Comparison between lipid and protein oxidation progress in the tail and claw muscles of signal crayfish (Pacifastacus leniusculus)

Microbial activity and then oxidation progress are the most important freshness indicators during post-mortem. In this study, we monitored proteomic and microbial changes, as well as biochemical degradation, in the tail and claw muscles of crayfish stored for 12 days at +4 °C. One specified protein band at 107 kDa in the claw muscle and two specified protein bands in the tail muscle at 140 and 36–40 kDa were identified. Western blotting indicated a higher amount of oxidised proteins in the tail compared to the claw muscle. Tail muscle showed higher oxidation progress and calpain activity than claw one. Both muscles were spoiled after 12 days with respect to total viable counts. In the first days, calpain activity is the main reason for protein degradation, while protein oxidation dominates for the rest of the time. Lipid–protein oxidation progress showed probably, protein oxidation started earlier than lipid oxidation in both muscles.     

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

This study proposes precise modeling for the proton exchange membrane fuel cells which present desirable advantages compared with other energy management systems. The presented model can be applied for the simulation of the actual behavior of the proton exchange membrane fuel cells such as the electrical, electrochemical, and mechanical. In the present literature, a newly presented optimizer namely Satin Bowerbird is implemented for the evaluation of the proton exchange membrane fuel cell performance criteria. The Satin Bowerbird optimizer is an evolutionary algorithm that imitates the mating process of the Bowerbirds in the mating season. The Satin Bowerbird optimizer is applied to the different commercial benchmark of proton exchange membrane fuel cell stack to assess the performance of the proposed algorithm. The statistical study is also carried out to show the superiority of the proposed method compared with other schemes. The standard deviation for the Satin Bowerbird optimizer is obtained 0.0941 which is the lowest value amongst the other well‐known approaches. Also, the lowest sum of squared error is calculated for the proposed algorithm. Moreover, the validation of the presented method is done with the experimental data which shows good agreement between the experimental and modeling data.     

Large-scale load capacity tests on a geosynthetic encased column

Stone columns have been used to minimize the settlement of embankments on soft soils but their use in very soft soils can become challenging, partly because of the low confinement provided by the surrounding soil. Geosynthetic encased columns (GECs) have been successfully used to enhance to reduce settlements of embankments on soft soils. This paper describes an investigation on the performance of encased columns constructed on a very soft soil using different types of encasement (three woven geotextiles with different values of tensile stiffness) and different column fill materials (sand, gravel and recycled construction and demolition waste, RCDW). The results of load capacity tests conducted on large-scale models constructed to simulate the different types of GECs indicate that the displacement method adopted during column installation can lead to an enhanced shear strength in the smear zone that develops within the very soft soil. In addition, breakage of the column fill material was found to affect the load-settlement response of gravel and RCDW columns. Furthermore, the excess pore water pressure generated in the surrounding soil during installation, was found to remain limited to radial distances smaller than three times the GEC diameter.     

Comprehensive review of water management and wastewater treatment in food processing industries in the framework of water-food-environment nexus

Food processing is among the greatest water-consuming industries with a significant role in the implementation of sustainable development goals. Water-consuming industries such as food processing have become a threat to limited freshwater resources, and numerous attempts are being carried out in order to develop and apply novel approaches for water management in these industries. Studies have shown the positive impact of the new methods of process integration (e.g., water pinch, mathematical optimization, etc.) in maximizing water reuse and recycle. Applying these methods in food processing industries not only significantly supported water consumption minimization but also contributed to environmental protection by reducing wastewater generation. The methods can also increase the productivity of these industries and direct them to sustainable production. This interconnection led to a new subcategory in nexus studies known as water-food-environment nexus. The nexus assures sustainable food production with minimum freshwater consumption and minimizes the environmental destructions caused by untreated wastewater discharge. The aim of this study was to provide a thorough review of water-food-environment nexus application in food processing industries and explore the nexus from different aspects. The current study explored the process of food industries in different sectors regarding water consumption and wastewater generation, both qualitatively and quantitatively. The most recent wastewater treatment methods carried out in different food processing sectors were also reviewed. This review provided a comprehensive literature for choosing the optimum scenario of water and wastewater management in food processing industries.     

Equivalent linear model for fully self‐centering earthquake‐resisting systems

Modern rocking and stepping cores have been known as the efficient self‐centering earthquake‐resisting systems (SC‐ERSs). The current article proposes an approximate equivalent linear (EL) model for rapid estimation of the SC‐ERS displacement. An equivalent damping ratio and effective stiffness are formulated for flag‐shaped hysteresis of a fully SC‐ERS. The approximate EL model is first established using secant stiffness and Jacobsen's damping model. Nonlinear response history analyses are carried out to compare exact and approximated peak displacements. Findings reveal that EL analysis of the SC‐ERS based on Jacobsen's damping leads to underestimation of the maximum inelastic displacement. Accordingly, a new optimal damping formula is proposed using a genetic algorithm and nonlinear regression analyses. The improved EL model is validated by practical examples, and the results show acceptable accuracy in design‐level displacement estimation.     

Short-term wind power forecasting using ridgelet neural network

Rapid growth of wind power generation in many countries around the world in recent years has highlighted the importance of wind power prediction. However, wind power is a complex signal for modeling and forecasting. Despite the performed research works in the area, more efficient wind power forecast methods are still demanded. In this paper, a new prediction strategy is proposed for this purpose. The forecast engine of the proposed strategy is a ridgelet neural network (RNN) owning ridge functions as the activation functions of its hidden nodes. Moreover, a new differential evolution algorithm with novel crossover operator and selection mechanism is presented to train the RNN. The efficiency of the proposed prediction strategy is shown for forecasting of both wind power output of wind farms and aggregated wind generation of power systems.     

Speed regulation in steering-based source seeking

The simplest strategy for extremum seeking-based source localization, for sources with unknown spatial distributions and nonholonomic unicycle vehicles without position measurement, employs a constant positive forward speed. Steering of the vehicle in the plane is performed using only the variation of the angular velocity. While keeping the forward speed constant is a reasonable strategy motivated by implementation with aerial vehicles, it leads to complexities in the asymptotic behavior of the vehicle, since the vehicle cannot settle—at best it can converge to a small-size attractor around the source. In this paper we regulate the forward velocity, with the intent of bringing the vehicle to a stop, or as close to a stop as possible. The vehicle speed is controlled using simple derivative-like feedback of the sensor measurement (the derivative is approximated with a washout filter) to which a speed bias parameter V_c is added. The angular velocity is tuned using standard extremum seeking. We prove two results. For V_c in a certain range around zero, we show that the vehicle converges to a ring around the source and on average the limit of the vehicle’s heading is either directly away or towards the source. For other values of V_c>0, the vehicle converges to a ring around the source and it revolves around the source. Interestingly, the average heading of this revolution around the source is more outward than inward—this is possible because the vehicle’s speed is not constant, it is lower during the outward steering intervals and higher during the inward steering intervals. The theoretical results are illustrated with simulations.     

Corrosion control in underground concrete structures using double waterproofing shield system (DWS)

High level of structural and waterproofing stability leads to long-term service life in underground infrastructures. Interaction between aggressive groundwater with tunnel causes corrosion and damage in concrete structure due to steel reinforcement corrosion and concrete cracks. This study introduces a double waterproofing shield system (DWS) as an innovative solution to waterproofing and structural failures in underground concrete structures. In this method, ordinary shotcrete mixture replaces by an organic polymer concrete (OPC) to construct a water-resistant temporary support right after each partial excavation. Two groups of specimens including reference concrete and OPC specimens were provided and tested in accordance with ASTM C 642. Waterproofing parameters including porosity, pore volume, permeability and hydraulic conductivity have been determined. Results show a remarkable reduction in mentioned parameters for OPC compared with ordinary concrete. Improvement in waterproofing performance of temporary support corresponds to a healthy final lining and increase in service life of the structure.     

Reactive power market development considering power system security

In the new open access environment, in pursuit of profit, power producers tend to operate closer to the security boundaries and consequently, the voltage instability, which is caused by insufficient reactive power support, threats system security and reliability. This paper presents a day-ahead reactive power market based on uniform auction price scheme considering voltage security. First, expected payment function (EPF), identified earlier in the literature to construct a bidding framework for synchronous generators, is modified. Then, the modified EPF is used as the objective function of optimal power flow problem to clear reactive power market. Finally, the OPF, including overload, voltage drop and voltage stability margin in its constraints, is solved by binary coded genetic algorithm. The validity of the proposed reactive power market is studied based on the IEEE 24-bus reliability test system.     

Electricity market price spike analysis by a hybrid data model and feature selection technique

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets.