Essential Metrics Research Articles

Unveiling the nexus of digital conversion and clean energy: An ISM-MICMAC and DEMATEL perspective

<abstract> <p>Our aim is to develop a hierarchical framework that assesses the interdependence of digital metrics impacting clean energy in the European energy market. The framework is evaluated to determine its applicability to clean energy and implementation. We utilize a taxonomy of digital metrics with the MICMAC ("Matrice d'Impacts Croisés-Multiplication Appliquée à un Classement") methodology and a questionnaire-based survey using DEMATEL to validate the framework. This results in an efficient hierarchy and contextual relationship between key metrics in the European energy industry. We investigate and simulate ten key metrics of digital conversion for clean energy in the energy domain, identifying the most significant effects, including the "decision-making process" the "sustainable value chain" the "sustainable supply chain", "sustainable product life cycle", and the "interconnection of diverse equipment". The MICMAC methodology is used to classify these parameters for a better understanding of their structure, and DEMATEL is employed to examine cause-and-effect relationships and linkages. The practical implications of this framework can assist institutions, experts, and academics in forecasting essential metrics and can complement existing studies on digital conversion and clean energy. By prioritizing these key parameters, improvements in convenience, efficiency, and the reduction of product fossilization can be achieved. The value and originality of this study lie in the novel advancements in analyzing digital conversion metrics in the European energy industry using a cohesive ISM, MICMAC, and DEMATEL framework.</p> </abstract>

Horseshoe crab egg availability for shorebirds in Delaware Bay: Dramatic reduction after unregulated horseshoe crab harvest and limited recovery after 20 years of management

Abstract The largest aggregation of spawning American horseshoe crabs in the world occurs in Delaware Bay and supports one of the largest concentrations of shorebirds in the western hemisphere where the birds feed on horseshoe crab eggs during migration. Unregulated harvest in the 1990s is associated with the decline of shorebird populations using the bay, but corresponding baseline information on the horseshoe crab egg food supply that supported peak shorebird populations has been lacking. Past and current measurements of horseshoe crab eggs in the bay indicate that abundance in the 1980s was an order of magnitude greater (x̄ = 156,600/m2) than present‐day estimates (2015–2021 x̄ = 10,243/m2). An additional egg prevalence index, which characterizes the timing and magnitude of horseshoe crab egg output, revealed a similar pattern of higher prevalence in the 1980s (0.89, 95% confidence interval 0.81–0.94) compared with the recent 2015–2021 interval (0.52, 95% confidence interval 0.43–0.60). Declines of egg and shorebird abundance occurred shortly after horseshoe crab harvest reached its peak. Red knot aerial survey counts have stabilized at 30% of the baseline population while ruddy turnstone counts are 40% of baseline estimates. Initially these species were evenly distributed between the New Jersey and Delaware coasts but both species have trended toward predominately occupying New Jersey beaches. A lack of recovery of horseshoe crab egg and shorebird abundance suggests that horseshoe crab harvest management has stabilized populations but progress toward population recovery has been limited. Impacts from bycatch, the pharmaceutical industry and other potential population impacts must be better quantified and mitigated, if necessary, to promote the recovery of horseshoe crab populations. Measurements of horseshoe egg prevalence and abundance are essential metrics for tracking the progress toward management goals for improving shorebird habitat quality.

A qualitative examination of the factors affecting the adoption of injury focused wearable technologies in recreational runners.

Understanding the perceived efficacy and ease of use of technologies will influence initial adoption and sustained utilization. The objectives of this study were to determine the metrics deemed important by runners for monitoring running-related injury (RRI) risk, and identify the facilitators and barriers to their use of injury focused wearable technologies. A qualitative focus group study was undertaken. Nine semi-structured focus groups with male (n = 13) and female (n = 14) recreational runners took place. Focus groups were audio and video recorded, and transcribed verbatim. Transcripts were thematically analysed. A critical friend approach was taken to data coding, and multiple methods of trustworthiness were executed. Excessive loading and inadequate recovery were deemed the most important risk factors to monitor for RRI risk. Other important factors included training activities, injury status and history, and running technique. The location and method of attachment of a wearable device, the design of a smartphone application, and receiving useful injury-related information will affect recreational runners' adoption of injury focused technologies. Overtraining, training-related and individual-related risk factors are essential metrics that need to be monitored for RRI risk. RRI apps should include the metrics deemed important by runners, once there is supporting evidence-based research. The difficulty and/or ease of use of a device, and receiving useful feedback will influence the adoption of injury focused running technologies. There is a clear willingness from recreational runners to adopt injury focused wearable technologies whilst running.

Robust stacking ensemble model for darknet traffic classification under adversarial settings

Encrypted traffic tunnelled by Tor or VPN is referred to as darknet traffic. The ability to detect, identify, and characterize darknet traffic is critical for detecting network traffic generated by a cyber-attack. Darknet classification models based on Machine Learning / Deep Learning (ML/DL) usually demonstrate high False Positive Rate (FPR) and lower F1-score which are essential metrics for network traffic analysis. Additionally, ML/DL models used in such tasks are susceptible to adversarial perturbed samples that can cause the network security solution to malfunction. This work proposes a Stacking Ensemble (SE) model to combine the predictions of three base learners, 1) Random Forest, 2) K-Nearest Neighbors, and 3) Decision Tree in the most efficient way for improving the overall performance of darknet characterization. A two-layered autoencoder-based defence mechanism consisting of a detector and denoiser is devised to increase the robustness of the network security system against adversarial attacks. The performance of the proposed model is demonstrated through extensive experiments using the CIC-Darknet-2020 dataset. The robustness of the model is tested against three highly transferable adversarial attacks, 1) Fast Gradient Sign Method (FGSM), 2) Basic Iterative Method (BIM), 3) DeepFool and a realistic 4) Boundary attack. The experimental results show that the SE model outperforms the baseline Deep Image and other competitor models by achieving an accuracy score of 98.89% and an FPR of 0.43% in the case of darknet traffic identification. Furthermore, an accuracy of 97.88% is achieved in the case of darknet traffic flow characterization. In addition, the statistical properties of the packet flow are identified as the most commonly affected features by adversarial attacks through the Mean Adversarial Perturbation (MAP) metric.

Essential Continuous Glucose Monitoring Metrics: The Principal Dimensions of Glycemic Control in Diabetes.

Background: With the proliferation of continuous glucose monitoring (CGM), a number of metrics were developed to assess quality of glycemic control. Many of them are highly correlated. Thus, we aim to identify the principal dimensions of glycemic control-a minimal set of metrics, necessary and sufficient for comprehensive assessment of diabetes management. Methods: Seventy-five thousand five hundred sixty-three 2-week CGM profiles recorded in six studies by 790 individuals with type 1 or type 2 diabetes were used to compute mean glucose (MG), percentage time >180 mg/dL (TAR), >250 mg/dL (TAR2), <70 mg/dL (TBR), <54 mg/dL (TBR2), and coefficient of variation (CV). The true dimensionality of the glycemic-metric space was identified in a training set (53,380 profiles) and validated in an independent test set (22,183 profiles). Results: Correlation analysis identified two blocks of metrics-(MG, TAR, TAR2) and (TBR, TBR2, CV)-each with high internal correlation, but insignificant between-block correlation, suggesting that the true dimensionality of the glycemic-metric space is 2. Principal component analysis confirmed two essential metrics quantifying exposure to hyperglycemia (i.e., treatment efficacy) and risk for hypoglycemia (i.e., treatment safety), and explaining ∼90% of the variance in the training and test data. Conclusion: Two essential metrics, treatment efficacy and treatment safety, are necessary and sufficient to characterize glycemic control in diabetes. Thus, quantitatively, diabetes treatment optimization is reduced to a two-dimensional problem, meaning that minimizing both exposure to hyperglycemia and risk for hypoglycemia will lead to improvement in any other metric of glycemic control.

Stochastic assessment of frequency support from wind power plants for power system with high wind penetration using correlation between wind farms

The increase of renewable energy use in a power generation decreases the flexibility of power supply. The intermittent and variable nature of renewable resources necessitates the use of a transmission system operator to prepare reserve generating power in case of power imbalance. Unfortunately, the generation and reserve margins of renewable energy generators are often insufficient. To ensure a balanced and resilient power system, surplus power generation from renewable resources must provide frequency support with the essential number of synchronous generators being remained in the network. Assessment of the amount of frequency support power from wind farm became available through forecast and real-time operation planning. However, the exact amount of back-up power that expected to be provided by individual wind farm can be barely determined due to the uncertainty and variation of renewable resources generation. A stochastic method for assessing the required frequency support operation of specific wind power plants is thus proposed in this paper. The stochastic correlation characteristics between a single wind farm and power system stability are applied in this method. The essential metrics for deloading operation and inertia response are calculated using the stochastic correlation model based on copula function.

Derivatives for Eigenvalues and Eigenvectors via Analytic Reverse Algorithmic Differentiation

Eigenvalue and eigenvectors are essential metrics to characterize dynamic system behavior and stability. When performing gradient-based design optimization, derivatives of these metrics are required. Analytic forward algorithmic differentiation (FAD) for a self-adjoint generalized eigenproblem has been a useful technique. However, reverse algorithmic differentiation (RAD) is preferred over FAD because it scales more favorably with the number of design variables. We propose two RAD formulas based on their mode-based FAD counterparts that project the derivative onto a reduced eigenvector space. One challenge for the mode-based derivative is that the reduced eigenvectors yield inexact gradient results. An approximation technique mitigates this issue. We verify the proposed methods by implementing a reverse Lanczos iteration and the adjoint of an Euler–Bernoulli beam test case.

Deep learning based wheat ears count in robot images for wheat phenotyping

The number of spikes, spikelets per spike, number of spikes per square meter are essential metrics for plant breeders and researchers in predicting wheat crop yield. Evaluating the crop yield based on wheat ears counting is still done manually, which is a labor-intensive, tedious and costly task. Thus, there is a significant need to develop a real-time wheat spikes/ears counting system for plant breeders for effective and efficient crop yield predictions. This paper proposed two deep learning-based methods based on EfficientDet and Faster-RCNN to detect and count the spikes. The images are taken using high-throughput phenotyping techniques under natural field conditions, and the algorithms localize and automatically count wheat spikes/ears. Faster R-CNN with Resnet50 as backbone architecture produced an overall accuracy of 88.7% on the test images. We also used recent stateof- the-art models EfficientDet-D5 and EfficientDet-D7, having backbone architectures EfficientNet-B5 and EfficientNet- B7, respectively. A comprehensive quantitative analysis is performed on the standard performance metrics. In the analysis, the EfficientDet-D5 model produces an accuracy of 92.7% on the test images, and EfficientDet-D7 produces an accuracy of 93.6%.

The Effect of Keeler’s Strategy on Cognitive Achievement and Learning Offensive Skills in Volleyball

Excellence in the sports field has become one of the essential metrics on which the progress of nationsand peoples is viewed. In order to draw up valid scientific policies represented in the use of modernstrategies and methods to prepare the appropriate educational and training curricula in order to reachthe learners to the highest levels, individual learning strategies must be used, which is self-learning asthey take into account the individual differences between the learners and reduce the time and effortrequired for this process. Keller’s strategy is for individual or personal learning. One of the strategiesthat achieve this goal.The researchers believe that learning with the Keeler strategy is a personal process, as learning cannotbe given or indoctrinated but must result from the educated person’s experience and ability to retrievehis memory to perform better than what he witnessed. The field of physical education and education.The researchers identified the research community with students of the second stage in the College ofPhysical Education and Sports Sciences - the University of Kufa for the academic year 2017-2018,numbering 30 students, divided into two groups, each group consisting of (12) students, one of whomwas chosen randomly to represent the experimental group to which the educational program willbe applied. Under consideration is what will be applied to the other, which is the control group, theprescribed curriculum, and according to the method used, and the seven female students were chosento represent the sample that will be conducted on the exploratory experiment.

WITHDRAWN: Non-intrusive load monitoring technique using deep neural networks for energy disaggregation

Households have increased energy usage in recent years to exceptionally high levels, where it is not feasible anymore. Owing to increased usage of power, there is a desperate need to find a way to use resources more sustainably. One of the key reasons why energy use is not sustainable is because the consumer does not know anything about the energy consumed by intelligent devices (dishwasher, refrigerator, washing machine etc). By letting household users know the energy consumption of intelligent devices. For energy monitoring firms, the energy used by the smart devices in a household needs to be analysed. In the presence of vampire loads such as electric heaters, we use deep neural networks to disaggregate the energy use of the dishwasher in a household. The training times of the deep neural networks are also being attempted. The object of the work is to calculate the efficiency and potentiality of deep neural networks on Non-intrusive load monitoring (NILM). In addition, the essential metrics chosen are RMSE and F1 in order to determine the efficiency of the proposed algorithms. Hence, from our research, we concluded that Gated recurrent Unit (GRU) is, in the presence of vapour loads of households, the best efficiency algorithm in our study, which breaks down the energy consumption of the dishwasher. GRU is however the strongest neural network when the RMSE and F1 scores are known as the metrics. However, if we take training time as the metric, we can take the best training time, that is, 19.34 min, as a simple recurrent neural network.

Satisfaction and Loyalty of Banking Customers in Indonesia

Improved customer satisfaction and loyalty are essential metrics in increasing a company's revenue and profit in the banking industry. This research aims to determine the effect of satisfaction on loyalty among bank customers in Indonesia. The study was conducted by face to face interview method (F2F) among 1910 bank customers in eight metropolis in Indonesia (2019). The sampling method used a random sampling followed by a booster sampling approach. The analysis was conducted using the Structural Equation Modeling (SEM) by WarpPLS approach. The results show that satisfaction positively and very significantly affects loyalty.

Comprehensive Strategy to Inculcate Professional Skills among First-Year Students: A Collaborative Learning Approach

Inculcating teamwork, communication skills, and lifelong learning are essential metrics of Outcome Based Engineering (OBE). Ensuring these in early professional courses will enhance the ability of the students to adopt the OBE effectively in the forthcoming semesters. Effective planning and deployment of pedagogy of engagement will aid the students and faculty members to attain the desired outcomes with effective utilization of resources. This article unearths the challenges and documents the lessons learned in deploying collaborative learning in the First Year (FY) engineering education. It establishes a comprehensive assessment methodology from OBE perspective. Further, it addresses professional skills such as teamwork, communication skills, and lifelong learning in a structured manner. The Action Research methodology is adopted by taking into account the OBE approach enshrined in the Programme Outcomes (POs) as suggested by the Indian statutory bodies. The collaborative learning approach of 'Student, Teams, Achievement, Division' (STAD) is utilized by establishing a heterogeneous group. The Define-Measure-Analyze- Improve-Control (DMAIC) approach is used for project deployment and sustainment. Statistical analysis is used to interpret the results effectively. The study shows that the desired outcomes are achieved with a minimum duration of the teaching-learning process. Moreover, statistical analysis shows that even weaker students can reach the set attainment target due to heterogeneous group formation and collaborative learning. Eventually, the structured approach assists in developing a framework for the effective Shreeranga Bhat Department of Mechanical Engineering, St Joseph Engineering College, Mangaluru, Karnataka, India shreerangab@sjec.ac.in deployment of collaborative learning by establishing robust assessment tools to address the POs related to professional skills. The study shows that collaborative learning can be effectively deployed in FY engineering education with specific reference to POs related to professional skills within the optimal duration, provided a structured approach is embraced. It is also observed that heterogeneous group formation assists slow learners to elevate themselves to progressive learners due to teamwork and the influence of wiser students.

An OGI model for personalized estimation of glucose and insulin concentration in plasma.

Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC 9.49±3.81 mU/L, and PGC 0.89±0.19 mmol/L. For human, the OGI with PFM has the promise to identify disturbances (95.46%±0.65% accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.

Digitalization of Global Economy: A Qualitative Study Exploring Key Indicators use to Measure Digital Progress in the Public Sector

Research background:The digitalization of the global economy is the most common phenomenon in the 21st century. Most Public sector organizations have already started their journeys towards digitalization, and many of them have dealt with their contemporary challenges with success. At the center of these transformations are metrics and indicators used for evaluating digital progress. Already existing measures focus on numerical measures of the presence of digital technology in the public sector (output measures), but do not evaluate the quality of the digitalized public sector (outcome measures).Purpose of the article: This paper attempts to evaluate metrics and indicators used for measuring the digital progress in the public sector.Methods:Three particular aspects of digitalization in the top five leading countries (Finland, Sweden, Denmark, Netherlands, and Malta) were examined: I. success rate of public sector IT projects (measured by how often projects are scrapped); II. The price comparability between the public sector and private sector IT projects; and III. The relative modernity of government IT systems (compared to private sector systems).Findings &amp; Value added:The findings of this paper present key metrics and indicators that can be used to evaluate public sector digital progress. Policymakers will need to redefine digitalization goals and areas of investments, while researchers can contribute more insights to the individual impact of these metrics and indicators on the development of a digital public sector. To this end, the paper contributes to a better understanding of the essential metrics and indicators to measure digitalization progress in the public sector.

Grafovske strukture za vizuelizaciju podataka

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

Systematic Review on Human Gait Analysis using Deep Learning Models

Human Gait is known as a behavioral characteristic of humans, compared with the other biometrics gait is found to be a difficult process to conceal. Human gait analysis is usually done by extracting the features from the body. Analysis of gait involves evaluating the individual by means of kinematic analysis while walking along a surface. The main objective and the purpose of gait recognition is to give the best method where risks are recognized in places where there is a need for high security in any public place and to detect diseases like Parkinson’s. In order to acquire a normal person’s identification and validation performance, various Deep Learning techniques are totally studied and modeled the biometrics of gait which is based on walking data. It is reviewed that among various essential metrics that are used, deep learning convolution neural networks are typically better Machine Learning models. The main objective of the present study was to examine in detail individual gait patterns. Finally, this paper recommends deep learning methods and suggests the directions for future gait analysis and also for its applications.

Establishing essential metrics for antifungal stewardship in hospitals: the results of an international Delphi survey.

Guidance on assessment of the quantity and appropriateness of antifungal prescribing is required to assist hospitals to interpret data effectively and structure quality improvement programmes. To achieve expert consensus on a core set of antifungal stewardship (AFS) metrics and to determine their feasibility for implementation. A literature review was undertaken to develop a list of candidate metrics. International experts were invited to participate in sequential web-based surveys to evaluate the importance and feasibility of metrics in the area of AFS using Delphi methodology. Three surveys were completed. Consensus was predefined as ≥80% agreement on the importance of each metric. Eighty-two experts consented to participate from 17 different countries. Response rate for each survey was >80%. The panel included adult and paediatric physicians, microbiologists and pharmacists with diverse content expertise. Consensus was achieved for 38 metrics considered important to routinely include in AFS programmes, and related to antifungal consumption (n = 5), quality of antifungal prescribing and management of invasive fungal infection (IFI) (n = 24), and clinical outcomes (n = 9). Twenty-one consensus metrics were considered to have moderate to high feasibility for routine collection. The identified core AFS metrics will provide a framework to comprehensively assess the quantity and quality of antifungal prescribing within hospitals to develop quality improvement programmes aimed at improving IFI prevention, management and patient-centred outcomes. A standardized approach will support collaboration and benchmarking to monitor the efficacy of current prophylaxis and treatment guidelines, and will provide important feedback to guideline developers.

Road-Based Multi-Metric Forwarder Evaluation for Multipath Video Streaming in Urban Vehicular Communication

In video streaming over vehicular communication, optimal selection of a video packet forwarder is a daunting issue due to the dynamic nature of Vehicular Ad-hoc NETworks (VANETs)and the high data rates of video. In most of the existing studies, extensive considerations of the essential metrics have not been considered. In order to achieve quality video streaming in vehicular network, important metrics for link connectivity and bandwidth efficiency need to be employed to minimize video packet error and losses. In order to address the aforementioned issues, a Road-based Multi-metric Forwarder Evaluation scheme for Multipath Video Streaming (RMF-MVS) has been proposed. The RMF-MVS scheme is adapted to be a Dynamic Self-Weighting score (DSW) (RMF-MVS+DSW) for forwarder vehicle selection. The scheme is based on multipath transmission. The performance of the scheme is evaluated using Peak Signal to Noise Ratio (PSNR), Structural SIMilarity index (SSIM), Packet Loss Ratio (PLR) and End-to-End Delay (E2ED) metrics. The proposed scheme is compared against two baseline schemes including Multipath Solution with Link and Node Disjoint (MSLND) and Multimedia Multi-metric Map-aware Routing Protocol (3MRP) with DSW (3MRP+DSW). The comparative performance assessment results justify the benefit of the proposed scheme based on various video streaming related metrics.

A Signaling Figure of Merit (s-FoM) for Advanced Packaging

In this letter, we propose a novel signaling figure of merit (s-FoM) for comparison of signaling schemes across different packaging technologies. It considers all the essential metrics that affect system performance and efficiency and combines them in a simple, easily quantifiable manner. These include energy per bit, data-bandwidth per unit shoreline, latency, length of the link, and real-estate overhead. This tool captures all the performance trade-offs and enables a fair comparison across different packaging platforms and integration schemes.

Label-free detection of thalassemia and other ROS impairing diseases.

Pathogenesis of different diseases showed that some of them, especially thalassemia (T) and rheumatoid arthritis (RA) have an implicit association with oxidative stress and altered levels of reactive oxygen species (ROS). Introducing ROS level and the balance between ROS and antioxidants as essential metrics, an attempt was made to classify T and RA from normal individuals (treated as controls)(C) using synchronous fluorescence spectroscopy (SFS) and Raman line intensity of water. This non-invasive and label-free approach was backed up by a categorization algorithm that helped in the prediction of disease types from serum samples. The predictive system constituted principal component analysis (PCA) with four parameters, namely spectral intensity ratios of reduced nicotinamide adenine dinucleotide (NADH) to tryptophan (Trp) (NADH/Trp), kynurenine (Kyn) to tryptophan (Kyn/Trp), kynurenine to NADH (Kyn/NADH), and logarithmic changes in Raman line intensity of water (Rline), with the index headers containing the disease types. Rline has a positive correlation with both Kyn/Trp and Kyn/NADH and a negative correlation with NADH/Trp ratio, implying its direct or indirect association with oxidative stress. In addition to the classification of T, RA, and C a sub-classification of T into beta major and E-beta in their post and pre-splenectomized surgical stages could also be realized. Furthermore, receiver operating characteristic (ROC) analysis was deployed to ascertain that the misclassification error (ME) was negligible for the disease types. Graphical Abstract A schematic representation of the workflow converging into the categorical classification of disease classes.