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A growing number of Chinese firms motivate their employees through employee stock ownership plans (ESOPs). Using a sample of listed firms in China, this paper examines the impact of ESOPs on firms’ total factor productivity (TFP), as well as the mechanisms of ESOPs. The empirical results show that ESOPs have a positive impact on firm TFP. The mechanism tests convey that ESOPs increase firm TFP by promoting research and development (R&D) investment and mitigating agency costs. These results are robust after accounting for endogeneity and using alternative metrics of TFP. In addition, we find that the positive effect of ESOPs on firm TFP is more pronounced in non-state-owned firms and firms with a less severe free-riding problem. Furthermore, the effect on firm TFP is positively associated with the subscription proportion of non-executive employees in ESOPs. Overall, the results of this study underscore the important role of employee ownership in firms’ productivity improvement.

Purpose The mechanism underlying malignant transformation of vocal fold leukoplakia (VFL) and the precise role of the expression of pepsin in VFL remain unclear. This study aimed to investigate the effects of acidified pepsin on VFL epithelial cell growth and migration, and also identify pertinent molecular mechanisms. Methods Immunochemistry and Western blotting were performed to measure glucose transporter type 1 (GLUT1), monocarboxylate transporters 4 (MCT4), and Hexokinase-II (HK-II) expressions. Cell viability, cell cycle, apoptosis, and migration were investigated by CCK-8 assay, flow cytometry and Transwell chamber assay, respectively. Glycolysis-related contents were determined using the corresponding kits. Mitochondrial HK-II was photographed under a confocal microscope using Mito-Tracker Red. Results It was found: the expression of pepsin and proportion of pepsin+ cells in VFL increased with the increased dysplasia grade; acidified pepsin enhanced cell growth and migration capabilities of VFL epithelial cells, reduced mitochondrial respiratory chain complex I activity and oxidative phosphorylation, and enhanced aerobic glycolysis and GLUT1 expression in VFL epithelial cells; along with the transfection of GLUT1 overexpression plasmid, 18FFDG uptake, lactate secretion and growth and migration capabilities of VFL epithelial cell were increased; this effect was partially blocked by the glycolysis inhibitor 2-deoxy-glucose; acidified pepsin increased the expression of HK-II and enhanced its distribution in mitochondria of VFL epithelial cells. Conclusion It was concluded that acidified pepsin enhances VFL epithelial cell growth and migration abilities by reducing mitochondrial respiratory complex I activity and promoting metabolic reprogramming from oxidative phosphorylation to aerobic glycolysis.

Learning from a few examples (one/few-shot learning) on the fly is a key challenge for on-device machine intelligence. We present the first chip-level demonstration of one-shot learning with Stanford Associative memory for Programmable, Integrated Edge iNtelligence via life-long learning and Search (SAPIENS), a resistive random access memory (RRAM)-based non-volatile associative memory (AM) chip that serves as the backend for memory-augmented neural networks (MANNs). The 64-kb fully integrated RRAM-CMOS AM chip performs long-term feature embedding and retrieval, demonstrated on a 32-way one-shot learning task on the Omniglot dataset. Using only one example per class for 32 unseen classes during on-chip learning, SAPIENS achieves 79% measured inference accuracy on Omniglot, comparable to edge software model accuracy using five-level quantization (82%). It achieves an energy efficiency of 118 GOPS/W at 200 MHz for in-memory L1 distance computation and prediction. Multi-bank measurements on the same chip show that increasing the capacity from three banks (24 kb) to eight banks (64 kb) improves the chip accuracy from 73.5% to 79%, while minimizing the accuracy excursion due to bank-to-bank variability.

A smart bioreactor (HPs-Ag), designed by protein-silver nanoclusters-PNIPAAm, could control the membrane permeability to determine the different molecular weight of materials and the speed of catalysis of enzyme reaction. Studies also showed that the molecular weight cutoff of the HPs-Ag membrane could be continuously turned up from 12 to 70 kDa. The catalytic reaction rate of the ALP loaded bared HPs-Ag by using pNPP as substrates was increased (18.14- and 9.23-fold ) with different concentrations of H2O2.

This paper deals with the Cauchy problem for the compressible Euler equations with time-dependent damping, where the time-vanishing damping in the form of μ ( 1 + t ) λ makes some fantastic variety of the dynamic system. For 0 λ 1 and μ > 0 , or λ = 1 but μ > 2 , the solutions are proved to exist globally in time, when the derivatives of the initial data are small, but the initial data themselves can be arbitrarily large. This is the so-called challenging case of global solutions with large initial data; while, when the initial Riemann invariants are monotonic and their derivatives with absolute value are large at least at one point, then the solutions are still bounded, but their derivatives will blow up at finite time, somewhat like the singularity formed by shock waves. For λ > 1 and μ > 0 , or λ = 1 but 0 μ ≤ 1 , the derivatives of solutions will blow up even for all initial data, including the interesting case of blow-up solutions with small initial data. Here the initial Riemann invariants are monotonic. In fact, such a blow-up phenomenon is determined by the mechanism of the dynamic system itself. In order to prove the global existence of solutions with large initial data, we introduce a new energy functional related to the Riemann invariants, which crucially enables us to build up the maximum principle for the corresponding Riemann invariants, and the uniform boundedness for the local solutions. Finally, numerical simulations in different cases are carried out, which further confirm our theoretical results.

Using the reduced resistor network developed in Part I of this two-part article, we present practical design guidelines from device to architecture levels to achieve ultrahigh-density 3-D vertical resistive switching memory (VRSM). We first design both hexagon and comb arrays using 7-nm FinFET as pillar driving transistors (pillar drivers). Small-footprint pillar drivers are necessary for a high pillar areal density competitive to 3-D NAND. We then organize the arrays into an architecture using the compact staircase and highly conductive wordplane connection (WPC) to maximize array efficiency and chip density. We investigate the memory and selector requirements, tolerance of parasitic resistances, latency, and energy consumption for both hexagon and comb architectures. The results indicate that the hexagon array with large low-resistance state (LRS) and nonlinearity (NL) is required for ultradense 3-D VRSM. Compared to the comb array, the hexagon array benefits from a continuous WP pattern and yields a better tolerance of parasitic resistances and a smaller latency. The energy consumptions of both architectures are similar. Compared to the most advanced 3-D NAND, 3-D VRSM has higher chip density and shows better potential for future ultradense storage.

Resistive switching memory (RSM) shows potentials for high-capacity storage because of its simple cell structure, small footprint, and good scalability. This two-part article discusses how to implement ultrahigh-density (~terabits) storage with RSM covering design considerations from device to memory array architecture. In Part I of this two-part article, an accurate and computationally efficient model is developed to study 3-D vertical RSM (VRSM). In this article, we use 3-D VRSM with a hexagon-patterned pillar layout (3-D hexagon VRSM) as an example to elaborate our approach. Using the parasitic resistance extracted from physics-based 2-D field solver as the reference, we develop a lumped resistor network for SPICE simulation to accurately capture all leakage currents of the array. This full resistor network is further simplified to a reduced network to achieve high computational efficiency and maintain the full network accuracy (with a relative error

OBJECTIVE: To study the clinical manifestations and surgical treatment of temporal bone cerebrospinal fluid leakage. METHODS: The clinical data of twenty seven cases with temporal bone cerebrospinal fluid leakage were analyzed retrospectively. Different surgical procedures were adopted according to the location of the leak and the hearing status of the affected ear.If the leakage location was clear before surgery, direct repair was performed via mastoid path or middle cranial fossa path with or without mastoid abdominal fat packing.For patients with unclear leak, large lesion or intractable CEREBROspinal fluid leakage of temporal bone, subtotal petrosal resection and abdominal fat packing were performed.Subtotal temporal bone resection and abdominal fat packing were performed for patients with cochlear involvement. RESULTS: In this study, cases of temporal bone CSF leakage including 13 cases of inner ear malformation; 5 cases secondary to head trauma or previous surgery, 5 cases of idiopathic intracranial hypertension or meningocele, 2 cases of langerhans histiocytosis in the temporal bone and 2 cases of inner ear inflammation. High resolution CT (HRCT) of temporal bone showed bone defect of inner auditory canal with vestibular communication in 13 patients with inner ear malformation.The temporal bone HRCT of the remaining 14 patients showed bone defects in the middle cranial fossa or posterior cranial fossa, while MRI of the temporal bone showed meningeal continuity interruption with cerebrospinal fluid inflow into the temporal bone or meningoencephalocele in 12 patients. The 26 patients were followed up from 6 months to 6 years. 1 patient lost follow-up.Meningitis recurred in only 1 patient with inner ear malformation, and subtotal resection of rock bone plus abdominal fat packing was performed.Postoperative hearing was preserved or improved in 7 patients.None of the patients had serious complications, and only 1 patient developed HB Grade Ⅱ facial paralysis after vestibular obliteration, and the facial paralysis recovered within one week. CONCLUSION: Temporal bone cerebrospinal fluid leakage is relatively rare. Surgical intervention should be taken as early as possible when conservative treatment is failure. Preoperative HRCT and MRI examination are necessary for the localization of the leakage, and individualized surgical approaches can be adopted according to the location of the leakage and the features of the lesion.

In this paper, we study the one-dimensional Euler–Poisson equations of bipolar hydrodynamic model for semiconductor device with time-dependent damping effect − J ( 1 + t ) λ for − 1 λ 1 , where the damping effect is time-gradually-degenerate for λ > 0 , and time-gradually-enhancing for λ 0 . Such a damping effect makes the hydrodynamic system possess the nonlinear diffusion phenomena time-asymptotic-weakly or strongly. Based on technical observation, and by using the time-weighted energy method, where the weights are artfully chosen, we prove that the system admits a unique global smooth solution, which time-asymptotically converges to the corresponding diffusion wave, when the initial perturbation around the diffusion wave is small enough. The convergence rates are specified in the algebraic forms O ( t − 3 4 ( 1 + λ ) ) and O ( t − ( 1 − λ ) ) according to different values of λ in ( − 1 , 1 7 ) and ( 1 7 , 1 ) , respectively, where λ = 1 7 is the critical point, and the convergence rate at the critical point is O ( t − 6 7 ln ⁡ t ) . All these convergence rates obtained in different cases are optimal in the sense when the initial perturbations are L 2 -integrable. Particularly, when λ = 1 7 , the convergence rate is the fastest, namely, the asymptotic profile of the original system at the critical point is the best.

Ternary content-addressable memory (TCAM) is specialized hardware that can perform in-memory search and pattern matching for data-intensive applications. However, achieving TCAMs with high search capacity, good area efficiency and good energy efficiency remains a challenge. Here, we show that two-transistor–two-resistor (2T2R) transition metal dichalcogenide TCAM (TMD-TCAM) cells can be created by integrating single-layer MoS2 transistors with metal-oxide resistive random-access memories (RRAMs). The MoS2 transistors have very low leakage currents and can program the RRAMs with exceptionally robust current control, enabling the parallel search of very large numbers of data bits. These TCAM cells also exhibit remarkably large resistance ratios (R-ratios) of up to 8.5 × 105 between match and mismatch states. This R-ratio is comparable to that of commercial TCAMs using static random-access memories (SRAMs), with the key advantage that our 2T2R TCAMs use far fewer transistors and have zero standby power due to the non-volatility of RRAMs. By integrating two-dimensional MoS2 transistors with metal-oxide resistive random-access memories, two-transistor–two-resistor ternary content-addressable memory cells can be created, which could be used to search large amounts of data in parallel.

We developed a Monte Carlo simulator to compute the state-dependent I-V characteristics of three-terminal (3T) RRAM devices. State switching in these devices is modeled using a combination of vacancy migration and trap-assisted-tunneling mechanisms. We describe key elements of the simulator, compute hysteresis curves under typical voltage cycling conditions, and demonstrate agreement with experimental results. We then study the response of 2T- and 3T-RRAMs under pulsed operation and show that 3T-RRAM conductance values have both greater dynamic range than 2T-RRAMs and the potential to deliver superior inference accuracy in neuromorphic applications.

Resistive-random-access-memory (ReRAM) based processing-in-memory (R$^2$PIM) accelerators show promise in bridging the gap between Internet of Thing devices' constrained resources and Convolutional/Deep Neural Networks' (CNNs/DNNs') prohibitive energy cost. Specifically, R$^2$PIM accelerators enhance energy efficiency by eliminating the cost of weight movements and improving the computational density through ReRAM's high density. However, the energy efficiency is still limited by the dominant energy cost of input and partial sum (Psum) movements and the cost of digital-to-analog (D/A) and analog-to-digital (A/D) interfaces. In this work, we identify three energy-saving opportunities in R$^2$PIM accelerators: analog data locality, time-domain interfacing, and input access reduction, and propose an innovative R$^2$PIM accelerator called TIMELY, with three key contributions: (1) TIMELY adopts analog local buffers (ALBs) within ReRAM crossbars to greatly enhance the data locality, minimizing the energy overheads of both input and Psum movements; (2) TIMELY largely reduces the energy of each single D/A (and A/D) conversion and the total number of conversions by using time-domain interfaces (TDIs) and the employed ALBs, respectively; (3) we develop an only-once input read (O$^2$IR) mapping method to further decrease the energy of input accesses and the number of D/A conversions. The evaluation with more than 10 CNN/DNN models and various chip configurations shows that, TIMELY outperforms the baseline R$^2$PIM accelerator, PRIME, by one order of magnitude in energy efficiency while maintaining better computational density (up to 31.2$\times$) and throughput (up to 736.6$\times$). Furthermore, comprehensive studies are performed to evaluate the effectiveness of the proposed ALB, TDI, and O$^2$IR innovations in terms of energy savings and area reduction., Comment: Accepted by 47th International Symposium on Computer Architecture (ISCA'2020)

Coming generations of information technology will process unprecedented amounts of loosely-structured data, including streaming video and audio, natural languages, real-time sensor readings, contextual environments, or even brain signals. The computational demands of these abundant-data applications, such as deep learning-based AI, far exceed the capabilities of today’s computing systems and cannot be met by isolated improvements in transistor or memory technologies, or integrated circuit architectures alone. This chapter will discuss how new computing nanosystems can deliver radical improvements in the energy efficiency and scalability of abundant-data applications, in the range of 1000×, through major advances across the computing stack: new transistor and memory technologies, new integration approaches, and new architectures for computation immersed in memory. Nanosystems with such massive benefits are essential for enabling new frontiers of applications across a wide range of domains, from highly energy-constrained and deeply-embedded computing systems all the way to the cloud.

One viable solution for continuous reduction in energy-per-operation is to rethink functionality to cope with uncertainty by adopting computational approaches that are inherently robust to uncertainty. It requires a novel look at data representations, associated operations, and circuits, and at materials and substrates that enable them. 3D integrated nanotechnologies combined with novel brain-inspired computational paradigms that support fast learning and fault tolerance could lead the way. Recognizing the very size of the brain’s circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with hypervectors as large randomly generated patterns. At its very core, HD computing is about manipulating and comparing these patterns inside memory. Emerging nanotechnologies such as carbon nanotube field-effect transistors (CNFETs) and resistive RAM (RRAM), and their monolithic 3D integration offer opportunities for hardware implementations of HD computing through tight integration of logic and memory, energy-efficient computation, and unique device characteristics. We experimentally demonstrate and characterize an end-to-end HD computing nanosystem built using monolithic 3D integration of CNFETs and RRAM. With our nanosystem, we experimentally demonstrate classification of 21 languages with measured accuracy of up to 98% on >20,000 sentences (6.4 million characters), training using one text sample (≈100,000 characters) per language, and resilient operation (98% accuracy) despite 78% hardware errors in HD representation (outputs stuck at 0 or 1). By exploiting the unique properties of the underlying nanotechnologies, we show that HD computing, when implemented with monolithic 3D integration, can be up to 420× more energy-efficient while using 25× less area compared to traditional silicon complementary metal–oxide–semiconductor (CMOS) implementations.

Neuromorphic computing systems, which use electronic synapses and neurons, could overcome the energy and throughput limitations of today’s computing architectures. However, electronic devices that can accurately emulate the short- and long-term plasticity learning rules of biological synapses remain limited. Here, we show that multilayer hexagonal boron nitride (h-BN) can be used as a resistive switching medium to fabricate high-performance electronic synapses. The devices can operate in a volatile or non-volatile regime, enabling the emulation of a range of synaptic-like behaviour, including both short- and long-term plasticity. The behaviour results from a resistive switching mechanism in the h-BN stack, based on the generation of boron vacancies that can be filled by metallic ions from the adjacent electrodes. The power consumption in standby and per transition can reach as low as 0.1 fW and 600 pW, respectively, and with switching times reaching less than 10 ns, demonstrating their potential for use in energy-efficient brain-like computing. Vertically structured electronic synapses, which exhibit both short- and long-term plasticity, can be created using layered two-dimensional hexagonal boron nitride.

Cyclone separation cleaning devices and a conical sieve for a rape combine harvester were designed to address problems of vibration and the complexity of conventional combine harvesters with vibration sieves. The cyclone separation cleaning devices consisted of a blower, cyclone separator, pipe, fan and other components. The working processes were examined and an innovative mechanical system was designed. Both single-factor and orthogonal experiments were carried out to obtain optimal parameter values. Based on the single-factor experiments, the proper rotational speed of the conical sieve was 40-60 r/min and the proper airflow speeds of the cyclone separator for the inlet and outlet were 3-5 m/s and 24-28 m/s, respectively. From the orthogonal experiments, the cleaning performance was optimal when the rotational speed of the conical sieve was 40 r/min and the airflow speeds of the cyclone separator for the inlet and outlet were 3 m/s and 24 m/s, respectively. The optimal cleaning ratio and total loss ratio parameters were 88.99% and 4.86%, respectively. This research could provide a reference for the improvement of cleaning systems for rape combine harvesters. Keywords: Agricultural engineering, Cleaning, Combine harvester, Conical sieve, Cyclone separation, Rape.

In this paper, we study the Cauchy problem for the hyperbolic p-system with time-gradually-degenerate damping term − 1 ( 1 + t ) λ u for 0 ⩽ λ 1 , and show that the damped p-system has a couple of global solutions uniquely, and such solutions tend time-asymptotically to the shifted nonlinear diffusion waves, which are the solutions of the corresponding nonlinear parabolic equation governed by the Darcy's law. We further derive the convergence rates when the initial perturbations are in L 2 . The approach adopted is the technical time-weighted energy method.