Search Results (2,899)

This study critically addresses the challenge of selecting optimal insulation materials for contemporary, energy-efficient building envelopes, a decision with profound environmental, structural, and occupational health consequences. The paper responds to the growing demand for sustainable, resilient solutions by comparing wool, a bio-based, regenerative material, and expanded polystyrene (EPS), a synthetic polymer widely implemented in the construction industry, and advanced laboratory testing (thermal conductivity, moisture buffering, freeze–thaw resistance) is discussed in a comprehensive synthesis of the recent literature. Also, field evaluations from European retrofits and pilot projects (UK, Denmark, Finland, Iceland, Norway, Sweden, Germany and France) further contextualize performance outcomes, and life cycle impacts are considered. Recent results reveal that wool insulation achieves a moisture buffering value (MBV) between 1.8 and 2.7 (g/m²) % RH, minimal vapor resistance (mvr = 1–2), and preserves functional and structural integrity through more than 100 freeze–thaw cycles, leading to significant stabilization of the interior microclimate and enhanced durability. In contrast, EPS delivers lower thermal conductivity (0.032–0.037 (W/mK), critical for reducing heating/cooling demand, but exhibits limited vapor permeability (lvp = 60–150 MN·s/(g·m)), increased risk of condensation and mold, and reduced compressive strength (<22% after 30 cycles), especially when ventilation details are inadequate. Hybrid envelope systems leveraging both EPS and wool are demonstrated to optimize energy efficiency (up to 23% seasonal savings) and reduce interior humidity fluctuations, while lifecycle and recycling assessments show wool panels to be markedly superior in carbon footprint reduction and circularity. The stratification of insulation layers incorporating wool for vapor and moisture control, and EPS for pure thermal resistance is emerging as best practice in sustainable retrofit and new-build projects. Recommendations highlight the necessity for rigorous laboratory validation, international standards alignment, and integrated material design for robust hygrothermal comfort and environmental performance. The review also covers wool- and EPS-based hybrid composites, showing how natural fibers can improve key mechanical properties without compromising thermal insulation performance or environmental benefits. Full article

The construction sector needs to transform to address increasing sustainability and resilience challenges driven by climate change and increasing demands from stakeholders such as governments and customers. While previous research has examined individual aspects of sustainable construction, there remains an important need for an integrated, performance-oriented framework to guide organisational capability development. This research study develops a novel Sustainability Performance-Led Progression Framework (SPL-PF) to support the systematic assessment of and improvement in sustainability and resilience performance within the construction sector. A structured literature review of global academic and industry sources (2020–2025) was conducted to identify key challenges and evidence-based strategies and solutions. Through systematic synthesis, ten challenge areas and forty-one success strategies were identified and consolidated into a staged maturity framework. The SPL-PF defines five progressive levels (compliance, integration, optimisation, collaboration, and innovative leadership) supported by performance criteria, measurement indicators, and an operational scoring approach. This framework enables organisations to benchmark current capability, prioritise interventions, and monitor continuous improvement across sustainability and resilience dimensions. Full article

Digital trade reflects the convergence of the new technological revolution and traditional trade. Investigating its effectiveness in pollution control and carbon mitigation (PCCM) is crucial for addressing global environmental challenges. This research exploits the rollout of cross-border e-commerce comprehensive pilot zones (CECPZs) as an exogenous policy shock, leveraging double machine learning (DML) methods to assess the impact of digital trade on PCCM using panel data from 280 Chinese prefecture-level cities from 2011 to 2023. The results reveal that digital trade significantly enhances PCCM, mainly by promoting technological innovation, intelligent industrial transformation, and public participation; government emphasis on new quality productive forces and digital government construction positively moderates the link between digital trade and PCCM, while intensified environmental regulation exerts a counteracting inhibitory effect. Heterogeneous outcomes reveal that the promoting effects of digital trade are more evident in large areas, as well as in cities that are neither traditional industrial bases nor resource-based. Further analysis shows that digital trade can deliver a triple dividend in the form of reduced pollution, lower carbon emissions, and sustained economic growth. These findings provide meaningful guidance for promoting a balanced and sustainable relationship between human activities and the natural environment in the digital era. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

Coal mining has historically been a central economic, cultural, and social cornerstone of Appalachian communities. The decline of the coal industry, driven by technological changes, competition from natural gas and renewable energy, environmental regulations, and evolving energy markets, has created major economic and environmental challenges for coal-dependent regions. This study examines Kentucky residents’ perceptions of coal decline and how socio-demographic factors shape environmental concern. Data was collected from 685 Kentucky residents through a statewide online survey conducted in December 2023. Ordered logistic regression was used to examine the influence of gender, age, rural residence, and political affiliation on concerns regarding climate change, environmental degradation, extinction of endangered species, air pollution, and water pollution. Respondents identified health and safety concerns, cleaner energy alternatives, government incentives, and technological changes as major contributors to coal decline, while climate change was viewed as less significant. The findings also reveal support for workforce retention and training in sectors such as construction, transportation, utility work, and renewable energy. Female respondents expressed high levels of environmental concern, while rural residents and Republicans reported lower concern regarding climate change and environmental degradation. Full article

Three-dimensional concrete printing (3DCP) has emerged as a promising digital construction technology that reduces material waste, eliminates formwork, and enables complex geometries. However, its sustainability remains constrained by the extensive use of ordinary Portland cement (OPC) and natural aggregates. This review comprehensively evaluates waste utilization in extrusion-based 3D printed concrete, classifying applications into three pathways: cement replacement in OPC-based systems, waste-derived precursors in alkali-activated/geopolymer binders, and fine aggregate replacement. Industrial, agricultural, and marine wastes are assessed regarding their effects on rheology, printability, mechanical performance, interlayer bonding, and durability. The reviewed literature investigated waste incorporation levels reaching up to 50% for cement replacement, up to 70% for alkali-activated/geopolymer systems, and up to 100% for aggregate replacement, depending on the material type and application pathway. Industrial wastes, particularly fly ash, slag, silica fume, and metakaolin, represent the most mature materials and generally improve printability and long-term performance. Agricultural and marine wastes show promising sustainability potential but remain insufficiently investigated. Despite encouraging laboratory-scale results, challenges related to material variability, early-age performance, standardization, and scalability continue to limit practical implementation. The review identifies critical research gaps and outlines future directions for developing sustainable and field-ready 3DCP technologies. Full article

In recent years, an essential approach for promoting and implementing efficient sustainable construction practices has been considered through the integration of Building Information Modeling (BIM) and Life-Cycle Assessment (LCA). The introduction of OpenBIM, which is characterized by its collaborative and interoperable nature, offers an ideal framework to enhance this integration. This paper conducts a systematic review of the literature concerning the practices applied to integrate BIM and LCA, focusing on the present trends, challenges, and opportunities as well as on how the concept of OpenBIM can be applied to tackle the identified issues and gaps. Based on an intense review of the literature to identify the ways currently used to exchange data, this paper proposes a robust framework to create Information Delivery Specifications (IDS) as a solution to the identified gaps to attain an effective implementation, ultimately contributing to sustainable buildings’ practices and enhancing the integration of OpenBIM and LCA. OpenBIM emphasizes interoperability and collaboration by using open standards like Industry Foundation Classes (IFCs), which, when combined with LCA, offer a powerful method for the practice of sustainable building and provide a transparent evaluation of the environmental impacts of building materials and processes. This paper explores the definitions, key concepts, types of the exchanged data, and methods of integration and therefore provides insights into their potential in addressing the gaps that the construction industry is currently facing. The framework of integrating OpenBIM and LCA will be developed as a tool; therefore, it will combine an automated validation option by using IDS, create an enriched IFC file(s), dynamically map the data to an external LCA repositories, and incorporate feedback and reporting mechanisms. All those will be combined to address the most persistent shortcomings in the reviewed studies related to the integration of BIM and LCA. The framework will promote a holistic approach covering the early design benchmark to the detailed Whole Building LCA (WBLCA), including the operational and end-of-life phases. This next-generation workflow will align closely to the principles of OpenBIM, leading to improvement in the efficiency, accuracy, and deeper understanding of the environmental impacts by stakeholders over the construction lifecycle of buildings. Full article

The mining industry faces increasing challenges related to the growing volume of tailings generated during mineral processing. This study presents a case study of the Complex Mine Waste Reduction (CMWR) concept implemented at the Polkowice Concentrator operated by KGHM Polska Miedź S.A. The approach integrates coarse ore sorting with tailings reprocessing for construction material production. Sorting improves flotation feed quality by rejecting low-grade gangue, while reprocessing converts fine tailings into value-added products. The combined implementation reduces tailing deposition by up to 22% and improves the operational copper recovery in flotation while maintaining overall process recovery at an essentially unchanged level. The results demonstrate the potential of integrated solutions for sustainable and circular mining. Full article

To tackle the environmental challenges associated with industrial oily wastewater discharges and recurrent marine oil spill incidents, developing high-efficiency oil–water separation technologies represents a pressing environmental challenge. This research presents a novel design approach comprising the deposition of a stable SiO₂ anchoring layer followed by the fabrication of a PDA/CS crosslinked coating, thereby achieving successful construction of a superhydrophilic/underwater superoleophobic (SH/UWSO) coating on stainless steel meshes (SSM). In the first step, SiO₂ microspheres were deposited via vapor deposition to create a micro-rough surface architecture. Subsequently, a dopamine/chitosan (DA/CS) reaction solution was introduced to form a Polydopamine/chitosan (PDA/CS) coating, yielding a SiO₂@PDA/CS-SSM separation membrane. The resulting membrane exhibited separation efficiencies surpassing 99% for various oil–water mixtures, achieving a flux of 1.24 × 10⁵ L·m⁻²·h⁻¹ in petroleum ether systems. Notably, the membrane maintained high efficiency and structural stability even after 25 separation cycles, immersion in strong acid and base solutions for 72 h, and 100 abrasion tests. The rational design of the anchoring and crosslinking layers endows SiO₂@PDA/CS-SSM with high efficiency and stability, making it an effective oil–water separation material. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

Recent advancements in computer vision, particularly in detection, segmentation, and classification, have significantly impacted various domains. However, these advancements are still strongly tied to RGB-based systems, which are insufficient for applications in industries such as waste sorting, pharmaceuticals, and defence, where material characterization beyond shape or visible colour is necessary. Hyperspectral (HS) imaging captures spatial and spectral information for each pixel and therefore offers a promising route for material-level classification. This study evaluates the potential of combining HS imaging with deep learning for plastic material classification. The work includes: (i) the design of an experimental setup with a HS line-scan camera, conveyor, and controlled illumination; (ii) the construction of an object-disjoint dataset of HDPE, PET, PP, and PS samples with semi-automated mask generation and Raman spectroscopy-based labelling; and (iii) the development of P1CH, a lightweight pixel-wise 1D convolutional hyperspectral classifier. On object-disjoint test images, P1CH achieved 97.44% all-pixel accuracy. A boundary sensitivity analysis, reported separately because semi-automated labels are uncertain at material/background interfaces, yielded 99.94% accuracy after excluding a pre-defined two-pixel border band. Additional ablation, baseline, and robustness analyses show that the proposed pixel-wise spectral approach is effective for small fragments, visually similar plastics, and overlapping materials, while black or very dark plastics remain challenging under the present camera and illumination configuration. Full article

This study aimed to design, develop, and evaluate a multi-axis welding positioner, designed as a laboratory simulator with 360° rotational capability and 90° tilting functionality to support outcome-based instruction in welding and fabrication technology courses. A developmental research design was employed to systematically address common challenges in instructional welding operations, such as limited workpiece maneuverability, inconsistent welding angles, operator fatigue, safety risks from manual repositioning, and the lack of affordable, adaptable positioning equipment. The study was conducted at Caraga State University–Cabadbaran Campus in Cabadbaran City, Agusan del Norte, and involved sixteen purposively selected experts in Welding and Fabrication Technology. These experts assessed the prototype during the design, development, and evaluation phases via a validated researcher-developed survey instrument. The welding positioner was evaluated based on the following criteria: design, construction and material availability, functionality, usability, safety, modularity, and ergonomics. Data were analyzed using descriptive statistics. Findings indicated that the prototype was highly functional, safe, and user-centered, enhancing welding accuracy and reducing operator fatigue. Of the evaluated parameters, Design, Construction, and Material Availability achieved the highest mean rating (3.61), reflecting strong structural quality and resource accessibility. Functionality received the lowest mean rating (3.51), signaling minor areas for improvement in responsiveness and component adjustability. The prototype, built from locally available, cost-effective materials, featured a motorized rotation system and a manual tilting mechanism that operated reliably during testing. The study concluded that the welding positioner met structural, ergonomic, and operational standards for use as a laboratory simulator in outcome-based welding instruction. Recommendations include integrating automated controls, enhancing portability, embedding digital monitoring features, and conducting extended performance evaluations in industrial settings. Full article

Defect detection in manufacturing has evolved from manual inspection to deep learning-based Automated Visual Inspection (AVI) systems; however, acquiring sufficient defect samples in real industrial environments remains challenging, causing severe data sparsity and class imbalance. We propose CASDA (Context-Aware Steel Defect Augmentation), a five-stage framework that classifies defect morphology and background surface properties, constructs a compatibility matrix encoding their contextual relationship, and synthesizes defect images via a ControlNet pipeline conditioned on a three-channel hint image. Experiments on the Severstal steel dataset demonstrate that CASDA achieves an 83.0% quality validation pass rate. Under multi-seed evaluation (seeds 42 and 456), CASDA improved EB-YOLOv8’s overall mAP@0.5 by 2.60 pp over the raw baseline and achieved a Class 2 AP gain of 22.09 pp over Copy-Paste, suggesting that context-aware synthesis produces more discriminative minority-class training samples than simple patch reuse under the tested settings. Performance gains are architecture-dependent; YOLO-MFD did not show overall improvement, indicating that augmentation sensitivity varies with backbone feature representation. Full article

The deep integration of sci-tech and industrial innovation, rooted in the fusion of exploratory and exploitative ambidextrous capabilities, is a common global challenge. Traditional actors like enterprises and universities struggle due to the inherent imbalance of ambidextrous capability. Developed countries (e.g., Germany’s Fraunhofer, Finland’s VTT) have achieved integration through new-type research organizations, but rely on a “static coordination” model across departments ill-suited for rapidly changing, multi-logic environments. In contrast, China’s new-type R&D institutions (NTRI), emerging as innovative organizations, are naturally equipped to handle such institutional complexity and have become key drivers of deep integration. This study takes NTRI as a longitudinal single-case study object. Based on ambidextrous innovation theory and resource action theory, it constructs an analytical framework of “identifying integration challenges—addressing integration challenges—achieving integrated innovation” to explore how NTRI build dynamic ambidextrous capability through resource actions to drive the internal mechanism of integrating sci-tech innovation and industrial innovation. The results show that: (1) Accurately identifying integration breakpoints, bottlenecks, and hurdles at different development phases and establishing integration goals are key prerequisites for achieving integrated innovation; (2) the process of achieving integrated innovation is essentially a dynamic reconstruction of ambidextrous capability, involving resource bricolage to reconfigure demand-driven ambidextrous linking capability, utilizing resource orchestration to fission context-synchronized ambidextrous integration capability, and executing resource concerto to leapfrog networked symbiotic ambidextrous empowerment capability; and (3) the integrated innovation of NTRI at different phases exhibits a dynamic evolution, evolving from unidirectional spillover-integrated innovation to bidirectional interactive integrated innovation, and ultimately to empowering symbiotic integrated innovation. Full article

Volatile fatty acids (VFAs) are essential precursors in chemical synthesis for various chemicals, polymers, pharmaceuticals, and fragrance compounds. Acidogenic anaerobic digestion (or arrested methanogenesis) is a promising method to stabilize organic wastes and convert them to value-added products such as VFAs. However, the VFAs’ accumulation could in turn suppress the fermentation process through product inhibition and limit the titer of VFA in the digestate. Therefore, in situ separation and recovery of VFAs from the fermentate is crucial to constructing an effective continuous VFA-producing system. Recent research has been dedicated to addressing these issues and advancing the utilization of biobased VFAs, particularly through process-intensified strategies employing novel green solvents such as natural deep eutectic solvents. Furthermore, in situ conversion of VFAs into esters is another potential strategy for VFA removal. However, VFA esterification in an aqueous medium is challenging due to the abundant water driving the reaction toward hydrolysis. Recent advances in free or immobilized enzyme catalysis in solvents have demonstrated improved ester yield by providing a hydrophobic space for the esterification reaction in aqueous solution. In this review, we present an overview of critical aspects on the state-of-the-art of green solvent-based process intensification strategies, including feedstock selection and pretreatment, operating condition optimization, advances in membrane- and solvent-based recovery methods, and biocatalytic in situ esterification. Lastly, we provide perspectives toward cost-effective, continuous, high-solid, environmental-benign, and industrial-relevant VFA production applications. Full article