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Ensuring a healthy and sustainable life and promoting the wellness of human beings at all ages are essential to sustainable development. For this purpose, consumer electronics play important roles, such as body-centric healthcare, health-related ambient monitoring, sustainable health management, etc. Undoubtedly, in modern health and sustainable applications, Consumer Electronics (CE) is at the forefront of bridging physical and digital worlds, offering innovative solutions to improve health outcomes and enhance sustainable practices.

This study primarily focuses on the efficiency of dye-sensitized solar cells (DSSCs), i.e., naturally sensitized solar cells, which were enhanced through the implementation of Zn2SnO4 compounds and zinc stannate (Zn2SnO4)/titanium dioxide (TiO2) nanofiber (NF) composites into the photoanode. The DSSCs’ photoanode properties of Zn2SnO4 compounds and Zn2SnO4/TiO2 NFs were also investigated using a variety of characterization techniques, including electrochemical impedance spectroscopy (EIS), X-ray diffraction (XRD), incident photon current efficiency (IPCE) measurement, and ${J}-{V}$ characterization, and. The findings demonstrate that adding Zn2SnO4 compound and Zn2SnO4/TiO2 NF composite to the photoanode enhances the DSSCs’ photovoltaic (PV) capabilities. The photoanode decorated with Zn2SnO4/TiO2 NFs displayed improved photoelectric conversion efficiency (PCE) ( $\eta $ ) in contrast to the reference TiO2 photoanode. The PV conversion efficiency ( $\eta $ ) has been reported to increase by around 51.07%. Based on these findings, the study demonstrates that Zn2SnO4/TiO2 NFs can be considered as candidate materials for boosting DSSCs’ PV efficiency.

As an emerging machine learning technique, lifelong learning is capable of solving multiple consecutive tasks based on previously accumulated knowledge. Although this is highly desired for streaming process prediction in industry, lifelong learning methods have so far failed to gain applications to mainstream adaptive predictive modeling of time-varying industrial processes. This is because when faced with a new data batch, existing lifelong learning approaches need both input and output data to construct local predictors before knowledge transfer can succeed. But in many process industries, the process output data are hard to measure online and it often takes time to acquire them from off-site laboratory analysis. This delayed acquisition of target output data makes it challenging to apply lifelong learning and other existing adaptive mechanisms to dynamic industrial processes with delayed process output measurement. To overcome this difficulty, this article proposes a novel lifelong learning framework that can rapidly predict new data batches with input data only before the arrival of the process output measurement. Specifically, we propose to incorporate process input information into lifelong learning via coupled dictionary learning, to enable the prediction of new batches without target output data. The input feature is linked with a local predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the local predictor for the new batch can be reconstructed by knowledge transfer given only process inputs. Two industrial case studies are used to evaluate the effectiveness of our proposed framework and reveal the intrinsic learning mechanism of our lifelong process modeling to perform knowledge base (KB) adaptation.

This article presents an observer-based adaptive fuzzy finite-time attitude control strategy for quadrotor unmanned aerial vehicles (UAVs). To estimate the information of angular velocity with the finite-time property, an adaptive neural network observer is first developed. Subsequently, an adaptive fuzzy-logic-system (FLS)-based nonsingular fast terminal sliding mode controller is proposed to compensate for the lumped disturbance and adjust the control gain online. To cope with the input saturation, an auxiliary system without the boundedness of the saturation difference is constructed. The theoretical analysis proves that all the system signals are bounded and the tracking errors can converge to small neighborhoods in finite time. Finally, comparative simulations and experiments are performed to manifest the feasibility and superiority of the proposed control strategy, in terms of strong robustness, singularity avoidance, free-chattering, fault tolerance, and saturation attenuation.

In the Cooperative Intelligent Transportation System (C-ITS) paradigm, vehicles could communicate with roadside units to augment their traffic knowledge. Smart roadside units could provide second-order information (e.g., vehicle count) from raw first-order data (e.g., visual feed, point clouds), and this “smart” feature is usually provided using deep neural network models. However, implementing these useful models implies a cost for computational complexity that could hinder the future deployment of smart roadside units needed for sustainability in transportation systems. In this paper, we propose to use model compression on deep image processing models to promote its feasibility for usage in smart sensors. We formulated a controllable convolutional model compression (CCMC) algorithm that can perform filter-wise evolutionary pruning on image processing networks, along with a predefined compression ratio. CCMC is applicable for image processing networks, which have multiple possible traffic data sources (e.g., road camera surveillance). Furthermore, CCMC has a definable target compression ratio that is useful for controlling the trade-off between resource consumption and output performance. We tested our proposed method on depth estimation, which is useful for scene understanding and mapping the locations of objects in the 3D space. Our experiments show that the pruned model has minimal performance discrepancy from the original one, supporting the sustainability features needed for intelligent transportation systems.