Petroleum source rocks characterization and hydrocarbon generation of the Upper Jurassic succession in Jabal Ayban field,Sabatayn Basin,Yemen

The Upper Jurassic samples of Jabal Ayban field, Sabatayn Basin, western Yemen were used to determine the source rock characteristics and petroleum generative potentials. Based on organic geochemical analysis, the Upper Jurassic source rocks have variable TOC content in the range of 0.82–13?wt%, indicating a fair to very good source rock generative potential. The bulk geochemical results showed that Safer Member contains Type III kerogen grading to mixed Type II–III, while the Lam and Meem members displaying generally Type II, mixed Type II–III and Type III kerogens, which have the ability to generate mixed oil and gas accumulations under thermal maturation level. Vitrinite reflectance in the range (0.35–0.78% Ro) and Tmax in the range (411–445?°C), generally proving that the Meem source rocks have sufficient thermal maturity for hydrocarbon-generation, whereas the Lam and Safer are immature source rocks. 1D basin modelling was performed to analyse the hydrocarbon generation and expulsion history of the study area. Calibration of the model with measured VR (% Ro) and (BHT) data shows that the paleo-heat-flow was high at Late Jurassic. The burial/thermal history models indicate that the source rocks in the Lam and Safer members are immature source rocks and the peak oil generation has not been reached yet. The models also indicate that the early hydrocarbon generation (0.64% Ro) in the Meem source rock occurred during late Jurassic at 150?Ma and the main hydrocarbon generation (0.79% Ro) has been reached approximately at 146?Ma. Therefore, the Meem Member can be consider as generative potentials of prospective source rock horizons in Jabal Ayban field, Sabatayn Basin.     

Adaptive frequency‐hopping schemes for CR‐based multi‐link satellite networks

In this paper, we study two dynamic frequency hopping (DFH)–based interference mitigation approaches for satellite communications. These techniques exploit the sensing capabilities of a cognitive radio to predict future interference on the upcoming frequency hops. We consider a topology where multiple low Earth orbit satellites transmit packets to a common geostationary equatorial orbit satellite. The FH sequence of each low Earth orbit–geostationary equatorial orbit link is adjusted according to the outcome of out‐of‐band proactive sensing scheme, performed by a cognitive radio module in the geostationary equatorial orbit satellite. On the basis of sensing results, new frequency assignments are made for the upcoming slots, taking into account the transmit powers, achievable rates, and overhead of modifying the FH sequences. In addition, we ensure that all satellite links are assigned channels such that their minimum signal‐to‐interference‐plus‐noise ratio requirements are met, if such an assignment is possible. We formulate two multi‐objective optimization problems: DFH‐Power and DFH‐Rate. Discrete‐time Markov chain analysis is used to predict future channel conditions, where the number of states are inferred using k‐means clustering, and the state transition probabilities are computed using maximum likelihood estimation. Finally, simulation results are presented to evaluate the effects of different system parameters on the performance of the proposed designs.     

Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter (called tweets). A dataset of the exchange rates between the United States Dollar (USD) and the Pakistani Rupee (PKR) was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related words. The dataset was collected in raw form, and was subjected to natural language processing by way of data preprocessing. Response variable labeling was then applied to the standardized dataset, where the response variables were divided into two classes: “1” indicated an increase in the exchange rate and “ −1” indicated a decrease in it. To better represent the dataset, we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector space. Clusters that were obtained using a sampling approach were then used for data optimization. Five machine learning classifiers—the simple logistic classifier, the random forest, bagging, naïve Bayes, and the support vector machine—were applied to the optimized dataset. The results show that the simple logistic classifier yielded the highest accuracy of 82.14% for the USD and the PKR exchange rates forecasting.     

Exploiting cognitive radios for reliable satellite communications

Satellite transmissions are prone to both unintentional and intentional RF interference. Such interference has significant impact on the reliability of packet transmissions. In this paper, we make preliminary steps at exploiting the sensing capabilities of cognitive radios for reliable satellite communications. We propose the use of dynamically adjusted frequency hopping (FH) sequences for satellite transmissions. Such sequences are more robust against targeted interference than fixed FH sequences. In our design, the FH sequence is adjusted according to the outcome of out‐of‐band proactive sensing, carried out by a cognitive radio module that resides in the receiver of the satellite link. Our design, called out‐of‐band sensing‐based dynamic FH, is first analyzed using a discrete‐time Markov chain (DTMC) framework. The transition probabilities of the DTMC are then used to measure the ‘channel stability’, a metric that reflects the freshness of sensed channel interference. Next, out‐of‐band sensing‐based dynamic FH is analyzed following a continuous‐time Markov chain model, and a numerical procedure for determining the ‘optimal’ total sensing time that minimizes the probability of ‘black holes’ is provided. DTMC is appropriate for systems with continuously adjustable power levels; otherwise, continuous‐time Markov chain is the suitable model. We use simulations to study the effects of different system parameters on the performance of our proposed design. Copyright © 2014 John Wiley & Sons, Ltd.     

Living-related liver transplantation in Africa

Liver transplantation is an effective therapy for end stage liver disease. Nevertheless in many areas of the world organ availability remains a major problem. We report here the success of the first living-related liver transplantation in Africa. The left lateral lobe of the mother was transplanted orthotopically to her 6 year old child suffering from liver cirrhosis complicating glycogen storage disease. The social and medical problems encountered are discussed. Living-related liver transplantation is a viable option in countries where cadaveric organ donation is either illegal or socially unacceptable.     

Optimal asynchronous agreement and leader election algorithm for complete networks with Byzantine faulty links

Summary.  We consider agreement and leader election on asynchronous complete networks when the processors are reliable, but some of the channels are subject to failure. Fischer, Lynch, and Paterson have already shown that no deterministic algorithm can solve the agreement problem on asynchronous networks if any processor fails during the execution of the algorithm. Therefore, we consider only channel failures. The type of channel failure we consider in this paper is Byzantine failure, that is, channels fail by altering messages, sending false information, forging messages, losing messages at will, and so on. There are no restrictions on the behavior of a faulty channel. Therefore, a faulty channel may act as an adversary who forges messages on purpose to prevent the successful completion of the algorithm. Because we assume an asynchronous network, the channel delays are arbitrary. Thus, the faulty channels may not be detectable unless, for example, the faulty channels cause garbage to be sent. We present the first known agreement and leader election algorithm for asynchronous complete networks in which the processors are reliable but some channels may be Byzantine faulty. The algorithm can tolerate up to [n−22] faulty channels, where n is the number of processors in the network. We show that the bound on the number of faulty channels is optimal. When the processors terminate their corresponding algorithms, all the processors in the network will have the same correct vector, where the vector contains the private values of all the processors. Received: May 1994/Accepted: July 1995     

Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19

Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for mixed or confusing opinions. The process of analyzing changes in sentiment and the combination of these categories is known as “sentiment analysis.” In this study, sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods. The deep learning-based model long-short-term memory (LSTM) performed better than machine learning approaches. Long short-term memory achieved 87% accuracy, and the support vector machine (SVM) classifier achieved slightly worse results than LSTM at 86%. The study also tested binary classes of positive and negative, where LSTM and SVM both achieved 90% accuracy.     

An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection

Diabetic retinopathy (DR) has become a serious threat in our society, which causes 45% of the legal blindness in diabetes patients. Early detection as well as the periodic screening of DR helps in reducing the progress of this disease and in preventing the subsequent loss of visual capability. This paper provides an automated diagnosis system for DR integrated with a user-friendly interface. The grading of the severity level of DR is based on detecting and analyzing the early clinical signs associated with the disease, such as microaneurysms (MAs) and hemorrhages (HAs). The system extracts some retinal features, such as optic disc, fovea, and retinal tissue for easier segmentation of dark spot lesions in the fundus images. That is followed by the classification of the correctly segmented spots into MAs and HAs. Based on the number and location of MAs and HAs, the system quantifies the severity level of DR. A database of 98 color images is used in order to evaluate the performance of the developed system. From the experimental results, it is found that the proposed system achieves 84.31% and 87.53% values in terms of sensitivity for the detection of MAs and HAs respectively. In terms of specificity, the system achieves 93.63% and 95.08% values for the detection of MAs and HAs respectively. Also, the proposed system achieves 68.98% and 74.91% values in terms of kappa coefficient for the detection of MAs and HAs respectively. Moreover, the system yields sensitivity and specificity values of 89.47% and 95.65% for the classification of DR versus normal.     

Petri nets as a framework for the reconstruction and analysis of signal transduction pathways and regulatory networks

Petri nets are directed, weighted bipartite graphs that have successfully been applied to the systems biology of metabolic and signal transduction pathways in modeling both stochastic (discrete) and deterministic (continuous) processes. Here we exemplify how molecular mechanisms, biochemical or genetic, can be consistently respresented in the form of place/transition Petri nets. We then describe the application of Petri nets to the reconstruction of molecular and genetic networks from experimental data and their power to represent biological processes with arbitrary degree of resolution of the subprocesses at the cellular and the molecular level. Petri nets are executable formal language models that permit the unambiguous visualization of regulatory mechanisms, and they can be used to encode the results of mathematical algorithms for the reconstruction of causal interaction networks from experimental time series data.     

Classification and Test Generation for Path-Delay Faults Using Single Struck-at Fault Tests

We classify all path-delay faults of a combinational circuit intothree categories: singly-testable (ST), multiply-testable (MT), and singly-testable dependent} (ST-dependent). The classification uses anyunaltered single stuck-at fault test generation tool. Only two runsof this tool on a model network derived from the original network areperformed. As a by-product of this process, we generate single andmultiple input change delay tests for all testable faults. With thesetests, we expect that most defective circuits are identified. All STfaults are guaranteed detection in the case of a single fault, andsome may be guaranteed detection through robust and validatablenon-robust tests even in the case of multiple faults. An ST-dependentfault can affect the circuit speed only if certain ST faults arepresent. Thus, if all ST faults are tested, the ST-dependent faultsneed not be tested. MT faults cannot be guaranteed detection, butaffect the speed only if delay faults simultaneously exist on a setof paths, none of which is ST. Examples and results on several ISCAS89 benchmarks are presented. The method of classification throughtest generation using a model network is complex and can be appliedto circuits of moderate size. For larger circuits, alternativemethods will have to be explored in the future.