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Abstract This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source—specifically, solar energy—to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100 W prototype demonstrated the converter’s ability to deliver stable 24 V and 48 V outputs from a 12 V input, with output voltage deviations kept within ± 1%, significantly improving upon the ± 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications.

Abstract This paper introduces a novel design for a universal DC-DC and DC-AC converter tailored for DC/AC microgrid applications using Approximate Dynamic Programming and Artificial Neural Networks (ADP-ANN). The proposed converter is engineered to operate efficiently with both low-power battery and single-phase AC supply, utilizing identical side terminals and switches for both chopper and inverter configurations. This innovation reduces component redundancy and enhances operational versatility. The converter's design emphasizes minimal switch usage while ensuring efficient conversion to meet diverse load requirements from battery or AC sources. A conceptual example illustrates the design's principles, and comprehensive analyses compare the converter's performance across various operational modes. A test bench model, rated at 3000W, demonstrates the converter's efficacy in all five operational modes with AC/DC inputs. Experimental results confirm the system's robustness and adaptability, leveraging ADP-ANN for optimal performance. The paper concludes by outlining potential applications, including microgrids, electric vehicles, and renewable energy systems, highlighting the converter's key advantages such as reduced complexity, increased efficiency, and broad applicability.

Abstract This research paper introduces an avant-garde poly-input DC–DC converter (PIDC) meticulously engineered for cutting-edge energy storage and electric vehicle (EV) applications. The pioneering converter synergizes two primary power sources—solar energy and fuel cells—with an auxiliary backup source, an energy storage device battery (ESDB). The PIDC showcases a remarkable enhancement in conversion efficiency, achieving up to 96% compared to the conventional 85–90% efficiency of traditional converters. This substantial improvement is attained through an advanced control strategy, rigorously validated via MATLAB/Simulink simulations and real-time experimentation on a 100 W test bench model. Simulation results reveal that the PIDC sustains stable operation and superior efficiency across diverse load conditions, with a peak efficiency of 96% when the ESDB is disengaged and an efficiency spectrum of 91–95% during battery charging and discharging phases. Additionally, the integration of solar power curtails dependence on fuel cells by up to 40%, thereby augmenting overall system efficiency and sustainability. The PIDC’s adaptability and enhanced performance render it highly suitable for a wide array of applications, including poly-input DC–DC conversion, energy storage management, and EV power systems. This innovative paradigm in power conversion and management is poised to significantly elevate the efficiency and reliability of energy storage and utilization in contemporary electric vehicles and renewable energy infrastructures.

Abstract Background Obesity is a metabolic syndrome where allelic and environmental variations together determine the susceptibility of an individual to the disease. Caloric restriction (CR) is a nutritional dietary strategy recognized to be beneficial as a weight loss regime in obese individuals. Preconceptional parental CR is proven to have detrimental effects on the health and development of their offspring. As yet studies on maternal CR effect on their offspring are well established but paternal CR studies are not progressing. In current study, the impact of different paternal CR regimes in diet-induced obese male Wistar rats (WNIN), on their offspring concerning metabolic syndrome are addressed. Methods High-fat diet-induced obese male Wistar rats were subjected to caloric restriction of 50% (HFCR-I) and 40% (HFCR-II) and then they were mated with normal females. The male parent’s reproductive function was assessed by sperm parameters and their DNMT’s mRNA expression levels were also examined. The offspring’s metabolic function was assessed by physiological, biochemical and molecular parameters. Results The HFCR-I male parents have shown reduced body weights, compromised male fertility and reduced DNA methylation activity. Further, the HFCR-I offspring showed attenuation of the AMPK/SIRT1 pathway, which is associated with the progression of proinflammatory status and oxidative stress. In line, the HFCR-I offspring also developed altered glucose and lipid homeostasis by exhibiting impaired glucose tolerance & insulin sensitivity, dyslipidemia and steatosis. However, these effects were largely mitigated in HFCR-II offspring. Regarding the obesogenic effects, female offspring exhibited greater susceptibility than male offspring, suggesting that females are more prone to the influences of the paternal diet. Conclusion The findings highlight that HFCR-I resulted in paternal undernutrition, impacting the health of offspring, whereas HFCR-II largely restored the effects of a high-fat diet on their offspring. As a result, moderate caloric restriction has emerged as an effective weight loss strategy with minimal implications on future generations. This underscores the shared responsibility of fathers in contributing to sperm-specific epigenetic imprints that influence the health of adult offspring.

Due to the dynamic nature and node mobility, assuring the security of Mobile Ad-hoc Networks (MANET) is one of the difficult and challenging tasks today. In MANET, the Intrusion Detection System (IDS) is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular operation. Different machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of MANET. However, it still has significant flaws, including increased algorithmic complexity, lower system performance, and a higher rate of misclassification. Therefore, the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning models. Here, the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields, which increases the overall intrusion detection performance of classifier. Then, a novel Adaptive Marine Predator Optimization Algorithm (AOMA) is implemented to choose the optimal features for improving the speed and intrusion detection performance of classifier. Moreover, the Deep Supervise Learning Classification (DSLC) mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training operations. During evaluation, the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

ABSTRACTIn our approach, a hybrid machine learning model is proposed which uses Enhanced Vector Space Model (EVSM) along with Hybrid Support Vector Machine (HSVM) classifier. Initially the social media-based information is retrieved using Enhanced Vector Space Model (EVSM). EVSMs are employed in order to characterize the text content by mapping them into high-dimensional vector spaces, capturing the relationships between words and their contextual meanings. Rigorous feature selection methods are employed to designate texts for review, and a multiclass semantic classification algorithm, specifically the HSVM classifier, is utilized for categorization. Decision tree algorithm is used along with SVM to refine the selection process. To enhance sentiment analysis accuracy, sentiment dictionaries are not only presented but also extended through the expansion of Stanford’s GloVE tool. To enhance precision, the proposed work introduces weight-enhancing methods for processing renowned text weights. Sentiments are classified into positive, negative, and neutral categories. Notably, the achieved results demonstrate improved accuracy, attributed to the incorporation of an emotional sentiment enhancement factor for determining weights and leveraging sentiment dictionaries for word availability. The accuracy is obtained to be 92.78% with 91.33% positive sentiment rate and 97.32% negative sentiment rate.

We report on the results of the first search for the production of axion-like particles (ALPs) via Primakoff production on nuclear targets, γA→aA, in the “SRC-CT” experiment using the GlueX detector at Jefferson Lab. This search uses an integrated luminosity of 100 pb⋅−1nucleon on a 12C target with a real photon beam of energies 6

Investigation on the simulation effects of free convection flow from a cold and heated cone with a downward pointing tip in various stagnant fluids is presented in this study. Examining the steady-state isotherm patterns around the cone to determine heat transfer characteristics. Results are presented for friction factor and Nusselt number distributions Over the surface of the cone for distinct dimensionless quantities like Pr and Gr to deliver an extensive class of dimensionless numbers. The commercial program ANSYS CFX 16.0 was used to solve the regulating equations which governs the motion vis., momentum mass and energy were computed. The temperature as well as flow fields are coupled using the Boussinesq approximation in order to simplify the governing equations and capture natural convection. Heat transmission has a rather minimal influence on the local Nusselt number for low Grashof and Prandtl values, according to the findings. It gains strength as the Grashof and Prandtl numbers grow, because of the extreme curvature of the isotherms, the maximum value of the local Nusselt number may be found around the corner of the vertical cone in both cones. The current simulations closely match the numerical values provided in the printed matter.

Trust detection and node authentication within fog computing for the Vehicle Ad-hoc Networks (VANETs) are used to determine whether the automobiles and other infrastructure elements including Roadside Units (RSUs) are valid and secure before permitting them to function on the system. It is essential for ensuring the privacy and authenticity of automobile networks, particularly for systems that require safety-critical communication. However, the distributed and decentralized design of fog computing increases difficulties in establishing security measures and coordination between various nodes that are responsible for trust detection and authentication. In fog-enabled VANETs, trust and privacy remain a key challenge. To overcome the existing challenges, a new system for node authentication and trust detection in fog computing for VANETs is developed. Initially, node authentication and trust detection in vehicular networks is conducted using Adaptive Deep Neural Networks (ADNN). Verification of the node’s authenticity and evaluating its trust scores will considerably minimize the chance of cyber attacks and fraudulent behavior across the system, thus enhancing the security of the entire system. Node authentication on the VANET model promotes safe interaction between vehicles. Trust detection in VANET guarantees the integrity of information transferred between vehicles. The parameters of the ADNN are optimally tuned with the help of an Enhanced Garter Snake Optimization Algorithm (EGSOA) to enhance the performance. Some of the models focus only on node authentication and do not consider privacy issues. Thus, it affects the users’ identities and personal information. So, in our model after completing the node authentication and trust detection, privacy preservation of data is performed using Optimal Key-aided Data Sanitization (OPDS). Here, the same EGSOA strategy is employed to get the sanitized key to increase security. The effectiveness of this newly developed fog computing framework for VANETs is evaluated against traditional models, with an expectation of achieving superior accuracy.

This paper aims to establish a virtual object management system, as well as optimal task scheduling using the foundation of Digital Twins (DT), to improve the user’s experience with management and to accomplish the task efficiently. On the other hand, offloading tasks using IoT gadgets to edge computing, fails to speed up control by users. The capabilities of the DT are provided by executing processes such as visualization, virtualization, synchronization, and simulation. The optimal selection of the virtual objects for the DT is done by utilizing the implemented Hybrid Energy Valley with Lévy Flight Distribution Optimization (HEV-LFDO) in order to optimally offload the task by the edge devices. The optimal selection of the virtual objects is done with the aid of the HEV-LFDO in the DT by considering the total cost of executing all tasks using the selected virtual objects and the decision variables to determine whether a virtual object is taken for executing a task or not as the constraint. The data for performing resource management is secured using the blockchain or distributed ledger technology. This accounts for the minimization of the local loss function. Finally, the secured data is considered for optimal resource management tasks. The optimal resource management is done using the same HEV-LFDO. This optimal resource management is carried out by considering the constraints like the cost of assigning a virtual object for the task to the edge device, and the cost of assigning the task to the edge device. These two costs are analyzed by taking the network’s bandwidth, energy consumption, and computational resources into consideration. Experimental verifications are conducted on the executed optimal resource management scheme to prove the ability of the implemented model to be integrated with the edge computing network. The overall processing time as well as the latency are also minimized by executing the optimal resource management scheme.

Abstract Ornamental fish keeping is the second most preferred hobby in the world and it provides a great opportunity for entrepreneurship development and income generation. Controlling the environment in ornamental fish farm is a considerable challenge because it is affected by a variety of parameters like water temperature, dissolved oxygen, pH, and disease occurrences. One particular interesting ornamental fish species is goldfish (Carassius auratus). Machine learning (ML) and deep learning technique have significant potential in analysing voluminous data collected from fish farm. Through this technique, the fish farmers can get insight on feeding behaviour, fish growth patterns, predict diseases/stress, and environmental factors affecting fish health. The aim of the study is to analyze the behavioural changes in goldfish due to alterations in environmental parameters (water temperature and dissolved oxygen). Decision tree, Naïve Bayes classifier, K-nearest neighbour (KNN), and linear discriminant analysis (LDA) were used to analyse the behavioural change data. To compare the performance between all four classifiers, cross validation and confusion matrix used. The cross-validation error of LDA, Naïve Bayes classification, KNN and decision tree was 19.86, 28.08, 30.14 and 13.78 respectively. Decision tree was proved to be the most accurate and effective classifier. Different temperature and DO range were taken to predict fish behaviour. Some findings are, the behaviour of fish was rest between temperature 37.85 °C and 40.535 °C, erratic when temperature was greater than or equal to 40.535 °C, gasping when temperature was between 37.85 and 40.535 °C and when DO concentration was less than 6.58 mg/L. Blood parameter analysis has been done to validate the change in external behaviours with change in physiological parameters.

According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper’s objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network’s ability to gather long-distance dependencies for AI, Expectation–Maximization is applied to the cascade network’s lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network’s ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network’s standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.

Introduction. This research paper describes a simple five-level single-phase pulse-width modulated inverter topology for photovoltaic grid applications. Multilevel inverters, as opposed to conventional two-level inverters, include more than two levels of voltage while using multiple power switches and lower-level DC voltage levels as input to produce high power, easier, and less modified oscillating voltage. The H-bridge multilevel inverter seems to have a relatively simple circuit design, needs minimal power switching elements, and provides higher efficiency among various types of topologies for multi-level inverters that are presently accessible. Nevertheless, using more than one DC source for more than three voltage levels and switching and conduction losses, which primarily arise in major power switches, continue to be a barrier. The novelty of the proposed work consists of compact modular inverter configuration to connect a photovoltaic system to the grid with fewer switches. Purpose. The proposed system aims to decrease the number of switches, overall harmonic distortions, and power loss. By producing distortion-free sinusoidal output voltage as the level count rises while lowering power losses, the constituted optimizes power quality without the need for passive filters. Methods. The proposed topology is implemented in MATLAB/Simulink with gating pulses and various pulse width modulation technique. Results. With conventional topology, total harmonic distortion, power switches, output voltage, current, power losses, and the number of DC sources are investigated. Practical value. The proposed topology has proven to be extremely useful for deploying photovoltaic-based stand-alone multilevel inverters in grid applications.

This study presents a GIS-based Multi-Criteria Decision Analysis (MCDA) spatial model to assess land suitability for oil palm (OP) cultivation in rainfed conditions. Initially, twelve parameters, viz., rainfall, number of rainy days, mean temperature, RH, ground water level, soil pH, salinity, soil depth, surface texture, stoniness, slope, and drainage, were selected for assessing OP suitability in one of the states (Kerala). However, subsequent ground verification revealed significant discrepancies, which prompted refining the model by focusing on key parameters with greater accuracy and relevance. Accordingly, only five the most critical parameters affecting OP cultivation under rainfed conditions were selected through the rank sum method, and weights were assigned ac-cording to their significance. This study was aimed at creating a comprehensive tool for informed decision making in agricultural planning. District-level spatial data from reliable sources were utilized for Multi-Criteria Decision Analysis. Thematic rasters, representing key factors influencing land suitability, were created in a GIS. Utilizing MCDA techniques, a digital suitability map was generated in ArcGIS 10.3, delineating three distinct classes over an extensive area of 10.5 million hectares. Further, with an aim to focus on actual locations that can be readily planted with oil palm, the suitable locations identified were restricted to eight selected land use/land cover (LULC) classes. This strategic limitation aimed to facilitate the expansion of OP cultivation exclusively to areas deemed most suitable based on the identified criteria. The validation of this developed model involved comparing the suitability map generated with the performance of existing oil palm plantations across diverse locations. The reasonable similarity between the model’s predictions and real-world plantation outcomes validated the effectiveness of this MCDA spatial model. This model not only helps identify suitable locations for rainfed oil palm cultivation but also serves as a valuable tool for strategic decision making in agricultural land use planning.

Introduction. This research presents a simple single-phase pulse-width modulated 7-level inverter topology for renewable system which allows home-grid applications with electric vehicle charging. Although multilevel inverters have appealing qualities, their vast range of application is limited by the use of more switches in the traditional arrangement. As a result, a novel symmetrical 7-level inverter is proposed, which has the fewest number of unidirectional switches with gate circuits, providing the lowest switching losses, conduction losses, total harmonic distortion and higher efficiency than conventional topology. The novelty of the proposed work consists of a novel modular inverter structure for photovoltaic energy system and electric vehicle applications with fewer numbers of switches and compact in size. Purpose. The proposed system aims to reduce switch count, overall harmonic distortions, and power loss. There are no passive filters required, and the constituted optimizes power quality by producing distortion-free sinusoidal output voltage as the level count increases while reducing power losses. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and various pulse-width modulation methodologies. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage, current, power losses and number of DC sources are investigated with conventional topology. Practical value. The proposed topology has proven to be extremely beneficial for implementing photovoltaic-based stand-alone multilevel inverter and electric vehicle charging applications.

Introduction. The advantages of a high-power quality waveform and a high voltage capability of multilevel inverters have made them increasingly popular in recent years. These inverters reduce harmonic distortion and improve the voltage output. Realistically speaking, as the number of voltage levels increases, so does the quality of the multilevel output-voltage waveform. When it comes to industrial power converters, these inverters are by far the most critical. Novelty. Multilevel cascade inverters can be used to convert multiple direct current sources into one direct current. These inverters have been getting a lot of attention recently for high-power applications. A cascade H-bridge multilevel inverter controller is proposed in this paper. A change in the pulse width of selective pulse width modulation modulates the output of the multilevel cascade inverter. Purpose. The total harmonic distortion can be reduced by using filters on controllers like PI and fuzzy logic controllers. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and pulse width modulation methodology and fuzzy logic controllers. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are analyzed with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid and industry.

Green synthetic methods are currently preferred in industry over other physicochemical methods. Herein, we present a facile, environmentally friendly, non-toxic approach for the fabrication of MgO using jamun fruit extract. The phytochemicals present in the fruit extract, such as kaemferol, glucoside, anthocyanins, ellagic acid, myricetin, and isoquercetin, facilitate the bio-reduction of Mg(NO3)2. Pure and Sm3+ (1–7 mol %) doped MgO nanomaterials were synthesized using this bio-mediated synthetic method. The structural and morphological properties of the synthesized nanomaterials were studied using Powder X-ray diffraction (PXRD), Field Emission Scanning Electron Microscopy (FE-SEM), Energy Dispersive Spectroscopy (EDS), and Diffused Reflectance Spectroscopy (DRS) techniques. The effect of Sm3+ ions on the host matrix for the photo-catalytic oxidation of Fast Orange-Red (FOR) dye was investigated under UV light irradiation. MgO: Sm3+(3 mol %) exhibited superior (94 %) degradation of the dye compared to pristine and other doped catalysts, attributed to the maximum migration of charge carriers at the catalyst's surface. Additionally, the 3 mol % Sm3+ doped MgO electrode demonstrated a smaller charge transfer resistance, indicating superior capacitive properties compared to pristine and other doped electrodes. The synthesized materials also exhibited effective bacterial activity against pathogens. This research demonstrates the potential of the synthesized nanomaterials for environmental pollution purification, as well as their utility as electrode materials for supercapacitors, batteries, sensors, and antibacterial applications.

Background: Ovarian cancer, colloquially termed the “silent killer” among gynecological malignancies, remains elusive due to its often-asymptomatic progression and diagnostic challenges. Central to its pathogenesis is the overactive PI3K/Akt/mTOR signaling pathway, responsible for various cellular functions, from proliferation to survival. Within this context, the phytochemical compounds mangiferin (derived from Mangifera indica) and curcumin (from Curcuma longa) stand out for their potential modulatory effects. However, their inherent bioavailability challenges necessitate innovative delivery systems to maximize therapeutic benefits.Objective: This study seeks to synergize the merits of nanotechnology with the therapeutic properties of mangiferin and curcumin, aiming to bolster their efficacy against ovarian cancer.Methods: Employing specific nanotechnological principles, we engineered exosomal and liposomal nano-carriers for mangiferin and curcumin, targeting the PI3K/Akt/mTOR pathway. Molecular docking techniques mapped the interactions of these phytochemicals with key proteins in the pathway, analyzing their binding efficiencies. Furthermore, molecular dynamics simulations, spanning 100 nanoseconds, verified these interactions, with additional computational methodologies further validating our findings. The rationale for the 100 nanoseconds time span lies in its sufficiency to observe meaningful protein-ligand interactions and conformational changes. Notably, liposomal technology provided an enhancement in drug delivery by protecting these compounds from degradation, allowing controlled release, and improving cellular uptake.Results: Our computational investigations demonstrated notable binding affinities of mangiferin and curcumin: PI3K at −11.20 kcal/mol, Akt at −15.16 kcal/mol, and mTOR at −10.24 kcal/mol. The adoption of exosome/liposome-mediated delivery significantly amplified the bioavailability and cellular uptake of these nano-formulated compounds, positioning them as potential stalwarts in ovarian cancer intervention. A brief explanation of exosome/liposome-mediated delivery involves the use of these vesicles to encapsulate and transport therapeutic agents directly to the target cells, enhancing drug delivery efficiency and minimizing side effects.Conclusion: Addressing ovarian cancer’s intricacies, dominated by the erratic PI3K/Akt/mTOR signaling, mandates innovative therapeutic strategies. Our pioneering approach converges nanotechnological liposomal delivery with mangiferin and curcumin’s natural efficacies. This confluence, validated by computational insights, heralds a paradigm shift in ovarian cancer treatment. As our findings underscore the collaborative potential of these phytochemicals, it beckons further exploration in translational studies and clinical applications, ensuring the best intersection of nature and technology for therapeutic advantage.

In an innovative approach to push the boundaries of antimicrobial and antioxidant strategies, we present the synthesis and characterization of a novel terpolymer derived from N-Phenyl-p-phenylenediamine and 2-aminopyrimidine with formaldehyde in the presence of dimethylformamide as a reaction medium through polycondensation technique. Leveraging this terpolymer as a ligand, we introduce an intriguing terpolymer-metal complex, created with Ni (II) metal ion. In our pursuit to validate the structure and properties of these substances, we performed meticulous characterizations using important spectral studies such as FTIR, electronic, and 1H NMR spectroscopy. This provided us with a unique fingerprint for the (N-Phenyl-p-phenylenediamine-2-aminopyrimidine-formaldehyde) terpolymeric ligand (PAF) and its metal complex. In addition, the molecular weights of PAF terpolymer were established using gel permeation chromatography.Upon investigation, PAF terpolymer and PAF-Ni complex exhibited impressive antimicrobial activity, tested by the disc-diffusion technique. Both demonstrated potency against a range of harmful bacterial and fungal strains, including Staphylococcus aureus, Escherichia coli, Candida albicans, and Aspergillus niger. In an extension to their biological applications, we evaluated the free radical scavenging activity of PAF terpolymer and PAF-Ni complex using the DPPH assay. The complex PAF-Ni showcased an enhanced scavenging activity 73.94% (IC50 = 17.58) compared to the ligand PAF 63.06% (IC50 = 27.61) at 100 μg/ml indicating its potential role in oxidative stress management.

Computational fluid dynamics technique have been employed to capture the plain convective flow past from a heated cone with a tip downward in various stagnant Newtonian fluids. Using detailed isotherm patterns around the cone under steady-state conditions examine the heat transfer characteristics that have been reported. The findings illustrate the distribution of the friction factor, average Nusselt number and local Nusselt number around the cone surface over a wide range of dimensionless values such as Grashof and Prandtl numbers. The guiding PDEs such as conservation of mass, momentum and energy are solved by finite volume method using commercial software of ANSYSCFX16.0. To simplify the governing equations while capturing the natural convection, Boussinesq approximation has been adopted to coupling the flow and temperature fields.This study reveals the contribution of various angles (φ = 15°,300and45°), diverse Prandtl numbers (Pr = 0.71,5,10,20,50) and distinct Grashof numbers (GrL = 104, 105, 106) for the efficacy of them over the slant surface of the cone. The heat transmission and parametric features of these processes have been discussed for how heat can be transferred from a heated cone when it is submerged in a liquid. Visualization of the effects of different parameters for different cone apex angles was done by displaying the results graphically. The present simulations are in a near match to the numeric values appeared in the literature.

The accurate and reliable derivation of the pap smear cell, which contains cytoplasm and nucleus regions, depends on the segmentation process employed in the cervical cancer detection mechanism. In this paper, an Improved Boykov’s Graph Cut-based Conditional Random Fields and Superpixel imposed Semantic Segmentation Technique (IBGC-CRF-SPSST) is proposed for efficient cervical cancer detection. This proposed IBGC-CRF-SPSST embeds the complete benefits of constraint association among pixels and superpixel edge data for accurate determination of the nuclei and cytoplasmic boundaries so as to ensure efficient differentiation of the healthy and unhealthy cancer cells. Finally, the pixel-level forecasting potential of Conditional Random Fields is included for enhancing the degree of semantic-based segmentation accuracy to a predominant level. The experimental evaluated results of the proposed IBGC-CRF-SPSST aim to produce an accuracy of 99.78%, a mean processing time of 2.18sec, a precision of 96%, a sensitivity of 98.92%, and a specificity of 99.32% value which is determined to be excellent and on par with the existing detection techniques used for investigating cervical cancer.

Introduction. Cascaded H-bridge multilevel inverters (CHB-MLI) are becoming increasingly used in applications such as distribution systems, electrical traction systems, high voltage direct conversion systems, and many others. Despite the fact that multilevel inverters contain a large number of control switches, detecting a malfunction takes a significant amount of time. In the fault switch configurations diode included for freewheeling operation during open-fault condition. During short circuit fault conditions are carried out by the fuse, which can reveal the freewheeling current direction. The fault category can be identified independently and also failure of power switches harmed by the functioning and reliability of CHB-MLI. This paper investigates the effects and performance of open and short switching faults of multilevel inverters. Output voltage characteristics of 5 level MLI are frequently determined from distinctive switch faults with modulation index value of 0.85 is used during simulation analysis. In the simulation experiment for the modulation index value of 0.85, one second open and short circuit faults are created for the place of faulty switch. Fault is identified automatically by means of artificial neural network (ANN) technique using sinusoidal pulse width modulation based on distorted total harmonic distortion (THD) and managed by its own. The novelty of the proposed work consists of a fast Fourier transform (FFT) and ANN to identify faulty switch. Purpose. The proposed architecture is to identify faulty switch during open and short failures, which has to be reduced THD and make the system in reliable operation. Methods. The proposed topology is to be design and evaluate using MATLAB/Simulink platform. Results. Using the FFT and ANN approaches, the normal and faulty conditions of the MLI are explored, and the faulty switch is detected based on voltage changing patterns in the output. Practical value. The proposed topology has been very supportive for implementing non-conventional energy sources based multilevel inverter, which is connected to large demand in grid.

Introduction. In recent years, a growing variety of technical applications have necessitated the employment of more powerful equipment. Power electronics and megawatt power levels are required in far too many medium voltage motor drives and utility applications. It is challenging to incorporate a medium voltage grid with only one power semiconductor that has been extensively modified. As a result, in high power and medium voltage settings, multiple power converter structure has been offered as a solution. A multilevel converter has high power ratings while also allowing for the utilization of renewable energy sources. Renewable energy sources such as photovoltaic, wind, and fuel cells may be readily connected to a multilevel inverter topology for enhanced outcomes. The novelty of the proposed work consists of a novel modular inverter structure for solar applications that uses fewer switches. Purpose. The proposed architecture is to decrease the number of switches and Total Harmonic Distortions. There is no need for passive filters, and the proposed design enhances power quality by creating distortion-free sinusoidal output voltage as the level count grows while also lowering power losses. Methods. The proposed topology is implemented with MATLAB / Simulink, using gating pulses and various pulse width modulation methodologies. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are compared with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid.

This paper proposes DC–AC Dual-leg dual-stage Conversion (DDC) and DC–AC Direct single-stage conversions (DSC). Conventional energy conversion system has only two-stage conversion, so it has some drawbacks such as huge power loss, less conversion range and lower power rating. So direct conversion, dual-leg step-up and step-up conversions are the solutions to get wide voltage conversion efficiently. The proposed converter can perform the power conversion from battery DC supply into AC with 1:1 ratio, step-up AC, and step-down AC in both directions. Also, it can perform rectifier operation from grid AC supply into DC with 1:1 ratio, step-down DC, and step-up DC. Step-up, step-down and ideal operations are possible within a single circuit; its operation is similar to solid-state DC–AC/AC–DC transformer. The ideal operation, Step-down to Step-up conversion and Step-up to Step-down conversion are possible on both sides, so this converter can handle a wide range of voltage. Power distribution is achieved with voltage regulation between battery/DC-load and AC-load/grid using the proposed control strategy with proper modulation. A prototype model of a 2-kW power rating validates the advantages and feasibility of the proposed methodology.

Cervical cancer is the most serious category of cancer that has very low survival rate in the women’s community around the globe. This survival probability of women society affected by this cervical cancer can be potentially enhanced if it is detected at an early stage as they do not provide any realizable degree of symptoms in the early phase. This cervical cancer needs to be detected at an early stage through periodical checkups. Hence, the objective of the proposed work focuses on the merits of Neutrosophic Graph Cut-based Segmentation (NGCS) facilitated over the pre-processed cervical images. This NGCS-based segmentation is mainly employed for investigating the overlapping contexts of cervical smear pre-processed images for better classification accuracy. This NGCS-based segmentation is responsible for partitioning the input preprocessed image into a diversified number of non-overlapping regions that aids in better perception at the convenience. In NGCS-based segmentation, the preprocessed input image is transformed into a Neutrosophic set and indeterminacy filter depending on the estimated indeterminacy value that integrates the intensity and spatial information the preprocessed image. The utilized indeterminacy filter plays the anchor role in minimizing the indeterminacy value associated with each intensity and spatial information. Then a graph is defined over the image with unique weights are assigned to each of the image pixels based on the estimated indeterminacy value. Finally, the maximum flow graph approach is applied over the graph for determining optimal segmentation results. The results of this NGCS-based cervical cancer detection technique is proved to be excellent on an average by 13% compared to the traditional graph cut oriented cancer detection approaches.

High voltage conversion dc/dc converters have perceived in various power electronics applications in recent times. In particular, the multi-port converter structures are the key solution in DC microgrid and electric vehicle applications. This paper focuses on a modified structure of non-isolated four-port (two input and two output ports) power electronic interfaces that can be utilized in electric vehicle (EV) applications. The main feature of this converter is its ability to accommodate energy resources with different voltage and current characteristics. The suggested topology can provide a buck and boost output simultaneously during its course of operation. The proposed four-port converter (FPC) is realized with reduced component count and simplified control strategy which makes the converter more reliable and cost-effective. Besides, this converter exhibits bidirectional power flow functionality making it suitable for charging the battery during regenerative braking of an electric vehicle. The steady-state and dynamic behavior of the converter are analyzed and a control scheme is presented to regulate the power flow between the diversified energy supplies. A small-signal model is extracted to design the proposed converter. The validity of the converter design and its performance behavior is verified using MATLAB simulation and experimental results under various operating states.

Maize is a short duration crop. Accurate and up-to-date assessment of the spatial distribution of cultivated area and its production is a key information requirement for all stakeholders including policy makers, farmers and consumers. The study utilized Sentinel-2A/2B satellite based NDVI (Normalized Difference Vegetation Index) data to study yield variability of maize in Siddipet district of Telangana state, in 2019. NDVI is a quantitative measurement of crop vigour, which denotes the crop biomass and health status. A regression analysis was developed between observed yield from crop cut experimental plots and seasonal maximum NDVI of maize area. Results showed that there was statistically significant relation (R2=0.87) between NDVI and observed yield at respective field plots. Validation of yield model with Root Mean Squared Error (RSME) computed for all observed and predicted yields was 0.50 explaining higher accuracy of the yield model. The average seasonal rainfall received in different mandals of Siddipet district was between 727 to 799 mm and during the crop growth period the yields were recorded in the range of 4.2 to 5.0 t ha-1. This showed that the distribution of rainfall has played a major role in yield prediction. The 31st Standard Week Rainfall (SWR) coinciding with the peak vegetative growth showed a significant positive correlation (r=0.68***) with maize yield data at p

Background: Entada phaseoloides is a well-known medicinal plant traditionally used in Ayurvedic medicine for centuries. Objective: To evaluate the anti-stress activity of seeds of E. phaseoloides in endoplasmic reticulum stress during chronic restrain stress in mice, based on our preliminary screening. Materials and Methods: Mice (n = 6/group) were restrained daily for 6 h in 50 ml polystyrene tubes for 28 days. Methanolic extract of E. phaseoloides (MEEP) (100 and 200 mg/kg, p.o.) and standard drug, imipramine (10 mg/kg i.p.) were administered daily 45 min prior to restrain from day 22–28. Then, forced swim test (FST) was performed to assess despair behavior. Lipid peroxidation (LPO) and antioxidant enzymes Reduced glutathione (GSH), Superoxide dismutase (SOD) were measured in the hippocampus of mice. 78 kDa Glucose-regulated Protein, 94 kDa Glucose-regulated Protein, C/EBP homologous protein, Caspase-12 expression were quantified by Real Time PCR. Results: MEEP significantly reduced the immobility time in FST (P

The growing need for access to safer food items is increasing, and hence, there is a need for a better supply chain management system in the food industry is increasing. The increased complexity of the existing systems tends to introduce more issues to the stakeholders, and also, the cost of product traceability is quite high. Hence, the industry is looking for effective solutions in relation to drug traceability, and the application of Blockchain technology enables the stakeholders in the food and beverage (F&B) sector to track the movement of goods, supported in gathering the required details so that the contaminated products can be identified and recalled without much delay and lesser recall costs to protect the lives of the individuals. The tampered food items are increasing and are impacting the supply chain process, brand name of the companies, and claim assurance. They create an adverse impact on the health of the individuals and cause higher economic loss to the health-care industry. The existing studies tend to focus on laying emphasis of the need for an enhanced, effective, and end tracking systems in the industry. The emergence of Blockchain technology enables centralized tracking of information support in enhancing the data privacy and increasing transparency and support in eradicating the tampered food products in the supply chain system. These approaches leverage the usage of smart contracts and decentralize the storage of information in a secure manner for enhanced product traceability in the F&B industry. The implementation of smart contracts generates better data governance, which tends to meet the needs and requirements of the stakeholders, and applies effective measures of food traceability. The primary objective of the study is to perform an analysis of Blockchain in enhancing drug traceability in the food sector. The researcher uses quantitative analysis for the study as it helps in understanding the critical determinants influencing drug traceability in food effectively, the survey method is used to gather the information, and past reviews are also used to possess a better understanding of the subject area effectively.

Multimodal medical image fusion is a current technique applied in the applications related to medical field to combine images from the same modality or different modalities to improve the visual content of the image to perform further operations like image segmentation. Biomedical research and medical image analysis highly demand medical image fusion to perform higher level of medical analysis. Multimodal medical fusion assists medical practitioners to visualize the internal organs and tissues. Multimodal medical fusion of brain image helps to medical practitioners to simultaneously visualize hard portion like skull and soft portion like tissue. Brain tumor segmentation can be accurately performed by utilizing the image obtained after multimodal medical image fusion. The area of the tumor can be accurately located with the information obtained from both Positron Emission Tomography and Magnetic Resonance Image in a single fused image. This approach increases the accuracy in diagnosing the tumor and reduces the time consumed in diagnosing and locating the tumor. The functional information of the brain is available in the Positron Emission Tomography while the anatomy of the brain tissue is available in the Magnetic Resonance Image. Thus, the spatial characteristics and functional information can be obtained from a single image using a robust multimodal medical image fusion model. The proposed approach uses a generative adversarial network to fuse Positron Emission Tomography and Magnetic Resonance Image into a single image. The results obtained from the proposed approach can be used for further medical analysis to locate the tumor and plan for further surgical procedures. The performance of the GAN based model is evaluated using two metrics, namely, structural similarity index and mutual information. The proposed approach achieved a structural similarity index of 0.8551 and a mutual information of 2.8059.

Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive and terminal interstitial lung disease (ILD) with a median survival of 3–5 years. The British Thoracic Society (BTS) established the UK IPF Registry in 2013 as a platform to collect data on clinical characteristics, treatments and outcomes for this cohort in the UK. Between 1 January 2013 and 31 October 2019, 2474 cases were registered. Most patients were male (79%) with a mean±sd age of 74±8.3 years and 66% were ex-smokers. Over time we observed an increase in the number of patients aged over 70 years. However, we have not seen a trend towards earlier presentation as symptoms of breathless and/or cough were present for >12 months in 63% of the cohort. At presentation, mean±sd % predicted forced vital capacity (FVC) was 78.2±18.3%, median 76.2% (interquartile range (IQR) 22.4%) and transfer factor of the lung for carbon monoxide (TLCO) 48.4±16.0, median 47.5 (IQR 20.1). Most cases were discussed at an ILD multidisciplinary meeting, with an increase over this time in the number of cases reported as having possible usual interstitial pneumonia (UIP) pattern on high-resolution computed tomography (HRCT) thorax. We noted a reduction in the number of patients undergoing surgical lung biopsy or bronchoalveolar lavage. Although more patients were prescribed anti-fibrotic therapies from 2013 to 2019, 43% were ineligible for treatment based upon National Institute for Health and Care Excellence (NICE) prescribing criteria. Hypertension, ischaemic heart disease, diabetes mellitus and gastro-oesophageal reflux were the most common comorbidities. In conclusion, we have presented baseline demographics as well as diagnostic and treatment strategies from the largest single-country IPF registry, reflecting changes in UK practices over this period.

Aluminophosphate, AlPO4-5, an AFI zeotype framework consisting of one-dimensional parallel micropores, and metal-substituted AlPO4-5 were prepared and studied for CO2 adsorption. Preparation of AlPO4-5 by using different activation methods (calcination and pyrolysis), incorporation of different metals/ions (Fe, Mg, Co, and Si) into the framework using various concentrations, and manipulation of the reaction mixture dilution rate and resulting crystal morphology were examined in relation to the CO2 adsorption performance. Among the various metal-substituted analogs, FeAPO-5 was found to exhibit the highest CO2 capacity at all pressures tested (up to 4 bar). Among the Fe-substituted samples, xFeAPO-5, with x being the Fe/Al2O3 molar ratio in the synthesis mixture (range of 2.5:100–10:100), 5FeAPO-5 exhibited the highest capacity (1.8 mmol/g at 4 bar, 25°C) with an isosteric heat of adsorption of 23 kJ/mol for 0.08–0.36 mmol/g of CO2 loading. This sample also contained the minimum portion of extra-framework or clustered iron and the highest mesoporosity. Low water content in the synthesis gel led to the formation of spherical agglomerates of small 2D-like crystallites that exhibited higher adsorption capacity compared to columnar-like crystals produced by employing more dilute mixtures. CO2 adsorption kinetics was found to follow a pseudo–first-order model. The robust nature of AlPO4-5–based adsorbents, their unique one-dimensional pore configuration, fast kinetics, and low heat of adsorption make them promising for pressure swing adsorption of CO2 at industrial scale.

In this paper, Carbon-doped Zinc Oxide (C-ZnO) samples were prepared using the solid-state reaction method. The influence of carbon-doping on the structural and dielectric properties of ZnO samples was studied. The shift in the highest peak position (101) in XRD patterns of carbon-doped samples was observed. The Raman peak at 581cm−1 in undoped ZnO was shifted and broadened in carbon-doped ZnO samples. The ZnO samples doped with carbon show higher values of dielectric constant (∼2400 at 1kHz) compared to pure ZnO(∼9 at 1kHz) which was due to increase in native point defects in the samples. The ac conductivity (σac) value of the carbon-doped sample was enhanced by 103 times for ((ZnO)0.9 C0.1) sample.

Gas separation and purification using polymeric membranes is a promising technology that constitutes an energy-efficient and eco-friendly process for large scale integration. However, pristine polymeric membranes typically suffer from the trade-off between permeability and selectivity represented by the Robeson's upper bound. Mixed matrix membranes (MMMs) synthesized by the addition of porous nano-fillers into polymer matrices, can enable a simultaneous increase in selectivity and permeability. Among the various porous fillers, metal-organic frameworks (MOFs) are recognized in recent days as a promising filler material for the fabrication of MMMs. In this article, we review representative examples of MMMs prepared by dispersion of MOFs into polymer matrices or by deposition on the surface of polymeric membranes. Addition of MOFs into other continuous phases, such as ionic liquids, are also included. CO2 separation from hydrocarbons, H2, N2, and the like is emphasized. Hybrid fillers based on composites of MOFs with other nanomaterials, e.g., of MOF/GO, MOF/CNTs, and functionalized MOFs, are also presented and discussed. Synergetic effects and the result of interactions between filler/matrix and filler/filler are reviewed, and the impact of filler and matrix types and compositions, filler loading, surface area, porosity, pore sizes, and surface functionalities on tuning permeability are discoursed. Finally, selectivity, thermal, chemical, and mechanical stability of the resulting MMMs are analyzed. The review concludes with a perspective of up-scaling of such systems for CO2 separation, including an overview of the most promising MMM systems.

This article contains data on in vitro cytotoxicity activity of chloroform, methanolic and water extracts of leaf and heartwood of Caesalpinia sappan L. a medicinal plant against Breast cancer (MCF-7) and Lung cancer (A-549) cells. This data shows that Brazilin A, a natural bioactive compound in heartwood of Caesalpinia sappan L.induced cell death in breast cancer (MCF-7) cells. The therapeutic property was further proved by docking the Brazilin A molecule against BCL-2 protein (an apoptotic inhibitor) using auto dock tools. Keywords: Chloroform extract, Methanol extract, Water extract, Caesalpinia sappan L., Cytotoxicity, MCF7, A549, MTT Assay

2,4 disubstitiuted furan derivatives (8Aâ8M) were prepared from furan-4-carboxylic acid ethyl ester (1). Antibacterial activity of new 2,4 di substituted furan derivatives was studied against Gram (+) and Gram (â) bacteria. 8F, 8E, 8D and 8J were found to be effective against Gram (â) bacteria whereas, 8A and 8L were effective against Gram (+) bacteria. Least MIC value was found as 100 µg/ml against Escherichia coli (for 8D and 8F), and Proteus vulgaris (for 8E and 8F). Antibacterial activity is correlated with different substituents. Products exhibited better antibacterial activities especially towards Streptococcus pyogenes, Proteus vulgaris, and Escherichia coli. Keywords: Pathogens, Antibacterial activity, Structure-activity relationship, Disubstitiuted furans, Synthesis

Abstract Quest for efficient ion conducting electrolyte thin film operating at intermediate temperature (~600 °C) holds promise for the real-world utilization of solid oxide fuel cells. Here, we report the correlation between mixed as well as preferentially oriented samarium doped cerium oxide electrolyte films fabricated by varying the substrate temperatures (100, 300 and 500 °C) over anode/ quartz by electron beam physical vapor deposition. Pole figure analysis of films deposited at 300 °C demonstrated a preferential (111) orientation in out-off plane direction, while a mixed orientation was observed at 100 and 500 °C. As per extended structural zone model, the growth mechanism of film differs with surface mobility of adatom. Preferential orientation resulted in higher ionic conductivity than the films with mixed orientation, demonstrating the role of growth on electrochemical properties. The superior ionic conductivity upon preferential orientation arises from the effective reduction of anisotropic nature and grain boundary density in highly oriented thin films in out-of-plane direction, which facilitates the hopping of oxygen ion at a lower activation energy. This unique feature of growing an oriented electrolyte over the anode material opens a new approach to solving the grain boundary limitation and makes it as a promising solution for efficient power generation.

The increasing global consumption of seafood has led to increased trade among nations, accompanied by mislabeling and fraudulent practices that have rendered authentication crucial. The multi-isotope ratio analysis is considered as applicable tool for evaluating geographical authentications but requires information and experience to select target elements such as isotopes, through a distinction method based on differences in habitat and physiology due to origin. The present study examined recombination conditions of multi-elements that facilitated geographically distinct classifications of the clams to sort out appropriate elements. Briefly, linear discriminant analysis (LDA) analysis was performed according to several combinations of five stable isotopes (carbon (δ13C), nitrogen (δ15N), oxygen (δ18O), hydrogen (δD), and sulfur (δ34S)) and two radiogenic elements (strontium (87Sr/86Sr) and neodymium (143Nd/144Nd)), and the geographical classification results of the Manila clam Ruditapes philippinarum from Democratic People’s Republic of Korea (DPR Korea), Korea and China were compared. In conclusion, linear discriminant analysis (LDA) with at least four elements (C, N, O, and S) including S revealed a remarkable cluster distribution of the clams. These findings expanded the application of systematic multi-elements analyses, including stable and radiogenic isotopes, to trace the origins of R. philippinarum collected from the Korea, China, and DPR Korea.

Mapping spatial variability of soil properties is the key to efficient soil resource management for sustainable crop yield. Therefore, the present study was conducted to assess the spatial variability of soil properties such as acidity (pH), salinity (electrical conductivity (EC)), organic carbon, available K, available P, exchangeable Ca2+, exchangeable Mg2+, available S and hot water soluble B in surface (0–20 cm) and subsurface (20–40 cm) soil layers of oil palm plantations in south Goa district of Goa located in west coastal area of India. A total of 128 soil samples were collected from 64 oil palm plantations of Goa located at an approximate interval of 1–2 km and analyzed. Soil was acidic to neutral in reaction. Other soil properties varied widely in both the soil layers. Correlations between soil pH and exchangeable Ca2+, between soil EC and available K, between available P and available S and between exchangeable Ca2+ and exchangeable Mg2+ in both the soil layers were found to be positive and significant (P < 0.01). Geostatistical analysis revealed a varied spatial distribution pattern for the measured soil properties. Best-fit models for measured soil properties were exponential, Gaussian, stable, K-Bessel and spherical with moderate to strong spatial dependency. The results revealed that site-specific fertilizer management options needed to be adopted in the oil palm plantations of the study area owing to variability in soil properties.

After in situ incubation at the site for a year, phytoplanktons in surface water were exposed to natural light in temperate lakes (every month); thereafter, the net production rate of photoprotective compounds (mycosporine-like amino acids, MAAs) was calculated using 13C labeled tracer. This is the first report describing seasonal variation in the net production rate of individual MAAs in temperate lakes using a compound-specific stable isotope method. In the mid-latitude region of the Korean Peninsula, UV radiation (UVR) usually peaks from July to August. In Lake Paldang and Lake Cheongpyeong, diatoms dominated among the phytoplankton throughout the year. The relative abundance of Cyanophyceae (Anabaena spiroides) reached over 80% during July in Lake Cheongpyeong. Changes in phytoplankton abundance indicate that the phytoplankton community structure is influenced by seasonal changes in the net production rate and concentration of MAAs. Notably, particulate organic matter (POM) showed a remarkable change based on the UV intensity occurring during that period; this was because of the fact that cyanobacteria that are highly sensitive to UV irradiance dominated the community. POM cultured in Lake Paldang had the greatest shinorine (SH) production rate during October, i.e., 83.83 ± 10.47 fgC·L−1·h−1. The dominance of diatoms indicated that they had a long-term response to UVR. Evaluation of POM cultured in Lake Cheongpyeong revealed that there was an increase in the net MAA production in July (when UVR reached the maximum); a substantial amount of SH, i.e., 17.62 ± 18.34 fgC·L−1·h−1, was recorded during this period. Our results demonstrate that both the net production rate as well as the concentration of MAAs related to photoinduction depended on the phytoplankton community structure. In addition, seasonal changes in UVR also influenced the quantity and production of MAAs in phytoplanktons (especially Cyanophyceae).

Considering the importance of ultra-performance liquid chromatography-electrospray ionization-quadrupole time of flight-tandem mass spectrometry (UPLC-ESI-QTOF-MS/MS) hyphenated techniques for analysis of secondary metabolites from crude extracts, the present study was aimed at identification of secondary metabolites in acetone extract of the lichen Usnea longissima. From our study, 19 compounds were tentatively identified through comparison of exact molecular masses from their MS/MS spectra, mass fragmentation studies and comparison with literature data. In addition, potent cytotoxic activity of U. longissima extract prompted us to isolate four compounds, 18R-hydroxy-dihydroalloprotolichesterinic acid (19), neuropogolic acid (20), barbatic acid (21), and usnic acid (22) from this extract which were adequately identified through mass spectrometry and NMR spectroscopy. All four compounds displayed cytotoxic activity. Barbatic acid (21) manifested doxorubicin equivalent activity against A549 lung cancer cell line with IC50 of 1.78 µM and strong G0/G1 accumulation of cells. Poly ADP-ribose polymerase (PARP) cleavage confirmed that it induced cytotoxic activity via apoptosis. Finally, our work has discerned the depside, barbatic acid (21) from crude extract as a candidate anti-cancer molecule, which induces cell death by stepping up apoptosis.

Background: Enterococcus, considered a normal commensal of intestinal tract is one of the fast emerging pathogen causing serious and life threatening hospital borne infections. This is attributed to acquisition of multi-drug resistance. Aims and Objectives: The present study was undertaken to isolate and characterize Enterococci from clinical specimens and determine the anti-microbial susceptibility pattern of these isolates. Methods: A total of 58 Enterococcal isolates from various clinical samples were speciated as per the scheme of Facklam and Collins. Anti-bacterial susceptibility pattern was determined by Kirby Bauer disc diffusion method with recommended drugs including high level aminoglycoside resistance. MIC for Vancomycin and Gentamicin was determined by E – test method. Results: Of the total 58 Enterococcal isolates, 52 were E. faecalis and 6 E. faecium. Antibiotic susceptibility tests showed high level resistance to penicillin, Ampicillin, Gentamicin and Streptomycin. All strains were sensitive to Vancomycin, Linezolid, Teicoplanin, E. faecium was more resistant than E. faecalis to the tested antibiotics. MIC for vancomycin was in range 1-4µg/ml. Conclusions: We conclude that Enterococcal strains with high rate of resistance to penicillin and aminoglycosides are prevalent in our nosocomial setting. Therefore, there is an urgent need for more rational and restricted use of antimicrobials in order to minimize the selection and spread of such strains.

This paper presents a methodology for maintenance scheduling (MS) of generators using binary particle swarm optimization (BPSO) based probabilistic approach. The objective of this paper is to reduce the loss of load probability (LOLP) for a power system. The capacity outage probability table (COPT) is the initial step in creating maintenance schedule using the probabilistic levelized risk method. This paper proposes BPSO method which is used to construct the COPT. In order to mitigate the effects of probabilistic levelized risk method, BPSO based probabilistic levelized risk method is embarked on a MS problem. In order to validate the effectiveness of the proposed algorithm, case study results for simple five unit system can accomplish a significant levelization in the reliability indices that make possible to evaluate system generation system adequacy in the MS horizon of the power system. The proposed method shows better performance compared with other optimization methods and conventional method with improved search performance.