Challenges Experienced by Nurse Educators in Promoting Acquisition of Clinical Reasoning Skills by the Undergraduate Nursing Students: A Qualitative Exploratory Study

Background: Clinical reasoning is an essential skill for nursing students since it is required to solve difficulties that arise in complex clinical settings. However, teaching and learning clinical reasoning skills is difficult because of its complexity. This study, therefore aimed at exploring the challenges experienced by nurse educators in promoting acquisition of clinical reasoning skills by undergraduate nursing students. Methods: A qualitative exploratory research design was used in this study. The participants were purposively sampled and recruited into the study. Data were collected using semi-structured interview guides. Thematic analysis method was used to analyze the collected data The principles of beneficence, respect of human dignity and justice were observed. Results: The findings have shown that clinical learning environment, lacked material and human resources. The students had no interest to learn the skill. There was also knowledge gap between nurse educators and clinical nurses. Lack of role model was also an issue and limited time exposure. Conclusion: The study revealed that nurse educators encounter various challenges in promoting the acquisition of clinical reasoning skills among undergraduate nursing students. Training institutions and hospitals should periodically revise the curriculum and provide sufficient resources to facilitate effective teaching and learning of clinical reasoning. Nurse educators must also update their knowledge and skills through continuous professional development if they are to transfer the skill effectively.

The Effect of the Menstrual Cycle on Cognitive Performance: Spatial Reasoning, Visual & Numerical Memory

The menstrual cycle has been a topic of interest in relation to behavior and cognition for many years, with historical beliefs associating it with cognitive impairment. However, recent research has challenged these beliefs and suggested potential positive effects of the menstrual cycle on cognitive performance. Despite these emerging findings, there is still a lack of consensus regarding the impact of the menstrual cycle on cognition, particularly in domains such as spatial reasoning, visual memory, and numerical memory. Hence, this study aimed to explore the relationship between the menstrual cycle and cognitive performance in these specific domains. Previous studies have reported mixed findings, with some suggesting no significant association and others indicating potential differences across the menstrual cycle. To contribute to this body of knowledge, we explored the research question of whether the menstrual cycles have a significant effect on cognition, particularly in the domains of spatial reasoning, visual and numerical memory in a regionally diverse sample of menstruating females. A total of 30 menstruating females from mixed geographical backgrounds participated in the study, and a repeated measures design was used to assess their cognitive performance in two phases of the menstrual cycle: follicular and luteal. The results of the study revealed that while spatial reasoning was not significantly related to the menstrual cycle (p = 0.256), both visual and numerical memory had significant positive associations (p < 0.001) with the luteal phase. However, since the effect sizes were very small, the importance of this relationship might be commonly overestimated. Future studies could thus entail designs with larger sample sizes, including neuro-biological measures of menstrual stages, and consequently inform competent interventions and support systems.

Ten misconceptions regarding decision-making in critical care

Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system.Patient complexity,illness severity,and the urgency in initiating proper treatment all contribute to decision-making errors.Clinician-related factors such as fatigue,cognitive overload,and inexperience further interfere with effective decision-making.Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error.This evidence-based review discusses ten common misconceptions regarding critical care decision-making.By understanding how practitioners make clinical decisions and examining how errors occur,strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

Using AI and Precision Nutrition to Support Brain Health during Aging

Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.

Data-Driven Model for Risk Assessment of Cable Fire in Utility Tunnels Using Evidential Reasoning Approach

Cable fire is one of the most important events for operation and maintenance(O&M)safety in underground utility tunnels(UUTs).Since there are limited studies about cable fire risk assessment,a comprehensive assessment model is proposed to evaluate the cable fire risk in different UUT sections and improve O&M efficiency.Considering the uncertainties in the risk assessment,an evidential reasoning(ER)approach is used to combine quantitative sensor data and qualitative expert judgments.Meanwhile,a data transformation technique is contributed to transform continuous data into a five-grade distributed assessment.Then,a case study demonstrates how the model and the ER approach are established.The results show that in Shenzhen,China,the cable fire risk in District 8,B Road is the lowest,while more resources should be paid in District 3,C Road and District 25,C Road,which are selected as comparative roads.Based on the model,a data-driven O&M process is proposed to improve the O&M effectiveness,compared with traditional methods.This study contributes an effective ER-based cable fire evaluation model to improve the O&M efficiency of cable fire in UUTs.

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.

International law obligations for the disposal of Fukushima nuclear-contaminated water under the principles of nuclear safety

The disposal of contaminated water from Japan’s Fukushima nuclear power plant is a significant international nuclear safety issue with considerable cross-border implications.This matter requires compliance not only with the law of the sea but also with the principles of nuclear safety under international law.These principles serve as the overarching tenet of international and China’s domestic nuclear laws,applicable to nuclear facilities and activities.The principle of safety in nuclear activities is fully recognized in international and domestic laws,carrying broad legal binding force.Japan’s discharge of nuclear-contaminated water into the sea violates its obligations under the principle of safety in nuclear activities,including commitments to optimum protection,as low as reasonably practicable,and prevention.The Japanese government and the International Atomic Energy Agency(IAEA)have breached the obligation of optimum protection by restricting the scope of assessments,substituting core concepts,and shielding dissenting views.In the absence of clear radiation standards,they have acted unilaterally without fulfilling the obligation as low as reasonably practicable principle.The discharge of Fukushima nuclear-contaminated water poses an imminent and unpredictable risk to all countries worldwide,including Japanese residents.Japan and the IAEA should fulfill their obligations under international law regarding disposal,adhering to the principles of nuclear safety,including optimum protection,the obligation as low as reasonably practicable,and prevention through multilateral cooperation.Specifically,the obligation to provide optimum protection should be implemented by re-evaluating the most reliable disposal technologies and methods currently available and comprehensively assessing various options.The standard of the obligation as low as reasonably practicable requires that the minimization of negative impacts on human health,livelihoods,and the environment should not be subordinated to considerations of cutting costs and expenses.Multilateral cooperation should be promoted through the establishment of sound multilateral long-term monitoring mechanisms for the discharge of nuclear-contaminated water,notification and consultation obligations,and periodic assessments.These obligations under international law were fulfilled after the accidents at the Three Mile Island and Chernobyl nuclear power plants.The implications of the principles of nuclear safety align with the concept of building a community of shared future for nuclear safety advocated by China.In cases of violations of international law regarding the disposal of nuclear-contaminated water that jeopardize the concept of a community of a shared future for nuclear safety,China can also rely on its own strength to promote the implementation of due obligations through self-help.

Hierarchical Restriction and Reduction of Overcriminalization of Teachers’Disciplinary Behaviors

The right to education is an important part of basic human rights. To transform from a designed vision to a reality in practice, teachers’ right to discipline, as a component of the right to education, needs tangible support from the criminal law. The criminal law cannot be absent from promoting the rule of law in education. However, in practice, teachers’ disciplinary behaviors are often ex-cessively criminalized, leading to problems such as over-expanding punishment and harming the innocent and even the malaise that en-danger substantive justice such as the tarnishing of teachers’ disci-plinary right and the imbalance of teachers’ disciplinary behaviors. Such overcriminalization has its social causes and normative crux, which is the ambiguity of regulations of teachers’ disciplinary right in terms of the pre-existing law and the unclear positioning of the jus-tification of teachers’ disciplinary behaviors in terms of the criminal law. Therefore, it is necessary to carry out a dual clarification of the chaotic parts of the two laws and determine the corresponding guiding principles, and test them one by one through the hierarchical theory of crime to make the path of exculpation clear. At the level of constituent elements, the exculpation is achieved through the normative judgment of the constituent elements;At the level of illegality, the exculpation is achieved by virtue of substantive considerations of reasons such as le-gal acts, legitimate defense, and victims’ commitments;At the level of accountability, the exculpation is achieved through the value screening of the culpability paradigm. We should reverse the trend of overcrim-inalization of teachers’ disciplinary behaviors by clearing the way of exculpation.

A reasoning diagram based method for fault diagnosis of railway point system

Railway Point System(RPS)is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations.For the fault diagnosis of RPS,most existing methods assume that sufficient samples of each failure mode are available,which may be unrealistic,especially for those modes of low occurrence frequency but with high risk.To address this issue,this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage.Specifically,the failure modes of RPS are distinguished through constructing a reasoning diagram,whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic.Then,an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem.Based on the results of decisions,the diagnostic rules are established to identify the failure modes.Finally,the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.

The Reasons for Admission of Elderly Subjects in Intensive Care at the CHU Ignace Deen

Objective: To determine the reasons for admission of elderly subjects and the prognosis in general intensive care. Patients and Methods: Observational descriptive and analytical study with prospective collection of data over a period of one year from January 1 to December 31, 2021. Patients aged 65 or over were included. Abstract: During the study period, 223 cases were collected out of 587 patients admitted, giving a prevalence of 37.9%. The average age was 74.127.39 ± years with extremes of 65 and 96 years and a male predominance (58.7%). The comorbidities were dominated by arterial hypertension (71.3%). The patients were: transferred from medical and surgical emergencies (75.8%). The average admission time was 48.8 ± 29.8 hours. One hundred and eight patients had a Glasgow score between 3 and 7. The reasons for admission were dominated by vascular causes (51.6%). Strokes of any type accounted for 43.9% of these reasons for admission. The average time for carrying out the biological assessments and imaging was 41.8 ± 27.3 hours with the extremes of 3 and 89 hours, 37.2% had a complete assessment within 24 hours. The average duration of hospitalization was 7.10 ± 8.87 days with extremes of 1 and 72 days. The mortality rate was 71.7%. Conclusion: This study has made it possible to take stock of the reasons for the admission of elderly subjects to intensive care. It appears that vascular causes are the main reasons for admission with heavy comorbidities which results in high mortality.

A Processor Performance Prediction Method Based on Interpretable Hierarchical Belief Rule Base and Sensitivity Analysis

The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.

Historical Changes of Ningxia Natural Disasters and Its Reason Analysis

A quantitative model is built and the historical variation of natural disasters is analyzed in this paper,by using materials as affected area(damaged area),affected population(dead and injured),casualty of livestock,collapsed houses(damaged houses),decrease of crops yield,economic loss(direct and indirect) and price index over the same period of Ningxia natural disasters(include drought,flood,gale and hail,frost,pest disasters and other disasters) during 1978-2007,and applying gray correlation analysis method.The long-term changes trend of natural disasters is analyzed by the application of the least squares method for linear trend,and the oscillation period is analyzed by using the maximum entropy statistical method.It is found that natural disasters have basically 2 to 3 years of variation period either in the whole region or in the individual regions;from the mid 1980s to the late 1990s,Ningxia is in an oscillation period with disasters attacked frequently in decadal and interannual scale under the background of climate;the increase of damage intensity of natural disasters slow down and tends to decrease since 2000.

DEBRA: On the Unsupervised Learning of Concept Hierarchies from (Literary) Text

With this work, we introduce a novel method for the unsupervised learning of conceptual hierarchies, or concept maps as they are sometimes called, which is aimed specifically for use with literary texts, as such distinguishing itself from the majority of research literature on the topic which is primarily focused on building ontologies from a vast array of different types of data sources, both structured and unstructured, to support various forms of AI, in particular, the Semantic Web as envisioned by Tim Berners-Lee. We first elaborate on mutually informing disciplines of philosophy and computer science, or more specifically the relationship between metaphysics, epistemology, ontology, computing and AI, followed by a technically in-depth discussion of DEBRA, our dependency tree based concept hierarchy constructor, which as its name alludes to, constructs a conceptual map in the form of a directed graph which illustrates the concepts, their respective relations, and the implied ontological structure of the concepts as encoded in the text, decoded with standard Python NLP libraries such as spaCy and NLTK. With this work we hope to both augment the Knowledge Representation literature with opportunities for intellectual advancement in AI with more intuitive, less analytical, and well-known forms of knowledge representation from the cognitive science community, as well as open up new areas of research between Computer Science and the Humanities with respect to the application of the latest in NLP tools and techniques upon literature of cultural significance, shedding light on existing methods of computation with respect to documents in semantic space that effectively allows for, at the very least, the comparison and evolution of texts through time, using vector space math.

On the “Reasonable” Handling of Personal Information Disclosed According to the Law

The model for protection of personal information dis-closed according to the law has changed from indirect protection to direct protection.The indirect protection model for traditional repu-tation rights and privacy rights was not enough to meet the practical needs of governance.However;due to the ambiguity in the application of the“reasonable”processing requirements,the direct protection model centered on Article 27 of the Personal Information Protection Law also is not enough to effectively respond to practical disputes.The essence of the problem is to resolve the tension between informa-tion circulation and risk control and reshape the legal order for the protection of personal information disclosed according to the law.The determination of“reasonable”should be centered on the scenario theory and holism interpretation and carried out by using the interpre-tation technique of the dynamic system under Article 998 of the Civil Code.With the support of scenario-based discussions and comparative propositions,the crawling and tag extraction of personal information.disclosed according to the law should be considered as reasonable processing;profiling and automated decision-making should not be covered in the scope of reasonable processing,in principle;for behav-iors such as correlation analysis,elements like information subject,identifiability and sensitivity should be comprehensively considered to draw open and inclusive conclusions in individual cases.

Threat Assessment Method Based on Intuitionistic Fuzzy Similarity Measurement Reasoning with Orientation

The aim of this paper is to propose a threat assessment method based on intuitionistic fuzzy measurement reasoning with orientaion to deal with the shortcomings of the method proposed in [Ying-Jie Lei et al., Journal of Electronics and Information Technology 29(9)(2007)2077-2081] and [Dong-Feng Chen et al., Procedia Engineering 29(5)(2012)3302-3306] the ignorance of the influence of the intuitionistic index's orientation on the membership functions in the reasoning, which caused partial information loss in reasoning process. Therefore, we present a 3D expression of intuitionistic fuzzy similarity measurement, make an analysis of the constraints for intuitionistic fuzzy similarity measurement, and redefine the intuitionistic fuzzy similarity measurement. Moreover, in view of the threat assessment problem, we give the system variables of attribute function and assessment index, set up the reasoning system based on intuitionistic fuzzy similarity measurement with orientation, and design the reasoning rules, reasoning algorithms and fuzzy-resolving algorithms. Finally, through the threat assessment, some typical examples are cited to verify the validity and superiority of the method.

Fuzzy multi-criteria decision-making approach with incomplete information based on evidential reasoning

The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evidential reasoning is proposed,in which the criteria values are integrated on the basis of analytical algorithm of evidential reasoning,and then nonlinear programming models of each alternative are developed with the incomplete information on weights.The genetic algorithm is employed to solve the models,producing the weights and the utility interval of each alternative,and the ranking of the whole set of alternatives can be attained.Finally,an example shows the effectiveness of the method.

Capability-based context ontology modeling and reasoning for C^4ISR communication

Modeling and reasoning on domain contexts play a key role for adding intelligence to communication services, and the approach of capability-based requirement engineering ensures the scientificity and accuracy of requirement elicitation. This paper presents a capability-based context ontology modeling approach for command and control, communication, computer, intelligence, surveillance and reconnaissance（C^4ISR） communication. Primarily, a capability-based C^4ISR communication meta-concept model and a C^4ISR communication context meta-ontology are constructed. The context ontology is established under the constraints of the C^4ISR communication context meta-ontology, and furthermore, algorithms are proposed to support context reasoning based on description logic. A case study is presented to demonstrate applicability of the proposed method.

Reasoning and Rehabilitation cognitive skills programme for mentally disordered offenders: Predictors of outcome

AIM To investigate factors predicting treatment completion and treatment outcome of the Reasoning and Rehabilitation Mental Health Programme(R&R2MHP) cognitive skills programme for mentally disordered offenders. METHODS Secondary analysis of data previously obtained from 97 male patients who were sectioned and detained under the United Kingdom Mental Health Act in low, medium and high security hospitals and who had completed R&R2MHP. Predictors of treatment completion included background variables and five outcome measures: Four self-reported measures of violent attitudes, social problem-solving skills, reactive anger and locus of control and an objective measure of behaviour on theward that was completed by staff. Completion of the 16 session programme, which was delivered on a weekly basis, was classified as ≥ 12 sessions.RESULTS It was found that the R&R2MHP is appropriate for delivery to participants of different ages, ethnic background, and at different levels of security without the completion rate or treatment effectiveness being compromised. Participants taking oral typical psychotropic medication were over seven times more likely to complete the programme than other participants. Behavioural disturbance on the ward prior to commencing the programme predicted non-completion(medium effect size). As far as treatment completion was concerned, none of the background factors predicted treatment effectiveness(age, ethnic background, level of security, number of previous convictions and number of previous hospital admissions). The best predictor of treatment effectiveness was attitude towards violence suggesting that this should be the primary outcome measure in future research evaluating outcomes of the R&R2MHP cognitive skills program. CONCLUSION The findings suggest that a stable mental state is a key factor that predicts treatment completion.

CASCADED FUZZY SYSTEM AND ITS ROBUST ANALYSIS BASED ON SYLLOGISTIC FUZZY REASONING

Syllogistic fuzzy reasoning is introduced into fuzzy system, and the new Cascaded Fuzzy System(CFS) is presented. The thoroughly theoretical analysis and experimental results show that syllogistic fuzzy reasoning is more robust than all other implication inferences for noise data and that CFS has better robustness than conventional fuzzy systems, which provide the solid foundation for CFS's potential application in fuzzy control and modeling and so on.

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.