Search Results (52,714)

Exploring the impact of urbanization on carbon emissions is crucial for formulating effective emission reduction policies. Using nighttime light data and attribute data from 68 Chinese cities (2002–2021), this paper develops an urban development evaluation system with the entropy method. The Lasso method is employed to select key factors affecting carbon emissions, and hierarchical regression models are utilized to analyze these factors across different city types. The results show the following: (1) The extraction of built-up areas using integrated nighttime light data yields an overall accuracy ranging from 70.90% to 98.87%, reflecting high precision. (2) Expanding cities have predominated over the past two decades, indicating a continued upward trend in urbanization in China. (3) Urban development is influenced by internal characteristics and geographic location: contracting cities are mainly inland heavy industrial centers, while expanding cities are located in economically advanced coastal regions. Additionally, it is also impacted by the growth of surrounding cities, exemplified by the imbalance between central cities and their peripheries within metropolitan areas. (4) The expansion of built-up areas is a significant factor affecting carbon emissions across all city types. For expanding cities, managing population growth and promoting tertiary sector development are recommended, while contracting cities should focus on judicious economic planning and virescence area protection. Full article

Previous research has highlighted significant phenotypic discrepancies between knockout and knockdown approaches in zebrafish, raising concerns about the reliability of these methods. However, our study suggests that these differences are not as pronounced as was once believed. By carefully examining the roles of maternal and zygotic gene contributions, we demonstrate that these factors significantly influence phenotypic outcomes, often accounting for the observed discrepancies. Our findings emphasize that morpholinos, despite their potential off-target effects, can be effective tools when used with rigorous controls. We introduce the concept of graded maternal contribution, which explains how the uneven distribution of maternal mRNA and proteins during gametogenesis impacts phenotypic variability. Our research categorizes genes into three types—susceptible, immune, and “Schrödinger” (conditional)—based on their phenotypic expression and interaction with genetic compensation mechanisms. This distinction provides new insights into the paradoxical outcomes observed in genetic studies. Ultimately, our work underscores the importance of considering both maternal and zygotic contributions, alongside rigorous experimental controls, to accurately interpret gene function and the mechanisms underlying disease. This study advocates for the continued use of morpholinos in conjunction with advanced genetic tools like CRISPR/Cas9, stressing the need for a meticulous experimental design to optimize the utility of zebrafish in genetic research and therapeutic development. Full article

(1) Background: In recent years, forest fires have become increasingly frequent both domestically and internationally. The pollutants emitted from the burning of fuel have exerted considerable environmental stress. To investigate the influence of forest fires on the atmospheric environment, it is crucial to analyze the variations in PM_2.5 emissions from various forest fuels under differing fire conditions. This assessment is essential for evaluating the effects on both the atmospheric environment and human health. (2) Methods: Indoor simulated combustion experiments were conducted on the branches, leaves, and bark of typical tree species in the Liangshui National Natural Reserve, including Pinus koraiensis (PK), Larix gmelinii (LG), Picea koraiensis (PAK), Betula platyphylla (BP), Fraxinus mandshurica (FM), and Populus davidiana (PD). The PM_2.5 concentrations emitted by six tree species under various combustion states were measured and analyzed, reflecting the impact of moisture content on the emission of pollutants from fuel combustion, as indicated by the emission factors for pollutants. (3) Results: Under different fuel loading and moisture content conditions, the mass concentration values of PM_2.5 emitted from the combustion of different organs of various tree species exhibit variability. (4) Conclusions: Among the various tree species, broad-leaved varieties release a greater quantity of PM_2.5 compared to coniferous ones. A positive correlation exists between the moisture content of the fuel and the concentration of PM_2.5; changes in moisture content notably influence PM_2.5 levels. The emission of PM_2.5 from fuel with varying loads increases exponentially. Utilizing the Response Surface Methodology (RSM) model for simulation, it was determined that both moisture content and fuel load exert a significant combined effect on the release of PM_2.5 during combustion. Full article

Post-stroke depression (PSD) represents a significant neuropsychiatric complication that affects between 39% and 52% of stroke survivors, leading to impaired recovery, decreased quality of life, and increased mortality. This comprehensive review synthesizes our current knowledge of PSD, encompassing its epidemiology, risk factors, underlying neurochemical mechanisms, and the existing tools for preclinical investigation, including animal models and behavioral analyses. Despite the high prevalence and severe impact of PSD, challenges persist in accurately modeling its complex symptomatology in preclinical settings, underscoring the need for robust and valid animal models to better understand and treat PSD. This review also highlights the multidimensional nature of PSD, where both biological and psychosocial factors interplay to influence its onset and course. Further, we examine the efficacy and limitations of the current animal models in mimicking the human PSD condition, along with behavioral tests used to evaluate depressive-like behaviors in rodents. This review also sets a new precedent by integrating the latest findings across multidisciplinary studies, thereby offering a unique and comprehensive perspective of existing knowledge. Finally, the development of more sophisticated models that closely replicate the clinical features of PSD is crucial in order to advance translational research and facilitate the discovery of future effective therapies. Full article

Sunflower is considered a plant with extraordinary adaptability. However, the conditions of growing sunflower function as a limiting factor in its production. The hybrids used in production tolerate weather variability to a different level and utilise the nutrient and water resources of the soil, while the yield is also affected by the number of plants per hectare. In this study, the authors attempted to observe the environmental effects influencing sunflower cultivation, the heterogeneous productivity zones of the given production site and the correlation of the number of seeding plants used under various farm practices. The average rainfall of 2021 and the dry weather of 2022 created suitable conditions for examining the yearly weather effect. In the selected experimental areas, three distinguishable zones were defined in terms of productivity. In each productivity zone, three crop density steps were used in four replicates. Based on the performed comparative tests, the rainy year of 2021 resulted higher yield than the drier year of 2022 in the average- and high productivity zones, while in the low-productivity zone, higher yields were harvested under the drier conditions of 2022 than in the rainy year of 2021. In 2021, with the improvement in productivity, the obtained yield was also higher. However, in 2022, this clarity could not be demonstrated. In the zones with low productivity, identical yield results were observed in both weather conditions. Based on the examination of the obtained results, it was shown that the effect of weather conditions and the given number of plants have a smaller influence on the yield results of low-productivity zones, while these factors have a greater influence on the yields of high-productivity zones. Full article

LMNA-related dilated cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C (LMNA) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. The molecular mechanisms of the disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA-related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA-mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (four from Patients and eight from Controls) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for cardiac progenitors to cardiomyocytes (CMs) and epicardium-derived cells (EPDCs). Data integration and comparative analyses of Patient and Control cells found cell type and lineage-specific differentially expressed genes (DEGs) with enrichment, supporting pathway dysregulation. Top DEGs and enriched pathways included 10 ZNF genes and RNA polymerase II transcription in pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CMs; LMNA and epigenetic regulation, as well as DDIT4 and mTORC1 signaling in EPDCs. Top DEGs also included XIST and other X-linked genes, six imprinted genes (SNRPN, PWAR6, NDN, PEG10, MEG3, MEG8), and enriched gene sets related to metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs, as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model. Full article

Wild edible mushrooms are an important food source globally and have a crucial role in forest ecosystems. However, there is limited research on the growth characteristics and the contribution of agronomic traits to biomass, and the environmental factors affecting mushroom growth are limited. This study was conducted in the Qilian Mountains, China, and focused on investigating the growth patterns and agronomic traits of Lyophyllum decastes and Coprinus comatus. The results revealed that the growth of these mushrooms followed a logical growth curve. By calculating the model parameters, we obtained the maximum daily growth of height (PH), pileus diameter (PD), and cluster perimeter (CP) of L. decastes on the 5th, 7th, and 7th days, respectively, with values of 0.55 cm d⁻¹, 0.54 cm d⁻¹, and 4.54 cm d⁻¹, respectively. However, the maximum daily growth of PH, pileus length (PL), and PD of the C. comatus appeared on the 3rd day, 2nd day, and 2nd day of the observation, respectively. This study identified near-surface relative humidity, air relative humidity, and rainfall as the primary factors influencing mushroom growth, as indicated by Pearson’s correlation analysis, redundancy analysis (RDA), and multiple linear and stepwise regression. Additionally, land surface temperature and air temperature were also identified as important factors affecting mushroom growth. By utilizing random forest and stepwise regression analysis, this study identified PH and stipe diameter (SD) as the most crucial agronomic traits affecting mushroom biomass. Overall, this study offers insights for industrial mushroom cultivation and basic fungal research. Full article

Background: this study aimed to identify the influence of postural stability on upper-limb bilateral deficit (BLD), and to compare the assessment of strength generated during elbow flexion functionally vs. analytically in the dominant and nondominant arms. Methods: Twenty men participated in two sessions to evaluate the maximum isometric strength of elbow flexion. This evaluation was performed unilaterally with the dominant arm, unilaterally with the non-dominant arm, and bilaterally, both in the sitting position (SiP) and the standing position (StP). Results: The BLD when peak force was considered was lower for StP (−6.44 ± 5.58%) compared to SiP (−10.73 ± 6.17%) (p = 0.007). Regarding peak force, statistically significant differences were observed for comparisons between dominance (p < 0.001) and Position*Dominance (p = 0.02), but mean force differences were only observed for the dominance factor (p < 0.001). Greater mean and peak forces were always produced bilaterally compared to unilaterally (p < 0.001). Conclusions: a decrease in postural stability by performing elbow flexion exercises in a standing position accentuates BLD of peak force. Full article

The primary goal of this research was to empirically identify and validate the factors influencing student engagement in a learning environment where AI-based chat tools, such as ChatGPT or other large language models (LLMs), are intensively integrated into the curriculum and teaching–learning process. Traditional educational theories provide a robust framework for understanding diverse dimensions of student engagement, but the integration of AI-based tools offers new personalized learning experiences, immediate feedback, and resource accessibility that necessitate a contemporary exploration of these foundational concepts. Exploratory Factor Analysis (EFA) was utilized to uncover the underlying factor structure within a large set of variables, and Confirmatory Factor Analysis (CFA) was employed to verify the factor structure identified by EFA. Four new factors have been identified: “Academic Self-Efficacy and Preparedness”, “Autonomy and Resource Utilization”, “Interest and Engagement”, and “Self-Regulation and Goal Setting.” Based on these factors, a new engagement measuring scale has been developed to comprehensively assess student engagement in AI-enhanced learning environments. Full article

Single-well productivity is a crucial metric for assessing the effectiveness of petroleum reservoir development. The accurate prediction of productivity is essential for achieving the efficient and economical development of oil–gas reservoirs. Traditional productivity prediction methods (empirical formulae and numerical simulation) are limited to specific reservoir types. There are few influencing factors, and a large number of ideal assumptions are made for the assumed conditions when predicting productivity. The application scenario is ideal. However, in tight oil reservoirs, numerous factors affect productivity, and their interactions exhibit significant complexity. Continuing to use traditional reservoir productivity prediction methods may result in significant calculation errors and lead to economic losses in oilfield development. To enhance the accuracy of tight reservoir productivity predictions and achieve economical and efficient development, this paper investigates the tight reservoir in the WZ block of the Beibuwan area, considering the impact of geological and engineering factors on productivity; the random forest tree and Spearman correlation coefficient are used to analyze the main influencing factors of productivity. The back propagation neural network optimized by particle swarm optimization was employed to develop a productivity prediction model (PSO-BP model) for offshore deep and ultra-deep tight reservoirs. The actual test well data of the oilfield are substituted into this model. The analysis results of the example application yielded an RMSE of 0.032, an MAE of 1.209, and an R² value of 0.919. Compared with traditional productivity prediction methods, this study concludes that the model is both reasonable and practical. The calculation speed is faster and the calculation result is more accurate, which can greatly reduce productivity errors. The model constructed in this paper is well suited for predicting the productivity of tight reservoirs within the WZ block. It offers substantial guidance for predicting the productivity of similar reservoirs and supports the economical and efficient development of petroleum reservoirs. Full article

This paper assesses the features/characteristics of a photovoltaic system’s data, investigates the relationship between the soiling and solar panel performance, and leverages real-world data obtained from a solar site in Shams Solar Facility located at the German University of Technology in Oman. Through an experimental approach, different parameters were scrutinized to unravel the dynamics at play. Due to the lack of studies on how to assess the features of a PV System’s data, and in order to model the PV System’s data, extensive analyses were conducted based on a big dataset containing 36,851 observations of each parameter (environmental factors) of the study. In addition, diverse environmental factors, operational conditions, and the collected data were analyzed by various mathematical/statistical measures, and inferential statistical measures were applied to obtain accurate and significant results that explain the level of each parameter (environmental factors), and are developed to examine the features/characteristics and performance of PV Systems and reveal the influence of soiling accumulation on the energy output. The research findings do not only deepen the understanding of the features of PV Systems data and the impact of soiling on solar panels, but also underscore the significance of considering geographical and climatic variations. This research contributes significantly to advancing knowledge within the realm of solar energy systems and provides actionable insights for optimizing the performance and reliability of PV installations in real-world settings. The discussion, conclusions, limitations, and future directions have been discussed. Full article

With the development of unconventional natural gas resources, plunger gas lift technology has gained widespread application. Accurately predicting gas production from unconventional gas reservoirs is a crucial step in evaluating the effectiveness of plunger gas lift technology and optimizing its design. However, most existing prediction methods are mechanism-driven, incorporating numerous assumptions and simplifications that make it challenging to fully capture the complex physical processes involved in plunger gas lift technology, ultimately leading to significant errors in capacity prediction. Furthermore, engineering design factors and production system factors associated with plunger gas lift technology can contribute to substantial deviations in gas production forecasts. This study employs three powerful regression algorithms, XGBoost, Random Forest, and SVR, to predict gas production in plunger gas lift wells. This method comprehensively leverages various types of data, including collected engineering design, production system, and production data, directly extracting the underlying patterns within the data through machine learning algorithms to establish a prediction model for gas production in plunger gas lift wells. Among these, the XGBoost algorithm stands out due to its robustness and numerous advantages, such as high accuracy, ability to effectively handle outliers, and reduced risk of overfitting. The results indicate that the XGBoost algorithm exhibits impressive performance, achieving an R² (coefficient of determination) value of 0.87 for six-fold cross-validation and 0.85 for the test set. Furthermore, to address the “black box” problem (the inability to know the internal working structure and workings of the model and to directly understand the decision-making process), which is commonly associated with conventional machine learning models, the SHAP (Shapley additive explanations) method was utilized to globally and locally interpret the established machine learning model, analyze the main factors (such as starting time of wells, gas–liquid ratio, catcher well inclination angle, etc.) influencing gas production, and enhance the credibility and transparency of the model. Taking plunger gas lift wells in southwest China as an example, the effectiveness and practicality of this method are demonstrated, providing reliable data support for shale gas production prediction, and offering valuable guidance for actual on-site production. Full article

The aim of this study was to study the impact of artificial intelligence (AI) on enterprises in terms of strategy, technology, business operations, and organizational management. This study used grounded theory analysis to identify the influencing factors of AI technology application maturity in Chinese enterprises. Taking Chinese film and television enterprises as an example, this study constructed an AI technology application maturity evaluation index system for enterprises based on the analytic network process (ANP) and evaluated the application maturity of AI technology in enterprises in terms of enterprise strategy, technology, business operations, and organizational management. To comprehensively evaluate and empirically analyze the application maturity of enterprise AI technology, this study calculated the index weight based on the ANP, and combined it with the fuzzy comprehensive evaluation method to construct a comprehensive evaluation model. The research results showed that intelligence strategy was the element that was believed to be most affected by the maturity of enterprise AI technology. For technology, intelligence technology and equipment were the elements that were believed to be affected the most. For business operations, smart shooting was the element that was believed to be affected the most. With respect to organizational management, corporate culture was the element that was believed to be most affected. The results showed that the proposed methods for evaluating the application maturity of enterprise AI technology are scientific and effective. The results of this study provide a reference for promoting the application of AI, implementing the intelligence transformation, and enhancing the core competitiveness of enterprises. Full article

The radon concentration activity in buildings is influenced by various factors, including meteorological elements like temperature, pressure, and precipitation, which are recognized as significant influencers. The fluctuations of indoor radon in premises are related to seasonal change. This study aimed to understand better the effects of environmental parameters on indoor radon concentration levels in the Aksu school. Indoor and outdoor temperature differentials heavily influence diurnal indoor radon patterns. The analysis indicates that the correlation between indoor radon and outdoor temperature, dew point, and air humidity is weak and negligible for atmospheric pressure, wind speed, and precipitation, as determined by the obtained values of R² and the Chaddock scale. The multiple regression model is characterized by the correlation coefficient r_xy = 0.605, which corresponds to a close relationship on the Chaddock scale. Full article

This review presents a comparative analysis of gelation properties in plant-based versus animal-based proteins, emphasizing key factors such as pH, ionic environment, temperature, and anti-nutritional factors. Gelation, a crucial process in food texture formation, is influenced by these factors in varying ways for plant and animal proteins. Animal proteins, like casein, whey, meat, and egg, generally show stable gelation properties, responding predictably to pH, temperature, and ionic changes. In contrast, plant proteins such as soy, pea, wheat, and oilseed show more variable gelation, often requiring specific conditions, like the presence of NaCl or optimal pH, to form effective gels. Animal proteins tend to gel more reliably, while plant proteins require precise environmental adjustments for similar results. Understanding these factors is crucial for selecting and processing proteins to achieve desired textures and functionalities in food products. This review highlights how changing these key factors can optimize gel properties in both plant- and animal-based proteins. Full article