Demonstration of On-Board Maneuver Planning using Autonomous S/W Architectures

The need for higher level of SIC autonomy is gaining increased importance in future space missions planning. These autonomous capabilities are needed not only to reduce the overall mission life-cycle cost but also to enable a new class of planetary missions (comets, asteroids, moons, etc.) requiring rendezvous and/or sample return. Some of the key candidate technologies identified for such missions include, autonomous approach/rendezvous/descent navigation, autonomous maneuver planning and implementation, and target based pointing and tracking. This paper presents the results of implementation and simulation of on-board maneuver planning, where a high level command is implemented while satisfying mission flight rules and spacecraft constraints. As part of this effort, a candidate spacecraft simulation is developed within a S/W architecture that provides a set of constructs suited for implementation of high level task decomposition and planning as well as on-line constraints checking and exception handling functions. The software architecture allows for future incremental refinements or addition of new autonomous capabilities as mission needs evolve

High-density SNP-based association mapping of seed traits in fenugreek reveals homology with clover

Fenugreek as a self-pollinated plant is ideal for genome-wide association mapping where traits can be marked by their association with natural mutations. However, fenugreek is poorly investigated at the genomic level due to the lack of information regarding its genome. To fill this gap, we genotyped a collection of 112 genotypes with 153,881 SNPs using double digest restriction site-associated DNA sequencing. We used 38,142 polymorphic SNPs to prove the suitability of the population for association mapping. One significant SNP was associated with both seed length and seed width, and another SNP was associated with seed color. Due to the lack of a comprehensive genetic map, it is neither possible to align the newly developed markers to chromosomes nor to predict the underlying genes. Therefore, systematic targeting of those markers to homologous genomes of other legumes can overcome those problems. A BLAST search using the genomic fenugreek sequence flanking the identified SNPs showed high homology with several members of the Trifolieae tribe indicating the potential of translational approaches to improving our understanding of the fenugreek genome. Using such a comprehensively-genotyped fenugreek population is the first step towards identifying genes underlying complex traits and to underpin fenugreek marker-assisted breeding programs

Potential application of microalgae in produced water treatment

The current study examines pollutant removal efficiency from the produced water of a local petroleum industry by five different local microalgae species. The five microalgae strains Monoraphidium, Chlorella, Neochloris, Scenedesmus, Dictyosphaerium, Chlorella and Dictyosphaerium species showed a significant amount of biomass generation within all different concentrations of produced water. Although the biomass yield of Neochloris strain was low, it was able to remove a higher amount of organic carbon than the other microalgae strains. Although biomass yield varied significantly among the microalgae strains, nitrogen removal efficiency was similar for all strains. Similar results were also obtained for most of the BTEX components. On an average, Dictyosphaerium sp. produced 0.5 g/L biomass density on different strength of produced water. Total nitrogen removal efficiency reached up to 63.76% when Scenedesmus sp. was grown in produced water. Only in case of phosphorus and various metals, removal efficiencies were better by Dictyosphaerium specie; reached up to 88.83%. Despite low biomass generation, Neochloris sp. removed 41.61% of total organic carbon from the different concentrations of produced water. Although benzene and ethylbenzene removal efficiency was 100% for all the different produced water, small amount of toluene and xylene remained in the produced water. Thus, the results indicate that microalgae strains can be used to remediate produced water effluents-derived from petroleum industries.Scopu

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis

In today’s digitalized era, the world wide web services are a vital aspect of each individual’s daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users’ personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), naïve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed

Association of Demographic Variables with the Awareness of Type 2 Diabetes Mellitus Patients (T2DM) among the Northwest Population in Saudi Arabia

The chronic hyperglycemia in diabetes is associated with long-term damage, dysfunction, and failure of different organs. Lack of patient education and knowledge about these complications can worsen the quality of a patient’s life. Hence, more efforts are needed to improve patient’s education especially in rural areas. Aim. Our objective is to explore the association between demographic variables and the knowledge of self-care practices in type 2 diabetes mellitus. Methods. We used observational cross-sectional descriptive study using a validated self-administered questionnaire in both Arabic and English languages as well. A descriptive correlation design analyzed the questionnaire completed by a convenience sample meeting the inclusion criteria. Results. A total of 100 patients met the inclusion criteria for the analysis out of 3251 patients who completed the questionnaire. The study population has low moderate knowledge in diabetes, moderate knowledge in self-care practices, and good knowledge about complications of nephropathy and cardiovascular disease. No significant association between demographic variables. However, better knowledge observed in male (p=0.028) and self-care practices with female (p=0.020). Further, educational status is significantly influencing the knowledge of diabetic patients. Conclusion. The study emphasizing irrespective of demographic variable and the importance of patient education to achieve well glycemic control

Towards Software Product Lines Optimization Using Evolutionary Algorithms

Software product line (SPL) engineering is a methodology that helps to develop a diversity of software products with minimum costs, less time and high quality by the reuse of core software assets which has been tested. Thus, testing is crucial for successfully deploying SPL. As the product features increases, testing process can be time-consuming. Testing in SPL is regarded as a combinatorial optimization problem. Evolutionary algorithms were reported to provide good results in such class of problems. This research provides a framework to compare the performance of different multi-objective Evolutionary Algorithms in software product line context. We report on the problem encoding, variation operators and different types of algorithms: Indicator Based Evolutionary Algorithm (IBEA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) and Strength Pareto Evolutionary algorithm II (SPEA-II). The framework will provide preliminary results on different Feature Models (FMs) to measure their feasibility to optimize SPL testing

Structural systematics of the [Cu(chelate)(3)][Y](2) series. An interesting crystallographic structural insight involving vibronic coupling and the Jahn-Teller effect (JTE). The syntheses and low temperature crystal structures of tris(2,2 ' bipyridyl)copper(II) tetraphenylborate and tris(2,2 ' bipyridyl)zinc(II) tetraphenylborate

The crystal structures of [Cu(bipy)(3)][BPh4](2), 1, and [Zn(bipy)(3)][BPh4](2), 2 have been determined at low temperature. 1 and 2 are closely related, but are not isostructural. Both contain a two-dimensional supramolecular construct (SC) involving a sandwich structure. 1 has a six-coordinate CuN6 chromophore with a regular elongated octahedral stereochemistry and rhombic in-plane bond lengths. The associated tetragonality value, T, of 1 is 0.8868. 2 involves a six-coordinate octahedral chromophore. Differences between 1 and 2 relate to the tendency of copper(II) complexes to undergo a Jahn-Teller (JT) distortion. The zinc( II) cation feels solely the host site strain, whereas the copper( II) cation also involves vibronic JT type coupling. The copper polyhedron geometry is characterized by both phenomena, with the vibronic interaction dominating. Scatter plot analysis involving the tris-chelate copper( II) series suggests that neither pure Q(theta) or Q(epsilon) components or the a(2u) mode operate in isolation over the entire series. All three operate in combination with varying quantifiable contributions, leading to distortion from the regular tetragonal octahedral stereochemistry

A comprehensive study of Al-Cu-Mg system reinforced with nano-ZrO2 particles synthesized by powder metallurgy technique

Abstract More focus has recently been placed on enhancing the strength, elastic modulus, coefficient of thermal expansion (CTE), wear and corrosion resistance, and other qualities of aluminum (Al) alloys by varying the quantity of ceramics added for a range of industrial uses. In this regard, Al-4.2-Cu-1.6Mg matrix nanocomposites reinforced with nano-ZrO2 particles have been created using the powder metallurgy approach. The microstructure and particle size distributions of the produced powders were analyzed using a diffraction particle size analyzer, XRD, TEM, and SEM. To achieve good sinterability, the powders were compacted and sintered in argon. The sintered nanocomposites' mechanical, elastic, and physicochemical characteristics were measured. Additionally, the behavior of corrosion, wear, and thermal expansion were examined. The results showed a decrease in the particle sizes of the Al-Cu-Mg alloy by adding ZrO2 nanoparticles up to 45.8 nm for the composite containing 16 wt.% ZrO2. By increasing the sintering temperature to 570 °C, the densification of nanocomposites was enhanced. Also, the coefficient of thermal expansion and wear rate remarkably decreased by about 28 and 37.5% by adding 16 wt.% ZrO2. Moreover, microhardness yield, strength, and Young’s modulus were enhanced to 161, 145, and 64%, respectively, after adding 16 wt.% ZrO2. In addition, increasing the exposure time was responsible for decreasing the corrosion rate for the same sample