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A Systematic Literature Review on Disruptions in Construction Supply Chain: Some Stylized Trends
No full textConstruction supply chains (CSC) are intricate systems characterized by fragmentation and inefficiencies, which have been exacerbated by external disruptions such as the COVID-19 pandemic, natural disasters, and geopolitical instabilities. These challenges underscore the urgent need for resilient and adaptive supply chain strategies that integrate emerging technologies. However, existing research remains fragmented, lacking a comprehensive framework that aligns digital innovations with broader operational, economic, and stakeholder considerations. To bridge this gap, this study employs a systematic literature review (SLR) complemented by natural language processing (NLP) techniques to analyze existing knowledge on CSC disruptions and resilience-building strategies. A total of 63 articles were reviewed, with thematic clustering and topic modeling applied to uncover key challenges and opportunities. Findings reveal two primary clusters: macro-level strategies focused on systemic resilience and micro-level solutions addressing operational inefficiencies. The results indicate that while Industry 4.0 and modular construction methods offer promising solutions, their integration into CSC frameworks remains inconsistent. By combining qualitative SLR insights with quantitative NLP analysis, this study provides a holistic perspective on CSC disruptions and potential resilience strategies, offering valuable implications for both academia and industry. In particular, it might support practitioners in identifying suitable solutions for material tracking and in turn reducing inefficiencies. In addition, the issue of collaboration and information sharing is stressed in order to achieve a more aware decision-making process and reduce the level of uncertainty with a consequent higher resilience along the supply chain in the construction industry
Enhancing Manufacturing Engineering Higher Education Through Mixed Reality and Gaussian Splatting: Preliminary Experimental Results
No full textThe integration of Mixed Reality (MR) technologies into higher manufacturing-engineering education can contribute to face the challenges of providing hands-on training with real manufacturing systems. This paper explores the potential of MR combined with Gaussian Splatting (GS) to create high-fidelity digital replicas of industrial machinery (e.g., lathes, milling machines, etc.), enhancing students’ understanding of manufacturing processes. GS is emerging as a breakthrough technique for real-time rendering of objects and environments. By delineating the scene as the realisation of an object in a defined temporal state, GS methodology represents a 3D high-fidelity digital scene as a collection of 3D Gaussian ellipsoids characterised by position, geometry, shape, colour and opacity. The integration of MR with GS allows trainees to engage with realistic virtual models, simulating a physical presence in a machining workshop. The capacity to digitally manipulate and analyse individual objects enhances the learning experience, addressing logistical and safety constraints by providing a risk-free and accessible training environment. A lathe is used as a case study, and the GS-based digital scene is compared with conventional CAD-based model in terms of qualitative performance
Boosting zero-shot learning through neuro-symbolic integration
Full text linkZero-shot learning (ZSL) aims to train deep neural networks to recognize objects from unseen classes, starting from a semantic description of the concepts. Neuro-symbolic (NeSy) integration refers to a class of techniques that incorporate symbolic knowledge representation and reasoning with the learning capabilities of deep neural networks. However, to date, few studies have explored how to leverage NeSy techniques to inject prior knowledge during the training process to boost ZSL capabilities. Here, we present Fuzzy Logic Prototypical Network (FLPN) that formulates the classification task as prototype matching in a visual-semantic embedding space, which is trained by optimizing a NeSy loss. Specifically, FLPN exploits the Logic Tensor Network (LTN) framework to incorporate background knowledge in the form of logical axioms by grounding a first-order logic language as differentiable operations between real tensors. This prior knowledge includes class hierarchies (classes and macroclasses) along with robust high-level inductive biases. The latter allow, for instance, to handle exceptions in class-level attributes and to enforce similarity between images of the same class, preventing premature overfitting to seen classes and improving overall performance. Both class-level and attribute-level prototypes through an attention mechanism specialized for either convolutional- or transformer-based backbones. FLPN achieves state-of-the-art performance on the GZSL benchmarks AWA2 and SUN, matching or exceeding the performance of competing algorithms with minimal computational overhead.
The code is available at https://github.com/FrancescoManigrass/FLP
Boosting biocompatibility and minimizing inflammation in electrospun polyvinylidene fluoride (PVDF) cardiac patches through optimized low-pressure plasma treatment
Full text link: Tailoring surface characteristics is key to guiding scaffold interaction with the biological environment, promoting successful biointegration while minimizing immune responses and inflammation. In cardiac tissue engineering, polyvinylidene fluoride (PVDF) is a material of choice for its intrinsic piezoelectric properties, which can be enhanced through electrospinning, also enabling the fabrication of nanofibrous structures mimicking native tissue. However, the inherent hydrophobicity of PVDF can hinder its integration with biological tissues. To overcome this limitation, electrospun PVDF patches were subjected to radio-frequency low-pressure O2 plasma treatment to enhance surface hydrophilicity and overall biocompatibility. A systematic experimental study identified optimal parameters, revealing that higher gas content and prolonged exposure are preferable to high power levels, which deteriorate the patch's morphological and mechanical properties. X-ray photoelectron spectroscopy confirmed the formation of oxygen-containing surface groups, resulting in the patch's superhydrophilicity. Preservation of the fibrous nanostructure and electroactive phase content was verified using scanning electron microscopy and infrared spectroscopy combined with differential scanning calorimetry, respectively. The optimized plasma treatment maintained the patch's elasticity and demonstrated long-term stability for up to 3 months. In vitro biocompatibility was assessed through indirect and direct tests using AC16 human cardiomyocytes and neonatal human dermal fibroblasts, revealing good cell viability, adhesion, and spreading over 7-days. Finally, plasma-treated patches demonstrated strong adhesion to the myocardial tissue and exhibited markedly reduced inflammatory response compared to the untreated controls, as shown by decreased CD45+ immune cell infiltration around the patch implanted in infarcted mice, highlighting the surface treatment's effectiveness in enhancing in vivo biocompatibility
Refined Two-Sided Learning Rate Tuning for Robust Evaluation in Federated Learning
No full textThis paper investigates the impact of client and server learning rates on training deep neural networks in Federated Learning (FL). While previous research has primarily focused on optimizing the initial values of these learning rates, we demonstrate that this approach alone is insufficient for maximizing model performance and training efficiency. To address this weakness, we propose a revised two-sided learning rate optimization strategy that integrates learning rate decay schedules as tunable variables and adjusts the learning rate configurations based on the target training budget, allowing for more effective optimization.
We conduct an extensive experimental evaluation to quantify the improvements offered by our approach. The results reveal that (i) integrating decay schedules into the tuning process leads to significant performance enhancements, and (ii) the optimal configuration of client-server decay schedules is strongly influenced by the training round budget. Based on these findings, we claim that performance evaluations of new FL algorithms should extend beyond the fine-tuning of the initial learning rate values, as done in the state-of-the-art approach, and include the optimization of decay schedules according to the available training budget
Quantum open system description of a hybrid plasmonic cavity
No full textWe present a unified quantum open system framework for lossy plasmonic cavities, treating coherent dynamics, relaxation, dephasing, and irreversible absorption on an equal footing. The Dyson equation for the cavity photon propagator in the random-phase approximation yields a complex self-energy S(ω) that accounts for both the renormalization and damping of hybrid plasmon-photon modes. It shows that increasing losses can drive a crossover from resolvable normal-mode splitting to a regime without resolved splitting, when the damping becomes comparable to or larger than the coherent hybridization scale. Tracing out the environment yields a Liouvillian for the upper polaritons (UPs) and lower polaritons (LPs) with leakage \Gamma = −2 ImS(ω), internal UP ↔ LP scattering, and dephasing. Closed-form dynamics for populations and interbranch coherence provide analytic steady-state values, line shapes, and UP-LP quench rates, valid at low polariton density and in the ultrastrong-coupling regime. The theory is directly applicable to spectra, time-domain probes, and dissipation engineering in plasmonic and nanophotonic cavities
Evaluation of Silicon-Rich Anodes for Low-Temperature Applications
No full textSilicon-rich anodes were investigated as promising alternatives to graphite for lithium-ion batteries operating at sub-zero temperatures. Micro-sized silicon particles were employed with a capacity-limitation strategy (1000 mAh g−1) to mitigate mechanical stress and volume expansion during cycling. Electrochemical performance was assessed in three-electrode half-cells and bi-layer pouch full-cells (Si - NMC811) at temperatures ranging from 25 °C down to −25 °C. Despite the increased polarization and hysteresis observed in the galvanostatic charge/discharge profiles at low temperatures, micro-Si anodes retained a reversible lithiation/delithiation behaviour and high coulombic efficiency. Full-cell response was mainly affected by the NMC cathode, while the Si anode exhibited good capacity retention. These results demonstrate that capacity-limited micro-silicon anodes enable stable and efficient operation under cold conditions, providing a scalable, safe, and cost-effective route toward next-generation lithium-ion batteries and reducing reliance on graphite now listed as a critical raw material in the E
Investigation of microfibers and microplastics in process water from spunlace nonwoven production
Full text linkMicroplastic (MP) pollution has become a pressing environmental issue due to its transfer to aquatic ecosystems through wastewater discharge, posing a growing risk to environmental sustainability. The textile industry, particularly nonwoven manufacturing, is one of the key contributors to this problem. The spunlace nonwoven process stands out in this context because of its high water demand and significant wastewater generation. Although several studies have investigated microplastics originating from textile effluents and disposable products, the contribution of the spunlace nonwoven production process, characterized by intensive water use and effluent discharge, to microplastic release has not yet been examined. Considering that spunlace fabrics are widely used in disposable Fast-Moving Consumer Goods (FMCG) such as wet wipes and hygiene products, and that their production volumes are continuously increasing, understanding the microplastic dynamics at different stages of the manufacturing process is crucial for evaluating the environmental sustainability of spunlace production. This study examines the formation and characteristics of microfibers (MFs) - including both synthetic (polyester) and cellulosic (viscose and cotton) types - in different stages of the process water used in a spunlace nonwoven production facility. Water and sludge samples were collected from the influent, effluent, and treatment stages of the process water system. The samples were pretreated with hydrogen peroxide (30% H2O2) and filtered to recover MPs and MFs. Mass- and count-based assessment and characterization analyses were performed on the particles obtained after filtration. By focusing on a previously unexamined stage of textile manufacturing, this work provides the first insights into process-related microfiber and microplastic formation in spunlace nonwoven production. The results highlight the importance of developing process and policy-level strategies to improve water reuse and wastewater management in the spunlace and nonwoven industry
Characterization of NTRM Systems for the Structural Strengthening of Masonry Cross Vaults
No full textTextile Reinforced Mortar (TRM) systems are a class of composite materials widely adopted for the structural strengthening of masonry constructions. They are usually made of inorganic matrix, which can be a lime-based or cement-based mortar, and are reinforced with layers of strengthening grids, often made of steel or glass fibres. In recent years, growing concern about sustainability has increased the interest towards materials with a lower environmental impact, by favouring the adoption of natural fibres in TRM systems. These systems are also known as Natural Textile Reinforced Mortar (NTRM). In this research, basalt-TRM composites are analysed for the structural strengthening of masonry cross vaults subjected to quasi-static shear settlement of two abutments. The mechanical characterization of the strengthening system is conducted by direct tensile tests on bare textiles and one-layer composite coupons. The durability of the strengthening system is assessed by applying an ageing protocol to both basalt textiles and composite coupons. In detail, the bare textiles were conditioned for 1000 h at 23° C in a 0.16% Ca(OH)2 alkaline solution, simulating the exposure to a lime-based-mortar aggressive environment. Meanwhile, the composite coupons were conditioned for 1000 h in water at a constant temperature of 23°. The tensile strength of conditioned samples is compared to those of the non-aged reference specimens. Then, the Finite Element (FE) simulation of a masonry cross vault is performed to assess the efficiency of the basalt-TRM strengthening system applied to the extrados, incorporating the mechanical properties of the composite material