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In Italy, the management of built assets confiscated from organized crime groups is particularly relevant. Returning these assets to the community is becoming increasingly important for Italian municipalities, thanks to the many social benefits that can be generated (e.g., new spaces to provide community services, a visible and tangible symbol of legality, etc.). The process of redeveloping confiscated buildings, due to procedural complexity and a lack of resources, is currently characterized, on one hand, by a limited number of projects actually implemented compared to the potential of the total number of buildings available and, on the other hand, by the lengthy duration of the redevelopment process (12 years on average), which significantly increases the time it takes for the asset to return social value to the community. The objective of this research was to study, develop, and describe a mechanism that (i) enables an increase in the number of redevelopment actions of confiscated assets, (ii) accelerates the attribution of social value to these assets over time and (iii) extends the social impact of the requalification interventions that provide an opportunity for the professional training of students and the professional qualification of vulnerable people. There are two main tasks of the research shown in this article: (i) to highlight the main critical issues and needs in the management of confiscated assets by Italian municipalities, through a survey conducted among key informants; (ii) to build and test an innovative ‘win–win model’ for the requalification process of confiscated buildings, aimed at overcoming obstacles and anticipating the delivery of social benefits to a large group of stakeholders, including vulnerable people, tested in a pilot project. This “win–win model” combines building requalification and training through the activation of a “construction site school”. All original contributions are derived from the research “Co-WIN”, funded by the “Polisocial Awards 2021”, which developed methods, strategies, and tools capable of reducing social imbalances, with an equity and sustainability perspective. The results illustrated the drivers and challenges for the renovation and reuse of confiscated built assets; the necessary changes in documents and procedures to activate and replicate the “Co-WIN model”; and a training program for the construction-site school based on the social categories involved. Finally, the discussion highlights the network and the mutual benefits for stakeholders, focusing on the social relevance and social impact achievable through applying the Co-WIN model to the requalification process of confiscated buildings. Full article

Aims: To investigate the global epidemiological characteristics of lower respiratory infection (LRI) burden caused by Streptococcus pneumoniae (SP) from 1990 to 2021. Methods: Using data from the Global Burden of Disease (GBD) study 2021, we systematically analyzed Streptococcus pneumoniae-related (SP-related) LRI burden, focusing on mortality, disability-adjusted life years (DALYs), and temporal trends by age, gender, geographic region, and socio-demographic index (SDI) quintiles. Decomposition analysis assessed the influence of epidemiological shifts, population growth, and aging on age-standardized mortality rates (ASMRs), while an autoregressive integrated moving average (ARIMA) model projected future trends. Results: Between 1990 and 2021, the global SP-related LRI death number decreased from 1,028,083 (95% uncertainty interval (UI): 923,782–1,146,074) to 505,268 (95% UI: 454,335–552,539), and the ASMR dropped from 19.28 (95% UI: 17.32–21.49) to 6.40 (95% UI: 5.76–7.00) per 100,000. The age distribution consistently exhibited a clear two-tiered pattern, gradually shifting from being predominantly composed of young children to being dominated by older adults. Disparities were stark across SDI quintiles, low-SDI regions exhibited up to 100-times-higher under-five mortality than high-SDI regions. Geographic distribution showed the highest ASMRs in sub-Saharan Africa and the lowest in Canada, the United States, and Australia, with Mongolia and Finland showing the largest reductions in mortality. Epidemiological changes were the most significant factor in ASMR reduction. Conclusions: The SP-related LRI burden has decreased globally but remains a major health concern, especially in low-SDI regions. Targeted public health interventions, particularly for neonates and elderly adults, are essential to address persistent disparities and further reduce mortality. Full article

This article provides a critical review of conceptually different approaches to automatic and transformer-based automatic item generation. Based on a discussion of the current challenges that have arisen due to changes in the use of psychometric tests in recent decades, we outline the requirements that these approaches should ideally fulfill. Subsequently, each approach is examined individually to determine the extent to which it can contribute to meeting the challenges. In doing so, we will focus on the cost savings during the actual item construction phase, the extent to which they may contribute to enhancing test validity, and potential cost savings in the item calibration phase due to either a reduction in the sample size required for item calibration or a reduction in the item loss due to insufficient psychometric characteristics. In addition, the article also aims to outline common recurring themes across these conceptually different approaches and outline areas within each approach that warrant further scientific research. Full article

Optimizing agricultural structure serves as a crucial pathway to promote sustainable rural economic development. This study focuses on a representative village in the mountainous region of North China, where agricultural production is constrained by perennial low-temperature conditions, resulting in widespread adoption of single-cropping systems. There exists an urgent need to enhance both economic returns and risk resilience of limited arable land through refined cultivation planning. However, traditional planting strategies face difficulties in synergistically optimizing long-term benefits from multi-crop combinations, while remaining vulnerable to climate fluctuations, market volatility, and complex inter-crop relationships. These limitations lead to constrained land productivity and inadequate economic resilience. To address these challenges, we propose an integrated decision-making approach combining stochastic programming, robust optimization, and data-driven modeling. The methodology unfolds in three phases: First, we construct a stochastic programming model targeting seven-year total profit maximization, which quantitatively analyzes relationships between decision variables (crop planting areas) and stochastic variables (climate/market factors), with optimal planting solutions derived through robust optimization algorithms. Second, to address natural uncertainties, we develop an integer programming model for ideal scenarios, obtaining deterministic optimization solutions via genetic algorithms. Furthermore, this study conducts correlation analyses between expected sales volumes and cost/unit price for three crop categories (staples, vegetables, and edible fungi), establishing both linear and nonlinear regression models to quantify how crop complementarity–substitution effects influence profitability. Experimental results demonstrate that the optimized strategy significantly improves land-use efficiency, achieving a 16.93% increase in projected total revenue. Moreover, the multi-scenario collaborative optimization enhances production system resilience, effectively mitigating market and environmental risks. Our proposal provides a replicable decision-making framework for sustainable intensification of agriculture in cold-region rural areas. Full article

With the increasingly frequent exploitation and transportation of offshore oil, the threat of oil spill accidents to the marine ecological environment has become increasingly serious. It is urgent to develop efficient and reliable oil film monitoring technology. Based on the marine radar oil spill data, an innovative OAT-NGN hybrid strategy segmentation algorithm was proposed. By integrating the local feature learning ability of a Neural Gas Network (NGN) and the global search strategy of the Oat optimization algorithm (OAT), the proposed method effectively meets the challenges of traditional oil film segmentation methods in complex sea conditions. Firstly, the raw data of marine radar were preprocessed by using co-frequency interference and speckle noise suppression. Then, the OAT algorithm guided the updating of neural weights in the NGN on a global scale for the exploration of a more optimal solution space during the optimization process. Finally, the oil spill segmentation results were projected to the polar coordinate system through post-processing technology. The experimental results showed that this method effectively balanced the problem of false detection and missing detection. Compared with existing methods, OAT-NGN shown stronger adaptability in complex scenarios. In order to improve the segmentation performance, its innovative dynamic weight adjustment mechanism and spatial constraint design provide a new technical path. Full article

In this paper, we introduce the martingale Hardy spaces and $BMO$ spaces generated by an operator T in continuous time and establish the atomic decomposition theorem of the space $H_p^T$ under the condition that T is predictable. We show that the $BM{O}_q$ spaces generated by the operator T are all equivalent and consider the sharp operator. Using the real interpolation method, we identify the interpolation spaces between the Hardy spaces and the $BMO$ spaces. With the aid of atomic decomposition, we establish some martingale inequalities between the Hardy spaces generated by two different operators. Full article

The sustainability of bridge infrastructure is becoming increasingly important due to rising environmental, economic, and social demands. However, most current assessment models remain fragmented, often overlooking the social pillar, underutilizing risk integration across the lifecycle, and failing to fully leverage digital tools such as Building Information Modeling (BIM) and Life Cycle Sustainability Assessment (LCSA), resulting in incomplete sustainability evaluations. This study addresses these limitations by introducing a practical and adaptable model that integrates BIM, LCSA, and expert-driven risk prioritization. Five Hungarian bridge projects were modeled using Tekla Structures and analyzed in OpenLCA to quantify environmental, economic, and social performance. A custom Sustainability Level Change (SLC) algorithm was developed to compare baseline scenarios (equal weighting) with risk-informed alternatives, simulating the impact of targeted improvements. The results demonstrated that prioritizing high-risk sustainability indicators leads to measurable lifecycle gains, typically achieving SLC improvements between +2% and +6%. In critical cases, targeted enhancement scenarios, applying 5% and 10% improvements to top-ranked, high-risk indicators, pushed gains up to +12%. Even underperforming bridges exhibited performance enhancements when targeted actions were applied. The proposed framework is robust, standards-aligned, and methodologically adaptable to various bridge types and lifecycle phases through its data-driven architecture. It empowers infrastructure stakeholders to make more informed, risk-aware, and data-driven sustainability decisions, advancing best practices in bridge planning and evaluation. Compared to earlier tools that overlook risk dynamics and offer limited lifecycle coverage, this framework provides a more comprehensive, actionable, and multi-dimensional approach. Full article

This study aimed to elucidate stage-specific dynamics, assembly mechanisms, and functional roles of bacterial communities during Macrobrachium rosenbergii larval development through high-resolution microbiota profiling. A high-frequency sampling strategy (126 samples across 11 zoeal stages and 1 post-larval stage within 21 days) and 16S rRNA absolute quantification sequencing were employed. Bacterial succession, persistent taxa, and ecological processes were analyzed using abundance-occupancy modeling, neutral community modeling, and PICRUSt2-based functional prediction. Absolute bacterial abundance exhibited a triphasic abundance trajectory. Initial accumulation: Linear increase (Dph 1–5, peak Δlog10 = +1.7). Mid-stage expansion: Peak abundance (log10 = 7.5 copies/g, Dph 7–8). Late-stage remodeling: Secondary peak (log10 = 7.1 copies/g, Dph 19). Eighty dominant amplicon sequence variants (ASVs) (dominant taxa: Herminiimonas, Maritalea, and Enterobacteriaceae) comprised >95% of the total abundance and coexisted via niche partitioning. Community construction was dominated by ecological drift/dispersal limitation (neutral model R² = 0.16, p < 0.01). Metabolic pathways (e.g., nutrient metabolism) shifted with dietary transition. “Phylogenetic replacement” underpinned microbiota resilience against environmental perturbations. Optimizing aquaculture environments offers a viable antibiotic-free strategy for microbial management, advancing our understanding of host microbe interactions and ecological niche differentiation in aquatic animals. Full article

Accurate and timely land monitoring is crucial for addressing global environmental, economic, and societal challenges, including climate change, sustainable development, and disaster mitigation. While single-source remote sensing data offers significant capabilities, inherent limitations such as cloud cover interference (optical), speckle noise (radar), or limited spectral information (LiDAR) often hinder comprehensive and robust characterization of land surfaces. Recent advancements in synergistic harmonization technology for land monitoring, along with enhanced signal processing techniques and the integration of machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize the comprehensive applications of synergistic harmonization technology for geosciences, with a particular focus on recent advancements. Most of the existing review papers focus on the application of a single technology in a specific area, highlighting the need for a comprehensive review that integrates synergistic harmonization technology. This review provides a comprehensive review of advancements in land monitoring achieved through the synergistic harmonization of optical, radar, and LiDAR satellite technologies. It details the unique strengths and weaknesses of each sensor type, highlighting how their integration overcomes individual limitations by leveraging complementary information. This review analyzes current data harmonization and preprocessing techniques, various data fusion levels, and the transformative role of machine learning and deep learning algorithms, including emerging foundation models. Key applications across diverse domains such as land cover/land use mapping, change detection, forest monitoring, urban monitoring, agricultural monitoring, and natural hazard assessment are discussed, demonstrating enhanced accuracy and scope. Finally, this review identifies persistent challenges such as technical complexities in data integration, issues with data availability and accessibility, validation hurdles, and the need for standardization. It proposes future research directions focusing on advanced AI, novel fusion techniques, improved data infrastructure, integrated “space–air–ground” systems, and interdisciplinary collaboration to realize the full potential of multi-sensor satellite data for robust and timely land surface monitoring. Supported by deep learning, this synergy will improve our ability to monitor land surface conditions more accurately and reliably. Full article

Temporal alterations in coastlines depict the significant changes in coastal areas, driven by both natural processes and human activities. For island nations, monitoring of the coastline is essential due to their vulnerability to such impacts. In this study, Funafuti Atoll, an archipelago of small and scattered islands around the capital of Tuvalu, is selected as the study region, and the aim is to extract coastlines of different islands and investigate coastal area changes between 2019 and 2023 using Sentinel-2 imagery. A simple linear iterative clustering-based superpixel segmentation and adaptive thresholding approach is employed for coastline extraction. Initially, superpixel segmentation is conducted to cluster 3-band image pixels into coherent regions, excluding the sea area. Subsequently, the normalized difference vegetation index (NDVI) is calculated, and the superpixels are used to obtain corresponding NDVI regions, on which adaptive Gaussian thresholding is applied to extract coastlines. Finally, the areas enclosed by the extracted coastline boundaries are utilized for change analysis. The results indicate that islands along the western rim of Funafuti exhibited significant alteration (an average decrease of −14.48%), whereas those along the eastern rim remained relatively stable due to the presence of coral rubble ridges and steep slopes. The change analysis revealed that from 2019 to 2020, approximately 15.1 hectares (ha) were eroded, resulting in a net area change rate of −4.14%. Between 2020 and 2021, erosion increased to 20.2 ha, yielding a net change of −7.75%. From 2021 to 2022, 13.2 ha were eroded, corresponding to a −1.74% change. From 2022 to 2023, a net gain of 10.3 ha occurred (+0.25%), primarily due to land reclamation along the lagoon-facing coast of Fongafale Island. Overall, all islands showed a decreasing area trend between 2019 and 2023, with an average net change of −12.97%. The coastal changes occurred along the sand-dominated coast with gentle slopes, possibly driven by the impact of tropical cyclones, prolonged swells, and coastal flooding, which act as the primary driving forces for the study region. Full article

Micro-crack defects on the surfaces of plate heat exchanger sheets often exhibit a linear grayscale pattern when clustered. In defect detection, traditional methods are more suitable than deep learning models in controlled production environments with limited computing resources to meet stringent national standards, which require low miss rates. However, deep learning models commonly suffer feature loss when detecting individual, small-scale defects, leading to higher leak detection rates. Moreover, in grayscale image line detection using traditional methods, the varying direction, width, and asymmetric grayscale profiles of defects can result in filled grayscale valleys due to width-adaptive smoothing coefficients, complicating accurate defect extraction. To address these issues, this study establishes a theoretical foundation for parameter selection in variable-width defect detection. We propose a directional gradient-based algorithm that mathematically constrains the Gaussian template width to cover variable-width defects with a fixed σ, reframing the detection defect from ridge edges to centrally symmetric double-ridge edges in gradient images. Experimental results show that, when tested in the defective boards library and under simulated factory CPU conditions, this algorithm achieves a miss detection rate of 14.55%, a false detection rate of 21.85%, and an 600 × 600 pixel image detection time of 0.1402 s. Compared to traditional line detection and deep learning object detection methods, this algorithm proves advantageous for detecting micro-crack defects on plate heat exchanger sheets in industrial production, particularly in data-scarce and resource-limited scenarios. Full article

Accurate information on waste composition is essential for strategic planning in waste management and developing environmental technologies. However, detailed analyses of individual waste containers are both time- and cost-intensive, resulting in a limited number of available samples. Therefore, it is crucial to apply statistical methods that enable reliable estimation of average waste composition and its variability, while accounting for territorial differences. This study presents a statistical approach based on territorial stratification, aggregating data from individual waste container analyses to higher geographic units. The methodology was applied in a case study conducted in the Czech Republic, where 19.4 tons of mixed municipal waste (MMW) were manually analyzed in selected representative municipalities. The method considers regional heterogeneity, monitors the precision of partial estimates, and supports reliable aggregation across stratified regions. Three alternative approaches for constructing interval estimates of individual waste components are presented. Each interval estimate addresses variability from the random selection of waste containers and the selection of strata representatives at multiple levels. The proposed statistical framework is particularly suited to situations where the number of samples is small, a common scenario in waste composition analysis. The approach provides a practical tool for generating statistically sound insights under limited data conditions. The main fractions of MMW identified in the Czech Republic were as follows: paper 6.7%, plastic 7.3%, glass 3.6%, bio-waste 28.4%, metal 2.1%, and textile 3.0%. The methodology is transferable to other regions with similar waste management systems. Full article

Biodegradable poly(lactic acid) (PLA)/poly(ε-caprolactone) (PCL) composite films were prepared with a compatibilizer (tributyl citrate, TBC) using a solvent casting method. Incorporation of 5% TBC (w/v, of PCL weight) improved tensile strength and elongation at break (21.93 ± 2.33 MPa and 21.02 ± 1.54%, respectively) and reduced water vapor permeability (from 0.12 ± 0.01 to 0.098 ± 0.01 g·mm·m²·h·kPa), indicating improved compatibility between PLA and PCL. Staphylococcus aureus phage PBSA08 demonstrated rapid and persistent bacteriolytic activity for up to 24 h, suggesting its potential as a promising antibacterial biological agent. To impart antibacterial properties to the developed PLA/PCL/TBC film, PBSA08 was loaded into sodium alginate (SA) and coated on the film surface. The optimal composition was 3% (w/v) SA and 3% (w/v) glycerol, which exhibited suitable dynamic behavior as a coating solution and excellent adhesion to the film surface. The phage-coated antibacterial films demonstrated progressive and significant inhibition against S. aureus starting from 10 to 24 h, with controlled phage-release properties. Overall, the developed active film might exert sustained and remarkable antibacterial effects through controlled release of biological agents (phage) under realistic packaging conditions. Full article

Strip mining creates widespread degraded landscapes that have low soil pH, high bulk density, impacted hydrologic processes, and an accumulation of heavy metals that limit revegetation efforts. To improve soil conditions and restoration success, soil amendments paired with native trees provide a potential solution. However, limited empirical studies have been conducted on the success of soil amendments to facilitate shortleaf pine (Pinus echinata Mill.) growth in the southeastern US. To fill this knowledge gap, a field trial was established on a reclaimed coal-mined site. Shortleaf pine seedlings were planted in a complete randomized block design with two soil amendment treatments: activated carbon and mycorrhizal inoculation, applied at a rate of 3.36 g/m² and 42.5 g per tree, respectively. Soil treatment did not impact tree survival which concluded with a 69 ± 3% (mean ± standard error) survival rate. Activated carbon increased soil electrical conductivity (p = 0.037) and the mycorrhizal amendment led to increased soil Ca content (p = 0.004). After the first growing season, trees in the mycorrhizal-amended soil were 12% shorter (p = 0.016) than trees in the activated carbon treatment. While soil amendment resulted in minimal improvements to soil parameters, shortleaf pine was found to be an effective species choice for post-mined site reforestation. Full article

Ludwig Wittgenstein’s later remarks on religious belief (from around the mid-1930s onwards) have often been and continue to be interpreted in connection with other terms he used in his later philosophy. The most common interpretations argue that Wittgenstein understands religious belief as a language-game or a group of language-games, as (part of) a form of life, and/or as a hinge/part of a world-picture. The term ‘Weltanschauung’ is also occasionally used to interpret Wittgenstein’s remarks on religious belief, and finally, Wittgenstein himself sporadically uses the term ‘style of thinking’ in connection with religious belief. In this paper, I will first conduct a meta-analysis of the secondary literature, presenting examples of the various lines of interpretation and criticism of these approaches. Subsequently, the various interpretations are examined to see whether or not they can actually be read from Wittgenstein’s remarks. The result of this investigation is that those interpretations which pursue a systematizing presentation of Wittgenstein’s remarks are, to a certain extent, reductive representations insofar as they can only draw on some of Wittgenstein’s remarks to support their interpretation, while other remarks stand in tension with the respective interpretation. Full article