Search Results (2,441)

Recycled aggregates (RAs) from construction and demolition waste (CDW) provide substantial circular-economy benefits, yet their elevated porosity, adhered mortar, and heterogeneity typically impair the mechanical performance and durability of recycled aggregate concrete (RAC). This PRISMA 2020-compliant systematic review synthesises 2180 records (2015–2026) to evaluate advanced strategies for enhancing RA quality prior to structural use. This paper critically compares removal-based treatments (mechanical, thermal, acid cleaning) with strengthening and densification approaches, including accelerated carbonation, pozzolanic and nano-silica coatings, polymer impregnation, microbial-induced calcium carbonate precipitation (MICP), and modified mixing methods such as triple-stage mixing (TSMA). Evidence shows that while all RA types (including recycled fine aggregate (RFA), recycled coarse aggregate (RCA), and their combination (RFCA)) can slightly reduce compressive strength and 30% replacement serves as a critical threshold, beyond this, strength loss accelerates, particularly in RCA and RFCA mixes. However, accelerated carbonation and TSMA consistently refine the interfacial transition zone, reduce water absorption by 17–30%, and recover 85–94% of natural aggregate concrete strength. Bio-deposition reduces water absorption by 13–21%, while acid/silica fume treatments improve late-age strength but carry environmental trade-offs. This review formulates a practice-oriented implementation framework for structural-grade RAC. Sustainability analyses indicate that carbonated RA can achieve net-positive CO₂ abatement when under low-carbon energy supply. A mechanistic schematic is presented to synthesise treatment-to-pore-structure/durability pathways across the four principal treatment routes, and a quantitative synthesis plot compares water absorption reductions across all treatment types using 13 data points drawn from included studies. A structured treatment comparison evaluates the energy intensity, industrial scalability, CO₂ footprint, and technology readiness level for each strategy. The remaining challenges include a lack of hybrid treatment studies, limited real-scale durability data, and insufficient mechanistic models linking treatment to pore structure evolution. This review recommends harmonised durability-based criteria and updates to standards (e.g., BS 8500, EN 12620) to support the scalable deployment of treated RA. Full article

NanoArduSiPM represents a paradigm shift in the ArduSiPM (Architected Detection Unit for Silicon Photomultipliers) roadmap, evolving from a standalone instrument into a high-density modular building block (36 mm × 42 mm × 3 mm, 7 g). This revision does not merely pursue miniaturization; it re-engineers the signal-processing chain to maintain high performance within a scaled-down footprint, enabling the transition from single-unit detection to scalable, distributed multi-detector systems. NanoArduSiPM is based on a three-layer architecture comprising an external scintillator and Silicon Photomultiplier (SiPM) detection module, a dedicated high-speed discrete analog front-end, and a System-on-Chip (SoC) for embedded acquisition and processing. The physical implementation adopts high-integrity PCB routing and rigorous isolation techniques designed to suppress digital–analog coupling, a critical requirement in such a compact form factor. This deterministic layout strategy provides the architectural foundation for time-tagging capabilities, currently under quantitative characterization, by addressing the fundamental sources of signal interference at the hardware level. Beyond hardware integration, NanoArduSiPM introduces the capability for extended firmware functionality, including event tagging via external inputs and the implementation of coincidence and veto logic. This framework supports the acquisition of multiple correlated histograms and allows multiple units to be interconnected on a shared SPI bus. By shifting from standalone operation to a coordinated, hierarchical architecture, NanoArduSiPM enables distributed detection schemes where event selection and correlation are handled natively within the system, reducing the dependency on external data acquisition electronics. The compact modular architecture, together with the high-performance discrete analog front-end and embedded data handling, makes NanoArduSiPM suitable for applications where low mass and low power consumption are critical, targeting applications such as space-based payloads, laboratory instrumentation, remote sensing, and large-scale distributed multi-channel detection systems. While no radiation-tolerance qualification of the complete system has been performed in this work, the microcontroller family used in the design is also available in radiation-tolerant variants, which may support future implementations targeting more demanding radiation environments. Full article

Nowadays, there is a deep interest in developing more compact user facilities, and plasma technology is one of the most promising techniques, not only for acceleration modules, but also for what is ancillary to the delivery of radiation to users, such as free electron lasers. In this regard, significant efforts have been made to miniaturize diagnostic stations, detection devices, and transfer lines, e.g., based on active plasma lenses. However, conventional undulators are still too cumbersome and expensive to meet the requirements of compactness and sustainability. For the aforementioned reasons, advanced undulator concepts have aroused great interest in pushing the frontier beyond conventional, magnet-based undulators. In this regard, a promising, very compact alternative is the use of the betatron motion of electrons in an ion channel to emulate an undulator device. This paper reports a feasibility study aiming to develop plasma-based undulator devices at SPARC_LAB as the test facility of the EuPRAXIA@SPARC_LAB project. In particular, this work provides a systematic assessment of free-electron-laser amplification in a plasma ion-channel undulator under experimentally realistic beam parameters, delivering quantitative predictions for gain and radiation performance in this configuration. Full article

Uplift modeling optimizes intervention-based campaigns by identifying customers whose behavior changes exclusively due to specific treatments, moving beyond standard baseline risk predictions. However, in real-world deployments, algorithms that maximize traditional causal ranking metrics (e.g., the Qini coefficient) often fail to be optimal in practice. The inherent variance of Conditional Average Treatment Effect (CATE) estimators exposes critical trade-offs between expected economic value, algorithmic stability, and policy interpretability. To address this gap, this study proposes a stability-aware, value-driven computational framework for selecting an uplift policy. The pipeline evaluates multiple causal and non-causal algorithmic families, including traditional baselines, multimodel approaches, and transformed-outcome variants, within a repeated-run validation protocol. Candidate policies are assessed primarily through incremental revenue and target-set stability, whereas a post hoc surrogate tree distillation step is used to translate the selected policy into interpretable rule-based customer segments. An empirical evaluation of the publicly available Telco Customer Churn dataset under two distinct regimes (a causally controlled semisynthetic scenario and an observational proxy scenario) reveals that the highest-yielding causal policy frequently suffers from severe targeting instability, inducing a clear risk–return trade-off. Furthermore, uplift models outperform traditional baselines in the causally controlled regime, whereas traditional baselines remain economically superior in the confounded proxy settings. Overall, this study establishes that jointly assessing economic utility, algorithmic stability, and transparent segmentation is essential for deploying robust and defensible causal machine learning in production environments. Full article

Opportunities abound in microfluidic technologies to impact how we understand extremely complex systems with many constituents which change with time and space. In these technologies, separation science plays a central role towards understanding everything from biology and healthcare to environmental monitoring to the search for life in the Solar system. Separations can amplify the capabilities of detection modalities by isolating targets and/or increasing their concentration while removing background constituents which can interfere with their sensing. In essence, separations increase the amount of information that can be gathered from a sample. The ideal features of next-generation separations capability are present in gradient insulator-based dielectrophoresis (g-iDEP), enabled by the length scale and precision of microfluidics. It acts through electric field interactions with particles, which enables unbiased (label-free) separations since all relevant particles, from atoms to cells, have an accessible response to electricity—either through linear (electrophoresis) or higher-order gradient (dielectrophoresis and related) effects. The technique isolates and concentrates, enabling improved detection function and multidimensional separations. Its foundational theoretical capabilities give it separations power on the order of 1:10⁸, beyond the resolving power of the best mass spectrometers and ultra-high resolution spectroscopies. Experimental evidence is amassing that shows it to be a powerful tool that can resolve tiny differences in cells (antibiotic resistance versus susceptible in unlabeled paired isolates across many species) and differentiate single-point mutations in proteins. Its capabilities are still emerging, and this work aims to quantify the current practice and connect those approaches to the ultimate capabilities of the technique towards quantifying the dynamic range and resolving power of the strategy as a whole. The technique uses two methods of quantifying the electrophysical properties of the target, voltage sweep and spatial methods. The voltage sweep method is lower-resolution and serves as a search mode, while the spatial method is higher-resolution and quantifies the properties over a smaller defined range determined via the sweep method. These quantification methods are examined by collating existing experimental data, performing relevant Monte Carlo simulations, and finite element model calculations. These are summarized to understand the mechanisms currently limiting the technique, facilitate quantitative comparisons with traditional separation science capabilities in terms of resolution and dynamic range, and compare them to the theoretical limits of the strategy. Full article

Against the backdrop of urbanization and global warming, reducing carbon emissions and achieving carbon neutrality have emerged as focal points in current urban ecological research. Urban green infrastructure (UGI) serves as the primary natural carbon sink within cities; therefore, investigating and optimizing its carbon sequestration services is a crucial step toward realizing carbon neutrality and fostering sustainable urban development. As the core components of urban ecosystems, urban parks provide essential ecosystem services that play a pivotal role in expanding carbon sinks, facilitating energy conservation and emission reduction, and enhancing urban climate resilience. This paper takes 20 parks in Linyi City’s central urban area as examples, systematically quantifies the carbon sequestration effect of urban parks in the central urban area of Linyi City from 2019 to 2024 using methods such as the Carnegie–Ames–Stanford Approach (CASA) and the gravity model, and quantitatively evaluates the equity of urban residents’ access to these services. The study shows that the overall annual average carbon sequestration rate of urban parks in Linyi City’s central area over nearly six years ranges from 202.02 gC·m⁻²·a⁻¹ to 279.31 gC·m⁻²·a⁻¹, while individual park annual averages range from 171.29 to 332.76 gC·m⁻²·a⁻¹, falling within the normal range for cities at the same latitude; in terms of vegetation carbon sequestration capacity, woody plant communities dominate in this region, with annual average carbon sequestration rates approximately 10% higher than those dominated by herbaceous vegetation. In terms of intrinsic activity performance of carbon sequestration, overall, woody-dominated plant communities exhibit greater stability and resilience under extreme weather conditions, experiencing smaller impacts on ecological functions but longer recovery cycles to peak levels. Regarding equity in the supply and demand of ecosystem services, the Gini coefficient in the study area is 0.59, indicating an extremely imbalanced state; within the same park service range, up to 60% of residents do not benefit from carbon sequestration ecosystem services. The urban supply–demand mismatch reveals that approximately 20% of the population resides in high-demand–low-supply areas, experiencing extreme ecological deprivation; only about 13% of the population falls into the high-demand–high-supply category, this group being the high-benefit recipients who enjoy both spatial convenience and high-quality ecological welfare. The theoretical implications for urban green space planning: according to the results, merely expanding park green space area to increase per capita access is myopic and inadvisable in central urban park planning. Instead, greater emphasis should be placed on enhancing ecological service levels beyond basic area requirements, comprehensively improving vegetation quality and ecosystem service capacity of parks. In old urban areas constrained by land use, the hierarchical structure of vegetation should be strengthened, and micro green spaces should have enhanced ecological service capabilities to improve residents’ access rights through higher service quality. In newly developed urban areas, planning should balance quantity and quality to serve more people and alleviate urban ecological pressures. Overall, by quantitatively assessing the carbon sequestration capacity and the socio-spatial equity of ecosystem services provided by urban parks in Linyi City, this study offers robust empirical evidence and methodological tools for sustainable urban planning, ultimately fostering the sustainable development of urban ecosystems. Full article

Inspired by the key hole concept in micro-surgery, we developed a “magnetic key hole concept” for basic studies of localized characteristics of soft magnetic laminations (electric steel, Fe-based amorphous ribbon) under exactly defined conditions of induction B(t). A material sample of 50 cm length and 10 cm width is magnetized in a novel multi-frequency SST that allows for exact sinus up to 10 kHz. A priori, the tester offers global results for permeability µ_G, power function p_G(t) and total loss P_G, as averaged over the entire sample material. But beyond that, a so-called Experimental Window (EW) offers additional information on local characteristics, as determined in a small central “key hole” region of defined magnetization. Here, a scanning adapter is mounted to study localized crystallographic features of the grain structure, as well as inhomogeneities, like failures, structure modifications, or specific technological treatment. Out of several types of sensor units a linear motor drive takes up a specific one within little manual effort. Already-developed sensor concepts concern the local tangential field, permeability, power, loss, and local widths of main domains and spike domains. The paper discusses several examples of analyses. Full article

This paper highlights the state of the art in Cooperative Dual-Manipulation (CDM) and Cooperative Multi-Manipulation (CMM), comparing advances in modeling, control, planning, sensing, vision, and end-effector technologies. Methods originally established in CDM have been extended or adapted to support higher complexity of CMM. A historical timeline visualizes the steady growth of cooperative manipulation (CM) and the recent acceleration of CMM driven by rising process complexity and the need for more flexible automation strategies. CM is becoming increasingly relevant as industrial processes demand higher payload capacity, larger workspaces, and greater flexibility. In addition, this paper categorizes existing applications by cooperation type and application domain. Here, a clear dominance of simultaneous object manipulation tasks is visible (fixation-fixation). However, fixation-tooling tasks, where one manipulator grasps the product while another performs a tool operation, and tooling-tooling tasks, where multiple manipulators perform tool operations simultaneously, remain significantly underrepresented. A similar imbalance is found for rigid/non-deformable object manipulation and flexible/deformable object manipulation, respectively. Based on this review, several research gaps are identified: (i) reliable flexible object manipulation methods; (ii) CM strategies for disassembly (e.g., battery pack deconstruction); (iii) complexity in control and planning for multi-manipulator systems; (iv) pathways to industrial deployment beyond laboratory demonstrators; and (v) task-specific tooling and end-effector innovation. Full article

The uplink/downlink (UL/DL) decoupled access, which allows users to associate with different base stations (BSs), including small BSs (SBSs) and macro BSs (MBSs), has emerged as a network architecture in heterogeneous cellular vehicle-to-everything (C-V2X) communications. It can be tailored to mitigate the signal interference and attenuation impairments that cell-edge vehicles face, while vehicles closer to a BS can opt for coupled access. Therefore, a UL/DL intelligent decoupled access network that integrates decoupled and coupled access approaches is urgently needed for C-V2X communications. In this paper, we present a novel framework for UL/DL intelligent decoupled access in C-V2X networks in the context of fifth-generation mobile communications (5G) and beyond 5G (B5G). We propose a joint optimization approach for radio resource allocation, power control, and user association to enhance the network throughput of UL and DL while meeting the service quality requirements of vehicle users. Specifically, we formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and transform it into a standard convex optimization problem by introducing various auxiliary variables. An efficient iterative algorithm based on successive convex optimization techniques is introduced to obtain a sub-optimal solution. The proposed framework uniquely integrates decoupled and coupled access modes within a unified optimization formulation, enabling dynamic mode selection based on network load. Extensive simulation results demonstrate a significant performance improvement of the proposed UL/DL intelligent decoupled access in C-V2X networks compared with benchmark schemes. Full article

Hybrid MTMS–silica aerogels incorporating calcium silicate hydrate (C–S–H), the primary hydration product in cementitious systems, were synthesized via sol–gel processing followed by freeze-drying. The influence of C–S–H loading on pore structure, density, wettability, and thermal transport was investigated. The lowest thermal conductivity (0.068 W/m·K) and tap density (0.30 g/cm³) were obtained at 10% C–S–H loading (wM-CSH10), while the thermal conductivity increases to approximately 0.075–0.082 W/m·K at higher C–S–H content. All samples exhibit mesoporous structures with pore diameters in the range of 10–21 nm. Increasing C–S–H content progressively densified the network, reduced mesopore volume, and enhanced high-temperature mass retention up to 540 °C. FTIR analysis confirmed Si–O–Ca interfacial interactions, while nitrogen adsorption demonstrated persistent mesoporosity across all compositions. Thermal conductivity showed a positive correlation with density, indicating that bulk densification governs heat transport in the hybrid system. Beyond structural modification, the incorporation of C–S–H introduces chemical and microstructural features relevant to cement-based materials, suggesting potential compatibility with cementitious matrices. The results highlight the compositional trade-off between insulation efficiency and structural stability and demonstrate the potential of C–S–H-modified MTMS–silica aerogels for future integration into cement-based composites. These findings provide fundamental insight into their possible use in thermal insulation applications, such as building envelope systems (walls, façades, and roofs used for thermal insulation). Full article

The rapid advancement of beyond-5G and 6G services is creating computational challenges that classical optimisation methods for Elastic Optical Networks (EONs) cannot effectively handle. Specifically, the multi-objective Routing and Spectrum Assignment (RSA) problem—aimed at minimising blocking probability, maximising spectral efficiency, and reducing fragmentation—poses significant challenges and is NP-hard, particularly in dynamic traffic. This paper introduces a hybrid framework that combines quantum and classical computing, dividing the optimisation tasks into classical pre-processing, a quantum optimisation core, and classical post-processing with Pareto frontier management. The RSA problem is modelled using a Quadratic Unconstrained Binary Optimisation (QUBO) formulation that accounts for blocking, efficiency, and a quadratic fragmentation metric. Simulations conducted on NSFNET and UBN topologies under Poisson traffic conditions revealed that even in realistic, noisy quantum environments, this hybrid method reduces the blocking probability by 14% and improves fragmentation by 7.3% compared to the top classical heuristics. A scaling analysis indicates a key point of around 220 variables where this hybrid strategy surpasses traditional meta-heuristics in both solution quality and execution time, emphasising its significant potential in the current NISQ era. Full article

The increasing demand for efficient and sustainable building design has led to the development of nD BIM, which integrates multiple dimensions such as time (4D), cost (5D), and environmental performance (6D and beyond). However, existing approaches often lack an integrated decision-making framework that supports the simultaneous evaluation of these criteria, particularly in the early design phase of building envelope systems. This study proposes a unified nD BIM-based decision-making framework for building assemblies, using authoring tools, namely the BIM approach and LCA methodology. The proposed framework is applied in an empirical case study, where several design variants of a multi-residential building are developed and analyzed through 4D and 5D BIM models to assess construction time and costs, while environmental impacts are evaluated using selected key indicators, e.g., Global Warming Potential (GWP), Acidification Potential (AP), and the consumption of non-renewable primary energy (PENRT). The outcomes of these analyses are integrated into a multi-criteria decision-making model based on a weighting system. The results demonstrate that an nD BIM-based unified weighted decision model enhances decision-making by enabling transparent comparison of design alternatives and identification of trade-offs, thereby supporting more efficient and sustainable building envelope design while improving decision quality and reducing uncertainty for designers, engineers, and project investors. Full article

Background: Dual-tracer positron emission tomography/magnetic resonance imaging (PET/MRI) using [¹⁸F]-fluorodeoxyglucose (¹⁸F-FDG) and [¹⁸F]-fluoroethylcholine (¹⁸F-FEC) may reveal complementary aspects of hepatocellular carcinoma (HCC) biology. This retrospective study evaluates whether PET parameters obtained from ¹⁸F-FDG and ¹⁸F-FEC correlate with MRI enhancement parameters, tumor histological grade, and survival in patients with HCC. Methods: We retrospectively evaluated 25 patients who underwent integrated PET/MRI. Lesions were analyzed on pre-contrast MRI and at early (3–5 min) and late (20–30 min) post-contrast phases. For ¹⁸F-FDG and ¹⁸F-FEC, standardized uptake values (SUV maximum, mean, and peak) and metabolic tumor volume were measured, along with lesion size, MRI enhancement ratios, AFP levels, and survival. For patient-level imaging analyses, the largest lesion per patient was defined as the index lesion. Correlation analysis using Spearman’s rank correlation coefficient, Kaplan–Meier analysis, log-rank testing, and exploratory Cox proportional hazards models were performed. Results:¹⁸F-FDG SUVmean (index lesion) showed the strongest inverse association with survival (r = −0.61, p = 0.003), followed by SUVpeak (r = −0.50, p = 0.012). ¹⁸F-FDG SUVmean differed across tumor grades (G1–G3; p = 0.040), without a consistent trend. In Cox regression, ¹⁸F-FDG SUVpeak was significantly associated with shorter overall survival (HR 1.22, 95% CI 1.05–1.42, p = 0.01), whereas SUVmean showed only a borderline association (p = 0.07). When split at the median, SUVmean was also significantly associated with shorter survival (p < 0.001; HR 10.39, 95% CI 2.75–39.3). ¹⁸F-FEC parameters were not associated with survival but showed a moderate correlation with AFP levels (r = 0.41, p = 0.01). Dynamic MRI early and delayed enhancement of the index lesion were strongly correlated (r = 0.70, p < 0.001) but not associated with survival. Conclusions: In HCC, ¹⁸F-FDG uptake provides prognostic information beyond MRI enhancement and histology, reflecting tumor aggressiveness and independently predicting survival. While ¹⁸F-FEC-PET complements lesion characterization and correlates with AFP, it does not show meaningful prognostic value. MRI enhancement parameters were not associated with survival in this cohort. ¹⁸F-FDG-based metabolic imaging may improve pre-treatment risk stratification, whereas dual-tracer PET/MRI should be considered a selective, exploratory approach rather than routine imaging. Full article

Background: While sphingolipid alterations in obesity and metabolic syndrome (MS) have been extensively studied in plasma, their intracellular regulation within immune cells remains poorly characterized. Here, we introduce a PBMC-based sphingolipidomic approach that provides a novel immunometabolic perspective beyond traditional analyses of circulating lipids. Methods: Targeted sphingolipidomics was performed in peripheral blood mononuclear cells (PBMCs) from normal-weight healthy (NWH) subjects, and patients with essential obesity (EO) or MS. Multivariate integration (principal component analysis [PCA], hierarchical clustering, and partial least squares discriminant analysis [PLS-DA]) was combined with selected univariate models to explore lipid patterns and associations with cardiometabolic variables. Results: PBMC profiling identified a selective intracellular sphingolipid signature, with 10 species significantly altered across groups (FDR < 0.05), including ceramides, dihydroceramides, glycosphingolipids, and ceramide ratio indices. PCA showed that the first two components explained ~72% of total variance, with PC2 driving group separation. EO and MS displayed partial overlap, consistent with a shared metabolic phenotype, while both differed from NWH. Multivariate models highlighted ceramide ratios (e.g., CER16/24, and CER18/24) as key discriminators. Associations with cardiometabolic variables were limited and modest (adjusted R² ≈ 0.06–0.09), indicating that lipid alterations reflect integrated metabolic dysregulation rather than single clinical drivers. Conclusions: PBMC-based sphingolipidomics reveals a distinct intracellular immunometabolic remodeling in EO and MS, capturing aspects not detectable in plasma. These findings support the relevance of immune cell lipid profiling as a potential source of integrative biomarkers and provide insight into immune–metabolic crosstalk underlying metabolic disease. Full article

In product manufacturing and operations, firms increasingly treat Environmental, Social, and Governance (ESG) ratings as strategically important. This differs from earlier views that framed ESG mainly as a burden, whereas recent studies suggest that ESG can enhance firm value. Using panel data on Chinese A-share listed firms over 2009–2022, this study examines whether ESG ratings affect product-market performance. A two-way fixed-effects model shows that better ESG ratings significantly increase market share, mainly by signaling stronger product quality and service capability. While findings from this emerging market context may have limited generalizability, results consistently show that ESG performance bolsters competitiveness, particularly in high-tech and consumer-facing sectors. Moreover, improvements in ESG ratings are positively associated with net market-share growth. The benefits extend beyond the focal firm and generate positive spillovers for downstream customers. The three ESG dimensions do not contribute equally: the Environmental (E) and Governance (G) dimensions exert stronger effects on product-market performance than the Social (S) dimension. This study provides a new perspective on understanding the value creation mechanism of ESG investment. Full article