Cardiovascular diseases continuing to be a major contributor to illness and death on a global scale and the implementation of stents has given rise to a revolutionary transformation in the field of interventional cardiology. The thrombotic and restenosis complications associated with stent implantation pose ongoing challenges. In recent years, bioactive coatings have emerged as a promising strategy to enhance stent hemocompatibility and reduce thrombogenicity. This review article provides an overview of the surface engineering techniques employed to improve the hemocompatibility of stents and reduce thrombus formation. It explores the mechanisms underlying thrombosis and discusses the factors influencing platelet activation and fibrin formation on stent surfaces. Various bioactive coatings, including anticoagulant agents, antiplatelet agents, and surface modifications, are discussed in detail, highlighting their potential in reducing thrombogenicity. Evaluation methods for assessing hemocompatibility and thrombogenicity are also reviewed, ranging from in vitro assays to animal models. Recent advances in the field, such as nanotechnology-based coatings and bioactive coatings with controlled drug release systems, are highlighted. Surface engineering of bioactive coatings holds great promise for enhancing the long-term outcomes of stent implantation by enhancing hemocompatibility and reducing thrombogenicity. Future research directions and potential clinical applications are discussed, underscoring the need for continued advancements in this field.

Many requirements engineering tools have been existing for gathering, documenting and tracing requirements that can even be further processed for such purposes as analysis and transformation. In this study, we analysed 56 different requirements engineering tools for a comprehensive set of features that are categorised into multiple viewpoints (i.e., project management, specification, collaboration, customisation, interoperability, methodology, knowledge management). The analysis results led to many interesting findings. Some of them are as follows: (i) the project planning and execution activities are rarely supported, (ii) multi-user access and versioning are highly supported, (iii) the top-popular specification technique is natural languages, while precise specification via modeling languages is rarely supported, (iv) requirements analysis is rarely supported, (v) requirements transformation is considered for generating documents only, (vi) tool customisation via the tool integration and API support is highly popular, while customising the notation set is rarely supported, (vii) exchanging requirements is popular in such standards as ReqIF and Excel/CSV, while no single standard are accepted by all the tools, (viii agile development is very common, while other methodologies (e.g., MDE and SPLE) are rarely supported, and (viii) user-guide, telephone, e-mail, videos are the top methods for sharing knowledge. The analysis results will be useful for different stakeholders including practitioners, tool vendors and researchers.

Carbon nanomaterial is widely used in structural health monitoring due to the advantage of sensitivity and good mechanical properties. This study presents a novel approach employing carbon nanocomposite materials (CNM) to characterize deformation and damage evolution in physical modelling. As the primary measurement method, the CNM is used to investigate the deformation characteristics of a 200-400 m thick sandstone bed at a 1 km deep longwall mine. The sandstone unit is identified as an ultra-thick key stratum (UTKS), with its thicknesses varying across different mining panels of the UTKS. The results of CNM monitoring show that the UTKS remains stable even after a consecutive excavation of 900 m in width. This stability impedes the upward propagation of overlying strata failure, leading to minimal surface subsidence. The study demonstrates the huge potential of CNM in the mining area, which can be useful for investigating material damage in physical modelling studies. The findings suggest that the cumulative extraction width in individual mining areas of the mine should be controlled to avoid a sudden collapse of the UTKS, and that special attention should be paid to where the UTKS's thickness changes substantially. The substantial variation in UTKS thickness significantly impacts the pattern of overburden subsidence.

This study explored drivers’ emotion-based impressions of car-borne central control platforms (CBCCPs) personal-use vehicles. Thus, this preference-based study examined experts’ and drivers’ opinions regarding CBCCPs’ appeal from the perspective of Miryoku engineering. To this end, this study analyzed data via the EGM (evaluation grid method (EGM) and quantification theory type I. Results: Drivers’ preferences for specific CBCCP design characteristics were categorized into the factors “legible,” convenient,” and “tasteful,” which comprised the core of the EGM semantic hierarchical diagram. In addition, the importance of CBCCPs’ appeal factors and characteristics was assessed through quantification theory type I.The findings of this study provide valuable insights for designers, manufacturers, and researchers interested in the design of CBCCPs. Additionally, the results of this study can contribute to research on applied psychology, human-computer interactions, and car interface design.

In this research, we present a comprehensive methodology to categorize perfumes based on their fragrance profiles and subsequently aid in creating innovative odoriferous molecules using advanced neural networks. Drawing from data on Parfumo and the Good Scents Company webpage (Parfumo, 2008; The Good Scents Company, 2021), the study employs sophisticated web scraping techniques to gather diverse perfume attributes. Following this, a k-means algorithm is applied for perfume clustering, paving the way for recommending similar scents to consumers. The process then bridges customer preferences to molecular design by incorporating their feedback into generating new molecules via graph neural networks (GNNs). Through converting the Simple Molecular Input Line Entry System (SMILES) representation into graph structures, the GNN facilitates the creation of new molecular designs attuned to consumer desires. The proposed approach offers promising avenues for consumers to pinpoint similar perfume choices, incorporating feedback, and for manufacturers to conceptualize new fragrant molecules with a high likelihood of market resonance.

Cell sheet (CS) engineering using human MSC is of significant interest for regenerative medicine and this rapidly growing field translates to clinical use in a number of indications. Nevertheless, little is known of factors that define the timing of CS assembly from primary cultures which is important for planning treatments in both – autologous and allogeneic modes of use. We used a comparative in vitro approach with primary donors’ (n=14) adipose-derived MSC and evaluated the impact of healthy subject’s characteristics (age and sex), MSC culture features (population doubling time and lag-phase) and extracellular matrix (ECM) composition along with factors related to connective tissue formations (-SMA and FAP-) on CS assembly duration.

Amid global energy challenges, hydrogen evolution reaction (HER) is gaining traction for green hydrogen production. While catalyst research is ongoing, recognizing electrolyte effects remains crucial for sustainable hydrogen production via renewable-powered water electrolysis. This review delves into the intricate effects of electrolytes on the kinetics of HER. It examines key factors, including pH, cations, anions, impurities, and electrolyte concentration. It is discussed that electrolyte pH alters catalyst-electrolyte interactions and proton concentrations, thereby influencing factors such as hydrogen binding energy, water adsorption, and the overall reaction kinetics. Moreover, it is briefed that electrolyte cations, such as Li+, can impact HER positively or negatively, offering opportunities for improvement based on the metal substrate. Interestingly, there is a potential that HER can be tuned using Li+ ions to modify M-H bond energy, demonstrating flexibility beyond pH levels and counter ions. Varied adsorption energies of metal cations on metal electrodes are also found to influence HER kinetics. The effects of electrolyte anions and impurities are also discussed, emphasizing both positive and negative impacts on HER kinetics. Moreover, it is pointed out that the electrolyte engineering approach enhances the HER kinetics of both metal and carbon-based electrodes without permanent catalyst surface modifications. This review underscores the importance of electrolyte composition, highlighting both challenges and potential solutions in advancing HER research for sustainable energy production.

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent Based Models and Simulation consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Agents generate large volumes of data over time and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents’ perspectives on key events by providing natural language narratives. However, these narratives need to be factual, transparent, and reproducible. To this end, we present a structured narrative prompt for sending queries to LLMs. Chi-square tests and Fisher’s Exact tests are applied to assess statistically significant difference in sentiment scores of the narrative messages between simulation generated narratives, ChatGPT-generated narratives, and real tweets. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. This structure is expected to be extensible across LLMs. In 14 out of 44 categories, ChatGPT generated narratives which has sentiment scores that were not discernibly different, in terms of statistical significance (alpha level of 0.05), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of a LLM based on simulated agents’ information; and (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect simulated agent’s experiences with real people.

In the present study, the efficiency of a biomimetic approach by coating demineralized bone matrix (DBM) amorphous calcium phosphate (DBM+CaP), including its combination with serum albumin (DBM+CaP+BSA), was investigated. The intact structure of DBM promotes the transformation of amorphous calcium phosphate (CaP) into DPCD with a characteristic plate shape and particle size of 5–35 µm. The inclusion of BSA in the coating resulted in a better and more uniform distribution of CaP on the surface of DBM trabeculae. MG63 cells showed that both the obtained forms of CaP and its complex with BSA did not exhibit cytotoxicity up to a concentration of 10 mg/ml in vitro. Ectopic (subcutaneous) implantation in rats revealed pronounced biocompatibility, as well as strong osteoconductive, osteoinductive, and osteogenic effects for both DBM+CaP and DBM+CaP+BSA, but more pronounced effects for DBM+CaP+BSA. In addition, for the DBM+CaP+BSA samples, a pronounced full physiological intrafibrillar biomineralization and proangiogenic effect with the formation of bone-morrow-like niches, accompanied by pronounced processes of intramedullary hematopoiesis, indicating a powerful osteogenic effect of this composite have been achieved.

The construction enterprise is a essential zone that contributes considerably to the worldwide financial system. but it faces numerous challenges, together with inefficiency in electric and electronics engineering tasks. This inefficiency results in delays, expanded charges, and decreased productivity. Device learning strategies have the capacity to deal with those challenges through optimizing the making plans and execution of electrical and electronics engineering tasks within the construction industry. This study paper targets to discover using machines gaining knowledge of strategies to maximise efficiency in electrical and electronics engineering projects inside the production industry. Mainly, the paper will be conscious of developing and imposing gadget mastering algorithms to optimize project scheduling, fabric procurement, and system utilization. The paper will even look at the ability of using predictive analytics to identify and mitigate dangers related to electric and electronics engineering tasks. The study could be based on a combination of literature review and empirical analysis. The literature evaluation will provide a top-level view of the demanding situations going through the development industry and the capability advantages of the usage of system getting to know strategies to deal with those challenges. The empirical evaluation will involve the improvement and testing of device mastering models on real-world information from electrical and electronics engineering initiatives inside the construction industry. The predicted results of this research are the development of a fixed of sensible recommendations for the use of machine gaining knowledge of strategies to optimize electric and electronics engineering projects within the construction industry. These suggestions will be beneficial for venture managers, engineers, and other stakeholders within the creation enterprise who're inquisitive about maximizing performance and decreasing charges of their tasks. Ordinary, this studies paper aims to contribute to the continued efforts to enhance the efficiency and productiveness of the development enterprise via exploring the ability of system studying techniques to optimize electrical and electronics engineering tasks.

This study assesses coastal flooding extension mapping based on two innovative approaches. The first is based on the coupling of two robust numerical models (SWASH and LISFLOOD), in this case, discharges were extracted from the wave overtopping results from SWASH 1D and set as boundary conditions for LISFLOOD on the crest of an existing seawall where overtopping typically occurs. The second, hereby called the ‘Tilted Bathtub Approach’ (TBTA), is based on wave run-up levels and buffering the affected area of a prior flooding event, adjusting it for expected sea-states according to different return periods. The proposed approaches are applied to a case study on the Northern Portuguese coast, at Furadouro beach, in the municipality of Ovar, which has been facing multiple flooding episodes throughout recent years, including a dramatic storm in February 2014. This event was used as validation for the proposed methods. A 30 years long hourly local wave climate time series was used both to perform an extreme value analysis in order to obtain expected sea-states according to different return periods, and also for performing a sensitivity test for established empirical formulas to estimate wave run-up at this particular case. Results indicate both approaches are valuable: they yield coherent flood extension predictions that align well with the real inundated area from the 2014 storm. The convergence of these findings underscores the potential for these methods in future coastal flood risk assessment and planning.

Collaborative robots have experienced low acceptance in applications, especially in industry. 1 This fact has attracted the attention of researchers and practitioners, who point to different causes 2 for this low acceptance. One of the main reasons is the difficulty in converging on suitable methods 3 for modeling collaborative interactions between robots and their surrounding context during the 4 requirements phase. These interactions must be elicited and modeled during requirements modeling 5 to maximize value creation through collaboration and must be formally verified, taking into account 6 the risks of human-robot interaction. However, such modeling is often not present in collaborative 7 robot design, and the choice of an appropriate approach remains an open problem. In this paper, 8 this problem is addressed from a requirements engineering perspective, a goal-oriented model and 9 a service-based approach supported by an additional verification method based on Petri nets is 10 proposed. A case study based on collaborative robots used in a hospital environment is presented.

In this paper, we investigate the structural, microstructural, dielectric, and energy storage properties of Nd and Mn co-doped Ba0.7Sr0.3TiO3 [(Ba0.7Sr0.3)1-xNdxTi1-yMnyO3 (BSNTM) ceramics (x = 0, 0.005, and y = 0, 0.0025, 0.005, and 0.01)] via a defect dipole engineering method. The complex defect dipoles (MnTi"-VO∙∙)∙ and (MnTi"-VO∙∙) between acceptor ions and oxygen vacancies capture electrons, enhancing the breakdown electric field and energy storage performances. XRD, Raman spectroscopy, and microscopic investigations of BSNTM ceramics revealed the formation of a tetragonal phase, increased oxygen vacancies, and reduced grain size with Mn dopant, respectively. The BSNTM ceramics with x=0.005 and y=0 exhibit a high dielectric constant of 2058 and a dielectric loss of 0.026 at 1 kHz. These values gradually decreased to 1876 and 0.019 for x=0.005 and y=0.01 due to the Mn2+ ions at Ti4+-site, which facilitates the formation of oxygen vacancies, and prevents the decrease of Ti4+. In addition, the defect dipoles act as a driving force for depolarization to tailor the domain formation energy and domain wall energy, which provides a high difference between the maximum polarization of Pmax and remnant polarization of Pr (ΔP=10.39 µC/cm2). Moreover, the complex defect dipoles with optimum oxygen vacancies in BSNTM ceramics can provide not only a high ΔP but also reduce grain size, which together improve the breakdown strength from 60.4 to 110.6 kV/cm, giving rise to a high energy storage density of 0.41 J/cm3 and high efficiency of 84.6% for x=0.005 and y=0.01. These findings demonstrate that defect dipoles engineering is an effective method to enhance the energy storage performance of dielectrics for capacitor applications.

Hydrogels prepared from natural polymer have attracted extensive attentions in biomedical fields such as drug delivery, wound healing, and regenerative medicine due to their good biocompatibility, degradability and flexibility. This review outlines the commonly used natural polymer in hydrogel preparation, including cellulose, chitosan, collagen/gelatin, alginate, hyaluronic acid and starch. The polymeric structure and process/synthesis of natural polymers are illustrated, and natural polymer-based hydrogels including the hydrogel formation and properties are elaborated. Subsequently, the biomedical application of hydrogels based on natural polymer in drug delivery, tissue regeneration, wound healing and other biomedical field is summarized. Finally, the future perspectives of natural polymers and hydrogels based on them are discussed. For natural polymer, novel technologies such as enzymatic and biological methods are developed to improve the structural properties and the development of new natural based polymers or natural polymer derivatives with high performance is still very important and challenging. For natural polymer-based hydrogels, novel hydrogel materials, like double-network hydrogel, multifunctional composite hydrogels and hydrogel microrobots are designed to meet the advanced requirements in biomedical application, and new strategies such as dual-crosslinking, microfluidic chip, micropatterning and 3D/4D bioprinting, have been explored to fabricate advanced hydrogel materials with designed properties for biomedical application. Overall, natural polymeric hydrogels have attracted increasing interests in biomedical application, and the development of novel natural polymer-based materials and new strategies/methods for hydrogel fabrication is badly desirable and still challenging.

Buildings can play an important role in reducing GHG emissions through increased energy efficiency. The European Commission issued a proposal in 2021 suggesting that all new buildings should be “zero-emission Buildings” (ZEB), aiming at a zero GHG emission building stock by 2050.The extent to which ZEB can contribute to reduced GHG emissions, however, varies between countries, due to different energy systems. It is also important to consider other environmental effects to avoid that climate benefits come with unintended consequences.Here, we explore the life-cycle environmental performance for a ZEB in a case where electricity and heating is largely fossil-free. The assessment concentrates on i) environmental impact of the use stage in relation to the product stage, ii) the interrelation between different energy sources, with attention on household electricity, and iii) the performance for more impact categories than primary energy use and climate change. While our results generally support the use of ZEBs from an environmental perspective, they also show that the climate benefit in this setting is marginal. However, given that energy systems are connected and energy savings in one place can reduce the demand for fossil energy elsewhere, the climate benefit of ZEBs is likely underestimated. Besides methdological implications for future studies, this indicates that current EU policy is promising, as incentives for implementation of ZEBs is unaffected by domestic effects.

Silk fibroin (SF) extracted from Bombyx mori cocoons is a biocompatible and biodegradable polymer shows potential as matrices for tissue engineering and regenerative medicine, or con-trolled drug delivery systems. Regenerated fibroin, obtained by removing sericin from natural silk, is water-soluble. Its aqueous solutions can be processed into a variety of materials, for example, fibrous matrices, using the electrospinning method. In this article, the concentration dependences of the properties of aqueous solutions of regener-ated fibroin, that determine the possibility of obtaining electrospun mats for tissue-engineering applications, are studied. A slowdown in the growth of viscosity after a concentration of fibroin of 30% and a decrease in the transparency of the solution were found; this may be due to the on-set of conformational rearrangements: the transition of fibroin from the random coils, charac-teristic for dilute solutions, to the anisotropic conformation of the helix and the appearance of β-pleated sheets. Fibrous mats from regenerated fibroin were obtained from solutions with a concentration of 10 and 20%. Two methods were used to prevent solubility of fibroin-based matrices: conversion of fibroin to the β-conformation by treatment with an ethanol solution and chemical cross-linking with gen-ipin (Gp). The structures of the fibrous samples were studied by AFM and confocal laser scan-ning microscopy. The composition of the moulding solution for the electrospinning of regener-ated fibroin fibrous matrices and the conditions for their modification have been optimised. It has been shown that the composition of fibroin with chitosan contributes to a decrease in the solubility of electrospun fibrous matrices by increasing the number of amino groups available for modification by Gp, and also leads to a decrease in the diameter of the fibers. Electrospun fiber matrices based on regenerated SF, modified by cross-linking with Gp in water-alcohol solutions, promote cell growth and proliferation and may be promising for tissue engineering.

Thermal fluctuations in two-dimensional (2D) isotropy systems at non zero finite temperatures can destroy the long-range (LR) magnetic order due to Mermin-Wanger theory. Interestingly, the magnetic anisotropy related to spin-orbit coupling (SOC) could stabilize magnetic order in 2D systems. Recently, 2D FexGeTe2 (3≤x≤7) with high curie temperature (TC), as a typical 2D van der Waals metallic ferromagnet, has not only made significant progress in synthetic methods and controlling ferromagnetism (FM), but also actively explored many device applications. In this Review, we introduce six experimental methods, ten ferromagnetic modulation strategies, and three spintronic devices of 2D FexGeTe2 materials. Overall, we have outlined the challenges and potential research directions in this field.

The construction safety risks (such as landslides or slumps) of the high-fill subgrade are characterized by uncertainty and variability. In order to ensure the construction safety of the high-fill subgrade of the expressway, a comprehensive risk assessment model of high-fill subgrade construction according to the combination weighting method based on game theory was established. Firstly, settlement rate, lateral differential settlement, horizontal displacement at the foot of the slope, deep horizontal displacement, and excess pore pressure were selected as evaluation indicators, and an evaluation index system of high-fill subgrade was constructed. Secondly, the subjective and objective weights were obtained via the analytic hierarchy process and entropy method. Furthermore, the optimized weights were obtained by introducing game theory, and the construction risk of the high-fill subgrade was evaluated and analyzed using the fuzzy comprehensive evaluation method. Finally, based on the high-fill section of the Zhijiang-Tongren Expressway in Hunan Province (China), the evaluation model was validated through field measurement data. The results show that the safety scores obtained using the three methods of analytic hierarchy process, entropy method, and combination weighting method based on game theory are similar. The combination weighting evaluation model can correct the evaluation result error caused by a single weight and avoid information delay caused by the manual processing and analysis of monitoring data. The high-fill subgrade safety evaluation grade based on the combination weighting method and game theory is consistent with the engineering situation, and its fault tolerance range is within 20%. The evaluation model proposed in this study can scientifically and effectively evaluate the safety risks of high-fill subgrade construction, significantly improving the safety and reliability during the construction process.

Tissue bioengineering has been assuming a pivotal role in implementing alternatives to conven-tional ossicular middle ear replacement prostheses to overcome extrusion while preserving acoustic properties. This in vitro study aims to explore for the first time in current literature the feasibility of a biohybrid middle ear prosthesis composed of titanium surrounded by bone ex-tracellular matrix as bio-coating. We have hereby studied the adhesion and proliferation of human adipose-derived mesenchymal stem cells (hASC) on titanium scaffolds in vitro. Moreover, we identified the osteogenic differentiation of hASC using immunofluorescence assay to analyze osteoblasts gene expression profile (Alp, Runx2, Col1a1, Osx, and Bglap) and we accounted the presence of collagen as a marker of hASC ability to secrete extracellular matrix. We utilized scanning electron microscopy to evaluate the presence of extracellular matrix on the scaffolds. Our preliminary data demonstrated the titanium ability to support human adipose-derived mesenchymal stem cell colonization, proliferation, and osteoblastic differentiation, to obtain a biohybrid device. Our experience seems encouraging, we advocate further in vivo research to corroborate our results towards bone transplantation.

With worsening environmental degradation and increasing international demand for human health, plant cell based technology will be more important. The aim of this review is to give an overview regarding key points of current progress and development trend of nature medical and edible plants from the framework of whole plant cell engineering including cell cultures, cell fusion and cell splitting derived biotechnology. Here we sum up three main application trends of medical plants cell cultures covering industrial output, technology upgrading and potential resource development. We also provide classification and typification insights to summarize the development of somatic hybridization in cereal, vegetables crops and other medicinal and edible plants as well as application of artificial polyploidy induction in major medicinal herbs. Development medicinal and edible homologous plants based on modern plant cell engineering will bring us a promising future.

Current stock market forecasting methods encompass fundamental, technical, emotional, and bargaining factors. Predominantly, price prediction hinges on order volume and price, although correlating these two within existing models proves challenging. This study employs Cycle Generative Adversarial Network (Cycle GAN) to unravel the intricate price-volume relationship, combining it with Bollinger Bands for trading signal analysis, overcoming hurdles in short-term forecasting prevalent in numerical analysis and AI. Focusing on TSMC (2330.TW) stock price, the research leverages Cycle GAN in deep learning to master the price-volume nexus, juxtaposed with LSTM and RNN. Historical TSMC closing prices and transaction counts are model inputs, scrutinizing their interconnectedness for predictions. This innovative approach aligns stock price, volume, market value, taxes, and prior changes via system engineering. By intertwining Bollinger Bands with stock price forecasts, trading signals are distilled, factoring in extended index %b for a comprehensive market picture.

Regenerative endodontic procedures (REPs) aim at recreating dental pulp tissue using biomaterials such as hydrogels. Their bioactivity is mostly linked to the nature of biomolecules or chemical compounds that will compose the endodontic hydrogel. However, many other parameters, such as hydrogel concentration, type, bioactive molecules solubility, of apex size were reported to influence the hydrogel behavior, leading to the inability to predict the clinical outcome. This lack of knowledge and the various parameters that should be considered, suggest that biological activity of endodontic hydrogel is impossible to anticipate and could hinder the bench to bedside transition. We formulated in this article our opinion that most of these parameters could be identified, described, and studied. A second part of the document lists some challenges and perspectives, including development of future mathematical models able to explain and predict the bioactivity of these endodontic hydrogel in clinic.

Conductive nanocomposites play a significant role in tissue engineering by providing a platform to support cell growth, tissue regeneration, and electrical stimulation. In this present study, a set of electroconductive nanocomposite hydrogels based on gelatin (G), chitosan (CH) and conductive carbon black (CB) was synthesized with the aim to develop novel biomaterials for tissue regeneration application. Incorporation of conductive carbon black (10, 15 and 20 wt %) significantly improved electrical conductivity and enhanced mechanical properties with the increased CB content. We employed an oversimplified unidirectional freezing technique to impart anisotropic morphology with interconnected porous architecture. An investigation into whether any anisotropic morphology affects the mechanical properties of hydrogel was conducted by performing compression and cyclic compression tests in each direction parallel and perpendicular to macroporous channels. Interestingly, nanocomposite with 10 % CB produced both anisotropic morphology and mechanical property, whereas anisotropic pore morphology diminished at higher CB concentration (15 and 20 %) imparting denser texture. Collectively, the nanocomposite hydrogels showed great structural stability as well as good mechanical stability and reversibility. Under repeated compressive cyclic at 50 % deformation, the nanocomposite hydrogels showed preconditioning, characteristic hysteresis, nonlinear elasticity, and toughness. Overall, the collective mechanical behavior resembled the mechanics of soft tissues. Electrical impedance associated to the hydrogels was studied in terms of modulus and phase in dry and wet condition. The electrical properties conducted in wet conditions, which is more physiologically relevant, showed low impedance at high frequencies due to capacitive currents. Overall, impedance of the nanocomposite hydrogels decreased with increased CB concentrations. These gelatin-chitosan–carbon black nanocomposite hydrogels show great promise for use as conducting substrates for the growth of electro-responsive cells in tissue engineering.

Excess energy from photosynthesis can be collected through biophotovoltaic platforms to generate green electrical power. This research evaluated the in-situ performance of different CAM (Crassulaceae acid metabolism) plants from Calama city located in the Atacama Desert for energy recovery as biophotovoltaic cell (BPV) using AISI 316L and Cu as electrodes. The species evaluated were Aloe perfoliata, Cereus jamacaru, Austrocylindropuntia subulata, Agave potatorum, Malephora crocea, and Kalanchoe daigremontiana. The results indicate that K. daigremontiana can be used as in-situ BFV because it has a maximum cell potential of 0.248 V and a minimum of 0.139 V with a recurrence close to 89% in the ranges [0.2 - 0.25] V. This is one of the few investigations that evaluate the potential of native CAM plants as BFV energy sources, providing new knowledge for the development of sustainable alternatives for horticultural crop production systems.

The aerospace industry demands high-performance materials that can withstand extreme conditions and maintain efficiency in a variety of applications. Bio-inspired polymer coatings have emerged as a promising approach to meet these demands, by drawing inspiration from natural systems to develop new coatings that exhibit enhanced properties, such as self-cleaning, anti-icing, thermal management, and corrosion resistance. In this review, recent developments in the field of bio-inspired polymer coatings for aerospace applications are presented, covering a range of coatings and their respective properties. We discuss the recent developments and applications of bio-inspired coatings, with a focus on their advantages and challenges in aerospace applications. We also highlight the potential for future research and development in this field, including the integration of advanced technologies such as nanotechnology and additive manufacturing. The review aims to provide insights into the current state-of-the-art of bio-inspired polymer coatings for aerospace applications and to inspire further research in this rapidly growing field.

Intracortical microelectrode arrays (MEAs) can be used in a range of applications, from basic neuroscience research to providing an intimate interface with the brain as part of a Brain-Computer Interface (BCI) system aimed to restore function for people living with neurological disorders or injuries. Unfortunately, MEAs tend to fail prematurely, leading to a loss in functionality for many applications. An important contributing factor in MEA failure is oxidative stress resulting from chronically inflammatory-activated microglia and macrophages releasing reactive oxygen species (ROS) around the implant site. Antioxidants offer a means for mitigating oxidative stress and improving tissue health and MEA performance. Here, we investigate using the clinically available antioxidant dimethyl fumarate (DMF) to reduce the neuroinflammatory response and improve MEA performance in a rat MEA model. Daily treatment of DMF for 16 weeks resulted in a significant improvement in the recording capabilities of MEA devices during the sub-chronic (Weeks 5-11) phase (42% active electrode yield vs. 35% for control). However, these sub-chronic improvements were lost in the chronic implantation phase, as a more exacerbated neuroinflammatory response occurs in DMF-treated animals by 16 weeks post-implantation. Yet, neuroinflammation was indiscriminate between treatment and control groups during the sub-chronic phase. Although worse for chronic use, a temporary improvement (<12 weeks) in MEA performance is meaningful. Providing short-term improvement to MEA devices using DMF can allow for improved use for limited-duration studies. Further efforts should be taken to explore the mechanism behind a worsened neuroinflammatory response at the 16-week time point for DMF-treated animals to assess its usefulness to specific applications.

MoS2 has long been considered as a promising catalyst for hydrogen production. At present, there are many strategies to further improve its catalytic performance, such as edge engineering, defect engineering, phase engineering and so on. However, at present, there is still a great deal of controversy about the mechanism of MoS2 catalytic hydrogen production. For example, it is generally believed that the base plane of MoS2 is inert, but it has been reported that the inert base plane can undergo a transient phase transition in the catalytic process to play the catalytic role, which is contrary to the common understanding that the catalytic activity is only at the edge. Therefore, it is necessary to further understand the mechanism of MoS2 catalytic hydrogen production. In this article, we summarized the latest research progress on the catalytic hydrogen production of MoS2, which is of great significance for revisited the mechanism of MoS2 catalytic hydrogen production.

Herein are reported the main pros and cons of using the conversational Artificial Intelligence (AI) called “ChatGPT” as a tool to write essays in the academic and scientific community. Also, selected mathematical and scientific problems were solved to determine the effectiveness of this platform. To this end, a systematic case study was designed and conducted to obtain data generated by the chatbot that was further analyzed. Six different topics were chosen for the essay evaluation, and the introduction section was created using this AI. Meanwhile, six mathematical problems related to integrals, z-transform, Laplace transform, and resolution of ordinary differential equations were solved. Furthermore, a scientific problem associated with the structural properties of material was also solved. After data analysis, we concluded that the conversational platform could quickly create written essays with well-structured words. Nonetheless, in almost all cases, the chatbot only used basic information to complete the introduction, and no critical writing was obtained. For the mathematical case study, we observed that under the current status of the ChatGPT, this platform has no ability or skills to solve complex equations. Thus, significant changes need to be conducted to improve the performance of ChatGPT for mathematical solutions.

Hydrogels (gels) are attractive tools for tissue engineering and regenerative medicine due to their potential for drug delivery and ECM-like composition. In this study, we use rheology to characterize GelMA/alginate gels loaded with human platelet lysate (PL). We then characterize these gels from a physicochemical perspective and evaluate their ability to transport PL proteins, their pore size, and their rate of degradation. Finally, their biocompatibility is evaluated. We describe how alginate changes the mechanical behavior of the gels from elastic to viscoelastic after ionic (calcium-mediated) crosslinking. In addition, we positively report the release of PL proteins and relate it to the degradation profile of the gels and the biocompatibility of the gels. Thus, the developed gels represent attractive substrates for both cell studies and bioactive materials.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Run-time validation techniques are required because complex Self-adaptive systems must consistently offer acceptable behavior for important services. The run-time testing can offer further confidence that a Self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for Self-adaptive systems. To ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the SPEA2 algorithm facilitates both the execution and adaptation of run-time testing operations.

Lipases have tremendous potential for industrial use, particularly, those mostly active against water-insoluble substrates, such as triglycerides composed of long-chain fatty acids. However, in most cases, mutants often need to be constructed to achieve optimal performance for such substrates. Protein engineering techniques have been reported as strategies for improving lipase characteristics by introducing specific mutations in the cap domain of esterases or in the lid domain of lipases, or through lid domain swapping. Here, we have improved the lipase character of a lipase, retrieved from the Marine Metagenomics MarRef Database and assigned to the Actinoalloteichus genus (WP_075743487.1), through site-directed mutagenesis and by substituting its lid domain (FRGTEITQIKDWLTDA) by the one of Rhizopus delemar (previously Rhizopus oryzae) lipase (FRGTNSFRSAITDIVF). Results demonstrated that the redesigned mutants gain activity against bulkier triglycerides such as glyceryl tridecanoate and tridodecanoate, olive oil, coconut oil, and palm oil. The absence of a residue in the new lid, present in the original lid, appears to be a key aspect to the increase in lipase character, although full activity recovery is only obtained with lid swapping.

The advancements achieved in Tissue Engineering are based on a careful and in-depth study of cell-tissue interaction. The choice of a certain biomaterial in Tissue Engineering is fundamental as it represents an interface for adherent cells in the creation of a microenvironment suitable for cell growth and differentiation. The knowledge of the biochemical and biophysical properties of the extracellular matrix is a useful tool for the optimization of polymeric scaffolds. The aim of this re-view is to analyse the chemical, physical and biological parameters on which it is possible to act in Tissue Engineering for the optimization of polymeric scaffolds. Understanding the scaffold impact on cell fate is of paramount importance for the successful advancement of Tissue Engineering.

Focal cartilage defects are a prevalent knee problem affecting people of all ages. Due to its avascular nature, cartilage has limited self-repair capacity, and osteochondral defects can lead to pain and long-term complications such as osteoarthritis. Autologous chondrocyte implantation (ACI) has been a successful surgical approach for repairing osteochondral defects over the past two decades. However, a major drawback of ACI is the de-differentiation of chondrocytes during their in vitro expansion. In this study, we isolated ovine chondrocytes and cultured them in a two-dimensional environment as for ACI procedures. We hypothesised that the 3D scaffolds would support the cells re-differentiation without the need for growth factors and so we encapsulated them into soft collagen and alginate (col/alg) hydrogels. Chondrocytes embedded into hydrogels were viable and proliferated. After 7 days they acquired a rounded morphology and started to aggregate. Gene expression studies showed that the genes associated with chondrogenesis started to be up regulated as early as day one. At 21 days chondrocytes had extensively colonized the hydrogel, forming large cell clusters and started to deposit collagen II and aggrecan with limited collagen type I deposition. These findings highlight the potential of soft col/alg hydrogels to enhance ACI outcomes by creating a favourable microenvironment for chondrocyte reprogramming and re-differentiation, eliminating the dependency on growth factors.

Amidst the strong demand for wood-based products, and the clamor of environmental consciousness, more functional and green solutions arise to meet both of these goals. Pursuant to this, the study looked into the potential of a bio-waste taken from Cocos nucifera tree known as phellem—the tree’s outermost skin made of dead tissues, to be a main component of a new construction material. This study builds on existing research showing phellem is impermeable, buoyant, elastic, has a slow burn rate, and repels water during precipitation, which supports the hypothesis that phellem may be a potential substitute material for dry wall boards. To test this, three (3) formulations were developed as experimental setups to test the physico-mechanical properties of coconut palm phellem (COPAP): Formulation A (40% COPAP and 60% cement); Formulation B ( 25% COPAP and 75% cement); and lastly, Formulation C that was (10% COPAP and 90% cement). The study tested (1) which among these formulations exhibited the best physico-mechanical properties, then it (2) compared the formulations with common dry wall products available in the market, and subsequently, (3) identified the possible architectural applications of COPAP board as a new building material. Of the three setups, Formulation C exhibited the best physico-mechanical properties in terms of compressive and flexural strength, fire resistivity, and water absorption. It fared better than the particle board in terms of compressive strength, better than wafer and particle board in fire resistivity, performed best in the water absorption test, and was almost equivalent in flexural strength to fiber cement board. With these findings, the study concluded that COPAP board can be used for projects that entail a high number of interior partitions, such as offices, BPO firms and commercial stalls in shopping malls, etc. The findings were conclusive only for interior applications, as the results clearly suggest that the COPAP boards are not load-bearing. Future investigations may explore and assess other properties of COPAP boards like thermal and sound insulation, resistance to rot and other properties that may prove its competitive advantage as a construction material.

This study focused on the optimization of the flexural strength of bio-composite samples of palm kernel, whelks, clams, periwinkles shells and bamboo fiber, reinforced with resin for engineering applications. The aim of the study was to: formulate different samples of bio-composite reinforce with resin for engineering applications and to evaluate the flexural strength, of the fabricated composite. The hand lay-up technique was used for the composites produced by incorporating different percentage compositions of the shells/fiber (10%, 15%, 20%, 25% and 30%) into varied proportions of epoxy resin and catalyst. The cured samples after 24hours were subjected to tensile, impact, flexural and water absorption test. The experiments were conducted using Taguchi optimization method L25 (5x5) with five design parameters and five level combinations in Minitab 18 statistical software. The results showed that the average values of flexural was 114.87MPa when compared to the unreinforced of 72.33MPa bio-composite. The study recommended that agricultural waste like palm kernel shells, whelk shells, clams, periwinkle shells and bamboo fiber, should be converted into important engineering applications.

The potential of sericin, a protein derived from silkworms, is explored in bone graft applications. Sericin's biocompatibility, hydrophilic nature, and cost-effectiveness make it a promising candidate for enhancing traditional graft materials. Its antioxidant, anti-inflammatory, and UV-resistant properties contribute to a healthier bone-healing environment, and its incorporation into 3D-printed grafts could lead to personalized medical solutions. However, despite these promising attributes, there are still gaps in our understanding. The precise mechanism through which sericin influences bone cell growth and healing is not fully understood, and more comprehensive clinical trials are needed to confirm its long-term biocompatibility in humans. Furthermore, the best methods for incorporating sericin into existing graft materials are still under investigation, and potential allergic reactions or immune responses to sericin need further study.

Mesenchymal stem cells (MSCs) are an attractive therapeutic tool for tissue engineering and regenerative medicine owing to their regenerative and trophic properties. The best-known and most widely used are bone marrow MSCs which are currently being harvested and developed from a wide range of adult and perinatal tissues. MSCs from different sources are believed to have different secretion potentials and production which may influence their therapeutic effects. To confirm it, we performed a quantitative proteomic analysis based on the TMT technique of MSCs from three different sources: Wharton’s jelly (WJ), dental pulp (DP) and bone marrow (BM). Our analysis focused on MSC biological properties of interest for tissue engineering. We identified a total of 611 differentially expressed human proteins. WJ-MSCs showed the greatest variation compared with the other sources. WJ produced more extracellular matrix (ECM) proteins and ECM-affiliated proteins and appeared more able to modulate the inflammatory and immune response. BM-MSCs displayed enhanced differentiation and paracrine communication capabilities. DP-MSC appeared to promote exosome production. The results obtained confirm the existence of differences between WJ, DP and BM-MSC and the need to select the MSC origin according to the therapeutic objective sought.

Background: Depression is a common mental health problem that can affect everyone at different stages of development. Though the prevalence rate of depression among university students is rising, exploration among engineering students is limited. The aim of the study was to examine the prevalence of depression and its associated factors of among engineering students in Thailand. Methods: 346students participated in this study. All completed the outcome inventory-depression subscale (OI-D) to evaluate the level and prevalence of depression. Other variables assessed included social skill, learning styles, relationship satisfaction, interpersonal difficulty, alcohol use, internet addiction, and neuroticism. Correlation and regression analysis were applied to test the association between sociodemographic and psychosocial factors and depression. Results. Of 346 students with the mean age was 20.25 (SD, 1.33), 52.31% was male. Based on the OI-D, 35.3% of participants exhibited symptoms indicative of major depression. Multiple regression showed that only neuroticism, interpersonal difficulties, social skills, and self-esteem appeared to be the significant predictors of depression. Conclusion: the prevalence of depression among engineering students is unexpectedly high. Intra and interpersonal factors were found to be associated with depression. Further study on identifying these risk factors should be encouraged.

The production of plants and crops is influenced by environmental stress, which is a serious scientific issue. Cotton is an essential crop for producing natural fibers that are used to make biofuel and edible oils. Salinity is the main element that influences cotton growth during the beginning of germination. The type of salt and the growth stage affects how sensitive a plant is to salt stress. Developing ways to enhance cotton performance in salty circumstances can be aided by an understanding of the response of cotton to salt, its mechanism of resistance, and its management methods. Osmotic and ionic imbalances originate due to the deposition of soluble salts under salinity stress in the plant's root zone. Soil salinity significantly reduces plant growth due to several factors, such as nutritional ion imbalance, which reduces K+, PO4-, and NO- absorption, excessive salt chloride concentrations, and osmotic stress, which hinders water availability. Research has revealed that compared to subsequent stages, the germination, emergence, and seedling phases are more vulnerable to salinity stress, which ultimately affects the seed cotton yield by delaying blooming, reducing fruiting positions, shedding fewer fruits, and reducing boll weight. The morphology, growth of cotton roots, shoots, yield, and fiber quality are all strongly impacted by salinity stress. It slows down plant feeding, cellular metabolism, and photosynthesis. The soil, water, and climate all affect how the plant responds to salinity stress. During salt stress, excessive exclusion of sodium or its compartmentalization is the key adaptation process in cotton. A major adaptive potential to create cotton types that can withstand salt is provided by the up-regulation of both physicochemical and non-enzymatic antioxidant genes. A successful strategy to increase cotton germination in saline soils is seed priming. Moreover, the transgenic strategy might be a viable choice for improving cotton yield in saline environments. Our review focuses on the impacts of salinity on cotton productivity as well as how plants react to salt stress. It also clarifies recent genetic advancements and molecular breeding for cotton's resistance to soil salt. To create salt-tolerant cultivars, a combination of traditional breeding and novel molecular approaches will be useful.

The results of applying drilling engineering tools in planned deviated and horizontal wells of oil and gas condensate fields have been examined. The basis for the implementation of a comprehensive approach was the information obtained from drilling horizontal wells in the West and East Siberia, Volga-Ural region, and Iraqi Kurdistan. A synthetic model is considered as an example of the data, incorporating geomechanical and geological properties. As a result of the conducted audit of the geomechanical model and drilling process, the methodologies used were further developed to obtain optimal drilling fluid densities, aiming to prevent cavity formation on well walls, hydraulic fractures, and other related issues. The implementation of the Python programming language was introduced in the stages of geomechanical model preparation, resulting in a time acceleration of up to 20%. During the pre-drilling stage, technological operations were synchronized with the geomechanical model, increasing the involvement of process participants through the synchronization of knowledge areas. The interrelation between drilling technology, drilling parameters, properties of flushing fluids, and rock stability analysis was established, ensuring a 5% increase in construction speed (through NPT reduction and modification of existing tools and technological operations) and accident-free drilling of complex deviated and horizontal wells, provided that the recommended drilling parameters are followed.

This comprehensive review paper provides a detailed analysis of the extraction methods employed in the dimension stone industry. Natural rock materials used in construction, have been historically extracted using primitive techniques such as thermal shock, hammer and chisel, and plug and feather. However, significant technological advancements have revolutionized the industry, leading to the widespread adoption of mechanized methods. One highly efficient technique is diamond wire cutting, which involves the utilization of diamond-engraved bits affixed to a tensioned steel wire. This method has demonstrated remarkable capabilities in minimizing waste generation and ensuring exceptional machining quality. Nonetheless, challenges such as high operating costs and wire breakage persist and require further investigation. In addition to diamond wire cutting, this review explores the utilization of circular diamond saws and frame sawing as alternative extraction methods. The geotechnical characteristics of the stone play a critical role in the selection of appropriate machinery, and researchers worldwide have conducted numerous studies to investigate the impact of each parameter on the extraction efficiency of these mechanized techniques. Moreover, controlled blasting with explosives emerges as a promising alternative for dimension stone extraction, warranting further exploration and optimization.

This paper examines some of the largely neglected areas of opportunity to utilize skeletal space structure systems in support of modular, industrialized, economical, sustainable and digital future of the construction industry. In this context, the feasibility of future use of skeletal space structures is studied for a few classes of engineering structures, namely, residential apartment buildings, and offshore platforms, along with their suitability for reconstruction, renovation, modernization and retrofit of damaged buildings and urban areas of cultural heritage significance. Finally, the particulars of lean project management of space structures are discussed with emphasis on engineering and economic factors, production management, environmental aspects, quality management, and reliability, maintainability and sustainability considerations. This article concludes that skeletal space structures can fulfil many of the essential construction requirements of modern societies, especially those facing environmental challenges, all while allowing for design flexibility and mass customization.

This research investigates the potential application of generative language models, especially ChatGPT, in aviation safety analysis as a means to enhance the efficiency of safety analyses and accelerate the time it takes to process incident reports. In particular, ChatGPT was leveraged to generate incident synopses from narratives, which were subsequently compared with ground truth synopses from the Aviation Safety Reporting System (ASRS) dataset. The comparison was facilitated by using embeddings from Large Language Models (LLMs), with aeroBERT demonstrating the highest similarity due to its aerospace-specific fine-tuning. A positive correlation was observed between synopsis length and their cosine similarity. In a subsequent phase, human factor issues involved in incidents as identified by ChatGPT were compared to human factor issues identified by safety analysts. A concurrence rate of 61% was found, with ChatGPT demonstrating a cautious approach towards attributing human factor issues. Finally, the model was used to attribute incidents to relevant parties. As no dedicated ground truth column existed for this task, a manual evaluation was conducted. ChatGPT attributed the majority of incidents to the Flight Crew, ATC, Ground Personnel, and Maintenance. This study opens new avenues for leveraging AI in aviation safety analysis.

In this study, Brillouin optical time-domain analysis (BOTDA) sensing technology was utilized for monitoring settlement in a similarity model of a highway subgrade. As contact winding cannot be used for an optical fiber that is buried directly in soil, uncoupling between. the fiber and the soil can occur. Thus, an optical-fiber-embedded beam (OFEB) was developed, and a method for measuring and calculating the beam deformation was proposed. A calibration test and a test on a similarity model of a subgrade were carried out to investigate the applicability and monitoring accuracy of the OFEB. It was concluded that the OFEB can accurately measure beam deflection, where the maximum relative error between measurements by the optical fiber and a displacement transducer was approximately 5%. The OFEB was embedded directly into a similarity model of a subgrade to monitor settlement. The deflection deformation of the OFEB was found to be close to the subgrade settlement over a certain settlement range with a relative error below 8.1%. Thus, the OFEB can be used to realize measure large-range distributed settlement in a subgrade. A numerical simulation was performed to identify appropriate beam dimensions and material design parameters, thereby extending the measurement range before decoupling of the OFEB and soil occurs. The enhancement of the measurement range and accuracy of the OFEB based on the preliminary experiments carried out in this study enables further investigation of settlement monitoring.

The role of mesenchymal-to-endothelial transition in the angiogenic response is controversial. Toward this, the present study aimed to determine if fibroblasts contribute to angiogenesis. Endothelial differentiation of fibroblasts was induced by culturing MRC-5 cells (human fetal lung fibroblast cells) on top of Matrigel hydrogel or embedded inside the hydrogel. The formation of ca-pillary-like networks in response to angiogenic signals was observed. The tube formation occurs quickly and can be visualized us-ing a phase-contrast inverted microscope, and/or the cells can be treated with DAPI before the assay and tubes can be visualized through fluorescence or confocal microscopy. Furthermore, fibroblasts cultured in a higher concentration invaded the Matrigel hydrogel and formed stem-cell-like spheroids. These spheroids embedded in matrigel matrices of varying densities sprouted to form 3D connective-tissue networks. Collectively, our results highlight the endothelial differentiation capacity of human lung fibroblasts. The results obtained in this work may have an impact on the search for alternative cell sources for vascular tissue engineering and the overcome of obstacles to vascularization of autologous tissue-engineered constructs and the production of functional grafts for clinical use.

Development of bioresponsive extrudable hydrogels for 3D bioprinting is imperative to address the growing demand for scaffold design and efficient and reliable methods of tissue engineering and regenerative medicine. This study proposed genipin-crosslinked gelatin-hyaluronic acid hydrogel bioink with different amounts of gelatin tailored for 3D bioprinting, focusing on high cell density loading and less artificial extra-cellular matrix (ECM) effect, as well as exploring their potential applications in tissue engineering. The bioresponsiveness of these hydrogel scaffolds was successfully evaluated in different physiological conditions. 3D and four-axis printing of complex structures such as shapes of hollow tube, star, pyramid, and four-axis tubular scaffolds prove the hydrogel’s high extrusion ability and post-printing shape fidelity. Cytocompatibility and high cell density 3D bioprinting using this moderately stable hydrogel exhibit high potential for precise cell-delivery modes in tissue engineering as well as regenerative medicine.

This research examines the impact of social equity on energy consumption. We constructed a digital twin for residential energy consumption by enriching the synthetic population with real-world surveys and feeding them with other environmental and appliance data to the energy modeling framework. We analyzed household hourly energy consumption data from Albemarle County and Charlottesville City in Virginia, USA, for the year 2019. We used clustering analysis to identify patterns in social equity and energy consumption. The results demonstrated the impact of different residential attributes on energy poverty. Statistical analyses, including ANOVA and Chi-Squared tests, were conducted to test for significant differences between racial groups in quantitative and categorical variables. The study found that race is significant in determining the location and quality of housing. People of color often live in areas with higher pollution and less access to green spaces. Additionally, income levels and the age of the house are influential factors in determining energy efficiency. Future work should focus on collecting and analyzing data at the country level and using qualitative data collection methods to gain a more comprehensive understanding of social equity issues concerning energy consumption. Overall, this study provides valuable insights into the relationship between different residential attributes and energy consumption, which can inform policy development to promote more equitable and sustainable communities.

In this study, 3D printing of poly-l-lactic acid (PLLA) scaffolds reinforced with graphene oxide (GO) nanoparticles via Digital Light Processing (DLP) was investigated to mimic bone tissue. Stereolithography is one of the most accurate additive manufacturing method, but the dominant available materials used in this method are toxic. In this research, a biocompatible resin (PLLA) was synthetized and functionalized to serve the purpose. Due to the low mechanical properties of the printed product with the neat resin, graphene oxide nanoparticles in three levels (0.5, 1, and 1.5 Wt.%) were added with the aim of enhancing the mechanical properties. At first, the optimum post cure time of the neat resin was investigated. Consequently, all the parts were post-cured for three hours after printing. Due to the temperature-dependent structure of GO, all samples were placed in an oven at 85 ° C for different time periods of 0, 6, 12, and 18 hours to increase mechanical properties. The compression test of heat treated samples reveals that the compressive strength of the printed parts containing 0.5,1, and 1.5 % of GO increased by 151,162 ad 235%, respectively. Scaffolds with the designed pore sizes of 750 microns and a porosity of 40% were printed. Surface hydrophilicity test was performed for all samples showing that the hydrophilicity of the samples increased with increasing GO percentage. The degradation behavior of the samples was evaluated in a PBS environment, and it revealed that by increasing GO, the rate of component degradation increased, but the heat treatment had the opposite effect and decreased the degradation rate. Finally, besides improving biological properties, a significant increase in mechanical properties under compression can introduce the printed scaffolds as a suitable option for bone implants.

To solve the benefit allocation problem between the subjects of the supply chain of general contracting projects. The Cobb-Douglas function is applied to establish the benefit distribution model when the two cooperate in the supply chain system, analyze the optimal benefit situation of one-time cooperation between the two parties under two modes of egoistic decision and collectivistic decision as well as the benefit distribution problem of multiple cooperation, and conduct numerical simulation to calculate the functional relationship between each decision variable and the contribution degree of the general contractor under different effort cost coefficients. The results show that：In the case of one-time cooperation between general contractors and subcontractors, regardless of the decision mode, the degree of effort of both parties is positively related to their own contributions and negatively related to each other's contributions; the overall benefit of the supply chain system is positively proportional to the degree of contribution of the general contractor and inversely proportional to the share of benefit distribution of the subcontractor; in multiple cooperation, the equilibrium result of both parties achieving Pareto optimality at each stage is that both parties choose to cooperate.

In this paper, a multirotor UAV platform based on the PX4 ecosystem was discussed, which in combination with the MATLAB Simulink programming environment enables utilization in various aspects of mechatronic engineering education. Extensive testing of the UAV platform control module was performed, in order to enable implementation in engineering education. In the design phase of the educational platform, take-off-the-shelf components were selected and tested, and airframe parts were manufactured using rapid prototyping technologies. The proposed educational platform with four rotors was presented and tested. The learning outcomes of the undergraduate mechatronics professional study program according to the Croatian Qualifications Framework and related to the considered platform are shown. A preliminary framework of laboratory exercises for the field of mechatronic systems control is investigated.

Computer simulation relies on well-defined experimental supported theories, models, and numerical methods. Computer simulation is an alternative to fill the gaps where the experiment is pricy or limited in engineering. Computer simulation is a high-fidelity computational technique that can work as an engineering innovation tool to try imaginative ideas beyond the available technologies. The accuracy of current computer simulation results is reliable enough to accelerate innovation for new materials and mechanical technologies without experimental validation. So, we may need to urge relying on computer simulation results without any experimental validation to accelerate engineering innovations. In addition, using high-fidelity computer simulation for engineering innovation acceleration without validation will be promoted if it is credited as a new classification. This subject and a related justification's classification in engineering have been discussed.

Effective marketing decision-making benefits from a rigorous, data-driven process that systematically evaluates alternatives based on insights and analysis. This study develops and evaluates a 5-stage decision-making process model integrating marketing engineering principles aimed to optimize marketing decisions. An experiment randomized 150 participants into groups following either the proposed model or an unaided approach. Results indicate the model-following group achieved significantly higher ROI from marketing decisions (19.3% vs 16.4%) and employed key elements like customer segmentation, experimentation, and optimization to a greater extent. However, limitations including the experiment's scenario, self-report measures, and cross-sectional design constrain implications. Future research employing probability sampling, multiple decision contexts, longitudinal designs, manipulation checks, and objective metrics can further validate the proposed model. Overall, the study advances the understanding of how structured decision processes based on marketing engineering principles may optimize marketing decisions and outcomes.

The four-way reversing valve is a key component determining whether a heat pump air conditioning system can work. Studying the four-way reversing valve is of great significance for improving the performance of the heat pump air conditioning system. In this paper, the control equation of the sliding block movement process is constructed to simulate the reversing process of the four-way reversing valve in fluid mechanics, and a detailed study is carried out from the sliding block speed, sliding block acceleration, sliding block force, refrigerant leakage, and other aspects. Research has shown that optimizing the structure of the slider can greatly improve the working performance of the four-way reversing valve. Increasing the height of the slider not only improves the pressure relief ability of the four-way reversing valve and prevents the slider from being damaged by high-pressure gas impact, but also reduces the leakage of refrigerant during the directional process; When reducing the length of the slider, although it will improve the pressure relief capacity of the four-way reversing valve, it also increases the amount of refrigerant leakage. The research results provide a theoretical basis and improvement direction for designers to improve the performance of four-way reversing valves.

Electrospinning is a simple versatile method to generate nanofibres. Remarkable progress has been made in the development of electrospinning process. The production of nanofibers is affected by many parameters, which influence the final material properties. Electrospun fibers have a wide range of applications such as energy storage and biomedical scaffolds. Polyacrylonitrile (PAN) has been widely used in the production of carbon nanofibers. PAN has received increasing interest in recent years, due to its excellent characteristics, such as spinnability, biocompatibility, and commercial viability, opening new applications in the biotechnological field. This paper provides an overview of the electrospinning process, its main parameters, and operation modes that affect nanofibers fabrication. A review of their biological applications is proposed. A focus is made on PAN fibers formation, functionalization, and application as scaffolds to allow cell growth. Overall, nanofibre scaffolds, made of stabilized PAN, appear as a powerful tool in medical applications needing controlled cell culture.

Currently, software development is more associated with families of configurable software rather than the single implementation of a product. Due to the numerous possible combinations in a software product line, testing these families of software product lines (SPLs) is a difficult undertaking (SPL). Moreover, the presence of optional features makes the testing of SPLs impractical. Several features are presented in SPLs, but due to the environment's time and financial constraints, these features are rendered unfeasible. Testing subsets of configured products is thus one approach to solving this issue. In order to reduce testing effort and get better results, alternative methods of testing SPLs are required, such as the combinatorial interaction testing (CIT) technique. Unfortunately CIT method produces unscalable solutions for large size SPLs with excessive constraints. The CIT method costs more because of feature combinations. The optimization of the various conflicting testing objectives, such as reducing the cost and configuration number, have also been considered. In this article, we have proposed a search-based software engineering solution using multi-objective optimization algorithms (MOEAs). In particular, the research is applied to different types of MOEA methods; Indicator-Based Evolutionary Algorithm (IBEA), Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), Non-Dominated Sorting Genetic Algorithm II (NSGAII), NSGAIII, and Strength Pareto Evolutionary Algorithm 2 (SPEA2). The results of the algorithms are examined in the context of distinct objectives and two quality indicators. The results revealed how feature model attributes, implementation context and the number of objectives affect the performance of the algorithms.

This paper proposes an AI-based approach to overcome the limitations of the SHM system in measuring global stress with limited sensors. Feature elements are selected based on correlation analysis among finite elements and used as stress-measured points. An ANN is used to establish the solution relationship between the feature and correlation elements. The proposed method is applied to the connector structure of an offshore platform, and an optimal ANN is established to optimize accuracy by considering factors like the number of sensors, neural network framework, and convergence criteria. The accuracy of the ANN is verified through a real-scale model test, demonstrating 93.6% accuracy. This technology represents a significant advancement, enhancing the practicality of the structural health monitoring (SHM) system from “point monitoring" to “field monitoring".

In this work, we report the electric energy storage properties of (1-x)Bi0.5(Na0.8K0.2)0.5TiO3-xBi0.2Sr0.7TiO3 (BNKT-BST; x = 0.15-0.50) relaxor ferroelectric ceramics enhanced via a domain engineering method. A rhombohedral–tetragonal phase, the formation of highly dynamic PNRs, and a dense microstructure are confirmed from XRD, Raman vibrational spectra, and microscopic investigations. The relative dielectric permittivity (2664 at 1 kHz) and loss factor (0.058) were gradually improved with BST (x=0.45). The incorporation of BST into BNKT can disturb the long-range ferroelectric order, lowering the dielectric maximum temperature Tm and inducing the formation of highly dynamic polar nano-regions. In addition, the Tm shifts toward high temperature with frequency and a diffuse phase transition, indicating relaxor ferroelectric characteristics of BNKT-BST ceramics, which is confirmed by the modified Curie Weiss law (γ=1.83). The rhombohedral–tetragonal phase, fine grain size, and lowered Tm with relaxor properties synergistically contribute to a high recoverable energy density Wrec of 0.81 J/cm3 and a high energy efficiency η of 86.95% at 90 kV/cm for x = 0.45.

The applicable conditions and scope of the underground sewage treatment plant include small amount of sewage, less available land on the ground, special requirements for the above-ground landscape, low groundwater level, easy-to-excavate geology, and the buried depth of the sewage return pipe is not very large. It is not under the main road, has certain distance from residential buildings, is not a depression and does not accumulate rainwater, and the sewage discharge requirements are not very strict. The underground sewage treatment plants are suitable in an area, where thermal insulation needs to be considered, less requisitionable land area, and higher environmental requirements The results of underground landscape design study showed that the design of principles, features, styles, water ecologies, combination between community and ecological friendly, operation and maintenance cost of the underground landscape should be emphasized. The construction of ecological complex will lead the sewage treatment plant exchange from minus assets to the ecological positive assets, which will meet the needs of future urban social economic development and environmental protection. The results of development direction exploration study showed that innovation on design concept, breakthroughs of key sewage treatment technology and variation of investment and operation mode can reduce the land occupation investment and operation cost, and the underground sewage plant with large scale and semi-underground mode owes broader prospects for development.

Most neuromorphic technologies use a point-neuron model, missing the spatiotemporal nature of neuronal computation performed in dendrites. Dendritic morphology and synaptic organization are structurally tailored for spatiotemporal information processing, enabling various computations like visual perception. Here, we report on a neuromorphic computational model termed ‘dendristor’, which integrates functional synaptic organization with dendritic tree-like morphology computation. The dendristor presents bioplausible nonlinear integration of excitatory and inhibitory synaptic inputs with silent synapses and diverse spatial distribution dependency. We show that the dendristor can emulate direction selectivity, which is the feature to react robustly to a preferred signal direction on the dendrite. We discover that silent synapses can remarkably enhance direction selectivity, turning out to be a crucial player in dendritic computation processing. Finally, we develop neuromorphic dendritic neural circuits that can emulate a cognitive function such as motion perception in the retina. Using dendritic morphology, we achieve visual perception of motion in 3D space by various mapping of spatial information on different dendritic branches. This neuromorphic dendritic computation innovates beyond current neuromorphic computation and provides solutions to explore new skylines in artificial intelligence, neurocomputation, and brain-inspired computing.

β-glucosidases (BGLs) play a crucial role in the degradation of lignocellulosic biomass as well as in industrial applications such as pharmaceuticals, foods, and flavors. However, the application of BGLs has been largely hindered by issues such as low enzyme activity, product inhibition, low stability, etc. Many approaches have been developed to engineer BGLs to improve these enzymatic characteristics to facilitate industrial production. In this article, we review the recent advances in BGL engineering in the field, including the efforts from our laboratory. We summarize and discuss the BGL engineering studies according to the targeted functions as well as the specific strategies used for BGL engineering.

Freeze-drying is a well-established process in biomedical engineering for the fabrication of three-dimensional open-porous medical devices, especially those based on biopolymers. One of the most used biopolymers in this field is collagen, the most abundant protein in the human body and the main component of the extracellular-matrix, as well as its derivatives. Freeze-dried collagen-based sponges with a wide variety of attributes can be produced by design and have led to a wide range of successful commercial medical devices, foremost for dental, orthopedic, hemostatic and neuronal applications. However, this is still considered a high-cost and time-consuming process that is often used in a non-optimized manner. By combining advances in other technological fields, the opportunity arises to further evolve this process in a sustainable manner, and optimize the resulting products as well as create new opportunities in this field.

2,7-bis(pyridin-4-ylethynyl)-9H-carbazole (1) was synthesized by reacting 4-ethynylpyridine hydrochloride with 2,7-dibromo-9H-carbazole. The full characterization of compound 1 is presented, and the crystal structure of its monohydrate co-crystal was determined by single-crystal XRD analysis.

Product assembly is usually one of the last steps in the entire production process. This activity is typically entrusted to assembly workers because it is generally not possible to automate every type of product. For complex products, assembly can take a long time until the fitter learns the procedure and is able to assemble the product on his own. This contribution presents a cus-tom-developed system that enables controlled assembly of the extruder and can be used for complex and diverse products. The system serves to guide the fitter precisely and shows him which part to use at which time. The proposed system will show and describe on the display all necessary assembly steps and parts. Two-step verification is used to ensure that the correct part is picked from the stack. The contribution is supported by the implementation of a case study in a small company with a sample of 30 employees, which demonstrates that the proposed system shortens the extruder assembly time and significantly reduces the error rate. The presented solution is scalable and flexible, as it can be easily adapted to display the assembly steps of another product.

Engineers make things, make things work, and make things work better and easier. This kind of knowledge is crucial for innovation, and much of the explicit knowledge developed by engineers is embodied in scientific publications. In this paper, we analyze the evolution of publications and citations in Engineering in a middle-income country such as Mexico. Using a database of all Mexican publications in Web of Science from 2004 to 2017, we explore the characteristics of publications that tend to have the greatest impact; this is the highest number of citations. Among the variables studied are the type of collaboration (no collaboration, domestic, bilateral, or multilateral), the number of coauthors and countries, the language of the article, controlling for a coauthor from the USA and the affiliation institution of the Mexican author(s). Our results emphasize the overall importance of joint international efforts and suggest that publications with the highest number of citations are those with multinational collaboration (coauthors from three or more countries), written in English, and when one of the coauthors is from the USA.

The main purpose of tissue engineering is to fabricate and exploit engineered constructs suitable for the effective replacement of damaged tissues and organs, and able to perfectly integrate with the host’s organism without eliciting any adverse reaction. Ideally, autologous materials represent the best option, but they are often limited due to the low availability of compatible healthy tissues. So far, one therapeutic approach relies on the exploitation of synthetic materials: they exhibit good features in terms of impermeability, deformability and flexibility, but present chronic risks of infections and inflammations. Alternatively, biological materials, including naturally derived ones and acellular tissue matrices of human or animal origin, can be used to induce cells growth and differentiation, which are needed for tissue regeneration: however, this kind of materials lacks satisfactory mechanical resistance and reproducibility, affecting their clinical application. In order to overcome the above-mentioned limitations, hybrid materials, which can be obtained by coupling synthetic polymers and biological materials, have been investigated with the aim to improve biological compatibility and mechanical features. Currently, the interest in these mate-rials is growing, but the ideal ones have not been found yet. The present review aims at exploring some applications of hybrid materials, with particular mention to urological and cardiovascular fields: in the first case, the efforts to find a construct that can guarantee impermeability, mechanical resistance and patency will be herein illustrated; in the second case, the search for impermeability, hemocompatibility and adequate compliance will be been disclosed.

In order to overcome the disadvantages of existing treatments in heart valve tissue engineering, decellularization studies are carried out. The main purpose of decellularization is to eliminate the immunogenicity of biologically derived grafts and to obtain a scaffold that allows recellularization while preserving the natural tissue architecture. SD and SDS are detergent derivatives frequently used in decellularization studies. The aim of our study is to decellularize the pulmonary heart valves of young Merino sheep by using low-density SDS and SD detergents together, and then to perform their detailed characterization to determine whether they are suitable for clinical studies. Pulmonary heart valves of 4-6 month old sheep were decellularized in detergent solution for 24 hours. The amount of residual DNA was measured to determine the efficiency of decellularization. Then, the effect of decellularization on the ECM by histological staining was examined. In addition, the samples were visualized by SEM to determine the surface morphologies of the scaffolds. Uniaxial tensile test was performed to examine the effect of decellularization on biomechanical properties. The results showed DNA removal of 94% and 98% from the decellularized leaflet and artery portions after decellularization relative to the control group. No cell nuclei were found in histological staining and it was observed that the 3-layer leaflet structure was preserved. As a result of the tensile test, it was determined that there was no statistically significant difference between the control and decellularized groups in the UTS and elasticity modulus, and the biomechanical properties did not change. In conclusion, we suggest that the pulmonary valves of decellularized young Merino sheep can be used as a initial matrix in heart valve tissue engineering studies.

Building Information Modelling (BIM) has been more popular in the construction industry as a consequence of the many advantages it offers, such as increased productivity, improved project outcomes, and reduced expenses. However, there are many obstacles that must be overcome before BIM can be widely used. This research uses a poll of construction industry experts to investigate the challenges associated with using BIM in projects. Cost of implementation, inadequate standards and procedures, and a lack of knowledge with BIM were some of the hurdles that were measured in the study. Statistical techniques were used to examine the data, and the findings indicate that the high cost of implementation and the absence of sufficient standards and procedures are the two main obstacles to the widespread adoption of BIM. Problems with supply chain management and a lack of experience with BIM are also mentioned as obstacles. The report also suggests solutions, such as a more focused teaching effort on BIM-related issues and the creation of a specialised BIM curriculum. Professionals in the construction industry may utilise the study’s results to their advantage by expanding their knowledge of the obstacles standing in the way of widespread adoption of BIM.

The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and in particular, a shift towards Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies that hinder their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP) in general and Large Language Models (LLMs) in particular can serve as an enabler for the conversion of NL requirements into semi-machine-readable requirements. This is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into semi-machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach is an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs were utilized. Two of these models were fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERT), specifically aeroBERT-NER and aeroBERT-Classifier, which were trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, was utilized to identify sentence chunks present in NL requirements. All three language models were utilized together to achieve the standardization of requirements. To demonstrate the effectiveness of the methodologies, requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs) were employed, and a total of two, five, and three boilerplate templates were identified for design, functional, and performance requirements, respectively.

3D bioprinting is the process of manipulating cell-laden bioinks to fabricate living structures. 3D bioprinting techniques have brought considerable innovation in biomedicine, especially in the field of tissue engineering, allowing the production of 3D organ and tissue models for in vivo transplantation purposes or for in-depth and precise in vitro analyses. Natural-derived hydrogels, especially those obtained from the decellularization of biological tissues, are promising bioinks for 3D printing purposes, as they present the best biocompatibility characteristics. Despite this, many natural hydrogels do not possess the necessary mechanical properties to allow a simple and immediate application in the 3D printing process. In this review, we focus on the bioactive and mechanical characteristics that natural hydrogels may possess to allow efficient production of organs and tissues for biomedical applications, emphasizing the reinforcement techniques to improve their biomechanical properties.

This thesis presents the effects of work done on a software project for generative models and spreadsheets, allowing for a quick creation of the conceptual model of the aircraft. The subject of the work is a response to the current trends and needs prevailing in the field of computer design engineering CAD and aviation. In the initial chapters, theoretical issues related to the work being carried out were introduced and the methodology of creating software for construction and verification of the structure of aircraft along with the needs of interchange between databases of generative models was presented. In the next stages, the concepts and selected solutions for the user interface supporting the knowledge base were presented along with a set of procedures for its operation. Furthermore, the method of database integration with the methods of determining design features for the developed generative models and with the Siemens NX system. Furthermore, problems encountered in software development, as well as solution examples for model application are specified. The results obtained and the models generated on their basis were subjected to a strength analysis using Autodesk Inventor software and analysed in terms of meeting the initial assumptions. In the end, conclusions and observations resulting from the effects of the work presented in the project were formulated.

This primer provides an overview of the rapidly evolving field of generative artificial intelligence, specifically focusing on large language models like ChatGPT (OpenAI) and Bard (Google). Large language models have demonstrated unprecedented capabilities in responding to natural language prompts. The aim of this primer is to demystify the underlying theory and architecture of large language models, providing intuitive explanations for a broader audience. Learners seeking to gain insight into the technical underpinnings of large language models must sift through rapidly growing and fragmented literature on the topic. This primer brings all the main concepts into a single digestible document. Topics covered include text tokenization, vocabulary construction, token embedding, context embedding with attention mechanisms, artificial neural networks, and objective functions in model training. The primer also explores state-of-the-art methods in training large language models to generalize on specific applications and to align with human intentions. Finally, an introduction to the concept of prompt engineering highlights the importance of effective human-machine interaction through natural language in harnessing the full potential of artificial intelligence chatbots. This comprehensive yet accessible primer will benefit students and researchers seeking foundational knowledge and a deeper understanding of the inner workings of existing and emerging artificial intelligence models. The author hopes that the primer will encourage further responsible innovation and informed discussions about these increasingly powerful tools.

Antimicrobial resistance (AMR) is a major global concern; antibiotics and other regular treatment methods have failed to overcome the increasing number of infectious diseases. Bacteriophages (phages) are viruses that specifically target/ kill bacterial hosts without affecting other human microbiome. Phage therapy provides optimism in the current global healthcare scenario with a long history of its applications in humans that has now reached various clinical trials. Phages in clinical trials have specific requirements of being exclusively lytic, free from toxic genes with an enhanced host range that adds an advantage to this requisite. This review explains in detail the various phage engineering methods and their potential applications in therapy. To make phages more efficient, engineering has been attempted using techniques like conventional homologous recombination, Bacteriophage Recombineering of Electroporated DNA (BRED), clustered regularly interspaced short palindromic repeats (CRISPR)-Cas, CRISPY-BRED/Bacteriophage Recombineering with Infectious Particles (BRIP), chemically accelerated viral evolution (CAVE), and phage genome rebooting. Phages are administered in cocktail form in combination with antibiotics, vaccines, and purified proteins, such as endolysins. Thus, phage therapy is proving to be a better alternative for treating life-threatening infections, with more specificity and fewer detrimental consequences.

Acute and chronic bone infections, especially those caused by methicillin-resistant Staphylococcus aureus (MRSA), remains a major complication and therapeutic challenge. It is documented that local administration of vancomycin offers better results than the usual routes of administration (e.g. intravenous) when ischemic areas are present. In this work, we evaluate the antimicrobial efficacy against S. aureus and S. epidermidis and analyzed the potential cytotoxic effect of a novel hybrid 3D-printed scaffold based on poly(caprolactone) and chitosan loaded with different vancomycin concentrations (1, 5, 10 and 20%). For this purpose, vancomycin release was measured by means of HPLC, and the biological response of ah-BM-MSCs in the presence of the scaffolds was evaluated in terms of cytotoxicity (LDH activity), proliferation (AlamarBlueⓇ) and osteogenic differentiation (ALP activity, Alizarin Red staining). In addition, two cold plasma treatments were evaluated to improve the adhesion of hydrophobic polymers to hydrogels. The hybrid PCL/CS/Van scaffolds tested were found to be biocompatible, bioactive, and bactericide, as demonstrated by no cytotoxicity or functional alteration and by bacterial inhibition. Our results suggest that the scaffolds developed would be excellent candidates to be used in a wide range of biomedical fields such as drug delivery systems or tissue engineering applications.

Infectious diabetic wounds can result in severe injuries or even death. Biocompatible wound dressings offer one of the best ways to treat these wounds, but creating a dressing with suitable hydrophilicity and biodegradation rate can be challenging. To address this issue, we used the electrospinning method to create a wound dressing composed of poly(glycerol sebacate) (PGS) and gelatin (Gel). We dissolved the PGS and Gel in acetic acid (75 v/v%) and added EDC/NHS solution as a crosslinking agent. Our measurements revealed that the scaffolds' fiber diameter ranged from 180.2 to 370.6 nm, and all the scaffolds had porosity percentages above 70%, making them suitable for wound healing applications. Additionally, we observed a significant decrease (p < 0.05) in the contact angle from 110.8° ± 4.3° for PGS to 54.9° ± 2.1° for PGS/Gel scaffolds, indicating an improvement in hydrophilicity of the blend scaffold. Furthermore, our cell viability evaluations demonstrated a significant increase (p < 0.05) in cultured cell growth and proliferation on the scaffolds during the culture time. Our findings suggest that the PGS/Gel scaffold has potential for wound healing applications.

In recent years, bone tissue engineering (BTE) has made significant progress in promoting the direct and functional connection between bone and graft, including osseointegration and osteoconduction, to facilitate the healing of damaged bone tissues. Herein, we introduce a new, environmentally friendly, and cost-effective method for synthesizing reduced graphene oxide (rGO) and hydroxyapatite (HAp). The method uses epigallocatechin-3-O-gallate (EGCG) as a reducing agent to synthesize rGO (E-rGO), and HAp powder is obtained from Atlantic bluefin tuna (Thunnus thynnus). The physicochemical analysis indicated that the E-rGO/HAp composites had exceptional properties for use as BTE scaffolds, as well as high purity. Moreover, we discovered that E-rGO/HAp composites facilitated not only proliferation, but also early and late osteogenic differentiation of human mesenchymal stem cells (hMSCs). Our work suggests that E-rGO/HAp composites may play a significant role in promoting the spontaneous osteogenic differentiation of hMSCs, and we envision that the E-rGO/HAp composites could serve as promising candidates for BTE scaffolds, stem cell differentiation stimulators, and implantable device components due to their biocompatible and bioactive properties. Overall, we suggest a new approach for developing cost-effective and environmentally friendly E-rGO/HAp composite materials for BTE application.

The sustainable metabolic engineering (SME) concept was defined by Stalidzans and Dace as an approach for the selection of most sustainable metabolic engineering designs taking the economic, environmental and social components of sustainability into account. In the centre of sustainability calculations is a genome scale metabolic model that provides full balance of all incoming and outgoing metabolic fluxes at steady state. Therefore, sustainability indicators are assigned for each exchange reaction enabling calculation of sustainability features of consumption or production of each metabolite. The further development of the SME concept depends on its implementation at the computational level to acquire applicable results – sustainable production strain designs. This study proposes for the first time a workflow and tools of SME implementation using constraint based stoichiometric modelling, genome scale metabolic models and growth coupled product synthesis approach. For SME application demonstration purposes, a relatively simple engineering task has been carried out. The most sustainable designs have been identified using Escherichia coli as chassis organism, glucose as a substrate and gene deletions as metabolic engineering tool. A growth coupled production design tool has been used to reduce the variability of sustainability. The 10 000 most sustainable designs were producing succinate as the main product with the number of deleted genes ranging from two to ten. A big number of similar designs has been identified due to the combinatorial explosion of different alternative combinations of gene deletion sets that result in interruption of the same metabolic pathways with the same impact on the metabolism.

It is known that people perceive animacy in objects. However, many studies on animacy and emotional expressions are limited in that the investigated motions were created by experimenters themselves. This makes the objective validity unclear. Moreover, it remains unclear what types of movements can express emotions with animacy due to the limited number of investigations examining both animacy and emotional expressions. Therefore, we investigated the motion elements for both animacy perception and emotional expressions using simple objects that lack features of specific living things, such as eyes, ears, tails, and voices in this study. First, we investigated the motion elements for animacy perception and emotional expressions using a robot simulator that enabled participants to create undulatory motions by tuning parameters for speed, height, and randomness. In total, 64 participants created motions in Normal (neutral), Joy, Sad, Relaxed, and Angry conditions. The results showed that the medians of speed and height in Normal, related only to animacy, were 0.5569[Hz] and 3.050cm at the edges/4.575cm at the center. The differences in Joy were 0.4028[Hz] and 3.348cm/5.022cm, in Sad were −0.1652[Hz] and −0.9982cm/−1.497cm, in Relaxed were −0.1979[Hz] and −0.4902cm/−0.7353cm, and in Angry were 0.5212[Hz] and 4.688cm/7.032cm. Second, we investigated whether the motion elements revealed in the first experiment were sufficient to express emotions with animacy, using a robot simulator that reflected the results of the motion element investigation. In total, 44 online participants observed the simulator. The results showed that participants could understand emotional arousal levels at the same time as animacy, but they did not fully understand emotional valence. Our findings provide design guidelines for robots that exhibit emotional expressions and closely interact with humans.

Mucosal vaccines offer several advantages over injectable conventional vaccines, such as adaptive immunity induction, secretory IgA production at the entry site of most pathogens, and needleless vaccinations. Despite their potential, only a few mucosal vaccines are currently used. Developing new effective mucosal vaccines strongly relies on identifying innovative anti-gens, efficient adjuvants, and delivery systems. Several approaches based on phages, bacteria, or nanoparticles have been proposed to deliver antigens to mucosal surfaces. Bacterial spores have also been considered antigen vehicles, and various antigens have been successfully exposed on their surface. Due to their peculiar structure, the spores conjugate the advantages of live microorganisms with synthetic nanoparticles. When mucosally administered, spores expressing anti-gens have been shown to induce antigen-specific, protective immune responses. This review accounts for recent progress in the formulation of spore-based mucosal vaccines, describing the spore's structure, specifically the spore surface, and the diverse approaches developed to improve its efficiency as a vehicle for heterologous antigen presentation.

A frequent problem in Geotechnics is the soils with inadequate physical-mechanical properties before the efforts to which they will be subjected by the constructions, incurring cost overruns caused by their engineering improvement. The need to improve the engineering properties of soils is not recent, currently observing that the most common alternatives are binders such as cement and lime. The climate change observed in recent decades and the uncontrolled emission of greenhouse gases have motivated Geotechnical and Geoenvironmental researchers to seek mechanisms for soil reinforcement from a more sustainable and environmentally friendly approach by proposing the use of recycled and waste materials. An alternative is natural fibers, which can be obtained as waste from many agro-industrial processes, implying their high availability and low cost. Sawdust as a by-product of wood processing has a rough texture that can generate high friction between the fiber and the matrix of the soils, leading to a significant increase in its shearing strength and bearing capacity. This concept of improving the properties of soils using natural fibers distributed randomly is inspired by the natural phenomenon of grass and/or plants that when growing on a slope can effectively stabilize a said slope.

For the emerging autonomous swarm technology, from the perspective of systems science and systems engineering, there must be some novel elements and methods to aggregate multiple systems into a group, which distinguish with the general components with specific functions, and we expect to provide the presentation of their existence in the swarm development processes. The inspiration of our methodology origins from the integration of swarm ontology, multi-paradigm modeling, multi-agent system, cyber-physical system, etc. Therefore, we choose the model-driven technology as a framework to acquire an approach of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism is defined and formed in parallel with ConOps analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by the nonlinear causation among individual and whole behaviors. This approach highlights to use the MBSE processes and their artifacts to embed the meso mechanism to integrate the operational and the functional, and which means to connect the macro and micro aspects in formalism and so to become a whole with its expected goals, and then to verify and validate within a L-V-C simulation environment.

Regeneration of biological tissues in medicine is challenging, and 3D bioprinting offers an innovative way to create functional multicellular tissues. One common way in bioprinting is bioink which is one type of the cell-loaded hydrogel. For clinical application, however, the bioprinting still suffers from satisfactory performance, e.g. in vascularization, effective antibacterial, immunomodulation and regulation of collagen deposition. Many studies have incorporated different bioactive materials into the 3D printed scaffolds to optimize the bioprinting. Here, we review a variety of additives added to the 3D bioprinting hydrogel. The underlying mechanisms and methodology for biological regeneration are important and will provide useful basis for future research.

Vehicle safety promises to be one of the Advanced Driver Assistance System (ADAS) biggest benefits. Higher levels of automation remove the human driver from the chain of events that can lead to a crash. Sensors play an influential role in vehicle driving as well as in ADAS by helping the driver to watch the vehicle’s surroundings for safe driving. Thus, the driving load is drastically reduced from steering as well as accelerating and braking for long-term driving. The baseline for the development of future intelligent vehicles relies even more on the fusion of data from surrounding sensors such as Camera, Lidar and Radar. These sensors not only need to perceive in clear weather but also need to detect accurately adverse weather and illumination conditions. Otherwise, a small error could have an incalculable impact on ADAS. As most of the current study is based on indoor or static testing. In order to solve this problem, this paper designs a series of dynamic test cases with the help of outdoor rain and intelligent lightning simulation facilities to make the sensor application scenarios more realistic. As a result, the effect of rainfall and illumination on sensor perception performance is investigated. As speculated, the performance of all automotive sensors is degraded by adverse environmental factors, but their behaviour is not identical. Future work on sensor model development and sensor information fusion should therefore take this into account.

Alkaloids are the most diversified nitrogen-containing secondary metabolites having antioxi-dant, and antimicrobial properties and are extensively used in pharmaceuticals to treat different types of cancer. Nicotiana serves as a reservoir of anticancer alkaloids and is also used as a model plant for the de novo synthesis of various anti-cancer molecules through genetic engineering. Up to 4% of the total dry weight of Nicotiana was found to be composed of alkaloids where nicotine, nornicotine, anatabine, and anabasine are reported as the dominant alkaloids. And, among the alkaloids present in Nicotiana, β-carboline (Harmane and Norharmane), Kynurenines are found to show anti-tumor effects, especially in the case of colon and breast cancer. Creating new or shunting of existing biosynthesis pathways in different species of Nicotiana resulted in de novo or increased synthesis of different anti-tumor molecules or their derivatives or precursors includ-ing Taxadiane (~22.5 µg/g), Artemisinin (~120 μg/g), Parthenolide (~2.05 ng/g), Costunolide (~60 ng/g), Etoposide (~1 mg/g), Crocin (~400 µg/g), Catharanthine (~60 ng/g), Tabersonine (~10 ng/g), Strictosidine (~0.23 mg/g), etc. Enrich precursor pool especially Dimethylallyl Diphos-phate (DMAPP) and down-regulating of other bi-product pathways, compartmentalization or metabolic shunting, or organelle-specific reconstitution of the precursor pool might trigger the enhanced accumulation of the targeted anticancer alkaloid in Nicotiana.

In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The new method known as deterministic artificial intelligence asserts physical-based process dynamics to achieve target trajectory tracking. There are two common autonomous trajectory generation algorithms: sinusoidal function and Pontryagin's based generation algorithm. This thesis aims to simulate model-following and deterministic artificial intelligence methods using sinusoidal and Pontryagin's methods and compare their performance difference when following challenging step function slew maneuver.

Considering the dynamic stall effects in engineering calculations is essential for correcting the aerodynamic loads acting on wind turbines, both during power production and stand-still cases, and impacts significantly the turbine aeroelastic stability. The employed dynamic stall model needs to be accurate and robust for a wide range of airfoils and range of angle of attack. The present studies are intended to demonstrate the performance of a recently implemented "IAG dynamic stall" model in a wind turbine design tool Bladed. The model is transformed from the indicial type of formulation into a state-space representation. The new model is validated against measurement data and other dynamic stall models in Bladed for various flow conditions and airfoils. It is demonstrated that the new model is able to reproduce the measured dynamic polar accurately without airfoil specific parameter calibration and has a superior performance compared to other models in Bladed.

In the history of civil aircraft transportation, ice formation has been identifies as a key factor in the safety of flight. Anti-icing and deicing system have emerged through the years with the aim to prevent or to eliminate ice formation on wing airfoil, control surface and probes. Modern flying machine demand more efficiency in order to reduce the carbon footprint and increase the sustainability of flight transport. In order to achieve this goal the need to have efficient aircraft with efficient and low power consuming system is fundamental. This paper proposes a new model for ice accretion using computational fluid dynamics (CFD). This model permits to simulate the shape of the ice formed over a profile varying boundary conditions (i.e.- speed, liquid water content and so on). The proposed model takes into account also the amount of heat transferred between water and the surrounding environment and includes the effects of air turbulence on the ice formation process. The CFD simulations have been validated with NASA experimental outcome and show good agreement. The proposed model can be also used to investigate the effects of various parameters such as air speed, Liquid Water Content and air temperature on the ice formation process. The results evidences that the proposed model can accurately predict ice formation process and is suitable to optimize the design of anti-icing or deicing system for aircraft and helicopters. This approach is not limited to aerospace but can also be exported to other applications such as transportation, wind turbine, energy management and infrastructure.

Recently, a paper was written to reformulate and strengthen the theory of semi-structured complex numbers H (a new number set invented to enable division by zero). Whilst the paper had profound results, the application of this number set in many areas of engineering and science has not been fully explored. Consequently, the aim of this paper was to establish the number set H as a useful mathematical tool by providing an amalgam of results arising from its application in several areas of engineering and science. In the process, this paper makes four major contributions: these are (1) determining the product of the gradients of a horizontal and vertical line; (2) developing new Euler formulas relating exponential, trigonometric and hyperbolic functions in the 3D semi-structured complex number Euclidean space; (3) establishing semi-structured complex numbers as a better extension of the complex number set than quaternions; (4) resolving singularities that may arise in engineering and science equations (because of division by zero) to develop reasonable conclusions in the absence of experimental data. These results and their applications provide a firm foundation to advance the number set H as a useful mathematical tool.

Industrialization, intensive farming, rapid population growth and urbanization are the source of a large number of pollutants entering the environment. The current concentration of xenobiotics released into the environment exceeds its natural ability to decompose them. Enzymatic degradation of pollutants seems to be an environmentally friendly process. Due to the wide spectrum of substrate specificity, from inorganic compounds to high molecular weight organic compounds such as PAH or dyes, as well as favorable biochemical properties, laccase has been used in the biological removal of xenobiotics from the environment. It is important to understand the degradation mechanisms of pollutants and to evaluate the final products in terms of their toxicity. The laccase oxidizes the substrates with the simultaneous reduction of molecular oxygen to water, which is the purest reaction co-substrate. That is why it is called a green biocatalyst. The trend is an increase in the production of enzymes related to the intensive development of industry, bioremediation or synthetic chemistry. This leads to the search for laccases with greater activity and stability under extreme conditions. The potential of laccases to degrade xenobiotics can be promoted by improving enzymatic catalytic characterization using protein engineering and other genetic engineering methods.

With the continuous improvement of people's quality of life and aesthetics, furniture products have higher requirements. Excellent solid wood chairs are the most representative products in furniture to enhance space and taste and whose design may significantly impact consumers' emo-tional experience and purchase decisions. This study aims to evaluate how the modeling imagery of solid wood chairs affects user preferences and emotional experiences. The development of the current era is inseparable from the analysis of big data. Firstly, a representative sample was ob-tained by multidimensional scaling (MDS), and the sample was analyzed and evaluated by fac-tor analysis. Moreover, five groups of adjective vocabulary were selected to describe the model-ing imagery of solid wood chairs, such as "balanced and coordinated", "unique and novel", "prac-tical and simple", "quality and detailed" and "traditional and plain". Further, the triangular fuzzy theory was applied to analyze and discuss the 12 types of solid wood chairs in the five groups of adjective vocabulary. Then the study verified that the differences in the evaluations of the 12 sam-ples in the "unique and novel" and "quality and detailed" groups were significant, but more minor differences in the groups of "traditional and plain", "balanced and coordinated" and "practical and simple". Through comprehensive comparison, five groups with similar modeling imagery were created, and solid wood chairs with different modeling imagery should be placed in suita-ble spaces. According to the results of this study, the evaluation of the modeling imagery of solid wood chairs can not only rely on subjective judgments but can be reasonably described through data analysis and mathematical algorithms. It can scientifically and effectively reflect the poten-tial perception needs of consumers on the modeling imagery of solid wood chairs, as well as help to improve the design efficiency of the furniture product development stage.

For technical or medical applications, the knowledge of the exact kinematics of the human hand is key to utilizing its capability to handle and manipulate objects and to communicate with other humans or machines. The optimal relationship between the number of measurement parameters, measurement accuracy as well as complexity, usability and cost of the measuring systems is hard to find. Biomechanic assumptions, the concepts of a biomechatronic system and the mechatronic design process as well as commercially available components are used to develop a sensorized glove. The proposed wearable can measure 14 of 15 angular values of a simplified hand model introduced in this paper. Additionally, five contact pressure values at the fingertips and inertial data of the whole hand with a degree of freedom of six are gathered. Due to the modular design and a hand size examination based on anthropometric parameters, the concept of the wearable is applicable for a large variety of hand sizes and adaptable to different use cases. Validations show a combined root-mean-square error of 0.99° to 2.38° for the measurement of all joint angles at one finger, surpassing the human perception threshold and the current state of the art in science and technology for comparable systems.

unlike the volume, the expression for the surface area of a regular conoid has not yet been obtained by means of direct integration or a differential geometry procedure. As this non-developable form is relatively used in engineering, the difficulty to determine its surface, represents a serious shortcoming for several problems which arise in radiative transfer, lighting and construction, to cite just a few. In order to solve the problem, I conceived the surface as a set of linearly dwindling ellipses which remain parallel to a circular directrix, a typical problem appears when searching the length of such ellipses. I employed a new procedure which, in principle, consists in dividing the surface into infinitesimal elliptic strips to which we have successively applied Ramanujan’s second approximation for the longitude of the ellipse. In this manner, we can obtain the perimeter of any transversal curve pertaining to the said form as a function of the radius of the directrix and the position of the ellipse’s center on the X-axis. Integrating the so-found perimeters of the differential strips for the whole span of the conoid, an unexpected solution emerges through the newly found number psi (ψ) which seems like a refined approximation to the third decimal of Pi but derived from a definite integral equation. As the strips are in truth slanted in the symmetry axis, their width is not uniform and we need to perform some adjustments in order to complete the problem with sufficient precision, but this is discussed separately in the annexes. Relevant implications for mathematical symmetry applied to multifarious architectural and engineering forms can be derived from this finding

Lithium-ion batteries (LIBs) have become the preferred battery system for portable electronic devices and transportation equipment because of their high specific energy, good cycling performance, low self-discharge and no memory effect. However, too low ambient temperature will seriously affect the performance of LIBs, almost cannot discharge at -40~-60 ℃. There are many factors affecting the low temperature performance of LIBs, among which the electrode material is an important factor. Thus it is urgent to develop electrode materials or modify existing materials with excellent low temperature LIBs performance. Carbon-based anode is a kind of LIBs candidate. In recent years, it has been found that the diffusion coefficient of lithium ion in graphite anode decreases more obviously at low temperature, which is one of the important bottlenecks limiting its low temperature performance. However, the structure of amorphous carbon materials is complex, amorphous carbon materials have good ionic diffusion properties, and its grain size, specific surface area, layer spacing, structural defects, surface functional groups and doping elements may have a greater impact on the low temperature performance of the material. In this work, the low temperature performance of LIBs is obtained by modifying the carbon-based material from the perspective of electronic modulation and structural engineering.

We have performed the first comparative analysis of the potential of two physiologically-diverse model cyanobacteria, Synechococcus PCC 7002 (S.7002) and Synechococcus PCC 7942 (S.7942), for the photosynthetic production of four chemically-different high-value terpenes: two monoterpenes limonene and pinene, and two sesquiterpenes bisabolene and farnesene. We showed, for the first time, that S.7002 and S.7942 can produce farnesene and bisabolene, respectively. Both cyanobac-teria produced farnesene (S.7942 better than S.7002) more efficiently than the other tested terpenes (especially pinene the weakest produced terpene). S.7002 produced limonene better than bisabo-lene, whereas S.7942 produced bisabolene better than limonene. These findings suggest that S.7942 is better suited to produce sesquiterpenes than monoterpenes. Also interestingly, higher levels of terpenes were produced by S.7942 and S.7002 expressing a terpene-synthase gene from both a RSF1010-derived replicating plasmid and a neutral chromosomal site, as compared to ei-ther the plasmid alone or the chromosome alone. These results suggest that in both cyanobacteria the production of terpenes is more limited by the activity of terpene synthases than the abondance of terpene precursors. Finally, higher levels of terpenes were produced by S.7002 growing on urea (a frequent pollutant) as compared to nitrate (or ammonium) the standard nitrogen sources for cyanobacteria (more expensive than urea).

In this study, we demonstrate how the incorporation of appropriate feature engineering together with the selection of a Machine Learning (ML) algorithm that best suits the available dataset, leads to the development of a predictive model for transformation temperatures that can be applied to a wide range of shape memory alloys. We develop a gradient boosting ML surrogate model capable of predicting Martensite Start, Martensite Finish, Austenite Start, and Austenite Finish transformation temperatures with an average accuracy of more than 95% by explicitly taking care of potential distribution changes when modeling different alloy systems. We included heat treatment, rolling, extrusion processing parameters, and alloy system categorical features in the model input features to achieve more accurate and realistic results. In addition, using Shapley values, which are calculated based on the average marginal contribution of features to all possible coalitions, this study was able to gain insights into the governing features and their effect on predicted transformation temperatures, providing a unique opportunity to examine the critical parameters and features in martensite transformation temperatures.

Requirements are predominantly written in Natural Language (NL), which makes them accessible to stakeholders with varying degrees of experience, as compared to a model-based language which requires special training. However, despite its benefits, NL can introduce ambiguities and inconsistencies in requirements, which may eventually result in system quality degradation and system failure altogether. The system complexity that characterizes current systems warrants an integrated and comprehensive approach to system design and development. This need has brought about a paradigm shift towards Model-Based Systems Engineering (MBSE) approaches to system design and a departure from traditional document-centric methods. While, MBSE shows great promise, the ambiguities and inconsistencies present in NL requirements hinder their conversion to models directly. The field of Natural Language Processing (NLP) has demonstrated great potential in facilitating the conversion of NL requirements into a semi-machine-readable format that enables their standardization and use in a model-based environment. A first step towards standardizing requirements consists of classifying them according to the ``type'' (design, functional, performance, etc.) they represent. To that end, a language model capable of classifying requirements needs to be fine-tuned on labeled aerospace requirements. This paper presents the development of an annotated aerospace requirements corpus and its use to fine-tune BERT$_\text{BASE-UNCASED}$ to obtain aeroBERT-Classifier: a new language model for classifying aerospace requirements into design, functional, or performance requirements. A comparison between aeroBERT-Classifier and bart-large-mnli (zero-shot text classification) showcased the superior performance of aeroBERT-Classifier on classifying aerospace requirements despite being fine-tuned on a small labeled dataset.

COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets’ inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.

Advanced state-of-the-art technologies, mainly computational intelligence including Machine/Deep Learning and Fuzzy Computing, through applied research, can provide added value to modern science and, in general, to entrepreneurship and the economy. Artificial intelligence is the field of computer science that attempts to model concepts such as learning, adaptability and perception to synthesize intelligent behavior in solving complex problems, utilizing elements of adaptation to the environment and philosophical reasoning. About the science of civil engineering and, in general, the construction industry, which is one of the most important in economic entrepreneurship, both in terms of the size of the workforce employed and the amount of capital invested, the penetration of artificial intelligence is possible to change industry business models, eliminate costly mistakes, reduce Jobsite injuries and generally make large-scale engineering projects more efficient. The purpose of the paper is to present the recent research on artificial intelligence methods (machine-deep learning, computer vision, natural language processing, fuzzy systems, robotics, etc.) and the corresponding related technologies related to it (extensive data analysis, blockchain, cloud computing, internet of things, augmented reality), in the fields of application of civil engineering science, including structural engineering, geotechnical engineering, hydraulics and water resources management, marine and coastal technology, transport and transportation infrastructure, planning and technical project management, critical infrastructure security and disaster mitigation..

The fundamental challenge for the structural design of complicated shapes and lightweight structures such as Tensile Membrane Structures (TMS) is a reliable estimation of lateral loading such as wind load. Wind pressure coefficients can’t be correctly calculated by standards for a large range of intricate forms of structures. Computational Fluid Dynamics (CFD), a strong computational technique for evaluating wind pressure distribution on complicated geometry, can be used to determine wind-related parameters. The average wind related-distributions for the tensile membrane surface are validated with a few contributions from prior studies, which are important to validate with benchmarks in experimental wind tunnel experiments. For the double-curved surface, the results show good agreement between experimental tests and CFD simulations. In this study, CFD simulation was used to calculate the mean surface pressure coefficient (Cp) for generic shapes and tensile membrane forms using the steady Reynolds-Averaged Navier-Stokes (RANS) technique. Wind force coefficients and the effect of wind directions are also explored across a wide range of classical and tensile forms. The purpose of the current research is to look at the effect of wind assault angle on the Cp distribution for tensile membrane structures and general shapes.

Thermoplastic molded regenerated silk was proposed as structural material in tissue engineering applications, mainly for application in bone. The protocol allows to obtain a compact non-porous material with a compression modulus in the order of a Giga Pascal starting from silk fibroin by compressing a silk fibroin lyophilized sponge of powder in mold at temperature higher than the glass transition temperature (Tg). The main purpose of the produced resin was the osteofixation and other structural applications in which the lack of porosity was not an issue. In this work, we introduced the use of citric acid in the thermoplastic molding protocol of SF to obtain a porosity inside the structural material. In addition to the previously developed protocols the addition of citric acid allowed us to obtain a structural material. The CA powder during the compression acted as a template for the pore formation. In addition, the CA was able to effectively crosslink the SF chain improving the mechanical strength. This effect was proved both evaluating the compression modulus and by studying the spectra obtained by Fourier infrared spectroscopy (FTIR). This protocol may be applied in the near future to the production of a structural bone scaffold.

Transition between the unfolded and native states of the ordered globular proteins is accompanied by accumulation of several intermediates, such as pre-molten globule, wet molten globule, and dry molten globule. Structurally equivalent conformations can serve as native functional states of intrinsically disordered proteins. This overview captures the characteristics and importance of these molten globules in both structured and intrinsically disordered proteins. It also discusses examples of engineered molten globules. The formation of these intermediates under the conditions of macromolecular crowding and their interactions with nanomaterials are also reviewed.