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1 – 10 of 157Lijuan Shi, Zuoning Jia, Huize Sun, Mingshu Tian and Liquan Chen
This paper aims to study the affecting factors on bird nesting on electronic railway catenary lines and the impact of bird nesting events on railway operation.
Abstract
Purpose
This paper aims to study the affecting factors on bird nesting on electronic railway catenary lines and the impact of bird nesting events on railway operation.
Design/methodology/approach
First, with one year’s bird nest events in the form of unstructured natural language collected from Shanghai Railway Bureau, the records were structured with the help of python software tool. Second, the method of root cause analysis (RCA) was used to identify all the possible influencing factors which are inclined to affect the probability of bird nesting. Third, the possible factors then were classified into two categories to meet subsequent analysis separately, category one was outside factors (i.e. geographic conditions related factors), the other was inside factors (i.e. railway related factors).
Findings
It was observed that factors of city population, geographic position affect nesting observably. Then it was demonstrated that both location and nesting on equipment part have no correlation with delay, while railway type had a significant but low correlation with delay.
Originality/value
This paper discloses the principle of impacts of nest events on railway operation.
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The authors investigate natural disasters’ impact on manufacturing and services foreign direct investment (FDI), both, in contemporaneous and time-lag contexts. Manufacturing and…
Abstract
Purpose
The authors investigate natural disasters’ impact on manufacturing and services foreign direct investment (FDI), both, in contemporaneous and time-lag contexts. Manufacturing and services FDI account for different types of technology transfers, respectively, through tangible physical assets and intangible knowledge assets. This paper aims to hypothesize that natural disasters that have pronounced physical impact, have different effect on different sectoral FDI.
Design/methodology/approach
The authors merge a data set from emergency events database, which covers natural disasters occurrences with a sector-level data on FDI for 69 countries for the period 1980-2011, distinguishing between four different kinds of natural disasters such as meteorological, climate, hydrological and geophysical, as well as between different geographical regions.
Findings
Controlling for commonly accepted determinants of FDI, such as output growth, quality of institutions and natural resource abundance, the authors find that manufacturing FDI is negatively affected immediately after the disaster and positively in the longer run- a finding that is in unison with the “creative destruction” growth theory. Services FDI, on the other hand, do not show such pattern. Meteorological disasters have no effect on services FDI and climate and hydrological disasters have long-lasting negative effects. For both, manufacturing and services FDI, geophysical disasters have a positive impact on FDI in the long run.
Research limitations/implications
The study is limited to 69 countries for the period 1980-2011.
Practical implications
FDI bears tangible and intangible knowledge assets and provides means of financing, even in countries with under-developed banking systems and stock markets. FDI is impacted by climate change, manifested by intensifying and increase of frequency of natural disasters.
Social implications
Natural disasters destroy infrastructure and displace people. The rebuilding of infrastructure and intangible capital present an opportunity for upgrading.
Originality/value
This is the first study that analyzes the impact of natural disasters on sector-level FDI in a multicounty and regional context.
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Mehmet Kursat Oksuz and Sule Itir Satoglu
Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response…
Abstract
Purpose
Disaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.
Design/methodology/approach
This study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.
Findings
Using a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.
Originality/value
This study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.
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The paper proposes an efficient and insightful approach for solving neutral delay differential equations (NDDE) with high-frequency inputs. This paper aims to overcome the need to…
Abstract
Purpose
The paper proposes an efficient and insightful approach for solving neutral delay differential equations (NDDE) with high-frequency inputs. This paper aims to overcome the need to use a very small time step when high frequencies are present. High-frequency signals abound in communication circuits when modulated signals are involved.
Design/methodology/approach
The method involves an asymptotic expansion of the solution and each term in the expansion can be determined either from NDDE without oscillatory inputs or recursive equations. Such an approach leads to an efficient algorithm with a performance that improves as the input frequency increases.
Findings
An example shall indicate the salient features of the method. Its improved performance shall be shown when the input frequency increases. The example is chosen as it is similar to that in literature concerned with partial element equivalent circuit (PEEC) circuits (Bellen et al., 1999). Its structure shall also be shown to enable insights into the behaviour of the system governed by the differential equation.
Originality/value
The method is novel in its application to NDDE as arises in engineering applications such as those involving PEEC circuits. In addition, the focus of the method is on a technique suitable for high-frequency signals.
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Noemi Manara, Lorenzo Rosset, Francesco Zambelli, Andrea Zanola and America Califano
In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme…
Abstract
Purpose
In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.
Design/methodology/approach
Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.
Findings
The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.
Originality/value
The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.
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Young-Tae Chang and Paul T.W. Lee
Port competition attracted much scholarly attention in Northwest Europe during the 1980s. Following the rise of powerful economies in East Asia, particularly during the 1980s and…
Abstract
Port competition attracted much scholarly attention in Northwest Europe during the 1980s. Following the rise of powerful economies in East Asia, particularly during the 1980s and 1990s, port competition has become an important phenomenon with the top five container ports in the world being located in the region. This paper aims to overview major port competition issues and outlines and analyzes the main alternative methodologies that researchers have employed to address them, referring to 70 items, mostly papers but including a few books and reports
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Yuhan Liu, Linhong Wang, Ziling Zeng and Yiming Bie
The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.
Abstract
Purpose
The purpose of this study is to develop an optimization method for charging plans with the implementation of time-of-day (TOD) electricity tariff, to reduce electricity bill.
Design/methodology/approach
Two optimization models for charging plans respectively with fixed and stochastic trip travel times are developed, to minimize the electricity costs of daily operation of an electric bus. The charging time is taken as the optimization variable. The TOD electricity tariff is considered, and the energy consumption model is developed based on real operation data. An optimal charging plan provides charging times at bus idle times in operation hours during the whole day (charging time is 0 if the bus is not get charged at idle time) which ensure the regular operation of every trip served by this bus.
Findings
The electricity costs of the bus route can be reduced by applying the optimal charging plans.
Originality/value
This paper produces a viable option for transit agencies to reduce their operation costs.
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Karlo Puh and Marina Bagić Babac
As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism…
Abstract
Purpose
As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.
Design/methodology/approach
This paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.
Findings
The performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.
Practical implications
The proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.
Originality/value
This study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.
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This paper aims to test the hypothesis that the effect of production slowdown on labour demand can be muted by labour hoarding.
Abstract
Purpose
This paper aims to test the hypothesis that the effect of production slowdown on labour demand can be muted by labour hoarding.
Design/methodology/approach
This study adopts a production function approach, using data from Malta, a small state in the EU.
Findings
The results confirm the hypothesis and indicate that firms are normally prepared to employ and dismiss more workers in the long run than in the short run.
Practical implications
This finding has important implications for developed countries, including that labour hoarding can be of certain relevance in times of economic slowdown as shocks are absorbed by internal flexibility.
Originality/value
The results of this study add on to the existing literature in two ways. First, this study compares two industries –manufacturing and financial services– for which the former sector received support to hoard labour after the financial turmoil of 2008. Consequently, the dominance of labour hoarding in manufacturing relative to financial services is uncovered and the effect of hoarding practices on labour demand is estimated. Second, Malta is an interesting case because it is one of the smallest economies in the world and faces a high degree of vulnerability because of constraints associated with small size and insularity. As a result, firms adopt policy-induced measures to minimise adjustment costs.
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Adrian Fernando Rivera, Neale R. Smith and Angel Ruiz
Food banks play an increasingly important role in society by mitigating hunger and helping needy people; however, research aimed at improving food bank operations is limited.
Abstract
Purpose
Food banks play an increasingly important role in society by mitigating hunger and helping needy people; however, research aimed at improving food bank operations is limited.
Design/methodology/approach
This systematic review used Web of Science and Scopus as search engines, which are extensive databases in Operations Research and Management Science. Ninety-five articles regarding food bank operations were deeply analyzed to contribute to this literature review.
Findings
Through a systematic literature review, this paper identifies the challenges faced by food banks from an operations management perspective and positions the scientific contributions proposed to address these challenges.
Originality/value
This study makes three main contributions to the current literature. First, this study provides new researchers with an overview of the key features of food bank operations. Second, this study identifies and classifies the proposed optimization models to support food bank managers with decision-making. Finally, this study discusses the challenges of food bank operations and proposes promising future research avenues.
Details