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As smart cities become a global topic, interest in smart mobility, the core of smart cities, is also growing. The technology that comes closest to general users is “autonomous driving”. In particular, the successful market entry and establishment of some private companies proved that “autonomous driving” is not technology of the future but imminent reality. However, safety in autonomous vehicles that rely on sensors instead of the driver’s five senses has been the focus of attention from the beginning and continues to be so. In this study, we attempted to counter this interest. Based on the actual data of thirty traffic accidents, assuming the AEBS (Autonomous Emergency Braking System) was installed to assist the driver in safe driving, it was reinterpreted through simulation to see what changes occurred in the accident. In the computer program, PC-Crash, the results were first analyzed through simulation using Euro NCAP (New Car Assessment Program)’s AEBS test standards. Subsequently, the other variables in the AEBS were controlled and the accident was reinterpreted by changing only the angle of the radar detection sensor. As a result, it was confirmed that a total of 27 accidents out of thirty accidents could have been prevented with the AEBS. In addition, it proved that the crash avoidance rate of vehicles gradually increased as the radar angle increased.

AbstractThis work reports the synthesis, characterization, photophysical, and photovoltaic properties of five new thieno[3,2‐b][1]benzothiophene isoindigo (TBTI)‐containing low bandgap donor–acceptor conjugated polymers with a series of comonomers and different side chains. When TBTI is combined with different electron‐rich moieties, even small structural variations can have significant impact on thin film morphology of the polymer:phenyl C70 butyric acid methyl ester (PCBM) blends. More importantly, high‐resolution electron energy loss spectroscopy is used to investigate the phase‐separated bulk heterojunction domains, which can be accurately and precisely resolved, enabling an enhanced correlation between polymer chemical structure, photovoltaic device performance, and morphology.

Heat energy storage systems were fabricated with the impregnation method using MgO and Mg(OH)2 as supporting materials and polyethylene glycol (PEG-6000) as the functional phase. MgO and Mg(OH)2 were synthesized from the salt Mg(NO3)·6H2O by performing hydrothermal reactions with various precipitating agents. The precipitating agents were NaOH, KOH, NH3, NH3 with pamoic acid (PA), or (NH4)2CO3. The result shows that the selection of the precipitating agent has a significant impact on the crystallite structure, size, and shape of the final products. Of the precipitating agents tested, only NaOH and NH3 with PA produce single-phase Mg(OH)2 as the as-synthesized product. Pore size distribution analyses revealed that the surfaces of the as-synthesized MgO have a slit-like pore structure with a broad-type pore size distribution, whereas the as-synthesized Mg(OH)2 has a mesoporous structure with a narrow pore size distribution. This structure enhances the latent heat of the phase change material (PCM) as well as super cooling mitigation. The PEG/Mg(OH)2 PCM also exhibits reproducible behavior over a large number of thermal cycles. Both MgO and Mg(OH)2 matrices prevent the leakage of liquid PEG during the phase transition in phase change materials (PCMs). However, MgO/PEG has a low impregnation ratio and efficiency, with a low thermal storage capability. This is due to the large pore diameter, which does not allow MgO to retain a larger amount of PEG. The latent heat values of PEG-1000/PEG-6000 blends with MgO and Mg(OH)2 were also determined with a view to extending the application of the PCMs to energy storage over wider temperature ranges.

Structural, optoelectronic, photovoltaic, and supplementary characterization data for “Benzylammonium-Mediated Formamidinium Lead Iodide Perovskite Phase Stabilization for Photovoltaics”, DOI:10.1002/adfm.202101163. Figure_2_XRD.zip: Data described in Figure 2 (XRD patterns) as Origin (.opj) software file. Figure_3_NMR_data.zip: Data described in Figure 3 (NMR spectra) in the file structure of the TopSpin software, which is available from Bruker. Figure_4_spectra.zip: Data described in Figure 4 (UV-vis absorption, PL and IPCE spectra) as Origin (.opj) software files. Figure_5_PV.zip: Data described in Figure 5 (photovoltaic characterization) as Origin (.opj) software files. Figure_6_spectra.zip: Data described in Figure 6 (PLQY and TRPL) as Origin (.opj) and *.csv files. Figure_7_stability.zip: Data described in Figure 7 (stability analysis) as Origin (.opj) software files. Figure_SI.zip: Data described in the Supporting Information Figures S1, S2, S3, S5, and S6 (XRD data, reciprocal space maps, radial profiles of q-maps, UV-vis absorption spectra, PL spectra, and additional photovoltaic characterization) as Origin (.opj), text (.txt), and image (.tiff) files.

Shading the greenhouses is necessary in summer to reduce the solar radiation load. This however generates a considerable amount of thermal radiation heat load that needs to be removed via cooling systems. This study aimed to evaluate the effect of different shading configurations on the solar and thermal radiation in a greenhouse. Nets at four different locations were employed to shade the roof and side-walls of a polycarbonate, mechanically ventilated greenhouse. The spectral radiative properties of all these plastic materials were measured in short and long wave spectrum bands. The net solar and thermal radiations and air temperature were measured outside and inside two identical shaded and unshaded greenhouses. The results showed that external roof-shading is desirable, as it reduced the generated thermal radiation in the greenhouse by 21% and 15% during the day and night time, respectively and reduced the greenhouse air temperature during the day. The internal shading (roof and side walls) is undesirable, since it drastically increased the generated thermal radiation in the greenhouse by 147% and strongly increased the greenhouse air temperature during the day. Shading the side-walls is not recommended because it significantly reduces the transmitted solar radiation in the morning and afternoon (when the outside irradiance is low) and is useless at around noon when the outside irradiance is extremely high.

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.