Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem

[Extract] Population based optimization algorithms are the techniques which are in the set of the nature based optimization algorithms. The creatures and natural systems which are working and developing in nature are one of the interesting and valuable sources of inspiration for designing and inventing new systems and algorithms in different fields of science and technology. Evolutionary Computation (Eiben& Smith, 2003), Neural Networks (Haykin, 99), Time Adaptive Self-Organizing Maps (Shah-Hosseini, 2006), Ant Systems (Dorigo & Stutzle, 2004), Particle Swarm Optimization (Eberhart & Kennedy, 1995), Simulated Annealing (Kirkpatrik, 1984), Bee Colony Optimization (Teodorovic et al., 2006) and DNA Computing (Adleman, 1994) are among the problem solving techniques inspired from observing nature. In this chapter population based optimization algorithms have been introduced. Some of these algorithms were mentioned above. Other algorithms are Intelligent Water Drops (IWD) algorithm (Shah-Hosseini, 2007), Artificial Immune Systems (AIS) (Dasgupta, 1999) and Electromagnetism-like Mechanisms (EM) (Birbil & Fang, 2003). In this chapter, every section briefly introduces one of these population based optimization algorithms and applies them for solving the TSP. Also, we try to note the important points of each algorithm and every point we contribute to these algorithms has been stated. Section nine shows experimental results based on the algorithms introduced in previous sections which are implemented to solve different problems of the TSP using well-known datasets

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

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

Parametric Costing Model Suitable for Economic Assessment of Heat Recovery Projects Implied by Site-Wide Energy Analysis Methods

RÉSUMÉ: Les industries peuvent être en mesure de réduire leurs coûts de production et de contribuer à la réalisation des objectifs de réduction des émissions en augmentant leur efficacité énergétique. L'objectif des études sur l'efficacité énergétique et l'intégration dans les usines de fabrication est d'examiner comment maximiser l'utilisation des fournisseurs d'énergie sur place tout en réduisant la dépendance à l'égard des sources d'énergie extérieures (par exemple, le pétrole, le gaz, le charbon, etc.). Plusieurs approches systématiques, telles que les méthodes Pinch et Bridge, ont été proposées et développées pour détecter les opportunités associées à l'intégration croissante entre les flux de processus par le biais de la modernisation du réseau d'échangeurs de chaleur. Cependant, les efforts se sont principalement concentrés sur la définition et/ou le raffinement des outils de visualisation utilisés pour obtenir des objectifs d'économie d'énergie et trouver des projets de modernisation basés sur des critères thermodynamiques, avec peu d'attention accordée à l'évaluation économique des projets de modernisation des HEN. Cette thèse propose un modèle de coût paramétrique amélioré qui peut être utilisé pour améliorer l'évaluation économique préliminaire des projets de récupération de chaleur suggérés par les méthodes d'analyse énergétique à l'échelle du site. Pour atteindre cet objectif, les paramètres clés de conception et de coût qui ont un impact sur les coûts d'investissement totaux directs des projets et sur les économies de coûts d'exploitation totales associées sont identifiés par une analyse détaillée de divers projets recommandés par des méthodes d'analyse énergétique à l'échelle du site. À la suite de cette étape, un modèle d'évaluation économique global est proposé pour chaque type de modification du réseau d'échangeurs de chaleur, qui met en corrélation les coûts d'achat et d'installation des éléments d'équipement associés à l'intérieur et à l'extérieur des limites de la batterie du projet. De plus, pour montrer que la nouvelle méthode d'évaluation globale des coûts est un outil fiable qui fournit à l'analyste des données économiques plus précises pour l'aider dans le processus décisionnel ouvert de la modification du réseau d'échangeurs de chaleur, un exemple a été donné pour comparer l'évaluation économique globale des projets à la méthode traditionnelle d'évaluation des coûts abordée dans la documentation sur les méthodes d'analyse énergétique à l'échelle du site, qui ne tient compte que du coût de l'échangeur de chaleur. ABSTRACT: Industries may be able to lower production costs and aid in satisfying emission reduction targets by increasing their energy efficiency. Examining how to maximize the use of on-site energy suppliers while decreasing reliance on outside energy supplies (e.g., oil, gas, coal, etc.) is the goal of energy efficiency and integration studies in manufacturing plants. Several systematic approaches, such as the Pinch and Bridge methods, have been proposed and developed to detect opportunities associated with the increasing integration between process streams through heat exchanger network (HEN) retrofitting; however, efforts have mostly focused on defining and/or refining visualization tools used to obtain energy saving targets and finding retrofit projects based on thermodynamic criteria, with little attention paid to the economic assessment of the HEN retrofit projects. This thesis proposes an improved parametric cost model that can be used to improve the preliminary economic assessment of heat recovery projects (HRPs) suggested by site-wide energy analytics methods (SWEAMs). To achieve this goal, key design and cost parameters that impact direct total investment costs (TICs) of HRPs and related total operating costs (TOCs) saving are identified through detailed analysis of diverse HRPs recommended by SWEAMs. As a result of this step, a Global Economic Assessment Model is proposed for each type of HEN modification that correlates the purchase and installation costs of HRP's ISBL and OSBL equipment items. Also, to show that the new global costing approach is a reliable tool that gives the analyst more accurate economic data to help with the open-ended decision-making process of the HEN retrofit, an example was given to compare the global economic assessment of the HRPs to the traditional costing method discussed in SWEAM-based literature, which only looked at the cost of the HX. If there is not enough data or time to conduct a rigorous economic assessment, factored-based cost models can estimate the cost of auxiliary equipment needed to install main items as a percentage of equipment (e.g. HX, pump) purchase cost. However, there are two major issues associated with existing models in conjunction with various HEN topology modifications: (1) cost factors only proposed for HX, whereas HEN topology modifications imply three main equipment, HX, pump/compressor, and piping system required for connecting different modules of the plant, and (2) HX cost factors are unreliable due to their insensitivity to equipment design alternatives and plant topology. These gaps motivate to propose enhanced factored-based cost models for eachviSWEAM’s recommendations named: (i) Adding new HX, (ii) modifying existing HX, (iii) stream splitting-mixing, and (iv) resequencing of existing HX(s)

Earthquake Damage Assessment Based on Deep Learning Method Using VHR Images

One of the numerous fundamental tasks to perform rescue operations after an earthquake is to check the status of buildings that have been destroyed. The methods to obtain the damage map are in two categories. The first group of methods uses data before and after the earthquake, and the second group only uses the data after the earthquakes that we want, to offer a flexible damage map according to information that we are available to achieve. In this paper, we work on VHR satellite images of Haiti and UNet which is a convolution network. The learning algorithm’s profound changes to improve the results were intended to identify the damage of the buildings caused by the earthquake. The deep learning algorithms require training data and that is one of the problems that we want to solve. As well as previous studies examining pixel by pixel degradation, ultimate precision to increase that shows the success of this approach felt and has been able to reach the overall accuracy of 68.71%. The proposed method for other natural disasters such as rockets, explosions, tsunamis, and floods also destroyed buildings in urban areas is to be used

Multimodal Few-Shot Target Detection Based on Uncertainty Analysis in Time-Series Images

The ability to interpret multimodal data, and map the targets and anomalies within, is important for an automatic recognition system. Due to the expensive and time-consuming nature of multimodal time-series data annotation in the training stage, multimodal time-series image understanding, from drone and quadruped mobile robot platforms, is a challenging task for remote sensing and photogrammetry. In this regard, robust methods must be computationally low-cost, due to the limited data on aerial and ground-based platforms, yet accurate enough to meet certainty measures. In this study, a few-shot learning architecture, based on a squeeze-and-attention structure, is proposed for multimodal target detection, using time-series images from the drone and quadruped robot platforms with a small training dataset. To build robust algorithms in target detection, a squeeze-and-attention structure has been developed from multimodal time-series images from limited training data as an optimized method. The proposed architecture was validated on three datasets with multiple modalities (e.g., red-green-blue, color-infrared, and thermal), achieving competitive results

A dynamic max-min ant system for solving the travelling salesman problem

In this paper, a modified max-min ant system, called dynamic max-min ant system (DMAS) is proposed to solve the travelling salesman problem (TSP). The proposed algorithm updates the value of t min, the lower bound of pheromone trails during its run. In addition, the used parameters for the DMAS are adjusted to improve the performance of the method. Furthermore, a local search based on 2-Opt is adjoined to the DMAS and the results are reported. Moreover, the DMAS is applied to some standard TSPs and its results are compared to some previous works. Results show that the proposed method outperforms several other well-known population-based methods in many cases. Also, in some standard problems, the proposed method improves the shortest known tour lengths. Moreover, experiments show that the standard deviation of tour lengths that are found by DMAS is very small, which exhibits the stability of the proposed algorithm. Copyrigh

Deep Learning-Based Change Detection Method for Environmental Change Monitoring Using Sentinel-2 Datasets

Change detection (CD) is an essential tool for the accurate understanding of land surface changes using Earth observation data and is extremely important for detecting the interactions between social and natural occurrences in geoscience. Binary change detection aims to detect changes and no changing areas, since improving the quality of the binary CD map is an important issue in remote sensing images; in this paper, a supervised deep learning (DL)-based change detection method was proposed to generate an accurate change map. Due to the good performance and great potential of DL in the domain of pattern recognition and nonlinear problem modeling, DL is becoming popular to resolve the CD problem using multitemporal remote sensing imageries. The purpose of using DL algorithms and especially convolutional neural networks (CNN) is to monitor the environmental change into change and no change classes. The Onera Satellite Change Detection (OSCD) datasets were used to evaluate the proposed method. Experimental results on the real dataset showed the effectiveness of the proposed algorithm. The overall accuracy and the kappa coefficient of the change map using the proposed method is over 95% and close to one, respectively