Betulin protects against isoproterenol-induced myocardial injury by inhibiting NF-κB signaling and attenuating cardiac inflammation and oxidative stress in rats

Objective:To investigate the cardioprotective potential of betulin in isoproterenol(ISO)-induced myocardial injury in rats.Methods:Wistar rats were divided into five groups(n=10):normal,ISO,nebivolol 5 mg/kg,and betulin(20&40 mg/kg).Nebivolol and betulin were administered orally for 29 days.ISO(85 mg/kg)was administered subcutaneously on day 27 and day 28 to induce myocardial injury.On day 29,blood was collected for determination of cardiac markers,and hemodynamic parameters were investigated.The levels of oxidative stress markers and the gene expressions of apoptotic markers and inflammatory mediators were evaluated.Moreover,2,3,5-triphenyltetrazolium chloride staining and histopathological analysis were also performed.Results:Betulin reduced the size of myocardial infarction,decreased elevated levels of cardiac enzymes,and maintained hemodynamic functions.It also inhibited ISO-induced upregulation of Bax,caspase-3,NF-κB,and IL-6,enhanced endogenous antioxidant enzymes,and reduced lipid peroxidation.Additionally,pretreatment with betulin alleviated myocardial ischemic damage,as reflected by reduced myonecrosis,edema,and inflammatory changes.Conclusions:Betulin exhibits strong cardioprotective activity against ISO-induced myocardial injury by anti-inflammatory,anti-apoptotic,and antioxidant activities.

Fundamentals,recent developments and prospects of lithium and non-lithium electrochemical rechargeable battery systems

The present and future energy requirements of mankind can be fulfilled with sustained research and development efforts by global scientists.The purpose of this review paper is to provide an overview of the fundamentals,recent advancements on Lithium and non-Lithium electrochemical rechargeable battery systems,and their future prospects.The initial part of this review paper is dedicated to the advancement and challenges faced by the conventional rechargeable batteries,such as lead-acid,Ni-Cd and Ni-MH batteries.The subsequent section of this review focuses on an in-depth analysis of two major categories of rechargeable batteries,namely lithium-based rechargeable battery systems and alternative non-Lithium rechargeable battery systems.The working principle,construction,and a few important research progress on Li-ion,Li-O_(2),Li-CO_(2) and Li-S batteries have been highlighted.The recent progress and challenges of the alternate batteries such as Na-ion,Na-S,Mg-ion,K-ion,Al-ion,Al-air,Zn-ion and Zn-air are also discussed in this review.The large gap between theoretical and practical electrochemical values for the alternate battery system must be filled by adopting a series of design architectures followed by modern instrumentation for developing next-generation batteries in a sustainable and efficient way.

Modulating Traffic Signal Phases to Realize Real-Time Traffic Control System

This paper proposes innovations to address challenges emanating from road traffic congestion. Improving economies create more car owners resulting in increased automobile manufacturing, increased vehicle population giving rise to higher emission of CO2 resulting in traffic congestion. Congested traffic has idling vehicles which emit higher CO2 and pollution. Besides, traffic congestion increases turnaround time, delivery time, commuting time and related logistical aspects. Commuting time negatively impacts working hours. Unless the traffic congestion is mitigated, the economy will take a beating creating a vicious ecology cycle. Building new roads, bridges or reconditioning of infrastructure is not always the best possible solutions. Efficient traffic management is a key to country’s economic growth. Various analytical models are employed to study, appreciate traffic congestion. The paper studies these models to infer that real time approach is the only solution. Several approaches are being worked on and few commercial systems too are available. These systems provide traffic information for course correction. However, it has latency and hence deviates from real time environment. Traffic congestion being highly dynamic in nature, it necessitates real time solution with real time inputs. It is proposed to integrate Real time traffic data with the traffic signal thus modulating the cycle timings at every junction. Deviation from static asymmetric cycle timing is implemented by assigning green phases based on density of vehicles. With minimalistic infrastructure and negligible incremental cost, the paper not only proposes to address traffic congestion but also paves the way for capturing traffic offenses, vehicle tracking and toll collection. The research is imminently realizable and makes a strong case for a PPP (Public Private Partnership) project.

Influence of Slope Angle on the Walking of Passive Dynamic Biped Robot

In this paper, we modeled a simple planer passive dynamic biped robot without knee with point feet. This model has a stable, efficient and natural periodic gait which depends on the values of parameters like slope angle of inclined ramp, mass ratio and length ratio. The described model actually is an impulse differential equation. Its corresponding poincare map is discrete case. With the analysis of the bifurcation properties of poincare map, we can effectively understand some feature of impulse model. The ideas and methods to cope with this impulse model are common. But, the process of analysis is rigorous. Numerical simulations are reliable.

An understanding of mitochondria and its role in targeting nanocarriers for diagnosis and treatment of cancer

Nanotechnology has changed the entire paradigm of drug targeting and has shown tremendous potential in the area of cancer therapy due to its specificity. In cancer, several targets have been explored which could be utilized for the better treatment of disease. Mitochondria, the so-called powerhouse of cell, portrays significant role in the survival and death of cells, and has emerged as potential target for cancer therapy. Direct targeting and nanotechnology based approaches can be tailor-made to target mitochondria and thus improve the survival rate of patients suffering from cancer. With this backdrop, in present review, we have reemphasized the role of mitochondria in cancer progression and inhibition, highlighting the different targets that can be explored for targeting of disease. Moreover, we have also summarized different nanoparticulate systems that have been used for treatment of cancer via mitochondrial targeting.

A Model with Traffic Routers, Dynamically Managing Signal Phases to Address Traffic Congestion in Real Time

On-road Vehicular traffic congestion has detrimental effect on three lifelines: Economy, Productivity and Pollution (EPP). With ever increasing population of vehicles on road, traffic congestion is a major challenge to the economy, productivity and pollution, notwithstanding continuous developments in alternative fuels, alternative sources of energy. The research develops accurate and precise model in real time which computes congestion detection, dynamic signaling algorithm to evenly distribute vehicle densities while ensuring avoidance of starvation and deadlock situation. The model incorporates road segment length and breadth, quality and achievable average speed to compute road capacity. Vehicles installed with GPS enabled devices provide their location, which enables computing road occupancy. Road occupancy is evaluated based on number of vehicles as well as area occupied by vehicles. Ratio of road occupancy and road capacity provides congestion index important to compute signal phases. The algorithm ensures every direction is serviced once during a signaling cycle ensuring no starvation. Secondly, the definition of minimum and maximum signal timings ensures against dead lock situation. A simulator is developed to validate the proposition and proves it can ease congestion by more than 50% which is better than any of the contemporary approaches offering 15% improvement. In case of higher congestion index, alternate routes are suggested based on evaluation of traffic density graphs for shortest route or knowledge database. The algorithm to compute shortest route is optimized drastically, reducing computation cost to 3*√2N vis-à-vis computation cost of N2 by classical algorithms. The proposal brings down the cost of implementation per traffic junction from USD 30,000 to USD 2000.

A Survey of High Performance Concrete Developments in Civil Engineering Field

High Performance concrete (HPC) has received increased attention in the development of infrastructure Viz., Buildings, Industrial Structures, Hydraulic Structures, Bridges and Highways etc. leading to utilization of large quantity of concrete. This paper presents a comprehensive coverage of High Performance concrete developments in civil engineering field. It highlights the High Performance concrete features and requirements over conventional concrete. Furthermore, recent trends with regard to High Performance Concrete development in this area are explored. This paper also includes effect of Mineral and Chemical Admixtures used to improve performance of concrete.

Shortest Alternate Path Discovery through Recursive Bounding Box Pruning

Congestion is a dynamic phenomenon and hence efficiently computing alternate shortest route can only help expedite decongestion. This research is aimed to efficiently compute shortest path for road traffic network so that congestion can be eased resulting in reduced CO2 emission and improved economy. Congestion detection is achieved after evaluating road capacity and road occupancy. Congestion index, a ratio of road occupancy to road capacity is computed, congestion index higher than 0.6 necessitates computation of alternate shortest route. Various algorithms offer shortest alternate route. The paper discusses minimization of graph based by removing redundant nodes which don’t play a role in computation of shortest path. The proposal is based on continuous definition of a bounding box every time a next neighboring node is considered. This reduces maximum number of contentious nodes repeatedly and optimizes the network. The algorithm is deployed from both the ends sequentially to ensure zero error and validate the shortest path discovery. While discovering shortest path, the algorithm also offers an array of shortest path in ascending order of the path length. However, vehicular traffic exhibits network duality viz. static and dynamic network graphs. Shortest route for static distance graph is pre-computed and stored for look-up, alternate shortest path based on assignment of congestion levels to edge weights is triggered by congestion index. The research also supports directed graphs to address traffic rules for lanes having unidirectional and bidirectional traffic.

A comprehensive review of approaches to detect fatigue using machine learning techniques

In the past decades,there have been numerous advancements in the field of technology.This has led to many scientific breakthroughs in the field of medical sciences.In this,rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent.So,this study aimed to understand what is fatigue,its repercussions,and techniques to detect it using machine learning(ML)approaches.This paper introduces,discusses methods and recent advancements in the field of fatigue detection.Further,we categorized the methods that can be used to detect fatigue into four diverse groups,that is,mathematical models,rule-based implementation,ML,and deep learning.This study presents,compares,and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue.Finally,the paper discusses the possible areas for improvement.

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization:a case study in Central Vietnam

This study represents a hybrid intelligence approach based on the differential evolution optimization and Least-Squares Support Vector Machines for shallow landslide prediction,named as DE-LSSVMSLP.The LSSVM is used to establish a landslide prediction model whereas the DE is adopted to search the optimal tuning parameters of the LSSVM model.In this research,a GIS database with 129 historical landslide records in the Quy Hop area(Central Vietnam)has been collected to establish the hybrid model.The receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to assess the performance of the newly constructed model.Experimental results show that the proposed model has high performances with approximately 82%of AUCs on both training and validating datasets.The model’s results were compared with those obtained from other methods,Support Vector Machines,Multilayer Perceptron Neural Networks,and J48 Decision Trees.The result comparison demonstrates that the DE-LSSVMSLP deems best suited for the dataset at hand;therefore,the proposed model can be a promising tool for spatial prediction of rainfall-induced shallow landslides for the study area.

A novel design of optimal intelligent fuzzy TID controller employing GA for nonlinear level control problem subject to actuator and system component fault

Purpose–The purpose of this article is about the design of controllers for conical two-tank noninteracting level(CTTNL)system in simulation.Local linearization around the equilibrium point has been done for the nonlinear CTTNL system to obtain a linearized model transfer function.Design/methodology/approach–This article deals with the design of novel optimal fractional-order tiltintegral-derivative(TID)controller using type-1 fuzzy set for the CTTNL prototype system.In this study,type-1 fuzzy TID controller parameters have been optimized through genetic algorithm(GA)and those set of values have been employed for the design of proportional-integral-derivative(PID)controller.Findings–A performance comparison between FTID and PID controller is then investigated.The analysis shows the superiority of FTID controller over PID controller in terms of integral absolute error(IAE),integral square error(ISE),integral of time multiplied absolute error(ITAE)and integral of time multiplied squared error(ITSE)integral errors.The transient and steady state performance of the FTID controller are superior as compared to conventional PID controller.In future,the FTID controller fault-tolerance capability tested on CTTNL system subject to actuator and system component(leak)faults.The detailed study of robustness in presence of model uncertainties will be incorporated as a scope of further research.Originality/value–A performance comparison between FTID and PID controller is then investigated.The analysis shows the superiority of FTID controller over PID controller in terms of IAE,ISE,ITAE and ITSE integral errors.Additionally,fault-tolerant performance of the proposed controller evaluated with fault-recovery time(F_(rt))parameter.The transient and steady state performance of the FTID controller are superior as compared to conventional PID controller.

A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model

Out-of-step protection of one or a group of synchronous generators is unreliable in a power system which has significant renewable power penetration. In this work, an innovative out-of-step protection algorithm using wavelet transform and deep learning is presented to protect synchronous generators and transmission lines. The specific patterns are generated from both stable and unstable power swing, and three-phase fault using the wavelet transform technique. Data containing 27,008 continuous samples of 48 different features is used to train a two-layer feed-forward network. The proposed algorithm gives an automatic, setting free and highly accurate classification for the three-phase fault, stable power swing, and unstable power swing through pattern recognition within a half cycle. The proposed algorithm uses the Kundur 2-area system and a 29-bus electric network for testing under different swing center locations and levels of renewable power penetration. Hardware-in-the-loop (HIL) tests show the hardware compatibility of the developed out-of-step algorithm. The proposed algorithm is also compared with recently reported algorithms. The comparison and test results on different large-scale systems show that the proposed algorithm is simple, fast, accurate, and HIL tested, and not affected by changes in power system parameters.

Adaptive virtual MIMO-based cross-layer design for wireless sensor networks via hybrid optimization model

In recent times,many MAC protocols were implemented for boosting and improving energy efficiency(EE)in WSNs.Moreover,the cooperative MIMO method is found to be much capable of enhancing the EE of WSNs if configured properly.This paper intends to propose cross-layer design for multihop virtual MIMO system to enhance the end-to-end(ETE)reliability,EE,and QoS of the adopted WSN.The protocol is set here to focus on the energy utilization for transmission of data packets by optimal selection of transmission constraints for each node of the network.Moreover,the protocol’s ETE latency and throughput are also modeled as the dependent variables of BER performance of every link.To discover the improved BER criteria of each link that meets the ETE QoS requirement in reduced energy utilization,this paper employs a new hybrid optimization algorithm named Lion Mutated Dragonfly Algorithm(LM–DA)that is a hybrid variant of both Lion Algorithm(LA)and Dragonfly Algorithm(DA).Finally,the performance of the adopted scheme is validated over other state-of-the-art models.The results state that the energy consumed by the adopted LM–DA approach is about 2.65%,1.77%,and 1.77%reduced over LA,PSO,and DA schemes,respectively.

Phasor Measurement Unit Incorporated Adaptive Out-of-step Protection of Synchronous Generator

The existing out-of-step(OOS)protection schemes have proven to be deficient in the prevention of significant outages.OOS protection schemes must not operate in stable power swing,and rapidly isolate an asynchronous generator or group of generators from the rest of the power system in case of unstable power swing.The paper proposes a novel phasor measurement unit(PMU)incorporating a polygon-shaped graphical algorithm for OOS protection of the synchronous generator.The unique PMU-based logic works further to classify the type of swing once the graphical scheme detects it,which can identify the complex power swing produced in the modern power system.The proposed algorithm can take the correct relaying decision in the event of power swing due to renewable energy integration,load encroachment,and transient faults.In this paper,the original and modified Kundur two-area system with a power system stabilizer(PSS)is used to test the proposed algorithm.In the end,it provides assessment results of the proposed relay on the Indian power system during the blackout in July 2012.The results demonstrate that the proposed algorithm is fast,accurate,and adaptive in the modern power system and shows better performance than the existing OOS protection schemes.