How Does Slack Influence Inflation?

Comprehensive assessment of sustainable utilization of regional water resources, including three subsystems: social economy, water resources and eco-environment, is a large complicated and systematic evaluating problem. The selection of the index system for the regional sustainable development of water resources was discussed herein. Furthermore, a neural network methodology for regional water resources evaluation was introduced. The method adopts the radial basis function (RBF) architecture and dynamically penalized rival competitive learning algorithm, which is fast and repetitive, compared with most traditional techniques. Additionally, a new approach to produce training samples, testing samples and examining samples randomly distributed between the critical values was established. The sample used consists of 16 indexes, and by using the proposed methodology the sustainable evaluation of regional water resources could be successfully done. Results showed that from 1994 to 2005, the comprehensive index of water resources sustainable utilization in Zhengzhou city ascended with a fast speed of 0.023 per year, from sustainable development level IV to level III. The calculation with the model showed that, the comprehensive index of water resources sustainable utilization in the future planning years (2010, 2015, and 2020) could be enhanced with a speed of 0.011 per year. A sensitive analysis indicates that it is necessary to increase eco-environmental water consumption rate, industrial output from each cubic meter of water consumption, public greenbelt area per capita, foodstuff output from each cubic meter of water consumption, and to ensure that natural growth rate of population decreases steadily, which are playing an important role in improving sustainable utilization of water resources in Zhengzhou city.

This paper presents a two-regime vector error-correction model (VECM) with a single cointegrating vector and a threshold effect in the error-correction term. We use a Hansen-Seo (2002) algorithm to extract maximum likelihood estimates in eight threshold cointegration model s that relate short-term to long-term interest rates in South Africa for the period 1990M1-2010M7. We employ a SupLM test to test for the presence of threshold. The Hansen-Seo algorithm yields both linear and non-linear estimates plus critical values used to test threshold effects. The method is applied by relating the South Africa Reserve Bank policy rate, the repo (short-term) to intermediate (TB rate, money market rate) and long-term rates (the 10-year government bond, the loan and deposit rates). In all cases, linear cointegration is rejected in favor of a threshold effect.

This study provides a candid, systematic, and critical review of recent evidence on this complex subject. Based on a review of the literature and some new empirical evidence, it finds that (1) in spite of an apparently strong theoretical presumption, it is difficult to detect a strong and robust causal relationship between financial integration and economic growth; (2) contrary to theoretical predictions, financial integration appears to be associated with increases in consumption volatility (both in absolute terms and relative to income volatility) in many developing countries; and (3) there appear to be threshold effects in both of these relationships, which may be related to absorptive capacity. Some recent evidence suggests that sound macroeconomic frameworks and, in particular, good governance are both quantitatively and qualitatively important in affecting developing countries’ experiences with financial globalization.

Under limited technological resources, optimising financing channel to R&D investment has important theoretical and practical implications. This paper analyzed the threshold effects of internal financing channels and external financing channels on the R&D investment using 692 manufacturing listed companies’ data from 2008 to 2015. And five threshold factors including the debt level, profit level, operational capacity, company’s size, and bank loan ratio were used to estimate the threshold effects of different financing channels. The results show that internal financing has a positive threshold effect on R&D investment when the debt level is lower than the threshold value; equity financing is negatively related to R&D investment; debt financing has a significant negative effect on the R&D investment under certain threshold levels; government subsidies promote the R&D investment extremely for the companies with certain size and scale effect. Besides, this paper proposed corresponding suggestions for different threshold values and financing channels.

A procedure is described by means of which the time interval during which a given temporary threshold shift produced by a high level traumatizing noise and the time required for the threshold to recover to near normal threshold values can be determined with useful accuracy. Initially, the subject's threshold values are ascertained for the noise and for the “test” tone. The levels of the two sounds are then raised to the desired intensity levels, e.g., the noise to 110 db and the test tone to 40 db above their respective threshold values. The two sounds are then presented alternately, the noise for ten seconds, the test tone for one second, this cycle being repeated until the subject reports inaudibility of the test tone. By instructing the subject to depress “yes” or “no” switches when the test tone is or is not heard and by connecting the switches to an appropriate recording mechanism, the time (in terms of ten-second intervals) over which 10-, 20-, 30-, and 40-db threshold shifts result are readily obtained. Also, by discontinuing the traumatic tone and attenuating the test tone (now presented continuously) in, e.g., 10-db steps whenever the subject responds with the “yes” button, the acceleration of the recovery curve is obtained. An almost infinite number of parameters result from varying the duration, intensity level, and frequency composition of the noise and the test tone. The procedure, with reasonable care, eliminates the possibility of producing permanent traumatic damage to the ear. Experimental results reveal marked individual differences in the length of time over which a given temporary threshold shift for the tone occurs. Curves of experimental data will be presented. It is anticipated that this procedure will constitute a significant noise susceptibility test by means of which “tender” ears can be differentiated from “tough” ears.

We analyse the nonlinear behaviour of the information content in the spread for future real economic activity. The spread linearly predicts one‐year‐ahead real growth in nine industrial production sectors of the USA and four of the UK over the last 40 years. However, recent investigations on the spread–real activity relation have questioned both its linear nature and its time‐invariant framework. Our in‐sample empirical evidence suggests that the spread–real activity relationship exhibits asymmetries that allow for different predictive power of the spread when past spread values were above or below some threshold value. We then measure the out‐of‐sample forecast performance of the nonlinear model using predictive accuracy tests. The results show that significant improvement in forecasting accuracy, at least for one‐step‐ahead forecasts, can be obtained over the linear model. Copyright © 2004 John Wiley & Sons, Ltd.

Effects of threshold on single-target detection by using modified amplitude-modulated joint transform correlator

The purpose of the paper is to explore how the perceived relationship quality is related to the innovation capacity in chains of the traditional food sector. Based on suggestions from theory and previous studies, empirical evidence is drawn from a survey of 90 traditional food chains including 270 chain partners from three European countries in four traditional food product categories. Heterogeneity across these chains is first examined based on cluster analysis that identifies three distinct clusters interpreted as reflecting three levels of intensity in innovation capacity: high, medium, and low. Next, we define measures of the chain relationship quality through characteristics such as trust, conflict and reputation. Results suggest that various aspects of chain relationship quality and relationship directions are differently important for the innovation capacity levels in traditional food chains. In particular the perception of the relationship quality between the food manufacturer and its supplier (and vice versa) is explored to be important. The better this relationship is perceived by one chain partner, the higher is the innovation capacity of the whole chain. Thus, our results strengthen the emerging conclusion that firms benefit from participating in networks but depend on its partner’s choices and perceptions. In future research, it should be explored how different national and cultural environments facilitate or hamper the innovation capacity in traditional food chains. It is also suggested to extend the complexity of the investigated system and to apply our novel approach to other food sectors, than the traditional food sector, in order to improve its generalizability.

Arid and semiarid areas generally face problems of natural resource degradation. In this paper, two indicators are proposed to characterize the utilization of rainwater: rainwater resource utilization quantity (RRU, mm) and rainwater resource utilization rate (RRUR, dimensionless ratio). A spatiotemporal assessment model (rainwater utilization assessment [RUA] model) for the RRUR and its influencing factors is constructed. The RUA model is based on Soil and Water Assessment Tool and Geographically Temporal Weighted Regression. The Zuli River Basin, located on the Loess Plateau in China, was selected as the study basin. The results show that the annual averages of the RRU and RRUR are 164 mm and 0.44, respectively, in the study basin. The RRU ranged from 71.4 to 243.2 mm, and the RRUR ranged from 0.32 to 0.53 during the evaluation period. Eight explanatory variables were selected, of which the leaf area index (LAI) and rainfall had a significant positive effect on RRUR, but there was spatial and temporal variability in the effects. The forest rehabilitation and crop rotations that can increase the LAI can effectively increase the RRUR, which increased by 0.10 and 0.11, respectively, and the increase in RRU was at least 36.42 mm, with a water volume of 3.85 × 108 m3. The impact of grassland rehabilitation and parallel terracing on the RRUR was small. We found that there is an interval effect and a threshold effect on rainfall utilization in the basin. The interval effect causes the RRUR to fluctuate within a certain range for rainfall intervals of 300–400 or 400–500 mm (Zuli River Basin). Threshold effects can guide the determination of the potential for rainwater utilization in different areas to ensure that the RRUR can be effectively enhanced with management measures.Recommendations for Resource Managers We have constructed an assessment model of rainwater resource utilization (RRU) and influencing factors. We have proposed two evaluation indices based on water balance, namely, the RRU quantity and rainwater resource utilization rate (RRUR). The leaf area index of vegetation is the most important influence on rainwater utilization, and the more severe the drought is, the more effective the regulation of water cycle processes by vegetation. The interval effect causes uncertainty in the RRUR for a certain interval of rainfall, with the RRUR fluctuating within a defined range. The threshold effect can guide us in determining the potential for rainwater utilization, and areas where the current RRUR is below the threshold can take management measures to ensure that the RRUR can be effectively increased.

In an era focused on value-based healthcare, the quality of healthcare and resource allocation should be underpinned by empirical evidence. Pragmatic clinical trials (pRCTs) are essential in this endeavor, providing randomized controlled trial (RCT) insights that encapsulate real-world effects of interventions. The rising popularity of pRCTs can be attributed to their ability to mirror real-world practices, accommodate larger sample sizes, and provide cost advantages over traditional RCTs. By harmonizing efficacy with effectiveness, pRCTs assist decision-makers in prioritizing interventions that have a substantial public health impact and align with the tenets of value-based health care. An international network for pRCT provides several advantages, including larger and diverse patient populations, access to a broader range of healthcare settings, sharing knowledge and expertise, and overcoming ethical and regulatory barriers. The hypothesis and study design of pRCT answers the decision-maker’s questions. pRCT compares clinically relevant alternative interventions, recruits participants from diverse practice settings, and collects data on various health outcomes. They are scarce because the medical products industry typically does not fund pRCT. Prioritizing these studies by expanding the infrastructure to conduct clinical research within the healthcare delivery system and increasing public and private funding for these studies will be necessary to facilitate pRCTs. These changes require more clinical and health policy decision-makers in clinical research priority setting, infrastructure development, and funding. This paper presents a comprehensive overview of pRCTs, emphasizing their importance in evidence-based medicine and the advantages of an international collaborative network for their execution. It details the development of PRIME-9, an international initiative across nine countries to advance pRCTs, and explores various statistical approaches for these trials. The paper underscores the need to overcome current challenges, such as funding limitations and infrastructural constraints, to leverage the full potential of pRCTs in optimizing healthcare quality and resource utilization.

This article comprehensively analyzes artificial intelligence (AI) implementation in modern manufacturing systems, focusing on smart factories and predictive maintenance applications. The article explores how AI-driven technologies transform traditional manufacturing processes through advanced data analytics, machine learning algorithms, and Internet of Things (IoT) integration. By examining the evolution of smart factory systems, this study investigates the role of predictive maintenance in reducing operational downtime and optimizing equipment performance. The article analysis encompasses real-time monitoring systems, automated quality control processes, and intelligent production scheduling, highlighting their collective impact on manufacturing efficiency. Through detailed case studies and empirical evidence, the article demonstrates how AI-powered solutions enhance decision-making capabilities, improve product quality, and streamline production workflows in manufacturing environments. The findings reveal significant improvements in operational efficiency, maintenance scheduling, and resource utilization across various manufacturing sectors. This article contributes to the growing knowledge of Industry 4.0 technologies and provides valuable insights for manufacturers seeking to implement AI-driven solutions in their operations. The article also addresses implementation challenges and offers strategic recommendations for successful AI integration in manufacturing processes, paving the way for future developments in smart manufacturing systems.

This comprehensive article examines performance optimization techniques for Presto, a distributed SQL query engine widely adopted for large-scale data analytics. Through systematic analysis of both theoretical frameworks and empirical evidence, we present a multifaceted approach to enhancing query performance and resource utilization. The article encompasses critical aspects including memory management strategies, parallel processing optimization, and storage connector configurations across diverse deployment scenarios. Our investigation reveals that strategic implementation of query planning algorithms, coupled with fine-tuned JVM configurations, can yield performance improvements of up to 40% in complex analytical workloads. The article also introduces a novel framework for workload-specific optimization patterns, validated through extensive testing across various data scales and query complexities. Through detailed case studies of large-scale deployments, we demonstrate how combined optimization techniques can significantly reduce query latency while maintaining system stability. Furthermore, we present empirical evidence supporting the effectiveness of adaptive resource allocation strategies in mixed-workload environments. These findings contribute to the growing body of knowledge in distributed query processing optimization and provide practical guidelines for organizations seeking to enhance their Presto deployments.

Cost and cost-effectiveness of three strategies for training clinicians in motivational interviewing

The Space Division Multiplexed-Elastic Optical Network (SDM-EON) has many parallel spatial channels, increasing the spectral capacity manifolds. The routing constraints in SDM-EON tend to leave the spectrum fragmented, leading to inefficient resource utilization. This work presents a Fragmentation Aware-resource provisioning technique to mitigate fragmentation effects while keeping the crosstalk-related impairments within thresh-old values. The proposed approach performs better than the benchmark technique in terms of connection blocking ratio and network resource utilization.

Purpose – The purpose of this study is to investigate nonlinearities in the behavior of investment expenditure. Conventional wisdom suggests that Tobin’s Q criterion is an important explanation of investment behaviour that bridges the financial and real sides of the economy. However, the empirical evidence in support of Q as a means of explaining aggregate business investment is rather weak. We answer a number of questions about the relationship between investment expenditure and Q. In particular, is the relationship governed by non-linearities? If so, what is the nature of the non-linearities present? Design/methodology/approach – The rationale for paying closer attention to non-linearities is based on the presence of information asymmetries and possible dependence of adjustments on non-linearities with respect to factors such as fixed costs, threshold effects and irreversibility, which are entertained in the investment literature. Using the non-linear vector error-correction model procedure advocated by Hansen and Seo, we show that in the context of the US economy, investment has a long-run relationship with Q that is based on threshold error correction. Findings – There are asymmetries present with respect to error correction or the speed of adjustment towards long-run equilibrium. We find that investment expenditure only responds significantly to long-run disequilibrium from Q during a particular regime. Such a regime is characterised by long-run disequilibrium based on high or rising investment expenditure compared with a relatively weak stock market. Originality/value – The authors provide new insights into the relationship between Tobin’s Q and real investment. In contrast to previous work, they find that error correction based on the adjustment of real investment is regime-specific and function of the size of departures from long-run equilibrium. The tests also allow for the identification of periods when error correction has occurred. Not only are these insights significant for future research on financial crises, market volatility and the impact of debt, but for policymaking purposes as well.