Suitable Metrics Research Articles

Cycle Registration in Persistent Homology With Applications in Topological Bootstrap.

We propose a novel approach for comparing the persistent homology representations of two spaces (or filtrations). Commonly used methods are based on numerical summaries such as persistence diagrams and persistence landscapes, along with suitable metrics (e.g., Wasserstein). These summaries are useful for computational purposes, but they are merely a marginal of the actual topological information that persistent homology can provide. Instead, our approach compares between two topological representations directly in the data space. We do so by defining a correspondence relation between individual persistent cycles of two different spaces, and devising a method for computing this correspondence. Our matching of cycles is based on both the persistence intervals and the spatial placement of each feature. We demonstrate our new framework in the context of topological inference, where we use statistical bootstrap methods in order to differentiate between real features and noise in point cloud data.

Characterizing neurologist EHR use: a cross-sectional analysis (P7-7.003)

<h3>Objective:</h3> We evaluated electronic health record (EHR) use metrics across two academic systems using the same EHR vendor. <h3>Background:</h3> The use of standardized EHR metrics allows direct comparisons of individual and departmental EHR use and the ability to track changes over time. We evaluated the following ambulatory EHR metrics normalized to 8 hours of scheduled patient time: total EHR time, work outside of work (WOW), time on note documentation, time on orders, time on inbox and note length in characters. <h3>Design/Methods:</h3> Ambulatory audit log data per encounter from the University of Illinois at Chicago (UIC) and the University of Texas Medical Branch (UTMB) was evaluated for August 2022. <h3>Results:</h3> Among 36 neurologists in the analysis (22 at UIC [61%], and 12 at UTMB [39%], respectively), the average EHR-Time was 2.25 (±1.29) hours. WOW time was 2.01 (±1.53) hours. Time in Notes was 0.85 (±0.72) hours. Time on Orders was 0.33 (±0.30) hours. Times on Inbox 0.23 (±0.19) hours. Note length in characters was 5,963 (±3,062). No statistical differences were noted by metric across each EHR institutions (p&lt;0.05, t-test). <h3>Conclusions:</h3> Normalized EHR metrics can be used to evaluate EHR use by neurologists across institutions. Future initiatives to improve documentation practices (reducing note bloat and comply with E/M coding changes) can be tracked across institutions with suitable metrics. <b>Disclosure:</b> Dr. Rodriguez-Fernandez has nothing to disclose. Dr. Hier has nothing to disclose. Dr. Loeb has received personal compensation in the range of $0-$499 for serving as an officer or member of the Board of Directors for Sturge Weber Foundation. Dr. Loeb has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for various Law firms. The institution of Dr. Loeb has received research support from Patrick Grange Memorial Foundation. Dr. Loeb has received intellectual property interests from a discovery or technology relating to health care.

Driver Classification Using Self-reported, Psychophysiological, and Performance Metrics Within a Simulated Environment

Driver classification provides an efficient approach to isolating unique traits associated with specific driver types under various driving conditions. Several past studies use classification to identify behavior and driving styles; however, very few studies employ both measurable physiological changes and environmental factors. This study looked to address the shortcomings in driver classification research using a data-driven approach to assess driving tasks performed under varying mental workloads. Psychophysiological and driving performance changes experienced by drivers when engaged in simulated tasks of varying difficulty were coupled with machine-learning techniques to provide a more accurate estimate of the ground truth for behavioral classification. A driving simulator study consisting of six tasks was carefully designed to incrementally vary complexity between individual tasks. Ninety drivers were recruited to participate in both the subjective and driving components of this research. Positive and Negative Affect Schedule (PANAS), Cognitive Reflection Task (CRT), Interpersonal Reactivity Index (IRI), Empathy Assessment Index (EAI), Psychological Entitlement Scale (PES), 18-point Need for Cognition (NFC), and a basic demographic survey were administered. Time-series clustering using the Dynamic Time Warping (DTW) score within hierarchical clustering was applied to determine difference in driving styles. The use of pre-driving psychometric questionnaires to determine the most suitable metrics for predicting driving style outside of the automobile suggest promising results. The results of the binary logistic regression indicate that an individual’s annual driving mileage, CRT score, age, IRI fantasy score, and EAI affective response score, contribute significantly towards predicting their driving style.

A comprehensive interpretation for medical VQA: Datasets, techniques, and challenges

Recent technological developments and improvement in the medical domain demands advancement, to address the issue of early disease detection. Also, the current pandemic has resulted in considerable progress of improvement in the medical domain, through online consultation by physicians for different diseases using clinical reports and medical images. A similar process is adopted in developing a Visual Question Answering (VQA) system in the medical field. In this paper, existing medical VQA datasets, appropriate techniques, suitable quantitative metrics, real time challenges and, the implementation of one VQA approach with algorithms and performance evaluation are discussed. The medical VQA datasets collected from multiple sources are represented in different perspectives (organwise, planewise, modality-type and abnormality-type) for a better understanding and visualization. Then the techniques used in VQA are subsequently grouped and explained, based on evolution, complexity in the dataset and the need for semantics in understanding the questions. In addition, the implementation of a VQA approach using VGGNet and LSTM is carried out for existing and improved datasets, and analyzed with accuracy and BLEU score metrics. The improved datasets, created through dataset reduction and augmentation approaches, resulted in better performance than the existing datasets. Finally, the challenges of the medical VQA domain are examined in terms of datasets, combining techniques, and modifying the parameters of existing performance metrics for future research.

The Challenge of Multiple Thermal Comfort Prediction Models: Is TSV Enough?

Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning (ML) is being increasingly used to predict occupant comfort, multiple TC metrics for the same indoor space may yield contradictory results. This poses the challenge of selecting the most suitable single TC metric or the minimal TC metric combination for a given indoor space. Ideally, it will be a metric that can be used to predict all other TC metrics and occupant behavior with high accuracy. This work addresses this problem by using a primary student thermal comfort dataset gathered from 11 schools and over 500 unique students. A comprehensive evaluation is carried out through hundreds of TC prediction models using several ML algorithms. It evaluates the ability of TC metrics to predict (a) other TC metrics, and (b) the adaptive behavior of primary students. An algorithm is proposed to select the most suitable single TC metric or the minimal TC metric input combination. Results show that ML models can accurately predict all TC metrics and occupant-adaptive behavior using a small subset of TC metrics with an average accuracy as high as 79%. This work also found Thermal Sensation Vote to be the most significant single TC predictor, followed by Thermal Satisfaction Level. Interestingly, satisfaction with clothing was found to be as equally relevant as thermal preference. Furthermore, the impact of seasons and choice of ML algorithms on TC metric and occupant behavior prediction is shown.

The importance of the language for the evolution of online communities: An analysis based on Twitter and Reddit

The study of Online Social Networks offers growing opportunities to examine a number of aspects of the real world and to better understand how human society works at scale. One crucial research direction, constantly evolving over time, concerns the language adopted by users and its impact on online communities. To provide a contribution in this setting, in this paper, we adopt a multi-relational model for social network and identify a new typology of relation, namely co-interest, as an explicit common declaration of engagement towards a given topic among pair of users. Thematic communities can be, hence, derived leveraging such relations. After that, exploiting Natural Language Processing and Machine Learning techniques, we identify and define suitable analysis metrics focusing on the characteristics of the language adopted in such communities. With the objective of analyzing how the adoption of different language may impact the formation of cohesive and strong communities, to make our study general, we consider two very popular and intrinsically different social platforms, namely Twitter and Reddit. The analysis carried out in this paper aims at comparing the two social media to find interesting and novel results about how their users compose textual comments and the corresponding impact on users’ interaction. Interestingly, the obtained results show that, although the main factor is related to the popularity of the user generating the content, the characteristics of the adopted language play a non negligible role in the formation of strong communities in social systems.

Formalising human resource practices in a family-centric business: the case of Precision Engineering

Learning outcomes After studying the case, the students will be able to: 1. understand the business and existing HR practices at Precision Engineering; 2. evaluate the factors affecting business that may require the company to formalise its HR practices; 3. create recruitment and selection-related solutions for HR 2.0 using appropriate models and theory to aid the company meet its business goals; 4. create training needs identification and evaluation practices for HR 2.0 using appropriate models and theory to aid the company meet its business goals; and 5. create performance planning and review-related solutions for HR 2.0 using appropriate models and theory to aid the company meet its business goals. The case helps students objectively assess HR practices related to three core verticals – recruitment and selection; training; and performance management systems. It also enables them to reassess these practices with the help of specific metrics and models. Case overview/synopsis Precision Engineering was a manufacturer of machined metal components in the Indian automotive components industry. It had been a family-run business since its inception in 1995. Precision was awarded the prestigious Automotive Component Manufacturers of India award in 2020 for excellence in HR. Ms Sakshi Kapoor, General Manager of Innovation, was ecstatic at the receipt of this award. She, however, was thoughtful about the informal human resource (HR) practices at the company. The top management had announced an aggressive growth plan and advised Ms Kapoor to leverage HR practices to facilitate these plans. Recruitment and selection, employee training and performance management systems needed to be formalised on a priority basis to strategically aid the future business agenda at Precision. Ms Kapoor faced the challenge of preparing the roadmap of HR 2.0 while preserving the employee-centric beliefs at Precision. The case initiates a discussion to achieve this goal by adopting suitable HR metrics and models. Complexity academic level It should be taught in the core course on Human Resource Management for first-year Masters in Business Administration (MBA) students. Alternatively, it could be used in elective courses such as Strategic Human Resource Management, Training and Development and Performance Management Systems for second-year MBA students. Supplementary materials Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management.

A Novel Method of Efficient Max-min Metric for Classification

Distance metric learning is an important method to study distance metrics that reflect the interaction between features and labels. Because of the high computational complexity and the fact that existing studies on algorithms that measure the similarities with Euclidean distances cannot reflect the real correlations between pairs of samples, learning a suitable distance metric is quite demanding for many data mining tasks. This paper innovatively proposes an extended efficient max-min metric (EMM) that maximizes the total distance between different pairs and minimizes the total distance between similar pairs as much as possible. Simultaneously, the adoption of the local preserving projection framework changes the solution process of the algorithm and improves the speed of the algorithm without losing accuracy. Because traditional EMM only considers pairwise constraints and ignores sample distribution, this study extends EMM based on sample distribution and successfully solves the multi-manifold problem. In the process of data realization, compared with the vector representation method, the use of high-order tensors will make the image representation more accurate and natural. To maintain the structure of higher-order tensors, a tensor-efficient max-min metric (TEMM) is proposed. In order to prove the accuracy and superiority of the research method in this paper, a large number of experiments have been carried out on image processing. The experimental results show that the method proposed in this paper has a good effect.

Standardized metrics to quantify solar energy-land relationships: A global systematic review

Ground-mounted solar energy installations, including photovoltaics (PV) and concentrating solar power (CSP), can have significant environmental, ecological, and sociocultural effects via land-use and land-cover change (LULCC). Research in disciplines ranging from engineering to environmental policy seeks to quantify solar energy-land (SE-land) interactions to better understand the comprehensive impacts of solar energy installations on society. However, increasing evidence shows that scholars across research disciplines employ disparate metrics to quantify SE-land interactions. While solar energy deployment helps to achieve progress toward sustainable development goals (SDG 7- affordable and clean energy), the inconsistent use of metrics to describe SE-land interactions may inhibit the understanding of the total environmental and ecological impacts of solar energy installations, potentially causing barriers to achieve concurrent SDG's such as life on land (SDG 15). We systematically reviewed 608 sources on SE-land relationships globally to identify and assess the most frequent metric terms and units used in published studies. In total, we identified 51 unique metric terms and 34 different units of measure describing SE-land relationships across 18 countries of author origin. We organized these findings into three distinct metric categories: (1) capacity-based (i.e., nominal), (2) generation-based, and (3) human population-based. We used the most frequently reported terms and units in each category to inform a standardized suite of metrics, which are: land-use efficiency (W/m2), annual and lifetime land transformation (m2/Wh), and solar footprint (m2/capita). This framework can facilitate greater consistency in the reporting of SE-land metrics and improved capacity for comparison and aggregations of trends, including SE-land modeling projections. Our study addresses the need for standardization while acknowledging the role for future methodological advancements. The results of our study may help guide scholars toward a common vernacular and application of metrics to inform decisions about solar energy development.

Effects of flexibility in coarse-grained models for bovine serum albumin and immunoglobulin G.

We construct a coarse-grained, structure-based, low-resolution, 6-bead flexible model of bovine serum albumin (BSA, PDB: 4F5S), which is a popular example of a globular protein in biophysical research. The model is obtained via direct Boltzmann inversion using all-atom simulations of a single molecule, and its particular form is selected from a large pool of 6-bead coarse-grained models using two suitable metrics that quantify the agreement in the distribution of collective coordinates between all-atom and coarse-grained Brownian dynamics simulations of solutions in the dilute limit. For immunoglobulin G (IgG), a similar structure-based 12-bead model has been introduced in the literature [Chaudhri et al., J. Phys. Chem. B 116, 8045 (2012)] and is employed here to compare findings for the compact BSA molecule and the more anisotropic IgG molecule. We define several modified coarse-grained models of BSA and IgG, which differ in their internal constraints and thus account for a variation of flexibility. We study denser solutions of the coarse-grained models with purely repulsive molecules (achievable by suitable salt conditions) and address the effect of packing and flexibility on dynamic and static behavior. Translational and rotational self-diffusivity is enhanced for more elastic models. Finally, we discuss a number of effective sphere sizes for the BSA molecule, which can be defined from its static and dynamic properties. Here, it is found that the effective sphere diameters lie between 4.9 and 6.1nm, corresponding to a relative spread of about ±10% around a mean of 5.5nm.

Transfer Learning in wastewater treatment plants control: Measuring the transfer suitability

The industrial sector is nowadays experiencing a digital transformation motivated by the Industry 4.0 paradigm. Concepts such as data-driven models, Artificial Neural Networks (ANNs), and Transfer Learning (TL) are part of the current vocabulary in the industrial management and control topics. For that reason, in this paper the application of TL techniques is proposed to derive new ANN-based control structures from pre-existing ones. Notice that if an ANN-based controller is transferred into a new industrial environment, its appropriate behaviour must be ensured, and what it is more important, this must be known a priori. Nevertheless, TL techniques do not always ensure this. That is why the Transfer Suitability Metric (TSM) is proposed here. Determining the similarity among environments, this metric tells if the controller can be transferred, transferred with certain limitations, or if it cannot be transferred at all. Here, the metric is applied over a Wastewater Treatment Plant (WWTP). The objective is to derive the control structure of one control loop, let us say the Dissolved Oxygen (DO), and then transfer it into another basic control loop in a WWTP, the Nitrate–nitrogen (NO), and vice-versa. Results show that with the help of the TSM, an improvement around a 68.54% and 80.53% in the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE) is obtained in the NO management, respectively. Moreover, a simplification and speed-up of the controller design process is achieved.

Nonstationary Reinforcement Learning: The Blessing of (More) Optimism

Motivated by operations research applications, such as inventory control and real-time bidding, we consider undiscounted reinforcement learning in Markov decision processes under model uncertainty and temporal drifts. In this setting, both the latent reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. We first develop the sliding window upper confidence bound for reinforcement learning with confidence-widening (SWUCRL2-CW) algorithm and establish its dynamic regret bound when the variation budgets are known. In addition, we propose the bandit-over-reinforcement learning algorithm to adaptively tune the SWUCRL2-CW algorithm to achieve the same dynamic regret bound but in a parameter-free manner (i.e., without knowing the variation budgets). Finally, we conduct numerical experiments to show that our proposed algorithms achieve superior empirical performance compared with existing algorithms. Notably, under nonstationarity, historical data samples may falsely indicate that state transition rarely happens. This thus presents a significant challenge when one tries to apply the conventional optimism in the face of uncertainty principle to achieve a low dynamic regret bound. We overcome this challenge by proposing a novel confidence-widening technique that incorporates additional optimism into our learning algorithms. To extend our theoretical findings, we demonstrate, in the context of single-item inventory control with lost sales, fixed cost, and zero lead time, how one can leverage special structures on the state transition distributions to achieve improved dynamic regret bound in time-varying demand environments. This paper was accepted by J. George Shanthikumar, data science. Funding: The authors acknowledge support from the Massachusetts Institute of Technology (MIT) Data Science Laboratory and the MIT–IBM partnership in artificial intelligence. W. C. Cheung acknowledges support from the Singapore Ministry of Education [Tier 2 Grant MOE-T2EP20121-0012]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4704 .

Adding wind power to a wind‐rich grid: Evaluating secondary suitability metrics

AbstractAs the quantity of renewable electricity generation from wind farms increases in a region, the costs associated with integrating it into the broader electricity system also grow. This is primarily due to the need for dispatchable generators that vary power output to compensate for wind farm power variations. Such “balancing services” are an economic cost to the system that is typically not passed on to wind farms. We propose including the use of technical merits other than capacity factor and cost of energy for evaluating new wind farm sites and present a new graphical geospatial method, with the intention of identifying sites that minimize the need for additional electricity balancing service and transmission congestion. Specifically, locations with low correlation to existing wind farms, locations with high correlation to load, locations with high characteristic power time‐shift from existing wind farms, and locations that relieve or do not negatively impact electricity transmission congestion are identified. A geospatial Venn diagram‐based method of visualization is presented. These methods will equip regional planners with new tools to encourage wind farm development in areas that benefit the electricity grid beyond the lowest bid price.

Combustion Variability Monitoring in Engines Using High-Speed Exhaust Temperature and Pressure Measurements

AbstractThe diagnostic merits of high-speed (HS) exhaust gas temperature and pressure measurements for indexing engine stability and identifying abnormal combustion cycles are explored through experimental investigations on a low speed, single-cylinder engine. Exhaust temperature and pressure are measured using a fine wire 50 μm thermocouple and a piezoresistive pressure transducer, respectively. Synchronously recorded cylinder pressure data is used to continuously index combustion variations, and then anomalous combustion event detection and engine stability monitoring are attempted using HS exhaust temperature and pressure measurements. Two types of abnormal combustion cycles, namely, misfiring and overload cycles are used to typify low and high intensity anomalous combustion cycles, respectively. The results demonstrate that if suitable cyclic exhaust pressure and temperature metrics are used, anomalous combustion cycles can be identified, and overall combustion variability levels can be indexed. The comparative diagnostic performance of HS measured exhaust pressure and temperature and slow speed measured exhaust temperature are also discussed. A thermodynamic simulation of the engine and the HS thermocouple is used to provide theoretical support for the discussion by comparing measured and simulated “actual” exhaust temperature and identifying the flow, heat transfer, and thermodynamic influences that act as limits to the thermocouple's dynamic performance.

A domain-agnostic approach for characterization of lifelong learning systems

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to “real world” events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of “Lifelong Learning” systems that are capable of (1) Continuous Learning, (2) Transfer and Adaptation, and (3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development — both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future.

A State-Of-The-Art Review on Coronary Artery Border Segmentation Algorithms for Intravascular Ultrasound (IVUS) Images.

Intravascular Ultrasound images (IVUS) is a useful guide for medical practitioners to identify the vascular status of coronary arteries in human beings. IVUS is a unique intracoronary imaging modality that is used as an adjunct to angioplasty to view vessel structures using a catheter with high resolutions. Segmentation of IVUS images has always remained a challenging task due to various impediments, for example, similar tissue components, vessel structures, and artifacts imposed during the acquisition process. Many researchers have applied various techniques to develop standard methods of image interpretation, however, the ultimate goal is still elusive to most researchers. This challenge was presented at the MICCAI- Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop in 2011. This paper presents a major review of recently reported work in the field, with a detailed analysis of various segmentation techniques applied in IVUS, and highlights the directions for future research. The findings recommend a reference database with a larger number of samples acquired at varied transducer frequencies with special consideration towards complex lesions, suitable validation metrics, and ground-truth definition as a standard against which to compare new and current algorithms.

A novel technique for the detection of Covid-19 patients with the applications of three-way decisions using variance-based criterion

Everyone is making constant efforts to establish an effective diagnostic approach, therapy and control of the spread of the pandemic. Due to a flexible formulation, the parameters prior to the normal distributions and explicitly formulate assumptions on the transition probabilities between these categories over time. The spread of the COVID-19 pandemic represents a serious threat for scientists and academics, health professionals and even governments today. The Hospital wards are classified into Intensive Care Unit (ICU), Regular Wards (RW) with Recovered (R) and Deceased (D).. The formulation may be truncated to include particular hypotheses with an epidemiological interpretation. The principles of Three-Way Decision Theory could be used to anticipate and diagnose COVID-19 patients were classified into one of three zones based on their symptoms: Positive, Negative, or Boundary, and treatment are recommended if necessary. The thresholds that distinguish the three zones are determined using a variance-based criterion. Examine the impact of nonpharmaceutical interventions and the findings from data gathered during the second wave of the pandemic in Trivandrum, India.The Three-Way Decision Theory model has a good fit and gives good predictive performance, especially for RW and ICU patients, according to suitable discrepancy metrics that were created to assess and compare models. 95 percent accuracy increased and calculated values for 10 days to demonstrate the temporal aspects of the expected daily reproduction number R

Evaluation of healing progression at surgical incision sites and the use of antiseptics for enhancing post-operative survival in subyearling Chinook salmon (Oncorhynchus tshawytscha).

In an attempt to develop more effective surgical implantation methods for fish, surgical incisions typical of those made for implanting micro-acoustic transmitters into the peritoneal cavity were evaluated on a weekly basis for healing progression using a suite of metrics. Additionally, four chemicals were evaluated at concentrations commonly used in aquaculture for their ability to prevent surgical site infection and thus to promote incision healing and survival. Chemical treatments included hydrogen peroxide (25, 50, and 100 mg l-1), salt (10 and 30 ppt), Argentyne (1:1, Argentyne:water), and PolyAqua (1/2 tsp 36 l-1). For all study fish, the presence of two intact sutures seven days post-surgery (generally associated with good suturing technique) was negatively correlated with survival. A generalized linear mixed effects model indicated that suture presence, increasing tagging temperature, and the presence of foreign material on sutures decreased survival by 0.56, 0.72 and 0.60 respectively. Conversely, evidence of suture tearing and increasing fork length at tagging increased survival by 0.24 and 0.17. The antiseptic treatments tested promoted neither faster healing of surgical incisions nor higher survival for fish held for 28 days compared to a reference group and two of the chemicals may be contraindicated for prophylactic use at published doses. These findings underscore the need for researchers to adopt a decidedly cautious approach to planning and interpretation of study outcomes that rely on telemetry tagging, carefully considering the study subjects, potential effects of the techniques used, and implications of the environmental conditions experienced.

On the Cyber-Physical Needs of DER-Based Voltage Control/Optimization Algorithms in Active Distribution Network

With the increasing penetration of distributed energy resources (DERs) and extensive usage of information and communications technology (ICT) in decision-making, the mechanisms to control/optimize transmission and distribution grid voltage would experience a paradigm shift. Given the introduction of inverter-based DERs with vastly different dynamics, real-world performance characterization of the cyber-physical system (CPS) in terms of dynamical performance, scalability, robustness, and resiliency with the new control algorithms require precise classification and identification of suitable metrics. It has been identified that classical controller definitions, along with three inter-disciplinary domains, such as (i) power system, (ii) optimization, control, and decision-making, and (iii) networking and cyber-security, would provide a systematic basis for the development of an extended metric for performance evaluation of DER controllers; while providing the taxonomy. Majority of the control algorithms operate in multiple time scales, and therefore, algorithmic time-decomposition facilitates a new way of performance analysis. Extended discussion on communication requirements with the algorithmic architectural subtleties is expected to identify the real-world deployment challenges of voltage control/optimization algorithms in terms of presence of cyber vulnerabilities and associated mitigation mechanisms affecting the controller performance with DERs. Detailed discussion provided identifies the modeling requirements of the CPS for real-world deployment, specific to voltage control, facilitating the development of a unified test bed.

Blockchain-Based Recommendation System: A Review Focusing On Security Aspect

Recently, blockchain technology has caused considerable interest in academic research in the recommendation system domain. These records frequently contain encryption to achieve a comprehensive and reliable recommendation system. These records frequently contain encryption keys. Additionally, recommendation systems concerning suitable evaluation metrics are crucial to providing an accurate recommender system. Despite the attempts to address these issues through various strategies, very few researchers have considered a success in encrypting, especially in safeguarding the subscribers’ data. Currently, blockchain technology has drawn a lot of interest amongst several researchers, including in the area of recommendation system. To define these gaps, the current study aims to review the adoption of blockchain as one significant upside plan to increase data protection in recommendation systems. The systematic literature review (SLR) method was used to analyze publications from different electronic databases and documents of studies from 2019 to 2022. This research contributed to an in-depth comprehension of blockchain-based security techniques for recommending systems in addition to offering an overview of currently available blockchainbased programs within industries. The report emphasizes and examines significant facts and information about the blockchain depends security techniques for recommendation networks. Researchers highlight interest in a higher rate of security data and highlight noteworthy elements determined by an independent evaluation technique