Yield response of lentil to directly applied and residual phosphorus in a Mediterranean environment

The response of lentil grown under rainfed conditions to directly applied and residual phosphorus (P) was described by a modified Mitscherlich equation, accounting for the effects of rainfall on (1) potential yield, and (2) the availability of soil-P to the crop. The response of lentil yield to directly applied and residual P was studied in two-course cereal–lentil rotational trials under rainfed conditions in a Mediterranean-type environment. Cereal crops were grown at different P application rates during 4 growing seasons at 3 sites, representing different rainfall zones in northwest Syria. Lentil (Lens culinaris Med.) was grown during 4 seasons at the same sites, each lentil crop following a cereal crop. In 3 out of 4 lentil-growing seasons, additional P was applied to lentil in subplots to compare the residual and direct effects of P application. The initial contents of extractable soil-P (P-Olsen) were low at all sites, in the range of 2–5 ppm P. Under the conditions of the experiments, lentil appeared to benefit slightly more from P applied to the preceding wheat crop (residual P) than from directly applied P. It is shown that the modified Mitscherlich equation could be used as a basis for P fertilizer recommendations for rainfed farming. As for lentil, it was concluded that a single application of P to the wheat crop in a wheat/lentil rotation could reduce the cost of lentil production, without reducing lentil yield.     

An integrated electrochemical-chemical method for the extraction of O2 from air

Oxygen can be extracted from ambient air by using a platinum cathode to reduce oxygen and by simultaneously evolving oxygen on a platinum anode. This leads to lower power consumption than when electrolyzing water to produce oxygen. A novel, integrated electrochemical-chemical method is described in this paper. The system consists of a Teflon-bonded graphite cathode which reduces oxygen via a two-electron process with the formation of HO ₂ ^– intermediate. By placing a NiCo₂O₄-covered nickel screen in the anode compartment, HO ₂ ^– is chemically decomposed to yield O₂; a separate Teflon-bonded NiCo₂O₄ anode evolves O₂ electrolytically. At 40° C and in 5 M KOH, the power consumption of this device is only 2.69 kWhr^–1 of O₂, significantly lower than conventional electrochemical oxygen extractors (4.38 kWhl^–1O₂).     

An Efficient Mechanism for Product Data Extraction from E-Commerce Websites

A large amount of data is present on the web which can be used for useful purposes like a product recommendation, price comparison and demand forecasting for a particular product. Websites are designed for human understanding and not for machines. Therefore, to make data machine-readable, it requires techniques to grab data from web pages. Researchers have addressed the problem using two approaches, i.e., knowledge engineering and machine learning. State of the art knowledge engineering approaches use the structure of documents, visual cues, clustering of attributes of data records and text processing techniques to identify data records on a web page. Machine learning approaches use annotated pages to learn rules. These rules are used to extract data from unseen web pages. The structure of web documents is continuously evolving. Therefore, new techniques are needed to handle the emerging requirements of web data extraction. In this paper, we have presented a novel, simple and efficient technique to extract data from web pages using visual styles and structure of documents. The proposed technique detects Rich Data Region (RDR) using query and correlative words of the query. RDR is then divided into data records using style similarity. Noisy elements are removed using a Common Tag Sequence (CTS) and formatting entropy. The system is implemented using JAVA and runs on the dataset of real-world working websites. The effectiveness of results is evaluated using precision, recall, and F-measure and compared with five existing systems. A comparison of the proposed technique to existing systems has shown encouraging results.     

Marker-Based and Marker-Less Motion Capturing Video Data: Person and Activity Identification Comparison Based on Machine Learning Approaches

Biomechanics is the study of physiological properties of data and the measurement of human behavior. In normal conditions, behavioural properties in stable form are created using various inputs of subconscious/conscious human activities such as speech style, body movements in walking patterns, writing style and voice tunes. One cannot perform any change in these inputs that make results reliable and increase the accuracy. The aim of our study is to perform a comparative analysis between the marker-based motion capturing system (MBMCS) and the marker-less motion capturing system (MLMCS) using the lower body joint angles of human gait patterns. In both the MLMCS and MBMCS, we collected trajectories of all the participants and performed joint angle computation to identify a person and recognize an activity (walk and running). Using five state of the art machine learning algorithms, we obtained 44.6% and 64.3% accuracy in person identification using MBMCS and MLMCS respectively with an ensemble algorithm (two angles as features). In the second set of experiments, we used six machine learning algorithms to obtain 65.9% accuracy with the k-nearest neighbor (KNN) algorithm (two angles as features) and 74.6% accuracy with an ensemble algorithm. Also, by increasing features (6 angles), we obtained higher accuracy of 99.3% in MBMCS for person recognition and 98.1% accuracy in MBMCS for activity recognition using the KNN algorithm. MBMCS is computationally expensive and if we re-design the model of OpenPose with more body joint points and employ more features, MLMCS (low-cost system) can be an effective approach for video data analysis in a person identification and activity recognition process.     

Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations among regular information and irregular information with incredible efficiency. Artificial intelligence, particularly machine learning methods can be used to develop an intelligent intrusion detection framework. There in this article in order to achieve this objective, we propose an intrusion detection system focused on a Deep extreme learning machine (DELM) which first establishes the assessment of safety features that lead to their prominence and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental results illustrate that the suggested framework outclasses traditional algorithms. In fact, the suggested framework is not only of interest to scientific research but also of functional importance.     

Causes and consequences of desertification in Kuwait: a case study of land degradation

Desertification in Kuwait is a process of environmental degradation under fragile ecological conditions and intensive human activities and the consequences of Gulf War. In Kuwait, very severe desertification prevails, due to increasing formation of new active sandy bodies, deterioration of many areas of natural vegetation cover to less than 10%, and limited water resources for large-scale forage production. Average annual desertified land in Kuwait is estimated to be 285 km². In Kuwait, three indicators of land degradation are encountered. These are vegetation, soil, and surface hydrological changes. Based on field measurements of soil compaction and vegetation changes, in the west Jahra area in the northern part of the country, degradation levels were assessed. Results of these measurements show that the average infiltration rate in compacted soil decreased by 53.8% in comparison with non-compacted soil, while the average soil penetration resistance in compacted soil increased by 154.1% in comparison with non-compacted soil. The bulk density in open sites was 23.4% higher than that in protected sites. The percentage of litter in open sites decreased by 77.3% in comparison with protected sites, while the percentage of total vegetation in open sites decreased by 6.1% in comparison with protected sites. Electronic Publication     

Network Quality Assessment in Heterogeneous Wireless Settings: An Optimization Approach

The identification of an effective network which can efficiently meet the service requirements of the target, while maintaining ultimate performance at an increased level is significant and challenging in a fully interconnected wireless medium. The wrong selection can contribute to unwanted situations like frustrated users, slow service, traffic congestion issues, missed and/or interrupted calls, and wastefulness of precious network components. Conventional schemes estimate the handoff need and cause the network screening process by a single metric. The strategies are not effective enough because traffic characteristics, user expectations, network terminology and other essential device metrics are not taken into account. This article describes an intelligent computing technique based on Multiple-Criteria Decision-Making (MCDM) approach developed based on integrated Fuzzy AHP-TOPSIS which ensures flexible usability and maximizes the experience of end-users in miscellaneous wireless settings. In different components the handover need is assessed and the desired network is chosen. Further, fuzzy sets provide effective solutions to address decision making problems where experts counter uncertainty to make a decision. The proposed research endeavor will support designers and developers to identify, select and prioritize best attributes for ensuring flexible usability in miscellaneous wireless settings. The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the usability and experience of end-users.     

Approximate Methods for the Estimation of Muskingum Flood Routing Parameters

There are a variety of techniques for estimating the parameters x and K of the Muskingum method of flood routing. One common difficulty in all the approaches is that different storm sequences along the same river reach would typically yield different parameter estimates. The a statistical analysis of these parameters also shows that they are highly variable. As a result achieving of a high level accuracy may not be the principle issue in describing x and K. This paper presents two approximate methods for estimating these parameters rather easily. The first method requires the computation of the slopes of the inflow and outflow hydrographs at their point of intersection, and the computation of the maximum storage within the reach. The second method requires the computation of the inflow and outflow hydrographs at two specific points. Three case studies investigated show that the first method gives estimates for the Muskingum parameters comparable to those derived by traditional estimation procedures for hydrographs showing linear characteristics.     

Prediction of the Phase Behavior of Hyperbranched Polymer Solutions by the Group Contribution Method

The purpose of this study is to predict the phase behavior of systems containing hyperbranched polymers by using a thermodynamic model based on the group contribution method, UNIFAC-FV, and considering an association term for the hydrogen bonding effect. After checking the validation of the model vapor-liquid equilibrium (VLE), bubble-point and liquid-liquid equilibrium (LLE), the cloud points for binary and ternary systems with different solvents were estimated from the proposed model. Furthermore, the activity of solvents in infinite dilution was predicted by this model. The results showed that the proposed model can estimate the phase behavior of solutions containing hyperbranched polymers with a good accuracy.