Intervention-Free Graphitization of Carbon Microspheres from a Non-Graphitizing Polymer at Low Temperature: Nanopores as Dynamic Nanoreactors.

Graphitizability of organic precursors is the topic of numerous investigations due to the wide applications of graphitic materials in the industry and emerging technologies of supercapacitors, batteries, etc. Most polymers, such as polydivinyl benzene (PDVB) are classified as non-graphitizings that do not convert to Graphite even after heating to 3000℃. Here, for the first time, the development of graphitic structure in the hierarchal porous sulfonated-PDVB microspheres without employing specific equipment or additives like metal catalysts, organic ingredients, or graphite particles, at 1100°C is reported. The abnormal additive-free graphitic structure formation is confirmed by Raman spectroscopy (ID/IG = 0.87), high-resolution transmission electron microscopy (HRTEM), and selected area diffraction patterns (SAED), as well as x-ray diffraction patterns (XRD), while preservation of aromatic compounds from the carbonization is detected by Fourier transform infrared (FTIR) analysis. Polymer evolution from room temperature to 1100°C is also studied by FTIR, Raman spectroscopy, and XRD techniques. Based on the obtained results, it is suggested that the hierarchal and complicated ink-bottle pore network with a high surface area besides super micropores in the sulfonated-PDVB microspheres has served as nano-sized reaction media. These pores, hereafter referred as "dynamic nanoreactors", are expected to have confined the in-situ produced thermal decomposition products containing broken bond benzene rings, while changing dimensionally and structurally during the designed carbonization regime. This confinement has led to the benzene rings fusion at 250°C, a remarkable extension of them at 450°C, their growth to graphene sheets at 900°C and finally, the stacking of curved graphene layers at 1100°C. The results of this research put stress on the capability of nanopores as nanoreactors to facilitate reactions of decomposition products at low temperatures and ambient pressures to form stacked layers of graphene; A transformation that normally requires catalysts and very high pressures for only specific polyaromatic hydrocarbons.

Applying human factors to improve patient safety, morale and team working for oral pathology and medicine specialists.

BACKGROUND: Human error is inevitable, and therefore can be considered as a 'normal' part of everyday life. Unfortunately, error can never be eliminated completely. However, learning from our mistakes can help reduce problems in future. Fifty years ago, most clinicians paid little or no attention to the human factors (HF) that can affect individual and team performance. It has only been in the last 20-25 years that colleagues in healthcare have truly begun recognizing the importance of HF and non-technical skills in medicine and dentistry and how their application can significantly improve patient safety and aid better team working and staff morale in the clinical setting and laboratory. DISCUSSION: Personal factors such as stress, tiredness, hunger and dehydration all reduce human performance and can raise the risk of mistakes. In addition, how we work and interact with the wider team is important since many errors can occur because of ineffective communication, steep hierarchal (authority) gradients and loss of situational awareness.  This short HF overview in the 50th commemorative special of JOPM issue is timely. It provides a contemporary overview of human factors and performance that the authors consider important for oral medicine and pathology colleagues and which can affect individuals and teams This article also discuss ways to reduce the chances of medical and dental error and improve patient safety.

Urban resilience as a mitigating factor against economically driven out-migration during COVID-19: The case of Eilat, a tourism-based city.

The COVID-19 pandemic has created an ongoing global crisis. The unprecedented shock has been particularly devastating for tourism-based cities and has tested their resilience. This study addresses the mitigating role of urban resilience in the interplay between acute crises and the phenomenon of urban outmigration. Leveraging a unique dataset collected during the first national lockdown that followed the outbreak of COVID-19 in the city of Eilat (Israel)-a geographically isolated single economic sector-based city with no feasible options to commute-we offer here a new conceptual framework and an empirical framework for measuring perceived resilience. Using validated psychometric questionnaires and employing the nested hierarchical modeling approach, we estimate the impact of perceived resilience on the decision to migrate from the city. We find that even though Eilat has all the attributes to experience significant out-migration, its residents are not inclined towards migration due to its prior investment in resilience measures, which strengthened the local community and created a unique credo shared by its residents. These findings call for policymakers to focus on long-term resilience schemes directed at increasing the appeal that cities have for their residents and ensuring their endurance in times of extreme hardship.

Analyzing brain structural differences among undergraduates with different grades of self-esteem using multiple anatomical brain network.

BACKGROUND: Self-esteem is the individual evaluation of oneself. People with high self-esteem grade have mental health and can bravely cope with the threats from the environment. With the development of neuroimaging techniques, researches on cognitive neural mechanisms of self-esteem are increased. Existing methods based on brain morphometry and single-layer brain network cannot characterize the subtle structural differences related to self-esteem. METHOD: To solve this issue, we proposed a multiple anatomical brain network based on multi-resolution region of interest (ROI) template to study the brain structural connections of self-esteem. The multiple anatomical brain network consists of ROI features and hierarchal brain network features that are extracted from structural MRI. For each layer, we calculated the correlation relationship between pairs of ROIs. In order to solve the high-dimensional problem caused by the large amount of network features, feature selection methods (t-test, mRMR, and SVM-RFE) are adopted to reduce the number of features while retaining discriminative information to the maximum extent. Multi-kernel SVM is employed to integrate the various types of features by appropriate weight coefficient. RESULT: The experimental results show that the proposed method can improve classification accuracy to 97.26% compared with single-layer brain network. CONCLUSIONS: The proposed method provides a new perspective for the analysis of brain structural differences of self-esteem, which also has potential guiding significance in other researches involved brain cognitive activity and brain disease diagnosis.

Digging for Significant Genes in Microarray Expression Data Based on Systematic Sampling and Hierarchal Clustering Algorithm.

Obesity is a worldwide health problem. Eating habits have changed during this decade and an increase in high-fat foods as well as sugar intake has been observed, which is associated with obesity and weight gaining. Therefore, in this chapter, we have analysed microarray expression data for obese and lean individuals. The microarray technology simultaneously records the expression levels of thousands of genes across related samples and during biological process. The microarray data sets are enriched with crucial information which have to be examined. In the study discussed in this chapter, the microarray data sets are pre-processed prior to analysis, in which upregulated and downregulated gene groups have been identified. Clustering is one of the learning techniques and it is applied in different fields of study. Clustering with microarray data can be accomplished based on genes or samples and depending on the type of datasets. Hierarchal clustering algorithm was used to detect gene patterns in our candidate datasets, since microarray data are considered big and complex. Systematic sampling technique was used to reduce the complexity of microarray datasets and to enhance the clustering quality. This technique is a simple and conductive sampling technique. The proposed algorithm, that is, Systematic Sampling with Hierarchal Clustering (SSHC), could detect significant gene patterns in the datasets, and the proposed system (SSHC) shows a better performance. The validity index utilized to evaluate the SSHC algorithm is adjusted Rand index (ARI).

Granular Data Access Control with a Patient-Centric Policy Update for Healthcare.

Healthcare is a multi-actor environment that requires independent actors to have a different view of the same data, hence leading to different access rights. Ciphertext Policy-Attribute-based Encryption (CP-ABE) provides a one-to-many access control mechanism by defining an attribute's policy over ciphertext. Although, all users satisfying the policy are given access to the same data, this limits its usage in the provision of hierarchical access control and in situations where different users/actors need to have granular access of the data. Moreover, most of the existing CP-ABE schemes either provide static access control or in certain cases the policy update is computationally intensive involving all non-revoked users to actively participate. Aiming to tackle both the challenges, this paper proposes a patient-centric multi message CP-ABE scheme with efficient policy update. Firstly, a general overview of the system architecture implementing the proposed access control mechanism is presented. Thereafter, for enforcing access control a concrete cryptographic construction is proposed and implemented/tested over the physiological data gathered from a healthcare sensor: shimmer sensor. The experiment results reveal that the proposed construction has constant computational cost in both encryption and decryption operations and generates constant size ciphertext for both the original policy and its update parameters. Moreover, the scheme is proven to be selectively secure in the random oracle model under the q-Bilinear Diffie Hellman Exponent (q-BDHE) assumption. Performance analysis of the scheme depicts promising results for practical real-world healthcare applications.

Thermo-catalytic co-pyrolysis of ironbark sawdust and plastic waste over strontium loaded hierarchical Y-zeolite.

The objective of this research is to synthesize hierarchical strontium loaded Y-zeolite and study its application for ironbark (IB) and plastic waste (PW) co-pyrolysis. Commercial parent Y-zeolite (Si/Al = 2.48) was modified via sequential dealumination-desilication using citric acid and NaOH. Further, strontium (8 wt %) was loaded into the modified Y-zeolite via wet and dry impregnation methods. The prepared catalyst was characterized by N2 adsorption-desorption isothermal, field emission scanning electron microscopy (FESEM) combined with energy dispersive x-ray spectroscopy (EDS), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and Thermogravimetric analyzer (TGA). After dealumination (treatment using 0.1 M of citric acid), the external surface area and Si/Al ratio increased from 53.5 to 147.4 m2/g and 2.48 to 5.36, respectively. However, the sequential desilication treatment reduced Si/Al ratio from 5.36 to 2.57. In addition, Y-zeolite enhanced the total aromatic percentage and reduced the acidic group in co-pyrolysis oil.

A Bayesian hierarchal modeling approach to shortening phase I/II trials of anticancer drug combinations.

In phase I/II anticancer drug-combination trials, trial design to evaluate toxicity and efficacy has been studied by dividing the trial into 2 stages, followed by seamless execution of the 2 stages. In the first stage, admissible dose combinations in toxicity are identified, followed by patient assignment among the identified admissible dose combinations using adaptive randomization in the second stage. When patients are assigned using adaptive randomization, it is desirable to determine a more appropriate dose combination by taking into consideration both drug efficacy and toxicity; however, during the course of this determination and evaluation of toxicity and efficacy, there remains a concern that the trial duration might be prolonged. Therefore, we proposed a trial design to assign patients adaptively to more appropriate dose combinations in both toxicity and efficacy and to shorten trial duration without compromising trial performance. When selecting the dose combination for subsequent cohorts, unobserved data are treated as missing data, which are imputed using a data augmentation algorithm involving a gamma process. Probabilities associated with toxicity and efficacy are estimated applying a Bayesian hierarchical model to the imputed data, thereby allowing more patients to be assigned more appropriate dose combinations in both toxicity and efficacy through adaptive randomization. Results of simulation studies suggested that the proposed approach shortened trial duration without significantly compromising the performance of the trial as compared with existing approaches. We believe that the proposed approach will expedite drug development time and reduce costs associated with clinical development.

Bayesian analysis of the piecewise diffusion decision model.

Most past research on sequential sampling models of decision-making have assumed a time homogeneous process (i.e., parameters such as drift rates and boundaries are constant and do not change during the deliberation process). This has largely been due to the theoretical difficulty in testing and fitting more complex models. In recent years, the development of simulation-based modeling approaches matched with Bayesian fitting methodologies has opened the possibility of developing more complex models such as those with time-varying properties. In the present work, we discuss a piecewise variant of the well-studied diffusion decision model (termed pDDM) that allows evidence accumulation rates to change during the deliberation process. Given the complex, time-varying nature of this model, standard Bayesian parameter estimation methodologies cannot be used to fit the model. To overcome this, we apply a recently developed simulation-based, hierarchal Bayesian methodology called the probability density approximation (PDA) method. We provide an analysis of this methodology and present results of parameter recovery experiments to demonstrate the strengths and limitations of this approach. With those established, we fit pDDM to data from a perceptual experiment where information changes during the course of trials. This extensible modeling platform opens the possibility of applying sequential sampling models to a range of complex non-stationary decision tasks.

A multi-criteria decision-making approach to rank supplier selection criteria for hospital waste management: A case from Pakistan.

To address environmental issues and cost effectiveness, waste management is necessary for healthcare facilities. Most importantly, segregation of hazardous and non-hazardous waste must be done as in many developing countries; disposal of both types of healthcare waste is done together, which is an unsafe practice. Waste generated in hospitals needs proper management to minimise hazards for patient and healthcare workers. At the same time, it is quite difficult for hospitals to find a systematic way to select appropriate suppliers for hospital waste management. Therefore, the purpose of this article is to identify, validate, and rank criteria that are essential for hospital waste management suppliers' selection. The analytical hierarchal process approach has been used and a survey from Pakistan's largest city (Karachi) has been considered to rank the most appropriate criteria that is necessary to select the supplier, especially in a developing country like Pakistan. Results show that waste management cost (45.5%) and suppliers' details (31.5%) are the top two main criteria for supplier selection; and storage cost (15.7%), waste handling cost (14.7%), and qualification of the suppliers (10.9%) are the top three most important overall sub-criteria for supplier selection for hospital waste management.

A new framework for modeling decisions about changing information: The Piecewise Linear Ballistic Accumulator model.

In the real world, decision making processes must be able to integrate non-stationary information that changes systematically while the decision is in progress. Although theories of decision making have traditionally been applied to paradigms with stationary information, non-stationary stimuli are now of increasing theoretical interest. We use a random-dot motion paradigm along with cognitive modeling to investigate how the decision process is updated when a stimulus changes. Participants viewed a cloud of moving dots, where the motion switched directions midway through some trials, and were asked to determine the direction of motion. Behavioral results revealed a strong delay effect: after presentation of the initial motion direction there is a substantial time delay before the changed motion information is integrated into the decision process. To further investigate the underlying changes in the decision process, we developed a Piecewise Linear Ballistic Accumulator model (PLBA). The PLBA is efficient to simulate, enabling it to be fit to participant choice and response-time distribution data in a hierarchal modeling framework using a non-parametric approximate Bayesian algorithm. Consistent with behavioral results, PLBA fits confirmed the presence of a long delay between presentation and integration of new stimulus information, but did not support increased response caution in reaction to the change. We also found the decision process was not veridical, as symmetric stimulus change had an asymmetric effect on the rate of evidence accumulation. Thus, the perceptual decision process was slow to react to, and underestimated, new contrary motion information.

"A 'Cool' Kid Wears a Brand, and Everyone's following Him" Hierarchal Social Status in Preadolescence: A New Developmental Perspective.

Inequality in hierarchical social status, especially among socially excluded children, profoundly affects preadolescents. Historically viewed through a lens of psychopathology and moral deficiencies, it challenges the education system's approaches and interventions. This article introduces a developmental perspective, highlighting the hierarchical social status' role in shaping classroom cohesion, strength, and distinctiveness. This study's phenomenological, qualitative methodology aimed to gain preliminary insight into the children's perspectives. Drawing from 12 focus group discussions involving 140 latency-age (grade 5) children in Israel, it uniquely reveals the dynamic nature of hierarchical social status influenced by children's connections with the group. This dynamism promotes group unity, strengthens bonds, and prioritizes collective concerns, contributing to the development of a "social self" in the latency phase. Beyond theory, this study proposes innovative interventions to address social status disparities.

Internalizing psychopathology and its links to suicidal ideation, dysfunctional attitudes, and help-seeking readiness in a national sample of college students.

BACKGROUND: Recent evidence suggests that multiple emotional disorders may be better assessed using dimensional models of psychopathology. The current study utilized a cross-sectional population survey of college students (N = 8613 participants) to examine the extent to which broad psychopathology factors accounted for specific associations between emotional problems and clinical and behavioral validators: suicidality, dysfunctional attitudes, and treatment seeking. METHODS: Confirmatory factor models were estimated to identify the best structure of psychopathology. Models were then estimated to examine the broad and specific associations between each psychopathology indicator and the clinical and behavioral validators. RESULTS: The hierarchical model of psychopathology with internalizing problems at the top, fear, and distress at the second level, and five specific symptom dimensions at the third level evidenced the best fit. The associations between symptom indicators of psychopathology and clinical and behavioral validators were relatively small and inconsistent. Instead, much of the association between clinical and behavioral validators and emotional problems operated at a higher-order level. LIMITATIONS: The cross-sectional nature of the survey precludes the ability to make conclusions regarding causality. CONCLUSIONS: Researchers should focus on investigating the shared or common components across emotional disorders, particularly concerning individuals presenting with higher rates of suicidal ideation dysfunctional attitudes, and help-seeking behavior. Using higher-order dimensions of psychopathology could simplify the complex presentation of multiple co-occurring disorders and suggest valid constructs for future investigations.

Spatial Assessment of Land Suitability Potential for Agriculture in Nigeria.

From land cover analysis, cropland expansion was a major driving factor for land use land cover changes in Nigeria from 2000 to 2020. This further highlights the food production needs in the country. While this land use change indicates a significant alteration in land cover, it was exigent to assess land suitability using a Multi-Criteria Decision Analysis (MCDA) combined with geospatial techniques to identify areas with agricultural suitability potential and to analyze cropland suitability. The results showed that the country had 8% of very high suitability, high suitability (25%), moderate suitability (29%), and marginal suitability (25%) croplands. However, low suitability accounts for 14% of the entire cropland. The spatial distribution of cropland suitability shows that most areas in the South East, South South, and South West, respectively, have the most suitable cropland as they meet the biophysical conditions for crop production, followed by the North Central regions, while most places in the North (North East and North West) have a higher share of moderate to low suitability. This study highlights the potential of the country to target localized self-sufficiency. Therefore, this study recommends using the cropland suitability map to launch food security programs across the six geopolitical zones to maximize their inherent environmental potentials to alleviate the country's food production needs.

Primary angiosarcoma of the breast: a literature review.

BACKGROUND AND OBJECTIVE: Primary angiosarcoma of the breast (PBA) is an extremely rare and heterogeneous disease. PBA is difficult to diagnose and has a poor prognosis. In order to better understand the disease and provide evidence-based treatment for PBA patients, a review of the published literature in the English language was conducted. METHODS: A literature review in agreement with the PRISMA protocol was conducted. Medline and Cochrane databases were searched for English articles on PBA patients in September 2023 with a predetermined strategy. The articles were categorized and assessed based on hierarchical levels of scientific evidence. KEY CONTENT AND FINDINGS: A total of 255 articles were identified, among these 137 publications which included 1,888 patients met the criteria for inclusion in the final analysis. No prospective, randomized trials exclusive to PBA have been recognized. This article provides an overview of the most current and comprehensive evidence concerning the epidemiology, etiology, genomic features, clinical presentations, diagnosis, treatment, and prognosis of PBA. CONCLUSIONS: Despite the fact that current evidence is largely derived from retrospective studies, database analyses, and case reports, we utilized this information to tackle important clinical questions concerning optimal patient management practices for PBA. Complete surgical excision continues to be the mainstay treatment for PBA. However, the effectiveness of adjuvant therapies is still unclear. This narrative review highlights the urgent need for more rigorously designed research to enhance the management and treatment strategies for PBA.

Hierarchal single-cell lineage tracing reveals differential fate commitment of CD8 T-cell clones in response to acute infection.

Generating balanced populations of CD8 effector and memory T cells is necessary for immediate and durable immunity to infections and cancer. Yet, a definitive understanding of CD8 differentiation remains unclear. We used CARLIN, a processive lineage recording mouse model with single-cell RNA-seq and TCR-seq to track endogenous antigen-specific CD8 T cells during acute viral infection. We identified a diverse repertoire of expanded T-cell clones represented by seven transcriptional states. TCR enrichment analysis revealed differential memory- or effector-fate biases within clonal populations. Shared Vb segments and amino acid motifs were found within biased categories despite high TCR diversity. Using single-cell CARLIN barcode-seq we tracked multi-generational clones and found that unlike unbiased or memory-biased clones, which stably retain their fate profiles, effector-biased clones could adopt memory- or effector-bias within subclones. Collectively, our study demonstrates that a heterogenous T-cell repertoire specific for a shared antigen is composed of clones with distinct TCR-intrinsic fate-biases.

The application of human reliability analysis to carpal tunnel decompression.

INTRODUCTION: Many surgical procedures are prone to human error, particularly in the learning phase of skills acquisition. Task standardisation has been suggested as an approach to reducing errors, but it fails to account for the human factors associated with learning. Human reliability analysis (HRA) is a structured approach to assess human error during surgery. This study used HRA methodologies to examine skills acquisition associated with carpal tunnel decompression. METHODS: The individual steps or subtasks required to complete a carpal tunnel decompression were identified using hierarchical task analysis (HTA). The systematic human error reduction and prediction approach (SHERPA) was carried out by consensus of subject matter experts. This identified the potential human errors at each subgoal, the level of risk associated with each task and how these potential errors could be prevented. RESULTS: Carpal tunnel decompression was broken down into 46 subtasks, of which 21 (45%) were medium risk and 25 (55%) were low risk. Of the 46 subtasks, 4 (9%) were assigned high probability and 18 (39%) were assigned medium probability. High probability errors (>1/50 cases) included selecting incorrect tourniquet size, failure to infiltrate local anaesthetic in a proximal-to-distal direction and completion of the World Health Organization (WHO) surgical sign-out. Three (6%) of the subtasks were assigned high criticality, which included failure to aspirate before anaesthetic injection, whereas 21 (45%) were assigned medium criticality. Remedial strategies for each potential error were devised. CONCLUSIONS: The use of HRA techniques provides surgeons with a platform to identify critical steps that are prone to error. This approach may improve surgical training and enhance patient safety.

Fallacies of Using the Win Ratio in Cardiovascular Trials: Challenges and Solutions.

The win ratio was introduced into cardiovascular trials as a potentially better way of analyzing composite endpoints to account for the hierarchy of clinical significance of their components and to facilitate the inclusion of recurrent events. The basic concept of the win ratio is to define a hierarchy of clinical importance within the components of the composite outcome, form all possible pairs by comparing every subject in the treatment group with every subject in the control group, and then evaluate each pair for the occurrence of the components of the composite outcome in descending order of importance, starting at the most important and progressing down the hierarchy if the outcome does not result in a win in either pair until pairs are tied for the outcome after exhaustion of all components. Although the win ratio offers a novel method of depiction of outcomes in clinical trials, its advantages may be counterbalanced by several fallacies (such as ignoring ties and weighting each hierarchal component equally) and challenges in appropriate clinical interpretation (establishing clinical meaningfulness of the observed effect size). From this perspective, we discuss these and other fallacies and provide a suggested framework to overcome such limitations to enhance utility of this statistical method across the clinical trial enterprise.

Lightweight 3D Graphene Metamaterials with Tunable Negative Thermal Expansion.

In this study, a 3D graphene metamaterial (GM) showing negative thermal expansion is prepared using a strategy of hyperbolically oriented freezing under a dual temperature gradient along orthogonal directions after the π-π stacking-derived assembly of 2D graphene sheets. As the fundamental construction element of the 3D GM, the graphene sheet displays anomalous shrinking deformation with a thermal expansion coefficient of (-6.12 ± 0.28) × 10-6 that is triggered by thermally induced out-of-plane vibrations of the CC bonds. A combination of numerical simulations and experimental investigations validates that anomalous negative thermal expansion (NTE) behavior can be effectively delivered to scalable 3D GM candidates at larger dimensions beyond the basic 2D graphene sheets at the microscale. The multiscale design and optimization of the structural characterization of the 3D GM further realize the desirable regulation of the NTE performance with the NTE coefficient ranging from negative ((-7.5± 0.65) × 10-6 K-1 ) to near-zero values ((-0.8 ± 0.25) × 10-6 K-1 ). This is attributed to the NTE-derived release regulation of the primary stress/strain of the microstructure, and the 3D GM exhibits high thermal stability while preserving the desirable structural robustness and fatigue resistance under thermo-mechanical coupling conditions. Therefore, this 3D GM offers promising potential for applications as protective skin, thermal actuator, smart switcher, and packing filler.