Search Results (40,576)

Human-induced climate change has rendered urban areas highly vulnerable to extreme events such as heatwaves, droughts, and floods. This study conducts a scoping review of extreme and compound climate hazards in Iranian urban areas under global warming conditions. Mapping the available literature, 92 authoritative scientific works published between 1999 and 2025 were analyzed. The review synthesizes evidence on the spatiotemporal patterns of heatwaves, drought, torrential rainfall, sea-level rise, and compound hazards across Iran. The results indicate that central, northwestern, eastern, and southern Iran experience the highest heatwave intensity and frequency, with short-duration heatwaves being more common than prolonged ones. Western Iran faces a high risk of torrential rainfall, but urbanization amplifies flood consequences by expanding impervious surfaces and accelerating surface runoff. Coastal areas show high vulnerability to compound flooding due to sea-level rise and storms. The review further reveals that Iran is experiencing hydroclimate whiplash (abrupt transitions between drought and flood) driven by global warming. The study concludes by presenting management suggestions and future research directions for integrated compound hazard management in Iran. Full article

Rapid urbanization in tropical Asia has led to a critical loss of green cover, exacerbating urban environmental challenges. While green roofs offer a promising Nature-based solution, their implementation in Asian countries is hindered by the prevalence of sloped roofs and high structural conversion costs. This research addresses this gap by developing a novel, lightweight vegetative roof tile designed as a direct structural replacement for conventional roofing materials in Sri Lanka. Existing roofing systems were studied, followed by a laboriousness study to determine the optimum tile dimensions. To meet these requirements, a modular tile measuring 900 mm × 1200 mm with a wave-shaped corrugated profile (a 10 mm rise and a 200 mm pitch) was engineered using SolidWorks 2024 and ABAQUS 2024 to meet Eurocode standards. Field investigations into plant health helped to finalize the depth of the roof tile as 2.5 cm. Following root penetration testing, fiber-reinforced plastic was selected for the tile structure to ensure durability while maintaining a total saturated weight of 52.5 kg/m². Biological testing demonstrated robust greening performance, with Axonopus compressus and Zoysia matrella achieving 100% survival rates and over 80% canopy coverage. This design methodology can be adapted across tropical Asia, contributing significantly to regional green infrastructure development and sustainable building practices. Full article

Climate change and increasing water scarcity are intensifying pressure on irrigated agriculture, which currently represents 70% of global freshwater withdrawals. Remote sensing technologies have become essential tools for monitoring soil moisture, evapotranspiration, crop growth, and irrigation performance across multiple spatial and temporal levels. This review synthesizes 83 peer-reviewed studies published between 2002 and 2025, focusing on the use of optical, thermal, and microwave sensors to support irrigation water management under climate variability. The analysis highlights progress in multi-sensor integration, UAV-based monitoring, crop and agro-hydrological modeling, and emerging machine learning approaches that enhance irrigation scheduling, soil moisture estimation, and crop water stress detection. Despite these advancements, several methodological challenges persist, including data integration constraints, sensor-specific limitations, model transferability issues, insufficient ground validation, and difficulties in translating remote sensing outputs into operational decision support systems. In addition, structural gaps at the policy level restrict the evaluation of irrigation efficiency and climate resilience. This review aims to clarify current limitations and outline priority research directions to enhance the climate resilience and sustainability of irrigated agricultural systems. Full article

The processing of two-dimensional (2D) spectral images constitutes a critical and multifaceted discipline in contemporary astronomical data analysis. As spectroscopic instruments evolve towards higher multiplexing, resolution, and sensitivity, the raw 2D data captured by detectors present increasingly complex challenges that transcend simple one-dimensional extraction. This review provides a systematic and comprehensive examination of the methodological evolution in this field over the past two decades. It gathered relevant studies by searching mainstream academic repositories and general search engines with the core keyword ‘2D Spectral Image’, and selected qualified references according to accessibility and research relevance. We categorize the landscape into three major paradigms: (1) physics-based modeling and algorithmic correction techniques for geometric distortion, scattered light, and sky background; (2) data-driven machine learning and deep learning approaches for image correction, spectral classification, and faint signal detection; and (3) the development of open-source software pipelines that democratize advanced processing. A central contribution of this review is a detailed comparative analysis of the performance metrics, underlying assumptions, and practical limitations of prominent algorithms. We highlight the transformative impact of convolutional neural networks (CNNs) and vision transformers (ViTs) on tasks such as celestial object classification and exoplanet detection, while also acknowledging the enduring importance of robust physical models for calibration and uncertainty quantification. The discussion culminates in an assessment of persistent challenges—including computational scalability, model generalizability, and interpretability—and outlines promising future directions at the intersection of AI, statistical inference, and large-scale survey science. Full article

Background/Objectives: Dietary assessment tools are essential for quantifying food and nutrient intake, characterising dietary patterns, and informing nutrition research. Food Frequency Questionnaires (FFQs) and Food Records (FRs) are widely implemented in observational studies, but completion rates vary, which may compromise data quality, introduce bias, and limit the interpretation of findings. This review is intended to synthesise evidence from observational studies on completion rates of these tools in populations with chronic conditions. Methods: A systematic search of Medline, PubMed, Scopus, and Google Scholar was completed. Eligible studies were observational studies using an FFQ or FR published from January 2015 to May 2025 in people with a chronic condition. Methodological quality was evaluated using the Hoy Risk of Bias tool for observational studies of prevalence. Subgroup meta-analyses estimated pooled mean completion proportions with 95% confidence intervals, and heterogeneity was assessed using the I² statistic. This study was registered with the International Prospective Register of Systematic Reviews and reported according to PRISMA guidelines. Results: Of the 8921 records screened, 88 studies (n = 84,579 participants) met inclusion criteria. The combined FFQ and food record mean pooled completion rate was 79.1% (95% Confidence Interval (CI): 74.37–83.43%). However, substantial heterogeneity was observed, indicating considerable variability across studies. Subgroup analyses highlighted important differences by tool type, format, age group, and disease category. FFQs demonstrated higher completion rates (80.6%) than FRs (74.3%). Electronic formats had higher completion rates than paper formats. Completion rates were higher in adults than in pediatric cohorts, and varied by chronic condition type, with kidney disease associated with the highest completion rates. Conclusions: These findings highlight the importance of considering tailored dietary data collection strategies, particularly for paediatric and medically complex populations, and provide direction for enhancing the feasibility of dietary assessment collection in future research. Full article

The grocerant represents an emerging hybrid retail–foodservice format integrating grocery shopping, prepared meals, and in-store dining. Although practically significant, the academic literature remains limited and dispersed. This PRISMA-informed semi-systematic review synthesizes 16 studies—including direct grocerant research and adjacent work on retail innovation, prepared foods, and digital food retail—to clarify the current state of knowledge. The review followed structured database searches, citation tracking, title/abstract screening, and full-text eligibility assessment. Three main perspectives emerged. First, consumer-focused studies emphasize customer experience, food healthiness, multidimensional perceived value (functional, hedonic, social, and financial), brand prestige, in-store dining behavior, and loyalty. Second, strategic research positions grocerants within retail format innovation and competitive convergence between grocery and restaurant sectors. Third, operational perspectives link grocerants to prepared-food systems, retail food environments, and omnichannel transformation. Major gaps include limited operational and comparative research, geographic concentration, and weak digital integration. The review suggests that grocerants function as evolving systems where convenience, experience, branding, and digital transformation converge. Full article

The experimental determination of the relationships between the stress distribution zone in the soil layer and the parameters of tillage working bodies is a labor-intensive process. Therefore, preliminary mathematical modeling of this process is recommended to minimize the total number of experiments. The research was conducted using the principles of classical mechanics and soil mechanics. Using an equation proposed by J. Boussinesq, a graphical–analytical method was developed to evaluate the stress state in the soil layer induced by a dihedral wedge. This method incorporates both the geometric parameters of the dihedral wedge and the physico-mechanical properties of the soil. A direct proportional relationship was established between the length of the dihedral wedge and the total area of the deformed soil mass. Specifically, increasing the length of the dihedral wedge by 83% (from 0.05 to 0.30 m) resulted in an 80% increase in the area of the deformed soil mass (from 0.02 to 0.10 m²). The proposed graphical–analytical method can be employed in the design of tillage implements. The results we obtained are consistent with the patterns previously reported by other researchers. The findings were used in the development of various types of flat-cutting working tools for shallow and deep tillage. Full article

Driven by the growing demand for sustainable polymers, polylactic acid (PLA) has attracted increasing attention due to its renewable origin and biodegradability. Lactide, the key cyclic monomer for PLA production via ring-opening polymerization (ROP), plays a decisive role in determining the molecular weight, stereoregularity, and final performance of PLA materials. However, current lactide synthesis processes still face significant challenges, including competing side reactions under high-temperature and high-vacuum conditions, difficulties in controlling stereochemical purity, and relatively high energy consumption. In this review, recent advances in lactide synthesis are systematically analyzed by examining the two principal industrial routes: the one-step process based on the direct dehydration–cyclization of lactic acid (LA), and the two-step process involving prepolymerization of LA followed by depolymerization/cyclization of oligomeric intermediates. The reaction mechanisms, key intermediates, and major side reactions—including racemization, transesterification, and deep polycondensation—are discussed, together with the regulatory roles of catalytic systems and reaction–separation coupling strategies. Comparative analysis reveals that the one-step route offers advantages in process integration and potential energy efficiency, whereas the two-step route provides superior control over stereochemical purity and process stability. Future research directions focusing on green catalysts, process intensification, and sustainable lactide production are also highlighted. Full article

This paper develops a boundary-sensitive asymptotic theory for nonparametric conditional U-statistics smoothed by support-adapted asymmetric kernels when the response variable is subject to Missing-at-Random observation. The problem lies at the intersection of three well-established but traditionally separate lines of research: conditional U-statistics, asymmetric smoothing on constrained supports, and incomplete-data inference under MAR sampling. The contribution of the paper is not a novelty claim concerning any of these components in isolation. Rather, it consists in deriving a kernel-specific and MAR-aware limit theory for their simultaneous occurrence, where the estimators are nonlinear complete-case ratios of localized U-statistics and the localization devices are point-dependent approximate identities adapted to the geometry of the covariate support. The analysis covers three principal classes of support-respecting smoothers: Dirichlet kernels on the simplex, Bernstein polynomial smoothers, and multivariate beta kernels on hypercubes, with an additional extension to mixed continuous–categorical regressors. These smoothing schemes are not translation-invariant, and their local moments, effective support, normalizing constants and $L^2$-masses vary with the evaluation point, especially near the boundary. Consequently, their incorporation into conditional U-statistics requires more than a direct transfer of ordinary asymmetric-kernel regression theory. The numerator and denominator of the estimators are localized U-statistics whose stochastic expansions are governed by Hoeffding projections, including canonical components that must be controlled uniformly over the conditioning domain. Under regularity, smoothness and positivity assumptions adapted to the MAR setting, we establish uniform consistency, weak and strong uniform convergence rates, stochastic expansions and asymptotic normality. The results are obtained both on fixed compact subsets and on interior regions approaching the boundary, thereby identifying how support geometry enters the bias and stochastic normalizations. A central feature of the theory is the separation between the deterministic effect of complete-case sampling and its stochastic effect. For the complete-case estimator, the natural deterministic equivalent is obtained by replacing the design density f with the effective complete-case density $pf$, where p is the propensity score. Thus, the MAR mechanism may enter higher-order deterministic bias constants through the local design tilt, whereas the leading stochastic dispersion reflects the loss of effective information through propensity score factors. The precise variance constants and normalizing rates remain kernel-specific, depending on the local $L^2$-structure of the Dirichlet, Bernstein or beta smoothing device. The paper should therefore be viewed as a MAR extension and refinement of the complete-data asymmetric-kernel conditional U-statistic theory. It provides a common probabilistic architecture for several boundary-adapted smoothing schemes while retaining the kernel-dependent bias operators, variance constants, boundary regimes and Hoeffding-projection structures required for sharp asymptotic interpretation. Numerical experiments illustrate the finite-sample behavior predicted by the theory and highlight the interaction between support-adapted smoothing, boundary effects and incomplete response observation. Full article

As micro, small and medium-sized enterprises (MSMEs) compete with limited resources, lightweight systems are needed to secure their digital assets. Fuzzy vaults (FVs) are useful for protecting secrets and, when applied to biometric systems, provide error-tolerance and privacy to enrolled biometric features. Combining multiple biometric traits also improves performance against attacks like spoofing in multimodal (MM) authentication systems. However, the design of the FV and the biometric-fusion method applied can limit the system’s effectiveness. This study systematically evaluates recent studies on FVs and MM systems and presents an up-to-date review to identify gaps, give directions for future studies, and, ultimately, improve the design of these systems. The research targeting MSMEs was carried out in two parts, with the first search focused on MM systems and the second on FVs, following the PRISMA guidelines. The main findings include the need to optimise the resource intensity of FV systems for the authentication of large numbers of individuals. It also found the need to make the model compatible with other biometric modalities as greater focus is on minutiae features. By reviewing these systems, we aim to foster the development of lightweight MM FV models to provide privacy and security in MSMEs. Full article

Hantaviruses (HTVs) are lethal zoonotic pathogens responsible for hemorrhagic fever with renal syndrome and HTV cardiopulmonary syndrome; however, no specific antiviral treatments or vaccines have been approved. Bee products, such as propolis, honey, royal jelly, bee venom, and bee pollen, demonstrate extensive antiviral, anti-inflammatory, antioxidant, and immunomodulatory properties against various RNA and DNA viruses. No published research has directly evaluated bee products in relation to HTV infection. This review proposes a hypothesis-driven mechanistic framework suggesting that bioactive compounds from bee products may concurrently inhibit HTV replication, alleviate the cytokine storm, diminish oxidative stress, and maintain endothelial barrier integrity. We explicitly recognize the lack of direct experimental evidence regarding bee products’ efficacy against HTVs. Considering the mechanistic similarities with other enveloped viral infections and the recognized functions of NF-κB, Nrf2, and endothelial signaling pathways in HTV pathogenesis, we present a scientifically substantiated rationale for forthcoming research endeavors. The diverse bioactive compounds present in bee products including bee pollen, bee venom, honey, propolis, and royal jelly could provide a multifaceted strategy for inhibiting HTV pathology. We propose systematic in vitro, in silico, and in vivo investigations to assess the potential of bee-derived flavonoids, peptides, and fatty acids as adjunctive therapeutic strategies for HTV disease. Full article

Web Application Programming Interfaces (Web APIs) have become fundamental components of modern software ecosystems. At the same time, they have emerged as major attack surfaces in web applications and distributed services. Although many web API vulnerabilities are well documented, a critical gap remains in understanding how insecure development practices, usability limitations, and developer-related issues contribute to recurring API security problems. To address this gap, this study presents a systematic review of web API security research using a PRISMA-guided methodology and a taxonomy-driven analytical approach. The review synthesizes findings from 50 selected studies covering web API architectural styles, usability concerns, authentication and access-control weaknesses, and common vulnerabilities. These vulnerabilities include SQL Injection (SQLi), Cross-Site Scripting (XSS), Broken Authentication, and Denial-of-Service (DoS) attacks within the context of the OWASP API Security Top 10 framework. The findings indicate that recurring web API vulnerabilities are associated not only with technical weaknesses but also with API usability issues, insecure development practices, inconsistent security guidance, and increasing implementation complexity. The review also identifies persistent research gaps involving usability-security integration, API evolution, secure-by-design development practices, and empirical validation of security tools and frameworks. By synthesizing these dimensions into a unified conceptual perspective, this study provides researchers and practitioners with a clearer understanding of the factors contributing to web API insecurity. The study also highlights directions for developing more resilient and developer-aware API security practices. Full article

Artificial intelligence has become constitutive of smart city governance, yet data and algorithmic challenges remain analytically separated in existing scholarship, obscuring their recursive coupling and consequences for the built environment. This review synthesises 82 peer-reviewed studies (2020–2025) drawn from a deduplicated corpus of 876 records, combining PRISMA-guided methodology with VOSviewer and CiteSpace bibliometric mapping. Annual output rose from 78 publications in 2020 to 224 in 2024, with ten leading countries contributing roughly 84% of the corpus. The keyword network organises into five thematic clusters spanning AI technical foundations, data governance, algorithmic governance, sustainability, and built-environment governance; emerging 2023–2025 couplings between digital twin and SDG 11, and between generative AI and SDG 11, mark a shifting research frontier, while the algorithmic governance → SDG 16 linkage constitutes the strongest single ribbon in the synthesis. The study advances a double-helix coupling mechanism specifying directional propagation, reverse modulation, and structural cross-linking between data and algorithmic strands, reframing building energy management, digital-twin operation, and smart infrastructure as governance arrangements whose sustainability legitimacy depends on the simultaneous integrity of both strands. Full article

Allergy is the most common hypersensitivity disorder, affecting around 30% of the global population. Due to its rapidly increasing prevalence and significant reduction in quality of life for patients, allergy represents a major public health problem, and the improvement of diagnostic and treatment options for allergic diseases is of utmost importance. Moreover, the development of preventive allergen-specific immunization strategies is an emerging research direction in mitigating allergy incidence, especially for respiratory diseases such as allergic rhinitis and asthma. Since environmental allergen exposures differ substantially depending on climatogeographical, ecological, and behavioral factors, investigating local IgE sensitization profiles could significantly contribute to optimizing allergy management. We performed a systematic database review to summarize available knowledge on IgE sensitization profiles in Russia across different regions of the country. The study was conducted in compliance with PRISMA and SWiM guidelines and registered in the PROSPERO database (CRD420250650847). We identified major differences in sensitization profiles across certain geographical areas, reported in 60 studies. However, heterogeneity of methods and gaps in the existing evidence were noted, and, as the available data appear insufficient for reliable profiling, an outline was proposed for systematic and methodologically harmonized studies necessary to develop further region-tailored approaches. Full article

Digital twin technology is becoming a core enabler for the intelligent transformation of civil engineering. This review adopts an integrated mixed-method design that combines a reproducible bibliometric protocol with structured content analysis to connect macro-level knowledge evolution with domain-specific engineering implementation. Based on the Web of Science Core Collection, the study analyzes publication trends, collaboration patterns, highly cited studies, keyword co-occurrence, network centrality, and citation bursts, and then reviews application status and technical pathways across five thematic areas: intelligent construction, bridge engineering, tunnel engineering, smart water conservancy, and other infrastructure. Key findings include: rapid growth in publication volume after 2021, three dominant keyword clusters (model/system construction, structural health monitoring and sensing, and AI-enabled optimization/decision-making), and an evolution of research frontiers from concept introduction to engineering scenario deepening and further to three-dimensional reconstruction, knowledge fusion, and intelligent decision-making. The content analysis shows differentiated technical pathways across sub-domains and identifies data heterogeneity/interoperability as the most urgent bottleneck because it constrains model updating, cross-platform integration, and engineering-scale deployment. Future directions should focus on data standardization, hybrid modeling, platform interoperability, artificial intelligence empowerment, and full-lifecycle cross-system coordination. This review provides a quantitatively supported panoramic reference for digital twin research in civil engineering. Full article