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ABSTRACTIntroductionPIM Kinases (PIM-1, PIM-2, and PIM-3) have been reported to play crucial role in signaling cascades that govern cell survival, proliferation, and differentiation. Over-expression of these kinases leads to hematological malignancies such as diffuse large B cell lymphomas (DLBCL), multiple myeloma, leukemia, lymphoma and prostate cancer etc. PIM kinases as biomarkers and potential therapeutic targets have shown promise toward precision cancer therapy. The selective PIM-1, PIM-2, and/or PIM-3 isoform inhibitors have shown significant results in patients with advanced stages of cancer including relapsed/refractory cancer.Areas coveredA comprehensive literature review of PIM Kinases (PIM-1, PIM-2, and PIM-3) in oncogenesis, the patented PIM kinase inhibitors (2016-Present), and their pharmacological and structural insights have been highlighted.Expert opinionRecently, PIM kinases viz. PIM-1, PIM-2, and PIM-3 (members of the serine/threonine protein kinase family) as therapeutic targets have attracted considerable interest in oncology especially in hematological malignancies. The patented PIM kinase inhibitors comprised of heterocyclic (fused)ring structure(s) like indole, pyridine, pyrazine, pyrazole, pyridazine, piperazine, thiazole, oxadiazole, quinoline, triazolo-pyridine, pyrazolo-pyridine, imidazo-pyridazine, oxadiazole-thione, pyrazolo-pyrimidine, triazolo-pyridazine, imidazo-pyridazine, pyrazolo-quinazoline and pyrazolo-pyridine etc. showed promising results in cancer chemotherapy.

Royal jelly is an anti-inflammatory, antioxidant, and neuroprotective bee product. There are several sources for royal jelly and one of them is Indian Royal Jelly (IRJ). However, the neuroprotective actions of IRJ and the underlying molecular mechanisms involved are not well known.

Ball bearing is used to provide free rotation around a fixed axis. Various kinds of faults exist in the bearing such as inner race fault, outer race fault and ball race fault. One very effective method to diagnose the bearing fault is vibration signal analysis. Empirical mode decomposition (EMD) has been used for ball-bearing fault diagnosis in mechanical systems using vibration signal analysis. Classification of the ball-bearing fault has always been a challenging task. Various classification schemes such as Extreme learning machine (ELM), K-means Clustering, and Support vector machine (SVM), have been reported in the literature for ball-bearing fault classification. SVM is restricted by multiclass classification efficiency, and ELM is restricted by the longer training. In this paper, the entropy analysis of the wavelet coefficient obtained from the third level decomposition of the residue signal (obtained after subtracting the highest frequency component from the raw signal) has been done for ball-bearing fault classification. A comparative analysis of the wavelet coefficient based on entropy measurement has been presented here. High fault classification accuracy has been achieved using the proposed method for the detection of ball-bearing fault. Shannon entropy, Average Shannon entropy, and Renyi’s entropy are parameters for the justification of the proposed approach. The result shows the best wavelet to be chosen among the available discrete wavelet based on various entropy measurements.

Bee propolis is a heterogeneous mixture of phytoactive metabolites with reported antioxidant, anti-inflammatory, and neuroprotective properties. The chemical composition is highly variable, complex and presents the greatest challenge for standardization. So, the study was aimed at standardization of selected phytochemicals in propolis collected from a region in India (IP). A thin-layer chromatography (TLC) method was developed for five markers with prior evidence of neuroprotective activity using orthogonal detection and subsequently validated as per the International Council for Harmonisation ICH Q2 (R1) guidelines. The methodology was assessed for greenness using different metrices and then applied to marketed samples. Furthermore, antioxidant and anticholinesterase activity were evaluated directly using TLC‒bioautography technique. The method was developed using toluene‒ethyl acetate‒formic acid (7.4:2.6:0.5, V/V) as the mobile phase, with RFvalues of 0.235, 0.353, 0.552, 0.606, and 0.655 for luteolin, p-coumaric acid, chrysin, galangin, and pinocembrin, respectively. The optimized method was found to be precise (%RSD ≤ 2.0%), accurate (90‒110%), linear over the concentration ranges (R2≥ 0.995), sensitive and robust. Pinocembrin (2.30 ± 0.12% w/w) and galangin (5.78 ± 0.30% w/w) were found in the highest concentration. TLC‒bioautography analysis confirmed the contribution of the selected phenolics in anticholinesterase and antioxidant activity. The analytical eco-score value of 76, analytical greenness calculator (AGREE) score of 0.62 and other assessed analytical pictograms indicated the methodology developed had low environmental impact. Overall, a simple, rapid, precise, and green TLC analytical methodology has been developed for IP for quality control and preliminary screening of neuroprotective activity.

This article proposes a total harmonic distortions (THD)-based passive islanding detection technique (IDT) to overcome the challenges in droop-controlled grid-forming inverters (GFMI). The conventional IDTs are ineffective in GFMI due to the impact of power control droop parameters on the nondetection zone (NDZ). Also, the active IDTs may cause disturbances in the working operation of the GFMI due to their opposite functionality. Thus, there is a need to develop adequate IDTs which can perform robustly even with unconventional droop parameters and helps in maintaining the GFMI functions. In this work, THD from the rms voltage and current at the point of common coupling are extracted using the fast Fourier transform analysis. Then, a novel islanding detection factor (IDF) with adaptive weights is calculated. Finally, the statistical feature extracted from the IDF is compared with the threshold value to detect islanding. The performance of the proposed IDT (P-IDT) is analyzed with an real-time digital simulator (RTDS) simulator in a developed RSCAD distribution system. The real-time (RT) performance validation of the P-IDT is executed with the RSCAD (Real-Time) software IEEE-13 node system and RT-LAB (Real-Time) software. The RT results for different islanding and nonislanding scenarios show the efficacy of P-IDT.The proposed method is superior in accuracy and detection time as compared to the other conventional techniques.

Distribution systems are most vulnerable to high-impedance faults (HIFs). These types of faults occur when the primary electrical conductor fails or comes into contact with high-impedance substances, such as asphalt, grass, sand, concrete, etc. The magnitude of fault current is particularly low for certain grounding materials due to the nonlinear and high impedance of resistive surfaces. As a result, traditional overcurrent relays usually fail to operate, which may lead to fire hazards. Also, they are unable to detect the characteristics of HIF accurately, leading to maloperation. The motive of this work is to overcome the above-mentioned challenges of conventional relays. This article proposes a linear time–frequency wavelet-based tool called synchro-squeezing transform (SST) for the distortion detection of HIF in power distribution networks. This study uses wavelet SST to redistribute signal energy in the frequency domain. The wavelet SST is applied to the measured phase current signal for maximum time–frequency ridge extraction. Then, the inverse SST operation is performed to reconstruct the signal from the time–frequency domain. The reconstruction error is calculated between the original signal and the reconstructed signal. The reconstruction error value is compared with the base value to discriminate HIF from other non-HIF events. The impact of distributed generation on HIF features are further explored by EV penetration in the RSCAD distribution system and validated on the IEEE-13 node test feeder using RTDS. The main contributions of the proposed method are viz., it can correctly distinguish HIF from severe faults, switching events, energization scenarios, and noisy environments. The algorithm has been compared with the previously reported HIF detection techniques, and the efficacy of SST is presented through real-time simulation results. SST has a fast detection time and less computation burden.

The variational nonlinear chirp mode decomposition (VNCMD) requires initialization of number of modes (NMs) and instantaneous frequency (IF). This article proposes an automated method for NM selection and IF initialization, which works on the scale-space representation-based automated boundary detection in a magnitude spectrum. The proposed automated VNCMD (AVNCMD) method is applied for bearing fault detection in which the kurtosis-based dominant mode selection method is recommended. The instantaneous amplitude and IF with spectral entropy are computed from the dominant mode. Features are given to a feed-forward neural network classifier. The methodology is investigated on two datasets for inner-race, outer-race, and ball-race faults detection. The proposed method classifies inner-race, outer-race, and ball-race bearing faults with 97.52% accuracy and classifies inner-race and outer-race bearing faults with 100% accuracy. The efficacy of the proposed method is compared with the existing methods to justify the superiority.

ABSTRACT External confinement using jackets can suitably increase the strength and ductility of the reinforced concrete cylindrical columns. This paper encompasses an experimental investigation for studying the effectiveness and suitability [...]

Climate change is one of the greatest environmental hazards to mankind. The emission of greenhouse gases has resulted in a continuous increase in the temperature of the atmosphere leading to Global warming. CO2 continues to be the leading contributor to the greenhouse effect, with transport being a major CO2 emission source. The majority of transport emissions are from road transport i.e. vehicular emissions. To control vehicular emission, first, an efficient emission monitoring system is required. Direct sensor installation in individual vehicles is neither cost-effective nor the data is easy to collect. In this paper, a scalable vehicle CO2 emission prediction model is proposed which uses vehicle On-Board Diagnostics (OBD-II) port data. The proposed system uses real-time in-vehicle sensor data to estimate CO2 emission of the vehicle using a Recurrent neural network (RNN) based Long short-term memory(LSTM) model. OBD-II dongles can be used to easily transmit the vehicle’s sensor data to the cloud, where the LSTM model uses this data to estimate the real-time CO2 emission of the vehicle. The proposed model provides a scalable and efficient system to monitor emissions at a vehicular level and, has been evaluated using public OBD-II dataset. The details of the data collection, sensors, and adapters used along with vehicle information are outlined in Section V-A.

People all over the world are affected by the chronic, disabling autoimmune illness known as rheumatoid arthritis (RA). Herbal products are more commonly used in treating RA as of their safety and efficacy. The selected formulation for the present study is ANTARTH capsules, which is a polyherbal formulation containing different herbs effective in the treatment of RA. The purpose of this study was to develop and validate a precise, accurate, novel high-performance thin-layer chromatography (HPTLC) method for the simultaneous estimation of quercetin, berberine, rutin, and curcumin and to determine the levels in a polyherbal formulation. A good chromatographic separation was accomplished using a mobile phase consisting of toluene‒ethyl acetate‒methanol‒formic acid (5:3:2:0.5, V/V) with wavelengths of 366 nm and 426 nm using an ultraviolet‒visible (UV‒VIS) detector. The retention factors for quercetin, berberine, rutin, and curcumin were found to be 0.57, 0.316, 0.093, and 0.663, respectively. A good linear regression relationship between peak areas and concentrations was obtained with correlation coefficients of 0.9988, 0.9987, 0.9977, and 0.9984 for quercetin, rutin, berberine, and curcumin, respectively. The method developed was found to be accurate, precise, and robust. The method was then applied in a marketed formulation (ANTARTH capsule), and the percent content was found to be 5.26 ± 1.09, 4.76 ± 0.83, 3.80 ± 0.94 and 7.62 ± 1.58 for quercetin, rutin, berberine, and curcumin, respectively.

The rapid integration of solar photovoltaic (PV) systems into the global energy landscape necessitates the development of accurate predictive models for PV system performance. A solar photovoltaic (PV) system is modelled either by its equivalent single-diode, double-diode or multiple-diode model. The model equations of these models need to be solved through iterative process which sometimes may not suitable for analysis. One cannot analyse a system using modal analysis, state-space analysis, linearisation, etc., when an iterative process is involved. Therefore, this paper suggests a multivariate regression based novel function of solar PV which replicates the actual PV system model. The model incorporates key factors such as solar irradiance, temperature, and system configuration to provide a comprehensive understanding of PV system behaviour. The proposed regressed model of the solar PV has been tested under various operating conditions and realistic solar data. To validate the accuracy and robustness of our multivariate regression model function, a feedforward back-propagation neural network has been used considering the data generated by the proposed regression model. It was also observed that the proposed model is good enough for the mathematical analysis of the PV integrated systems.

AbstractThe potential of Indian propolis as a suitable therapeutic agent is still to be explored to the fullest. As variation in the chemical composition across different geographical regions has been a major concern, it is, therefore, worthwhile to systematically investigate, and assess the quality of Indian propolis and set nationalized standards. The propolis collected from three different regions were authenticated for the major plant source, followed by the evaluation of physicochemical properties. Different extraction methods were compared, with their influence on balsam content, polyphenol, and flavonoid content. The sample coded HAR using ultrasonication gave the highest extraction yield of 71.87%. Further, the process variables in the ultrasonication method (i.e. Amplitude, extraction time, the concentration of ethanol) at three levels were optimized using the Box-Behnken response design. The model was found significant with the concentration of ethanol having a maximum influence on the responses. The model was successfully verified by setting the amplitude at 48%, extraction time to 5.6 mins, and ethanol concentration to 87% v/v. The extraction efficiency of 83.86±1.17% was achieved with a substantial diminution of extraction time. The total phenolic, total flavonol, and total flavanone content of the optimized batch were 28.74±0.13 mgGAE/g, 4.66±0.78 mgQE/g, and 3.16±0.14 mgPE/g of propolis respectively. Further HPTLC fingerprint image of the optimized extract revealed the presence of quercetin, p-coumaric acid, kaempferol, chrysin, pinocembrin, and galangin.

Owing to its use of solar radiation and ambient energy, solar based heat pump water heaters are environmentally friendly. Several recommendations for system optimization are offered based upon the experimental study and thermodynamics analysis to enhance the efficiency of the model. The goal of this work was to create and maintain a solar based heat pump prototype as well as to propose a comprehensive theoretical examination into the social-economic and energy efficiency of a new solar based heat pumping (SHP) model for water heater application. The ultimate focus of the performance evaluation and interpretation is to evaluate the viability and logical consistency of deploying a solar-based heat pump as an auxiliary water heating mechanism during local weather scenarios. Subsequently, a modelling approach is created for optimizing the performance. Moreover, the suggested system’s performance characteristics are assessed using the Solar Collector Efficiency (SCE) and Coefficient of Performance (COP). The optimal case’s simulation results demonstrate that the solar-based heat pump’s average COP is about 9.0. The comfortable level of temperature could be accomplished when a pleasant indoor temperature reaches 92.6 percent of the whole heating season. Traditional air source heat pump systems may not be as promising for domestic use in cold temperature areas as the optimal proposed system does.

Machine learning (ML) is a type of artificial intelligence (AI) that empowers a framework to gain information instead of through express programming. The application of ML techniques is not limited to one sector, and it has been used in healthcare, fraud detection, automation etc. The bibliometric analytical approach has not yet been applied to the construct 'application of ML in banking and finance'. The study attempts to connect ML into banking and finance which is intrinsically a multidisciplinary study. The study identifies and put across a comprehensive analysis of the publications made in this construct so as to make significant contributions on the existing body of knowledge in this field with facts that are statistically backed using bibliometric analysis. This study will provide insights to faculty, researchers and industry experts about the research trends and the possible avenues for future research in this construct.

The paper presents wide-area back-up protection (WABP) and faulted line identification (FLI) scheme for series-compensated power transmission network. The proposed scheme uses the Koopman mode technique on fault current data obtained from phasor measurement units for WABP and FLI. The main advantage of the proposed scheme is that it is based on spatiotemporal information system (STIS). As we know that in real-world applications, often time and space exist together, and hence, dealing with spatial aspect without considering temporal aspect is of limited use. Similarly, the introduction of series compensation in a power transmission network is common nowadays due to many technical benefits. The FLI in a power network with series-compensation is more complicated due to complex transients produced by series capacitor and the metal–oxide varistor. The proposed scheme is developed using STIS for WABP and FLI under stressed conditions including power swing and load encroachment. The New England 39 bus system is used as the test system, which is developed on Dig-Silent power factory commercial software (PF4C) platform for evaluating the performance of the proposed technique. Further, the efficacy of the proposed scheme is also tested for other critical situations such as symmetrical fault during power swing and multievent detection and found to work effectively.

Structural retrofitting and upgradation of existent structures is amongst one of the major challenges that modern civil engineers are currently facing. To this effect, self-compacting concrete (SCC) mixes can be used efficiently as a material for retrofitting of various structural members. In this study, the efficacy and applicability of SCC mixtures as retrofitting material for column specimens using jacketing techniques was investigated. It was found that retrofitting of damaged primary column samples making use of SCC jacket and reinforced with welded wire mesh restores and enhances the ultimate load carrying capacity of primary column specimen from 18 to 47% of the ultimate load carrying capability. It also enhanced the strain at ultimate load from 59 to 66% with respect to primary column specimens depending upon strength of concrete used in jacket and number of curing days. Two SCC mixes—OSCC1 and OSCC2—were used for retrofitting and it was reported that the use of OSCC1 as retrofitting material improved the load carrying capability of column specimens by 47%, whereas the use of OSCC2 as retrofitting material improved the load carrying capability of column specimens by 35% at 90 days of curing. Experimental results were then compared with analytical results revealing that out of the analytical models, Mander and Legeron model predict the ultimate load carried by the column specimens retrofitted with SCC more accurately. Saatcioglu model overestimated the strain, whereas Legeron model underestimated the axial strain at ultimate load. However, Mander model predicted the strains closer to experimental results.

The paper presents an on-line sequential extreme learning machine (OS-ELM) based fast and accurate adaptive distance relaying scheme (ADRS) for transmission line protection. The proposed method develops an adaptive relay characteristics suitable to the changes in the physical conditions of the power systems. This can efficiently update the trained model on-line by partial training on the new data to reduce the model updating time whenever a new special case occurs. The effectiveness of the proposed method is validated on simulation platform for test system with two terminal parallel transmission lines with complex mutual coupling. The test results, considering wide variations in operating conditions of the faulted power network, indicate that the proposed adaptive relay setting provides significant improvement in the relay performance.

Self-compacting concrete (SCC) is being used worldwide in the construction industry for new construction as structural and architectural concrete. The properties of SCC are very sensitive to constituent materials. Thus, proportioning and optimization of constituent materials including admixtures is key issue in the mix design of SCC. This paper focuses mainly on an experimental study to select optimum proportions of indigenously available aggregates for SCC and optimize fly ash content in SCC, keeping in view its sensitiveness to water/powder ratio and superplasticizer dosage. The results show that to develop SCC with indigenous materials, the coarse aggregate content should be in the range of 31.63 %–33.97 % of concrete volume and fine aggregates in the range of 38.76 %–47.09 % of mortar volume depending upon powder content. In addition, SCC with 25 %–30 % of fly ash content with respect to powder content requires the lowest amount of superplasticizer dosage and water/powder ratio.

Concrete is extensively used construction material in the infrastructure development industry. With increase in technical knowhow, the need of research for high performance concretes such as self-compacting concrete (SCC) has increased in the last decade. The adaptability of SCC is due to its fluidic behavior in fresh state. However, to develop SCC using indigenous materials, the lack of standardized mix design procedures is the biggest impediment. Although with the advent of chemical admixtures, it is possible to achieve concrete with high fluidity, but at the same time durability issues require more attention. To have these fresh state properties SCC mixes are typically designed with high powder contents, and chemical admixtures. Proportioning and optimization of these materials is a key issue in the mix design of SCC. This paper focuses mainly on experimental study to optimize dosages of superplasticizer for mortar of SCC and then in concrete mixture itself.

Lacrimal drainage system probing and syringing is a commonly performed diagnostic procedure in the evaluation of tearing. However, we believe that the technique is not described in adequate detail in the medical literature. We describe our technique for reliable, safe, and pain-free assessment of the lacrimal drainage system.