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The objective is to develop a portable device capable of promptly identifying root rot in the field. This study employs hyperspectral imaging technology to detect root rot by analyzing spectral variations in chili pepper leaves during times of health, incubation, and disease under the stress of root rot. Two types of chili pepper seeds (Manshanhong and Shanjiao No. 4) were cultured until they had grown two to three pairs of true leaves. Subsequently, robust young plants were infected with Fusarium root rot fungi by the root-irrigation technique. The effective wavelength for discriminating between distinct stages was determined using the successive projections algorithm (SPA) after capturing hyperspectral images. The optimal index related to root rot between each normalized difference spectral index (NDSI) was obtained using the Pearson correlation coefficient. The early detection of root rot illness can be modeled using spectral information at effective wavelengths and in NDSI, together with the application of partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LSSVM), and back-propagation (BP) neural network technology. The SPA-BP model demonstrates outstanding predictive capabilities compared with other models, with a classification accuracy of 92.3% for the prediction set. However, employing SPA to acquire an excessive number of efficient wave-lengths is not advantageous for immediate detection in practical field scenarios. In contrast, the NDSI (R445, R433)-BP model uses only two wavelengths of spectral information, but the prediction accuracy can reach 89.7%, which is more suitable for rapid detection of root rot. This thesis can provide theoretical support for the early detection of chili root rot and technical support for the design of a portable root rot detector. [ABSTRACT FROM AUTHOR]

A 68-year-old male patient came to the orthopedics department because of swelling and pain in his left shoulder joint. He received more than 15 intraarticular steroid injections in the shoulder joint at a local private hospital. MRI showed that the synovial membrane of the joint capsule was thickened and swollen, and there were extensive "rice body-like" low T2 signal shadows filling. Arthroscopic removal of rice bodies and subtotal bursectomy were performed. The observation channel was placed through the posterior approach, and a large amount of rice bodies in yellow bursa fluid were observed to flow out. Rice bodies with a diameter of approximately 1– 5 mm filled the joint cavity were seen in the observation channel. The histopathological examination of the rice body showed that it was mainly composed of fibrin without a clear tissue structure. Bacterial and fungal cultures of synovial fluid suggested Candida parapsilosis infection, so the patient received antifungal treatment. However, the shoulder swelled again after three weeks, MRI revealed that there was significant fluid accumulation in the subacromial-subdeltoid region with necrotic synovial tissue floating and ultrasound examination showed joint cavity effusion, synovial hyperplasia, and some synovium looked like "floating weeds". After 2 weeks, there were recurrent rice bodies in the articular cavity. Arthroscopic surgery was performed again to clean the joint and a catheter was placed for irrigation and drainage, and a large amount of necrotic synovial tissue floating as seen in ultrasound. Finally, patient received sensitive antifungal treatment and did not relapse within 6 months. During the recurrence in the current case, we recorded the process of rice body formation, which has for the first time been reported. [ABSTRACT FROM AUTHOR]

The development of a reliable optoelectronic memristor is crucial for the success of neuromorphic vision systems, but current devices are limited by stochastic resistance switching and uncontrollable ion dynamics. To address these challenges, a core–shell nanosphere composite was synthesized using CsPbBr3 quantum dots and block copolymer polystyrene-poly2-vinyl pyridine. This memristor exhibits both negative differential resistance and resistive switching memory behavior and has demonstrated exceptional stability, withstanding over 5000 cycles and remaining stable for over 5 million seconds. It is also tunable by light irradiation, making it ideal for optoelectronic logic operations and multi-derivative state applications. The memory behavior is attributed to an electrochemical metalization mechanism involving the formation and annihilation of pyridine groups, conductive metal filaments, and bromide ion vacancies in S2VP filaments. This work presents an effective method for fabricating robust multi-derivative perovskite optoelectronic devices for neuromorphic computing systems. Additionally, machine learning algorithms have demonstrated accurate digital image classification and recognition using photoelectric pulse-driven neuromorphic computation with an accuracy of over 97%. [ABSTRACT FROM AUTHOR]

According to Moore's Law's development law, traditional floating gate memory is constrained by charge tunneling, and its size is approaching the physical limit, which is insufficient to meet the requirements of large data storage. The introduction of new information storage devices may be the key to overcoming the bottleneck. Resistive random access memory (RRAM) has garnered interest due to its fast switching speed, low power consumption, and high integration density. The resistive switching (RS) behaviors can be demonstrated in many materials, including transition metal oxides, perovskite oxides and organic matter, etc. Among these materials, graphene oxide (GO) with its unique physical, chemical properties and excellent mechanical properties is attracting significant attention for use in RRAM owing to its RS operation and potential for integration with other graphene-based electronics. However, there is unacceptable variability in RS reliability, including retention and endurance, which is the key factor that affects the development of memristors. In addition, the RS mechanism of GO-based RRAM has not been systematically discussed. In this article, we discuss systematically several typical models of the switching mechanism of GO-based RRAM and a summary of methods for improving the device's RS performance. This article concludes by discussing the applications of GO-RRAM in artificial neural networks, flexible devices, and biological monitoring. [ABSTRACT FROM AUTHOR]

Current resistive access memories (RRAM) suffer from severe resistive switching variability issues due to the large stochasticity in the formation and disruption process of conductive filaments. Here, a material strategy is applied in designing an RRAM device that can substantially improve the switching uniformity by utilizing a silver imidazole complex as the switching medium. In the silver imidazole, Ag species has a moderate reduction Gibbs energy which can participate in the redox reaction to change the resistance state of the device. Moreover, as a resistive layer, the migration range of silver ions is limited in the grain of silver imidazole, which shortens the migration distance of silver ions and reduces the randomness caused by silver ion migration, thus improving the uniformity and stability of the device. Through this facile material engineering strategy, the RRAM exhibits enhanced performance with low spatial variation of 7.86%, low temporal variation of 1.78% and low operation voltage of 0.50 V. The high switching uniformity of silver imidazole RRAM allows us to employ them as building blocks for logic gates with high yield. The self‐reduction and grain boundary confinement effect of switching materials for RRAM design may open the way to the development of large‐scale circuits for non‐volatile computing and machine learning. [ABSTRACT FROM AUTHOR]

In this work, we demonstrated the high switching uniformity and 50 fJ/bit energy consumption in an amorphous silicon-based resistive switching (RS) device by inserting the AgInSbTe (AIST) layer between the silicon insulating layer and Ag top electrodes. The improved RS performance is attributed to the introduction of an Ag ion reservoir layer, which helps to suppress conducting filament overgrowth. After insertion of the AIST layer, the cumulative probability of low/high resistance states decreased from 176.8%/46.2% to 3.1%/11.9%, respectively. The advantages of promoting Ag dissolution enable the realization of fast switching speed (<50 ns) and low set voltage (∼70 mV), which gives our device low energy consumption (∼50 fJ/bit). Moreover, the multi-step of set/reset analytical model of our dual-layer RS device was developed based on the formation and dissolution of the Ag-ion-based conductive filaments. Our work presents an effective method for obtaining high-performance Si-based memory for practical applications. [ABSTRACT FROM AUTHOR]

To deal with the growing demand for data storage and processing, phase‐change memory (PCM) provides one of the most promising candidates for next‐generation nonvolatile data storage and neuromorphic computing applications. A lot of effort has been made toward optimizing the materials and device design; thus, excellent device performances have been achieved including high density, fast switching speed, great endurance, and retention. In addition, the widely tunable optical characteristics of PCMs are irresistibly attractive for optoelectronic or all‐optical applications with unprecedented bandwidth, low energy consumption, and multilevel data storage. Herein, the materials system and switching mechanisms on experimental and modeling methods for PCM designs and applications are discussed. Electric‐domain and optical‐domain PCM‐based artificial synapses/neurons and their applications in neuromorphic computing are also reviewed. Finally, the future prospects and challenges of PCM‐based applications on materials, devices, algorithms, and system levels are highlighted. [ABSTRACT FROM AUTHOR]

The close replication of synaptic functions is an important objective for achieving a highly realistic memristor-based cognitive computation. The emulation of neurobiological learning rules may allow the development of neuromorphic systems that continuously learn without supervision. In this work, the Bienenstock-Cooper-Munro learning rule, as a typical case of spike-rate-dependent plasticity, is mimicked using a generalized triplet-spike-timing-dependent plasticity scheme in a WO3−x memristive synapse. It demonstrates both presynaptic and postsynaptic activities and remedies the absence of the enhanced depression effect in the depression region, allowing a better description of the biological counterpart. The threshold sliding effect of Bienenstock-Cooper-Munro rule is realized using a history-dependent property of the second-order memristor. Rate-based orientation selectivity is demonstrated in a simulated feedforward memristive network with this generalized Bienenstock-Cooper-Munro framework. These findings provide a feasible approach for mimicking Bienenstock-Cooper-Munro learning rules in memristors, and support the applications of spatiotemporal coding and learning using memristive networks. Designing reliable and energy efficient neuromorphic computing systems for spatiotemporal coding remains a challenge. Here, the authors demonstrate a type of spike-rate-dependent plasticity based on a triplet learning scheme in a WO3−x-based second-order memristor network for spatiotemporal patterns. [ABSTRACT FROM AUTHOR]

Arbuscular mycorrhizal fungi (AMF) play an important role in nutrient cycling processes and plant stress resistance. To evaluate the effect of Rhizophagus irregularis CD1 on plant growth promotion (PGP) and Verticillium wilt disease, the symbiotic efficiency of AMF (SEA) was first investigated over a range of 3% to 94% in 17 cotton varieties. The high-SEA subgroup had significant PGP effects in a greenhouse. From these results, the highest-SEA variety of Lumian 1 was selected for a two-year field assay. Consistent with the performance from the greenhouse, the AMF-mediated PGP of Lumian 1 also produced significant results, including an increased plant height, stem diameter, number of petioles, and phosphorus content. Compared with the mock treatment, AMF colonization obviously inhibited the symptom development of Verticillium dahliae and more strongly elevated the expression of pathogenesis-related genes and lignin synthesis-related genes. These results suggest that AMF colonization could lead to the mycorrhiza-induced resistance (MIR) of Lumian 1 to V. dahliae. Interestingly, our results indicated that the AMF endosymbiont could directly inhibit the growth of phytopathogenic fungi including V. dahliae by releasing undefined volatiles. In summary, our results suggest that stronger effects of AMF application result from the high-SEA. [ABSTRACT FROM AUTHOR]

[Objective] Observe effects of Didang Qigui decoction on crystalline lens, lens epithelial cells and the level of HSP70 in rats with streptozotocin induced diabetic cataract. [Methods] 10 SD rats were randomly selected as the blank control group, and the rest of rats were injected 60 mg/kg of streptozotocin(STZ) into the enterocoelia at one time to reproduce diabetic rats models. 55 of the model animals were selected and randomly divided into model group, diamicron group, Didang Qigui decoction group, Didang Qigui decoction high dose group, Didang Qigui decoction and diamicron group, and corresponding interference was given to each group respectively. Turbidity of the lens was examined under the slit lamp respectively at 2, 4, 6, 8 week; after 8 weeks, morphologic changes of crystalline lens were observed under the microscope, and the expressions of HSP70 in lens epithelial cells(LEC) were detected. [Results] Crystalline lens of rats in the blank control group were always transparent and complete. Those of rats in the model group were gradually changed until becoming complete turbid. The rest of treatment groups with turbidity degree of rats' lens from heavy to light were Didang Qigui decoction high dose group, Didang Qigui decoction group, diamicron group, and Didang Qigui decoction and diamicron group. Forms of HE staining lens epithelial cells were also changed. If rats in the blank control group were taken as standards, expressions of HSP70 in LEC of crystalline lens in the model group gradually rose. In the treatment groups, the rate of HSP70 positive expression of rats in the Didang Qigui decoction high dose group was higher than that rate in diamicron group, idang Qigui decoction group, and Didang Qigui decoction and diamicron group. The discrepancy was very obvious (P < 0. 05). [Conclusions] By reducing blood glucose, gathering of sugar metabolites and osmotic pressure in crystalline lens, Didang Qigui decoction can effectively protect lens injury in experimental diabetic rats and inhibit the occurrence and development of cataract in diabetic rats, so it exerts certain preventive and therapeutic effects on early diabetic cataract in rats. [ABSTRACT FROM AUTHOR]

Memristive Devices: Analog-Digital Hybrid Memristive Devices for Image Pattern Recognition with Tunable Learning Accuracy and Speed (Small Methods 10/2019) Keywords: analog resistive switching; digital resistive switching; memristors; pattern recognition; spike-timing-dependent plasticity Analog resistive switching, digital resistive switching, memristors, pattern recognition, spike-timing-dependent plasticity. [Extracted from the article]

Brain‐inspired memristive artificial neural networks (ANNs) have been identified as a promising technology for pattern recognition tasks. To optimize the performance of ANNs in various applications, a recognition system with tunable accuracy and speed is highly desirable. A single WO3−x‐based memristor is presented in which analog and digital resistive switching (A‐RS and D‐RS) coexist according to a selectively executed forming process. The A‐RS and D‐RS mechanisms can be attributed to the modulation of the Schottky barrier on the interface and the formation/rupture of conducting filaments inside the film, respectively. More importantly, a new analog–digital hybrid ANN is developed based on the coexistence of A‐RS and D‐RS in the WO3−x memristor, enabling tunable learning accuracy and speed in pattern recognition. The spike‐timing‐dependent plasticity learning rules, as a learning base for image pattern recognition, are demonstrated using A‐RS and D‐RS devices with obviously different fluctuations and rates of change. The learning accuracy/speed can be improved by increasing the proportion of A‐RS/D‐RS in the crossbar array. A convenient method is provided for selecting an optimized pattern recognition scheme to meet different application situations. [ABSTRACT FROM AUTHOR]

As one key issue of resistive switching (RS) memory, the cycling endurance is poorly understood in hybrid perovskite‐based memory devices. Here, the cycling failure and the corresponding cycling‐induced degradation of CH3NH3PbI3‐based resistive random access memory devices are discussed. The high resistance state clearly decreases with the number of operation cycles, finally triggering irreversible failure in the collapse of switching window. By monitoring the I–V curves for all cycles, a negative set event is observed to be the critical turning point that considerably accelerates the cycling degradation rate. The decrease of |Vset| and |Vreset| indicates a reduction of the migration barrier of iodine vacancies (VI), which accounts for the appearance of a negative set after cycling. The understanding of RS cycling degradation can promote the optimization of device endurance by slowing the defect accumulation rate. The endurance degradation of CH3NH3PbI3‐based RRAM is attributed to the excessive defect accumulation during cycling operation. During the reset process, a negative set event is observed to accelerate the endurance degradation rate. Moreover, the decrease of transition voltages suggests a reduction of the migration barrier of iodine vacancies with cycling operation. [ABSTRACT FROM AUTHOR]

In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity.