Key Indicators Research Articles

Physics-Informed Neural Networks With Weighted Losses by Uncertainty Evaluation for Accurate and Stable Prediction of Manufacturing Systems.

The state prediction of key components in manufacturing systems tends to be risk-sensitive tasks, where prediction accuracy and stability are the two key indicators. The physics-informed neural networks (PINNs), which integrate the advantages of both data-driven models and physics models, are deemed as an effective approach and research trends for stable prediction; however, the potential advantages of PINN are limited for the situations with inaccurate physics models or noisy data, where the balancing of the weights of the data-driven model and physics model is very important for improving the performance of PINN, and it is also a challenge urgently to be addressed. This article proposed a kind of PINN with weighted losses (PNNN-WLs) by uncertainty evaluation for accurate and stable prediction of manufacturing systems, where a novel weight allocation strategy based on uncertainty evaluation by quantifying the variance of prediction errors is proposed, and an improved PINN framework is established for accurate and stable prediction. The proposed approach is verified with open datasets on tool wear prediction, and experimental results show that the prediction accuracy and stability could be obviously improved over existing methods.

Метод расчета ставок роялти на основе Big Data и Fuzzy Logic

The article by an expert from the Intellectual Property Valuation School and the Experimental Economics Laboratory of CEMI RAS is devoted to the development and application of the "LABRATE ROYALTY PRO" method for calculating royalty rates, based on Big Data analysis and Fuzzy Logic. Using the serial case of "Rikor Electronics" as an example, the problem of determining a fair (market) royalty rate, dependent on three key indi-cators — Licensor's Share (LS), Return on Sales (ROS), and EBIT Margin (EM) — has been addressed. Tradi-tional methods based on production or cost profitability do not correlate with sales revenue indicators, necessitat-ing adaptation to Russian realities and international practices. The article provides a detailed algorithm for calcula-tion, based on the analysis of companies' financial indicators, industry statistics, and the use of Big Data and Fuzzy Logic technologies. Examples from judicial practice confirm the effectiveness of the proposed approach in ensuring objective and fair decisions on determining royalty rates, the value of trademark usage rights, and com-pensation calculations. Special attention is given to the importance of precise and clear descriptions of the re-search objects and source data.

Water Salinity Sensing with UAV-Mounted IR-UWB Radar

The quality of surface water is closely related to human’s production and livelihood. Water salinity is one of the key indicators of water quality assessment. Recently, there has been an increased salinization problem of surface water in many regions of the world, making it necessary to timely monitor the salinity of surface water. Water salinity sensing could be challenging when it comes to surface water with complicated basin and tributaries, where existing methods fail to satisfy both efficiency and accuracy requirements. To address this problem, we propose a novel water salinity sensing system USalt, which leverages the high mobility of UAV and the contactless sensing ability of IR-UWB radar, and realizes fast and accurate water salinity sensing for surface water. Specifically, we design novel methods to eliminate the contamination in raw received radar signals and extract salinity-related features from radar signals. Furthermore, we adopt a neural network model ssNet to precisely estimate water salinity using the extracted features. To efficiently adapt ssNet to different environments, we customize meta learning and design a meta-learning framework mssNet. Extensive real-world experiments carried out by our UAV-based system illustrate that USalt can accurately sense the salinity of water with an MAE of 0.39 g/100 mL.

An adaptive weighted ensemble learning network for diabetic retinopathy classification.

Diabetic retinopathy (DR) is one of the leading causes of blindness. However, because the data distribution of classes is not always balanced, it is challenging for automated early DR detection using deep learning techniques. In this paper, we propose an adaptive weighted ensemble learning method for DR detection based on optical coherence tomography (OCT) images. Specifically, we develop an ensemble learning model based on three advanced deep learning models for higher performance. To better utilize the cues implied in these base models, a novel decision fusion scheme is proposed based on the Bayesian theory in terms of the key evaluation indicators, to dynamically adjust the weighting distribution of base models to alleviate the negative effects potentially caused by the problem of unbalanced data size. Extensive experiments are performed on two public datasets to verify the effectiveness of the proposed method. A quadratic weighted kappa of 0.8487 and an accuracy of 0.9343 on the DRAC2022 dataset, and a quadratic weighted kappa of 0.9007 and an accuracy of 0.8956 on the APTOS2019 dataset are obtained, respectively. The results demonstrate that our method has the ability to enhance the ovearall performance of DR detection on OCT images.

Structural Toughness of Fluorinated-Nitrided Interphase Passivation Layers for Lithium-Ion Batteries: A Reactive Molecular Dynamics Study.

The performance of lithium-ion batteries largely depends on the stability of the solid electrolyte interphase (SEI) layer formed on the anode surface. Strategies to improve SEI robustness often rely on optimizing its composition through electrolytic additives. Recently, the amalgamation of fluorinated cosolvents with nitride sources as additives has been shown to enable the construction of sustainable fluorinated-nitrided SEI layers (FN-SEI). Furthermore, the presence of lithiophilic nitrides embedded in lithium fluoride (LiF) was found to contribute toward stability of a beneficial amorphous phase for interfacial passivation. However, there is a lack of understanding on how key indicators of mechanical longevity, like plasticity and fracture resistance, may evolve in such multiphase SEI building blocks. Herein, in conjunction with first-principles calculations, a reactive force field (ReaxFF) has been developed for deriving new mechanistic insights into the intriguing FN-SEI. Our studies demonstrate that owing to a significant elasticity mismatch, the hard nitride phases have a propensity to affect the native deformation modes when embedded in a soft amorphous LiF-rich matrix. Impact of the volume fraction and distribution of the nitride (Li3N) phases are discussed from the perspective of how they interfere with the propagation of shear bands. Interestingly, brittle-ductile-brittle regimes are recognized along the nitride infusion window, providing a glimpse into the effect of phase distribution on the structural toughness of the LiF-Li3N-enhanced SEI.

In‐Network Quantum Computing for Future 6G Networks

AbstractIn light of the imperative for expeditious data processing and enhanced global connectivity, the domain of communication technology is experiencing a rapid progression from the Fifth Generation (5G) to the forthcoming Sixth Generation (6G) within the research community. Furthermore, 6G promises to significantly augment the synergy between the human, digital, and physical realms, thereby necessitating the formulation of novel Key Performance Indicators (KPIs) as well as Key Values Indicators (KVIs), and the assimilation of commensurate technologies. Among these technologies, quantum computing is evolving rapidly due to its inherent advantages from quantum mechanics. Nevertheless, scant attention is directed toward a comprehensive exploration of the consequences attendant to its present‐day application. The principal objective of this article resides in its endeavor to underscore, from a compensatory perspective, the convergence of 6G and quantum computing while concurrently considering the Sustainable Development Goals and its KVIs.

Stay Alert: Drowsiness Detection with IoT Technology

The "Drowsiness Detection using IoT with Eye Blink Sensor and Buzzer" project aims to address the critical issue of driver fatigue and drowsiness, a major cause of road accidents. Leveraging Internet of Things (IoT) technologies and a combination of eye blink sensors and a buzzer, this system provides a real-time solution for monitoring and alerting drivers when signs of drowsiness are detected. The project focuses on enhancing road safety by preventing accidents caused by driver fatigue Drowsiness detection is a critical aspect of ensuring safety, particularly in domains such as transportation where fatigue-related accidents pose significant risks. In recent years, the proliferation of Internet of Things (IoT) devices has enabled innovative approaches to drowsiness detection systems. This abstract outlines a novel framework for drowsiness detection utilizing IoT technologies. Our proposed system integrates various sensors, including facial recognition cameras, heart rate monitors, and accelerometers, into a networked IoT infrastructure. These sensors continuously monitor key physiological and behavioral indicators associated with drowsiness, such as eye movement patterns, blink frequency, heart rate variability, and head position changes.

The effects of the COVID-19 pandemic on connectivity, operational efficiency, and resilience of major container ports in Southeast Asia

The COVID-19 pandemic in 2020 has had significant impacts on the connectivity and performance of container ports in Southeast Asia. The application of social network analysis (SNA) in conjunction with key indicators, such as container throughput, total capacity of ships, number of ship calls, and berth duration, provides a comprehensive understanding of the development of the regional port system. While major ports in the region, namely Singapore, Port Kelang, Manila, and Laem Chabang, have exhibited signs of recovery owing to the expeditious and timely implementation of targeted policies to mitigate the adverse effects of the pandemic, smaller ports in the region, such as Bangkok, Penang, Tanjung Priok, and Tanjung Perak, are grappling with enduring repercussions and encountering challenges in resilience. These challenges stem from inadequate infrastructure, limited accommodation capacity, low loading/unloading productivity, and other significant risk events associated with the pandemic, all of which have weakened their competitiveness. In contrast, ports in Vietnam, including Cai Mep, Hai Phong, and Ho Chi Minh, as well as Malaysia's Tanjung Pelepas, have demonstrated that they are less impacted by the COVID-19 pandemic. This can be attributed to the sustained growth of their respective economies and the continued surge in domestic production to meet the unabated demand for exports. Based on these findings, several policy recommendations can be suggested for port authorities to effectively manage and mitigate the impact of risky events, such as pandemics, in the future while also enhancing the competitiveness of ports.

Sustainable Gender Equality: A Comparative Perspective on STEM Education and Employment in Jordan

Gender equality is a concept that is synonymous with debates towards economic and societal advancement, as manifested through the United Nation’s Sustainable Development Goal 5. This study sets out to identify the key indicators driving gender equality in Science, Technology, Engineering, and Mathematics (STEM) education and employment in Jordan. This area is both critical and timely in light of emerging challenges facing technological advancement, progressive economies, and wider societal inclusion strategies within the professional environment. This study predominantly employs a quantitative methodology, utilising a survey to evaluate and rank a comprehensive set of indicators/challenges associated with gender equality in the published literature. Feedback is gathered from both male and female participants. The findings of this study reveal certain categories and indicators that are highly ranked compared with others, with practical aspects such as workplace conditions and professional treatment, societal norms and gender stereotypes, and professional perceptions and underlying gender bias being more predominant in Jordan. This study also revealed differing challenges facing gender equality in employment compared with the ones faced in education. This finding resonates with the historical trajectory of academic progress in STEM fields in Jordan, and its divergence emphasises the need for a nuanced exploration to advance gender equality in STEM effectively.

Institutional Quality and Economic Performance: A Study of Asian Countries

The objective of this article is to investigate the influence of institutional quality on the economic performance of 47 Asian countries, categorized into four income levels (low-income, lower-middle-income, upper-middle-income and high-income countries) over the period 1981–2021. The study utilizes six governance indicators, and a composite governance index is constructed to evaluate institutional quality. Economic performance is assessed through three key indicators: real gross domestic product (GDP), real GDP per capita (GDPPC) and the human development index (HDI). Except for HDI data, all variable data were sourced from the World Bank, while HDI data were obtained from the UNDP. The study employed panel regression, panel co-integration techniques and the autoregressive distributed lag model. The findings indicate that governance indicators have a positive and significant impact on GDP, GDPPC and HDI in Asian countries. Moreover, the study reveals that the influence of governance indicators on economic performance is more pronounced in high-income countries compared to other income levels in Asia. Consequently, the research emphasizes the importance of focusing on governance indicators for fostering economic growth and enhancing HDI in Asian countries.

Assessing Soil Quality Under Different Land-Uses Through Constructing Minimum Datasets from Soil Profiles in a Fragile Ecosystem of Northeastern Region of India

ABSTRACT The conversion of forest to agricultural land has been a major worldwide concern and a cause of deterioration of soil quality. The study examined the influence of land uses on the soil quality index (SQI) using minimum datasets (MDS) constructed from soil profiles through two methods namely, additive (SQIa) and weighted (SQIw). In this study, 29 geo-referenced soil profiles (116 layers) up to a depth of 150 cm were collected in Northeastern India, representing forest, jhum, jhum fallow, and agricultural land uses. Principal component analysis (PCA) with multiple correlation analysis is found to be a suitable statistical method for selecting the key soil quality indicators. From PCA, organic carbon (OC), sand, clay, available P and available S were selected as soil quality indicators in MDS. Our results showed that these MDS significantly varied within the profiles depending on the land-uses. The estimated values of SQIs showed that SQIa was higher than SQIw across the soil depths and land-uses. For the surface soil, SQIa values were in the order of forest (2.97) > jhum fallow (2.82) > agriculture (2.68) > jhum land (1.97) and for profile soil jhum fallow (2.38) > forest (2.37) > agriculture (2.33) > jhum (1.88). Similarly, SQIw followed the trend of forest > jhum fallow > agriculture > jhum for all the three studied depths. The statistical analysis showed that the surface, subsurface, and profile soil had no significant impact on SQIw for agriculture, fallow jhum, and forest. The overall data indicated that jhum land had a significantly lower SQI than other land-uses for all soil depths. From this study, the information generated can be useful to assess the soil quality in different land-uses for managing soil health in similar areas of the Northeastern region of India. The findings are also useful in the ecosystems in which soils develop in an acid edaphic environment.

The Role and Impact of Private Higher Education Institutions on South Africa's Educational Sector: A Comprehensive Analysis

This study presents a comprehensive analysis of the role and impact of private higher education institutions on South Africa's educational sector. Private institutions have gained prominence in recent years, raising questions about their influence on the overall educational landscape. To address this, a mixed method approach was employed, combining a quantitative analysis, and a comparative analysis. Quantitative analysis examined student enrollment trends, graduation rates, and staffing in private higher education institutions, comparing them with public institutions to assess the influence of private institutions on key educational indicators. The comparative analysis on the other hand, compared the student employability and curricula in private institutions. The literature review analyzed the differences in quality, diversity student support services, leadership, and challenges of PHIEs. This comprehensive analysis contributes to understanding the complex dynamics of private institutions in South Africa's educational sector. It sheds light on their influence on educational quality, student enrollment, accessibility, and the overall educational landscape. The findings inform policymakers, educators, and stakeholders, facilitating evidence-based decision-making to further enhance the educational sector in South Africa.

Band Selection via Band Density Prominence Clustering for Hyperspectral Image Classification

Band clustering has been widely used for hyperspectral band selection (BS). However, selecting an appropriate band to represent a band cluster is a key issue. Density peak clustering (DPC) provides an effective means for this purpose, referred to as DPC-based BS (DPC-BS). It uses two indicators, cluster density and cluster distance, to rank all bands for BS. This paper reinterprets cluster density and cluster distance as band local density (BLD) and band distance (BD) and also introduces a new concept called band prominence value (BPV) as a third indicator. Combining BLD and BD with BPV derives new band prioritization criteria for BS, which can extend the currently used DPC-BS to a new DPC-BS method referred to as band density prominence clustering (BDPC). By taking advantage of the three key indicators of BDPC, i.e., cut-off band distance bc, k nearest neighboring-band local density, and BPV, two versions of BDPC can be derived called bc-BDPC and k-BDPC, both of which are quite different from existing DPC-based BS methods in three aspects. One is that the parameter bc of bc-BDPC and the parameter k of k-BDPC can be automatically determined by the number of clusters and virtual dimensionality (VD), respectively. Another is that instead of using Euclidean distance, a spectral discrimination measure is used to calculate BD as well as inter-band correlation. The most important and significant aspect is a novel idea that combines BPV with BLD and BD to derive new band prioritization criteria for BS. Extensive experiments demonstrate that BDPC generally performs better than DPC-BS as well as many current state-of-the art BS methods.

State of Governance, Development and Diplomacy in Republic Nepal

A new constitution was promulgated in 2015. There are several major problems in the execution of the constitution in achieving the aspired goals. It excludes the participation of large sections of society. All three branches of government, the executive, legislative and judiciary, lack coordination, autonomy, and effectiveness in carrying out their respective duties. They are marred by corruption, nepotism, and non-adherence to the rules, norms and values that govern them. The economy is in shambles with all the key economic indicators showing negative trends. The economy, development works and delivery of basic needs/services are in dire need of major improvement. Over the years, development strategy and priority have prevented the economy from being self-reliant and sustainable. Nepal’s foreign policy execution in recent years has become more and more unbalanced and ineffective threatening its autonomy, independence, sovereignty and non-aligned policy. The need of the day is a system that promotes unity and inclusiveness of all sections of society.

Perovskite Quantum Dots for the Next‐Generation Displays: Progress and Prospect

AbstractThe “Nobel Prize in Chemistry 2023” is awarded to Moungi G. Bawendi, Louis E. Brus, and Alexey I. Yekimov for discovering and synthesizing Quantum Dots (QDs). Colloidal QDs possess fascinating size‐, morphological‐, composition‐, and assembly‐tunable electronic and optical properties, which makes them star materials for various optoelectronic applications, especially as luminescent materials for next‐generation wide color gamut ultra‐high‐definition displays. Perovskite QDs (PQDs) have gained widespread attention in recent years. In less than ten years, research on perovskite‐related materials and devices has basically been perfected in terms of quantum yield and external quantum efficiency (EQE). However, on the eve of its industrial application, some key technical indicators and technical processes need to be met and resolved. The development and transformation of QD materials and then focuses on the progress of luminescence linewidth and EQE of the PQD light‐emitting diode. Finally, several application avenues are reviewed for PQDs, and some challenges and opportunities in the field are proposed.

The impact of natural resources on environmental degradation: a review of ecological footprint and CO2 emissions as indicators

Environmental degradation resulting from the overexploitation of natural resources has become a pressing global concern. This review paper aims to investigate the relationship between natural resources and environmental degradation, with a specific focus on carbon dioxide (CO2) emissions and ecological footprint (EF) as indicators. The study method involved an exhaustive search across prominent research databases, including ScienceDirect, Web of Science, Scopus, and Springer, using carefully defined search terms. A total of more than 160 research papers related to the search terms were obtained from the four sources of the database during the initial search. After applying sorting, filtering, and removing duplication and repetitions, we were left with 75 research papers that had a direct link to the topic under investigation. From these 75 papers, we further applied inclusion criteria to identify the most relevant studies for our review, resulting in the final inclusion of 50 research papers. The selected papers were thoroughly assessed for their methodological robustness, relevance, and adherence to the research questions. The review encompasses studies from diverse geographical regions and periods, shedding light on both positive and negative associations between natural resources and the two key indicators of environmental degradation (CO2 emissions and EF). The review identified diverse findings in the literature, highlighting both positive and negative associations between natural resources and environmental degradation indicators (CO2 and EF). The results of this comprehensive review will contribute to a better understanding of the complex interplay between natural resources and environmental sustainability and will offer valuable insights for policymakers and researchers alike.

Evolutionary modeling approach based on multiobjective genetic programming for strip quality prediction

In the iron and steel industry, hardness is one of the key indicators of strip quality in the continuous annealing production line (CAPL). However, the complex production process and the strong coupled nonlinearity between process parameters make it difficult to develop accurate mechanism models and pose a challenge for data-driven modeling approaches. More importantly, most of the data-driven learning methods lack interpretability and cannot characterize the mathematical relationship between process parameters and product quality, which in turn makes it extremely hard to understand the process mechanism. Therefore, this paper proposes an interpretable modeling approach (IMA) based on feature decomposition and ensemble to construct interpretable analytical models between process parameters and strip quality. In the IMA, a data-mechanism fusion-based feature decomposition (DM_FD) method is first applied to cope with high-dimensional input feature problems. Then, an improved multiobjective genetic programming algorithm (iMOGP) is developed to construct interpretability sub-models. Finally, a sparse optimization ensemble method (SOE) is used to integrate the sub-models to achieve interpretability and good generalization. Experimental results based on practical strip data demonstrate that the proposed IMA can cope well with high-dimensional input features and achieve model interpretability compared with commonly used machine learning methods and genetic programming (GP)-based modeling methods while ensuring better accuracy and generalization.

A stakeholder approach to the formation of and the use of key indicators in the management of organizations and in assessing the financial benefits of their activities

A stakeholder approach to the formation of and the use of key indicators in the management of organizations and in assessing the financial benefits of their activities

To do or not to do: Pyongyang’s seventh nuclear test calculations

ABSTRACT A nuclear test often is as political as it is technical. The intricacies of domestic and external politics play into Pyongyang’s decisions on nuclear testing, going far beyond the technical aspects of such tests. This article explores the repercussions of the second North Korea-US summit in Hanoi in 2019 across all policy realms in North Korea and how domestic and external factors could inform and shape Kim Jong-un’s decision on a seventh nuclear test. In order to delve into what to look out for in 2024 and beyond, this article focuses on Pyongyang’s approach to the civilian economy as opposed to national defense and the reorientation of its foreign and South Korea policies as potential key indicators of Kim’s thinking on nuclear testing.

Enhancing pedagogical strategies through technology integration in basic teacher education program

This study investigated enhancing pedagogical strategies through technology integration in basic teacher education programs. It sheds light on its significant influence on both educational management and instructional approaches. The investigation centred on experiences within Ghana's College of Education (CoE), contributing insights that align with the objectives of Sustainable Development Goal 4. A mixed-method approach was employed, integrating both quantitative and qualitative methods. The participants of the study consisted of 485 participants (469 tutors and 16 principals) selected from sixteen colleges by using cluster and simple random sampling techniques. Quantitative data were gathered by employing a structured questionnaire, while qualitative data were acquired through semi-structured interviews. The quantitative data underwent analysis using both descriptive and inferential statistics, while the qualitative data were subjected to an inductive analysis approach. The results revealed the noteworthy influence of age, gender, higher qualifications, and professional experience of principals and instructors on technology integration in education. Furthermore, key indicators such as smartphone usage and Microsoft Office 365 tools were found to be insufficient for facilitating technology integration. This inadequacy was attributed to managerial challenges like infrastructural limitations and resistance to change. It was recommended that the Ministry of Education should institute regular in-service training initiatives on technology integration. This step will empower the staff at CoE with the requisite knowledge and competencies to proficiently infuse technology in their roles.