Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norm's life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work.
Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.
In recent decades, the application of fibre-reinforced polymer (FRP) composites for strengthening structural elements has become an efficient option to meet the increased cyclic loads or repair due to corrosion or fatigue cracking. Hence, the objective of this study is to explore the existing FRP reinforcing techniques to care for fatigue damaged structural steel elements. This study covers the surface treatment techniques, adhesive curing, and support conditions under cyclic loading including fatigue performance, crack propagation, and failure modes with finite element (FE) simulation of the steel bridge girders and structural elements. FRP strengthening composites delay initial cracking, reduce the crack growth rate, extend the fatigue life, and decrease the stiffness decay with residual deflection. Prestressed carbon fibre-reinforced polymer (CFRP) is the best strengthening option. End anchorage prevents debonding of the CRRP strips at the beam ends by reducing the local interfacial shear and peel stresses. Hybrid-joint, nanoadhesive, and carbon-flex can also be attractive for strengthening systems.
Thermoelectricity, by converting heat energy directly into useable electricity, offers a promising technology to convert heat from solar energy and to recover waste heat from industrial sectors and automobile exhausts. In recent years, most of the efforts have been done on improving the thermoelectric efficiency using different approaches, that is, nanostructuring, doping, molecular rattling, and nanocomposite formation. The applications of thermoelectric polymers at low temperatures, especially conducting polymers, have shown various advantages such as easy and low cost of fabrication, light weight, and flexibility. In this review, we will focus on exploring new types of polymers and the effects of different structures, concentrations, and molecular weight on thermoelectric properties. Various strategies to improve the performance of thermoelectric materials will be discussed. In addition, a discussion on the fabrication of thermoelectric devices, especially suited to polymers, will also be given. Finally, we provide the challenge and the future of thermoelectric polymers, especially thermoelectric hybrid model.
An immense problem affecting environmental pollution is the increase of waste tyre vehicles. In an attempt to decrease the magnitude of this issue, crumb rubber modifier (CRM) obtained from waste tyre rubber has gained interest in asphalt reinforcement. The use of crumb rubber in the reinforcement of asphalt is considered as a smart solution for sustainable development by reusing waste materials, and it is believed that crumb rubber modifier (CRM) could be an alternative polymer material in improving hot mix asphalt performance properties. In this paper, a critical review on the use of crumb rubber in reinforcement of asphalt pavement will be presented and discussed. It will also include a review on the effects of CRM on the stiffness, rutting, and fatigue resistance of road pavement construction.
An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs.
Cloud computing is currently emerging as an ever-changing, growing paradigm that models "everything-as-a-service." Virtualised physical resources, infrastructure, and applications are supplied by service provisioning in the cloud. The evolution in the adoption of cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, the increasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the best services. By employing successful service provisioning, the essential services required by customers, such as agility and availability, pricing, security and trust, and user metrics can be guaranteed by service provisioning. Hence, continuous service provisioning that satisfies the user requirements is a mandatory feature for the cloud user and vitally important in cloud computing service offerings. Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements, necessary metrics, and pricing mechanisms. We synthesize and summarize different provision techniques, approaches, and models through a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematic review. Finally, future research directions and open research issues are identified.
In the original particle swarm optimisation (PSO) algorithm, the particles' velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm's best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well.
This paper presents a new systematic approach to analyze all possible array configurations in order to determine the most optimal dense-array configuration for concentrator photovoltaic (CPV) systems. The proposed method is fast, simple, reasonably accurate, and very useful as a preliminary study before constructing a dense-array CPV panel. Using measured flux distribution data, each CPV cells' voltage and current values at three critical points which are at short-circuit, open-circuit, and maximum power point are determined. From there, an algorithm groups the cells into basic modules. The next step is I-V curve prediction, to find the maximum output power of each array configuration. As a case study, twenty different I-V predictions are made for a prototype of nonimaging planar concentrator, and the array configuration that yields the highest output power is determined. The result is then verified by assembling and testing of an actual dense-array on the prototype. It was found that the I-V curve closely resembles simulated I-V prediction, and measured maximum output power varies by only 1.34%.
A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.
The world's tropical rainforests are decreasing at an alarming rate as they are converted to agricultural land, pasture, and plantations. Decreasing tropical forests affect global warming. As a result, afforestation progams have been suggested to mitigate this problem. The objective of this study was to determine the carbon and phosphorus accumulation of a rehabilitated forest of different ages. The size of the study area was 47.5 ha. Soil samples were collected from the 0-, 6-, 12-, and 17-year-old rehabilitated forest. Twenty samples were taken randomly with a soil auger at depths of 0-20 and 20-40 cm. The procedures outlined in the Materials and Methods section were used to analyze the soil samples for pH, total C, organic matter, total P, C/P ratio, yield of humic acid (HA), and cation exchange capacity (CEC). The soil pH decreased significantly with increasing age of forest rehabilitation regardless of depth. Age did not affect CEC of the rehabilitated forest. Soil organic matter (SOM), total C, and total P contents increased with age. However, C/P ratio decreased with time at 0-20 cm. Accumulation of HA with time and soil depth was not consistent. The rehabilitated forest has shown signs of being a C and P sink.
Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.
Reinforced concrete (RC) box girders are a common structural member for road bridges in modern construction. The hollow cross-section of a box girder is ideal in carrying eccentric loads or torques introduced by skew supports. This study employed acoustic emission (AE) monitoring on multicell RC box girder specimens subjected to laboratory-based torsion loading. Three multicell box girder specimens with different cross-sections were tested. The aim is to acquire AE analysis data indicative for characterizing torsion fracture in the box girders. It was demonstrated through appropriate parametric analysis that the AE technique could be utilized to effectively classify fracture developed in the specimens for describing their mechanical behavior under torsion. AE events localization was presented to illustrate the trend of crack and damage propagation in different stages of fracture. It could be observed that spiral-like patterns of crack were captured through AE damage localization system and damage was quantified successfully in different stages of fracture by using smoothed b-value analysis.
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
We propose an Advanced Mobility Handover scheme (AMH) in this paper for seamless mobility in MIPv6-based wireless networks. In the proposed scheme, the mobile node utilizes a unique home IPv6 address developed to maintain communication with other corresponding nodes without a care-of-address during the roaming process. The IPv6 address for each MN during the first round of AMH process is uniquely identified by HA using the developed MN-ID field as a global permanent, which is identifying uniquely the IPv6 address of MN. Moreover, a temporary MN-ID is generated by access point each time an MN is associated with a particular AP and temporarily saved in a developed table inside the AP. When employing the AMH scheme, the handover process in the network layer is performed prior to its default time. That is, the mobility handover process in the network layer is tackled by a trigger developed AMH message to the next access point. Thus, a mobile node keeps communicating with the current access point while the network layer handover is executed by the next access point. The mathematical analyses and simulation results show that the proposed scheme performs better as compared with the existing approaches.
Little is known about the relationships between adverse health effects and unhealthy behaviors among medical students using Facebook. The aim of this study was to determine the associations between adverse health effects and unhealthy behaviors with Facebook use. A cross-sectional study was conducted in a private university in Malaysia among 316 medical students. A self-administered questionnaire was used. It included questions on sociodemographics, pattern of Facebook use, social relationship, unhealthy behaviors, and health effects. Mean age was 20.5 (±2.7) years. All students had a Facebook account. The average daily Facebook surfing hours were 2.5 (±1.7). Significant associations were found between average hours of Facebook surfing and the following factors: isolation from family members and community, refusing to answer calls, musculoskeletal pain, headache, and eye irritation (P < 0.005). The average hours spent on Facebook were significantly associated with holding urination and defecation while online, surfing Facebook until midnight, and postponing, forgetting, or skipping meals (P < 0.005). The average hours spent on Facebook were associated with adverse health effects and unhealthy behaviors among medical students, as well as social isolation from the family and community.
Automatic estimation of a speaker's age is a challenging research topic in the area of speech analysis. In this paper, a novel approach to estimate a speaker's age is presented. The method features a "divide and conquer" strategy wherein the speech data are divided into six groups based on the vowel classes. There are two reasons behind this strategy. First, reduction in the complicated distribution of the processing data improves the classifier's learning performance. Second, different vowel classes contain complementary information for age estimation. Mel-frequency cepstral coefficients are computed for each group and single layer feed-forward neural networks based on self-adaptive extreme learning machine are applied to the features to make a primary decision. Subsequently, fuzzy data fusion is employed to provide an overall decision by aggregating the classifier's outputs. The results are then compared with a number of state-of-the-art age estimation methods. Experiments conducted based on six age groups including children aged between 7 and 12 years revealed that fuzzy fusion of the classifier's outputs resulted in considerable improvement of up to 53.33% in age estimation accuracy. Moreover, the fuzzy fusion of decisions aggregated the complementary information of a speaker's age from various speech sources.
The presented study established Agrobacterium-mediated genetic transformation using protocorm-like bodies (PLBs) for the production of transgenic Vanda Kasem's Delight Tom Boykin (VKD) orchid. Several parameters such as PLB size, immersion period, level of wounding, Agrobacterium density, cocultivation period, and concentration of acetosyringone were tested and quantified using gusA gene expression to optimize the efficiency of Agrobacterium-mediated genetic transformation of VKD's PLBs. Based on the results, 3-4 mm PLBs wounded by scalpel and immersed for 30 minutes in Agrobacterium suspension of 0.8 unit at A 600 nm produced the highest GUS expression. Furthermore, cocultivating infected PLBs for 4 days in the dark on Vacin and Went cocultivation medium containing 200 μM acetosyringone enhanced the GUS expression. PCR analysis of the putative transformants selected in the presence of 250 mg/L cefotaxime and 30 mg/L geneticin proved the presence of wheatwin1, wheatwin2, and nptII genes.
We apply the classical theory of hyperrings to vague soft sets to derive the concepts of vague soft hyperrings, vague soft hyperideals, and vague soft hyperring homomorphism. The properties and structural characteristics of these concepts are also studied and discussed. Furthermore, the relationship between the concepts introduced here and the corresponding concepts in classical hyperring theory and soft hyperring theory is studied and investigated.
This study investigated the allelopathic effect of Axonopus compressus litter on Asystasia gangetica and Pennisetum polystachion. In experiment 1 the bioassays with 0, 10, 30, and 50 g L⁻¹ of aqueous A. compressus litter leachate were conducted. Experiment 2 was carried out by incorporating 0, 10, 20, 30, 40, and 50 g L⁻¹ of A. compressus litter leachate into soil. In experiment 3, the fate of A. compressus litter leachate phenolics in the soil was investigated. A. compressus leachates did not affect the germination percentage of A. gangetica and P. polystachion, but delayed germination of A. gangetica seeds and decreased seed germination time of P. polystachion. A. compressus litter leachates affected weeds hypocotyl length. Hypocotyl length reductions of 18 and 31% were observed at the highest concentration (50 g L⁻¹) compared to the control in A. gangetica and P. polystachion, respectively. When concentration of A. compressus litter leachate-amended soil increased A. gangetica and P. polystachion seedling shoot length, root length, seedling weight and chlorophyll concentration were not affected. The 5-week decomposition study of A. compressus showed that the phenolic compounds in A. compressus litter abruptly decreased about 52% after two weeks and remained steady until the end of the incubation.