Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities. This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals. This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance. The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity. The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments. The accuracy for the proposed method is 80% when SNR=1 dB, 84% when SNR=5 dB, and 88% when SNR=10 dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.
The effects of feeding different levels of whole linseed on fatty acid (FA) composition of muscles and adipose tissues of goat were investigated. Twenty-four Crossed Boer bucks were assigned randomly into three treatment diets: L0, L10, or L20, containing 0%, 10%, or 20% whole linseed, respectively. The goats were slaughtered after 110 days of feeding. Samples from the longissimus dorsi, supraspinatus, semitendinosus, and subcutaneous fat (SF) and perirenal fat (PF) were taken for FA analyses. In muscles, the average increments in α-linolenic (ALA) and total n-3 PUFA were 6.48 and 3.4, and 11.48 and 4.78 for L10 and L20, respectively. In the adipose tissues, the increments in ALA and total n-3 PUFA were 3.07- and 6.92-fold and 3.00- and 7.54-fold in SF and PF for L10 and L20, respectively. The n-6 : n-3 ratio of the muscles was decreased from up to 8.86 in L0 to 2 or less in L10 and L20. The PUFA : SFA ratio was increased in all the tissues of L20 compared to L0. It is concluded that both inclusion levels (10% and 20%) of whole linseed in goat diets resulted in producing meat highly enriched with n-3 PUFA with desirable n-6 : n-3 ratio.
Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.
Runoff potentiality of a watershed was assessed based on identifying curve number (CN), soil conservation service (SCS), and functional data analysis (FDA) techniques. Daily discrete rainfall data were collected from weather stations in the study area and analyzed through lowess method for smoothing curve. As runoff data represents a periodic pattern in each watershed, Fourier series was introduced to fit the smooth curve of eight watersheds. Seven terms of Fourier series were introduced for the watersheds 5 and 8, while 8 terms of Fourier series were used for the rest of the watersheds for the best fit of data. Bootstrapping smooth curve analysis reveals that watersheds 1, 2, 3, 6, 7, and 8 are with monthly mean runoffs of 29, 24, 22, 23, 26, and 27 mm, respectively, and these watersheds would likely contribute to surface runoff in the study area. The purpose of this study was to transform runoff data into a smooth curve for representing the surface runoff pattern and mean runoff of each watershed through statistical method. This study provides information of runoff potentiality of each watershed and also provides input data for hydrological modeling.
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.
Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.
Humans have been using natural products for medicinal use for ages. Natural products of therapeutic importance are compounds derived from plants, animals, or any microorganism. Ginger is also one of the most commonly used condiments and a natural drug in vogue. It is a traditional medicine, having some active ingredients used for the treatment of numerous diseases. During recent research on ginger, various ingredients like zingerone, shogaol, and paradol have been obtained from it. Zingerone (4-(4-hydroxy-3-methoxyphenyl)-2-butanone) is a nontoxic and inexpensive compound with varied pharmacological activities. It is the least pungent component of Zingiber officinale. Zingerone is absent in fresh ginger but cooking or heating transforms gingerol to zingerone. Zingerone closely related to vanillin from vanilla and eugenol from clove. Zingerone has potent anti-inflammatory, antidiabetic, antilipolytic, antidiarrhoeic, antispasmodic, and so forth properties. Besides, it displays the property of enhancing growth and immune stimulation. It behaves as appetite stimulant, anxiolytic, antithrombotic, radiation protective, and antimicrobial. Also, it inhibits the reactive nitrogen species which are important in causing Alzheimer's disease and many other disorders. This review is written to shed light on the various pharmacological properties of zingerone and its role in alleviating numerous human and animal diseases.
Deploying large numbers of mobile robots which can interact with each other produces swarm intelligent behavior. However, mobile robots are normally running with finite energy resource, supplied from finite battery. The limitation of energy resource required human intervention for recharging the batteries. The sharing information among the mobile robots would be one of the potentials to overcome the limitation on previously recharging system. A new approach is proposed based on integrated intelligent system inspired by foraging of honeybees applied to multimobile robot scenario. This integrated approach caters for both working and foraging stages for known/unknown power station locations. Swarm mobile robot inspired by honeybee is simulated to explore and identify the power station for battery recharging. The mobile robots will share the location information of the power station with each other. The result showed that mobile robots consume less energy and less time when they are cooperating with each other for foraging process. The optimizing of foraging behavior would result in the mobile robots spending more time to do real work.
Introduction. Trawling is a method of catching fish in a large volume where fish nets are pulled through water using one or two boats. Bottom trawling is where the nets are pulled over on the seabed. The gear of the bottom trawling would impact the exposed subsea pipeline, on the seabed. Subsea pipelines transport crude oil and gas from the offshore platform to shore facility. This study assesses the risk of fish trawling activities to the subsea pipelines at Sabah and Labuan offshore. The specification of trawl equipment used by local trawlers in Sabah was determined by the on-site survey. The frequency of a fish trawler crossing over the pipelines was calculated based interview on operation and site survey. The calculation of the pull-over load of the otter board was calculated using the DNVGL algorithm. The severity and frequency index of the risk matrix was developed based on literature review. Results showed that the pull-over load of the otter board would not damage the pipelines. The risk posed by the fish trawler activity to the pipelines is low and moderate.
Ultrahigh-molecular-weight polyethylene/high-density polyethylene (UHMWPE/HDPE) blends prepared using polyethylene glycol PEG as the processing aid and hydroxyapatite (HA) as the reinforcing filler were found to be highly processable using conventional melt blending technique. It was demonstrated that PEG reduced the melt viscosity of UHMWPE/HDPE blend significantly, thus improving the extrudability. The mechanical and bioactive properties were improved with incorporation of HA. Inclusion of HA from 10 to 50 phr resulted in a progressive increase in flexural strength and modulus of the composites. The strength increment is due to the improvement on surface contact between the irregular shape of HA and polymer matrix by formation of mechanical interlock. The HA particles were homogenously distributed even at higher percentage showed improvement in wetting ability between the polymer matrix and HA. The inclusion of HA enhanced the bioactivity properties of the composite by the formation of calcium phosphate (Ca-P) precipitates on the composite surface as proven from SEM and XRD analysis.
We study a fuzzy fractional differential equation (FFDE) and present its solution using Zadeh's extension principle. The proposed study extends the case of fuzzy differential equations of integer order. We also propose a numerical method to approximate the solution of FFDEs. To solve nonlinear problems, the proposed numerical method is then incorporated into an unconstrained optimisation technique. Several numerical examples are provided.
Seagrass is a valuable marine ecosystem engineer. However, seagrass population is declining worldwide. The lack of seagrass research in Malaysia raises questions about the status of seagrasses in the country. The seagrasses in Lawas, which is part of the coral-mangrove-seagrass complex, have never been studied in detail. In this study, we examine whether monthly changes of seagrass population in Lawas occurred. Data on estimates of seagrass percentage cover and water physicochemical parameters (pH, turbidity, salinity, temperature, and dissolved oxygen) were measured at 84 sampling stations established within the study area from June 2009 to May 2010. Meteorological data such as total rainfall, air temperature, and Southern Oscillation Index were also investigated. Our results showed that (i) the monthly changes of seagrass percentage cover are significant, (ii) the changes correlated significantly with turbidity measurements, and (iii) weather changes affected the seagrass populations. Our study indicates seagrass percentage increased during the El-Nino period. These results suggest that natural disturbances such as weather changes affect seagrass populations. Evaluation of land usage and measurements of other water physicochemical parameters (such as heavy metal, pesticides, and nutrients) should be considered to assess the health of seagrass ecosystem at the study area.
Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.
Gymnema sylvestre (R.Br.) is an important diabetic medicinal plant which yields pharmaceutically active compounds called gymnemic acid (GA). The present study describes callus induction and the subsequent batch culture optimization and GA quantification determined by linearity, precision, accuracy, and recovery. Best callus induction of GA was noticed in MS medium combined with 2,4-D (1.5 mg/L) and KN (0.5 mg/L). Evaluation and isolation of GA from the calluses derived from different plant parts, namely, leaf, stem and petioles have been done in the present case for the first time. Factors such as light, temperature, sucrose, and photoperiod were studied to observe their effect on GA production. Temperature conditions completely inhibited GA production. Out of the different sucrose concentrations tested, the highest yield (35.4 mg/g d.w) was found at 5% sucrose followed by 12 h photoperiod (26.86 mg/g d.w). Maximum GA production (58.28 mg/g d.w) was observed in blue light. The results showed that physical and chemical factors greatly influence the production of GA in callus cultures of G. sylvestre. The factors optimized for in vitro production of GA during the present study can successfully be employed for their large-scale production in bioreactors.
An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.
Intercell interference is one of the most challenging issues in femtocell deployment under the coverage of existing macrocell. Allocation of resources between femtocell and macrocell is essential to counter the effects of interference in dense femtocell networks. Advances in resource management strategies have improved the control mechanism for interference reduction at lower node density, but most of them are ineffective at higher node density. In this paper, a dynamic resource allocation management algorithm (DRAMA) for spectrum shared hybrid access OFDMA femtocell network is proposed. To reduce the macro-femto-tier interference and to improve the quality of service, the proposed algorithm features a dynamic resource allocation scheme by controlling them both centrally and locally. The proposed scheme focuses on Femtocell Access Point (FAP) owners' satisfaction and allows maximum utilization of available resources based on congestion in the network. A simulation environment is developed to study the quantitative performance of DRAMA in hybrid access-control femtocell network and compare it to closed and open access mechanisms. The performance analysis shows that higher number of random users gets connected to the FAP without compromising FAP owners' satisfaction allowing the macrocell to offload a large number of users in a dense heterogeneous network.
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.
Curcuma zedoaria also known as Temu putih is traditionally used in food preparations and treatment of various ailments including cancer. The cytotoxic activity of hexane, dichloromethane, ethyl acetate, methanol, and the methanol-soxhlet extracts of Curcuma zedoaria rhizomes was tested on two human cancer cell lines (Ca Ski and MCF-7) and a noncancer cell line (HUVEC) using MTT assay. Investigation on the chemical components in the hexane and dichloromethane fractions gave 19 compounds, namely, labda-8(17),12 diene-15,16 dial (1), dehydrocurdione (2), curcumenone (3), comosone II (4), curcumenol (5), procurcumenol (6), germacrone (7), zerumbone epoxide (8), zederone (9), 9-isopropylidene-2,6-dimethyl-11-oxatricyclo[6.2.1.0(1,5)]undec-6-en-8-ol (10), furanodiene (11), germacrone-4,5-epoxide (12), calcaratarin A (13), isoprocurcumenol (14), germacrone-1,10-epoxide (15), zerumin A (16), curcumanolide A (17), curcuzedoalide (18), and gweicurculactone (19). Compounds (1-19) were evaluated for their antiproliferative effect using MTT assay against four cancer cell lines (Ca Ski, MCF-7, PC-3, and HT-29). Curcumenone (3) and curcumenol (5) displayed strong antiproliferative activity (IC50 = 8.3 ± 1.0 and 9.3 ± 0.3 μg/mL, resp.) and were found to induce apoptotic cell death on MCF-7 cells using phase contrast and Hoechst 33342/PI double-staining assay. Thus, the present study provides basis for the ethnomedical application of Curcuma zedoaria in the treatment of breast cancer.
Ammonia loss significantly reduces the urea-N use efficiency in crop production. Efforts to reduce this problem are mostly laboratory oriented. This paper reports the effects of urea amended with triple superphosphate (TSP) and zeolite (Clinoptilolite) on soil pH, nitrate, exchangeable ammonium, dry matter production, N uptake, fresh cob production, and urea-N uptake efficiency in maize (Zea mays) cultivation on an acid soil in actual field conditions. Urea-amended TSP and zeolite treatments and urea only (urea without additives) did not have long-term effect on soil pH and accumulation of soil exchangeable ammonium and nitrate. Treatments with higher amounts of TSP and zeolite significantly increased the dry matter (stem and leaf) production of Swan (test crop). All the treatments had no significant effect on urea-N concentration in the leaf and stem of the test crop. In terms of urea-N uptake in the leaf and stem tissues of Swan, only the treatment with the highest amount of TSP and zeolite significantly increased urea-N uptake in the leaf of the test crop. Irrespective of treatment, fresh cob production was statistically not different. However, all the treatments with additives improved urea-N uptake efficiency compared to urea without additives or amendment. This suggests that urea amended with TSP and zeolite has a potential of reducing ammonia loss from surface-applied urea.