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Mechanical forces are essential for coordinating cardiac morphogenesis, but much remains to be discovered about the interactions between mechanical forces and the mechanotransduction pathways they activate. Due to the elaborate and fundamentally multi-physics and multi-scale nature of cardiac mechanobiology, a complete understanding requires multiple experimental and analytical techniques. We identify three fundamental tools used in the field to probe these interactions: high resolution imaging, genetic and molecular analysis, and computational modeling. In this review, we focus on computational modeling and present recent studies employing this tool to investigate the mechanobiological pathways involved with cardiac development. These works demonstrate that understanding the detailed spatial and temporal patterns of biomechanical forces is crucial to building a comprehensive understanding of mechanobiology during cardiac development, and that computational modeling is an effective and efficient tool for obtaining such detail. In this context, multidisciplinary studies combining all three tools present the most compelling results.

Abstract: Osteoporosis is becoming more prevalent in the ageing society, however, its treatment is still a problem for both society and individuals. Traditional Chinese Medicine (TCM) has a long history in treating osteoporosis and is receiving increasing attention. Multiple formulas of TCM showed satisfactory effects in treating osteoporosis in both animal models and clinical patients. However, because TCM usually consists of multiple plant and/or animal products, it is difficult to clarify the mechanism of TCM according to the requirements of Western medicine regarding purity, efficacy, dosage, and safety. With increasing researchers have started to investigate the TCM using modern scientific tools such as bioinformatics and network pharmaceutics in osteoporosis and the addition of TCM in the latest version of International Statistical Classification of Diseases and Related Health Problems (ICD-11 version, 2019) by WHO, TCM is showing large potential in treating osteoporosis although there is still a long way. The review aimed to summarize recent advancements of TCM treating osteoporosis.

Traditional machine learning models generally use weak supervision model, which is difficult to adapt to the scene of multi classification for emotional text. Therefore, a multi model ensemble learning algorithm for emotional text classification is proposed. The algorithm takes the labeled emotional text data as the training sample, uses the improved TF-IDF algorithm to train the word vector space model, selects three weakly supervised machine learning algorithms, linear SVC, xgboost and logistic regression, to construct the base classifier, and uses the random forest algorithm to construct the meta classifier. It realizes the function of dividing emotional text into three categories: positive, neutral and negative. From the simulation and test results, the AUC values of the multi model ensemble learning algorithm model for each category are 0.93, 0.94 and 1.00, and the AP values are 0.87, 0.86 and 1.00, and the indicators of accuracy and recall are better than the single machine learning model, which realizes the high performance and high accuracy for emotional text classification. &nbsp

Bullying is a type of youth violence and an adverse childhood experience that can result in trauma and have immediate and long-term consequences for all involved. It can happen at school or elsewhere - including online entertainment and social and learning environments. Some children are at increased risk for bullying victimization, such as those targeted because of their racial/ethnic background or cultural identity. This study assessed U.S. parents and caregivers' self-reported changes in concern about their children's involvement in bullying during Fall 2020 compared to the prior year, which was marked by extraordinary historical circumstances (e.g., COVID-19 pandemic, heightened awareness of racial inequities, schools transitioning to virtual learning). Secondary analyses of data from the 2020 Fall ConsumerStyles and

Taxi sharing is a promising method to save resource consumption and alleviate traffic congestion while satisfying people’s commuting needs. Existing research methods include taxi dispatching methods based on intelligent algorithms, single vehicle route recommendation algorithms, and route recommendation algorithms based on vehicle traffic history. However, these studies either focus on how to efficiently dispatch satisfactory vehicles for passengers, ignoring the effect of efficient routes on vehicle travel efficiency, or let vehicles follow the shortest detour distance recommended by the system, ignoring the traffic congestion caused by the influx of vehicles into the same road. To address the abovementioned problems, a noncooperative game for a taxi sharing model (NCG-TSM) in urban road networks is proposed in this paper combined with the traffic conditions of the optional routes, and a distribution estimation algorithm for the shared taxi game is designed to make multivehicle route selections reach Nash equilibrium. The effectiveness of NCG-TSM is verified through simulation experiments. When the number of vehicles reaches the congestion capacity of the road segment, compared to the three common frameworks, the travel time cost and fuel consumption cost can be reduced by 5.8% to 9.1% and 3.5% to 8.9%, respectively. Besides, the occupancy rate has been improved, especially compared to the BMP framework, by 5.5% to 40%.

Background: Feeding and eating disorders related to cognitive and psycho-behavioral symptoms are strongly associated with health status in persons with dementia (PWD). Non-pharmacological interventions have been the priority selection to address this significant issue. However, the direct targets of non-pharmacological interventions are unclear and there is no consistent evidence of recommendations on the intervention of different dementia stages and the settings of intervention practice. Objective: To provide caregivers with a set of self-help non-pharmacological interventions for feeding and eating disorders in PWD. Methods: Based on the process of evidence summary, a systematic literature search was performed on dementia websites and seven databases. Two researchers screened the studies independently and appraise the quality. The evidence was graded by Joanna Briggs Institute Grades of Recommendation. Results: Twenty-eight articles were included. Twenty-three non-pharmacological intervention recommendations were categorized into six themes containing oral nutritional supplementation, assistance with eating and drinking, person-centered mealtime care, environmental modification, education or training, and multi-component intervention. These interventions corresponded to three direct targets including improving engagement, making up for loss ability, and increasing food intake directly. They were applied to different stages of dementia and most interventions were targeted at PWD in long-term care institutions. Conclusion: This article summarized the direct targets and the specific implementation of recommendations at different stages of dementia to provide caregivers with self-help non-pharmacological interventions. The practice of recommendations was more applicable to institutionalized PWD. When applied to PWD at home, caregivers need to identify the specific feeding and eating conditions at different stages and adopted the interventions in conjunction with the wishes of the PWD and professional advice.

We propose a hybrid approach for solving problems of mixed-integer linear programming with two types of variables (MILPTTV). The hybrid approach includes three algorithms: (1) Benders decomposition; (2) the ant colony algorithm; (3) the feasible heuristic method. We decompose the programming problem into a master problem and a subproblem using Benders decomposition. The ant colony algorithm generates an initial solution for the master problem. A feasible heuristic is used to obtain feasible solutions as input to the subproblem, whereas the subproblem is solved to optimality by using a linear programming solver that is named Linprog function in MATLAB R2016a. Over successive iterations, the master problem is refined by adding cuts from the subproblem and the master problem is solved by a linear programming solver Cplexmilp function. We compare the performance of the proposed hybrid approach against a general Benders decomposition approach as well as against a mixed-integer programming solver CPLEX (version 12.6) that is invoked by MATLAB on MIPLIB test problem mas76, mas74 and some large-scale numerical examples.

Due to the complexity of the design of multilayer electromagnetic (EM) wave absorbing materials, it is difficult to establish the relationship between material parameters (type and filling ratios) and EM properties using traditional trial and error methods. Based on the measured EM parameters within a few materials and Boltzmann mixing theory, a database of EM parameters was thereafter built up. In this study, the genetic algorithm (GA) was used to design the multilayer wave-absorbing cement mortar. In order to verify this method, a multilayer mortar was fabricated and measured. The simulated and measured results are well consistent, which convincingly verifies computer-aided design. In addition, the optimized result expresses that the first layer as a matching layer guides EM waves into the interior of the material, while the other layers as absorption layers attenuate EM waves. The multilayer material may not meet the impedance gradient principle but still exhibits better EM wave absorption performance. The reflection loss (RL) of all optimized three layer sample is below –6.89 dB in the full frequency band and the minimum RL is –26.21 dB. This composite absorbing material and the GA method provide more design ideas for the design of future cement-based wave-absorbing materials and save a lot of time and material cost.

Green supply chain management attaches great importance to the coordinated development of social economy and ecological environment, and requires enterprises to consider environmental protection factors in product design, packaging, procurement, production, sales, logistics, waste and recycling. Suppliers are the “source” of the entire supply chain, and the choice of green suppliers is the basis of green supply chain management, and their quality will directly affect the environmental performance of enterprises. The green supplier selection is a classical multiple attribute group decision making (MAGDM) problems. Interval-valued intuitionistic fuzzy sets (IVIFSs) are the extension of intuitionistic fuzzy sets (IFSs), and are utilized to depict the complex and changeable circumstance. To better adapt to complex environment, the purpose of this paper is to construct a new method to solve the MAGDM problems for green supplier selection. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, the original MABAC method based on the cumulative prospect theory (CPT) is extended into IVIFSs (IVIF-CPT-MABAC) method for MAGDM issues. Meanwhile, the method to evaluate the attribute weighting vector is calculated by CRITIC method. Finally, a numerical example for green supplier selection has been given and some comparisons is used to illustrate advantages of IVIF-CPT-MABAC method and some comparison analysis and sensitivity analysis are applied to prove this new method’s effectiveness and stability.