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In this paper, we mainly considered the problem of nonovershooting control of high order systems with or without time delay by simple controllers. As basic principles for nonovershooting control systems, three propositions are offered and proved. Under direction of these principles, a nonovershooting dominant pole control structure having three dominant poles, i.e., one real pole and a pair of complex conjugate poles on its left, is proposed. While its zeroes and nondominant poles are on the left side of these three dominant poles with sufficient distance. The controllers adopted are composed by first order filter and PD-PID controller. Dominance of the three dominant poles can be checked and ensured through the computational method we offered. Two illustrating examples are given to show the effectiveness of our method.

Hydrophobic base stacking is a major contributor to DNA double-helix stability. We report the discovery of specific unstacking effects in certain semihydrophobic environments. Water-miscible ethylene glycol ethers are found to modify structure, dynamics, and reactivity of DNA bymechanisms possibly related to a biologically relevant hydrophobic catalysis. Spectroscopic data and optical tweezers experiments show that base-stacking energies are reduced while base-pair hydrogen bonds are strengthened. We propose that a modulated chemical potential of water can promote “longitudinal breathing” and the formation of unstacked holes while base unpairing is suppressed. Flow linear dichroism in 20% diglyme indicates a 20 to 30% decrease in persistence length of DNA, supported by an increased flexibility in single-molecule nanochannel experiments in poly(ethylene glycol). A limited (3 to 6%) hyperchromicity but unaffected circular dichroism is consistent with transient unstacking events while maintaining an overall average B-DNA conformation. Further information about unstacking dynamics is obtained from the binding kinetics of large thread-intercalating ruthenium complexes, indicating that the hydrophobic effect provides a 10 to 100 times increased DNA unstacking frequency and an “open hole” population on the order of 10−2 compared to 10−4 in normal aqueous solution. Spontaneous DNA strand exchange catalyzed by poly(ethylene glycol) makes us propose that hydrophobic residues in the L2 loop of recombination enzymes RecA and Rad51 may assist gene recombination via modulation of water activity near the DNA helix by hydrophobic interactions, in the manner described here. We speculate that such hydrophobic interactions may have catalytic roles also in other biological contexts, such as in polymerases.

The role of hydrophobic interactions in DNA strand exchange has been studied using fluorescence-labeled DNA oligomers in a FRET assay. Strand exchange was found to be accelerated in the presence of polyethylene glycol, which provides a crowded and hydrophobic environment possibly mimicking that of the catalytically active recombinase-DNA complexes. Circular dichroism spectroscopy shows that B-DNA conformation is conserved, so the increased rate of exchange is not simply caused by melting of DNA duplexes. A hydrophobic environment increases the base pairing accuracy of DNA strand exchange, which causes mismatched duplexes to quickly be replaced in the presence of matching strands. It is inferred that these effects are caused by a decrease in water activity which weakens the DNA stacking forces, and by favorable hydrophobic interactions between PEG and DNA chains, with the result that DNA breathing and subsequent strand invasion is facilitated. Linear dichroism and dynamic light scattering were also used to study some other biomolecular systems where hydrophobic interactions are important: lipid membranes, DNA-protein complex, DNA nanoconstructs anchored to membrane surface, and to study fusion of liposomes induced by shearing forces. A DNA hexagon construct was found to adopt different orientations at the membrane surface depending on the number of attached anchors, but the construct itself was inferred to have a metastable shape due to internal flexibility. Finally, an example of assembly of protein subunits to a membrane surface was considered in shape of the ATP synthase system for which we propose that the activation energy of ATP synthesis may be reduced through coupled reactions between three active sites. The results are interesting in more general contexts of methodological improvements for studying biomolecular assembly, including linear dichroism spectroscopy of transmembrane proteins.

Rapid developments in the Internet of Things and Artificial Intelligence trigger higher requirements for image perception and learning of external environments through visual systems. However, limited by von Neumann's bottleneck, the physical separation of sense, memory, and processing units in a conventional personal computer‐based vision system tend to consume a significant amount of energy, time latency, and additional hardware costs. By integrating computational tasks of multiple functionalities into the sensors themselves, the emerging bio‐inspired neuromorphic visual systems provide an opportunity to overcome these limitations. With high speed, ultralow power and strong adaptability, it is highly desirable to develop a neuromorphic vision system that is based on highly precise in‐sensor computing devices, namely retinomorphic devices. We here present a timely review of retinomorphic devices for visual in‐sensor computing. We begin with several types of physical mechanisms of photoelectric sensors that can be constructed for artificial vision. The potential applications of retinomorphic hardware are, thereafter, thoroughly summarized. We also highlight the possible strategies to existing challenges and give a brief perspective of retinomorphic architecture for in‐sensor computing. image

The burgeoning interest in two-dimensional semiconductors stems from their potential as ultrathin platforms for next-generation transistors. Nonetheless, there persist formidable challenges in fully obtaining high-performance complementary logic components and the underlying mechanisms for the polarity modulation of transistors are not yet fully understood. Here, we exploit both ferroelectric domain-based nonvolatile modulation of Fermi level in transitional metal dichalcogenides (MoS 2 ) and quantum tunneling through nanoscale hexagonal boron nitride (h-BN). Our prototype devices, termed as vertical tunneling ferroelectric field-effect transistor, utilizes a Van der Waals MoS 2 /h-BN/metal tunnel junction as the channel. The Fermi level of MoS 2 is bipolarly tuned by ferroelectric domains and sensitively detected by the direct quantum tunneling strength across the junction, demonstrating an impressive electroresistance ratio of up to 10 9 in the vertical tunneling ferroelectric field-effect transistor. It consumes only 0.16 fJ of energy to open a ratio window exceeding 10 4 . This work not only validates the effectiveness of tailored tunnel barriers in manipulating electronic flow but also highlights a new avenue for the design flexibility and functional versatility of advanced ferroelectric memory technology. The authors propose vertical tunneling ferroelectric field-effect transistors based on asymmetric MoS 2 /h-BN/metal tunnel junction as channel. The Fermi level of MoS 2 is bipolarly tuned by ferroelectric domains and detected by the quantum tunneling strength across the junction.

Among today’s nonvolatile memories, ferroelectric-based capacitors, tunnel junctions and field-effect transistors (FET) are already industrially integrated and/or intensively investigated to improve their performances. Concurrently, because of the tremendous development of artificial intelligence and big-data issues, there is an urgent need to realize high-density crossbar arrays, a prerequisite for the future of memories and emerging computing algorithms. Here, a two-terminal ferroelectric fin diode (FFD) in which a ferroelectric capacitor and a fin-like semiconductor channel are combined to share both top and bottom electrodes is designed. Such a device not only shows both digital and analog memory functionalities but is also robust and universal as it works using two very different ferroelectric materials. When compared to all current nonvolatile memories, it cumulatively demonstrates an endurance up to 10 10 cycles, an ON/OFF ratio of ~10 2 , a feature size of 30 nm, an operating energy of ~20 fJ and an operation speed of 100 ns. Beyond these superior performances, the simple two-terminal structure and their self-rectifying ratio of ~ 10 4 permit to consider them as new electronic building blocks for designing passive crossbar arrays which are crucial for the future in-memory computing. Designing efficient high-density crossbar arrays are nowadays highly demanded for many artificial intelligence applications. Here, the authors propose a two-terminal ferroelectric fin diode non-volatile memory in which a ferroelectric capacitor and a fin-like semiconductor channel are combined to share both top and bottom electrodes with high performance and easy fabrication process

Recently, the increasing demand for data-centric applications is driving the elimination of image sensing, memory and computing unit interface, thus promising for latency- and energy-strict applications. Although dedicated electronic hardware has inspired the development of in-memory computing and in-sensor computing, folding the entire signal chain into one device remains challenging. Here an in-memory sensing and computing architecture is demonstrated using ferroelectric-defined reconfigurable two-dimensional photodiode arrays. High-level cognitive computing is realized based on the multiplications of light power and photoresponsivity through the photocurrent generation process and Kirchhoff’s law. The weight is stored and programmed locally by the ferroelectric domains, enabling 51 (>5 bit) distinguishable weight states with linear, symmetric and reversible manipulation characteristics. Image recognition can be performed without any external memory and computing units. The three-in-one paradigm, integrating high-level computing, weight memorization and high-performance sensing, paves the way for a computing architecture with low energy consumption, low latency and reduced hardware overhead.It remains challenging to integrate memory, sensing and computing in one device. Here a compact in-memory sensing and computing architecture based on ferroelectric-defined reconfigurable two-dimensional photodiode arrays has been reported.

Hyperspectral imagery (HSI) with high spectral resolution contributes to better material discrimination, while the spatial resolution limited by the sensor technique prevents it from accurately distinguishing and analyzing targets. Though generative adversarial network-based HSI super-resolution methods have achieved remarkable progress, the problems of treating vital and unessential features equally in feature expression and training instability still exist. To address these issues, an attention-enhanced generative adversarial network (AEGAN) for HSI spatial super-resolution is proposed, which elaborately designs the enhanced spatial attention module (ESAM) and refined spectral attention module (RSAM) in the attention-enhanced generator. Specifically, the devised ESAM equipped with residual spatial attention blocks (RSABs) facilitates the generator that is more focused on the spatial parts of HSI that are difficult to produce and recover, and RSAM with spectral attention refines spectral interdependencies and guarantees the spectral consistency at the respective pixel positions. Additionally, an especial U-Net discriminator with spectral normalization is enclosed to pay more attention to the detailed informations of HSI and yield to stabilize the training. For producing more realistic and detailed super-resolved HSIs, an attention-enhanced generative loss is constructed to train and constrain the AEGAN model and investigate the high correlation of spatial context and spectral information in HSI. Moreover, to better simulate the complicated and authentic degradation, pseudo-real data are also generated with a high-order degradation model to train the overall network. Experiments on three benchmark HSI datasets illustrate the superior performance of the proposed AEGAN method in HSI spatial super-resolution over the compared methods.

COP1 and COP9 signalosome (CSN) are the substrate receptor and deneddylase of CRL4 E3 ligase, respectively. How they functionally interact remains unclear. Here, we uncover COP1–CSN antagonism during glucose-induced insulin secretion. Heterozygous Csn2 WT/K70E mice with partially disrupted binding of IP 6 , a CSN cofactor, display congenital hyperinsulinism and insulin resistance. This is due to increased Cul4 neddylation, CRL4 COP1 E3 assembly, and ubiquitylation of ETV5, an obesity-associated transcriptional suppressor of insulin secretion. Hyperglycemia reciprocally regulates CRL4-CSN versus CRL4 COP1 assembly to promote ETV5 degradation. Excessive ETV5 degradation is a hallmark of Csn2 WT/K70E , high-fat diet-treated, and ob/ob mice. The CRL neddylation inhibitor Pevonedistat/MLN4924 stabilizes ETV5 and remediates the hyperinsulinemia and obesity/diabetes phenotypes of these mice. These observations were extended to human islets and EndoC-βH1 cells. Thus, a CRL4 COP1 -ETV5 proteolytic checkpoint licensing GSIS is safeguarded by IP 6 -assisted CSN-COP1 competition. Deregulation of the IP 6 -CSN-CRL4 COP1 -ETV5 axis underlies hyperinsulinemia and can be intervened to reduce obesity and diabetic risk. Mediators of insulin signalling are targets of cullin-RING ubiquitin ligases (CRL) that mediate protein degradation, but the role of protein degradation in insulin signalling is incompletely understood. Here, the authors identified a glucose-responsive CRL4-COP1-ETV5 proteolytic axis that promotes insulin secretion, and is inhibited under hypoglycemia.

Differential calculus is the cornerstone of many disciplines, spanning the breadth of modern mathematics, physics, computer science, and engineering. Its applications are fundamental to theoretical progress and practical solutions. However, the current state of digital differential technology often requires complex implementations, which struggle to meet the extensive demands of the ubiquitous edge computing in the intelligence age. To face these challenges, we propose an in-memory differential computation that capitalizes on the dynamic behavior of ferroelectric domain reversal to efficiently extract information differences. This strategy produces differential information directly within the memory itself, which considerably reduces the volume of data transmission and operational energy consumption. We successfully illustrate the effectiveness of this technique in a variety of tasks, including derivative function solving, the moving object extraction and image discrepancy identification, using an in-memory differentiator constructed with a crossbar array of 1600-unit ferroelectric polymer capacitors. Our research offers an efficient hardware analogue differential computing, which is crucial for accelerating mathematical processing and real-time visual feedback systems. Here, authors develop an in-memory differentiator using a 40×40 array of ferroelectric capacitors. This device efficiently performs real-time differential computation and motion extraction, demonstrating low energy consumption and high operational frequency, with potential applications in edge computing.