Resource orchestration in Indian ethnic entrepreneurial enterprises through generation change in Malaysia

Ethnic entrepreneurial enterprises are continuously evolving, especially when generations change. As these changes take place, resources are also orchestrated differently. However, research gap exists on how resources are orchestrated in ethnic entrepreneurial enterprises through generational change. We answer this question by adopting a qualitative approach based on data from eleven ethnic entrepreneurial enterprises that have experienced generational succession. The data was then analysed by adopting a novel approach of artificial intelligence. Our results suggest that the orchestration in class and ethnic resources has equipped the later generation ethnic entrepreneurs with capabilities to expand and develop their ethnic entrepreneurial enterprises. We emphasize the importance of orchestrating resources in ethnic entrepreneurial enterprises for product innovation, market growth and business development as generations change. The use of artificial intelligence technique enables underlying patterns in ethnic entrepreneurship to be discovered, which assist practitioners in making the best decisions concerning entrepreneurial efforts. This study invites entrepreneurs to comprehend the importance of orchestrating resources for entrepreneurial decision-making in business expansion and development, especially in ethnic entrepreneurial enterprises. With novelty in the methodological application, we extend a cordial invitation to erudite scholars to apply artificial intelligence technique within qualitative research to achieve precision and nuances

Flowers amongst the weeds benefit-finding during the Covid-19 pandemic in England

Preliminary research suggests that in addition to negative experiences, many individuals experienced positive outcomes connected to the COVID-19 pandemic. However, most research has studied posttraumatic growth, which can only account for cognitive positive change, which is a limitation. Therefore, this study aimed to explore experiences of benefit-finding, which includes both practical and cognitive positive changes, relating to living through the COVID-19 pandemic in England within a general population sample.230 participants were recruited via non-randomised convenience sampling. Experiences of benefit-finding were assessed by qualitative self-report via an online questionnaire, distributed as part of a larger mixed methods pandemic study. Results were analysed via inductive content analysis.Approximately 70% of participants reported perceiving at least one benefit because of living through the COVID-19 pandemic. The most commonly reported perceived benefit was having more time to oneself, followed by having more time with family. Other benefits reported included changes to working and education styles, life slowing down and benefits of nature. Overall, the results presented that many individuals felt that the COVID-19 pandemic presented a greater opportunity to make decisions more in line with personal wants/goals. In this way, the COVID-19 pandemic may have presented a unique opportunity for life-crafting.This research provides unique evidence of both benefit-finding and life-crafting in the otherwise negative circumstances of the COVID-19 pandemic in England. Such evidence presents use for understanding factors to support wellbeing in challenging circumstances and for the formulation of potential wellbeing interventions

Machine learning applications for wind resource mapping in Ajman, UAE towards sustainable energy solutions

Accurate site-specific Sustainable Wind Resource Assessment (SWRA) remains a critical contemporarysustainable wind power development issue. Especially in regions like the Emirate of Ajman and theUnited Arab Emirates (UAE), site-specific wind data collection faces high challenges and constraints.Mainly due to the excessively high costs of measuring wind speeds at wind turbine hub heights level,leading to dependence on publicly available NASA satellite data or any other freely available wind data that requiresextensive error correction for reliable application in SWRA.This research develops a comprehensive methodology for site-specific SWRA in the Emirate of Ajman throughfive integrated objectives: developing machine learning (ML)-based error correction methodology for NASAsatellite wind data, determining site-specific surface parameters, predicting future wind speed trends using ARIMAmodelling, analysing wind potential variations, and creating GIS-based wind resource maps. A systematic mixed-methodsapproach was used, integrating multiple ML algorithms (Random Forest, Support Vector Machine, Gradient Boosting) for NASAwind speed data correction, determination of site-specific parameters (wind shear coefficients, roughness length, air density),statistical analysis of wind patterns, and GIS-based wind resource mapping. Ground-based measurements from strategicallylocated onshore monitoring stations validated the methodology and established site-specific correction factors across Ajman'sdiverse terrain. Results showed clear spatial and temporal variations in wind resources, with annual wind speeds rangingfrom 3.33- 3.74 m/s at 50m to 4.75-5.2 m/s at 100m height. Spring emerged as the optimal season, with wind speedsreaching 5.69-6.16 m/s at 100m height. The Random Forest model achieved the highest accuracy (R² = 0.5772) insatellite data correction. Surface roughness length varied from 0.0002 (offshore) to 0.50 (urban areas), while air densityranged between 1.146-1.166 kg/m³. Offshore locations showed higher wind power density, reaching 126.12 W/m².This study establishes Ajman's first validated, GIS integrated SWRA methodology, contributing to practical and theoreticaladvances in SWRA. While supporting the feasibility of hybrid wind-solar systems and offshore installations, the findingsalign with the UAE's Net Zero 2050 strategy and establish a systematic approach that other regions can follow to improvesatellite-derived wind speed data accuracy

Air Quality Management Strategic Framework For Future Sustainable Cities

Pollution can originate from fixed, mobile, and local sources, due to human activity or naturally occurring processes. The well-developed cities contribute to over 70% of global carbon emissions. This paper analyses the parameters that contribute to achieving healthy, environmentally sustainable cities. A strategic planning framework is introduced to implement efficient and effective strategies for future sustainable cities. Therefore, the paper aims to identify and examine successful factors within a framework to reduce negative environmental impacts from air pollution designed for future sustainable cities. Data is collected from locations that reflect the nature of human settlement and well-being, and Advanced SWOT analysis was conducted to see the success factors. Data analyses reveal many factors that must be monitored to achieve SDG 11 targets. The results show how air quality affects people's health and social living conditions in urbanised areas. Comparisons between pre-and-post Covid 19 indicated the impact of the pandemic on air quality and showed evidence of possible reductions in air pollution when activities are reduced. The method used for this research is analysing the data recorded from a network of environmental stations constructed at different sites in the Emirate of Ajman. The achieved framework consists of strategies categorised into five main categories, formed by different functional layers, to demonstrate actions needed by the government. Recommendations have been drawn from the findings, and if considered, it could be possible to achieve sustainable air quality. Keywords: Aerodynamics, Forebody and afterbody, Next keyword, Projectile, Supersonic speed

Special Floating Wind-H2 Design For Ajman - UAE: Navigating The Turbulent Waters Of The Energy Transition And Building Climate Resilience

A Floating Wind-H2 System can support efforts toward a net-zero future through its synergistic potential. This paper overviews the powerful synergy between floating wind and hydrogen technologies. Floating wind technology has made significant advances in terms of turbine size and efficiency, opening a vast offshore wind resource. H2's multifaceted advantages and the trajectory of these systems within the broader energy landscape are analysed in a multidimensional approach to assessing their transformative potential. Floating Wind-H2 Systems are examined, revealing current and prospects based on existing projects. As floating wind technology evolves, they shed light on H2's nuanced role in the energy transition and provide insights into the feasibility and impact of integrating these technologies at scale. Using Ajman, UAE, as a case study to illustrate regional adaptation, this paper discusses the significance of floating wind and hydrogen technologies for achieving global net-zero targets. The comparison of centralised onshore and decentralised offshore electrolysis demonstrated the importance of flexible solutions, particularly in regions like Ajman with limited land availability. An innovative approach to clean energy infrastructure is demonstrated by the proposed wind-H2 system in Ajman, which relocates offshore facilities to overcome land constraints, reduce costs, and facilitate free trade. The system can produce more than 5 Tons of H2 per day considering power generation of 3.4 MW per wind turbine for 3 turbines

Addiction recovery stories: Emma Roughley in conversation with Lisa Ogilvie

PurposeThe purpose of this paper is to examine recovery through lived experience. It is part of a series that explores candid accounts of addiction and recovery to identify important components in the recovery process.Design/methodology/approachThe growth, connectedness, hope, identity, meaning in life,empowerment (G-CHIME) model comprises six elements important to addiction recovery (growth, connectedness, hope, identity, meaning in life and empowerment). It provides a standard against which to consider addiction recovery. It has been used in this series, as well as in the design of interventions that improve well-being and strengthen recovery. In this paper, a firsthand account is presented, followed by a semistructured e-interview with the author of the account. Narrative analysis is used to explore the account and interview through the G-CHIME model.FindingsThis paper shows that addiction recovery is a remarkable process that can be effectively explained using the G-CHIME model. The significance of each component in the model is apparent from the account and e-interview presented.Originality/valueEach account of recovery in this series is unique and as yet untold

Enhanced Intrusion Detection in IoT Networks Using Hybrid Machine Learning Technique

This study introduces a unique framework for an Intrusion Detection System (IDS) that employs an advanced machine learning approach to improve the Internet of Things (IoT) networks. IoT devices, now increasingly prevalent, rely on data which is a subject of interest to hackers/attackers who explore the present rise in network security vulnerabilities. There is therefore the need for a more robust and accurate intrusion detection system. The integration of Random Forest (RF) algorithms with Deep Neural Networks (DNNs) provides a significant increase in model evaluation metrics and robustness. A comprehensive CICIoT2023 dataset was adopted and used to meticulously train and evaluate the IDS model, resulting in an exceptional and effective system of identifying and preventing potential threats. Also, the study analysis highlights areas of improvement, particularly in detecting specific attack types such as SQL injection. Whilst these findings push the boundaries of IoT security using state-of-the-art machine learning techniques, they have also underlined the need for further studies to address the obvious gaps

Improving Telemedicine with Digital Twin-Driven Machine Learning: A Novel Framework

The convergence of digital twin technology and machine learning has ushered in a transformative era in patient monitoring and diagnosis within the healthcare sector. This review article explores the comprehensive integration of digital twin-driven machine learning frameworks, aiming to elucidate the core objectives, pivotal findings, and overarching implications. Our primary objectives encompass the exploration of digital twin technology's adaptation to healthcare, the augmentation of medical assessments through machine learning algorithms, the enabling of real-time monitoring with early anomaly detection capabilities, and the personalization of treatment plans rooted in patient profiles generated by digital twins. The key findings underscore the successful adaptation of digital twin technology for healthcare applications, emphasizing its potential to capture dynamic patient data and history. The synergy between machine learning and digital twins enhances the precision of diagnostics and predictive analytics, thus improving healthcare outcomes. Real-time monitoring, made possible through digital twins, ensures proactive patient care with timely interventions. Moreover, personalizing treatment plans, tailored to individual patient profiles, offers a promising avenue for more effective and less invasive interventions. The implications of this review extend to the transformative potential of digital twin-driven machine learning in healthcare, with the ability to revolutionize patient care, diagnostics, and monitoring. The review highlights data security and ethical challenges, stressing the need for standardized protocols to protect patient information. Ongoing research and innovation are crucial for maximizing these frameworks' potential, improving patient outcomes, and enhancing healthcare quality

Integrated Intrusion Detection And Prevention Model For Moodle Learning Management System

This study developed and evaluated an integrated intrusion detection and prevention (IDP) model for Moodle LearningManagement System (LMS), utilizing Snort 3, Open-Source Security (OSSEC), ModSecurity, and Moodle's securitysettings. The increasing security threats facing LMS platforms was addressed in the study by leveraging the strengths ofeach tool: Snort 3 for network-level detection, OSSEC for host-based monitoring, ModSecurity for web applicationprotection, and Moodle’s native security features for enhanced control. An experimental approach was adopted,beginning with a literature review to identify vulnerabilities, followed by system design, tool configuration, andintegration. The model was tested against simulated attacks, with performance measured by detection accuracy. Theresults demonstrated the model's effectiveness in identifying and mitigating common security threats within Moodle LMSsuch as distributed denial of service, brute force attack, SQL injection and aggressive scan. The study concludes byrecommending the deployment of the IDP model in a live environment for both private/individual owned and publicowned Moodle platforms, for the provision of a robust framework for enhancing security. This work contributes to thebroader field of LMS security through the provision of a comprehensive, multi-layered approach to protectingeducational platforms from cyber threats

Multifunctional Smart Grid Control based on Power Electronic Systems

There is a worldwide switch to electricity generation plants based on renewable energy sources(RES) to decarbonise electricity generation. In contrast to the fossil fuel based traditionalpower plants, power plants based on RES are widely distributed and often connected to thedistribution system operator (DSO) grid level, which leads to a structural change of theelectricity network. The rising numbers of installed RES and the high fluctuation of powergeneration increase the stress on this grid level. To improve the stability, reliability andefficiency of the DSO grid level, it is necessary to transfer and adapt ancillary service functionsknown from the transmission system operator (TSO) to the DSO grid level.To provide ancillary services at the DSO grid level under high fluctuations, unbalanced gridconditions and harmonic distortions, a new Multifunctional Energy and Power Server (MEPS)based on modern power electronic is introduced in this research. The system topology consistsof a series- and a parallel-connected inverter branch. This structure is known as the UnifiedPower Quality Conditioner (UPQC) and the Unified Power Flow Controller (UPFC), whichare used in active power filters and in power flow control in electrical grids. The systemapproach developed in this work for implementing grid service functions aims to combine thevarious approaches for this in a single system.The series branch consists of an inverter system connected by a transformer in series to theupstream network and is able to compensate for asymmetrical and harmonic distorted voltages.The parallel branch consists of a second inverter system, which is connected in parallel to thegrid and is able to compensate for asymmetrical and harmonic distorted currents. Incombination with a battery system, the parallel branch can also provide active power-basedfunctions, such as primary control and power fluctuation compensation. All these grid-specificdynamic control functions are implemented based on symmetrical components (SC) withindividual controller loops for the positive, negative and zero sequences in the fundamentaland harmonic frequency range. To use the SC for real-time control, all measured voltages andcurrents are separated into different harmonic components using the heterodyne method. Thecombination of the heterodyne method with the SC transformation allows for the individualand decoupled control under asymmetrical and harmonic distorted conditions.The simulation and application tests carried out during the research show, that unbalanced andharmonic distorted voltages and currents can be controlled selectively and in a decoupledmanner. By considering the effective impedance of the grid connection point individually, forevery harmonic frequency under control, allows for a stable operation and good transientresponse - also at difficult impedance characteristics, such as found at a 3-leg, 4-wire splitcapacitor inverter topology.Finally, several inverters were connected in parallel to increase the output power forexperiments under real grid conditions. The successful operation of a whole system consistingof several inverters demonstrates the flexibility and scalability of the approach. The control hasa positive impact on the capacity and stability of the examined grid area by reducing powerfluctuations and unsymmetrical and harmonic voltages and currents. The experiments confirmthe effectiveness of the decoupled control also under real grid conditions