Search Results (10,527)

The exact failure probability of a series structural system requires multidimensional numerical integration that becomes computationally infeasible beyond ten to fifteen elements—a limitation routinely encountered in the probabilistic assessment of real structures such as timber trusses, bridge decks, and lattice girders. This paper presents a systematic formal analysis and comparison of the principal approximation methods available in the literature: simple bounds by Thoft-Christensen and Murotsu, Sørensen’s complement-product reformulation, and Ditlevsen’s narrow bounds based on pairwise failure event correlations. A new approximation is proposed, defined as the arithmetic mean of the upper and lower reliability bounds evaluated over the j most critical failure modes. Four formal propositions are established: (1) the approximation is bounded by the upper and lower bounds (consistency); (2) it is monotonically increasing in each component failure probability (monotonicity); (3) it converges to the exact midpoint as j → m (convergence); and (4) under positive inter-element correlation—the typical case for timber trusses manufactured from the same batch—the approximation overestimates the system reliability index below a crossover correlation ρ* (an unsafe-side error) and underestimates it above ρ* (a safe-side error), the practical timber truss regime falling on the unsafe side. Numerical validation against the exact solution for a ten-element series system across correlation levels ρ = 0 to 0.99, against five additional synthetic test systems, and against a classical equicorrelated benchmark from the reliability literature shows reliability-index errors of, at most, about 2.6%, anti-conservative below the crossover correlation and conservative above it. The approximation is applied to six timber truss configurations (spans 15–20 m, GL24h glulam, C24 structural timber, punched metal plate and bolt connections) within a probabilistic robustness assessment framework. Practical guidelines are provided for selecting the parameter j and for applying the approximation in robustness classification workflows. Full article

The reuse of small-capacity lithium-ion cells recovered from disposable e-cigarettes is limited by the lack of rapid and low-cost screening methods. This study evaluates a low-complexity 3-30-3 pre-sorting methodology based on a 30 s DC load pulse and a 3 min rest period applied to series connected cells under identical current conditions. Generic 13350-type cells with a nominal capacity of approximately 850 mAh were tested, and cell suitability was assessed from voltage drop, effective direct-current resistance, and stability over repeated load cycles. In the tested batch, the coefficient of variation of the effective resistance reached 28–35%, indicating substantial cell-to-cell variation within this pilot sample. Repeated loading improved discrimination between stable and degraded cells and enabled selection of a compatible subset for assembly into a 3S3P battery pack. The assembled pack operated successfully as a power source for a FläktWoods 227VM pressure controller in biomass drying monitoring. The proposed method does not replace full battery diagnostics, but it provides a practical first-stage filter that can reduce testing time by more than 90% and improve the safety of second-life cell grouping. Full article

The increasing discharge of pharmaceutical contaminants, particularly antibiotics like metronidazole (MNZ), into water systems poses significant ecological and public health risks due to their high solubility and low biodegradability. This study developed and characterized a highly porous activated carbon derived from maize cob (MC-AC). The synthesized material was characterized using FTIR, FESEM, PXRD, HRTEM, and BET analysis. Batch adsorption experiments were conducted, and the removal efficiency of MC-AC for MNZ was 98.6%. Optimization and modeling of the process variables of pH (3–11), contact time (0–75 min), concentration (0–70 mg/L), temperature (25–35 °C), and adsorbent dosage (0.5–1.5 g/L) were investigated using the Box–Behnken design (BBD) of response surface methodology, and 29 runs were obtained. The BBD model determined an optimal removal efficiency of 94.6 for metronidazole. Furthermore, non-linearized kinetic and isotherm models were used to determine the adsorption mechanism and mode of metronidazole from water. From the investigation, it was observed that both the Freundlich and pseudo-second-order models exhibited high correlation coefficients. The models with the best performance and low error metrics were determined by R², MSE, RMSE, SAE, and SSE. Therefore, the adsorption mode was multilayer heterogeneous, and the mechanism was chemisorption. Therefore, this study provides a unique alternative for using the Box–Behnken design, kinetic, and isotherm models to understand the removal of metronidazole from water using maize cob-activated carbon. Full article

Vehicle-ejected debris detection is a practical but insufficiently studied problem in intelligent traffic enforcement. Unlike static road litter, objects thrown from moving vehicles are usually small, irregular, transient, and easily confused with road textures, shadows, lane markings, and light reflections. In current traffic management, such violations still rely heavily on manual video review or offline inspection, while task-specific datasets and edge-deployable detection solutions remain limited. To address this gap, this study constructs a vehicle-ejected debris dataset containing 4328 annotated image samples collected from real road scenarios. The dataset covers urban and suburban roads, daytime and nighttime illumination, near-range and distant small-object cases, and hard negative samples. To meet the coupled requirements of vehicle-mounted small-object detection and edge-side INT8 deployment, this study develops a hardware-aware lightweight detection framework based on YOLOv8m. The original CSPDarknet backbone is replaced with the convolutional variant of MobileNetV4 to reduce feature-extraction cost, while a scale-specific Channel Alignment Module is inserted between the heterogeneous MobileNetV4 backbone and the YOLOv8m PANet neck to preserve multi-scale feature compatibility. The alignment module uses only BPU-friendly convolution, batch normalization, and activation operations, thereby avoiding deployment-unfriendly operators while maintaining compatibility with INT8 quantization and edge acceleration. The trained FP32 model is quantized to INT8 and deployed on the RDK X5 BPU using the Horizon OpenExplorer toolkit. Experimental results and repeated-seed validation show that the proposed model achieves a consistent accuracy–efficiency advantage on the constructed dataset. In a representative run, the proposed model obtains 93.1% mAP₅₀, while reducing the number of parameters from 25.9 M to 13.1 M and GFLOPs from 78.9 to 39.6 compared with the YOLOv8m baseline. After INT8 deployment, the model reaches 112.6 FPS on the RDK X5 platform with only a minor accuracy decrease. These results indicate that the proposed framework can serve as a practical edge-deployable perception module for real-time vehicle-ejected debris monitoring under vehicle-mounted traffic-enforcement scenarios. It should be noted that this work focuses on single-frame debris detection, while event-level ejection verification, temporal consistency analysis, offending-vehicle attribution, and enforcement decision-making remain beyond the scope of this study. Full article

Recommendation systems are widely used in e-commerce, social media, and content distribution, yet LLM-based recommendation workflows still face recurring challenges in data completeness, sample balance, and output stability. In addition, the conventional “user-platform” structure leaves limited room for user-side mediation of exposure and preference expression. This paper presents ABP, a workflow-level extension of $i^2$Agent in the User–Agent–Platform setting. ABP contains three modules: Adaptive Description Enrichment (ADE), Batch-balanced Sampling Strategy (BSS), and Prompt-driven Workflow Optimization (PWO). ADE repairs missing or rigid item text with richer natural-language descriptions, BSS builds balanced comparative inputs for user profiling, and PWO strengthens multi-stage reasoning with structured output constraints. Experiments on four real-world datasets show that ABP achieves strong ranking results under the reported protocol. Across the 16 reported dataset–metric pairs, the five-run mean results of ABP show an average relative improvement of 24.62% over $i^2$Agent, with especially large gains on Amazon Book and Amazon Movietv. Under a fixed five-run protocol, ABP shows limited run-to-run dispersion on Amazon Book, Amazon Movietv, and Yelp, while Goodreads exhibits comparatively larger but still bounded variation. Overall, these results suggest that carefully designed workflow improvements can improve LLM-based recommendation quality in the reported setting while maintaining the agent’s role as a user-side mediation layer in the User–Agent–Platform setting. Full article

Railway logistics terminals are crucial nodes in the national logistics system. Prior to the market-oriented reform of railway logistics, the warehousing operations at these stations primarily focused on temporary storage services before and after shipment, making it difficult to provide customers with integrated warehousing and transportation logistics services. With the advancement of railway marketization reforms, railway logistics terminals have gradually begun to offer socialized warehousing services, acquiring the capability to provide integrated warehousing and transportation services. In response to market development needs and the requirements for green development in railway logistics, an optimization design model for obtaining green railway logistics solutions is established, considering factors such as carbon emission costs, integrated warehousing and transportation logistics service costs, fuzzy constraints on logistics network capability, transportation time windows and the customer’s risk tolerance level. The model takes minimizing carbon emission costs and railway logistics service costs as dual-objective functions and uses a standardized weighting method to convert the dual-objective functions into a single-objective function for solving using triangular fuzzy numbers to characterize the ability constraints of network nodes, making the model more realistic. This model aims to minimize these costs and assesses the impact of factors such as changes in delivery time limits, shipment quantity, shipment batches, the superposition of multiple goods batches and customer preferences on the railway logistics solution for different scenarios. Research indicates that reasonably designing delivery time limits and aligning shipment times with railway transportation time windows can effectively reduce carbon emissions and logistics costs, and the risk tolerance level has a significant impact on the reliability of railway logistics solutions. Full article

In response to the challenges encountered in high-throughput resilience assessment of zonal ship central cooling systems, including numerical stiffness in physics-based dynamic models, abnormal solver termination, and insufficient continuity in batch simulation campaigns, a physics-informed co-simulation framework for resilience-oriented assessment is proposed. With control–physics orthogonal decoupling as its core, the framework separates the control-scheduling layer from the thermo-hydraulic solver at the software-execution level, while retaining information exchange through standardized interfaces. In addition, physics constraint-based pre-filtering, process-level fault isolation, and automatic recovery mechanisms are integrated to improve the robustness and continuity of automated batch assessment. A hierarchical reduced-order thermo-hydraulic model of the zonal ship central cooling system is established. Subsequently, the numerical stiffness characteristics of the fluid network and heat-transfer units under valve topology switching conditions are analyzed. A standalone C++ solver kernel is generated from a Simulink prototype model, and a Java/Web-based collaborative scheduling platform is constructed. Cross-environment consistency tests show that the C++ solver reproduces the Simulink prototype results under representative fast hydraulic and slow thermal scenarios, with steady-state and transient discrepancies below 0.05% and 1.08%, respectively. Physics constraint-based pre-filtering intercepted 42.6% of infeasible samples and reduced the total wall-clock runtime of the tested optimization task by approximately 38%. In 1000 fault-injection tests, the process-isolation mechanism isolated 12 abnormal solver terminations, while the main scheduling process remained alive and the remaining batch tasks were completed under the tested conditions. Finally, an abrupt pulse thermal-load increase in the forward zone was used as a representative scenario to demonstrate automatic extraction of temperature trajectories and quantitative evaluation using the cumulative temperature-exceedance severity (CTS) index. The results indicate that the proposed framework can support offline resilience-oriented assessment, reconfiguration-strategy screening, and batch evaluation of shipboard fluid–thermal systems under the tested conditions. Full article

Per- and polyfluoroalkyl substances (PFAS), and the short-chain representative perfluorohexanoic acid (PFHxA), are persistent environmental pollutants that pose serious health risks due to their resistance to degradation, mobility, and widespread presence in aquatic systems. This study investigates the adsorption of PFHxA onto graphene-based materials synthesised from graphite using a scalable, resource-efficient route and compares their performance with three commercial reduced graphene oxides. The graphene samples were characterised by BET surface area analysis, SEM, XPS, and Raman spectroscopy, revealing significant differences in surface area, pore volume, and surface chemistry that govern adsorption behaviour. Batch adsorption experiments in different water matrices (tap water, river water, and treated wastewater) under controlled pH conditions showed that graphene materials with higher surface area and optimised oxygen-containing functional groups achieved enhanced PFHxA removal, even in complex, real-world waters. Based on the physicochemical properties of both the adsorbent and adsorbate, hydrophobic interactions may contribute to adsorption alongside pore-filling effects, hydrogen bonding, and other intermolecular forces. Among the tested sorbents, the SG-X material, with its high BET surface area and hydrophobic character, and the CG-A material, which retained high performance across a broad pH range, exhibited the most promising adsorption capacities and operational robustness. These findings demonstrate the potential of engineered graphene-based adsorbents as a sustainable remediation option for short-chain PFASs, supporting circular and low-chemical-intensity approaches to protecting water quality under diverse environmental conditions. Full article

Landfill wastewater is a serious environmental problem and represents a high-concentration source of valuable organic compounds such as humic acids (HAs). The nanofiltration (NF) concentrate generated during treatment poses an even more significant environmental challenge, and the recovery of these substances is compatible with circular economy principles but requires innovative, pollution-resistant separation technologies. This study presents a novel hybrid approach for HA recovery by integrating naturally occurring clay minerals, such as halloysite nanotubes (HNTs), as a dynamic coating layer onto tube-shaped ceramic ultrafiltration membranes. The research was conducted in two stages: batch adsorption–desorption experiments followed by membrane integration. In the first stage, the batch adsorption studies showed that HA adsorption by HNTs followed the Freundlich isotherm model. The maximum HA adsorption capacity for HNTs increased with increasing initial concentration. In desorption studies, recovery rates of 74.6% were achieved with 1.5 N sodium hydroxide (NaOH) and 67.5% with 1.5 N potassium hydroxide (KOH). In membrane studies, the optimum HNT coating concentration was determined as 0.05 g/L. While an average removal efficiency of 85.3% was obtained in synthetic HA filtration, the desorption efficiency after regeneration was around 35–37%. In experiments with real NF concentrate, HA removal efficiencies ranged from 19 to 64% for concentrations of 5, 10, and 20 mg/L, with the highest desorption efficiency (59.3%) obtained in the 10 mg/L NF concentrate. The results reveal that the complex structure and competing components in the real wastewater matrix limit the removal and recovery performance compared to synthetic solutions. Full article

Vision–Language Models (VLMs) have emerged as powerful tools for image understanding tasks, yet their practical deployment remains hindered by significant architectural heterogeneity across model families. This paper introduces UVLM (Unified Vision–Language Model), a pip-installable Python (v3.9+) package that provides a unified interface for loading, configuring, and running multiple VLM architectures on custom image analysis tasks. UVLM currently supports two major model families which differ fundamentally in their vision encoding, tokenization, and decoding strategies: LLaVA-NeXT and Qwen2.5-VL. The package abstracts these differences behind a single inference function and eliminates all architecture-specific code from the user’s workflow. UVLM is organized as eight modular Python components (model loading, dual-backend inference, response parsing, consensus validation, batch processing, prompt assembly, model registry, and utilities) and can be deployed in three modes: Google Colab for zero-install cloud access, local Jupyter notebooks for on-premises GPU use, and as a programmatic API for integration into automated pipelines. Key features include a multi-task prompt builder supporting four response types (numeric, category, boolean, text), a consensus validation mechanism based on majority voting, a flexible token budget (up to 1500 tokens) for custom reasoning strategies, and built-in truncation detection. The package is designed for extensibility: adding a new VLM family requires implementing one backend-specific inference section and adding entries to the model registry, without modifying any other module. An illustrative example on 120 street-view images across 16 model configurations is provided to demonstrate the software’s evaluation workflow. Full article

Iron (Fe) contamination of water sources has become an increasing environmental concern, creating the need for effective, environmentally friendly, and cost-effective technologies for Fe(III) removal from aqueous systems. In the present work, the possibility of using the sulfuric acid-activated carbon made of Vigna trilobata rind waste for treating water contaminated with Fe ions was explored. The characteristics of the synthesized material were identified by physical, chemical, and spectroscopic methods, and its Fe ion sorption efficiency was studied experimentally in batch mode under various conditions. Equilibrium, kinetics, and thermodynamics of Fe ion removal by the prepared adsorbent were determined. The obtained adsorbent had a BET surface area of 20.55 m² g⁻¹ and showed high experimental adsorption capacity with the highest observed uptake of 19.81 mg g⁻¹. Based on the experimental results, the equilibrium data could be best described by the Langmuir equation (R² = 0.978). Kinetic analysis showed that the rate-limiting step in Fe ion sorption was intraparticle diffusion (R² = 0.921). Thermodynamic calculations indicated that the adsorption process occurred spontaneously (ΔG° = −4.31 to −6.53 kJ mol⁻¹) and endothermically (ΔH° = +7.11 kJ mol⁻¹). A comparative analysis showed that the sorption capacity of the studied adsorbent corresponded to that reported for the analogous materials produced from other biomasses. Full article

Accurate modeling of degradation progression in rotating machinery remains challenging in real industrial systems, where data are inherently limited and imbalanced because of safety-critical operations and associated risks, and the cost of acquiring fault data is high. These conditions make it difficult for data-driven approaches to reliably capture the evolution of degradation over time. To address this challenge, this study proposes a hybrid framework that models degradation progression as a set of distinct behavioral regimes driven by loss of lubrication. The proposed framework first applies adaptive scaling guided by an $\alpha$ parameter derived from Root Mean Square (RMS) deviation of the vibration signals relative to the baseline condition, aiming to mitigate data leakage during preprocessing while improving robustness to data imbalance. It then performs baseline-referenced deep feature learning using a lightweight Long Short-Term Memory (LSTM) model trained only on baseline data. The trained model is subsequently used to encode the entire dataset into latent representations, which are finally clustered using Mini-Batch K-Means to organize distinct degradation-related behavioral regimes. Results on both real-world and experimental datasets demonstrate that the learned latent representations strongly agree with the degradation regimes associated with baseline characterization and $\alpha$-guided progression patterns, achieving an Adjusted Rand Index (ARI) of 1.0 across both datasets with respect to the internally defined reference stages. Full article

With the increase in Qatar’s population, the generation of plastic waste has grown, resulting in high levels of environmental pollution. Polyhydroxyalkanoates are sustainable bio-alternatives to petrochemical plastics. Despite their market potential, the industrial implementation of PHAs is still limited. This study aimed to develop sustainable processes for PHA accumulation by screening and isolating novel haloarchaeal strains from Qatari extreme environments with the ability to convert carbon sources to PHAs. In total, 24 positive haloarchaeal members, belonging to Natrinema, Haloarcula, and Halostagnicola genera, were identified for the first time in Qatari ecosystems through 16S rRNA and phaC/phaE gene sequence analyses. Among them, the promising PHA-producing archaeon Haloarcula sp. PLQ exhibited the highest production, reaching a PHB concentration of 496 ± 24 mg L⁻¹ and a cell dry weight of 1109.8 ± 58.6 mg L⁻¹, corresponding to a maximum yield of 44.69 wt % ± 2.13 under optimal conditions. Polymer characterization confirmed the production of poly(3-hydroxybutyrate). In addition, the thermal properties analyzed by TGA (Tonset = 250 °C; Td = 270 °C) and DSC (Tm = 169 °C) confirmed a PHB-like film with thermal behavior comparable to standard PHB. Therefore, future pilot-scale studies on the pure culture of a promising strain for PHA production from renewable feedstocks under non-sterile, batch, or continuous fermentation will be conducted. Full article

Since 2024, most electricity production on Réunion Island has been achieved through combustion of biomass, using either imported wood pellets or locally produced bagasse. Their combustion generates two types of ash, depending on the biomass source: Wood Biomass Fly Ash (WBFA) and SugarCane Bagasse Ash (SCBA). Their chemical compositions differ significantly, leading to different potential applications. The composition of SCBA is similar to that of Coal Fly Ash (CFA), with low variability between batches. Therefore, SCBA could be used as an alternative to CFA, as a Supplementary Cementitious Material (SCM) or in composite cements. SCBA also meets most of the requirements of the NF EN 450-1 standard. However, grinding of SCBA appears necessary to achieve mechanical performance required by the standard. In contrast, WBFA exhibits variable chemical composition, mainly due to differences in pellet origin prior to combustion. Nevertheless, WBFA contains significant levels of chloride ions and sulfate, which may act as activators for materials such as GGBS or metakaolin (MK). Although the high unburned carbon content of WBFA increases water demand, their incorporation into GGBS-based binders (SSC or CEM III) or metakaolin-based systems shows promising potential, particularly for improving early strength. Full article

Nano bioinks have recently emerged as a promising class of biomaterials for advanced bioprinting applications, offering new opportunities in regenerative medicine, controlled drug delivery, and biosensing technologies. These materials are typically developed by integrating nanostructures such as nanoparticles, nanosheets, and nanofibers into polymeric or hydrogel matrices to enhance mechanical strength, bioactivity, and printing performance. Various fabrication approaches such as direct blending, in-situ polymerization, and surface functionalization are used to incorporate nanomaterials into bioink formulations. Subsequent crosslinking strategies are employed to improve print fidelity and structural stability while maintaining cell viability and biological functionality during the bioprinting process. Despite significant progress in recent years, several challenges continue to hinder the clinical translation of nano bioinks. Achieving consistent batch-to-batch reproducibility, ensuring long-term biocompatibility, and optimizing rheological properties for reliable printing remain critical issues. In addition, regulatory pathways and ethical considerations related to the biomedical use of nano-enabled bioinks are still insufficiently addressed in the literature. This review provides a comprehensive overview of recent advances in the design and fabrication of nano bioinks, highlighting key synthesis strategies, functional nanomaterials used in bioink formulations, and their emerging applications in tissue engineering, drug delivery, and biosensing. Furthermore, the review discusses the major technical, regulatory, and translational challenges that need to be addressed to facilitate the safe and effective implementation of nano bioinks in future biomedical applications. Full article