Capacity Data Research Articles

Theoretical Frameworks of Leadership in Nursing: Critical Reflections

ObjectivesThe objective of this article is to present a comprehensive view on the topic of nursing leadership discussing the theoretical frameworks that underpin its manifestation, the leadership styles in nursing, the overall landscape in nursing leadership including the context where leadership is being practiced, and finally looking at the educational pathways for building leadership capacity and sustainability Data SourcesRelevant studies on the topic as well professional associations, national and international organizations’ evidence have formed the basis for this article. ConclusionThe role of the nurse leaders is complex and demanding within the context of health care. Its value has been increasingly drawing the attention of national and international organizations. The nurse leader is performing tasks within rapidly shifting environments that require constant changes to best address the organizational goals but remain true to the values and perspectives envisioned by the nursing profession. Through the implementation of corresponding competency frameworks, the capacity and preparedness of nurse leaders can be cultivated ideally in a multidisciplinary context. Implications for Nursing PracticeNurse leaders are increasingly assuming positions of authority across the structures of health care organizations. There is need to timely and appropriately increase the preparedness of nurse leaders to assume and sustain these challenging and dynamic roles.

Temperature sensor integrated curl resonator chipless RFID tag for high capacity data identification applications

This paper proposes a compact multi-bits chipless RFID tag using fully printed resonators with dual performance of tagging and sensing temperature. Each bit is modelled as curl resonator coupled to microstrip line and is printed on metal-backed Rogers RO4003 substrate. The proposed model is characterized by high attenuation of − 10 dB with narrow bandwidth and power reflection of − 3.2 dB. A parametric study on the effect of the tag dimensions of the transmission coefficient response is introduced. The resonance frequency of each bit is controlled via controlling the curl arm’s length. The multi-bits RFID tag is modelled using multi-curl resonators arranged next to microstrip line with different arm lengths. A multi-bits chipless RFID tags with 3-bits, 6-bits and 12-bits are designed in the frequency band from 3 to 7 GHz. The dimensions of 6-bits code are 60 × 17 mm2 and for 12-bits code are 29.75 × 34 mm2. A temperature sensing capability is achieved via integrated Stanyl polyamide material with the curl resonator. The first bit is used for temperature sensing while the other bits are used for data encoding. The proposed chipless RFID tags are simulated using commercial software based on well defined numerical techniques; i.e. high-frequency structure simulator (HFSS) based on finite element method (FEM) and verified with CST-microwave studio (CST-MWS) based on the finite integral technique (FIT).

Dual particle swarm optimization based data-driven state of health estimation method for lithium-ion battery

Accurate estimation of Li-ion battery state of health (SOH) is essential to ensure battery safety and vehicle operation. Here, this paper proposes a dual particle swarm optimization algorithm-extreme gradient boosting algorithm (DP-X) with the battery's charging voltage and incremental capacity (IC) data. First, the features are extracted from the voltage curve and the IC curve of each charging cycle through curve compression and interpolation. Then, this paper utilizes the PSO-XGBoost (P-X) algorithm to optimize the selected features and reduce the dimensionality of the features. Finally, the P-X algorithm was applied to combine with the optimized features to adjust the model's hyperparameters and estimate the SOH. Experimental results show that the maximum SOH estimation error of the dual P-X algorithm is less than 2 %.

TLS and FEM combined methods for deformation analysis of tunnel structures

With the increasing of tunnel mileage, one of the most vital problems of broad-scale structures is the intelligent identification and location of structural defects, which require massive data storage capacity and high data processing efficiency. The full-field point cloud data collected by terrestrial laser scanning (TLS) can be extracted to construct three-dimensional measured models more accurately, therefore, the fusion of TLS and the finite element method (FEM) is particularly beneficial to the deformation analysis of tunnel structures. This paper combines numerical simulation and full-field measurement to interactively verify the reliability of deformation analysis based on FEM simulation and point cloud processing. The innovation of this paper is that a three-dimensional FEM is validated through the measured point cloud data of the tunnel structure. The result of this study indicated that FEM simulation and the structural health monitoring analysis are consistent.

Data Partition and Rate Control for Learning and Energy Efficient Edge Intelligence

The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the wireless network edge, which is dubbed as edge intelligence (EI). The communication bottleneck between the data resource and the server results in deteriorated learning performance as well as tremendous energy consumption. To tackle this challenge, we explore a new paradigm called learning-and-energy-efficient (LEE) EI, which simultaneously maximizes the learning accuracies and energy efficiencies of multiple tasks via data partition and rate control. Mathematically, this results in a multi-objective optimization problem. Moreover, the continuously varying communication rates introduce infinite variables, which further complicates the problem. To solve this complex problem, we consider the case with infinite server buffer capacity and one-shot data arrival at sensor. First, the number of variables is reduced to a finite level by exploiting the optimality of constant-rate transmission in each epoch. Second, the optimal solution of the multi-objective problem is found by applying the stratified sequencing or merging of objectives. By assuming higher priority of learning efficiency in stratified sequencing, the optimal data partition is derived in closed form by the Lagrange method, while the optimal rate control is proved to have the structure of directional water filling (DWF), based on which a string-pulling (SP) algorithm is proposed to obtain the numerical values. The DWF structure of rate control is also proved to be optimal in merging of objectives, which combines different objectives in a weighted manner. By exploiting the optimal rate changing properties, the SP algorithm is further extended to tackle the more challenging cases with limited server buffer capacity or bursty data arrival at sensor. The performance of the proposed joint data partition and rate control design is examined by extensive experiments based on public datasets.

Prediction of Li-ion battery state of health based on data-driven algorithm

Li-ion battery state of health (SOH) is a key parameter for characterizing actual battery life. SOH cannot be measured directly. In order to further improve the accuracy of Li-ion battery SOH estimation, a combined model based on health feature parameters combined with EMD-ICA-GRU is proposed to predict the SOH of Li-ion batteries. The capacity regeneration phenomenon and data noise are decomposed by empirical mode decomposition (EMD), and then the SOH-related health indicators are deeply mined using incremental capacity analysis (ICA), and the peaks of IC curves and their corresponding voltages are extracted as the input of the model. Then, gated recurrent units (GRUs) are formed into a combined SOH estimation model by adaptive weighting factors. Finally, it is validated against the NASA lithium battery dataset. Experimental results show that the mean squared error (MSE) of the proposed combined model can reach about 0.3%, and it has stronger generalization and prediction accuracy than other algorithms driven by independent estimation data.

A Transferred Recurrent Neural Network for Battery Calendar Health Prognostics of Energy-Transportation Systems

Battery-based energy storage system is a key component to achieve low carbon industrial and social economy, where battery health status plays a vital role in determining the safety and reliability of energy-transportation nexus. This article proposes a transferred recurrent neural network (RNN)-based framework to achieve efficient calendar capacity prognostics under both witnessed and unwitnessed storage conditions. Specifically, this transferred RNN framework contains a base model part and a transfer model part. The base model is first trained by using the easily collected and time-saving accelerated ageing dataset from high temperature and state-of-charge (SOC) cases. Then the transfer part is tuned by using only a small portion of starting capacity data from unwitnessed condition of interest. The developed framework is evaluated under a well-rounded ageing dataset with three different storage SOCs (20%, 50%, and 90%) and temperatures (10 °C, 25 °C, and 45 °C). Experimental results demonstrate that the derived transferred RNN framework is capable of providing satisfactory calendar capacity health prognostics under different storage cases. A model structure with the impact factor terms of SOC and temperature outperforms other counterparts especially for the unwitnessed conditions. The proposed framework could assist engineers to significantly reduce battery ageing experiment burden and is also promising to capture future capacity information for battery health and life-cycle cost analysis of energy-transportation applications.

Measurement of Warehouse Layout at Rice Refinery Using Shared Storage Method

PT. Kilang Padi Mutiara Refinery is a company engaged in rice production that produces rice, groats, and bran. The main product of this factory is rice. It can be seen in storage warehouse activities that they still experience problems with poor product placement due to irregular distances between placement and delivery areas caused by the preparation of products that are not based on inventory so that the products in the warehouse become narrower and seem messy, this triggers the accumulation of excess products. to damage the finished product packaging. Based on the existing problems, in this study, planning for layout improvements using the shared storage method was carried out to increase the effectiveness of the distance between doors to the storage area and better product placement. the steps of re-planning using the shared storage method begin by knowing the warehouse area, warehouse capacity, and other warehouse data, as well as collecting data by determining space requirements, determining space allowances, and the distance of the entrance to the storage area. From the data processing, the distance becomes more regular and can be known every distance. Based on the comparison of the initial layout and the proposal, it is known that the distance in the initial layout is 719.56 meters, while for the proposed layout using the shared storage method, the results are 593.33 meters with 13 product areas, with this loading and unloading activities become better and more effective.

Driving Mechanisms of the Evolution and Ecological Water Demand of Hulun Lake in Inner Mongolia

Hulun Lake is located in the Hulun Buir Grassland in Inner Mongolia and is an important component of the northern ecological barrier of China. Fluctuations in its area directly affect the stability of the surrounding ecological environments. In this study, Hulun Lake was taken as the study object, and meteorological data, water body area, water level, reservoir capacity, runoff volume, and social statistical data were used to study the dynamic changes in Hulun Lake and the critical driving forces. We constructed a balance analysis equation, determined the role of groundwater in the water cycle, and examined the minimum ecological water demand of Hulun Lake. The results of the analyses revealed that during the last 55 years, the variation trends of the water level, area, and reservoir capacity were generally consistent and could be divided into six stages. The increased temperature decreased precipitation, and increased evaporation resulted in decreased water flow from the major rivers supplying the lake, which was the main cause of the decrease in the water level and area of Hulun Lake. Groundwater was involved in the water cycle of Hulun Lake. After deducting the seepage and surface drainage, we found that the groundwater recharge volume was around 792 million m3 yearly. Based on the environmental, ecological, and production functions of Hulun Lake, the minimum ecological water demand is 11.272 billion m3.

Quantifying the cost savings of global solar photovoltaic supply chains.

Achieving carbon neutrality requires deploying renewable energy at unprecedented speed and scale1,2, yet countries sometimes implement policies that increase costs by restricting the free flow of capital, talent and innovation in favour of localizing benefits such as economic growth, employment and trade surpluses3,4. Here we assess the cost savings from a globalized solar photovoltaic (PV) module supply chain. We develop a two-factor learning model using historical capacity, component and input material price data of solar PV deployment in the United States, Germany and China. We estimate that the globalized PV module market has saved PV installers US$24 (19-31) billion in the United States, US$7 (5-9) billion in Germany and US$36 (26-45) billion in China from 2008 to 2020 compared with a counterfactual scenario in which domestic manufacturers supply an increasing proportion of installed capacities over a ten-year period. Projecting the same scenario forwards from 2020 results in estimated solar module prices that are approximately 20-25 per cent higher in 2030 compared with a future with globalized supply chains. International climate policy benefits from a globalized low-carbon value chain4, and these results point to the need for complementary policies to mitigate welfare distribution effects and potential impacts on technological crowding out.

Attribution of climate change and human activities to streamflow variations with a posterior distribution of hydrological simulations

Abstract. Hydrological simulations are a main method of quantifying the contribution rate (CR) of climate change (CC) and human activities (HAs) to watershed streamflow changes. However, the uncertainty of hydrological simulations is rarely considered in current research. To fill this research gap, based on the Soil and Water Assessment Tool (SWAT) model, in this study, we propose a new framework to quantify the CR of CC and HAs based on the posterior histogram distribution of hydrological simulations. In our new quantitative framework, the uncertainty of hydrological simulations is first considered to quantify the impact of “equifinality for different parameters”, which is common in hydrological simulations. The Lancang River (LR) basin in China, which has been greatly affected by HAs in the past 2 decades, is then selected as the study area. The global gridded monthly sectoral water use data set (GMSWU), coupled with the dead capacity data of the large reservoirs within the LR basin and the Budyko hypothesis framework, is used to compare the calculation result of the novel framework. The results show that (1) the annual streamflow at Yunjinghong station in the Lancang River basin changed abruptly in 2005, which was mainly due to the construction of the Xiaowan hydropower station that started in October 2004. The annual streamflow and annual mean temperature time series from 1961 to 2015 in the LR basin showed significant decreasing and increasing trends at the α= 0.01 significance level, respectively. The annual precipitation showed an insignificant decreasing trend. (2) The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was 42.6 % due to CC, and the remaining 57.4 % was due to HAs, such as the construction of hydropower stations within the study area. (3) The comparison with the other two methods showed that the CR of CC calculated by the Budyko framework and the GMSWU data was 37.2 % and 42.5 %, respectively, and the errors of the calculations of the new framework proposed in this study were within 5 %. Therefore, the newly proposed framework, which considers the uncertainty of hydrological simulations, can accurately quantify the CR of CC and HAs to streamflow changes. (4) The quantitative results calculated by using the simulation results with the largest Nash–Sutcliffe efficiency coefficient (NSE) indicated that CC was the dominant factor in streamflow reduction, which was in opposition to the calculation results of our new framework. In other words, our novel framework could effectively solve the calculation errors caused by the “equifinality for different parameters” of hydrological simulations. (5) The results of this case study also showed that the reduction in the streamflow in June and November was mainly caused by decreased precipitation and increased evapotranspiration, while the changes in the streamflow in other months were mainly due to HAs such as the regulation of the constructed reservoirs. In general, the novel quantitative framework that considers the uncertainty of hydrological simulations presented in this study has validated an efficient alternative for quantifying the CR of CC and HAs to streamflow changes.

Does Exercise-Based Cardiac Rehabilitation Reduce Mortality and Hospitalization Rates After Heart Valve Surgery?: A Cochrane Review Summary With Commentary.

Does Exercise-Based Cardiac Rehabilitation Reduce Mortality and Hospitalization Rates After Heart Valve Surgery?: A Cochrane Review Summary With Commentary.

Tracking Efficiency Improvement According to Incident Beam Size in QPD-Based PAT System for Common Path-Based Full-Duplex FSO Terminals.

Free space optical (FSO) communication can support various unmanned aerial vehicles’ (UAVs) applications that require large capacity data transmission. In order to perform FSO communication between two terminals, it is essential to employ a pointing, acquisition, and tracking (PAT) system with an efficient and optimal performance. We report on the development of a common optical-path-based FSO communication system, tailored for applications in UAVs. The proposed system is equipped with a quadrant photodiode (QPD)-based PAT system without an additional beacon beam subsystem. The presented approach reduced the structural complexity and improved the tracking efficiency for the same size, weight, and power (SWaP). To achieve a robust FSO link in a dynamic UAV environment, the observability and controllability were obtained based on the linearized control according to the incident beam size on the QPD, which was verified by optical simulation and experiments. As a result, the QPD-based PAT system for implementing FSO links demonstrated an up to 4.25 times faster tracking performance. Moreover, the FSO link experimentally confirmed the 1.25 Gbps full-duplex error-free communication at a 50 m distance.

The Effects of Ionic Liquid Addition on the Heat of Polymerization of Additive Manufacturing Resins

Ionic liquids can be utilized in stereolithography (SLA) additive manufacturing resins to adjust multiple parameters of the resulting polymer, such as chemical stability, temperature stability, and other key physical properties. While studies into these properties are ongoing, little is known about the effects the presence of these ILs has on the polymerization process. This study intends to use a new technique incorporating acoustic levitation, FLIR thermography, and differential scanning calorimetry (DSC) to provide insight into heat of polymerization of the resins utilizing an approach previously developed in the Duranty laboratory. This technique allows a small amount of liquid to levitate in standing sound waves generated by transducers at the top and bottom of a 3D printed scaffolding. This novel approach maximizes the probability of heat transfer via convection (through the air/surrounding medium) while preventing heat transfer via conduction (through direct contact) that is normally observed in container methods, allowing for direct thermal imaging of the captured droplets. Two 405 nm lasers, mounted at the top and bottom of the apparatus, are utilized to mimic the lasers of SLA 3D printing which initiate the polymerization process. This apparatus allows for the direct observation of the temperature changes during the polymerization process.A variety of ionic liquids were used in this investigation in order to study to effects of structure changes and ion effects. The study observed the effects of alkyl chain length, functional groups on the alkyl chain, anion effects, aromatic vs. non-aromatic, and potential co-polymer ionic liquids. Each ionic liquid was added to the commercial Clear V4 resin from Formlabs in the amount of 10 to 50 wt% ionic liquid. Once the mixtures were homogenous, 5 µL of each IL/resin mixture was suspended in the acoustic field. Thermographic data was collected before, during, and after the polymerization process which was initiated by exposure to 405 nm lasers. The heats of polymerization for each sample were calculated using heat capacity data collected via DSC along with the change in temperature of the droplet observed by FLIR thermographic imaging of the suspended droplet during polymerization. Thermal degradation temperatures of the resulting polymer matrices were determined via thermal gravimetric analysis (TGA). The combination of this data revealed that the addition of some of the ionic liquids targeted in this study reduced the heat of polymerization, while others increased it. Therefore, the cation and anion structures greatly impact the heat of polymerization. Figure 1

A description of the thermodynamic properties of pure indium in the solid and liquid states from 0 K

Thermodynamic data for the crystalline phase of indium were critically assessed using an extended Einstein model from 0 K. The heat capacity between 6 K and 354 K was measured using an automated vacuum adiabatic calorimeter to compensate for a lack of published data between 21 K and 273 K. Data for the liquid phase were described using a two state model. During the assessment, careful analysis of the experimental data was carried out. In order to derive a precise value for So298 an additional technique using multiple Einstein functions was used, which allows the experimental heat capacity and enthalpy data for the solid phase to be approximated accurately from 0 K up to the melting point.

Remaining useful life prediction with imprecise observations: An interval particle filtering approach

Particle Filtering (PF) has been widely used for predicting Remaining Useful Life (RUL) of industrial products, especially for those with nonlinear degradation behavior and non-Gaussian noise. Traditional PF is a recursive Bayesian filtering framework that updates the posterior probability density function of RULs when new observation data become available. In engineering practice, due to the limited accuracy of monitoring/inspection techniques, the observation data available for PF are inevitably imprecise and often need to be treated as interval data. In this article, a novel Interval Particle Filtering (IPF) approach is proposed to effectively leverage such interval-valued observations for RUL prediction. The IPF is built on three pillars: (i) an interval contractor that mitigates the error explosion problem when the epistemic uncertainty in the interval-valued observation data is propagated; (ii) an interval intersection method for constructing the likelihood function based on the interval observation data; and (iii) an interval kernel smoothing algorithm for estimating the unknown parameters in the IPF. The developed methods are applied on the interval-valued capacity data of batteries and fatigue crack growth data of railroad tracks. The results demonstrate that the developed methods could improve the performance of RUL predictions based on interval observation data.

Signatures of spin-liquid state in a 3D frustrated lattice compound KSrFe2(PO4)3 with S = 5/2

A quantum spin-liquid is a spin disordered state of matter in which spins are strongly correlated and highly entangled with low-energy excitations. It has been often found in two-dimensional S = ½, highly frustrated spin networks but rarely observed in three-dimensional (3D) frustrated quantum magnets. Here, KSrFe2(PO4)3, forming a complicated 3D frustrated lattice with a spin moment S = 5/2, is investigated by thermodynamic, neutron diffraction measurements and electronic structure calculations. Despite the relatively sizable Curie–Weiss temperature θCW = −70 K, a conventional magnetic long-range order is confirmed to be absent down to 0.19 K. The magnetic heat capacity data follow the power-law behavior at the lowest temperature region, supporting gapless excitations in a 3D spin-liquid state. Strong geometrical spin frustration responsible for the spin-liquid feature is understood as originating from the almost comparable five competing nearest-neighbor antiferromagnetic exchange interactions, which form the complicated 3D frustrated spin network. All these results suggest that the compound KSrFe2(PO4)3, representing a unique 3D spin frustrated network, could be a rare example of forming a gapless spin-liquid state even with a large spin moment of S = 5/2.

Identifying the Leverage Points in the Household Solid Waste Management System for Harare, Zimbabwe, Using Network Analysis Techniques

Managing household solid waste (HSW) has gone beyond what the Harare local government can handle. Inadequate knowledge of the interactions existing between issues that affect the efficient running of waste management systems is one of the major hindrances in waste management planning in developing countries like Zimbabwe. The complexity of the waste management system for a given municipal area needs to be identified and understood to generate appropriate and efficient waste management strategies. Network analysis (NA) is a methodology extensively used in research to help reveal a comprehensive picture of the relationships and factors related to a particular phenomenon. The methodology reduces the intricacy of large systems such as waste management to smaller and more understandable structures. In this study, NA, which was done mainly using the R software environment, showed a result of 1.5% for network density, thus signifying that for Harare, waste management strategies need to be ‘seeded’ in various parts of the system. The Pareto principle and the 3Rs (Reduce, Reuse, Recycle) concept were applied to suggest the issues to prioritize and generate strategies that could potentially affect significant change to the city’s waste management system. The key issues identified, in their order of importance, are an increase in uncollected waste, low waste collection efficiency, increase in illegal waste dumping, the deteriorating country’s economy, reduced municipal financial capacity, reduced municipal workforce capacity, inadequate or unreliable waste data, increase in waste volume, increase in the number of street vendors, no waste planning and monitoring unit, no engineered landfills in the city, increase in waste collection pressure, low waste collection frequency, increase in the unemployment rate, reduced municipal technical capacity, few waste collection vehicles, limited vehicles maintenance, distinct socio-economic classes, high vehicles breakdown, and increase in population.

A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study.

BackgroundDisease surveillance is a critical function of public health, provides essential information about the disease burden and the clinical and epidemiologic parameters of disease, and is an important element of effective and timely case and contact tracing. The COVID-19 pandemic demonstrates the essential role of disease surveillance in preserving public health. In theory, the standard data formats and exchange methods provided by electronic health record (EHR) meaningful use should enable rapid health care data exchange in the setting of disruptive health care events, such as a pandemic. In reality, access to data remains challenging and, even if available, often lacks conformity to regulated standards.ObjectiveWe sought to use regulated interoperability standards already in production to generate awareness of regional bed capacity and enhance the capture of epidemiological risk factors and clinical variables among patients tested for SARS-CoV-2. We described the technical and operational components, governance model, and timelines required to implement the public health order that mandated electronic reporting of data from EHRs among hospitals in the Chicago jurisdiction. We also evaluated the data sources, infrastructure requirements, and the completeness of data supplied to the platform and the capacity to link these sources.MethodsFollowing a public health order mandating data submission by all acute care hospitals in Chicago, we developed the technical infrastructure to combine multiple data feeds from those EHR systems—a regional data hub to enhance public health surveillance. A cloud-based environment was created that received ELR, consolidated clinical data architecture, and bed capacity data feeds from sites. Data governance was planned from the project initiation to aid in consensus and principles for data use. We measured the completeness of each feed and the match rate between feeds.ResultsData from 88,906 persons from CCDA records among 14 facilities and 408,741 persons from ELR records among 88 facilities were submitted. Most (n=448,380, 90.1%) records could be matched between CCDA and ELR feeds. Data fields absent from ELR feeds included travel histories, clinical symptoms, and comorbidities. Less than 5% of CCDA data fields were empty. Merging CCDA with ELR data improved race, ethnicity, comorbidity, and hospitalization information data availability.ConclusionsWe described the development of a citywide public health data hub for the surveillance of SARS-CoV-2 infection. We were able to assess the completeness of existing ELR feeds, augment those feeds with CCDA documents, establish secure transfer methods for data exchange, develop a cloud-based architecture to enable secure data storage and analytics, and produce dashboards for monitoring of capacity and the disease burden. We consider this public health and clinical data registry as an informative example of the power of common standards across EHRs and a potential template for future use of standards to improve public health surveillance.

State of health prediction of lithium-ion batteries based on autoregression with exogenous variables model

The gradually decreasing capacity of lithium-ion batteries can serve as a health indicator for tracking their degradation. Therefore, it is important to predict the capacity of future cycles to assess the health condition of lithium-ion batteries. According to electrochemical theory and the characteristics of the data curves, this paper proposes several ideas for feature extraction. A novel fusion prognostic framework is proposed, in which a data-driven time series prediction model is adopted and combined with extracted features for lithium-ion battery capacity prediction. The proposed method is based on an autoregression with an exogenous-variable model that can self-adaptively update at each cycle and then predict the state of health in the next cycle and cycles in the near future. Under the assumption that the historical capacity data is available, the experimental results showed that by using the proposed autoregression with exogenous variables model, the root mean square error, mean absolute error, and mean absolute percentage error of the prediction results were 0.000963, 0.000562, and 0.000584, respectively, which indicated that the prediction results were precise.