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This paper reports on a study designed to explore the relationship between environmental scanning and business expansion strategies in the international hotel sector. Unit level perspectives on environmental scanning and business expansion strategies of trans‐national hotel companies (TNHCs) operating in China and Eastern Europe are presented and analysed. (For the purpose of this study transnational hotel companies are defined as those companies operating hotel units in more than two countries.) The research, based on a survey of 50 general managers, concludes that, due to the complexity and the increasing uncertainty of the international hotel sector, TNHCs and their managers at unit level need to be more conscious of the significance of trends in the business environment when deciding on their business expansion strategies.

Discusses the telecommunications infrastructure of the USA and issues surrounding its restructuring. Describes the role and impact of broadband Integrated Services Digital Network (ISDN) in applications development and the societal implications of this change. Points out that global development of broadband technologies makes personal access to multimedia applications possible and promotes new information‐sharing partnerships. Argues for an holistic, ethical approach to future development of ISDN.

It is a challenge in the design to determine the feasible anode position and the supply current when the hull is protected by the impressed current cathodic protection method. It is difficult to obtain these parameters through traditional experimental methods due to the huge hull surface area and geometric complexity. This study aims to solve the problem by finite element method.

First, a great number of experiments need to be conducted; second, experiments are empirical; finally, there exist measurement errors, etc. All these factors make the experimental results less reliable. The application of the finite element method, combined with other technologies, is expected to overcome these deficiencies. In this paper, the combined Matlab and Comsol method was used to calculate various anode positions and corresponding protection areas with a series of input current conditions. The calculation is implemented via the script in Matlab.

As a result, the best design can be obtained. The results show that the method provided in this paper can replace the experiment to a certain extent, save human and material resources and reduce the design time. The method also can be applied to other similar fields, having a good universality.

This optimization method can be extended to other areas of relevant production and research, having a good universality.

The purpose of this paper is to propose a web intrusion detection system (IDS), SensorWebIDS, which applies data mining, anomaly and misuse intrusion detection on web environment.

SensorWebIDS has three main components: the network sensor for extracting parameters from real‐time network traffic, the log digger for extracting parameters from web log files and the audit engine for analyzing all web request parameters for intrusion detection. To combat web intrusions like buffer‐over‐flow attack, SensorWebIDS utilizes an algorithm based on standard deviation (δ) theory's empirical rule of 99.7 percent of data lying within 3δ of the mean, to calculate the possible maximum value length of input parameters. Association rule mining technique is employed for mining frequent parameter list and their sequential order to identify intrusions.

Experiments show that proposed system has higher detection rate for web intrusions than SNORT and mod security for such classes of web intrusions like cross‐site scripting, SQL‐Injection, session hijacking, cookie poison, denial of service, buffer overflow, and probes attacks.

Future work may extend the system to detect intrusions implanted with hacking tools and not through straight HTTP requests or intrusions embedded in non‐basic resources like multimedia files and others, track illegal web users with their prior web‐access sequences, implement minimum and maximum values for integer data, and automate the process of pre‐processing training data so that it is clean and free of intrusion for accurate detection results.

Web service security, as a branch of network security, is becoming more important as more business and social activities are moved online to the web.

Existing network IDSs are not directly applicable to web intrusion detection, because these IDSs are mostly sitting on the lower (network/transport) level of network model while web services are running on the higher (application) level. Proposed SensorWebIDS detects XSS and SQL‐Injection attacks through signatures, while other types of attacks are detected using association rule mining and statistics to compute frequent parameter list order and their maximum value lengths.

This paper aims to provide a comparison between the mechanical performance and microstructural aspects of stainless steel 17-4 PH processed using, respectively, two technologies: atomic diffusion additive manufacturing (ADAM) and metal fused filament fabrication (MFFF).

Different tensile specimens have been printed using an industrial system and a consumer three-dimensional (3D) printer, varying two main 3D printing parameters. Mechanical and microstructural tests are executed to make a comparison between these two technologies and two different feedstock material, to identify the main differences.

These 3D printing processes make parts with different surface quality, mechanical and microstructural properties. The parts, printed by the industrial system (ADAM), showed lower values of roughness, respect those produced using the 3D consumer printer (MFFF). The different sintering process parameters and the two debinding methods (catalytic or solvent based) affect the parts properties such as porosity, microstructure, grain size and amount of δ-ferrite. These proprieties are responsible for dissimilar tensile strength and hardness values. With the aim to compare the performances among traditional metal additive technology, MFFF and ADAM, a basic analysis of times and costs has been done.

The application of two metal extrusion techniques could be an alternative to other metal additive manufacturing technologies based on laser or electron beam. The low cost and printing simplicity are the main drivers of the replacements of these technologies in not extreme application fields.

Prior studies identified a need for further comparison of data-sharing practices across different disciplines and communities. Toward addressing this need, the purpose of this paper is to examine the data-sharing practices of the earthquake engineering (EE) community, which could help inform data-sharing policies in EE and provide different stakeholders of the EE community with suggestions regarding data management and curation.

This study conducted qualitative semi-structured interviews with 16 EE researchers to gain an understanding of which data might be shared, with whom, under what conditions and why; and their perceptions of data ownership.

This study identified 29 data-sharing factors categorized into five groups. Requirements from funding agencies and academic genealogy were frequent impacts on EE researchers’ data-sharing practices. EE researchers were uncertain of data ownership and their perceptions varied.

Based on the findings, this study provides funding agencies, research institutions, data repositories and other stakeholders of the EE community with suggestions, such as allowing researchers to adjust the timeframe they can withhold data based on project size and the amount of experimental data generated; expanding the types and states of data required to share; defining data ownership in grant requirements; integrating data sharing and curation into curriculum; and collaborating with library and information schools for curriculum development.

Community gardens are increasingly used as interventions during the resettlement of refugees and other migrants. Little is known about how garden programs might support their mental health and wellbeing. Given the links between climate change and forced migration, community gardens are especially relevant, as they can also support climate change mitigation. This study aims to document psychosocial outcomes of gardening programs for refugees and migrants, and mechanisms leading to these outcomes.

The authors searched major databases and the grey literature up to 2021, resulting in the inclusion of 17 peer-reviewed and 4 grey literature articles in a thematic, qualitative analysis.

Four consistent themes arose from the analysis: community gardening programs promoted continuity and adaptation (81% of articles), social connectedness (81%), overall wellbeing (95%) and a sense of meaning and self-worth (67%). The results suggest that community gardens can strengthen psychosocial pillars that are key to the recovery and resettlement of refugees and migrants. The land-based and social nature of community gardening may enable connections to the land and others, nurture a sense of belonging in the host country and provide a link to the past for those from agricultural backgrounds.

Further participatory action research is needed to develop guidelines for the successful implementation of community gardens by resettlement organisations.

This review indicates that community gardens can be effective psychosocial interventions as part of a network of services supporting the resettlement of refugees and migrants.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.