We will consider a class of neutral functional differential equations. Some infinite integral conditions for the oscillation of all solutions are derived. Our results extend and improve some of the previous results in the literature.
The aim of this study was to isolate and identify Actinobacteria from Malaysia mangrove forest and screen them for production of antimicrobial secondary metabolites. Eighty-seven isolates were isolated from soil samples collected at 4 different sites. This is the first report to describe the isolation of Streptomyces, Mycobacterium, Leifsonia, Microbacterium, Sinomonas, Nocardia, Terrabacter, Streptacidiphilus, Micromonospora, Gordonia, and Nocardioides from mangrove in east coast of Malaysia. Of 87 isolates, at least 5 isolates are considered as putative novel taxa. Nine Streptomyces sp. isolates were producing potent antimicrobial secondary metabolites, indicating that Streptomyces isolates are providing high quality metabolites for drug discovery purposes. The discovery of a novel species, Streptomyces pluripotens sp. nov. MUSC 135(T) that produced potent secondary metabolites inhibiting the growth of MRSA, had provided promising metabolites for drug discovery research. The biosynthetic potential of 87 isolates was investigated by the detection of polyketide synthetase (PKS) and nonribosomal polyketide synthetase (NRPS) genes, the hallmarks of secondary metabolites production. Results showed that many isolates were positive for PKS-I (19.5%), PKS-II (42.5%), and NRPS (5.7%) genes, indicating that mangrove Actinobacteria have significant biosynthetic potential. Our results highlighted that mangrove environment represented a rich reservoir for isolation of Actinobacteria, which are potential sources for discovery of antimicrobial secondary metabolites.
Current study presents RSM based optimized production of biodiesel from palm oil using chemical and enzymatic transesterification. The emission behavior of biodiesel and its blends, namely, POB-5, POB-20, POB-40, POB-50, POB-80, and POB-100 was examined using diesel engine (equipped with tube well). Optimized palm oil fatty acid methyl esters (POFAMEs) yields were depicted to be 47.6 ± 1.5, 92.7 ± 2.5, and 95.4 ± 2.0% for chemical transesterification catalyzed by NaOH, KOH, and NaOCH3, respectively, whereas for enzymatic transesterification reactions catalyzed by NOVOZYME-435 and A. n. lipase optimized biodiesel yields were 94.2 ± 3.1 and 62.8 ± 2.4%, respectively. Distinct decrease in particulate matter (PM) and carbon monoxide (CO) levels was experienced in exhaust emissions from engine operating on biodiesel blends POB-5, POB-20, POB-40, POB-50, POB-80, and POB-100 comparative to conventional petroleum diesel. Percentage change in CO and PM emissions for different biodiesel blends ranged from -2.1 to -68.7% and -6.2 to -58.4%, respectively, relative to conventional diesel, whereas an irregular trend was observed for NOx emissions. Only POB-5 and POB-20 showed notable reductions, whereas all other blends (POB-40 to POB-100) showed slight increase in NOx emission levels from 2.6 to 5.5% comparative to petroleum diesel.
This paper deals with the performance and emission analysis of a multicylinder diesel engine using biodiesel along with an in-depth analysis of the engine heat losses in different subsystems followed by the energy balance of all the energy flows from the engine. Energy balance analysis allows the designer to appraise the internal energy variations of a thermodynamic system as a function of ''energy flows" across the control volume as work or heat and also the enthalpies associated with the energy flows which are passing through these boundaries. Palm and coconut are the two most potential biodiesel feed stocks in this part of the world. The investigation was conducted in a four-cylinder diesel engine fuelled with 10% and 20% blends of palm and coconut biodiesels and compared with B5 at full load condition and in the speed range of 1000 to 4000 RPM. Among the all tested blends, palm blends seemed more promising in terms of engine performance, emission, and heat losses. The influence of heat losses on engine performance and emission has been discussed thoroughly in this paper.
An experiment was conducted in Field Laboratory, Department of Entomology at Bangladesh Agricultural University, Mymensingh, during 2013 to manage the mango hopper, Idioscopus clypealis L, using three chemical insecticides, Imidacloprid (0.3%), Endosulfan (0.5%), and Cypermethrin (0.4%), and natural Neem oil (3%) with three replications of each. All the treatments were significantly effective in managing mango hopper in comparison to the control. Imidacloprid showed the highest efficacy in percentage of reduction of hopper population (92.50 ± 9.02) at 72 hours after treatment in case of 2nd spray. It also showed the highest overall percentage of reduction (88.59 ± 8.64) of hopper population and less toxicity to natural enemies including green ant, spider, and lacewing of mango hopper. In case of biopesticide, azadirachtin based Neem oil was found effective against mango hopper as 48.35, 60.15, and 56.54% reduction after 24, 72, and 168 hours of spraying, respectively, which was comparable with Cypermethrin as there was no statistically significant difference after 168 hours of spray. Natural enemies were also higher after 1st and 2nd spray in case of Neem oil.
This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.
Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.
Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.
Two strains of Pleurotus giganteus (commercial and wild) were tested for their ability to induce neurite outgrowth in rat pheochromocytoma (PC12) and mouse neuroblastoma-2a (N2a) cells. Treatment with the mushroom extracts resulted in neuronal differentiation and neuronal elongation, but not nerve growth factor (NGF) production. Linoleic acid (4.5-5.0%, w/w) which is a major fatty acid present in the ethanol extract promoted NGF biosynthesis when augmented with low concentration of NGF (5 ng/mL). The two strains of mushroom were found to be high in protein (154-192 g kg(-1)), total polysaccharides, phenolics, and flavonoids as well as vitamins B1, B2, and B3. The total phenolics present in the mushroom extracts were positively correlated to the antioxidant activity (free radical scavenging, ferric reducing power, and lipid peroxidation inhibition). To conclude, P. giganteus could potentially be used in well-balanced diet and as a source of dietary antioxidant to promote neuronal health.
Cloud computing is a significant shift of computational paradigm where computing as a utility and storing data remotely have a great potential. Enterprise and businesses are now more interested in outsourcing their data to the cloud to lessen the burden of local data storage and maintenance. However, the outsourced data and the computation outcomes are not continuously trustworthy due to the lack of control and physical possession of the data owners. To better streamline this issue, researchers have now focused on designing remote data auditing (RDA) techniques. The majority of these techniques, however, are only applicable for static archive data and are not subject to audit the dynamically updated outsourced data. We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete. Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost. The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.
In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well.
One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations.
Reconfigurable ring filter based on single-side-access ring topology is presented. Using capacitive tuning elements, the electrical length of the ring can be manipulated to shift the nominal center frequency to a desired position. A synthesis is developed to determine the values of the capacitive elements. To show the advantage of the synthesis, it is applied to the reconfigurable filter design using RF lumped capacitors. The concept is further explored by introducing varactor-diodes to continuously tune the center frequency of the ring filter. For demonstration, two prototypes of reconfigurable ring filters are realized using microstrip technology, simulated, and measured to validate the proposed concept. The reconfigured filter using lumped elements is successfully reconfigured from 2 GHz to 984.4 MHz and miniaturized by 71% compared to the filter directly designed at the same reconfigured frequency, while, for the filter using varactor-diodes, the frequency is chosen from 1.10 GHz to 1.38 GHz spreading over 280 MHz frequency range. Both designs are found to be compact with acceptable insertion loss and high selectivity.
The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.
Nitric oxide (NO), produced by endothelial nitric oxide synthase (eNOS), is a major antiatherogenic factor in the blood vessel. Oxidative stress plays an important role in the pathogenesis of various cardiovascular diseases, including atherosclerosis. Decreased availability of endothelial NO promotes the progression of endothelial dysfunction and atherosclerosis. Rutin is a flavonoid with multiple cardiovascular protective effects. This study aimed to investigate the effects of rutin on eNOS and NO production in cultured human umbilical vein endothelial cells (HUVEC). HUVEC were divided into four groups: control; oxidative stress induction with 180 μM H₂O₂; treatment with 300 μM rutin; and concomitant induction with rutin and H₂O₂ for 24 hours. HUVEC treated with rutin produced higher amount of NO compared to control (P < 0.01). In the oxidative stress-induced HUVEC, rutin successfully induced cells' NO production (P < 0.01). Rutin promoted NO production in HUVEC by inducing eNOS gene expression (P < 0.05), eNOS protein synthesis (P < 0.01), and eNOS activity (P < 0.05). Treatment with rutin also led to increased gene and protein expression of basic fibroblast growth factor (bFGF) in HUVEC. Therefore, upregulation of eNOS expression by rutin may be mediated by bFGF. The results showed that rutin may improve endothelial function by augmenting NO production in human endothelial cells.
A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.
During the process and operation of the dyes, the wastes produced were commonly found to contain organic and inorganic impurities leading to risks in the ecosystem and biodiversity with the resultant impact on the environment. Improper effluent disposal in aqueous ecosystems leads to reduction of sunlight penetration which in turn diminishes photosynthetic activity, resulting in acute toxic effects on the aquatic flora/fauna and dissolved oxygen concentration. Recently, photodegradation of various synthetic dyes has been studied in terms of their absorbance and the reduction of oxygen content by changes in the concentration of the dye. The advantages that make photocatalytic techniques superior to traditional methods are the ability to remove contaminates in the range of ppb, no generation of polycyclic compounds, higher speed, and lower cost. Semiconductor metal oxides, typically TiO2, ZnO, SnO, NiO, Cu2O, Fe3O4, and also CdS have been utilized as photocatalyst for their nontoxic nature, high photosensitivity, wide band gap and high stability. Various process parameters like photocatalyst dose, pH and initial dye concentrations have been varied and highlighted. Research focused on surface modification of semiconductors and mixed oxide semiconductors by doping them with noble metals (Pt, Pd, Au, and Ag) and organic matter (C, N, Cl, and F) showed enhanced dye degradation compared to corresponding native semiconductors. This paper reviews recent advances in heterogeneous photocatalytic decolorization for the removal of synthetic dyes from water and wastewater. Thus, the main core highlighted in this paper is the critical selection of semiconductors for photocatalysis based on the chemical, physical, and selective nature of the poisoning dyes.
This study describes the hydrothermal synthesis of a novel carbon/palmitic acid (PA) microencapsulated phase change material (MEPCM). The field emission scanning electron microscopy (FESEM) and high resolution transmission electron microscopy (HRTEM) images confirm that spherical capsules of uniform size were formed with a mean diameter of 6.42 μm. The melting and freezing temperature were found to be slightly lower than those of pure PA with little undercooling. The composite retained 75% of the latent heat of pure PA. Thermal stability of the MEPCM was found to be better than that of pure PA. The thermal conductivity of MEPCM was increased by as much as 41% at 30°C. Due to its good thermal properties and chemical and mechanical stability, the carbon/PA MEPCM displays a good potential for thermal energy storage systems.
Cross-linked resin-captured palladium (XL-QPPd) was readily prepared by simple physical adsorption onto the high loading QuadraPure macroporous resin and a subsequent reduction process. To enhance the mechanical stability, entrapped palladium nanocatalysts were cross-linked with succinyl chloride. Both transmission electron microscopy images and X-ray diffraction analysis revealed that the palladium nanoparticles were well dispersed with diameters ranging in 4-10 nm. The catalyst performed good catalytic activity in microwave-promoted Suzuki cross-coupling reactions in water under aerobic condition with mild condition by using various aryl halides and phenylboronic acid. In addition, the catalyst showed an excellent recyclability without significant loss of catalytic activity.
This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.