Search results

The sharing economy enables apartment owners to generate income from their assets. “Agoda Homes” is an online travel agent (OTA) that directly competes with Airbnb. A destination has to discover its competitiveness, but few studies have provided an overview of accommodation attributes in each destination, which are crucial to shaping its brand image. This paper aims to illustrate firm-generated content or attributes that apartment owners list about their properties on an OTA platform to comprehend factual information about apartments in each destination with various star ratings and user ratings and to formulate a research model for future studies.

Informational content and accommodation attributes for apartments are automatically collected using a Web scraping tool (the Data Miner). Descriptive statistics and text analysis (word cloud and word frequency) are used to analyze data.

Findings reveal the primary location, facilities, cleanliness and safety attributes for all apartments in each destination, along with star ratings and user ratings. A research framework for scholars is also suggested. Guidelines for stakeholders in the tourism industry are additionally furnished.

This work concentrates on apartments, which have received less attention in the tourism literature. The study gathers factual data from a website to mitigate respondent bias issues inherent in the traditional survey methods.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

Land value in urban areas in India is influenced by regulations, bylaws and the amenities associated with them. Planning interventions play a significant role in enhancing the quality of the neighbourhood, thereby resulting in a change in its value. Land is a distinct commodity due to its fixed location, and planning interventions are also specific to certain locations. Consequently, the factors influencing land value will vary across different areas. While recent literature has explored some determinants of land value individually, conducting a comprehensive study specific to each location would be more beneficial for making informed policy decisions. Therefore, this article aims to examine and identify the critical factors that impact the value of residential land in the National Capital Territory of Delhi, India.

The study employed a combination of semi-structured and structured interview methods to construct a Relative Importance Index (RII) and ascertain the critical determinants affecting residential land value. A sample of 36 experts, comprising property valuers, urban planners and real estate professionals operating within the National Capital Territory of Delhi, India, were selected using snowball sampling techniques. Subsequently, rank correlation and ANOVA methods were employed to evaluate the obtained results.

Location and stage of urban development are the most critical determinants in determining residential land values in the National Capital Territory of Delhi, India. The study identifies a total of 13 critical determinants.

A scenario planning approach can be developed to achieve an equitable distribution of values and land use entropy. A land value assessment model can also be developed to assist professional valuers.

There has been a lack of emphasis on assessing the impact of planning interventions and territorial regulation on land values in the context of Delhi. This study will contribute to policy decision-making by developing a rank list of planning-based determinants of land value.

Because of the global increase of climate change effects, floods are becoming more frequent and severer, especially in urban areas of coastal cities and islands where floodplains have turned into buildings because of rapid urbanisation, leading to a higher risk of damages. Urban heritage areas should be highly considered in the time of evacuation because of the vulnerability of streets and buildings and limitations on taking counteractions. Given these limitations, this study aims to propose a network of potential evacuation routes based on spatial configuration analysis of the heritage areas.

Penang Island's heritage site, namely, George Town, located on the northwest coast of Malaysia, is chosen as the case study. By using an approach of spatial configuration analysis using space syntax techniques in addition to considering the potential starting points for evacuation and flood risk map of the area, this study analysed the area's street network values for evacuation function during flood crisis time.

Potential evacuation routes were identified for flood disasters in the George Town heritage area. Furthermore, the proposed evacuation routes were evaluated in terms of time for evacuation by metric step-depth analysis of space syntax.

A few studies have focused on practical guidelines for evacuation routes based on spatial configuration analysis, an important yet neglected approach in this regard, especially concerning urban island areas. This study can contribute to providing strategies to reduce vulnerability and casualties in urban heritage areas.

The use of continuous fiber-reinforced filaments improves the mechanical properties obtained with the fused filament fabrication (FFF) process. Yet, there is a lack of simulation tailored tools to assist in the design for additive manufacturing of continuous fiber composites. To build such models, a precise elastic model is required. As the porosity caused by interbead voids remains an important flaw of the process, this paper aims to build an elastic model integrating this aspect.

To study the amount of porosity, which could be a failure initiator, this study proposes a two step periodic homogenization method. The first step concerns the microscopic scale with a unit cell made of fiber and matrix. The second step is at the mesoscopic scale and combines the elastic material of the first step with the interbead voids. The void content has been set as a parameter of the model. The material models predicted with the periodic homogenization were compared with experimental results.

The comparison between periodic homogenization results and tensile test results shows a fair agreement between the experimental results and that of the numerical simulation, whatever the fibers’ orientations are. Moreover, a void content reduction has been observed by increasing the crossing angle from one layer to another. An empiric law giving the porosity according to this crossing angle was created. The model and the law can be further used for design evaluation and optimization of continuous fiber-reinforced FFF.

A new elastic model considering interbead voids and its variation with the crossing angle of the fibers has been built. It can be used in simulation tools to design high performance fused filament fabricated composite parts.

This study employs Stein Rokkan's methodological approach to analyse state formation in the Greater Middle East. It develops a conceptual framework distinguishing colonial, populist and democratic pacts, suitable for analysis of state formation and nation-building through to the present period. The framework relies on historical institutionalism. The methodology, however, is Rokkan's. The initial conceptual analysis also specifies differences between European and the Middle Eastern state formation processes. It is followed by a brief and selective discussion of historical preconditions. Next, the method of plotting singular cases into conceptual-typological maps is applied to 20 cases in the Greater Middle East (including Afghanistan, Iran and Turkey). For reasons of space, the empirical analysis is limited to the colonial period (1870s to the end of World War 1). Three typologies are combined into one conceptual-typological map of this period. The vertical left-hand axis provides a composite typology that clarifies cultural-territorial preconditions. The horizontal axis specifies transformations of the region's agrarian class structures since the mid-19th century reforms. The right-hand vertical axis provides a four-layered typology of processes of external intervention. A final section presents selected comparative case reconstructions. To the authors' knowledge, this is the first time such a Rokkan-style conceptual-typological map has been constructed for a non-European region.

The purpose of this study is to examine how well reinforced concrete structures can be shielded against concrete carbonation using anti-carbonation coatings based on synthetic polymer.

Applying free radical polymerization, an acrylate terpolymer emulsion that a surfactant had stabilized was created. A thermogravimetric analysis, minimum film-forming temperature, Fourier transform infrared spectroscopy and particle size distribution are used to characterize the prepared eco-friendly water base acrylate terpolymer emulsion. Using three different percentages of the acrylate terpolymer emulsion produced, 35%, 45% and 55%, the anti-carbonation coating was formed. Tensile strength, tensile strain, elongation, crack-bridging ability, carbon dioxide permeability, chloride ion diffusion, average pull-off adhesion strength, water vapor transmission, gloss, wet scrub resistance, QUV/weathering and storage stability are the characteristics of the anti-carbonation coating.

The formulated acrylate terpolymer emulsion enhances anti-carbonation coating performance in CO₂ permeability, Cl-diffusion, crack bridging, pull-off adhesion strength and water vapor transmission. The formed coating based on the formulated acrylate terpolymer emulsion performed better than its commercial counterpart.

To protect the steel embedded in concrete from corrosion and increase the life span of concrete, the surface of cement is treated with an anti-carbonation coating based on synthetic acrylate terpolymer emulsion.

In addition to saving lives from building collapse, it maintains the infrastructure for the long run.

The anti-carbonation coating, which is based on the synthetic acrylate terpolymer emulsion, is environmentally benign and stops the entry of carbon dioxide and chlorides, which are the main causes of steel corrosion in concrete.

This study aims to analyse how the adoption of Industry 4.0 technologies can help different types of agri-food supply chains introduce and manage innovations in response to the challenges and opportunities that emerged following the COVID-19 pandemic.

A systematic literature review methodology was used to bring together the most relevant contributions from different disciplines and provide comprehensive results on the use of I4.0 technologies in the agri-food industry.

Four technological clusters are identified, which group together the I4.0 technologies based on the applications in the agri-food industry, the objectives and the advantages provided. In addition, three types of agri-food supply chains have been identified and their configuration and dynamics have been studied. Finally, the I4.0 technologies most suited for each type of supply chain have been identified, and suggestions on how to effectively introduce and manage innovations at different levels of the supply chain are provided.

The study highlights how the effective adoption of I4.0 technologies in the agri-food industry depends on the characteristics of the supply chains. Technologies can be used for different purposes and managers should carefully consider the objectives to be achieved and the synergies between technologies and supply chain dynamics.

The study explores the possibility of developing a valid scale for integrated management of heritage sites to bring a holistic approach to heritage properties' conservation practices.

The study followed the exploratory sequential method. An in-depth interview was adopted for exploring indicators, and a questionnaire survey was administered for descriptive analysis.

Cultural resources conservation strategies have been analyzed from a tourist, local communities and stakeholder's perspective with local-specific indicators. The study indicates that a multi-dimensional approach that integrates tourists, local communities and other stakeholders-based indicators can be developed at the destination level for the integrated management of heritage properties. Tourist-centric, local community-specific and stakeholder-oriented approaches could act as catalysts for more pragmatic conservation practices in the local areas based on the site-specific indicators.

The study is limited to lesser-known heritage sites located in the southern provincial states of India. The technical conservation strategies on the structure and architecture are not part of the study. Theoretical implications on the study of this kind can contribute to the literature as it throws light on future studies seeking local-centric conservation and management practices of heritage sites hitherto less explored in the domain of conservation science. The scale provides insight into the appropriate form of intervention that the local communities, tourists and other stakeholders can do at the heritage sites, hence the possibility of garnering the attention of other discipline strivings towards the conservation of heritage sites and to apply along with other relevant variables. It is expected that the study might expedite the knowledge accumulation in conservation science.

The scale can be used in a similar context for the integrated management of heritage sites. The study can assist the policymakers and planners in seeking the support of stakeholders, local communities and tourists for the implementation of heritage conservation and management programs. Such a local-centric management strategy promoting responsible consumption and production could contribute to SDG 12. Further, the study can also contribute towards SDG 11.4, which calls for strengthening the effort to protect and safeguard cultural and natural heritage. This scale can be a tool for destination management organizations (DMOs) to understand the level of intervention of local communities, tourists and other stakeholders at the heritage site.

The integrated management approach of heritage conservation immensely helps the lesser-known heritage sites the world over as such structures are out of the focal point of government funding and other conservation efforts. The synergy of the integrated approach could protect lesser-known unfunded heritage sites, and thereby, the cultural reflections of the community concerned can be made available for future visitors’ consumption.

The study attempted to understand the conservation approaches for lesser-known heritage sites with the support of both demand and supply-side stakeholders. Such a collaborative approach is the first of this kind in the conservation of heritage sites in India.

The variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.