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Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

The author studies forms over finite fields obtained as the determinant of Hermitian matrices and use these determinatal forms to define and study the base polynomial of a square matrix over a finite field.

The authors give full proofs for the new results, quoting previous works by other authors in the proofs. In the introduction, the authors quoted related references.

The authors get a few theorems, mainly describing some monic polynomial arising as a base polynomial of a square matrix.

As far as the author knows, all the results are new, and the approach is also new.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

Using an integrated theoretical model, this study aims to examine the moderating role of gamification in influencing intentions to use mobile payment applications in Bahrain.

The current examination happens to be the first approximation in the context of Bahrain wherein an extended TPB-based model integrating variables from TAM and UTAUT2 is used along with gamification and situational influence to examine the intentions to use m-payment applications.

The findings revealed that among the variates of the TPB, AT and PB significantly affect the intentions (IN) to use m-payment applications in Bahrain, but SN fails to affect intentions, similarly SI also fails to affect intentions thereby proving that the pandemic fails to drive the intention of the population under study toward using m-payment applications. However, when the application offers gamification (GM) features, SI significantly affects intentions through GM, thus experience along with situation drives intentions and this becomes the major theoretical contribution of the study.

This examination offers useful practical implications in the form of the findings revealing that GM affects intentions to use m-payment applications and that GM moderates the relationship between perceived risk (PR) and IN, as well as SI and IN, which can be used by the service providers to improve the user experience and achieve better acceptance of their application.

The novelty of the study lies in testing the integrated theoretical model in the context of a GCC nation, Bahrain.

The purpose of this research is to provide a critical review of the intellectual capital (IC) research in hospitality and tourism (HT) literature.

This study uses 141 research papers published on IC in HT between 2003 and 2021 to offer the findings of a systematic review of publications that cover the issue of IC as a holistic concept, rather than just a component of it, within the sector.

The progress on the topic is addressed. The authors' findings also reveal the related research productivity, main themes compared to other service sectors and methodologies applied in the knowledge field. In order to provide a tangible structure in the field, a research agenda is offered.

This study analyzed the development of IC research in the HT literature by focusing on journal articles in the Scopus database. The findings could aid researchers in (re)designing their study goals so they may add to both general IC literature and literature related to HT.

A strong positive relationship between IC and HT organizations’ performance has been demonstrated, but no study has previously mapped the research constituents of publications in IC research. To contribute to the endeavor of knowledge consolidation on this subject, the authors' paper covers the research that has been done so far on the under-researched issue of IC in HT from a new perspective.

This paper argues that cultures with the same climate have similar handicrafts as they have similar cultivation and identical raw materials. This study focuses on how mountainous, coastal and hot regions partaking in similar crafts and cultural heritage use palm leaves and analyses the resulting handicrafts' similarities.

A review of mapping these samples establishes this similarity in the traditional industries of some civilizations' cultural heritage from countries sharing similar climates.

The handwoven crafts using palm leaves were significant patrimonial artifacts in different societies' and communities' cultural heritage. Our studies revealed that climate plays an active role in influencing all aspects of humanity’s life. It affects the construction methods and style, agriculture and lifestyles.

Traditional handwoven palm leaf product models, especially plates and baskets, are studied from South America, Africa, Gulf Countries and Asia.

Additionally, this paper focuses on preserving these treasures as an essential part of interior elements as accessories for most inhabitants of these areas.

Cultural heritage also embraces intangible aspects such as skills passed down through generations within a particular society. The tangible and intangible elements complement each other and contribute to an overall legacy.

Cultural heritage reflects a society’s way of life carried down through the years across lands, items, customs and aesthetic concepts. People are the gatekeepers of society, as they preserve their way of life for future generations to emulate. Tangible artistic and cultural heritage comprises artifacts. It comprises all human evidence and expressions, such as traditional handicrafts, pictures, documents, books and manuscripts.

This study aims to evaluate the efficiency of various techniques for enhancing indoor air quality (IAQ) in construction. It analyzed the alterations in the concentration of indoor air pollutants over time for each product employed in controlling pollution sources and removing it, which included eco-friendly substances and adsorbents. The study will provide more precise and dependable data on the effectiveness of these control methods, ultimately supporting the creation of more efficient and sustainable approaches for managing indoor air pollution in buildings.

The research investigates the impact of eco-friendly materials and adsorbents on improving indoor air quality (IAQ) in Dubai's tall apartment buildings. Field experiments were conducted in six units of The Gate Tower, comparing the IAQ of three units built with “excellent” grade eco-friendly materials with three built with “good” grade materials. Another experiment evaluated two adsorbent products (H and Z) in the Majestic Tower over six months. Results indicate that “excellent” grade materials significantly reduced toluene emissions. Adsorbent product Z showed promising results in pollutant reduction, but there is concern about the long-term behavior of adsorbed chemicals. The study emphasizes further research on household pollutant management.

The research studied the effects of eco-friendly materials and adsorbents on indoor air quality in Dubai's new apartments. It found that apartments using “excellent” eco-friendly materials had significantly better air quality, particularly reduced toluene concentrations, compared to those using “good” materials. However, high formaldehyde (HCHO) emissions were observed from wood products. While certain construction materials led to increased ethylbenzene and xylene levels, adsorbent product Z showed promise in reducing pollutants. Yet, there is a potential concern about the long-term rerelease of these trapped chemicals. The study emphasizes the need for ongoing research in indoor pollutant management.

The research, while extensive, faced limitations in assessing the long-term behavior of adsorbed chemicals, particularly the potential for rereleasing trapped pollutants over time. Despite the study spanning a considerable period, indoor air pollutant concentrations in target households did not stabilize, making it challenging to determine definitive improvement effects and reduction rates among products. Comparisons were primarily relative between target units, and the rapid rise in pollutants during furniture introduction warrants further examination. Consequently, while the research provides essential insights, it underscores the need for more prolonged and comprehensive evaluations to fully understand the materials' and adsorbents' impacts on indoor air quality.

The research underscores the importance of choosing eco-friendly materials in new apartment constructions for better IAQ. Specifically, using “excellent” graded materials can significantly reduce harmful pollutants like toluene. However, the study also highlights that certain construction activities, such as introducing furniture, can rapidly elevate pollutant levels. Moreover, while adsorbents like product Z showed promise in reducing pollutants, there is potential for adsorbed chemicals to be rereleased over time. For practical implementation, prioritizing higher-grade eco-friendly materials and further investigation into furniture emissions and long-term behavior of adsorbents can lead to healthier indoor environments in newly built apartments.

The research offers a unique empirical assessment of eco-friendly materials' impact on indoor air quality within Dubai's rapidly constructed apartment buildings. Through field experiments, it directly compares different material grades, providing concrete data on pollutant levels in newly built environments. Additionally, it explores the efficacy of specific adsorbents, which is of high value to the construction and public health sectors. The findings shed light on how construction choices can influence indoor air pollution, offering valuable insights to builders, policymakers and residents aiming to promote public health and safety in urban living spaces.

This study aims to quantify sectoral energy and carbon intensity, revisit the validity of the Environmental Kuznets Curve (EKC) and explore the relationship between economic diversification and CO₂ emissions in Bahrain.

Three stages were followed to understand the linkages between sectoral economic growth, energy consumption and CO₂ emissions in Bahrain. Sectoral energy and carbon intensity were calculated, time series data trends were analyzed and two econometric models were built and analyzed using the autoregressive distributed lag method and time series data for the period 1980–2019.

The results of the analysis suggest that energy and carbon intensity in Bahrain’s industrial sector is higher than those of its services and agricultural sectors. The EKC was found to be invalid for Bahrain, where economic growth is still coupled with CO₂ emissions. Whereas CO₂ emissions have increased with growth in the manufacturing, and real estate subsectors, the emissions have decreased with growth in the hospitability, transportation and communications subsectors. These results indicate that economic diversification, specifically of the services sector, is aligned with Bahrain’s carbon neutrality target. However, less energy-intensive industries, such as recycling-based industries, are needed to counter the environmental impacts of economic growth.

The impacts of economic diversification on energy consumption and CO₂ emissions in the Gulf Cooperation Council petroleum countries have rarely been explored. Findings from this study contribute to informing economic and environment-related policymaking in Bahrain.

Linkages play a strategic role in improving actor disposition and resource density in university–industry ecosystems. Due to the importance of interconnected ecosystems for the development of engaged universities, applying the service-dominant logic (SDL) perspective, the authors aim at developing theory on linkages that lead to a higher level of actor commitment and in turn result in increased actor disposition and greater resource density.

Data from semistructured interviews from four in depth case studies, known internationally as successful cases of university linkages development (Columbia Lab-to-Market Accelerator Network, Oxford University Innovation, Auckland Uniservices and the Münster Center for Interdisciplinarity), undergo qualitative analysis according to the Gioia methodology.

The results represent a contribution to the theory, as they highlight the strategic role of linkages in improving actor disposition and increasing resource density. Due to its shown importance, linkage is an element to be considered on its own in the innovation ecosystems configurations in the context of universities.

The results of the research have implications for university management, since they focus on how resources are mobilized and linked. The interactive roles of actors in ecosystems imply that the locus of value creation moves beyond the borders of the organization toward the linkages. Strategies for managing university–industry linkages (UIL) are presented.

To date there has not been sufficient theoretical or empirical contribution in the university–industry ecosystem context about the role of linkages to improve resource density through increased actor disposition.

Interior workspace environments use exclusively artificial light, resulting in a loss of biological connection and natural light quality, as well as greater energy consumption. The purpose of the study is to identify a suitable system that can provide natural light to such interior spaces throughout the day while supplementing it with artificial light when necessary. The fundamental aim is to provide insights into the most effective solutions for energy-efficient lighting design in the UAE's environment, with the potential to lower energy consumption related to interior lighting.

The study adopted an empirical approach to gather and analyze primary data based on field measurements to understand and assess existing lighting conditions, as well as DIALux lighting simulation software to test the efficacy of the proposed HLS in terms of natural light delivery, illumination quality and energy consumption. A branch of a local bank in the United Arab Emirates, situated inside one of the shopping malls where there is no natural light penetration, has been chosen as a case study.

The findings of comparing the base case to four probable scenarios that used HLS revealed that the third scenario, which uses 100% pure sunshine and 35% artificial LED light during daylight operations and 100% LED light during night duty, is considered to be optimal in terms of illumination quality and energy efficiency.

The study demonstrated the potential of innovative lighting to improve the visual working environment in interior spaces with limited access to direct natural lighting, especially in arid regions, where sunlight is plentiful throughout the year. The study contributes new insights into the establishment of lighting-related recommendations and standards for the UAE context. This may include advice for sustainable construction practices, lighting guidelines or incentives to encourage the use of hybrid lighting technology in commercial and institutional buildings.