Search Results (7,612)

Background: The transition from the parenteral to subcutaneous application of systemic oncological therapy represents one of the most important innovations in modern oncology, as it affects the quality of life of patients as well as the organization of work and the management of health services. The introduction of this change requires effective leadership, interdisciplinary cooperation, and the adaptation of existing processes in healthcare organizations. Methods: We conducted a systematic review of the professional and scientific literature, considering the purpose and goal of this research. We used electronic databases: Wiley Online Library, PubMed, COBBIS.SI, and Google Scholar web browser. Papers from 2017 to 2025 were considered and processed using meta-synthesis. Results: Recent studies confirm that the subcutaneous administration of immunotherapy and targeted therapy is as effective and safe as parenteral immunotherapy, while significantly reducing treatment time and improving patient experience. Discussion: The transition to subcutaneous application provides an opportunity to transform oncology care. From a management perspective, the introduction of subcutaneous application requires systematic change management, staff training, process adaptation, and interdisciplinary cooperation. The sustainable implementation of innovations depends on organization, communication, and management support. Full article

The expansion of Moso bamboo (Phyllostachys edulis) significantly alters the spatial configuration of surrounding trees, leading to dynamic shifts in the spatial distribution of coarse woody debris (CWD). Investigating the spatial patterns of CWD during bamboo expansion can reveal the dynamic mechanisms of forest communities in this process, thereby providing scientific insights for forest management and conservation. In this study, conducted within the Yangjifeng Biodiversity Monitoring Large Plot, all trees with a diameter at breast height (DBH) ≥ 1 cm within the plots were tagged and important variables were measured, including DBH, tree height, and spatial attributes. Coarse woody debris (CWD) with a DBH ≥ 10 cm was also measured, including DBH, diameters at both ends, length, and spatial attributes. Based on the importance values of tree species in each 20 m × 20 m plot—where an importance value comprehensively measured a species’ relative abundance, frequency, and dominance in the community—the sample plots were divided into three continuous sample transects: evergreen broad-leaved forest (EBF), bamboo–broadleaf mixed forest (BMF), and Phyllostachys edulis forest (PEF). Ripley’s ‘g’-function was employed to analyze the spatial patterns and associations of CWD across these three forest types. A random sampling approach was used to collect CWD samples for the measurement and calculation of carbon storage. Three key findings emerged. (1) With the expansion of Moso bamboo, the biomass and carbon storage of standing dead trees both decrease. The biomass is highest in EBF, followed by BMF and PEF. However, carbon storage is greatest in BMF and lowest in PEF. (2) With the expansion of Moso bamboo, the distribution of CWD became increasingly uniform in PEF; analysis of the overall spatial pattern of CWD indicated that with increasing spatial scale, CWD changed from an aggregated to a random distribution across all three forest types, with a pattern scale of approximately 10 m. In EBF, the CWD of Moso bamboo exhibited a random distribution at all spatial scales. Additionally, CWD across different diameter classes, decay stages, and types changed from aggregated to random distributions as the spatial scale increased, with a reduction in aggregation intensity correlated with larger diameter classes. (3) Finally, we found an almost exclusively negative spatial association between living trees and CWD across all scales, and this negative correlation may be attributed to the absence of new tree growth following tree mortality. Collectively, our findings demonstrate that during Moso bamboo expansion, the spatial distribution of CWD changes from aggregated to random, while maintaining a significantly negative spatial association with living trees. This reveals the dynamic changes in the spatial patterns of CWD during community development, thereby providing a scientific basis for the forest management of subtropical evergreen broadleaf forests and bamboo management and control. Full article

Critical infrastructure restoration (CIR) is a disaster-management and sustainability challenge because prolonged disruption of energy, water, transport, communications, healthcare, and public-administration services can amplify social, economic, and environmental losses. This PRISMA 2020-reported systematic review synthesizes post-2016 scientific literature and official policy, legal, standards, and technical documents on CIR and AI decision support. The review identified 55 records, removed 1 duplicate, excluded 1 ineligible record, and retained 53 core sources for qualitative synthesis, including 31 scholarly publications and 22 official documents. Manual screening was used; no automated screening or AI-assisted exclusion tools were applied. The results are organized around four research questions covering regulatory frameworks, recovery practices, supporting systems, and AI model families. The synthesis shows that CIR is shaped by layered governance through NIS2, the CER Directive, the AI Act, and national measures; by operational recovery practices such as continuity planning, cyber crisis coordination, interdependency mapping, and model-supported restoration; by digital platforms including SCADA/ICS, IoT sensing, GIS/common operating pictures, decision-support systems, simulation environments, and digital twins; and by AI methods ranging from classical machine learning and computer vision to reinforcement learning and generative assistants. However, evidence maturity remains uneven, with many AI applications still simulation-based, sector-specific, or weakly validated in real restoration settings. The review contributes an integrated CIR-oriented framework showing that AI creates practical value when embedded in interoperable, human-supervised, regulation-aware, and empirically validated restoration architectures that support sustainable service continuity rather than isolated automation. Full article

Eukaryotic organisms play a critical role in maintaining agricultural ecosystem functions and crop health. Irrigation practices and water salinity significantly affect eukaryotic communities, yet the interactive effects of drip irrigation depth and water salinity on these communities remain unclear. This study aimed to investigate the interactive effects of drip irrigation depth and water salinity on the diversity, community structure, and functional groups of winter wheat rhizosphere eukaryotes, and to examine their relationships with soil environmental factors. A two-year field experiment was conducted in Cangzhou, Hebei Province, with two drip irrigation depths (5 cm shallow, 25 cm deep) and two irrigation water salinity levels (2 g·L⁻¹, 3 g·L⁻¹). High-throughput sequencing was used to analyze rhizosphere microbial communities, and α/β diversity, species composition, LEfSe differential analysis, and redundancy analysis (RDA) were performed to assess the effects of environmental factors. Results showed that both irrigation depth and water salinity significantly influenced α/β diversity and community structure of soil eukaryotes. The 5 cm shallow + 2 g·L⁻¹ salinity treatment favored species richness, while the 25 cm deep + 3 g·L⁻¹ treatment promoted community evenness. Dominant taxa responded selectively, with Annelida markedly suppressed and groups such as Streptophyta and Chytridiomycota enriched under different treatments. Network analysis revealed that key microbial taxa occupied central positions in interspecies interactions. RDA indicated that soil pH, nitrogen, potassium, and organic matter were important drivers of community structure. In conclusion, drip irrigation depth and water salinity synergistically shape soil eukaryotic community structure. These findings provide a scientific basis for optimizing drip irrigation depth, utilizing brackish water, and enhancing agricultural ecosystem functions. Full article

To scientifically evaluate the health of river aquatic ecosystems in the Qianxinan Buyi and Miao Autonomous Prefecture, southwestern Guizhou, systematic surveys of benthic macroinvertebrate and periphytic algal communities were conducted in representative rivers during October 2024 (autumn) and April 2025 (spring), coupled with concurrent water quality monitoring. Reference sites were selected based on water quality indicators and habitat conditions. Core parameters were identified through correlation analysis, discriminatory ability analysis, and distribution range analysis to construct a Benthic Index of Biotic Integrity (B-IBI) and a Periphytic Algae Index of Biotic Integrity (P-IBI) suitable for the region. These indices were then applied to assess the ecological health of the rivers. Additionally, stepwise regression analysis was employed to investigate the key environmental drivers influencing the two biotic integrity indices. The results indicated that: (1) In terms of species composition, the benthic macroinvertebrate community structure was relatively simple, dominated by arthropods, particularly chironomid larvae. Bacillariophyta and Cyanophyta consistently dominated the periphytic algae community. (2) Assessments using both B-IBI and P-IBI showed that the overall river health in spring was slightly better than in autumn. However, more than half of the sampling sites were rated as “fair” or worse in both seasons. The reference sites (S2, S10) consistently exhibited “excellent” or “good” health, while the impaired sites showed significant spatial heterogeneity. Discrepancies between B-IBI and P-IBI ratings at some sites revealed differential responses of the two biological communities to environmental stressors. (3) Stepwise regression analysis unveiled a seasonal shift in key environmental drivers. The primary factor affecting the B-IBI in autumn was biochemical oxygen demand (BOD₅), which shifted to total phosphorus (TP) and ammonia nitrogen (NH₄⁺-N) in spring. For the P-IBI, the main factor changed from dissolved oxygen (DO) in autumn to chemical oxygen demand (COD) in spring. These findings confirm the applicability of the B-IBI and P-IBI systems in this region, and indicate that multi-assemblage integrated assessments can contribute to understanding the health status of river ecosystems in the Qianxinan Prefecture. This study could serve as a scientific reference for the protection, management, and restoration of local river ecosystems. Full article

In sociotechnical transitions, landscape disruptions, such as climate change, exert pressure on incumbent regimes and can trigger the emergence of niche innovations. Renewable energy communities represent one such innovation, increasingly central to European energy policy. This paper applies a critical realist method to examine the energy community niche in Portugal, drawing on a content analysis of the scientific literature and recent Horizon Europe research projects involving Portuguese actors. The analysis reveals three distinct research pathways structuring knowledge production in this niche—technology-driven, socio-governance-oriented, and infrastructure-focused. It also reveals a systemic R&D bias: incumbent actors occupy dual positions—simultaneously at the regime level and within the niche—playing central roles in learning and network formation while exhibiting limited capacity to translate innovation into institutional change and large-scale diffusion. Building on these critical realist findings, we then apply the Strategic Niche Management framework as an evaluative lens, revealing structural misalignments between components of the sociotechnical system. Together, these two analytical steps offer a novel reading of the Portuguese energy community niche, contributing to the theoretical debate on incumbent roles in transition dynamics and identifying concrete shortcomings for future R&D agenda-setting. Full article

This study systematically compared the structural, functional, pathogenic, and assembly-mechanism characteristics of bacterial communities between urban and rural rivers across China, based on integrated water quality data from 421 sampling sites and 16S rRNA gene sequences from 475 sampling sites. The results revealed that urban rivers had significantly higher nutrient concentrations and bacterial α-diversity, along with lower β-diversity. Urban rivers were enriched with organic matter-degrading phyla such as Chloroflexi and Acidobacteriota and might exhibit more complex co-occurrence networks (average degree: 85.41). In contrast, rural rivers were enriched with phyla including Firmicutes and Cyanobacteria, as well as genera such as Exiguobacterium and Limnohabitans, and might display higher network modularity (modularity: 0.59) and greater spatial heterogeneity in community composition. Functional prediction indicated stronger carbon-cycling potential in urban rivers, whereas nitrogen-cycling functions did not differ between the two river types. Regarding pathogen composition, urban rivers contained a higher number of pathogen species than rural rivers. It was suggested that stochastic processes dominated community assembly in both systems; however, heterogeneous selection contributed more strongly in urban rivers (14.7%). Overall, this work elucidated systematic differences in bacterial community structure, function, pathogen profile, and assembly mechanisms between urban and rural rivers, offering a scientific foundation for differentiated watershed management. Full article

Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., “Parsha (Scab),” “Brown spotting,” “White-Gray spotting,” and “Rust,” which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management. Full article

To elucidate the mammalian community structure and interspecific relationships within the alpine ecosystem of the Qinghai–Tibet Plateau, this study was conducted in the Selin co National Nature Reserve for Black-necked Cranes, Tibet. Based on infrared camera monitoring data collected from June 2023 to July 2024, we analyzed mammalian species diversity and their spatial association patterns. A total of 150 infrared cameras were deployed, of which 128 were effectively retrieved, yielding 13,301 effective camera-trap days and 31,170 photographs of mammals. In total, 21 mammal species were recorded, belonging to 5 orders, 9 families, and 17 genera. The species accumulation curve approached an asymptote, indicating adequate sampling effort. Relative abundance analysis showed that Bharal (Pseudois nayaur) was the dominant species (RAI = 13.72), followed by Plateau Pika (Ochotona curzoniae) (RAI = 8.44), Moupin Pika (Ochotona thibetana) (RAI = 5.93), and Red Fox (Vulpes vulpes) (RAI = 5.50), while Snow Leopard (Panthera uncia) exhibited a moderate abundance level (RAI = 3.69). Significant differences in species diversity were observed among habitat types. Alpine meadow and meadow–desert ecotone exhibited higher diversity indices, whereas alpine desert and alpine bare rock habitats showed lower diversity. Interspecific association analysis identified 30 significant species pairs (p < 0.05), among which positive associations accounted for 93.3% and negative associations for 6.7%. The constructed association network comprised 16 nodes and 30 edges, with Chiru (Pantholops hodgsonii), Snow Leopard, and Red Fox serving as key hub species. Predator–prey pairs exhibited clear spatial coupling, while positive associations among herbivores mainly reflected shared utilization of similar habitat resources. The association structure varied across habitats, being most complex in alpine meadow, whereas no significant associations were detected in alpine desert. Overall, the mammalian community in this region is characterized by “low species richness and high endemism,” with interspecific relationships dominated by positive associations. Habitat heterogeneity plays a critical role in shaping the structure of the association network. These findings provide a scientific basis for biodiversity conservation and alpine ecosystem management on the Qinghai–Tibet Plateau. Full article

Changsha’s historic district, a key part of China’s Historic and Cultural Cities, faces the challenge of balancing spatial constraints and historical preservation while optimizing its existing infrastructure. The public service facilities in Changsha’s historic district are the subject of this study. The study goes beyond traditional facility classification techniques and divides these facilities into four categories: medical and welfare, cultural and historical, education and sports, and environmental sanitation. It is based on field research and multi-source geospatial data and considers a variety of factors, including the distribution of cultural resources, traditional living zones, historic street networks, and cultural and tourism functions of the historic district. Based on this foundation, we propose a configuration evaluation framework that encompasses three dimensions—“spatiality, accessibility, and relatedness”—to systematically analyze the spatial distribution of facilities, walkability, and the community network structure by integrating kernel density, accessibility, and modularity algorithms. The results offer methodological guidance and practical support for facility planning research in the context of historic and cultural city preservation, as well as a scientific foundation for the focused optimization and renovation of public service facilities in historic districts. Full article

Improving the comprehensiveness of old community renewal is a key approach to enhancing residents’ quality of life and the community environment. Currently, research on improving comprehensiveness from both supply and demand perspectives remains limited. This study constructs an evaluation system comprising 27 indicators that cover three dimensions: physical infrastructure, community services, and community governance. Adopting the approach of “single indicator, two-way assessment, and comprehensive evaluation,” this study conducts evaluations from both supply and demand perspectives. On the supply side, facility coverage is calculated through field surveys, POI data, and ArcGIS spatial analysis; on the demand side, resident satisfaction is measured via questionnaires, and an evaluation framework for supply–demand matching is constructed using the IPA model. An empirical analysis using Community X in Beijing as a case study reveals that the completeness of community renewal exhibits significant hierarchical differentiation: supply–demand matching and conditions are favorable for basic services, elderly care and services for special groups, and cultural services; supply and demand for buildings, infrastructure, and public safety are balanced and moderately complete; environmental facilities exhibit oversupply and excessive completeness; and long-term management and resident participation suffer from insufficient supply and lack of completeness, emerging as core constraints. Based on these findings, targeted optimization strategies are proposed, which can provide scientific guidance for the development of comprehensive communities and the renewal of existing urban stock. Full article

The chiral-induced spin selectivity (CISS) effect enables the spin-selective transport of electrons through chiral systems, linking handedness with spin polarization. This review provides a comprehensive examination of the emerging field of chiral electrocatalysis, detailing also the extensive experimental and theoretical endeavor conducted to gain a deeper understanding of the fundamental physical principles and mechanistic characteristics of this phenomenon. In particular, the CISS effect has garnered significant attention within the scientific community due to its potential for broad applicability across several fields, ranging from spintronics to biology. Among them, the prospective harnessing of the CISS effect into electrocatalytic processes offers an innovative strategy to improve the performance of energy conversion and storage technologies. This review deeply examines the practical applications of the CISS effect across different electrocatalytic reactions, with particular emphasis on its influence on the oxygen reduction reaction (ORR) and its critical role in energy conversion systems where the ORR reaction is a key process, such as in metal–air batteries, whose safety and performance can be enhanced through spin-selective electron transport. Full article

The Garisenda Tower, along with the neighboring Asinelli Tower, is arguably the symbol of the city of Bologna. They are the sole remnants of about one hundred towers that formed the city’s skyline in medieval times. As such, the monitoring of their state of health has been of great interest to the scientific community for more than a century—one example being the studies of Prof. Cavani in the early 1900s. The Garisenda Tower, famous for its impressive lean, is the object of Structural Health Monitoring (SHM) involving a multitude of devices. Some examples are a 30 m long pendulum installed on the inside of the tower to measure the planar displacement of the tower’s top; Fiber-Optical Strings (FOSs) installed in the walls of the basement to measure their vertical deformation; and piezoelectric acoustic emission (AE) sensors, also installed on the walls of the tower’s basement to detect elastic waves generated by micro-cracking. This rich experimental setup allows for the investigation of the tower’s stability and damage assessment. In this work, attention is focused on two analyses: The first is a Principal Component Analysis (PCA) study that investigates the correlation between AE data and other SHM data, such as in situ temperature, pendulum displacement, and AE rate. The second analysis corresponds with numerical finite element (FE) studies that assess damage in the base of the tower. Initially, the Smeared Cracking material model is used to understand which zones of the tower are more damaged. Moreover, a possible critical scenario due to increasing tower tilt is investigated. Finally, a viscoelastic formulation of the materials at the base of the tower is used to account for creep to understand the possible viscous effects at the base of the tower. Full article

The basic renovation of old urban communities is an important livelihood project for urban renewal, but there are many problems in the decision-making of renovation schemes, such as strong dependence on experience, lack of quantitative basis for multi-objective trade-off, and difficulty in describing residents’ risk attitude. Combining Case-Based Reasoning (CBR) and utility theory, this paper constructs a set of intelligent decision support models driven by data and knowledge. First of all, through literature analysis and expert investigation, a decision-making index system is established, which includes four dimensions and 16 quantitative indicators: policy and financial support, residential conditions and needs, residents’ consensus and social coordination, and implementation management and long-term maintenance. Secondly, the framework representation method is used to describe the reconstruction case, a hybrid retrieval strategy combining inductive retrieval and nearest-neighbor retrieval is designed, and the subjective and objective data combination weights are calculated by using AHP and the entropy method. On this basis, a loss utility function and risk aversion coefficient based on accident and public opinion data (a = 0.02) are introduced to modify the similarity calculation results to describe the risk avoidance behavior of decision-makers. Through 40 real renovation projects, a case base is built, and two types of target cases, “typical inclusive” (F5) and “key renovation” (F35), are selected for empirical verification. The results show that the model can effectively retrieve similar cases, and the similarity ranking changes in line with risk aversion expectations after utility correction. Taking F5 as an example, by reusing and revising the reconstruction scheme of a similar case, targeted suggestions are generated, which give consideration to safety, economy and operability. This model provides a new quantifiable and reusable method for scientific decision-making in basic renovation of old residential areas. Full article

In this paper, we propose a new digital filtering approach based on the FIR-Median Hybrid (FMH) structure, which incorporates an Unbiased Finite Impulse Response (UFIR) filter as its core component. The proposed filter employs spatially symmetric window configurations to reduce Gaussian noise while preserving edges in images. Although the scientific community is rapidly adopting machine-learning- and deep-learning-based filters, there are several reasons to continue developing filters based on traditional methods. For example, these methods are well understood and rely on a strong mathematical foundation. Moreover, the structure of the proposed filter is simple; thus, this type of filter may be appealing to engineers unfamiliar with the machine-learning field. The performance of the proposed filter was assessed using two datasets: the first consisted of a set of artificial binary images, and the second comprised a subset of the BOWS image dataset. We conducted three main experiments. In the first experiment, we fine-tuned the filter considering three window-shape configurations. In the second experiment, Gaussian noise was added to the images, and the proposed filter was compared against other filters using edge-preservation-oriented metrics such as the Structural Similarity Index Measure (SSIM), the Normalized Step Edge Response (NSER), and the Gradient Conduction Mean Square Error (GcMSE), among others. The third experiment evaluated the performance of the best-performing window-shape configurations. This final test was assessed quantitatively using the Friedman test to identify the best-performing structure, whereas qualitative assessment was conducted using a Mean Opinion Score (MOS) test. The results show that the proposed filter achieved improved performance according to the PSNR, SNR, RMSE, and GcMSE metrics. These findings suggest that the proposed filter can be used in practical applications such as image enhancement, computer vision, and edge-detection-based preprocessing. Full article