Investigating the Diffusion of Workload-Induced Stress—A Simulation Approach

Work-induced stress is widely acknowledged as harming physical and psychosocial health and has been linked with adverse outcomes such as a decrease in productivity. Recently, workplace stressors have increased due to the COVID-19 pandemic. This study aims to contribute to the literature base in a couple of areas. First, it extends the current knowledge base by utilising generative additive modelling (GAMs) to uncover the nature of the relationship between workload (a key workplace stressor) and productivity based on real-world event logs. Additionally, it uses recursive partitioning modelling to shed light on the factors that drive the relationship between these variables. Secondly, it utilises a simulation-based approach to investigate the diffusion of workload-induced stress in the workplace. Simulation is a valuable tool for exploring the effect of changes in a risk-free manner as it provides the ability to run multiple scenarios in a safe and virtual environment with a view to making recommendations to stakeholders. However, there are several recognised issues with traditional simulation approaches, such as inadequate resource modelling and the limited use of simulations for operational decision making. In this study, we propose an approach which extracts the required parameters from an event log and subsequently utilises them to initialise a workload-induced stress diffusion simulation model accurately. We also explore the effects of varying the parameters to control the spread of workload-induced stress within the network. With suitable amendments, this approach can be extended to model the spread of disease (e.g., COVID-19), diffusion of ideas, among other things, in the workplace

Comparative analysis of clustering-based remaining-time predictive process monitoring approaches

Predictive process monitoring aims to accurately predict a variable of interest (e.g. remaining time) or the future state of the process instance (e.g. outcome or next step). Various studies have been explored to develop models with greater predictive power. However, comparing the various studies is difficult as different datasets, parameters and evaluation measures have been used. This paper seeks to address this problem with a focus on studies that adopt a clustering-based approach to predict the remaining time to the end of the process instance. A systematic literature review is undertaken to identify existing studies that adopt a clustering-based remaining-time predictive process monitoring approach and performs a comparative analysis to compare and benchmark the output of the identified studies using five real-life event logs

Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach

Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches

An Exploration of Ethical Decision Making with Intelligence Augmentation

In recent years, the use of Artificial Intelligence agents to augment and enhance the operational decision making of human agents has increased. This has delivered real benefits in terms of improved service quality, delivery of more personalised services, reduction in processing time, and more efficient allocation of resources, amongst others. However, it has also raised issues which have real-world ethical implications such as recommending different credit outcomes for individuals who have an identical financial profile but different characteristics (e.g., gender, race). The popular press has highlighted several high-profile cases of algorithmic discrimination and the issue has gained traction. While both the fields of ethical decision making and Explainable AI (XAI) have been extensively researched, as yet we are not aware of any studies which have examined the process of ethical decision making with Intelligence augmentation (IA). We aim to address that gap with this study. We amalgamate the literature in both fields of research and propose, but not attempt to validate empirically, propositions and belief statements based on the synthesis of the existing literature, observation, logic, and empirical analogy. We aim to test these propositions in future studies

Dermatological emergencies: current trends in management

Emergencies in dermatology are well recognized and are associated with significant morbidity and mortality. Early recognition of these conditions with institution of prompt medical care can help in reducing the morbidity and mortality associated with these conditions. This article reviews relevant dermatologic emergencies with respect to this environment with emphasis on current trends in management. Prompt and aggressive management of dermatologic emergencies are important to reduce mortality related to these skin disorders so as to prevent skin failure. Dermatologic emergencies are clinical conditions which lead to increased morbidity and mortality. Recognizing them and the urgency required in their management would help in reducing the attendant skin failure thatmay arise from these conditions.Keywords: Dermatologic emergency, skinfailure, management

Spatial Modelling and Analysis of an Electrical Distribution System

The distribution of electrical energy to end-users in Nigerian communities is faced with diverse spatial problems leading to low voltage, overload on equipment, difficulties in fault tracing and delay in fault clearing. The traditional management system is not only manual but also has flaws such as difficulties in searching and updating previous records as well as no real-time information on their distribution assets. In this study, a geospatial technique was employed for effective management of the electricity distribution system in Malete, Kwara State, Nigeria. Spatial information of distribution asset was collected from the field and used in the structural modelling of the distribution network in the ArcMap software. Analysis of the model and the available information showed that 80 % of the transformers were not properly located at the centroid of the load. The span of the network fairly conformed to standard as only 45.73 % were within 45-50 m. The result also showed that 40 % of the transformers were overloaded with an unbalanced load, hence the need for restructuring of the network

The oncological and reproductive outcomes of fertility-preserving treatments for stage 1 grade 1 endometrial carcinoma: a systematic review and meta-analysis.

INTRODUCTION: The number of patients desiring fertility-preserving treatment for endometrial cancer rather than standard surgical management continues to increase. OBJECTIVE: We aimed to evaluate the efficacies of fertility-preserving treatments on the live birth rate, remission and relapse rates for women with stage 1a grade 1 endometrial carcinoma to support patient counselling. METHODS: We performed a meta-analysis for our primary outcomes of overall remission and relapse rate, and for secondary analysis, we divided papers into treatment type: systemic progestins, intrauterine progestins or hysteroscopic resection and adjuvant hormonal treatment. RESULTS: Thirty-five observational studies met inclusion criteria, with a total of 624 patients. Overall, conservative treatment of endometrial cancer showed a remission rate of 77% (95% CI: 70-84%), a relapse rate of 20% (95% CI: 13-27%) and a live birth rate of 20% (95% CI: 15-25%) with more favourable outcomes for the hysteroscopic resection group. CONCLUSIONS: Hysteroscopic resection and adjuvant hormonal treatment had the most favourable fertility and oncological outcomes. Further high-quality prospective multi-centre trials are warranted to determine the optimal treatment regimen and dosage and risk stratification for these patients

Process modeling and optimization of magnetic field pretreatment of sweet pepper and fluted pumpkin leaf

Modeling and optimization of magnetic field (MF) pretreatment of sweet pepper (SP) and fluted pumpkin leaf (FPL) were done with Response Surface Methodology. Three pretreatment factors combined were: types of MF (static, pulse and alternating), MF strength (5 - 30 mT) and pretreatment time (5 - 25 min). All the MF pretreated, control (blanched) and fresh samples were dried at 50 ˚C and analyzed for fibre, vitamin C, potassium, microbial load and colour; data obtained were used for modeling and optimization of the process. Results showed that the selected 30 developed model equations reliably described the characteristics of the process with adequate precision values of greater than four (4) and significant probability values (P ≤ 0.05) in all cases. The best optimized process conditions for the MF pretreatment process are Static MF at 14.31 mT magnetic field strength and 16.40 min pretreatment time for SP and Alternating MF at 10.42 mT magnetic field strength and 9.96 min pretreatment time for FPL. Magnetic field (non-thermal) pretreatment was able to achieve all the optimization goals better than blanching (thermal) pretreatment

Acute and chronic effects of Δ⁹-tetrahydrocannabinol (THC) on cerebral blood flow:A systematic review

Acute and chronic exposure to cannabis and its main psychoactive component, Δ 9-tetrahydrocannabinol (THC), is associated with changes in brain function and cerebral blood flow (CBF). We therefore sought to systematically review the literature on the effects of THC on CBF following PRISMA guidelines. Studies assessing the acute and chronic effects of THC on CBF, perfusion and volume were searched in the PubMed database between January 1972 and June 2019. We included thirty-four studies, which altogether investigated 1259 humans and 28 animals. Acute and chronic THC exposure have contrasting and regionally specific effects on CBF. While acute THC causes an overall increase in CBF in the anterior cingulate cortex, frontal cortex and insula, in a dose-dependent manner, chronic cannabis use results in an overall reduction in CBF, especially in the prefrontal cortex, which may be reversed upon prolonged abstinence from the drug. Future studies should focus on standardised methodology and longitudinal assessment to strengthen our understanding of the region-specific effects of THC on CBF and its clinical and functional significance. </p