Search Results (1,967)

Wild orchids, valued for their beauty and economic importance, are facing the challenges of distribution contraction and range shifts from climate change. The rare Cymbidium cyperifolium (class II in the List of National Key Protected Wild Plants in China, Vulnerable on the China Biodiversity Red List) remains understudied regarding its responses to climate variability. Utilizing an enhanced MaxEnt model, we predicted suitable habitats under diverse climate scenarios, revealing a potential distribution of 52.37 × 10⁴ km², concentrated in eastern Yunnan, western Guangxi, the Guizhou border, and southern Hainan. Cymbidium cyperifolium is sensitive to climate change, and temperature annual range (Bio 7) contributes a significant 77.42% of the distribution probability (i.e., habitat suitability), highlighting temperature’s pivotal influence on its distribution. Although the overall potential distribution area and low-suitability regions in China are predicted to decrease, medium and high-suitability areas are expected to expand. The center of mass of the high-altitude habitat is concentrated in southeastern Yunnan Province, migrating just slightly, yet tending westward and northeastward. Based on these findings, we recommend the expansion of existing protected areas or the establishment of new ones for C. cyperifolium, particularly in eastern Yunnan and western Guangxi. Additionally, our research can serve as a reference for the ex situ conservation of C. cyperifolium and other orchids with similar ecological habits, underscoring the broader implications in biodiversity preservation efforts. Full article

This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms. Full article

Background: Head and neck squamous cell carcinoma (HNSCC) is a prevalent malignancy with poor clinical outcomes. microRNA-7-5p (miR-7-5p) has been described as both a tumour suppressor and an oncomiR depending on the tissue context, but its role in HNSCC remains unclear. This study aimed to clarify the clinical significance and biological function of miR-7-5p in HNSCC by integrating data from multiple sources. Methods: A systematic review of the literature was conducted to identify studies analysing miRNA expression in human head and neck tissues. A meta-analysis of individual patient data from Gene Expression Omnibus (GEO), ArrayExpress, and The Cancer Genome Atlas (TCGA) was performed to assess miR-7-5p expression in tumours and normal tissues, and its associations with clinical parameters and prognostic outcomes. Bioinformatics analyses were used to predict miR-7-5p target genes, classify hub genes, and perform gene ontology enrichment analysis. MicroRNA in situ hybridisation (miRNA ISH) and real-time quantitative PCR (RT-qPCR) were conducted on tissue samples, HNSCC cell lines, and an in vitro model of oral oncogenesis to validate miR-7-5p expression patterns. Results: miR-7-5p was significantly upregulated in tumours compared to normal tissues and associated with larger tumour size, HPV-negative status, poor disease-specific survival, and shorter progression-free intervals. Bioinformatics analysis highlighted miR-7-5p target genes enriched in pathways related to cell growth, survival, and tumourigenesis. Despite evidence supporting the anti-cancer role of exogenous miR-7-5p in preclinical models, the observed endogenous upregulation in tumours suggests that miR-7-5p expression may represent a compensatory or stress-responsive mechanism during tumourigenesis, rather than acting as a primary oncogenic driver. Conclusions: This study provides new insights into the complex role of miR-7-5p in HNSCC, supporting its potential as both a biomarker and a therapeutic target. Understanding the context-specific functions of miR-7-5p is essential for its development as an RNA-based therapeutic in HNSCC. Full article

Three-dimensional concrete printing (3DCP), an innovative fabrication technique, has emerged as an environmentally friendly digital manufacturing process for using recycled waste materials in the construction industry. The aim of this review paper is to critically evaluate the current state of research on the use of recycled materials such as aggregates and powders in 3DCP, correlating the environmental, economic, and performance parameter effects. This review comprehensively evaluates the potential benefits of incorporating recycled waste materials in 3D printing by critically reviewing the existing peer-reviewed articles through a scientometric review. The resulting bibliometric analysis identified 73 relevant papers published between 2018 and 2024. Through the critical review, five main research categories were identified: recycled materials in 3DCP arising mainly from construction demolition in powder and aggregate forms, which investigates the types of recycled materials used, their extraction methods, morphology and physical and chemical properties. The morphology properties of the materials used displayed high irregularities in terms of shape and percentage of adhered mortar. In the second category, printability and performance, the buildability, rheological properties and the mechanical performance of 3DCP with recycled materials were investigated. Category 3 assessed the latest developments in terms of 3D-printed techniques, including Neural Networks, in predicting performance. Category 4 analysed the environmental and economic impact of 3DCP. The results indicated anisotropic behaviour for the printed samples influencing mechanical performance, with the parallel printing direction showing improved performance. The environmental performance findings indicated higher global warming potential when comparing 3DCP to cast-in situ methods. This impact was reduced by 2.47% when recycled aggregates and binder replacements other than cement were used (fly ash, ground slag, etc.). The photochemical pollution impact of 3DPC was found to be less than that of cast-in situ, 0.16 to 0.18 C2H4-eq. This environmental impact category was further reduced up to 0.10 C2H4-eq following 100% replacement. Lastly, category 5 explored some of the challenges and barriers for the implementation of 3DCP with recycled materials. The findings highlighted the main issues, namely inconsistency in material properties, which can lead to a lack of regulation in the industry. Full article

Background/Objectives: Germ Cell Neoplasia In Situ (GCNIS) is considered the precursor lesion for the majority of testicular germ cell tumors (TGCTs). The aim of this study was to evaluate whether first-order radiomics features derived from volumetric diffusion tensor imaging (DTI) metrics—specifically apparent diffusion coefficient (ADC) and fractional anisotropy (FA) histogram parameters—can detect GCNIS. Methods: This study included 15 men with TGCTs and 10 controls. All participants underwent scrotal MRI, including DTI. Volumetric ADC and FA histogram metrics were calculated for the following tissues: group 1, TGCT; group 2: testicular parenchyma adjacent to tumor, histologically positive for GCNIS; and group 3, normal testis. Non-parametric statistics were used to assess differences in ADC and FA histogram parameters among the three groups. Pearson’s correlation analysis was followed by ordinal regression analysis to identify key predictive histogram parameters. Results: Widespread distributional differences (p < 0.05) were observed for many ADC and FA variables, with both TGCTs and GCNIS showing significant divergence from normal testes. Among the ADC statistics, the 10th percentile and skewness (p = 0.042), range (p = 0.023), interquartile range (p = 0.021), total energy (p = 0.033), entropy and kurtosis (p = 0.027) proved the most significant predictors for tissue classification. FA_energy (p = 0.039) was the most significant fingerprint of the carcinogenesis among the FA metrics. These parameters correctly characterized 88.8% of TGCTs, 87.5% of GCNIS tissues and 100% of normal testes. Conclusion: Radiomics features derived from volumetric ADC and FA histograms have promising potential to differentiate TGCTs, GCNIS, and normal testicular tissue, aiding early detection and characterization of pre-cancerous lesions. Full article

Stomoxys calcitrans (Linnaeus, 1758) is a hematophagous fly species of veterinary importance, known for its negative effects on animal health and productivity. The stress caused by their painful bites results in losses in milk and meat production. Despite its impact, data on its ecology and activity in Algeria are lacking. Such knowledge is needed to evaluate its potential effects on livestock production and rural health, and to support surveillance, outbreak prediction, and control strategies. This study aimed to investigate the monthly and daily dynamics of S. calcitrans in livestock farms in the Batna region and evaluate the influence of climatic factors on its abundance. From July 2022 to July 2023, Vavoua traps were placed monthly from 7 a.m. to 6 p.m. on four farms in the Batna region, representing different livestock types. Captured flies were identified, sexed, and counted every two hours. Climatic data were collected both in situ and from NASA POWER datasets. Fly abundance was analyzed using non-parametric statistics, Spearman’s correlation, and multiple regression analysis. A total of 1244 S. calcitrans were captured, mainly from cattle farms. Activity occurred from August to December, with a peak in September. Males were more abundant and exhibited a bimodal activity in September. Fly abundance was positively correlated with temperature and precipitation and negatively correlated with wind speed and humidity. This study presents the first ecological data on S. calcitrans in northeastern Algeria, highlighting its seasonal dynamics and the climatic drivers that influence it. The results highlight the species’ preference for cattle and indicate that temperature and rainfall are key factors influencing its abundance. These findings lay the groundwork for targeted control strategies against this neglected pest in Algeria. Full article

Crops nitrogen supply through the in situ mineralization of soil organic matter is a critical process for plant nutrition. However, accurately estimating the contribution of mineralization remains challenging. The complexity of biological, chemical, and physical processes in the soil, influenced by environmental conditions, makes it difficult to precisely quantify the amount of nitrogen available for crops. In this study, we created a database by collecting results from 121 mineralization monitoring experiments carried out between 2015 and 2021 on different experimental plots across Wallonia, Southern Belgium, and assessed the efficiency of predictive mineralization methods. The most impactful analytical parameters on in situ mineralization (ISM), determined using LIXIM program, appeared to be potentially mineralizable nitrogen (PMN) (r = 0.79). PMN, estimated by anaerobic soil incubation, also allowed the effective consideration of the after-effects of grassland termination and manure inputs. A multiple linear regression (MLR) combining PMN, POxC, pH, TOC:N, and TOC:clay significantly improved the prediction of soil nitrogen mineralization available for crops, achieving r = 0.87 (vs. r = 0.58 for the current method), while reducing dispersion by 41% (RMSE 56.35 → 33.13 kg N·ha⁻¹). The use of a more flexible Bootstrap Forest model (BFM) further enhanced performance, reaching r = 0.92 and a 50.8% reduction in dispersion compared to the current method (RMSE 56.35 → 27.76 kg N·ha⁻¹), i.e., about 16% lower RMSE than the MLR. Those models provided practical and efficient tools to better manage nitrogen resources in temperate agricultural systems. Full article

Background: Although telomere length maintenance is a common characteristic of hematological malignancies, the role of telomere length as a prognostic factor to stratify acute lymphoblastic leukemia (ALL) patients depending on their risk of relapse remains elusive. Methods: This knowledge gap motivated us to examine telomere length values in children with ALL at the time of diagnosis and after treatment using quantitative polymerase chain reaction (qPCR) (n = 35). To achieve high-resolution precision and cell specificity, a quantitative fluorescence in situ hybridization (qFISH) technique was developed (n = 5). Results: The results demonstrated statistically significant evidence of telomere shortening in the lymphoblasts of children with ALL but not in the lymphocytes of children after remission following treatment. Our findings also suggested a significant association between telomere shortening and a high risk of relapse disease. Last but not least, our preliminary results showed a trend that telomere shortening was more pronounced in children with B-ALL compared to those with T-ALL in a non-significant manner. Conclusions: Consequently, the current study provides preliminary insights into the potentially substantial prognostic value of telomere length in the progression of pediatric ALL, with the possibility of predicting treatment response. To clarify the application of telomere length as a possible biomarker for disease progression and treatment response in children with ALL, the telomere length values of additional participants need to be examined in further studies. Full article

Solar biomass gasification results in reducing CO₂ emissions while saving biomass resources and producing higher-quality syngas when compared with conventional autothermal processes that require partial feedstock combustion for supplying the process heat. However, the solar process suffers from inherent barriers related to the variability of solar energy caused by cloud passages and shutdowns at night. The concept of hybrid solar gasification thus appears attractive for continuous and stabilized operation under intermittent or variable solar irradiation. This study addresses the dynamic simulation and control of hybrid solar–autothermal biomass gasification for continuous and stabilized syngas fuel production. A hybridization path with a constant H₂ + CO production was retained, and this control strategy was implemented in a second-by-second dynamic optimization problem using a model predictive control (MPC) algorithm. Its feasibility was demonstrated both at the small scale and industrial scale, and daily to yearly performance results were provided. For a 10 MW hybrid gasifier, the yearly solar heat share was 22% for a controlled 1000 NL/s production rate of H₂ + CO (corresponding to the complete allothermal gasification of ~2 t/h of wood at 1200 K), and this decreased with increasing H₂ + CO production objectives (17.4% at 1300 NL/s). A total of 24,200 t of wood feedstock and 8290 t of O₂ were required annually to generate 1410 t of H₂ and 19,200 t of CO, with a 1.03 average H₂:CO molar ratio. In addition, solar-only gasification and hybridization with external heating were also assessed. External auxiliary heating might be as efficient as in situ oxy-combustion and would not affect syngas composition by contamination from combustion products throughout hybridization. However, similar to external heat storage, the related thermal efficiency and heat losses must be considered. Full article

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article

The discrepancy between design-stage predictions and actual building energy performance, known as the “performance gap,” poses a barrier to achieving energy efficiency goals, especially in modern buildings with high-performance envelopes and complex façades. Characterization of façade elements, both on site and in laboratory facilities, can help ensure envelope quality and mitigate this gap. Although glazed envelopes are increasingly used in contemporary architecture, current regulations lack standardized procedures for experimental heat transfer assessment in buildings. This paper explores how existing standards for heat flux measurements in opaque envelopes could be adapted to transparent façades. A detailed uncertainty analysis is provided to define measurement conditions that ensure accurate conductance results. A sensitivity analysis—based on both analytical error propagation and Monte Carlo simulations—identifies minimum sensor precision, temperature gradients, and test durations needed for reliable in situ assessments. Results show that uncertainty is mainly driven by small temperature gradients and systematic sensor errors. Measurements taken over six hours with a minimum 5 K gradient yield acceptable uncertainty. The proposed framework supports the development of rigorous experimental protocols for assessing the conductance of transparent façade elements, accounting for real-world conditions and measurement limitations. Full article

Electrochemical impedance spectroscopy (EIS) is widely used at the laboratory level for monitoring/diagnostics of battery cells, but the design and validation of in situ, online measurement systems based on EIS face challenges due to complex hardware–software interactions and non-idealities. This study aims to develop an integrated co-simulation framework to support the design, debugging, and validation of EIS measurement systems devoted to the online monitoring of battery cells, helping to predict experimental results and identify/correct the non-ideality effects and sources of uncertainty. The proposed framework models both the hardware and software components of an EIS-based system to simulate and analyze the impedance measurement process as a whole. It takes into consideration the effects of physical non-idealities on the hardware–software interactions and how those affect the final impedance estimate, offering a tool to refine designs and interpret test results. For validation purposes, the proposed general framework is applied to a specific EIS-based laboratory prototype, previously designed by the research group. The framework is first used to debug the prototype by uncovering hidden non-idealities, thus refining the measurement system, and then employed as a digital model of the latter for fast development of software algorithms. Finally, the results of the co-simulation framework are compared against a theoretical model, the real prototype, and a benchtop instrument to assess the global accuracy of the framework. Full article

The lake surface water temperature (LSWT) is one of the key indicators for monitoring and predicting changes in lake ecosystems, as it regulates numerous physical and biogeochemical processes. However, current LSWT measurements mainly rely on infrared thermometry and traditional in situ sensors, and lack effective short-term LSWT forecasting and early warning capabilities. To overcome these limitations, we established a high-frequency, real-time, and accurate monitoring and forecasting method for the LSWT based on a novel hyperspectral proximal sensing system (HPSs). An LSWT inversion method was constructed based on a deep neural network (DNN) algorithm with a satisfactory accuracy of R² = 0.99, RMSE = 0.92 °C, MAE = 0.64 °C. An analysis of data collected from October 2021 to December 2023 revealed distinct seasonal fluctuations in the LSWT in the northern region of Lake Taihu, with the LSWT ranging from 2.61 °C to 38.52 °C. The hourly LSWT for the next three days was forecasted based on a long short-term memory (LSTM) model, with the accuracy having an R² = 0.99, an RMSE = 1.01 °C, and an MAE = 0.87 °C. This study complements lake water quality monitoring and early warning systems and supports a deeper understanding of dynamic processes within lake physical systems. Full article

Thin liquid film corrosion is a critical failure mechanism for the atmospheric environment and industrial infrastructure. This review systematically examines relevant methods and recent advances in characterizing and simulating this phenomenon. Various measurement methods for liquid film thickness, composition, and conductivity are investigated, with particular focus on the advantages of non-contact optical technology and X-ray fluorescence (XRF) in in situ monitoring and analysis. For corrosion simulation, the finite element method (FEM), cellular automaton (CA), and molecular dynamics (MD) are widely used. Their combination has synergistic potential in revealing essential corrosion mechanisms and establishing prediction models across scales. Full article

Artificial intelligence (AI), particularly machine learning (ML), is equipping laser micro/nano processing with significant intelligent capabilities, demonstrating exceptional performance in areas such as manufacturing process modeling, process parameter optimization, and real-time anomaly detection. This transformative potential is driving the development of next-generation laser micro/nano processing technologies. The key challenges confronting traditional laser manufacturing stem from the complexity of laser–matter interactions, resulting in difficult-to-control processing outcomes and the accumulation of micro/nano defects across multi-step processes, ultimately triggering catastrophic process failures. This review provides an in-depth exploration of how machine learning effectively addresses these challenges through the integration of data-driven modeling with physics-driven modeling, coupled with intelligent in situ monitoring and adaptive control techniques. Systematically, we summarize current representative breakthroughs and frontier advances at the intersection of machine learning and laser micro/nano processing research. Furthermore, we outline potential future research directions and promising application prospects within this interdisciplinary field. Full article