A Review on Technologies used in MERN stack

MERN stack is one of the well known web stack that has acquired significance over other stack. This is a direct result of its UI delivering and execution, Cost-Adequacy, Open Source and is not difficult to switch among customer and server. Its essential target is to improve the general exhibition of the application. This stack, as well as utilizing superior execution and tweaked advances, considers web applications and programming to be grown rapidly. MERN stack is an assortment of strong and amazing innovations used to foster adaptable expert web applications, containing front-end, back-end, and data set parts. It is an innovation stack that is an easy to understand full-stack JavaScript structure for building dynamic sites and applications. This is the explanation it is the most favored stage by new businesses. This paper will depict MERN Stack involving 4 advancements to be specific: Mongo DB, Express, Respond and Node.js. Every one of these 4 incredible advancements gives a start to finish system for the designers to work in and every one of these advances have a major influence in the improvement of web applications. Index Terms: MERN STACK, Mongo DB, Express JS, React JS, Node JS platform

Assessment of early warning signs in hospital projects' front-end phase

PurposeThe front-end phase plays an important role in achieving project success, and establishment of performance measurement systems considering project challenges or pitfalls is a way of keeping track of this phase. Early warning signs, a type of proactive performance indicators, may serve as means for improving decision-making and project processes aiming for short- and long-term project success. In this paper, the authors present findings from a study on early warning signs (EWS) in hospital projects' front-end. A preliminary systematisation of identified signs as a contribution to front-end improvement is provided.Design/methodology/approachThe paper is based on a mixed methods approach, using a sequential, exploratory research design comprising document studies, interviews and a survey.FindingsThe authors identified 62 challenges for hospital projects' front-end performance and further established four categories of EWS as follows: (1) structure and tools, (2) context and frame factors, (3) management and (4) relational factors and properties. This mirrors the presence of hard and soft issues from previous studies. There is need for clarifying terminology and raising consciousness on EWS. Processual approaches to identify EWS are considered more useful than subsequent established indicators.Originality/valueThe findings from this paper provide insight into EWS in hospital projects' front-end phase. This adds to the general understanding of EWS and contributes to more knowledge on the front-end phase in general.

Suppression of Frequency Modulation to Amplitude Modulation Conversion with Modified Group Velocity Dispersion Compensation Device in the Front End of High-Power Lasers

The group velocity dispersion (GVD) occurring in the front end of high-power lasers is one of the primary factors leading to the conversion of frequency modulation (FM) to amplitude modulation (AM). In this paper, we propose a modified, active, closed-loop feedback compensation device for GVD-induced FM–AM conversion, using a two-dimensional, electric, adjustable mirror mount and parallel grating pair to improve the long-term stability, efficiency of adjustment, and accuracy of compensation. Experimental results of a 12 h FM–AM depth test revealed that the depth varied between 2.28% and 5.22%. Moreover, we formulated a mathematical relationship between the dispersion parameters and temperature in optical fibers to analyze the intrinsic effect of temperature on FM–AM. The related simulation and experimental results consistently validated the quantitative relationship between the temperature and FM–AM depth.

Resource management framework using simulation modeling and multi-objective optimization: a case study of a front-end department of a public hospital in Thailand

Background The overcrowded patients, which cause the long waiting time in public hospitals, become significant problems that affect patient satisfaction toward the hospital. Particularly, the bottleneck usually happens at front-end departments (e.g., the triage and medical record department) as every patient is firstly required to visit these departments. The problem is mainly caused by ineffective resource management. In order to support decision making in the resource management at front-end departments, this paper proposes a framework using simulation and multi-objective optimization techniques considering both operating cost and patient satisfaction. Methods To develop the framework, first, the timestamp of patient arrival time at each station was collected at the triage and medical record department of Thammasat University Hospital in Thailand. A patient satisfaction assessment method was used to convert the time spend into a satisfaction score. Then, the simulation model was built from the current situation of the hospital and was applied scenario analyses for the model improvement. The models were verified and validated. The weighted max–min for fuzzy multi-objective optimization was done by minimizing the operating cost and maximizing the patient satisfaction score. The operating costs and patient satisfaction scores from various scenarios were statistically compared. Finally, a decision-making guideline was proposed to support suitable resource management at the front-end departments of the hospital. Result The three scenarios of the simulation model were built (i.e., a real situation, a one-stop service, and partially shared resources) and ensured to be verified and valid. The optimized results were compared and grouped into three situations which are (1) remain the same satisfaction score but decrease the cost (cost decreased by 2.8%) (2) remain the same satisfaction score but increase the cost (cost increased up to 80%) and (3) decrease the satisfaction score and decrease the cost (satisfaction decreased up to 82% and cost decreased up to 59%). According to the guideline, the situations 1 and 3 were recommended to use in the improvement and the situation 2 was rejected. Conclusion This research demonstrates the resource management framework for the front-end department of the hospital. The experimental results imply that the framework can be used to support the decision making in resource management and used to reduce the risk of applying a non-improvement model in a real situation.

A Novel Piezoelectric RF-MEMS Resonator with Enhanced Quality Factor

This work presents a novel ultra-high frequency (UHF) Lamb mode Aluminum Nitride (AlN) piezoelectric resonator with enhanced quality factors (Q). With slots introduced in the vicinity of the tether support end, the elastic waves leaking from the tether sidewalls can be reflected, which effectively reduces the anchor loss while retaining size compactness and mechanical robustness. Comprehensive analysis was carried out to provide helpful guidance for obtaining optimal slot designs. For various resonators with frequencies ranging from 630 MHz to 1.97 GHz, promising Q enhancements up to 2 times have all been achieved. The 1.97 GHz resonator implemented excellent f × Q product up to 6.72 × 1012 and low motional resistance down to 340 Ω, which is one of the highest performances among the reported devices. The devices with enhanced Q values as well as compact size could have potential application in advanced RF front end transceivers.

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

Applications of mobile robots are continuously capturing the importance in numerous areas such as agriculture, surveillance, defense, and planetary exploration to name a few. Accurate navigation of a mobile robot is highly significant for its uninterrupted operation. Simultaneous localization and mapping (SLAM) is one of the widely used techniques in mobile robots for localization and navigation. SLAM consists of front- and back-end processes, wherein the front-end includes SLAM sensors. These sensors play a significant role in acquiring accurate environmental information for further processing and mapping. Therefore, understanding the operational limits of the available SLAM sensors and data collection techniques from a single sensor or multisensors is noteworthy. In this article, a detailed literature review of widely used SLAM sensors such as acoustic sensor, RADAR, camera, Light Detection and Ranging (LiDAR), and RGB-D is provided. The performance of SLAM sensors is compared using an analytical hierarchy process (AHP) based on various key indicators such as accuracy, range, cost, working environment, and computational cost.

ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks

An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model.

Implementação de processo de identificação e rastreio do material processado em um centro de material e esterilização

Objetivo: Implementar e descrever o método de identificação e rastreio informatizadodo material processado no Centro de Material e Esterilização em um Hospital Regional no DistritoFederal. Método: Estudo observacional, experimentaledescritivo, de aspecto prospectivo que realizou a implementação de uma tecnologia de saúde no CME de um hospital público, realizado em oito etapas. Resultados: Foi realizada exploração de campo antes da implementação do sistema para reconhecimento dos métodos já utilizados, aidentificação de bandejas em categorias e sua respectiva nomenclatura e numeração deforma sequencial, confecção dos rótulos, registro das etapas de processamento e instalação doSoftware Access Front-End , criação de códigos de barras para os materiais processados nosetor, alimentaçãodo banco de dados SQL SERVER versão 2008 R.2 com cadastro dos servidores, materiais processados no setor e setores consumidores, treinamentodos profissionais e atualização do manual de normas e rotinas do setor. A pesquisa foi realizada em dois meses. Conclusão: O sistema implementado trouxe benefícios ao CME do hospital nos aspectos de organização e padronização na realização das etapas e registros, além da possibilidade de rastreio dos materiais.

Time-Domain Joint Training Strategies of Speech Enhancement and Intent Classification Neural Models

Robustness against background noise and reverberation is essential for many real-world speech-based applications. One way to achieve this robustness is to employ a speech enhancement front-end that, independently of the back-end, removes the environmental perturbations from the target speech signal. However, although the enhancement front-end typically increases the speech quality from an intelligibility perspective, it tends to introduce distortions which deteriorate the performance of subsequent processing modules. In this paper, we investigate strategies for jointly training neural models for both speech enhancement and the back-end, which optimize a combined loss function. In this way, the enhancement front-end is guided by the back-end to provide more effective enhancement. Differently from typical state-of-the-art approaches employing on spectral features or neural embeddings, we operate in the time domain, processing raw waveforms in both components. As application scenario we consider intent classification in noisy environments. In particular, the front-end speech enhancement module is based on Wave-U-Net while the intent classifier is implemented as a temporal convolutional network. Exhaustive experiments are reported on versions of the Fluent Speech Commands corpus contaminated with noises from the Microsoft Scalable Noisy Speech Dataset, shedding light and providing insight about the most promising training approaches.