Search results

The purpose of this paper is to examine the current state of the literature on the variables associated with self-harm and aggression in women who committed a criminal offence.

Studies were identified through online databases, namely, PsycINFO, PubMed, ERIC and EBSCOhost, as well as manual searches of reference lists of the selected studies. The target population included women who committed a criminal offence and have engaged in self-harm and aggressive behaviors during their incarceration, either in correctional institutions or in forensic psychiatric settings.

Of the 1,178 studies identified, nine met inclusion criteria. The studies were conducted in six different countries and included data from 6360 female participants. Few studies examine self-harm and aggression in women who committed a criminal offence which speaks to the still sparse literature on this topic. This review of the association between self-harm and aggression in women offenders highlights the finding that a small group of women is often involved in both self-harm and aggression. The authors have identified possible psychological factors associated with women engaging in both self-harm and aggression. The findings also reveal a possible connection between types of aggressive behaviors and specific time periods during sentences or stays in forensic psychiatry.

The findings of this scoping review have clinical implications which may be considered by both researchers and the case management teams of women involved in both self-harm and aggression.

Despite the limited number of studies examining self-harm and aggression in women, this scoping review highlights gaps in the literature as well as notable psychological correlates of women who engage in self-harm and aggression.

The Covid-19 pandemic has created an environment of high uncertainty and caused major disruptions in supply chains. The new normal that has emerged during the pandemic is leading to a need to identify new solutions to improve supply chain crisis management in the future. Practitioners require adapted recommendations for solutions to implement. These recommendations are laid out in this paper.

A combination of a systematic literature review (SLR), qualitative semi-structured interviews and a questionnaire survey of supply chain practitioners is applied. The interviews provide insights into supply chain practitioners' views of their approaches and, together with the solutions proposed in the literature, provide future recommendations for action for supply chain managers.

During the pandemic, companies experienced disruptions in supply, production and demand, as well as interruptions in transportation and distribution. The majority of the solutions proposed in the literature, coincide with the opinions of practitioners. These include collaborative risk management, real-time monitoring and information sharing, supply network management, scenario planning and “what-if” simulations.

Although the number of interviews conducted and questionnaires completed is limited, they still serve to supplement the SLR with important practical insights and recommendations.

This paper presents a review of recent academic literature focusing on the impact of Covid-19 on supply chains and the existing solutions to mitigate that impact and manage future crises. It has been expanded to include industry perspectives and experiences. The findings of this study present recommended practices and strategies for better managing supply chains during a crisis.

This bibliography is offered as a practical guide to published papers, conference proceedings papers and theses/dissertations on the finite element (FE) and boundary element (BE) applications in different fields of biomechanics between 1976 and 1991. The aim of this paper is to help the users of FE and BE techniques to get better value from a large collection of papers on the subjects. Categories in biomechanics included in this survey are: orthopaedic mechanics, dental mechanics, cardiovascular mechanics, soft tissue mechanics, biological flow, impact injury, and other fields of applications. More than 900 references are listed.

This study examined the factors influencing willingness-to-repurchase for each class of airline service, and integrate the constructs of service quality, satisfaction and willingness-to-repurchase which were rooted on Engel-Kollat-Blackwell (EKB) model. The study focuses on the domestic and international arrival of passengers at Murtala Muhammed International Airport in Lagos and Nnamdi Azikwe International Airport in Abuja. Information was gathered from domestic and foreign passengers who had post-purchase experience and had used the airline's services more than once. The survey data were obtained concurrently from arrival passengers at two major international airports using an electronic questionnaire through random and purposive sampling techniques. The data was analysed using the ordinal logit model and structural equation model. From the 606 respondents, 524 responses were received but 489 responses were valid for data analysis and reporting and were obtained mostly from economy and business class passengers. The study found that the quality of seat pitch, allowance of 30 kg luggage permission, availability of online check-in 24 hours before the departing flight, quality of space for legroom between seats, and the quality of seats that can be converted into a fully flatbed are the major service factors influencing willingness-to-repurchase economy and business class tickets. Also, it was found that passengers' willingness to repurchase is influenced majorly by service quality, but not necessarily influenced by satisfaction. These results reflect the passengers' consciousness of COVID-19 because the study was conducted during the heat of COVID-19 pandemic. Recommendations were suggested for airline management based on each class.

Despite the efforts of governments and firms, consumer resistance toward digital innovations in the retail finance space continues to manifest rather visibly. Yet, the causes of consumer resistance toward innovations such as online procurement of financial products continue to remain under-explored. The present study attempts to address this gap by examining barriers that may constitute Indian consumers' resistance to buying financial products marketed digitally, using insurance as an exemplar. Precisely, the study measures five classic innovation resistance theory (IRT) barriers constituting consumers' resistance toward procuring digitally marketed insurance and examines the influence of consumers' demographic characteristics, measured through age and gender.

The conceptual model, resting on the theoretical proposition of IRT, was tested using data collected from 420 smartphone users. Given that, the data did not satisfy the multivariate assumptions of normality, homoscedasticity and linearity, artificial neural network approach was used for analysis. The analysis served as the basis for determining the relative importance of the five barriers in influencing consumer resistance.

The results indicated that the image barrier was the most influential barrier impacting consumer resistance, followed by usage, tradition, risk and value barriers. Moreover, as revealed by the values of correlations, the direction of influence was positive. Notably, the relationship of all barriers except tradition with consumer resistance was found to be nonlinear.

The study makes a novel contribution in two ways – one by extending IRT to a new area, i.e., resistance to buying financial products online, thereby further enhancing its applicability, and the other by exploring consumer resistance to e-procurement of life and nonlife insurance, which to the best of the authors' knowledge, has not been examined so far despite the established exigency.

Additive manufacturing (AM) enables the production of lightweight parts with complex shapes and small dimensions. Recent improvements in AM techniques have allowed a significant growth of AM for industrial applications. In particular, AM is suitable for the production of materials shaped in lattice, which are very attractive for their lightweight design and their multi-functional properties. AM parts are often characterised by geometrical imperfections, residual porosity, high surface roughness which typically lead to stress/strain localisations and decreasing the resistance of the structure. This paper aims to focus on the study of the effects of geometrical irregularities and stress concentrations derived from them.

In this paper, several technique were combined: 3D tomography, experimental tests, digital image correlation and finite elements (FE) models based on both the as-designed and the as-manufactured geometries of lattice materials. The Digital Image Correlation technique allowed to measure local deformations in the specimen during the experimental test. The micro-computed tomography allowed to reconstruct the as-manufactured geometries of the specimens, from which the geometrical quality of the micro-structure is evaluated to run FE analyses.

Experimental and numerical results were compared by means of a stress concentration factor. This factor was calculated in three different specimens obtained from three-different printing processes to compare and understand their mechanical properties. Considering the as-designed geometry, it is not possible to model geometrical imperfections, and a FE model based on an as-manufactured geometry is needed. The results show that the mechanical properties of the printed samples are directly related to the statistical distribution of the stress concentration factor.

In this work, several techniques were combined to study the mechanical behaviour of lattice micro-structures. Lattice materials obtained by different selective laser melting printing parameters show different mechanical behaviours. A stress concentration factor can be assumed as a measure of the quality of these mechanical properties.

The purpose of this paper is to share the lessons learned in implementing specific design patterns within the “Dr Internet” massive open online course (MOOC).

MOOCs are boasting considerable participant numbers, but also suffer from declining participant activity and low completion rates. Learning analytics results from earlier xMOOCs indicate that this might be alleviated by certain instructional design patterns – critical aspects include shorter course duration, narrative structures with suspense peaks, and a course schedule that is diversified and stimulating. To evaluate their impact on retention, the authors have tried to implement these patterns in the design of the “Dr Internet” MOOC.

Statistical results from the first run of the case study MOOC do not indicate any strong influences of these design patterns on the retention rate.

With inconclusive statistical results from this case study, more research with higher participant numbers is needed to gain insight on the effectiveness of these design patterns in MOOCs. When interpreting retention outcomes, other influencing factors (course content, pacing, timing, etc.) need to be taken into account.

This publication reports about a case study MOOC and gives practical hints for further research.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.