Dynamics of Decision Making in Traditional Companies Using Three-Level Quadratic Programming Problem with Random Rough Coefficient in Constraints

This paper presents three-level quadratic programming problem with random rough coefficient in constrains. At the first phase of the solution algorithm, and to avoid the complexity of this problem, we begin with converting the rough nature in constraints into equivalent crisp form. At the second phase, a membership function is constructed to develop a fuzzy model for obtaining the optimal solution of the three-level quadratic programming problem. An auxiliary problem is discussed as well as an example is presented

Measuring the technical efficiency of local banks in UAE using rough bi-level linear programming technique

The aim of this paper is to use a bi-level linear programming technique with rough parameters in the constraints, for measuring the technical efficiency of local banks in UAE and Egypt, while the proposed linear objective functions will be maximized for different goals. Based on Dauer's and Krueger's goal programmingmethod, the described approach was developed to deal with the bi-level decision-making problem. The concept of tolerance membership function together was used to generate the optimal solution for the problem under investigation. Also an auxiliary problem is discussed to illustrate the functionality of the proposed approach

Neoadjuvant chemotherapy and the complexity of operative procedure in advanced epithelial ovarian cancer: a retrospective analysis

Introduction: Complete tumor resection for epithelial ovarian cancer (EOC) generally incorporates complex surgical maneuvers, especially bowel resection. This study retrospectively analyzed the impact of neoadjuvant chemotherapy (NAC) on complexity of surgical procedures for EOC (represented by bowel resection) and postoperative morbidity. Methods: We retrospectively recruited all patients with Fédération Internationale de Gynécologie et d'Obstétrique (FIGO) stages IIIC–IVB EOC who were treated in our center between 2011 and 2016. Patients were divided into those who received primary debulking followed by chemotherapy (group A), and those who received NAC followed by interval debulking (group B). Patient age, tumor stage, grade, dates of commencement and completion of therapy, intraoperative events, completion of surgical resection, and postoperative events were evaluated. Results: Of 92 patients, 42 were assigned to group A and 50 to group B. Their FIGO stages were group A—stages IIIC: 34 (80.9%), IVA: 6 (14.3%), and IVB: 2 (4.8%); and group B—stages IIIC: 45 (90%), IVA: 5 (10%), and IVB: 0 (0%). The 2 groups did not significantly differ in completeness of surgical cytoreduction or rates of bowel resection, intraoperative complications, or postoperative morbidities. Conclusion: NAC did not reduce rates of bowel resection, intraoperative complications, and postoperative morbidity in advanced EOC compared with primary surgical cytoreduction. Future prospective studies will be required to corroborate our results

Distributionally Robust Deep Learning using Hardness Weighted Sampling

Limiting failures of machine learning systems is vital for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM)aiming at addressing this need. However, its use in deep learning has been severely restricted due to the relative inefficiency of the optimizers available for DRO in comparison to the wide-spread variants of Stochastic Gradient Descent (SGD) optimizers for ERM. We propose SGD with hardness weighted sampling, a principled and efficient optimization method for DRO in machine learning that is particularly suited in the context of deep learning. Similar to a hard example mining strategy in essence and in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning, requiring minimal overhead computation. In contrast to typical ad hoc hard mining approaches, and exploiting recent theoretical results in deep learning optimization, we prove the convergence of our DRO algorithm for over-parameterized deep learning networks with ReLU activation and finite number of layers and parameters. Our experiments on brain tumor segmentation in MRI demonstrate the feasibility and the usefulness of our approach. Using our hardness weighted sampling leads to a decrease of 2% of the interquartile range of the Dice scores for the enhanced tumor and the tumor core regions. The code for the proposed hard weighted sampler will be made publicly available

Structural Insights and Docking Analysis of Adamantane-Linked 1,2,4-Triazole Derivatives as Potential 11 -HSD1 Inhibitors

The solid-state structural analysis and docking studies of three adamantane-linked 1,2,4- triazole derivatives are presented. Crystal structure analyses revealed that compound 2 crystallizes in the triclinic P-1 space group, while compounds 1 and 3 crystallize in the same monoclinic P21/c space group. Since the only difference between them is the para substitution on the aryl group, the electronic nature of these NO2 and halogen groups seems to have no influence over the formation of the solid. However, a probable correlation with the size of the groups is not discarded due to the similar intermolecular disposition between the NO2/Cl substituted molecules. Despite the similarities, CE-B3LYP energy model calculations show that pairwise interaction energies vary between them, and therefore the total packing energy is affected. HOMO-LUMO calculated energies show that the NO2 group influences the reactivity properties characterizing the molecule as soft and with the best disposition to accept electrons. Further, in silico studies predicted that the compounds might be able to inhibit the 11 -HSD1 enzyme, which is implicated in obesity and diabetes. Self- and cross-docking experiments revealed that a number of non-native 11 -HSD1 inhibitors were able to accurately dock within the 11 -HSD1 X-ray structure 4C7J. The molecular docking of the adamantane-linked 1,2,4-triazoles have similar predicted binding affinity scores compared to the 4C7J native ligand 4YQ. However, they were unable to form interactions with key active site residues. Based on these docking results, a series of potentially improved compounds were designed using computer aided drug design tools. The docking results of the new compounds showed similar predicted 11 -HSD1 binding affinity scores as well as interactions to a known potent 11 -HSD1 inhibitor

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.</p

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of a state-of-the-art backbone AI for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

The Effect of a Whole Brain Teaching Based Instruction on Developing Number Competencies and Arithmetic Fluency in Kindergarten Children

Young children need to be taught using effective interventions. A good teaching method is one that will increase children's motivation to learn, keep them aware of their understanding and encourage them to reflect on what they learn, if this teaching is based on relevant and visible training. Otherwise, they may suffer from delays in mathematics. One of these effective interventions is Whole Brain Teaching (WBT). The participants were 90 kindergarten children recruited from two public kindergarten schools in Matrouh city, Egypt. Two classes, with 45 children in KG1, were randomly selected using the fishbowl method. Children aged 4 and above (KG1 children) were targeted. In order to analyze the data from the pre- and post-test, the author used the Statistical Package for the Social Sciences (SPSS) V18.0. two- way ANOVA analysis and t-test. The findings of the study confirmed that adopting Whole Brain Teaching (WBT) approach helped in the increasing of student's involvement. Third, the intervention allowed children to see, say, hear and move physically, and this resulted in the emotional involvement in lessons presented. Gradually, as lessons progress, children become more fluent. The effectiveness of WBT had great results which were obvious on children’s learning, affection and behaviour. During and after the presentation and application of the intervention, children maintained behavioural engagement. Prior to the intervention, the teacher took a long time trying to manage children