Search Results (1,673)

Lithium-ion batteries (LiBs) are utilized in numerous applications due to advancements in technology, and the recovery of end-of-life (EoL) LiBs is imperative for environmental and economic reasons. Pyrometallurgical and hydrometallurgical methods have been used in the recovery of metals such as Li, Co, and Ni in the EoL LiBs. Hydrometallurgical methods, which have been demonstrated to exhibit higher recovery efficiency and reduced energy consumption, have garnered increased attention in recent research. Inorganic acids, including HCl, HNO₃, and H₂SO₄, as well as organic acids such as acetic acid and citric acid, are employed in the hydrometallurgical recovery of these metals. It is imperative to acknowledge the environmental hazards posed by these acids. Consequently, solvometallurgical processes, which involve the use of organic solvents with minimal or no water, are gaining increasing attention as alternative or complementary techniques to conventional hydrometallurgical processes. In the context of solvent systems that have been examined for a range of solvometallurgical methods, deep eutectic solvents (DESs) have garnered particular interest due to their low toxicity, biodegradable nature, tunable properties, and efficient metal recovery potential. In this study, the leaching process of black mass containing graphite, LCO, NMC, and LMO was carried out in a short time using the ternary DES system. The ternary DES system consists of choline chloride (ChCl), glycolic acid (GLY), and ascorbic acid (AA). As a result of the leaching process of cathode powders in the black mass without any pre-enrichment process, Li, Co, Ni, and Mn elements passed into solution with an efficiency of over 95% at 60 °C and within 1 h. Moreover, the kinetics of the leaching process was investigated, and Density Functional Theory (DFT) calculations were used to explain the leaching mechanism. Full article

Contemporary evaluation and surgical approaches in congenital aortic valve disease have yielded limited success. The ability to evaluate and understand detailed flow characteristics surrounding surgical repair may be beneficial. This study explores the feasibility and utility of echocardiographic-based blood speckle imaging (BSI) in assessing pre- and post-operative flow characteristics in those with non-stenotic congenital aortic root disease undergoing aortic valve repair or valve-sparing root replacement (VSRR) surgery. Transesophageal echocardiogram was performed during the pre-operative and post-operative assessment surrounding aortic surgery for ten patients with non-stenotic congenital aortic root disease. BSI, utilizing block-matching algorithms, enabled detailed visualization and quantification of flow parameters from the echocardiographic data. Post-operative BSI unveiled enhanced hemodynamic patterns, characterized by quantified changes suggestive of the absence of stenosis and no more than trivial regurgitation. Rectification of an asymmetric jet and the reversal of flow on the posterior aspect of the ascending aorta resulted in a reduced oscillatory shear index ($OSI$) of $0.0543\pm 0.0207$ (pre-op) vs. $0.0275\pm 0.0159$ (post-op) and $p=0.0044$, increased peak wall shear stress of $1.9423\pm 0.6974$ (pre-op) vs. $3.6956\pm 1.4934$ (post-op) and $p=0.0035$, and increased time-averaged wall shear stress of $0.6885\pm 0.8004$ (pre-op) vs. $0.8312\pm 0.303$ (post-op) and $p=0.23$. This correction potentially attenuates cellular alterations within the endothelium. This study demonstrates that children and young adults with non-stenotic congenital aortic root disease undergoing valve-preserving operations experience significant improvements in flow dynamics within the left ventricular outflow tract and aortic root, accompanied by a reduction in $OSI$. These hemodynamic enhancements extend beyond the conventional echocardiographic assessments, offering immediate and valuable insights into the efficacy of surgical interventions. Full article

A novel composite patch osteosynthesis technique (CPT) has demonstrated promising ex vivo biomechanical performance in small tubular bones. To bridge the gap toward clinical evaluations, this study compared the stability of the CPT to a stainless-steel locking plate (LP) in an experimental in vivo ovine bilateral proximal phalanx fracture model. Eight sheep underwent a midline osteotomy with a 4.5 mm circular unicortical defect in the lateral proximal phalanx of both front limbs, treated with the CPT (n = 8) or the LP (n = 8). A half-limb walking cast, or a custom off-loading hoof shoe, was used for postoperative protection. Implant stability was assessed by post-surgery X-ray evaluations and post-euthanasia (16 weeks) dual-energy X-ray absorptiometry (DXA). At week one, all CPT implants demonstrated mechanical failure, while all LPs remained overall intact. Mean BMD was 0.45 g/cm² for CPT and 0.60 g/cm² for LP in the fracture area (p = 0.078), and 0.37 g/cm² vs. 0.41 g/cm² in the distal epiphysis (p = 0.016), respectively. In conclusion, the CPT demonstrated indications of inferior stability compared to the LP in this fracture model, which may limit its clinical applicability in weight-bearing or high-load scenarios and in non-compliant patients. Full article

With the rapid development of new energy vehicles, the echelon utilization of power batteries has become a key pathway to promoting efficient resource recycling and environmental sustainability. To address the limitation of the existing studies that overlook the dynamic strategic interactions among multiple stakeholders, this paper constructs a tripartite evolutionary game model involving the government, battery recycling enterprises, and consumers. By incorporating consumers’ battery usage levels into the strategy space, the model captures the behavioral evolution of all these parties under bounded rationality. Numerical simulations are conducted to analyze the impact of government incentives and penalties, consumer usage behaviors, and enterprise recycling modes on system stability. The results show that a “low-subsidy, high-penalty” mechanism can more effectively guide enterprises to prioritize echelon utilization and that moderate consumer usage significantly improves battery reuse efficiency. This study enriches the application of the evolutionary game theory in the field of battery recycling and provides quantitative evidence and practical insights for policy formulation. Full article

The recent advancements and emergence of rapidly evolving AI models, such as large language models (LLMs), have sparked interest among researchers and professionals. These models are ubiquitously being fine-tuned and applied across various fields such as healthcare, customer service and support, education, automated driving, and smart factories. This often leads to an increased level of complexity and challenges concerning the trustworthiness of these models, such as the generation of toxic content and hallucinations with high confidence leading to serious consequences. The European Union Artificial Intelligence Act (AI Act) is a regulation concerning artificial intelligence. The EU AI Act has proposed a comprehensive set of guidelines to ensure the responsible usage and development of general-purpose AI systems (such as LLMs) that may pose potential risks. The need arises for strengthened efforts to ensure that these high-performing LLMs adhere to the seven trustworthiness aspects (data governance, record-keeping, transparency, human-oversight, accuracy, robustness, and cybersecurity) recommended by the AI Act. Our study systematically maps research, focusing on identifying the key trends in developing LLMs across different application domains to address the aspects of AI Act-based trustworthiness. Our study reveals the recent trends that indicate a growing interest in emerging models such as LLaMa and BARD, reflecting a shift in research priorities. GPT and BERT remain the most studied models, and newer alternatives like Mistral and Claude remain underexplored. Trustworthiness aspects like accuracy and transparency dominate the research landscape, while cybersecurity and record-keeping remain significantly underexamined. Our findings highlight the urgent need for a more balanced, interdisciplinary research approach to ensure LLM trustworthiness across diverse applications. Expanding studies into underexplored, high-risk domains and fostering cross-sector collaboration can bridge existing gaps. Furthermore, this study also reveals domains (like telecommunication) which are underrepresented, presenting considerable research gaps and indicating a potential direction for the way forward. Full article

With the advance of Artificial Intelligence, Deep Neural Networks are widely employed in various sensor-based systems to analyze operational conditions. However, due to the inherently nondeterministic and probabilistic natures of neural networks, the assurance of overall system performance could become a challenging task. In particular, soft errors could weaken the robustness of such networks and thereby threaten the system’s safety. Conventional fault-tolerant techniques by means of hardware redundancy and software correction mechanisms often involve a tricky trade-off between effectiveness and scalability in addressing the extensive design space of Deep Neural Networks. In this work, we propose a Reinforcement-Learning-based approach to protect neural networks from soft errors by addressing and identifying the vulnerable bits. The approach consists of three key steps: (1) analyzing layer-wise resiliency of Deep Neural Networks by a fault injection simulation; (2) generating layer-wise bit masks by a Reinforcement-Learning-based agent to reveal the vulnerable bits and to protect against them; and (3) synthesizing and deploying bit masks across the network with guaranteed operation efficiency by adopting transfer learning. As a case study, we select several existing neural networks to test and validate the design. The performance of the proposed approach is compared with the performance of other baseline methods, including Hamming code and the Most Significant Bits protection schemes. The results indicate that the proposed method exhibits a significant improvement. Specifically, we observe that the proposed method achieves a significant performance gain of at least 10% to 15% over on the test network. The results indicate that the proposed method dynamically and efficiently protects the vulnerable bits compared with the baseline methods. Full article

Traditional supervisory control methods for the nonblocking control of discrete event systems often suffer from exponential computational complexity. Reinforcement learning-based approaches mitigate state explosion by sampling many random sequences instead of computing the synchronous product of multiple modular supervisors, but they struggle with limited reachable state spaces. A primary novelty of this study is to use the K-means clustering method for online inference with the learned state-action values. The clustering method divides all events at a state into the good group and the bad group. The events in the good group are allowed by the supervisor. The obtained supervisor policy can ensure both system constraints and larger control freedom compared to conventional RL-based supervisors. The proposed framework is validated by two case studies: an industrial transfer line (TL) system and an automated guided vehicle (AGV) system. In the TL case study, nonblocking reachable states increase from 56 to 72, while in the AGV case study, a substantial expansion from 481 to 3558 states is observed. Our new method achieves a balance between computational efficiency and nonblocking supervisory control. Full article

The squared Bessel process plays a central role in stochastic analysis, with broad applications in mathematical finance, physics, and probability theory. While explicit expressions for its transition probability density function (TPDF) under constant parameters are well known, analytical results in the case of time-dependent dimensions remain scarce. In this paper, we address a significantly challenging problem by deriving an analytical formula for the TPDF of a conic combination of independent squared Bessel processes with time-dependent dimensions. The result is expressed in terms of a Laguerre series expansion. Furthermore, we obtain closed-form expressions for the conditional moments of such conic combinations, represented via generalized hypergeometric functions. These results also yield new analytical formulas for the TPDF and conditional moments of both squared Bessel processes and Bessel processes with time-dependent dimensions. The proposed formulas provide a unified analytical framework for modeling and computation involving a broad class of time-inhomogeneous diffusion processes. The accuracy and computational efficiency of our formulas are verified through Monte Carlo simulations. As a practical application, we provide an analytical valuation of an interest rate swap, where the underlying short rate follows a conic combination of independent squared Bessel processes with time-dependent dimensions, thereby illustrating the theoretical and practical significance of our results in mathematical finance. Full article

Bamboo residues represent an abundant, renewable biomass feedstock that can be converted into hard carbon—an emerging anode material for sodium-ion batteries. This study presents a detailed techno-economic analysis of hard carbon production from bamboo residues across China’s ten most bamboo-rich provinces. Regional feedstock availability was estimated from provincial production statistics, while average transportation distances were derived using a square-root-area-based approximation method. The process includes hydrothermal pretreatment, acid washing, carbonization, graphitization, and ball milling. Material and energy inputs were estimated for each stage, and both capital and operating expenses were evaluated using a discounted cash flow model assuming a 15% internal rate of return. The resulting minimum selling price of bamboo-derived hard carbon ranges from 14.47 to 18.15 CNY/kg. Assuming 10% of bamboo residues can be feasibly collected and processed, these ten provinces could collectively support an annual hard carbon production capacity of approximately 1.04 million tons. The results demonstrate that bamboo residues are a strategically distributed and underutilized resource for producing cost-competitive hard carbon at scale, particularly in provinces with existing bamboo industries and supply chains. Full article

Hollow fibre renewal liquid membranes (HFRLMs) are susceptible to third-phase formation during rare earth element (REE) extraction using D2EHPA (bis(2-ethylhexyl phosphoric acid)), potentially leading to membrane fouling and decreased mass transfer efficiency. This study investigated the effects of various parameters, such as the composition of the aqueous feed and organic phases, on the third-phase formation and limiting organic concentration (LOC) of REE(III) in D2EHPA. Higher concentrations of REEs and a higher pH in the feed phase correlated with decreased mass transfer, while yttrium showed a greater propensity to induce third-phase formation compared to other REEs. Conditions favouring the use of linear aliphatic diluents, low extractant concentrations (5–10 v/v% D2EHPA) and the absence of modifiers also contributed to third-phase formation. The addition of tri-n-butyl phosphate (TBP) mitigated third-phase formation without evidence of synergy with D2EHPA. These findings provide key insights into formulating extraction systems that prevent third-phase formation in HFRLM processes. Full article

This study aimed to explore the independent and interactive effects of varying squat depths and movement speeds on dynamic postural stability during the Part the Wild Horse’s Mane (PWHM) movement. Thirteen male participants (age: 25.86 ± 1.35 years; height: 174.26 ± 6.09 cm; body mass: 68.64 ± 8.15 kg) performed the PWHM movement at three different squat heights, high squat (HS), middle squat (MS), low squat (LS), and two different speeds, fast and slow. Dynamic postural stability (DPSI) was assessed through the center-of-mass (CoM) trajectory and the center-of-pressure (CoP) trajectory. The analyses used two-factor repeated-measures ANOVA and statistical nonparametric mapping, with key metrics including anteroposterior stability (APSI), mediolateral stability (MLSI), vertical stability (VSI), DPSI indices, and the path lengths of the CoP and CoM. LS exhibited significantly greater CoP and CoM path lengths compared with MS and HS (p < 0.01). Furthermore, fast movements demonstrated higher VSI and DPSI than slow movements (p < 0.05). Tai Chi with different squat depths and speeds can affect postural stability. To reduce the fall risk, older adults and individuals with balance impairments should prioritize slower Tai Chi movements, particularly when using high squat postures. Full article

The integration of Artificial Intelligence into industrial maintenance remains challenging due to the scarcity of high-quality data representing faulty conditions. Machine Learning models trained on laboratory testbed data often fail to generalize effectively in real workshop environments. This study evaluated the effectiveness of Transfer Learning models in handling this domain shift challenge compared with Machine Learning models. Their potential to address the generalization gap was assessed by analyzing the model adaptability from lab-recorded data to data from emulated workshop conditions, where real-world variability was replicated by embedding synthetic noise into the lab-recorded data. The case study focuses on detecting rotor unbalance through bearing vibration signals at varying speeds. A Support Vector Classifier was trained on the transformed features for both models for binary classification. Model performance was assessed under varying data availability and noise conditions to evaluate the impact of these factors on classification accuracy, sensitivity, and specificity. The results show that Transfer Learning outperforms Machine Learning, achieving up to 30% higher accuracy under high-noise conditions. Although the Machine Learning model exhibits greater sensitivity, it misclassifies balanced cases and reduces specificity. In contrast, the Transfer Learning model maintains high specificity but has difficulty detecting mild unbalance levels, particularly when data availability is limited. Full article

In this paper, we study the Bochner modular relation (Lambert series) for the kth power of the product of two Riemann zeta-functions with difference $\alpha$, an integer with the Voronoĭ function weight $V_k$. In the case of $V_1\left(x\right)=e^{-x}$, the results reduce to Bochner modular relations, which include the Ramanujan formula, Wigert–Bellman approximate functional equation, and the Ewald expansion. The results abridge analytic number theory and the theory of modular forms in terms of the sum-of-divisor function. We pursue the problem of (approximate) automorphy of the associated Lambert series. The $\alpha =0$ case is the divisor function, while the $\alpha =1$ case would lead to a proof of automorphy of the Dedekind eta-function à la Ramanujan. Full article

Designed ankyrin repeat protein (DARPin) Ec1, a small scaffold protein (18 kDa), binds with high affinity the epithelial cell adhesion molecule (EpCAM) that is overexpressed in several carcinomas. To enhance the targeted delivery of cytotoxic drugs using Ec1, we investigated the potential of fusing Ec1 with an albumin-binding domain (ABD) to improve its circulation time and decrease renal uptake. Two fusion proteins were created, Ec1-ABD, with the ABD at the C-terminus, and ABD-Ec1, with the ABD at the N-terminus. Both variants were labeled with ¹¹¹In. ABD-fused variants bound specifically to EpCAM-expressing cells with picomolar affinity. Adding human albumin reduced the affinity. This effect was more pronounced for Ec1-ABD; however, the affinity remained in the subnanomolar range. The position of the ABD did not influence the internalization rate of both variants by human cancer cells. In mouse models with human cancer xenografts, both variants demonstrated over 10-fold lower renal uptake compared to the Ec1. Tumor uptake of the ABD-fused variants was higher than the uptake of Ec1. ABD-Ec1 provided two-fold higher tumor uptake, indicating fusion with an ABD as a promising way to modulate the targeting properties of an Ec1-based construct. However, the effect of fusion depends on the order of the domains. Full article

B Cell Maturation Antigen (BCMA) has gained considerable attention as a target in directed therapies for multiple myeloma (MM) treatment, via immunoglobulin-based bispecific T cell engagers or CAR T cell strategies. We describe the development of alternative, non-immunoglobulin BCMA-recognising affinity proteins, based on the small (58 aa) three-helix bundle affibody scaffold. A first selection campaign using a naïve affibody phage library resulted in the isolation of several BCMA-binding clones with different kinetic profiles. One clone showing the slowest dissociation kinetics was chosen as the template for the construction of two second-generation libraries. Characterization of output clones from selections using these libraries led to the identification of clone 1-E6, which demonstrated low nM affinity to BCMA and high thermal stability. Biosensor experiments showed that 1-E6 interfered with the binding of BCMA to both its natural ligand APRIL and to the clinically evaluated anti-BCMA monoclonal antibody belantamab, suggesting overlapping epitopes. A fluorescently labelled head-to-tail homodimer construct of 1-E6 showed specific binding to the BCMA⁺ MM.1s cell line in both flow cytometry and fluorescence microscopy. Taken together, the results suggest that the small anti-BCMA affibody 1-E6 could be an interesting alternative to antibody-based affinity units in the development of BCMA-targeted therapies and diagnostics. Full article