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1 – 10 of 674Stefania Stellacci, Leonor Domingos and Ricardo Resende
The purpose of this research is to test the effectiveness of integrating Grasshopper 3D and measuring attractiveness by a categorical based evaluation technique (M-MACBETH) for…
Abstract
Purpose
The purpose of this research is to test the effectiveness of integrating Grasshopper 3D and measuring attractiveness by a categorical based evaluation technique (M-MACBETH) for building energy simulation analysis within a virtual environment. Set of energy retrofitting solutions is evaluated against performance-based criteria (energy consumption, weight and carbon footprint), and considering the preservation of the cultural value of the building, its architectural and spatial configuration.
Design/methodology/approach
This research addresses the building energy performance analysis before and after the design of retrofitting solutions in extreme climate environments (2030–2100). The proposed model integrates data obtained from an advanced parametric tool (Grasshopper) and a multi-criteria decision analysis (M-MACBETH) to score different energy retrofitting solutions against energy consumption, weight, carbon footprint and impact on architectural configuration. The proposed model is tested for predicting the performance of a traditional timber-framed dwelling in a historic parish in Lisbon. The performance of distinct solutions is compared in digitally simulated climate conditions (design scenarios) considering different criteria weights.
Findings
This study shows the importance of conducting building energy simulation linking physical and digital environments and then, identifying a set of evaluation criteria in the analysed context. Architects, environmental engineers and urban planners should use computational environment in the development design phase to identify design solutions and compare their expected impact on the building configuration and performance-based behaviour.
Research limitations/implications
The unavailability of local weather data (EnergyPlus Weather File (EPW) file), the high time-resource effort, and the number/type of the energy retrofit measures tested in this research limit the scope of this study. In energy simulation procedures, the baseline generally covers a period of thirty, ten or five years. In this research, due to the fact that weather data is unavailable in the format required in the simulation process (.EPW file), the input data in the baseline is the average climatic data from EnergyPlus (2022). Additionally, this workflow is time-consuming due to the low interoperability of the software. Grasshopper requires a high-skilled analyst to obtain accurate results. To calculate the values for the energy consumption, i.e. the values of energy per day of simulation, all the values given per hour are manually summed. The values of weight are obtained by calculating the amount of material required (whose dimensions are provided by Grasshopper), while the amount of carbon footprint is calculated per kg of material. Then this set of data is introduced into M-MACBETH. Another relevant limitation is related to the techniques proposed for retrofitting this case study, all based on wood-fibre boards.
Practical implications
The proposed method for energy simulation and climate change adaptation can be applied to other historic buildings considering different evaluation criteria and context-based priorities.
Social implications
Context-based adaptation measures of the built environment are necessary for the coming years due to the projected extreme temperature changes following the 2015 Paris Agreement and the 2030 Agenda. Built environments include historical sites that represent irreplaceable cultural legacies and factors of the community's identity to be preserved over time.
Originality/value
This study shows the importance of conducting building energy simulation using physical and digital environments. Computational environment should be used during the development design phase by architects, engineers and urban planners to rank design solutions against a set of performance criteria and compare the expected impact on the building configuration and performance-based behaviour. This study integrates Grasshopper 3D and M-MACBETH.
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Guilherme Dayrell Mendonça, Stanley Robson de Medeiros Oliveira, Orlando Fontes Lima Jr and Paulo Tarso Vilela de Resende
The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport…
Abstract
Purpose
The objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.
Design/methodology/approach
The research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).
Findings
Shipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.
Originality/value
These findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.
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Su-Lan Pan, Lingqiong Wu and Alastair M. Morrison
The purpose of this study is to review empirical studies on the relationship between climate change and tourism for a period of 15 years, from 2007 to 2021. The main variables…
Abstract
Purpose
The purpose of this study is to review empirical studies on the relationship between climate change and tourism for a period of 15 years, from 2007 to 2021. The main variables analyzed were research subjects, topics and economic development levels.
Design/methodology/approach
Literature review was used to analyze articles published on climate change and tourism from 2007 to 2021. A staged article selection process was followed using the Scopus database. Statistical comparison tests found differences among sub-groupings of articles.
Findings
The research articles on climate change and tourism continued their upward trajectory until 2021. The 893 articles analyzed were published in 254 different journals, with over 60% from non-tourism or cross-disciplinary journals. Significant differences were found by time period and between developed and developing countries.
Research limitations/implications
Gaps in the literature were detected with respect to policy analysis and it was concluded that the research for developing nations remains insufficient. More research should be encouraged to focus on the situation and solutions to climate change and tourism in developing countries. Additional research is also needed on biodiversity declines in destinations because of climate change.
Originality/value
This research dealt exclusively with empirical research studies in academic articles. It compared results across three different time periods and between developing and developed countries. Statistical tests supported the comparisons.
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The aim of this research paper is to investigate entrepreneurial opportunities through digital technology among agrifood businesses. Specifically, the research paper uses resource…
Abstract
Purpose
The aim of this research paper is to investigate entrepreneurial opportunities through digital technology among agrifood businesses. Specifically, the research paper uses resource bricolage theory to evaluate the various activities that agrifood businesses conduct through digital technology, and whether these businesses realise their full potential from these activities.
Design/methodology/approach
Data are gathered from 22 semi-structured interviews with representatives of small agrifood businesses. Maximum variation sampling was used to ensure that respondents were representative of different types of agrifood businesses across the food supply chain. Interview data were analysed through thematic analysis.
Findings
Agrifood businesses engage in a range of activities through digital technology, however, findings point to a continuum of different attitudes among respondents towards the adoption of digital technology, ranging from passive to proactive attitudes. Notable themes from the research identified efficiency and productivity, usability, marketing and connectivity as issues in the adoption of digital technology by agrifood businesses. However, these businesses were less likely to engage in cutting-edge technology activities.
Originality/value
This research contributes to emerging research on digital entrepreneurship, but particularly on digital entrepreneurship in the agrifood sector. This builds on existing debates relating to the passive nature of agrifood businesses towards growth opportunities. The use of research bricolage is also a novel theoretical approach to research on this topic. The development of a digital technology adoption continuum provides businesses and policymakers with a deeper understanding of how digital entrepreneurship opportunities can be harnessed.
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Sayamol Charoenratana and Samridhi Kharel
As climate change increasingly affects rural food production, there is an urgent need to adopt agricultural adaptation strategies. Because the agricultural sector in Nepal is one…
Abstract
Purpose
As climate change increasingly affects rural food production, there is an urgent need to adopt agricultural adaptation strategies. Because the agricultural sector in Nepal is one of the most vulnerable to the effects of climate change, the adaptation strategies of household farmers in rural areas are crucial. This study aims to address the impacts of agricultural climate change adaptation strategies in Nepal. The research empirically analyzed climate hazards, adaptation strategies and local adaptation plans in Mangalsen Municipality, Achham District, Sudurpashchim Province, Nepal.
Design/methodology/approach
This study used a purposive sampling of household lists, categorized as resource-rich, resource-poor and intermediate households. The analysis used primary data from 110 household surveys conducted among six focus groups and 30 informants were selected for interviews through purposive random sampling.
Findings
Climate change significantly impacts rainfall patterns and temperature, decreasing agriculture productivity and increasing household vulnerability. To overcome these negative impacts, it is crucial to implement measures such as efficient management of farms and livestock. A comprehensive analysis of Nepalese farmers' adaptation strategies to climate change has been conducted, revealing important insights into their coping mechanisms. By examining the correlation between farmers' strategies and the role of the local government, practical policies can be developed for farmers at the local level.
Originality/value
This study represents a significant breakthrough in the authors' understanding of this issue within the context of Nepal. It has been conclusively demonstrated that securing land tenure or land security and adopting appropriate agricultural methods, such as agroforestry, can be instrumental in enabling Nepalese households to cope with the effects of climate change effectively.
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Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled…
Abstract
Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled technology to improve guest experiences and streamline operations. This technology allows travellers to manage their travel plans, request services and get information through natural voice commands on any voice-enabled device. Voice assistants are also multilingual, allowing hotels to customise responses to guests who do not speak the local language. Angie, a multilingual, in-room voice assistant, is an example of this technology. It can fulfil guest requests, answer common questions about the property and create streamlined access to a wide range of hotel amenities, such as ordering room service or requesting extra towels. Hotels can control questions and responses to assist stretched staff and provide upsell and advertising revenue through digital promotions or recommended onsite amenities or discounts. In addition, voice command technology can be used to book travel and find things to do at a destination. Google Assistant can help with travel plans like booking a hotel, checking flight status and finding things to do at a destination. In conclusion, voice command and voice search technology are transforming the hospitality and tourism industry by improving guest experiences, reducing operational costs and increasing revenue.
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Tamara Apostolou, Ioannis N. Lagoudis and Ioannis N. Theotokas
This paper aims to identify the interplay of standard Capesize optimal speeds for time charter equivalent (TCE) maximization in the Australia–China iron ore route and the optimal…
Abstract
Purpose
This paper aims to identify the interplay of standard Capesize optimal speeds for time charter equivalent (TCE) maximization in the Australia–China iron ore route and the optimal speeds as an operational tool for compliance with the International Maritime Organization (IMO) carbon intensity indicator (CII).
Design/methodology/approach
The TCE at different speeds have been calculated for four standard Capesize specifications: (1) standard Capesize with ecoelectronic engine; (2) standard Capesize with non-eco engine (3) standard Capesize vessel with an eco-electronic engine fitted with scrubber and (4) standard Capesize with non-eco engine and no scrubber fitted.
Findings
Calculations imply that in a highly inflationary bunker price context, the dollar per ton freight rates equilibrates at levels that may push optimal speeds below the speeds required for minimum CII compliance (C Rating) in the Australia–China trade. The highest deviation of optimal speeds from those required for minimum CII compliance is observed for non-eco standard Capesize vessels without scrubbers. Increased non-eco Capesize deployment would see optimal speeds structurally lower at levels that could offer CII ratings improvements.
Originality/value
While most of the studies have covered the use of speed as a tool to improve efficiency and emissions in the maritime sector, few have been identified in the literature to have examined the interplay between the commercial and operational performance in the dry bulk sector stemming from the freight market equilibrium. The originality of this paper lies in examining the above relation and the resulting optimal speed selection in the Capesize sector against mandatory environmental targets.
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Tuncer Akay and Cevahir Tarhan
One of the sectors most affected by the variable weather events caused by climate change and global warming is the aviation sector. Especially in aircraft accidents, weather…
Abstract
Purpose
One of the sectors most affected by the variable weather events caused by climate change and global warming is the aviation sector. Especially in aircraft accidents, weather events increasing with climate change and global warming are effective. The purpose of this study is to determine how much the change in weather conditions caused by global warming and climate changes affect the aircraft in the world between the years 2010 and 2022.
Design/methodology/approach
In this study, it was investigated which weather events were more effective in aircraft crashes by determining the rates of air events and aircraft crashes in aircraft crashes with a passenger capacity of 12 or more that occurred between 2010 and 2022.
Findings
It is clearly seen that increasing weather conditions with global warming and climate change increase the effect of weather conditions in aircraft crashes.
Originality/value
The difference of this study from other studies is the evaluation of the data of the past 12 years, in which the increasing consequences of global warming and climate change have been felt more. It also reveals the necessity of further research on the effects of weather conditions on aircraft.
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Priyanka Chawla, Rutuja Hasurkar, Chaithanya Reddy Bogadi, Naga Sindhu Korlapati, Rajasree Rajendran, Sindu Ravichandran, Sai Chaitanya Tolem and Jerry Zeyu Gao
The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives…
Abstract
Purpose
The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.
Design/methodology/approach
This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.
Findings
The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.
Research limitations/implications
The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.
Practical implications
To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.
Social implications
To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.
Originality/value
The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.
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Yan Zhou and Chuanxu Wang
Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to…
Abstract
Purpose
Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.
Design/methodology/approach
This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.
Findings
The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.
Originality/value
Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.
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