Search Results (3,328)

A conserved comparison ledger is an abstract system in which each state carries a positive ratio $r\left(s\right)$, every measurement factors through that ratio, and pairwise comparisons are scored by an admissible cost J. A separate classification fixes the admissible costs with polynomial combiner and, under a units normalization, selects the representative $J_{\mathrm{cost}}\left(x\right)=\frac{1}{2}(x+x^{-1})-1.$ This paper installs that classified cost in a ledger and determines what the ledger assumptions force. The argument has three structural steps and one neutrality axiom: H1 forces the scale ratio to $\sigma =\phi$; H2, using that classification, forces $J=J_{\mathrm{cost}}$; H3 reads $r\left(s\right)$ from a constrained cost minimizer of an internal positive vector, and A3 imposes zero total log-charge; together they force $r\equiv 1$, make the measurement map constant, and collapse the observational quotient to a singleton. The assumptions are also sharp in the following sense: removing H1, H2, H3, or A3 admits an explicit ledger satisfying the remaining meaningful assumptions while losing the corresponding conclusion. The examples verify joint satisfiability, include non-uniform internal data whose variational minimum gives $r=1$, and show nontrivial sector dynamics when A3 is not imposed. A final example realizes the full axiom package on the patch space of the Fibonacci quasicrystal, with the scale and the charge-sector structure supplied intrinsically by the tiling and the remaining installations stated explicitly. We frame this result as a coherence theorem for a deliberately chosen axiom system: each hypothesis is individually natural and, by design, controls a single output, so their joint forcing of $(\sigma ,J,r)$ exhibits the internal consistency of the axiom package rather than asserting that any independently given system must realize it. Full article

The treatment of coffee silverskin (CSS), a waste stream from the coffee industry, through deep eutectic solvents (DES) was studied. Choline chloride-based DES mixtures were checked by using a lactic acid (LA) hydrogen bond donor. The effect of time, temperature and biomass:DES ratio on the dissolution of lignin and hemicellulose was analyzed. Maximum hemicellulose reduction was from 28.15% to 3.37% at 60 °C and 1 h and maximum lignin reduction was from 33.1% to 20.03% at 90 °C and 5 h. Additionally, it was found that the optimum operating conditions obtained were 3 h, 120 °C and biomass:DES ratio (1:10), which led to 8.02%, 29.31% and 36.48% hemicellulose, lignin and cellulose content, respectively. Consequently, DES pretreatment can be considered a promising approach to obtain cellulose-enriched coffee silverskin solids under lower-temperature conditions than many conventional lignocellulosic biomass pretreatments. However, further downstream validation is required to assess their suitability for paper, textile, or food-packaging-related applications. Full article

Cannabidiol (CBD)-infused bottled water has recently become one of the new types of functional beverages in the expanding cannabinoid and nutraceutical market. This paper presents preliminary experimental observations combined with a literature-based analysis to investigate quality and stability parameters of CBD-infused bottled water. Physicochemical characterization, microbial quality analysis and stability monitoring of CBD under storage conditions were included in the experimental work. The results indicated that the formulation maintained the properties of its initial dispersion during the first period of storage. An increase in transparency and a decrease in CBD concentration were noted. Microbial counts increased during storage, suggesting that microbiological stability is limited by time under the tested conditions. Reviewing the literature underlines the significance of cannabinoid stability in aqueous beverages aided by nanoemulsion delivery systems, packaging materials and controlled storage environments. The results show that CBD can be infused into bottled water but challenges regarding formulation and storage exist. The research was not conducted to address long shelf-life, commercial scalability or compliance with regulations but to provide primary information on quality and stability factors influencing CBD beverages. More research using standardized procedures is required to understand safety, efficacy and stability over time. Full article

Electric vehicles equipped with in-wheel motors provide packaging, controllability, and drivetrain-simplification advantages; however, the increase in wheel-side unsprung mass can intensify vibration transmission to the chassis, seat, and occupant. This paper presents a coordinated active seat and active suspension control strategy for an integrated 8-DOF quarter-car model that includes an in-wheel motor, an active seat suspension, and a 4-DOF seated driver body model. The proposed controller combines a Harmony Search (HS)-optimized proportional–integral–derivative (PID) feedback baseline with a repeatable-disturbance feedforward compensation term. The HS-PID loop provides baseline transient attenuation, while the feedforward term compensates the repeatable component of the bump-induced disturbance transmitted through the coupled seat–vehicle system. The controller is evaluated against passive suspension, active-seat-only control, active-vehicle-suspension-only control, and an HS-PID baseline under repeated bump/shock excitation. The results show that coordinated actuation reduces occupant displacement and acceleration responses relative to the benchmark cases. The discussion explains the active-seat-only peak-acceleration amplification, the different magnitudes of displacement and acceleration improvements, and the practical implications of suspension stroke and actuator-force limits. The reported conclusions are therefore confined to the repeated bump/shock condition considered in this numerical study; broader ride-comfort generalization requires standardized whole-body vibration metrics, random-road validation, speed variation, parametric uncertainty analysis, and drivetrain energy evaluation. Full article

The growing demand for environmentally sustainable materials has accelerated the development of bio-based coatings as alternatives to conventional petroleum-derived surface treatments. Among renewable biopolymers, lignin has emerged as a particularly attractive candidate owing to its abundance, renewable origin, aromatic structure, antioxidant activity, ultraviolet shielding capability, and diverse functional groups suitable for chemical modification. As a major by-product of the pulp, paper, and biorefinery industries, lignin represents an underutilized renewable resource with significant potential for value-added coating applications. This review provides an overview of recent advances in lignin-based coatings for sustainable and biodegradable materials. The chemical structure, physicochemical properties, industrial sources, extraction technologies, purification methods, and functionalization strategies of lignin are discussed. Particular attention is given to nanostructured lignin systems, including lignin nanoparticles (LNPs) and chemically modified derivatives, which have demonstrated improved compatibility and performance in coating formulations. Fabrication technologies such as solution casting, dip coating, spray coating, layer-by-layer (LbL) assembly, extrusion processing, and nanocomposite approaches are examined. Mechanical, barrier, thermal, UV-shielding, antioxidant, antimicrobial, hydrophobic, and environmental performance are comparatively assessed. Lignin nanoparticles and chemically modified lignins generally show improved functionality, while waterborne coatings for paper and fiber-based packaging appear closest to practical application. However, lignin heterogeneity, durability, scalability, and limited regulatory evaluation and end-of-life assessment remain major barriers to commercialization. Full article

Simultaneous Localization and Mapping (SLAM) is essential for autonomous systems navigating in human-centric environments, yet conventional systems fail when people and objects move through the scene. This paper introduces SAR-SLAM (Semantic-Aware Recognition SLAM), an RGB-D SLAM framework that robustly handles dynamic scenes containing moving people and objects using dual semantic geometric processing. First, we employ YOLOv8-based semantic segmentation to identify dynamic objects and generate initial detection masks. Second, we apply RANSAC-based Homography analysis to perform geometric motion verification, distinguishing truly moving objects from stationary ones by analyzing feature correspondence patterns. Third, an adaptive fusion mechanism combines both semantic and geometric evidence while incorporating temporal consistency and coverage constraints to maintain system stability. The system is implemented as a modular ROS2 package, enabling smooth integration with robotic systems and compatibility with existing navigation frameworks. SAR-SLAM reduces Absolute Trajectory Error by up to 96% over ORB-SLAM3 on the dynamic sequences of the TUM RGB-D benchmark, and remains competitive with state-of-the-art dynamic SLAM methods across a range of dynamic scenarios. Full article

This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues. Full article

This study aims to systematically investigate packaging waste repurposing in humanitarian logistics. It aims to bridge the theory–practice gap by providing a consolidated evidence-based analysis of how packaging is repurposed, the driving motivations, and the design characteristics that enable such transformations. A systematic review and thematic analysis of 34 documents from well-known international relief organizations was conducted. The documents were thoroughly reviewed, and codes were extracted and grouped into themes. The findings reveal that packaging repurposing is a widespread practice driven by three interconnected streams: (1) designer-led (top-down) initiatives rooted in Non-Governmental Organizations (NGOs) policies and design innovation, (2) user-led (bottom-up) initiatives that are induced by the survival and livelihood of affected communities, and (3) hybrid initiatives that involve the participation of the NGOs and affected people. Moreover, repurposing is highly dependent on specific design enablers, such as structural and material properties, to facilitate it. Lastly, a hierarchy of material transformation is proposed, ranging from low-energy mechanical adaptations to high-energy physio-chemical processes. This paper provides a novel integrated framework that links material, drivers, and design enablers for packaging repurposing in humanitarian logistics. It proposes a transformation spectrum to guide repurposing strategies based on resource and tool availability. This study consolidates fragmented knowledge across different sources and offers practical insights into the applicability of repurposing in various domains such as procurement, packaging design, and logistics planning. Full article

To achieve high interconnect density in 2.5D packages, various microscale structures such as through-silicon vias (TSVs), microbumps, and redistribution layers (RDLs) are employed. These features typically exist at the micron scale, whereas other package components span millimeter to centimeter scales, resulting in a wide range of physical dimensions within the package. Although finite element analysis (FEA) has proven effective for evaluating the mechanical and thermal characteristics of 2.5D packages, the inherent multi-scale nature poses significant computational challenges and numerical convergence issues, severely hindering the design and analysis of increasingly dense packages. To address this problem, this paper proposes an efficient numerical homogenization method for the mechanical and thermal analysis of 2.5D packages. The method employs periodic boundary conditions (PBCs) based on the concept of referential statistical volume elements (rSVEs). In this approach, typical microstructures—including TSVs, microbumps, and RDL traces together with the surrounding matrix material—are treated as a homogeneous medium, and the equivalent material properties of the multi-scale structures are evaluated. These properties include the stiffness matrices (from which the equivalent Young’s modulus, shear modulus, and Poisson’s ratio can be derived), coefficients of thermal expansion, and thermal conductivity. Validation results demonstrate that the proposed method ensures continuity of displacement, stress, strain, and heat flux across opposite surface pairs of the rSVEs. Compared with experimental measurements and other existing homogenization techniques, the method accurately determines the equivalent material properties of complex multi-scale structures without being restricted to specific geometries, while significantly improving computational efficiency. Finally, the proposed numerical homogenization method is successfully applied to wafer warpage analysis during the manufacturing process and to thermal analysis under operating conditions. The results indicate that the method achieves high computational efficiency while maintaining accuracy in both mechanical and thermal analyses of 2.5D packages, thereby laying a solid foundation for the development of next-generation 2.5D package structures. Full article

Traditional methods for wine bottle packaging leakage detection often suffer from low efficiency, high false-positive rates, or an inability to detect micro-leakages. This paper proposes a near-infrared laser leakage detection system based on tunable diode laser absorption spectroscopy at 1392 nm, combined with a LightGBM machine learning model. The system detects gaseous ethanol vapor escaping from leaking bottles, addressing the spectral interference caused by ambient water vapor. A total of 1410 samples were collected, and each raw 2000-point spectral contour was compressed into a 200-dimensional feature vector through baseline correction, Z-score normalization, and uniform down-sampling. A two-stage hyperparameter optimization strategy yielded the optimal LightGBM configuration with a 5-fold cross-validation. For the binary classification task, the model achieved an AUC of 0.9949 and an inference speed of 0.0058 ms per sample on a CPU, outperforming Random Forest, PLS, and four deep learning models. For the regression task, the model achieved an R² of 0.5854 ± 0.0919. An anti-interference experiment on 422 samples under varying flow rates, temperatures, and commercial wine types confirmed the model’s robustness, achieving an overall accuracy of 0.94 and an alcohol recall of 0.99. To further validate the system under realistic conditions, a simulated micro-leakage test was conducted using a negative-pressure extraction method: 320 samples were collected from artificially damaged commercial wine bottles placed in a custom-built acrylic vacuum chamber that replicates the production line enclosure. The model achieved an accuracy of 0.95 with zero false negatives. The complete detection cycle takes no more than 5 s per bottle, enabling non-destructive, rapid, and online packaging integrity assessment. The results demonstrate that the proposed system provides a low-cost and reliable solution for wine bottle leakage detection suitable for industrial deployment. Full article

This paper proposes a level-based scientific maturation master plan (SMMP) for strengthening research projects prior to manuscript submission. A weak manuscript is often not simply a weak text but an immature project that has been translated too early into publication form. Contemporary research management is better at registering deadlines, deliverables, resources, and visible publication signals than at diagnosing the internal maturity of a scientific object. The result is false readiness: a project may have a topic, structure, literature, methodological vocabulary, and a polished manuscript but still lack a mature problem, a coherent conceptual architecture, a testable design, sufficient evidence, and a disciplined contribution. To address this gap, this paper proposes a nine-level SMMP, moving from thematic impulses to peer review and publication readiness. The model integrates noncompensatory gates, evidence packages, red flags, maturation debt, bottlenecks, the publication maturation gap, and peer-review readiness. Methodologically, the paper is a conceptual design study supplemented by a proof-of-concept documentary application to publicly available CORDIS project biographies. The framework shows how to distinguish publication polish from scientific maturation and how to translate expert criticism into concrete presubmission actions. Full article

Field-programmable gate arrays (FPGAs) with on-package high-bandwidth memory (HBM) are an attractive substrate for low-latency, precision-customizable AI inference, yet high-level synthesis (HLS) flows expose little control over how tensors are distributed across many independent memory channels. The AMD Alveo V80 spreads its nominal 820 GB/s across 64 pseudo-channels, each capped at 12.8 GB/s, so delivered bandwidth is governed by an explicit tensor-to-channel partitioning decision that existing framework-based flows do not surface. This paper presents a methodology for HLS-based inference on the V80 built on two coupled contributions: a design-time partitioning framework that assigns each tensor to on-chip (BRAM/URAM) storage, a single HBM channel, or a stripe across several channels according to its reuse and access pattern, and a three-stage HLS flow that separates functional baseline, unroll-and-partition throughput extraction, and precision-aware DSP packing. The methodology is developed through five model kernels—a parameterized GEMM, ResNet-18, ViT-Small, a BERT attention block, and GPT-2 Small—and realized as nine engines spanning GEMM (FP32, INT8, INT4), convolution, attention, layer normalization, SoftMax, pooling, and embedding lookup. All nine engines are synthesized in Vitis HLS 2024.2 and placed, routed, and executed on the physical Alveo V80 at 400 MHz (2.5 ns period), and every engine closes timing with positive worst-case slack. We report per-engine cycle counts, post-route utilization, and post-route dynamic-power estimates, and compare engine latency and energy against NVIDIA Titan RTX (GPU) and Intel Xeon W-3223 (CPU) baselines on identical kernels. A central result, confirmed on silicon, is that INT8 roughly halves the GEMM DSP58 footprint relative to FP32 (27 to 14 slices), whereas INT4 yields no further compute reduction and acts purely as a memory-placement lever. The full-model compositions, the roofline classification, and the tensor-to-channel placement framework are analytical, design-time results rather than end-to-end measured performance. The bandwidth-scaling premise underlying the placement framework is confirmed directly on the V80: a 1-to-64 channel sweep shows aggregate HBM bandwidth scaling near-linearly to within 5–13% of the device peak, and placing the GPT-2 LM-head operand on a single channel versus the eight the framework assigns it yields a measured 6.81× speedup—a direct on-board test of the striping decision. The resulting design guidelines target HLS practitioners working with HBM-equipped FPGAs. Full article

The power density of modern high-power integrated processors keeps rising rapidly. Among them, chiplet-based high-power AI accelerators exhibit local peak heat flux exceeding 1 kW/cm², which leads to concentrated hotspots, severe internal temperature gradients, device performance degradation and reliability deterioration. Conventional heat dissipation approaches are limited by the bottleneck of series interfacial thermal resistance and fail to meet the cooling demands of complex integrated architectures. Chip-level thermal management serves as a core method to suppress hotspots near heat sources and reduce overall system thermal resistance, which guarantees long-term stable operation of high-power integrated processors and plays a vital role in improving the energy efficiency and service life of computing platforms. This paper systematically reviews mainstream chip-level thermal management technologies for high-power integrated processors, covering heterogeneous integration of high-thermal-conductivity substrates, embedded microchannel liquid cooling, solid-state active heat pumps, multi-physics co-design and advanced packaging manufacturing processes. The basic working principles and state-of-the-art research progress of each cooling technology are elaborated in detail. The common engineering bottlenecks, including ultra-high heat flux endurance, packaging process compatibility, fluid leakage risks and multi-layer interfacial thermal resistance, are summarized, and the future development trends of this field are clarified. This review can provide comprehensive theoretical guidance for structural design and large-scale engineering implementation of near-junction thermal management solutions for various high-power integrated processors, especially high-computing-power AI accelerators. Full article

Rolling-element bearings remain a leading cause of unplanned downtime in industrial machinery, while vibration-based condition monitoring has matured, the post-2018 literature has converged on machine-learning classifiers whose interpretability layer remains restricted to expert analysts. This paper presents an operator-centred visualization framework supported by two time-frequency methods: the Zhao–Atlas–Marks Distribution (ZAMD), a Cohen’s-class representation with a cross-term-suppressing cone kernel, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), evaluated through its Hilbert spectral analysis output. Both methods produce two-dimensional time-frequency artefacts with a similar visual structure—impact-related energy bursts that recur at the characteristic fault frequencies—and are presented in side-by-side form for each fault class. A four-stage framework wraps either method with the characteristic fault frequencies (supplied as a comparison reference) and colour-coded, healthy baseline-referenced scaling. The framework is demonstrated on a laboratory bearing rig (KOYO 6302, 600 RPM) across inner-race, outer-race, and ball-spin fault classes. A preliminary readability assessment of annotated ZAMD-generated artefacts, with twelve non-specialist participants from a brewing and packaging industrial context, recorded 89.8% aggregate classification accuracy (194 of 216 trials) at a mean response time of 15.4 s. Because no label-free or alternative-format control conditions were included, this result characterises the annotated artefact as a whole and does not isolate the contribution of the time-frequency representation from that of the annotation layer; it is established for the ZAMD engine only. The two methods are compared as visualization engines—qualitatively, through the structure of their side-by-side time-frequency artefacts, and quantitatively, through computational cost—whereas the non-specialist readability assessment characterises the ZAMD-based framework specifically. CEEMDAN is positioned as a candidate alternative engine whose time-frequency output is shown to be structurally similar but whose operator readability has not been tested with human participants and is identified as future work. 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