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An agro-waste corncob was used to prepare activated carbon and loaded with ZnO nanoparticles by using the impregnation method. The FTIR band at 492 cm−1 of zinc oxide nanoparticle-loaded corncob activated carbon (ZnO@CCAc-7) shows the spreading vibrational modes of ZnO. The crystallinity and crystal structure of different prepared materials were analysed using X-ray diffraction (XRD). The specific surface area of ZnO@CCAc-7 was observed to be 148.3 m2/g, which was nearly two times larger than that of efficient corncob activated carbon. The fundamental peaks of carbon, oxygen and ZnO nanoparticle with their atomic percent of C 66.66, O 27.10 and ZnO 6.24 were observed in the SEM-EDX spectrum. The thermal stability of the prepared adsorbent was also analysed using thermogravimetric analysis (TGA). The maximum COD reduction using ZnO@CCAc-7 adsorbent was obtained to be 76.4% at the peak values of pH 6, dose 1.50 g/50 mL, time 160 minutes and temperature 30 ͦC. The various adsorption models Hill, Khan, Redlich–Peterson, Toth, Koble–Corrigan and Freundlich were found close-fitted to the adsorption process. It was observed that the COD removal adsorption technique ensued to a pseudo-first-order rate of kinetics. The negative value of Gibb's free ΔG0 and positive values of the enthalpy change (∆H° = 25.64 kJmol-1) indicates that the process of COD removal using ZnO@CCAc-7 adsorbent was spontaneous and endothermic in nature. Furthermore, response surface methodology with a subset of Central composite design was also studied in detail to optimise the COD removal efficiency. [ABSTRACT FROM AUTHOR]

Polymorphous AdenoCarcinoma (PAC), a slow growing tumor primarily found in minor salivary glands, predominantly palate. Herein, we report a case of 52-year female with palatal swelling. This report highlights the recent updates on molecular and IHC findings of PAC and its sub variant CASG. [ABSTRACT FROM AUTHOR]

The current study was carried out to evaluate various combinations of potting media and ornamental plants for outdoor vertical gardening as, these days to use vertical space is trending with increasing urbanization. This study will help to workout best potting media ratio for them. We will conclude to highlight a best combination of potting media, among, T1: Soil + Farm Yard Manure + Cocopeat (50:25:25), T2: Soil + Farm Yard Manure + Cocopeat (25:50:25), T3: Soil + Farm Yard Manure + Cocopeat (25:25:50), T4: Soil + Vermicompost + Cocopeat (25:50:25), and T5 (Control): Soil + Farm Yard Manure (50:50) and best plant species among Syngonium podophyllum, Dracaena compacta (Red), Kalanchoe blossfeldiana, Dracaena reflexa, and Pedilanthus tithymaloides. Each treatment contained 15 plants replicated thrice under the Completely Randomized Block Design. The potting media ratio combination in T4: Soil + Vermicompost + Cocopeat (25:50:25) proved to be the best for improvement of vegetative parameters and for the attractive foliage of the selected ornamental plants, having more ratio of vermicompost thus capable of providing good amount of all beneficial benefits with more moisture retention capacity and nutrients value than other combination of media used. The minimum value of all the observations recorded was found to be in T5 (Control): Soil + Farm Yard Manure (50:50). [ABSTRACT FROM AUTHOR]

We studied a candidate TPS (thermal protection system) material for reusable re-entry space vehicle applications. The material was based on a high-temperature-resistant material called Cerakwool. A total of six specimens were fabricated with substrate densities of 0.45 g/cm3, 0.40 g/cm3, and 0.35 g/cm3, with two specimens for each density. All specimens were coated with high-emissivity TUFI (toughened unpiece fibrous insulation), with coating thicknesses ranging from 445 to 1606 µm. The specimens were tested using an HVOF (high-velocity oxygen fuel) material ablation test facility. For each density specimen pair, one specimen was tested at 1 MW/m2 and the remaining one was tested at 0.65 MW/m2. The average stagnation point temperature for specimens tested at 1 MW/m2 was ~893 °C, approximately 200 °C higher than those tested at 0.65 MW/m2. This suggests a ~200 °C increase in stagnation point temperature for a 0.35 MW/m2 rise in incident heat flux. During the tests, internal temperatures were measured at three locations. For all tested specimens, regardless of heat flux test conditions and density, the temperature at ~40 mm from each specimen's stagnation point remained around or below 50 °C, well within the 180 °C design limit set for the TPS back face temperature. Post-test visual inspections revealed no signs of ablation or internal damage, confirming the material's reusability. [ABSTRACT FROM AUTHOR]

Polymer nanocomposite is commonly used to develop structural components of space, aircraft, biomedical, sensor, automobile, and battery sector applications. It remarkably substitutes the heavyweight metallic and nonmetallic engineering materials. The machining principles of polymer nanocomposites are intensely different and complex from traditional metals and alloys. The nonhomogeneity, abrasive, and anisotropic nature differs its machining aspect from conventional metallic materials. This investigation aims to execute the CNC drilling of modified nanocomposite using Graphene–carbon (G-C) @ epoxy matrix. The process constraints, namely, cutting speed (S), feed (F), and wt.% of graphene oxide (GO) vary up to three levels and are designed according to the response surface methodology (RSM) array. The nonlinear model is created to predict surface roughness (Ra) and delamination (Fd) on regression analysis. It has been found that the average error for Ra is 0.94% and for Fd it is 3.27%, which is acceptable in model predictions. The metaheuristics-based evolutionary Dragonfly algorithm (DA) evaluated the optimal parametric condition. The optimal setting prediction for the DA is observed as cutting speed (S)-37.68m/min, feed (F)-80mm/min, and wt.% of graphene oxide (GO)-1%. This algorithm demonstrates a higher application potential than the previous efforts in controlling Ra and Fd values. Both the drilling response values are found to be minimized when the cutting speed increases and the feed decreases. The best fitness value for the DA is 1.626 for surface roughness and 5.086 for delamination. This study agreed with the prediction model’s outcomes and the process parameters’ optimal condition. The defects generated during the sample drilling, such as fiber pull out, uncut/burr, and fiber breakage, were examined using FE-SEM analysis. The optimal findings of the DA module significantly controlled the damages during machining. [ABSTRACT FROM AUTHOR]

ABSTRACT The detection of brain tumours presents a significant challenge in the medical domain, where prompt and precise diagnosis is crucial as patient outcomes depend on it. Conventional deep neural networks perform well in carrying out various imaging tasks within the healthcare sector; however, their effectiveness often falls short of expectations in practical applications due to the substantial computational resources required and issues with reliability. In this research, an optimised and effective deep learning model founded on the DenseNet‐169 architecture is introduced for the classification of magnetic resonance imaging brain tumours, which is particularly advantageous for smart healthcare systems and information and communication technology (ICT) settings with limited computational capabilities. The model compression methodologies, including pruning and quantization, have been employed to significantly diminish the dimensions and intricacy of the model while achieving a classification accuracy of 97.07%. Furthermore, this endeavour necessitates the enhancement of the model's interpretability through the utilisation of explainable artificial intelligence methodologies such as Gradient‐weighted Class Activation Mapping (Grad‐CAM) and SHapley Additive exPlanations (SHAP), which will aid clinicians in highlighting crucial areas of the images and validating feature importance concerning the decisions rendered by the model. A comparative performance evaluation is conducted against DenseNet‐169, ResNet‐50 and various other models to delineate the superior efficacy of our model, rendering it exceptionally adept for knowledge‐driven, real‐time brain tumour diagnosis within smart healthcare and ICT systems where resources are constrained. [ABSTRACT FROM AUTHOR]

In the event of a fire, the performance of concrete becomes pivotal for structural integrity. Concrete undergoes significant changes in its mechanical properties when exposed to high temperatures. This study investigates the mechanical and micro‐characterization of concrete incorporating ground granulated blast furnace slag (GGBFS) under ambient and exposed conditions. The study examines alkali‐activated concrete (AAC) by implementing sodium hydroxide (NaOH) and sodium silicate (Na2SiO3) as alkali activators. Three different fractions of GGBFS and alkali activators (AAs) were employed in the concrete mixes. The mechanical properties of the AAC were evaluated under ambient and elevated temperatures, and the results were compared with those of Portland slag cement (PSC). The study's findings indicate that AAC outperforms PSC, and the study recommends using 93% GGBFS with 7% AAs as a suitable replacement for PSC. The mechanical properties of AAC are enhanced by 20%–47% at ambient conditions. The strength loss for the AAC with increased temperature is also less than that of the PSC concrete. [ABSTRACT FROM AUTHOR]

Recently, metal-supported solid oxide fuel cells (MS–SOFCs) have been in the spotlight again for their design, thanks to their inexpensive materials, robustness, resistance to thermal cycling, and benefits of manufacturability. Hydrogen energy electrochemical devices, like MS–SOFCs, have a lot of potential. They are very ideal substitutes for solid oxide fuel cells (SOFCs) that utilize electrolytes or ceramic electrodes as their carrier basis, because of their greater durability, mechanical stability, heat cycle resistance, and rapid startup time. Even though MS–SOFCs have several advantages over conventional ceramic-based SOFCs, researchers are still struggling to perfect them due to issues such as selecting the appropriate metal-based material for the electrodes (anode, electrode) and comprehending how they deteriorate. This limitation might be evaded by optimizing the pore former filling and the diameter of the metallic supports (130–250μm). Optimization methods, such as particle swarm optimization, as well as penetration cycle numbers (1–15), as well as the impacts of fire temperatures (400–900∘C), were investigated to aid in optimizing the catalyst infiltration procedure. The enhanced cell outperformed its original performance by a factor of three, reaching an ideal energy density of 0.9W cm−2 at 700∘C when powered by hydrogen. The improved cells had a 2% degradation rate per 100h at 550∘C, a 4.5% degradation rate at 600∘C, and a 5.5% degradation rate at 700∘C. We used electrochemical impedance spectroscopy and scanning electron microscopy to look at the catalyst’s mass shipping, coarsening, and chromium poisoning. [ABSTRACT FROM AUTHOR]

Colloidal cesium lead halide (CsPbX3, X = Cl, Br, I) perovskite nanocrystals (NCs) hold promises in photovoltaic and optoelectronic applications. However, their poor long‐term stability results in degradation of optical properties under ambient conditions. Various methods have been explored to address these issues. In this study, we demonstrate that following 3‐precursor hot‐injection method employing ethylenediammonium dihalide (EDAX2) as an additional ligand/halide source one can obtain phase‐pure, monodispersed, stable, and highly luminescent CsPbX3 NCs. Our violet‐emitting CsPbCl3, blue‐emitting CsPbClBr2, Green‐emitting CsPbBr3, yellow‐emitting CsPbBr2I, and red‐emitting CsPbI3 NCs show PLQYs of 53%, 97%, 96%, 93%, and 95%, respectively. Moreover, the CsPbI3 NCs, which are known for their phase instability, exhibit stability for up to two months under ambient conditions. The enhanced stability and photoluminescence are attributed to strong binding of the bidentate ligand (EDA2+) to the surface of the NCs and effective removal of the halide‐deficiency trap states by X−. [ABSTRACT FROM AUTHOR]

Background: The pararectus approach is a minimally invasive surgical approach for anterior acetabulum fracture, with an advantage of the medial window of the modified Stoppa approach (MSA). However, it is unclear whether the pararectus approach is superior to MSA. We aimed this systematic review and meta-analysis to compare the outcomes and complications of pararectus and MSA. Methods: We performed a data search by conducting an electronic search across databases of PubMed, Embase, Scopus, Cinahl, CNKI, and Cochrane Library and included seven comparative studies for analysis. Statistical analysis was performed using the RevMan software 5.4.1. The risk of bias was evaluated using the Cochrane Collaboration's risk of bias tool for RCTs and the MINORS tool for non-RCTs. Results: Two randomized control trials (RCTs), one prospective study, and four retrospective studies were included. Meta-analysis revealed a better Matta's reduction quality [OR 1.58, 95% CI 1.06, 2.37; p = 0.03] and radiological outcome [OR 2.18, 95% CI 1.03, 4.60; p = 0.04] in MSA than in pararectus approach. However, the pararectus approach has less intraoperative blood loss [MD – 9.79 (95% CI − 176.75, − 6.83; p = 0.03)] and a shorter hospital stay [MD − 2.61 (95% CI − 5.03, −.18; p = 0.04)] than MSA. Both approaches have failed to show a difference concerning overall complication rates [OR 0.66 (95% CI 0.28, 1.55; p = 0.34)], postoperative infection, DVT, duration of surgery [MD − 15.09 (95% CI − 35.38, 5.20; p = 0.15)], functional outcome, and incision length. Conclusion: The pararectus approach offers an advantage with lesser operative blood loss and shorter hospital stay, whilst MSA stands out with better reduction quality and radiological outcomes. Nevertheless, both approaches exhibit no difference in complication rates, duration of surgery, incision length, and functional outcome. Hence, the pararectus approach can be considered an alternative to MSA; however, the existing literature fails to demonstrate a distinct advantage over MSA. [ABSTRACT FROM AUTHOR]

The swift societal evolution and ceaseless advancement of human value of life have been set forth for reliability as well as rapidity of railway transportation. Latest advances in machine learning approaches as well as surging accessibility of numerous information sources is produced state‐of‐the‐art probabilities for significant, precise train delay identification. In this method called, Barzilai Borwein Incremental Grey Polynomial Regression (BBI‐GPR) is introduced for predicting train arrival/departure delays, which utilized for later delay management in an accurate manner with this method comprised into three sections such as, pre‐processing, feature selection and classification. First, with the raw ETA train delay dataset as input, Barzilai–Borwein Feature Rescaling‐based Pre‐processing is applied to model computationally efficient feature rescaled and normalized values. Second with processed features as input, Incremental Maximum Relevance Minimum Redundant‐based Feature Selection is applied to select error minimized optimal features. Finally, with optimal features selected as input, Grey Polynomial Regression‐based Prediction algorithm is employed to analyse train delay. For confirming proposed BBI‐GPR, as well as analyse its performance, compare standard train delay prediction method with existing machine learning‐based regression method. Results show that new variants outperform existing train delay prediction method by minimizing train delay prediction time, error rate by 25% and 27% respectively, with improved accuracy rate of 7%, therefore paving ways for efficient train delay prediction. [ABSTRACT FROM AUTHOR]

Background: Valgus knee deformity poses great challenges in total knee arthroplasty (TKA) and requires precision in balancing of soft tissue and implant component positioning. The surgical approach used for TKA has a determinantal impact on intraoperative soft tissue balancing, postoperative knee function, and complications. We executed a systematic review and meta-analysis of current literature, which included a maximum number of studies with quantitative analysis of all possible outcomes to substantiate the current evidence of the advantage of lateral versus medial approach in TKA for valgus knee deformity. Methodology: We performed a meticulous primary electronic search across PubMed, Emabse, Scopus, and Cochrane Library databases, and looked for the comparative studies that evaluated the medial versus lateral approach in TKA for valgus knees. Statistical analyses were executed with RevMan-5.4.1. Results: On the evaluation of four randomized controlled trials (RCTs), two prospective and five retrospective comparative studies, our analysis revealed a better functional outcome in terms of Knee Society Score [MD 2.24, 95% CI 0.42, 4.05; p = 0.02] with the medial approach. However, comparable results were observed for two approaches with regard to Knee Society Function [MD 0.69, 95% CI − 1.77, 3.15; p = 0.58], knee flexion range of motion (ROM) [MD 3.30, 95% CI − 1.34, 7.95; p = 0.16], overall complications [OR 0.60, 95% CI 0.27,1.34; p = 0.22], wound-related complications, infection, nerve injury, periprosthetic fracture, post-operative valgus, blood loss, duration of surgery, postoperative pain, and patellar tilt for TKA in valgus knee. Conclusion: Evidence from the currently available published data suggests that the lateral approach, compared to the medial approach in TKA for the valgus knee, does not show clear superiority. [ABSTRACT FROM AUTHOR]

Attack detection in cyber security systems is one of the complex tasks which require domain specific knowledge and cognitive intelligence to detect novel and unknown attacks from large scale network data. This research explores how the network operations and network security affects the detection of unknown attacks in network systems. A hash based profile matching technique is presented in this paper for attack detection. The main objective of this work is to detect unknown attacks using a profile matching approach in Hypervisors. Hypervisors are characterized by their versatile nature since they allow the utilization of available system resources. The virtual machines (VMs) in the hypervisors are not dependent on the host hardware and as a result, hypervisors are considered advantageous. In addition, hypervisors have direct access to the hardware resources such as memory, storage and processors. However, hypervisors are more susceptible to the security threats which attack each and every VM. A SHA3-512 hashing algorithm used for generating hash values in hypervisor and the proposed model is used to verify whether the profile is malicious or benign. The performance of the hashbased profile matching technique is compared with traditional hash techniques namely SHA-256 and MD5 algorithm. Results show that the proposed SHA3-512 algorithm achieves a phenomenal performance in terms of phenomenal accuracy and zero false positive rates. Simulation results also show that the computation time required by Sha3-512 algorithm is lower compared to SHA-256 and MD5 algorithms. The performance analysis validates that the hash based approach achieves reliable performance for attack detection. The effectiveness of the hashing technique was determined using three different evaluation metrics namely attack DR, FPR, and computational time. Simulation results show that the existing SHA3- 512 algorithm detection rate of 97.24% with zero false positive rate and faster computational time compared to SHA 256 and MD5 algorithms. [ABSTRACT FROM AUTHOR]

Maternal separation can have long-lasting effects on an individual's susceptibility to stress later in life. Maternal separation during the postnatal period is a commonly used paradigm in rodents to investigate the effects of early life stress on neurobehavioural changes and stress responsiveness. However, maternal separation during stress hyporesponsive and responsive periods of postnatal development may differ in its effects on stress resilience. Therefore, we hypothesised that late maternal separation (LMS) from postnatal day 10 to 21 in mice may have different effect on resilience than early maternal separation during the first week of postnatal life. Our results suggested that male LMS mice are more resilient to chronic variable stress (CVS)-induced anxiety and depressive-like behaviour as confirmed by the open field, light-dark field, elevated plus maze, sucrose preference and tail suspension tests. In contrast, female LMS mice were equally resilient as non-LMS female mice. We found increased expression of NPY, NPY1R, NPY2R, NPFFR1, and NPFFR2 in the hypothalamus of male LMS mice whereas the opposite effect was observed in the hippocampus. LMS in male and female mice did not affect circulating corticosterone levels in response to psychological or physiological stressors. Thus, LMS renders male mice resilient to CVS-induced neurobehavioural disorders in adulthood. HIGHLIGHTS: Sexual dimorphism exists in the effects of late maternal separation (LMS) LMS provides resilience to stress-induced anxiety and depression in male mice LMS upregulates NPY and NPVF system in the hypothalamus of male mice No effect of LMS on stress-induced corticosterone levels [ABSTRACT FROM AUTHOR]

In the current era there are huge quantities of waste organic matter available, creating a big burden to the environment. To address these issues, researchers started to apply effective and microbial induced biotechnological processes that can mitigate these waste matters. In this context, different nature of microbial systems are involved in hydrolysing the waste organic material into fermentable sugar. These can be easily consumed by specific microbial systems like Saccharomyces cerevisiae MTCC 3821 and Clostridium acetobutylicum that produced bioethanol and biobutanol, respectively. Saccharomyces cerevisiae was cultured in specific media and incubated at rotary shaker with 150 rpm at 30°C for 72 to 96 hours. Ethanol concentrations from different waste matters were found in the range of 1.2-1.5 g.L-1. Ethanol synthesis was done by shake flask experiment with addition of glucose (50 g.L-1) to waste organic hydrolyzed solution. Non-glucose media produced less than 3 g.L-1 ethanol but glucose media produced 4.5 g.L-1. Next, Clostridium acetobutylicum was grown in culture media containing waste organics as sole carbon substrate with pH 7 and then was incubated in anaerobic conditions at 35°C for 72 hours, produced butanol (0.7 to 1.25 g.L-1). This research work promoted biofuels synthesis by keeping a waste mitigation strategy. [ABSTRACT FROM AUTHOR]

An ankle-foot orthosis (AFO) with plantar flexion resistance (PFR) can improve the first rocker function during gait, but the incremental changes in the resistive moment on balance and gait have not been well identified. To investigate the effect of changing the PFR moment of dynamic AFO (DAFO) on measures of the center of pressure (COP) and clinical gait outcomes in individuals with post-stroke hemiparesis. In this randomized repeated measure study of 36 stroke individuals, the customized DAFO using foot drop ankle units set in three PFR situations (low, medium, and high) was evaluated. The balance parameters for COP measures were investigated by HUMAC® Balance & Tilt System. Gait parameters and ankle kinematics were recorded using the 3D motion analysis through force platform and optoelectronic system. The comparison was made using a parametric ANOVA test and the P value was set at 0.05 for statistical significance. Significant differences were observed for COP average velocity (1.30 ± 0.64, 1.10 ± 0.05, and 1.37 ± 0.43), COP path length (43.3 ± 4.6, 33.4 ± 4.3, and 36.3 ± 5.4), walking velocity (11.0 ± 3.1, 13.2 ± 4.4, and 9.9 ± 3.5), and cadence (31.5 ± 2.0, 33.0 ± 3.1, and 29.0 ± 1.6) respectively for low, medium and high PFR settings (P < 0.05). Except for the COP path length and cadence, posthoc multiple comparisons revealed significant differences between low and medium (P < 0.05) and medium and high (P < 0.05) PFR grades. PFR with medium resistance demonstrated near-normal maximal peak ankle dorsiflexion (mean deviation of 8 degrees, P < 0.05). Medium PFR grade should be encouraged since it can enhance balance parameters like path length and average velocity of COP, increase cadence and average velocity during gait, and improve maximal peak ankle dorsiflexion. • Changes in resistive moment of plantar-flexion affects gait and balance in stroke. • The influence of PFR moment on ankle kinematics, gait and balance was examined. • Dynamic AFOs with dorsiflexion assist Klenzak joints allow for adjustable PFR. • Moderate PFR setting may improve the ankle kinematics, gait and balance parameters. • High PFR may increase non-symmetrical ankle dorsiflexion during swing. [ABSTRACT FROM AUTHOR]

This study explores the synergistic effects of chitosan-coated cadmium sulphide (CdS) nanoparticles (NPs) at varying concentrations on their structural, optical, and photocatalytic properties. CdS NPs are known for their promising photocatalytic potential, but their practical application often requires stability enhancement and reduced toxicity. Chitosan, a natural biopolymer, offers unique advantages such as biocompatibility and heavy metal adsorption capabilities, making it an attractive candidate for surface modification of CdS NPs. Our investigation reveals that chitosan-coated CdS NPs exhibit concentration-dependent changes in their crystalline structure, bandgap energy, particle size, and vibrational characteristics. Notably, CdS NPs synthesized with 1.5 g chitosan concentration display the smallest bandgap and particle size, suggesting optimal photocatalytic activity. This research provides valuable insights into tailoring CdS NPs for efficient visible light photocatalysis, with implications in environmental remediation and energy conversion. [Display omitted] • Synergistic effects of chitosan-coated cadmium sulphide (CdS) nanoparticles (NPs). • Chitosan-coated CdSNP exhibit dose-dependent changes crystalline structure. • Bandgap energy, particle size, and vibrational characteristics were validated. • 1.5 g chitosan displayed smallest bandgap, size, at optimal photocatalytic activity. • Provides insights into tailoring CdS NPs for efficient visible light photocatalysis. [ABSTRACT FROM AUTHOR]

White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. Automatic WMH segmentation is essential for evaluating the natural disease course and the efficacy of clinical interventions, especially drug discovery. This work presents a hybrid method, which combines the novel metaheuristic Slime Mold Algorithm with the Harris Hawk's Optimization algorithm to train the Deep Convolutional Neural Network (DCNN) as a robust model to optimize the Kapur's entropy for WMH segmentation. We named it as Slime Mold Harris Hawk's Optimization-based DCNN (SMHHO-DCNN). The proposed method was tested on the open WMH segmentation challenge MICCAI 2017 and in-house dataset. However, the WMH regions would appear blurry and globally inconsistent. In order to solve the fuzzy problem, we improved the network architecture by incorporating skip connections across the mirrored layers with in encoder and decoder stacks. As a result, the segmented WMH output appears more realistic and coherent with its surrounding contexts, which enables the hybrid attention module to further enhance the precision of WMH localization by extracting the supporting information of high-level characteristics and low-level features. As a result, that the proposed hybrid optimization, skip connections, has increased the segmentation performance of Dice score to 87.2%, precision 90.3%, recall 89.5%, and f1-score 88.8% for MRI dataset which is superior compared to other approaches. The proposed technique outperformed existing state-of-the-art methodologies in both qualitative and quantitative measurements across a wide range of medical modalities. Furthermore, the dataset contains T1-w, T2-w, and FLAIR images, demonstrating the adaptability and robustness of the model. [ABSTRACT FROM AUTHOR]

BACKGROUND In this study, we wanted to evaluate the prevalence of genitourinary tract infection in diabetic patients and to know the clinical presentation, bacteriological profile and antibiotic sensitivity pattern of organism causing Genitourinary tract infection. METHODS This was a hospital based study conducted among 344 patients who were screened for genitourinary tract infection by culture of urine and discharge, in the Department of Medicine with the assistance of Department of Microbiology at VIMSAR, Burla, over a period of two years from December 2015 to November 2017, after obtaining clearance from institutional ethics committee and written informed consent from the study participants. RESULTS Nephropathy was found to in maximum no. of cases (64%) followed by retinopathy and neuropathy. Most common urinary tract complications were found to be cystitis and pyelonephritis (31%). E. Coli was isolated in highest no. of cases (33%) followed by Klebsiella (25%). Amp-ampicillin, Nal-nalidixic acid, Nitro- nitrofurantoin, Cipciprofloxacin, Ctx-cotrimoxazole, Nor-norfloxacin, Gen-gentamicin, Cefocefotaxime, Ami-amikasin. Nitrofurantoin and Amikasin are found to be having highest sensitivity among the antibiotic tested and cefotaxime and cotrimoxazole having least sensitivity. CONCLUSION Abdominal imaging is recommended as a screening in a patient with diabetes mellitus presenting with systemic signs of diabetes. The fact that antibiotic sensitivity changes with time; therefore, knowledge of bacteria involved and their current sensitivity pattern will help us not only providing best initial empirical therapy but also preventing long term morbidity. [ABSTRACT FROM AUTHOR]

The cast IN 713 C alloy is extensively used in the aerospace and automobile industries. Even with improved casting methods, the inhomogeneity in the microstructure is still inevitable and causes strain localizations during cyclic loading. So far, the effect of microstructural variables on the low cycle fatigue (LCF) deformation behavior of IN 713 C alloy has rarely been studied. In this study, IN 713 C alloy is LCF tested at 650°C and 750°C for strain amplitudes of 0.35% to 0.6%. The results showed that the deformation heterogeneity resulted from carbides, γ′ precipitates, and γ–γ′ eutectics distribution, governing the LCF failure micro‐mechanisms. The principle fatigue cracks were seen to be initiated from the precipitate‐free zone and oxidized‐surface carbides. It is corroborated that the higher GNDs were observed at carbide/matrix, eutectic/matrix, γ′/matrix interfaces, and in the interdendritic areas where voids and cracks formed. The higher GNDs at these sites facilitate crack initiation and propagation. Highlights: The fatigue life of alloy IN 713 C is highly sensitive to the microstructure.Higher tendency for carbide cracking was observed at high Δε/2.Cracks are seen to be initiated in PFZ and at oxidized MC carbides.Higher GND density was observed near carbides and eutectics. [ABSTRACT FROM AUTHOR]

Biomarkers play a crucial role in advancing precision medicine by enabling more targeted and individualized approaches to diagnosis and treatment. Various biofluids, including serum, plasma, cerebrospinal fluid (CSF), saliva, tears, pancreatic cyst fluids, and urine, have been identified as rich sources of potential for the early detection of disease biomarkers in conditions such as cancer, cardiovascular diseases, and neurodegenerative disorders. The analysis of plasma and serum in proteomics research encounters challenges due to their high complexity and the wide dynamic range of protein abundance. These factors impede the sensitivity, coverage, and precision of protein detection when employing mass spectrometry, a widely utilized technology in discovery proteomics. Conventional approaches such as Neat Plasma workflow are inefficient in accurately quantifying low-abundant proteins, including those associated with tissue leakage, immune response molecules, interleukins, cytokines, and interferons. Moreover, the manual nature of the workflow poses a significant hurdle in conducting large cohort studies. In this study, our focus is on comparing workflows for plasma proteomic profiling to establish a methodology that is not only sensitive and reproducible but also applicable for large cohort studies in biomarker discovery. Our investigation revealed that the Proteograph XT workflow outperforms other workflows in terms of plasma proteome depth, quantitative accuracy, and reproducibility while offering complete automation of sample preparation. Notably, Proteograph XT demonstrates versatility by applying it to various types of biofluids. Additionally, the proteins quantified widely cover secretory proteins in peripheral blood, and the pathway analysis enriched with relevant components such as interleukins, tissue necrosis factors, chemokines, and B and T cell receptors provides valuable insights. These proteins, often challenging to quantify in complex biological samples, hold potential as early detection markers for various diseases, thereby contributing to the improvement of patient care quality. [ABSTRACT FROM AUTHOR]

The current breeding effort aims to develop new wheat genotypes with higher genetic potential, thereby contributing to food security. Ten wheat genotypes were crossed in a half-diallel mating design. The F1 progeny from the 45 crosses, along with their ten parents, were evaluated in a randomized block design to estimate combining ability and the nature of gene action. Analysis of variance for combining ability revealed significant mean squares for both general and specific combining abilities across all studied traits. This indicates the importance of both additive and non-additive gene effects in the inheritance of these traits. The GCA/SCA ratio suggested the predominance of non-additive genetic action for all studied traits. Only five parents (HD3086, UP 2748, WH 1105, UP 2565, and HD 2967) exhibited good general combining ability for yield and associated traits. However, but none of them were found good combiners for all traits. Twenty-one crosses exhibited significant specific combining ability for yield and associated traits. The crosses UP 2748 x HD 3059, UP2565 x HD3086, WH 1105 x UP 2526, WH 1105 x WH 1021, and HD 2967 x WH 1021 were identified as good specific combiners for higher grain yield and associated traits. Utilizing these crosses in breeding programs that exploit heterosis can lead to the creation of new wheat genotypes with a broader genetic base. This approach, which involves crossing high-performing parents for some traits with low-performing parents for others, has the potential to produce offspring with superior characteristics in the next generation due to interactions between dominant genes. [ABSTRACT FROM AUTHOR]

This study explores the reinforcement potential of nano graphene in a poly latic acid (PLA) matrix. The present work introduces a framework to select reinforcement for the formation of composite that will be feasible to manufacture by using additive manufacturing. Main objective of this research work is to develop the composition of PLA-nano graphene composite to make it feasible to be printed by fused deposition modeling based additive manufacturing. In order to achieve the same, the problem of selecting a reinforcement and constituents of composite is discussed in detail. Based on the literature review, four compositions (1.5 %, 3 %, 4.5 %, and 6 % nano-graphene by weight to PLA) are prepared to find out the optimum composition. In this study, the effects of nano graphene loading on the melt flow behaviour of PLA-graphene composites for extrusion-based fused deposition modeling (FDM) were investigated. The objective was to determine the optimal nano graphene loading that would yield the optimum melt flow index with desirable best mechanical properties. Tests have been performed for three samples of each of four compositions 1.5 %, 3 %, 4.5 %, and 6 % nano Graphene by weight to PLA. The results found that with increasing nano graphene content, both the melt flow index as well as mechanical properties increased significantly. However, when the nano graphene loading reached 6 wt%, a decrement in these parameters was observed due to the agglomeration of fillers onto each other within the composite structures. Therefore, it is concluded that out of the remaining compositions (1.5 %, 3 %, and 4.5 % nano graphene by weight to PLA), 4.5 wt% is an optimal loading level for obtaining desired processing performance and mechanical properties from PLA-graphene composites for FDM application. [ABSTRACT FROM AUTHOR]

The exposure of zero trust security in the Industrial Internet of Things (IIoT) increased in importance in the era where there is a huge risk of injection of malicious entities and owning the device by an unauthorized user. The gap in the existing approach of zero trust security is that continuous verification of devices is a time-consuming process and adversely affects the promising nature of the zero-trust model. Every time the node enters, even if the node is a member of the network, authorization of the node is necessary to ensure authenticity. This verification section of zero trust hinders the seamless working of the IIoT infrastructure. Therefore, the main objective of this paper is to propose the solution for the above-mentioned problem by enabling "device profiling" via deep reinforcement learning so that the same device can be identified and permitted access without hindering the working of Industrial Internet of Things infrastructure. The overall proposed approach works in different phases including the compression function for ensuring data confidentiality and integrity, then the device profiling is performed based on the features a device possesses, and lastly, deep reinforcement learning for anomaly detection. To test and validate the proposed approach, extensive experimentations were performed using measures such as false positive rate, data confidentiality rate, data integrity rate, and network access time, and results showed that the proposed technique titled "MMODPAD-DRL" outperforms the existing approaches in false positive rate by 27%, data confidentiality rate by 4% and data integrity rate by 3%, in addition, lessen the network access time by 20%. [ABSTRACT FROM AUTHOR]

This is a prime time to develop and implement the "waste to energy" projects across the globe to attain a sustainable environment. Anaerobic digestion (AD) has attracted the scientific community due to its simplicity and easiness to handle, and has the potential to utilize any kind of organic waste to produce a mixture of combustible gases, i.e., biogas and digested slurry, which has further applications in agriculture, solid biofuels, and purification. The process, in turn, reduces the local waste and helps in recycling in a manner that reduces greenhouse gas (GHG) emission, conserves the resources, and establishes a circular economy in the time of undetermined future for the production of energy and safe disposal of the waste. However, the conventional processes encounter with the low biogas yield and long retention time, which discourage the developers. To enhance biogas yield and quality, the momentum of research has increased towards implementation of advanced techniques for development of efficient processes. The present article summarizes the effect of different operational parameters on AD and impact of advanced techniques for enhanced biomethane/biogas. The article further covers the life cycle assessment (LCA) and techno-economic aspect (TCA) of the AD process. This will provide the comparison of different advanced techniques in terms of biomethane/biogas yield. [ABSTRACT FROM AUTHOR]

• Auxins have an essential role in callus induction. • Application of ABA causes high quality callus. • 3 % of sucrose has a positive effect on callus features. • Modeling by ML methods is required to predict the results. Saffron is a spice derived from its flower (Crocus sativus L.) and it is grown for its medicinal and culinary uses worldwide. Callus is an intermediate stage between explant and in vitro organogenesis. The fate of embryogenesis and organogenesis depends on the quality of the callus. Callogenesis is a complex biological process and is affected by various intrinsic and extrinsic factors. Machine learning (ML), can effectively cope with the time and cost constraints of experiments. In this study, we emphasized the effects of sucrose and phytohormones (auxin, and abscisic acid) on callogenesis with the aid of a machine-learning algorithm. Among the different tested hormone combinations, maximum callus proliferation was observed in the MS media plate containing 3 % sucrose, supplemented with 100 µM ABA, 2 and 4 mg/L NAA, and 2,4-D. A total of eight different ML tools were used to predict the efficacy of inputs on callogenesis. Out of all applied algorithms, Xtreme Gradient Boosting (XGB) and Gradient Boosted Regression (GBR) provide better prediction performance than other models on callus induction and proliferation. SHAP analysis reveals the significance of sucrose, plant growth regulators (NAA, 2,4-D, and ABA), and time on callogenesis. Therefore, machine learning programs can effectively save cost and time by guiding experiments and facilitating optimization. [ABSTRACT FROM AUTHOR]

The current study focuses on exploring the effectiveness of Turtle Shell Powder (TSP) as a new and unique approach to use it as a filler material while fabricating kenaf/epoxy composites and investigating its potential application. The FRP composite of kenaf/epoxy reinforced with TSP filler was manufactured at various loading (1%, 3% and 5% by weight). The influence of TSP filled KFRP composite was studied and compared with the results of mechanical properties. SEM was employed to conduct morphological traits of the tensile fracture for the K0 and K5 samples with this EDAX also analysed. A notable enhancement observed from the result is the 5% TSP reinforced composite (K5) compared to other samples. This enhancement was ascribed to the improved dispersion and interfacial bonding between the reinforcement and the matrix in the composites, leading to more efficient load transfer mechanisms. The performance of TSP particles dramatically improves the strength of the laminated composites, and the inclusion of ply orientation further strengthens the interfacial bonding between TSP, epoxy and kenaf components. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been experienced as the most significant global disaster after the Spanish flue in 1918. Millions of people lost their life due to a lack of preparedness and ineffective strategies for managing humanitarian supply chains (HSC). Based on the learnings from this pandemic outbreak, different strategies for managing the effective HSC have been explored in the present context of pandemics through a systematic literature review. The findings highlight some of the major challenges faced during the COVID-19 pandemic, such as lack of planning and preparedness, extended shortages of essential lifesaving items, inadequate lab capacity, lack of transparency and visibility, inefficient distribution network, high response time, dependencies on single sourcing for the medical equipment and medicines, lack of the right information on time, and lack of awareness about the protocol for the treatment of the viral disease. Some of the significant learnings observed from this analysis are the use of multiple sourcing of essential items, joint procurement, improving collaboration among all stakeholders, applications of IoT and blockchain technologies for improving tracking and traceability of essential commodities, application of data analytics tools for accurate prediction of next possible COVID wave/disruptions and optimization of distribution network. Limited studies are focused on finding solutions to these problems in managing HSC. Therefore, as a future scope, researchers could find solutions to optimizing the distribution network in context to pandemics, improving tracing and tracking of items during sudden demand, improving trust and collaborations among different agencies involved in HSC. [ABSTRACT FROM AUTHOR]

In this work, the effects of adding Multi-Walled Carbon Nanotubes (MWCNT) along with leftover cooking oil on a diesel engine's efficiency, emissions, and combustion properties were studied. After TEM examination, using an ultrasonicator, the MWCNT was combined with biodiesel fuel made from leftover cooking oil at concentrations of 0 and 25, parts per million. A constant speed of 1500 rpm was used to test these blends while varying engine loads. The experimental results indicated that the cylinder peak pressure, peak pressure rise rate, and heat release rate were slightly lower when MWCNTs were added to WCO fuel compared to pure WCO fuel. The innovative fuel mix significantly decreased CO and BSFC. Not only that, but the specific fuel consumption was lower than with pure WCO fuel, and the brake thermal efficiency was significantly altered.25 ppm was found to be the proper dose level of MWCNTs with WCO fuel based on engine capability and pollutant comparisons. All engine performance metrics significantly increased as a result. [ABSTRACT FROM AUTHOR]

This study demonstrates the development of a unique hybrid thermoplastic composite using reduced Graphene oxide (rGO) content and Zirconia (ZrO2) nanoparticles into the Ultra-High Molecular Weight Polyethylene (UHMWPE) biomaterials for continuous loading conditions. Specimens with different loadings of rGO (0 to 1.5 wt.%) and ZrO2 (5 to 10 wt.%) were fabricated using liquid phase ultrasonication followed by the hot press moulding method. The samples were analyzed using Thermogravimetric Analysis (TGA), Impact (Izod) testing, and Dynamic Mechanical Analysis (DMA). The developed material feasibility was assessed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) analyses. The findings revealed that the 1 wt.% rGO/5 wt.% ZrO2/UHMWPE sample improved the storage modulus by 66.15%, and the Impact absorbed energy by 11.33% compared to the pristine UHMWPE. The proposed nanocomposite could be endorsed for artificial joints, prostheses, and other Artificial Bio-Bearing (ABB) applications. [ABSTRACT FROM AUTHOR]

Introduction: "Imposter syndrome" or "Imposter Phenomenon" is not a mental disorder but is considered as a psychological pattern in which people doubt their accomplishments and is associated with certain poor psychological functioning like low self-esteem can be detrimental to medical students. Objective: To find out the prevalence of Imposter phenomenon as well as it association with self-esteem among the final year medical students. Methods: A cross sectional study was carried-out among 249 medical students of a private medical college in the U.T of Puducherry using Clance Imposter Phenomenon scale. Results: 58.2% and 41.8% of the participants were in Part-1 and part-2 of final year respectively. It was observed that every participant had some degree of Imposter phenomenon. Low self -esteem, was found among 12.85% of the study participants. In this study there was no association between the low self-esteem and Imposter Syndrome. Conclusion: It is very clear that the burden of Imposter syndrome is quite high among the medical students as the reasons are varied. Extensive research to elaborate these is the need of the hour, as this information helps in designing the preventive measures for the students who are the future of the nation and the foundation stones of progress. [ABSTRACT FROM AUTHOR]

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype–genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity. [ABSTRACT FROM AUTHOR]

We consider the question of the number of exactly solvable complex but PT-invariant reflectionless potentials with N bound states. By carefully considering the Xm rationally extended reflectionless potentials, we argue that the total number of exactly solvable complex PT-invariant reflectionless potentials are 2[(2N − 1)m + N]. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerators (TENGs) can play a pivotal role in harnessing non-utilized reciprocating motion and convert it into electrical energy that can later be stored in a battery or capacitor to power various Internet of Things-based smart electronic and wearable devices. Herein, we designed a cost-effective instrumental test bed focused on investigating the output performance of a horizontal contact separation mode triboelectric nanogenerator by varying the input parameters, such as applied force, motor speed, triboplate separation, and frequency of instrumental setup. The test bed mainly consists of three major parts: (i) application of force, (ii) tapping of TENG sample, and (iii) output parameters measurement. The output performance in terms of open circuit output voltage (VOC), short circuit current (ISC), and power density of polydimethylsiloxane-based TENG was monitored and optimized by varying the input parameters. A low-cost current measuring circuitry using an operational amplifier integrated circuit has been proposed with 92% accuracy. The maximum value of VOC and ISC was observed to be 254 V and 31.8 µA at a motor speed of 600 rpm, the distance between both the plates was 6 mm, the input applied force of 40 N, and the striking frequency of 3 Hz. The maximum power density of 2.1 W/m2 was obtained at an input impedance of 8 kΩ. The durability of the test bed as well as the TENG sample was also measured for 25 h. The degree of uncertainty was measured for VOC, ISC, and applied force and calculated to be 1.62%, 7.45%, and 6.27%, respectively. [ABSTRACT FROM AUTHOR]

The rising global temperature is one of the primary concerns of the world as it impacts the economy, environment and healthcare of any country which are more pronounced a regional level. Assessment of regional impacts of climate change at a local level requires fine resolution of climate data for which a robust and fast downscaling method is needed. In this study, we use three deep learning‐based methods, namely long short‐term memory network (LSTM), deep neural network (DNN) and recurrent neural network (RNN), to downscale CMIP6 13 GCMS models data (1.25° × 1.25° resolution) global climate model (GCM) maximum temperature (Tmax) at a regional scale of 0.5° × 0.5° spatial resolution for the period 1991–2010 over the Indo‐Gangetic Plain (IGP). In addition to the temperature prediction, heat wave events have been also analysed in the study. The study found that LSTM method performs better than DNN and RNN in downscaling of all GCM model datasets when evaluated against observed maximum temperature data from the India Meteorological Department (IMD) in terms of RMSE (0.9–3.5), average of all grid MAE value between (1.2 and 2.68), correlation (0.68–0.9) along with and spatiotemporal variability. LSTM also performed better in heat wave prediction over the region with similar temporal range (12–36 events) and spatial occurrence as compared to the observation (12–28 events). Overall, the study concludes that LSTM performs better than two methods for Indo‐Gangetic Plain with best hyper parameter tuning. Hence, we propose to utilize a deep learning framework based on LSTM for downscaling GCM dataset at a finer resolution. [ABSTRACT FROM AUTHOR]

Network traffic analysis is referred to as the method to monitor the availability of the network and identify the anomalous activities like operational issues and security. Network traffic mainly occurs due to the transmission of a large amount of data over the computer network at the same time. The grouping and classification process can evaluate the network traffic and the endless determination of network traffic patterns is the most important challenge during network traffic classification. The existing approaches have some limitations because the time consumption was not able to reduce and it has low classification accuracy in the network traffic analysis. To overcome these problems, the Dove Swarm Optimized Reinforced Deep Belief Network (DSO-RDBN) is proposed which is utilized for optimizing the hyperparameters. There are no effectual training algorithms that can optimize the different hyperparameters associated with the RDBN architecture such as momentum coefficients, learning rate, number of hidden nodes, and number of hidden layers without manual tuning. The manual selection of hyperparameters leads to some delay in the network traffic classification process so the DSO algorithm is implemented for addressing the hyperparameter tuning problem. For network traffic classification analysis, the USTC-TFC2016 test dataset is used and this dataset has two categories Benign and Malicious. The performance metrics such as accuracy, precision, sensitivity, F-measure, and false alarm range (FAR) are applied for improving the performance compared to state-of-art methods. The performance rate of accuracy, precision, sensitivity, F-measure and FAR is 97.2%, 97.6%, 97.5%, 97.6% and 68% respectively. [ABSTRACT FROM AUTHOR]

Apart from our imagination, the nanotechnology industry is rapidly growing and promises that the substantial changes that will have significant economic and scientific impacts be applicable to a wide range of areas, such as aerospace engineering, nanoelectronics, environmental remediation, and medical healthcare. In the medical field, magnetic materials play vital roles such as magnetic resonance imaging (MRI), hyperthermia, and magnetic drug delivery. Among them, manganese oxide garnered great interest in biomedical applications due to its different oxidation states (Mn2+, Mn3+, and Mn4+). Manganese oxide nanostructures are widely explored for medical applications due to their availability, diverse morphologies, and tunable magnetic properties. In this review, cogent contributions of manganese oxides in medical applications are summarized. The crystalline structure and oxidation states of Mn oxides are highlighted. The synthesis approaches of Mn-based nanoparticles are outlined. The important medical applications of manganese-based nanoparticles like magnetic hyperthermia, MRI, and drug delivery are summarized. This review is conducted to cover the future impact of MnOx in diagnostic and therapeutic applications. [ABSTRACT FROM AUTHOR]

Nano and dusty fluids provide new possibilities to improve energy transfer efficiencies. Motivated by immiscible fluids duct flow framework in the filtration of hydrocarbon materials, the objective of this manuscript is to present the numerical study of three immiscible fluids namely, copper oxide-ethylene glycol (CuO-EG) water nano, Saffman dusty, and plain Newtonian fluids in the horizontal channel. For the formulation of the mathematical model, the dusty fluid is positioned between the immiscible layers of the nanofluid and Newtonian fluid. The interface between the immiscible fluids is continuous and non-deformable in nature. The flow is driven by an externally time-dependent pressure gradient. The novel radial basis function pseudospectral (RBFPS) method is employed to solve the governed partial differential equations, and the resulting outcomes are compared with cubic B-spline differential quadrature and finite difference method. The impact of several crucial fluid parameters, comprising the volume percentage of CuO nanoparticles, the dust particle concentration parameter, and various others on velocity profiles, are presented graphically. It is observed that despite being present only in the lower zone, the CuO nanoparticles have an impact on the flow velocities in other zones also. The fluid and particle velocities decrease as the volume fraction of CuO increases. [ABSTRACT FROM AUTHOR]

The present paper employed the Applied Element Method (AEM) to investigate the damage patterns in high-strength concrete (HSC) frames under extreme loads. Unlike existing computational techniques, AEM offers a distinct advantage in accurately identifying structural damage in all stages of loading conditions. The research focuses on analysing the impact of displacement-controlled in-plane cyclic loads on single- and multi-storey reinforced concrete frames with and without infill walls. AEM analysis coincided with experimental results, theoretical predictions, and FEMA 356 code provisions, affirming a reliable examination of cyclic damage patterns. The study reveals distinct damage patterns, shedding light on infilled frames' superior in-plane lateral load resistance compared to bare frames. Interestingly, multi-storey frames with soft-storey configurations exhibited higher resistance to lateral loads than those with infilled walls due to premature collapse of HSC columns. The study highlights the crucial role of robust, strong infill walls in reinforcing slender HSC frames. The results provide valuable insights for designing resilient structures under extreme operating conditions, thus contributing to advancing structural engineering practices. [ABSTRACT FROM AUTHOR]

This study aims to gain insight into the effect of the synthesized poly‐ionic liquids (PILs) (1‐vinyl‐3‐alkyl imidazolium bromide) (alkyl=hexyl (A), octyl (B), and decyl (C)) on various cellular components of blood by flow cytometry. PILs may have some adverse effect on blood depending on the nature and the dose administered. So, it is critical to understand the potential influences of foreign polymeric entities on blood before they are used clinically. The complete blood counts (CBCs) are done using an automated analyzer based on the flow cytometry principle. The leukocyte viability with different PIL samples is performed by flow cytometry using 7‐AAD. The auto‐hemolysis is performed by incubating the PILs with defibrinated blood. The CBC is mostly the same when incubated with various PILs. However, RBCs show an increase in mean corpuscular volume (MCV), indicating cellular swelling. The viability with phosphate saline buffer (PBS), dimethyl sulphoxide (DMSO), and PIL (A, B, and C) is 99.94 %, 99.76 %, 99.71 %, 98.87 %, and 99.42 %, respectively. The effect on leukocyte size & internal structures are noted. The auto‐hemolysis tests of RBC by PILs are very similar to control auto‐hemolysis. [ABSTRACT FROM AUTHOR]

Green methanol, ethanol, and diesel-based hydrotreated vegetable oils are some of the renewable liquid fuels that show satisfactory performance in diesel engines. A notable advantage of these fuels is that they are renewable and do not require significant modifications in the existing engines for successful operation. Suitable fuel systems, especially their material compatibility, remain unresolved, and therefore, it is a weak link in their large-scale adaptation. Elastomer-based sealing O-rings lose their mechanical properties after a short exposure time to these fuels, adversely impacting their functionality. This research study evaluated the long-term material compatibility of different elastomer-based sealing materials by immersing the O-rings in these test fuels (hydrotreated vegetable oil, methanol, ethanol, and diesel) for different time intervals (i.e., up to 15 months). The material compatibility was assessed mainly by investigating these changes in various mechanical properties of these O-rings, namely tensile strength (ΔTs), elongation at break (ΔEb), Shore A hardness (ΔH), and mass (ΔM). The degradation of mechanical properties was studied and analyzed during the immersion interval from 0.9 to 15.2 months and compared with O-rings kept in a normal atmosphere. It was noted that individual fuels affect various mechanical properties significantly. In a short interval of 0.9 months (28 days), significant changes in the mechanical properties of the sealing O-rings were observed. [ABSTRACT FROM AUTHOR]

This study presents the tuning strategy of PI controllers gain parameters to improve the performance of vector-controlled permanent magnet synchronous motor drive. In the proposed work PI controller parameters are optimized using Particle Swarm Optimization (PSO) and Whale Optimisation Algorithm (WOA). Nonlinear programming techniques, namely PSO and WOA optimisation, are employed to enhance the convergence rate of speed, torque, and flux errors using an offline tuning method. The simulation results of speed, torque, and flux show that the transient and steady state responses have been greatly improved and verified for different operating conditions. It has been observed that the quality of stator current, developed output voltage of inverter, and ripples in electromagnetic torque are also improved, which shows the feasibility and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

The migration of neural progenitor cells (NPCs) to their final destination during development follows well-defined pathways, such as along blood vessels. Cells originating from the highly malignant tumor glioblastoma (GBM) seem to exploit similar routes for infiltrating the brain parenchyma. In this report, we have examined the migration of GBM cells using three-dimensional high-resolution confocal microscopy in brain tumors derived from eight different human GBM cell lines xenografted into immunodeficient mice. The primary invasion routes identified were long-distance migration along white matter tracts and local migration along blood vessels. We found that GBM cells in the majority of tumors (6 out of 8) did not exhibit association with blood vessels. These tumors, derived from low lamin A/C expressing GBM cells, were comparatively highly diffusive and invasive. Conversely, in 2 out of 8 tumors, we noted perivascular invasion and displacement of astrocyte end-feet. These tumors exhibited less diffusive migration, grew as solid tumors, and were distinguished by elevated expression of lamin A/C. We conclude that the migration pattern of glioblastoma is distinctly tumor cell-specific. Furthermore, the ability to invade the confined spaces within white matter tracts may necessitate low expression of lamin A/C, contributing to increased nuclear plasticity. This study highlights the role of GBM heterogeneity in driving the aggressive growth of glioblastoma. [ABSTRACT FROM AUTHOR]