Marine Fish Oil Replacement with Lard or Basa Fish (Pangasius bocourti) Offal Oil in the Diet of Tiger Puffer (Takifugu rubripes): Effects on Growth Performance, Body Composition, and Flesh Quality.

Lard (LD) and Basa fish offal oil (BFO) have similar fatty acid profiles, both containing high contents of saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA). The present study aimed to investigate the efficacy of partial or complete replacement of marine fish oil (MFO, herring oil) by LD or BFO in the diets of tiger puffer. The control diet contained 49.1% crude protein and 9.28% crude lipid content including 6% added MFO. In other diets, 1/3, 2/3, and 3/3 of the added MFO was replaced by LD or BFO, respectively. Each diet was fed to triplicate tanks of juvenile fish (initial body weight, 13.88 g). A 46-day feeding trial was conducted in a flow-through seawater system. Each diet was fed to triplicate 200-L rectangular polyethylene tanks, each of which was stocked with 30 fish. Fish were fed to satiation three times a day. The complete replacement of added MFO (replacing 65% of the total crude lipid) had no adverse effects on fish growth performance in terms of survival (>94%), weight gain (360-398%), feed intake (2.37-3.04%), feed conversion ratio (0.84-1.02), and somatic indices. The dietary LD or BFO supplementation also had marginal effects on fish body proximate composition, biochemical parameters, muscle texture, and water-holding ability, as well as the hepatic expression of lipid metabolism-related genes. Partial (2/3) replacement of added MFO by LD or BFO did not significantly reduce the muscle n-3 LC-PUFA content, indicating the n-3 LC-PUFA sparing effects of SFA and MUFA in LD and BFO. In general, dietary LD or BFO reduced the peroxidation level and led to significant changes in the muscle volatile flavor compound profile, which were probably attributed to the change in fatty acid composition. The results of this study evidenced that LD and BFO are good potential lipid sources for tiger puffer feeds.

A near-zero quiescent power breeze wake-up anemometer based on a rolling-bearing triboelectric nanogenerator.

Wind sensors have always played an irreplaceable role in environmental information monitoring and are expected to operate with lower power consumption to extend service lifetime. Here, we propose a breeze wake-up anemometer (B-WA) based on a rolling-bearing triboelectric nanogenerator (RB-TENG) with extremely low static power. The B-WA consists of two RB-TENGs, a self-waking-up module (SWM), a signal processing module (SPM), and a wireless transmission unit. The two RB-TENGs are employed for system activation and wind-speed sensing. Once the ambient wind-speed exceeds 2 m/s, the wake TENG (W-TENG) and the SWM can wake up the system within 0.96 s. At the same time, the SPM starts to calculate the signal frequency from the measured TENG (M-TENG) to monitor the wind speed with a sensitivity of 9.45 Hz/(m/s). After the wind stops, the SWM can switch off the B-WA within 0.52 s to decrease the system energy loss. In quiescent on-duty mode, the operating power of the B-WA is less than 30 nW, which can greatly extend the service lifetime of the B-WA. By integrating triboelectric devices and rolling bearings, this work has realized an ultralow quiescent power and self-waked-up wireless wind-speed monitoring system, which has foreseeable applications in remote weather monitoring, IoT nodes, and so on.

Self-Powered Wireless Temperature and Vibration Monitoring System by Weak Vibrational Energy for Industrial Internet of Things.

Developing self-powered smart wireless sensor networks by harvesting industrial environmental weak vibration energy remains a challenge and an impending need for enabling the widespread rollout of the industrial internet of things (IIoT). This work reports a self-powered wireless temperature and vibration monitoring system (WTVMS) based on a vibrational triboelectric nanogenerator (V-TENG) and a piezoelectric nanogenerator (PENG) for weak vibration energy collection and information sensing. Therein, the V-TENG can scavenge weak vibration energy down to 80 µm to power the system through a power management module, while the PENG is able to supply the frequency signal to the system by a comparison circuit. In an industrial vibration environment where the vibration frequency and amplitude are 20 Hz and 100 µm, respectively, the WTVMS can upload temperature and frequency information on the equipment to the cloud in combination with the narrowband IoT technology to realize real-time information monitoring. Furthermore, the WTVMS can work continuously for more than 2 months, during which the V-TENG can operate up to 100 million cycles, achieving ultrahigh stability and durability. By integrating weak vibration energy harvesting and active sensing technology, the WTVMS can be used for real-time online monitoring and early fault diagnosis of vibration equipment, which has great application prospects in industrial production, machinery manufacturing, traffic transportation, and intelligent IIoT.

An efficient tomato-detection method based on improved YOLOv4-tiny model in complex environment.

Automatic and accurate detection of fruit in greenhouse is challenging due to complicated environment conditions. Leaves or branches occlusion, illumination variation, overlap and cluster between fruits make the fruit detection accuracy to decrease. To address this issue, an accurate and robust fruit-detection algorithm was proposed for tomato detection based on an improved YOLOv4-tiny model. First, an improved backbone network was used to enhance feature extraction and reduce overall computational complexity. To obtain the improved backbone network, the BottleneckCSP modules of the original YOLOv4-tiny backbone were replaced by a Bottleneck module and a reduced version of BottleneckCSP module. Then, a tiny version of CSP-Spatial Pyramid Pooling (CSP-SPP) was attached to the new backbone network to improve the receptive field. Finally, a Content Aware Reassembly of Features (CARAFE) module was used in the neck instead of the traditional up-sampling operator to obtain a better feature map with high resolution. These modifications improved the original YOLOv4-tiny and helped the new model to be more efficient and accurate. The experimental results showed that the precision, recall, F 1 score, and the mean average precision (mAP) with Intersection over Union (IoU) of 0.5 to 0.95 were 96.3%, 95%, 95.6%, and 82.8% for the improved YOLOv4-tiny model, respectively. The detection time was 1.9 ms per image. The overall detection performance of the improved YOLOv4-tiny was better than that of state-of-the-art detection methods and met the requirements of tomato detection in real time.

The Fabrication, Properties, and Application of a Printed Green Ag NWs-Based Flexible Electrode and Circuit.

A suitable conductive ink for office inkjet printers is important for the convenient design of flexible electrodes for triboelectric nanogenerators (TENG). Ag nanowires (Ag NWs) easily printed with an average short length of 1.65 µm were synthesized by using soluble NaCl as a growth regulator and adjusting the amount of chloride ion. The water-based Ag NWs ink with a low solid content of 1% but with low resistivity was produced. The printed flexible Ag NWs-based electrodes/circuits showed excellent conductivity with RS/R0 values kept at 1.03 after bending 50,000 times on PI substrate and an excellent anticlimate property in acidic conditions for 180 h on polyester woven fabric. The sheet resistance was reduced to 4.98 Ω/sqr heated at 30-50 °C for 3 min by a blower due to the formed excellent conductive network when compared to Ag NPs-based electrodes. Finally, the integration of printed Ag NWs electrode and circuits was applied to the TENG, which can be used to predict a robot's out-of-balance direction by the change of the TENG signal. In all, a suitable conductive ink with a short length of Ag NWs was fabricated, and flexible electrodes/circuits can be conveniently and easily printed by office inkjet printers.

Accurate and fast detection of tomatoes based on improved YOLOv5s in natural environments.

Uneven illumination, obstruction of leaves or branches, and the overlapping of fruit significantly affect the accuracy of tomato detection by automated harvesting robots in natural environments. In this study, a proficient and accurate algorithm for tomato detection, called SBCS-YOLOv5s, is proposed to address this practical challenge. SBCS-YOLOv5s integrates the SE, BiFPN, CARAFE and Soft-NMS modules into YOLOv5s to enhance the feature expression ability of the model. First, the SE attention module and the C3 module were combined to form the C3SE module, replacing the original C3 module within the YOLOv5s backbone architecture. The SE attention module relies on modeling channel-wise relationships and adaptive re-calibration of feature maps to capture important information, which helps improve feature extraction of the model. Moreover, the SE module's ability to adaptively re-calibrate features can improve the model's robustness to variations in environmental conditions. Next, the conventional PANet multi-scale feature fusion network was replaced with an efficient, weighted Bi-directional Feature Pyramid Network (BiFPN). This adaptation aids the model in determining useful weights for the comprehensive fusion of high-level and bottom-level features. Third, the regular up-sampling operator is replaced by the Content Aware Reassembly of Features (CARAFE) within the neck network. This implementation produces a better feature map that encompasses greater semantic information. In addition, CARAFE's ability to enhance spatial detail helps the model discriminate between closely spaced fruits, especially for tomatoes that overlap heavily, potentially reducing the number of merging detections. Finally, for heightened identification of occluded and overlapped fruits, the conventional Non-Maximum-Suppression (NMS) algorithm was substituted with the Soft-NMS algorithm. Since Soft-NMS adopts a continuous weighting scheme, it is more adaptable to varying object sizes, improving the handling of small or large fruits in the image. Remarkably, this is carried out without introducing changes to the computational complexity. The outcome of the experiments showed that SBCS-YOLOv5s achieved a mean average precision (mAP (0.5:0.95)) of 87.7%, which is 3.5% superior to the original YOLOv5s model. Moreover, SBCS-YOLOv5s has a detection speed of 2.6 ms per image. Compared to other state-of-the-art detection algorithms, SBCS-YOLOv5s performed the best, showing tremendous promise for tomato detection in natural environments.

A Flexible Tribotronic Artificial Synapse with Bioinspired Neurosensory Behavior.

As key components of artificial afferent nervous systems, synaptic devices can mimic the physiological synaptic behaviors, which have attracted extensive attentions. Here, a flexible tribotronic artificial synapse (TAS) with bioinspired neurosensory behavior is developed. The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor, which can tune the carriers transport through the migration/accumulation of ions. The TAS successfully demonstrates a series of synaptic behaviors by external stimuli, such as excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memory process from sensory memory to short-term memory and long-term memory. Moreover, the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm, and the TAS still exhibits excellent durability after 1000 bending cycles. Finally, Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell, respectively. This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems, which is great significance to the practical application of artificial limbs, robotics, and bionics in future.

Tomato Pest Recognition Algorithm Based on Improved YOLOv4.

Tomato plants are infected by diseases and insect pests in the growth process, which will lead to a reduction in tomato production and economic benefits for growers. At present, tomato pests are detected mainly through manual collection and classification of field samples by professionals. This manual classification method is expensive and time-consuming. The existing automatic pest detection methods based on a computer require a simple background environment of the pests and cannot locate pests. To solve these problems, based on the idea of deep learning, a tomato pest identification algorithm based on an improved YOLOv4 fusing triplet attention mechanism (YOLOv4-TAM) was proposed, and the problem of imbalances in the number of positive and negative samples in the image was addressed by introducing a focal loss function. The K-means + + clustering algorithm is used to obtain a set of anchor boxes that correspond to the pest dataset. At the same time, a labeled dataset of tomato pests was established. The proposed algorithm was tested on the established dataset, and the average recognition accuracy reached 95.2%. The experimental results show that the proposed method can effectively improve the accuracy of tomato pests, which is superior to the previous methods. Algorithmic performance on practical images of healthy and unhealthy objects shows that the proposed method is feasible for the detection of tomato pests.

TomatoDet: Anchor-free detector for tomato detection.

The accurate and robust detection of fruits in the greenhouse is a critical step of automatic robot harvesting. However, the complicated environmental conditions such as uneven illumination, leaves or branches occlusion, and overlap between fruits make it difficult to develop a robust fruit detection system and hinders the step of commercial application of harvesting robots. In this study, we propose an improved anchor-free detector called TomatoDet to deal with the above challenges. First, an attention mechanism is incorporated into the CenterNet backbone to improve the feature expression ability. Then, a circle representation is introduced to optimize the detector to make it more suitable for our specific detection task. This new representation can not only reduce the degree of freedom for shape fitting, but also simplifies the regression process from detected keypoints. The experimental results showed that the proposed TomatoDet outperformed other state-of-the-art detectors in respect of tomato detection. The F1 score and average precision of TomatoDet reaches 95.03 and 98.16%. In addition, the proposed detector performs robustly under the condition of illumination variation and occlusion, which shows great promise in tomato detection in the greenhouse.

Ferromagnetic-Based Charge-Accumulation Triboelectric Nanogenerator With Ultrahigh Surface Charge Density.

An encouraging micro-energy harvesting technology, the triboelectric nanogenerator (TENG), has been proven to transfer ambient environmental micro-energy into electricity, but a low surface charge density results in low performance and limits the practical application of TENG. Here, a ferromagnetic-based charge-accumulation TENG (FC-TENG) is proposed with ultrahigh surface charge density and performances. The FC-TENG introduces a ferromagnetic media to enhance the output charge by magnetization effect. Meanwhile, the charge can also be continuously accumulated by the charge pump effects. Based on these two effects, an ultra-high surface charge density of 2.85 mC m-2 is obtained under ambient atmospheric conditions using an ultra-thin PET film (3 µm) and deposited Permalloy ferromagnetic electrodes. Meanwhile, the surface charge density of the FC-TENG can always maintain more than 1.5 mC m-2 , even if the relative humidity arrives at 90%. This work provides a prospective technical mode to enhance the surface charge density of TENG, which would shed a new insight and guidance on the high-performance TENG for various environmental conditions such as the ocean, industrial manufacturing, aerospace, and rail traffic.

Self-Powered and Autonomous Vibrational Wake-Up System Based on Triboelectric Nanogenerators and MEMS Switch.

With the extensive application of wireless sensing nodes, the demand for sustainable energy in unattended environments is increasing. Here, we report a self-powered and autonomous vibrational wake-up system (SAVWS) based on triboelectric nanogenerators and micro-electromechanical system (MEMS) switches. The energy triboelectric nanogenerator (E-TENG) harvests vibration energy to power the wireless transmitter through a MEMS switch. The signal triboelectric nanogenerator (S-TENG) controls the state of the MEMS switch as a self-powered accelerometer and shows good linearity in the acceleration range of 1-4.5 m/s2 at 30 Hz with a sensitivity of about 14.6 V/(m/s2). When the acceleration increases, the S-TENG turns on the MEMS switch, and the wireless transmitter transmits an alarm signal with the energy from E-TENG, using only 0.64 mJ. Using TENGs simultaneously as an energy source and a sensor, the SAVWS provides a self-powered vibration monitoring solution for unattended environments and shows extensive applications and great promise in smart factories, autonomous driving, and the Internet of Things.

An ultraweak mechanical stimuli actuated single electrode triboelectric nanogenerator with high energy conversion efficiency.

Triboelectric nanogenerator (TENG) as a new energy harvester has attracted significant attention due to its excellent output performance and high energy conversion efficiency at low-frequency, small-amplitude and weak-force compared with a traditional electromagnetic generator. Here, an ultraweak mechanical stimuli actuated single electrode triboelectric nanogenerator (UMA-TENG) has been studied with an atomic force microscope. The electrical output and force curve of UMA-TENG were studied at first, as well as the maximum output performance and highest energy conversion efficiency. Then the influence of the driving frequency, separation distance and motion amplitude was investigated, respectively. Moreover, by introducing an external switch to reach a cycle of maximized energy output, the maximum energy conversion efficiency of the UMA-TENG was up to 73.6% with an input mechanical energy of 48 pJ. This work demonstrates that the TENG shows excellent performance in ultraweak mechanical stimuli and could broaden the applications of the TENG in sensors, actuators, micro-robotics, micro-electro-mechanical-systems, and wearable electronics.

Raindrop energy-powered autonomous wireless hyetometer based on liquid-solid contact electrification.

Triboelectric nanogenerators (TENGs) can directly harvest energy via solid-liquid interface contact electrification, making them very suitable for harvesting raindrop energy and as active rainfall sensors. This technology is promising for realizing a fully self-powered system for autonomous rainfall monitoring combined with energy harvesting/sensing. Here, we report a raindrop energy-powered autonomous rainfall monitoring and wireless transmission system (R-RMS), in which a raindrop-TENG (R-TENG) array simultaneously serves as a raindrop energy harvester and rainfall sensor. At a rainfall intensity of 71 mm/min, the R-TENG array can generate an average short-circuit current, open-circuit voltage, and maximum output power of 15 µA, 1800 V, and 325 µW, respectively. The collected energy can be adjusted to act as a stable 2.5 V direct-current source for the whole system by a power management circuit. Meanwhile, the R-TENG array acts as a rainfall sensor, in which the output signal can be monitored and the measured data are wirelessly transmitted. Under a rainfall intensity of 71 mm/min, the R-RMS can be continuously powered and autonomously transmit rainfall data once every 4 min. This work has paved the way for raindrop energy-powered wireless hyetometers, which have exhibited broad prospects in unattended weather monitoring, field surveys, and the Internet of Things.

Recent Progress of Switching Power Management for Triboelectric Nanogenerators.

Based on the coupling effect of contact electrification and electrostatic induction, the triboelectric nanogenerator (TENG) as an emerging energy technology can effectively harvest mechanical energy from the ambient environment. However, due to its inherent property of large impedance, the TENG shows high voltage, low current and limited output power, which cannot satisfy the stable power supply requirements of conventional electronics. As the interface unit between the TENG and load devices, the power management circuit can perform significant functions of voltage and impedance conversion for efficient energy supply and storage. Here, a review of the recent progress of switching power management for TENGs is introduced. Firstly, the fundamentals of the TENG are briefly introduced. Secondly, according to the switch types, the existing power management methods are summarized and divided into four categories: travel switch, voltage trigger switch, transistor switch of discrete components and integrated circuit switch. The switch structure and power management principle of each type are reviewed in detail. Finally, the advantages and drawbacks of various switching power management circuits for TENGs are systematically summarized, and the challenges and development of further research are prospected.

Triboelectric Nanogenerators as Active Tactile Stimulators for Multifunctional Sensing and Artificial Synapses.

The wearable tactile sensors have attracted great attention in the fields of intelligent robots, healthcare monitors and human-machine interactions. To create active tactile sensors that can directly generate electrical signals in response to stimuli from the surrounding environment is of great significance. Triboelectric nanogenerators (TENGs) have the advantages of high sensitivity, fast response speed and low cost that can convert any type of mechanical motion in the surrounding environment into electrical signals, which provides an effective strategy to design the self-powered active tactile sensors. Here, an overview of the development in TENGs as tactile stimulators for multifunctional sensing and artificial synapses is systematically introduced. Firstly, the applications of TENGs as tactile stimulators in pressure, temperature, proximity sensing, and object recognition are introduced in detail. Then, the research progress of TENGs as tactile stimulators for artificial synapses is emphatically introduced, which is mainly reflected in the electrolyte-gate synaptic transistors, optoelectronic synaptic transistors, floating-gate synaptic transistors, reduced graphene oxides-based artificial synapse, and integrated circuit-based artificial synapse and nervous systems. Finally, the challenges of TENGs as tactile stimulators for multifunctional sensing and artificial synapses in practical applications are summarized, and the future development prospects are expected.

Diseases Detection of Occlusion and Overlapping Tomato Leaves Based on Deep Learning.

Background: In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise. Results: Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model. Conclusion: The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions: (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment.

Performance improvements in all-solution processed inverted QLEDs realized by inserting an electron blocking layer.

Highly efficient, all-solution processed inverted quantum dot light-emitting diodes (QLEDs) are demonstrated by employing 1,3,5-tri(m-pyrid-3-yl-phenyl)benzene (TmPyPB) layer as electron blocking layer. Electron injection from ZnO electron transport layer to quantum dots (QDs) emission layer (EML) can be adjusted by thickness of TmPyPB layer, enabling the balanced charge carriers in QDs EML. With optimal thickness of this TmPyPB adjuster, 59.7% increment in the device current efficiency (from 8.2 to 13.1 cd A-1) and 46.2% improvement in the maximum luminance (from 31916 to 46674 cd m-2) are achieved, compared with those of the control QLED which has double hole transport layer structure. On the other hand, we find luminescence quenching process, which often happens at the interface of ZnO nanoparticles and QDs, is not obvious in our QLEDs, in which the ZnO layer is fabricated in precursor method, and this conclusion is verified through Time Resolution Photoluminescence test. In a word, this strategy provides a direction for optimizing charge carrier balance in all-solution processed inverted QLED.

Frequency Band Characteristics of a Triboelectric Nanogenerator and Ultra-Wide-Band Vibrational Energy Harvesting.

Micromechanical vibration, as one of the most prevalent forms of energy in an ambient environment, has surpassing application potentials as the power source for self-powered electronics. A triboelectric nanogenerator (TENG) can effectively convert vibrational energy to electricity, which has the unique benefit of a wide-band over a traditional vibration energy harvester due to the contact electrification mechanism. Herein, the frequency band characteristics of vibrational TENG (V-TENG) were systematically elaborated. The mechanical model of V-TENG was established to explore its working mechanism for wide-band vibrational energy harvesting. By simulation analysis and experimental validation, the bandwidth dependence of V-TENG on acceleration magnitude, proof mass, stiffness, and gap distance was investigated in detail. With optimized structural parameters, an ultra-wide-band vibration energy harvester (UVEH) was developed by a tandem spring-mass structure. Within the ultra-wide-band range from 3 to 45 Hz, the UVEH can invariably illuminate 36 serial light-emitting diodes (LEDs) and charge a 33 µF capacitor to 1.5 V within 35 s. This work has quantitatively studied frequency band characteristics of V-TENG and provided a promising strategy for wide-band vibrational energy harvesting from a machine, bridge, water wave, and human motion.

Comparison of applied torque and energy conversion efficiency between rotational triboelectric nanogenerator and electromagnetic generator.

Triboelectric nanogenerator (TENG) is regarded as an equally important mechanical energy harvesting technology as electromagnetic generator (EMG). Here, the input mechanical torques and energy conversion efficiencies of the rotating EMG and TENG are systematically measured, respectively. At constant rotation rates, the input mechanical torque of EMG is balanced by the friction resisting torque and electromagnetic resisting torque, which increases with the increasing rotation rate due to Ampere force. While the input mechanical torque of TENG is balanced by the friction resisting torque and electrostatic resisting torque, which is nearly constant at different rotation rates. The energy conversion efficiency of EMG increases with the increasing input mechanical power, while that of the TENG remains nearly constant. Compared with the EMG, the TENG has a higher conversion efficiency at a low input mechanical power, which demonstrates a remarkable merit of the TENG for efficiently harvesting weak ambient mechanical energy.

Network Topology Optimization of Triboelectric Nanogenerators for Effectively Harvesting Ocean Wave Energy.

The emerging triboelectric nanogenerator (TENG) network shows great potential in harvesting the ocean wave energy, which can help to achieve large-scale clean wave power generation. However, due to the lack of an effective networking strategy and theoretical guidance, the practicability of the TENG network is heavily restricted. In this paper, based on the typical spherical TENG, we investigated the networking design of TENGs. Four fundamental forms of electrical networking topology are proposed for large-scale TENG networks, and the influences of cable resistance and output phase asynchrony of each unit to the network output were systematically investigated. The research results show that the forms of electrical networking topology can produce an important influence on the output power of large-scale TENG networks. This is the first strategy analysis for the TENG network, which provides a theoretical basis and a universal method for the optimization design of large-scale power networks.