Spatio-temporal modelling of breast cancer incidence between 2000 and 2021 at sub-national levels in Iran: Bayesian disease mapping

While in Iran trends in breast cancer incidence are generally monitored at the national level, little is known about sub-national variation in these trends. This project aims to assess levels and trends (2000-2021) of relative risk of breast cancer incidence and mortality at sub-national levels in Iran and their relation to key socioeconomic dimensions, to understand the full extent of geographical and social inequalities in the country associated with breast cancer morbidity and mortality. Data from the national cancer registry system of the Iranian Ministry of Health have been used, which is gathered on cancer incidence at provincial and district levels by age and sex. Census and Household Expenditure and Income Survey (HEIS) datasets have been then used to extract related covariates. The relative risk of breast cancer incidence was estimated in women aged 30+ years for all 316 districts in Iran from 2000 to 2010 using a Bayesian spatio-temporal model. Then, I’ve propagated uncertainty from the spatio-temporal model into the prediction model for the years from 2011 to 2021. The national relative risk of breast cancer incidence in Iran increased from 0.21 (95% credible interval (CrI): 0.19, 0.22) in 2000 to 0.66 (0.63, 0.68) in 2010 and 1.23 (1.18, 1.28) in 2021. The relative risk of breast cancer incidence was highest in Yazd (1.96 [1.63, 2.33]), Shiraz (1.90 [1.72, 2.09]) and Shemiranat (1.90 [1.12, 2.91]) in 2010 and Tehran (3.99 [3.86, 4.33]), Bushehr (3.89 [3.07, 4.77]) and Abadan (3.67 [2.99, 4.39]) in 2021. In contrast, Savojbolagh, Saravan and Nikshahr were found to have the lowest relative risks in both 2010 (0.11 [0.05, 0.20], 0.17 [0.08, 0.30] and 0.20 [0.09, 0.36], respectively) and 2021 (0.19 [0.10, 0.33], 0.34 [0.18, 0.54] and 0.35 [0.17, 0.62], respectively). The relative risk of breast cancer incidence was 60% higher across districts in the highest YOS quintile (average years of schooling: 3.9) than those in the lowest YOS quintile (average years of schooling: 2.2; relative index of inequality: 1.6). Results show that the relative risk of breast cancer incidence has increased over time (2000-2021) at national and sub-national levels in Iran. Breast cancer is one of the few diseases with a positive education gradient, with higher relative risk of breast cancer incidence among higher- compared to lower-educated women, probably due to better awareness of diagnostic approaches and access to those. While social inequalities are a major barrier to reducing the prevalence and incidence of breast cancer, it is important to track the progress made at the district level based on the characteristics of specific policies aimed at reducing health inequalities. A scaling-up in the quality of healthcare services, national and sub-national policies addressing prevention and treatment, and more specialised training programs in women's health are needed

A Simple Method for Finding Optimal Paths of Hot and Cold Streams inside Shell and Tube Heat Exchangers to Reduce Pumping Cost in Heat Exchanger Network Problems

In this paper, a simple method is presented for the synthesis and retrofit of heat exchanger networks (HENs) by considering pressure drop as well as finding proper path of streams inside heat exchangers (HEs) to reduce the pumping cost of network. Generally, HEN problems lead to MINLP models which have convergence difficulties due to the existence of both continuous and integer variables. In this study, instead of solving these variables simultaneously, a combination of Genetic Algorithm (GA) with Quasi Linear Programming (QLP) and Integer Linear Programming (ILP) was used for solving the problem. GA was used to find optimal HENs structure and streams paths, whereas continuous variables were solved by QLP. For the retrofit of HENs, a modified ILP model was used. Results show that the proposed method has the ability to reduce the cost of annual pumping due to considering optimal paths for streams in the HEs compared to the literature. This work is licensed under a Creative Commons Attribution 4.0 International License

Study on Transpiration Rates of Vicia Villocea and Bromus Inermis Species

Ecohydrology is concerned with the interaction between the hydrological and plant processes. Some aspects of the hydrologic cycle, such as transpiration and interception have received little attention owing to difficulties in field measurements. Quantifying the components of water balance for a watershed is crucial for understanding the dominant hydrologic processes occurring in a basin (Flerchinger & Cooley, 2000). Water use by vegetation is controlled by the water uptake by roots, the transfer of liquid water through plants and vapour loss from the leaf surfaces by the opening and closure of the stomata (Roberts, 2000) i.e. transpiration. Comparison of transpiration of rangelands species is a prerequisite for improving range management. The present study is a preliminary comparison in transpiration between two important Iranian rangeland species, viz. the legume, Vicia villocea and the grass, Bromus inermis

Field trial of a 15 Tb/s adaptive and gridless OXC supporting elastic 1000-fold all-optical bandwidth granularity

An adaptive gridless OXC is implemented using a 3D-MEMS optical backplane plus optical modules (sub-systems) that provide elastic spectrum and time switching functionality. The OXC adapts its architecture on demand to fulfill the switching requirements of incoming traffic. The system is implemented in a seven-node network linked by installed fiber and is shown to provide suitable architectures on demand for three scenarios with increasing traffic and switching complexity. In the most complex scenario, signals of mixed bit-rates and modulation formats are successfully switched with flexible per-channel allocation of spectrum, time and space, achieving over 1000-fold bandwidth granularity and 1.5 Tb/s throughput with good end-to-end performance

A qualitative interview study to determine barriers and facilitators of implementing automated decision support tools for genomic data access

Data access committees (DAC) gatekeep access to secured genomic and related health datasets yet are challenged to keep pace with the rising volume and complexity of data generation. Automated decision support (ADS) systems have been shown to support consistency, compliance, and coordination of data access review decisions. However, we lack understanding of how DAC members perceive the value add of ADS, if any, on the quality and effectiveness of their reviews. In this qualitative study, we report findings from 13 semi-structured interviews with DAC members from around the world to identify relevant barriers and facilitators to implementing ADS for genomic data access management. Participants generally supported pilot studies that test ADS performance, for example in cataloging data types, verifying user credentials and tagging datasets for use terms. Concerns related to over-automation, lack of human oversight, low prioritization, and misalignment with institutional missions tempered enthusiasm for ADS among the DAC members we engaged. Tensions for change in institutional settings within which DACs operated was a powerful motivator for why DAC members considered the implementation of ADS into their access workflows, as well as perceptions of the relative advantage of ADS over the status quo. Future research is needed to build the evidence base around the comparative effectiveness and decisional outcomes of institutions that do/not use ADS into their workflows.</p

Leveraging algorithms to improve decision-making workflows for genomic data access and management

Studies on the ethics of automating clinical or research decision making using artificial intelligence and other algorithmic tools abound. Less attention has been paid, however, to the scope for, and ethics of, automating decision making within regulatory apparatuses governing the access, use, and exchange of data involving humans for research. In this article, we map how the binary logic flows and real-time capabilities of automated decision support (ADS) systems may be leveraged to accelerate one rate-limiting step in scientific discovery: data access management. We contend that improved auditability, consistency, and efficiency of the data access request process using ADS systems have the potential to yield fairer outcomes in requests for data largely sourced from biospecimens and biobanked samples. This procedural justice rationale reinforces a broader set of participant and data subject rights that data access committees (DACs) indirectly protect. DACs protect the rights of citizens to benefit from science by bringing researchers closer to the data they need to advance that science. DACs also protect the informational dignities of individuals and communities by ensuring the data being accessed are used in ways consistent with participant values. We discuss the development of the Global Alliance for Genomics and Health Data Use Ontology standard as a test case of ADS for genomic data access management specifically, and we synthesize relevant ethical, legal, and social challenges to its implementation in practice. We conclude with an agenda of future research needed to thoughtfully advance strategies for computational governance that endeavor to instill public trust in, and maximize the scientific value of, health-related human data across data types, environments, and user communities

Heavy Metals Residue in Cultivated Mango Samples from Iran

Background: Heavy metals contaminations are recognized as the serious risk to our environment. The aim of the present study was to analyze heavy metals residue in cultivated mango samples from Iran. Methods: Totally, 72 mango samples were randomly collected among six different mango genotypes cultivated in Southern Iran from June to July 2015. Lead, chromium, cadmium, and arsenic were determined using an atomic absorption spectrometer. Analysis of variance was performed with SAS 9.0. Descriptive statistics, multivariate analysis, and Duncan multiple range tests were done with a significance level of p<0.05. Results: Measurement of heavy metals in all the mango samples showed various level ranges of lead (0.008-0.05 ppm), chromium (0.01-0.1 ppm), cadmium (0.002-0.014 ppm), and arsenic (0.01-0.04 ppm). Heavy metal levels were significantly (p0.05) was seen between heavy metals residue and variety in genotypes of mango samples. Conclusion: The average amount of heavy metals residue in mango samples found in the current study were generally below the maximum acceptable levels indicating acceptable safety of these products

The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy

NiTi-based alloys are one of the most well-known alloys among shape memory alloys having a wide range of applications from biomedical to aerospace areas. Adding a third element to the binary alloys of NiTi changes the thermomechanical properties of the material remarkably. Two unique features of stability and high transformation temperature have turned NiTiHf as a suitable ternary shape memory alloys in various applications. Selective laser melting (SLM) as a layer-based fabrication method addresses the difficulties and limitations of conventional methods. Process parameters of SLM play a prominent role in the properties of the final parts so that by using the different sets of process parameters, different thermomechanical responses can be achieved. In this study, different sets of process parameters (PPs) including laser power, hatch space, and scanning speed were defined to fabricate the NiTiHf samples. Changing the PPs is a powerful tool for tailoring the thermomechanical response of the fabricated parts such as transformation temperature (TTs), density, and mechanical response. In this work, an artificial neural network (ANN) was developed to achieve a prediction tool for finding the effect of the PPs on the TTs and the size deviation of the printed parts

Experimental Study on the Optimization of Dielectric Barrier Discharge Reactor for NOx Treatment

In this paper, a comprehensive study of a DBD reactor is conducted to investigate the optimum operating conditions of the reactor for NOx treatment. For each parameter, the objective is to find the maximum NOx removal efficiency with the minimum consumed power. Different effective parameters of the reactor i.e. electrode length and diameter, electrode and dielectric materials as well as parameters of power generator, i.e. voltage and frequency, are investigated. The results show that for this configuration, the electrode with 20 cm length and 10 mm diameter has the best performance. Aluminum as the inside electrode material and quartz as the dielectric material are selected. Furthermore, the optimum value for the pulse frequency is 16.6 kHz. For the mentioned optimum conditions, the NOx removal efficiency achieved is equal to almost 82% at the input power of 486 W. Furthermore, the highest achieved NOx removal is almost 92% at the input power of 864 W. The results of this paper can be used to reduce the energy consumption of NTP systems to acceptable levels