Higher College of Technology

About: Higher College of Technology is a based out in . It is known for research contribution in the topics: Computer science & Higher education. The organization has 369 authors who have published 379 publications receiving 729 citations.

Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity

Abstract: The establishment of enduring connections that adapt to the dynamic environmental conditions are considered as a major trend in today’s research. This paper aims at re-establishing internet connectivity to users under dynamic environmental conditions (natural disasters) by utilizing an energy management approach. The proposed model follows a series of steps that initially harvest energy for the nodes in disaster areas. Once the nodes are energized by harvesting power from a healthy node or drone, an efficient clustering using Binary Flower Pollination algorithm is carried out. The clustering is carried out based on the energy level of each node within the coverage area of a healthy node. Subsequently, a heap sort algorithm is employed to find the shortest path. Finally, the connection is established between the users using a two-zone model that consists of normal, super, and advanced nodes. This proposed method is evaluated against benchmark methods in terms of energy efficiency.

Ten people-centered rules for socially sustainable ecosystem restoration

Abstract: As the UN Decade on Ecosystem Restoration begins, there remains insufficient emphasis on the human and social dimensions of restoration. The potential that restoration holds for achieving both ecological and social goals can only be met through a shift towards people‐centered restoration strategies. Towards this end, this paper synthesizes critical insights from a special issue on “Restoration for whom, by whom” to propose actionable ways to center humans and social dimensions in ecosystem restoration, with the aim of generating fair and sustainable initiatives. These rules respond to a relative silence in di Sacco et al.’s (2021) “Ten golden rules for reforestation to optimize carbon sequestration, biodiversity recovery and livelihood benefits” on socio‐political issues, and offer complementary guidance to their piece. Arranged roughly in order from pre‐intervention, design/initiation, implementation, through the monitoring, evaluation and learning phases, the ten people‐centered rules are: 1) Recognize diversity and interrelations among stakeholders; 2) Actively engage communities as agents of change; 3) Address socio‐historical contexts; 4) Unpack and strengthen resource tenure for marginalized groups; 5) Advance equity across its multiple dimensions and scales; 6) Generate multiple benefits; 7) Promote an equitable distribution of costs, risks, and benefits; 8) Draw on different types of evidence and knowledge; 9) Question dominant discourses; and 10) Practice inclusive and holistic monitoring, evaluation and learning. We contend that restoration initiatives are only tenable when the issues raised in these rules are respectfully addressed.

Regression-BPNN modelling of surfactant concentration effects in electroless NiB coating and optimization using genetic algorithm

Abstract: Critical micelle concentration (CMC) is an important factor to avoid the formation of micelles from monomeric surfactant molecules. Trisodium citrate stabilized electroless Ni B (ENi-B) coating on aluminium alloy (Al7075-T6) is attempted with the addition of amphoteric surfactant, 3-(N, N-Dimethylmyristylammonio) propanesulfonate (3-DMAPS), to enhance the surface finish (Ra) of the coatings. The main aim of the study is to investigate the influence of surfactant concentration on average surface roughness in ENi-B bath and determine the CMC of 3-DMAPS at minimum Ra. Mathematical models relating the concentration of amphoteric surfactant (0–0.162 g/L) as an independent variable and Ra as a dependent variable are developed using univariate regression analysis (Linear, quadratic, power, and exponential models) and back propagation neural network (BPNN) algorithm. The coefficient of determination (R2) is used to evaluate the goodness of fit between the models, and the BPNN model is found to be the best fit (R2 > 0.98). The minimum Ra of 0.171 ± 0.001 μm was achieved at the CMC of 0.049 g/L (0.135 mM) from the genetic algorithm (GA) using the validated models developed by quadratic regression analysis and BPNN as fitness functions. SEM, XRD and AFM techniques were carried out for the characterization of ENi-B coatings with and without surfactant at CMC.