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Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

Technological capabilities of manufacturing microelectromechanical system (MEMS) gyroscopes are still insufficient if compared to manufacturing high-efficient gyroscopes and accelerometers. This creates weaknesses in their mechanical structure and restrictions in the measurement accuracy, stability and reliability of MEMS gyroscopes and accelerometers. This paper aims to develop a new architectural solutions for optimization of MEMS gyroscopes and accelerometers and propose a multi-axis MEMS inertial module combining the functions of gyroscope and accelerometer.

The finite element modeling (FEM) and the modal analysis in FEM are used for sensing, drive and control electrode capacitances of the multi-axis MEMS inertial module with the proposed new architecture. The description is given to its step-by-step process of manufacturing. Algorithms are developed to detect its angular rates and linear acceleration along three Cartesian axes.

Experimental results are obtained for eigenfrequencies and capacitances of sensing, drive and control electrodes for 50 manufactured prototypes of the silicon electromechanical sensor (SES). For 42 SES prototypes, a good match is observed between the calculated and simulated capacitance values of comb electrodes. Thus, the mean-square deviation is not over 20 per cent. The maximum difference between the calculated and simulated eigenfrequencies in the drive channel of 11 SES prototypes is not over 3 per cent. The same difference is detected for eigenfrequencies in the first sensing channel of 17 SES prototypes.

This study shows a way to design and optimize the structure and theoretical background for the development of the MEMS inertial module combining the functions of gyroscope and accelerometer. The obtained results will improve and expand the manufacturing technology of MEMS gyroscopes and accelerometers.

The purpose of this study is to analyze the technological change under development linked to the convergence of the Internet of Things (IoT) and digital transformation (DT) from the perspective of a scientific mapping in a context marked by the occurrence of an unexpected event that accelerated this process such as the SARS-CoV-2 pandemic and its variants.

The study was developed under the longitudinal scientific mapping approach and considered the period 1990–2021 using as a basis the descriptors DT and IoT. The steps followed were identification and selection of keywords; design and application of an algorithm to identify these selected keywords in titles, abstracts and keywords using terms in Web of Science (WoS) to contrast them; and performing a data processing based on the journals in the Journal Citation Report during 2022. The longitudinal study uses scientific mapping to analyze the evolution of the scientific literature that seeks to understand the acceleration in the integration of technology and its impact on the human factor, processes and organizational culture.

This study showed that the technologies converging around IoT form the basis of the main DT processes being experienced on a global scale; furthermore, it was shown that the pandemic accelerated the convergence and application of new technologies to support the major changes required for a world with new needs. Finally, China and the USA differ significantly in the production of scientific knowledge with respect to the first eight followers.

The knowledge gap addressed by this study is to identify the production of scientific knowledge related to IoT and its impact on DT processes at the scale of individuals, organizations and the new way of delivering value to society. This knowledge about researchers, institutions, countries and the derivation is multiple indicators allows improving decision-making at multiple scales on these issues.

This paper describes a new method designed for a quartz tuning‐fork temperature sensor. This sensor is designed with a new cut ZYtw(115°/15°) and it is shown that this worked best in flexural vibration mode. The way for raising signal to noise ratio and reducing equivalent resistor of the sensor were analyzed in theory. A manufacturing method has been determined to form and adjust the precise frequency, which could improve sensitivity and reduce non‐linearity.

This study aims to investigate the ammonia-sensing performance of polyaniline/polyethylene oxide (PANI/PEO) and polyaniline/polyethylene oxide/zinc oxide (PANI/PEO-ZnO) composite nanofibers at room temperature.

Gas sensor structures were fabricated using microfabrication techniques. First, onto the SiO₂ wafer, gold electrodes were fabricated via thermal evaporation. PANI/PEO nanofibers were produced by the electrospinning method, and the ZnO layer was deposited by using radio frequency (RF) magnetron sputtering on the electrospun nanofibers as a sensing layer. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray diffraction were performed to characterize the analysis of nanofibers. After all, gas sensing analysis of PANI/PEO and PANI/PEO/ZnO nanofibers was conducted using an experimental setup at room temperature conditions. Furthermore, the impact of humidity (17%–90% RH) on the sensor resistance was actively investigated.

FTIR analysis confirms the presence of functional groups of PANI, PEO and ZnO in nanofiber structure. SEM micrographs demonstrate beads-free, thinner and smooth nanofibers with ZnO contribution to electrospun PANI/PEO nanofibers. Moreover, according to the gas sensing results, the PANI/PEO nanofibers exhibit 115 s and 457 s response time and recovery time, respectively. However, the PANI/PEO/ZnO nanofibers exhibit 245 s and 153 s response time and recovery time, respectively. PANI/PEO/MOx composite nanofibers ensure stability to the NH₃ gas owing to the high surface/volume ratio and decrease in the humidity dependence of gas sensors, making gas sensors more stable to the environment.

In this study, ZnO was deposited via RF magnetron sputtering techniques on PANI/PEO nanofibers as a different approach instead of in situ polymerization to investigate and enhance the sensor response and recovery time of the PANI/PEO/ZnO and PANI/PEO composite nanofibers to ammonia. These results indicated that ZnO can enhance the sensing properties of conductive polymer-based resistive sensors.

This study aims to investigate the literature concerning the five basic tastes and find out who contributed to these publications, where they were published and what concepts were investigated.

The Web of Science was searched to identify the relevant articles. For each paper, the full record and cited references were analyzed.

Sweetness received the most attention, with 6,445 publications, 144,648 citations and h-index of 137. It was followed by bitterness (5,606 publications and 125,525 citations), sourness (1,841 publications and 40,696 citations), umami (1,569 publications and 39,120 citations) and saltiness (1,547 publications and 33,627 citations). Though umami taste had similar publication number as salty and sour tastes, it had the highest number of average citations per publication (24.9). The USA, Japan, Germany and England were major contributors to research on every basic taste. Chemical Senses was the major outlet of taste papers. Terms from the titles suggested that multiple tastes were often co-investigated. Ikeda (1909) and Kodama (1913) were identified as the seminal references that laid the foundation of umami research.

Umami, although only added relatively late to the family of basic tastes, is a highly investigated principle that receives similar amount of attention as some other basic tastes, such as sourness and saltiness.

This paper aims to study the working principle of the capacitive pressure sensor and explore the distribution of pressure acting on the surface of the capacitor. Herein, a kind of high sensitivity capacitive pressure sensor was prepared by overlaying carbon fibers (CFs) on the surfaces of the thermoplastic elastomer (TPE), the TPE with high elasticity is a dielectric elastomer for the sensor and the CFs with excellent electrical conductivity were designed as the conductor.

Due to the excellent mechanical properties and electrical conductivity of CFs, it was designed as the conductor layer for the TPE/CFs capacitive pressure sensor via laminating CFs on the surfaces of the columnar TPE. Then, a ‘#' type structure of the capacitive pressure sensor was designed and fabricated.

The ‘#' type of capacitive pressure sensor of TPE/CFs composite was obtained in high sensitivity with a gauge factor of 2.77. Furthermore, the change of gauge factor values of the sensor under 10 per cent of applied strains was repeated for 1,000 cycles, indicating its outstanding sensing stability. Moreover, the ‘#' type capacitive pressure sensor of TPE/CFs was consisted of several capacitor arrays via laminating CFs, which could detect the distribution of pressure.

The TPE/CFs capacitive pressure sensor was easily fabricated with high sensitivity and quick responsiveness, which is desirably applied in wearable electronics, robots, medical devices, etc.

The outcome of this study will help to fabricate capacitive pressure sensors with high sensitivity and outstanding sensing stability.