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Currently, the Tang tomb mural cultural relic resources are presented in a multi-source and heterogeneous manner, with a lack of effective organization and sharing between resources. Therefore, this study aims to propose a multidimensional knowledge discovery solution for Tang tomb mural cultural relic resources.

Taking the Tang tomb murals collected by the Shaanxi History Museum as an example, based on clarifying the relevant concepts of Tang tomb mural resources and considering both dynamic and static dimensions, a top-down approach was adopted to first construct an ontology model of Tang tomb mural type cultural relics resources. Then, the actual case data was imported into the Neo4J graph database according to the defined pattern hierarchy to complete the static organization of knowledge, and presented in a multimodal form in knowledge reasoning and retrieval. In addition, geographic information system (GIS) technology is used to dynamically display the spatiotemporal distribution of Tang tomb mural resources, and the distribution trend is analysed from a digital humanistic perspective.

The multi-dimensional knowledge discovery of Tang tomb mural cultural relics resources can help establish the correlation and spatiotemporal relationship between resources, providing support for semantic retrieval and navigation, knowledge discovery and visualization and so on.

This study takes the murals in the collection of the Shaanxi History Museum as an example, revealing potential knowledge associations in a static and intelligent way, achieving knowledge discovery and management of Tang tomb murals, and dynamically presents the spatial distribution of Tang tomb murals through GIS technology, meeting the knowledge presentation needs of different users and opening up new ideas for the study of Tang tomb murals.

Using a real-time road network combined with historical traffic data for Al-Salt city, the paper aims to propose a new federated genetic algorithm (GA)-based optimization technique to solve the dynamic vehicle routing problem. Using a GA solver, the estimated routing time for 300 chromosomes (routes) was the shortest and most efficient over 30 generations.

In transportation systems, the objective of route planning techniques has been revised from focusing on road directors to road users. As a result, the new transportation systems use advanced technologies to support drivers and provide them with the road information they need and the services they require to reduce traffic congestion and improve routing problems. In recent decades, numerous studies have been conducted on how to find an efficient and suitable route for vehicles, known as the vehicle routing problem (VRP). To identify the best route, VRP uses real-time information-acquired geographical information systems (GIS) tools.

This study aims to develop a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences.

Furthermore, developing a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. An adaptive genetic algorithm (GA) is used to determine the optimal time route, taking into account factors that affect vehicle arrival times and cause delays. In addition, ArcGIS' Network Analyst tool is used to determine the best route based on the user's preferences using a real-time map.

The construction industry’s daily processes demand heavy data usage and communication between project participants to meet client requirements. Thus, the application of information technology in project implementation has been increasing in the construction sector (CS) lately. However, the same cannot be seen in public sectors responsible for implementing government projects in South Africa. This study aimed to investigate the causes and effects of the underutilisation of information communication technology (ICT) in the building section of a public sector in a municipality in South Africa.

A qualitative approach was adopted for the study, using a public sector in one of the municipalities as a case study. Face-to-face interviews were conducted among the building unit workers, using unstructured interview questions. The data collected were analysed using the ATLAS.ti software.

The findings indicate a lack of understanding of existing and newly available ICT software and hardware technology among staff within the building technology due to lack of digitalisation in construction projects implementation, inadequate system upgrades, lack of adequate ICT resources, lack of financial resources for internet and software application subscriptions and lack of ICT training leading. The issues mentioned above have led to the outsourcing of projects professionals, slow pace of electronic emails, untrained professionals, usage of different and unlicensed software, resulting in the underutilisation of ICT within the whole building section. This change also adversely affects all officials, especially the junior officials who have graduated using the most recent ICT technology during their studies.

The building department of only one public sector was used for the study; therefore, the findings may not be generalisable. The case study public sector’s name is withheld for confidentiality purposes.

Adequate change management and continuous development, combined with the allocation of proper resources, would be necessary for all staff members. Enormous investments had to be made in the ICT equipment by providing a sufficient budget in the building section of the public sectors. The building section within public sectors should provide change management to all aged skills staff by attending seminars to learn new ICT technology applied within its work environment.

The study established the causes of the underutilisation of ICT in the CS, especially in the public work departments and municipalities, and how this contributes to service delivery.

The study, within the Increasing Resilience of Cultural Heritage (ResCult) project, aims to support civil protection to prevent, lessen and mitigate disasters impacts on cultural heritage using a unique standardised-3D geographical information system (GIS), including both heritage and risk and hazard information.

A top-down approach, starting from existing standards (an INSPIRE extension integrated with other parts from the standardised and shared structure), was completed with a bottom-up integration according to current requirements for disaster prevention procedures and risk analyses. The results were validated and tested in case studies (differentiated concerning the hazard and type of protected heritage) and refined during user forums.

Besides the ensuing reusable database structure, the filling with case studies data underlined the tough challenges and allowed proposing a sample of workflows and possible guidelines. The interfaces are provided to use the obtained knowledge base.

The increasing number of natural disasters could severely damage the cultural heritage, causing permanent damage to movable and immovable assets and tangible and intangible heritage. The study provides an original tool properly relating the (spatial) information regarding cultural heritage and the risk factors in a unique archive as a standard-based European tool to cope with these frequent losses, preventing risk.

The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021).

In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation.

The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices throughout the city. The results of hot spots show that the highest concentration of hot spots (the highest price) is in the northern part of the city, and the highest concentration of cold spots (the lowest price) is in the southern part of Tehran city. Calculating the area and estimating the quantitative values of data-free points by the use of the Kriging interpolation method indicates that 9.95% of Tehran’s area has a price of less than US$800, 17.68% of it has a price of US$800 to US$1,200, 25.40% has the price of US$1,200 to US$1,600, 17.61% has the price of US$1,600 to US$2,000, 9.54% has the price of US$2,000 to US$2,200, 6.69% has the price of US$2,200 to US$2,600, 5.38% has the price of US$2,600 to US$2,800, 4.59% has the price of US$2,800 to US$3,200 and finally, the 3.16% has a price more than US$3,200. The highest price concentration (above US$3,200) is in five neighborhoods (Zafaranieh, Mahmoudieh, Tajrish, Bagh-Ferdows and Hesar Bou-Ali). The findings from the study of changes in housing prices in the period (1991–2021) indicate that the southern part of Tehran has grown slightly compared to the average range, and the western part of Tehran, which includes the 21st and 22nd regions with much more growth than the average price.

There is massive inequality in housing prices in different areas and neighborhoods of Tehran city in 2021. In the period under study, spatial inequality in the residential system of Tehran intensified. The considerable increase in housing prices in the housing market of Tehran has made this sector a commodity, intensifying the inequality between owners and non-owners. This increase in housing price inequality has caused an increase in the informal living for the population of the southern part. This population is experiencing a living situation that contrasts with the urban plans and policies.

Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours.

This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed.

Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk.

The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

This study aims to predict the consequences associated with the propagation of the flood wave that may occur after the failure of the Taksebt dam and suggest an efficient emergency action plan for mitigation purposes.

To achieve the objectives of this study, the hydrodynamic model HEC-RAS 2D was used for the flood routing of the dam-break wave, which gave an estimate of the hydraulic characteristics downstream the Taksebt dam.

Geospatial analysis of the simulation results conducted in a geographic information system (GIS) environment showed that many residential areas are considered to be in danger in case of the Taksebt dam-break event. Based on the obtained results, an emergency actions plan was suggested to moderate the causalities in the downstream area at risk.

Overall, this study showed that the integration of 2D hydraulic modeling and GIS provides great capabilities in providing realistic view of the dam-break wave propagation that enhances assessing the associated risks and proposing appropriate mitigation measures.

This study uses a bibliometric approach to analyze the overall status of e-government research by revealing patterns and trends that would help gain a broad understanding of global developments in the field and future directions.

All documents related to e-government published from 2000 to 2019 were extracted from the Scopus and the Digital Government Reference Library databases. Bibexcel, Biblioshiny, and VOSviewer were used to perform the analyses and visualize the science mapping.

The results showed that 21,320 documents related to e-government research were published and cited 263,179 times. The annual growth rate of e-government research has reached 21.50%. The regression analysis showed that the growth rate is expected to increase gradually over the coming years. Despite the significant role that conference papers play in the e-government literature, the impact of articles far exceeds conference papers. The University of Albany (SUNY) has played an important role in e-government research in terms of production and impact. Furthermore, the study revealed some countries that are expected to play a prominent role in e-government research, as well as several topics that may attract more attention soon.

The results presented in this study and the comprehensive picture obtained about the e-government field make it an effective supplement to the expert evaluation. Thus, researchers, research managers, policymakers, institutions, funding agencies, and governments can rely on it.

The new Italian National Recovery and Resilience Plan (NRRP) has prioritised a new healthcare model that will establish the additional community healthcare facilities (CHFs). The paper proposes a methodology for supporting decision-making on location of the future facilities according to new parameters that consider how proximity to healthcare benefits communities. Rethinking the spatial parameters for locating future CHFs, focusing on fragile areas, creates a novel decision support system.

The methodology is based on multifactor analysis and on geographic information system (GIS) mapping to simulate the potential and risks associated with the proposed location of CHFs, focusing on territorial contexts of metropolitan cities, medium-sized cities, and Inner Areas, characterized by different fragilities. This method aims to innovate urban planning practices by updating the practice of per-capita urban planning standards and promoting implementation of the 15-minute city model.

The method defines new spatial parameters useful to inform the appropriate location of CHFs in Italy's Inner Areas. This offers a new integrated approach to spatial design mixing urban planning and healthcare dimensions.

The methodology will bring about an integrated urban planning approach, which guides both transformative urban choices and health services' implementation according to the needs of communities.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.