Do OPEC+ policies help predict the oil price: A novel news-based predictor.

The OPEC+, composed of the Organization of the Petroleum Exporting Countries (OPEC) and non-OPEC oil-producing countries, exerts considerable influence over the global crude oil market. However, existing literature lacks a comprehensive application of this factor in oil price forecasting, primarily due to the complexity of measuring such policy evolutions. To address this research gap, this study develops a news-based OPEC+ policy index based on text mining methods for comprehensive analysis and forecasting of the oil price. First, by crawling and mining news headlines related to OPEC+ production decisions, a dynamic and high-frequency (weekly) OPEC+ policy index is established. Second, the linear and nonlinear relationship between the proposed OPEC+ policy index and the WTI crude oil futures price is thoroughly examined, assessing the potential predictive power of the index in explaining the movements of the crude oil price. Third, the forecasting efficacy of the constructed index on the oil price is rigorously evaluated across eight econometric and machine learning models. Key findings include: (1) The proposed weekly OPEC+ policy index demonstrates strong concordance with OPEC+ production change decisions, exhibiting notable peaks and troughs corresponding to OPEC+ Ministerial Meetings. (2) The relationship analysis demonstrates a strong linear and nonlinear association between the proposed OPEC+ policy index and the crude oil price. (3) For oil price prediction, models incorporating our proposed OPEC+ policy index demonstrate superior performance compared to models without this index. In particular, the index exhibits a more significant predictive effect within three-week forecasting horizons and performs exceptionally well during periods of pandemic and the Russia-Ukraine conflict. In addition, the OPEC+ policy index also exhibits a significant predictive effect on the daily crude oil price and natural gas price, further confirming the robust and powerful forecasting capability of this index within the energy system.

Quantifying US air pollution policy: How political and regional factors influence pollutant mitigation.

Air pollution control in the United States has evolved into a comprehensive policy system spanning from the federal to the state level over time. A unified quantitative analysis of policy intensity can shed light on the policy evolution across different levels, the influence of partisan and regional factors on policy, and the relationships with emissions of major pollutants. By harnessing the policy text of the Clean Air Act (CAA) at the federal level and State Implementation Plans (SIPs) at the state governments (1955-2020), we deployed a Natural Language Processing approach to define a policy intensity index to systematically quantify the US air policy landscape. Our findings highlight that the 1970 CAA amendment carries the most vigorous intensity as it established a holistic control system for the first time. Subsequent years witnessed a general trend of partisan polarization, eventually leading to a graduate convergence between red and blue states. Blue states demonstrated a closer alignment with federal directives and a superior efficacy in pollutant reduction. Regionally, the Northeast displays the highest overall policy intensity, and the West exhibits the highest coordination with the federal benchmarks, making these regions outperform others in air pollution control. Our study not only discusses policy implications for air pollutant reductions considering partisan and regional differences but also provides a novel measurement tool to quantify policies for assessing disparities and synergies.

AI-Based Optimal Treatment Strategy Selection for Female Infertility for First and Subsequent IVF-ET Cycles.

Over the last 20 years, China's infertility rate has risen from 3% to 12.5%-15%. Infertility has become the third largest disease following cancer and cardiovascular disease. Then, the in vitro fertilization and embryo transfer (IVF-ET) becomes more and more important in infertility treatment field. However, the reported success rate for IVT-ET is 30%-40% and costs are gradually rising. Meanwhile, to increase success rates and decrease costs, the optimal selection of the IVF-ET treatment strategy is crucial. In a clinical work, the IVF-ET treatment strategy selection is always based on the experience of the doctor without a uniform standard. To solve this important and complex problem, we proposed an artificial intelligence (AI)-based optimal treatment strategy selection system to extract implicit knowledge from clinical data for new and returning patients, by mimicking the IVF-ET process and analysing a myriad of treatment decisions. We demonstrated that the performance of the model was different in 10 AI classification algorithms. Hence, we need to select the optimal method for predicting patient pregnancy result in different IVF-ET treatment strategies. Moreover, feature ranking is determined in the proposed model to measure the importance of each patient characteristics. Therefore, better advice can be provided for individual patient characteristics, doctors can provide more valid suggestions regarding certain patient characteristics to improve the accuracy of diagnosis and efficiency.

Dependences and risk spillover effects between Bitcoin, crude oil and other traditional financial markets during the COVID-19 outbreak.

This study examines the relationship and risk spillover between Bitcoin, crude oil, and six traditional markets (the US stock, Chinese stock, gold, bond, currency, and real estate markets) from 2019 to 2020, during which the coronavirus disease 2019 (COVID-19) outbreak occurred as well. We first discuss the static relationship between Bitcoin and these markets using a quantile-on-quantile model and examine the dynamic relationship using a time-varying copula model. A conditional value-at-risk model is subsequently used to estimate the risk spillover between the markets studied. The empirical results reveal that the relationship between these markets is always time-varying, and the COVID-19 outbreak has revealed such changes in the relationship between Bitcoin and other traditional financial markets. The risk of all single markets has enhanced because of the pandemic. Further, the risk spillover of these markets has also changed dramatically since the COVID-19 outbreak during which the Bitcoin market has played an important role and exerted a significant impact on the crude oil market, and the four other markets (US stock, gold, Chinese stock, and real estate markets). Overall, our findings indicate that investors and policymakers need to be made aware of the risk spillover between Bitcoin, crude oil, and other traditional markets and that flexible hedge strategies and policies should be implemented in response to the challenges and economic recession observed following the COVID-19 outbreak.

Quantifying the spatiotemporal evolution characteristics of medical waste generation during the outbreak of public health emergencies.

Based on the medical waste quantity and patient data during the corona virus disease 2019 (COVID-19) outbreak in China, this study used scenario analysis to quantitatively analyze the temporal and spatial evolution of medical waste generation during the pandemics. First, the results show that the estimated medical waste per capita reached 15.4 kg/day if only patients were considered in Scenario 1, while the figures were reduced to 3.2 kg/day in Scenario 2 and 2.5 kg/day in Scenario 3 when the effects of both the patient type and the number of medical staffs were considered. The estimated results also demonstrated that the per capita medical waste related to the epidemic showed the characteristics of a U-shaped and trailing phenomenon over time. Then, the amount of medical waste related to the COVID-19 generated that generated due to COVID-19 was estimated in Hubei, Heilongjiang, Zhejiang, Henan and Hunan provinces under Scenario 2 and Scenario 3. The results indicated that the spatiotemporal evolution characteristics of five provinces show the significant differences, and the patient type has a remarkable influence on the generation of medical waste. Finally, a novel decomposition-ensemble approach was designed to make a better short-term forecasting effect for future medical waste generation in different provinces. Supplementary Information: The online version contains supplementary material available at 10.1007/s10163-022-01523-5.

Multi-data source-based recycling value estimation of wasted domestic electrical storage water heater in China.

A domestic electrical storage water heater (i.e., DESWH) is one of the 14 products listed in China's Waste Electrical and Electronic Products Disposal Catalogue (Batch 2). Due to the lack of systematic quantitative analysis on the waste quantity and recovery value of a DESWH, a multi-data source-based hybrid methodology based on quarterly sales data, survey data and internet data is proposed. In the methodology, the seasonal Mann-Kendall trend test is used to identify the seasonal trait of the quarterly sales data for DESWHs, and an accurate prediction of the sales volume of DESWHs is obtained via a generalised seasonal grey model with dynamic seasonal adjustment factors. Then the lifespan distribution of DESWHs is fitted based on the survey data, and the quantity of wasted DESWHs is estimated from 2012Q1 to 2038Q4. Finally, on the basis of the data crawled from the internet, the weight distribution of DESWHs is constructed, and the recycling value of wasted DESWHs is then calculated. The empirical results show that the waste quantity of DESWHs will increase greatly from 2012Q1 to 2022Q4 and that the recycling value of wasted DESWHs may be worth 18.48 billion yuan in China. The results show that the wasted DESWHs have a great recycling value, and that the proposed multi-data source based hybrid methodology can be used as an effective estimation method for the recycling value of electronic waste.

In Vitro Efficacy of Flomoxef against Extended-Spectrum Beta-Lactamase-Producing Escherichia coli and Klebsiella pneumoniae Associated with Urinary Tract Infections in Malaysia.

The increasing prevalence of extended-spectrum ß-lactamase (ESBL)-producing Enterobacteriaceae has greatly affected the clinical efficacy of ß-lactam antibiotics in the management of urinary tract infections (UTIs). The limited treatment options have resulted in the increased use of carbapenem. However, flomoxef could be a potential carbapenem-sparing strategy for UTIs caused by ESBL-producers. Here, we compared the in vitro susceptibility of UTI-associated ESBL-producers to flomoxef and established ß-lactam antibiotics. Fifty Escherichia coli and Klebsiella pneumoniae strains isolated from urine samples were subjected to broth microdilution assay, and the presence of ESBL genes was detected by polymerase chain reactions. High rates of resistance to amoxicillin-clavulanate (76-80%), ticarcillin-clavulanate (58-76%), and piperacillin-tazobactam (48-50%) were observed, indicated by high minimum inhibitory concentration (MIC) values (32 µg/mL to 128 µg/mL) for both species. The ESBL genes blaCTX-M and blaTEM were detected in both E. coli (58% and 54%, respectively) and K. pneumoniae (88% and 74%, respectively), whereas blaSHV was found only in K. pneumoniae (94%). Carbapenems remained as the most effective antibiotics against ESBL-producing E. coli and K. pneumoniae associated with UTIs, followed by flomoxef and cephamycins. In conclusion, flomoxef may be a potential alternative to carbapenem for UTIs caused by ESBL-producers in Malaysia.

Forecasting the electronic waste quantity with a decomposition-ensemble approach.

Waste electrical and electronic equipment (viz., WEEE or e-waste) is the fastest-growing type of hazardous solid waste in the worldwide. The accurate prediction of the amount of e-waste might help improve the efficiency of e-waste disposal. In this study, a novel decomposition-ensemble-based hybrid forecasting methodology that integrates variational mode decomposition (VMD), exponential smoothing model (ESM), and grey modeling (GM) methods (named VMD-ESM-GM) is proposed for e-waste quantity prediction. For verification purposes, sample data from Washington State, US, and UK Environment Agency are analyzed. Compared to benchmark models, the proposed VMD-ESM-GM methodology not only obtains a satisfactory prediction result for e-waste data but also predicts the future fluctuation trend of e-waste. These results indicate that the proposed VMD-ESM-GM methodology based on the decomposition-ensemble principle is a suitable model for the prediction of the e-waste quantity and could help decision-makers develop both e-waste recycling plans and circular economy plans.

Diagnostic approaches and contribution of next-generation sequencing technologies in genomic investigation of Vibrio parahaemolyticus that caused acute hepatopancreatic necrosis disease (AHPND).

A unique strain of Vibrio parahaemolyticus (designated as VPAHPND) causes acute hepatopancreatic necrosis disease (AHPND), a deadly bacterial disease associated with mass mortality in cultured shrimps since 2009. AHPND is responsible for severe economic losses worldwide, causing multimillion-dollar loss annually. Because of the rapid and high mortality rates in shrimps, substantial research has been carried out to develop rapid detection techniques. Also, recent technological advances such as the next-generation sequencing (NGS) have made it possible to elucidate relevant information about a pathogen in a single assay. This review summarizes the current research pertaining to VPAHPND, focusing on diagnosis and contribution of NGS technologies in the genomic studies of AHPND.

Response of resources and environment carrying capacity under the evolution of land use structure in Chongqing Section of the Three Gorges Reservoir Area.

In this study, a comprehensive model for suitable carrying capacity of resources and environment was proposed based on ecological footprint method. Using the spatiotemporal distribution data of land use in Chongqing Section of Three Gorges Reservoir Area from 2001 to 2016, the response changes of carrying capacity of resources and environment under the evolution of land use structure were investigated. The analytical results showed that the suitable carrying capacity of resources and environment in Chongqing decreased first and then increased. In the early stage of the Three Gorges Project, some districts and counties exhibited the phenomenon of suitable carrying capacity deficit, especially in the northeast of Chongqing. In the main urban area of Chongqing, the suitable carrying capacity was also mainly restricted by the ecological resources conditions, the deficit was getting worse with the increase of population density. In the later stage, by restoring ecology and improving the living and economic conditions, the phenomenon of deficit was gradually alleviated. These findings will provide some references for the protection of ecological environment and the development of social economy in Chongqing Section of the Three Gorges Reservoir Area.

Comparative genomic provides insight into the virulence and genetic diversity of Vibrio parahaemolyticus associated with shrimp acute hepatopancreatic necrosis disease.

Acute hepatopancreatic necrosis disease (AHPND) is an important shrimp disease of economic importance which causes mass mortality of cultivated penaeid shrimps in Southeast Asian countries, Mexico and South America. This disease was originally caused by Vibrio parahaemolyticus (VPAHPND) which is reported to harbour a transferable plasmid carrying the virulent PirAB-like toxin genes (pirABvp). However, little is known about the pathogenicity of VPAHPND. To extend our understanding, comparative genomic analyses was performed in this study to identify the genetic differences and to understand the phylogenetic relationship of VPAHPND strains. Seven Vibrio parahaemolyticus strains (five VPAHPND strains and two non-VPAHPND strains) were sequenced and 31 draft genomes of V. parahaemolyticus were retrieved from NCBI database and incorporated into the genomic comparison to elucidate their genomic diversity. The study showed that the genome sizes of the VPAHPND strains were approximately 5 Mbp. Ten sequence types (STs) were identified among the VPAHPND strains using in silico-Multilocus Sequence Typing analysis (MLST) and ST 970 was the predominant ST. Phylogenetic analysis based on MLST and single nucleotide polymorphisms (SNP) showed that the VPAHPND strains were genetically diverse. Based on the comparative genomic analysis, several functional proteins were identified from diiferent categories associated with virulence-related proteins, secretory proteins, conserved domain proteins, transporter proteins, and phage proteins. The CRISPR analysis showed that VPAHPND strains contained less number of CRISPRs elements than non-VPAHPND strains while six prophages regions were identified in the genomes, suggested the lack of CRISPR might promote prophage insertion. The genomic information in this study provide improved understanding of the virulence of these VPAHPND strains.

Privacy Preservation in Distributed Subgradient Optimization Algorithms.

In this paper, some privacy-preserving features for distributed subgradient optimization algorithms are considered. Most of the existing distributed algorithms focus mainly on the algorithm design and convergence analysis, but not the protection of agents' privacy. Privacy is becoming an increasingly important issue in applications involving sensitive information. In this paper, we first show that the distributed subgradient synchronous homogeneous-stepsize algorithm is not privacy preserving in the sense that the malicious agent can asymptotically discover other agents' subgradients by transmitting untrue estimates to its neighbors. Then a distributed subgradient asynchronous heterogeneous-stepsize projection algorithm is proposed and accordingly its convergence and optimality is established. In contrast to the synchronous homogeneous-stepsize algorithm, in the new algorithm agents make their optimization updates asynchronously with heterogeneous stepsizes. The introduced two mechanisms of projection operation and asynchronous heterogeneous-stepsize optimization can guarantee that agents' privacy can be effectively protected.