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Nowadays, searching the relevant documents from a large dataset becomes a big challenge. Automatic query expansion is one of the techniques, which addresses this problem by refining the query. A new query expansion approach using cuckoo search and accelerated particle swarm optimization technique is proposed in this paper. The proposed approach mainly focused to find the most relevant expanded query rather than suitable expansion terms. In this paper, Fuzzy logic is also employed, which improves the performance of accelerated particle swarm optimization by controlling various parameters. We have compared the proposed approach with other existing and recently developed automatic query expansion approaches on various evaluating parameters such as average recall, average precision, Mean-Average Precision, F-measure and precision-recall graph. We have evaluated the performance of all approaches on three datasets CISI, CACM and TREC-3. The results obtained for all three datasets depict that the proposed approach gets better results in comparison to other automatic query expansion approaches. [ABSTRACT FROM AUTHOR]

The identification and apprehending of suspects by law enforcement authorities rely heavily on facial sketches. The sketch artist creates sketches based on the witnesses' memories. Sketch artists are few and limited in their availability. It is also evident that as time passes, the eyewitness forgets many of the important details, which can be expensive in time-sensitive investigations. The sketch was used to obtain the suspect's image through the state-of-the-art sketch-photo retrieval model, which missed the relevance of time sensitivity. A linguistic description-based suspect face image retrieval approach is presented in this study. In the proposed approach, the facial attribute-value pair is extracted from eyewitness descriptions. Facial attribute saliency is also studied in this work and validated with the Fuzzy Analytic Hierarchy Process (FAHP) model. A weighted score is computed to retrieve the suspect face images. The effectiveness of the proposed method is assessed by comparing it to existing linguistic sketch-based retrieval methods as well as the sketch to photo retrieval models. As compared to state-of-the-art approaches, experimental results give an accuracy of 94.98%. [ABSTRACT FROM AUTHOR]

Faces are the most common biometric used for the identification of a person. Law enforcement agencies use face as a key point to identify the suspect involved in unlawful activities. Forensic sketches are normally developed by the sketch artist based on verbal details provided by an eyewitness about the suspect. In a forensic sketch, the facial description depends on the memory of the eyewitness; therefore, there is uncertainty in facial attributes. In the recent past, lots of sketch-to-photograph retrieval methods are proposed by many researchers; however, they have ignored the uncertainty of facial attributes for suspect face retrieval. Recently, linguistic information is also utilized for suspect face retrieval. In this paper, we have provided an extensive review of the available methods for suspect face retrieval using visual and linguistic information. The review focuses firstly on the traditional methods and their categorization also shows the evolution of suspect face retrieval approaches over the years. We have also shown the summary of the performance of representative state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work, fake news identification poses many challenges due to the lack of a comprehensive publicly available benchmark dataset. There is no large-scale dataset that consists of Indian news only. So, this paper presents IFND (Indian fake news dataset) dataset. The dataset consists of both text and images. The majority of the content in the dataset is about events from the year 2013 to the year 2021. Dataset content is scrapped using the Parsehub tool. To increase the size of the fake news in the dataset, an intelligent augmentation algorithm is used. An intelligent augmentation algorithm generates meaningful fake news statements. The latent Dirichlet allocation (LDA) technique is employed for topic modelling to assign the categories to news statements. Various machine learning and deep-learning classifiers are implemented on text and image modality to observe the proposed IFND dataset's performance. A multi-modal approach is also proposed, which considers both textual and visual features for fake news detection. The proposed IFND dataset achieved satisfactory results. This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models. [ABSTRACT FROM AUTHOR]

The present world is intimidated by the problem of water scarcity that is to be addressed immediately. So, it is wise to treat wastewater to meet the massive need for drinking water for the fast-growing population. The magnificent application of Internet of Things (IoT) technology in many smart cities has derived fruitful results. This research study has proposed a real-time system using IoT that regularly monitors specific crucial parameters of a wastewater treatment plant and informs any plant's dysfunction to the operator. Furthermore, the large stream of data sets generated by IoT sensors in real-time can be analyzed and processed by complex event processing (CEP). This study was experimented with Smart Treatment (SMARTreat) architecture and its application in a simple water system of an industrial estate in South India. The proposed architecture showed outstanding results and has received positive comments from the water treatment plant managers. [ABSTRACT FROM AUTHOR]

Biodiesel is a biological renewable source produced from the conversion of triglycerides to alkyl esters. Palm oil is one of the most used lipid feedstocks for biodiesel production. It becomes necessary to optimize the transesterification reaction parameters to reduce the cost and enhance the quality of biodiesel. This study focuses on the use of homogenous sulfuric acid as a catalyst for the transesterification of palm fatty acids to methyl esters in a batch-scale reactor. A novel examination of transesterification reaction input parameters using the technique for order performance by similarity to ideal solution optimization technique and the effect of these parameters on yield, viscosity, and density of palm biodiesel using 3D surface graphs is investigated in this research. The present optimization approach is implemented to find out the optimum ranking of biodiesel production. From the experimental and numerical simulation, optimum results were observed at the catalyst concentration of 6% (w/w), reaction temperature of 70 °C, the reaction time of 120 min, and alcohol to oil molar ratio of 30:1 at which yield of 95.35%, viscosity of 5.0 cSt, and density of 880 kg/m3 of palm biodiesel were obtained. The different physicochemical properties of produced palm methyl esters are obtained within standards set by international authorities. Selected optimized process parameters can be used for commercial-scale biodiesel production. [ABSTRACT FROM AUTHOR]

Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks. [ABSTRACT FROM AUTHOR]

In the reported study, a dynamic analytical model is developed to propose the energy, exergy, environmental impact, and economic analyses of the water heating system at Jaipur (India) with an evacuated tube compound parabolic concentrator field of a total area of 81 m2. Consequently, the model is used to perform parametric studies to report the effect of operating and meteorological parameters on the productivity and performance of the system. Moreover, the system's performance, environmental impact, and economic aspects have been investigated and compared under different meteorological conditions at four different Rajasthan (India) locations using TMY2 weather data files. Results clarified that Jodhpur receives the highest solar radiation intensity from these four locations. The model results were validated with the experimental data, and a good agreement has prevailed. Consequently, the results indicate the highest annual energy and exergy gain for Jodhpur with 79.72 MWh and 9.311 MWh, respectively, followed by Jaisalmer, Barmer, and Jaipur. The economic analysis results clarified that the simple payback period ranged from 4.5 to 4.75 years and the discounted payback period ranged from 6.6 to 7 years based on a 6% discount rate. At the same time, the levelized cost of heating for the given system is around 0.023 $/kWh which is very economical closest to that of CNG as a fuel which costs around 0.059 $/kWh. The internal rate of return is reported to be 16.76, 16.82, 16.77, and 16.75% for Barmer, Jodhpur, Jaipur, and Jaisalmer, respectively, and savings of 74.4, 78.1, 75.4, and 73.8 tonnes of CO2 emission to the environment. [ABSTRACT FROM AUTHOR]

Privacy is a significant problem in communications networks. As a factor, trustworthy knowledge sharing in computer networks is essential. Intrusion Detection Systems consist of security tools frequently used in communication networks to monitor, detect, and effectively respond to abnormal network activity. We integrate current technologies in this paper to develop an anomaly-based Intrusion Detection System. Machine Learning methods have progressively featured to enhance intelligent Anomaly Detection Systems capable of identifying new attacks. Thus, this evidence demonstrates a novel approach for intrusion detection introduced by training an artificial neural network with an optimized Bat algorithm. An essential task of an Intrusion Detection System is to maintain the highest quality and eliminate irrelevant characteristics from the attack. The recommended BAT algorithm is used to select the 41 best features to address this problem. Machine Learning based SVM classifier is used for identifying the False Detection Rate. The design is being verified using the KDD99 dataset benchmark. Our solution optimizes the standard SVM classifier. We attain optimal measures for abnormal behavior, including 97.2 %, the attack detection rate is 97.40 %, and a false-positive rate of 0.029 %. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are generally employed for observing and monitoring specific environments. WSNs are made from a huge amount of low-cost sensor nodes separated and distributed in different environments for distributing data through sensor nodes. The collection of data by the various sensors were transmitted into the Base Station. An enhanced Trust-Based Adaptive Acknowledgment based Intrusion-Detection System was proposed from positive distributions in WSNs. A Kalman filter algorithm is used in Multi-objective Particle Swarm Optimization to predict trust nodes over the WSN. Simulations were carried out for non-malicious (0% malicious) networks, and various ranges of malicious nodes in the network were investigated. The outcomes show that the proposed MPSO achieves an improvement of 3.3% than PSO at 0% malicious nodes concerning the PDR. Similarly, at 30% malicious, the PDR of MPSO achieves better by 3.5% than PSO in WSN. [ABSTRACT FROM AUTHOR]

The climate has changed absolutely in every area in just a few years as digitized, making high-speed internet service a significant need in the future. Future Internet is supposed to face exponential growth in traffic, and highly complicated infrastructure, threatening to make conventional NTC approaches unreliable and even counterproductive. In recent days, AI Stimulated state-of-the-art breakthroughs with the ability to tackle extensive and multifarious challenges, and the network community is initiated by considering the NTC prototype from legacy rule-based towards a novel AI-based. Design and execution are applied to interdisciplinary become more essential. A smart home network supports various applications and smart devices within the proposed work, including e-health devices, regular computing devices, and home automation devices. Many devices accessible through the Internet by Home GateWay for Congestion (HGC) in a smart home. Throughout this paper, a Software-Defined Network Home GateWay for Congestion (SDNHGC) architecture for improved management of remote smart home networks and protection of the significant network's SDN controller. It enables effective network capacity regulation, focused on real-time traffic analysis and core network resource allocation. It cannot control the Network in dispersed smart homes. Our innovative SDNHGC expands power across the connectivity network, a smart home network enabling improved end-to-end monitoring of networks. The planned SDNHGC directly will gain centralized device identification by classifying traffic through a smart home network. Several of the current traffic classifications approach, checking deep packets, cannot have this real-time device knowledge for encrypted data to solve this issue. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a severe progressive neurodegenerative as well as disruptive behavior disorder affecting innumerable people throughout the world. The discovery of potential biomarkers in the clinical scenario would lead to the development of effective methods of diagnosis and would provide an understanding of the prognosis of the disease. Moreover, breakthrough inventions for the treatment and prevention of this mysterious disease could evolve as a result of a thorough understanding of the clinical biomarkers. In this review, we have discussed about specific biomarkers of SZ an emphasis has been laid to delineate (1) diagnostic biomarkers like neuroimmune biomarkers, metabolic biomarkers, oligodendrocyte biomarkers and biomarkers of negative and cognitive symptoms, (2) therapeutic biomarkers like various neurotransmitter systems and (3) prognostic biomarkers. All the biomarkers were evaluated in drug-naïve (at least for 4 weeks) patients in order to achieve a clear comparison between schizophrenic patients and healthy controls. Also, an attempt has been made to elucidate the potential genes which serve as predictors and tools for the determination of biomarkers and would ultimately help in the prevention and treatment of this deadly illness. [ABSTRACT FROM AUTHOR]

The use of an automatic query expansion technique is to enhance the performance of the Information Retrieval System. Selecting the candidate terms for query expansion is an essential task to make query more precise to extract the most suitable documents. This paper provides a method to select the best terms for query enhancement. Firstly, the effect of abbreviation resolution, Lexical Variation, Synonyms, n-gram pseudo-relevance feedback, Co-occurrence method on baseline approaches of query expansion is analyzed.. In this work, we used the Okapi BM25 algorithm for ranking. We used Concept-based normalization to deal with concept terms. Here our results show the improvement in results than the baseline approach. A new combined technique that integrates lexical variation, synonyms, n-gram pseudo relevance feedback for query enhancement is proposed. For experimental purpose three English written datasets CACM, CISI, and TREC-3 is used. The obtained results show improvement in the performance of query expansion concerning mean average precision, F-measure, and precision-recall curve. [ABSTRACT FROM AUTHOR]

Globally, cities are emerging into Smart Cities (SC) as a result of sustainable cities and the adaption of recent Internet of Things (IoT) technology. It is becoming essential to involve students in sustainability as engineering and technology are crucial elements in fixing the past adverse effects on our globe. Engineering e-learners are being educated on the sustainable development of SC in many Smart e-learning Tools (SeT) and infrastructure faculties around the world, especially in developing Asian countries such as India. This research paper presents an advanced solution for interactive Smart Learning Environment (SLE) systems based on new IoT technologies in the Virtual Reality (VR) and Augmented Reality (AR) found in Smart Learning Environments (SLE) for SC people. The proposed IoT-Ve-LS system provides an optimized solution for online classes to attend classes using VR/AR glasses to feel the interactions between Smart Digital Devices (SDD) as practically as in practice. The new Virtual e-Learning System (Ve-LS) is experimental, allows automatic Information and Communications Technology (ICT) development, and offers an extraordinary SLE for increased global recognition. This paper focuses on IoT-Ve-LS, a tool for SLE. The IoT-Ve-LS domain has been fast-growing through the emerging technological trends of the IoT. The IoT-Ve-LS method used in the design and implementation allows flexible usage and integration of the online courses by SLE. The impacts of empirical E-learning evaluation on implementing IoT techniques in online tutoring have been analysed to find out its research hypothesis. Our IoT-sensor-based Reservoir Computing allows the classification of short-term learning language sentences relatively quickly, highlighting the minimal training time and optimized solution of real-time cases for controlling temporal and sequential signals at the cloud computing level. The triangulation analysis in information gathering endorses the theoretical models that use computable and personalized approaches. [ABSTRACT FROM AUTHOR]

This research presents a mathematical model using the exergy analysis of evacuated tube compound parabolic concentrator under the meteorological conditions of Jaipur–India. Moreover, the effect of hourly variation of solar radiation intensity and ambient temperature over the exergetic efficiency of evacuated tube compound parabolic concentrator are analyzed. The maximum exergetic efficiency was 12.83%, whereas day-wise efficiency varied from 4.70 to 8.45% on average. The maximum energy and exergy gain recorded are 252.2 and 46.84 kW h per day. Exergy destructions are highest during energy transfer from the absorber to the receiver tubes (46%), followed by exergy destructions due to optical (36%). [ABSTRACT FROM AUTHOR]

The advent of cloud infrastructure in which third-party cloud services may retain sensitive consumer and company data in storage environments underlines the need to advocate for encryption and multi-tenant shared processing as a primary security mechanism. Digital information movement, storage, and processing are widely defined in terms of "Data in Motion," "Data at Rest," and "Data in Usage," respectively. The implementation of security methods for each of these states can be viewed similarly. Transit data applies to data when being moved from one source of data to another. Transit data contains data sent across the network from back-end clients, programs, and repositories. There could be two data centers inside the same organizational network in the cloud, as a member of completely separate networks. This paper presents a novel architecture data-in-transit defender (DiTD), to protect data in transit; DiTD provides a novel security framework based on high-performance cloud computing. This protocol enables more efficient use of the key strength and time of symmetric block encrypted data, public-key cryptography (PUKC), cryptographic hash, and brief key exchange function. [ABSTRACT FROM AUTHOR]

Classifying the remote sensing images requires a deeper understanding of remote sensing imagery, machine learning classification algorithms, and a profound insight into satellite images' know-how properties. In this paper, a convolutional neural network (CNN) is designed to classify the multispectral SAT-4 images into four classes: trees, grassland, barren land, and others. SAT-4 is an airborne dataset that captures the images in 4 bands (R, G, B, infrared). The proposed CNN classifier learns the image's spectral and spatial properties from the ground truth samples provided. The contribution of this paper is three-fold. (1) A classification framework for feature extraction and normalization is built. (2) Nine different architectures of CNN models are built, and multiple experiments are conducted to classify the images. (3) A deeper understanding of the image structure and resolution is captured by varying different optimizers in CNN. The correlation between images of varying classes is identified. The experimental study shows that vegetation health is predicted most accurately by the proposed CNN models. It significantly differentiates the grassland vegetation from tree vegetation, which is better than other classical methods. The tabulated results show that a state-of-the-art analysis is done to learn varying land cover classification models. [ABSTRACT FROM AUTHOR]

Streams are temporally ordered, rapid changing, ample in volume, and infinite in nature. It is nearly impossible to store the entire data stream due to its large volume and high velocity. In this work, the principle of parallelism is employed to accelerate stream data computing. GPU-based high-performance computing (HPC) framework is proposed for accelerated processing of big-data streams using the in-memory data structure. We have implemented three parallel algorithms to prove the viability of the framework. The contributions of Mille Cheval are: (1) the viability of streaming on accelerators to increase throughput, (2) carefully chosen hash algorithms to achieve low collision rate and high randomness, and (3) memory sketches for approximation. The objective is to leverage the power of a single node using in-memory computing and hybrid computing. HPC does not always require high-end hardware but well-designed algorithms. Achievements of Mille Cheval are: (1) relative error is 1.32 when error rate and overestimate rate are chosen as 0.001 and (2) the host memory space requirement is just 63 MB for 1 terabyte of data. The proposed algorithms are pragmatic. It is evident from experimental results that the framework demonstrates 10X speed-up as compared with CPU implementations and 3X speed-up as compared with GPU implementations. [ABSTRACT FROM AUTHOR]

To increase the Information Retrieval System's efficiency, there is a requirement to expand the native user query. There are many approaches to enhance the user query in which the primary method consists of semantic-based query expansion (QE). In a semantic-based QE approach, relevant documents are retrieved by considering all the similar terms of a given user query. The semantic-based QE approach helps in dealing with the limitation of low recall and low precision value of the Information retrieval system and deals with ambiguity and vagueness. Query Expansion techniques contain the semantic concepts that are relevant to semantic computing, computational intelligence, and information retrieval area. Computational intelligence technique is required during automatic query expansion for advanced information processing. This paper presents a comprehensive survey of the methods proposed by several researchers considering semantic-based query expansion. It also discusses the merits and demerits of each technique in a detailed manner. This paper presents the view of several semantic query expansion core techniques. This paper also discusses the various keyholes present in today's era. [ABSTRACT FROM AUTHOR]

Internet users are getting more and more dependent for information regarding their daily lives. Most of the users are connected to each other using social networks. Social networking sites not only helps the users to connect and talk to each other but also share information with each other. Twitter [1] users attach their location information with the post or tweet to show their presence at the location. But, not all users tags or integrate the location information within the post. If a person wants to obtain the latest updates about an event then he/she have to go through all the tweets about that event, which is impossible because nearly 500 million tweets are posted on Twitter on a daily basis. Using Twitter the users can post up to 140 characters in their posts or tweet. Also, the tweets that originate from the location of the event are latest and contain new facts and the rest of the tweets convey that information only. Non-geo-tagged tweets are eliminated by the traditional systems. This paper presents a method to tag the non-geo-tagged tweets with the location then the user would be able to obtain the latest information by including the new. The proposed method performs better than previous methods and yields better results. [ABSTRACT FROM AUTHOR]

The rapid depletion of conventional fuel reserves and the increase in environmental pollution prompted the search for a sustainable energy solution. Biodiesel is one of the most promising energy substitutes with similar properties as conventional diesel fuel. Surplus availability of palm oil makes it suitable for biodiesel production. Due to the lack of availability of review articles that cover the entire process of palm biodiesel production and its optimum use in diesel engines, the authors were motivated to write this article. Cultivation parameters of palm trees, extraction of oil, and physicochemical properties of palm oil–based biodiesel are explained in this review. The production of palm biodiesel from raw oil can be done through pyrolysis, micro-emulsification, blending, hydro-esterification, and transesterification processes. For high biodiesel yield and less cost of operation, the transesterification method is adopted. The performance and emission parameters of diesel engines that operated on palm biodiesel and its blends are also explained. There is a decrease in brake thermal efficiency and an increase in brake-specific fuel consumption observed with the use of palm biodiesel in diesel engines. A reduction in CO and HC emissions and an increase in NOx emissions are found due to the oxygenating nature of palm biodiesel. This article provides the scientific approach to find out the optimum parameters for palm biodiesel production and its efficient use in compression ignition engines. [ABSTRACT FROM AUTHOR]

The intermittent nature of solar energy is a dominant factor in exploring well-designed thermal energy storages for consistent operation of solar thermal-powered vapor absorption systems. Thermal energy storage acts as a buffer and moderator between solar thermal collectors and generators of absorption chillers and significantly improves the system performance. Vapor absorption chillers are available in half, single, double, and triple-effect modes of operation and operate at temperatures ranging from 75 to 220 °C to produce a cooling effect with COPs ranging from 0.3 to 1.8. Thus, the selection of appropriate solar collectors and thermal energy storages are two significant decisions affecting the consistency of output of a vapor absorption refrigeration system. The present review of state of the art is focused on the appropriate selection, from among different types of solar collectors available to meet the demand of capacity and degree of thermal energy required in operating absorption chillers at optimum performance. Characteristics of various thermal energy storage systems and their integration with solar thermal collectors and absorption chillers are also investigated to meet the demand for heat during non-sunshine hours or periods of low solar intensity. In the latter section, economic feasibility is explored so that a sustainable solar cooling system can be proposed which can work consistently with the best performance throughout its entire life. [ABSTRACT FROM AUTHOR]

Kinesins are one of the neoteric and efficacious targets recently reported to play an important role in the initiation and progression of chronic pain. Kinesins are anterograde microtubule-based motor proteins that are involved in trafficking of receptors including nociceptors and progression of pain. The specific kinesin and regulatory proteins interplay is crucial for the delivery of nociceptors to the synapse. If this complex and less understood interplay is inhibited, it may result in a decrease in central sensitization, and thus attenuation of pain. This review is focused on the transportation process of receptors/cargos, the role of regulatory proteins influencing the respective kinesin, and their relationship with chronic pain. The review also features specific strategies adopted by researchers for targeting kinesin and chronic pain. Considering the recent preclinical success of kinesin inhibition in pain, it is expected that inhibitors for kinesin or enzymes responsible for kinesin activation could be developed or repurposed as alternative, safe, and potential therapies for the treatment of chronic pain. [ABSTRACT FROM AUTHOR]

Vegetable oils are found suitable alternate of diesel fuel as per the results of short-run studies. Long-run studies with vegetable oil as a fuel pointed out the problems related to wear and maintenance of the engine. A single cylinder, variable compression ratio diesel engine was tested for 512 h (32 cycles of 16 h per day) to investigate longevity implications of fueling Thumba vegetable oil. Results of the study revealed that a very little damage was observed over the running surface of the cylinder liner, piston rings, valves, and valve seats. Wear in the piston outer diameter was observed to be 13 to 30 microns. Cylinder wear was about 80 microns. The closed gap in the oil piston ring increased up to 200 microns. Heavy carbon deposition was found on different internal parts of the engine, which indicates poor combustion of fuel. Amount of copper (66 mg/kg) and silicon (112 mg/kg) dissolved in the lubricating oil was found more than permissible limits (Cu 50 mg/kg, Si 25 mg/kg), after 450-h engine test run. But all the dissolve materials remain in allowable limits when the durability test conducted with diesel. Smoke, CO, HC, and NOX emissions were found to increase initially then decrease in the further engine running hours. But these emissions were found inferior to the engine emissions fueled with diesel in all the running hours. CO2 emissions were found superior throughout the test with the preheated T20 Thumba oil blend than diesel. The maximum reduction in the viscosity of the lubricating oil, during endurance testing, was found 60 centipoises but it was found 25 centipoises when the test conducted with diesel. [ABSTRACT FROM AUTHOR]

Increasing environmental concern, human health and the continuous upgradation in the stringent standards of vehicular emissions have shown much interest in cleaner diesel fuels. Out of various strategies to mitigate the diesel engine emissions, use of water blended diesel in the form of emulsion has grabbed sufficient attention of the fuel research community. Various researches have shown that water-emulsified diesel has sufficient potential to improve the engine performance simultaneously with a significant reduction in the levels of nitrogen oxides (NOx) and particulate matter (PM) emissions. Micro-explosion phenomenon of combustion in emulsion fuel helps to provide efficient and complete combustion which in turn improves brake thermal efficiency. The current study presents a comprehensive review of the usage of water-emulsified diesel fuel in CI engines. Focusing on the performance, combustion, and emission analysis, it also talks in detail about the principle and the chemistry involved in making of a stable and homogeneous water-diesel emulsion compatible for CI engine. The literature survey concludes two crucial points. First, the water-blended diesel emulsion serves as an economical, fuel efficient, and cleaner combustion technology. Second, the optimum blend ratio, emulsifier quantity, and proper process differs in almost all the research papers and hence needed to be standardized. [ABSTRACT FROM AUTHOR]

Regression testing is an expensive procedure that is implemented during maintenance phase of the Software Development Life Cycle of evolving software. During this process, test case prioritization is one of the strategies followed in which test cases are organized in a fashion so as to enhance efficiency in achieving some performance goal. During the process, there could be several aspects to be kept in mind due to resources constraints such as fault severity detected per unit of test cost, severity detection per test case execution, and execution time of test cases to detect all the faults. Keeping all such constraints in mind, the test case prioritization problem becomes a multi-objective problem where some of the objectives have to be maximized and the remaining ones minimized. In this study, experiments were performed on different versions of five web applications. The problem instance was found to vary from 5 × 5 test cases versus fault matrix, to 125 × 125 matrix. Random approach, 2-opt algorithm, improved 2-opt algorithm, greedy approach, additional greedy approach, Weighted Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) were applied to a generate prioritized test sequence which maximizes the Cost Cognizant Average Percentage of Fault Detection value, severity detection and minimizes test case execution cost to expose all the faults. The performances of these algorithms are compared, keeping these parameters in mind, and it is concluded that the performance of NSGA-II algorithm is better than that of all the other tested algorithms throughout all the experiments. [ABSTRACT FROM AUTHOR]

In this paper, the effect of injection pressure on the performance, emission, and combustion characteristics of a diesel-acetylene fuelled single cylinder, four-stroke, direct injection (DI) diesel engine with a rated power of 3.5 kW at a rated speed of 1500 rpm was studied. Experiments were performed in dual-fuel mode at four different injection pressures of 180, 190, 200, and 210 bar with a flow rate of 120 LPH of acetylene and results were compared with that of baseline diesel operation. Experimental results showed that highest brake thermal efficiency of 27.57% was achieved at injection pressure of 200 bar for diesel-acetylene dual-fuel mode which was much higher than 23.32% obtained for baseline diesel. Carbon monoxide, hydrocarbon, and smoke emissions were also measured and found to be lower, while the NOx emissions were higher at 200 bar in dual fuel mode as compared to those in other injection pressures in dual fuel mode and also for baseline diesel mode. Peak cylinder pressure, net heat release rate, and rate of pressure rise were also calculated and were higher at 200 bar injection pressure in dual fuel mode. [ABSTRACT FROM AUTHOR]

The continuous rise in the cost of fossil fuels as well as in environmental pollution has attracted research in the area of clean alternative fuels for improving the performance and emissions of internal combustion (IC) engines. In the present work, n-butanol is treated as a bio-fuel and investigations have been made to evaluate the feasibility of replacing diesel with a suitable n-butanol-diesel blend. In the current research, an experimental investigation was carried out on a variable compression ratio CI engine with n-butanol-diesel blends (10-25% by volume) to determine the optimum blending ratio and optimum operating parameters of the engine for reduced emissions. The best results of performance and emissions were observed for 20% n-butanol-diesel blend (B20) at a higher compression ratio as compared to diesel while keeping the other parameters unchanged. The observed deterioration in engine performance was within tolerable limits. The reductions in smoke, nitrogen oxides (NO), and carbon monoxide (CO) were observed up to 56.52, 17.19, and 30.43%, respectively, for B20 in comparison to diesel at rated power. However, carbon dioxide (CO) and hydrocarbons (HC) were found to be higher by 17.58 and 15.78%, respectively, for B20. It is concluded that n-butanol-diesel blend would be a potential fuel to control emissions from diesel engines. [ABSTRACT FROM AUTHOR]

The present investigation aims to depict the effect of compression ratio on performance, emission and combustion characteristics of diesel-acetylene-fuelled stationary compression ignition engine. The optimum values for compression ratio, injection timing and injection pressure for diesel were experimentally found, and baseline data were established as 20, 23° before top dead centre and 210 bar, respectively. In order to investigate the effect of acetylene fuelling, acetylene gas was inducted at four different flow rates of 60, 120, 180 and 240 litres per hour at compression ratio 20. It was observed that the flow rate of 120 litres per hour gave the best performance with highest brake thermal efficiency of 25.09%. In order to find the optimum compression ratio for acetylene fuelling at 120 litres per hour, experimentation was done at different compression ratios of 18, 19, 20, 21 and 22. Experimental results showed that highest brake thermal efficiency of 25.72% was achieved at compression ratio 21 for diesel-acetylene fuelling which was much higher than 23.32% for pure diesel. Carbon monoxide, hydrocarbon and smoke emissions were also measured and found to be lower, while the NOx emissions were higher at optimized values in dual-fuel mode as compared to those for pure diesel. Peak cylinder pressure and net heat release rate were also calculated and found to be higher in dual-fuel mode compared to diesel mode. [ABSTRACT FROM AUTHOR]

Old engines (Euro III or earlier) produce more emissions, and it will be difficult to entirely stop their usage especially in developing and under-developed nations; hence, it is desired that appropriate emission reduction technologies are tested on such engines to analyze their feasibility and economical acceptability. While most such studies have been conducted on constant speed stationary engines and modern engines, this study tried to analyze the effectiveness of an uncoated wall-flow type ceramic diesel particulate filter on a Euro-I, water-cooled, direct injection, variable speed, compression ignition engine in a laboratory set-up in India. Also, this study focused on diesel particulate filter regeneration by two methods: active regeneration by diesel injection in the particulate filter using an electronic control unit and off-board regeneration by taking out and heating the diesel particulate filter in an electrical resistance furnace at 650 °C for 10 h. The results, in the form of smoke emission, NO emission and engine performance, obtained using both the regeneration methods were analyzed, and conclusions were drawn. It was found that using diesel particulate filter, particulate matter emissions (smoke) were almost entirely eliminated. It was also found that off-board regeneration had numerous advantages compared to active regeneration. Since a furnace would be needed for off-board regeneration, an exchange process for diesel particulate filter is suggested. [ABSTRACT FROM AUTHOR]

The present experimental investigation aims to depict the effect of varying injection opening pressure (IOP) using diesel-mahua oil (raw) blends as fuel on the characteristics of a small agricultural engine. The effect of preheated mahua oil and mahua oil methyl ester (MOME) as fuel on engine characteristics has also been investigated. Series of short-term tests were conducted on a single cylinder, 3.7 kW, water cooled, stationary diesel engine and performance and emission characteristics were determined using different diesel-mahua oil blends and also with varying IOP. Optimal IOP as well as blend ratio was determined for maximum efficiency and minimum emission values. Further, a novel pipe in pipe counter flow heat exchanger was designed and fabricated to arrange for preheating the raw mahua oil with exhaust gases of the engine. MOME was also prepared and tested as fuel in engine. Characterisation for major properties of all test fuels was conducted. Performance and emission characteristics of the diesel engine for raw mahua oil blends (unheated) at both rated and optimal IOP were compared with those of diesel, MOME and preheated raw mahua oil. At IOP of 226 bar, maximum brake thermal efficiency was obtained for M10, M20 and M30 blends. Significant reduction in carbon monoxide and hydrocarbon emission were observed. Nitrogen oxides emissions increased marginally. It was also observed that preheated mahua oil as fuel increases efficiency and reduces emissions. The use of M10 even at rated injection pressure and M20 and M30 at 226 bar results in minimum loss of efficiency and generates higher environmental benefits. [ABSTRACT FROM AUTHOR]

Objective: There are many studies on changes in Doppler waveforms of hepatic veins in cirrhotic liver. It is postulated that dampening of phasic oscillations appears with worsening of liver function. Our aim was to reevaluate the significance of Doppler waveforms of hepatic vein in cirrhotic patients and to correlate with hepatic blood flow. Patients and method: One hundred and thirty-five consecutive patients of liver cirrhosis and 60 age and sex matched non-cirrhotic controls were enrolled in this study. Doppler waveforms were obtained from right hepatic vein during normal respiration. Other parameters measured were flow volume of portal trunk, right portal vein and proper hepatic artery. Result: Waveforms of the hepatic vein were classified into triphasic, biphasic and flat patterns. Flat waveform was rare and appeared in only 3% cases. There was no correlation between liver dysfunction and patterns of waveforms. Inflow, particularly to the right lobe, was significantly elevated in cases associated with the non-triphasic waveforms. Conclusion: This study shows that the flat waveforms have no diagnostic value. Role of hepatic blood flow seems to be important suggesting hemodynamic changes rather than liver dysfunction as a plausible cause of change in waveforms. [ABSTRACT FROM AUTHOR]

The original version of this article unfortunately contained mistakes. [ABSTRACT FROM AUTHOR]