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The purpose of this paper is to improve the efficiency of dye-sensitized solar cells (DSSCs) which present promising low-cost alternative to the conventional silicon solar cells mainly due to comparatively low manufacturing cost, ease of fabrication and relatively good efficiency. One of the undesirable factor in DSSCs is the electron recombination process that takes place at the transparent conductive oxide/electrolyte interface, on the side of photoelectrode. To reduce this effect in the structure of the solar cell, a TiO₂ blocking layer (BL) by atomic layer deposition (ALD) was deposited.

Scanning electron microscope, Raman and UV-Vis spectroscopy were used to evaluate the influence of BL on the photovoltaic properties. Electrical parameters of manufactured DSSCs with and without BL were characterized by measurements of current-voltage characteristics under standard AM 1.5 radiation.

The TiO₂ BL prevents the physical contact of fluorine-doped tin oxide (FTO) and the electrolyte and leads to increase in the cell’s overall efficiency, from 5.15 to 6.18%. Higher density of the BL, together with larger contact area and improved adherence between the TiO₂ layer and FTO surface provide more electron pathways from TiO₂ to FTO which facilitates electron transfer.

This paper demonstrates that the introduction of a BL into the photovoltaic device structure is an important step in technology of DSSCs to improve its efficiency. Moreover, the ALD is a powerful technique which allows for the highly reproducible growth of pinhole-free thin films with excellent thickness accuracy and conformality at low temperature.

In the past several years, CH₃NH₃PbI₃ perovskite material has been extensively evaluated as an absorber layer of perovskite solar cells due to its excellent structural and optical properties, and greater than 22% conversion efficiency. However, improvement and future commercialization of solar cells based on CH₃NH₃PbI₃ encountered restrictions due to toxicity and instability of the lead element. Recently, studies on properties of lead-free and mixture of lead with other cations perovskite thin films as light absorber materials have been reported. The purpose of this paper was the fabrication of CH₃NH₃Sn₁-xPbxI₃ thin films with different SnI₂ concentrations in ambient condition, and study on the structural, morphological, optical, and photovoltaic performance of the studied solar cells. The X-ray diffraction studies revealed the formation of both CH₃NH₃PbI₃ and CH₃NH₃SnI₃ phases with increasing the Sn concentration, and improvement in crystallinity and morphology was also observed. All perovskite layers had a relatively high absorption coefficient >104 cm⁻¹ in the visible wavelengths, and the bandgap values varied in the range from 1.46 to 1.63 eV. Perovskite solar cells based on these thin films have been fabricated, and device performance was investigated. Results showed that photo-conversion efficiency (PCE) for the pure CH₃NH₃PbI₃sample was 1.20%. With adding SnI₂, PCE was increased to 4.48%.

In this work, the author mixed tin and lead with different percentages in the perovskite thin film. Also, the preparation of these layers and also other layers to fabricate solar cells based on them were conducted in an open and non-glove box environment. Finally, the effect of [Sn/Pb] ratio in the CH₃NH₃Sn₁-xPbxI₃ layers on the structural, morphological, optical, electrical and photovoltaic performance have been investigated.

CH₃NH₃Sn₁-xPbxI₃ (x = 0.0, 0.25, 0.50, 0.75, 1.0) perovskite thin films have been grown by a spin-coating technique. It was found that as tin concentration increases, the X-ray diffraction and FESEM images studies revealed the formation of both CH₃NH₃PbI₃ and CH₃NH₃SnI₃ phases, and improvement in crystallinity, and morphology; all thin films had high absorption coefficient values close to 104 cm⁻¹ in the visible region, and the direct optical bandgap in the layers decreases from 1.63 eV in pure CH₃NH₃SnI₃ to 1.46 eV for CH₃NH₃Sn0.0.25Pb0.75I3 samples; all thin films had p-type conductivity, and mobility and carrier density increased; perovskite solar cells based on these thin films have been fabricated, and device performance was investigated. Results showed that photo-conversion efficiency (PCE) for the pure CH₃NH₃PbI₃sample was 1.20%. With adding SnI2, PCE was increased to 4.48%.

The preparation method seems to be interesting as it is in an ambient environment without the protection of nitrogen or argon gas.

The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process.

In this study, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples.

The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error.

This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts.

The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation.

This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

The first purpose of this paper is to propose a scalable video coding scheme providing flexibility in video transmission, especially under wireless environment. The second purpose is to analyze the problem of lengthening the key frame interval in distributed video coding (DVC), and propose an approach to improve the rate‐distortion (RD) performance of DVC for long group‐of‐frames (GOF) size.

In the proposed scheme, a base layer is first obtained from an H.264 coder. When a DVC coder is then used to code the enhancement layer, information in processing the base layer is extracted and analyzed to make multiple side‐information available and reduce error accumulation for DVC coding, thus further improving the performance of the DVC coder.

By dividing video into base and enhancement layers, the combined video coding architecture enables a flexible video transmission. In addition, several methods are used to improve the RD performance in DVC coding. Simulation shows that the proposed scheme outperforms non‐scalable DVC for long GOF size.

Prediction from the decoding loop in base layer encoder largely reduces enhancement layer spatial redundancy. Multiple side‐information provides better estimation for DVC reconstruction. Long prediction loop is more reliable because error accumulation is effectively compensated.

AS readers of ‘Aircraft Engineering’ will be aware there has been a considerable amount of discussion, criticism of decisions and controversey over means to improve passenger survivability. The Civil Aviation Authority (CAA) has published a review of those improvements that relate specifically to aircraft fires. In particular and although the House of Commons Transport Committee recommended the mandatory carriage of the best smoke hoods currently available, the CAA feels that it is an unnacceptable safety risk and will not require their provision. On the subject of water sprays, the Authority considers that serious consideration should now be given to introducing regulations requiring the installation of such systems in large transport aircraft. These two issues are among a number discussed in the report but are those which have attracted the most publicity.

Workpiece sorting is a key link in industrial production lines. The vision-based workpiece sorting system is non-contact and widely applicable. The detection and recognition of workpieces are the key technologies of the workpiece sorting system. To introduce deep learning algorithms into workpiece detection and improve detection accuracy, this paper aims to propose a workpiece detection algorithm based on the single-shot multi-box detector (SSD).

Propose a multi-feature fused SSD network for fast workpiece detection. First, the multi-view CAD rendering images of the workpiece are used as deep learning data sets. Second, the visual geometry group network was trained for workpiece recognition to identify the category of the workpiece. Third, this study designs a multi-level feature fusion method to improve the detection accuracy of SSD (especially for small objects); specifically, a feature fusion module is added, which uses “element-wise sum” and “concatenation operation” to combine the information of shallow features and deep features.

Experimental results show that the actual workpiece detection accuracy of the method can reach 96% and the speed can reach 41 frames per second. Compared with the original SSD, the method improves the accuracy by 7% and improves the detection performance of small objects.

This paper innovatively introduces the SSD detection algorithm into workpiece detection in industrial scenarios and improves it. A feature fusion module has been added to combine the information of shallow features and deep features. The multi-feature fused SSD network proves the feasibility and practicality of introducing deep learning algorithms into workpiece sorting.

The purpose of this paper is to compare the single-sided packaging structure and double-sided packaging structure of high-power module and study the overall heat dissipation performance and reliability of the module.

In this paper, the single-sided packaging structure and double-sided packaging structure of power module are designed based on Wolfspeed products. This paper is analyzed by finite element method. First, the heat dissipation performance of single-sided packaging structure and double-sided packaging structure is analyzed; second, the deformation and stress of single-sided packaging structure and double-sided packaging structure are compared and analyzed; and finally, the cumulative plastic deformation of single-sided packaging and double-sided packaging structures are compared and analyzed, and the fatigue life of the structure is calculated based on the plastic deformation.

In the heat transfer simulation, under the same power input, the heat dissipation performance of single-sided packaging structure is not as good as that of double-sided packaging structure. Under the reliability simulation of the same temperature cycle standard, the maximum equivalent stress of single-sided packaging structure is lower than that of double-sided packaging structure, but the fatigue life prediction based on plastic strain shows that the fatigue life of double-sided packaging structure is not different from that of single-sided packaging structure.

This paper creatively simulates the thermal characteristics and reliability of single-sided packaging structure and double-sided packaging structure and proves the advantages of double-sided packaging structure compared with single-sided packaging structure from the aspects of heat transfer performance and reliability.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.