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Decarbonizing the electricity sector requires massive investments in generation and transmission infrastructures that may impact both water and land resources. Characterizing these effects is key to ensure a sustainable energy transition. Here, we identify and quantify the unintended consequences of decarbonizing the China Southern Power Grid, China’s second-largest grid. We show that reaching carbon neutrality by 2060 is feasible; yet, doing so requires converting 40,000 square kilometers of land to support solar and wind as well as tapping on rivers to build ~32 gigawatts of hydropower. The impact of wind and solar development would span across multiple sectors, since crop and grassland constitute 90% of the identified sites. The construction of new dams may carry major externalities and trickle down to nearby countries, as most dams are located in transboundary rivers. Curbing the international footprint of this decarbonization effort would require additional investments (~12 billion United States dollars) in carbon capture technologies.

 

Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potential to) leverage color information. To promote advances in such systems, we propose an efficient multi-modal keypoint detector that can extract both geometry-salient and color-salient keypoints in colored point clouds. The proposed CEntroid Distance (CED) keypoint detector comprises an intuitive and effective saliency measure, the centroid distance, that can be used in both 3D space and color space, and a multi-modal non-maximum suppression algorithm that can select keypoints with high saliency in two or more modalities. The proposed saliency measure leverages directly the distribution of points in a local neighborhood and does not require normal estimation or eigenvalue decomposition. We evaluate the proposed method in terms of repeatability and computational efficiency (i.e. running time) against state-of-the-art keypoint detectors on both synthetic and real-world datasets. Results demonstrate that our proposed CED keypoint detector requires minimal computational time while attaining high repeatability. To showcase one of the potential applications of the proposed method, we further investigate the task of colored point cloud registration. Results suggest that our proposed CED detector outperforms state-of-the-art handcrafted and learning-based keypoint detectors in the evaluated scenes. The C++ implementation of the proposed method is made publicly available at https://github.com/UCR-Robotics/CED_Detector. 

Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications. 

Thermal conductive gap filler materials are used as thermal interface materials (TIMs) in electronic devices due their numerous advantages, such as higher thermal conductivity, ease of use, and conformity. Silicone is a class of synthetic materials based on a polymeric siloxane backbone which is widely used in thermal gap filler materials. In electronic packages, silicone-based thermal gap filler materials are widely used in industries, whereas silicone-free thermal gap filler materials are emerging as new alternatives for numerous electronics applications. Certainly, characterization of these TIMs is of immense importance since it plays a critical role in heat dissipation and long-term reliability of the electronic packages. Insubstantial studies on the effects of various chemical compounds on the properties of silicone-based and silicone-free TIMs has led to this study, which focuses on the effect of thermal aging on the mechanical, thermal, and dielectric properties of silicone-based and silicone-free TIMs and the chemical compounds that cause the changes in properties of these materials. Characterization techniques such as dynamic mechanical analysis (DMA), thermomechanical analysis (TMA), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and broadband dielectric spectroscopy (BbDS) are used to study the mechanical, thermal, and dielectric characteristics of these TIMs, which will guide towards a better understanding of the applicability and reliability of these TIMs. The experiments demonstrate that upon thermal aging at 125 °C, the silicone-free TIM becomes hard, while silicone-based TIM remains viscoelastic, which indicates its wide applicability to higher temperature applications for a long time. Though silicone-based TIM displays better mechanical and thermal properties at elevated temperatures, dielectric properties indicate low conductivity for silicone-free TIM, which makes it a better candidate for silicone-sensitive applications where higher electric insulation is desired.