M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol for WSNs

In this research work, we advise gateway based energy-efficient routing protocol (M-GEAR) for Wireless Sensor Networks (WSNs). We divide the sensor nodes into four logical regions on the basis of their location in the sensing field. We install Base Station (BS) out of the sensing area and a gateway node at the centre of the sensing area. If the distance of a sensor node from BS or gateway is less than predefined distance threshold, the node uses direct communication. We divide the rest of nodes into two equal regions whose distance is beyond the threshold distance. We select cluster heads (CHs)in each region which are independent of the other region. These CHs are selected on the basis of a probability. We compare performance of our protocol with LEACH (Low Energy Adaptive Clustering Hierarchy). Performance analysis and compared statistic results show that our proposed protocol perform well in terms of energy consumption and network lifetime.Comment: IEEE 8th International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA'13), Compiegne, Franc

Invariant Solutions for Nonhomogeneous Discrete Diffusion Equation

One-dimensional optimal systems for nonhomogeneous discrete heat equation with different source terms are calculated. By utilizing these optimal systems invariant solutions are found. Also generating solutions are calculated, using the elements of the symmetry algebra

Deep COLA: A Deep COmpetitive Learning Algorithm for Future Home Energy Management Systems

A smart grid ecosystem requires intelligent Home Energy Management Systems (HEMSs) that allow the adequate monitoring and control of appliance-level energy consumption in a given household. They should be able to: i) profile highly non-stationary and non-linear measurements and ii) conduct correlations of such measurements with diverse inputs (e.g. environmental factors) in order to improve the end-user experience, as well as to aid the overall demand-response optimisation process. However, traditional approaches in HEMS lack the ability to capture diverse variations in appliance-level energy consumption due to unpredictable human behaviour and also require high computation to process large datasets. In this paper, we go beyond current profiling schemes by proposing Deep COLA; a novel Deep COmpetitive Learning Algorithm that addresses the limitations of existing work in terms of high dimensional data and enables more efficient and accurate clustering of appliancelevel energy consumption. The proposed approach reduces human intervention by automatically selecting load profiles and models variations and uncertainty in human behaviour during appliance usage. We demonstrate that our proposed scheme is far more computationally efficient and scalable data-wise than three popular conventional clustering approaches namely, K-Means, DBSCAN and SOM, using real household datasets. Moreover, we exhibit that Deep COLA identifies per-household behavioral associations that could aid future HEMSs

Behavior of a Competitive System of Second-Order Difference Equations

We study the boundedness and persistence, existence, and uniqueness of positive equilibrium, local and global behavior of positive equilibrium point, and rate of convergence of positive solutions of the following system of rational difference equations: xn+1=(α1+β1xn-1)/(a1+b1yn), yn+1=(α2+β2yn-1)/(a2+b2xn), where the parameters αi, βi, ai, and bi for i∈{1,2} and initial conditions x0, x-1, y0, and y-1 are positive real numbers. Some numerical examples are given to verify our theoretical results

Co adaptation of LiCl tolerant Solanum tuberosum L. callus cultures to NaCl stress

In this research, co-adaptation of the Calli of Solanum tuberosum, raised from petioles, to the presence of lithium (LiCl) and sodium chloride (NaCl) was studied. The cultures were adapted with LiCl in the absence of an osmotic stress and the response of adapted and unadapted calli to salinity was investigated. Undifferentiated callus growth was induced in S. tuberosum by the addition of 2 mg/l 2,4 dichlorophenoxy acetic acid (2,4-D), 0.25 mg/l kinetin to Murashige and Skoog medium. Subcultures were subjected to an incremental increase in LiCl to obtain adapted lines. Adapted and undapted calli were grown with LiCl and NaCl and the tissue content of Na+, K+, Ca2+, Mg2+ and proline levels were determined. Either 40 mM LiCl or 100 mM NaCl inhibited unadapted calli by more than 50%, while adapted calli grew normally under these conditions. The adapted calli exhibited a lower K+ content with or without salt and showed a lower accumulation of Na+ at 100 mM NaCl. The tissue K+ and Mg2+ contents decreased and their proline levels increased with salinity. A co-adaptation phenomenon is induced by LiCl that involves a regulation of K+ and Na+ contents and an accumulation of proline, which also brings about tolerance to osmotic effects of salt. This data is highly useful for devising breeding and molecular modification strategies for stress tolerance.Key words: Cations, proline, osmotic adjustment, salt tolerance, Solanum tuberosum

Magnetic phase transitions and entropy change in layered NdMn1.7Cr0.3Si2

A giant magnetocaloric effect has been observed around the Curie temperature, TC ∼ 42 K, in NdMn1.7Cr0.3Si2 with no discernible thermal and magnetic hysteresis losses. Below 400 K, three magnetic phase transitions take place around 380 K, 320 K and 42 K. Detailed high resolution synchrotron and neutron powder diffraction (10-400 K) confirmed the magnetic transitions and phases as follows: TN intra ∼ 380 K denotes the transition from paramagnetism to intralayer antiferromagnetism (AFl), TN inter ∼ 320 K represents the transition from the AFl structure to the canted antiferromagnetic spin structure (AFmc), while TC ∼ 42 K denotes the first order magnetic transition from AFmc to canted ferromagnetism (Fmc + F(Nd)) due to ordering of the Mn and Nd sub-lattices. The maximum values of the magnetic entropy change and the adiabatic temperature change, around TC for a field change of 5 T are evaluated to be −ΔSM max ∼ 15.9 J kg−1 K−1 and ΔTad max ∼ 5 K, respectively. The first order magnetic transition associated with the low levels of hysteresis losses (therma

The electrokinetic impact on heavy metals remediation of Tasik Chini iron ore mine tailings, at Pahang state, Peninsular Malaysia

The improper disposal of mining tailings is a severe threat to the surrounding environment because it comprises high concentrations of heavy metals contamination. Any precious metal extraction (mining) produces millions of tons of waste; iron ore extraction is common globally, unlike other metals extraction. The iron ore tailings contain heavy metals such as Arsenic (As), Cobalt (Co), Manganese (Mn), Lead (Pb), Copper (Cu), and Zinc (Zn). This study focuses on extracting hazardous metals such as As, V, and Zn from the disposed waste and improving its geotechnical properties. Nine samples were collected from Tasik Chini Iron ore mine, Pekan district, Pahang State, Malaysia. The initial data were prepared for elemental analysis by following ICP-OES analysis. The results showed that As, Co, Mn, Pb, Cu, and Zn concentrations exceeded the standard guidelines. In recent years, sustainable remediations techniques (EKR) have attracted extensive attention, including the electrokinetic remediation technique. The (EKR) method was applied to extract these metals from iron ore tailings specimens. A comprehensive approach of EKR shows an outstanding result where the highest removal efficiency of As was 68.4 %, Co 64.5%, Mn 67.8%, Pb 67.1%, and Cu was 64.1% and Zn 64.9% with the voltage gradient of 100 and 150 V for 4 and 8 hours constantly. Increasing the voltage gradient could be a cost-effective long-term solution for the remediation of iron ore tailings. The existing method was experienced as an effective and green technique for extracting heavy metals and recycling the mining waste materials