Search Results (9,513)

The construction industry is one of the primary global contributors to carbon emissions, with both construction materials and operational energy recognized as critical factors in achieving net-zero goals. Given that structural systems are embodied carbon-intensive, significant early-stage carbon reductions are possible. This paper introduces the dual-layer sustainable optimization framework (DLSOF), a methodology that integrates system-level substitution with span-level optimization and a single life-cycle assessment (LCA) approach focused on embodied carbon (EC) that is applicable to various construction types and climate regions. To validate DLSOF, two representative models of reinforced concrete buildings were selected for analysis: one focused on alternate structural systems and the other on span optimization for a standard slab configuration. The results indicate that, in most cases, span optimization achieves a reduction in embodied carbon of 33%, whilst system-level substitution, in most cases, achieves a reduction of approximately 30%. The dual-layer approach, in comparison to conventional baseline designs, achieves approximately a 52% reduction in embodied carbon. Uncertainty analysis indicates variability in design and data inputs, but the overall trend of embodied carbon reduction remains consistent. The results highlight the critical nature of the early structural design stage. For engineers, the DLSOF provides a practical design pathway, and it offers flexibility to accommodate diverse sustainability goals across varying geographical contexts. This study establishes a replicable and transferable model for low-carbon structural design by systematically integrating design optimization with embodied carbon assessment. Full article

The increasing availability of microwave bistatic remote sensing data highlights the need for reliable and computationally efficient scattering models to support data interpretation, system design, and mission planning. This is particularly relevant in urban and maritime environments, where the electromagnetic (EM) interaction between buildings and ships with the surrounding environment significantly affects the observed bistatic signatures. This paper presents a fully analytical model for EM bistatic scattering from a canonical target, represented as a parallelepiped with smooth dielectric faces located over a lossy random rough surface. The formulation is developed within the framework of the Kirchhoff Approximation and accounts for both single- and multiple-bounce scattering mechanisms arising from the mutual interaction between the target and the underlying surface. Reflections from the target walls are modeled using the Geometrical Optics solution, while scattering from the rough surface is described through the zeroth-order Physical Optics approximation. The resulting closed-form expressions provide both coherent and incoherent components of the scattered field as explicit functions of system and scene parameters. The proposed closed-form model enables fast and reliable evaluation of bistatic scattering from parallelepiped-like structures, such as buildings and large ships interacting with surrounding rough surfaces. This capability is particularly beneficial for the design and optimization of bistatic remote sensing missions in urban and maritime contexts as well as the development and assessment of inversion methods and large-scale analyses. Validation against numerical simulations and experimental results available in the literature demonstrates the effectiveness of the proposed approach across different operating conditions. Full article

Due to the complex operating environment of tracked vehicles, experimental braking tests using real vehicles are typically costly and time-consuming. Furthermore, limitations in testing environments make it difficult to comprehensively evaluate a system’s braking performance across diverse operating scenarios. To overcome these limitations, this paper proposes the construction of a high-precision digital model to simulate the real braking process of tracked vehicles in a virtual environment and validates the model through experiments. The results show that braking pressure changes continuously and proportionally with the pedal angle, the system response time is less than 0.3 s, braking pressure builds up rapidly, and the output process is smooth, with no significant overshoot. Under different braking percentage conditions, the simulation accuracy of both braking pressure and response time exceeds 95%, indicating that the established model accurately reflects actual braking performance and provides a theoretical basis for optimizing tracked vehicle braking systems. Finally, by rationally designing the parameters of the accumulator and electro-hydraulic proportional valve and reducing the brake cylinder volume, it is possible to improve braking performance. This provides a theoretical basis for the optimization of tracked vehicle braking systems. Full article

This paper examines whether broad enterprise AI adoption in Europe is best understood as an isolated technology decision or as the outcome of a wider bundle of digital capabilities. Using harmonized Eurostat data for European enterprises, the analysis builds a repeated cross-section at the country–size-class–year level and models high AI adoption with a combination of random forest and elastic-net estimation. The dependent variable captures enterprises using at least one AI technology, while the explanatory set focuses on cloud adoption, cloud CRM, cloud ERP, cloud database hosting, cloud security, cloud software use, e-sales intensity, and enterprise size. The findings reveal a stable predictive structure and consistent classification performance across specifications. Across models, cloud CRM and e-sales emerge as the strongest predictors of high AI adoption, followed by general cloud use and selected data-related cloud capabilities. This ordering remains largely stable in threshold-sensitivity checks based on alternative definitions of high adoption. The pattern also remains visible when country controls are removed, which suggests that the result is not merely a reflection of national heterogeneity. The paper contributes by shifting attention from broad claims about “digital readiness” to a narrower and more operational notion of capability complementarity: AI uptake tends to cluster where firms already possess customer-facing, cloud-based, and commercially digital infrastructures. In that sense, the paper offers a transparent, reproducible, and policy-relevant account of the digital foundations of enterprise AI adoption in Europe. Full article

The integration of Building Information Modeling [BIM] and Digital Twins [DT] has emerged as a central driver of digital transformation in the architecture, engineering, and construction sector. Yet, its systemic impact remains constrained by conceptual fragmentation and uneven institutional adoption. This study synthesizes contemporary BIM–DT scalability and each to identify dominant technological and application dimensions, examine the governance conditions shaping scalability, and develop an analytical framework that advances understanding beyond technology-centered syntheses. A two-stage analytical design was employed, combining bibliometric keyword co-occurrence analysis of 1295 Scopus-indexed records with systematic qualitative synthesis of 56 peer-reviewed journal articles published between 2020 and 2025, following PRISMA guidelines. Six interrelated analytical dimensions characterize the current BIM–DT research landscape: BIM–DT integration advancements and applications; interoperability and visualization; safety enhancement; energy efficiency; data-driven decision making; and stakeholder collaboration. Across these dimensions, a persistent misalignment emerges between technological capability and organizational readiness, with deficiencies in standards, governance, and sociotechnical coordination constituting the principal barriers to large-scale deployment. The findings reframe BIM–DT convergence not as a discrete technological upgrade but as the emergence of a coordinated socio-technical information ecosystem spanning the full building lifecycle. By foregrounding governance conditions, data stewardship, and institutional coordination, this study extends understanding of how digital twins expand BIM from design coordination to operational governance and establishes a foundation for more systematic implementation of intelligent, resilient, and sustainable built-environment systems. Full article

Social media platforms have become critical infrastructures for customer relationship management (CRM), requiring scalable and intelligent solutions to handle high-volume interactions. In the luxury fragrance sector, digital promotion poses a unique challenge because olfactory attributes cannot be experienced online. As a result, physical fragrance samples remain essential, generating large volumes of sample requests or inquiries across social media. However, many requests remain unmanaged due to limitations in manual CRM (i.e., human-driven processes), revealing a design gap that may negatively affect perceived responsiveness and service quality. This study uses qualitative content analysis with NVivo 12 to examine large-scale sample request interactions on the Facebook pages of four luxury fragrance brands. Data was collected via NCapture and analyzed to identify recurring patterns, linguistic structures, and customer expressions related to sample requests. Findings confirm frequent repetitive requests, highlighting inefficiencies in traditional CRM systems under high demand. This research proposes an AI-driven CRM Sample Request Management Architecture (CRM–SRMA) that systematically captures and processes customer sample requests, collects the necessary mailing information, and seamlessly transfers validated data to the final dispatching stage. The proposed system also models individual fragrance preferences by analyzing customers’ interactions with samples, particularly in terms of top, middle, and base notes. By leveraging this information, the architecture enables the targeted promotion of new fragrance releases that closely align with customers’ demonstrated olfactory preferences. The insights of this research provide a scalable, intelligent mechanism that enables luxury social media managers and CRM systems to manage high-volume interactions while maintaining service quality. By automating sample request processing, the mechanism improves responsiveness and reduces operational burden. It also supports long-term relationship building through preference tracking and updating customers with any new relevant-fragrance releases. Although focused on fragrances, the mechanism is adaptable to other luxury cosmetic categories, thereby ideally enhancing overall social media-based customer service. Full article

Unconventional aircraft configurations show significant potential to reduce aviation’s environmental footprint. Computerized conceptual design environments enable the design of unconventional aircraft concepts and the comparison of their performance and environmental impact against conventional Tube-And-Wing aircraft and other competing unconventional layouts. However, no environment has yet been specifically developed to support the Twin-Fuselage configuration. This paper addresses this gap by analyzing the advantages of the Twin-Fuselage configuration, identifying a potentially relevant design space, and compiling the existing conceptual-level design methods applicable to this layout. Building on these results, a roadmap for the conception of computerized conceptual design environments supporting Twin-Fuselage aircraft is presented. A structured environment architecture is proposed considering current trends and limitations of state-of-the-art environments supporting other unconventional configurations. The proposed modules for each discipline are also outlined. Finally, the main research gaps in Twin-Fuselage aircraft conceptual design are identified, highlighting and prioritizing the developments needed to enable a fully operational Twin-Fuselage-supporting computerized conceptual design environment. Full article

In this paper, we introduce, for the first time, a novel class of (center-radius order $(P,\mathrm{m})$-superquadratic interval-valued functions) $\mathfrak{cr}$-$(P,\mathrm{m})$-superquadratic $\mathcal{IVF}$s, and systematically investigate their fundamental structural properties. Building upon these properties, we establish new Jensen and Hermite–Hadamard ($\mathcal{HH}$) type inequalities, together with their fractional extensions formulated via Riemann–Liouville ($\mathcal{RL}$) fractional integral operators within the setting of interval calculus. The validity and sharpness of the derived results are illustrated through numerical examples and graphical representations. Moreover, the theoretical developments are further enriched by applications in information theory, leading to meaningful generalizations and notable improvements over several existing results reported in the literature. Full article

Though the European Commission has repeatedly stated that the necessary energy transition in Europe should leave “no one behind”, this paper describes a building refurbishment case that has entailed economic hardships for the low-income families involved. The project is located in the area of P. D. Orcasitas in southern Madrid, led by a grassroots neighbours’ movement, comprising one hundred and seven housing blocks, containing more than 2000 dwellings. The main source of funding for the operation consists of subsidies granted by the Madrid City Council; however, Spanish legislation requires the state Agency of Tax Administration to classify these subsidies as capital gains derived from lucrative transfers. Based on the tax data of vulnerable beneficiaries, the conclusion is that the recipients have ended up returning part of the subsidies to the State through their Income Tax Return. In addition, the Spanish Social Security Institute requires the return of social benefits associated with non-contributory retirement pensions and the Minimum Living Income. Apart from tax accounting, regulations are revised to draw conclusions. Unlike most actuations of this kind, in this case the negative effects are obvious. Although intended to alleviate fuel poverty, the initiative has exacerbated vulnerability due to the impact of the imposed penalties on household income. In conclusion, unless preventive measures are implemented, the mandatory refurbishment of inefficient buildings may place an undue burden on vulnerable low-income occupants and hinder the effective implementation of energy-efficiency regulations. Full article

Multi-object tracking (MOT) is a key technology for enabling autonomous navigation of unmanned surface vehicle (USV) as it provides continuous perception of surrounding maritime targets and supports navigation decision-making. However, videos acquired on maritime platforms typically suffer from challenges such as platform-induced jitter and nonlinear object motion, which significantly degrade tracking performance. To address these challenges, this paper builds upon ByteTrack by incorporating an adaptive Kalman filtering scheme and proposing a density-aware association strategy, resulting in a novel tracker termed the Marine-Aware Adaptive Tracker (MAAT). Specifically, an adaptive Kalman filter is introduced to increase the contribution of high-confidence detections during the state update process, thereby enhancing the stability and robustness of state estimation. Furthermore, to better mitigate the frequent identity switches caused by severe platform jitter from the USV observation platform, a density-aware association strategy is proposed. This strategy dynamically adjusts the composition of the cost matrix according to the density of high-confidence targets, enabling more reliable data association under varying scene conditions. Finally, the proposed tracking algorithm is evaluated against several state-of-the-art methods on the Singapore Maritime Dataset. It achieves competitive performance, attaining 44.37 MOTA and 43.857 IDF1. Moreover, MAAT operates in real time, running at 41.4 FPS. The experimental results demonstrate that MAAT is capable of performing accurate and real-time multi-object tracking in dynamic maritime environments with surface fluctuations, thereby providing effective technical support for intelligent maritime surveillance applications. Full article

The COVID-19 pandemic had a global impact on students transitioning into higher education. During quarantine measures, students often turned to social media for connectedness and peer support in an adverse time. The aim of this research was to explore the perceived impact of social media use during COVID-19 on the psychological resilience of students transitioning into higher education. Data were collected via a qualitative online survey completed by 51 students across the United Kingdom. Following a reflexive thematic analysis of the survey data, three themes were generated: (1) A challenging transition: restricted visits, remote learning, and seeking connection. (2) Facing adversity: becoming resilient in a transitional period. (3) A valued resource: social media as a facilitator of resilience. The findings suggest that social media helped students build virtual connections to overcome feelings of isolation during this transition. Several participants perceived their psychological resilience to have increased through developing strategies to regulate their emotional and mental well-being. Nonetheless, some participants believed that their psychological resilience either decreased or remained untouched. Furthermore, it was explicitly argued that social media played a facilitating role in enhancing participants’ perceived psychological resilience through operating as a ‘coping mechanism’, which fostered a sense of community and togetherness amongst like-minded students. Full article

The Third Plague Pandemic originated in 19th-century Yunnan, China, yet the confluence of factors that enabled the pandemic strain Yersinia pestis 1.ORI to emerge and spread globally remains unclear. Using a One Health framework, this study investigates how human-driven ecological and socioeconomic changes disrupted zoonotic barriers in Yunnan. We conduct an interdisciplinary historical analysis, triangulating evidence from Qing dynasty gazetteers, environmental reconstructions, and biological data on plague ecology, including host–vector dynamics, to model conditions for spillover and spread and to build a convergent, validated case. The analysis identifies a mid-19th-century convergence that created a high-risk interface: widespread deforestation from mining and agriculture, rapid population growth, increased synanthropic rat densities, and the turmoil of the Panthay Rebellion. Socioeconomic stressors—labour migration into mining valleys, currency devaluation undermining food security, and comorbidities such as malnutrition, heavy metal contamination, and opium use—may have further increased host susceptibility. This socio-ecological context catalysed spillover and establishment of the 1.ORI strain in commensal rat populations. The findings show the pandemic’s origin reflects spatiotemporal convergence rather than a single cause, while noting uncertainty in quantifying historical ecological and health parameters; the case offers a framework for assessing contemporary pandemic risks. It underscores how layered pressures operate across timescales. Full article

Wastewater heat recovery has emerged as a vital strategy for building energy conservation, due to its significant potential and the inherent thermal stability of sewage as a heat source. Enhancing synergy between such waste heat and other clean energy sources is a key research focus. This study developed a solar-assisted sewage-source coupled heating system for a Chinese university dormitory and established a multiobjective optimization framework integrating economic, environmental, and energy efficiency indicators via a combined weighting approach of the Analytic Hierarchy Process and Entropy Weight Method. Optimization was conducted using the Hooke–Jeeves algorithm, Particle Swarm Optimization algorithm, and the Hooke–Jeeves–Particle Swarm Optimization hybrid algorithm (shorten as HJ–PSO), with subsequent comparative performance analysis. The HJ–PSO hybrid performed best: 24% lower operating costs, a 4.8-year shorter dynamic payback period, 26.35% fewer carbon dioxide emissions, 38.65% lower overall energy consumption, and an 11.18% higher system coefficient of performance. Supported by relevant policies, the system is low-carbon and economically viable, enabling grid peak shaving. This research provides theoretical and engineering references for renewable energy heating systems. Full article

Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership. Full article

Unmanned Aerial Vehicles (UAVs/drones) have emerged as transformative tools for coastal environmental monitoring, yet the field’s intellectual evolution and operational maturity remain incompletely characterized. This study employs citation network analysis via Litmaps to map the structure, consolidation, and knowledge diffusion patterns of coastal drone research from 2013 to 2024. A corpus of 47 influential articles was identified through systematic citation connectivity criteria, revealing three distinct phases: Seminal (≤2016), Consolidation (2017–2022), and Innovation (≥2023). Results demonstrate that foundational RGB photogrammetry protocols established in 2013–2016 remain standard references in 2024, indicating methodological maturity rather than obsolescence. However, substantial geographic concentration exists (Mediterranean institutions dominate early development), with application imbalances: temporal monitoring (46.8%) dominates while policy-relevant erosion/risk assessment comprises only 8.5%. Despite documented technical adequacy (sub-centimeter accuracy, 70–80% cost reduction vs. alternatives), the transition to operational coastal programs faces institutional rather than technological barriers. The analysis concludes that realizing UAV operational potential requires coordinated institutional development across management agencies, research institutions, capacity-building programs, and equitable data governance frameworks. Full article