Modeling a cooperation environment for flexibility enhancement in smart multi-energy industrial systems

Environmental aspects have been highlighted in architecting future energy systems where sustainable development plays a key role. Sustainable development in the energy sector has been defined as a potential solution for enhancing the energy system to meet the future energy requirements without interfering with the environment and energy provision. In this regard, studying the cross-impact of various energy vectors and releasing their inherent operational flexibility is main topic. Thecoordinationofvariousenergyvectorsundertheconceptofmulti-energysystem (MES)hasintroducednewsourcesofoperationalflexibilitytothesystemmanagers. MES considers both interactions among the energy carriers and the decision makers in an interdependent environment to increase the total efficiency of the system and reveal the hidden synergy among energy carriers. This thesis addresses a framework for modeling multi-energy players (MEP) that are coupled based on price signal in multi-energy system (MES) in a competitive environment. MEP is defined as an energy player who can consume or deliver more than one type of energy carriers. At first, the course of evolution for the energy system from today independent energy systems to a fully integrated MES is presented and the fractal structure is described for of MES architecture. Moreover, the operational behavior of plug-in electric vehicles’ parking lots and multi-energy demands’ external dependency are modeled in MES framework to enhance the operational flexibility of local energy systems (LES). In the fractal environment, there exist conflicts among MEPs’ decision making in a same layer and other layers. Realizing the inherent flexibility of MES is the main key for modeling the conflicts in this multi-layer structure. The conflict between two layers of players is modeled based on a bi-level approach. In this problem, the first level is the MEP level where the player maximizes its profit while satisfying LES energy exchange. The LES’s exchange energy price is the output of this level. In the lower level, the LESs schedule their energy balance, based on the upper level input price signal. The problem is transformed into a mathematical program with equilibrium constraint (MPEC) through duality theory. In the next step, high penetration of multi-energy players in the electricity market is modeled and their impacts on electricity market equilibrium are investigated. In such a model, MEP participates in the local energy and wholesale electricity markets simultaneously. MEP and the other players’ objectives in these two markets conflict with each other. Each of these conflicts is modeled based on bi-level programming. The bi-level problems are transformed into a single level mixed-integer linear problem by applying duality theory

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

The Effect of a Words-in-Noise Training Method on Speech Perception in Noise of Children with Unilateral Hearing Loss

Background and Aim: Despite more affordable and advanced technologies for early detection of congenital hearing loss, unilateral hearing loss is the prevalent form of hearing loss affecting school-aged children. This study aimed to examine the impact of Words-in-Noise (WIN) training on speech perception of noise in children with unilateral hearing loss. Methods: Thirteen children aged 8 to 12 years with unilateral hearing loss underwent a WIN training program in noise. The participants were tested before and after training on word identification in noise and cortical auditory evoked potentials. Results: A comparison of the mean signal-to-noise ratio 50% between pre- and post-training indicated that signal-to-noise ratio 50% score decreased after training sessions. WIN training reduced the latency in N1 and P2 waves in the Fz electrode and the N1 wave in the Pz electrode and increased the amplitude of the waves in the Fz and Pz electrodes. The observed data suggest that all participants’ performance improved on word identification in noise and some electrophysiological parameters. Cortical auditory evoked potentials components changes did not correlate with the WIN scores. Conclusion: The Persian version of the WIN training improved speech perception ability in the presence of competitive noise in children with unilateral hearing loss. Therefore, this software solution can partially solve speech comprehension problems with noise in these children

Measuring Iran’s success in achieving Millennium Development Goal 4: a systematic analysis of under-5 mortality at national and subnational levels from 1990 to 2015

Background Child mortality as one of the key Millennium Development Goals (MDG 4—to reduce child mortality by two-thirds from 1990 to 2015), is included in the Sustainable Development Goals (SDG 3, target 2—to reduce child mortality to fewer than 25 deaths per 1000 livebirths for all countries by 2030), and is a key indicator of the health system in every country. In this study, we aimed to estimate the level and trend of child mortality from 1990 to 2015 in Iran, to assess the progress of the country and its provinces toward these goals. Methods We used three different data sources: three censuses, a Demographic and Health Survey (DHS), and 5-year data from the death registration system. We used the summary birth history data from four data sources (the three censuses and DHS) and used maternal age cohort and maternal age period methods to estimate the trends in child mortality rates, combining the estimates of these two indirect methods using Loess regression. We also used the complete birth history method to estimate child mortality rate directly from DHS data. Finally, to synthesise different trends into a single trend and calculate uncertainty intervals (UI), we used Gaussian process regression. Findings Under-5 mortality rates (deaths per 1000 livebirths) at the national level in Iran in 1990, 2000, 2010, and 2015 were 63·6 (95% UI 63·1–64·0), 38·8 (38·5–39·2), 24·9 (24·3–25·4), and 19·4 (18·6–20·2), respectively. Between 1990 and 2015, the median annual reduction and total overall reduction in these rates were 4·9% and 70%, respectively. At the provincial level, the difference between the highest and lowest child mortality rates in 1990, 2000, and 2015 were 65·6, 40·4, and 38·1 per 1000 livebirths, respectively. Based on the MDG 4 goal, five provinces had not decreased child mortality by two-thirds by 2015. Furthermore, six provinces had not reached SDG 3 (target 2). Interpretation Iran and most of its provinces achieved MDG 4 and SDG 3 (target 2) goals by 2015. However, at the subnational level in some provinces, there is substantial inequity. Local policy makers should use effective strategies to accelerate the reduction of child mortality for these provinces by 2030. Possible recommendations for such strategies include enhancing the level of education and health literacy among women, tackling sex discrimination, and improving incomes for families

Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper

Optimisation-based integrated decision model for ambulance routing in response to pandemic outbreaks

Pandemics and sudden disease outbreaks place considerable stress on hospital resources. Their increasing numbers in recent years has necessitated investment in disaster risk management strategies, particularly in the healthcare sector. The sudden surge of patients, particularly in requesting ambulance services, overwhelms hospital systems and compromises health service delivery. Failure of health planners to respond immediately to a sudden disease outbreak can result in insufficient distribution of healthcare services and can thereby exacerbate the death toll dramatically. The current research aims to develop an optimisation-based integrated decision model to assist healthcare decision-makers with immediate and effective planning for ambulances to move critical patients from their residences to hospitals, considering the available capacities of each hospital. Several lemmas for the problem are proposed, and based on these; several local search methods are developed to improve the performance of the proposed optimisation method. To confirm the efficacy of the proposed approach, a comprehensive comparison is conducted. In conclusion, sensitivity analyses are performed to discuss some practical insights. The proposed models can be adopted to develop decision tools that enable hospital system managers to optimize their resources to changing healthcare needs in disease outbreaks

Reliability estimation using an integrated support vector regression – variable neighborhood search model

As failure and reliability predictions play a significant role in production systems they have caught the attention of researchers. In this study, Support Vector Regression (SVR), which is known as a powerful neural network method, is developed as a way of forecasting reliability. Generally, SVR is applied in many research environments, and the results illustrate that SVR is a successful method in solving non-linear regression problems. However, SVR parameters tuning is a vital task for performing an accurate reliability estimation. We propose variable neighborhood search (VNS) for continuous space, including some simple but efficient shaking and local search as its main operators, to tune the SVR parameters and create a novel SVR-VNS hybrid system to improve the reliability of estimation accuracy. The proposed method is validated with a benchmark from the former literature and compared with conventional techniques, namely RBF (Gaussian), AR (autoregressive), MLP (logistic), MLP (Gaussian), and SVMG (SVM with genetic algorithm). The experimental results indicate that the proposed model has a superior performance for prediction reliability than other techniques

The Alignment of Australia’s National Construction Code and the Sendai Framework for Disaster Risk Reduction in Achieving Resilient Buildings and Communities

The risks associated with extreme weather events induced by climate change are increasingly being recognized, and must be addressed through each country’s construction regulations, building codes, and standards. Ensuring that buildings and cities are resilient against disasters is becoming more important. Few studies have analyzed the impact of global polices and frameworks in reducing disaster risks and increasing resilience in built environments. This research reviews disasters associated with climate change in the Sendai Framework for Disaster Risk Reduction 2015–2030, analyzing how Australia’s National Construction Code is aligned with the framework and the potential implications for reducing disaster risk. Decision-makers in construction companies in Sydney, Australia, were surveyed. The results show there is a statistically significant link among the National Construction Code, the Sendai Framework, and building resilience. The Sendai Framework is an effective mediator in this three-pronged relationship that can further enhance building resilience in Australia. Stakeholders in the construction industry will need to incorporate disaster risk reduction practices, especially authorities, such as local governments, building commissioners, and building certifiers that are responsible for the approval, quality, and defects mitigation of development applications and best practices. Overall, implementation of the Sendai Framework will help develop more regulations and standards for resilient buildings, set targets, and make improvements over time in the Australian construction industry