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This study aims to introduce a new methodology for generating synthetic images for facility management purposes. The method starts by leveraging the existing 3D open-source BIM models and using them inside a graphic engine to produce a photorealistic representation of indoor spaces enriched with facility-related objects. The virtual environment creates several images by changing lighting conditions, camera poses or material. Moreover, the created images are labeled and ready to be trained in the model.

This paper focuses on the challenges characterizing object detection models to enrich digital twins with facility management-related information. The automatic detection of small objects, such as sockets, power plugs, etc., requires big, labeled data sets that are costly and time-consuming to create. This study proposes a solution based on existing 3D BIM models to produce quick and automatically labeled synthetic images.

The paper presents a conceptual model for creating synthetic images to increase the performance in training object detection models for facility management. The results show that virtually generated images, rather than an alternative to real images, are a powerful tool for integrating existing data sets. In other words, while a base of real images is still needed, introducing synthetic images helps augment the model’s performance and robustness in covering different types of objects.

This study introduced the first pipeline for creating synthetic images for facility management. Moreover, this paper validates this pipeline by proposing a case study where the performance of object detection models trained on real data or a combination of real and synthetic images are compared.

The purpose of this study is to investigate the authors' previously prepared and fully characterized poly (methacrylamide)-chitosan nanoparticles (CNPs) graft copolymer having 50.2% graft yield with respect to flocculation efficiency for ferric laurate aqueous dispersions. This was done to compare the ability of the latter cheap, biodegradable and ecofriendly hybrid natural-synthetic polymeric substrate as a flocculant in comparison with higher cost, nonbiodegradable and harmful polyacrylamide as a well-known synthetic flocculant counterpart.

The graft copolymerization process was carried out at 450°Cfor 120 min using (1.0 g) CNPs, methacrylamide (1.5 g), 100 mmol/l potassium chromate and 80 mmol/l mandelic acid. Fourier transform infrared spectroscopy, thermogravimetric analysis, elemental analysis and specific viscosity were used to characterize and analyze the resultant copolymer. The flocculation efficiency was conferred in terms of transmittance % and weight removal %. The main factors influencing the flocculation process, such as flocculent dose, flocculation medium pH, stirring speed, flocculation temperature and grafting extent, were comprehensively discussed.

The flocculation efficiency of the prepared copolymers revealed the following findings: increased by increasing the flocculant dose, pH, temperature and stirring speed to a maximum values denoted at 30 ppm, 6.0, 30°C and 50 r/min, respectively, then decreased thereafter; increased by increasing the extent of grafting within the range studied; showed a comparable flocculation efficiency in comparison with polyacrylamide as a synthetic polymeric flocculent; and, finally, a preliminary bridging mechanism representing the attraction between the anionic suspended particles ferric laurate and cationic poly (MAam)-CNPs graft copolymer has been projected.

The advancement addressed here is undertaken with using the authors’ poly (MAam)-CNPs graft copolymers having different extent of grafting (a point which is not cited in the literature especially for the authors’ prepared copolymer) as a hybrid natural-synthetic polymeric substrate as a flocculant for ferric laurate aqueous dispersions in comparison with the high cost and nondegradable polyacrylamide synthetic flocculant.

The purpose of this paper is to identify and describe the influence of the knowledge base (KB) of the company on driving forces of innovation processes in knowledge-intensive services (KIS) and to compare the level of innovativeness of the final services.

The paper investigates through qualitative research 11 KIS organisations with different KB.

The research results identified and described the influence of the KB on driving forces of innovations processes and its results in companies with four newly identified KBs (analytical, synthetic, symbolic and compliance).

Further research, based on a larger number of companies, is needed to confirm the results of this research and to complement the effect of the KB on driving forces of innovation.

This research can help organisations understand how to develop strategic plans and new ideas for innovative services depending on the KB of the organisation.

The description of successful innovation processes and results in several leading companies presented in the study may help other companies in identifying knowledge-integration practices to improve performance and innovation processes that support multiplicity, productivity and creativity.

The study systemised the sources of new ideas for innovation in companies with different KB, several driving forces of innovation were identified and how these forces are affected by each KB; lastly, innovation results were compared in companies with different KB.

The objective of this paper is to analyze the relationship between innovation and the development of artificial intelligence (AI) and digital technologies in Europe. The use of digital technologies among European companies is studied through a composite index, while the relationship between innovation and AI is studied through a log-linear regression model. The results of the model have made possible to develop interesting indications for economic and industrial policy.

The use of digital technologies among European companies is studied through a composite index of AI and information technology (ICT) (using the Fair and Sustainable Welfare methodology) with the aim of measuring territorial gaps and to know which European countries are more or less inclined to its use, while the relationship between innovation and AI is studied through a log-linear regression model.

In the paper, two different methodologies were used to analyze the relationship between innovation and the development of digital technologies in Europe. The synthetic indicator made possible to develop a taxonomy between the different countries, the log-linear model made possible to identify and explain the determinants of innovation.

The description of the biunivocal relationship between innovation and AI is a topical and relevant issue that is treated in the paper in an original way using a synthetic indicator and a log-linear model.

本文旨在探討在歐洲、創新與人工智能和數字技術的發展之間的關係。研究人員透過一個綜合指數、去探討歐洲公司之間數字技術的使用狀況。至於創新與人工智能之間的關係, 則以對數線性回歸模型來進行研究。從模型所得的結果, 為我們提供了建議、去訂定適切的經濟和產業政策。

研究人員透過一個人工智能和資訊科技的綜合指數, 去探討歐洲企業之間數字技術的使用狀況 (研究人員使用了公平和可持續福利方法論), 其目標為測量領土差距, 以及確定哪些歐洲國家、大體上傾向於使用數字技術;至於創新與人工智能之間的關係, 則以對數性回歸模型來進行研究。

本文使用了兩個不同的方法、去探討在歐洲、創新與數字技術發展之間的關係。有關的合成指標, 使研究人員可製定一個不同國家間的分類法;而有關的對數線性模型, 則讓研究人員可確立並說明創新的決定因素。

本文使用了合成指標和對數線性模型、去探討創新與人工智能之間的一對一的關係, 這是時下受到關注和適宜的課題;就研究法而言, 本研究確是新穎獨創的。

Everyone is extremely concerned about environmental protection and health safety due to the rise in living standards. Plant-derived natural dyes have garnered much industrial attention in food, pharmaceutical, textile, cosmetics, etc. owing to their health and environmental benefits. The present study aims to focus on the elimination of the use of synthetic dyes and provides brief information about natural dyes, their sources, extraction procedures with characterization and various advantages and disadvantages.

In producing natural colors, extraction and purification are essential steps. Various conventional methods used till date have a low yield, as these consume a lot of solvent volume, time, labor and energy or may destroy the coloring behavior of the actual molecules. The establishment of proper characterization and certification protocols for natural dyes would improve the yielding of natural dyes and benefit both producers and users.

However, scientists have found modern extraction methods to obtain maximum color yield. They are also modifying the fabric surface to appraise its uptake behavior of color. Various extraction techniques such as solvent, aqueous, enzymatic and fermentation and extraction with microwave or ultrasonic energy, supercritical fluid extraction and alkaline or acid extraction are currently available for these natural dyes and are summarized in the present review article.

If natural dye availability can be increased by the different extraction measures and the cost of purified dyes can be brought down with a proper certification mechanism, there is a wide scope for the adoption of these dyes by small-scale dyeing units.

Synthetic patient data produced by Synthea was described in Chapter 6. That data is used to create a baseline for all patients, palliative patients, and deceased palliative patients. Distributions of comorbidities across the patient groups are examined and demographic characteristics. The factors used in palliative care groupings are presented with the synthesized data fields used. The size of the palliative population is again estimated to establish validity.

The coal-based synthetic natural gas slag (CSNGS) is a solid waste remaining from the incomplete combustion of raw coal to produce gas. With the continuous promotion of efficient and clean utilization of coal in recent years, the stockpiling of CSNGS would increase gradually, and it would have significant social and environmental benefits with reasonable utilization of CSNGS. This study prepared a new geopolymer by mixing CSNGS with PC42.5 cement in a certain mass ratio as the precursor, with sodium hydroxide and sodium silicate solution as the alkali activators.

The formulation of coal-based synthetic natural gas slag geopolymer (CSNGSG) was determined by an orthogonal test, and then the strength mechanism and microstructure of CSNGSG were characterized by multi-scale tests.

The results show that the optimum ratio of CSNGSG was a sodium silicate modulus of 1.3, an alkali dosage of 21% and a water cement ratio of 0.36 and the maximum unconfined compressive strength of CSNGSG at 7 d was 26.88 MPa. The increase of curing temperature could significantly improve the compressive strength of CSNGSG, and the curing humidity had little effect on the compressive strength of CSNGSG. The development of the internal strength of CSNSG at high temperatures consumed SiO₂, Al₂O₃ and CaO and the intensity of corresponding crystalline peaks decreased.

Moreover, the vibration of chemical bonds in different wavenumbers also revealed the reaction mechanism of CSNSG from another perspective. Finally, the relevant test results indicated that CSNGS had practical application value as a raw material for the preparation of geopolymer cementing materials.

This chapter more clearly identifies the distinction between Electronic Health Record (EHR) and Electronic Medical Record (EMR), and states their value in obtaining individual-level data. Synthetic medical records may be used as a surrogate for EHR data in order to ensure digital data privacy is maintained during the development of the LHS. Synthea is an open-source simulation tool available through GitHub.¹ Extensive descriptive analysis of synthesized data is provided as a foundation for the analysis in Chapter 7.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.