Analysis of the implementation of an innovative IT solution to improve waiting times, communication with primary care and efficiency in Rheumatology

Objective To describe in detail an innovative program based on telemedicine for semi-automated prioritization of referrals from Primary Care (PC) to Rheumatology, for reproducibility purposes, and to present the results of the implementation study. Methods The context and situation were carefully analyzed, paying attention to all processes in place, referral numbers, waiting times, and number of complementary tests prior to discharge from Rheumatology. The composition of the team, aims, users, scope, and implementation phases were defined. Eight process indicators were established and measured before and 32 months after the program implementation. Results The program, which includes IT circuits, algorithms based on response to specific guideline-based checklists, e-consultation, and appointments based on priority, was fully implemented in our health area after a pilot study in two PC centers. After implementation, 6185 rheumatology referrals showed an e-consultation response delay of 8.95 days, and to first face-to-face visit (after e-consultation) of 12.6 (previous delay before program implementation was 83.1 days). Resolution by e-consultation reached 20% (1195 patients did not need seeing the rheumatologist to have the problem solved), and 1369 patients (32%) were discharged after the first visit. The overall resolution rate was 44.0% (2564 discharges/5830 e-consultations). From a random sample of 100 visits, only 10% of patients needed additional complementary tests to make a diagnosis and decision by Rheumatology (20.9% decrease from previous period). Conclusion A careful analysis of the situation and processes, with implementation of simple IT circuits, allows for the improvement of the efficiency and resolution of problems in Rheumatology.

Decoupling countermands nonselective response inhibition during selective stopping

Response inhibition is essential for goal-directed behavior within dynamic environments. Selective stopping is a complex form of response inhibition where only part of a multi-effector response must be cancelled. A substantial response delay emerges on unstopped effectors when a cued effector is successfully stopped. This stopping-interference effect is indicative of nonselective response inhibition during selective stopping which may, in-part, be a consequence of functional coupling. The present study examined selective stopping of (de)coupled bimanual responses in healthy human participants of either sex. Participants performed synchronous and asynchronous versions of an anticipatory stop-signal paradigm across two sessions while mu (µ) and beta (β) rhythm were measured with electroencephalography. Results showed that responses were behaviorally decoupled during asynchronous go trials and the extent of response asynchrony was associated with lateralized sensorimotor µ and β desynchronization during response preparation. Selective stopping produced a stopping-interference effect and was marked by a nonselective increase and subsequent rebound in prefrontal and sensorimotor β. In support of the coupling account, stopping-interference was smaller during selective stopping of asynchronous responses, and negatively associated with the magnitude of decoupling. However, the increase in sensorimotor β during selective stopping was equivalent between the stopped and unstopped hand irrespective of response synchrony. Overall, the findings demonstrate that decoupling facilitates selective stopping after a global pause process and emphasizes the importance of considering the influence of both the go and stop context when investigating response inhibition.

Multi-objective Optimization of High-Speed Solenoid Valve for Biodiesel Electronic Unit Pump

Background: A larger response delay of a high-speed solenoid valve will cause inaccurate fuel injection timing and imprecise cycle injection quantity, resulting in diesel engine emission and increased fuel consumption. Objective: Biodiesel as an ideal alternative fuel has exceptional advantages in energy conservation, emission reduction, and low-carbon environmental protection; however, matching with Electronic Unit Pump (EUP) and its impacts on solenoid valve operation need to be further studied. Method: In the present work, a numerical model of EUP fueled with biodiesel was established in an AMESim environment, which was validated by the experiment. Then, combined with the Design of Experiments (DOE) method, key parameters influencing the solenoid valve response delay were predicted: armature residual air gap, spring preload, poppet valve half-angle, valve needle diameter, and poppet valve diameter. Finally, taking the response delay time of solenoid valve as targets, multi-objective optimization model for high-speed solenoid valve was established using NSGA-II (non-dominated sorting genetic algorithm-II) genetic algorithm in modeFRONTIER platform. Results: The optimized results showed that the delay time of the solenoid valve closing is reduced by 6%, the opening delay time is reduced by 20.8%, the injection pressure peak is increased by 1.8MPa, which is beneficial to accurate injection quantity and the application of biodiesel in diesel engines.

Research on Mobile English Learning System Based on iOS

In order to improve the use experience of mobile learning and reduce the response delay caused by the slow positioning of system resources, research on mobile English learning systems based on iOS is proposed. Firstly, the value of realizing mobile learning is objectively analyzed, and the practical significance of the research is clarified from the perspectives of social development and learning conditions. On this basis, taking the intelligent client equipped with an iOS operating system as the hardware condition of the system, the system architecture, including data access layer, business logic layer, and presentation layer, is constructed by MVC. In the model module, it is divided into ten difficulty levels by using the frequency of vocabulary, the length of words, and the number of syllables of a single word. The learning resources of the reading class are uniformly represented by the vector space model, and the improved TF-IDF is used to calculate the weight of eigenvalues in the resources. According to the calculation results, the resources are also divided into ten difficulty levels, and the divided resources correspond to their respective databases and use regular expressions to find the learning resources that match the access request. Finally, with the support of the iOS protocol, the feedback mechanism between each module is constructed by Express.js. By analyzing the access requests sent and received by the view, the timely call to the target database resources is realized to improve the response rate of the system. The test results show that the function of the designed system can be realized stably, and the module response delay time is stable within 1s; under the ideal network environment, the buffer delay is in the range of 0.4–0.6 s, and the loading delay is no more than 1.8 s when the network speed is 3 Mpbs, which has a good user experience.

Research and Application of Full-Link Management Intellectual Inspection Robot with Remote Pre-Control and Information Digitization

With the development of intelligent power grid maintenance, inspection robot has been widely used for its integrated perception and remote pre-control. Aiming at the problems of the weak information interaction capacity and the low correlation with digital requirements of inspection operation, a kind of Full-Link Management Intellectual Inspection Robot is proposed. The article introduces the system structure, function modules, engineering application and debugging process. It is with low response delay and strong anti-interference ability. The correct recognition rate of violations reached 88.2 %, meanwhile provided with the function of evaluation and early warning of workers’ unsafe behavior. The Full-Link Management Intellectual Inspection Robot has a broad application foreground in power grid maintenance.

Automatic Control of Electronic Information Transmission Integrity Considering Gabp Neural Algorithm

With the rapid development of the economy and society, information has become an essential social resource and creates wealth continuously. However, open electronic information can be easily lost during transmission control. In this paper, the state of the information network is determined through a series of indexes based on the GABP neural algorithm. At the same time, the rate of information transmission is adjusted based on the state of the information network through self-adaptation, and the integrity of information transmission is controlled by the genetic particle swarm algorithm. Based on reasonable control of the integrity of information transmission, the research on the automatic control technology of open electronic information transmission integrity is accomplished under the condition that the bandwidth and response delay indexes are meeting. The study results indicate that the neural algorithm based on the GABP network can automatically control the electronic information transmission integrity and improve the reliability of information transmission effectively.

Timely surveillance and temporal calibration of disease response against human infectious diseases

Background Disease surveillance and response are critical components of epidemic preparedness. The disease response, in most cases, is a set of reactive measures that follow the outcomes of the disease surveillance. Hence, timely surveillance is a prerequisite for an effective response. Methodology/principal findings We apply epidemiological soundness criteria in combination with the Latent Influence Point Process and time-to-event models to construct a disease spread network. The network implicitly quantifies the fertility (whether a case leads to secondary cases) and reproduction (number of secondary cases per infectious case) of the cases as well as the size and generations (of the infection chain) of the outbreaks. We test our approach by applying it to historic dengue case data from Australia. Using the data, we empirically confirm that high morbidity relates positively with delay in disease response. Moreover, we identify what constitutes timely surveillance by applying various thresholds of disease response delay to the network and report their impact on case fertility, reproduction, number of generations and ultimately, outbreak size. We observe that enforcing a response delay threshold of 5 days leads to a large average reduction across all parameters (occurrence 87%, reproduction 83%, outbreak size 80% and outbreak generations 47%), whereas extending the threshold to 10 days, in comparison, significantly limits the effectiveness of the response actions. Lastly, we identify the components of the disease surveillance system that can be calibrated to achieve the identified thresholds. Conclusion We identify practically achievable, timely surveillance thresholds (on temporal scale) that lead to an effective response and identify how they can be satisfied. Our approach can be utilized to provide guidelines on spatially and demographically targeted resource allocation for public awareness campaigns as well as to improve diagnostic abilities and turn-around times for the doctors and laboratories involved.

Service-Oriented Real-Time Smart Job Shop Symmetric CPS Based on Edge Computing

Symmetry is one of the most important notions in the digital twins-driven manufacturing cyber–physical system (CPS). Real-time acquisition of production data and rapid response to changes in the external environment are the keys to ensuring the symmetry of the CPS. In the service-oriented production process, in order to solve the problem of the service response delay of the production nodes in a smart job shop, a CPS based on mobile edge computing (MEC) middleware is proposed. First, the CPS and MEC for a service-oriented production process are analyzed. Secondly, based on MEC middleware, a CPS architecture model of a smart job shop is established. Then, the implementation of MEC middleware and application layer function modules are introduced in detail. By designing an MEC middleware model and embedding function modules such as data cache management, redundant data filtering, and data preprocessing, the ability of data processing is sunk from the data center to the data source. Based on that, the network performances, such as network bandwidth, packet loss rate, and delay, are improved. Finally, an experiment platform of the smart job shop is used to verify different data processing modes by comparing the network performance data such as bandwidth, packet loss rate, and response delay.

Benzothiazolylhydrazone-Based Turn-on Fluorescent Probe for Detecting Cu2+: S-donor as a Cu2+-induced Fluorescence Quenching Inhibitor

The fluorescent turn-on detection of metal ions is highly desirable for public health and environmental security. Herein, we report a rationally designed fluorescent probe (1) for the detection of Cu2+ synthesized by integrating 2-hydrazinylbenzothiazole with 3-acetyl-7-hydroxycoumarin. The probe alone is non-fluorescent due to the isomerization of C=N in the excited state. The addition of Cu2+ can cause a delayed fluorescence enhancement. A comparative study of 1 and its analogues indicated that the turn-on fluorescence response was thanks to the sulfur atom coordinating to Cu2+. The response delay of 1 in sensing Cu2+ was ascribed to the gradual transition from N-coordination to S-coordination (N and S in thiazole moiety). The proposed new function of S-donor would provide a new approach for the turn-on fluorescence sensation of Cu2+.