Search Results (11,325)

The growing demand for lithium-ion batteries (LIBs) is driving a rapid increase in the volume of spent cells which—as hazardous waste—must be managed effectively in accordance with circular-economy principles. Hydrometallurgical recycling allows the recovery of critical metals at far lower environmental cost than primary mining. This paper presents a method for obtaining metallic nickel from sulfate leach solutions produced by leaching the so-called “black mass” derived from shredded LIBs. Nickel electrodeposition was performed on a stainless-steel cathode with Ti/Ru-Ir anodes at 60 °C and pH 3.0–4.5. Two process variants were examined. Variant A—with a decreasing Ni²⁺ concentration (49 → 25 g L⁻¹)—achieved a current efficiency of 60–88%, but the deposits were non-uniform and prone to flaking. Variant B—in which the bath was stabilized by the continuous dissolution of Ni(OH)₂ (maintaining Ni²⁺ at 35–40 g L⁻¹) and amended with PEG-4000, H₃BO₃ and Na₂SO₄—reached higher efficiency (78–93%) and produced uniform, bright deposits up to 0.5 mm thick with a purity >90%. The results confirm that keeping the nickel concentration constant and appropriately modifying the electrolyte significantly improve both the qualitative and economic aspects of recovery, highlighting electrolysis as an efficient way to process LIB waste and close the nickel stream within the material cycle. Full article

Urban UAV imagery presents challenges for reliable feature matching owing to complex 3D structures and depth discontinuities. Conventional 2D-based outlier rejection methods often fail to maintain geometric consistency under significant altitude variations or viewpoint differences, resulting in the rejection of valid correspondences. To overcome these limitations, a depth-guided local outlier rejection methodology is proposed which integrates monocular depth estimation, DBSCAN-based clustering, and local geometric model estimation. Depth information estimated from single UAV images is combined with feature correspondences to form pseudo-3D coordinates, enabling spatially localized registration. The proposed method was quantitatively evaluated in terms of Precision, Recall, F1-score, and Number of Matches, and was applied as a depth-guided front-end to three representative 2D-based outlier rejection schemes (RANSAC, LMedS, and MAGSAC++). Across all image sets, the depth-guided variants consistently achieved higher Recall and F1-score than their conventional 2D counterparts, while maintaining comparable Precision and keeping mismatches low. These results indicate that introducing depth-guided pseudo-3D constraints into the outlier rejection stage enhances geometric stability and correspondence reliability in complex urban UAV imagery. Accordingly, the proposed methodology provides a practical and scalable solution for accurate registration in depth-varying urban environments. Full article

This review describes the dramatic transformation that has occurred in the last ten years in the therapeutic landscape for hematologic malignancies, such as leukemias, lymphomas, myelomas, and myelodysplastic syndromes. Treatment paradigms have quickly changed from depending solely on cytotoxic chemotherapy to embracing precision medicine, driven by a previously unprecedented understanding of disease biology and precise molecular changes. The development of powerful immunotherapies (such as CAR T-cell therapy and bispecific antibodies) and innovative targeted agents (like BTK inhibitors, BCL-2 inhibitors, and immunomodulatory medications) is at the heart of this revolution. In addition to evaluating new and synergistic combination strategies, this paper examines the clinical utility, efficacy, and recent developments of these novel agents. It also addresses important issues like managing acquired drug resistance, minimizing financial burden, and adapting clinical trial designs to keep pace with innovation. These advancements are collectively redefining clinical practice, leading to deeper and more durable responses, and significantly improving the prognosis and quality of life for patients. Full article

Background/Objectives: Tuberculosis remains a significant global health problem, particularly in resource-limited environments. Its mortality and spread can be considerably decreased by early and precise detection via chest X-ray imaging. This study introduces a novel approach based on hybrid deep learning for Tuberculosis detection from chest X-ray images. Methods: The introduced approach combines GhostNet, a lightweight convolutional neural network tuned for computational efficiency, and MobileViT, a transformer-based model that can capture both local spatial patterns and global contextual dependencies. Through such integration, the model attains a balanced trade-off between classification accuracy and computational efficiency. The architecture employs feature fusion, where spatial features from GhostNet and contextual representations from MobileViT are globally pooled and concatenated, which allows the model to learn discriminative and robust feature representations. Results: The suggested model was assessed on two publicly available chest X-ray datasets and contrasted against several cutting-edge convolutional neural network architectures. Findings showed that the introduced hybrid model surpasses individual baselines, attaining 99.52% accuracy on dataset 1 and 99.17% on dataset 2, while keeping low computational cost (7.73M parameters, 282.11M Floating Point Operations). Conclusions: These outcomes verify the efficacy of feature-level fusion between a convolutional neural network and transformer branches, allowing robust tuberculosis detection with low inference overhead. The model is ideal for clinical deployment and resource-constrained contexts due to its high accuracy and lightweight design. Full article

This study investigated the statistical association between public incentives and industrial innovation as reflected in firms’ financial performances. In particular, the analysis was carried out considering a Regional Operational Program, namely, the 2007–2013 ERDF Regional Program in Lombardy, and investigating a dataset of Lombardy-based companies that received support through the mentioned initiative. For each of them, balance sheet variables before and after the acquisition of the incentive and the development of the related innovation project were detected and analyzed by means of both standard and normalized linear regression. Notably, normalized regressions showed that higher subsidy intensity was positively associated with subsequent changes in revenues and intangible assets, especially among manufacturing firms, thereby supporting policies that target sectors with a high innovation capacity. Furthermore, this research underscores the importance of tailoring policy instruments to local and sectoral contexts, recognizing the limitations of one-size-fits-all approaches. In keeping with this exploratory stance, this study does not build a counterfactual control group and makes no causal claims; it simply documents balance sheet associations that may inform future, impact-oriented research. Given the absence of a control group, the design is observational; all findings describe associations and do not allow causal inference. Full article

To address the pressing need for accurate and efficient detection of corn diseases, we propose a novel, lightweight object detection framework, CBS-YOLOv8 (C2f-BiFPN-SCConv YOLOv8), which builds upon the YOLOv8 architecture to enhance performance for corn disease detection. The model incorporates two key components, the GhostNetV2 block and SCConv (Selective Convolution). The GhostNetV2 block improves feature representation by reducing computational complexity, while SCConv optimizes convolution operations dynamically, adjusting based on the input to ensure minimal computational overhead. Together, these features maintain high detection accuracy while keeping the network lightweight. Additionally, the model integrates the C2f-GhostNetV2 module to eliminate redundancy, and the SimAM attention mechanism improves lesion-background separation, enabling more accurate disease detection. The Bi-directional Feature Pyramid Network (BiFPN) enhances feature representation across multiple scales, strengthening detection across varying object sizes. Evaluated on a custom dataset of over 6000 corn leaf images across six categories, CBS-YOLOv8 achieves improved accuracy and reliability in object detection. With a lightweight architecture of just 8.1M parameters and 21 GFLOPs, it enables real-time deployment on edge devices in agricultural settings. CBS-YOLOv8 offers high detection performance while maintaining computational efficiency, making it ideal for precision agriculture. Full article

The built environment (BE) encompasses an enormous volume and substantial material mass. However, structures within it typically serve single, limited functions. Enhancing these structures with multifunctional capabilities holds significant potential for achieving broader sustainability goals and creating impactful environmental benefits. Among these potential multifunctional applications, carbon capture, reduction, and storage are especially critical, given the current built environment’s substantial contribution of approximately 40% of global energy and CO₂ emissions. Keeping this potential in view, this comprehensive review critically evaluates carbon management strategies for the built environment via three interrelated approaches: carbon capture (via photosynthesis, passive concrete carbonation, and microbial biomineralization), carbon storage (employing carbonation curing, mineral carbonation, and valorization of construction and demolition waste), and carbon reduction (integrating industrial waste, alternative binders, and bio-based materials). The review also evaluates the potential of novel direct air-capture materials, assessing their feasibility for integration into construction processes and existing infrastructure. Key findings highlight significant advancements, quantify CO₂ absorption potentials across various construction materials, and reveal critical knowledge gaps, thereby providing a strategic roadmap for future research direction toward a low-carbon, climate-resilient built environment. Full article

Background/Objectives: This study aimed to examine the effect of different forgetting strategies on intentional forgetting, specifically comparing the passive decay strategy (‘forgetting by keeping the mind blank’) and the active rehearsal strategy (‘forgetting by rehearsing other words’). Methods: An item-method directed forgetting paradigm was used in a between-subjects design while the electroencephalogram (EEG) was recorded. Results: Behavioral results showed that both strategies produced a robust directed forgetting (DF) effect, but participants in the active rehearsal group recognized more to-be-remembered (TBR) words. Event-related potential (ERP) results indicated that both groups exhibited a DF effect in the cue-induced P2–P3 complex. Compared to the passive decay group, the active rehearsal group did not show a DF effect in the cue-induced later positive component (LPC); instead, a significant DF effect appeared in the P600 during the test phase. Time–frequency results showed that the passive decay group exhibited a significant DF effect in the 9–25 Hz frequency band during the late stage of cue processing, while the active rehearsal group showed a reversed DF effect in the 8–16 Hz frequency band during the mid-stage of cue processing. Conclusions: These findings indicate that forgetting strategies do not affect the recognition performance of to-be-forgotten (TBF) words. The active rehearsal strategy led participants to shift attention from TBF to TBR words, resulting in better TBR recognition performance in this group. Full article

Traditionally, the dynamics of power systems have been governed by synchronous generators and their associated rotating masses. However, with the increasing penetration of renewable generation and power electronic interfaces, the inertia contributed by rotating machines has been gradually displaced. This makes it imperative to study alternative elements capable of mitigating the reduction in inertia in modern power systems. This article addresses the problem of optimal sizing and placement of Battery Energy Storage Systems to enhance frequency response in power grids through the application of optimization techniques such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Several inertia scenarios are analyzed, where the algorithms determine the optimal locations for Battery Energy Storage Systems units while minimizing the total installed Battery Energy Storage Systems capacity. As key contributions, this study models Battery Energy Storage Systems units, which emulate inertial responses based on the system’s Rate of Change of Frequency, and evaluates the impact of Battery Energy Storage Systems on frequency stability by analyzing parameters such as the frequency nadir, zenith, and steady-state frequency according to the installed Battery Energy Storage System’s size and location. A comparative analysis of the optimization scenarios shows that the Particle Swarm Optimization algorithm with 50% rotational inertia is the most efficient, requiring the lowest total installed power (277.11 MW). It is followed by the Particle Swarm Optimization algorithm with 100% rotational inertia (285.79 MW) and Genetic Algorithms with 50% rotational inertia (285.57 MW). In contrast, Genetic Algorithms with 25% rotational inertia demand the highest total installed Battery Energy Storage Systems power (307.44 MW), a result directly associated with a significant reduction in system inertia. Overall, an inverse relationship is observed between the available inertia level and the required Battery Energy Storage Systems capacity: the lower the inertia, the greater the power that the Battery Energy Storage Systems must supply to keep the system frequency within acceptable operational limits. Full article

A system-level scheme that couples a multi-dimensional attention-fused vision model and an improved Dijkstra planner is proposed for basketball robots in complex scenes. Fast-moving object detection, cluttered background recognition, and real-time path decision are targeted. For vision, the proposed YOLO11 with Multi-dimensional Attention Fusion (YOLO11-MAF) is equipped with four modules: Coordinate Attention (CoordAttention), Efficient Channel Attention (ECA), Multi-Scale Channel Attention (MSCA), and Large-Separable Kernel Attention (LSKA). Detection accuracy and robustness for high-speed basketballs are raised. For planning, an improved Dijkstra algorithm is proposed. Binary heap optimization and heuristic fusion cut time complexity from $O\left(V^2\right)$ to $O\left(\right(V+E\left)\log V\right)$. Redundant expansions are removed and planning speed is increased. A complete robot platform integrating mechanical, electronic, and software components is constructed. End-to-end experiments show the improved vision model raises mAP@0.5 by 0.7% while keeping real-time frames per second (FPS). The improved path planning algorithm cuts average compute time by 16% and achieves over 95% obstacle avoidance success. The work offers a new approach for real-time perception and autonomous navigation of intelligent sport robots. It lays a basis for future multi-sensor fusion and adaptive path planning research. Full article

Mechanical amplifiers can enhance the travel range of piezoelectric actuators, thereby expanding the applications of these actuators. Various amplification mechanisms have been proposed for piezoelectric actuators with different design requirements. For instance, rhombus- and bridge-type amplification mechanisms are compact and can therefore be applied in many applications with size restrictions. However, the amplification ratio of a single-stage rhombus- or bridge-type mechanism is limited. In this study, a novel three-stage amplifier was developed to achieve a high amplification ratio while keeping the device compact. A piezoelectric actuator integrated with this amplifier had a travel range of 207.5 μm, an amplification ratio of 13.7, and dimensions of 33.5 mm × 34.2 mm × 10 mm. Moreover, this actuator was used to construct a compact jetting dispenser with dimensions of 69 mm × 72 mm × 20 mm. Experimental results suggested that this dispenser can generate uniform and stable droplets, confirming the practical utility of the developed piezoelectric actuator. Full article

Antimicrobial resistance (AMR) has become a major health crisis worldwide, and it is expected to surpass cancer as one of the leading causes of death by 2050. Conventional antibiotics are struggling to keep pace with the rapidly evolving resistance trends, underscoring the urgent need for novel antimicrobial therapeutic strategies. Antimicrobial peptides (AMPs) function through diverse, often membrane-disrupting mechanisms that can address the latest challenges to resistance. However, the identification, prediction, and optimization of novel AMPs can be impeded by several issues, including extensive sequence spaces, context-dependent activity, and the higher costs associated with wet laboratory screenings. Recent developments in artificial intelligence (AI) have enabled large-scale mining of genomes, metagenomes, and quantitative species-resolved activity prediction, i.e., MIC, and de novo AMPs designed with integrated stability and toxicity filters. The current review has synthesized and highlighted progress across different discriminative models, such as classical machine learning and deep learning models and transformer embeddings, alongside graphs and geometric encoders, structure-guided and multi-modal hybrid learning approaches, closed-loop generative methods, and large language models (LLMs) predicted frameworks. This review compares models’ benchmark performances, highlighting AI-predicted novel hybrid approaches for designing AMPs, validated by in vitro and in vivo methods against clinical and resistant pathogens to increase overall experimental hit rates. Based on observations, multimodal paradigm strategies are proposed, focusing on identification, prediction, and characterization, followed by design frameworks, linking active-learning lab cycles, mechanistic interpretability, curated data resources, and uncertainty estimation. Therefore, for reproducible benchmarks and interoperable data, collaborative computational and wet lab experimental validations must be required to accelerate AI-driven novel AMP discovery to combat multidrug-resistant Gram-negative pathogens. Full article

Anomaly detection in safety-critical systems often operates under severe label constraints, where only a small subset of normal and anomalous samples can be reliably annotated, while large unlabeled data streams are contaminated and high-dimensional. Deep one-class methods, such as deep support vector data description (DeepSVDD) and deep semi-supervised anomaly detection (DeepSAD), address this setting. However, they treat samples largely in isolation and do not explicitly leverage the manifold structure of unlabeled data, which can limit robustness and interpretability. This paper proposes Anomaly-Aware Graph-based Semi-Supervised Deep Support Vector Data Description (AAG-DSVDD), a boundary-focused deep one-class approach that couples a DeepSAD-style hypersphere with a label-aware latent k-nearest neighbor (k-NN) graph. The method combines a soft-boundary enclosure for labeled normals, a margin-based push-out for labeled anomalies, an unlabeled center-pull, and a k-NN graph regularizer on the squared distances to the center. The resulting graph term propagates information from scarce labels along the latent manifold, aligns anomaly scores of neighboring samples, and supports sample-level interpretability through graph neighborhoods, while test-time scoring remains a single distance-to-center computation. On a controlled two-dimensional synthetic dataset, AAG-DSVDD achieves a mean F1-score of $0.88\pm 0.02$ across ten random splits, improving on the strongest baseline by about $0.12$ absolute F1. On three public benchmark datasets (Thyroid, Arrhythmia, and Heart), AAG-DSVDD attains the highest F1 on all datasets with F1-scores of $0.719$, $0.675$, and $0.8$, respectively, compared to all baselines. In a multi-sensor fire monitoring case study, AAG-DSVDD reduces the average absolute error in fire starting time to approximately 473 s (about 30% improvement over DeepSAD) while keeping the average pre-fire false-alarm rate below $1\%$ and avoiding persistent pre-fire alarms. These results indicate that graph-regularized deep one-class boundaries offer an effective and interpretable framework for semi-supervised anomaly detection under realistic label budgets. Full article

In the elastic–plastic analysis of structures, the deformation problem of cantilever beams is a classical problem, in which it is usually assumed that the material constituting the beam has an identical elastic modulus and identical yield strength when it is tensioned and compressed. These characteristics are manifested graphically as the symmetry of tension and compression. In this work, we will give up the general assumption and consider that the material has the property of tension–compression asymmetry, that is, the material presents different moduli in tension and compression and different yield strengths in tension and compression. First, the elastic–plastic response of the cantilever beam with a concentrated force acting at the fixed end in the loading stage is theoretically analyzed. When the plastic hinge appears at the fixed end, the maximum deflection at the free end is derived, and in the unloading stage the residual deflection at the free end is also given. At the same time, the theoretical solution obtained is validated by the numerical simulation. The results indicate that when considering the tension–compression asymmetry of materials, the plastic zone length from the fixed end no longer keeps the classical value of 1/3 and will become bigger; the tension–compression asymmetry will enlarge the displacement during the elastic–plastic response; and the ultimate deflection in loading and the residual deflection in unloading are both greater than the counterparts in the classical problem. The research results provide a theoretical reference for the fine analysis and optimal design of cantilever beams. Full article

Installation in sand is sensitive to its evolving pore structure, yet design models rarely update permeability for real-time fabric changes. This study tracks the stress-dependent pore size distribution of coarse sand under laterally constrained compression using high-resolution X-ray nano-CT. Scans taken at six axial stress levels show that the distribution shifts toward smaller radii while keeping its log-normal shape. A single shifting factor, defined as the current median radius normalized by the initial value, captures this translation. The factor decays with axial stress according to a power law, and the exponent as well as the reference pressure are calibrated from void ratio data. The resulting closed-form expression links mean effective stress to pore radius statistics without extra fitting once the compressibility constants are known. This quantitative relation between effective stress and pore size distribution has great potential to be embedded into coupled hydro-mechanical solvers, enabling engineers to refresh hydraulic permeability at every computation step, improving predictions of excess pore pressure and soil resistance during suction anchor penetration for floating wind foundations. Full article