The Behavioral Role of Digital Economy Adaptation in Sustainable Financial Literacy and Financial Inclusion.

The basic aim of this research was to investigate the impact of the behavioral biases on financial inclusion in Pakistan while considering the moderating effect of financial literacy in this relation, in the context of behavioral perspective. This study focused on the significant behavioral phenomenon, including self-control, optimism, herding, and loss aversion with a perspective of the digital economy. To test the proposed hypothesis, the primary data collection method was used. A structured questionnaire was designed to collect data from 102 individual households through the convenience sampling technique. SmartPLS was used to analyze collected data. This study found the negative impact of self-control, optimism, and herding on financial inclusion. In contrast, loss aversion contributes to the uplift of financial inclusion in Pakistan. Similarly, financial literacy proved to have a decreasing effect on financial inclusion because of religious concerns. The moderation effect of financial literacy was also significantly positive except for loss aversion. The behavioral phenomenon proved to have a significant impact on financial inclusion. This research shows that individual households who do not use developed technological services and products from formal financial inclusion can overcome the behavioral biases that hinder them from making informed financial decisions. This research work will significantly help households use financial services to improve their standard of living and overall long-term financial well-being. This research is essential because many households are not using bank services and have low financial knowledge in Pakistan. The key contribution of this research study is that it found the relation between behavioral factors and financial inclusion. Financial literacy also has a moderating effect on their relations.

Refining Safe Contrast Limits for Preventing Acute Kidney Injury After Percutaneous Coronary Intervention.

Background Contrast-associated acute kidney injury (CA-AKI) is associated with substantial morbidity and may be prevented by using less contrast during percutaneous coronary intervention (PCI). However, tools for determining safe contrast volumes are limited. We developed risk models to tailor safe contrast volume limits during PCI. Methods and Results Using data from all PCIs performed at 18 hospitals from January 2015 to March 2018, we developed logistic regression models for predicting CA-AKI, including simpler models ("pragmatic full," "pragmatic minimum") using only predictors easily derivable from electronic health records. We prospectively validated these models using PCI data from April 2018 to December 2018 and compared them to preexisting safe contrast models using the area under the receiver operating characteristic curve (AUC). The model derivation data set included 20 579 PCIs with 2102 CA-AKI cases. When applying models to the separate validation data set (5423 PCIs, 488 CA-AKI cases), prior safe contrast limits (5*Weight/Creatinine, 2*CreatinineClearance) were poor measures of safety with accuracies of 53.7% and 56.6% in predicting CA-AKI, respectively. The full, pragmatic full, and pragmatic minimum models performed significantly better (accuracy, 73.1%, 69.3%, 66.6%; AUC, 0.80, 0.76, 0.72 versus 0.59 for 5 * Weight/Creatinine, 0.61 for 2*CreatinineClearance). We found that applying safe contrast limits could meaningfully reduce CA-AKI risk in one-quarter of patients. Conclusions Compared with preexisting equations, new multivariate models for safe contrast limits were substantially more accurate in predicting CA-AKI and could help determine which patients benefit most from limiting contrast during PCI. Using readily available electronic health record data, these models could be implemented into electronic health records to provide actionable information for improving PCI safety.

Relationship between economic growth and CO2 emissions: does governance matter?

The environmental stress due to massive CO2 emissions is a critical issue for scholars and policymakers. Many studies have discussed various determinants of environmental pollution both from theoretical and empirical perspectives. However, how governance influences the nexus between economic growth and CO2 emissions is rarely studied. Hence this study investigates the relationship between economic growth, governance and CO2 emissions within the framework of the Environmental Kuznets Curve for five high CO2 emission countries over 1996-2017. Empirical results from the panel data estimation methods reflect that measures of governance influence emission levels differently in high CO2 emission countries. Overall, political, economic and institutional governance enhance environmental quality. According to the results, provision of good governance needs to exercise effective measures to enhance and protect environmental quality.

Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax.

OBJECTIVE: The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. METHODS AND MATERIALS: A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. RESULTS: We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. CONCLUSION: Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.

A Granular Ontology Model for Maternal and Child Health Information System.

In several developing countries, maternal and child health indicators trail behind the international targets set by the UN as Millennium or Sustainable Development Goals. One of the reasons is poor and nonstandardized maternal health record keeping that affects data quality. Effective decision making to improve public healthcare depends essentially on the availability of reliable data. Therefore, the aim of this research is the design and development of the standard compliant data access model for maintaining maternal and child health data to enable the effective exchange of healthcare data. The proposed model is very granular and comprehensive in contrast with existing systems. To evaluate the effectiveness of the model, a web application was implemented and was reviewed by healthcare providers and expectant mothers. User feedback highlights the usefulness of the proposed approach as compared to traditional record-keeping techniques. It is anticipated that the proposed model will lay a foundation for a comprehensive maternal and child healthcare information system. This shall enable trend analysis for policy making to help accelerate the efforts for meeting global maternal and child health targets.

Recommendations service for chronic disease patient in multimodel sensors home environment.

With advanced technologies in hand, there exist potential applications and services built around monitoring activities of daily living (ADL) of elderly people at nursing homes. Most of the elderly people in these facilities are suffering from different chronic diseases such as dementia. Existing technologies are mainly focusing on non-medication interventions and monitoring of ADL for addressing loss of autonomy or well-being. Monitoring and managing ADL related to cognitive behaviors for non-medication intervention are very effective in improving dementia patients' conditions. However, cognitive functions of patients can be improved if appropriate recommendations of medications are delivered at a particular time. Previously we developed the Secured Wireless Sensor Network Integrated Cloud Computing for Ubiquitous-Life Care (SC(3)). SC(3) services were limited to monitoring ADL of elderly people with Alzheimer's disease and providing non-medication recommendations to the patient. In this article, we propose a system called the Smart Clinical Decision Support System (CDSS) as an integral part of the SC(3) platform. Using the Smart CDSS, patients are provided with access to medication recommendations of expert physicians. Physicians are provided with an interface to create clinical knowledge for medication recommendations and to observe the patient's condition. The clinical knowledge created by physicians as the knowledge base of the Smart CDSS produces recommendations to the caregiver for medications based on each patient's symptoms.

The N-terminal juxtamembrane segment of the V1a vasopressin receptor provides two independent epitopes required for high-affinity agonist binding and signaling.

It is fundamentally important to define how agonist-receptor interaction differs from antagonist-receptor interaction. The V1a vasopressin receptor (V1aR) is a member of the neurohypophysial hormone subfamily of G protein-coupled receptors. Using alanine-scanning mutagenesis of the N-terminal juxtamembrane segment of the V1aR, we now establish that Glu54 (1.35) is critical for arginine vasopressin binding. The mutant [E54A]V1aR exhibited decreased arginine vasopressin affinity (1700-fold) and disrupted signaling, but antagonist binding was unaffected. Mutation of Glu54 had an almost identical pharmacological effect as mutation of Arg46, raising the possibility that agonist binding required a mutual interaction between Glu54 and Arg46. The role of these two charged residues was investigated by 1) substituting Glu54; 2) inserting additional Glu/Arg in transmembrane helix (TM) 1; 3) repositioning the Glu/Arg in TM1; and 4) characterizing the reciprocal mutant [R46E/E54R]V1aR. We conclude that 1) the positive/negative charges need to be precisely positioned in this N terminus/TM1 segment; and 2) Glu54 and Arg46 function independently, providing two discrete epitopes required for high-affinity agonist binding and signaling. This study explains why Glu and Arg, part of an -R(X3)L/V(X3)E(X3)L- motif, are conserved at these loci throughout this G protein-coupled receptor subfamily and provides molecular insight into key differences between agonist and antagonist binding requirements.