Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL. 

/pdf/a-survey-on-deep-learning-tools-dealing-with-data-scarcity-3amzf9cp.pdf

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Performance evaluation of an automotive thermoelectric generator with inserted fins or dimpled-surface hot heat exchanger

Experimental investigation on thermoelectric generator with non-uniform hot-side heat exchanger for waste heat recovery

Performance investigation of a thermoelectric generator system applied in automobile exhaust waste heat recovery

Parametric study of a thermoelectric module used for both power generation and cooling

Flexible pressure sensors are essential components for wearable smart devices and intelligent systems. Significant progress has been made in this area, reporting on excellent sensor performance and fascinating sensor functionalities. Nevertheless, geometrical and morphological engineering of pressure sensors is usually neglected, which, however, is significant for practical application. Here, we present a digitized manufacturing methodology to construct a new class of iontronic pressure sensors with optionally defined configurations and widely modulated performance. These pressure sensors are composed of self-defined electrode patterns prepared by a screen printing method and highly tunable pressure-sensitive microstructures fabricated using 3D printed templates. Importantly, the iontronic pressure sensors employ an iontronic capacitive sensing mechanism based on mechanically regulating the electrical double layer at the electrolyte/electrode interfaces. The resultant pressure sensors exhibit high sensitivity (58 kPa−1), fast response/recovery time (45 ms/75 ms), low detectability (6.64 Pa), and good repeatability (2000 cycles). Moreover, our pressure sensors show remarkable tunability and adaptability in device configuration and performance, which is challenging to achieve via conventional manufacturing processes. The promising applications of these iontronic pressure sensors in monitoring various human physiological activities, fabricating flexible electronic skin, and resolving the force variation during manipulation of an object with a robotic hand are successfully demonstrated.

/pdf/digitized-construction-of-iontronic-pressure-sensor-with-261lf721.pdf

Digitized Construction of Iontronic Pressure Sensor with Self-Defined Configuration and Widely Regulated Performance

Cryogenic Printing of Bioactive Materials for Bone Tissue Engineering: A Review

This article investigates the negative effect of land use and climate changes on water resources by the SWAT and SWAT‐DEEP∖LMSFO model. Due to the importance of runoff impact on water resources in this article, the hybrid hydrological–deep neural networks optimized by the improved SFO based on logistic map (LMSFO) algorithm have been used to provide more accurate results for runoff estimation. This method improves runoff simulation. Firstly, runoff under the influence of land use and climate change is estimated by the SWAT model. Once again, runoff is estimated by the SWAT‐DEEP/LMSFO model. In the DEEP/LMSFO model, the primary runoff has been estimated by an un‐calibrated SWAT model. Then the Primary runoff simulated is entered as an input into the DEEP model. Finally, the runoff is estimated by a test‐training method. The results of the SWAT model and the DEEP/LMSFO model show that there is an inverse correlation between land use so that reducing land cover increases runoff. The results show that climate change and land‐use change can affect annual runoff changes in the coming years.

Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

The temperature distribution and electrical performance of fluid heat exchanger-based thermoelectric generator

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Digitized Construction of Iontronic Pressure Sensor with Self-Defined Configuration and Widely Regulated Performance

Cryogenic Printing of Bioactive Materials for Bone Tissue Engineering: A Review

Using an optimized soil and water assessment tool by deep belief networks to evaluate the impact of land use and climate change on water resources

The temperature distribution and electrical performance of fluid heat exchanger-based thermoelectric generator