Covering tour problem with varying coverage: Application to marine environmental monitoring

In this paper, we present a novel variant of the Covering Tour Problem (CTP), called the Covering Tour Problem with Varying Coverage (CTP-VC). We consider a simple graph = ( ,), with a measure of importance assigned to each node in . A vehicle with limited battery capacity visits the nodes of the graph and has the ability to stay in each node for a certain period of time, which determines the coverage radius at the node. We refer to this feature as stay-dependent varying coverage or, in short, varying coverage. The objective is to maximize a scalarization of the weighted coverage of the nodes and the negation of the cost of moving and staying at the nodes. This problem arises in the monitoring of marine environments, where pollutants can be measured at locations far from the source due to ocean currents. To solve the CTP-VC, we propose a mathematical formulation and a heuristic approach, given that the problem is NP-hard. Depending on the availability of solutions yielded by an exact solver, we evaluate our heuristic approach against the exact solver or a constructive heuristic on various instance sets and show how varying coverage improves performance. Additionally, we use an offshore CO2 storage site in the Gulf of Mexico as a case study to demonstrate the problem’s applicability. Our results demonstrate that the proposed heuristic approach is an efficient and practical solution to the problem of stay-dependent varying coverage. We conduct numerous experiments and provide managerial insights.publishedVersio

A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems

Many problem-specific heuristic frameworks have been developed to solve combinatorial optimization problems, but these frameworks do not generalize well to other problem domains. Metaheuristic frameworks aim to be more generalizable compared to traditional heuristics, however their performances suffer from poor selection of low-level heuristics (operators) during the search process. An example of heuristic selection in a metaheuristic framework is the adaptive layer of the popular framework of Adaptive Large Neighborhood Search (ALNS). Here, we propose a selection hyperheuristic framework that uses Deep Reinforcement Learning (Deep RL) as an alternative to the adaptive layer of ALNS. Unlike the adaptive layer which only considers heuristics’ past performance for future selection, a Deep RL agent is able to take into account additional information from the search process, e.g., the difference in objective value between iterations, to make better decisions. This is due to the representation power of Deep Learning methods and the decision making capability of the Deep RL agent which can learn to adapt to different problems and instance characteristics. In this paper, by integrating the Deep RL agent into the ALNS framework, we introduce Deep Reinforcement Learning Hyperheuristic (DRLH), a general framework for solving a wide variety of combinatorial optimization problems and show that our framework is better at selecting low-level heuristics at each step of the search process compared to ALNS and a Uniform Random Selection (URS). Our experiments also show that while ALNS can not properly handle a large pool of heuristics, DRLH is not negatively affected by increasing the number of heuristics.publishedVersio

Evaluation of Different Best Management Practices for Erosion Control on Machine Operating Trails

Ground-based mechanized forest operations often lead to increased runoff and soil loss on unbound forest roads and machine operating trails, which in turn can impede the technical trafficability of machines and cause negative impacts on the environment. The aim of this study was to evaluate the effectiveness of three Best Management Practice (BMP) treatments used to control erosion occurring on machine operating trails. The treatments included water bar, water bar and hardwood brush (H-brush), and water bar and softwood brush (S-brush). For a more comprehensive assessment of both brush treatments, two levels of brush thickness were tested; 0.5 m and 1.0 m. Results indicate that the most effective BMP treatments were the water bar and softwood brush followed by the water bar and hardwood brush and finally the least effective was the water bar. The average runoff rates and soil loss from the machine operating trails with the water bar treatment (52.64 l per plot, 8.49 g m-2) were higher than runoff and soil loss at the trails protected with hardwood brush (23.75 l per plot, 4.5 g m-2), and the trails protected by the hardwood brush had higher runoff and soil loss compared to trails covered by softwood brush (15.83 l per plot, 2.98 g m-2). Furthermore, results of this study showed that regardless of the treatment, the amount of runoff and soil loss decreased consistently as the thickness of the brush mat increased. Overall, erosion control techniques similar to either H-brush or S-brush that provide direct soil coverage should be used for erosion control, and final selection should be based on costs, availability of material, or landowner objectives

Identification of a novel tailor-made chitinase from white shrimp fenneropenaeus merguiensis

Fenneropenaeus merguiensis (commonly named banana shrimp) is one of the most important farmed crustacean worldwide species for the fisheries and aquaculture industry. Besides its nutritional value, it is a good source of chitinase, an enzyme with excellent biological and catalytic properties for many industrial applications. In the present study, a putative chitinase-encoding cDNA was synthesized from mRNA from F. merguiensis hepatopancreas tissue. Subsequently, the corresponding cDNA was cloned, sequenced and functionally expressed in Escherichia coli, and the recombinant F. merguiensis chitinase (rFmCHI) was purified by His-tag affinity chromatography. The bioinformatics analysis of aminoacid sequence of rFmCHI displayed a cannonical multidomain architecture in chitinases which belongs to glycoside hydrolase family 18 (GH18 chitinase). Biochemical characterization revealed rFmCHI as a monomeric enzyme of molecular weight 52 kDa with maximum activity at 40 °C and pH 6.0 Moreover, the recombinant enzyme is also stable up to 60 °C, and in the pH range 5.0-8.0. Steady-state kinetic studies for colloidal chitin revealed KM, Vmax and kcat values of 78.18 μM, 0.07261 μM. min−1 and 43.37 s−1, respectively. Overall, our results aim to demonstrate the potential of rFmCHI as suitable catalyst for bioconversion of chitin waste

Evaluating the Effectiveness of Mulching for Reducing Soil Erosion in Cut Slope and Fill Slope of Forest Roads in Hyrcanian Forests

Forest operations often enhance runoff and soil loss in roads and skid trails, where cut slopes and fill slopes are the most important source of sediment. This study evaluated the effectiveness of four erosion control treatments applied to cut slope and fill slope segments of forest roads of different ages in the Hyrcanian forest in northern Iran. The treatment combinations, each replicated three times, included four classes of mulch cover (bare soil [BS], wood chips cover [WCH], sawdust cover [SC], and rice straw cover [RSC]), two levels of side slope (cut slope and fill slope), two levels of side slope gradient (20–25% and 40–45%), and three levels of road age (three, 10 and 20 years after construction). Mulch cover treatments significantly reduced average surface runoff volume and sediment yield compared to BS. Regardless of erosion control treatment, greater surface runoff volume and soil loss under natural rainfall occurred on steeper slope gradients in all road age classes and decreased with increasing road age on both slope gradients. On cut slopes, average runoff and soil loss from the plots covered with WCH (17.63 l per plot, 2.43 g m–2) was lower than from those covered with SC (22.81 l per plot, 3.50 g m–2), which was lower than from those covered with RSC (29.13 l per plot, 4.41 g m–2 and BS (34.61 l per plot, 4.94 g m–2). On fill slopes, average runoff and soil loss from the plots covered with WCH (14.13 l per plot, 1.99 g m–2) was lower than from plots covered with SC (20.01 l per plot, 3.23 g m–2), which was lower than from plots covered with RSC (24.52 l per plot, 4.06 g m–2) and BS (29.03 l per plot, 4.47 g m–2). Surface cover successfully controlled erosion losses following road construction, particularly on steep side slopes with high erosion potential

Modeling hydrogen solubility in hydrocarbons using extreme gradient boosting and equations of state

Due to industrial development, designing and optimal operation of processes in chemical and petroleum processing plants require accurate estimation of the hydrogen solubility in various hydrocarbons. Equations of state (EOSs) are limited in accurately predicting hydrogen solubility, especially at high-pressure or/and high-temperature conditions, which may lead to energy waste and a potential safety hazard in plants. In this paper, five robust machine learning models including extreme gradient boosting (XGBoost), adaptive boosting support vector regression (AdaBoost-SVR), gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), and multi-layer perceptron (MLP) optimized by Levenberg–Marquardt (LM) algorithm were implemented for estimating the hydrogen solubility in hydrocarbons. To this end, a databank including 919 experimental data points of hydrogen solubility in 26 various hydrocarbons was gathered from 48 different systems in a broad range of operating temperatures (213–623 K) and pressures (0.1–25.5 MPa). The hydrocarbons are from six different families including alkane, alkene, cycloalkane, aromatic, polycyclic aromatic, and terpene. The carbon number of hydrocarbons is ranging from 4 to 46 corresponding to a molecular weight range of 58.12–647.2 g/mol. Molecular weight, critical pressure, and critical temperature of solvents along with pressure and temperature operating conditions were selected as input parameters to the models. The XGBoost model best fits all the experimental solubility data with a root mean square error (RMSE) of 0.0007 and an average absolute percent relative error (AAPRE) of 1.81%. Also, the proposed models for estimating the solubility of hydrogen in hydrocarbons were compared with five EOSs including Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), Redlich–Kwong (RK), Zudkevitch–Joffe (ZJ), and perturbed-chain statistical associating fluid theory (PC-SAFT). The XGBoost model introduced in this study is a promising model that can be applied as an efficient estimator for hydrogen solubility in various hydrocarbons and is capable of being utilized in the chemical and petroleum industries