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What happens when patients suddenly stop their medications? We study the health consequences of drug interruptions caused by large, abrupt, and arbitrary changes in price. Medicare's prescription drug benefit as-if-randomly assigns 65-year-olds a drug budget as a function of their birth month, beyond which out-of-pocket costs suddenly increase. Those facing smaller budgets consume fewer drugs and die more: mortality increases 0.0164 percentage points per month (13.9%) for each $100 per month budget decrease (24.4%). This estimate is robust to a range of falsification checks and lies in the 97.8th percentile of 544 placebo estimates from similar populations that lack the same idiosyncratic budget policy. Several facts help make sense of this large effect. First, patients stop taking drugs that are both high value and suspected to cause life-threatening withdrawal syndromes when stopped. Second, using machine learning, we identify patients at the highest risk of drug-preventable adverse events. Contrary to the predictions of standard economic models, high-risk patients (e.g. those most likely to have a heart attack) cut back more than low-risk patients on exactly those drugs that would benefit them the most (e.g. statins). Finally, patients appear unaware of these risks. In a survey of 65-year-olds, only one-third believe that stopping their drugs for up to a month could have any serious consequences. We conclude that far from curbing waste, cost sharing is itself highly inefficient, resulting in missed opportunities to buy health at very low cost ($11,321 per life-year). [ABSTRACT FROM AUTHOR]

In the dynamic and intricate shared economy, efficient resource management and forecasting are still crucial. In this research, a new prediction model is presented that aims to improve the operational efficiency of several shared economy services. Our method creatively combines Long Short-Term Memory (LSTM) networks with Genetic Algorithms (GA) to assess user demand and optimize resource deployment. We apply this methodology especially to e-scooter sharing services, but the underlying ideas and methods can be applied to other shared economy platforms as well, like peer-to-peer lending services, car sharing, and vacation rentals. The model starts with a GA to adjust the hyperparameters of the LSTM network, making the network better suited to handle specific characteristics of common economic data. Capturing the complex temporal and spatial patterns of user behavior and demand on these platforms requires this optimization. The LSTM element then predicts changes in service demand due to its capacity to analyze sequential records. Further to being useful for analyzing sequential data and predicting destiny wishes, this predictive functionality is important for a shared economy platform to correctly manage stock, allocate assets, and predict personal wishes. We use a large dataset to check our technique, demonstrating the predictive accuracy of the model and demand and its potential to aid strategic choice-making. Compared to traditional fashions, the consequences show a large development in forecast accuracy and resource allocation efficiency. Our methodology creates a robust basis for statistics-driven insights that decorate customer happiness and decorate the long-term increase of the shared financial system. This work highlights the blended ability of GA and LSTM inside the shared economy and paves the manner for future enhancements in using modern-day gadget mastering techniques to optimize and alter various shared services. In quick, effective useful resource control and forecasting in the shared economic system is tough, however our specific forecasting model combining GA-LSTM gives a manner ahead. Our technique, as it should be, predicts fluctuations in provider demand; it was first refined the usage of GA to regulate the LSTM hyperparameters. The consequences show how correct and powerful our version is and spotlight how it can enhance customer pleasure and operational performance. This research paves the manner for future trends within the software of gadgets, gaining knowledge of methods and supports the continuing enlargement and development of shared economic system offerings. [ABSTRACT FROM AUTHOR]

In this work, a beneficial approach for efficient depolymerization of lignin and controllable product distribution is provided. Lignin, an abundant aromatic biopolymer, has the potential to produce various biofuels and chemical adsorption agents and is expected to benefit the future circular economy. Microwave-ultrasonic (MW/US) assisted efficient depolymerization of lignin affords some aromatic materials used in manufacturing the starting material to be investigated. Some nano organometallic surfactants (NOMS) based on Ni2+, Cu2+, Co2+, Fe3+, and Mn2+ besides 2-hydroxynaphth-sulphanilamide are synthesized to enhance oil recovery (EOR). In this work, the assessment of the NOMS's efficiency was improving the heavy oil recovery via the study of the dynamic interfacial tension (IFT), contact angle, and chemical flooding scenarios. The NOMS-Ni2+ exhibited the maximum reduction of viscosity and yield values. Dropping the viscosity to 819.9, 659.89, and 499.9 Pa s from blank crude oil viscosity of 9978.8, 8005.6, and 5008.6 Pa s respectively at temperatures of 40, 60, and 80 °C was investigated. The reduction of τB values was obtained also by OMS-Ni2+. The minimum IFT was recorded against the Ni2+ derivatives (0.1 × 10–1 mN m−1). The complete wettability alteration was achieved with the NOMS-Ni2+ surfactant (ɵ ≅ 6.01). The flooding test has been steered in 3 sets using the sand-packed model as a porous media at surfactant concentrations (1, 1.5, 2 and 2.5%) at 50 °C and 499 psi as injection pressure. The best value (ORs) formed for NOMS-Ni2+ were 62, 81, 85.2, and 89% respectively as compared with other NOMS-M2+ at the same concentrations. The mechanism of alternating wettability was described in the text. The rheology of the used heavy crude oil was investigated under temperatures of 40, 60, and 80 °C. [ABSTRACT FROM AUTHOR]

Oil is one of the main resources used by all countries in the world. The ever-growing demand for oil and oil products forces oil companies to increase production and refining. In order to increase net profit, oil producing companies are constantly upgrading equipment, improving oil production technologies, and preparing oil for further processing. When considering the elements of primary oil refining in difficult conditions, such as hard-to-reach or in remote locations, developers face strict limitations in energy resources and dimensions. Therefore, the use of traditional systems causes a number of difficulties, significantly reducing production efficiency. In this study, the authors solve the problem of improving the characteristics of the oil separation process. In their work, the authors analyzed the separation columns of primary oil distillation, identified the shortcomings of the technological process, and searched for technological solutions. Having identified the lack of technical solutions for monitoring the state of the temperature field of the separation column, the authors developed their own hardware–software complex for monitoring the separation column (RF patents No. 2020665473, No. 2021662752 were received). The complex was tested and successfully implemented into production. The study provides an assessment of the economic efficiency of implementation for a year and a forecast of the economic effect for 10 years. [ABSTRACT FROM AUTHOR]

Migration forecasts are crucial for proactive immigration and integration management. While the demand for accurate migration forecasts continues to grow, the current state of migration forecasting is still unsatisfactory, because they tend to lack precision. We introduce an alternative method to forecast migration movements: prediction markets. While prediction markets are mainly unknown in migration studies, they are established in the political economy of forecasting election outcomes. For its application to a complex phenomenon in a more constrained information environment such as migration movements, we argue that prediction markets allow to balance complementarities of current qualitative and quantitative approaches if they provide solutions to avoid thin trading and integrate expert knowledge into the market. We apply the prediction market to forecast immigration in four West European countries in 2020 and find encouraging results. We discuss the strengths and limitations of prediction markets to migration forecasting, including ethical considerations, and guide its future application. [ABSTRACT FROM AUTHOR]

This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study's overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos. [ABSTRACT FROM AUTHOR]

The article "Climate-Related Financial Stability Risks for the United States: Methods and Applications" examines various models used to study climate-related financial stability risks (CRFSRs) in the U.S. It discusses the strengths and weaknesses of ten types of models and their suitability for assessing CRFSRs, noting a high level of uncertainty in current assessments. The text also delves into the methodology of natural capital analysis, emphasizing the importance of measuring firms' exposure to natural resource degradation and the impacts on business models. It concludes by suggesting ways to combine existing methodologies for a more comprehensive understanding of climate risks and vulnerabilities, highlighting the need for further research in integrating climate-related risks into modeling frameworks. [Extracted from the article]

Modelling and predicting agricultural production processes have high cognitive and practical values. Plant growth, the threat of pathogens and pests, and the structure of agricultural machinery treatments are mostly non-linear, measurable processes that can be described mathematically. In this paper, a multiple regression analysis was carried out in the first step to check the non-linearity of the data and yielded a coefficient of determination of R2 = 0.9741 and the coefficient of determination corrected for degrees of freedom was R2adj = 0.9739. An artificial neural network model, called CH-NET, is then presented to predict the yield loss of carrot roots by leaving root mass in the field during harvest at the mechanical heading stage. The proposed network model has an architecture consisting of an input layer, three hidden layers with 12 neurons each, and an output layer with one neuron. Twelve input criteria were defined for the analysis and testing of the network, eight of which related to carrot root parameters and four to the heading machine. The training, testing, and validation database of the CH-NET network consisted of the results of field trials and tests of the operation of the patented (P.242097) root heading machine. The proposed CH-NET neural network model achieved global error (GE) values of 0.0931 t·ha−1 for predicting carrot root yield losses for all twelve criteria adopted. However, when the number of criteria is reduced to eight, the error increased to 0.0991 t·ha−1. That is, the prediction was realised with an accuracy of 90.69%. The developed CH-NET model allows the prediction of economic losses associated with root mass left in the field or contamination of the raw material with undercut leaves. The simulations carried out showed that minimum root losses (0.263 t·ha−1) occur at an average root head projection height of 38 mm and a heading height of 20 mm from the ridge surface. [ABSTRACT FROM AUTHOR]

The article discusses potential changes to the UK's fiscal debt rule in the upcoming Budget, focusing on the implications of shifting to a public sector net financial liabilities (PSNFL) target. By excluding certain factors like losses on gilt sales and redemptions, the Chancellor could create fiscal headroom for increased public spending and investment. Different balance sheet measures are explored, with PSNFL emerging as a likely option for guiding fiscal sustainability. The article presents scenarios illustrating the effects of changing the debt rule, emphasizing the importance of a robust framework for fiscal policy. [Extracted from the article]

The article "World Economic Prospects Monthly | Global" examines the impact of heightened tensions in the Middle East on global growth forecasts. The US has seen positive data surprises, leading to an increase in GDP growth forecasts, while China's economic slowdown has prompted bold policy support with uncertain effectiveness. The oil price surge from Middle East tensions poses short-term risks, but the long-term outlook remains stable. The text also delves into economic forecasts for various regions, including the Eurozone, Japan, the UK, Emerging Markets, India, Latin America, Central Eastern Europe, and Romania, highlighting key changes and policy outlooks. [Extracted from the article]

Wastewater treatment plants (WWTPs) connected to combined sewer systems must cope with high flows during wet-weather conditions, often leading to bypass and thus pollution of water bodies. Radar rainfall forecasts coupled with a rainfall-runoff model provides flow and volume forecasts that can be used for deciding when to switch from normal to wet-weather operation, which temporarily allows for higher inflow. However, forecasts are by definition uncertain and may lead to potential mismanagement, e.g., false alarms and misses. Our study focused on two years of operational data from the Damhuså sewer catchment and WWTP. We used the Relative Economic Value (REV) framework to optimize the control parameters of a baseline control strategy (thresholds on flow measurements and radar flow prognosis) and to test new control strategies based on volume instead of flow thresholds. We investigated two situations with different objective functions, considering higher negative impact from misses than false alarms and vice versa, and obtained in both cases a reduction of the rate of false alarms, higher flow thresholds and lower bypass compared to the baseline control. We also assess a new control strategy that employs thresholds of predicted accumulated volume instead of predicted flow and achieved even better results. [ABSTRACT FROM AUTHOR]

It is intuitive and theoretically sound to combine experts' forecasts based on their proven skills, while accounting for correlation among their forecast submissions. Simpler combination methods, however, which assume independence of forecasts or equal skill, have been found to be empirically robust, in particular, in settings in which there are few historical data available for assessing experts' skill. One explanation for the robust performance by simple methods is that empirical estimation of skill and of correlations introduces error, leading to worse aggregated forecasts than simpler alternatives. We offer a heuristic that accounts for skill and reduces estimation error by utilizing a common correlation factor. Our theoretical results present an optimal form for this common correlation, and we offer Bayesian estimators that can be used in practice. The common correlation heuristic is shown to outperform alternative combination methods on macroeconomic and experimental forecasting where there are limited historical data. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Supplemental Material: The data file is available at https://doi.org/10.1287/mnsc.2021.02009. [ABSTRACT FROM AUTHOR]

The U.S. Survey of Professional Forecasters produces precise and timely point forecasts for key macro‐economic variables. However, the accompanying density forecasts are mostly conducted for "fixed events." For example, in each quarter, panelists predict output growth and inflation for the current calendar year and the next, hence the forecast horizon changes with each survey round. This limits the forecasts' usefulness to policymakers, researchers, and market participants. We propose a density combination approach that weights fixed‐event density forecasts, aiming at obtaining a correctly calibrated fixed‐horizon density forecast. We show that our method produces competitive density forecasts relative to widely used alternatives. [ABSTRACT FROM AUTHOR]

The article introduces the Kansas City Fed's Measure of Policy Rate Uncertainty (KC PRU), which aims to measure uncertainty about future policy rates using financial options prices. The KC PRU is calculated daily and has shown correlations with changes in policymakers' communication strategies and macroeconomic events. The measure indicates a recent increase in policy rate uncertainty, attributed to factors such as inflation outlook, large increases in the funds rate, and regional banking stresses. Overall, the KC PRU provides a timely and consistent measure of policy rate uncertainty, reflecting historical movements and recent developments. [Extracted from the article]

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In this study, we investigate an appointment sequencing and scheduling problem with heterogeneous user delay tolerances under service time uncertainty. We aim to capture the delay tolerance effect with heterogeneity, in an operationally effective and computationally tractable fashion, for the appointment scheduling problem. To this end, we first propose a Tolerance-Aware Delay (TAD) index that incorporates explicitly the user tolerance information in delay evaluation. We show that the TAD index enjoys decision-theoretical rationale in terms of Tolerance sensitivity, monotonicity, and convexity and positive homogeneity, which enables it to incorporate the frequency and intensity of delays over the tolerance in a coherent manner. Specifically, the convexity of TAD index ensures a tractable modeling of the collective delay dissatisfaction in the appointment scheduling problem. Using the TAD index, we then develop an appointment model with known empirical service time distribution that minimizes the overall tolerance-aware delays of all users. We analyze the impact of delay tolerance on the sequence and schedule decisions and show that the resultant TAD appointment model can be reformulated as a mixed-integer linear program (MILP). Furthermore, we extend the TAD appointment model by considering service time ambiguity. In particular, we encode into the TAD index a moment ambiguity set and a Wasserstein ambiguity set, respectively. The former captures effectively the correlation among service times across positions and user types, whereas the latter captures directly the service time data information. We show that both of the resultant TAD models under ambiguity can be reformulated as polynomial-sized, mixed-integer conic programs (MICPs). Finally, we compare our TAD models with some existing counterpart approaches and the current practice using synthetic data and a case of real hospital data, respectively. Our results demonstrate the effectiveness of the TAD appointment models in capturing the user delay tolerance with heterogeneity and mitigating the worst-case delays. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"], the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2], and the Major Program of National Natural Science Foundation of China [Grant 72192843]. S. Wang was also supported by a grant from MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0025) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0025). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Biomass is inherently organic and renewable, promoting a circular economy ecosystem. As global consumption patterns change, circular economy strategies have turned into sustainable net-zero strategies for developing countries and developed countries, and its value chain is now included in important biomass energy policies. Many countries are actively transforming their economic growth patterns, developing their own circular economy, targeting ecological sustainable development, and adjusting domestic industrial structures. The concept of a circular society, synergistic with the social economy and developed on the basis of the circular economy, has production and consumption at its core. This research aims to verify the important roles that biomass plays in the circular economy and to initiate a virtuous resource circulation model, promote material recycling and reuse, form a "resources-products-renewable resources" model, and promote better resource use efficiency. It discusses the important roles that the bioeconomy plays when achieving a circular economy and also proposes new economic and policy concepts. The key conclusions cover: (1) the biomass energy–circular economy business model; (2) recognizing the co-benefit of consumers and a prosumer circular economy; and (3) challenges to a renewable cycle under economic applications. [ABSTRACT FROM AUTHOR]

The present work aims to explore Spirulina biomass' functional and technological marvels and its components, such as C-phycocyanin (C-PC), in modern food systems from a circular economy perspective, evaluating a decade of insights and innovations. This comprehensive review delves into the pivotal studies of the past decade, spotlighting the vital importance of maintaining stability in various food matrices to unleash the full biological impacts. Through the lens of food science intertwined with circular economy principles, this analysis meets health and environmental requisites and explores the harmonious synergy between food systems, economy, and industry. While Spirulina has typically served as a supplement, its untapped potential as a fundamental food ingredient has been unveiled, showcasing its abundant nutritional and functional attributes. Technological hurdles in preserving the vibrant color of C-PC have been triumphantly surmounted through simple temperature control methods or cutting-edge nanotechnology applications. Despite the gap in sensory acceptance studies, the emergence of blue foods introduces groundbreaking functional and innovative avenues for the food industry. [ABSTRACT FROM AUTHOR]

This study harnesses advanced time series models ARIMA, ETS, and SARIMA, coupled with neutrosophic statistics, to forecast unemployment trends through interval-based predictions. Transforming these predictions into neutrosophic forms enables the quantification of indeterminacy, providing a nuanced interpretation of potential economic scenarios. The integration of neutrosophic statistics enhances the interpretative power and accuracy of these models, offering a deeper insight into the inherent uncertainties of economic forecasting. The approach reveals not only the variabilities and potential outcomes within the unemployment rates but also strengthens the decision-making processes by presenting data that encompass both precision and indeterminacy. This paper underscores the importance of advanced statistical methods in economic predictions, suggesting further exploration into other economic metrics and advocating for a broader application of neutrosophic statistics to enhance the reliability of economic forecasting across diverse contexts. [ABSTRACT FROM AUTHOR]

In the following article, we analyse the relationships among banking stability, the efficiency of the energy system and climate risks at a global level. We present a detailed analysis of the literature relating to the relationship between the banking system and Environmental, Social and Governance (ESG) models. In our research, we try to verify whether it is possible to achieve energy efficiency, stability of the banking system and reduction of climate risk together, i.e. the “Green Trilemma”. The econometric analysis is conducted through the following models: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squared and Weighted Least Squared-WLS. To estimate the variables, we used World Bank data. The analysis shows that ESG growth is negatively associated with energy efficiency and positively associated with banking stability and climate risk. It therefore follows that the Green Trilemma hypothesis is rejected. Countries can only target banking stability and climate risk through ESG models. [ABSTRACT FROM AUTHOR]

Stock market indices are important tools to measure and compare stock market performance. The Selective Stock Price (SSP) index reflects fluctuations in a set value of financial instruments of Santiago de Chile's stock exchange. Stock indices also reflect volatility linked to high uncertainty or potential investment risk. However, economic shocks are altering volatility. Evidence of long memory in SSP time series also exists, which implies long-term persistence. In this paper, we studied the volatility of SSP time series from January 2010 to September 2023 using fractionally heteroskedastic models. We considered the Autoregressive Fractionally Integrated Moving Average (ARFIMA) process with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) innovations—the ARFIMA-GARCH model—for SSP log returns, and the fractionally integrated GARCH, or FIGARCH model, was compared with a classical GARCH one. The results show that the ARFIMA-GARCH model performs best in terms of volatility fit and predictive quality. This model allows us to obtain a better understanding of the observed volatility and its behavior, which contributes to more effective investment risk management in the stock market. Moreover, the proposed model detects the influence volatility increments of the SSP index linked to external factors that impact the economic outlook, such as China's economic slowdown in 2012 and the subprime crisis in 2008. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and the current wars in some countries have put incredible pressure on the global economy. Challenges for the U.S. include not only economic factors, major disruptions, and reorganizations of supply chains, but also those of national security and global geopolitics. This unprecedented situation makes predicting economic crises for the coming years crucial yet challenging. In this paper, we propose a method based on various machine learning models to predict the probability of a recession for the U.S. economy in the next year. We collect the U.S.'s monthly macroeconomic indicators and recession data from January 1983 to December 2023 to predict the probability of an economic recession in 2024. The performance of the individual economic indicator for the coming year was predicted separately, and then all of the predicted indicators were used to forecast a possible economic recession. Our results showed that the U.S. will face a high probability of being in a recession period in the last quarter of 2024. [ABSTRACT FROM AUTHOR]

This research aims to study and develop a model to demonstrate the causal relationships of factors used to forecast CO2 emissions from energy consumption in the industrial building sector and to make predictions for the next 10 years (2024–2033). This aligns with Thailand's goals for sustainability development, as outlined in the green economy objectives. The research employs a quantitative research approach, utilizing Linear Structural Relationships based on a Latent Growth Model (LISREL-LGM model) which is a valuable tool for efficient country management towards predefined green economy objectives by 2033. The research findings reveal continuous significant growth in the past economic sector (1990–2023), leading to subsequent growth in the social sector. Simultaneously, this growth has had a continuous detrimental impact on the environment, primarily attributed to the economic growth in the industrial building sector. Consequently, the research indicates that maintaining current policies would result in CO2 emissions from energy consumption in the industrial building sector exceeding the carrying capacity. Specifically, the growth rate (2033/2024) would increase by 28.59%, resulting in a surpassing emission of 70.73 Mt CO2 Eq. (2024–2033), exceeding the designated carrying capacity of 60.5 Mt CO2 Eq. (2024–2033). Therefore, the research proposes strategies for country management to achieve sustainability, suggesting the implementation of new scenario policies in the industrial building sector. This course of action would lead to a reduction in CO2 emissions (2024–2033) from energy consumption in the industrial building sector to 58.27 Mt CO2 Eq., demonstrating a decreasing growth rate below the carrying capacity. This underscores the efficacy and appropriateness of the LISREL-LGM model employed in this research for guiding decision making towards green economy objectives in the future. [ABSTRACT FROM AUTHOR]

Big data technology has revolutionized the research paradigm of economic forecasting regardless of the data source, forecasting method, or forecasting result. This study evaluates the current literature on economic forecasting using big data and employs bibliometric approaches to offer a comprehensive analysis. Additionally, utilizing the advanced structural variation analysis technique, we can identify papers with transformative potential in this domain. This study provides valuable suggestions for enhancing scholars' understanding of significant research, novel breakthroughs, and emerging trends in the role of big data in economic forecasting.

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production. Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of "expert interviews + big data technology" for economic forecasting. Results: The forecasting accuracy on four provincial companies has reached over 90%. Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development. [ABSTRACT FROM AUTHOR]

Economic growth is essential for regional economic performance, with gross regional domestic product (GRDP) being a key indicator of economic development over time. In this research case, the GRDP data of various provinces on Java Island from 2010 to 2023 will be used as the variable being studied. The data obtained from the GRDP variable contain spatial and temporal information, requiring an appropriate model to forecast spatiotemporal data, namely, the Generalized Space-Time Autoregressive (GSTAR) model. However, in estimating the parameters, the GSTAR model is unable to detect correlated residuals between equations, resulting in inefficient estimators. Therefore, an appropriate estimation method is needed to address correlated residuals within the seemingly unrelated regression (SUR) framework, namely, the Generalized Least Square (GLS) estimation method. The GSTAR-SUR method is applied to forecast the economic growth rate of Java Island. The optimal model, GSTAR-SUR (1[sub 1])-I(1) with inverse distance location weights, demonstrates high accuracy with a mean absolute percentage error (MAPE) of 8.451%. Forecasts for Banten, DKI Jakarta, West Java, Central Java, East Java, and DI Yogyakarta predict consistent monthly GRDP increases through December 2024. [ABSTRACT FROM AUTHOR]

The ontology behind quantum mechanics has been the subject of endless debate since the theory was formulated some 100 years ago. It has been suggested, at one time or another, that the objects described by the theory may be individual particles, waves, fields, ensembles of particles, observers, and minds, among many other possibilities. I maintain that these disagreements are due in part to a lack of precision in the use of the theory's various semantic designators. In particular, there is some confusion about the role of representation, reference, and denotation in the theory. In this article, I first analyze the role of the semantic apparatus in physical theories in general and then discuss the corresponding ontological implications for quantum mechanics. Subsequently, I consider the extension of the theory to quantum fields and then analyze the semantic changes of the designators with their ontological consequences. In addition to the classical arguments to rule out a particle ontology in the case of non-relativistic quantum field theory, I show how the existence of black holes makes the proposal of a particle ontology in general spacetimes unfeasible. I conclude by proposing a provisional pluralistic ontology of fields and spacetime and discussing some prospects for possible future ontological economies. [ABSTRACT FROM AUTHOR]

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market's complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector's significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning's pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder's effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry. [ABSTRACT FROM AUTHOR]

Introduction: Coastal erosion is widely distributed globally, with sandy coasts being particularly prominent, causing significant economic losses to coastal areas. This study focuses on the coastal areas of Liaoning Province, China, which represent typical sandy beach erosion. Methods: Based on the assessment of coastal erosion disaster losses, a multivariate variable-weight combination prediction model is utilized to predict the losses caused by coastal erosion disasters, providing an effective mathematical modeling approach for predicting such losses. Furthermore, the cluster analysis method is employed to delineate vulnerability risk zones for coastal erosion along the Liaoning Province coast. Results and discussion: The research findings indicate the following: (1) From 2023 to 2025, the losses due to coastal erosion disasters in Liaoning Province are projected to decrease significantly froma scale of 36 million RMB yuan to 5million RMB yuan; (2) Based on themagnitude of disaster vulnerability risk, the coastal areas of Liaoning Province are categorized into four risk zones: high-risk zones (Jinzhou District), moderately high-risk zones (Zhuanghe City, Wafangdian City, Ganjingzi District), medium-risk zones (Lvshunkou District, Bayuquan), and low-risk zones (Gaizhou City, Suizhong County, Xingcheng City). Recommendations are proposed accordingly: (1) Effective measures should be implemented to mitigate the losses incurred by coastal erosion on land, buildings, infrastructure, agricultural output; (2) In the selection of disaster prevention and control measures, economic factors should be carefully considered in line with the specific circumstances of the losses. Specifically, the economic efficiency of disaster prevention andmitigationmeasures should be taken into account; (3) Precise efforts should be made to implement coastal erosion disaster prevention and control measures in different regions. [ABSTRACT FROM AUTHOR]

A growing number of industries have started to adapt to the circular economy since the concept's introduction. Therefore, in order to accurately evaluate the development level of circular economy, the circular economy prediction model based on support vector machine-Gaussian K-nearest neighbor is proposed. This model first uses the improved K-nearest neighbor algorithm based on Gaussian function to classify the index data of various levels, and then uses Support Vector Machine to make predictions based on relevant data. According to the experimental findings, the model's average prediction accuracy for each level of indicator was approximately 98.1%, 98.8%, 94.9%, and 95.9% for the levels of industrial development, resource consumption, ecological protection, and resource recycling and reuse, respectively. This prediction accuracy was higher than that of the multi-vector autoregressive model and the grey prediction model. The average prediction accuracy of the multi-vector autoregressive model, the grey prediction model, and the support vector machine-Gaussian K-nearest neighbor-based model in predicting the overall development level of the circular economy were about 94.3%, 96.2%, and 99.3%, respectively, with average recalls of about 86.6%, 87.7%, and 89.1%, and the average F1-measure was about 0.88, 0.89, and 0.92. Moreover, the average relative error based on the support vector machine-Gaussian K-nearest neighbour model was only approximately 0.6%, which was lower than the 3.7% and 2.8% for the multi-vector autoregressive model and the grey prediction model, respectively. Meanwhile, compared with the existing time series analysis techniques, the proposed SVM-Gaussian K nearest neighbor model fitted up to 0.95, which achieved good prediction performance. According to the above data, the support vector machine-Gaussian K-nearest neighbour model has the highest accuracy in predicting the amount of development of the circular economy. [ABSTRACT FROM AUTHOR]

This article provides an overview of labor force and macroeconomic projections from 2023 to 2033 based on data from the U.S. Bureau of Labor Statistics. It highlights that demographic trends, such as the aging population, will result in slower labor force and employment growth during this period. The article also mentions that the decline in the labor force participation rate is influenced by the aging population and a stalling participation rate for prime-age workers. As a result, GDP growth is projected to slow as well. The article provides data and references for further research on the topic. [Extracted from the article]

An econometric model was developed to analyze the contribution of various factors to the gross value added to the agricultural, manufacturing, and service sectors in the United States. The study found that variables such as rare earth element mining, the employment it generates, the domestic consumption, imports, and prices of certain elements significantly impact economic sectors. The models showed a good fit and met the necessary statistical assumptions. Rare earth elements are essential for a wide range of technological products, with China being the leading producer and consumer. This has raised concerns about the dependence on other countries. These elements significantly impact the economy's primary, secondary, and tertiary sectors used in agriculture, manufacturing, and services. Rare earth elements' mining and processing are complex and expensive processes, and demand is expected to continue to increase in the future. [ABSTRACT FROM AUTHOR]

This study presents a technology used for the prediction of economic losses to facilities in a given area during an earthquake, thereby enabling the efficient application of performance-based maintenance and seismic strengthening. We also propose an algorithm for the establishment of a reinforcement plan that minimizes earthquake-induced economic losses within a constrained budget. The algorithm incorporates fragility functions from prior research and utilizes an optimization technique for budget allocation, leveraging the target damage ratio concept and constrained optimization. Based on the fragility curve, the probability of occurrence for each damage state for a specific PGA value and the damage rate for each damage state are calculated. From these values, the expected damage ratio (EDR) is estimated. An optimization-based budget allocation algorithm is developed to find the elements that would result in the lowest damage rate for a limited cost. To validate the applicability of the model, we created a hypothetical city with a 30 km × 30 km area containing bridges, embankments, and buildings. The estimated pre- and post-reinforcement damage was assessed in two earthquake scenarios, allowing us to test the effectiveness of the optimization-based budget allocation model in reducing damage. These results suggest that the proposed model offers a viable strategy for efficient seismic strengthening within budgetary constraints. [ABSTRACT FROM AUTHOR]

The concept of the circular economy represents the most relevant mainstream approach to reducing the negative environmental impact of waste. Anaerobic digestion has proved to be one of the leading and widely adopted techniques for sewage sludge treatment under the principles of the circular economy. The purpose of this study is to forecast environmental and economic indicators through modeling the extensive utilization of biogas technologies with a case study of an administrative territorial unit. The proposed methodological framework involves the use of averaged specific indicators and is based on the relationship between inhabitants, waste generation rates, biogas yield, greenhouse gas emission mitigation and biogas energy potential. The widespread use of anaerobic digestion techniques according to the proposed methodology in the instant scenario will ensure the biogas yield of 10 million Nm3 within the considered administrative territory unit with a population of 4.2 million P.E., which ultimately can be expressed in electricity and thermal generation potential of 20.8 and 24.8 million kWh*y, respectively, annual greenhouse gas elimination of 119.6 thousand tons of CO2 equivalent and capital investment attraction of EUR 65.18 million. Furthermore, all sewage sludge will be subjected to disinfection and stabilization procedures to ensure its safe utilization. The findings of this study offer an opportunity for a wide range of stakeholders to assess the environmental and economic benefits of the widespread adoption of biogas technologies. The developed methodology can be utilized to inform management decisions through the use of the instant and scenario forecasts. [ABSTRACT FROM AUTHOR]

Intelligent forecasting of economic indexes has been an important demand for sustainable management of smart cities. Existing methods for this purpose were mostly established upon the basis of economic mechanism. Econometric models are the most general technical means in this area. However, in era of digital economy, increasing amount of big data has brought great change to traditional production. It is becoming more difficult for conventional technological forecasting methods to deal with multi-dimensional economic indexes. To deal with such challenge, this paper introduces the artificial intelligence algorithms to implement automatic information processing, and proposes a deep neural network-based intelligent forecasting method for multi-dimensional economic indexes in smart cities. Specifically, a deep neural network with three-layer structure is developed as the backbone methodology. For empirical analysis, the real-world data from "Chengdu–Chongqing Economic Circle" in China from 2012 to 2022 are selected as the main simulation scenario. Four major indexes are selected as the main research object: gross product (GDP), per capita GDP, GDP growth rate and the proportion of tertiary industry in GDP. The experimental results show that the proposal can well deal with such forecasting problem from a data-driven perspective, with a proper forecasting effect on historical data. [ABSTRACT FROM AUTHOR]

This study delves into the global evolution of 43 Sustainable Development Goals (SDG) indicators, spanning 7 major health themes across 185 countries to evaluate the potential progress loss due to the COVID-19 pandemic. Both the cross-country and temporal variability of the dataset are employed to estimate an empirical model based on an extended version of the Preston curve, which links well-being to income levels and other key socioeconomic health determinants. The approach reveals significant global evolution trends operating in each SDG indicator assessed. We extrapolate the model yearly between 2020 and 2030 using the IMF's pre-COVID-19 economic growth projections to show how each country in the dataset are expected to evolve in these health topics throughout the decade, assuming no other external shocks. The results of this baseline scenario are contrasted with a post-COVID-19 scenario, where most of the pandemic costs were already known. The study reveals that economic growth losses are, on average, estimated as 42% and 28% for low- and lower middle-income countries, and of 15% and 7% in high- and upper middle-income countries, respectively, according to the IMF's projections. These disproportional figures are shown to exacerbate global health inequalities revealed by the curves. The expected progress loss in infectious diseases in low-income countries, for instance, is an average of 34%, against a mean of 6% in high-income countries. The theme of Infectious diseases is followed by injuries and violence; maternal and reproductive health; health systems coverage; and neonatal and infant health as those with worse performance. Low-income countries can expect an average progress loss of 16% across all health indicators assessed, whereas in high-income countries the estimated loss is as low as 3%. The disparity across countries is even more pronounced, with cases where the estimated progress loss is as high as nine times worse than the average loss of 8%. Conversely, countries with greater fiscal capacity are likely to fare much better under the circumstances, despite their worse death count, in many cases. Overall, these findings support the critical importance of integrating the fight against inequalities into the global development agendas. [ABSTRACT FROM AUTHOR]

Using a two-step approach GMM, this study examines the short- and long-term effects of fiscal deficit on the economic growth of 42 Sub-Saharan African nations between 2011 and 2021. The World Development Index, the most reliable source, is where the panel data is taken from. Using the Levin-Lin-Chu and Hadri LM tests for unit root, it was determined that there is no risk of a random walk in the data. The study's findings indicate that while the fiscal deficit has short-term, positive, and significant benefits on the economic growth of SSA countries, it has long-term, negative repercussions. According to the system GMM's results, an increase in the fiscal deficit of SSA countries is linked to a short-term increase in economic growth of 0.036 percent, while an increase in the fiscal deficit of one percentage point is linked to a long-term decline in economic growth of SSA countries of 0.013 percent, holding all other factors constant. The study's findings also showed that the budget deficit has a larger positive short-run coefficient than a negative long-run coefficient. The study also revealed that while real effective exchange rates and inflation short-term hinder economic growth, gross fixed capital creation and real interest rates are the primary drivers of economic expansion. Long-term economic growth in the SSA countries is also found to be positively and significantly impacted by gross fixed capital formation. According to the study, SSA nations should manage their fiscal deficits and, in the long run, provide more funds for gross fixed capital development. [ABSTRACT FROM AUTHOR]

Residential areas in developing arid and semi-arid countries are highly vulnerable to flooding, and water shortages have forced their inhabitants to live close to rivers. While climate change is expected to cause more extreme weather conditions in the future, adopting private loss-reduction measures can diminish flood risk. Although the number of flood models has grown significantly for developing arid and semi-arid lands, these models suffer from being incapable of performing micro-scale flood risk analysis and including household behaviors. This research work presents a novel socio-economic simulation model in the framework of flood risk management (FRM) to couple household adaptive responses with flood risk analysis. Agent-based modeling (ABM) embeds human behaviors in a flood-simulating environment. The focus of this research is the upper Kan catchment in Iran with a long history of severe flash flooding. Our results show the ability of the developed framework to address household participation in FRM activities through private precautionary measures. Moreover, the results indicate the importance of presenting such micro-level behaviors in flood modeling for a more realistic flood risk assessment. It is also demonstrated that household adaptation in a continuous way can lead to less flood risks in the region, even under climate change and the future economy. Finally, the results reveal that the remaining and diminished regional flood risks are influenced by the behavioral framework through which the individuals make decisions in adopting flood-loss-reduction measures. A similar pattern is, however, observed in household contributions over time among the discussed behavioral approaches. [ABSTRACT FROM AUTHOR]