Feasible Mode Research Articles

The Impacts of Recessive Curriculum on Maintaining a Stable Community in China

As is known to all, recessive curriculum is the one which is presented in education institution through indirect and hidden way. Although such concept was launched as early as in 1960s, it developed with the huge impacts on shaping one’s value, which actually plays significant role in individuals’ character. Therefore, a stable and harmonious society can be maintained. The aim of this thesis is to illustrate the specific influence of recessive curriculum on establishing one’s value and devising a feasible mode to stress the application of recessive curriculum.

Value Analysis and Mode Construction of Tutorial System for P2P Undergraduates in Computer Science—-Based on the Reform Practice of Tutorial System in the College of Computer Science, Xi’an University of Technology

With the implementation of credit system, undergraduate tutorial system is becoming a new system of student education management in colleges and universities. The implementation of tutorial system of undergraduate students is based on educational theories, such as teachers teaching to educate people, teaching students in accordance of their aptitudes, and constructing a harmonious teacher-students’ relationship. This paper puts forward a feasible operation mode for the optimization of the tutorial system for undergraduate students, which has achieved good results.

4056 Dementia family caregivers’ mobile app use and intention to adopt mHealth apps

OBJECTIVES/GOALS: To describe preliminary results of Alzheimer’s and dementia caregivers’ (CGs) mobile app use and intention to adopt mHealth apps for their own chronic condition self-management. To discuss implications for designing and implementing mHealth interventions for CGs. METHODS/STUDY POPULATION: This study aims to recruit 110 racially and ethnically diverse family caregivers (CGs) who have a chronic condition, provide care for persons with Alzheimer’s disease or related dementias, and have access to a mobile device. This is a cross-sectional correlational study collecting data with computer-assisted telephone interviews stored through REDCap. The study survey was created using existing surveys about mobile app use; relevant, well-validated research instruments; and questions from the U.S. Census and other national surveys. CGs are being actively recruited from the Baltimore-Washington metropolitan area using various recruitment strategies that have been effective in prior studies. RESULTS/ANTICIPATED RESULTS: The majority of CGs used websites (86%), mobile devices (68%) or apps (53%) to manage their own health. CGs using health-related apps were tracking their exercise (60%), diet (60%), medical records (50%), and physical health measures (50%). More than 4 out of 5 (82%) predicted they would use mobile apps to self-manage their chronic condition, though only 68% actually planned to use them. 86% of CGs were using mobile apps for non-health related purposes, with the most popular app being weather (90%), followed by social media (74%), music/entertainment (68%), and banking/business apps (63%). CGs used weather and social media apps most often (2 or more times/day) and spent 9 hours/week on apps. DISCUSSION/SIGNIFICANCE OF IMPACT: Websites and mobile apps appear to be feasible modes to deliver health interventions to CGs. Researchers should consider including features of apps most frequently used by CGs, such as the weather, ways to connect with others, and music/entertainment, when delivering mHealth interventions to CGs.

An Intuitionistic Fuzzy Based Decision-Making Method for River Operation Management: Practice from China

River course is the path of carrying river flow and the blood of modern economic and social development. River operation management has attracted great attention from governments and water conservancy circles all over the world. In China, the river operation management mode refers to the combination of two dimensions: The organization method of river operation management and the bearing and use method of river maintenance fund. Based on the practice of China, we used a two-dimensional matrix method to construct a feasible mode set, including 12 modes, according to the various organization methods of river operation management and the bearing and use methods of river maintenance fund over the years in China. We also compared and analyzed the advantages, disadvantages, and applicable conditions of these 12 river operation management modes. In particular, we investigated the main rivers of 19 provinces and municipalities in China, identified the main factors of the river operation management mode, further identified 5 key indexes, and constructed a decision-making index system for the river operation management mode. We used the intuitionistic fuzzy hybrid average (IFHA) and intuitionistic fuzzy weighted average (IFWA) operators to construct a set of river operation management mode selection method based on intuitionistic fuzzy decision-making. A case study was conducted to select the operation management mode for the Heilongjiang section of Songhua River, using the method put forward in this paper. This study can promote water resource management research and prepare for a possible future sustainability emergency.

Analytical cooperative entry guidance for Rendezvous and formation flight

The cooperative entry guidance design is difficult for Hypersonic Glide Vehicles (HGVs), because the motions of hypersonic glider in longitudinal- and lateral-planes are seriously coupled as they lack adjustable thrust to offset the complex influence of lateral maneuvers on longitudinal motion, whereas formation flight requires that the two motions can be controlled independently. However, by exploring and exploiting the weak capability of HGVs in controlling the longitudinal and lateral motions independently, we propose a feasible flight mode named Rendezvous & Formation Flight (R&FF) mode, in which the vehicles are only required to fly in formation through a relatively short range. This flight mode is also of great value in fast cooperative detection of key area. The flight mode divides the entry flight into Rendezvous Phase (RP) and Formation Flight Phase (FFP), and the corresponding guidance scheme is designed to be of two layers: offline planning layer and online guidance layer. In the offline planning layer, the coordinated reference trajectories of all the vehicles for all the phases are fast planned beforehand by the mix use of the trajectory simulations and analytical solutions to 3-D hypersonic glide problem. In the online guidance layer, a weak-tracking guidance is posed for RP, which allows the HGVs to fly relatively freely for anti-interference, but should meet the rendezvous conditions strictly. By contrast, in FFP, a strong-tracking guidance is put forward to track the 3-D reference trajectories closely in order to maintain the formation configuration.

Development of a theory-driven implementation strategy for cancer management guidelines in sub-Saharan Africa

BackgroundDespite recent international efforts to develop resource-stratified clinical practice guidelines for cancer, there has been little research to evaluate the best strategies for dissemination and implementation in low- and middle-income countries (LMICs). Guideline publication alone is insufficient. Extensive research has shown that structured, multifaceted implementation strategies that target barriers to guideline use are most likely to improve adherence; however, most of this research has been conducted in high-income countries. There is a pressing need to develop and evaluate guideline implementation strategies for cancer management in LMICs in order to address stark disparities in cancer outcomes.MethodsIn preparation for the launch of Tanzania’s first National Cancer Treatment Guidelines, we developed a theory-driven implementation strategy for guideline-based practice at Ocean Road Cancer Institute (ORCI). Here, we use the Intervention Mapping framework to provide a detailed stepwise description of our process. First, we conducted a needs assessment to identify barriers and facilitators to guideline-based practice at ORCI. Second, we defined both proximal and performance objectives for our implementation strategy. Third, we used the Capability, Opportunity, Motivation and Behavior/Behavior Change Wheel (COM-B/BCW) framework to categorize the barriers and facilitators, choose behavior change techniques most likely to overcome targeted barriers and leverage facilitators, and select a feasible mode of delivery for each technique. Fourth, we organized these modes of delivery into a phased implementation strategy. Fifth, we operationalized each component of the strategy. Sixth, we identified the indicators of the process, outcome, and impact of our intervention and developed an evaluation plan to measure them using a mixed methods approach.DiscussionWe developed a robust, multifaceted guideline implementation strategy derived from a prominent behavior change theory for use in Tanzania. The barriers and strategies we generated are consistent with those well established in the literature, enhancing the validity and generalizability of our process and results. Through our rigorous evaluation plan and systematic account of modifications and adaptations, we will characterize the transferability of “proven” guideline implementation strategies to LMICs. We hope that by describing our process in detail, others may endeavor to replicate it, meeting a widespread need for dedicated efforts to implement cancer guidelines in LMICs.

Finite horizon constrained control and bounded-error estimation in the presence of missing data

In this paper, we propose an optimization-based design technique for constrained control and bounded-error state estimation for affine systems in the presence of intermittent measurements. We treat the affine system as a switched system where the measurement equation switches between two modes based on whether a measurement exists or is missing, and model potential missing data patterns with a finite-length language that constrains the feasible mode sequences. Then, we introduce a novel property, equalized recovery, that generalizes the equalized performance property and that allows us to tolerate missing observations. By utilizing Q-parametrization, we show that a finite horizon optimal estimator/controller can be constructed using time-based and prefix-based approaches, where the latter implicitly estimates the specific missing data pattern (i.e., mode sequence), within the given language, according to the prefix observed so far. We illustrate with numerical examples that the proposed approaches can provide desirable performance guarantees.

Feasible Prestress Modes for Cable-Strut Structures with Multiple Self-Stress States Using Particle Swarm Optimization

A prestressed cable-strut structure is usually regarded as a mechanism before being prestressed. Under the action of initial prestresses, the internal infinitesimal mechanisms can be rigidified, resulting in achieving the desired structural stiffness. Therefore, feasible prestress design is a key to develop and analyze novel prestressed cable-strut structures. In this study, an effective optimization method is presented to determine the optimal feasible prestress modes of a cable-strut structure with predefined geometry and multiple self-stress states. Two optimization models based on the self-stress states and the integral self-stress states are presented to compute the optimal feasible prestress modes. Thereafter, the multiobjective optimization problem is converted into a single objective optimization problem by the weight coefficient method, and the particle swarm optimization algorithm is applied to find feasible solutions. Illustrative examples verify the feasibility of the presented optimization algorithms to calculate feasible prestress modes. In comparison with the conventional optimization methods, the proposed method shows satisfactory accuracy and efficiency.

An integrated computational intelligence technique based operating parameters optimization scheme for quality improvement oriented process-manufacturing system

The analysis and improvement of product quality for process industry is an increasing concern for academia and industry. As the outputs of a manufacturing system mainly depend on corresponding input conditions, so it is of high significance to develop an optimization scheme to actively and accurately determine operating parameters to obtain desired quality. However, the widely employed single-model modeling mode for whole production process neglects the natural characteristics within process manufacturing system such as multistage manufacturing and hysteresis. Additionally, the popular data-driven modeling techniques in current works, especially black-box machine learning models have been restricted to satisfying the requirements regarding excellent approximation capability and explicit mathematical expression simultaneously. To fill up above research gap, it is meaningful to develop a new data-driven optimization scheme in this work to effectively and accurately determine the optimum operating parameters considering the abovementioned characteristics and requirements. Firstly, two different connecting strategies are discussed to determine the more accurate and feasible quality propagation mode between adjacent stages. Then, two computational intelligence (CI) techniques, i.e., Multi-Gene Genetic Programming (MGGP) and Multi-objective Particle Swarm Optimization (MOPSO) algorithm are exploited to construct correlation model with explicit mathematical expression and derive the optimal operating parameters, respectively. Afterwards, the fuzzy Multi-criteria Decision Making (FMCDM) method is further proposed to select the optimal solution from the obtained Pareto solutions sets. The application of the proposed scheme in a coal preparation process indicates that the proposed scheme is promising and competitive on prediction accuracy and optimization efficiency over baseline methods, and can significantly improve the final product quality comparing with initial parameters setting. Moreover, the feasible quality specification for intermediate product can also be obtained by our proposed scheme which is beneficial for early detection of quality abnormality and timely parameters adjustment.

Methyl Scanning and Revised Binding Mode of 2-Pralidoxime, an Antidote for Nerve Agent Poisoning.

Organophosphorus nerve agents (OPNAs) inhibit acetylcholinesterase (AChE) and, despite the Chemical Weapons Convention arms control treaty, continue to represent a threat to both military personnel and civilians. 2-Pralidoxime (2-PAM) is currently the only therapeutic countermeasure approved by the United States Food and Drug Administration for treating OPNA poisoning. However, 2-PAM is not centrally active due to its hydrophilicity and resulting poor blood-brain barrier permeability; hence, these deficiencies warrant the development of more hydrophobic analogs. Specifically, gaps exist in previously published structure activity relationship (SAR) studies for 2-PAM, thereby making it difficult to rationally design novel analogs that are concomitantly more permeable and more efficacious. In this study, we methodically performed a methyl scan on the core pyridinium of 2-PAM to identify ring positions that could tolerate both additional steric bulk and hydrophobicity. Subsequently, SAR-guided molecular docking was used to rationalize hydropathically feasible binding modes for 2-PAM and the reported derivatives. Overall, the data presented herein provide new insights that may facilitate the rational design of more efficacious 2-PAM analogs.

A hybrid symmetry–PSO approach to finding the self-equilibrium configurations of prestressable pin-jointed assemblies

For pin-jointed assemblies with many members or self-stress states, the form-finding problem using conventional methods generally involves considerable computational complexities due to the large size of the solution spaces. Here, we propose an improved form-finding method for prestressable pin-jointed structures by combining symmetry-based qualitative analysis with particle swarm optimization. Expressed in the symmetry-adapted coordinate system, the nodal coordinate vectors of a structure with specific symmetry and topology are independently extracted from the key blocks of the small-sized force density matrices associated with rigid-body translations. Then, the first block of the equilibrium matrix is computed, in which the null space reveals integral self-stress states. Particle swarm optimization is introduced and adapted to find feasible prestress modes, where the uniformity and unilaterality conditions of the members are considered. Besides, the QR decomposition with column pivoting is adopted for efficient computations on the null space of these blocks. The QR decompositions of the small-sized blocks of the force density matrix and the equilibrium matrix are performed iteratively, to simultaneously find a stable self-equilibrium configuration and a feasible prestress mode. Representative examples show the presented method is computationally efficient and accurate for the form-finding of symmetric tensegrities and prestressed cable–strut structures.

How to complete the tautomerization and substrate-assisted activation prior to C–C bond fission by meta-cleavage product hydrolase LigY?

Two feasible binding modes could complete the C–C bond fission of the substrate. One is the bidentate mode and five-coordination, and the other is the monodentate mode and five-coordination.

Proactive empty vehicle rebalancing for Demand Responsive Transport services

Abstract Worldwide, ridesharing business is steadily growing and has started to receive attention also by public transport operators. With future fleets of Autonomous Vehicles, new business models connecting schedule-based public transport and feeder fleets might become a feasible transport mode. However, such fleets require a good management to warrant a high level of service. One of the key aspects of this is proactive vehicle rebalancing based on the expected demand for trips. In this paper we model vehicle rebalancing as the Dynamic Transportation Problem. Results suggest that waiting times can be cut by around 30 % without increasing the overall vehicle miles travelled for a feeder fleet in rural Switzerland.

Social Impacts on Development of Rural River Transportation in Indonesia

Indonesia is a maritime country that consists of thousands islands connected by seas and other type of inland water such as rivers, lakes, swamps and canals. Basically, water transportation particularly river transportation becomes one of the most important transportation modes to connect one area to another area because the absence of the availability of land transport infrastructure. Besides that, river transportation is the major transport mode in rural area where there is no a feasible transport mode to support their daily activities. However, the development of river transportationin rural area gives social impacts not only for people who live in rural area, but also for the area development itself as a whole. Those impacts influence people's life in many aspects such as education, health, environment, culture and even political system. Then, this paper presents social impacts caused by the development of rural river transportation that case study in Indonesia. Furthermore, the integrated and participatory approach is required to optimize the development of rural river transportation in Indonesia

Collaborative robot and mixed reality assisted microgravity assembly for large space mechanism

Gravity balance has a great impact on the assembly and deployment experiment of the large space mechanism to achieve the perfect performance in space. Traditional gravity balance methods have their advantages in the deployment experiment, but not suitable for the assembly process which has a more stringent location requirement. This paper proposed an advanced microgravity assembly method for a large space mechanism. The architecture of the system for microgravity assembly is proposed. The collaborative robot and mixed reality (MR) are integrated into this system, which can realize human-robot collaboration assembly, improve assembly efficiency and reduce errors. The microgravity assembly is realized by the six-dimensional force sensor which makes the robot work in force control and installed at the end of the collaborative robot. The prototype system is verified in the laboratory environment, which provides a new and feasible assembly mode for intelligent collaborative assembly for large complex space products.

Strategic Interaction Multi-Agent Deep Reinforcement Learning

Despite the proliferation of multi-agent deep reinforcement learning (MADRL), most existing typical methods do not scale well to the dynamics of agent populations. And as the population increases, the dimensional explosion of joint state-action and the complex interaction between agents make learning extremely cumbersome, which poses the scalability challenge for MADRL. This paper focuses on the scalability issue of MADRL with homogeneous agents. In a natural population, local interaction is a more feasible mode of interplay rather than global interaction. And inspired by the strategic interaction model in economics, we decompose the value function of each agent into the sum of the expected cumulative rewards of the interaction between the agent and each neighbor. This novel value function is decentralized and decomposable, which enables it to scale well to the dynamic changes in the number of large-scale agents. Hereby, the corresponding strategic interaction reinforcement learning algorithm (SIQ), is proposed to learn the optimal policy of each agent, wherein a neural network is employed to estimate the expected cumulative reward for the interaction between the agent and one of its neighbors. We test the validity of the proposed method in a mixed cooperative-competitive confrontation game through numerical experiments. Furthermore, the scalability comparison experiments illustrate that the scalability of the SIQ algorithm outperforms the independent learning and mean field reinforcement learning algorithms in multiple scenarios with different and dynamically changing numbers.

Improvement of freight transportation technologiy in the multimodal railway-water transport

Analysis of arrangement of the local operation during freight transportation by the multimodal railway-water transport to Tobolsk river port of the Ob-Irtysh bassin is presented. Dynamics of growth of construction cargo loading and increase of the volumes of their transportation to the port enhance the working capacity of Sverdlovsk railroad, which increases the loading level of its processing and carrying capacities. It should be noted that the level of quality and reliability of such transportation is reduced in the period of the transport infrastructure repair, when the number of the freight trains temporarily removed from operation is grown at the approaches to the port railway station. The search for the possible solutions of arranging the local operation was conducted at the route limited in the carrying and processing capabilities, in particular at the interaction of two types of transport: railway and inland water transport. Effective solutions for automation of the processes of adaptive approval of the volume of transported cargo are proposed, as well as the methods and options of their transportation at all stages of transportation services, forecasting and planning of the regional freight transportation as a part of the inter-industry transportation. The “Electronic service of the complex transportation services” system is presented, which provides for selection of the best option of effect on the main quality criteria of the regional freight transportation and ensures self-arrangement and self-support of the process when its conditions are changed under the impact of external and internal factors. The system calculates the economically feasible mode of transportation, performs adaptive approval of the volumes of transported cargo at all stages of the technological cycle of rendering complex transportation services. The system provides for information and analytical support of the functions of planning, arrangement of local operation, and collection, analysis, and processing of statistical data. Methods of the system construction are used for control of the transportation services when arranging the car traffic volume of inert cargo making the forecast of their delivery. The “Electronic service of the complex transportation services” system has passed the state registration.

Dynamic Social-Aware Computation Offloading for Low-Latency Communications in IoT

Internet of Things (IoT) as a prospective platform to develop mobile applications, is facing with significant challenges posed by the tension between resource-constrained mobile smart devices and low-latency demanding applications. Recently, mobile edge computing (MEC) is emerging as a cornerstone technology to address such challenges in IoT. In this paper, by leveraging social ties in human social networks, we investigate the optimal dynamic computation offloading mode selection to jointly minimize the total tasks' execution latency and the mobile smart devices' energy consumption in MEC-aided low-latency IoT. Different from the previous studies, which mostly focus on how to exploit social tie structure among mobile smart device users to construct the permutation of all the feasible modes, we consider dynamic computation offloading mode selection with social awareness-aided network resource assignment, involving both the computing resources and transmit power from heterogeneous mobile smart devices. On the one hand, we formulate the dynamic computation offloading mode selection into the infinite-horizon time-average renewal-reward problems subject to time average latency constraints on a collection of penalty processes. On the other hand, an efficient solution is also developed, which elaborates on a Lyapunov optimization-based approach, i.e., drift-plus-penalty (DPP) algorithm. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed dynamic social-aware computation offloading mode selection method considering different configurations of the IoT network parameters.

Novel Derivatives of Rhodanine-3-Hippuric Acid as Active Inhibitors of Aldose Reductase: Synthesis, Biological Evaluation, and Molecular Docking Analysis

Inhibitors of aldose reductase provide a feasible mode of action against diabetic complications. Based on the marketed aldose reductase inhibitor epalrestat containing rhodanine nucleus, rhodanine-3-hippuric acid and its 5-arylidene derivatives were synthesized. The structure of newly synthesized compounds was confirmed by IR, 1H NMR, and 13C NMR spectrometry. In vitro aldose reductase inhibitory activity of the synthesized compounds was assayed and the results showed that most of the derivatives were potent against aldose reductase with IC50 values ranging from 0.2 to 2.36 µM. Two of the compounds with the highest efficacies among the tested compounds (IC50 values of 0.2 and 0.6 µM) were more potent than epalrestat. Molecular docking studies were undertaken to explore the binding modes of all compounds into the active site of aldose reductase in order to rationalize the inhibitory efficacy of these derivatives. Rhodanine-3-hippuric acid and all its arylidene derivatives fulfil Lipinski’s rule and show good drug-likeness property. In conclusion, novel compounds synthesized in the present study have proved to be potential drugs for diabetic complications.

A randomized trial of telemedicine for migraine management

To determine whether synchronous video-based telemedicine visits with specialists are feasible and to evaluate clinical effectiveness, patient perceptions, and other benefits of telemedicine visits for follow-up migraine care in a tertiary headache center. A one-year, randomized clinical trial. Fifty patients were screened and 45 entered the study (43 women, two men). Out of 96 scheduled visits, 89 were successfully conducted using telemedicine. Eighteen patients (out of 22) in the telemedicine cohort and 12 patients (out of 23) in the in-office cohort completed the study. In this small study, clinical outcomes, namely improvement in MIDAS, number of headache days, and average severity at 12 months for participants in the telemedicine group, were not different from those in the in-office group. Convenience was rated higher and visit times were shorter in the telemedicine group. In this cohort of patients with severe migraine-related disability, telemedicine was a feasible mode of treatment and an effective alternative to in-office visits for follow-up migraine care. Physician productivity could be higher with telemedicine, and patients may get better access because of its convenience. This study is listed on ClinicalTrials.gov (NCT01706003).