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Background: Globally, healthcare systems are experiencing a workforce crisis which has been exacerbated by the COVID19 pandemic. Numerous reports have documented the deterioration of healthcare professional wellbeing with burnout being called the new pandemic. Rehabilitation Medicine Physicians are among the most likely specialties to experience burnout. There is a strong association between staff and patient experience and global and national policies and strategies have recognized the importance of ensuring staff wellbeing in healthcare organisations. Although there have been many publications on interventions focusing on wellbeing, these have been directed mainly at personal rather than organisational factors. Maslow's hierarchy of needs, an integrated hierarchy of human needs, has been utilised as a framework to assess wellbeing in doctors but not heretofore in Rehabilitation Medicine. This study aimed to explore Rehabilitation Medicine Physician wellbeing in a complex specialist rehabilitation setting., Methods: Qualitative deductive content analysis (an approach to reanalyzing existing data in a new context) was the approach used. Maslow's hierarchy of needs was used as the categorization matrix. Qualitative data from three different data sources were systematically analysed., Results: Using Maslow's five needs (psychological, safety, social, esteem, and self-actualisation needs), the analysis demonstrated that Rehabilitation Medicine Consultants' needs were not being met at any of the five levels. The data revealed what constitutes relative deprivation and organisational injustice., Conclusions: In order to enable the flourishing of the Rehabilitation Medicine Consultants, the organisation needs to focus on satisfying not just basic needs but creating the conditions for them to function at the highest level. The adapted Maslow framework provides a scaffolding for interventions to support such flourishing., Competing Interests: Declarations. Ethics approval and consent to participate: Ethical approval was obtained from Henley Business School Research Ethics Committee and informed consent to participate was obtained from all of the participants in the study. The study adhered to the Declaration of Helsinki. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs' evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care., (©Kuo Zhang, Xiangbin Meng, Xiangyu Yan, Jiaming Ji, Jingqian Liu, Hua Xu, Heng Zhang, Da Liu, Jingjia Wang, Xuliang Wang, Jun Gao, Yuan-geng-shuo Wang, Chunli Shao, Wenyao Wang, Jiarong Li, Ming-Qi Zheng, Yaodong Yang, Yi-Da Tang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.01.2025.)

Financial regulators and investors are increasingly concerned about the effects of climate change on investments and seek to capture the climate-related and environmental risks of investments. Whilst energy companies have attracted most of the attention due to the contribution of the Energy sector to environmental degradation, climate-related and environmental risks actually affect companies in every sector. In this paper, we propose novel measures termed as climate Value-at-Risk (VaR) and climate Expected Shortfall (ES) that capture the risk attributed to transition risk factors proxied by environmental scores. We compare the average ratio of climate VaR and ES to total risk in various equity sectors, which enables us to identify the sectors in which climate and environmental risk factors contribute most to the total risk. Our analysis considers different risk measurements and various significance levels. Our findings show heterogeneity in sensitivity to climate and environmental risk factors in various sectors. The Health Care sector is the least cost-effective in reducing climate-related and environmental risks, and the Energy sector benefits most from improving the firms' environmental scores., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: The focus in reverse shoulder arthroplasty (RSA) has been on the lateralization and distalization of prosthesis positioning, influenced by implant design and surgical technique. There's no consensus on the optimal amount of lateralization and distalization or the best radiographic parameters for evaluating placement. This study examines the correlation and the predictive value between previously described modified distalization shoulder angle (DSA) and lateralization shoulder angle (LSA) with postoperative outcomes, which aim to differentiate the contributions of the humeral and glenoid components in the global distalization and lateralization of the RSA. The LSA was divided into the glenoid lateralization angle and the humeral lateralization angle; the modified distalization shoulder angle was divided into the glenoid distalization angle and the humeral distalization angle. Our hypothesis was that these new angles play a marginal role in predicting clinical outcomes., Materials and Methods: Retrospective analysis of 83 RSA patients from 2017 to 2021 at San Pietro Fatebenefratelli Hospital, Rome. Angles were measured using true anteroposterior radiographs. Clinical outcomes were assessed using the Constant-Murley Score, a visual analog scale (VAS) for pain, and range of motion., Results: A weak correlation was found between the modified angles and clinical outcomes. Modified DSA (mDSA) correlated positively with internal rotation and negatively with VAS score. Glenoid distalization angle correlated positively with abduction, forward flexion, and Constant-Murley Score. Humeral distalization angle correlated positively with internal rotation and negatively with VAS. Glenoid lateralization angle and humeral lateralization angle showed negative correlation with internal rotation and positive correlation with VAS., Conclusion: Modified DSA and LSA show marginal correlation with postoperative outcomes and have limited predictive value. Further research with larger, diverse populations is needed to refine these metrics and their clinical utility., (Copyright © 2024 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.)

Psychological contract theory has largely neglected the role of previous experience. In this study, we examine how previous work experience influences outcomes of communication with organizational insiders during organizational socialization among healthcare staff. We develop a model based on signalling theory, within which information acquisition during socialization is associated with psychological contract fulfilment, which is in turn is related to better health, happiness, and social relationships. Moderated mediation analysis based on data collected at entry and three months later confirms indirect effects between three types of information acquisition and three employee outcomes via the mediating role of psychological contract fulfilment. Importantly, these indirect effects are present only for inexperienced newcomers. Our findings build on signalling theory and add to knowledge about how the psychological contract forms during early socialization. They also suggest that organizations should pay particular attention to inducting inexperienced newcomers., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Purpose: The aim of this study was to explore and understand the leadership experiences of medical consultants prior to a major hospital move. Health and care is becoming increasingly complex and there is no greater challenge than the move to a new hospital. Effective leadership has been identified as being essential for successful transition. However, there is very little evidence of how medical consultants experience effective leadership., Design/methodology/approach: A qualitative methodology was utilized with one-to-one semi-structured interviews conducted with ten medical consultants. These were transcribed verbatim and analyzed using inductive thematic analysis. The research complied with the consolidated criteria for reporting qualitative research (COREQ)., Findings: Four themes were found to influence medical consultants' experience of leadership: collaboration, patient centredness, governance and knowledge mobilization. Various factors were identified that negatively influenced their leadership effectiveness. The findings suggest that there are a number of factors that influence complexity leadership effectiveness. Addressing these areas may enhance leadership effectiveness and the experience of leadership in medical consultants., Research Limitations/implications: This study provides a rich exploration of medical consultants' experience of collective leadership prior to a transition to a new hospital and provides new understandings of the way collective leadership is experienced in the lead up to a major transition and makes recommendations for future leadership research and practice., Practical Implications: The findings suggest that there are a number of factors that influence complexity leadership effectiveness. Addressing these areas may enhance leadership effectiveness and the experience of leadership in medical consultants., Social Implications: Clinical leadership is associated with better outcomes for patients therefore any interventions that enhance leadership capability will improve outcomes for patients and therefore benefit society., Originality/value: This is the first research to explore medical consultants' experience of collective leadership prior to a transition to a new hospital., (© Áine Carroll, Jane McKenzie and Claire Collins.)

Competing Interests: Competing interests: MK, OK, and BL declare that they have no competing interests related to this manuscript. Their main goal is to provide impartial and comprehensive information on the topic, without any financial or non-financial conflicts that might impact the interpretation or presentation of the research findings. The authors have no affiliations or financial associations with any organisations or entities that have a direct or indirect interest in the subject matter discussed. Their sole commitment is to deliver accurate and pertinent information for the benefit of the readers and the wider healthcare community.

Background: The optimal treatment for complex proximal humerus fractures (PHFs) lacks consensus, with reverse total shoulder arthroplasty (RTSA) often being a final resort rather than a primary approach. This study aimed to compare outcomes and satisfaction rates of primary RTSA for PHFs versus salvage RTSA for previously unsuccessful treatments. We hypothesized that primary RTSA would yield superior clinical outcomes, functional scores, and patient satisfaction. Methods: A retrospective analysis of RSA procedures between 2011 and 2021 was conducted, focusing on primary RTSA for PHFs or salvage RTSA for failed osteosynthesis. Patients meeting inclusion criteria underwent clinical and radiological follow-up for at least two years. Demographic characteristics, outcomes scores, and range of motion (ROM) were assessed. Results: Of 63 patients, 42 underwent primary RTSA and 21 underwent salvage RTSA. The median follow-up was 50 months. Statistically significant differences favored primary RTSA in forward flexion, abduction, internal rotation, and Constant shoulder score. Patient satisfaction levels did not significantly differ between groups. Complications occurred in 7.15% of primary RTSA cases and 14.28% of salvage RTSA cases. Conclusions: Primary RTSA may yield slightly better outcomes and lower complication rates compared to salvage RTSA. Further prospective studies are necessary to validate these findings.

To explore the influencing factors of consumers' impulsive purchase behavior in live streaming, based on the Stimulus-Organism-Response framework, we construct the model of how streamers influence consumers' impulsive purchase behavior by consulting literature. Collected data by means of a questionnaire, and made an empirical study by using the structural equation model to explore the mechanism of streamer affecting consumers' impulsive purchase behavior. The results show that streamer characteristics (personal charisma, professionalism) and streamer performance (interactivity, entertainment) affect consumers' impulsive purchase behavior by affecting consumers' trust and flow experience. The empirical results have important theoretical and practical significance., (© 2024. The Author(s).)

Green innovation (GI) is increasingly recognised as an effective strategy for tackling climate change, mitigating environmental issues, and promoting sustainable development. Using panel data of the Chinese listed firms from 2007 to 2019, this study adopts the difference-in-differences approach to assess the impact of the green finance policy (GFP) initiated by the Chinese government in 2012 on the green innovation performance of firms. The findings reveal that the GFP significantly boosts the green innovation performance of heavily polluting enterprises (HPEs). Notably, this effect is more pronounced in state-owned enterprises and firms with high dependence on external finance. Compared with penalty-based regulations, incentive-based and voluntary environmental regulations demonstrate more significant moderating effects on the relationship between the GFP and green innovation performance for HPEs. We also identify improved efficiency in the usage of green investments as a potential mechanism through which the GFP enhances the green innovation performance of HPEs. Further comparative analysis shows that green enterprises can achieve simultaneous improvement in both the quality and quantity of green innovation, whereas HPEs predominantly exhibit enhancements in innovation quantity. To maximise the GFP's positive effects, it is recommended to facilitate more targeted bank lending towards HPEs to support their structural transformation. Additionally, the coordinated deployment of diverse environmental policy instruments is advised to exploit their synergistic effects., Competing Interests: Declaration of competing interest Dr. Dan Luo would like to acknowledge the financial report provided by the National Natural Science Foundation of China (71972010). The remaining authors declare that they have no conflicts of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Air pollution has become a global public health hazard leading to debilitating effects on physical, mental, and emotional health. Management research has just begun to explore the effects of air pollution on employees' work life. Drawing from the transactional theory of stress (Lazarus & Folkman, 1984) and crossover theory (Westman, 2001), we argue that appraisal of air pollution is an important factor that influences leaders and their behavior with subordinates. Specifically, we propose that when leaders appraise severe air pollution, they are more likely to behave abusively toward their subordinates and engage in laissez-faire leadership. We also propose that this relationship is mediated by leaders' experience of somatic complaints and negative affect. We test our model using an experience sampling study in India of leaders and followers who were located in different cities from each other. Overall, our results highlight how air pollution appraisals can harm not only the leader experiencing the pollution but also subordinates of those leaders. In other words, our counterintuitive finding is that subordinates may be harmed by air pollution to which they are not even directly exposed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Previous research on the psychological effect of job change has revealed a honeymoon-hangover pattern during the turnover process. However, there is a dearth of evidence on how individuals react and adapt to multiple job changes over their working lives. This study distinguishes adaptation to a single job change in the short term from adaptation to the process of job change in the long term. Drawing on two large-scale, long-running panel data sets from Britain and Australia, it examined how job satisfaction trajectory evolved as individuals made a series of consecutive job changes since they first entered the labor market. Our fixed effect analyses show that in both countries, individuals experienced a stronger honeymoon effect with each successive job change, before gradually reverting to their baseline job satisfaction. In short, the amplitude of the honeymoon-hangover effect increased across multiple job changes. By distinguishing "adaptation to change" from "change in adaptation," this study generates original insights into the role of job mobility in facilitating career development and extends set point theory from understanding the impact of single life events to recurring life events. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul. Our main motivation behind this study is to apply sentiment analysis to financial-related tweets in Turkish. We import 17189 tweets posted as "#Borsaistanbul, #Bist, #Bist30, #Bist100″ on Twitter between November 7, 2022, and November 15, 2022, via a MAXQDA 2020, a qualitative data analysis program. For the lexicon-based side, we use a multilingual sentiment offered by the Orange program to label the polarities of the 17189 samples as positive, negative, and neutral labels. Neutral labels are discarded for the machine learning experiments. For the machine learning side, we select 9076 data as positive and negative to implement the classification problem with six different supervised machine learning classifiers conducted in Python 3.6 with the sklearn library. In experiments, 80 % of the selected data is used for the training phase and the rest is used for the testing and validation phase. Results of the experiments show that the Support Vector Machine and Multilayer Perceptron classifier perform better than other classifiers with 0.89 and 0.88 accuracy and AUC values of 0.8729 and 0.8647 respectively. Other classifiers obtain approximately a 78,5 % accuracy rate. It is possible to increase sentiment analysis accuracy with parameter optimization on a larger, cleaner, and more balanced dataset by changing the pre-processing steps. This work can be expanded in the future to develop better sentiment analysis using deep learning approaches., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

Background: Numerous mobile health apps are marketed globally, and these have specific features including physical activity tracking, motivational feedback, and recipe provision. It is important to understand which features individuals prefer and whether these preferences differ between consumer groups., Objective: In this study, we aimed to identify consumers' most preferred features and rewards for a mobile app that targets healthy eating and physical activity and to reduce the number of individual mobile health app features to a smaller number of key categories as perceived by consumers. In addition, we investigated the impact of differences in consumers' BMI and self-efficacy on their intention to use and willingness to pay for such an app. Finally, we identified the characteristics of different target groups of consumers and their responses toward app features via cluster analysis., Methods: A total of 212 participants from France, Italy, the United Kingdom, and Germany were recruited via the web to answer questions about app features, motivation, self-efficacy, demographics, and geographic factors. It is important to note that our study included an evenly distributed sample of people in the age range of 23 to 50 years (23-35 and 35-50 years). The app features in question were generated from a 14-day cocreation session by a group of consumers from the United Kingdom and the Republic of Ireland., Results: "Home work out suggestions," "exercise tips," and "progress charts" were the most preferred app features, whereas "gift vouchers" and "shopping discounts" were the most preferred rewards. "Connections with other communication apps" was the least preferred feature, and "charitable giving" was the least preferred reward. Importantly, consumers' positive attitude toward the "social support and connectedness and mindfulness" app feature predicted willingness to pay for such an app (β=.229; P=.004). Differences in consumers' health status, motivational factors, and basic demographics moderated these results and consumers' intention to use and willingness to pay for such an app. Notably, younger and more motivated consumers with more experience and knowledge about health apps indicated more positive attitudes and intentions to use and willingness to pay for this type of app., Conclusions: This study indicated that consumers tend to prefer app features that are activity based and demonstrate progress. It also suggested a potential role for monetary rewards in promoting healthy lifestyle behaviors. Moreover, the results highlighted the role of consumers' health status, motivational factors, and socioeconomic status in predicting their app use. These results provide up-to-date, practical, and pragmatic information for the future design and operation of mobile health apps., (©Bahram Mahmoodi Kahriz, Sarah Snuggs, Anumeha Sah, Sophie Clot, Daniel Lamport, Joseph Forrest, Agnes Helme-Guizon, Marie-Claire Wilhelm, Cindy Caldara, Camille Valentine Anin, Julia Vogt. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 11.12.2023.)

The term 'digital coaching' is widely used but ill-defined. The present study therefore investigates how digital coaching is defined and how it differentiates from face-to-face coaching and other digital-technology-enabled (DT-enabled) formats, such as digital training, digital mentoring, or digital consulting. A qualitative inductive approach was chosen for more in-depth and open-minded content. Based on previous studies on the importance of asking coaches working in the field, 260 coaches working in the field of digital coaching were surveyed. The given answers depict the importance of differing between forms of DT-enabled coaching. Thus, digital coaching is a DT-enabled, synchronous conversation between a human coach and a human coachee, which is different to artificial intelligence (AI) coaching and coaching that is supported by asynchronous digital and learning communication technologies. Due to this definition and differentiation, future studies can explore the digital coaching process and its effectiveness - particularly in comparison to other formats. Furthermore, this clear definition enables practitioners to maintain professional standards and manage client's expectations of digital coaching while helping clients understand what to expect from digital coaching., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Diller and Passmore.)

Competing Interests: Competing interests: None declared.

Vickrey auctions (VA) and Becker-DeGroot-Marschak auctions (BDM) are strategically equivalent demand-revealing mechanisms, differentiated only by a human opponent in the VA, and a random-number-generator opponent in the BDM. Game parameters are such that players are incentivized to reveal their private subjective values (SV) and behavior should be identical in both tasks. However, this has been repeatedly shown not to be the case. In this study, the neural correlates of outcome feedback processing during VA and BDM were directly compared using electroencephalography. Twenty-eight healthy participants bid for household products which were then divided into high- and low-SV categories. The VA included a human opponent deception to induce a social environment, while in reality a random-number-generator was used in both tasks. A P3 component peaking at 336 ms over midline parietal sites showed more positive amplitudes for high bid values, and for win outcomes in the VA but not the BDM. Both auctions also elicited a Reward Positivity potential, maximal at 275 ms along the central midline electrodes, that was not modulated by auction task or SV. Further, an exploratory N170 potential in the right occipitotemporal electrodes and a vertex positive potential component were stronger in the VA relative to the BDM. Results point to an enhanced cortical response to bid outcomes during VA task in a potential component associated with emotional control, and to the occurrence of face-sensitive potentials in VA but not in BDM auction. These findings suggest modulation of bid outcome processing by the social-competitive aspect of auction tasks. Directly comparing two prominent auction paradigms affords the opportunity to isolate the impact of social environment on competitive, risky decision-making. Findings suggest that feedback processing as early as 176 ms is facilitated by the presence of a human competitor, and later processing is modulated by social context and subjective value., (© 2023 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)

Environmental pollution has become a global issue attracting ever-increasing attention. Green technology innovation (GTI) is considered an effective strategy in countering this problem and helping achieve sustainability goals. However, the market failure suggests that intervention from the government is necessary to promote the effectiveness of technological innovation and hence, its positive social impacts on emissions reduction. This study investigates how the environmental regulation (ER) influences the relationship between green innovation and CO 2 emissions reduction in China. Employing data from 30 provinces from the period 2003 to 2019, the Panel Fixed-effect model, the Spatial Durbin Model (SDM), the System Generalised Method of Moments (SYS-GMM) and the Difference-In-Difference (DID) models are applied to take issues relating to endogeneity and spatial impact into consideration. The results indicate that environmental regulations positively moderate the impact of green knowledge innovation (GKI) on CO 2 emissions reduction but have a much weaker moderation effect when green process innovation (GPI) is considered. Among different types of regulatory instruments, investment-based regulation (IER) is the most effective in promoting the relationship between green innovation and emissions reduction, followed by command-and-control-based regulation (CER). Expenditure-based regulation (EER) is less effective and can encourage short-termism and opportunistic behaviour among firms, who can accept the paying of fines as a cheaper cost over the short-term than investment in green innovation. Moreover, the spatial spillover effect of green technological innovation on carbon emissions in neighbouring regions is confirmed, in particular when IER and CER are implemented. Lastly, the heterogeneity issue is further examined by considering differences in the economic development and the industrial structure across different regions, and the conclusions reached remain robust. This study identifies that the market-based regulatory instrument, IER, works best in promoting green innovation and emissions reduction among Chinese firms. It also encourages GKI which may assist firms in achieving long-term sustained growth. The study recommends further development of the green finance system to maximise the positive impact of this policy instrument., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Forming and comparing subjective values (SVs) of choice options is a critical stage of decision-making. Previous studies have highlighted a complex network of brain regions involved in this process by utilising a diverse range of tasks and stimuli, varying in economic, hedonic and sensory qualities. However, the heterogeneity of tasks and sensory modalities may systematically confound the set of regions mediating the SVs of goods. To identify and delineate the core brain valuation system involved in processing SV, we utilised the Becker-DeGroot-Marschak (BDM) auction, an incentivised demand-revealing mechanism which quantifies SV through the economic metric of willingness-to-pay (WTP). A coordinate-based activation likelihood estimation meta-analysis analysed twenty-four fMRI studies employing a BDM task (731 participants; 190 foci). Using an additional contrast analysis, we also investigated whether this encoding of SV would be invariant to the concurrency of auction task and fMRI recordings. A fail-safe number analysis was conducted to explore potential publication bias. WTP positively correlated with fMRI-BOLD activations in the left ventromedial prefrontal cortex with a sub-cluster extending into anterior cingulate cortex, bilateral ventral striatum, right dorsolateral prefrontal cortex, right inferior frontal gyrus, and right anterior insula. Contrast analysis identified preferential engagement of the mentalizing-related structures in response to concurrent scanning. Together, our findings offer succinct empirical support for the core structures participating in the formation of SV, separate from the hedonic aspects of reward and evaluated in terms of WTP using BDM, and show the selective involvement of inhibition-related brain structures during active valuation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Newton-Fenner et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Machine Learning (ML), among other things, facilitates Text Classification, the task of assigning classes to textual items. Classification performance in ML has been significantly improved due to recent developments, including the rise of Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Transformer Models. Internal memory states with dynamic temporal behavior can be found in these kinds of cells. This temporal behavior in the LSTM cell is stored in two different states: "Current" and "Hidden". In this work, we define a modification layer within the LSTM cell which allows us to perform additional state adjustments for either state, or even simultaneously alter both. We perform 17 state alterations. Out of these 17 single-state alteration experiments, 12 involve the Current state whereas five involve the Hidden one. These alterations are evaluated using seven datasets related to sentiment analysis, document classification, hate speech detection, and human-to-robot interaction. Our results showed that the highest performing alteration for Current and Hidden state can achieve an average F 1 improvement of 0.5% and 0.3%, respectively. We also compare our modified cell performance to two Transformer models, where our modified LSTM cell is outperformed in classification metrics in 4/6 datasets, but improves upon the simple Transformer model and clearly has a better cost efficiency than both Transformer models.

This paper explores the link between personal experience with COVID-19 and US retail investors' financial decision-making during the first COVID-19 wave. Do retail investors that have personally experienced COVID-19 change their investments after the pandemic outbreak, and if so, why? We use a cross-sectional dataset from an online survey of US retail investors collected in July and August 2020 to assess if and how respondents change their investment decisions after the COVID-19 outbreak. On average retail investors increase their investments during the first wave of COVID-19 by 4.7%, while many of them decrease their investments suggesting a high heterogeneity of investor behaviours. We provide the first evidence that personal experience with the virus can have unexpected positive effects on retail investments. Investors who have personal experience with COVID-19, who are in a vulnerable health category, who tested positive, and who know someone in their close circle of friends or family who died because of COVID-19, increase their investments by 12%. We explain our findings through terror management theory, salience theory and optimism bias, suggesting that reminders of mortality, focussing on selective salient investment information, and over-optimism despite personal vulnerable health contribute to the increase in retail investments. Increased levels of savings, saving goals and risk capacity are also positively associated with increased investments. Our findings are relevant to investors, regulators, and financial advisors, and highlight the importance of providing retail investors with access to investment opportunities in periods of unprecedented shocks such as COVID-19., (© 2023 The Authors. Published by Elsevier Inc.)

Background: Continuous glucose monitoring (CGM) for diabetes combines noninvasive glucose biosensors, continuous monitoring, cloud computing, and analytics to connect and simulate a hospital setting in a person's home. CGM systems inspired analytics methods to measure glycemic variability (GV), but existing GV analytics methods disregard glucose trends and patterns; hence, they fail to capture entire temporal patterns and do not provide granular insights about glucose fluctuations., Objective: This study aimed to propose a machine learning-based framework for blood glucose fluctuation pattern recognition, which enables a more comprehensive representation of GV profiles that could present detailed fluctuation information, be easily understood by clinicians, and provide insights about patient groups based on time in blood fluctuation patterns., Methods: Overall, 1.5 million measurements from 126 patients in the United Kingdom with type 1 diabetes mellitus (T1DM) were collected, and prevalent blood fluctuation patterns were extracted using dynamic time warping. The patterns were further validated in 225 patients in the United States with T1DM. Hierarchical clustering was then applied on time in patterns to form 4 clusters of patients. Patient groups were compared using statistical analysis., Results: In total, 6 patterns depicting distinctive glucose levels and trends were identified and validated, based on which 4 GV profiles of patients with T1DM were found. They were significantly different in terms of glycemic statuses such as diabetes duration (P=.04), glycated hemoglobin level (P<.001), and time in range (P<.001) and thus had different management needs., Conclusions: The proposed method can analytically extract existing blood fluctuation patterns from CGM data. Thus, time in patterns can capture a rich view of patients' GV profile. Its conceptual resemblance with time in range, along with rich blood fluctuation details, makes it more scalable, accessible, and informative to clinicians., (©Nicholas Berin Chan, Weizi Li, Theingi Aung, Eghosa Bazuaye, Rosa M Montero. Originally published in JMIR AI (https://ai.jmir.org), 26.05.2023.)