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The dawn of mass plastic production in the early 20th century has accelerated the penetration of microplastics into the environment, making them known to be an insidious threat to diverse ecosystems. The current scenario for microplastics is dire, as they pervade living organisms and disrupt vital functions while also affecting the environment by altering soil quality. They pose an inherent risk to human health, making their elimination a multifaceted challenge. Due to factors such as small size, low biodegradability, and ubiquity, microplastics are particularly challenging to detect, and methods for their elimination from terrestrial and marine environments are an ever‐evolving field of research. The detection of such microplastics has necessitated the use of various biotechnological techniques to monitor microplastic pollution. The control of microplastic pollution in natural ecosystems can be met by replacing mass‐produced single‐use plastics with viable bio‐based alternatives. The main objectives of this review are to discuss how microplastic pollution is currently monitored, assessed, and controlled using satellite‐generated imagery complemented with a range of biotechnological and bioremedial techniques. This review also traces the development of a range of bio‐based plastic alternatives that are observed to potentially replace single‐use plastics. Further discussion on the various challenges and prospects for the mitigation of microplastics will also be conducted, stressing the importance of future research. [ABSTRACT FROM AUTHOR]

The generalized travelling salesman problem (GTSP), a generalization of the well-known travelling salesman problem (TSP), is considered for our study. Since the GTSP is NP-hard and very complex, finding exact solutions is highly expensive, we will develop genetic algorithms (GAs) to obtain heuristic solutions to the problem. In GAs, as the crossover is a very important process, the crossover methods proposed for the traditional TSP could be adapted for the GTSP. The sequential constructive crossover (SCX) and three other operators are adapted to use in GAs to solve the GTSP. The effectiveness of GA using SCX is verified on some GTSP Library (GTSPLIB) instances first and then compared against GAs using the other crossover methods. The computational results show the success of the GA using SCX for this problem. Our proposed GA using SCX, and swap mutation could find average solutions whose average percentage of excesses from the best-known solutions is between 0.00 and 14.07 for our investigated instances. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) are very goodmetaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems. A simpleGA begins with a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes. It uses a crossover operator to create better offspring chromosomes and thus, converges the population. Also, it uses a mutation operator to explore the unexplored areas by the crossover operator, and thus, diversifies the GA search space. A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution. However, appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem. In this present paper, we aim to study the benchmark traveling salesman problem (TSP). We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances. The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem. The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances. [ABSTRACT FROM AUTHOR]

The fixed fleet heterogeneous open vehicle routing problem (HFFOVRP) is one of the most practical versions of the vehicle routing problem (VRP) defined because the use of rental vehicles reduces the cost of purchasing and routing for shipping companies nowadays. Also, applying a heterogeneous fleet is recommended due to the physical limitations of the streets and efforts to reduce the running costs of these companies. In this paper, a mixed-integer linear programming is proposed for HFFOVRP. Because this problem, like VRP, is related to NP-hard issues, it is not possible to use exact methods to solve real-world problems. Therefore, in this paper, a hybrid algorithm based on the ant colony algorithm called MACO is presented. This algorithm uses only global updating pheromones for a more efficient search of feasible space and considers a minimum value for pheromones on the edges. Also, pheromones of some best solutions obtained so far are updated, based on the quality of the solutions at each iteration, and three local search algorithms are used for the intensification mechanism. This method was tested on several standard instances, and the results were compared with other algorithms. The computational results show that the proposed algorithm performs better than these methods in cost and CPU time. Besides, not only has the algorithm been able to improve the quality of the best-known solutions in nine cases but also the high-quality solutions are obtained for other instances. [ABSTRACT FROM AUTHOR]

The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm. [ABSTRACT FROM AUTHOR]

We study the capacitated vehicle routing problem (CVRP) which is a well-known NP-hard combinatorial optimization problem (COP). The aim of the problem is to serve different customers by a convoy of vehicles starting from a depot so that sum of the routing costs under their capacity constraints is minimized. Since the problem is very complicated, solving the problem using exact methods is almost impossible. So, one has to go for the heuristic/metaheuristic methods and genetic algorithm (GA) is broadly applied metaheuristic method to obtain near optimal solution to such COPs. So, this paper studies GAs to find solution to the problem. Generally, to solve a COP, GAs start with a chromosome set named initial population, and then mainly three operators-selection, crossover andmutation, are applied. Among these three operators, crossover is very crucial in designing and implementing GAs, and hence, numerous crossover operators were developed and applied to different COPs. There are two major kinds of crossover operators-blind crossovers and distance-based crossovers. We intend to compare the performance of four blind crossover and four distance-based crossover operators to test the suitability of the operators to solve the CVRP. These operators were originally proposed for the standard travelling salesman problem (TSP). First, these eight crossovers are illustrated using same parent chromosomes for building offspring(s). Then eight GAs using these eight crossover operators without any mutation operator and another eight GAs using these eight crossover operators with a mutation operator are developed. These GAs are experimented on some benchmark asymmetric and symmetric instances of numerous sizes and various number of vehicles. Our study revealed that the distance-based crossovers are much superior to the blind crossovers. Further, we observed that the sequential constructive crossover with and without mutation operator is the best one for theCVRP. This estimation is validated by Student's t-test at 95% confidence level. We further determined a comparative rank of the eight crossovers for the CVRP. [ABSTRACT FROM AUTHOR]

Group buying is attracting a lot of attention, but studies on group buying of bundled products are still in their infancy. In this study, a group purchasing structure is proposed in which each purchaser needs a portion of the items in a bundle and has his or her preferences. However, when the buyers of a bundle are scattered across different locations, the shipper must travel long distances between buyers, which increases shipping costs. Therefore, besides bundle prices, shipping costs are an inseparable expenditure for group purchasing. It is assumed that shipping costs increase on certain days, e.g. around Christmas. A tailored mathematical formulation is formulated to maximize the total group saving. Considering the complexity of the model, a genetic algorithm (GA) is developed as a solution algorithm, and compared with the CPLEX solver. The results confirm the performance of the GA, which provides up to 24% better solutions than CPLEX. [ABSTRACT FROM AUTHOR]

Kalij pheasant Lophura leucomelanos is habitat indicator and the information about its habitat characteristics and suitability is lacking. In the current study, presence of kalij pheasant was recorded from 166 sites of Mirpur Division Azad Jammu and Kashmir, Pakistan. The maximum abundance was recorded at Gaian site (2.33/ha). Estimation of Habitat Suitability Index (HSI) from 166 sites revealed that ten sites fell under the category of highly suitable habitat based on parameters including water, food, vegetation, disturbance, hunting and predation pressure. Kalij pheasant was distributed between 381-1689m (asl) elevation. Species presence data along with GIS database were used to model the habitat suitability of kalij pheasant through MaxEnt software, version 3.4.4. The model showed an average Area Under the Curve value (AUC) (0.802), showing the model precision for suitable habitat mapping. The analysis for the contribution of environmental variables through Jackknife test showed that temperature was the prime environmental variable. Results revealed that out of total, 4388 km², 406.03 km² area was calculated to be highly suitable for kalij pheasant. Identification of hotspots and potential habitats for kalij pheasant can be considered as an important initiative to conserve the species. [ABSTRACT FROM AUTHOR]

The complexity challenges of mechatronic systems justify the need of numerical simulation to efficiently assess their reliability. In the case of solder joints in electronic packages, finite element methods (FEM) are commonly used to evaluate their fatigue response under thermal loading. Nevertheless, Experience shows that the prediction quality is always affected by the variability of the design variables. This paper aims to benefit from the statistical power of the R software and the efficiency of the finite element software ANSYS©, to develop a probabilistic approach to predicting the solder joint reliability in Mechatronic Packaging taking into account the uncertainties in material properties. The coupling of the two software proved an effective evaluation of the reliability of the T-CSP using the proposed method. [ABSTRACT FROM AUTHOR]

Brakes are the most important component of any automobile. Brakes provide the ability to reduce or bring automobile to a complete stop. The process of braking is usually achieved by applying pressure to the brake discs. The main objective of this research paper is to propose an appropriate design and to perform analysis of a suitable brake rotor to enhance the performance of the high-speed car. The design of the brake disc is modelled using Solid works and the analysis is carried out using Ansys software. The analysis has been conducted by considering stainless steel and grey cast iron using same brake rotor design so that optimal choice of brake disc can be considered. The analysis considered involves static structural analysis and steady state thermal analysis considering specific parameters on brake rotor to increase the life of brake rotor. From the analysis it is found that the performance and life of disc brake depends upon heat dissipation. From the analysis results it can be concluded that grey cast iron has performed better as compared to stainless steel as this material has anti-fade properties which improves the life of the brake rotor. [ABSTRACT FROM AUTHOR]

This article argues that most analyses of the Kargil conflict concede the important role played by the United States in understanding how India regained control of the Kargil heights, but fail to explain how India's intra-war compellent threat forced Washington to bring irresistible pressure to bear on Islamabad. The Indian decision to threaten asymmetrical escalation was the result of domestic pressures and military difficulties facing the Vajpayee-led caretaker government. The article shows that Washington pursued an 'impartially' interventionist strategy until it came under Indian pressure to forsake its 'balanced' approach towards ending the conflict. The article also shows how the 'asymmetry of motivation' between New Delhi and Washington was an important factor in terminating hostilities in India's favor. [ABSTRACT FROM AUTHOR]