mental time travel used as a sensitising and interpretive lens. Three themes were identified: “Fading and mourning”, “Ambivalent choices”, and “Acceptance and meaning reconstruction”. Caregivers’ anticipatory grief was reflected in memories of the patient’s former health and shared life, present perceptions of relational separation and role disruption, and imagined futures marked by loss. They also described a continuing tension between holding on and letting go, while gradually reconstructing meaning through companionship, filial responsibility, acceptance of death, and the pursuit of a good death and good farewell. Interpreted through the lens of mental time travel, anticipatory grief among primary family caregivers of patients with advanced lung cancer is a cognitively and temporally organized experience, not merely an emotional response. Supportive care should attend to caregivers’ memories of past relationships, present role changes, future-oriented fears, and culturally embedded meaning-making.
To improve the adaptability of temperature regulation strategies for bamboo water-based paint roll coating processes, this study investigates a dynamic weight fusion-based ANFIS–Fuzzy-PID strategy for temperature control. By employing a dynamic weighted fusion mechanism that integrates the adaptive learning capability of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) with the nonlinear regulation characteristics of Fuzzy-PID, the proposed method enables adaptive coordination between different control strategies and reduces the dependence on manual parameter tuning. Simulation results demonstrate that the proposed control method achieves a balanced overall control performance by coordinating the complementary characteristics of ANFIS and Fuzzy-PID. Specifically, the rise time is reduced to 10.2162s, representing a 90.91% reduction compared with the standalone ANFIS controller, while the overshoot is reduced by 91.6% compared with the standalone Fuzzy-PID controller. The steady-state error remains within ±0.5%, although it is not the minimum among all compared controllers. These results indicate that the proposed ANFIS–Fuzzy-PID strategy does not optimize a single performance index exclusively, but instead achieves a compromise among response speed, overshoot suppression, and steady-state accuracy through dynamic weight adjustment. This study demonstrates the complementary characteristics of ANFIS and Fuzzy-PID control methods within the considered simulation framework and provides a potential reference for intelligent temperature regulation strategies for temperature control in bamboo water-based paint roll coating processes.
Reduced-order models (ROMs) are critically important for the efficient simulation of complex physical systems. However, ROM performance often degrades under out-of-training operating conditions. To overcome this challenge, we propose a transferable reduced-order modeling framework called the dilated vector-quantized variational autoencoder (DVQVAE), which is developed for compact field representation, structural analysis, and adaptation across operating conditions. By integrating multiscale convolutions with vector quantization, the DVQVAE compresses high-dimensional physical field data while retaining important spatial structures. The resulting discrete latent variables can be examined through code maps and masking tests, which provide structural information about the learned representation without assuming that each code corresponds to a unique physical mode. Furthermore, a KAN-transformer is introduced to model the nonlinear evolution of the latent variables. Validated across a series of benchmark cases in fluid dynamics and electromagnetics, our framework consistently outperforms conventional ROMs in terms of reconstruction accuracy, structural interpretability, and transfer efficiency—offering a new paradigm for efficient, structurally interpretable, and transferable ROMs. These capabilities demonstrate the potential of our method as a practical surrogate model for accelerating simulation-driven engineering tasks across diverse physical systems.
The mortality rates of patients admitted to intensive care units (ICUs) with sepsis-associated liver injury (SALI) are relatively high; early identification of SALI patients with poor prognosis is necessary. The Prognostic Nutritional Index (PNI) is associated with the mortality rate in conditions such as cancer, cardiovascular disease, and sepsis. However, the correlation between the PNI and mortality rate of patients with SALI remains unclear. Therefore, this study aimed to elucidate the association between the PNI and 28-day mortality in patients with SALI. The admission data of patients diagnosed with SALI admitted to the ICU from 2008 to 2022 were obtained from the Medical Information Mart for Intensive Care-IV database and retrospectively analyzed. The PNI at admission was recorded. Multivariate Cox regression analysis was used to determine the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). To further validate our findings, we employed a restricted cubic spline (RCS) model, survival curve analysis, receiver operating characteristic (ROC) analysis, and subgroup analysis. This study included 375 patients diagnosed with SALI. The patients were divided into two groups according to the survival status of SALI patients within 28 days: 217 patients were in the survivor group and 158 patients died within 28 days (non-survivor group). Multivariate Cox regression analysis revealed that for every one unit increase in the PNI, the 28-day mortality risk decreased by 7.1%, with an HR of 0.929 (95% CI 0.904–0.955; P<0.001). The RCS model revealed no statistically significant evidence of a non-linear association between PNI and SALI patients’ prognosis. Kaplan–Meier analysis revealed that the group with a lower PNI had a higher 28-day mortality rate. ROC analysis revealed that the optimal cutoff value for the PNI was 32.1. Subgroup analysis showed no significant interaction between the PNI and each subgroup. A lower PNI was significantly associated with an elevated risk of mortality within 28 days in patients with SALI. This finding suggests that PNI may be an independent risk factor for adverse outcomes in patients with SALI. Further studies on the PNI may contribute to advances in the prevention and treatment of SALI.
Alpine lakes on the Qinghai-Tibet Plateau are important regions for studies of the regional carbon cycle and are highly sensitive to climate change. However, the variation patterns of lake surface greenhouse gases and their environmental regulation mechanisms remain unclear. In this study, Bird Island of Qinghai Lake was selected as the study area. Based on the measured concentrations of lake surface CO2, CH4, and H2O corresponding to Sentinel-2 satellite overpass periods throughout 2021, combined with Sentinel-2 remote sensing imagery and ERA5-Land meteorological data, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models, together with the SHAP method, were used to identify and predict the influencing factors and their contribution characteristics to the variations in lake surface CO2, CH4, and H2O. The results showed that CO2, CH4, and H2O exhibited distinct seasonal variations. The annual variation in CO2 concentration generally showed a “V”-shaped pattern, with concentrations during the growing season being lower than those during the non-growing season. The annual variation in CH4 concentration was relatively small and exhibited a “wave”-shaped pattern. The H2O concentration showed a distinct unimodal pattern, and its high-value period was generally consistent with the low-value period of CO2. Temperature and MNDWI were the main predictive factors affecting the variations in CO2, CH4, and H2O concentrations, while H2O concentration was also affected by Radiation. This study combined observational data, remote sensing imagery, and machine learning, providing new insights into the environmental predictive factors of greenhouse gas concentrations in lakes under climate change.
This study maps iron oxide–bearing zones in the northern Markazi Behsud District of Wardak Province, Central Afghanistan, using a comparative analysis of ASTER and Sentinel‑2 MSI data. Multiple remote sensing techniques, False Color Composites (FCC), Band Ratios (BR), Principal Component Analysis (PCA), the Crosta method, and the Spectral Angle Mapper (SAM), were applied to identify iron oxides and discriminate minerals such as magnetite, hematite, and goethite. This study newly established band ratios based on mineral spectral properties enhanced detection performance. The newly established spectral ratios, derived explicitly from diagnostic ferric and ferrous absorption features, differ from conventional band ratio schemes by offering improved sensitivity to iron‑bearing mineral variations and more robust delineation of hydrothermal alteration zones in multispectral data. Sentinel‑2 MSI produced markedly superior results compared to ASTER due to its higher spatial resolution and seamless single‑tile coverage. Among all methods, the Crosta technique and SAM were the most effective, with SAM showing the strongest agreement with USGS permissive mineralization zones. The integrated results delineate a continuous iron‑bearing belt approximately 54 km long and 6.8 km wide along the Baba Mountains. The overall accuracy of 71.3% demonstrates the strong capability of Sentinel‑2 MSI, particularly when combined with SAM, for mapping iron oxide–rich zones in structurally complex terrains.
Molecular structure profoundly influences chemical behavior, which can be modeled using topological indices. This article introduces two new indices, the first and second edge–edge Zagreb indices ($$M_{ee}^1(G)$$ and $$M_{ee}^2(G)$$). We analyze their chemical relevance and show that they demonstrate comparable or improved descriptive performance for key physico-chemical properties of octane isomers within the investigated dataset. Additionally, we assess the capability of $$M_{ee}^1(G)$$ and $$M_{ee}^2(G)$$ to distinguish between molecular isomers.
Food supply chains face increasing challenges related to transparency, traceability, and data integrity. This study proposes a blockchain-based traceability framework integrating the InterPlanetary File System (IPFS), Internet of Things (IoT) sensors, and a hybrid GM-PBFT consensus mechanism to ensure secure and efficient data sharing across stakeholders. The proposed system combines on-chain and off-chain storage to reduce blockchain overhead while preserving data immutability. A MobileNetV1-based model is incorporated for product quality assessment. Experimental evaluation using Hyperledger Fabric and Ethereum test networks demonstrates improved performance, achieving 99.3% traceability accuracy, 37% faster recall response, and reduced data tampering risk. The results confirm that the proposed approach enhances transparency, scalability, and trust in agri-food supply chains. The proposed system utilizes a hybrid blockchain architecture, combining Hyperledger Fabric (permissioned blockchain) for secure enterprise-level transactions and Ethereum Layer-2 network for scalability and smart contract execution.
