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Purpose Focusing on the emerging economies, this study proposes a comprehensive framework for selecting sustainable battery suppliers in the electric vehicle (EV) industry. The study examines how EV manufacturers can prioritise battery suppliers to create a robust and eco-friendly supply chain. Design/methodology/approach This study integrates the technology-organisation-environment (TOE) framework and the dynamic capability (DC) theory to develop a robust and comprehensive framework for the evaluation of battery suppliers. The fuzzy ordinal priority approach (OPA-F) is used to prioritise the criteria and sub-criteria finalised under the integrated framework. Fuzzy linear programming problems are formulated under this approach with the linguistic opinion of experts as the input parameters. To obtain the global weight of sub-criteria, multiplicative aggregation is performed on the defuzzified local weights of criteria and sub-criteria. Findings As per the findings of this study, the battery policy adherence, environment compliance and safety certification emerged as the most important sub-criteria, whereas the liquidity ratio, debt-to-equity ratio and creditworthiness emerged as the least important. These reveal that the environment and technological criteria have great influence on battery supplier selection decisions. Originality/value By utilising OPA-F and combining the TOE framework and DC theory, this study offers a theoretical and practical contribution that enables efficient decision-making. The framework provides manufacturers and policymakers with practical insights on improving operational resilience and sustainability in EV battery supply chains, particularly in emerging markets.

A new method to improve the strength of permanent magnet synchronous machine (PMSM) is proposed in this manuscript. It utilizes the principal of space vector pulse width modulation (SVPWM) & proportional integral control (PI). The SVPWM allows the motor to have a high voltage with low harmonic distortion than the typical sinusoidal pulse width modulation. The control technique which is used in this paper is the voltage-frequency control process which depends on space vector pulse width modulation. The triggering pulses of space vector pulse width modulation inverter which is been given to the motor enables the motor to have a bigger torque at high speeds & elevated efficiency. It also increases the use of DC link voltage & low output harmonic distortion than the typical sine pulse width modulation inverter. The suggested permanent magnet synchronous machine module that involves a field-oriented control method not only links torque & flux, but also facilitates control allocation. Finally investigation of the suggested model is examined and its results are been validated by the simulation.

(1) Background: Intervertebral disc degeneration has been linked to obesity; its potential mechanical effects on the intervertebral disc remain unknown. This study aimed to develop and validate a patient-specific model of L3–L4 vertebrae and then use the model to estimate the impact of increasing body weight on disc degeneration. (2) Methods: A three-dimensional model of the functional spinal unit of L3–L4 vertebrae and its components were developed and validated. Validation was achieved by comparing the range of motions (RoM) and intradiscal pressures with the previous literature. Subsequently, the validated model was loaded according to the body mass index and estimated stress, deformation, and RoM to assess disc degeneration. (3) Results: During validation, L3–L4 RoM and intradiscal pressures: flexion 5.17° and 1.04 MPa, extension 1.54° and 0.22 MPa, lateral bending 3.36° and 0.54 MPa, axial rotation 1.14° and 0.52 MPa, respectively. When investigating the impact of weight on disc degeneration, escalating from normal weight to obesity reveals an increased RoM, by 3.44% during flexion, 22.7% during extension, 29.71% during lateral bending, and 33.2% during axial rotation, respectively. Also, stress and disc deformation elevated with increasing weight across all RoM. (4) Conclusions: The predicted mechanical responses of the developed model closely matched the validation dataset. The validated model predicts disc degeneration under increased weight and could lay the foundation for future recommendations aimed at identifying predictors of lower back pain due to disc degeneration.

Mycobacterium tuberculosis ’s ( Mtb ) autarkic lifestyle within the host involves rewiring its transcriptional networks to combat host-induced stresses. With the help of RNA sequencing performed under various stress conditions, we identified that genes belonging to Mtb sulfur metabolism pathways are significantly upregulated during oxidative stress. Using an integrated approach of microbial genetics, transcriptomics, metabolomics, animal experiments, chemical inhibition, and rescue studies, we investigated the biological role of non-canonical L -cysteine synthases, CysM and CysK2. While transcriptome signatures of RvΔcysM and RvΔcysK2 appear similar under regular growth conditions, we observed unique transcriptional signatures when subjected to oxidative stress. We followed pool size and labelling ( 34 S) of key downstream metabolites, viz. mycothiol and ergothioneine, to monitor L-cysteine biosynthesis and utilization. This revealed the significant role of distinct L-cysteine biosynthetic routes on redox stress and homeostasis. CysM and CysK2 independently facilitate Mtb survival by alleviating host-induced redox stress, suggesting they are not fully redundant during infection. With the help of genetic mutants and chemical inhibitors, we show that CysM and CysK2 serve as unique, attractive targets for adjunct therapy to combat mycobacterial infection.

In this paper, we consider constrained optimization problems with convex objective and smooth convex functional constraints. We propose a new stochastic gradient algorithm, called the Stochastic Moving Ball Approximation (SMBA) method, to solve this class of problems, where at each iteration we first take a (sub)gradient step for the objective function and then perform a projection step onto one ball approximation of a randomly chosen constraint. The computational simplicity of SMBA, which uses first-order information and considers only one constraint at a time, makes it suitable for large-scale problems with many functional constraints. We provide a convergence analysis for the SMBA algorithm using basic assumptions on the problem, that yields new convergence rates in both optimality and feasibility criteria evaluated at some average point. Our convergence proofs are novel since we need to deal properly with infeasible iterates and with quadratic upper approximations of constraints that may yield empty balls. We derive convergence rates of order O ( k - 1 / 2 ) when the objective function is convex, and O ( k - 1 ) when the objective function is strongly convex. Preliminary numerical experiments on quadratically constrained quadratic problems demonstrate the viability and performance of our method when compared to some existing state-of-the-art optimization methods and software.

The overall transcriptional reduction, irrespective of disease severity (Figure 1C), is well correlated with the phenomenon of fading host cell functionality and prominent viral protein synthesis, and may be associated with interference in host cellular processes and responses.1 The results indicate a diverse transcriptomic profile in response to SARS-CoV-2, in line with the variable prognosis seen in many COVID-19 patients. IFN-mediated activation of the JAK-STAT signaling pathway may play a role in inducing necroptosis (Figure 2B), and is implicated in Acute Respiratory Distress Syndrome (ARDS) development and protection from severe COVID-19 along with OAS1.2,3 Though not a part of this network, all the MHC class 1 and some MHC class 2 genes (HLA-A,B,C,E,F, and HLA-DQB1, DR-B1, DR-B5), involved in T-cell mediated cell death and the antibody-mediated adaptive immune response, were also upregulated along with RFX5, that binds to MHC-II promoters. The effect on GABAergic interneurons in the olfactory bulb, connecting sensory neurons in the olfactory epithelium, might increase the potential for neurological complications observed in COVID-19 patients.7 Further studies are underway to delineate the implications for neuronal infectivity via the olfactory and respiratory tracts and the nasopharyngeal compartment,6 which are predominantly epithelial cells.

Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles. Specifically, the often large number of non-informative genes on the microarray adversely affects the performance and efficiency of classification algorithms. Furthermore, the skewed ratio of sample to variable poses a risk of overfitting. Thus, in this context, feature selection methods become crucial to select relevant genes and, hence, improve classification accuracy. In this study, we investigated feature selection methods based on gene expression profiles and protein interactions. We found that in our setup, the addition of protein interaction information did not contribute to any significant improvement of the classification results. Furthermore, we developed a novel feature selection method that relies exclusively on observed gene expression changes in microarray experiments, which we call \"relative Signal-to-Noise ratio\" (rSNR). More precisely, the rSNR ranks genes based on their specificity to an experimental condition, by comparing intrinsic variation, i.e. variation in gene expression within an experimental condition, with extrinsic variation, i.e. variation in gene expression across experimental conditions. Genes with low variation within an experimental condition of interest and high variation across experimental conditions are ranked higher, and help in improving classification accuracy. We compared different feature selection methods on two time-series microarray datasets and one static microarray dataset. We found that the rSNR performed generally better than the other methods.

Fungal virulence is regulated by a tight interplay of transcriptional control and chromatin remodelling. Despite compelling evidence that lysine acetylation modulates virulence of pathogenic fungi such as Candida albicans , the underlying mechanisms have remained largely unexplored. We report here that Gcn5, a paradigm lysyl-acetyl transferase (KAT) modifying both histone and non-histone targets, controls fungal morphogenesis – a key virulence factor of C . albicans . Our data show that genetic removal of GCN5 abrogates fungal virulence in mice, suggesting strongly diminished fungal fitness in vivo . This may at least in part arise from increased susceptibility to killing by macrophages, as well as by other phagocytes such as neutrophils or monocytes. Loss of GCN5 also causes hypersensitivity to the fungicidal drug caspofungin. Caspofungin hypersusceptibility requires the master regulator Efg1, working in concert with Gcn5. Moreover, Gcn5 regulates multiple independent pathways, including adhesion, cell wall-mediated MAP kinase signaling, hypersensitivity to host-derived oxidative stress, and regulation of the Fks1 glucan synthase, all of which play critical roles in virulence and antifungal susceptibility. Hence, Gcn5 regulates fungal virulence through multiple mechanisms, suggesting that specific inhibition of Gcn5 could offer new therapeutic strategies to combat invasive fungal infections.

Due to the restructuring of the power system, customers always try to obtain low-cost power efficiently and reliably. As a result, there is a chance to violate the system security limit, or the system may run in risk conditions. In this paper, an economic risk analysis of a power system considering wind and pumped hydroelectric storage (WPHS) hybrid system is presented with the help of meta-heuristic algorithms. The value-at-risk (VaR) and conditional value-at-risk (CVaR) are used as the economic risk analysis tool with two different confidence levels (i.e., 95% and 99%). The VaR and CVaR with higher negative values represent the system in a higher-risk condition. The value of VaR and CVaR on the lower negative side or towards a positive value side indicates a less risky system. The main objective of this work is to minimize the system risk as well as minimize the system generation cost by optimal placement of wind farm and pumped hydro storage systems in the power system. Sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth flame optimization algorithms (MFO) are used to solve optimal power flow problems. The novelty of this paper is that the MFO algorithm is used for the first time in this type of power risk curtailment problem. The IEEE 30 bus system is considered to analyze the system risk with the different confidence levels. The MVA flow of all transmission lines is considered here to calculate the value of VaR and CVaR. The hourly VaR and CVaR values of the hybrid system considering the WPHS system are reported here and the numerical case studies of the hybrid WPHS system demonstrate the effectiveness of the proposed approach. To validate the presented approach, the results obtained by using the MFO algorithm are compared with the SQP and ABC algorithms’ results.

Porous structure offers the advantage of minimizing stress shielding phenomena and supports bone ingrowth, thereby improving the long-term durability of scaffolds. Unlike conventional techniques, additive manufacturing is capable of fabricating complex pore architecture in an exceedingly controlled fashion. In this paper, implicit surface modeling technique is used to develop the triply periodic minimal surfaces-based scaffolds of varying architectures. Sheet and solid-based wrapped package graph (IWP) and diamond are investigated by finite element analysis of lattices under compression. Parameters of mathematical trigonometric functions are varied to tune the structural characteristics like pore size, porosity on the elastic moduli, and strength of the scaffold. Results indicate that morphological features could be effectively controlled to achieve the desired bone mimicking architectures. In terms of biomechanical performances, IWP and diamond structures achieved the responses similar to surrounding bone tissue and have the good agreement with the data available in the literature for the range of elastic modulus of bone for various anatomical locations; the numerical results show that the architecture, pore size, and porosity have a major impact on performances of scaffolds. Also, in the biomechanical and clinical context, this work highlights the limitations and capabilities of additively manufactured porous scaffolds, thus proposing a permissible design space for the scaffold fabricated by additive manufacturing.