Multidimensional Metrics Research Articles

Development of preferred sizes for building-integrated lighting units (BILUs)

Building-Integrated Lighting Unit (BILU) is a new type of LED lighting product that is organically integrated with building elements and components. To promote the digital design, standardized production, and assembly construction of new lighting forms, it is necessary to establish Preferred Sizes (PSs) of BILU that are coordinated with building modules. With the aim of reduction in quantity and strength in universality, PSs were selected from BILU modular arrays in this paper. Based on the preliminary survey, a set of target sizes involving building, furniture, and home decoration elements was established to cover all application scenarios of PSs. Through a traversal search of feasible splicing methods of all PSs, the distribution of alternative PSs was analyzed with their universality quantified. Then, multidimensional metrics of alternative PSs were established, including the average feasible scheme proportion and feasible PS proportion of Line-BILU, as well as the feasible coverage-rate proportion and average coverage rate of Surface-BILU. A weighted Euclidean distance method was applied in the multi-criterion decision-making, realizing a quantitative optimization of PSs in multiple scenarios and under multiple objectives. A total of nineteen Line-BILU preferred sizes and sixty-five Surface-BILU preferred sizes were finally obtained, with their feasibility and advantages validated based on existing modular coordination standards and actual lighting project examples. These results can provide data support for establishing product specifications and design standards of BILUs, promote building industrialization from the lighting perspective, and stimulate the sustainable construction of architectural lighting.

Challenges for climate change adaptation in Latin America and the Caribbean region

The limited success of international efforts to reduce global warming at levels established in the Paris Agreement, and the increasing frequency and strength of climate impacts, highlight the urgent need of adaptation, particularly in developing countries. Unfortunately, current levels of adaptation initiatives are not enough to counteract the observed impacts and projected risks from climate change in Latin America and the Caribbean (LAC). In this paper, we review and highlight relevant issues that have limited the capacity to transform climate knowledge and parties’ ambitions into action in the region. Current vulnerabilities and climatic impact-drivers in LAC are diverse, complex, and region-specific and their effects are expected to be exacerbated by climate change. However, the advancement of regional and domestic climate agendas has been hindered by scientific gaps, political support, institutional capacity, and financial, technical, human, and economic limitations that are common to many LAC countries. Transforming climate data into multidimensional metrics with useful thresholds for different sectors and understanding their contribution for feasible adaptation strategies are delayed by regional and local conundrums such as lack of inclusive governance, data availability, equity, justice, and transboundary issues. We discuss ways to move forward to develop local and regional climate resilient development actions and a more sustainable future in LAC. The climate science community in LAC needs to strengthen its local, national, and international connections and with decision/policymakers and society to establish a three-way engagement by proposing suitable adaptation actions and international negotiations to reduce the risks and vulnerability associated with climate extremes, climate variability and climate change in the region. The discussions and insights presented in this work could be extrapolated to other countries in the Global South.

Isochrony in barks of Cape fur seal (Arctocephalus pusillus pusillus) pups and adults.

Animal vocal communication often relies on call sequences. The temporal patterns of such sequences can be adjusted to other callers, follow complex rhythmic structures or exhibit a metronome-like pattern (i.e., isochronous). How regular are the temporal patterns in animal signals, and what influences their precision? If present, are rhythms already there early in ontogeny? Here, we describe an exploratory study of Cape fur seal (Arctocephalus pusillus pusillus) barks-a vocalisation type produced across many pinniped species in rhythmic, percussive bouts. This study is the first quantitative description of barking in Cape fur seal pups. We analysed the rhythmic structures of spontaneous barking bouts of pups and adult females from the breeding colony in Cape Cross, Namibia. Barks of adult females exhibited isochrony, that is they were produced at fairly regular points in time. Instead, intervals between pup barks were more variable, that is skipping a bark in the isochronous series occasionally. In both age classes, beat precision, that is how well the barks followed a perfect template, was worse when barking at higher rates. Differences could be explained by physiological factors, such as respiration or arousal. Whether, and how, isochrony develops in this species remains an open question. This study provides evidence towards a rhythmic production of barks in Cape fur seal pups and lays the groundwork for future studies to investigate the development of rhythm using multidimensional metrics.

Research on the Characteristics and Evaluation Metrics of Green Highway Corridors

This paper reviews the key characteristics and the progress of research on the evaluation metrics for green highway corridors. Initially, it defines the concept of green highway corridors. Subsequently, the paper thoroughly discusses the multidimensional metrics used to evaluate the success of these corridors. Finally, it suggests recommendations for optimizing the design and evaluation methods of green highway corridors. This review aims to serve as a reference for researchers in the fields of highway engineering and environmental science, and to promote the development of green transportation infrastructure.

Textual Information Processing Based on Multi-Dimensional Indicator Weights

With the rapid advancement of artificial intelligence and wireless communication technologies, the abundance of textual information has grown significantly, accompanied by a plethora of multidimensional metrics such as innovation, application prospects, key technologies, and expected outcomes. Extracting valuable insights from these multifaceted indicators and establishing an effective composite evaluation weighting framework poses a pivotal challenge in text information processing. In response, we propose a novel approach in this paper to textual information processing, leveraging multi-dimensional indicator weights (MDIWs). Our method involves extracting semantic information from text and inputting it into an LSTM-based textual information processor (TIP) to generate MDIWs. These MDIWs are then processed to create a judgment matrix following by eigenvalue decomposition and normalization, capturing intricate semantic relationships. Our framework enhances the comprehension of multi-dimensional aspects within textual data, offering potential benefits in various applications such as sentiment analysis, information retrieval, and content summarization. Experimental results underscore the effectiveness of our approach in refining and utilizing MDIWs for improved understanding and decision-making. This work contributes to the enhancement of text information processing by offering a structured approach to address the complexity of multidimensional metric evaluation, thus enabling more accurate and informed decision-making in various domains.

Credit Risk Prediction Based on Psychometric Data

In today’s financial landscape, traditional banking institutions rely extensively on customers’ historical financial data to evaluate their eligibility for loan approvals. While these decision support systems offer predictive accuracy for established customers, they overlook a crucial demographic: individuals without a financial history. To address this gap, our study presents a methodology for a decision support system that is intended to assist in determining credit risk. Rather than solely focusing on past financial records, our methodology assesses customer credibility by generating credit risk scores derived from psychometric test results. Utilizing machine learning algorithms, we model customer credibility through multidimensional metrics such as character traits and attitudes toward money management. Preliminary results from our prototype testing indicate that this innovative approach holds promise for accurate risk assessment.

Hemoglobin albumin lymphocyte and platelet score and all-cause mortality in coronary heart disease: a retrospective cohort study of NHANES database.

Anemia, inflammatory status, and malnutrition are all important factors in the prognosis of cardiovascular disease (CVD), and their interactions are also noteworthy. A recent scoring system, the hemoglobin albumin lymphocyte and platelet (HALP) score, combining multi-dimensional metrics, has been used in the prognoses of many diseases except coronary heart disease (CHD). Herein, this study aims to explore the association between HALP score and all-cause mortality in patients with CHD. Demographic and clinical data of adult patients with CHD were extracted from the National Health and Nutrition Examination Surveys (NHANES) database from 2003 to 2018 in this retrospective cohort study. Weighted univariate and multivariate COX proportional hazard models were used for covariates screening and exploration of the association between HALP score and all-cause mortality. The evaluation indexes were hazard ratios (ORs) and 95% confidence intervals (CIs). Kaplan-Meier (KM) curve and the receiver operator characteristic (ROC) curve were used to assess the predictive performance of HALP on CHD prognosis. In addition, subgroup analyses of age and congestive heart failure (CHF) were also performed. Among the eligible patients, 657 died of all-cause mortality. After adjusting for the covariates including age, education level, PIR, marital status, smoking, physical activity, total energy intake, CHF, stroke, hypertension, DM, CKD, cancer or malignancy, monocyte, drug for CVD, treatment for anemia, anticoagulants drug, and adrenal cortical steroids, we found that HALP score was negatively associated with the risk of all-cause mortality [HR = 0.83, 95% CI: (0.74-0.93)]. Compared with patients with high HALP scores, those who had lower HALP scores seemed to have a higher risk of all-cause mortality (all P < 0.05). HALP score has a potential predictive value on CHD prognosis with an area under the curve (AUC) of 0.61. Furthermore, in patients aged <65 years, with or without CHF, a lower HALP score was also associated with a higher risk of all-cause mortality (all P < 0.05). HALP score has a potential predictive value on CHD prognosis; however, the causal association between HALP score and mortality in patients with CHD needs further exploration.

CodePlex: Software Complexity Measuring Tool based on ECB Measure

The surge in popularity of object-oriented programming as a predominant paradigm in software development has spurred numerous studies to introduce metrics for assessing the complexity of object-oriented programs. These metrics typically fall into two primary categories: those focusing on object-oriented aspects and those centered on cognitive aspects, delineating their principal areas of concern when evaluating program complexity. Within the realm of cognitive aspects, the majority of metrics have historically been confined to the consideration of no more than three complexity variables. However, the ECB (Enhanced Cognitive Based) measure stands as a notable exception, capable of encompassing and addressing four or more intricate facets in the assessment of software program intricacy and difficulty. This research paper undertakes the exploration of the incorporation of these multidimensional metrics as refinements to the existing weighted composite complexity CB measure, originally introduced by Chhillar and Bhasin. In doing so, it endeavors to furnish a more comprehensive and holistic framework for the evaluation of program complexity, accommodating both object-oriented and cognitive dimensions. Furthermore, the study assumes the pivotal role of empirically validating the practical effectiveness of the ECB measure, seeking to bridge the chasm between theoretical metrics and their tangible applicability in real-world settings. Such an endeavor holds profound significance for software developers and researchers, proffering invaluable insights that can advance our understanding and management of intricate object-oriented programs.

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

Identification of urban-rural integration types in China – an unsupervised machine learning approach

PurposeDevelopment of urban-rural integration is essential to fulfill sustainable development goals worldwide, and comprehension about urban-rural integration types has been highlighted as increasingly relevant for an efficient policy design. This paper aims to utilize an unsupervised machine learning approach to identify urban-rural integration typologies based on multidimensional metrics regarding economic, population and social integration in China.Design/methodology/approachThe study introduces partitioning around medoids (PAM) for the identification of urban-rural integration typologies. PAM is a powerful tool for clustering multidimensional data. It identifies clusters by the representative objects called medoids and can be used with arbitrary distance, which help make clustering results more stable and less susceptible to outliers.FindingsThe study identifies four clusters: high-level urban-rural integration, urban-rural integration in transition, low-level urban-rural integration and early urban-rural integration in backward stage, showing different characteristics. Based on the clustering results, the study finds continuous improvement in urban-rural integration development in China which is reflected by the changes in the predominate type. However, the development still presents significant regional disparities which is characterized by leading in the east regions and lagging in the western and central regions. Besides, achievement in urban-rural integration varies significantly across provinces.Practical implicationsThe machine learning techniques could identify urban-rural integration typologies in a multidimensional and objective way, and help formulate and implement targeted strategies and regionally adapted policies to boost urban-rural integration.Originality/valueThis is the first paper to use an unsupervised machine learning approach with PAM for the identification of urban-rural integration typologies from a multidimensional perspective. The authors confirm the advantages of this machine learning techniques in identifying urban-rural integration types, compared to a single indicator.

Cross-Scale Dynamicity of Entropy and Connectivity in the Sleeping Brain.

Introduction: The concept of local sleep refers to the phenomenon of local brain activity that modifies neural networks during unresponsive global sleep. Such network rewiring may differ across spatial scales; however, the global and local alterations in brain systems remain elusive in human sleep. Materials and Methods: We examined cross-scale changes of brain networks in sleep. Functional magnetic resonance imaging data were acquired from 28 healthy participants during nocturnal sleep. We adopted both metrics of connectivity (functional connectivity [FC] and regional homogeneity [ReHo]) and complexity (multiscale entropy) to explore the global and local functionality of the neural assembly across nonrapid eye movement sleep stages. Results: Long-range FC decreased with sleep depth, whereas local ReHo peaked at the N2 stage and reached its lowest level at the N3 stage. Entropy exhibited a general decline at the local scale (Scale 1) as sleep deepened, whereas the coarse-scale entropy (Scale 3) was consistent across stages. Discussion: The negative correlation between Scale-1 entropy and ReHo reflects the enhanced signal regularity and synchronization in sleep, identifying the information exchange at the local scale. The N2 stage showed a distinctive pattern toward local information processing with scrambled long-distance information exchange, indicating a specific time window for network reorganization. Collectively, the multidimensional metrics indicated an imbalanced global-local relationship among brain functional networks across sleep-wake stages.

Metrics for evaluating the performance of complex engineering system health monitoring models

Recent efforts to apply prognostics and health management (PHM) practices towards the risk management of complex engineering systems (CES) traditionally structured using probabilistic risk assessments (PRA) provide more opportunities for modeling and dynamically monitoring system-level heath. However, it is unclear what metrics should be used to assess and compare the performance of these models. PHM performance metrics center around prediction accuracy without considering operational realities of the system, while risk management models are not easily compared. A new set of model metrics is needed for this evolving space.This paper identifies and defines multi-dimensional metrics to assess the performance, effectiveness and efficiency of system-level health monitoring models based on alignment with specific phases of model design and operation. Using high-interest model aspects and metrics from PHM and PRA, a taxonomy of measurable characteristics is presented for systematically comparing model designs. The verification process for this taxonomy is described as well as an illustrative example for utilizing the metrics in model design decision. The comprehensive set of metrics presented in this work enables rigorous and scientific comparisons of developed and proposed system models, enabling both PRA and PHM communities to analytically assess system-level health monitoring models beyond accuracy-driven measurements.

Modeling urban systems: Comparing the efficacy of simulated interventions on the urban food environment

• Food access metrics using more than store proximity lead to tailored food access interventions. • Modeling disruptors on the food system inexpensively tests out food access solutions. • Interventions in different case study regions have distinct effects. • Further work needed on interplay between choices of where to shop and how to travel. Inequitable access to food has negative effects on the health and quality of life of urban residents, especially in regions known as Food Deserts (FDs). Interventions to increase food access are inconsistently effective when applied to different regions in the U.S. This work seeks to answer two research questions: (1) can differences in interventions efficacy be explained by local demographic factors and geographies?; and (2) can multi-dimensional metrics be used to isolate what demographic factors drive food inaccessibility? Results indicate that the efficacy of interventions does depend on local demographic factors and infrastructure, especially bus service. Additionally, demographic factors can act either to increase or impede food access. After distance to the store, the presence of a child under 5 in the household was the most significant limiting factor to accessing healthy food. Conversely, a household size exceeding 4 members had the greatest positive impact on food access. These results show how the combination of simulation modeling and multi-dimensional metrics can inform decision makers about what demographic factors drive inaccessibility, and the interventions that would be most effective and sustainable.

A unifying framework for analyzing temporal changes in functional and taxonomic diversity along disturbance gradients.

Frameworks exclusively considering functional diversity are gaining popularity, as they complement and extend the information provided by taxonomic diversity metrics, particularly in response to disturbance. Taxonomic diversity should be included in functional diversity frameworks to uncover the functional mechanisms causing species loss following disturbance events. We present and test a predictive framework that considers temporal functional and taxonomic diversity responses along disturbance gradients. Our proposed framework allows us to test different multidimensional metrics of taxonomic diversity that can be directly compared to calculated multidimensional functional diversity metrics. It builds on existing functional diversity-disturbance frameworks both by using a gradient approach and by jointly considering taxonomic and functional diversity. We used previously unpublished stream insect community data collected prior to, and for the two years following, an extreme flood event that occurred in 2013. Using 14 northern Colorado mountain streams, we tested our framework and determined that taxonomic diversity metrics calculated using multidimensional methods resulted in concordance between taxonomic and functional diversity responses. By considering functional and taxonomic diversity together and using a gradient approach, we were able to identify some of the mechanisms driving species losses following this extreme disturbance event.

Traces of SARS-CoV-2 RNA in Peripheral Blood Cells of Patients with COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the third virus that caused coronavirus-related outbreaks over the past 20 years. The outbreak was first reported in December 2019 in Wuhan, China, but rapidly progressed into a pandemic of an unprecedented scale since the 1918 flu pandemic. Besides respiratory complications in patients with COVID-19, clinical characterization of severe infection cases showed several other comorbidities, including multiple organ failure, and septic shock. To better understand the systemic pathogenesis of COVID-19, we interrogated the virus's presence in the peripheral blood cells, which might provide a form of trafficking or hiding to the virus. By analyzing >2 billion sequence reads of high-throughput transcriptome sequence data from 180 samples of patients with active SARS-CoV-2 infection or healthy controls collected from 6 studies, we found evidence of traces of SARS-CoV-2 RNA in peripheral blood mononuclear cells in two samples from two independent studies. In contrast, the viral RNA was abundant in bronchoalveolar lavage specimens from the same patients. We also devised a “viral spike-to-actin” RNA normalization as a metric to compare across various samples and minimize errors caused by intersample variability in total human RNA abundance. Our observation suggests immune presentation and discounts the possibility of extensive viral infection of lymphocytes or monocytes.

Resilience Assessment of Water Quality Sensor Designs under Cyber-Physical Attacks

Water distribution networks (WDNs) are critical infrastructure for the welfare of society. Due to their spatial extent and difficulties in deployment of security measures, they are vulnerable to threat scenarios that include the rising concern of cyber-physical attacks. To protect WDNs against different kinds of water contamination, it is customary to deploy water quality (WQ) monitoring sensors. Cyber-attacks on the monitoring system that employs WQ sensors combined with deliberate contamination events via backflow attacks can lead to severe disruptions to water delivery or even potentially fatal consequences for consumers. As such, the water sector is in immediate need of tools and methodologies that can support cyber-physical quality attack simulation and vulnerability assessment of the WQ monitoring system under such attacks. In this study we demonstrate a novel methodology to assess the resilience of placement schemes generated with the Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Tool (TEVA-SPOT) and evaluated under cyber-physical attacks simulated using the stress-testing platform RISKNOUGHT, using multidimensional metrics and resilience profile graphs. The results of this study show that some sensor designs are inherently more resilient than others, and this trait can be exploited in risk management practices.

Network Service Embedding Across Multiple Resource Dimensions

Network Function Virtualization (NFV) poses the need for efficient embeddings of network services, usually defined in the form of service graphs, associated with resource and bandwidth demands. As the scope of NFV has been expanded in order to meet the requirements of virtualized cellular networks and emerging 5G services, the diversity of resource demands across dimensions, such as CPU, memory, and storage, increased. This requirement exacerbates the already challenging problem of network service embedding (NSE), rendering most existing NSE methods inefficient, as they commonly account for a single resource dimension ( i.e. , typically, the CPU). In this context, we investigate methods for NSE optimization across multiple resource dimensions. To this end, we study a range of multi-dimensional mapping efficiency metrics and assess their suitability for heuristic and exact NSE methods. Utilizing the most suitable and efficient metrics, we propose two heuristics and a mixed integer linear program (MILP) for optimized multi-dimensional NSE. In addition, we devise a virtual network function (VNF) bundling scheme that generates (resource-wise) balanced VNF bundles in order to augment VNF placement. Our evaluation results indicate notable resource efficiency gains of the proposed heuristics compared to a single-dimensional counterpart, as well as a minor degree of sub-optimality in relation to our proposed MILP. We further demonstrate how the bundling scheme affects the embedding efficiency, when coupled with our most efficient heuristic. Our study also uncovers interesting insights and potential implications from the utilization of multi-dimensional metrics within NSE methods.

Does Firm's Value Matter With Firm's Patent Quality in Technology-Intensive Industries?

During the contemporary studies on the measurement of the patent quality, two typical research circles gradually protruded. One research circle concerns on the detailed metrics and measuring methods on the patent quality, whose members could be mostly from public policy, governance, and so forth. Another research circle pays more attention on the relationships between firm's performance, technology capability, and patent count or quality. Sometimes, the research fruits from the first community occasionally fail to timely transmit to second research community; especially the possible effects of firm's patent quality on firm's market value under the context of technology management in China were much less mentioned during the past decades. In this article, an integrated or multidimensional metrics on the patent quality evaluation are utilized to reshape the analytical model, and the possible relationships between firm's value and firm's patent quality are further explored based on a quantitative means. In empirical analyses, two technology-intensive industries in China, i.e., China's telecommunication equipment industry and China's pharmaceutical industry are conducted for case studies. The selected industries are relatively competitive, and they are all technology intensive or patent intensive. Through the theoretical hypotheses designing, and regression analyses based on the unbalanced panel data, some interesting phenomenon and conclusions could be revealed.

Evaluating wider impacts of books via fine-grained mining on citation literatures

Citations are commonly used to measure academic impacts of scientific publications, including books. However, citation frequencies of books are single numerical evaluation metrics. It neglects details about books (e.g. contents), which may lead to the decline in comprehensiveness of evaluation results. Hence, fine-grained mining on books’ citation information to integrate frequency metrics and content metrics can obtain more reliable evaluation results. Books’ citation literatures (i.e. literatures cited books) present citation frequencies of books, and reflect citation intentions, topics and domains simultaneously. Existing research focused on analysing citation frequencies, authors or citation contexts of citation literatures to conduct citation analysis. It may be costly for collecting citation contexts and neglected latent information of citation literatures, such as impact scopes or topics of books reflected by citation literatures. Therefore, in this paper, we conducted fine-grained analysis on books’ citation literatures to assess whether citation literatures could be systematically used for indicators of books’ wider impacts. Specifically, we firstly collected books and corresponding information about their citation literatures. Then, we extracted multi-dimensional metrics via multi-granularity mining on citation literatures, and got assessment results by integrating content-level and frequency-level metrics. Finally, we compared assessment results based on citation literatures and existing metrics for assessing books’ impacts to verify assessment results. Experimental results infer that citation literatures are a promising source for book impact assessment, especially books’ academic impacts.

A new deal for translation quality

Given the rich body of technical developments and a relatively long history of industrial use of Machine Translation (MT), it is astonishing of how little interest the topic of MT quality has received so far. In this paper, we present three ways of performing MT quality evaluation from our own research: (1) using TQ-AutoTest, a framework for semi-automatic testing and comparison of different translation engines; (2) applying the multidimensional quality metrics for analytical markup of translation errors; and (3) performing task-based user testing. We will set these three in perspective as they serve different needs and different people’s interest in translation quality assessment. This paper deals with the translation of text in the first place. Still, we hope that the methods, insights, and observations we report transfer to broader applications of translation in the field of Media Accessibility.