Proposing a Hybrid Approach to Predict, Schedule and Select the Most Robust Project Portfolio under Uncertainty

Suitable project portfolio selection in inconsistent economy that can reduce the portfolio risks and increasing utilities for investors has gained significant research attentions.   This article addresses the project portfolio selection in which conventional certain (1) prediction, (2) optimization and (3) clustering approaches cannot be used to face uncertainty. To predict the real value of affecting project risk parameters, neural network has been used; Then to determine the optimized sequences and procedures, the proposed model have been evaluated using the multi-objective shuffle frog leaping algorithm (SFLA) by robust optimization approach; To suggest different risk criteria, K-means algorithm utilized to categorize the candidate projects and differentiating the clusters.  As the proposed hybrid methodology is studied on 420 different construction projects in an Iranian construction company in two economic stable years and an instable year in Iran real estate market. The results show 96 percent prediction-optimization capability due to different desired criteria

Lymphatic mapping and sentinel node biopsy in endometrial cancer — a feasibility study using cervical injection of radiotracer and blue dye

BACKGROUND: The aim of this study was to evaluate the feasibility and accuracy of sentinel lymph node (SLN) detection using preoperative lymphoscintigraphy and intra-operative gamma probe/blue dye for endometrial cancer patients. MATERIAL AND METHODS: Twenty four consecutive patients with endometrial cancer were recruited. All patients underwent lymphatic mapping and sentinel node biopsy using combined intracervical radiotracer and blue dye injections. Pelvic lymph node dissection was performed for all patients. Para-aortic lymphadenectomy was done in high risk patients. All SLNs were examined by frozen section and Hematoxylin and Eosin (H&E) permanent sections. RESULTS: Pre-operative lymphoscintigraphy showed at least one SLN in 21/24 patients. Intra-operatively, at least one SLN could be harvested by gamma probe and/or blue dye methods. A total of 95 SLNs were detected. Four SLNs were detected only by blue dye, 42 only by radiotracer, and 49 were hot/blue. Median number of SLN per patient was 3. Three patients had positive pelvic lymph nodes. All of them had positive SLN (no false negative case). Frozen section could identify SLN involvement in two of three patients with positive pathology. CONCLUSION: Lymphatic mapping and sentinel node biopsy is feasible and accurate in endometrial cancer patients using combined radiotracer and blue dye methods. Frozen section accuracy was lower and underscores the importance of expert pathologists for SLN mapping technique.

Cathodic synthesis of Al-Ce-Mn Oxide nanohybride powder with improved surface for effective removal of fluoride from aqueous media

Al-Ce-Mn oxide samples were synthesized by the cathodic electrochemical method at current densities of 5, 15, and 35 mAcm-2. The XRD, SEM, and EDX techniques were used for the characterization of samples. The SEM images show that at high current density the one-dimensional(nanowire) structure and at low current density two-dimensional (nanosheet) structure were obtained. Moreover, the particle sizes are decreased with increasing the current density. The samples were applied for the uptake of fluorine (F-) ions from solutions. The influence of the contact time, initial fluoride concentration, and solution pH on the adsorption was investigated. The results showed more than 80 % of F-  ions were uptake from solution during the three hours initial contact times and the uptake capacity has little change at pH below 6 and it has a sharp decline with increasing solution pH. The kinetic data were well fitted to the pseudo-second-order model and the equilibrium adsorption data was well described by the Langmuir isotherm model.  The adsorption capacity was 48 mg/g at pH 6 and room temperature

GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation

Similar item recommendation is a critical task in the e-Commerce industry, which helps customers explore similar and relevant alternatives based on their interested products. Despite the traditional machine learning models, Graph Neural Networks (GNNs), by design, can understand complex relations like similarity between products. However, in contrast to their wide usage in retrieval tasks and their focus on optimizing the relevance, the current GNN architectures are not tailored toward maximizing revenue-related objectives such as Gross Merchandise Value (GMV), which is one of the major business metrics for e-Commerce companies. In addition, defining accurate edge relations in GNNs is non-trivial in large-scale e-Commerce systems, due to the heterogeneity nature of the item-item relationships. This work aims to address these issues by designing a new GNN architecture called GNN-GMVO (Graph Neural Network - Gross Merchandise Value Optimizer). This model directly optimizes GMV while considering the complex relations between items. In addition, we propose a customized edge construction method to tailor the model toward similar item recommendation task and alleviate the noisy and complex item-item relations. In our comprehensive experiments on three real-world datasets, we show higher prediction performance and expected GMV for top ranked items recommended by our model when compared with selected state-of-the-art benchmark models.Comment: 9 pages, 3 figures, 43 citation

Immobilized nickel hexacyanoferrate nano particles on graphen for effective removal of Cs(I) ions from radionuclide wastes

In the current work synthesis and modification of graphene oxide with Nickel Hexa Ferrocyanide (NiHCF) nanoparticles has been reported. The Graphene oxide- Nickel Hexa Ferrocyanide (GO-NiHCF) was used as an adsorbent to remove Cesium (Cs) ions from a simulated solution. The obtained product was characterized with XRD, SEM, TGA, FTIR, and BET techniques. The SEM images and XRD pattern confirms the successful immobilization of Nickel Hexa Ferrocyanide on graphene oxide sheet. The cesium removal ability of GO-NiHCF was evaluated in batch mode. Effect of various parameters such as pH, initial concentration, contact time, and interferences ions were studied. The results cleared that the maximum adsorption for Cs removal was 240 mg g-1. Equilibrium modeling studies suggest that the data are reasonably and relatively fitted well to the Langmuir adsorption isotherm. Kinetic studies show that sorption process is fairly rapid and the kinetic data are fitted well to the pseudo-second order rate model. This composite offers strong potential in the field of elimination of Cs that requires rapid and complete decontamination

New metal organic framework (MOF) nanoparticle for gas separation by matrix membranes

{[Dy(BTC)(H2O)]•DMF}n metal organic framework nanoparticles was synthezed through solvthermal method. The product was characterized by XRD, TG, BET, and SEM techniques. SEM images showed that the synthesized sample has semi-cubic particles with average size of 70 nm in length.For improve the gas separation performance,the MOF nano particles were dispersed in polydimethylsiloxane (PDMS) for preparation of mixed matrix membrane (MMM) on support of polyethersulphone (PES). The performance of obtained MMM in separation of NO, N2 and O2 gas were investigated, and the effect of MOF nanoparticles (5, 10, and 15% wt)and feed pressure (100-250 kPa) on permeability and selectivity were studied. It was found that the membrane performance is evaluated by addition of MOF nano particles in membrane (polymeric matrix), and the feed pressure have not important effect on separation. The performance (NO/N2 and NO/O2 selectivity) increased as the loading of MOF particles (up to 15% wt) being dispersed within the polymer matrices

The association of cardio-metabolic risk factors and history of falling in men with osteosarcopenia: a cross-sectional analysis of Bushehr Elderly Health (BEH) program

Osteosarcopenia, defined as sarcopenia plus osteopenia/osteoporosis, may increase the risk of fractures and affects morbidity and mortality in the older population. Falling is also common in the elderly and increases the risk of fractures and mortality. We examined the association of cardio-metabolic risk factors with a history of falling in osteosarcopenic men. Methods We used the baseline data of the Bushehr Elderly Health (BEH) program. Osteosarcopenia was defined as having both sarcopenia (reduced skeletal muscle mass plus low physical performance and/or low muscle strength) and osteopenia/osteoporosis (T-score ≤ − 1.0). Falling was defined as a self-reported history of an unintentional down on the ground during the previous year before the study. We used logistic regression analysis to estimate the adjusted odds ratio (AOR) with a 95% Confidence Interval (CI) to quantify the associations. Results All elderly men diagnosed with osteosarcopenia (n = 341), with a mean age of 73.3(±7.4) years, were included. Almost 50(14.7%) participants reported falling. Age showed a positive association with falling (AOR: 1.09, 95%CI: 1.04–1.14). An increase of 10 mmHg in systolic blood pressure(SBP), reduces the odds of falling by 26%(AOR:0.74, 95%CI:0.62–0.89), while a positive association was detected for fasting plasma glucose (FPG), as 10 mg/dl increase in the FPG, raises the chance of falling by 14%(AOR = 1.14, 95%CI:1.06,1.23). Hypertriglyceridemia was inversely associated with falling (AOR = 0.33, 95% CI: 0.12, 0.89). Conclusions Falling is a major public health problem in rapidly aging countries, especially in individuals with a higher risk of fragility fractures. Older age-raised fasting plasma glucose and low SBP are associated with falling in osteosarcopenic patients. Considering the higher risk of fracture in osteosarcopenic men, comprehensive strategies are needed to prevent fall-related injuries in this high-risk population

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation