Investigation of key performance indicators for performance management of the manufacturing industry in the era of the COVID-19 pandemic

The execution of constructive Key Performance Indicators (KPIs) is a critical tool for the Performance Management (PM) of the manufacturing industry to regulate operations. The companies rely on the PM strategies grounded on conventional KPIs assessment to achieve sustainability although the current dynamic manufacturing environment is undergoing complexities. The KPIs used in the past for PM are not mutually dependent, as they have not been adequately measured and updated to address emergency situations like the COVID-19 pandemic, particularly for the Leather Products Industry (LPI). Monitoring of plentiful KPIs is inconceivable and literature is also not available. Realizing these gaps, this study accumulates suggestions from a wide-ranging context of 25 experts’ feedback. Initially, a set of KPI was identified through literature review and experts survey. Later, employing a Pareto analysis, 15 KPIs were identified from 48 KPIs. Then the finalized KPIs investigated utilizing linguistic Z-digits and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to find the “Cause-Effect” relationship. An industrial chronology is conferred to demonstrate the potency and functionality of the suggested method. The upshot signifies the “Target fulfillment within the delivery time during COVID-19” as the most important KPI for the studied case. The outcomes will assist the LPI managers to dictate crucial KPIs suitably and flourish the PM in attaining the goals and objectives

Evaluation and Damage Detection of Highway Bridges with Distinct Vulnerabilities

Bridge failures over the past few decades have shown conventional bridge monitoring is insufficient to effectively evaluate the safety of this important piece of infrastructure. Therefore, new methods for bridge monitoring and special considerations in bridge design are needed to ensure the health of these structures as they continue to age and prevent the possibility of catastrophic collapses. The objective of this research is to explore new means for detecting damage in bridge members during normal operations that are both accurate and affordable at the same time. However, to make any damage detection method effective and efficient, the behavior of intact and damaged bridges needs to be investigated, preferably using simple analytical models. Therefore, to achieve the objective of this research, a two-fold investigation was performed. One was to study the bridge behavior subjected to various damage scenarios and identify possible failure mechanisms. Achieving this objective leads to a method for bridge evaluation after damage and determines its level of vulnerability to such damage; in other words, it defines the redundancy and reliability of the structure. The other was to develop an effective non-destructive method for damage detection based on the bridge behavior after the damage. vii Two types of bridges were selected and studied for this purpose, twin steel box girder bridges (TSBG) that are classified as fracture critical and prefabricated bridge systems containing cast-in-place joints. These bridges are designed with distinct vulnerabilities that make them susceptible to certain types of damages. The results of the current study confirmed that concrete deck failure is the dominant failure mode of the TSBG bridge after the occurrence of a fracture in one of the girders. Therefore, an improved simple and unified yield line analysis method was developed to determine the bridge deck capacity. An extensive analytical evaluation and availability of a simple model for load-carrying capacity developed in this study facilitated a comprehensive and coherent reliability approach to assess the safety of TSBG bridges after the complete fracture of one steel girder. Although the results of this research cannot readily be generalized for all TSBG bridges without further evaluation, this study shows that simply supported twin steel box girder bridges could indeed be safe and potentially removed from the fracture critical list. Moreover, the TSBG bridge dynamic analysis after damage showed that bridge frequencies are sufficiently sensitive for identifying partial or full-depth girder fracture in the simple span bridges. However, these significant damages may cause very small changes in the natural frequencies of continuous span bridges. The results show a significant change in the vibration mode shapes after damage in both simple and continuous span bridges. The mode shapes are sensitive enough to detect damage at the inflicted locations, in most cases providing better resolution when compared to the frequency changes. Investigation on the performance of the full-depth precast-prestressed voided slab bridge shows the vulnerability of such bridge decks to damage at the deck longitudinal joints. Using the FE analysis and load testing results, a new damage detection method for viii structural health monitoring of bridges with precast deck panels was also introduced. This method, applicable to all bridges with modular precast deck units, can effectively identify locations and significance of potential deck joint damage based on the measured changes in bridge response and model updating. A damage detection software tool was also developed in this case that is patent pending

Connectedness and frequency connection among green bond, cryptocurrency and green energy-related metals around the COVID-19 outbreak

We investigate the return interdependence among green bonds, cryptocurrency indices and green energy-related metals. We apply time-varying parametric vector autoregression (TVP-VAR) conenctedness, wavelet coherence, Wavelet Quantile Correlation （WQC) and Quantile on Quantile (QQR) Connectedness Methods. Our empirical findings show that return connectedness has become even stronger after the outbreak of COVID-19, with both green bonds and cryptocurrency indices acting as net receivers of return spillovers. Surprisingly, Copper functioned as a net sender of return spillovers over the entire observation period. Findings revealed that the cryptocurrency index exhibited a consistent positive correlation with the green energy-related metals market at medium to short-term frequencies, whereas green bonds showed a negative correlation with metals market at short-term frequencies and a positive correlation at long-term frequencies

A decomposition and decoupling analysis for carbon dioxide emissions: evidence from OECD countries

Despite the huge difference in their climatic regimes, the OECD countries are among the world’s largest energy consumers and emitters of greenhouse gases, particularly carbon dioxide. Nonetheless, no studies have been conducted to decompose and decouple the long-term influential primary factors of carbon emissions for these countries. In this research, the Log Mean Divisia Method I is used to inspect the contribution of several influencing factors to fill this knowledge gap. Moreover, Tapio (Transp Policy 12(2):137–151, 2005) decomposition analysis (DA) is performed to investigate the driving forces of CO2 emissions over the 1990–2019 years. The study provides an in-depth analysis of how to reduce CO2 emissions and the factors that contribute to their variation, which is crucial for both global and regional climate change policies. DA shows that, up to 2004, the activity effect and the population effect drove the emissions to increase; while, in more recent years, the activity effect was able to curb the emissions. Decoupling analysis show the prevalence of the expansive negative decoupling regime for the 1990–2004 and 2015–2019 periods, while several countries were in the strong decoupling phase over the central period (2005–2009). According to the results, further efforts to increase energy efficiency, political support for digitalization and decentralized energy systems, and setting up a unique emission trading system are recommended for air pollution reduction

Enhancing cardiovascular risk assessment with advanced data balancing and domain knowledge-driven explainability

In medical risk prediction, such as predicting heart disease, machine learning (ML) classifiers must achieve high accuracy, precision, and recall to minimize the chances of incorrect diagnoses or treatment recommendations. However, real-world datasets often have imbalanced data, which can affect classifier performance. Traditional data balancing methods can lead to overfitting and underfitting, making it difficult to identify potential health risks accurately. Early prediction of heart attacks is of paramount importance, and researchers have developed ML-based systems to address this problem. However, much of the existing ML research is based on a single dataset, often ignoring performance evaluation across multiple datasets. As the demand for interpretable ML models grows, model interpretability becomes central to revealing insights and feature effects within predictive models. To address these challenges, we present a novel data balancing technique that uses a divide-and-conquer strategy with the -Means clustering algorithm to segment the dataset. The performance of our approach is highlighted through comparisons with established techniques, which demonstrate the superiority of our proposed method. To address the challenge of inter-dataset discrepancies, we use two different datasets. Our holistic pipeline, strengthened by the innovative balancing technique, effectively addresses performance discrepancies, culminating in a significant improvement from 81% to 90%. Furthermore, through advanced statistical analysis, it has been determined that the 95% confidence interval for the AUC metric of our method ranges from 0.8187 to 0.8411. This observation serves to underscore the consistency and reliability of our approach, demonstrating its ability to achieve high performance across a range of scenarios. Incorporating Explainable AI (XAI), we examine the feature rankings and their contributions within the best performing Random Forest model. While the domain expert feedback is consistent with the explanatory power of XAI, some differences remain. Nevertheless, a remarkable convergence in feature ranking and weighting is observed, bridging the insights from XAI tools and domain expert perspectives

Impacts of the changing climate on agricultural productivity and food security: Evidence from Ethiopia

This study investigates the influence of climate change on agriculture productivity and food security in the context of Ethiopia. We use 2011–2020 state level data set of four major seasonal crops of Cash and Food in Ethiopia, namely, barley, wheat, maize, and sorghum. Methodologically, we apply the productivity function and the Ricardian approaches in the modelling for simulating the association of climate change with agriculture productivity. This study documents the interconnectedness among changes in climate, security of food and agriculture, indicating how the prior changes bring the latter kind of alterations. In general, agriculture in Ethiopia is prone to changes in climate and variations in the levels of precipitation, posing threats to food security of the rural population. The specific findings of this study highlight sorghum and barley as the majorly impacted stable crops through changes in meteorology. Furthermore, the study shows that barley production has vital contribution to causing insecurity of food in Ethiopia. The study ends with recommending some policy prescriptions and adaptation methods for mitigating the detrimental effects of climate change on production of agriculture and security of food in Ethiopia