19672 research outputs found
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A new black fly species of Simulium (Nevermannia) (Diptera: Simuliidae) from Vietnam
No full textSimulium pumatense sp. nov. is described from Vietnam, and is placed in the Simulium feuerborni species-group of the subgenus Simulium (Nevermannia) Enderlein. Its morphological characteristics include the relatively smaller numbers of the following three numerical features: inner teeth of the female mandible (15–18), minute conical processes (16) on the female cibarium, and male upper-eye facets (in 15 vertical columns and 16 horizontal rows). Keys are constructed to distinguish this species from four species of the same group in Vietnam. Our molecular analysis of the DNA barcoding COI gene shows that this species is most closely related to cytoform A of the S. feuerborni complex from Thailand
A hybrid approach to building face shape classifier for hairstyle recommender system
No full textIdentifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This framework enables an automatic hairstyle recommendation with a single face image. This has a direct impact on beauty industry service providers. It can simulate how the user looks like when she is wearing the chosen hairstyle recommended by the expert system. The model used in this framework is based on Support Vector Machine. The framework is evaluated on hand-crafted, deep-learned (VGG-face) features and VGG-face fine-tuned version for the face shape classification task. In addition to evaluating these individual features by a well-designed framework, we attempted to fuse these three descriptors together in order to improve the performance of the classification task. Two combination techniques were employed, namely: Vector Concatenation and Multiple Kernel Learning (MKL) techniques. All the hyper-parameters of the model were optimised by using Particle Swarm Optimisation. The results show that combining hand-crafted and VGG-face descriptors with MKL yielded the best results at 70.3% of accuracy which was statistically significantly better than using individual features. Thus, combining multiple representations of the data with MKL can improve the overall performance of the expert system. In addition, this proves that hand-crafted descriptor can be complementary to deep-learned descriptor
Two New isoquinoline alkaloids from the bark of Alphonsea cylindrica King and their antioxidant activity
No full textTwo new isoquinoline alkaloids, iraqiine (1) and kareemine (2), along with five known alkaloids, muniranine (3), kinabaline (4), O-methylmoschatoline (5), atherospermidine (6) and N-methylouregidione (7), were purified from the dichloromethane crude extract of Alphonsea cylindrica bark. The structures of these compounds were elucidated through extensive 1D and 2D NMR, IR and LC–MS studies and comparison with previously reported data. Compounds (1–5 and 7) were tested for their antioxidant activity using in vitro DPPH radical scavenging assay, and 1, 3 and 4 showed the highest antioxidant activities with IC50 values of 48.77 ± 1.01, 44.51 ± 1.12 and 64.28 ± 0.93 μg/ml, respectively
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
No full textActivity detection and classification using different sensor modalities have emerged as revolutionary technology for real-time and autonomous monitoring in behaviour analysis, ambient assisted living, activity of daily living (ADL), elderly care, rehabilitations, entertainments and surveillance in smart home environments. Wearable devices, smart-phones and ambient environments devices are equipped with variety of sensors such as accelerometers, gyroscopes, magnetometer, heart rate, pressure and wearable camera for activity detection and monitoring. These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. Recently, deep learning algorithms for automatic feature representation have also been proposed to lessen the burden of reliance on handcrafted features and to increase performance accuracy. Initially, one set of sensor data, features or classifiers were used for activity recognition applications. However, there are new trends on the implementation of fusion strategies to combine sensors data, features and classifiers to provide diversity, offer higher generalization, and tackle challenging issues. For instances, combination of inertial sensors provide mechanism to differentiate activity of similar patterns and accurate posture identification while other multimodal sensor data are used for energy expenditure estimations, object localizations in smart homes and health status monitoring. Hence, the focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices. First, data fusion methods and modalities were presented and also feature fusion, including deep learning fusion for human activity recognition were critically analysed, and their applications, strengths and issues were identified. Furthermore, the review presents different multiple classifier system design and fusion methods that were recently proposed in literature. Finally, open research problems that require further research and improvements are identified and discussed
Numerical investigation on the effect of different parameters in enhancing heat transfer performance of photovoltaic thermal systems
No full textPhotovoltaic thermal (PV/T) collectors that supply both electricity and heat are growingly becoming popular in household and other applications. However, efficient heat removal from backside of PV module is still a challenge that hampers its electrical as well as thermal performance. In the present research, an absorber-plate less thermal collector has been introduced and mathematical model of such a PV/T system has been developed, which is employed in COMSOL Multiphysics® software to simulate the heat transfer phenomenon in the system. Effect of different flow parameters on heat transfer and PV/T performance is thus studied numerically in the developed simulation model. Also, the effect of irradiation level and depth of the flow channel has been examined on the thermal as well as electrical performance of the module. Results reveal that PV/T electrical and thermal efficiency increase with both of Reynolds and Prandtl number. Heat transfer rate is observed to increase as high as 25.5% with increasing Reynolds number. A maximum reduction in cell temperature of 10.2 °C is obtained by increasing the channel depth. Elimination of absorber plate from thermal collector simplified the design reducing its weight and cost as well
NSGA-II and MOPSO Based Optimization for Sizing of Hybrid PV/ Wind / Battery Energy Storage System
No full textThis paper presents a Stand-alone Hybrid Renewable Energy System (SHRES) as an alternative to fossil fuel based generators. The Photovoltaic (PV) panels and wind turbines (WT) are designed for the Malaysian low wind speed conditions with battery Energy Storage (BES) to provide electric power to the load. The appropriate sizing of each component was accomplished using Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) techniques. The optimized hybrid system was examined in MATLAB using two case studies to find the optimum number of PV panels, wind turbines system and BES that minimizes the Loss of Power Supply Probability (LPSP) and Cost of Energy (COE). The hybrid power system was connected to the AC bus to investigate the system performance in supplying a rural settlement. Real weather data at the location of interest was utilized in this paper. The results obtained from the two scenarios were used to compare the suitability of the NSGA-II and MOPSO methods. The NSGA-II method is shown to be more accurate whereas the MOPSO method is faster in executing the optimization. Hence, both these methods can be used for techno-economic optimization of SHRES
Synergistic effect of (3-Aminopropyl)Trimethoxysilane treated ZnO and corundum nanoparticles under UV-irradiation on UV-cutoff and IR-absorption spectra of acrylic polyurethane based nanocomposite coating
No full textZnO and corundum (α–Al2O3) nanoparticles were successfully synthesized by aqueous precipitation and sintering techniques respectively. ZnO nanoparticles were effectively coated with (3-aminopropyl)trimethoxysilane (APTMS) by polycondensation method to prevent the photocatalytic activity of ZnO during a UV-weathering study. X-ray diffractogram and FTIR were used to confirm the crystalline structure of as prepared nanoparticles, blue shift of the Al–O bond and the formation of a secondary amine via polycondensation of APTMS over ZnO surface. The prepared APTMS-ZnO, corundum and commercially available surface modified hydrophobic SiO2 (M − SiO2) nanoparticles were used to prepare the acrylic polyurethane (AP: Poly-Macrynal® SM 510 N coating resign) bases nanocomposite coating on a polyurethane substrate. Individual and mixed nanoparticles were dispersed into acrylic polyurethane to prepare the coating layer on polyurethane film substrate separately. IR-active and UV–visible regions of the FTIR and UV–Vis spectroscopies were used to investigate the synergistic effect of the nanoparticles on a selected range of the radiative spectrum, especially the UV-resistant and IR-absorption properties of the coated films with and without exposure of UV-irradiations. Polyurethane substrate coated with APTMS-ZnO (2 wt%) based acrylic polyurethane-based nanocomposite coating (APUC) layer containing 2 wt% corundum (D50) and 6 wt% M − SiO2 (F50) exhibited 98.77% and 97.60% of UV-resistant property respectively. These results indicate that the visible light transparency and transmittance ability reduced significantly after 500 h of UV-irradiation exposure. Both of the activity and deformation have great impact on the IR-absorption property of the APUC
Association between subclinical hypothyroidism and depression: an updated systematic review and meta-analysis
No full textBackground: Although depression is associated with changes in the hypothalamic-pituitary-thyroid axis, its relationship with subclinical hypothyroidism (SCH) is controversial. To date, there is a lack of data on the improvement of depressive symptoms with levothyroxine therapy among individuals with coexistent SCH. Methods: We conducted a meta-analysis to evaluate the association between SCH and depression including 1) the prevalence of depression in SCH (with a sub-analysis of the geriatric cohort), 2) thyroid stimulating hormone (TSH) level among patients with depression and 3) the effect of levothyroxine therapy among patients with SCH and coexistent depression. Results: In a pooled analysis of 12,315 individuals, those with SCH had higher risk of depression than euthyroid controls (relative risk 2.35, 95% confidence intervals [CI], 1.84 to 3.02; p < 0.001). Geriatric cohort with SCH had a 1.7-fold higher risk of depression compared with healthy controls (odds ratio 1.72, CI, 1.10 to 2.70; p = 0.020). There was no difference in the mean TSH level between individuals with depression and healthy controls (2.30 ± 1.18 vs. 2.13 ± 0.72 mIU/L, p = 0.513). In individuals with SCH and coexistent depression, levothyroxine therapy was neither associated with improvement in the Beck Depression Inventory scoring (pooled d + = - 1.05, CI -2.72 to 0.61; p = 0.215) nor Hamilton Depression Rating Scale (pooled d + = - 2.38, CI -4.86 to 0.10; p = 0.060). Conclusion: SCH has a negative impact on depression. Early and routine screening of depression is essential to prevent morbidity and mortality. However, the use of levothyroxine among patients with SCH and coexistent depression needs to be individualized
Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks
No full textRecently, considerable advancement has been achieved in semisupervised multitask feature selection methods, which they exploit the shared information from multiple related tasks. Besides, these algorithms have adopted manifold learning to leverage both the unlabeled and labeled data since its laborious to obtain adequate labeled training data. However, these semisupervised multitask selection feature algorithms are unable to naturally handle the multiview data since they are designed to deal single-view data. Existing studies have demonstrated that mining information enclosed in multiple views can drastically enhance the performance of feature selection. Multiview learning is capable of exploring the complementary and correlated knowledge from different views. In this paper, we incorporate multiview learning into semisupervised multitask feature selection framework and present a novel semisupervised multiview multitask feature selection framework. Our proposed algorithm is capable of exploiting complementary information from different feature views in each task while exploring the shared knowledge between multiple related tasks in a joint framework when the labeled training data is sparse. We develop an efficient iterative algorithm to optimize it since the objective function of the proposed method is non-smooth and difficult to solve. Experiment results on several multimedia applications have shown that the proposed algorithm is competitive compared with the other single-view feature selection algorithms
A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
No full textFog computing is a paradigm that extends cloud computing and services to the edge of the network in order to address the inherent problems of the cloud, such as latency and lack of mobility support and location-awareness. The fog is a decentralized platform capable of operating and processing data locally and can be installed in heterogeneous hardware which makes it ideal for Internet of Things (IoT) applications. Intrusion Detection Systems (IDSs) are an integral part of any security system for fog and IoT networks to ensure the quality of service. Due to the resource limitations of fog and IoT devices, lightweight IDS is highly desirable. In this paper, we present a lightweight IDS based on a vector space representation using a Multilayer Perceptron (MLP) model. We evaluated the presented IDS against the Australian Defense Force Academy Linux Dataset (ADFA-LD) and Australian Defense Force AcademyWindows Dataset (ADFA-WD), which are new generation system calls datasets that contain exploits and attacks on various applications. The simulation shows that by using a single hidden layer and a small number of nodes, we are able to achieve a 94% Accuracy, 95% Recall, and 92% F1-Measure in ADFA-LD and 74% Accuracy, 74% Recall, and 74% F1-Measure in ADFA-WD. The performance is evaluated using a Raspberry Pi