Failure to mitigate thermal runaway (TR) early can result in TR propagation due to cell-to-cell heat interactions, resulting in module/pack fires. In this study, a comparative investigation is conducted on the TR cell position prediction performance by different machine learning (ML) algorithms for 32-cell cylindrical air-cooled LiB modules in aligned, staggered, and cross arrangement cells. The ML models are trained on temperature data from the sensors optimized using the Pearson Correlation Coefficient approach with a threshold of 0.85. The air temperature data in the battery module under different operating and faulty conditions were generated from experimentally validated numerical models and recorded by the sensors initially dispersed in single mid-plane and multiple arbitrary planes of the battery domain. The base ML algorithms chosen for the study comprise the k-Nearest Neighbors, Random forest, Gradient boosting, and Long short-term memory classification algorithms. The developed models are further subjected to 5-fold cross-validation and external testing using random test cases and subsequently compared for prediction accuracy with the error metrics, training, and prediction times. A Stacked Ensemble learning model is built and tested for accuracy based on the base models to improve the overall predictive accuracy. The study concludes that the RF model outperformed other base models owing to its 100% accuracy across all cell arrangements, high consistency across validation folds, and the lowest training and prediction time of 22 s and 0.59 s, respectively. The study identifies the best-fit ML model for early fault detection and preventing catastrophic accidents.

The rise of severe accidents caused due to thermal runaway (TR) and its propagation in lithium-ion battery (LiB) modules is one of the most challenging factors that decelerate the rapid expansion of the electric vehicle (EV) industry. Timely detection of the TR undergoing cells in the module is crucial as the heat generated during TR is adequate to trigger the TR of the surrounding cells. In this study, an accurate machine learning (ML) based faulty cell position prediction model is developed for the air-cooled cylindrical LiB modules with the cells in aligned, staggered, and cross arrangements. The CFD model used for data generation is validated with the in-house experiments on an aligned surrogate 32-cell module for multiple failure positions. Further, to predict the TR cell position in the battery module, the random forest classification (RFC) model is developed based on the temperature distribution data obtained from the optimized temperature sensors derived for the two types of initial temperature sensor distributions (single and multiple-planes) using a heat map approach. The model developed is tested for varying design and operating conditions, and the prediction results, along with the error metrics and the prediction timings, are compared. It is revealed that except for the cross-cell arrangement in the single-plane temperature sensors distribution scenario, the RFC model produces higher accuracy when tested on the optimized temperature sensor layouts for the multiple-plane sensor distribution. The results of this study can allow early failure detection in battery modules, resulting in increased safety and cost savings.

The 5,6-Bis(diisopropylphosphino)acenaphthene L-stabilized Sb(I) cationic compound [LSb][OTf] (OTf = CF3SO3) 1 possessing two lone pairs of electrons on the Sb(I) center showed nucleophilic behavior toward methyl trifluoromethanesulfonate forming the oxidized product [LSbMe][OTf]2 2 (OTf = CF3SO3). Reaction of compound 1 with Lewis acids such as GaCl3 and AlBr3 led to changes in the counteranions only giving products [LSb][GaCl4] 3 and [LSb][SbBr4] 4, respectively. A metathesis reaction was observed when compound 1 was reacted with PI3. The Sb(I) cation in 1 underwent the metathesis reaction with the P(I) cation, forming the more stable product [LP][OTf] 5. The Sb(I) center in 1 was completely oxidized to Sb(V) by reacting with two equivalents of o-chloranil to give the Bis(phosphine)-stabilized Bis(perchloro catecholato)stibonium cation [L(O2C6Cl4)2Sb][OTf] 6. Both compounds 1 and 6 were employed as proof-of-concept Lewis acid catalysts for the hydrosilylation of p-methyl benzaldehyde. All the compounds were characterized using single-crystal X-ray diffraction analysis, multinuclear nuclear magnetic resonance spectroscopy, mass spectrometry, and absorbance spectroscopy. Density functional theory calculations were performed on the relevant compounds.

Immersion cooling is a promising thermal management system for LiBs where the cells are submerged in a thermally conductive coolant, thus improving heat dissipation and prolonging battery life. The present study investigates an aligned arranged 4S4P immersion-cooled LiB module to develop a best-fit machine learning (ML) model that could predict the fault position in the module depending on the inputs from the temperature sensors that are optimized under different operating and fault conditions. The Pearson Correlation Coefficient (PCC) feature selection approach is used to optimize the sensors, while the data is generated from numerical simulations on a 4S4P immersion-cooled LiB module. The model validation through internal experimental trials is performed on a 2S2P battery module. The four ML classification algorithms, which include the K Nearest Neighbors, Random Forest (RF), Extreme Gradient, and Long Short Term Memory (LSTM), are trained on the optimized sensors data and are further tested internally and externally to assess their performance on unseen data within and outside the training range. A 5-fold cross-validation process is also implemented, and following a comprehensive comparison of the predictions based on the accuracies and model elapsed time, the best-fit model is identified. The results conclude that while the LSTM model slightly outweighs the other models with an accuracy of 98.18% for the specific external test cases, the RF model is chosen as the best-fit model with a prediction accuracy of 99.7% based on the error metrics and low training time for the internal testing. The outcomes of this present work contribute to the early identification of battery module failures, enhancing safety and reducing costs.

Nanotechnology is a fast-growing field as it has vast area of applications in physiochemical as well as biological science. The method of synthesis of nanoparticles has very big impact on their properties. Therefore, researchers are having very keen interest in the different method of synthesis of nanomaterials and their application. Out of physical, chemical, and microbial synthesis the green synthesized nanoparticles are cost-effective, easy to produce, have no hazardous impacts on surrounding as well as on the human and animals. The mostly used metal oxide nanoparticles are Ag (silver), Au (gold), Zn (zinc), Pb (lead), Cu (copper), Cd (cadmium), Ce (cerium) and so on in different areas. Nanoparticles have very significant role in agriculture as well, in disease suppression and crop growth. The relationship between plant nutrition and disease suppression makes them more promising tool to be used in agriculture. Zinc (Zn), boron (B), iron (Fe), manganese (Mn), molybdenum (Mo), copper (Cu), and chlorine (Cl) are an essential nanonutrient required by the plant for healthy crop growth. The antifungal potential of these metals (Zn, Cu, Fe, etc.) has been reported in various studies. Therefore, nutrients can be supplied in the form of nanoparticles in agricultural sector that can suppress the disease and increase the crop growth. This chapter deals with the green synthesized nanomaterials, various essential nanonutrients and their role in agriculture reported till date.

Abstract The attenuation characteristics of NW Himalaya and NE India region has been estimated using recorded accelerograms. In this analysis frequency dependent attenuation of P-wave, S wave and coda wave was estimated and the scattering & intrinsic attenuation are separated using Wennerberg (1993) method. The frequency dependent relation of scattering attenuation (QS) and intrinsic attenuation (QI) have also been developed for these seismically active regions. The frequency dependent relations for NW Himalaya are Qα = (67 ± 0.22) f (0.96 ± 0.001), Qβ = (68 ± 0.29) f (0.95 ± 0.002), QC = (107 ± 0.10) f (1.06 ± 0.0003), QI = (95 ± 0.06) f (1.01 ± 0.0002) and QS = (306 ± 6.85) f (0.80 ± 0.009) for P-wave, S-wave, coda wave, intrinsic and scattering attenuation respectively. Similarly, for NE India region the frequency dependent relations are Qα = (89 ± 0.22) f (0.85 ± 0.001), Qβ = (92 ± 0.10) f (0.86 ± 0.0004), QC = (141 ± 0.34) f (1.02 ± 0.0009), QI = (120 ± 0.21) f (0.98 ± 0.0004) and QS = (306 ± 0.66) f (0.77 ± 0.0009) for P-wave, S-wave, coda wave, intrinsic and scattering attenuation respectively. The ratio of Qβ and Qα is greater than 1 for entire frequencies range considered in this analysis which indicates that both NW Himalaya and NE India regions are partially saturated with fluids. The estimated values of intrinsic (QI) and scattering (QS) attenuation shows that intrinsic attenuation is dominant in both NW Himalaya as well as NE India region. The important fact is that in both regions’ QC lies in between QI and QS, which agrees with theoretical and experimental measurements. The frequency dependent of Q’s in this study suggests that both regions are highly heterogenous and low value of Q0’s (i.e. Qα, Qβ and QC) for both regions exhibits high degree of seismicity.

Diabetes and its complications, such as delayed wound healing, are increasing at an alarming rate in India, putting an enormous strain on the country’s limited healthcare resources. Hence, the present study proposes to screen/identify the possible mechanisms and to study the effect of the flavonoid-enriched fraction of Selaginella bryopteris extract against human keratinocyte cell lines (HaCaT) and streptozocin (STZ)-induced diabetic wounds in a male Wistar rat model. Chemical profiling was performed by an MTT assay. The obtained GC–MS analysis results showed the presence of amentoflavone, gallic acid, imidazole, palmitic acid, catechine, L-fucitol, lupeol, and myo-inositol as the major bioactive phytoconstituents. S. bryopteris induces the generation of ROS, the condensation of chromatin in the nucleus, and changes in the membrane potential of mitochondria in HaCaT cell lines. An S. bryopteris-dependent induction of apoptosis-mediated cell death in HaCaT cell lines was confirmed by an AO/PI analysis. Mitochondrial depolarization was reflected in JC-1 staining of cells. The wound size was reduced and epithelialization was enhanced. Keratinocyte migration decreased interleukins, TNF-α, IL-2, and IL-6 and the expression of pro-apoptotic (p53, caspase-3, caspase-9, and Bax) and anti-apoptotic (Bcl-2) genes in a dose-dependent manner. Keratinocyte migration increased antioxidant enzyme levels (CAT, SOD, MDA, and GSH). Wound healing is facilitated through the mitochondria-mediated apoptosis pathway, revealing a new area of diabetic wound therapy.

The invention of magnetorheological abrasive finishing (MRAF) techniques has been prompted by the ever-increasing demand for nano-finished exterior surfaces of cylindrical work samples. In the present study, a novel form of the MRAF system to nano-finish the exterior cylindrical surfaces of various diameters has been devised and their finishing mechanism is discussed. The finishing experiments have been carried out to examine the finishing capabilities of newly devised MRAF system. Taguchi's parameter design approach has been employed to find the optimal combination of finishing parameters to achieve high-quality surface of S.S.-316 L cylindrical work-sample. The obtained results have been analyzed statistically which demonstrate that the chosen parameters namely F-to-S iron ratio, d.c. supply, work sample rotational speed and linear feed rate have significant effect on the percentage change in average surface roughness (%Delta R-a). The effect of finishing time on R-a has also been explored, with the findings revealing that a minimum R-a value of 0.074 mu m was attained after 60 minutes of finishing time, demonstrating the developed MRAF process's superior capabilities.

Machining of metal matrix composites (MMCs) has now become an essential task in shaping them to their final usable forms for various industrial applications. These machining operations may range from drilling of simple through holes to cutting and shaping of the composites into complex shapes. Determination of the optimal process parameters during their machining presents a combinatorial optimization problem, which may turn out to be more complex due to involvement of various material dependent parameters of the MMCs, like particle size, reinforcement percentage etc. The importance of optimization increases manifold due to conflicting settings of different process parameters for attaining the desired quality characteristics. In this paper, two nature-inspired optimization algorithms, i.e. multi-objective antlion optimization (MOALO) and multi-objective dragonfly algorithm (MODA) are applied for optimization of various process parameters during machining of MMCs. To mitigate uncertainties in global optimality of the predicted Pareto optimal fronts arising due to stochastic nature of the metaheuristics, an ensemble approach integrating MOALO and MODA techniques is proposed here. It is demonstrated with the help of two case studies that MOALO-MODA ensemble is far superior to its individual counterparts and thus, can be employed for development of robust and reliable Pareto optimal fronts.

The present study examines the magnitude and pattern of non-farm employment among schedule caste respondents in western plain zone of Punjab. The study was based on multi stage random sampling technique. Both primary and secondary sources of data was used for the study. For analysis of the data simple percentages, averages and regression analysis was used. The study found that 63 percent of scheduled caste respondents adopted both farm and non-farm activities for their livelihood. The average operated area came out to be one acre among selected scheduled caste respondents. Among non-farm activities in the study area, 41 percent scheduled casted respondents were engaged as casual labourers. .Due to lack of skill they are left with no other option but to work for daily wages in unskilled jobs. Pattern of income from farm and non-farm activities shows that the income of respondents engaged in both farm and non-farm activities was double than the income of respondents engaged in farm activities only. The average per monthly income of SC respondents engaged in farm activities only was Rs. 7764 whereas average income of SC respondents engaged in both farm and non-farm activities was Rs. 13382. The study suggested that skill development centres specific for the need of the SC respondents should be opened in their areas.The average operated area was marginally low in the study area so the only hope to increase the income of SC respondents is non-farm activities in the rural areas. The priority should be attached to the removal of barriers of any kind for the people to enter into the non-farm activities besides improving infrastructure facilities like banking, roads, market and communication facilities in the rural areas, facilitating more poor agricultural labourers and jobless people to take up some kind of non-farm activities for their livelihood.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

Heat generating components produce varying magnitudes of heat during their working. To extend the working life and also to increase thermal reliability, intervention in the form of thermal management is essential. Thermal management of such systems is a complex task that uses heat removal and temperature control strategies to keep the components at optimal conditions. When applied to failure mechanisms in electronics, the Arrhenius equation, which relates the rate of chemical reactions to temperature, shows that a 15 degrees C increase in temperature reduces component life by half, and thermal cycling between the minimum and maximum environmental temperatures reduces component life by a factor of 8. In the present work, convective flow across a two-dimensional 4x4 circular tube bank in aligned configurations with arbitrary heating is considered for the prediction of the location of the heated component and also the temperature distribution across the tube bank. The tube spacing in the transverse and longitudinal directions is 22 mm for each tube bank configuration, and the tube diameter is set to 19 mm. Cross flow over the tube banks is modeled and solved using computational fluid dynamics simulations to investigate the temperature distribution of the tube bank. A support vector regression (SVR) algorithm is trained by utilizing the data from the simulations. The trained model predicts the temperature distribution of the tube banks and the heater location under different operating conditions like varying mass flow rate, heat generation, inlet air temperature, etc. The results of the present study may be particularly applied for engineering applications, which include heat-generating components like battery modules, electronic components, nuclear fuel rods, etc., to understand thermal reliability and failure propagation in similar scenarios.

Intrusive marketing is invasive in nature and it has negative impact on the consumers. Due to its turning off effect, customers may develop an antipathy to the brand. Hence, the marketing campaign may lead to just opposite of its objectives. With the large-scale proliferation of mobile communication, there has been a stringent shift from the traditional marketing to mobile phone-based marketing. However, due to the more personalised nature of device, customers are generally more sensitive towards the marketing campaigns delivered on their mobile phones. Any unwanted marketing campaign may elicit negative response and marketers need to be very careful in drafting their strategies to identify what and how to reduce the intrusive nature of their advertisements. The existing frameworks and models for marketing campaigns do not cover the intrusiveness of mobile marketing campaigns. Present work identifies the factors leading to the non-intrusive campaigns and their possible consumer effects. A strategic framework has been proposed for the development of non-intrusive mobile marketing campaigns along with technology-based solutions and content specific suggestions.

The current study is dedicated for solving the time-fractional (2+1)-dimensional Navier-Stokes equation. The Lie symmetry approach is applied on the governed time-fractional equation to fulfill this need. In the direction of exact solutions of the time-fractional equation first of all invariance condition is obtained in the presence of the Lie group. Consequently, infinitesimals are obtained with the help of the invariant condition. Moreover, these infinitesimals are utilized to obtain the subalgebras. Further, under the each subalgebras similarity variable and similarity solutions are obtained which are used to find the reduced equations. These reduced equations are solved for exact solutions. The solutions of the reduced equations are further used to find the exact solutions of the main time-fractional (2+1)-dimensional Navier-Stokes equation with the help of similarity solutions under each subalgebra.

BACKGROUND:Hemodynamic changes are the most common predicted response after laryngoscopy and intubation during general anesthesia. We compared the efficacy of buprenorphine with fentanyl to attenuate this stress response.METHODS:One hundred and thirty patients of either sex between the age group of 18-70 years, admitted for the routine surgical procedure under general anesthesia were enrolled in this double blind, randomized, clinical study. Patients were randomly assigned into two equal groups (60 patients in each group): group F received fentanyl 2 μg/kg, and group B received buprenorphine 2.5 μg/kg; both via intravenous route. Each group received a total volume of 10 mL by adding normal saline to the total drug volume, given over 60 seconds, 5 minutes before intubation. Thereafter patients were induced using routine balanced anesthesia technique, and the hemodynamic parameters were observed at baseline (0 minute), 1, 3, and 5 minutes after the administration of the study drug and again at 1, 3, 5, 7, and 10 minutes after intubation. Continuous variables were presented as mean with an 80% confidence interval, and a t-test was applied for comparing the difference of means between two groups after we checked the normality condition. Chi-square test was applied to test the independence of attributes of categorical variables. Repeated measures two-way analysis of variance was performed to compare the outcome variables between the two groups.RESULTS:In both groups, mean arterial blood pressure (MAP) and heart rate (HR) were statistically insignificant up to 5 minutes after study drug, thereafter mean HR and MAP at 1, 3, 5, 7, and 10 minutes after intubation, were statistically significant between the two groups, and P value was less than 0.05.CONCLUSIONS:The dose of 2.5 μg/kg buprenorphine is an effective alternative to fentanyl 2 μg/kg for attenuating the hemodynamic response accompanying laryngoscopy and tracheal intubation without causing any hemodynamic adverse effect.

Objective: Human papillomavirus (HPV)-associated uteri cervix carcinoma continues to be the 2nd highest cause of death among women in India. This study aims to identify the mode of HPV transmission in different communities such as Hindu, Muslim, Christian and Banjaran women of Bihar, India. Different patterns of life and cultural variations exist among Muslims, Hindus, Christians, and Banjarans. For example, Muslim wash their genital parts after urination and maintain genital hygiene, whereas Banjaran tribes, Christians, and Hindu communities do not maintain hygiene. Thus, the present study was undertaken to evaluate high-risk HPV (HR-HPV) infection among healthy women. We access to genuine reason for the cause of HPV transmission in women. Methods: Ethical clearance was obtained from MCS and RC Patna, India. A total 154 urine samples have been used for the detection of HR-HPV through a real-time PCR technique. The DNA extraction was done from collected non-invasive urine samples. The estimation and purification of DNA purity was performed by QuantiFluor® dsDNA system and detected HPV-16 and HPV-18. Results: Overall, the prevalence of HR-HPV infection was detected to be 12.34% (19/154) whereas HPV-16 was found to be 9.9% (14/154) and HPV-18 was found to be 3.25% (5/154) in women. The lowest (2%; 1/50) prevalence of HR-HPV was observed in the Muslim community, while higher (25%, 16%, and 14.71%) prevalence was found in the Banjaran, Christian, and Hindu communities, respectively. Conclusion: Our study indicates that personal hygiene possibly reduces HPV infection in women and the evidence suggests that male circumcision has a protective role of HPV infection in Muslim community. Therefore, personal hygiene and circumcision may reduce the risk of HPV acquisition and transmission as well as cervical cancer development in women.

One major element in geological CO2 storage is the ability to track the CO2 migration and its related pressure buildup in the reservoir. Lack of information, especially for saline storage, requires extensive fluid flow simulations to understand risk and design mitigation plans. The ultimate goal is to run these simulations fast and accurately for visualization and demonstration to various stakeholders. We are evaluating the performance of several deep learning algorithms that use numerical simulations to train and predict the system behavior of an offshore Gulf of Mexico reservoir. The simulation responses of interest were the pressure and saturation and the water production rate. We used the global root-mean-squared error (RMSE) and forecast time as quantitative performance metrics. Comparison of different neural networks shows that pressure and saturation can be predicted with an RMSE of less than 2 psi and 0.05, respectively. In addition, models offer 50–2,000X speedup.

Magnetorheological finishing fluid (MRFF) is the key element of the magnetorheological abrasive finishing (MRAF) process. The environment-friendly nature of MRFF is equally important along with the rheological performance. The current study involves monodispersed and bidispersed MRFF samples synthesized employing coconut oil. The rheological performance of these samples was evaluated at low and high magnetic fields. The magnetorheological results show that the bidispersed MRFF shows better performance vis-à-vis monodispersed MRFF under low magnetic field.