Ensuring sustainable electricity supply is a key issue facing decision-makers. Due to its impact on system reliability, balancing supply and demand is essential. Electricity subsidy policies in many countries have led to high consumption and low power plant efficiency, and may cause an imbalance in the future. This research analyzes different electricity subsystems using system dynamics to investigate how and when low energy prices lead to an unstable situation. Simulation results using Iran's data show demand surpasses supply with a continuation of current trends. However, when the prices increase 90%, demand is met, and subsidy decreases by about 20%.

Clustering is a widely used data mining technique with a diverse set of applications. Since clustering is an NP-hard problem, finding high-quality solutions for large-scale clustering problems can be an arduous and computationally expensive task. Therefore, many metaheuristics are utilized to solve these problems efficiently. In this paper, a modified unconscious search (US) and its k-means hybrid for data clustering are proposed with two main modifications:(1) generating initial population by combining solutions of k-means and random solutions,(2) replacing the usual local search step of the original US by an existing Heuristic Search method. Modified US is tested on the seven following well-known benchmarks from the UCI machine learning d

As the share of variable renewable energy in power systems increases, incorporating the operational constraints in generation expansion planning has become necessary to address flexibility issues. Accordingly, to preserve chronology and to make the technically detailed planning model tractable, the model should be solved for some representative periods. Different approaches have been introduced to select representative periods; however, most of them ignore the importance of the net load. In this research, a method is proposed to include the extreme days with higher and lower levels of the net load in representative days by using self-organizing map clustering. Further, the impacts of the extreme days, with different weighting approaches, on

We determine optimal pricing and order quantity of two substitute products in two markets, one of them is seasonal, with a decreasing market potential over time, and the other is nonseasonal. The two markets are sealed, that is, the prices in one market are irrelevant to consumers in the other market. Still, the two markets are linked through inventories and the order quantity policy. In the paper, we develop a nonlinear model, in which seasonal demand depends on time and on both products' prices, while nonseasonal demand is time‐invariant and is the function of both products' prices. Then, we provide an algorithm to compute the optimal solution. An illustrative example is given along with a sensitivity analysis. Among other results, we

This paper deals with the coordination of pricing and order quantity decisions for two seasonal and substitutable goods in one firm. We assume that the customers are price sensitive and they are willing to buy the cheaper products, which is known as one way and customers-based price driven substitution. First, a mathematical model is developed for one firm, which contains two replaceable products considering seasonality. The model aims to maximize the profit by determining optimal dynamic prices, order quantities and the number of periods for both of the products. Then, we show that the objective function is strictly concave of price and has a unique maximum solution. Next, an exact algorithm based on the Karush Kuhn Tucker (KKT) conditions

Concerns about environmental and social issues have led international organizations to pressure companies to invest in sustainable development. Therefore, economic, environmental and social dimensions should be taken into account simultaneously in energy supply planning and policy making. Additionally, one of the world's major sources of energy is natural gas with valuable hydrocarbon components. Since the change in demand for one of these components and the consequent change in natural gas extraction affect the other components, integrated planning for the supply chain of these components is required. Accordingly, the purpose of this study is to design and plan an optimal and sustainable supply chain for natural gas components in order to

Different mechanisms including knee dislocation, replacement surgery, nerve tumor, lumbar disc herniation, sharp injury, and gunshot wound lead to foot drop. Several surgical techniques have been used for treatment of foot drop, however, they have had sub-optimal outcomes. Soleus branch of tibial nerve is a good donor for nerve transfer for treatment of foot drop. In this is retrospective study, we reviewed medical records of 6 consecutive patients with sustained foot drop following injury to lumbar root or peroneal nerve, who underwent transfer of the soleus branch of tibial nerve to deep peroneal nerve during 2014–2016. The mean age of the patients was 44.8?years and duration of injury to surgery and follow-up was 8.3 and 14.6?months, r

Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-sensitive learning for credit card fraud detection. In the first step, useful features are identified using genetic algorithm. Next, the optimal resampling strategy is determined based on the design of experiments (DOE) and response surface methodologies. Finally, the cost sensitive C4.5 algorithm is used as the base learner in the Adaboost algorithm. Usi

Conclusion: The current study suggests formulating a predictive radiological index to identify PLC injury successfully. These guidelines can be very helpful in emergency room decision-makings, especially when the cost, availability, and time of performing MRI are important concerns in patients with multiple trauma.

Background. Spondylolisthesis is the forward slippage of the upper to the lower vertebrae, which affects spinal cord. Spinal fusion is an important method for the stability of the spine leading to pain and disability reduction in patients with chronic low back pain in spondylolisthesis. The aim of this study was to evaluate postoperative changes in spinopelvic parameters, pain, and disability in low-grade spondylolisthesis patients undergoing posterior lateral fusion (PLF) and posterior lateral interbody fusion (PLIF). Materials and methods. In the present study, 68 patients who underwent PLF and PLIF due to low-grade spondylolisthesis were recruited. The spinopelvic parameters, visual analogue scale (VAS) score and Oswestry disability inde

Natural gas is a green fossil fuel with the highest demand growth. Natural gas industry is composed of different sectors that form a complex and large supply chain. These sectors make their own decisions individually, resulting in implementation of non-optimal decisions. The aim of this study is to design and optimize an integrated natural gas supply chain (NGSC) formulated as a mixed integer linear programming (MILP) model. The model minimizes total cost and optimizes gas flow between supply chain nodes through pipelines, location-allocation of facilities and pipeline routes along with their capacities, number of pipelines, capacity expansion, extraction, production, storage, exports and imports. The proposed model is applied to a real wor

Natural gas is the most promising fossil fuel in the transition to a low-carbon energy future, and many countries have long term plans to increase its share in their energy supply mix through pricing regulations. While these policies encourage substitution of natural gas with more polluting fossil fuels, its over consumption and inefficient use can lead to misallocation of resources and CO2 emission increase. This paper develops a supply-demand model to optimally allocate natural gas to various demand sectors through determining a price path for each sector. The dynamic effects of price on demand, and income on supply are modeled using system dynamics. The model is applied to a case study on the optimal consumption share of each demand sect

Spontaneous cervical epidural hematoma (SCEH), which is a rare disease, is manifested as by a sudden quadriplegia or paraplegia and other neurological deficits. SCEH can compress the spinal cord resulting in its clinical manifestations. The reported etiological risk factors are anticoagulants, coagulopathies, vascular malformations, infections, and herniated discs. Here, we report a 77-year-old woman with a presenting chief complaint of left hemiparesis and a history of hypertension. The medical drugs in use were aspirin and antihypertensives. The initiating presentations were hemiparesis, in favor of ischemic stroke, so the patient admitted to neurology ward and received anticoagulant therapy with the initial diagnosis of stroke. Although

Petroleum industry is the world's biggest energy source, and its associated industries such as production, distribution, refining and retail are considered as the largest ones in the world. Having the increasing price and governments job creation and international environmental policies, the petroleum companies try to maximize the number of created job, and their profit and minimize the air pollution simultaneously. To meet these objectives, an effective detailed and precise planning is needed. On the other hand, the dynamic environment and the presence of various stakeholders add to the complexity of planning and design of petroleum supply chain. Therefore, the multi-period, multi-objective, multi-level and multi-product dynamic sustainabl

The p-hub median problem aims at locating p-hub facilities in a network and allocating non-hub nodes to the hubs such that the overall transportation cost is minimized. One issue of major importance in this problem remarks the requirement to deal with uncertain factors such as weather conditions and traffic volume. These lead to uncertainty in travel time between origin and destination points. In today’s competitive markets in which customers look for robust delivery services, it is important to minimize the upper bound of uncertainty in the network routes. In this paper, a robust bi-objective uncapacitated single allocation p-hub median problem (RBUSApHMP) is introduced in which travel time has non-deterministic nature. The

Under restructuring of electric power industry and changing traditional vertically integrated electric utility structure to competitive, market clearing price (MCP) prediction models are essential for all generation company (GenCos) for their survival under new deregulated environment. In this paper, a hybrid model is presented to predict hourly electricity MCP. The model contains a Neural Network (NN), Particle swarm optimization (PSO) and Genetic Algorithm (GA). The NN is used as the major forecasting module to predict the electricity MCP values and PSO applied to improve the traditional neural network learning capability and optimizing the weights of the NN and GA applied to optimize NN architecture. The main contribution includes: prese

Minimizing the passenger waiting times is an important aim of the rail companies to improve the service efficiency. The present study contributes to this aim by: (1) presenting novel mixed-integer nonlinear programming formulations for the train timetabling problem, (2) designing efficient algorithms to solve large instances of the problem. The model addresses the strict vehicle capacity constraint and the period-dependent arrival rate and alighting ratio. The basic model is then improved by embedding heuristic rules in the mathematical formulation. Due to the complexity of the problem, the sizes of the instances solved optimally are small and not practical for the real implementation. In order to tackle large-sized problem ins