Solar energy is vast, abundant, cost free, green renewable source of energy. Due to the aforementioned qualities, the world is today researching and exploring the most feasibly optimized way of ...harnessing this energy and solar tracking system is a result of this quest. This paper begins with a brief introduction to the solar PV cells and the materials used in their construction. It also discusses the types of solar PV systems and types of solar tracking systems. It mainly focuses on the design and performance analysis of the various dual-axis tracking solar systems proposed in recent years. Although the choice on the use of trackers mainly depends upon the physical features of the land but in general this system has proved to be more efficient and advantageous than its single-axis and fixed counterparts.
Spectral unmixing-based estimation of material abundances in hyperspectral imagery has a variety of applications in mineralogy, environmental monitoring, agriculture, food processing, pharmacy, etc. ...A substantial body of literature is available on different inversion algorithms, optional pre-processing such as dimensionality reduction, and algorithms for endmembers extraction. The quality of abundance estimation depends on the number of materials, size, the geometrical orientation of materials, the source of endmembers, and the inversion algorithm used. However, there is a lack of studies on one-to-one assessment of the retrieval of abundances under various scenarios of spectral material distributions, the spatial resolution of the imagery, and the potential of in-situ reflectance measurements as candidate endmembers. The unavailability of comprehensive benchmark data coupled with pixel-to-pixel ground truth data has impeded comprehensive assessment of the first principles of spectral unmixing from a verifiable experimental perspective. The objective of this research is assessing the dynamics of material abundance as a function of the source of endmembers, spatial resolution, number of materials, and the size of materials. Linear and its sparse-based spectral unmixing algorithms were implemented on the datasets acquired for the estimation of abundances, considering the different scenarios of material distributions, spatial resolution, and the source of endmembers. We validated the results using pixel-to-pixel ground truth maps prepared for the different cases of spectral unmixing. The results provide answers to some critical open challenges in spectral unmixing, such as, (i) for an unambiguous detection, the fractional distribution of material has to be at least 1% of the pixel, (ii) endmembers from the in-situ spectra based on the external spectral library can offer reasonably good abundance estimates (an error of up to 20% compared to the image-based endmembers), and (iii) geometric orientations of materials in the ground sampling distance influence the abundance estimations. The benchmark dataset generated in this work is a valuable resource for addressing intriguing questions in spectral unmixing using hyperspectral imagery from a multi-resolution perspective.
In today’s digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to ...network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification.
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•An innovative hybrid feature extraction method for training of DDoS attack detection model.•We propose a robust CNN model integrated with an Inception mechanism designed to classify various types of DDoS attacks.•Evaluated the network performance of the proposed system in terms of various parameters and compared the results with existing studies and baseline models.•The proposed model outperforms more than 96% in all performance metrics (Precision, Recall, Accuracy, and F1 Score).•The proposed model achieves the fastest prediction time of 0.189 ms among existing models in the literature.
Bull Trench Kiln-based Brick industries are major contributors of Particulate Matter Pollutants (PM2.5 and PM10), which leads to deterioration of air quality, and hence needs to be monitored. As the ...static air monitoring stations require huge infrastructure and are sparsely located, it is necessary to find alternate ways using recent technologies like IoT, Machine Learning, and Serverless Computing. IoT devices being low cost and portable could be deployed near brick industries, however, they are sensitive to fluctuating ambient air temperature or humidity and hence have to be calibrated and there is a need for powerful back-end infrastructures for storage and analyses of data. The existing approach of transmitting IoT-based data to the cloud is time-consuming, energy inefficient, prone to scaling problems, has poor resource utilization, and is expensive. In this study, the IoT data are calibrated using ML Classifier algorithms and an Automatic Function Triggerer - Function-as-a-Service (AFT-FaaS) method is proposed, which is based on event-driven, Serverless method using AWS Lambda along with the Three-Tier Serverless architecture (Edge Tier, Fog Tire, and Cloud Tier) to deal with the challenges of delay, under-resource allocation, and to reduce the expenditures. The data transmitted to the cloud using the proposed AFT-FaaS method are indexed in ElasticSearch and Kibana is used for the analysis and visualization of data. While performing calibration, Random Forest Classifier was chosen as it had accuracy of above 98%. Finally, an in-depth pricing analysis is performed to better understand the difference between different configurations deployed for Serverless and non-Serverless Computing methods, which shows there is a significant reduction (7 times) of expenses incurred while using Serverless Computing than the non-Serverless Computing methods.
Motivational deficits are a central feature of the negative syndrome in schizophrenia. They have consistently been associated with reduced willingness to expend physical effort in return for monetary ...rewards on effort based decision making (EBDM) paradigms. Nevertheless, the mechanisms underlying such altered performance are not well characterised, and it remains unclear if they are driven purely by negative symptoms, or also in part by cognitive impairment, antipsychotic treatment or even positive symptoms. Here we investigated the impact of all these factors using a paradigm that has not previously been used to measure EBDM in schizophrenia.
Forty treatment resistant schizophrenia (TRS) patients on clozapine and matched controls (N = 80) completed a well validated EBDM task which offers monetary rewards in return for physical effort. Choice and reaction time data was analysed using logistic regressions, as well as Bayesian hierarchical drift diffusion modelling (HDDM). Behavioural parameters were compared between groups and their association with negative symptoms, cognitive function and serum clozapine levels were assessed.
Overall, TRS patients accepted significantly less offers than controls during effort-based decision making, suggesting they were less motivated. They demonstrated reduced sensitivity to increasing rewards, but surprisingly were also less averse to increasing effort. Despite a positive correlation between negative symptoms and cognitive function in TRS, reward sensitivity was associated only with cognitive performance. In contrast, reduced effort aversion correlated with negative symptom severity. Clozapine levels and positive symptoms were not associated with either behavioural parameter.
Motivational deficits in TRS are characterised by both diminished reward sensitivity and reduced effort aversion during EBDM. Cognitive dysfunction and negative symptom severity account for distinct aspects of these behavioural changes, despite positive associations between themselves. Overall, these findings demonstrate that negative symptoms and cognitive impairment have significant independent contributions to EBDM in TRS, thereby opening the possibility of individualised treatment targeting these mechanisms to improve motivation.
Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and ...disseminate a wide range of malware on Android smartphones. The researchers have conducted numerous studies on the detection of Android malware, but the majority of the works are based on the detection of generic Android malware. The detection based on malware categories will provide more insights about the malicious patterns of the malware. Therefore, this paper presents a detection solution for different Android malware categories, including adware, banking, SMS malware, and riskware. In this paper, a novel Huffman encoding-based feature vector generation technique is proposed. The experiments have proved that this novel approach significantly improves the efficiency of the detection model. This method makes use of system call frequencies as features to extract malware’s dynamic behavior patterns. The proposed model was evaluated using machine learning and deep learning methods. The results show that the proposed model with the Random Forest classifier outperforms some existing methodologies with a detection accuracy of 98.70%.
ABSTRACT ALMA Cycle 2 observations of long-wavelength dust emission in 145 star-forming galaxies are used to probe the evolution of the star-forming interstellar medium (ISM). We also develop a ...physical basis and empirical calibration (with 72 low-z and z ∼ 2 galaxies) for using the dust continuum as a quantitative probe of ISM masses. The galaxies with the highest star formation rates (SFRs) at = 2.2 and 4.4 have gas masses up to 100 times that of the Milky Way and gas mass fractions reaching 50%-80%, i.e., gas masses 1-4× their stellar masses. We find a single high-z star formation law: yr−1-an approximately linear dependence on the ISM mass and an increased star formation efficiency per unit gas mass at higher redshift. Galaxies above the main sequence (MS) have larger gas masses but are converting their ISM into stars on a timescale only slightly shorter than those on the MS; thus, these "starbursts" are largely the result of having greatly increased gas masses rather than an increased efficiency of converting gas to stars. At z > 1, the entire population of star-forming galaxies has ∼2-5 times shorter gas depletion times than low-z galaxies. These shorter depletion times indicate a different mode of star formation in the early universe-most likely dynamically driven by compressive, high-dispersion gas motions-a natural consequence of the high gas accretion rates.
Nowadays, land use and land cover (LULC) change is a major problem for decision-makers and ecologists on account of its impact on natural ecosystems. In this manuscript, LU/LC change classification ...and prediction using deep convolutional spiking neural network (DCSNN) and enhanced Elman spike neural network (EESNN) (LU/LC-DCSNN-EESNN) is proposed. The input images are gathered from IRS Satellite Resourcesat-1 LISS-III with Cartosat-1 digital elevation model (DEM) satellite imagery of the Javadi Hills, Tamil Nadu. After that, the images are pre-processed using the fast discrete curvelet transform and wrapping (FDCT-WRP) method is used for extracting the region of interest (ROI) coordinates of Javadi Hills satellite image. Then, for categorizing the area of forest and non-forest, the DCSNN is used. The categorized images are given to post-classification process for eradicating the noise and misclassification errors by Markov chain random field (MCRF) co-simulation approach. The LU/LC changes are predicted using EESNN method. The performance metrics, like precision, accuracy, f1 score, error rate, specificity, recall, kappa coefficient and ROC, are analyzed. The proposed LU/LC-DCSNN-EESNN method has attained 19.45%, 20.56% and 23.67% higher accuracy, 19.45%, 32.56% and 17.45% higher F-measure, and 16.78%, 22.09% and 32.39% lower error rate compared with the existing methods.
The use of submillimeter dust continuum emission to probe the mass of interstellar dust and gas in galaxies is empirically calibrated using samples of local star-forming galaxies, Planck observations ...of the Milky Way, and high-redshift submillimeter galaxies. All of these objects suggest a similar calibration, strongly supporting the view that the Rayleigh-Jeans tail of the dust emission can be used as an accurate and very fast probe of the interstellar medium (ISM) in galaxies. We present ALMA Cycle 0 observations of the Band 7 (350 GHz) dust emission in 107 galaxies from z = 0.2 to 2.5. Three samples of galaxies with a total of 101 galaxies were stellar-mass-selected from COSMOS to have M super(*) Asymptotically = to 10 super(11)M sub(middot in circle): 37 at z ~ 0.4, 33 at z ~ 0.9, and 31 at z = 2. A fourth sample with six infrared-luminous galaxies at z = 2 was observed for comparison with the purely mass-selected samples. From the fluxes detected in the stacked images for each sample, we find that the ISM content has decreased by a factor ~6 from1 to 2 x 10 super(10)M sub(middot in circle) at both z = 2 and 0.9 down to ~2 x 2 shows a further ~4 times increase in M sub(ISM) compared with the equivalent non-infrared-bright sample at the same redshift. The gas mass fractions are ~2% + or - 0.5%, 12% + or - 3%, 14% + or - 2%, and 53% + or - 3% for the four subsamples (z = 0.4, 0.9, and 2 and infrared-bright galaxies).
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