Practical Issues Research Articles

Students' reflections on their experience with ChatGPT

AbstractBackgroundThe emergence of Generative Artificial Intelligence has brought a number of ethical and practical issues to higher education. Solid experimental evidence is yet inadequate to set the functional rules for the new technology.ObjectivesThe objective of this study is to analyse the experience of undergraduate students’ interaction with ChatGPT and contribute to identifying the problems arising from the widespread use of artificial intelligence.MethodsJunior university students (N = 25) were assigned the task of working on their seminar essays with the aid of ChatGPT. Most students were novices with this tool (the study was conducted in the spring of 2023). Their essays were analysed qualitatively, according to the principles of the general inductive approach.Results and ConclusionsThe initial attitudes towards artificial intelligence were almost equally distributed from enthusiastic to indifferent and cautious, with one student refusing to interact with the chatbot on ideological grounds. After the first experience, most of the students declared themselves adopters of the new technology. We have found some evidence for enhancing critical thinking competence when using ChatGPT, as well as examples of unquestioned reliance on its outputs. The tendency to personification of the chatbot was apparent in the students' essays.ImplicationsThe findings show how easily the students embrace artificial intelligence and suggest a failure of any attempts for its strict regulation. This, on the other hand, underlines the need for emphasis on personal and research‐oriented approaches in teaching and learning.

Design and Implementation of Improved Gate Driver Circuit for Sensorless Permanent Magnet Synchronous Motor Control

Reliable motor control is important for electric vehicle applications. The control process requires accurate measurements of the current and rotor position information to establish correct motor control design, particularly in sensorless permanent magnet synchronous motor control systems. Practical issues regarding the motor control circuit, such as the effects of parasitic element behavior on the switching components in the insulated gate bipolar transistor-driven inverter, were discussed in this study. It analyzed the effects of parasitic elements that can cause the ringing of switching losses and affect the spike of the signal in the motor current, which must be avoided in the implementation of motor control. The gate driver circuit topology was improved to reduce this effect in motor control devices. The proposed gate driver circuit design with the ringing suppression circuit configuration achieved good performance by keeping the signal spike at less than 10% in the motor current. Furthermore, a signal spike or noise was not observed in the estimation results of rotor position when using current information as the parameter control process. Both conditions were verified by experiments on the designed motor control devices. Under these conditions, signal precision can be achieved in motor control.

Attitudes of Greek Foreign Language Teachers Regarding the Role of Authentic Assessment in Foreign Language Teaching

The evaluation of student performance, which is closely tied to all school functions and is a fundamental responsibility of teachers in their role as educators, has been a challenging and contentious issue in educational policy and practice in recent years. It plays a crucial role in the educational process, as it is essential for fostering creativity and human development within the school environment. Between traditional assessment based mainly on written and oral measurable tests and authentic assessment that highlights the role of the pupil through the self-evaluation or other techniques such as the materials folder, teachers are called upon to undertake a difficult task. In this article the results of a qualitative research in relation to the role of authentic assessment in foreign language teaching that took place in the Attica Region in Greece from March 10 to April 10, 2022 were presented. A total of 20 foreign language teachers (English, French, German) participated in semi-structured interviews and expressed their views on the relevant questions. The research problem posed in this study was to investigate the views and attitudes of foreign language teachers (English, French, German) regarding the framework of authentic assessment in foreign language teaching. The survey asked teachers to share their opinions on the role of authentic assessment in foreign language teaching, their preferred assessment criteria, the use of alternative forms of assessment, and their satisfaction with their training in authentic assessment. In general, it emerged that permanent secondary school teachers with many years of service prefer traditional forms of assessment and choose techniques that result in measurable criteria. In contrast, younger contractual teachers are more receptive to alternative forms of evaluation. However, no significant difference was observed on the issue of training. This survey aimed to gather language teachers' perspectives on authentic assessment in order to enhance textbooks and training programs.

Canadian intensive care unit nurses' responses to moral distress during the COVID-19 pandemic, and their recommendations for mitigative interventions.

To describe intensive care unit nurses' experiences of moral distress during the COVID-19 pandemic, and their recommendations for mitigative interventions. Interpretive description. Data were collected with a purposeful sample of 40 Canadian intensive care unit nurses between May and September 2021. Nurses completed a demographic questionnaire, the Measure of Moral Distress-Healthcare Professionals survey and in-depth interviews. Quantitative data were analysed using descriptive statistics. Qualitative data were categorized and synthesized using reflexive thematic analysis and rapid qualitative analysis. Half of the nurses in this sample reported moderate levels of moral distress. In response to moral distress, nurses experienced immediate and long-term effects across multiple health domains. To cope, nurses discussed varied reactions, including action, avoidance and acquiescence. Nurses provided recommendations for interventions across multiple organizations to mitigate moral distress and negative health outcomes. Nurses reported that moral distress drove negative health outcomes and attrition in response to moral events in practice. To change these conditions of moral distress, nurses require organizational investments in interventions and cultures that prioritize the inclusion of nursing perspectives and voices. Nurses engage in a variety of responses to cope with moral distress. They possess valuable insights into the practice issues central to moral distress that have significant implications for all members of the healthcare teams, patients and systems. It is essential that nurses' voices be included in the development of future interventions central to the responses to moral distress. This study adheres to COREQ guidelines. What Problem did the Study Address? Given the known structural, systemic and environmental factors that contribute to intensive care unit nurses' experiences of moral distress, and ultimately burnout and attrition, it was important to learn about their experiences of moral distress and their recommendations for organizational mitigative interventions. Documentation of these experiences and recommendations took on a greater urgency during the context of a global health emergency, the COVID-19 pandemic, where such contextual influences on moral distress were less understood. What Were the Main Findings? Over half of the nurses reported a moderate level of moral distress. Nurses who were considering leaving nursing practice reported higher moral distress scores than those who were not considering leaving. In response to moral distress, nurses experienced a variety of outcomes across several health domains. To cope with moral distress, nurses engaged in patterns of action, avoidance and acquiescence. To change the conditions of moral distress, nurses desire organizational interventions, practices and culture changes situated in the amplification of their voices. Where and on Whom Will the Research Have an Impact on? These findings will be of interest to: (1) researchers developing and evaluating interventions that address the complex phenomenon of moral distress, (2) leaders and administrators in hospitals, and relevant healthcare and nursing organizations, and (3) nurses interested in leveraging evidence-informed recommendations to advocate for interventions to address moral distress. What Does this Paper Contribute to the Wider Global Community? This paper advances the body of scientific work on nurses' experiences of moral distress, capturing this phenomenon within the unique context of a global health emergency. Nurses' levels of moral distress using Measure of Moral Distress-Healthcare Professional survey were reported, serving as a comparator for future studies seeking to measure and evaluate intensive care unit nurses' levels of moral distress. Nurses' recommendations for mitigative interventions for moral distress have been reported, which can help inform future interventional studies. No patient or public contribution.

Gender-Based Violence and Harassment in Bangladesh’s Ready-Made Garments (RMG) Industry: Exploring Workplace Well-Being Issues in Policy and Practice

Gender-based violence and harassment (GBVH) in the workplace are major issues that affect the work environment and employee well-being. These issues have received little attention from managers in the ready-made garments (RMG) factories or policymakers more generally in Bangladesh, despite the comprehensive approach to restructuring the industry following two devastating industrial accidents. This research examines the role played by RMG factory managers in eliminating gender-based violence and increasing positive work experiences and well-being in the workplace, and the policy initiatives from the government with the aim to stop workplace violence. The study uses qualitative methods—document analysis and semi-structured interviews. The findings reveal that measures to protect workers from GBVH are absent in employment regulation. Though there are high court directives on workplace harassment, these have not been implemented effectively. The findings show that women workers have faced longstanding GBVH, which has impacted their well-being. The increasing rate of women leaving RMG sector jobs reflects how women workers’ well-being issues have been disregarded within the factories.

Exploring Hesitancy, Motivations, and Practical Issues for COVID-19 Vaccination Among Vaccine-Hesitant Adopter Parents Using the Increasing Vaccination Model

IntroductionCOVID-19 vaccination coverage among children remains low, and many parents report being hesitant to get their child(ren) vaccinated. This study explores factors influencing hesitancy and the facilitators that helped hesitant adopter parents choose to vaccinate their child(ren) against COVID-19 despite their hesitancy. MethodWe use a qualitative descriptive design with individual interviews (n=20) to explore COVID-19 vaccine hesitancy and facilitators of vaccination among hesitant adopter parents. The Increasing Vaccination Model domains (thoughts and feelings, social processes, and practical issues) provided the framework for initial coding, and the research team identified nine emergent themes. ResultsFindings document the factors influencing hesitancy and the facilitators motivating COVID-19 vaccination among hesitant adopter parents. DiscussionFindings fill the gap in the literature by providing hesitant adopters’ lived experience, perspectives on vaccine hesitancy, and the influential factors that helped participants overcome their hesitancy and choose to vaccinate their child(ren) against COVID-19.

Phenomenological Approach for the Application of the FOAP Concept of the Vicious Circle of Ill-Health

Background: The Field (F) Observation(O) for Accurate(A) Practicum(P) Concept of Vicious Circle of Ill- Health is an informative and innovative concept that focuses on how human activities within the environment reinforce each other in ways that contribute to vicious circle of ill-health. It is intended to change the perspective of community dwellers to embrace the new ideas of adapting healthy environmental practices to prevent and/or control the vicious circle of ill-health among them.Method: This study used the innovative qualitative research method that focused on the realist and meta-narrative reviews. The following databases were used as search engines: Google Scholar, OpenMD, RefSeek, BASE, Semantic Scholar, Academia, ResarchGate, and Google Search Engine.Result: The four stages of the phenomenological approach to the FOAP concept were vividly explained with stated characteristics. These four stages, when cleverly used, can make huge impacts on the lives of community dwellers. The various risk factors of vicious circle of ill-health, under the illustration of the Causal Theory, were greatly elaborated with all cited evidences to back the various claims.Conclusion: Most Liberians look healthy in an unhealthy environment; they get sick from contracting preventable diseases from the unhealthy environment; they go to hospitals or clinics for quality treatment against the preventable diseases; and they return to the same unhealthy environment that made them sick in the first place. This is the vicious circle of ill-health the FOAP Concept addresses. The FOAP Concept is a novel tool that will help national stakeholders and community dwellers successfully address the issue of unhealthy environmental practices.

Characterization of chemical composition of high viscosity heavy oils: Macroscopic properties, and semi-quantitative analysis of molecular composition using high-resolution mass spectrometry

Heavy oil is an important resource in current petroleum exploitation, and the chemical composition information of heavy oil is crucial for revealing its viscosity-inducing mechanism and solving practical exploitation issues. In this study, the techniques of high-temperature gas chromatography and high-resolution mass spectrometry equipped with an electrospray ionization source were applied to reveal the chemical composition of typical heavy oils from western, central, and eastern China. The results indicate that these heavy oils display significant variations in their bulk properties, with initial boiling points all above 200 °C. Utilizing pre-treatment and ESI high-resolution mass spectrometry, an analysis of the molecular composition of saturated hydrocarbons, aromatic hydrocarbons, acidic oxygen compounds, sulfur compounds, basic nitrogen compounds, and neutral nitrogen compounds within the heavy oil was conducted. Ultimately, a semi-quantitative analysis of the molecular composition of the heavy oil was achieved by integrating the elemental content. The semi-quantitative analysis results of Shengli-J8 heavy oil and a conventional Shengli crude oil show that Shengli-J8 heavy oil lacks alkanes and low molecular weight aromatic hydrocarbons, which contributes to its high viscosity. Additionally, characteristic molecular sets for different heavy oils were identified based on the semi-quantitative analysis of molecular composition. The semi-quantitative analysis of molecular composition in heavy oils may provide valuable reference data for establishing theoretical models on the viscosity-inducing mechanism in heavy oils and designing viscosity-reducing agents for heavy oil exploitation.

Slimming Neural Networks Using Adaptive Connectivity Scores.

In general, deep neural network (DNN) pruning methods fall into two categories: 1) weight-based deterministic constraints and 2) probabilistic frameworks. While each approach has its merits and limitations, there are a set of common practical issues such as trial-and-error to analyze sensitivity and hyper-parameters to prune DNNs, which plague them both. In this work, we propose a new single-shot, fully automated pruning algorithm called slimming neural networks using adaptive connectivity scores (SNACS). Our proposed approach combines a probabilistic pruning framework with constraints on the underlying weight matrices, via a novel connectivity measure, at multiple levels to capitalize on the strengths of both approaches while solving their deficiencies. In SNACS, we propose a fast hash-based estimator of adaptive conditional mutual information (ACMI), that uses a weight-based scaling criterion, to evaluate the connectivity between filters and prune unimportant ones. To automatically determine the limit up to which a layer can be pruned, we propose a set of operating constraints that jointly define the upper pruning percentage limits across all the layers in a deep network. Finally, we define a novel sensitivity criterion for filters that measures the strength of their contributions to the succeeding layer and highlights critical filters that need to be completely protected from pruning. Through our experimental validation, we show that SNACS is faster by over 17× the nearest comparable method and is the state-of-the-art single-shot pruning method across four standard Dataset-DNN pruning benchmarks: CIFAR10-VGG16, CIFAR10-ResNet56, CIFAR10-MobileNetv2, and ILSVRC2012-ResNet50.

Using a Novel Gameplay Intervention to Target Intrusive Memories After Work-Related Trauma: Iterative Qualitative Analysis of Intensive Care Unit Staff Experiences.

Many intensive care unit (ICU) staff experience intrusive memories following work-related traumatic events, which can lead to long-term mental health outcomes and impact work functioning. There is a need for interventions that target intrusive memories in this population; however, factors such as mental health stigma and difficulty in fitting interventions into busy schedules can pose barriers. The Brief Gameplay Intervention For National Health Service Intensive Care Unit Staff Affected By COVID-19 Trauma (GAINS) study tested a brief, digital imagery-competing task intervention (including computer gameplay) with the aim of reducing the recurrence of intrusive memories, which holds promise for overcoming some of these barriers. This substudy aims to explore barriers and facilitators to the uptake and practical use of the intervention by ICU staff, along with its acceptability, and iteratively explore the impact of intervention optimizations to further refine the intervention. The GAINS study is a randomized controlled trial comparing access to a brief digital imagery-competing task intervention for 4 weeks with usual care followed by delayed access to the intervention. The participants were ICU staff who worked during the COVID-19 pandemic and experienced intrusive memories. All participants were sent a questionnaire at 4 weeks to gather data about intervention acceptability. Nested within the randomized controlled trial, a subset of 16 participants was interviewed, and data were analyzed using thematic analysis drawing from a framework approach. Both quantitative and qualitative data indicated high acceptability of the intervention. Intervention use data show that, on average, staff were able to target approximately 73% (3.64/4.88) of their intrusive memories and engaged with the Tetris component for the full 20 minutes per session. Overall, on the acceptability questionnaire, staff found the intervention easy to use, helpful, and highly acceptable. The interviews generated four themes: approach to the intervention, positives of the intervention, negatives of the intervention, and improvements and optimizations. Findings highlighted barriers that ICU staff experienced: stigma, feeling weak for seeking help, not wanting colleagues to know they were struggling, and skepticism. However, they provided suggestions on how barriers could be overcome and discussed the advantages of the intervention when compared with other treatments. Although participants described many positive aspects of the intervention, such as being easy to use, enjoyable, and leading to a reduction in the frequency or intensity of intrusive memories, they also raised practical issues for implementation. The intervention has the potential to overcome stigma and reduce the frequency of intrusive memories after traumatic events among ICU staff. Further refinement is needed to improve the adoption and reach of this intervention. A limitation is that we could not interview the National Health Service staff who were unable or unwilling to take part in the trial.

Решение обратной задачи теплопередачи в конденсаторе турбоустановки со встроенным теплофикационным пучком

For power plants reduction of heat losses is one of the main ways to save energy. Energy saving issue is one of the priority areas of the development of science, engineering, and technology in the Russian Federation. Efficiency upgrading of the equipment operation are associated with the condensers of turbine units, since more than 50 percent of the fuel energy is released into the environment with cooling water. The operating efficiency of a condenser with built-in heating beams significantly affects the vacuum in the condenser and, thus, the operating efficiency of the entire unit. Diagnostics of the condition and improvement of operating modes of the condenser of a turbine unit with a built-in heating unit is an urgent scientific and practical issue. To develop a model of a condenser with a built-in heating beam, the methodology of matrix formalization of the description of heat and mass transfer processes has been used. To solve the problem of diagnosing the condition of equipment, methods of least squares and mathematical programming have been used. Within the framework of the matrix formalization methodology, the authors have developed an approach to solve inverse problems of diagnosing and designing multi-flow heat exchange equipment considering the phase transition in coolants. The authors have obtained and analyzed solutions of the inverse problem from the point of view of diagnosing the state of heat exchange equipment using the example of a turbine unit condenser with a built-in heating beam. Analysis of the calculated results has shown an adequate description by the model of the standard data for the analyzed condenser. Analysis results have shown the possibility to use the model for runtime diagnosis of the condition of power equipment and the efficient use of energy resources due to preventing ineffective modes. The proposed approach allows us to formulate and solve inverse problems of diagnosing the state of equipment of condensing units for various purposes.

Deep Learning Methods for Diagnosing Thyroid Cancer

Abstract One of the prevalent, life-threatening disorders that has been on the rise in recent years is thyroid nodule. A frequent diagnostic technique for locating and identifying thyroid nodules is ultrasound imaging. However, it takes time and presents difficulties for the specialists to evaluate all of the slide images. Automated, reliable, and objective methods are required for accurately evaluating ultrasound images. Recent developments in deep learning have completely changed several facets of image analysis and computer-aided diagnostic (CAD) techniques that deal with the issue of identifying thyroid nodules. We reviewed the literature on the potential, constraints, and present deep learning applications for thyroid cancer detection and discussed the study's goals. We provided an overview of latest developments in the deep learning techniques for thyroid cancer diagnosis and addressed some of the difficulties and practical issues that can restrict the development of deep learning and its incorporation into healthcare setting.

Investigation into quality of life of patient receiving chemotherapeutic regimen in selected hospital in South East Nigeria: A pilot study

In 2008 approximately 12.7million cancers were diagnosed and 7.6million people died due to cancer worldwide. People living with cancer experience a variety of reactions as such quality of life and chemotherapy for cancer patients and its associated side effects are topical issues in oncology practice. A survey was conducted among 264 breast cancer patients selected by Purposive sampling technique, the instrument for the study was (EORTC QLQ - C30 & BR 23). This instrument was used to determine the quality of life of cancer patients and breast cancer in particular. It has a total of 53 items. The first 30 items (i.e. EORTC QLQ - C30), bothers on patients perception of health, the last 23 items which are numbered 31 – 53 (i.e. BR 23). QOL of breast cancer patients using EORTC QLQE30-BR23, revealed that they have trouble taking a long walk, they have you had pains, they need to rest with a mean of 1.9, 2.7 and 2.8 respectively. Also, 15.9% of the respondents had an excellent quality of life in the past week, 78.0% have good quality of life in the past week, it also further revealed that 14.8% had an overall excellent quality of life during the past week. Also, findings revealed that most of the respondents 57.95% had a very bad/poor QOL. Furthermore, findings further revealed that the patients’ perception of overall health past weeks was good, 206(78.03%). Cancer patients experienced many adverse reactions that affected their QOL. There is a need to develop strategies for managing their adverse event resulting from interventions for cancer, thus improving the patient quality of life.

Study of medium and long-term free flow capacity and queue discharge rates on roads.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city's main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning.

Biomimetic fabrication of sr-silk fibroin co-assembly hydroxyapatite based microspheres with angiogenic and osteogenic properties for bone tissue engineering

Bone defects caused by trauma, tumor resection, or developmental abnormalities are important issues in clinical practice. The vigorous development of tissue engineering technology provides new ideas and directions for regenerating bone defects. Hydroxyapatite (HAp), a bioactive ceramic, is extensively used in bone tissue engineering because of its excellent osteoinductive performance. However, its application is challenged by its single function and conventional environment-unfriendly synthesis methods. In this study, we successfully "green" synthesized sr-silk fibroin co-assembly hydroxyapatite nanoparticles (Sr-SF-HA) using silk fibroin (SF) as a biomineralized template, thus enabling it to have angiogenic activity and achieving the combination of organic and inorganic substances. Then, the rough composite microspheres loaded with Sr-SF-HA (CS/Sr-SF-HA) through electrostatic spraying technology and freeze-drying method were prepared. The CCK-8 test and live/dead cell staining showed excellent biocompatibility of CS/Sr-SF-HA. Alkaline phosphatase (ALP) staining, alizarin red staining (ARS), immunofluorescence, western blotting, and qRT-PCR test showed that CS/Sr-SF-HA activated the expression of related genes and proteins, thus inducing the osteogenic differentiation of rBMSCs. Moreover, tube formation experiments, scratch experiments, immunofluorescence, and qRT-PCR detection indicated that CS/Sr-SF-HA have good angiogenic activity. Furthermore, in vivo studies showed that the CS/Sr-SF-HA possesses excellent biocompatibility, vascular activity, as well as ectopic osteogenic ability in the subcutaneous pocket of rats. This study indicates that the construction of CS/Sr-SF-HA with angiogenic and osteogenic properties has great potential for bone tissue engineering.

Transforming Mining Waste to Wealth: A Novel Process for the Sustainable Recovery and Utilization of Iron Tailings through HCl Leaching and MOFs Absorption

Rapid economic development and increased demand for mineral products in China have led to extensive extraction of various ores, resulting in significant environmental challenges associated with the generation of industrial solid waste, particularly iron tailings. Despite being a major mining nation, China faces issues of wasteful practices, with substantial amounts of valuable elements lost during the processing of iron ore. This study addresses the urgent need for sustainable solutions by proposing an innovative approach for the recovery of valuable elements from iron tailings. The proposed process involves a sequential application of acid leaching, chemical precipitation, and Metal-Organic Frameworks (MOFs) ion adsorption. The pre-treated iron tailings were leached in HCl solution with pH 1.5 at 70 °C for 2 h, and the co-leaching efficiency of 98.1% V, 98.2% Mo, 99.3% Fe, and 98.7% Mg was obtained. Chemical precipitation is then employed to isolate Fe, Mg V, and Mo and promote the formation of targeted compounds, ensuring concentration and purity. The integration of MOF ion adsorption, known for its high surface area and tunable pore structures, provides an efficient platform for selectively capturing and recovering target ions. 97.7% V and 96.3% Mo were selectively extracted from Zirconium 1,4-carboxybenzene metal-organic framework (UiO-66) adsorption system with pH 5.0 at 30 °C for 6 h, and 91.7% V and 90.3% Mo were selectively extracted from 2-methylimidazole zinc salt metal-organic framework (ZIF-8) adsorption system with pH 5 at 30 °C for 6.0 h. This three-stage process offers an efficient method for the recovery of valuable elements from iron tailings.

Fibrous Material Structure Developments for Sustainable Heterogeneous Catalysis – An Overview

AbstractThe continuous development of advanced catalysts to increase process yield and selectivity is crucial. A high specific surface area and a good active phase dispersion are generally essential to create catalytic materials with a large number of active sites. Notably, materials with a fibrous morphology are appealing because of their large surface‐to‐volume ratio and flexibility. This contribution highlights the morphology of different types of fibrous structures currently under investigation, all the way from the nanoscale to the macroscale and back, where the distinction lies in the length and diameter of the fibers, as well as in the connection between the structures. Fibers with at least one submicron to nanoscale characteristic result in a higher yield, but can display practical usability issues when unbound. Therefore, fibrous structure catalysts with a balance between the small diameter and handleability are important for industrial viability. By combining different morphologies, the best of both nanomaterials and macroscopic integer materials can be combined into advanced catalytic materials. This overview showcases the large potential of these materials but makes clear that further research is needed to keep expanding the use and effectiveness of fibrous structures in catalysis.

Role of Artificial Intelligence in Global Finance

Artificial Intelligence (AI) has become an integral part of every financial sector. It is found to be the key player in the banking and other financial service institutions. More than 80 percent of banks have understood the potential benefits of application of AI in the industry. The use of AI in financial institutions helps to resolve the practical issues in taking financial decisions. The aim of this paper is to understand the applicability of Artificial Intelligence in financial sector and to study the uses of AI in financial credit decisions and risk management.

Recombinations, chains and caps: resolving problems with the DCJ-indel model

One of the most fundamental problems in genome rearrangement studies is the (genomic) distance problem. It is typically formulated as finding the minimum number of rearrangements under a model that are needed to transform one genome into the other. A powerful multi-chromosomal model is the Double Cut and Join (DCJ) model.While the DCJ model is not able to deal with some situations that occur in practice, like duplicated or lost regions, it was extended over time to handle these cases. First, it was extended to the DCJ-indel model, solving the issue of lost markers. Later ILP-solutions for so called natural genomes, in which each genomic region may occur an arbitrary number of times, were developed, enabling in theory to solve the distance problem for any pair of genomes. However, some theoretical and practical issues remained unsolved. On the theoretical side of things, there exist two disparate views of the DCJ-indel model, motivated in the same way, but with different conceptualizations that could not be reconciled so far. On the practical side, while ILP solutions for natural genomes typically perform well on telomere to telomere resolved genomes, they have been shown in recent years to quickly loose performance on genomes with a large number of contigs or linear chromosomes. This has been linked to a particular technique, namely capping. Simply put, capping circularizes linear chromosomes by concatenating them during solving time, increasing the solution space of the ILP superexponentially. Recently, we introduced a new conceptualization of the DCJ-indel model within the context of another rearrangement problem. In this manuscript, we will apply this new conceptualization to the distance problem. In doing this, we uncover the relation between the disparate conceptualizations of the DCJ-indel model. We are also able to derive an ILP solution to the distance problem that does not rely on capping. This solution significantly improves upon the performance of previous solutions on genomes with high numbers of contigs while still solving the problem exactly and being competitive in performance otherwise. We demonstrate the performance advantage on simulated genomes as well as showing its practical usefulness in an analysis of 11 Drosophila genomes.

Racial and Ethnic Disparities in COVID-19 Treatments in the United States.

Racial and ethnic disparities in patient outcomes following COVID-19 exist, in part, due to factors involving healthcare delivery. The aim of the study was to characterize disparities in the administration of evidence-based COVID-19 treatments among patients hospitalized for COVID-19. Using a large, US hospital database, initiation of COVID-19 treatments was compared among patients hospitalized for COVID-19 between May 2020 and April 2022 according to patient race and ethnicity. Multivariate logistic regression models were used to examine the effect of race and ethnicity on the likelihood of receiving COVID-19 treatments, stratified by baseline supplemental oxygen requirement. The identified population comprised 317,918 White, 76,715 Black, 9297 Asian, and 50,821 patients of other or unknown race. There were 329,940 non-Hispanic, 74,199 Hispanic, and 50,622 patients of unknown ethnicity. White patients were more likely to receive COVID-19 treatments, and specifically corticosteroids, compared to Black, Asian, and other patients (COVID-19 treatment: 87% vs. 81% vs. 85% vs. 84%, corticosteroids: 85% vs. 79% vs. 82% vs. 82%). After covariate adjustment, White patients were significantly more likely to receive COVID-19 treatments than Black patients across all levels of supplemental oxygen requirement. No clear trend in COVID-19 treatments according to ethnicity (Hispanic vs. non-Hispanic) was observed. There were important racial disparities in inpatient COVID-19 treatment initiation, including the undertreatment of Black patients and overtreatment of White patients. Our new findings reveal the actual magnitude of this issue in routine clinical practice to clinicians, policymakers, and guideline developers. This is crucial to ensuring equitable and appropriate access to evidence-based therapies.