•First BAT for generating all d-MP sets.•First BAT for all-level d-MP problems.•Three MP models for d-MP Problems.•Inequality BAT (IBAT) with fixed ones in vectors.•Hybrid IBAT: Integration of IBAT, ...sequential inequality, MP-to-arc transformation, cycle test, and LPM.
In various network applications like wireless sensors, utilities, IoT, and transport systems, multistate flow networks (MFNs) serve as valuable models. A d-level minimal path (d-MP) is a unique type of MFN characterized by having a maximum flow of d without any redundant arcs. Assessing MFN reliability is critical and often relies on the d-MP algorithm, a foundational method for calculating reliability. Existing d-MP algorithms, however, lack the capability to concurrently identify all-level d-MPs. We propose a novel algorithm, the Hybrid Inequality Binary-Addition-Tree (IBAT), which overcomes existing limitations by concurrently discovering all-level d-MPs (decision-making points), thus enabling more informed decision-making. This hybrid IBAT combines the IBAT with several key techniques: the path-based layered-search algorithm (PLSA), sequential verification, the MP-to-arc state transformation, the cycle test, and the logarithmic prime pairwise comparison method (LPM). In contrast to existing methods, our BAT-based approach consistently showcases superior performance in the parallelized retrieval of all-level d-MPs, as substantiated through experiments conducted on 12 benchmark MFNs. Compared to existing methods, our BAT-based approach demonstrates superior performance in parallelized retrieval of all-level d-MPs in the execution times in discovering d-MPs across all levels, as validated by experiments on 12 benchmark MFNs.
•A novel self-boundary search (SBS) and a two-variable update mechanism (UM2) are proposed.•The performance of the proposed BSO is ascertained by comparing the results with existing algorithms.•The ...proposed BSO is the best soft computing algorithm in the RRAP.
A new methodology called boundary simplified swarm optimization (BSO) is proposed by integrating a novel self-boundary search (SBS) and a two-variable update mechanism (UM2) to improve simplified swarm optimization (SSO) in solving mixed-integer programing problems that include both discrete and continuous variables. To balance the exploration and exploitation ability, the proposed SBS is implemented to update the current best solution (called gBest) based on the boundary conditions and analytical calculations to enhance the exploitation ability of gBest; the UM2 updates the solutions (called non-gBest) that are not gBest to fix the over-exploration of the SSO, in which all variables need to update without exploiting the information of the neighborhood area. The performance of the proposed BSO is ascertained by comparing the results with existing algorithms using four reliability redundancy allocation benchmark problems in the existing literature.
Calcium sulfate, an injectable and biodegradable bone‐void filler, is widely used in orthopedic surgery. Based on clinical experience, bone‐defect substitutes can also serve as vehicles for the ...delivery of drugs, for example, antibiotics, to prevent or to treat infections such as osteomyelitis. However, antibiotic additions change the characteristics of calcium sulfate cement. Moreover, high‐dose antibiotics may also be toxic to bony tissues. Accordingly, cefazolin at varying weight ratios was added to calcium sulfate samples and characterized in vitro. The results revealed that cefazolin changed the hydration reaction and prolonged the initial setting times of calcium sulfate bone cement. For the crystalline structure identification, X‐ray diffractometer revealed that cefazolin additive resulted in the decrease of peak intensity corresponding to calcium sulfate dihydrate which implying incomplete phase conversion of calcium sulfate hemihydrate. In addition, scanning electron microscope inspection exhibited cefazolin changed the morphology and size of the crystals greatly. A relatively higher amount of cefazolin additive caused a faster degradation and a lower compressive strength of calcium sulfate compared with those of uploaded samples. Furthermore, the extract of cefazolin‐impregnated calcium sulfate impaired cell viability, and caused the death of osteoblast‐like cells. The results of this study revealed that the cefazolin additives prolonged setting time, impaired mechanical strength, accelerated degradation, and caused cytotoxicity of the calcium sulfate bone‐void filler. The aforementioned concerns should be considered during intra‐operative applications.
Air pollution has become an extremely serious problem, with particulate matter having a significantly greater impact on human health than other contaminants. The small diameter of fine particulate ...matter (PM2.5) allows it to penetrate deep into the alveoli as far as the bronchioles, interfering with a gas exchange within the lungs. Long-term exposure to particulate matter has been shown to cause the cardiovascular disease, respiratory disease, and increase the risk of lung cancers. Therefore, forecasting air quality has also become important to help guide individual actions. This paper aims to forecast air quality for up to 48 h using a combination of multiple neural networks, including an artificial neural network, a convolutional neural network, and a long-short-term memory to extract spatial-temporal relations. The proposed predictive model considers various meteorology data from the previous few hours as well as information related to the elevation space to extract terrain impact on air quality. The model includes trends from multiple locations, extracted from correlations between adjacent locations, and among similar locations in the temporal domain. Experiments employing Taiwan and Beijing data sets show that the proposed model achieves excellent performance and outperforms current state-of-the-art methods.
Tandem solar cells have the potential to improve photon conversion efficiencies (PCEs) beyond the limits of single‐junction devices. In this study, a triple‐junction tandem design is demonstrated by ...employing three distinct organic donor materials having bandgap energies ranging from 1.4 to 1.9 eV. Through optical modeling, balanced photon absorption rates are achieved and, thereby, the photocurrents are matched among the three subcells. Accordingly, an efficient triple‐junction tandem organic solar cell can exhibit a record‐high PCE of 11.5%.
Network structures and models have been widely adopted, and many are based on the binary-state network. Reliability is the most commonly used tool to evaluate network performance. Efficient ...algorithms to evaluate binary-state network reliability are continually being developed. The motivation of this study is to propose an efficient algorithm called the quick BAT to evaluate binary-state network reliability. The propose quick BAT is based on the binary-addition tree algorithm (BAT) and employs three novel concepts: the first connected vector, the last disconnected vector, and super vectors. These super vectors narrow the search space and the calculations of their occurrent probabilities simplify the probability calculations to reduce the run time of the algorithm. Moreover, we show that replacing each undirected arc with two directed arcs, which is required in traditional direct methods, is unnecessary in the proposed algorithm. We call this novel concept the undirected vectors. The advantage and performance of the proposed quick BAT algorithm was verified experimentally by solving 20 benchmark problems and compared to the binary decision diagram (BDD), quick inclusion–exclusion technology (QIE), and BAT.
•Novel STSP approach: integrates time and reliability, addresses TSP shortcomings.•TR-STSP: optimizes routes, minimizes costs, maximizes reliability.•Tri-objective model: equal focus on mean, ...deviation, reliability.•Permutation BAT: enhances solution efficiency, identifies Pareto solutions.•Realistic solutions for dynamic, uncertain route optimization.
This paper presents a novel approach to addressing the Stochastic Traveling Salesman Problem (STSP), a classical problem in combinatorial optimization, by integrating travel time and reliability factors into the decision-making process. Traditional TSP models primarily focus on minimizing the total travel distance or cost without considering the reliability of each route. In real-world situations, especially in logistics and network design, it's just as important to have reliable routes. A reliable route means there's a good chance it will be completed successfully and on time. Our research extends the conventional STSP framework by incorporating a reliability metric for each route, alongside the standard travel time metric. A tri-objective optimization model is proposed to minimize the mean and standard deviation of travel time and maximize route reliability simultaneously. A new algorithm called Permutation Binary-Addition-Tree (BAT) is proposed to solve the problem more efficiently when there is uncertainty. Our approach marks a significant step towards more realistic and practical solutions for route optimization problems in dynamic and uncertain environments. We also present a complexity analysis of our model against traditional cost-only TSP solutions, demonstrating the efficacy of considering reliability in route planning.
In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough ...information. However, comprehensive data collection from multiple sensors for high degree-of-freedom structures is not typically available in practice. We propose a method that reconciles the two seemingly conflicting difficulties. Takens’ embedding theorem is used to augment the dimensions of data collected from a single sensor. Taking advantage of the success of machine learning in image classification, high-dimensional reconstructed attractors were converted into images and fed into a convolutional neural network (CNN). Attractor classification was performed for 10 damage cases of a 3-story shear frame structure. Numerical results show that the inherently high dimension of the CNN model allows the handling of higher dimensional data. Information on both the level and the location of damage was successfully embedded. The same methodology will allow the extraction of data with unsupervised CNN classification to be consistent with real use cases.
Rodent virus communities (viromes) are overrepresented with zoonotic viruses, and as such are a key host system for the study of zoonotic viruses. However, the extent of viral diversity beyond ...characterized zoonotic viruses, and the factors that modulate the viromes of rodents remain opaque. In this issue of Molecular Ecology, Raghwani et al. (2023) use rodents as a model to understand the role of seasonality in dictating virome abundance and composition—a factor known to play an important role in most animal one‐host, one‐pathogen systems. These data are not only highly relevant to rodents, but have broad applications across understanding and disentangling animal virome ecology.
PDF
