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Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation. The thermal distribution, considered a steady condition in one dimension, is described by a unique implementation of a physics-informed neural network (PINN) as part of machine-learning intelligent strategies for analyzing heat transfer in a convective wavy fin. This novel research explores the use of PINNs to examine the effect of the nonlinearity of temperature equation and boundary conditions by altering the hyperparameters of the architecture. The non-linear ordinary differential equation (ODE) involved with heat transfer is reduced into a dimensionless form utilizing the non-dimensional variables to simplify the problem. Furthermore, Runge–Kutta Fehlberg’s fourth–fifth order (RKF-45) approach is implemented to evaluate the simplified equations numerically. To predict the wavy fin's heat transfer properties, an advanced neural network model is created without using a traditional data-driven approach, the ability to solve ODEs explicitly by incorporating a mean squared error-based loss function. The obtained results divulge that an increase in the thermal conductivity variable upsurges the thermal distribution. In contrast, a decrease in temperature profile is caused due to the augmentation in the convective-conductive variable values.

The thermal distribution in a convective-radiative concave porous fin appended to an inclined surface has been examined in this research. The equation governing the temperature and heat variation in fin with internal heat generation is transformed using non-dimensional variables, and the resulting partial differential equation (PDE) is tackled using an analytical scheme, generalized residual power series method (GRPSM). Moreover, a graphical discussion is provided to examine the consequence of diverse non-dimensional variables including the parameters of convection-conduction, ambient temperature, radiation, heat generation, and porosity effect on the thermal field of the fin. Also, a graph is plotted to analyze the variations in unsteady temperature gradient using the finite difference method (FDM) and generalized residual power series method (GRPSM). The major result of this investigation unveils that as the convection-conduction parameter scale upsurges, the distribution of temperature in the fin diminishes. For the heat-generating parameter, the thermal distribution inside the fin increases.

Polymyxin B remains a key treatment option for infections caused by multidrug-resistant gram-negative bacilli, particularly in critically ill patients. However, its optimal dosing strategy recommendation remains uncertain, especially in those undergoing renal replacement therapy. This study aimed to compare the clinical and microbiological outcomes of low, usual and high dose polymyxin B in a real-world ICU population. This 5-year retrospective cohort study included critically ill adult patients with gram-negative sepsis who received polymyxin B. Patients were categorized into low-, usual- and high-dose groups based on loading and total daily maintenance dose. Pairwise propensity score matching was performed to adjust for baseline differences. Primary outcome was 28-day all-cause mortality. Secondary outcomes included microbiological clearance, ventilator-free days, ICU-free days, and vasopressor-free days. Subgroup and sensitivity analyses were conducted, including within patients requiring dialysis. All the statistical analysis was performed using R software. A total of 674 patients were included. After matching, usual-dose polymyxin B (61%) was associated with significantly higher 28-day mortality compared to the low-dose group (48.04%) (HR = 1.47;95% CI:[1.11-1.95];p = 0.007). Vasopressor, ventilator and ICU-free days were also significantly higher in the low-dose group were compared to the other groups. No significant survival advantage was observed with high-dose regimens. Among dialysis-dependent patients (n = 254), mortality did not differ significantly across dosing groups, though microbiological clearance was better with low dosing. Sensitivity and subgroup analysis also supported the results to be robust. Low dose polymyxin B regimens were associated with lower mortality and comparable clinical outcomes compared to higher doses and may be feasible in critically ill patients with renal impairment. However, these findings should be interpreted cautiously given the observational design and residual confounding, warranting confirmation in future randomized trials.

Most of the enterprises are transforming their conventional networks into Software-Defined Network (SDN) to avail the cost efficiency and network flexibility. But recent attacks and security breaches against SDNs expose the security weakness of the technology. Distributed Denial of Service (DDoS) is the most common attack launched against various SDN architecture layers. Hence, DDoS has been claimed to be the most dangerous attack and threat to SDN. The existing mitigation techniques are traffic volumetric methods, entropical methods and traffic flow analysis methods. They depend on traffic sampling to achieve truly inline against DDoS detection accuracy in real time. However, traffic sampling-based methods are expensive with chances for incomplete approximation of underlying traffic patterns being very high. Early detection of DDoS attack in the controller is critical and requires highly adaptive and accurate methods. In this paper, an effective and accurate DDoS detection method using Lion optimization algorithm is proposed. The proposed detection technique is robust enough to detect DDoS attack within the least magnitude of attack traffic. Further, to evaluate the performance, the proposed method is compared with the state-of-the-art techniques. The outcome of this paper is current method limitation and scope for improvement depicted from overall study and analysis. The experimental results have proved that the proposed method outperforms the existing state-of-the-art methods with 96% accuracy.

Fuchs endothelial corneal dystrophy (FECD) is a progressive and debilitating disorder of the corneal endothelium (CE) that affects approximately 4% of individuals over the age of 40. Despite the burden of the disease, the pathogenesis of FECD remains poorly understood, and treatment options are limited, highlighting the need for deeper investigation into its underlying molecular mechanisms. Over the past decade, studies have indicated independent contributions of endoplasmic reticulum (ER) and mitochondrial stress to the pathogenesis of FECD. However, there are limited studies suggesting ER-mitochondria crosstalk in FECD. Recently, our lab established the role of chronic ER stress in inducing mitochondrial dysfunction for corneal endothelial cells (CEnCs), indicating the existence of ER-mitochondria crosstalk in FECD. This paper aims to provide a comprehensive overview of the current understanding of how ER and mitochondrial stress contribute to FECD pathogenesis. The paper also reviews the literature on the mechanisms of ER-mitochondria crosstalk in other diseases relevant to FECD.

The present study represents a formulation of nanocurcumin based hybrid virosomes (NC-virosome) to deliver drugs at targeted sites. Curcumin is a bioactive component derived from Curcuma longa and well-known for its medicinal property, but it exhibits poor solubility and rapid metabolism, which led to low bioavailability and hence limits its applications. Nanocurcumin was prepared to increase the aqueous solubility and to overcome all the limitations associated with curcumin. Influenza virosomes were prepared by solubilization of the viral membrane with 1,2-distearoyl- sn -glycerol-3-phosphocholine (DSPC). During membrane reconstitution, the hydrophilic nanocurcumin was added to the solvent system, followed by overnight dialysis to obtain NC-virosomes. The same was characterized using a transmission electron microscope (TEM) and scanning electron microscope (SEM), MTT assay was used to evaluate it's in vitro-cytotoxicity using MDA-MB231 and Mesenchyme stem cells (MSCs). The results showed NC-virosomes has spherical morphology with size ranging between 60 and 90 nm. It showed 82.6% drug encapsulation efficiency. The viability of MDA-MB231 cells was significantly inhibited by NC-virosome in a concentration-dependent manner at a specific time. The IC50 for nanocurcumin and NC-virosome was 79.49 and 54.23 µg/ml, respectively. The site-specific drug-targeting, high efficacy and non- toxicity of NC-virosomes proves its future potential as drug delivery vehicles.

BackgroundCoronaviruses mainly affect the respiratory system; however, there are reports of SARS-CoV and MERS-CoV causing neurological manifestations. We aimed at discussing the various neurological manifestations of SARS-CoV-2 infection and to estimate the prevalence of each of them.MethodsWe searched the following electronic databases; PubMed, MEDLINE, Scopus, EMBASE, Google Scholar, EBSCO, Web of Science, Cochrane Library, WHO database, and ClinicalTrials.gov. Relevant MeSH terms for COVID-19 and neurological manifestations were used. Randomized controlled trials, non-randomized controlled trials, case-control studies, cohort studies, cross-sectional studies, case series, and case reports were included in the study. To estimate the overall proportion of each neurological manifestations, the study employed meta-analysis of proportions using a random-effects model.ResultsPooled prevalence of each neurological manifestations are, smell disturbances (35.8%; 95% CI 21.4–50.2), taste disturbances (38.5%; 95%CI 24.0–53.0), myalgia (19.3%; 95% CI 15.1–23.6), headache (14.7%; 95% CI 10.4–18.9), dizziness (6.1%; 95% CI 3.1–9.2), and syncope (1.8%; 95% CI 0.9–4.6). Pooled prevalence of acute cerebrovascular disease was (2.3%; 95%CI 1.0–3.6), of which majority were ischaemic stroke (2.1%; 95% CI 0.9–3.3), followed by haemorrhagic stroke (0.4%; 95% CI 0.2–0.6), and cerebral venous thrombosis (0.3%; 95% CI 0.1–0.6).ConclusionsNeurological symptoms are common in SARS-CoV-2 infection, and from the large number of cases reported from all over the world daily, the prevalence of neurological features might increase again. Identifying some neurological manifestations like smell and taste disturbances can be used to screen patients with COVID-19 so that early identification and isolation is possible.

Objective Rhinoplasty also popularly known among people as plastic surgery performed on nose.it is the most commonly performed plastic surgery procedures for both functional and aesthetic purposes according to the statistics of the American Society of Plastic Surgeons [ 1 ]. This study is aimed to investigate the changes in the psychological impact of rhinoplasty on day-to-day life & on quality of life after the rhinoplasty surgery. Method This study contained a total of 19 (15 men and 4 women) between 16 and 35 years old (mean, 24.8). The study used ROE questionnaire, a simple, reliable, validated, and widely used inventory. For statistical analysis, paired Student’s t test and the Mann-Whitney test were applied. Student’s t test was used to compare preoperative and postoperative scores. Results The scores of both the cosmetic and the post-traumatic patients were significantly improved by rhinoplasty ( p  < 0.0001). There was no significant difference when the authors compared the improvement scores of subgroups ranged by age, sex, primary versus secondary rhinoplasty, time between first consultation and surgery, posttraumatic versus non–posttraumatic patients, and functional versus nonfunctional indications. Conclusions With rhinoplasty being one of the most complex aesthetic procedures, it is crucial to measure outcomes from the patient’s perspective to determine surgical success. Rhinoplasty is a beneficial procedure since the majority of patients showed greater satisfaction with appearance, treatment outcome, and quality-of-life postoperatively. These factors can be used during pre- operative management of outcome expectations.

Purpose This paper aims to map the global landscape of humanoid robotics research (2000–2024) using scientometric techniques to identify key trends, thematic evolutions, and influential contributors, thereby guiding future research and policy directions. Design/methodology/approach The study retrieved 7065 Scopus-indexed publications on humanoid robots, then applied bibliometric analyses and text mining, including topic modelling (LDA), co-word mapping, citation and authorship metrics (Lotka’s Law), and country-level contributions. Visualisation tools (i.e. Google Colab) were used to illustrate publication trends, thematic clusters, and collaboration networks. Findings The results reveal a steady annual increase in publications, with notable surges post-2010. Key research themes include human–robot interaction (HRI), locomotion, AI integration, and ethical/social implications. The US, Japan, and Germany emerged as leading contributors, while co-authorship analysis highlighted a skewed distribution among prolific scholars. Topic modelling showed growing emphasis on social robotics and ethical frameworks. Research limitations/implications The analysis is limited to publications indexed in English within Scopus, potentially excluding significant regional or non-English research. Citation lag may under-represent recent contributions. Future studies should extend to additional databases and include non-English sources to capture diverse scholarly perspectives. Practical implications Insights inform funding agencies on strategic investments in themes, guide policymakers in developing robotics ethics regulations, and help researchers identify emerging subfields and collaboration opportunities. Originality/value This is one of the first quantitative scientometric studies focused exclusively on humanoid robotics over 25 years, offering a comprehensive, data-driven panorama of scholarly activity. This work advances knowledge by linking research outputs with thematic dynamics and providing actionable recommendations.

Evolutionary algorithms are a large class of optimization techniques inspired by the ideas of natural selection, and can be employed to address challenging problems. These algorithms iteratively evolve populations using crossover, which combines genetic information from two parent solutions, and mutation, which adds random changes. This iterative process tends to produce effective solutions. Inspired by this, the current study presents the results of thermal variation on the surface of a wetted wavy fin using a genetic algorithm in the context of parameter estimation for artificial neural network models. The physical features of convective and radiative heat transfer during wet surface conditions are also considered to develop the model. The highly nonlinear governing ordinary differential equation of the proposed fin problem is transmuted into a dimensionless equation. The graphical outcomes of the aspects of the thermal profile are demonstrated for specific non-dimensional variables. The primary observation of the current study is a decrease in temperature profile with a rise in wet parameters and convective-conductive parameters. The implemented genetic algorithm offers a powerful optimization technique that can effectively tune the parameters of the artificial neural network, leading to an enhanced predictive accuracy and convergence with the numerically obtained solution.