Search Results (17,428)

Physical fit analysis has become an important complementary approach in forensic anthropology, particularly in cases involving fragmented human remains, where fragment reassociation is often critical to human identification. This review examines the role of physical fit analysis in fragment reassociation and its contribution to identification in forensic contexts, including disaster victim identification. To this end, the available literature was critically reviewed, with emphasis on physical fit, fracture matching, fracture morphology, and the interpretation of skeletal discontinuities in fragmented remains. The reviewed studies indicate that physical fit analysis can support the recognition of corresponding fragments, the reconstruction of disrupted skeletal regions, and the integration of anthropological findings with other identification methods, especially in cases involving highly fragmented or commingled remains. At the same time, the literature reveals important limitations, including methodological heterogeneity, limited validation studies, and interpretive challenges related to preservation and fracture complexity. Overall, physical fit analysis represents a promising adjunct in forensic anthropology for fragment reassociation and human identification, although further standardization and validation remain necessary to strengthen its forensic applicability. Full article

In the context of China’s carbon peaking and carbon neutrality goals, clarifying whether the Yellow River Basin can achieve a decoupling of economic growth from carbon emissions and sustain regional development through deep decarbonization is critical to both ecological protection and high-quality development of the region. This study integrates the Tapio decoupling model, the geographically and temporally weighted regression (GTWR) model, and an author-developed LEAP-YRB v5 macro-sectoral hybrid model to examine 95 prefecture-level cities from 2010 to 2022 and to project energy consumption and carbon emissions for the nine YRB provincial-level regions from 2022 to 2060. The results show that: (1) the urban decoupling status fluctuated among expansive coupling, strong decoupling, and weak decoupling, with weak decoupling becoming dominant and increasing to 62 cities in 2022; (2) per capita GDP and urbanization tended to increase the decoupling index and therefore inhibited decoupling, whereas more intensive construction-land use promoted decoupling, and industrial structure upgrading and green patents showed context-dependent effects; and (3) the basin cannot peak its emissions under the business-as-usual scenario, while the policy-driven scenario peaks at approximately 3.357 billion tons of CO₂ around 2030. Under the carbon-neutrality-oriented scenario, net emissions decline substantially to 825 million tons by 2060, indicating deep decarbonization but not full carbon neutrality. Full neutrality would require additional carbon sinks, cross-regional clean-electricity integration, stronger power-sector decarbonization, or negative-emission technologies beyond the endogenous measures represented in the model. Full article

Emissivity is a critical parameter in infrared temperature measurement and varies significantly among different materials. Infrared thermography has been widely used for the inspection of substation equipment. However, substations contain a large number of devices with complex structures, making it impractical to assign a separate emissivity value to each device or component. This limitation can significantly affect temperature measurement accuracy. To address this issue, this paper proposes an intelligent multi-emissivity temperature correction method for infrared images of substation equipment. First, a temperature–emissivity correction function is established. Then, a total of 2189 infrared images of substation equipment are collected, and the main equipment components are annotated at the pixel level. Subsequently, an equipment component segmentation model based on DeepLabv3+ is trained. Finally, different emissivity values are assigned to different component regions for temperature correction, and corrected infrared pseudo-color images are regenerated. In the experiment, the temperature values before and after correction are compared with thermocouple measurements. In the present validation experiment, the average deviation between the corrected infrared temperature and the thermocouple measurement was reduced by 79.2% compared with that before correction. Full article

Agricultural carbon emissions represent a major source of greenhouse gases and play a critical role in achieving global climate mitigation and sustainable agricultural development targets. In China, the rapid transformation of agricultural production systems has led to substantial spatial heterogeneity in emission patterns and driving mechanisms of agricultural carbon emissions, while the underlying processes at the county scale remain insufficiently understood. This study investigated the spatiotemporal evolution and potential influencing factors of agricultural carbon emissions at the county level from 2000 to 2022 in Guangdong Province, China. First, agricultural carbon emissions were estimated based on a multi-source accounting framework covering land management, crop cultivation, animal production, and straw burning based on internationally recognized emission accounting methods and IPCC global warming potentials. Then, spatial clustering characteristics were analyzed using local spatial autocorrelation (LISA) to identify heterogeneous emission patterns. Finally, an interpretable machine learning framework combining Random Forest (RF) and SHapley Additive exPlanations (SHAP) was employed to quantify the nonlinear effects and relative contributions of multiple socioeconomic and agricultural drivers. The results showed that agricultural carbon emissions in Guangdong Province exhibited a fluctuating but overall decreasing trend, declining from 50.89 Mt CO₂-eq in 2000 to 39.24 Mt CO₂-eq in 2022, with an overall reduction of 22.9%. High-emission clusters were primarily concentrated in western and northern Guangdong, while low-emission areas were mainly located in the Pearl River Delta (PRD). The RF models demonstrated satisfactory predictive performance, with spatial cross-validated R² values ranging from 0.75 to 0.91 across different years. SHAP analysis suggested that ploughing area, fertilizer and pesticide usage, agricultural machinery power, and primary industry GDP were the dominant factors associated with agricultural carbon emissions, whereas urbanization consistently showed a negative association. Furthermore, these drivers exhibited pronounced nonlinear responses and distinct regional heterogeneity, particularly between the PRD and the western and northern parts of Guangdong Province. These findings suggested that agricultural carbon emissions are jointly influenced by agricultural production intensity, mechanization, and socioeconomic transition and can provide a scientific basis for developing region-specific low-carbon agricultural policies and promoting the sustainable transformation of agricultural systems. Full article

Cyanobacterial blooms occur frequently in lakes worldwide, disrupting aquatic ecosystem balance and directly threatening drinking water safety and fisheries production. Establishing a reliable bloom early-warning system has therefore become an urgent priority for lake water management. This study adopts a structured narrative review approach to synthesize the major early-warning methods, including indicator threshold methods, statistical and empirical models, mechanistic models, machine learning, and remote sensing monitoring. These methods are compared in terms of their fundamental principles, data requirements, predictive capabilities, applicability, interpretability, and computational and maintenance requirements. Emerging trends in multi-source data fusion, multi-model integration, and the development of integrated early-warning systems are also summarized. The findings indicate that each method has distinct strengths and limitations with respect to forecasting lead time, spatial coverage, process interpretation, and operational costs, and that no single method can simultaneously meet the requirements of multiscale bloom monitoring and forecasting. Integrating multi-source data from in situ monitoring, remote sensing observations, and meteorological and hydrological measurements, while coordinating statistical models, mechanistic models, and artificial intelligence algorithms according to specific forecasting objectives, represents an important pathway for improving the robustness and operational applicability of early-warning systems. Given the pronounced seasonal ice cover, substantial hydrological variability, limited monitoring data, and marked regional heterogeneity of some cold and arid region lakes, future research should strengthen high-frequency monitoring during critical periods, promote coordination between remote sensing and in situ observations, and conduct local calibration of early-warning thresholds and model parameters. Season-specific models should also be developed to account for environmental differences among ice-covered, ice-off transition, and open-water periods. Overall, early warning of cyanobacterial blooms in lakes is evolving from the application of individual methods toward the integration of multi-source monitoring, multi-model integration, and decision support, thereby providing a reference for bloom risk prevention and water environment management across different types of lakes. Full article

Secondary inorganic aerosols (SIAs) are critical components of regional air pollution, yet uncertainties in bottom–up emission inventories lead to biases in the simulation of nitrate (PNO₃⁻) and ammonium (PNH₄⁺). This study utilizes a joint NO_x−NH₃ inversion inventory constrained by satellite observations to investigate the response characteristics of SIAs to precursor reductions in the Beijing–Tianjin–Hebei (BTH) region during July 2020. Results indicate that the a priori emission inventory significantly underestimated NH₃ emissions in the BTH region, with a posteriori emission in cities such as Shijiazhuang and Handan increasing to 2–3 times the a priori levels, while NO_x emissions were slightly underestimated. Sensitivity analysis conducted via Comprehensive Air Quality Model with extensions (CAMx)—Decoupled Direct Method (DDM) reveals that the sensitivities of PNO₃⁻ and PNH₄⁺ to precursor variations are higher in south–central BTH; specifically, the sensitivity of PNO₃⁻ concentration to NO_x emission abatement under the a posteriori inventory rises substantially compared with a priori estimates, with relative growth rates spanning 33% to 308% across the study domain. Scenario simulations demonstrate that synergistic NO_x and NH₃ control is the most effective strategy, reducing PNO₃⁻ and PNH₄⁺ concentrations by 2.27 ug/m³ and 0.77 ug/m³, respectively. Full article

Reasonable carbon allowance allocation for power transmission and transformation projects is critical for regional emission reduction, yet existing methods rarely address spatial heterogeneity or the fairness-efficiency trade-off at the city level. This study proposes a framework integrating entropy weighting with Zero-Sum Gains Data Envelopment Analysis (ZSG-DEA), applied to cities in Liaoning, China, to achieve objective initial distribution and efficiency-driven adjustments under a binding carbon cap. Empirical results reveal that regional quota levels are shaped not by economic scale or historical emissions alone, but by grid hub functions, load intensity, new energy transmission demand, and network density. The integrated approach avoids subjective bias and optimizes allocation efficiency, offering a replicable pathway for carbon quota allocation in similar contexts. Full article

Banten Province, Indonesia, is a critical yet overlooked frontier of urbanisation. Despite being one of Southeast Asia’s fastest-urbanising regions, its secondary cities, towns and peri-urban hinterlands remain marginal in urban studies and sustainability science. This neglect is consequential. Future urban growth will increasingly concentrate in such settlements, yet debates on urbanisation in Indonesia and Southeast Asia remain focused on metropolitan regions such as Jakarta, one of the world’s largest urban areas. This paper addresses this gap by combining a cross-sector participatory horizon scan with a responsive development pathway for under-resourced territories. Thirty-four participants from academia, government and professional associations co-produced insights into Banten’s urban trajectories, triangulated with literature and secondary data. To our knowledge, this study draws on the first expert workshop on Banten’s urbanisation of this scale, cross-sectoral reach and disciplinary breadth, spanning six domains: economic development, physical infrastructure, social development, cultural heritage, environment and biodiversity, and disaster vulnerability. The deliberations also identified nine cross-cutting issues—including governance deficits, environmental injustice and climate risk—that underpin territorial inequality. From these findings, the paper synthesises a responsive development pathway that supports coordinated planning and governance across Banten’s urbanising settlements and offers a transferable model for negotiating extended urbanisation in rapidly urbanising Global South regions. Full article

Reliable estimation of winter wheat yield is critical to food system stability and farmland management. Integrating multi-spectral remote sensing data with agronomic parameters represents a primary strategy for improving yield estimation accuracy. However, existing research often overlooks parameters reflecting crop population structure and fails to account for dynamic shifts in the contributions of multidimensional agronomic variables across growth stages, thereby limiting prediction accuracy and model stability. To address these limitations, a winter wheat yield estimation model was developed. This model integrates multi-source and multi-temporal data, incorporates stem tiller density, a key population structure parameter, and accounts for dynamic variation across growth stages. Unmanned aerial vehicle multi-spectral images were collected at four key growth stages: jointing (stem elongation with detectable nodes), booting (flag leaf sheath swelling preceding heading), heading (spike emergence) and filling (grain filling with dry matter accumulation). Three growth indicators, stem tiller density, leaf area index and above-ground biomass, were measured. Two comprehensive growth indicators were derived using the coefficient of variation and the CRITIC weighting methods (CGI_CR). Correlation and feature importance analyses were used to identify sensitive vegetation indices (VIs), which were subsequently integrated with the comprehensive growth indicators. Single-stage, multi-source feature fusion and multi-temporal yield estimation models were established using the Kernel Extreme Learning Machine and its optimised algorithm using the Crested Porcupine Optimizer. The results showed the following: (1) among the individual growth stages, features from the filling stage achieved the highest prediction accuracy; (2) the fusion of multi-source features (VIs + CGI_CR) enhanced the prediction accuracy of the model, achieving a validation set R² of 0.884 and a relative prediction deviation of 2.916 at the filling stage; and (3) the multi-temporal model further improved predictive performance, with the validation R² reaching 0.920, indicating that information from different growth stages contributed complementarily to yield prediction and improved overall model performance. By contrast, the model exhibited relatively weak predictive capability at the early growth stages and was better-suited to early risk identification. Meanwhile, its generalisation ability under cross-regional and inter-annual conditions still requires further validation. Overall, integrating multi-source and multi-temporal data can enhance the precision and stability of predicting winter wheat yield, thereby facilitating precision agriculture management. Full article

Background/Objectives: Substance use behaviors share a complex, overlapping polygenic architecture, yet translating genome-wide association study (GWAS) findings into actionable biological mechanisms remains challenging. This study aimed to characterize the genetic architecture of five substance use traits (alcohol consumption, alcohol dependence, nicotine use, illicit drug use, and behavioral disinhibition) and identify shared and distinct gene expression signatures within the neural circuits governing addiction. Methods: We reanalyzed 7188 individuals from the Minnesota Center for Twin and Family Research (MCTFR) cohort utilizing longitudinal composite phenotypes spanning five substance-use domains and general behavioral disinhibition. Post-QC, 6874 individuals were retained for downstream analysis. Following genomic imputation and linear mixed model GWAS (GEMMA), we utilized the SNipar framework to partition polygenic risk scores (PRS) into direct and indirect genetic effects, investigating intergenerational shifts in genetic penetrance and effects of assortative mating. Finally, we integrated our summary statistics with brain tissue reference panels to perform a transcriptome-wide association study (TWAS) modeling genetically regulated gene expression within neural circuits relevant to addiction. Results: Partitioning of polygenic risk revealed that while surface-level parental DNA correlations were modest (r = 0.08), underlying latent genetic correlations approached unity (R_δ ≈ 0.99), indicating that addiction risk clustering in families is driven by intense assortive mating and concentrated biological inheritance. Multi-phenotype TWAS identified several significant gene–phenotype associations—notably ADAM32 and SLC9A3, which demonstrated pleiotropic effects across multiple substance use categories. Crucially, these significant TWAS signals were enriched in striatal structures (caudate, putamen, substantia nigra) and frontal cortical regions. Conclusions: Our findings support a model of shared genetic liability across diverse substance use behaviors, mediated by specific gene expression patterns in the mesolimbic dopamine system and frontal cortex. By integrating multi-phenotype GWAS and TWAS, this study highlights pleiotropic candidate genes and provides critical insights into the tissue-specific neurobiological pathways underlying addiction vulnerability. Full article

Background: Transmitted drug resistance (TDR) remains a critical challenge in HIV-1 management, particularly in treatment-naïve populations. Baseline genotypic resistance testing is recommended to optimize antiretroviral therapy (ART), yet data from Saudi Arabia remain limited. This study aimed to characterize HIV-1 genetic diversity and baseline resistance-associated mutations among newly diagnosed, ART-naïve individuals. Methods: We conducted a multi-centre, retrospective cross-sectional study across three hospitals in Saudi Arabia between January 2023 and December 2024. Adult, treatment-naïve patients with confirmed HIV infection who underwent genotyping using Sanger sequencing were included. Mutations in reverse transcriptase (RT), protease (PI), and integrase (INSTI) genes were analyzed using the Stanford HIV Drug Resistance Database. Demographic, clinical, and virological data were collected, and comparative analyses between regions were performed. Results: A total of 614 patients were included, predominantly male (85.5%) and aged 25–44 years. Most patients presented with high viral loads (≥10,000 copies/mL, 89.7%), and 25.3% had CD4 counts <200 cells/mm³. HIV-1 subtype distribution was highly diverse, with subtype C (19.5%), CRF02_AG (15.6%), and subtype G (12.7%) predominating. Although mutations were frequently detected (RT: 85.2%, PI: 94.7%, INSTI: 36.4%), the majority were subtype-associated polymorphisms rather than major drug resistance mutations. Clinically significant mutations including M184V/I (1.17%), K103N (0.83%), and K65R (0.17%) were observed at low frequencies. No major INSTI resistance mutations were detected. Multi-class mutation patterns were common but largely driven by accessory variants. Conclusions: Despite the high prevalence of detected mutations, clinically significant TDR remained low, occurring in approximately 2.8% of patients. Most detected variants were polymorphic or accessory mutations, while susceptibility to INSTIs remained largely preserved. Continued baseline genotyping and molecular surveillance remain important to monitor emerging resistance patterns. Full article

Arid inland saline lakes are key components of basin ecosystems. As the largest saline lake in Xinjiang and a critical ecological barrier in northwest China, Ebinur Lake’s area dynamics are vital to regional sustainable development. This study integrates Landsat imagery (1992–2024) with meteorological and socio-economic data to investigate optimal water extraction methods, spatio-temporal lake area variations, and driving mechanisms. Multiple methods were employed, including water index comparison, Mann–Kendall test, Pearson correlation, and ridge regression. Results show that: (1) the Normalized Difference Water Index (NDWI) maintains high, stable classification accuracy across years and months, making it suitable for long-term monitoring; (2) from 1992 to 2024, lake area demonstrates a significant fluctuating downward trend without abrupt change points, indicating continuous degradation. During the growing season (April–October), it first decreases and then increases, with larger early-season areas, minima in August and September, coinciding with peak agricultural irrigation demand; (3) regarding driving mechanisms, socio-economic factors dominate (approximately 70%), while meteorological factors play a weakly regulatory role (about 30%). Population growth and increased water consumption are the primary drivers, with obvious seasonal differences. Meteorological changes, socio-economic development, and ecological measures jointly influence lake area. Although extreme events (e.g., anomalous precipitation) induce short-term fluctuations, they do not alter the long-term degradation trend dominated by human activities. This study provides methodological support for long-term monitoring of arid saline lakes and scientific evidence for ecological conservation and water resource management in the Ebinur Lake Basin. Full article

Water scarcity and increasing energy demand are critical challenges that often characterize Mediterranean regions, especially islands such as Crete. A sustainable solution for a combined water and energy supply lies in the domain of hybrid renewable energy systems. This research study evaluates a large-scale wind-driven desalination system with pumped-hydro energy storage for the Regional Unit of Rethymno, Crete, focusing on climate-driven demand and air temperature variation. The proposed system integrates wind energy production, seawater desalination, pumped-hydro storage, and water supply both for domestic and for irrigation purposes. Four scenarios, each with increasing air temperature, are examined in order to assess their effect on water demand and system performance. The analysis evaluates electricity allocation, the production of desalinated water, domestic and irrigation coverage, as well as the economic performance of the system. The results indicate that domestic water demand is almost fully covered in all four scenarios, reaching nearly 99.9%, while irrigation water coverage decreases from 82% under present conditions to 67% under higher-temperature scenarios. Wind-generated electricity is mainly used for water-related processes, with a constant share supplied to the grid. The economic assessment indicates that the system can operate under break-even conditions using realistic water and electricity prices. Full article

Background: Malnutrition is common among patients with cancer and is associated with poorer clinical outcomes and increased healthcare costs. However, nutritional care remains suboptimal in routine oncology practice. This study aimed to assess the implementation of nutritional screening, assessment and management across oncology centers in Lombardy (Italy) and to identify critical areas to support the development and implementation of a regional Diagnostic–Therapeutic Care Pathway (PDTA). Methods: A 21-item questionnaire developed by the Oncology Commission of the Clinical Nutrition Network of Lombardy Region was administered to all regional oncology centers. Data were collected via the Welfare General Directorate platform from 13 November 2024 to 31 December 2024, and analyzed in aggregate form. Results: In total, 55 out of 59 centers responded (93.2%). Nutritional care was available in 78% of centers. Systematic nutritional risk screening was reported by 42% of centers, while 51% performed a comprehensive nutritional assessment in all patients with a positive screening result. Nutritional assessment was scheduled at both initial and follow-up visits in 53% of centers. In the perioperative setting, prehabilitation programs were available in 49% of centers, and preoperative immunonutrition was administered in 35%. Tools for assessing patient-reported outcomes and quality of life were available in only 15% and 20% of centers, respectively. Conclusions: The survey suggests some improvements compared to previous Italian surveys but highlights persistent gaps in the organization and delivery of nutritional care for patients with cancer. Full article

The leading-edge (LE) of hepatocellular carcinoma (HCC) is a critical region driving malignant progression and is closely associated with high patient mortality and marked intratumoral heterogeneity. Multi-omics integration identified elevated expression of SPARC and IGFBP7 in the LE region, which was associated with stromal remodeling-related transcriptional programs and an immune-depleted microenvironment. Cell-cell communication and pathway analyses further suggested potential links between LE-associated stromal states and pro-invasive signaling programs. Furthermore, we developed SpaPred, which demonstrated favorable performance in inferring the spatiotemporal heterogeneity of HCC at the spatial resolution. This model overcomes the limitations of existing algorithms in analyzing the composition of tissue spatial structures. Finally, integration of in silico trajectory-perturbation and pharmacogenomic drug-response analyses prioritized Oxaliplatin, Belinostat, and Temsirolimus as candidate compounds associated with LE-related transcriptional programs. These drug predictions are computational and require experimental validation. Collectively, SpaPred provides a hypothesis-generating framework for investigating spatial heterogeneity and candidate therapeutic vulnerabilities in HCC. Full article