ALBERT

Description: The present study examines the mediating role of affective and cognitive trust, and the moderating role of continuous commitment on participative leadership and organizational citizenship behavior (OCB) relationships. Four hundred employees were recruited from the hotel industry in Pakistan. The bootstrapping method was used for an estimation of the mediation effect by the process macro. This study employs confirmatory factor analysis and structural equation modeling. Results revealed that affective trust significantly mediates the relationship between participative leadership and OCB. Moreover, continuous commitment significantly plays a moderating role in the relationship between participative leadership and OCB. This study provides unique insight into the OCB in the hotel industry. Affective and cognitive trust as a mediator and moderator of continuance commitment are examined, which is the novel characteristic of this study. Managerial implications, limitations, and future directions are discussed.

Description: We first formulate the mixed backward in time problem in the context of thermoelasticity for dipolar materials. To prove the consistency of this mixed problem, our first main result is regarding the uniqueness of the solution for this problem. This is obtained based on some auxiliary results, namely, four integral identities. The second main result is regarding the temporal behavior of our thermoelastic body with a dipolar structure. This behavior is studied by means of some relations on a partition of various parts of the energy associated to the solution of the problem.

Description: In recent years, Energy Efficiency (EE) has become a critical design metric for cellular systems. In order to achieve EE, a fine balance between throughput and fairness must also be ensured. To this end, in this paper we have presented various resource block (RB) allocation schemes in relay-assisted Long Term Evolution-Advanced (LTE-A) networks. Driven by equal power and Bisection-based Power Allocation (BOPA) algorithm, the Maximum Throughput (MT) and an alternating MT and proportional fairness (PF)-based SAMM (abbreviated with Authors’ names) RB allocation scheme is presented for a single relay. In the case of multiple relays, the dependency of RB and power allocation on relay deployment and users’ association is first addressed through a k-mean clustering approach. Secondly, to reduce the computational cost of RB and power allocation, a two-step neural network (NN) process (SAMM NN) is presented that uses SAMM-based unsupervised learning for RB allocation and BOPA-based supervised learning for power allocation. The results for all the schemes are compared in terms of EE and user throughput. For a single relay, SAMM BOPA offers the best EE, whereas SAMM equal power provides the best fairness. In the case of multiple relays, the results indicate SAMM NN achieves better EE compared to SAMM equal power and BOPA, and it also achieves better throughput fairness compared to MT equal power and MT BOPA.

Description: Norway is the largest sheep meat producer among Nordic countries with more than 1.3 million lambs and sheep slaughtered in 2017. The sheep industry is limited by the need for in-house feeding during the winter months. In summer, Norwegian sheep are mainly kept on rangeland pastures, with sufficient feed for almost double the current sheep population. Lambs are slaughtered over a three- to four-month period from September to December with a peak in September–October, providing a surplus of lamb, much of which is subsequently frozen, followed by eight months during which fresh produce is in limited supply. Norwegian consumers eat an average of 5.4 kg of sheep meat per person per year, much of which is purchased as a frozen product. The Muslim (4.2% of the population) preference for year-round halal meat, with an increased demand on the eve of the Muslim meat festival (Eid al-Adha), has the potential to boost demand, particularly in Oslo. This paper provides an overview of the Norwegian sheep farming system, the current market value chains, and the potential to meet the demand for halal meat in Norway (specifically during the Muslim meat festival—Eid al-Adha) to the advantage of both consumers and sheep farmers.

Description: Urban stormwater management is essential to improve the management of floodwaters in municipalities in urban areas. However, relying on sponge city options for site planning in an attempt to decrease the impacts of flooding is challenging due to the magnitude of flooding in urban China. The merits of exfiltration technology being used in Canada are described as having significant potential; this technology encourages passage from the stormwater pipe down to a second, lower pipe, to facilitate exfiltration to the vadose zone and, ultimately, to replenish groundwater. For example, for a small urban catchment, stormwater runoff from a 2-h long, 5-yearly storm, is demonstrated as being able to exfiltrate approximately 53% of the stormwater. Overall, the potential exists to exfiltrate stormwater from the lower pipe and it is estimated that 71% of the water entering the storm sewer is exfiltrated to the vadose zone, for a small catchment. The exfiltration pipe technology increases groundwater recharge which provides an opportunity to help manage subsidence in China. However, attention must be paid to the quality of the infiltrating water since, as true for any sponge city initiative, poor quality infiltrating water may deteriorate the quality of the groundwater.

Description: Various state-of-the-art gridded satellite precipitation products (GPPs) have been derived from remote sensing and reanalysis data and are widely used in hydrological studies. An assessment of these GPPs against in-situ observations is necessary to determine their respective strengths and uncertainties. GPPs developed from satellite observations as a primary source were compared to in-situ observations, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). These products were compared to in-situ data from 51 stations, spanning 1998–2016, across Pakistan on daily, monthly, annual and interannual time scales. Spatiotemporal climatology was well captured by all products, with more precipitation in the north eastern parts during the monsoon months and vice-versa. Daily precipitation with amount larger than 10 mm showed significant (95%, Kolmogorov-Smirnov test) agreement with the in-situ data, especially TMPA, followed by CHIRPS and MSWEP. At monthly scales, there were significant correlations (R) between the GPPs and in-situ records, suggesting similar dynamics; however, statistical metrics suggested that the performance of these products varies from north towards south. Temporal agreement on an interannual scale was higher in the central and southern parts which followed precipitation seasonality. TMPA performed the best, followed in order by CHIRPS, MSWEP and PERSIANN-CDR.

Description: An efficient algorithm for the persistence operation of data routing is crucial due to the uniqueness and challenges of the aqueous medium of the underwater acoustic wireless sensor networks (UA-WSNs). The existing multi-hop algorithms have a high energy cost, data loss, and less stability due to many forwarders for a single-packet delivery. In order to tackle these constraints and limitations, two algorithms using sink mobility and cooperative technique for UA-WSNs are devised. The first one is sink mobility for reliable and persistence operation (SiM-RPO) in UA-WSNs, and the second is the enhanced version of the SiM-RPO named CoSiM-RPO, which utilizes the cooperative technique for better exchanging of the information and minimizes data loss probability. To cover all of the network through mobile sinks (MSs), the division of the network into small portions is accomplished. The path pattern is determined for MSs in a manner to receive data even from a single node in the network. The MSs pick the data directly from the nodes and check them for the errors. When erroneous data are received at the MS, then the relay cooperates to receive correct data. The proposed algorithm boosts the network lifespan, throughput, delay, and stability more than the existing counterpart schemes.

Description: The rapid decrease in conventional energy resources and their harmful impact on the environment has brought the attention of the researchers towards the use of renewable energy technologies. The renewable energy systems are connected to Direct Current (DC) micro-grids via power electronic converters where the load conditions are unknown and network parameters are uncertain. These conditions call for the use of robust control techniques such as Sliding Mode Control (SMC) in order to regulate the grid voltage. However, SMC has a drawback of operating the power converter at variable switching frequency which results in degrading the power quality. This paper introduces a fixed frequency sliding mode controller that does not suffer from this predicament. A novel double integral type switching manifold is proposed to achieve voltage regulation of a DC micro-grid, in the presence of unknown load demands and un-modeled dynamics of the network. Rigorous mathematical analysis is carried out for the stability of the closed loop system and the technique is experimentally validated on position of a DC micro-grid using a specially designed test rig. For benchmarking purposes, a conventional Proportional Integral (PI) controller is also implemented. An improvement of 2.5% in rise time, 6.7% in settling time and reduction of voltage dip by 31.7% during load transaction is achieved as compared to the PI controller. The experiment confirms the hypothesis that fixed frequency SMC shows better performance than its counterpart in the phase of introduced disturbances.

Description: The rapid proliferation of wireless sensor networks over the past few years has posed some serious technical challenges to researchers. The primary function of a multi-hop wireless sensor network (WSN) is to collect and forward sensor data towards the destination node. However, for many applications, the knowledge of the location of sensor nodes is crucial for meaningful interpretation of the sensor data. Localization refers to the process of estimating the location of sensor nodes in a WSN. Self-localization is required in large wireless sensor networks where these nodes cannot be manually positioned. Traditional methods iteratively localize these nodes by using triangulation. However, the inherent instability in wireless signals introduces an error, however minute it might be, in the estimated position of the target node. This results in the embedded error propagating and magnifying rapidly. Machine learning based localizing algorithms for large wireless sensor networks do not function in an iterative manner. In this paper, we investigate the suitability of some of these algorithms while exploring different trade-offs. Specifically, we first formulate a novel way of defining multiple feature vectors for mapping the localizing problem onto different machine learning models. As opposed to treating the localization as a classification problem, as done in the most of the reported work, we treat it as a regression problem. We have studied the impact of varying network parameters, such as network size, anchor population, transmitted signal power, and wireless channel quality, on the localizing accuracy of these models. We have also studied the impact of deploying the anchor nodes in a grid rather than placing these nodes randomly in the deployment area. Our results have revealed interesting insights while using the multivariate regression model and support vector machine (SVM) regression model with radial basis function (RBF) kernel.

Description: Information, Vol. 9, Pages 143: An Extended-Tag-Induced Matrix Factorization Technique for Recommender Systems Information doi: 10.3390/info9060143 Authors: Huirui Han Mengxing Huang Yu Zhang Uzair Aslam Bhatti Social tag information has been used by recommender systems to handle the problem of data sparsity. Recently, the relationships between users/items and tags are considered by most tag-induced recommendation methods. However, sparse tag information is challenging to most existing methods. In this paper, we propose an Extended-Tag-Induced Matrix Factorization technique for recommender systems, which exploits correlations among tags derived by co-occurrence of tags to improve the performance of recommender systems, even in the case of sparse tag information. The proposed method integrates coupled similarity between tags, which is calculated by the co-occurrences of tags in the same items, to extend each item’s tags. Finally, item similarity based on extended tags is utilized as an item relationship regularization term to constrain the process of matrix factorization. MovieLens dataset and Book-Crossing dataset are adopted to evaluate the performance of the proposed algorithm. The results of experiments show that the proposed method can alleviate the impact of tag sparsity and improve the performance of recommender systems.