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The purpose of this paper is to scrutinize the structure of the federal crop insurance program and test whether participating private insurers screen insurance buyers better than the federal agency.

This paper regresses the claim payout on the risk share of private insurers in insurance pools and other relevant variables. The claim payout should be negatively related with the private insurers’ risk share if private insurers screen insurance buyers better than the federal agency.

The payout rates are significantly lower for reinsurance funds with higher risk shares of AIPs, and the relationship between the two variables is not affected much by the aggregate yield (similar relationship in good crop years and bad crop years).

The federal government could improve the effectiveness and the efficiency of the crop insurance program by restructuring its delivery system.

The novel contributions of this paper include estimating the economic significance of private insurers’ screening advantage and showing that the economic significance is similar in good crop years and bad crop years.

Micro-, small-, and medium-sized enterprise (MSME) sectors have a strong influence on the Egyptian economy. Yet, effective formulation of integrated cluster along the value chain can lead MSMEs to become international actors in export-oriented activities. An Agro-Industrial Park is an integrated cluster taking into consideration synergetic agglomeration of agribusiness MSMEs assisting in decreasing deprivation of rural territories. This research aims to present the needs of agriculture MSMEs in Egypt, to describe challenges faced by this specific sector, and to set guidelines for a business model integrating small farmers and producers into an integrated agribusiness cluster. The researcher followed purposive/nonprobability sampling technique of 100 MSMEs research participants. A combination of questionnaires and open-ended interviews were the research instruments of choice along with secondary data. This research focused on agribusiness only and specifically MSMEs in Egypt. There is no agribusiness-integrated cluster in Egypt, even though the agricultural land increased significantly in the past 5 years, leading us to believe that it is imperative to develop agribusiness-integrated clusters in the coming few years.

The purpose of the study is to gain insights into the post-acquisition integration practices of Indian multinational companies involved in cross-border acquisitions.

The study is based on the primary data from a sample of Indian companies engaged in cross-border acquisitions. A survey (based on a structured questionnaire) method has been used to collect the relevant data.

Majority of the sample companies have successfully managed the post-acquisition phase and realized the anticipated synergies. These companies recognize the importance of cultural integration, people/HR integration and formal communication channel during the post-acquisition phase. Despite the integration strategy being profoundly influenced by cultural differences between two companies, the emphasis on these cultural differences especially during the due-diligence stage is lacking. These aspects have not been examined adequately during the due-diligence phase.

Based on the findings, a model of cross-border acquisition and integration process (AIP) in the context of emerging economies has been proposed; the model is expected to be applicable across all industries and organizations, especially in emerging economies. The proposed model should essentially help senior and middle managers to develop successful integration strategies. Moreover, the study holds immense potential for practitioners and academicians by providing them with a new thought on executing successful acquisitions.

To the best of the authors’ knowledge, no study has examined the post-acquisition integration approach in emerging economies. Moreover, rarely has any AIP model applicable across several organizations of all sizes and types from emerging economies been suggested in the literature. The suggested AIP model is the unique proposition of the paper.

This paper aims to present a survey concerning the accuracy of thermoplastic polymeric parts fabricated by additive manufacturing (AM). Based on the scientific literature, the aim is to provide an updated map of trends and gaps in this relevant research field. Several technologies and investigation methods are examined, thus giving an overview and analysis of the growing body of research.

Permutations of keywords, which concern materials, technologies and the accuracy of thermoplastic polymeric parts fabricated by AM, are used for a systematic search in peer-review databases. The selected articles are screened and ranked to identify those that are more relevant. A bibliometric analysis is performed based on investigated materials and applied technologies of published papers. Finally, each paper is categorised and discussed by considering the implemented research methods.

The interest in the accuracy of additively manufactured thermoplastics is increasing. The principal sources of inaccuracies are those shrinkages occurring during part solidification. The analysis of the research methods shows a predominance of empirical approaches. Due to the experimental context, those achievements have consequently limited applicability. Analytical and numerical models, which generally require huge computational costs when applied to complex products, are also numerous and are investigated in detail. Several articles deal with artificial intelligence tools and are gaining more and more attention.

The cross-technology survey on the accuracy issue highlights the common critical aspects of thermoplastics transformed by AM. An updated map of the recent research literature is achieved. The analysis shows the advantages and limitations of different research methods in this field, providing an overview of research trends and gaps.

This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.

A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.

The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.

The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field.

Articles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis.

Synthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models.

To the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions.

本研究旨在对酒店需求预测进行全面回顾, 以确定其关键基础和演变以及未来的研究方向和趋势, 以推动该领域的发展。

使用严格和透明的纳入和排除的标准对酒店需求建模和预测的文章进行识别和选择。通过内容分析, 最终有 85个实证研究作为综合分析的样本。

综合文献发现, 基于历史需求数据的酒店预测在研究中占主导地位, 近年来预订/取消数据和组合数据逐渐引起研究关注。在模型演化方面, 时间序列和基于人工智能的模型是最受欢迎的酒店需求预测模型。审查结果表明, 许多研究都集中在混合模型和基于 AI 的模型上。

本研究是第一次从数据源和方法发展的角度对酒店需求预测文献进行系统回顾, 并指出未来的研究方向。

Este estudio tiene como objetivo proporcionar una revisión amplia de la previsión sobre la demanda hotelera a la hora de identificar sus fundamentos clave, la evolución y las direcciones y tendencias de investigación futuras para avanzar en el campo de estudio.

Se identificaron y seleccionaron de forma rigurosa artículos sobre modelado y previsión de la demanda hotelera utilizando criterios transparentes de inclusión y exclusión. Se obtuvo una muestra final de 85 estudios empíricos para su análisis integral a través del análisis de contenido.

La síntesis de la literatura destaca que la previsión hotelera basada en datos históricos de demanda ha dominado la investigación, y los datos de reserva/cancelación, así como los datos combinados han atraído gradualmente en los últimos años la atención de la investigación. En términos de evolución del modelo, las series temporales y los modelos basados en IA son los modelos más populares para la previsión de la demanda hotelera. Los resultados de la revisión muestran que numerosos estudios se han centrado en modelos híbridos y basados en IA.

Este estudio es la primera revisión sistemática de la literatura sobre la previsión de la demanda hotelera desde la perspectiva de la fuente de datos y el desarrollo metodológico e indica futuras líneas de investigación.

Various iron loss models can be used for the simulation of electrical machines. In particular, the effect of rotating magnetic flux density at certain geometric locations in a machine is often neglected by conventional iron loss models. The purpose of this paper is to compare the adapted IEM loss model for rotational magnetization that is developed within the context of this work with other existing models in the framework of a finite element simulation of an exemplary induction machine.

In this paper, an adapted IEM loss model for rotational magnetization, developed within the context of the paper, is implemented in a finite element method simulation and used to calculate the iron losses of an exemplary induction machine. The resulting iron losses are compared with the iron losses simulated using three other already existing iron loss models that do not consider the effects of rotational flux densities. The used iron loss models are the modified Bertotti model, the IEM-5 parameter model and a dynamic core loss model. For the analysis, different operating points and different locations within the machine are examined, leading to the analysis of different shapes and amplitudes of the flux density curves.

The modified Bertotti model, the IEM-5 parameter model and the dynamic core loss model underestimate the hysteresis and excess losses in locations of rotational magnetizations and low-flux densities, while they overestimate the losses for rotational magnetization and high-flux densities. The error is reduced by the adapted IEM loss model for rotational magnetization. Furthermore, it is shown that the dynamic core loss model results in significant higher hysteresis losses for magnetizations with a high amount of harmonics.

The simulation results show that the adapted IEM loss model for rotational magnetization provides very similar results to existing iron loss models in the case of unidirectional magnetization. Furthermore, it is able to reproduce the effects of rotational flux densities on iron losses within a machine simulation.

The purpose of this paper is to examine the effect of varying compaction pressure on magnetic properties of self-developed soft magnetic composite (SMC) cores. The change in shape of ferromagnetic hysteresis curves has – in turn – the impact on the values of hysteresis model parameters. The phenomenological GRUCAD model is chosen for description of hysteresis curves.

Several cylinder-shaped cores have been made from a mixture of iron powder and suspense polyvinyl chloride using a hydraulic press with a form and a band with a thermocouple for controlling heat treatment conditions. The only varying parameter in the study is the compaction pressure. The magnetic properties of developed cores have been measured using a computer-acquisition card and LabView software. The obtained hysteresis curves are fitted to the equations of the phenomenological GRUCAD model. This description is compliant with the laws of irreversible thermodynamics. The variations of model parameters are presented as functions of compacting pressure.

The compaction pressure has a significant impact on magnetic properties of self-developed SMC cores. The paper provides a number of charts useful for checking how the parameters of the hysteresis model are affected.

The present paper is limited to modelling symmetrical loops only. Description of more complex magnetization cycles is postponed to another, forthcoming paper.

The GRUCAD hysteresis model may be a useful tool for the designers of magnetic circuits. Its parameters depend on the processing conditions (in this study – the compaction pressure) of the SMC cores.

Modelling of magnetic properties of SMC cores has been carried so far using some well-known description like Preisach, Takács and Jiles–Atherton proposals. The GRUCAD model has a number of advantages, and it may be a useful alternative to the latter formalism. So far it has been used for description of hysteresis curves in conventional materials like non-oriented and grain-oriented electrical steels. In the present work, it is applied to novel SMC materials.

It has been revealed that application of the differential form of Eringen’s nonlocal elasticity theory to some cases (e.g. cantilevers) leads to paradoxical results, and recourse must be made to the integral version of Eringen’s nonlocal model. The purpose of this paper, within the framework of integral form of Eringen’s nonlocal theory, is to study the bending behavior of nanoscale plates with various boundary conditions using the isogeometric analysis (IGA).

The shear deformation effect is taken into account according to the Mindlin plate theory, and the minimum total potential energy principle is utilized in order to derive the governing equations. The relations are obtained in the matrix-vector form which can be easily employed in IGA or finite element analysis. For the comparison purpose, the governing equations are also derived based on the differential nonlocal model and are then solved via IGA. Comparisons are made between the predictions of integral nonlocal model, differential nonlocal model and local (classical) model.

The bending analysis of nanoplates under some kinds of edge supports indicates that using the differential model leads to paradoxical results (decreasing the maximum deflection with increasing the nonlocal parameter), whereas the results of integral model are consistent.

A new nonlocal formulation is developed for the IGA of Mindlin nanoplates. The nonlocal effects are captured based on the integral model of nonlocal elasticity. The formulation is developed in matrix-vector form which can be readily used in finite element method. Comparisons are made between the results of differential and integral models for the bending problem. The proposed integral model is capable of resolving the paradox appeared in the results of differential model.

To understand the behavior of the magnetization processes in ferromagnetic materials in function of temperature, a temperature-dependent hysteresis model is necessary. This study aims to investigate how temperature can be accounted for in the energy-based hysteresis model, via an appropriate parameter identification and interpolation procedure.

The hysteresis model used for simulating the material response is energy-consistent and relies on thermodynamic principles. The material parameters have been identified by unidirectional alternating measurements, and the model has been tested for both simple and complex excitation waveforms. Measurements and simulations have been performed on a soft ferrite toroidal sample characterized in a wide temperature range.

The analysis shows that the model is able to represent accurately arbitrary excitation waveforms in function of temperature. The identification method used to determine the model parameters has proven its robustness: starting from simple excitation waveforms, the complex ones can be simulated precisely.

As parameters vary depending on temperature, a new parameter variation law in function of temperature has been proposed.

A complete static hysteresis model able to take the temperature into account is now available. The identification is quite simple and requires very few measurements at different temperatures.

The results suggest that it is possible to predict magnetization curves within the measured range, starting from a reduced set of measured data.