Real-Time, Adaptive AI Driven Business Simulation::Design Science Research on a Dynamic Learning Platform

This working paper presents a design science research (DSR) investigation into the development and evaluation of an innovative real-time, adaptive AI-driven business simulation platform. Traditional business simulations typically operate with static scenarios and predefined parameters that fail to capture the dynamic complexity of contemporary business environments. Using a rigorous DSR methodology spanning four design cycles over twenty-four months, we developed and refined a prototype system that integrates machine learning algorithms, natural language processing, and knowledge graph technologies to create dynamically evolving simulation scenarios. The platform was evaluated across diverse contexts including MBA education programmes, corporate strategy training, and entrepreneurial incubators, involving 287 participants across multiple evaluation phases. Our findings demonstrate the system's efficacy in enhancing strategic decision-making capabilities, improving knowledge transfer, and fostering adaptive reasoning skills among users. The paper lays the groundwork for next-generation business education and strategy testing environments that more authentically reflect the complex, evolving nature of real-world business ecosystems

Causal AI for Business Decision Making:A Multi-Domain Investigation, Practical Applications and Implementation Challenges

This working paper presents an investigation into the emerging field of Causal Artificial Intelligence (Causal AI) and its transformative potential for business decision-making processes. While traditional machine learning methodologies excel at identifying correlational patterns within complex datasets, they fundamentally lack the capacity to address critical "why" questions essential for strategic business decisions. Our research employs a multi-domain case study methodology across financial services, retail, healthcare, and manufacturing sectors to examine how contemporary organisations are implementing causal inference frameworks to enhance decision-making robustness. The findings reveal that successful causal AI implementation necessitates not merely technical sophistication but also substantial organisational readiness factors, including causal literacy among executive decision-makers, integrated decision processes, and appropriate governance frameworks. Furthermore, we identify significant implementation challenges regarding data quality requirements, model validation approaches, and ethical considerations specific to causal reasoning systems. The paper concludes with a proposed developmental trajectory model for organisational adoption of causal AI and practical recommendations for businesses seeking to transcend correlation- based analytics paradigms

Impact of a Recipe Kit Scheme (BRITE Box) on Cooking and Food‐Related Behaviours of Children and Families: Exploring Parental/Carer Views

Background: Dietary intakes in UK children fail to meet national recommendations, especially in low‐income groups. Involving children in food preparation and cooking may enhance acceptability of a wider range of foods, enhance their skills and increase their enjoyment of food. An innovative recipe meal kit scheme, Building Resilience in Today's Environment (BRITE) Box, was developed during the pandemic primarily to address food insecurity (FI). Administered via schools, it offers pre‐weighed ingredients sufficient for a meal for a family of five, plus a child‐focused recipe, weekly during school termtimes. Methods: Qualitative and quantitative exploration of BRITE Box using questionnaires and semi‐structured interviews among parents/carers of children receiving the boxes was conducted at two timepoints a year apart. Results: A total of 154 parents/carers completed questionnaires and 29 were interviewed. Responses indicated multiple benefits of the scheme, including increased confidence in cooking among both children and parents/carers. Both questionnaire responses and interviews suggested improvements in a range of food‐related behaviours, including cooking and eating together and talking more about food. Parents/carers suggested that their children were more willing to eat vegetables and healthy foods and to try new foods and flavours. They also reported greater use of leftovers thereby potentially reducing food waste. Improved behaviours, willingness to try new foods and flavours, reduced food waste and lower stress of trying to think of new and acceptable family meals are likely to have contributed to the positive impact on their mental health reported by BRITE Box parents/carers. Conclusions: Meal kits for children may improve dietary diversity, enhance enjoyment and skills and impact positively on a range of family food‐related behaviours. We argue that BRITE Box has the potential for widespread positive impacts on cooking and food‐related behaviours in children and families, meriting wider study and dissemination as a positive approach to healthy eating in children

Knowledge-Grounded Attention-Based Neural Machine Translation Model

Neural machine translation (NMT) model processes sentences in isolation and ignores additional contextual or side information beyond sentences. The input text alone often provides limited knowledge to generate contextually correct and meaningful translation. Relying solely on the input text could yield translations that lack accuracy. Side information related to either source or target side is helpful in the context of NMT. In this study, we empirically show that training an NMT model with target-side additional information used as knowledge can significantly improve the translation quality. The acquired knowledge is leveraged in the encoder-/decoder-based model utilizing multiencoder framework. The additional encoder converts knowledge into dense semantic representation called attention. These attentions from the input sentence and additional knowledge are then combined into a unified attention. The decoder generates the translation by conditioning on both the input text and acquired knowledge. Evaluation of translation from Urdu to English with a low-resource setting yields promising results in terms of both perplexity reduction and improved BLEU scores. The proposed models in the respective group outperform in LSTM and GRU with attention mechanism by +3.1 and +2.9 BLEU score, respectively. Extensive analysis confirms our claim that the translations influenced by additional information may occasionally contain rare low-frequency words and faithful translation. Experimental results on a different language pair DE-EN demonstrate that our suggested method is more efficient and general

Automated Detection and Severity Prediction of Wheat Rust Using Cost‐Effective Xception Architecture

Wheat crop production is under constant threat from leaf and stripe rust, an airborne fungal disease caused by the pathogen Puccinia triticina. Early detection and efficient crop phenotyping are crucial for managing and controlling the spread of this disease in susceptible wheat varieties. Current detection methods are predominantly manual and labour‐intensive. Traditional strategies such as cultivating resistant varieties, applying fungicides and practicing good agricultural techniques often fall short in effectively identifying and responding to wheat rust outbreaks. To address these challenges, we propose an innovative computer vision‐based disease severity prediction pipeline. Our approach utilizes a deep learning‐based classifier to differentiate between healthy and rust‐infected wheat leaves. Upon identifying an infected leaf, we apply Grabcut‐based segmentation to isolate the foreground mask. This mask is then processed in the CIELAB color space to distinguish leaf rust stripes and spores. The disease severity ratio is calculated to measure the extent of infection on each test leaf. This paper introduces a ground‐breaking disease severity prediction method, offering a low‐cost, accessible and automated solution for wheat rust disease screening in field conditions using digital colour images. Our approach represents a significant advancement in crop disease management, promising timely interventions and better control measures for wheat rust

When rudeness goes home: the impact of supervisor incivility on employees’ work–family conflict

This study uses the frustration-aggression theory to examine how supervisor incivility relates to employee interpersonal incivility toward customers and co-workers and work–family conflict. Work–family conflict refers to the incompatibility between the demands and responsibilities of an individual’s work role and their family role. Service employees from the banking industry (N = 750) participated in the study’s daily multisource questionnaires over a continuous two-week period. The study’s findings, based on multilevel structural equation modeling, show that supervisors’ hostility toward subordinates increases employee interpersonal deviance toward customers and co-workers, as well as work–family conflict. Additionally, the study found in a parallel mediation that the association between supervisor-initiated incivility and work–family conflict is mediated by employee interpersonal deviance toward co-workers and customers. These findings indicate the detrimental effects of incivility from supervisors on both the workplace and employees’ personal lives. The study suggests that workplace incivility causes a negative spiral of mistreatment where a target of incivility may respond by mistreating other, resulting in a toxic work environment. However, organizations can mitigate the negative effects of incivility and promote productivity and success for employees and organization by investing in employee well-being and creating a respectful work culture

Employing Federated Learning for the Implication of Digital Twin

Rapid development of wireless technologies, such as the Internet of Things (IoT) and widely deployed fifth generation (5G) networks, are proved to be the embarkment of envisioning, and planning for sixth generation (6G) mobile networks. It is believed that 6G will provide extremely high data rates, low latency, and improved edge intelligence for hundreds of billions of end devices, connected to the 6G network. Thus, a huge amount of data will be generated from these devices, which requires tremendous computation and communication resources to be provided by edge servers. The gap between users’ requirements and edge servers’ capability of service provisions in 6G systems is mitigated with digital twin with close monitoring, real-time interaction, and reliable communication between the digital space and the physical systems, which can in turn optimize the running of the physical systems. This article entails the basic knowledge of Digital Twin (DT) for 6G Wireless networks. Moreover, the properties of Federated Learning that can enhance the DT for 6G to provide high level performance in communication by presenting the case studies of smart manufacturing and smart cities

A study on the marine propulsion plant system by simulating the numerical modelling on Simulink/Matlab: a case study of passenger ship

The improvement of ship performance and propulsive efficiency has been addressed in this article. In this research, the comparative study has been investigated for a certain passenger vessel with the research results of Stapersma and Woud in their research “Matching Propulsion Engine with Propulsor” that has been published on Journal of Marine Engineering & Technology. After that, the marine propulsion plant system will be researched to enlarge the operational ranges between marine propeller-shaft system- marine diesel engines. A case study of passenger ship has been applied from this research namely Sea life Legend 02 in Quang Ninh province, Vietnam. The marine propulsion plant system of passenger ship will be designed in the Simulink/Matlab platform. Each functional block will be presented for the devices of the marine propulsion plant system, including the diesel engine, generator, shaft system, and marine propeller. The improvement of marine propulsion plant will be conducted on this proposed numerical model. The collected results have been shown the priority features of the marine propulsion plant system and they are fundamental to enlarge the propulsion performance of ship. The research results would be analyzed and validated with the actual marine propulsion plant system. This article is significant for ship-operators and ship-owners in the management of marine propulsion plant system for ships nowadays