Search Results (5,936)

Against the backdrop of China’s “dual carbon” goals and urban green transition, improving urban land use efficiency is essential for shifting land development from extensive expansion to intensive and low-carbon use. Using the Low-Carbon City Pilot Program as a quasi-natural experiment, this study examines panel data from 285 prefecture-level cities in China from 2007 to 2023. We apply a multi-period difference-in-differences model, a threshold regression model, and a spatial Durbin model to assess the program’s impact on urban land use efficiency. The results show that the pilot program significantly improves urban land use efficiency, and the effect persists over time. This finding remains robust across a series of robustness checks. Heterogeneity analysis shows that the efficiency gains are stronger in cities with lower air pollution control pressure, higher industrial pollution control pressure, and lower fiscal pressure. Further threshold analysis shows that digital connectivity is a key condition for strengthening the policy effect. The spatial analysis suggests that the policy effect shows some spatial association. However, the decomposed indirect and total effects are not robust, so the spatial results should be interpreted with caution. This study provides empirical evidence on how low-carbon city pilots affect urban land governance and land use efficiency. Its conclusions, however, remain subject to limitations related to efficiency measurement, policy identification, and the availability of city-level data. Full article

Adhesives and sealants are critical yet still underrepresented components in packaging science. Existing reviews mainly address specific chemistries, sealing technologies, or application niches, whereas integrated analyses of adhesive and sealant families within a unified packaging-system framework remain limited. This review addresses this gap by proposing a three-dimensional classification framework—functional role, material chemistry and activation mechanism, and performance constraints—that connects functional roles, processing routes, regulatory constraints, and circularity requirements. The framework is applied across natural, synthetic, hot-melt, pressure-sensitive, and tie-layer adhesives, as well as conventional thermoplastic, barrier-oriented, and biodegradable sealant systems. Special attention is given to hybrid systems operating at the boundary between bonding and sealing, and to the performance–recyclability trade-offs that arise in multilayer architectures. Structure–property–function relationships are analysed qualitatively with respect to bond and seal strength, seal initiation temperature, hot-tack behaviour, and end-of-life compatibility. Part I establishes the classification and functional groundwork for the two-part review; Part II will extend the analysis to quantitative performance data, advanced materials, and emerging technologies. Full article

Deep ConvNets suffer from gradient signal degradation as network depth increases, limiting effective feature learning in complex architectures. ResNet addressed this through residual connections, but these fixed short circuits cannot adapt to varying input complexity or selectively emphasize task-relevant features across network hierarchies. This study introduces GradAttn, a variation of the residual approach in CNNs that replaces the fixed residual connections with attention-controlled gradient flow. By extracting multi-scale CNN features at different depths and regulating them through self-attention, GradAttn dynamically weights shallow texture features and deep semantic representations. For representational analysis, we evaluated three GradAttn variants across eight diverse datasets: from natural images and medical imaging to fashion recognition. The results demonstrate that GradAttn outperforms ResNet-18 on five of eight datasets, achieving up to $+11.07\%$ accuracy improvement on FashionMNIST while maintaining a comparable network size. Gradient flow analysis reveals that controlled instabilities, introduced by attention, often coincide with improved generalization, challenging the assumption that perfect stability is optimal. Furthermore, positional encoding’s effectiveness turned out to be dataset-dependent, with CNN hierarchies frequently encoding sufficient spatial structure. These findings render attention mechanisms as enablers of learnable gradient control, offering a new way for adaptive representation learning in deep neural architectures. Full article

Rainfall prediction is essential for the enhanced understanding of several issues related to water resources and agriculture, such as flood and drought alerts and flood management. Neural network models are frequently used due to their capability of effectively handling large datasets and addressing the non-stationarity of rainfall data series, resulting in better accuracy and affordable solutions. However, further study is necessary to comprehend the dynamic nature and extreme events of rainfall. Therefore, we implemented a novel wavelet Fourier-enhanced network (W-FENet) that included a Fourier enhancement module (FEMEX) and an improved U-Net mechanism to strengthen the predictive accuracy of daily rainfall. The adopted U-Net structure facilitated efficient multiscale feature extraction and preservation of temporal rainfall information through encoder–decoder connections and residual learning. The results of the developed models for one-day-ahead rainfall prediction were evaluated against two traditional neural network models, i.e., artificial neural networks and long short-term memory networks. Mongla, being a coastal station and having a highly non-linear rainfall pattern, operated by the Bangladesh Meteorological Department, was selected as the study area. Four preprocessing techniques were incorporated to enhance the robustness of the models: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), and successive variational mode decomposition (SVMD). The SVMD-enhanced W-FENet model (abbreviated as W5) demonstrated significant improvements over existing literature with RMSE = 2.226 mm, MAE = 1.131 mm, PCC = 0.988, NSE = 0.974, and WI = 0.993 at the testing phase. Full article

Natural products are the fundamentals of drug discovery due to their exceptional structural diversity and biological activity’s evolutionary optimization. The review provides a critical and integrative analysis of natural products in pharmaceutical chemistry, highlighting their significance for current biomedicine and pharmacotherapy. The review is organized around a system that connects structure, function, and translation, focusing on structural analysis, scaffold design, and mechanistic understanding in major disease-relevant therapeutic areas. Investigations on representative compounds like paclitaxel, artemisinin, and curcumin are presented to explain the way molecular architecture regulates pharmacological activity, drug selectivity, and clinical performance. The review evaluates significant medicinal chemistry strategies, including semisynthetic modification, prodrug design, and scaffold optimization, and their crucial roles in enhancing potency, pharmacokinetics, and safety. We critically examine the latest advancements in drug delivery technologies, particularly those based on nanotechnology and carrier-free methods, regarding their translational potential and regulatory concern. Current challenges pertaining to pharmacokinetics and ADMET properties, as well as the standardization of analysis, are also examined, emphasizing their impact on reproducibility in research. Researchers investigate the role and limitations of emerging fields such as genome mining, synthetic biology, and network pharmacology in enhancing discovery pipelines. Thus, this review integrates chemical, pharmacological, and translational approaches and suggests an effective strategy to overcome challenges in the development of natural products as the next generation of precision medicine therapeutic agents. Full article

Fracture–vuggy carbonate reservoirs contain discrete caves, fractures, conduits, and vugs, which makes recoverable-reserve evaluation strongly dependent on connected volume rather than on total pore volume alone. This study develops a unit-scale dynamic reserve-updating method for the S48 unit, Tahe Oilfield, by coupling a water-body-corrected material-balance equation, a heterogeneity-corrected waterflood characteristic curve, and iterative geological-model calibration. The main methodological contribution is to convert static fracture–vug architecture into dynamically constrained connected subsystems: the parameter Rwo quantifies connected/injected water volume at the fracture–vug unit scale, whereas the coefficient M corrects the apparent slope of waterflood curves for non-uniform sweep and preferential pathways. The revised workflow was calibrated against pressure, production, injection-response, and history-matched simulation data. Sensitivity analysis indicates that the estimated reserve-utilization degree increased from 48.77% +/− 4.8 percentage points during natural depletion to 74.1% +/− 6.7 percentage points after gas injection, reflecting staged reserve mobilization within the tested uncertainty range. The method is intended for field-scale reserve updating in reservoirs with sufficient pressure-production data; its transferability remains limited by static-model quality, channeling intensity, and the single-unit validation scope of this study. Full article

Traumatic brain injury (TBI) is one of the leading causes of acquired epilepsy, with a significant proportion of patients developing post-traumatic epilepsy (PTE) even months or years after the initial injury. The identification of reliable imaging biomarkers able to predict epileptogenesis remains a major clinical challenge. In recent years, diffusion-weighted imaging (DWI) and structural connectome analysis have emerged as promising tools to investigate brain network alterations associated with late seizure development. Machine learning approaches may further support the detection of predictive patterns in complex neuroimaging data. The goal of this study is to perform a binary classification between seizure-free and late seizure-affected patients following TBI, with a specific focus on the identification of the anatomical regions potentially connected with late seizure development. A dataset of 59 diffusion weighted images (DWI) scans from the EpiBioS4Rx project, including 42 seizure-free and 17 late seizure-affected TBI patients, was analyzed. A Random Forest classification algorithm was applied, incorporating network feature importance based on the Gini index to investigate model’s decisions and allow a clinical interpretation. The model reported a 69% ± 0.03 accuracy for discrimination and a 73% AUC ± 0.05. Despite the limited and imbalanced nature of the dataset, and the fact that the performance does not significantly exceed chance once all data-dependent steps are taken into account, our approach allows us to achieve accurate classification results compared to the literature and to identify brain regions potentially associated with epileptogenesis. Full article

The intermittent nature of renewable power sources, nonlinear load effects, and harmonic distortions induced by power electronic converters complicate the maintenance of high energy quality in microgrid-connected hybrid renewable power systems. In a range of operating conditions, conventional strategies-including fractional-order proportional-integral (FOPI) controllers-frequently prove ineffective in delivering both robust harmonic mitigation and expeditious dynamic response. To surmount these constraints, the present paper puts forth an intelligent control solution that is predicated on a fractional-order fuzzy logic (FOFL). The FOFL is integrated into a multi-converter HRPS, comprising a photovoltaic generator, a lithium-ion battery power storage system, and a wind turbine equipped with a permanent magnet synchronous generator. A multifunctional voltage source inverter has been developed to control these parts, which are interfaced via a common DC bus. Through the implementation of MATLAB 2021 simulation studies, the efficacy of the suggested algorithm is verified and evaluated in comparison to the FOPI. The findings indicate that the FOFL enhances system efficacy by minimizing harmonic distortion, improving energy quality, and achieving a faster dynamic response under various circumstances. In the context of grid-connected microgrid environments, the FOFL has been demonstrated to offer superior overall energy management, robustness, and adaptability when compared to other evaluated strategies. Full article

Revealing correlations for styles in texts has been an interesting topic, providing an example of trans-disciplinarity between physics and linguistics. Typical cases can be seen for sound correlations in verses as well as for word correlations in prose. Of these, long-range correlations are of particular interest because of their connection to the Markovian nature in human cognition. For a famous novel written in an archaic style of Japanese, we conduct an analysis of a series of kanji compounds in the text. Here, kanji is a Japanese name for Chinese characters. Specifically, we focus on the number (equivalent to the length) of a compound. Subsequently, the sequence of numbers is expanded into 6-bit binary codes (equivalent to 64 modes). Replacing each compound in the text with an oscillator in a string, for the chain of the kanji compounds, one can find an analogy with the so-called Fermi-Pasta-Ulam-Tsingou (FPUT) model. Comparative analyses for 16 modern translations by humans and machines show, without exception, a strong dominance for a particular mode and its stability bearing a remote resemblance to a soliton, and at the same time, reproduce a statistical property far from a sort of ergodicity. Furthermore, comparison between the human and machine translations shows that the entropy of the latter is higher than that of the former because machines are subjected to neither a psychological bias nor an inspection by editors. Lastly, in addition to the above translated texts, 6 codices of the classic are also analyzed, and their results are compared with those of the modern translations. Note that the original of the classic has not been found yet. Full article

Background/Objectives: Yes-associated protein 1 (YAP1) is a transcriptional cofactor that coordinates the complex interplay between cell proliferation, survival, differentiation, metabolism, biomechanics, and tissue regeneration. Previous studies have shown that YAP1 activity is reduced during aging, and replacing YAP1 function has been shown to rejuvenate old cells by mitigating senescence and its associated inflammation. Methods: As YAP1 is now confirmed to exert a profound regenerative influence on multiple organs, we wanted to gain more insight into the molecular signature of YAP1 expression relevant to brain cells. Since proteomics is a very powerful tool for discoveries, we generated SH-SY5Y cells stably expressing GFP-YAP1 and screened 8000 human proteins using multiplex arrays that utilize biotin-label-based antibody arrays. Results: We found YAP1 expression in astrocytes, microglia, neuronal and neuroblastoma cell lines, as well as human neurons. Importantly, YAP1 protein levels were significantly reduced selectively in the nuclear fractions of the brains of patients with Alzheimer’s disease (AD) relative to normal control (NC) subjects. The screen resulted in the identification of 283 differentially expressed proteins. In line with YAP1’s known role in the regulation of actin and cytoskeleton, we found a 2.53-fold upregulated level of Rho guanine nucleotide exchange factor 1 (ARHGEF1), a guanine nucleotide exchange factor (GEF) for the RhoA GTPase, which is crucial for dendritic spine regulation. A 6.19-fold upregulated level of NECAP endocytosis-associated 2 (NECAP2), the highest known increase for any protein in this screen, plays an essential role in clathrin-mediated endocytosis. Most importantly, another upregulated protein was Neudesin Neurotrophic Factor (NENF) (3.07-fold increase), also known as Neudesin, which primarily acts as a neurotrophic factor, and it promotes neuronal survival, enhances cell proliferation, and neurogenesis in neural progenitor cells. Neural Precursor Cell Expressed, Developmentally Down-Regulated 9(NEDD9) levels were also upregulated by 2.46-fold, and it affects neuronal cell number and synaptic connections through its role in neurite formation. However, it should be noted that these proteomic results are preliminary in nature as they are derived from single-sample data. The upregulated levels of ARHGEF1 and NEDD9 were confirmed by immunoblots. We also found a drastic reduction in the levels of p16INK4a, a marker of senescence. Conclusions: Thus, the anti-senescence effect of YAP1 may be mediated through p16INK4a, which in turn may be crucial for YAP1’s regenerative functions through NENF and NEDD9. Full article

International evidence has been positioning the built environment as an active component of the development of students with Autism Spectrum Disorder (ASD); nevertheless, a gap persists in the empirical quantification of that relationship and, above all, in its dimensional hierarchization, a gap that becomes more pronounced in urban educational contexts with limited infrastructure such as those in Latin America. Within this framework, and with the aim of contributing empirical evidence to guide design decisions in comparable contexts, the present study analyzed the association between multisensory architecture and the cognitive development of students with ASD at a Special Basic Education Center (CEBE) in San Miguel, Metropolitan Lima, organizing the findings into a dimensional hierarchy that makes it possible to compare the relative strength of each spatial criterion. To address this objective, a non-experimental, cross-sectional, and correlational design was adopted, in which cognitive development was assessed through proxy informants (specifically, immediate family members with daily and sustained contact with the students), given that students with ASD present limitations for standardized verbal self-reporting. On this basis, a sample of 101 proxy informants completed, through the QuestionPro platform, a structured questionnaire of 24 Likert-scale items previously validated by expert judgment, exploratory factor analysis, and internal consistency analysis; inferential analysis was then conducted using Spearman’s rho, in keeping with the non-normal nature of the data. The results revealed a positive and statistically significant association between multisensory architecture and cognitive development, and they further allowed that relationship to be dimensionally ordered: on the built-environment side, physical-spatial conditions reached the greatest magnitude of association, followed by environmental conditions and, lastly, functional conditions; on the cognitive side, concentration emerged as the dimension most sensitive to the environment, followed by self-regulation and accessibility. Taken together, this empirical hierarchization offers architects, educational administrators, and therapeutic teams a practical reference for prioritizing design decisions in contexts with limited infrastructure and, to that extent, contributes to the fulfillment of Sustainable Development Goals 3 and 11, which connect health with inclusive urban environments. Full article

The Indonesian government should seriously consider the use of renewable energy, given the natural potential that can still be utilized as an environmentally friendly power source. The utilization of renewable energy can be achieved by harnessing available natural resources. Pico hydro power plants (PLTPHs) can serve as an alternative electricity generator for use in Indonesia due to the existing natural potential. The output from this power plant can be utilized directly or stored in batteries. Directly measuring the generator’s performance on-site is deemed less effective. Therefore, a monitoring system is introduced as a solution to allow remote monitoring and display parameters such as voltage, current, frequency, and power of the generator online. This system is designed to display the micro hydro generator’s output parameter data on the Blynk application. The display on the Blynk application can be monitored via a connected mobile phone. Testing of the monitoring system was carried out by comparing two sets of measurements: one through the PZEM-004T sensor system and the other through a kWh meter (Kilowatt-hour meter). For the AC output from the battery with a 12-watt lamp load (tested 4 times), the reading error values obtained were a voltage reading error of 0.2%, a current reading error of 19.4%, a frequency reading error of 0.67%, and a power reading error of 18.2%. Full article

This work develops a geometric and statistical framework for analyzing the structure of biological sequences and explores its implications for understanding the emergence and evolution of life. Motivated by questions concerning the transition from prebiotic chemistry to living systems, the quantification of negentropy in organic matter, and the distinction between random and biologically viable sequences, we introduce mathematical descriptors that measure deviation from linearity and related geometric irregularities of self-replicating macromolecules. These descriptors reveal a pronounced geometric separation between biological DNA and random sequences, underscoring the non-random structural organization characteristic of living systems. Using these descriptors, we compare a broad range of species across the Tree of Life and examine how geometric complexity varies between primitive and more advanced organisms. We further investigate whether these measures provide a natural way to compare organismal complexity, characterize the structure of viable sequence space, and identify potential constraints on evolutionary trajectories. The framework also offers an initial perspective on how natural selection and stochastic mutations may jointly influence genomic organization. Finally, we outline speculative connections between increasing geometric irregularity and the emergence of biological complexity, suggesting that such geometric transitions may offer insight into the origins of life and the theoretical limits of evolutionary development. Full article

Rail transport is the basis for the proper functioning of a transport system that is sustainable for future generations. It is safe and environmentally friendly; moreover, it is suitable for carrying a large number of passengers. Train connections should be operated following the requirements of the traveling public, as well as with the potential to reach those who have hitherto preferred individual car transport. The study aimed to identify the needs of current as well as potential rail users and to propose measures for improving service provision and supporting more sustainable transport possibilities. Given the ecological nature of rail transport and the high numbers of tourists using individual car transport in the summer and winter seasons, the study sought solutions to shift transport from road to rail infrastructure. Visitors to the area were approached directly during their visit as part of a transport–sociological survey conducted during periods of peak visitation, specifically in the summer and winter seasons. Drawing on findings from previous studies and the results of the transport–sociological survey, four universal variants were developed. The study applies to the method of practical permeability indicators. It evaluates variants of measures involving timetable adjustments, line modifications, and construction of new stations. It assesses their impact on reducing travel times and proper timetable management. The result of the study is to propose building a station on the railway infrastructure, which brings fundamental changes in increasing the practical capacity of the line and meets the goal of sustainability concerning increasing the number of connections and thus increasing the number of public service opportunities. The study addresses the growing pressure of individual car transport in a protected natural area and the need to shift demand towards more sustainable rail transport. Full article

Currently, there is a lack of sufficient research regarding spirituality in the lives of young people in youth care contexts. In this study, youth care refers to various forms of either voluntary or mandatory support and care for young people (children and teenagers) and their educators for growing-up problems, parenting problems, and psychological, psychosocial, and behavioral problems or intellectual disabilities. The available research is not systematically gathered in an overview. Against this background, this article presents a systematic literature review based on the following main research question: What insights can be distilled from scholarly peer-reviewed journal articles published from January 2000–July 2025 regarding spiritual formation in youth care? The results of the review study were based on 41 journal articles. Half of these articles thematize the foster care context. The other articles are spread over other youth care contexts, including psychiatric care, child and youth welfare, residential care, social work, and services for unaccompanied minors. Most of the articles presented empirical research. Three major themes can be defined that connect most articles: (a) the discussion of religion and spirituality as naturally present in the lives of children and the need or right to recognize that dimension and to facilitate continuity in it; (b) the question or the hypothesis that religion and spirituality can promote well-being, including the finding that this does not always appear unambiguous, up to and including attention to the harmful effects of religion and spirituality; and (c) the question of whether and how religion and spirituality can be used more instrumentally in youth care services to provide the best possible care to young people. The article discusses these findings, and recommendations for youth care professionals and follow-up research are presented. Full article