Search Results (12,039)

Climate change presents urgent and complex challenges to agricultural sustainability and food security, particularly in regions reliant on resource-intensive staple crops. Smart agriculture—through the integration of crop modeling, satellite remote sensing, and artificial intelligence (AI)—offers data-driven strategies to enhance productivity, optimize input use, and mitigate greenhouse gas (GHG) emissions. This study introduces Farmdee-Mesook, a mobile-first smart agriculture platform designed specifically for Thai rice farmers. The platform leverages AquaCrop simulation, open-access satellite data, and localized agronomic models to deliver real-time, field-specific recommendations. Usability-focused design and no-cost access facilitate its widespread adoption, particularly among smallholders. Empirical results show that platform users achieved yield increases of up to 37%, reduced agrochemical costs by 59%, and improved water productivity by 44% under alternate wetting and drying (AWD) irrigation schemes. These outcomes underscore the platform’s role as a scalable, cost-effective solution for operationalizing climate-smart agriculture. Farmdee-Mesook demonstrates that digital technologies, when contextually tailored and institutionally supported, can serve as critical enablers of climate adaptation and sustainable agricultural transformation. Full article

The body-in-white (BIW) is an important part of the electric vehicle body, its mass accounts for about 30% of the vehicle mass, and reducing its mass can significantly contribute to energy savings and emission reduction. In this paper, a collaborative optimization method combining the response surface method and genetic algorithm (RSM-GA) is developed to perform the lightweight optimization of the body-in-white of an electric vehicle. Seventeen design variables were screened by relative sensitivity calculations based on modal and stiffness sensitivity analysis, and the data samples were collected using the Taguchi experiment and Hammersley experiment during the designing of the experiment methods. To further maintain the accuracy rate, the least squares regression, moving least squares method, and radial basis function are applied to fitting data to obtain the response surface, and the error analysis of the fitting results is carried out to correct the response surface. Finally, the genetic algorithm based on the response surface is employed to optimize the structure of the body-in-white, and the results are compared with those of the adaptive response surface method and sequential quadratic programming method. Through comparison, the paper found that the optimization effect obtained by the proposed method has a relatively high accuracy rate. Full article

Fusarium root rot, caused by Fusarium equiseti, poses a significant threat to soybean production. This study aimed to explore the genetic basis of resistance to Fusarium equiseti root rot (FERR) by evaluating the resistance phenotype of 346 soybean germplasms and conducting a genome-wide association study (GWAS) using 698,949 SNP markers obtained from soybean germplasm resequencing data. GWAS analysis identified 101 SNPs significantly associated with FERR resistance, distributed across nine chromosomes, with the highest number of SNPs on chromosomes 13 and 20. Further gene-based association and allele variation analyses identified candidate genes whose mutations are closely related to FERR resistance. To accelerate soybean FERR resistance breeding screening, we developed CAPS markers S13_14464319-CAPS1 and S15_9215524-CAPS2, targeting these SNP sites, and KASP markers based on the S15_9205620-G/A, providing an effective tool for marker-assisted selection (MAS). This study offers a valuable theoretical foundation and molecular marker resources for the functional validation of FERR resistance genes and soybean disease resistance breeding. Full article

Energy-intensive industries (EIIs) face mounting pressure to reduce greenhouse gas emissions while maintaining international competitiveness—a balance that is central to achieving the EU’s 2030 and 2050 climate objectives. In this context, energy management (EM) emerges as a strategic instrument to decouple industrial growth from fossil energy consumption. This study proposes a redefinition of EM to support carbon-neutral supply chains within the European Union’s EIIs, addressing critical limitations of conventional EM frameworks under increasingly stringent carbon regulations. Using a modified systematic literature review based on PRISMA methodology, complemented by expert insights from EU Member States, this research identifies structural gaps in current EM practices and highlights opportunities for integrating sustainable innovations across the whole industrial value chain. The proposed EM concept is validated through an analysis of 24 EM definitions, over 170 scientific publications, and over 80 EU legal and strategic documents. The framework incorporates advanced digital technologies—including artificial intelligence (AI), the Internet of Things (IoT), and big data analytics—to enable real-time optimisation, predictive control, and greater system adaptability. Going beyond traditional energy efficiency, the redefined EM encompasses the entire energy lifecycle, including use, transformation, storage, and generation. It also incorporates social dimensions, such as corporate social responsibility (CSR) and stakeholder engagement, to cultivate a culture of environmental stewardship within EIIs. This holistic approach provides a strategic management tool for optimising energy use, reducing emissions, and strengthening resilience to regulatory, environmental, and market pressures, thereby promoting more sustainable, inclusive, and transparent supply chain operations. Full article

Ships play a crucial role in modern society, serving purposes such as marine transportation, tourism, and exploration. Malfunctions or defects in the main engine, which is a core component of ship operations, can disrupt normal functionality and result in substantial financial losses. Consequently, early fault diagnosis of abnormal engine conditions is critical for effective maintenance. In this paper, we propose a deep hybrid model for fault diagnosis of ship main engines, utilizing exhaust gas temperature data. The proposed model utilizes both time-domain features (TDFs) and time-series raw data. In order to effectively extract features from each type of data, two distinct feature extraction networks and an attention module-based classifier are designed. The model performance is evaluated using real-world cylinder exhaust gas temperature data collected from the large ship low-speed two-stroke main engine. The experimental results demonstrate that the proposed method outperforms conventional methods in fault diagnosis accuracy. The experimental results demonstrate that the proposed method improves fault diagnosis accuracy by 6.146% compared to the best conventional method. Furthermore, the proposed method maintains superior performanceeven in noisy environments under realistic industrial conditions. This study demonstrates the potential of using exhaust gas temperature using a single sensor signal for data-driven fault detection and provides a scalable foundation for future multi-sensor diagnostic systems. Full article

With the intensification of urbanization, resulting in the growing building stock, building operations have become the main contributors to greenhouse gas emissions. However, the relationship between urban form and carbon emissions remains unclear, which limits the sustainable development of cities. This study reviews the definition of carbon sources, data characteristics, and evaluation methods of carbon emissions. In addition, the impact of urban form on building carbon emissions at the macro, meso, and micro scales is reviewed, and low-carbon design strategies for urban form are discussed. Finally, the existing problems in this field are pointed out, and future research directions are proposed. Our review found that small and medium-sized compact cities tend to have less carbon emissions, while large cities and megacities with compact urban forms have more carbon emissions. The carbon reduction design of urban form at the meso scale is often achieved by improving the microclimate. Developing a research framework for the impact mechanism of building carbon emissions in a coordinated manner with multi-scale urban forms can effectively promote the development of low-carbon sustainable cities. This review can assist urban planners and energy policymakers in selecting appropriate methods to formulate and implement low-carbon city analysis and planning projects based on limited available resources. Full article

Developing accurate activity data on tree biomass using remote sensing tools such as LiDAR and drone-mounted sensors is essential for improving carbon accounting in the agricultural sector. However, direct biomass measurements of perennial fruit trees remain limited, especially for validating remote sensing estimates. This study evaluates the potential of terrestrial laser scanning (TLS) and drone-mounted RGB sensors (Drone_RGB) for estimating biomass in two major perennial crops in South Korea: apple (‘Fuji’/M.9) and citrus (‘Miyagawa-wase’). Trees of different ages were destructively sampled for biomass measurement, while volume, height, and crown area data were collected via TLS and Drone_RGB. Regression analyses were performed, and the model accuracy was assessed using R², RMSE, and bias. The TLS-derived volume showed strong predictive power for biomass (R² = 0.704 for apple, 0.865 for citrus), while the crown area obtained using both sensors showed poor fit (R² ≤ 0.7). Aboveground biomass was reasonably estimated (R² = 0.725–0.865), but belowground biomass showed very low predictability (R² < 0.02). Although limited in scale, this study provides empirical evidence to support the development of remote sensing-based biomass estimation methods and may contribute to improving national greenhouse gas inventories by refining emission/removal factors for perennial fruit crops. Full article

Swirl spray combustion has attracted significant attention due to its common usage in gas turbines. However, the high pressure in many practical applications remains a major obstacle to the deep understanding of flame stability and pollutant formation. To address this concern, this study investigated a swirl spray flame fueled with n-decane at elevated pressure. Planar laser-induced fluorescence (PLIF) of OH and polycyclic aromatic hydrocarbons (PAHs) were used simultaneously, enabling the distinction of the locations of OH, PAHs, and mixtures of them, providing detailed information on flame structure and evolution of PAHs. The effects of swirl number and ambient pressure on reaction zone characteristics and PAHs’ formation were studied, with the swirl number ranging from 0.30 to 1.18 and the pressure ranging from 1 to 3 bar. The data suggest that the swirl number changes the flame structure from V-shaped to crown-shaped, as observed at both atmospheric and elevated pressures. Additionally, varying swirl numbers lead to the initiation of flame divergence at distinct pressure levels. Moreover, PAHs of different molecular sizes exhibit significant overlap, with larger PAHs able to further extend downstream. The relative concentration of PAH increased with pressure, and the promoting effect of pressure on producing larger PAHs was significant. Full article

In response to the global imperative of reducing greenhouse gas emissions, the optimisation of renewable energy systems under regionally favourable conditions has become increasingly essential. Solar irradiance forecasting plays a pivotal role in enhancing energy planning, grid reliability, and long-term sustainability. However, in the context of Turkey, existing studies on solar radiation forecasting often rely on traditional statistical approaches and are limited to single-site analyses, with insufficient attention to regional diversity and deep learning-based modelling. To address this gap, the present study focuses on Turkey’s Mediterranean region, characterised by high solar potential and diverse climatic conditions and strategically relevant to national clean energy targets. Historical data from 2020 to 2023 were used to forecast solar irradiance patterns up to 2026. Five representative locations—Adana, Isparta, Fethiye, Ulukışla, and Yüreğir—were selected to capture spatial and temporal variability across inland, coastal, and high-altitude zones. Advanced deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), were developed and evaluated using standard performance metrics. Among these, BiLSTM achieved the highest accuracy, with a correlation coefficient of R = 0.95, RMSE = 0.22, and MAPE = 5.4% in Fethiye, followed by strong performance in Yüreğir (R = 0.90, RMSE = 0.12, MAPE = 7.2%). These results demonstrate BiLSTM’s superior capacity to model temporal dependencies and regional variability in solar radiation. The findings contribute to the development of location-specific forecasting frameworks and offer valuable insights for renewable energy planning and grid integration in solar-rich environments. Full article

Background: Necrotizing enterocolitis (NEC) is a severe gastrointestinal disease and a major cause of morbidity and mortality among preterm infants. Traditional diagnostic methods such as abdominal radiography have limited sensitivity in early disease stages, prompting interest in bowel ultrasound (BUS) as a complementary imaging modality. Objective: This scoping review aims to synthesize existing literature on the role of ultra sound in the early detection, diagnosis, and management of NEC, with emphasis on its diagnostic performance, integration into clinical care, and technological innovations. Methods: Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Embase, Cochrane Library, and Google Scholar for studies published between January 2000 and December 2025. Inclusion criteria encompassed original research, reviews, and clinical studies evaluating the use of bowel, intestinal, or Doppler ultrasound in neonates with suspected or confirmed NEC. Data were extracted, categorized by study design, population characteristics, ultrasound features, and diagnostic outcomes, and qualitatively synthesized. Results: A total of 101 studies were included. BUS demonstrated superior sensitivity over radiography in detecting early features of NEC, including bowel wall thickening, portal venous gas, and altered peristalsis. Doppler ultrasound, both antenatal and postnatal, was effective in identifying perfusion deficits predictive of NEC onset. Neonatologist-performed ultrasound (NEOBUS) showed high interobserver agreement when standardized protocols were used. Emerging tools such as ultra-high-frequency ultrasound (UHFUS) and artificial intelligence (AI)-enhanced analysis hold potential to improve diagnostic precision. Point-of-care ultrasound (POCUS) appears feasible in resource-limited settings, though implementation barriers remain. Conclusions: Bowel ultrasound is a valuable adjunct to conventional imaging in NEC diagnosis. Standardized protocols, validation of advanced technologies, and out come-based studies are essential to guide its broader clinical adoption. Full article

Members of Sideritis sect. Empedoclia (Lamiaceae), known as ‘mountain tea’, are widely used medicinal plants. Their taxonomic classification is complex due to frequent hybridization and subtle morphological distinctions. This study examines 12 populations of eight native Sideritis taxa from Greece: S. clandestina subsp. clandestina, S. clandestina subsp. peloponnesiaca, S. euboea, S. raeseri subsp. raeseri, S. raeseri subsp. attica, S. scardica, S. sipylea, and S. syriaca subsp. syriaca. The objectives were to (1) monitor non-polar secondary metabolites (mainly terpenoids) using gas chromatography; (2) shed light on their phylogenetic relationships; (3) evaluate the correlation between genetic and chemical data. Diterpenes, particularly sideridiol, siderol, 7-epicandicandiol, and ent-3α,18-dihydroxy-kaur-16-ene, were the most abundant chemical compounds. Categorical Principal Component Analysis revealed that S. raeseri subsp. attica is chemically distinct, while the rest are grouped into two clusters: one comprising S. clandestina and S. sipylea, and the other including all the rest. Genetic analysis based on chloroplast DNA (matK, psbA-trnH, trnL-F), showed that S. sipylea and S. syriaca subsp. syriaca were the most phylogenetically distant groups. Our study enhances the understanding of Sideritis chemovariability and phylogeny, supporting also taxonomic, authentication, and breeding efforts. Full article

Background and Objectives: Immunodeficiency associated with aging comorbidities increases the vulnerability of geriatric patients to sepsis. Early recognition and management of sepsis are essential in this population. This study evaluated the relationships between RDW, MPV, and MPV/PLT ratios and mortality in geriatric sepsis patients. Materials and Methods: This retrospective study was conducted between 2020 and 2024 in the Intensive Care Unit of the Department of Anesthesiology and Reanimation at a university hospital. Patients aged ≥ 65 years with a SOFA score of ≥2 were included. Demographic data (sex, age, height, weight, and BMI), hemogram parameters (RDW, MPV, and PLT), blood gas, and biochemical values were analyzed. Furthermore, their comorbidities; site of infection; ICU length of stay; vital signs; and SOFA, APACHE II, and SAPS II scores, recorded within the first 24 h following ICU admission, were evaluated. Statistical analysis was performed using the chi-square test, Student’s t-test, the Mann–Whitney U test, the Monte Carlo exact test, and ROC analysis. A p-value of <0.05 was considered statistically significant. Results: A total of 247 patients were included, with 46.2% (n = 114) classified as non-survivors during the 28-day follow-up period. Among them, 64.9% (n = 74) were male, with a mean age of 78.22 ± 8.53 years. Significant differences were also found in SOFA, APACHE-II, and SAPS-II scores between non-survivors and survivors (SOFA: 7.64 ± 3.16 vs. 6.78 ± 2.78, p = 0.023; APACHE-II: 21.31 ± 6.36 vs. 19.27 ± 5.88, p = 0.009; SAPS-II: 53.15 ± 16.04 vs. 46.93 ± 14.64, p = 0.002). On days 1, 3, and 5, the MPV/PLT ratio demonstrated a statistically significant predictive value for 28-day mortality. The optimal cut-off values were >0.03 on day 1 (AUC: 0.580, 95% CI: 0.516–0.642, sensitivity: 72.81%, specificity: 65.91%, p = 0.027), >0.04 on day 3 (AUC: 0.602, 95% CI: 0.538–0.663, sensitivity: 60.53%, specificity: 60.61%, p = 0.005), and >0.04 on day 5 (AUC: 0.618, 95% CI: 0.554–0.790, sensitivity: 66.14%, specificity: 62.88%, p = 0.001). Conclusions: The MPV and MPV/PLT ratios demonstrated statistically significant but limited predictive value for 28-day mortality in geriatric patients with sepsis. In contrast, the limited prognostic value of RDW may be related to variability in the inflammatory response and other underlying conditions. The correlations found between SOFA, APACHE II, and SAPS II scores highlight their importance in mortality risk prediction. Full article

The Moxi area in the Sichuan Basin hosts abundant deep geothermal resources, but their thermal regime and accumulation mechanisms remain poorly understood. Using 2D/3D seismic data, drilling records, and temperature measurements (DST), we analyze deep thermal fields, reservoir–caprock systems, and structural features. The following are our key findings: (1) Heat transfer is conduction-dominated, with thermal anomalies in Late Permian–Early Cambrian strata. Four mudstone/shale caprocks and three carbonate reservoirs occur, with the Longtan Formation as the key seal. Reservoir geothermal gradients (25.05–32.55 °C/km) exceed basin averages. (2) Transtensional strike-slip faults form E-W/NE/NW networks; most terminate at the Permian Longtan Formation, with few extending into the Lower Triassic while penetrating the Archean–Lower Proterozoic basement. (3) Structural highs positively correlate with higher geothermal gradients. (4) The deep geothermal reservoirs and thermal accumulation mechanisms in the Moxi area are jointly controlled by crustal thinning, basement uplift, and structural architecture. Mantle-derived heat converges at basement uplift cores, generating localized thermal anomalies. Fault networks connect these deep heat sources, facilitating upward fluid migration. Thick Longtan Formation shale seals these rising thermal fluids, causing anomalous heating in underlying strata and concentrated thermal accumulation in reservoirs—enhanced by thermal focusing effects from uplift structures. This study establishes a theoretical framework for target selection and industrial-scale geothermal exploitation in sedimentary basins, highlighting the potential for repurposing oil/gas infrastructure. Full article

The commercialization of Km 0 agricultural and livestock products represents a strategic opportunity to enhance rural economic resilience and reduce greenhouse gas emissions in the food sector. This paper presents an original, policy-oriented framework that connects Km 0 distribution models with measurable CO₂ reductions, proposing a structured system of economic incentives to support their adoption. Grounded in a mixed-methods approach, including normative analysis, empirical modeling, and a regional case study in Galicia, Spain, we demonstrate the alignment of Km 0 policies with the EU’s Common Agricultural Policy (CAP) 2023–2027 and the Sustainable Development Goals (SDGs). Findings reveal substantial potential for environmental mitigation, improved farm profitability, and revitalization of rural economies. This work provides a comprehensive roadmap for integrating Km 0 into national agricultural strategies, supported by data-driven justification and scalable implementation models. Full article

Electricity markets in Europe have undergone major changes in the last decade, mainly due to the increasing share of variable renewable energy sources (RES), changing demand patterns, and geopolitical factors—particularly the war in Ukraine, tensions over energy imports, and disruptions in natural gas supplies. These changes have led to increased electricity price volatility, reducing the reliability of traditional forecasting tools. This research analyses the potential of static and adaptive linear regression as electricity price forecasting tools in the context of three countries with different energy mixes: Germany, France and the Czech Republic. The static regression approach was compared with an adaptive approach based on incremental model updates at monthly intervals. Testing was carried out in three different scenarios combining stable and turbulent market periods. The quantitative results showed that the adaptive model achieved a lower MAE and RMSE, especially when trained on data from high-volatility periods. However, models trained under turbulent conditions performed poorly in stable environments due to a shift in market dynamics. The results supported several of the hypotheses formulated and demonstrated the need for localised, flexible and continuously updated forecasting. Limitations of the adaptive approach and suggestions for future research, including changing the length of training windows and the use of seasonal models, are also discussed. The research confirms that modern markets require adaptive analytical approaches that account for changing RES dynamics and country specificities. Full article