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The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

Comfortable outdoor workspaces are important for employees in business parks and urban areas. Prioritizing a pleasant thermal environment is essential for employee productivity, as well as the improvement of outdoor spaces between office buildings to enhance social activities and quality of outdoor workplaces in a hot arid climate has been subjected to very little studies Thus, this study focuses on business parks (BPs) landscape elements. The objective of this study is to enhance the user's thermal comfort in the work environment, especially in the outdoors attached to the administrative and office buildings such as the BPs.

This research follows Four-phases methodology. Phase 1 is the investigation of the literature review including the Concept and consideration of BP urban planning, Achieving outdoor thermal comfort (OTC) and shading elements analysis. Phase 2 is the case study initial analysis targeting for prioritizing zones for shading involves three main methods: social assessment, geometrical assessment and environmental assessment. Phase 3 entails selecting shading elements that are suitable for the zones requiring shading parametrize the selected shading elements. Phase 4 focuses on the optimization of OTC through shading arrangements for the prioritized zones.

Shading design is a multidimensional process that requires consideration of various factors, including social aspects, environmental impact and structural integrity. Shading elements in urban areas play a crucial role in mitigating heat stress by effectively shielding surfaces from solar radiation. The integration of parametric design and computational optimization techniques enhances the shading design process by generating a wide range of alternative solutions.

While conducting this research, it is important to acknowledge certain limitations that may affect the generalizability and scope of the findings. One significant limitation lies in the use of the shade audit method as a tool to prioritize zones for shading. Although the shade audit approach offers practical benefits for designers compared to using questionnaires, it may have its own inherent biases or may not capture the full complexity of human preferences and needs.

Few studies have focused on optimizing the type and location of devices that shade outdoor spaces. As a result, there is no consensus on the workflow that should regulate the design of outdoor shading installations in terms of microclimate and human thermal comfort, therefore testing parametric shading scenarios for open spaces between office buildings to increase the benefit of the outer environment is very important. The study synthesizes OTC strategies by filling the research gap through the implementation of a proper workflow that utilizes parametric thermal comfort.

The buildings should be designed by respecting the environmental and climatic conditions they are in and their orientation. Then, the characteristics of the building envelope (BE) play an important role in building energy consumption and user comfort. In fact, the type and material of glazing is one of the crucial parameters for BE. The transparency ratio of BE also determines the façade performance. The aim of this study is to analyze the different renovation scenarios for BE with high transparency of an educational building (EB) in hot summer weather to obtain indoor thermal comfort (ITC) for users.

The methodology includes thorough measurement of existing ITC using TESTO-440 and simulation of each retrofit scenario using DesignBuilder building energy modeling (BEM) simulation software with Energyplus to determine optimal thermal comfort. Since the study focuses on the impact of the transparent BE on summer ITC, four main scenarios, naturally ventilated (NV) façade, film-coated glass façade, replacement of glazing with opaque units, sun-controlled façade with overhang and solar shading, were simulated. The results were analyzed comparatively on both performance and cost to find the best renovation solutions.

A total of 7 different renovation scenarios were tested. Simulation results show that passive systems such as NV have limited contribution to indoor air temperature (IAT) improvement, achieving only a 4 °C reduction while offering the lowest cost. A film coating resulted in a reduction of 3–6 °C, but these applications have the highest cost and least impact on ITC. It was found that exterior coating leads to better results in film coating. Preventing and limiting the increase in IAT was achieved by reducing the transparency ratio of BE. The best results were obtained in these scenarios, and it was possible to reduce IAT by more than 10 °C. The best performance/cost value were also obtained by decreasing transparency ratio of roof and sun control.

Since the high transparency ratio has a negative impact on summer comfort, especially in hot climate zones, summer ITC was prioritized in the renovation solutions for the case building.

The study’s findings present a range of solutions for improving the ITC of highly transparent buildings. The solutions can help building managers see the differences in renovation costs and their impacts on ITC to decrease the cooling load of the existing buildings.

The purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.

At present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.

Robot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.

This review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

Owing to dual constraints including liability of foreignness and liability of origin when emerging multinationals internationalize, they inevitably face the challenge of overseas legitimation. However, few studies have explored how latecomers cross the threshold of legitimacy in the dynamic context of transnational operation. The purpose of this paper is to unravel the evolution process, triggers and specific strategies of overseas legitimacy threshold crossing of emerging multinationals.

Through the longitudinal case study of Haier Group and Goldwind Sci & Tech Co., Ltd, this study investigates the periodical characteristics of overseas legitimacy threshold crossings and the co-evolution among critical factors influencing the legitimation process in the host country.

First, it summarizes that the legitimacy threshold in the host country experiences a sequential process from pragmatic legitimacy to normative legitimacy, and finally cognitive legitimacy. It is an inevitable choice for emerging multinational enterprises to realize and sustain legitimation from passive adaptation to active creation. Second, it reveals that the triggers for crossing the threshold of overseas legitimacy include periodically dynamic factors – international network linkage and resource system reconfiguration, as well as cross-stage spiral interaction effects. Third, it determines the specific strategies for crossing the threshold of overseas legitimacy, namely, replacement, upgrading and reconstruction of organizational identity, and reveals the important role of insisting on the country-of-origin Facebook in promoting the legitimation.

This study enriches the legitimacy threshold crossing literature from an evolutional perspective, especially the traditional static legitimacy research. This study also reveals the key impacting factors – international network linkage and resource system reconfiguration – and their evolution process interacted with the legitimation process.

The emerging multinationals should break the stereotypes from developed markets in that only creating new cognitive patterns through active legitimate strategies can they truly cross the legitimacy threshold in the host country. The emerging multinationals also need to retain their own home country legitimacy traits – Facebook and balance the relation between the image of the home country and the image of host country.

This paper investigates the process of overseas legitimacy threshold crossing for emerging multinationals in a dynamic context of transnational operation, particularly with respect to the evolutionary role played by international network linkage and resource system reconfiguration.

Despite the growing attention on the relevance of improved building management systems with cognition in recent years in the architecture, engineering, construction and operation (AECO) community, no review has been conducted to understand the human-environment interaction features of cyber-physical systems (CPS) and digital twins (DTs) in developing the concept of a cognitive building (CB). Thus, this paper aims to review existing studies on CPS and DTs for CB to propose a comprehensive system architecture that considers human-environment interactions.

Scientometric analysis and content analysis were adopted for this study.

The scientometric analysis of 1,042 journal papers showed the major themes of CPS/DTs for CB, and these can be categorized into three key technologies to realize CB in the AECO community: CPS, DTs and cognitive computing (CC). Content analysis of 44 relevant publications in the built environment assisted in understanding and evidently confirming the claim of this study on the integration of CPS and DTs for CB in construction by also involving the CC. It is found and confirmed that CB can be realized with CPS and DTs along with the CC. A CB system architecture (CBSA) is proposed from the three key technologies considering the human-environment interactions in the loop. The study discovered the potential applications of the CBSA across the building lifecycle phases, including the design, construction and operations and maintenance, with the potential promise of endowing resilience, intelligence, greater efficiency and self-adaptiveness. Based on the findings of the review, four research directions are proposed: human-environment interactions, CB for sustainable building performance, CB concept for modular buildings and moving beyond CB.

This study stands out for comprehensively surveying the intellectual core and the landscape of the general body of knowledge on CPS/DTs for CB in the built environment. It makes a distinctive contribution to knowledge as it does not only propose CBSA by integrating CPS and DTs along with CC but also suggests some potential practical applications. These may require expert judgments and real case examples to enhance reproducibility and validation.

The paper’s main goal is to examine the relationship between the video marketing of financial technologies (Fintechs) and their vulnerable website customers’ brand engagement in the ongoing coronavirus disease 2019 (COVID-19) crisis.

To extract the required outcomes, the authors gathered data from the five biggest Fintech websites and YouTube channels, performed multiple linear regression models and developed a hybrid (agent-based and dynamic) model to assess the performance connection between their video marketing analytics and vulnerable website customers’ brand engagement.

It has been found that video marketing analytics of Fintechs’ YouTube channels are a decisive factor in impacting their vulnerable website customers’ brand engagement and awareness.

By enhancing video marketing analytics of their YouTube channels, Fintechs can achieve greater levels of vulnerable website customers’ engagement and awareness. Higher levels of vulnerable customers’ brand engagement and awareness tend to decrease their vulnerability by enhancing their financial knowledge and confidence.

Fintechs should aim to increase the number of total videos on their YouTube channels and provide videos that promote their customers’ knowledge of their services to increase their brand engagement and awareness, thus reducing their vulnerability. Moreover, Fintechs should be aware not to over-post videos because they will be in an unfavorable position against their competitors.

This research offers valuable insights regarding the importance of video marketing strategies for Fintechs in promoting their vulnerable website customers’ brand awareness during crisis periods.

The amount of energy consumption of buildings has obtained international concern so the concept of zero energy building becomes a target for building designers. There are various definitions and evaluation methods for efficient buildings. However, detailed research about the critical parameters that have a major effect through the operational time to reduce the energy consumption is not emphasized as this paper represents. The main aim of this study is to identify the effect of applicable interventions on energy consumption parameters with their sensitivity to each other to reach zero energy building. Relatedly, the cost of energy reduction is also determined.

Energy consumption parameters were defined as area lightings, space heating, space cooling, ventilation fans, pumps, auxiliary equipment and related miscellaneous equipment. The effect of each applied intervention on energy consumption was classified as high, medium, low, very low, no effect and negative effect by utilizing a sensitivity analysis. The base case's energy model is created by utilizing energy performance software such as e-Quest. Accordingly, energy performance improvement scenarios are developed by applying interventions such as lamp replacements, sensors, heat pumps and photovoltaic panels’ integration. Furthermore, sensitivity analyses of each intervention were developed for consumed energy and its cost.

Results indicated the electric consumption is more effective than gas consumption on primary energy and energy cost. Solar systems decline primary energy by 78.53%, lighting systems by 13.47% and heat pump by 5.48% in this building; therefore, integrating mentioned strategies could rise the improvement rate to 100%, in other words, zero amount of energy is using from the grid that means saving $ 5,750.39 in one year.

The study can be applied to similar buildings. It is worthwhile to investigate suggested methods in diverse buildings with different functions and climates in future works.

This study aims to investigate of energy consumption of an educational building in the Mediterranean climate to convert an existing building into a zero energy building by saving energy and renewable sources. Subsequent purposes are analyzing the effect of each strategy on energy consumption and cost.

The novelty of this study is filling gaps in sensitivity analysis of energy consumption parameters by not only identifying their effect on overall energy consumption but also identifying their effect on each other. Some interventions may have a positive effect on overall consumption while having a negative effect on each other. Identifying this critical effect in detail not only further improves the energy performance, but also may affect the decision-making of the interventions.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.