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Objectives. This study aimed to investigate 12-month and 7-day prevalence of musculoskeletal symptoms among workers of the pulp and paper industry, and assess the contribution of age, gender, body mass index (BMI) and physical activity (PA). Methods. The sample comprised 904 workers. Musculoskeletal symptoms were assessed using the Nordic musculoskeletal questionnaire. χ2 tests assessed the association between musculoskeletal symptoms and age/gender/BMI/PA, and multivariate logistic regression models predicted the 12-month occurrence of symptoms in the five most prevalent regions. Results. The five most affected body regions were the lower back, shoulders, neck, knees and wrists/hands. Older workers presented more symptoms in all body regions, except dorsal; female gender was associated with neck symptoms (p = 0.001); overweight was associated with a higher prevalence of lower back (p = 0.01), knee (p = 0.017) and ankle/foot (p = 0.037) symptoms; and a moderate PA level was associated with thigh/hip symptoms (p = 0.006). Age was a significant predictor in all five most affected regions; gender was a significant predictor for shoulders, neck and wrists/hands; and BMI was a significant predictor for lower back symptoms. Conclusion. The 12-month and 7-day prevalence of musculoskeletal symptoms among workers of the pulp and paper industry is higher in the lower back, shoulders, neck, knees and wrists/hands. [ABSTRACT FROM AUTHOR]

Drawing on language expectancy theory, we predict that linguistic errors in ICO white papers negatively impact investors' willingness to financially contribute to ICO projects. We manually annotate a sample of 546 ICO white papers according to 13 different error subcategories related to spelling and grammar. The error-annotated data are subsequently submitted to regression analyses which confirm that linguistic errors discourage potential investments in ICOs. Specifically, our analyses reveal the presence of 'high penalty' vs. 'low penalty' errors which result in higher vs. lower financial investment losses for the ICOs. The negative impact of language errors is stronger when ICO white papers are (1) written in native English-speaking countries and (2) from countries without cryptocurrency regulation. Results from an experiment confirm that this relationship is not driven by the entrepreneur- or investor-specific characteristics. Overall, we highlight that the reader identifies linguistic errors as a major 'red flag' that ultimately affects financial decision-making. [ABSTRACT FROM AUTHOR]

Power and distribution transformers reliability is limited by the electrical insulation system quality, which also operates additionally as a heat transfer medium. On the other hand, cellulose insulation decomposes due to overheating of the transformers causes reduction in their life time. Hence, it is very important to check the insulation levels and the oil insulation quality of the transformers. Several efforts have been made over the past years to study the electrical, physical, and chemical properties of the insulating oils. To manage the life of transformers, to reduce their failures, some measurements are being adopted. In this article, new diagnostic and monitoring technique using Polynomial regression modeling has been developed for these goals. This article contains study done on ten power transformers, which are in operation in the Egyptian networks. The transformers oils are monitored in this study for periods up to 10 years. The results of the used Polynomial regression modeling technique prove a good agreement with the experimental oil characteristic. In this article also, the remaining service life of the transformer is estimated by using the degree of polymerization of paper insulation. [ABSTRACT FROM AUTHOR]

Copyright of Ecoscience (Ecoscience) is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The compression strength of a corrugated board box is a direct measure of its stacking strength. The edgewise compression strength of corrugated board is the major contributor to the box stacking strength. This relation can be further extended to the critical strength properties of paper substrates. It was, therefore, the aim of this study to establish predictive mathematical correlations between corrugated board and paper compression properties after exposure to certain climate conditions. This would allow packaging technologists in the field to select adequate paper substrates, with certain strength characteristics, to manufacture corrugated board with the necessary compression strength for achieving a desired carton performance for certain climate conditions. The results of this study were used to determine the linear regression constants in the Maltenfort model by correlating the measured board edgewise compression strength (ECT) with the predicted strength, using the paper components' compression strengths, measured with the short-span compression test (SCT) and the ring crush test (RCT) methods. The obtained predictive mathematical models were as follows: ECT = 0.6982 (SCTL1 + SCTL2 + SCTL3 + α1SCTF1 + α2SCTF2) and ECT = 1.028 (RCTL1 + RCTL2 + RCTL3 + α1RCTF1 + α2RCTF2). The obtained models showed very high coefficients of determination (R²SCT = 0.9758, R²RCT = 0.9625), which confirmed that they can be used successfully to predict the compression strength of corrugated board, using the measured paper compression strength (SCT or RCT) after exposure to constant climatic conditions. The results also indicated that the obtained plant-specific predictive mathematical models compared well to the overall linear models and that no major difference existed between the plants' capabilities to convert the paper compression strength into corrugated board edgewise crush strength. This suggested that a single set of predictive mathematical models can be utilised with a good degree of confidence in the South African corrugating industry. [ABSTRACT FROM AUTHOR]

A number of academic disciplines permit doctoral students to choose between completing a traditional dissertation or a multi-paper option that replaces the dissertation. We surveyed members of ASC and ACJS and gathered data on their perceptions of the suitability of a multi-paper option in lieu of the dissertation for criminology and criminal justice graduate students. Responses revealed a fair degree of opposition to the multi-paper option. We also estimated a series of regression models in an effort to model the correlates of support for the multi-paper option. Faculty in Ph.D. programs were most opposed to the option, suggesting preference for the status quo. Finally, we included several responses to open-ended questions, thus shedding light on respondents’ reason for supporting or opposing the multi-paper option. [ABSTRACT FROM AUTHOR]

The author investigated the impact of assessment methods on student performance on accounting tests. Specifically, the author used analysis of variance to determine whether the use of computer-based tests instead of paper-based tests affects students' traditional test scores in accounting examinations. The author included 2 independent variables, student gender and student class, as covariates. The findings indicate that there was no significant difference in the values of the students' performance according to the 2 methods of assessment. The findings also revealed that neither student gender nor class was correlated to the test scores in either form of testing. [ABSTRACT FROM AUTHOR]

The present paper proposes an automated approach to estimate the aerosol reflectance at the Advanced Very High Resolution Radiometer (AVHRR) red channel. The aerosol dominant pixels were separated through two orthogonal transforms. The aerosol reflectance ratio at these pixels was estimated through regression. The results are validated with in situ measurements. The retrieved water-leaving reflectance matched the modelled values with a relative error below 45%. The smallest error values were at the stations with the closest sampling time to image acquisition. However, a weak correlation of 16% was found between water-leaving reflectance and aerosol signals. This suggested that these errors could be attributed to the spatial and temporal variability between the two sampling methods (ship measurement and pixel reflectance). [ABSTRACT FROM AUTHOR]

The defense-growth nexus is investigated empirically using time series data for the US and allowing the effect of defense spending on growth to be non-linear. Using recently developed econometric methods involving threshold regressions, evidence of a level-dependent effect of military expenditure on GDP growth is found: the positive externality effect of defense spending prevails for relatively lower levels of defense spending (with respect to the history of defense spending in the US) and reverts its influence for higher levels. [ABSTRACT FROM AUTHOR]

Spatial homoscedasticity of the error term is commonly assumed in mixed geographically weighted regression analysis. In many practical situations, however, rarely can we know a priori whether the error term is homoscedastic or not. Therefore, it is necessary to first detect the spatial heteroscedasticity of the error term before the mixed geographically weighted regression model is used. In this paper, a statistic is constructed based on the square root of the absolute value of the residuals obtained by the two step estimation. The three moment $ \chi ^2 $ χ 2 approximation is applied to compute the p value of the test. Furthermore, simulation results show that the proposed test method has high accuracy and satisfactory power in detecting spatial heteroscedasticity, even when the model errors follow non-normal distributions. A real-world data set is analysed to demonstrate the application of the proposed test method and the paper is ended with a conclusion. [ABSTRACT FROM AUTHOR]

In this paper, we consider the nonparametric regression model $ Y_{ni}= g(t_{ni})+\varepsilon _i $ Y ni = g (t ni) + ϵ i ( $ 1\leq i\leq n $ 1 ≤ i ≤ n), where $ \varepsilon _{i}=\sum _{j=-\infty }^{\infty }a_je_{i-j} $ ϵ i = ∑ j = − ∞ ∞ a j e i − j and $ e_{i} $ e i are identically distributed and asymptotically negatively associated (ANA or $ \rho ^- $ ρ − , for short) sequences. This paper obtains the Berry-Esseen bounds of the wavelet estimator of $ g(\cdot) $ g (⋅) , and the rate of the normal approximation is shown as $ O(n^{-1/4}) $ O (n − 1 / 4) under certain conditions. Finally, the simulation study is provided to verify the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

Till now, studies have determined the existence of process innovation using survey data; however, in this paper, we use secondary data to empirically establish its presence in low-tech industries. Our empirical approach consists of data envelopement analysis in the first step followed by regression analysis in the second step. We use a cross-sectional dataset, i.e., Annual Survey of Industries, 2017–2018 for our study. Our results suggest that among Indian low-tech firms, investment in computer equipment and software is the most popular strategy to trigger process innovation followed by investment in plant and machinery. We find that these factors have a greater impact on firms that export products and import inputs, than on firms that export products but do not import inputs. In other words, the export-import orientation of firms induces a differential impact of these factors on process innovation. One policy implication from the study is that government should encourage and incentivize low-tech firms to improve the capabilities of its labour force. This is the first paper in the Indian context that uses secondary data to empirically determine the existence of process innovation in low-tech industries. [ABSTRACT FROM AUTHOR]

Scholars have been interested in examining what drives pro-environmental behavior. However, only a few scientific studies have been devoted to analyzing and understanding the pro-environmental behavior of those that are on vacation. Therefore, the current paper contributes to the existing literature by employing a text-mining approach to conduct a full-text analysis of 210 articles and (1) describes pro-environmental conceptualization, (2) presents the important topics and studies that have emerged from the literature, and (3) suggests directions for future research. The eight core topics that were uncovered contributed to discussion of the content of publications, related theories, core constructs, methodologies, main authors, and journals. The paper shows that the literature on pro-environmental behavior uses more quantitative than qualitative approaches and uses structural equations or regression analysis to explore the data. The findings also show that researchers tend to employ well-known theories arising from psychology, sociology, and biology. [ABSTRACT FROM AUTHOR]

Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models - Random Forest (RF), K-nearest neighbours (KNN), Gradient Boosting (GB) - and a proposed hybrid model based on Lévy Jaya Algorithm (LJA) and GB (LJA-GB) are employed to estimate the effect of trash racks on flow properties at power intakes. The experimental data which are prepared for the proposed study in this paper were obtained through a rectangular laboratory tank 8.3m³ with various submergence depths and Froude numbers on nine trash racks with 63.7%-84.1% opening, made out of 2, 2.5, 4, and 6 mm thick copper wire. The outcomes revealed that the proposed LJA-GB model shows the best overall performance among the four models used for estimation. Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared (R²) (0.9899) and Willmott's index (WI) values (0.9508) in the testing stage metrics for Vxy/Vi estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), R² (0.9971), WI (0.9727) for Nt estimation. Whereas the RF and KNN models exhibited poor performance in both stages of estimation. [ABSTRACT FROM AUTHOR]

Network screening is a key aspect of the safety management process. The current standard methods in the Highway Safety Manual assume that intersections and roadway segments are independent, even though interactions between sites likely exist. One way to account for these interactions is by studying roadway corridors made of consecutive intersections and segments. This paper developed a roadway corridor definition to identify urban and suburban arterial corridors in Florida with context classification and lane count as defining features. Applying this definition to 10 Florida counties resulted in 1105 corridors with 14,097 fatal or serious injury crashes from 2017 to 2021. Historic crashes and a developed regression model were used to estimate the expected crash frequency for fatal and serious injury crashes with the empirical Bayes method. After performing k-means clustering on expected crash frequency per mile, 25 high-risk corridors were identified. The difference between the expected and predicted crash frequencies (potential for safety improvement) for each high-risk corridor provided a list of priority corridors for safety improvements. Countermeasures focused on speed reduction in suburban corridors would likely lead to the greatest reductions in fatal and serious injury crashes. This corridor approach allows agencies to quickly identify high-risk areas for priority treatment. [ABSTRACT FROM AUTHOR]

It is found that alphabetized coauthored papers with two authors are more highly cited than non-alphabetized coauthored papers in both economics and agricultural economics. Alphabetized coauthored papers with more than two authors are not cited more, suggesting an optimal team size of two, ceteris paribus. We speculate that the preponderance of non-alphabetized papers in agricultural economics, as compared to economics, is due in some part to the differential importance of nonmarket-based criteria to evaluate research in this area as opposed to economics departments. [ABSTRACT FROM AUTHOR]

A stability indicating UPLC method was developed for the quantitative determination of Zolpidem Tartrate (ZT) and its product related variants present in drug substance and drug products. The liquid chromatographic separation was achieved on Acquity UPLC HSS T3-C18 column. All the eight potential variants and the drug were eluted within 8 minutes. A narrow range pH gradient, in combination with organic modifier was used to obtain separation (Rs > 1.5) between critical pairs. There was no placebo interference during the determination of analytes. The method had shown a good and consistent recoveries [98.91 ± 0.55 (mean ± RSD)] with a high degree of intra- and inter-day precision (ANOVA: FCalculated = 0.6498 < FCritical = 3.6823) for the drug. Linear regression analysis data revealed the best R2values (0.9980–0.9998) and F Siginificance values (p < α = 0.05) for the drug and impurities. The stability-indicating capability of the method was verified by subjecting the drug to hydrolytic, oxidative, photolytic, and thermal stress conditions. LC-MS was used to identify the mass numbers of the degradation products. Them/zvalues 281 and 227 were matched with zolpidem acid and zolpyridine, respectively. Possible degradation pathways were discussed for the product related variants formed during stress studies. [ABSTRACT FROM PUBLISHER]

The existence of differences in geopolitical preferences between the titular nationalities and ethnic minorities has been thoroughly documented for the Association Agreement countries and other post-Soviet states. This paper goes beyond simply identifying the existence of this "ethnic gap" in geopolitical preferences, by also testing some of the common theories that try to explain it. To do so, I use different regression analyses based on survey data from Georgia (2015–2021). The results of a first multivariate analysis that aims to explain the ethnic gap show that the linguistic differences between the titular nationalities and the ethnic groups explain a higher percentage of the gap in support for the EU than differences related to ideology, values, and information. Furthermore, a second analysis that divides the sample into different ethnic groups reveals that certain variables influence the geopolitical preferences of members of the titular nationalities and members of each of the studied ethnic minorities in different ways. [ABSTRACT FROM AUTHOR]

The paper investigates how parliamentary efforts to represent the interests of female electorates influence the legislators' re-election chances. Taiwan is chosen as the case study and, for empirical analysis, I utilise an original bill co-sponsorship dataset that consists of roughly 400,000 cosponsors for all bills submitted between 1992 and 2016. The findings, based on regression analyses, show that making more legislative effort on women's issues – by prioritising them over other issues – results in electoral losses, and this negative effect is more pronounced among female legislators. The paper contributes to the gender politics literature by theorising and testing a hitherto underexplored relationship between two representational processes: how the substantive representation women by female legislators affects their descriptive representation. It also contributes to legislative and electoral studies by demonstrating that legislators' policy-vote trade-offs are policy-sensitive and gendered, thus calling for a more nuanced approach to be taken in future research. [ABSTRACT FROM AUTHOR]

The mechanical properties of cemented paste backfill (CPB) directly determine the effect of backfilling in coal mining. However, the current studies on the mechanical properties of CPB are all based on standard cylinder or cube specimens, which cannot accurately characterise the distribution of mechanical properties in different spatial positions of CPB in situ. In addition, high-water-content material (HWM) has developed rapidly as a new binder in recent years. Therefore, in this paper, the uniaxial compressive strength (UCS) and elastic modulus of CPB with gangue as the aggregate and HWM as the binder (GHW-CPB) were investigated under different gangue particle sizes. The following conclusions were drawn: (1) The spatial evolution and distribution characteristics of mechanical properties of GHW-CPB are analysed. (2) The spatial evolution regression models of mechanical properties of GHW-CPB are established. (3) We improved the GHW-CPB spatial evolution regression models for mechanical properties due to their limitations and confirmed its validity and rationality. Through the research results of this paper, the failure position of the CPB in the goaf can be accurately predicted, so that manual interventions can be carried out in the vulnerable areas in advance to reduce the risk of coal mining production. [ABSTRACT FROM AUTHOR]

This paper explores whether university–industry collaboration can facilitate university technology license with the moderating effects of external and internal environments. Here, the university technology license is measured by the license of university non-collaborative-patent. We set up panel data during 2010–2016 of 118 top universities in China and adopted negative binomial regression for empirical analysis. Results show that university–industry collaboration is positively related to non-collaborative-patent license and market for technology has no moderating effect while the R&D strength can moderate the relationship. Specifically, funding has a positive effect on non-collaborative-patent license while research full-time equivalent of R&D personnel negatively moderates the relationship between collaboration and non-collaborative-patent license. This paper creates a new direction to study influencing factors of university technology license from the perspective of collaborators and environment and verified the driving effect of university–industry collaboration on university technology license. Moreover, it sheds light on theoretical streams of university technology license in developing countries. [ABSTRACT FROM AUTHOR]

Multivariate regression is a widely used technique for large-scale statistical applications. In this paper, we propose a novel method for the multivariate regression model, called multivariate weighted lasso (MWL). We consider the heteroscedasticity in the noise matrix and introduce an adjustable weight to calibrate the fitting residuals of different responses. The proposed method shows an advantage in complex multivariate regressions with heteroscedasticity. To implement the procedure, an efficient algorithm is provided based on the multivariate coordinate descent. We provide theoretical guarantees for the proposed method. Numerical simulations and financial applications are conducted using the proposed and other existing methods. The results indicate that the proposed method performs well in both variable selection and coefficient estimation. [ABSTRACT FROM AUTHOR]

In this paper, we develop a Bayesian two-stage cure rate model whose biological destructive mechanism (immune system) of the competing risk factors of death is suitable for detecting the impact on the long-term survival function of three sources of variance well-known in accident theory: randomness, liability and proneness. From a survival analysis viewpoint, proneness means individual effect or destructive mechanism and liability corresponds to external effects or covariates. The flexibility of the generalized Waring frailty distribution in capturing these variance components separately enables one to understand the nature of overdispersion of the risk factors involved in studying risk of death after a long-term treatment of the patient. A new cure rate, involving covariate and destructive mechanism, is developed here under a competing cause scenario. A simulation study and an application to colorectal cancer data set are finally presented to demonstrate the usefulness of the proposed model and the inferential results developed here. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between news media sentiment and spillover effects in the cryptocurrency market. By employing a time-varying parameter vector autoregressive model, we initially develop measures of spillover specific to individual cryptocurrencies. Subsequently, we employ unique data on cryptocurrency-specific sentiment to assess its impact on these spillover measures using panel fixed effects regression analysis. Our findings indicate that news media sentiment plays a significant role in explaining the spillover dynamics within the cryptocurrency market. Unlike traditional assets, it appears that only positive sentiment affects the spillovers among cryptocurrencies, suggesting an asymmetric effect. Taking into account various characteristics of cryptocurrencies, we find that sentiment's impact on spillover is more pronounced in community-based coins than in those driven by firms. An examination of news content suggests that sentiment pertaining to emotional and risk aspects of cryptocurrencies predominantly influences these spillovers. Additionally, a comparative analysis of sentiment derived from social media and traditional news sources reveals a stronger influence of the former on spillover effects. Through extensive robustness checks, our research consistently affirms the pivotal role of sentiment in driving spillovers among cryptocurrency returns, underlining the importance of sentiment analysis in understanding the dynamics of the cryptocurrency market. [ABSTRACT FROM AUTHOR]

Counts of a specific characteristic were obtained within regions defined on an object that was manufactured in a proprietary setting. The count regions were altered during production and resulted in misaligned or overlapping count data. A closed-formula maximum likelihood estimator (MLE) of the new region means is derived using all of the available count data and an independent Poisson model. The MLE is shown to be preferable to estimators constructed using generalized linear models for the overlapping data setting. This closed-form estimator extends to over-dispersed overlapping count data as the quasi-MLE and also performs well with correlated overlapping count data. Standard errors for the estimator are approximated and are validated with a simulation study. Additionally, the methods are extended to overlapping multinomial data. Illustrative examples of the methods are provided throughout the paper and are reproducible with the supplemental R code. Proofs of the paper's results are also included in the supplemental material. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) and Developmental Language Disorder (DLD) have traditionally been considered separate disorders, although some similarities and overlaps in certain aspects of language have been detected. In this paper, we compare the deficits in receptive grammar in these two disorders. We analyse the proportion of grammatical errors in relation to semantic complexity in 84 children divided into four groups: children with autism language impairment (ALI), with autism language normal (ALN), with DLD, and with typical development (TD), all groups with the same age of receptive vocabulary. The results show significant differences in the comprehension of grammatical structures, both simple (canonical and non-reversible) and complex (non-canonical and reversible). Children with ASD and DLD show different language profiles depending on the syntactic complexity. In the simplest structures, no differences are found between the groups, starting at an equivalent vocabulary age of 7:8 years. However, there are differences between the ALI and DLD groups with respect to the TD group in the more complex structures, starting at an equivalent vocabulary age of 3 years. Therefore, both groups ALI and DLD present the greatest difficulties compared to ALN and TD. The paper discusses the importance of attending to these differences, since the repercussion of comprehension difficulties increases as children grow. [ABSTRACT FROM AUTHOR]

This paper derives a general Bartlett correction formula to improve the inference based on the likelihood ratio test in a multivariate model under a quite general parameterization, where the mean vector and the variance-covariance matrix can share the same vector of parameters. This approach includes a number of models as special cases such as non-linear regression models, errors-in-variables models, mixed-effects models with non-linear fixed effects, and mixtures of the previous models. We also employ the Skovgaard adjustment to the likelihood ratio statistic in this class of multivariate models and derive a general expression of the correction factor based on Skovgaard approach. Monte Carlo simulation experiments are carried out to verify the performance of the improved tests, and the numerical results confirm that the modified tests are more reliable than the usual likelihood ratio test. Applications to real data are also presented for illustrative purposes. [ABSTRACT FROM AUTHOR]

In this paper we propose a new parameter estimator that ensures global exponential convergence of linear regression models requiring only the necessary assumption of identifiability of the regression equation, which we show is equivalent to interval excitation of the regressor vector. An extension to – separable and monotonic – nonlinear parameterisations is also given. The estimators are shown to be robust to additive measurement noise and – not necessarily slow-parameter variations. Moreover, a version of the estimator that is robust with respect to sinusoidal disturbances with unknown internal model is given. Simulation results that illustrate the performance of the estimator compared with other algorithms are given. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of e-commerce supply chain finance (SCF) on small and medium enterprises' (SMEs) financing performance. Using the 423 observations panel dataset of Chinese technology-based SMEs for the period from 2011 to 2020, the descriptive, correlation statistics and regression analysis are conducted. The findings indicate that e-commerce SCF platforms facilitate SME financing by increasing the coverage breadth and usage depth. Particularly, we found that comparing to e-commerce SCF usage depth, the coverage breadth has more positive impacts on SMEs' financing performance. In addition, this research also reveals the positive moderating effects of green innovation on this relation. The results of the study empirically demonstrate how SCF platforms could better help SME satisfy their financing needs. [ABSTRACT FROM AUTHOR]

The media has been repeatedly demonstrated to have a large effect on voting behaviour and voter information worldwide, and to be crucial in the establishment of collective identities. Relatively unexplored in the field of regional politics are the effects of media on substate party system divergence and non-statewide party success. This article takes Europe as its focus and demonstrates how strongly regionalized media environments contribute to the development of distinctive party systems at the regional level. I argue that the effects of media works chiefly through the establishment of a 'banal regionalism' and by increasing voter information, thereby boosting issues traditionally associated with regionalist success such as socio-cultural distinctiveness and regional autonomy. The paper demonstrates this through a regression analysis of 69 European 'Small Worlds' and an illustrative case study of the United Kingdom. [ABSTRACT FROM AUTHOR]

This study identifies and analyzes the relative importance of the multi-faceted factors that drive the decision to automate container terminals and the realized benefits, thus establishing how accurately terminal operators predicted the benefits of automation. The empirical analysis relies on a survey-based approach and the input of senior representatives of terminal operating entities in charge of the fully and semi-automated container terminals. The analysis of the findings, using statistical tools (i.e., descriptive statistics, Pearson correlations, ANOVA, Kruskal-Wallis), reveals that most of the benefits assumed by an individual terminal operator materialized once the automated terminal was in operation. It also concludes that expectations often exceed benefits and vice versa. A stepwise regression analysis enables the search of causal relationships between drivers and realized benefits with key characteristics of automated container terminals, such as their organizational features, technical dimensions, and the maritime and urban markets they serve. This article is a revised and expanded version of a paper Container Terminal Automation: Assessment of Drivers and Benefits presented at the International Association of Maritime Economists Annual Conference, Busan, Korea, 2022. [ABSTRACT FROM AUTHOR]

By utilizing a socioeconomic framework, this paper contributes to the water stress literature. Employing the instrumental variable quantile regression model on 81 countries from 2000 to 2019, our findings reveal financial development can alleviate high levels of water stress. Good governance consistently reduces water stress but human development exacerbates water stress, indicating a conflicting relationship between improving human development and reducing water stress. The moderating effect of good governance reveals that when combined with human development, it can effectively reduce water stress. [ABSTRACT FROM AUTHOR]

Clientelism is a widespread and persistent practice in Latin America with significant ramifications for political actors and systems. This paper analyses its impact upon ideological overlapping, which is one of the aspects of ideological competition that has received disparate levels of attention depending on the region of study. The hypothesis examined is that clientelism reduces the ideological differentiation of political parties because, firstly, a clientelist environment makes it difficult for political actors to use left/right categories, thereby contributing to a reduction in parties' ideological identities. Secondly, the use of clientelist practices by parties fosters ideological overlapping with other parties, given the lack of incentives for them to differentiate themselves ideologically from one another. To test this relationship, a multilevel regression analysis was performed of 86 Latin American parties since 2009. [ABSTRACT FROM AUTHOR]

The debate on the changes in livelihood assets as a function of health shocks remains inconclusive, thus spurring attention from scientists and development practitioners across the globe. This paper analyses COVID-19-induced changes in the livelihood assets of rural households in Ghana. While content analysis was employed in qualitative data analysis, the quantitative data set was analysed using a binary logistic regression model. The analyses led to the following conclusions: The COVID-19 pandemic led to a more significant decline in financial assets than social assets. Although several socio-economic factors determine changes in the livelihood assets of households, the assets base of migrants was disproportionately affected by the pandemic. Further, women were disproportionately affected since market access restrictions significantly affected their income and savings and, consequently, their ability to buy farm necessities. These results suggest the need to emphasise the resilience of financial assets in times of pandemics, especially for migrants. This study provides new insights to inform the sustainable livelihoods framework, emphasising pandemics and changing livelihood strategies. Studies to uncover the coping strategies of migrants in the context of health shocks are required to complement this position. [ABSTRACT FROM AUTHOR]

Due to the randomness of weather conditions, the complexity of power demands and the nonlinearity of high-dimensional data, traditional regression analysis, and time series forecasting methods can no longer meet the current requirements of high-precision wind power load forecasting. Therefore, it is necessary to use an accurate short-term load forecasting method for power grid dispatching and planning. Taking the onshore wind power load data of Valencia, Spain (from January 1, 2015, to December 31, 2018), as the research object, this paper proposes a hybrid model of bidirectional gated recurrent units and squeeze-and-excitation networks (BiGRU-SENet) based on an attention mechanism, which is good at handling the nonlinearity of high-dimensional time series data, to predict short-term power loads. The results showed that (1) the attention mechanism could significantly improve the predictive performance of BiGRU by weighting the input of different time steps. (2) The GRU-CNN was more suitable for short-term load forecasting than CNN-GRU, which could make full use of time series information and not lose the key information at the beginning. (3) SENet enabled CNN to perform feature-wise recalibration, further focusing on effective features. (4) Compared with the second-best model (BiGRU-Attention-CNN), BiGRU-Attention-SENet was 12.849% and 3.096% lower in terms of the mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively. In terms of computational overheads, BiGRU-Attention-SENet achieved greater improvement in prediction accuracy by adding relatively fewer computational overheads (the parameters increased by 0.338%, and the FLOPs increased by 0.621%). (5) Through fine tuning, the pre-trained model achieved almost the same accuracy as the no pre-trained one in a shorter time, which proved that BiGRU-Attention-SENet had strong generalization ability. The hybrid model proposed in this paper has application prospects in power load forecasting. [ABSTRACT FROM AUTHOR]

In this paper, we investigate whether the use of accounting conservatism in India decreases IPO underpricing, which is of attention to stakeholders and supervisors. Furthermore, the study examines how asymmetry information affects the implication of accounting conservatism for IPOs. Based on a regression analysis of 527 firms that went public through IPOs of "A" shares listed on the national stock exchanges between 2000 and 2020, the paper also examines whether the relationship between conservatism and under-pricing is robust to alternative measures of accounting conservatism, mean regressions, sample exclusions, and endogenous treatment models. The research study finds that accounting conservatism is negatively associated with the degree of IPO under-pricing, and the association between accounting conservatism and IPO under-pricing is more perceptible when information asymmetry is high. The paper's originality should shed light on what drives IPO underpricing and how it could be affected by accounting conservatism in an Indian economy, and provide find evidence that legal origin, a factor linked to the practice of conservatism, influences the relationship between under-pricing and conservatism. [ABSTRACT FROM AUTHOR]

In view of the fact that Industry 4.0 is becoming increasingly essential, the implementation of Industry 4.0 technologies is believed to be an essential strategic component in further increasing the efficiency of manufacturing processes and in determining decisions concerning globalisation strategies. Thus, this paper provides an empirical analysis of the essential constructs of Industry 4.0, and drivers and barriers for Industry 4.0. The paper is based on 270 valid answers to a questionnaire-survey distributed among Danish manufacturers. Regression analyses were used to test the proposed hypotheses. The results of the analyses reveal that the identified drivers and barriers for Industry 4.0 have a positive impact on the perceived relevance of Industry 4.0 among companies. Furthermore, the analyses show that the perceived relevance of Industry 4.0 among companies has a positive impact on companies that have moved manufacturing back and on companies that have moved manufacturing out and back. Conversely, the perceived relevance of Industry 4.0 among companies has a negative impact on companies that have remained domestic. The perceived relevance of Industry 4.0 has no impact on companies that have moved manufacturing out. This research, in general, offers to both researchers and practitioners an increased consciousness concerning the relevant drivers and barriers for Industry 4.0 in decision processes about where to locate manufacturing. [ABSTRACT FROM AUTHOR]

This paper aims to discover whether the rate of unemployment in the first two decades of the 21st century influenced electoral support for the radical left parties (RLPs). The election results for 37 RLPs were analysed at the level of the NUTS 2 regions. The impact of unemployment was examined using four indicators – general, youth, long-term and male unemployment rates. The aim was achieved through a multilevel regression analysis conducted for parliamentary elections between 2002 and 2019. The results suggest that RLPs are more successful in regions with a higher unemployment rate (general, long-term, male) across the years under review. A 1% rise in the long-term unemployment rate led to an increase in support for RLPs of 0.2% to 1.9% in most NUTS 2 regions (179 out of 198). This effect is most pronounced in the regions where the centre-periphery cleavage associated with historically strong leftist tendencies can be observed, particularly in the case of German, Greek, Portuguese and Spanish regions. The main thrust of the paper is in the analysis of electoral support in parliamentary elections at the regional level, which is less represented in the thematic literature compared with the individual or national level. [ABSTRACT FROM AUTHOR]

In this paper, the sufficient and necessary conditions for the strong consistency of least squares estimators in simple linear EV regression models are proved under widely orthant dependent random errors. The results obtained in the paper improve and extend some existing ones in the literature. Simulation studies are also carried out to support the consistency of the estimators under dependence assumption and finite samples. [ABSTRACT FROM AUTHOR]

In this paper, we analyse the impact of immigrant preference status on tourism for a global sample from 1990 to 2019, every 5 years. We construct an international migration preference network and calculate some corresponding structural parameters. Then, the paper compares the impact of ordinary immigration relations and important immigration relations on tourism. We obtain the following results: (1) Countries are exchanging increasingly closely through migration, and the immigrant preference network extracts the backbone structure of the network. (2) In the migration network, when a country has more immigration and emigration, it will promote the development of its inbound tourism. (3) In the migration preference network, in addition to the impact of the number of immigrants on tourism, we find that countries with stronger immigration controls are more likely to attract tourists, as are countries in a tight immigrant community. As a result, we find important immigration relationship can reveal some problems generally immigrants can't reflect. [ABSTRACT FROM AUTHOR]

Due to the growing demand for customised products, companies have faced increasing product and process complexity levels. To address this issue, manufacturing processes should become more flexible. One of the most promising technologies to achieve this goal is collaborative robotics (or 'cobots'). In collaborative assembly processes, human and robot combine their skills. However, the co-existence of humans and cobots in the same workspace may influence the operators' perception of assembly complexity. The analysis and control of assembly complexity are crucial to achieving better performances in terms of process quality and operators' well-being. Many qualitative methods have been proposed in the literature to provide a holistic assessment of assembly complexity. This paper proposes a novel method to define a quantitative scale of perceived assembly complexity, based on Thurstone Law of Comparative Judgements. This method was applied to an experimental case-study concerning the assembly of three different products in two modalities (i.e. manual and collaborative). Regression analysis showed that the perceived complexity may be related to the occurrence of process failures and to the perceived workload. The method also proved capable of identifying assembly processes where cobot assistance was helpful, providing process designers with a supporting tool to minimise perceived complexity. [ABSTRACT FROM AUTHOR]

Achieving inclusive outcomes through innovation is such that small, micro and medium enterprises (SMMEs) can no longer be ignored. This study aims to investigate the determinants of innovation activities among SMMEs in a developing country context using evidence from South Africa. Using the binary logistic regression model, this paper analysed a dataset of 643 SMMEs located across eight district municipalities in South Africa. The study findings revealed that there are dynamic innovation activities occurring among SMMEs located in rural and/or informal settings, focusing on making incremental changes to production and delivery processes adopted from elsewhere. The study identified several factors that hinder or enhance innovation activities among SMMEs, revealing that the factors associated with innovation activities vary depending on the nature of the innovation activity. Empirical insights from this study could better inform strategies used by policy makers to promote innovation that strengthen the competitiveness of SMMEs and innovation that is socially inclusive. [ABSTRACT FROM AUTHOR]

Body circumference fitting model is a key technology for personalization in smart manufacturing by the apparel industry. First, the paper collected front and side images plus manual measurement data of 122 young men. Second, through contour recognition and feature point detection of the collected images, the width and thickness of each body part plus the height were estimated. Then, a correlation analysis of these known factors was conducted. The paper proposed that weight was included in initial regression model for circumferences as an additional independent variable along with width and thickness. Finally, according body type classification, a multivariate regression model was established. After validation, the values predicted by the multivariate regression model were within ±1.5 cm of the manual values for 90.9% of the individuals on average. The paper provides theoretical support for body circumference fitting and may also be beneficial to the development of the smart manufacturing of garments. [ABSTRACT FROM AUTHOR]

The paper presents evidence on the relationship concerning military spending among Southeast Mediterranean countries (Greece – Turkey, Israel – Egypt, Israel – Turkey) over the period 1962–2020. We account for the presence of nonlinearities in the bilateral relationships of defence spending by applying Threshold Autoregressive methodologies and utilize kink regression analysis to detect the existence of a country's military spending threshold that signals a threat to another country of the region. Our empirical results show: First, there is a nonlinear strategic interaction between the countries examined, in the sense that their defence spending policy is cointegrated. Second, there is no arms race among the countries examined, but only unilateral effects. Third, there is consistent evidence of a possible military spending equilibrium, in the absence of friction between the countries involved (peace threshold). Our findings have important policy implications as they indicate, first, that each country in the region should not determine its level of military spending considering only operational factors, but also considering the signaling of its military spending on its neighboring countries and, second, that there is space for peaceful solutions regarding disputes in the Southeast Mediterranean region. [ABSTRACT FROM AUTHOR]

Using the multivariate quantile model, this paper develops a global economic policy uncertainty (EPU) spillover measure for each country and investigates the spillover effects on the country-level stock market idiosyncratic volatility across a sample of 23 economies. The regression results show that global EPU spillovers have a positive and significant effect on the country-level stock market idiosyncratic volatility. We find that the effect of developed-market-generated EPU spillovers on the country-level stock market idiosyncratic risk is noticeably larger compared to the effect of emerging-market-generated EPU spillovers. Furthermore, the significant and positive effect of the EPU spillovers on the country-level stock market idiosyncratic volatility continues when we utilize various economic, financial, and political risk factors as controls, as well as when we use alternative measures of stock market idiosyncratic volatility as the dependent variable in our regression analyses. [ABSTRACT FROM AUTHOR]

In real applied fields such as clinical medicine, environmental sciences, psychology as well as economics, we often encounter the task of conducting statistical inference for longitudinal data with ordinal responses. The traditional methods of longitudinal data analysis are often inclined to model continuous responses, which are no longer suitable for such ordinal data. Logistic regression and probit regression are two considerable methods which are frequently used to model ordinal longitudinal responses. However, such modelling methods just depict the mean feature of latent outcome variable and may produce non-robust results when encountering nor-normal errors or outliers. As a proper alternative of mean regression models, composite quantile regression (CQR) method is usually employed to derive robust estimation. The target of this paper is to investigate the CQR estimation approach for ordinal latent longitudinal model. The joint Bayesian hierarchical model is established and a relative CQR estimation approach is suggested to conduct posterior inference for the considered model. Further, in longitudinal data modelling, excessive predictors may be brought into in the models which result in the decrease of the model prediction precision. Bayesian $ L_{1/2} $ L 1 / 2 regularized prior is incorporated into ordinal longitudinal CQR model to conduct variable selection simultaneously. Finally, simulation studies and two ordinal longitudinal data analysis are hired to illustrate the considered method. [ABSTRACT FROM AUTHOR]

This paper evaluates the thermal performance of ducts partially or fully buried in loose-fill attic insulation. The overall thermal resistance between the ducts and the attic is referred to as an effective R-value. This paper shows a strong dependency of assumed attic temperature on the effective R-value. Based on the results, the effective R-value can be about twice as much with an attic temperature of 130 °F [54.4 °C], compared to when the attic temperature is 80 °F [26.7 °C]. Thus, this paper provides a polynomial regression equation based on a large set of simulations to determine the effective R-value of buried ducts depending on attic temperature and whether the HVAC system runs in cooling or heating mode. Further, the work presented in this paper investigated the potential impact of convective airflow within the attic insulation, particularly around the ducts. The analysis was based on computational fluid dynamics (CFD) and indicated that convectional forces are presented around the exterior surface of the ducts, but with negligible impact on the overall heat balance between the duct and the attic space. [ABSTRACT FROM AUTHOR]

This article mainly studies the strong convergence properties for randomly weighted maximum partial sums of arrays of row-wise extended negatively dependent (END, for short) random variables, including the complete moment convergence and complete f-moment convergence. The results obtained in this paper extend the corresponding ones in the literature. As an application, we investigate the complete consistency of the estimators of nonparametric regression models based on END random errors by using the complete convergence that we established. Then we provide comprehensive simulation studies to demonstrate the validity of the theoretical results based on the finite sample performance. Finally, an application to a real data set is illustrated. [ABSTRACT FROM AUTHOR]

The proper scheduling of track maintenance and renewal operations is dependent on the accuracy of predictive maintenance outputs which, in turn, rely on the appropriate modelling of the track geometry degradation process. This paper explores the incidence and representativeness of the alerts generated due to local defects of track geometry and the modelling of their degradation rates between consecutive tamping operations. A software tool was developed and used to process track inspection data collected over 12 years of operation of the main Portuguese line (Lisbon-Porto) on a total of 727 km of single-track lines where speeds vary between 30 and 220 km/h. Linear regressions are found to be suitable to model the degradation rates of local defects of longitudinal levelling, alignment and twist in the vast majority of cases, although 20–30% of the time degradation patterns prove to be non-linear even in the short term. The local defects of alignment degrade more rapidly and trigger 80% of the unplanned maintenance needs. Modelling the evolution of local defects, and not only the standard deviation on sections 200 m long, allows us to anticipate unplanned maintenance and tackle unavailability costs. [ABSTRACT FROM AUTHOR]

The presence of unburned carbon in coal-burning systems undoubtedly causes a loss in the amount of energy that can be obtained from the system, and also reveals an inadequacy in terms of the usability of the ashes. The expensiveness of existing unburned carbon prediction methods is one of the reasons why these technologies cannot be used. This situation requires working on alternative non-combustible carbon technologies. In this paper, a new approach is presented for estimating unburned carbon in a small-scale coal burner system using the Gaussian regression model and CCD camera-acquired flame image. The proposed approach evaluates brightness, fluctuation amplitude, area, and radiation signal properties of the flame image. The proposed non-combustible carbon estimation technique does not require prior knowledge of CCD camera features. In the feature acquisition phase, results were obtained for each natural component of the flame image in RGB colour space separately, in pairs, all together and for three artificial colour channels (grey image). With the proposed method, the unburned carbon estimation was obtained with an accuracy of R = 0.9664 when all colour channels of the RGB image were used together. This result shows that unburned carbon can be estimated from the instantaneous flame images obtained with the CCD camera. [ABSTRACT FROM AUTHOR]