Attitudinal and behavioural determinants influencing decision makers when adopting integration technologies in local government

Over the last few years, the advent of innovative or revolutionary integration technologies has influenced pivotal decisions within top management to strategically transform Local Government Authorities (LGAs). These technologies may represent a huge cost for adopting LGAs, but may also offer the chance to achieve competitive advantage through superior service delivery. With the emergence of electronic Government (e- Government), LGAs are turning to integration technologies to fully automate and e-enable their business processes and integrate their IT infrastructures. While prior research on the adoption of integration technologies in the private and public domain has considered several determinants (e.g. benefits, barriers, costs), little attention has been given to investigate the attitudinal and behavioural determinants influencing top management’s decision making process for the adoption of integration technologies in LGAs. Notwithstanding, the implications of this research have yet to be assessed, leaving scope for timeliness and novel research. Therefore, it is of high importance to investigate this area within LGAs and contribute to the area of strategic decision making by examining attitudinal and behavioural determinants of top management in relation to integration technologies adoption

A new methodology called dice game optimizer for capacitor placement in distribution systems

Purpose. Shunt capacitors are installed in power system for compensating reactive power. Therefore, feeder capacity releases, voltage profile improves and power loss reduces. However, determination optimal location and size of capacitors in distributionsystems is a complex optimization problem. In order to determine the optimum size and location of the capacitor, an objective function which is generally defined based on capacitor installation costs and power losses should be minimized According to operational limitations. This paper offers a newly developed metaheuristic technique, named dice game optimizerto determine optimal size and location of capacitors in a distribution network. Dice game optimizer is a game based optimization technique that is based on the rules of the dice game.Цель. Шунтирующие конденсаторы в энергосистеме устанавливаются для компенсации реактивной мощности. Следовательно, снижается емкость фидера, улучшается профиль напряжения и снижаются потери мощности. Однако определение оптимального местоположения и размера конденсаторов в системах распределения является сложной задачей оптимизации. Чтобы определить оптимальный размер и расположение конденсатора, целевую функцию, которая обычно определяется на основе затрат на установку конденсатора и потерь мощности, следует минимизировать в соответствии с эксплуатационными ограничениями. Данная статья предлагает недавно разработанный метаэвристический метод, называемый оптимизатором игры в кости, для определения оптимального размера и расположения конденсаторов в распределительной сети. Оптимизатор игры в кости – это игровой метод оптимизации, основанный на правилах игры в кости

Securing Autonomous Vehicles Against GPS Spoofing Attacks: A Deep Learning Approach

With the rapid advancement of technology and multimedia systems, ensuring security has become a critical concern. Connected and Autonomous Vehicles (CAVs) are vulnerable to various hacking techniques, including jamming and spoofing. Global Positioning System (GPS) location spoofing poses a significant threat to CAVs, compromising their security and potentially endangering pedestrians and drivers. To address this issue, this research proposes a novel methodology that uses deep learning (DL) algorithms, such as Convolutional Neural Networks (CNN), and machine learning (ML) algorithms, such as Support Vector Machine (SVM), to protect CAVs from GPS location spoofing attacks. The proposed solution is validated using real-time simulations in the CARLA simulator, and extensive analysis of different learning algorithms is conducted to identify the most suitable approach across three distinct trajectories. Training and testing data include GPS coordinates, spoofed coordinates, and localization algorithm values. The proposed machine learning algorithm achieved 99% and 96% accuracy for the best and worst case scenarios, respectively. In case of deep learning, it achieved as high as 99% for best and 82% for the worst case scenario

Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing

Nowadays, researchers are interested on granular computing in order to solve the big data problem. The volume of Big Scholarly Data (BSD) is rapidly growing. In order to evaluate the research performance, it’s becoming essential to evaluate the impact of BSD. Traditionally, journals have been ranked by their journal impact factor (JIF). However, several impact evaluation methods have been used by different BSD digital systems, such as the citation analysis, G-Index, H-index, i10-index, jurnal impact (JIF), and the Eigenfactor. In this paper, a detailed study of these different impact evaluation methods is shown along with their advantages and disadvantages. From this study, we can say that although the evaluation methods appear highly correlated but they lead to large differences in BSD impact evaluation. We conclude that no one evaluation method is superior and the present research gap is to develop standard rubrics and standard benchmarks in order to evaluate these existing methods. Furthermore, we have hypothetically modeled a new fuzzy granular approach as evolving structural fuzzy model (ESFM) which consider the concept of granular computing. Therefore, information granules exhibit the expressive and functional depiction of the global concept

Conotoxins: structure, therapeutic potential and pharmacological applications.

Cone snails, also known as marine gastropods, from Conus genus produce in their venom a diverse range of small pharmacologically active structured peptides called conotoxins. The cone snail venoms are widely unexplored arsenal of toxins with therapeutic and pharmacological potential, making them a treasure trove of ligands and peptidic drug leads. Conotoxins are small disulfide bonded peptides, which act as remarkable selective inhibitors and modulators of ion channels (calcium, sodium, potassium), nicotinic acetylcholine receptors, noradrenaline transporters, N-methyl-D-aspartate receptors, and neurotensin receptors. They are highly potent and specific against several neuronal targets making them valuable as research tools, drug leads and even therapeutics. In this review, we discuss their gene superfamily classification, nomenclature, post-translational modification, structural framework, pharmacology and medical applications of the active conopeptides. We aim to give an overview of their structure and therapeutic potential. Understanding these aspects of conopeptides will help in designing more specific peptidic analogues

Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques

Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims

Alignments in quasar polarizations: pseudoscalar-photon mixing in the presence of correlated magnetic fields

We investigate the effects of pseudoscalar-photon mixing on electromagnetic radiation in the presence of correlated extragalactic magnetic fields. We model the Universe as a collection of magnetic domains and study the propagation of radiation through them. This leads to correlations between Stokes parameters over large scales and consistently explains the observed large-scale alignment of quasar polarizations at different redshifts within the framework of the big bang model.Comment: 12 pages, 5 figures, version published in PR

A novel green antenna phase-shift system with data acquisition boards

A novel green phase shifter system is proposed in this research. The system is developed by a combination of reconfigurable beam steering antennas and data acquisition (DAQ) boards. A combination of two reconfigurable beam steering antennas, located side-by-side, forms a spatial configuration structure with a fabricated ‘green’ element plank of rice husk placed in between. The concept of a spatial configuration technique has been ‘mutated’ by shifting the structure of spiral feed line and aperture slots of first beam steering antenna by as much as 45 ◦ . The PIN diode switches connected to the DAQ boards enable the intelligent capability of the spatial antennas. The activation of certain degree radiation patterns of either the first beam steering antenna or the second beam steering antenna depends on the memory of the DAQ boards — Beam Manager. When an intruder comes from the cardinal angles of 0◦/ 360◦, 90◦, 180◦, or 270◦, its range and angles’ location will be automatically detected by the first antenna through the output ports of the 1st DAQ: P1.0, P1.1, P1.2, and P1.3. The second antenna is then activated by the output ports of the 2nd DAQ: P2.0 up to P2.3, to adaptively maneuver the beam towards four different ordinal directions of 45◦, 135◦, 225◦, and 315◦