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Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

Unmanned Aerial Vehicle (UAV)-assisted edge computing supports latency-sensitive applications by offloading computational tasks to ground-based servers. However, determining optimal resource allocation under strict latency constraints and stochastic channel conditions remains challenging. This paper addresses the joint computation partitioning and power allocation problem for UAV-assisted edge computing systems. We formulate the problem as a Constrained Markov Decision Process (CMDP) that explicitly models latency constraints, rather than relying on implicit reward shaping. To solve this CMDP, we propose Constrained Soft Actor–Critic (C-SAC), a deep reinforcement learning algorithm that combines maximum-entropy policy optimization with Lagrangian dual methods. C-SAC employs a dedicated constraint critic network to estimate long-term constraint violations and an adaptive Lagrange multiplier that automatically balances energy efficiency against latency satisfaction without manual tuning. Extensive experiments demonstrate that C-SAC achieves an 18.9% constraint violation rate. This represents a 60.6-percentage-point improvement compared to unconstrained Soft Actor–Critic, with 79.5%, and a 22.4-percentage-point improvement over deterministic TD3-Lagrangian, achieving 41.3%. The learned policies exhibit strong channel-adaptive behavior with a correlation coefficient of −0.894 between the local computation ratio and channel quality, despite the absence of explicit channel modeling in the reward function. Ablation studies confirm that both adaptive mechanisms are essential, while sensitivity analyses show that C-SAC maintains robust performance with violation rates varying by less than 2 percentage points even as channel variability triples. These results establish constrained reinforcement learning as an effective approach for reliable UAV edge computing under stringent quality-of-service requirements.

Microfiltration flat sheet membranes of polyether sulfone (PES) were fabricated by incorporating varying concentrations of polymer and investigated the influence of substituting solvents. The membranes were prepared via immersion precipitation method. Different solvents that included NMP (N-methyl-2-pyrrolidone), DMF (dimethylformamide), and THF (tetrahydrofuran) were used to analyse their effect on the performance and morphology of the prepared membranes. Two different coagulation bath temperatures were used to investigate the kinetics of membrane formation and subsequent effect on membrane performance. The maximum water flux of 141 ml/cm2.h was observed using 21% of PES concentration in NMP + THF cosolvent system. The highest tensile strength of 29.15 MPa was observed using membrane prepared with 21% PES concentration in NMP as solvent and coagulation bath temperature of 25°C. The highest hydraulic membrane resistance was reported for membrane prepared with 21% PES concentration in NMP as solvent. Moreover, the lowest contact angle of 67° was observed for membrane prepared with 15% of PES concentration in NMP as solvent with coagulation bath temperature of 28°C. Furthermore, the Hansen solubility parameter was used to study the effect on the thermodynamics of membrane formation and found to be in good correlation with experimental observation and approach in the present work.

Sentiment analysis, the meta field of Natural Language Processing (NLP), attempts to analyze and identify the sentiments in the opinionated text data. People share their judgments, reactions, and feedback on the internet using various languages. Urdu is one of them, and it is frequently used worldwide. Urdu-speaking people prefer to communicate on social media in Roman Urdu (RU), an English scripting style with the Urdu language dialect. Researchers have developed versatile lexical resources for features-rich comprehensive languages, but limited linguistic resources are available to facilitate the sentiment classification of Roman Urdu. This effort encompasses extracting subjective expressions in Roman Urdu and determining the implied opinionated text polarity. The primary sources of the dataset are Daraz (an e-commerce platform), Google Maps, and the manual effort. The contributions of this study include a Bilingual Roman Urdu Language Detector (BRULD) and a Roman Urdu Spelling Checker (RUSC). These integrated modules accept the user input, detect the text language, correct the spellings, categorize the sentiments, and return the input sentence’s orientation with a sentiment intensity score. The developed system gains strength with each input experience gradually. The results show that the language detector gives an accuracy of 97.1% on a close domain dataset, with an overall sentiment classification accuracy of 94.3%.

Background: Signet ring cell colorectal cancer (SRCCa) has a bleak prognosis. Employing molecular pathology techniques we investigated the potential of precision medicine in this disease. Methods: Using test ( n =26) and validation ( n =18) cohorts, analysis of mutations, DNA methylation and transcriptome was carried out. Microsatellite instability (MSI) status was established and immunohistochemistry (IHC) was used to test for adaptive immunity (CD3) and the immune checkpoint PDL1. Results: DNA methylation data split the cohorts into hypermethylated ( n =18, 41%) and hypomethylated groups ( n =26, 59%). The hypermethylated group predominant in the proximal colon was enriched for CpG island methylator phenotype (CIMP), BRAF V600E mutation and MSI ( P <0.001). These cases also had a high CD3 + immune infiltrate ( P <0.001) and expressed PDL1 ( P =0.03 in intra-tumoural lymphoid cells). The hypomethylated group predominant in the distal colon did not show any characteristic molecular features. We also detected a common targetable KIT mutation (c.1621A>C) across both groups. No statistically significant difference in outcome was observed between the two groups. Conclusions: Our data show that SRCCa phenotype comprises two distinct genotypes. The MSI + /CIMP + / BRAF V600E + /CD3 + /PDL1 + hypermethylated genotype is an ideal candidate for immune checkpoint inhibitor therapy. In addition, one fourth of SRCCa cases can potentially be targeted by KIT inhibitors.

In the era of Industry 5.0, the digital revolution stands as the paramount tool for achieving efficiency and elevating the standards of quality and quantity. This study delves deeply into the invaluable applications of digital twins within real production settings, highlighting their transformative potential across a multitude of industries. Focusing particularly on textiles, machinery, and electronics manufacturing, the authors illustrate how digital twins enhance productivity, anticipate challenges, bolster the food supply chain, refine healthcare services, and propel sustainability initiatives within each sector. Through concrete examples, we demonstrate how digital twins can markedly decrease waste, energy consumption, and production downtime, all while elevating product quality and enabling virtualization. By virtually simulating physical systems, numerous operational issues can be mitigated, underscoring the pivotal role of digital twins in fostering hyper-personalization, sustainability, and resilience the foundational tenets of Industry 5.0. Nevertheless, this evaluation acknowledges the inherent challenges associated with the widespread adoption of digital twins, including concerns regarding data infrastructure, cybersecurity, and workforce adaptation. By presenting a balanced assessment of both the advantages and disadvantages, this review aims to guide future research and development endeavors, paving the way for the successful integration of this revolutionary technology as we journey toward Industry 5.0.

Delayed STEMI presentation can cause extensive myocardial necrosis, left ventricular thrombus, cardiogenic shock, and progression to end‐stage heart failure despite reperfusion. Early recognition and timely transfer to specialized shock centers are critical. In refractory cases, advanced mechanical circulatory support (VA‐ECMO with Impella) may serve as a bridge to life‐saving heart transplantation.

Intrusion detection system (IDS) is a well-known and effective component of network security that provides transactions upon the network systems with security and safety. Most of earlier research has addressed difficulties such as overfitting, feature redundancy, high-dimensional features and a limited number of training samples but feature selection. We approach the problem of feature selection via sparse logistic regression (SPLR). In this paper, we propose a discriminative feature selection and intrusion classification based on SPLR for IDS. The SPLR is a recently developed technique for data analysis and processing via sparse regularized optimization that selects a small subset from the original feature variables to model the data for the purpose of classification. A linear SPLR model aims to select the discriminative features from the repository of datasets and learns the coefficients of the linear classifier. Compared with the feature selection approaches, like filter (ranking) and wrapper methods that separate the feature selection and classification problems, SPLR can combine feature selection and classification into a unified framework. The experiments in this correspondence demonstrate that the proposed method has better performance than most of the well-known techniques used for intrusion detection.

Stroke is one of the most feared complications of aortic valve replacement. Although the outcomes of transcatheter aortic valve implantation (TAVI) improved substantially over time, concerns remained about a potentially higher incidence of stroke with TAVI compared with surgical replacement (SAVR). However, comparative data are sparse. We performed a meta-analysis comparing the incidence of stroke among patients undergoing TAVI versus SAVR. Of the 5067 studies screened, 28 eligible studies (22 propensity-score matched studies and 6 randomized trials) were analyzed. Primary endpoints were 30-day stroke and disabling stroke. Secondary endpoints were 1-year stroke and disabling stroke. A total of 23,587 patients were included, of whom 47.27% underwent TAVI and 52.72% underwent SAVR. For each endpoint, pooled estimates of odds ratio (OR) with 95% confidence interval (CI) were calculated. The pooled estimates for stroke (2.7% vs 3.1%, OR 0.86; 95% CI 0.72 to 1.02; p=0.08) and disabling stroke (2.5% vs 2.9%, OR 0.96; 95% CI 0.57 to 1.62; p=0.89) were comparable following TAVI versus SAVR at 30 days. Similarly, the pooled estimates for stroke (5.0% vs 4.6%, OR 1.01; 95% CI 0.79 to 1.28; p=0.96) and disabling stroke (4.1% vs 4.5%, OR 0.92; 95% CI 0.92 to 1.39; p=0.71) were similar at 1 year. A sensitivity analysis including only RCTs yielded similar results. Our meta-analysis documents comparable rates of strokes and disabling strokes following TAVI or SAVR both at 30 days and 1 year.