Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Inferring Socioeconomic Characteristics from Travel Patterns

Nowadays, crowd-based big data is widely used in transportation planning. These data sources provide valuable information for model validation; however, they cannot be used to estimate travel demand forecasting models, because these models need a linkage between travel patterns and the socioeconomic characteristics of the people making trips and such a connection is not available due to privacy issues. As such, uncovering the correlation between travel patterns and socioeconomic characteristics is crucial for travel demand modelers to be able to leverage such data in model estimation. Different age, gender, and income groups may have specific travel behavior preferences. To extract and investigate these patterns, we used two data sets: one from the National Household Travel Survey 2009 and the other from the Metropolitan Washington Council of Government Transportation Planning Board 2007-2008 household survey. After preprocessing the data, a range of machine learning algorithms were used to synthesize the socioeconomic characteristics of travelers. After comparison, we found that the CatBoost model outperformed the other models. To further improve the results, a synthetic population and Bayesian updating were used, which considerably improved the estimation of income. This study showed that the conventional inference of travel demand from socioeconomic patterns can be reversed, creating an opportunity to utilize the plethora of crowd-based mobility data

Operations planning for agricultural harvesters using ant colony optimization

An approach based on ant colony optimization for the generation for optimal field coverage plans for the harvesting operations using the optimal track sequence principle B-patterns was presented. The case where the harvester unloads to a stationary facility located out of the field area, or in the field boundary, was examined. In this operation type there are capacity constraints to the load that a primary unit, or a harvester in this specific case, can carry and consequently, it is not able to complete the task of harvesting a field area and therefore it has to leave the field area, to unload, and return to continue the task one or more times. Results from comparing the optimal plans with conventional plans generated by operators show reductions in the in-field nonworking distance in the range of 19.3-42.1% while the savings in the total non-working distance were in the range of 18-43.8%. These savings provide a high potential for the implementation of the ant colony optimization approach for the case of harvesting operations that are not supported by transport carts for the out-of-the-field removal of the crops, a practice case that is normally followed in developing countries, due to lack of resources

Optimal route planning of agricultural field operations using ant colony optimization

Farming operations efficiency is a crucial factor that determines the overall operational cost in agricultural production systems.  Improved efficiency can be achieved by implementing advanced planning methods for the execution of field operations dealing, especially with the routing and area coverage optimisation aspects. Recently, a new type of field area coverage patterns, the B-patterns, has been introduced.  B-patterns are the result of a combinatorial optimisation process that minimizes operational criterions such as, the operational time, non-working travelled distance, fuel consumption etc.  In this paper an algorithmic approach for the generation of B-patterns based on ant colony optimisation is presented.  Ant colony optimization metaheuristic was chosen for the solution of the graph optimisation problem inherent in the generation of B-patterns.  Experimental results on two selected fields were presented for the demonstration of the effectiveness of the proposed approach. Based on the results, it was shown that it is feasible to use ant colony optimization for the generation of optimal routes for field area coverage while tests made on the resulting routes indicated that they can be followed by any farm machine equipped with auto-steering and navigation systems

Effects of dietary Thymus Vulgaris essential oil on the liver in mice

Introduction: Thymus vulgaris (thyme) is commonly used in folk medicine for several therapeutic purposes. The aim of this study was to evaluate the effects of peritoneal injection of essential oil of thymus vulgaris on the liver as an organ involved in metabolism. It also examines toxic effects following a high dose of the drug. Methods and Results: Twenty-four mice were divided randomly into three groups. Group 1 was the control group without treatment, group 2 has received a dose of thyme essential oil, 0.4 mg/kg intraperitonealy for 10 days and group 3 has received a dose of thyme essential oil, 0.6 mg/kg intraperitonealy for 10 days. A score of liver damage severity was semi-quantitatively assessed using the modified Histological Activity Index ‘(modified HAI). Statistical analysis was performed by using computer program SPSS (19). The results showed the statistically significant increase (P≤ 0.05) in the histopathological scoring in Group2 when compared with Group1. Conclusions: The histopathological examination of control group reveals normal hepatic tissue, no portal or periportal inflammation, necrosis, congestion, Infiltration of WBC and fibrosis. While there was a significant loss in hepatic architecture in Group 2 which showed portal inflammation with periportal interface hepatitis (piecemeal necrosis) centrilobular necrosis, congestion, infiltration of WBC and bridging necrosis. The results were revealed statistically significant increase (P≤ 0.05) in the histopathological scoring in Group3 when compared with Group2. The histopathological examination of thyme treated group (Gp3) showed significant toxic effects with moderate acute inflammation of mononuclear cells. It has been determined that Thymus vulgaris (thyme) leads to histological damage including portal inflammation with centrilobular necrosis. The histological alterations may occur through oxidative properties

APPLYING NANOPARTICLES FOR TREATING GIARDIA INFECTION: A SYSTEMATIC REVIEW

At present, chemotherapy with some drugs such as nitroimidazoes derivatives is the preferred treatment for giardiasis. However, these agents are associated with adverse side effects ranging from nausea to possible genotoxicity. The present investigation was designed to systematically review the in vitro, in vivo, and clinical studies about the efficacy of nanoparticles against giardiasis. The study was carried out based on the 06-PRISMA guideline and registered in the CAMARADES-NC3Rs Preclinical Systematic Review and Meta-analysis Facility (SyRF) database. The search was performed in five English databases, including Scopus, PubMed, Web of Science, EMBASE, and Google Scholar, without time limitation for publications around the world about anti-Giardia effects of all organic and inorganic nanoparticles without date limitation in order to identify all the published articles. The searched words and terms were “Giardiasis”, “Giardia lamblia”, “Giardia intestinalis”, “Giardia duodenalis”, “nanoparticles”, “nanomedicine”, “in vitro”, in vivo”, and “clinical trial”. Out of 312 papers, 10 papers, including 4 in vitro (40.0%), 5 in vivo (50.0%), and 1 in vitro/in vivo (10.0%) up to 2021 met the inclusion criteria for discussion in this systematic review. The most common type of nanoparticles was metal nanoparticles (5 studies, 50.0%) such as silver, gold, etc., followed by organic nanoparticles such as chitosan nanoparticles (4 studies, 40.0%). The results of this review study showed the high efficacy of a wide range of organic and non-organic NPs against giardiasis, indicating that nanoparticles could be considered as an alternative and complementary resource for treating giardiasis, since they have no significant toxicity. However, more studies are required to elucidate this conclusion, especially in clinical systems

The global, regional, and national burden of stomach cancer in 195 countries, 1990-2017 : a systematic analysis for the Global Burden of Disease study 2017

Background: Stomach cancer is a major health problem in many countries. Understanding the current burden of stomach cancer and the differential trends across various locations is essential for formulating effective preventive strategies. We report on the incidence, mortality, and disability-adjusted life-years (DALYs) due to stomach cancer in 195 countries and territories from 21 regions between 1990 and 2017. Methods: Estimates from GBD 2017 were used to analyse the incidence, mortality, and DALYs due to stomach cancer at the global, regional, and national levels. The rates were standardised to the GBD world population and reported per 100 000 population as age-standardised incidence rates, age-standardised death rates, and age-standardised DALY rates. All estimates were generated with 95% uncertainty intervals (UIs). Findings: In 2017, more than 1·22 million (95% UI 1·19–1·25) incident cases of stomach cancer occurred worldwide, and nearly 865 000 people (848 000–885 000) died of stomach cancer, contributing to 19·1 million (18·7–19·6) DALYs. The highest age-standardised incidence rates in 2017 were seen in the high-income Asia Pacific (29·5, 28·2–31·0 per 100 000 population) and east Asia (28·6, 27·3–30·0 per 100 000 population) regions, with nearly half of the global incident cases occurring in China. Compared with 1990, in 2017 more than 356 000 more incident cases of stomach cancer were estimated, leading to nearly 96 000 more deaths. Despite the increase in absolute numbers, the worldwide age-standardised rates of stomach cancer (incidence, deaths, and DALYs) have declined since 1990. The drop in the disease burden was associated with improved Socio-demographic Index. Globally, 38·2% (21·1–57·8) of the age-standardised DALYs were attributable to high-sodium diet in both sexes combined, and 24·5% (20·0–28·9) of the age-standardised DALYs were attributable to smoking in males. Interpretation: Our findings provide insight into the changing burden of stomach cancer, which is useful in planning local strategies and monitoring their progress. To this end, specific local strategies should be tailored to each country's risk factor profile. Beyond the current decline in age-standardised incidence and death rates, a decrease in the absolute number of cases and deaths will be possible if the burden in east Asia, where currently almost half of the incident cases and deaths occur, is further reduced. Funding: Bill & Melinda Gates Foundation

Estimating global injuries morbidity and mortality : methods and data used in the Global Burden of Disease 2017 study

Background: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future

Global injury morbidity and mortality from 1990 to 2017 : results from the Global Burden of Disease Study 2017

Correction:Background Past research in population health trends has shown that injuries form a substantial burden of population health loss. Regular updates to injury burden assessments are critical. We report Global Burden of Disease (GBD) 2017 Study estimates on morbidity and mortality for all injuries. Methods We reviewed results for injuries from the GBD 2017 study. GBD 2017 measured injury-specific mortality and years of life lost (YLLs) using the Cause of Death Ensemble model. To measure non-fatal injuries, GBD 2017 modelled injury-specific incidence and converted this to prevalence and years lived with disability (YLDs). YLLs and YLDs were summed to calculate disability-adjusted life years (DALYs). Findings In 1990, there were 4 260 493 (4 085 700 to 4 396 138) injury deaths, which increased to 4 484 722 (4 332 010 to 4 585 554) deaths in 2017, while age-standardised mortality decreased from 1079 (1073 to 1086) to 738 (730 to 745) per 100 000. In 1990, there were 354 064 302 (95% uncertainty interval: 338 174 876 to 371 610 802) new cases of injury globally, which increased to 520 710 288 (493 430 247 to 547 988 635) new cases in 2017. During this time, age-standardised incidence decreased non-significantly from 6824 (6534 to 7147) to 6763 (6412 to 7118) per 100 000. Between 1990 and 2017, age-standardised DALYs decreased from 4947 (4655 to 5233) per 100 000 to 3267 (3058 to 3505). Interpretation Injuries are an important cause of health loss globally, though mortality has declined between 1990 and 2017. Future research in injury burden should focus on prevention in high-burden populations, improving data collection and ensuring access to medical care.Peer reviewe

Estimating global injuries morbidity and mortality : methods and data used in the Global Burden of Disease 2017 study

Background While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.Peer reviewe