A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem

Multiple work shifts are commonly utilized in construction projects to meet project requirements. Nevertheless, evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. Tradeoff optimization among project duration (time), project cost, and the utilization of evening and night work shifts while maintaining with all job logic and resource availability constraints is necessary to enhance overall construction project success. In this study, a novel approach called “Multiple Objective Symbiotic Organisms Search” (MOSOS) to solve multiple work shifts problem is introduced. The MOSOS algorithm is new meta-heuristic based multi-objective optimization techniques inspired by the symbiotic interaction strategies that organisms use to survive in the ecosystem. A numerical case study of construction projects were studied and the performance of MOSOS is evaluated in comparison with other widely used algorithms which includes non-dominated sorting genetic algorithm II (NSGA-II), the multiple objective particle swarm optimization (MOPSO), the multiple objective differential evolution (MODE), and the multiple objective artificial bee colony (MOABC). The numerical results demonstrate MOSOS approach is a powerful search and optimization technique in finding optimization of work shift schedules that is it can assist project managers in selecting appropriate plan for project

Optimizing Multiple-Resources Leveling in Multiple Projects Using Discrete Symbiotic Organisms Search

Resource leveling is used in project scheduling to reduce fluctuation in resource usage over the period of project implementation. Fluctuating resource usage frequently creates the untenable requirement of regularly hiring and firing temporary staff to meet short-term project needs. Construction project decision makers currently rely on experience-based methods to manage fluctuations. However, these methods lack consistency and may result in unnecessary waste of resources or costly schedule overruns. This research introduces a novel discrete symbiotic organisms search for optimizing multiple resources leveling in the multiple projects scheduling problem (DSOS-MRLMP). The optimization model proposed is based on a recently developed metaheuristic algorithm called symbiotic organisms search (SOS). SOS mimics the symbiotic relationship strategies that organisms use to survive in the ecosystem. Experimental results and statistical tests indicate that the proposed model obtains optimal results more reliably and efficiently than do the other optimization algorithms considered. The proposed optimization model is a promising alternative approach to assisting project managers in handling MRLMP effectively

Fuzzy clustering chaotic-based differential evolution for resource leveling in construction projects

 Project scheduling is an important part of project planning in many management companies. Resource lev­eling problem describes the process of reducing the fluctuations in resource usage over the project duration. The goal of resource leveling is to minimize the incremental demands that cause fluctuations of resources, and thus avoid unde­sirable cyclic hiring and firing during project execution. In this research, a novel optimization model, named as Fuzzy Clustering Chaotic-based Differential Evolution for solving Resource leveling (FCDE-RL), is introduced. Fuzzy Cluster­ing Chaotic-based Differential Evolution (FCDE) is developed by integrating original Differential Evolution with fuzzy c-means clustering and chaotic techniques to tackle complex optimization problems. Chaotic was exploited to prevent the optimization algorithm from premature convergence. Meanwhile, fuzzy c-means clustering acts as several multi-par­ent crossover operators to utilize the information of the population efficiently to enhance the convergence. Experimental results revealed that the new optimization model is a promising alternative to assist project managers in dealing with construction project resource leveling. First published online: 13 Jul 201

Combining machine learning models via adaptive ensemble weighting for prediction of shear capacity of reinforced‑concrete deep beams

This study presents a novel artificial intelligence (AI) technique based on two support vector machine (SVM) models and symbiotic organisms search (SOS) algorithm, called “optimized support vector machines with adaptive ensemble weight- ing” (OSVM-AEW), to predict the shear capacity of reinforced-concrete (RC) deep beams. This ensemble learning-based system combines two supervised learning models—the support vector machine (SVM) and least-squares support vector machine (LS-SVM)—with the SOS optimization algorithm as the optimizer. In OSVM-AEW, SOS is integrated to simulta- neously select the optimal parameters of SVM and LS-SVM, and control the coordination process of the learning outputs. Experimental results show that OSVM-AEW achieves the greatest evaluation criteria for coefficient of correlation (0.9620), coefficient of determination (0.9254), mean absolute error (0.3854 MPa), mean absolute percentage error (7.68%), and root- mean-squared error (0.5265 MPa). This paper demonstrates the successful application of OSVM-AEW as an efficient tool for helping structural engineers in the RC deep beams design process

A DSC/TGA STUDY OF THE HETEROGENEOUS NUCLEATION OF CRYSTALLIZATION IN POLYPROPYLENE COPOLYMER

The study is aimed at the evaluation of the influence of nucleating agent (t-butyl)benzoic aluminum (NA) in changing clarity, crystallization temperature (Tp) and radiation resistant properties of polypropylene copolymer, PP (co) with 6% of ethylene. It has been shown that crystallization temperature of PP (co)+NA did not change when the content of the NA less than 2%, but the super cooling temperature is a little bit increased when the content of the NA was 2 or higher than 2%. Clarity of the blend comprising of PP (co)+NA decreases with the addition of 2% or more NA (t-butyl)benzoic aluminum. However, the increase of the supercooling temperature makes possible to improve radiation resistance of the material

Chaotic initialized multiple objective differential evolution with adaptive mutation strategy (CA-MODE) for construction project time-cost-quality trade-off

Time, cost and quality are three factors playing an important role in the planning and controlling of construc­tion. Trade-off optimization among them is significant for the improvement of the overall benefits of construction pro­jects. In this paper, a novel optimization model, named as Chaotic Initialized Multiple Objective Differential Evolution with Adaptive Mutation Strategy (CA-MODE), is developed to deal with the time-cost-quality trade-off problems. The proposed algorithm utilizes the advantages of chaos sequences for generating an initial population and an external elitist archive to store non-dominated solutions found during the evolutionary process. In order to maintain the exploration and exploitation capabilities during various phases of optimization process, an adaptive mutation operation is introduced. A numerical case study of highway construction is used to illustrate the application of CA-MODE. It has been shown that non-dominated solutions generated by CA-MODE assist project managers in choosing appropriate plan which is other­wise hard and time-consuming to obtain. The comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previ­ous results verify the efficiency and effectiveness of the proposed algorithm. First published online: 24 Aug 201

Optimization model for construction project resource leveling using a novel modified symbiotic organisms search

In the construction industry, determining project schedules has become one of the most critical subjects among project managers. These schedules oftentimes result in significant resource fluctuations that are costly and impractical for the construction company. Thus, construction managers are required to adjust the resource profile through a resource leveling process. In this paper, a novel optimization model is presented for resource leveling, called the “modified symbiotic organisms search” (MSOS). MSOS is developed based on the standard symbiotic organisms search, but with an improvement in the parasitism phase to better tackle complex optimization problems. A case study is employed to investigate the performance of the proposed optimization model in coping with the resource leveling problem. The experimental results show that the proposed model can find a better quality solution in comparison with existing optimization models

Is Chytridiomycosis an Emerging Infectious Disease in Asia?

The disease chytridiomycosis, caused by the fungus Batrachochytrium dendrobatidis (Bd), has caused dramatic amphibian population declines and extinctions in Australia, Central and North America, and Europe. Bd is associated with >200 species extinctions of amphibians, but not all species that become infected are susceptible to the disease. Specifically, Bd has rapidly emerged in some areas of the world, such as in Australia, USA, and throughout Central and South America, causing population and species collapse. The mechanism behind the rapid global emergence of the disease is poorly understood, in part due to an incomplete picture of the global distribution of Bd. At present, there is a considerable amount of geographic bias in survey effort for Bd, with Asia being the most neglected continent. To date, Bd surveys have been published for few Asian countries, and infected amphibians have been reported only from Indonesia, South Korea, China and Japan. Thus far, there have been no substantiated reports of enigmatic or suspected disease-caused population declines of the kind that has been attributed to Bd in other areas. In order to gain a more detailed picture of the distribution of Bd in Asia, we undertook a widespread, opportunistic survey of over 3,000 amphibians for Bd throughout Asia and adjoining Papua New Guinea. Survey sites spanned 15 countries, approximately 36° latitude, 111° longitude, and over 2000 m in elevation. Bd prevalence was very low throughout our survey area (2.35% overall) and infected animals were not clumped as would be expected in epizootic events. This suggests that Bd is either newly emerging in Asia, endemic at low prevalence, or that some other ecological factor is preventing Bd from fully invading Asian amphibians. The current observed pattern in Asia differs from that in many other parts of the world

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security