Explore the vast range of titles available.

An exact two-dimensional rotation–strain model describing the motion of Hookean incompressible viscoelastic materials is constructed by the polar decomposition of the deformation tensor. The global existence of classical solutions is proved under smallness assumptions only on the size of the initial strain tensor. The proof of global existence utilizes the weak dissipative mechanism of motion, which is revealed by passing the partial dissipation to the whole system.

With the rapid increase in lithium consumption for electric vehicle applications, its price soared during the past decade. To secure a reliable and cost-effective supply chain, it is critical to unlock alternative lithium extraction resources beyond conventional brine. In this study, we develop an electrochemical method to directly leach lithium from α-phase spodumene. We find the H 2 O 2 promoter can significantly reduce the leaching potential by facilitating the electron transfer and changing the reaction path. Upon leaching, β-phase spodumene shows a typical phase transformation to HAlSi 2 O 6 , while leached α-phase remains its original crystal phase with a lattice shrinkage. To demonstrate the scale-up potential of electrochemical leaching, we design a catalyst-modified high-throughput current collector for high loading of suspended spodumene, achieving a leaching current of 18 mA and a leaching efficiency of 92.2%. Electrochemical leaching will revolutionize traditional leaching and recycling processes by minimizing the environmental footprint and energy consumption. Sustainable harvesting of lithium is critical to the success of the entire battery industry. Here, the authors report an electrochemical leaching method which can directly extract lithium from natural state spodumene ores with low energy consumption, environmental impact, and high efficiency.

The p-median problem is one of the earliest location-allocation models used in spatial analysis and GIS. It involves locating a set of central facilities (the location decision) and allocating customers to these facilities (the allocation decision) so as to minimize the total transportation cost. It is important not only because of its wide use in spatial analysis but also because of its role as a unifying location model in GIS. A classical way of solving the p-median problem (dating back to the 1970s) is to formulate it as an Integer Linear Program (ILP), and then solve it using off-the-shelf solvers. Two fundamental properties of the p-median problem (and its variants) are the integral assignment property and the closest assignment property. They are the basis for the efficient formulation of the problem, and are important for studying the p-median problems and other location-allocation models. In this paper, we demonstrate that these fundamental properties of the p-median can be proven mechanically using integer linear programming and theorem provers under the program-as-proof paradigm. While these theorems have been proven informally, mechanized proofs using computers are fail-safe and contain no ambiguity. The presented proof method based on ILP and the associated definitions of problem data are general, and we expect that they can be generalized and extended to prove the theoretical properties of other spatial-optimization models, old or new.

Near-infrared-visible (NIR-VIS) heterogeneous face recognition matches NIR to corresponding VIS face images. However, due to the sensing gap, NIR images often lose some identity information so that the NIR-VIS recognition issue is more difficult than conventional VIS face recognition. Recently, NIR-VIS heterogeneous face recognition has attracted considerable attention in the computer vision community because of its convenience and adaptability in practical applications. Various deep learning-based methods have been proposed and substantially increased the recognition performance, but the lack of NIR-VIS training samples leads to the difficulty of the model training process. In this paper, we propose a new Large-Scale Multi-Pose High-Quality NIR-VIS database ‘LAMP-HQ’ containing 56,788 NIR and 16,828 VIS images of 573 subjects with large diversities in pose, illumination, attribute, scene and accessory. We furnish a benchmark along with the protocol for NIR-VIS face recognition via generation on LAMP-HQ, including Pixel2-Pixel, CycleGAN, ADFL, PCFH, and PACH. Furthermore, we propose a novel exemplar-based variational spectral attention network to produce high-fidelity VIS images from NIR data. A spectral conditional attention module is introduced to reduce the domain gap between NIR and VIS data and then improve the performance of NIR-VIS heterogeneous face recognition on various databases including the LAMP-HQ.

Diversified application scenarios and business models are effective ways to improve the utilization and economic benefits of energy storage systems. In response to the current problems of single application scenarios, high idle rates, and imperfect price formation mechanisms faced by energy storage on the power generation side, a robust two-stage optimization operation strategy for shared energy storage is proposed, taking into account leasing demand and multiple uncertainties, from the perspective of the sharing concept. A multi-scenario application framework for shared energy storage is established to provide leasing services for wind farm clusters, as well as auxiliary services for participating in the electric energy markets and frequency regulation markets, and the participation sequence is streamlined. Based on the operating and opportunity costs of shared energy storage, a pricing mechanism for leasing services is designed to explore the driving forces of wind farm clusters participating in leasing services from the perspective of cost assessment. Considering the uncertainty of wind power output and market electric prices, as well as the market operational characteristics, an optimized operation model for shared energy storage in the day-ahead and real-time stages is constructed. In the day-ahead stage, a Stackelberg game model is introduced to depict the energy sharing between wind farm clusters and shared energy storage, forming leasing prices, leasing capacities, and energy storage pre-scheduling plans at different time periods. In the real-time stage, the real-time prediction results of wind power output and electric prices are integrated with scheduling decisions, and an improved robust optimization model is used to dynamically regulate the pre-scheduling plan for leasing capacity and shared energy storage. Based on actual data from the electricity market in Guangdong Province, effectiveness verification is conducted, and the results showed that diversified application scenarios improve the utilization rate of shared energy storage in the power generation side by 52.87%, increasing economic benefits by CNY 188,700. The proposed optimized operation strategy has high engineering application value.

Lagrangian Relaxation (LR) is an effective method for solving spatial optimization problems in geospatial analysis and GIS. Among others, it has been used to solve the classic p-median problem that served as a unified local model in GIS since the 1990s. Despite its efficiency, the LR algorithm has seen limited usage in practice and is not as widely used as off-the-shelf solvers such as OPL/CPLEX or GPLK. This is primarily because of the high cost of development, which includes (i) the cost of developing a full gradient descent algorithm for each optimization model with various tricks and modifications to improve the speed, (ii) the computational cost can be high for large problem instances, (iii) the need to test and choose from different relaxation schemes, and (iv) the need to derive and compute the gradients in a programming language. In this study, we aim to solve the first three issues by utilizing the computational power of GPGPU and existing facilities of modern deep learning (DL) frameworks such as PyTorch. Based on an analysis of the commonalities and differences between DL and general optimization, we adapt DL libraries for solving LR problems. As a result, we can choose from the many gradient descent strategies (known as “optimizers”) in DL libraries rather than reinventing them from scratch. Experiments show that implementing LR in DL libraries is not only feasible but also convenient. Gradient vectors are automatically tracked and computed. Furthermore, the computational power of GPGPU is automatically used to parallelize the optimization algorithm (a long-term difficulty in operations research). Experiments with the classic p-median problem show that we can solve much larger problem instances (of more than 15,000 nodes) optimally or nearly optimally using the GPU-based LR algorithm. Such capabilities allow for a more fine-grained analysis in GIS. Comparisons with the OPL solver and CPU version of the algorithm show that the GPU version achieves speedups of 104 and 12.5, respectively. The GPU utilization rate on an RTX 4090 GPU reaches 90%. We then conclude with a summary of the findings and remarks regarding future work.

Plant cells constantly face genetic invasions from both external and internal sources. Viruses and transgenes represent major external threats, while transposable elements (TEs) are endogenous sources of invasive DNA. The early recognition and activation of innate defence mechanisms are therefore critical for maintaining genome integrity. Emerging evidence suggests that foreign genetic elements are detected and processed by the ribosome-associated RNA quality control system, a key cellular pathway responsible for resolving aberrant transcripts with translation defects. One such pathway, known as No-Go RNA Decay (NGD), facilitates RNA cleavage and ribosome dissociation at stalled ribosomes. Genetic and biochemical studies indicate that NGD plays a crucial role in plant antiviral defence and TE regulation, positioning it as a potential first line of defence against invasive genetic elements. This review explores recent advances in plant NGD research, shedding light on the fundamental question of how cells distinguish self from non-self nucleic acids.

Beta-blocker (BB) is suggested to have anticancer efficacy. However, the potential influence of BB use on overall survival (OS) in patients with lung cancer remains undetermined. We aimed to evaluate the above relationship in an updated meta-analysis. Observational studies comparing OS between users and non-users of BB with lung cancer were identified by search of PubMed, Embase, and Cochrane's Library. A random-effect model was used to pool the results. Ten retrospective cohort studies with 30870 patients were included. Overall, BB use was not associated with significantly improved OS in lung cancer (hazard ratio [HR] = 1.02, 95% confidence interval [CI]: 0.98 to 1.06, p = 0.33) with moderate heterogeneity (I2 = 29%). Stratified analyses showed similar results in patients with non-small cell lung cancer and small cell lung cancer, in studies with BB use before and after the diagnosis of lung cancer, and in studies with or without adjustment of smoking. Use of BB was associated with improved OS in patients with stage III lung cancer (HR = 0.91, 95% CI: 0.85 to 0.98, p = 0.02) and in patients that did not receive surgery resection (HR = 0.78, 95% CI: 0.64 to 0.96, p = 0.02), while use of non-selective BB was associated with worse OS (HR = 1.14, 95% CI: 1.01 to 1.28, p = 0.03). This meta-analysis of retrospective cohort studies does not support a significant association between BB use and improved OS in lung cancer.

Additive manufacturing (AM) is one of the pillars of Industry 4.0 to attain a circular economy. The process involves a layer-by-layer deposition of material from a computer-aided-design (CAD) model to form complex shapes. Fast prototyping and waste minimization are the main benefits of employing such a technique. AM technology is presently revolutionizing various industries such as electronics, biomedical, defense, and aerospace. Such technology can be complemented with standardized frameworks to attract industrial acceptance, such as in the construction industry. Off-site construction has the potential to improve construction efficiency by adopting AM. In this paper, the types of additive manufacturing processes were reviewed, with emphasis on applications in off-site construction. This information was complemented with a discussion on the types and mechanical properties of materials that can be produced using AM techniques, particularly metallic components. Strategies to assess cost and material considerations such as Production line Breakdown Structure (PBS) and Value Stream Mapping are highlighted. In addition, a comprehensive approach that evaluates the entire life cycle of the component was suggested when comparing AM techniques and conventional manufacturing options.

Diabetic osteoporosis (DOP) is a metabolic disease which is characterized by impaired bone microarchitecture and reduced bone mineral density resulting from hyperglycemia. Curcumin, an effective component extracted from Curcuma longa, exhibits antioxidation, regulation of bone metabolism and hypoglycemic effects. The BMSC-mediated osteogenesis and angiogenesis coupling seems to be important in bone formation and regeneration. We aimed to explore the effect of curcumin on BMSC-mediated osteogenesis-angiogenesis coupling in high glucose conditions and underlying mechanisms. Our results showed that high glucose impaired the osteogenic and proangiogenic ability of BMSCs and that curcumin pretreatment rescued the BMSC dysfunction induced by high-concentration glucose. Inhibition of the high glucose-activated NF-κB signaling pathway has been found to contribute to the protective effects of curcumin on high glucose-inhibited coupling of osteogenesis and angiogenesis in BMSCs. Furthermore, accelerated bone loss and decreased type H vessels were observed in diabetic osteoporosis mice models. However, curcumin treatment prevented bone loss and promoted vessel formation in diabetic osteoporosis mice. Based on these results, we concluded that curcumin ameliorated diabetic osteoporosis by recovering the osteogenesis and angiogenesis coupling of BMSCs in hyperglycemia, partly through inhibiting the high glucose-activated NF-κB signaling pathway.