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The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

Chinese multinational enterprises’ (MNEs) unprecedented, aggressive cross-border mergers and acquisitions (CBMAs) have led to several studies examining Chinese CBMAs, which importantly has also led to some degree of “theorising”. This study aims to undertake a “non-theoretical” fact-finding exercise before any theorising and empirical “causal” examination for a better understanding of the phenomenon (the rise of Chinese CBMAs).

This study uses a “stylised facts” approach which documents “empirical regularities” concerning Chinese CBMAs and thus guides new research questions.

Several facts are documented. Firstly, both the value and frequency of Chinese CBMAs are catching up to greenfield investments, with CBMA deals being larger in scale but lower in frequency. Secondly, Chinese CBMAs show a global reach away from the regional orientation of their early years. Thirdly, Chinese MNEs are possibly transforming their value chain with industrial upgrading as an aim. Fourthly, Chinese “full” acquisitions of targets have surged, especially in OECD countries, suggestive of Chinese MNEs’ “radical” acquisition approaches.

The gathered facts lend support to the view of the need for such fact-finding exercises to explicate and shed “new” light on the phenomenon (beyond our “current” views/beliefs). An understanding of the underlying trends beyond bare facts can also identify new knowledge, which can in turn provide new directions for research.

This paper aims at developing an improved method, based on binary search algorithm (BSA) for the steady-state analysis of self-excited induction generators (SEIGs), which are increasingly used in wind energy electric conversion systems. The BSA is also compared with linear search algorithm (LSA) to bring out the merits of BSA over LSA.

All the parameters of SEIG, including the varying core loss of the machine, have been considered to ensure accuracy in the predetermined performance values of the set up. The nodal admittance method has been adopted to simplify the equivalent circuit of the generator and load. The logic and steps involved in the formulation of the complete procedure have been illustrated using elaborate flowcharts.

The proposed approach is validated by the experimental results, obtained on a three-phase 240 V, 5.0 A, 2.0 kW SEIG, which closely match with the corresponding predicted performance values. The analysis is shown to be easy to implement with reduced computation time.

A novel improved and simplified technique has been formulated for estimating the per unit frequency (a), magnetizing reactance (X_m) and core loss resistance (R_m) of the SEIG using the nodal admittance of its equivalent circuit. The accuracy of the predetermined performance is enhanced by considering the SEIG’s varying core loss. Only simple MATLAB programming has been used for adopting the algorithms.

This study investigates if the causal relationships between the exchange rates and selected inbound markets’ tourism demand are temporary or permanent, and compares market reactions in Türkiye.

Tourism demand is examined with a regional approach, focusing on the geographical markets, namely Europe, Commonwealth of Independent States (CIS) members and Asian countries, as the top inbound tourism markets, in addition to the total number of inbound tourists to Türkiye. Granger, frequency-domain causality, asymmetric Toda–Yamamoto, and asymmetric frequency-domain causality tests were employed to investigate and compare markets on exchange rate–tourism demand relationship for 2008M01-2020M02.

The results indicate that exchange rates affect European tourism demand both in the short and long run. The meaning of this Frequency Domain Causality (FDC) analysis finding shows that the exchange rate has both permanent and temporary effects on European tourists. The relationships are statistically insignificant for CIS members and Asian countries. The exchange rates also permanently affect total inbound tourism demand, but the independent variable has no short-run (temporary) effects on total demand. Asymmetric causality tests confirmed a permanent causality relationship from the positive and negative components of exchange rates to the positive and negative components of European and total tourism demand.

The Granger causality test provides information on the presence of a causal relation, while the FDC test, an extended version of Granger causality, enlightens the short- (temporary) and long-run (permanent) relationships and allows for analyzing the duration of the impact. In addition, asymmetric causality relationships are also investigated in the study. Besides, this study is the first in the literature to examine the relationship between tourism demand and the exchange rate regionally (continentally) for Türkiye.

This paper aims to analyze deeply the effect of surface roughness conditions of the common interface of the two-layered riveted cantilever beams on their frictional damping during free lateral vibration at first mode. Here, the product, (µ × α), and damping ratio, ξ, are the parameters whose variations are analyzed in this investigation. For this, the influencing parameters considered are the natural frequency of vibration, f; the amplitude of initial excitation, y; and surface roughness value, Ra.

For experimentally evaluating logarithmic damping decrement, d, the frequency response function analyzer for the case of free lateral vibrations was used. Later, for evaluating the product, µ × α (where µ is the kinematic coefficient of friction and α is the dynamic slip ratio), and then, the damping ratio, ξ, the empirical relation suggested for logarithmic damping decrement, d, of riveted cantilever beams was used. After this, the full and reduced quadratic models of the product, µ × α, ξ, response surface methodology (RSM) with the help of Design Expert 11 software was used. Corresponding main effects plots, surface plots and prediction comparison plots were obtained to observe the variations of the product, µ × α, ξ for the variations of influencing parameters: f, y and Ra. Finally, a machine learning technique such as artificial neural networks (ANNs) using “nntool” present in MATLAB R13a software was used to predict the ξ for the different combinations of f, y and Ra.

The full and reduced quadratic regression models for the product, (µ × α) and the damping ratio, ξ of riveted cantilever beams for free lateral vibrations of the first mode in terms of the parameters: f, y and Ra were obtained. In addition, the main effects plots, surface plots and prediction comparison plots for the product, µ × α, ξ, with the corresponding experimental values of the product, µ × α, ξ, were obtained. Also, the execution of ANNs using MATLAB R13a software is proved to be the more accurate tool for the prediction of damping ratios in comparison to quadratic regression equations obtained from Design Expert 11 software. In the end, the assumption that the effect of surface roughness value on the product, (µ × α), and the damping ratio, ξ, is negligible is proved to be true using the main effects plots for the product, (µ × α) and ξ obtained from the Design Expert 11 software.

Obtaining the full and reduced quadratic regression equations for the product, (µ × α), and ξ of the two-layered riveted cantilever beams in terms of parameters: f, y and Ra was done. In addition, the conditions for the corresponding minimum and maximum values of the product, (µ × α), and ξ were obtained. Later, the main effects plots, surface plots and comparison plots of the predicted product, (µ × α), and ξ versus experimental product, (µ × α), and ξ were also obtained. Finally, the predicted values of the product, (µ × α), and ξ using the ANNs tool are observed to be the more accurate values in comparison to that obtained from RSM using the Design Expert 11 software.

The purpose of this study is to numerically model the rigid pavement resting on two-parameter soil and to examine its modal parameters.

This study is carried out using a one-dimensional beam element with three rotational and three translational degrees of freedom based on the finite element method. MATLAB programming is used to perform the free vibration analysis of the rigid pavement.

Cyclic frequency and their corresponding mode shapes were determined. It has been investigated how cyclic frequency changes as a result of variations in the thickness, span length of pavement, shear modulus, modulus of subgrade, different boundary conditions and element discretization. Thickness of the pavement and span length has greater effect on the cyclic frequency. Maximum increase of 29.7% is found on increasing the thickness, whereas the cyclic frequency decreases by 63.49% on increasing span length of pavement.

The pavement's free vibration is the sole subject of the current investigation. This study limits for the preliminary design phase of rigid pavements, where a complete three-dimensional finite element analysis is unnecessary. The current approach can be extended to future research using a different method, such as finite element grilling technique, mesh-free technique on reinforced concrete pavements or jointed concrete pavements.

The finite element approach adopted in this paper involves six degrees of freedom for each node. Furthermore, to the best of the authors’ knowledge, no prior study has done seven separate parametric investigations on the modal analysis of rigid pavement resting on two-parameter soil.

This paper aims to present the simulation, fabrication and testing of a novel ultra-wide band (UWB) band-pass filters (BPFs) with better transmission and rejection characteristics on a low-loss Taconic substrate and analyze using the coupled theory of resonators for UWB range covering L, S, C and X bands for radars, global positioning system (GPS) and satellite communication applications.

The filter is designed with a bent coupled transmission line on the top copper layer. Defected ground structures (DGSs) like complementary split ring resonators (CSRRs), V-shaped resonators, rectangular slots and quad circle slots (positioned inwards and outwards) are etched in the ground layer of the filter. The circular orientation of V-shaped resonators adds compactness when linearly placed. By arranging the quad circle slots outwards and inwards at the corner and core of the ground plane, respectively, two filters (Filters I and II) are designed, fabricated and measured. These two filters feature a quasi-elliptic response with transmission zeros (TZs) on either side of the bandpass response, making it highly selective and reflection poles (RPs), resulting in a low-loss filter response. The transmission line model and coupled line theory are implemented to analyze the proposed filters.

Two filters by placing the quad circle slots outwards (Filter I) and inwards (Filter II) were designed, fabricated and tested. The fabricated model (Filter I) provides transmission with a maximum insertion loss of 2.65 dB from 1.5 GHz to 9.2 GHz. Four TZs and five RPs are observed in the frequency response. The lower and upper stopband band width (BW) of the measured Filter I are 1.2 GHz and 5.5 GHz of upper stopband BW with rejection level greater than 10 dB, respectively. Filter II (inward quad circle slots) operates from 1.4 GHz to 9.05 GHz with 1.65 dB maximum insertion loss inside the passband with four TZs and four RPs, which, in turn, enhances the filter characteristics in terms of selectivity, flatness and stopband. Moreover, 1 GHz BW of lower and upper stopbands are observed. Thus, the fabricated filters (Filters I and II) are therefore evaluated, and the outcomes show good agreement with the electromagnetic simulation response.

The limitation of this work is the back radiation caused by DGS, which can be eradicated by placing the filter in the cavity and retaining its performance.

The proposed UWB BPFs with novel resonators find their role in the UWB range covering L, S, C and X bands for radars, GPS and satellite communication applications.

To the best of the authors’ knowledge, for the first time, the authors develop a compact UWB BPFs (Filters I and II) with BW greater than 7.5 GHz by combining reformed coupled lines and DGS resonators (CSRRs, V-shaped resonators [modified hairpin resonators], rectangular slots and quad circle slots [inwards and outwards]) for radars, GPS and satellite communication applications.

With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction.

In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting.

Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters.

This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction.

Even if there is a wealth of research highlighting the key role of values and cultural significance for heritage management and, defining specific interventions on built heritage, seldom the relation to their leading values and values hierarchy have been researched. How do values and interventions relate? What values trigger most and least interventions on heritage? How do these values relate and characterize interventions? And what are the values hierarchy that make the interventions on built heritage differ?

This paper conducts a systematic content analysis of 69 international doctrinal documents – mainly adopted by Council of Europe, UNESCO, and ICOMOS, during 1877 and 2021. The main aim is to reveal and compare the intervention concepts and their definitions, in relation to values. The intensity of the relationship between intervention concepts and values is determined based on the frequency of mentioned values per intervention.

There were three key findings. First, historic, social, and aesthetical values were the most referenced values in international doctrinal documents. Second, while intervention concepts revealed similar definitions and shared common leading values, their secondary values and values hierarchy, e.g. aesthetical or social values, are the ones influencing the variation on their definitions. Third, certain values show contradictory roles in the same intervention concepts from different documents, e.g. political and age values.

This paper explores a novel comparison between different interventions concepts and definitions, and the role of values. The results can contribute to support further research and practice on clarifying the identified differences.

Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches.

The work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information.

The proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators.

Fuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.