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This paper aims to reduce and eliminate the abnormal peaks which, because of the reflection in the process of laser detection, make it easier to proceed with further analysis.

To solve the above problem, an abnormal data correction algorithm based on histogram, K-Means clustering and improved robust locally weighted scatter plot smoothing (LOWESS) is put forward. The proposed algorithm does section leveling for shear plant first and then applies histogram to define the abnormal fluctuation data between the neighboring points and utilizes a K-Means clustering to eliminate the abnormal data. After that, the improved robust LOWESS method, which is based on Euclidean distance, is used to remove the noise interference and finally obtain the waveform characteristics for next data processing.

The experiment result of liner tool mark laser test data correction demonstrates the accuracy and reliability of the proposed algorithm.

The study enables the following points: the detection signal automatic leveling; abnormal data identification and demarcation using K-Means clustering and histogram; and data smoothing using LOWESS.

In the literature on determinants of disclosure, scholars generally tend to investigate the existence of relations in “global” terms by considering the whole range of observed values pertaining to both dependent and independent variables involved in the descriptive model. Despite the different methodologies used coherently to this approach, a hypothesis can be only accepted or rejected entirely. This paper aims to contribute to the literature by proposing a data-driven method based on smooth curves, which allow scholars to detect the existence of local relations, significant in a limited interval of the dependent variable.

The employment of smooth curves is simplified by conducting a study on goodwill disclosure. The model derived by the adoption of the locally weighted scatterplot smoothing (LOWESS) curves may provide an accurate description about complex relations between the extent of disclosure and its expected determinants, whose shape is not completely captured by traditional statistic techniques.

The model based on LOWESS curves provided a comprehensive description about the complexities characterizing the relationship between disclosure and its determinants. The results show that in some cases, the extent of disclosure is influenced by multi-faceted local relations.

The exemplificative study provides evidences useful for standard setters to improve their comprehension about the inclination of companies in disclosing information on goodwill impairment.

The adoption of smooth curves is coherent with an inductive research approach, where empirical evidence is generalized and evolves into theoretical explanations. The method proposed is replicable in all the field of studies, when extant studies come to unclear and contradicting results as a consequence of the complex relations investigated.

The purpose of this paper is to assess the relationship between South Africa's foreign direct investment (FDI) and economic fundamentals at the municipal level.

The paper develops a data set and an econometric model to analyze FDI flows at the municipal level in South Africa.

The empirical results derived from municipal level data support the findings in some of the established literature (which for the most part uses country‐level data) and indicates: that FDI tends to flow to areas with high factor (capital, labour and land) productivity; and that increases in higher labour productivity lead to higher investment.

This paper has used a cross‐section of municipalities. A further area of research would be to carry out a similar exercise with panel data.

These findings indicate that FDI flows can be considered at the municipal level and this justifies the need for careful selection of the geographic basis for economic policy and development planning.

Whereas most studies on FDI use country‐level data as standard geographic units of analysis, this paper analyzes FDI flows at the municipal level.

The article aims to investigate the effects of ownership and capital structure on postacquisition operating performance.

The article extends the ongoing literature from an operating loss perspective and provides empirical evidence on the probability of acquirers’ operating loss in relation to ownership and capital structure. The operating performance of publicly listed manufacturing firms in China was tracked up to five years since the completion of the mergers and acquisitions (M&A) during 2003–2014.

The empirical results show that, in a five-year postacquisition period, state-owned enterprises (SOEs) are more likely to experience operating loss than non-SOEs. The likelihood of the operating loss is negatively associated with ownership concentration, implying that concentrated ownership may serve as an effective corporate governance mechanism in the emerging economy and improve postacquisition performance. The rise in leverage increases the likelihood of postacquisition operating loss, indicating that the costs of debt may outweigh the benefits.

The findings contribute to the literature on ownership, debt governance and post-M&A performance from an emerging economy perspective.

An accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.

During the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.

The proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.

The model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.

The model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.

The model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

New Hampshire’s mountains and winter climate support a ski industry that contributes substantially to the state economy. Through more than 70 years of history, this industry has adapted and changed with its host society. The climate itself has changed during this period too, in ways that influenced the ski industry’s development. During the 20th century, New Hampshire’s mean winter temperature warmed about 2.1° C (3.8° F). Much of that change occurred since 1970. The mult‐decadal variations in New Hampshire winters follow global temperature trends. Snowfall exhibits a downward trend, strongest in southern New Hampshire, and also correlates with the North Atlantic Oscillation. Many small ski areas opened during the early years while winters were cold and snowy. As winters warmed, areas in southern or lowelevation locations faced a critical disadvantage. Under pressure from both climate and competition, the number of small ski areas leveled off and then fell steeply after 1970. The number of larger, chairliftoperating ski areas began falling too after 1980. Aprolonged warming period increased the importance of geographic advantages, and also of capital investment in snowmaking, grooming and economic diversification. The consolidation trend continues today. Most of the surviving ski areas are located in the northern mountains. Elsewhere around the state, one can find the remains of “lost” ski areas in places that now rarely have snow suitable for downhill skiing. This case study demonstrates a general approach for conducting integrated empirical research on the human dimensions of climate change.

This study aims to examine the relation between population composition and financial market variables in post-war Japan.

Cointegration and Granger causality tests are applied to annual data for the period 1948-2015.

Accounting for nonstationarity, this study finds long-run equilibrium relations between real financial price (stock and house) indices and the proportion of population in the prime earning (45-64) or retirement (65+) age. Granger causality tests that account for possibly nonstationary variables find some evidence of dynamic causation running from the 45-64 cohort to the real financial price indices. No such evidence is found for the 65+ cohort.

This study complements the existing literature primarily based on US data with analysis of Japanese data that has some unique population composition features.

This paper aims to examine the impact of child health (measured by nutritional status) on schooling performance of Bangladeshi children.

The data set used in this study comes from a survey titled “Micronutrient and Gender Study (MNGS) in Bangladesh”. The survey was administered by the International Food Policy Research Institute (IFPRI). The author controls for the potential endogeneity of child health by an instrumental variables approach. The results indicate that the impact of child health on school achievement will be overestimated if endogeneity of child health is ignored.

The results reveal that child health has significant effects on school enrolment and grade attainment, although it does not affect the current school attendance. The impact of child health is stronger for school enrolment compared to grade attainment.

This study improves the understanding on the relationship between child health and schooling in several ways. First, the author controls for the potential endogeneity of child health by an instrumental variables approach. The chosen instrumental variables (i.e. heights of father and mother) are strong predictors of child health and satisfy the validity test. Second, this study examines the effects of child health on wide ranges of schooling measures: enrolment, attendance and attainment.

This article's purpose is to examine the effect of a Classification-Based Forest Management (CFM) program on farmers' income and determine whether its effect varies with the degree of farmers' concurrent occupations.

The authors use representative panel survey data from Longquan to explore the welfare effects of CFM on farmers. The analysis uses differences-in-differences with propensity score matching (PSM-DID) estimation techniques to deal with endogeneity problems when farmers make the decision to participate in CFM.

The results show that CFM has a positive effect on part-time forestry households (where forestry income accounts for between 5 and 50% of total income). In contrast, it has a negative impact on full-time forestry households (forestry income accounts for more than 50%), and no clear effect on nonforestry households whose forestry income is less than 5%. This research also shows that the positive effect of CFM on farmers' total income is mainly due to increase of off-farm income driven by CFM, while the negative effects consist of CFM's reduction of forestry income.

The extent of CFM's economic benefits to farmers is uncertain and largely unexplored. This paper analyzes the impact of CFM on income structure to explore the mechanisms explaining its effects on farmers' income. There are still challenges in ensuring the reliability and accuracy of CFM assessment. This paper collected natural experimental data and used the estimation technology of PSM-DID to solve the possible endogeneity problems.