Powerful Control Research Articles

Dual-Channel Surface Waves Directional Radiation with Customizable Intensity and Switchable Pattern.

Achieving the conversion from surface waves (SWs) to propagating waves has captivated long-standing interest, and various ingenious metasurfaces benefiting from the powerful control capability for electromagnetic waves are able to realize efficient SWs directional radiation. Nevertheless, most existing schemes still suffer from the bottlenecks of single radiation channel, uncontrollable radiation intensity, and immutable radiation pattern, which immensely hinder their practical application in high-integration intelligent devices. Herein, a series of appealing strategies are proposed to achieve the dual-channel SWs directional radiation with customizable radiation intensity and switchable radiation pattern. The dual-channel SWs radiation metadevice based on the phase modulation metasurface is designed to directionally radiate SWs in left-handed circular polarized channel and right-handed circular polarized channel and possesses the broadband frequency scanning characteristic. More strikingly, the intensity-customizable dual-channel SWs radiation metadevice loaded with lumped resistors can control the realized gain of two circular polarized radiation beams, and the pattern-switchable dual-channel SWs radiation metadevice loaded with PIN diodes can dynamically adjust the radiation direction of the radiation beams. Numerous simulations and experiments of the proof-of-concept prototypes with modular design corroborate the theoretical predictions. Our methodology shows unprecedented flexibility in regulating SWs directional radiation and has enormous potential in engineering applications.

Effectiveness of ethyl acetate extract from Aspergillus flavipes AUMC 11390 culture filtrate on biological and physiological performance of the spiny bollworm, Earias insulana, (Boisd.) (Lepidoptera: Nolidae)

BackgroundSpiny bollworm, Earias insulana is a serious cotton pest in Egypt. Besides the economic losses it caused, treatment with chemical insecticides has negative effects on human health and the environment, thus the development of a powerful safe control strategy rather than chemical pesticides is an international goal.ResultsEthyl acetate extract from Aspergillus flavipes AUMC 11390 culture filtrate has an insecticidal activity against E. insulana causing larval and pupal mortality of 58.33, and 15.59%, respectively, compared with controls, in addition, reduction in adult's emergency and deformation of emerged adults. The impact of fungal extract treatment extended to adult stages by diminishing the male and the female longevity, the number of produced eggs and the hatchability percent. Furthermore, A. flavipes AUMC 11390 ethyl acetate extract caused a strong disturbance on some insect enzymes including amylase, invertase, trehalase, GOT, GPT and acetylcholinesterase, alongside total lipid and total protein. Analysis of ethyl acetate fungal extract revealed the presence of one hydrocarbon 3-Eicosene and four long-chain alcohols namely hexadecanol, 1-hexadecanol, 1-octadecanol, and 1-pentadecanol which are known for their insecticidal activity.ConclusionA. flavipes AUMC 11390 culture filtrate might represent a promising source for different important bioactive compounds that could be used as a potential biocontrol agent involved in E. insulana management strategies.

The Plasmodium falciparum exported J domain proteins fine-tune human and malarial Hsp70s: pathological exploitation of proteostasis machinery.

Cellular proteostasis requires a network of molecular chaperones and co-chaperones, which facilitate the correct folding and assembly of other proteins, or the degradation of proteins misfolded beyond repair. The function of the major chaperones, heat shock protein 70 (Hsp70) and heat shock protein 90 (Hsp90), is regulated by a cohort of co-chaperone proteins. The J domain protein (JDP) family is one of the most diverse co-chaperone families, playing an important role in functionalizing the Hsp70 chaperone system to form a powerful protein quality control network. The intracellular malaria parasite, Plasmodium falciparum, has evolved the capacity to invade and reboot mature human erythrocytes, turning them into a vehicles of pathology. This process appears to involve the harnessing of both the human and parasite chaperone machineries. It is well known that malaria parasite-infected erythrocytes are highly enriched in functional human Hsp70 (HsHsp70) and Hsp90 (HsHsp90), while recent proteomics studies have provided evidence that human JDPs (HsJDPs) may also be enriched, but at lower levels. Interestingly, P. falciparum JDPs (PfJDPs) are the most prominent and diverse family of proteins exported into the infected erythrocyte cytosol. We hypothesize that the exported PfJPDs may be an evolutionary consequence of the need to boost chaperone power for specific protein folding pathways that enable both survival and pathogenesis of the malaria parasite. The evidence suggests that there is an intricate network of PfJDP interactions with the exported malarial Hsp70 (PfHsp70-x) and HsHsp70, which appear to be important for the trafficking of key malarial virulence factors, and the proteostasis of protein complexes of human and parasite proteins associated with pathology. This review will critically evaluate the current understanding of the role of exported PfJDPs in pathological exploitation of the proteostasis machinery by fine-tuning the chaperone properties of both human and malarial Hsp70s.

Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling

Central air conditioning in large buildings is an important demand-response resource due to its large load power and strong controllability. Demand-response-oriented air conditioning load modeling needs to calculate the room temperature. The room temperature calculation models commonly used in the existing research cannot easily and accurately calculate the room temperature change of large buildings. Therefore, in order to obtain the temperature change of a large building and its corresponding power potential, this paper first proposes a building model based on CNN (convolutional neural network). Then, in order to fully apply the demand-response potential of the central air conditioning load, this paper puts forward an evaluation method of the load-reduction potential of the central air conditioning cluster based on pre-cooling and develops an economic load-reduction strategy according to the different energy consumption of different buildings in the pre-cooling stage. Finally, multiple building examples with different building parameters and temperature comfort ranges are set up, and the economic advantages of the proposed strategy are illustrated by Cplex solution examples.

ENHANCING QUALITY AND EFFICIENCY: THE POWER OF POKA-YOKE

Poka-Yoke is known as mistake-proofing or error-proofing. This concept can be applied to the process to prevent mistakes from occurring, stop the error from further processing, and warn that the error has occurred. Poka-yoke is a simple and powerful quality control tool that can be used in any industry to reduce or eliminate errors. In the advanced technology world, this has become the need of the hour. In this paper, the author wants to talk about the evolution, challenges, and benefits of implementation with examples. KEYWORDS: Poka-yoke, mistake-proofing, Toyota Production System, Constraints, Bar-code, Error prevention and Employee empowerment.

CFPy-A Python Package for Pre- and Postprocessing of the Conduit Flow Process of MODFLOW.

The conduit flow process (CFP) for MODFLOW's groundwater flow model is an advanced approach for investigating complex groundwater systems, such as karst, with coupled discrete-continuum models. CFP represents laminar and turbulent flow in a discrete pipe network coupled to a matrix continuum. However, the preprocessing demand is comparatively high to generate the conduit network and is usually performed with graphical user interfaces. To overcome this limitation and allow a scalable, reproducible, and comprehensive workflow, existing and new routines were aggregated to a Python package named CFPy, to allow script-based modeling that harmonizes well with the available and widely used FloPy package. CFPy allows information about the location and geometry of the conduit network to be considered by user-specific approaches or by sophisticated methods such as stochastic conduit network generators. The latter allows the automatic generation of many model variants with differing conduit networks for advanced investigations like multi-model approaches in combination with automatic parameter estimation. Additional postprocessing routines provide powerful control and valuable insights for CFP applications. In this methods note, a general technical description of the approach is complemented with two examples that guide users and demonstrate the main capabilities of CFPy.

Pressure tuning of minibands in MoS2/WSe2 heterostructures revealed by moiré phonons.

Moiré superlattices of two-dimensional heterostructures arose as a new platform to investigate emergent behaviour in quantum solids with unprecedented tunability. To glean insights into the physics of these systems, it is paramount to discover new probes of the moiré potential and moiré minibands, as well as their dependence on external tuning parameters. Hydrostatic pressure is a powerful control parameter, since it allows to continuously and reversibly enhance the moiré potential. Here we use high pressure to tune the minibands in a rotationally aligned MoS2/WSe2 moiré heterostructure, and show that their evolution can be probed via moiré phonons. The latter are Raman-inactive phonons from the individual layers that are activated by the moiré potential. Moiré phonons manifest themselves as satellite Raman peaks arising exclusively from the heterostructure region, increasing in intensity and frequency under applied pressure. Further theoretical analysis reveals that their scattering rate is directly connected to the moiré potential strength. By comparing the experimental and calculated pressure-induced enhancement, we obtain numerical estimates for the moiré potential amplitude and its pressure dependence. The present work establishes moiré phonons as a sensitive probe of the moiré potential as well as the electronic structures of moiré systems.

Research on The Internal Control Framework of Large State-owned Enterprises Under Information-based Environment

In the information society and digital era, the information construction of the enterprise internal control framework is particularly important for enterprises to enhance their endogenous power and improve their management efficiency. In order to achieve increasingly powerful control functions and build an efficient overall enterprise internal control framework, we must rely on scientific and precise control means and efficient operation mechanisms. Under such circumstances, large state-owned enterprises should absorb and apply more advanced management concepts and actively innovate and optimize their management mode in order to improve their management level and enhance their comprehensive competitiveness and thus achieve long-term development in the fierce market competition. An important way for large state-owned enterprises to strengthen their management is to build an internal control system. The internal control system can regulate the operation of power, strengthen risk prevention and supervision of internal personnel and be a stimulus to large enterprises to reduce operating costs and increase economic efficiency.

A real-time operational management strategy for vehicular hybrid propulsion system based on GRNN-AECMS

The driving characteristics of a hybrid propulsion system (HPS) depend on operational management strategies. However, the differences in output and response of various types of power sources make real-time management of operating conditions complex. In this study, an operational management strategy, which combines adaptive equivalent consumption minimization strategy (AECMS) and general regression neural network (GRNN), is proposed for optimizing power controls within hybrid power sources. Under this strategy, the optimal function of the AECMS is adaptively calculated by the load profile changes based on GRNN’s prediction results, and it is more suitable for the HPS. Furthermore, the proposed GRNN-AECMS method can minimize fuel consumption and meet requirements for power tracking. From simulation results, fuel consumption under GRNN-AECMS method can be reduced by 2.715 kg, or 31.8%.

Temporal Defocusing as a Depth Diagnostic of Submerged Sources of Transient Acoustic Emission from Solar Flares

Helioseismic holography applied to HMI observations of a sunquake associated with the SOL20140207T10:29M1.9 flare hosted by NOAA AR11968 shows the signature of a compact submerged acoustic source. In the 9 – 11-mHz bandpass, this source appears to be at a depth of 2 Mm. This is nearly double the depth of the highly impulsive acoustic transient, referred to as an ultra-impulsive transient source recently found in the SOL20110730T02:09M9.3 flare, emerging from NOAA AR11261 in 2011. The latter source was compound, having multiple surface components overlaying a single submerged component. Many of the sources observed have evidence of being constituted of multiple components, some of which are staggered in depth. The helioseismic source of the flare of 2014-02-07 is distinguished by the apparent absence of a strong shallow compact overlying source component matching the character of those apparent in the flare of 2011-07-30. This suggests that the volume several Mm beneath active-region photospheres could possibly harbor many more ultra-impulsive transient acoustic sources than the rarities that have so far appeared in familiar surface-focal-plane diagnostics. Based on weak surface signatures that appear in place of the strong ones, we find a temporal delay between weak surface and strong submerged sources similar to that found between surface-and-submerged sources in the flare of 2011-07-30. While it remains highly speculative based on the limited statistics we have at this point, this temporal delay supports a model in which the submerged source is perturbed by some presently invisible triggering disturbance that propagates downward from the flaring outer atmosphere at ∼ 5 km s−1. This is slower than the sound speed anywhere in the 0 – 2-Mm depth range. However, this potentially could be an Alfvèn speed if submerged magnetic flux densities along which the trigger propagates are as high as possible. The standard local-helioseismic diagnostics we have used in the past have been heavily reinforced in this study by a powerful new control resource found in the recognition of temporal defocusing of compact transient sources. In particular, its tight relationship to the standard spatial defocusing upon which helioseismic holography has capitalized from its early advent.

The role of the dashboard as a tool to management control in activating the corporate governance

This study aims to identify the role of the dashboard, as oneof the modern tools to management control, in activating corporategovernance by demonstrating its role as a powerful control tool and akey means of delivering appropriate information about theorganization's performance.The study found that the dashboard as an information system, which is based on financial and qualitative indicators, has an effective role in promoting best practices and measuring performance to maximizeaccountability and transparency. In addition to that, designing itaccording to the requirements and principles of governance contributesto a good evaluation of the corporation's performance and limitsagency problems by reducing information asymmetries betweendifferent levels of management, shareholders and all stakeholders. Thissupports an effective corporate governance system.

Bias-Free Predictive Control of Power Converters with LCL Filter in Micro-Energy Systems

Power converters with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> filters have been widely adopted in systems, e.g., micro-energy systems, grid-tied renewable energy systems, etc. Such a system forms a high-order <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple input and multiple output</i> (MIMO) dynamic system, requiring complicated active damping and showing slow control dynamics, applying classical cascaded linear controllers. Model predictive control (MPC) is a powerful control strategy, which inherently suits for MIMO systems with constraints. However, for such systems, inevitable tracking biases are seen when using the classical MPC. In addition, the nonlinear nature of the underlying system leads to difficulty for a deep analysis of an MPC controller design. In this work, we mathematically reveal the cause of tracking biases when applying classical MPC, and develop an equivalent modeling method to eliminate it at both parameter and model uncertainties, forming a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robust and bias-free MPC</i> . The proposed solution remains fast in control dynamics and simple in structure. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Both</i> simulation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> experimental data confirm the effectiveness of the proposed solution in mitigating tracking bias and good robustness at model deviations and grid disturbances.

Coordinated active and reactive power management for enhancing PV hosting capacity in distribution networks

AbstractThis paper proposes an operational planning model based on optimal active and reactive power control strategies to enhance solar photovoltaics (PV)’ hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two‐stage stochastic model assigns decision‐making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15‐bus and IEEE 33‐bus distribution systems considering different active‐reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active‐reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

Shank3 deficits in the anteromedial bed nucleus of the stria terminalis trigger an anxiety phenotype in mice.

Anxiety disorders are the most prevalent co-morbidity factor associated with the core domains of autism spectrum disorders (ASD). Investigations on potential common neuronal mechanisms that may explain the co-occurrence of ASD and anxiety disorders are still poorly explored. One of the key questions that remained unsolved is the role of Shank3 protein in anxiety behaviours. Firstly, we characterize the developmental trajectories of locomotor, social behaviour and anxiety traits in a mouse model of ASD. We highlight that the anxiety phenotype is a late-onset emerging phenotype in mice with a Shank3Δe4-22 mutation. Consequently, we used an shRNA strategy to model Shank3 insufficiency in the bed nucleus of the stria terminalis (BNST), a brain region exerting a powerful control on anxiety level. We found that Shank3 downregulation in the anteromedial BNST (amBNST) induced anxiogenic effects and enhanced social avoidance after aversive social defeat. Associated with these behavioural defects, we showed alteration of glutamatergic synaptic functions in the amBNST induced by Shank3 insufficiency during adolescence. Our data strongly support the role of Shank3 in the maturation of amBNST, and its key role in anxiety control. Our results may further help to pave the road on a better understanding of the neuronal mechanisms underlying anxiety disorders implicated in ASDs.

Pontus: Finding Waves in Data Streams

The bumps and dips in data streams are valuable patterns for data mining and networking scenarios such as online advertising and botnet detection. In this paper, we define the wave, a data stream pattern with a serious deviation from the stable arrival rate for a period of time. We then propose Pontus, an efficient framework for wave detection and estimation. In Pontus, a lightweight data structure is utilized for the preliminary processing of incoming packets in the data plane to take advantage of its high processing speed; then, the powerful control plane carries out computationally intensive wave detection and estimation. In particular, we propose the Multi-Stage Progressive Tracking strategy which detects waves in stages and removes any disqualified items promptly to save memory. Hash collisions are addressed by a Stage Variance Maximization technique to reduce estimation error. Moreover, we prove the theoretical error bound and establish upper bounds of false positive and false negative. Experiment results show that the software version of Pontus can achieve around 97% F1-Score even under scarce memory when baselines fail. Furthermore, the implemented prototype of Pontus based on P4 achieves 842x higher throughput than the baseline strawman solution.

Strong Ligand Control for Noble Metal Nanostructures.

ConspectusNanosynthesis is the art of creating nanostructures, with on-demand synthesis as the ultimate goal. Noble metal nanoparticles have wide applications, but the available synthetic methods are still limited, often giving nanospheres and symmetrical nanocrystals. The fundamental reason is that the conventional weak ligands are too labile to influence the materials deposition, so the equivalent facets always grow equivalently. Considering that the ligands are the main synthetic handles in colloidal synthesis, our group has been exploring strong ligands for new growth modes, giving a variety of sophisticated nanostructures. The model studies often involve metal deposition on seeds functionalized with a certain strong ligand, so that the uneven distribution of the surface ligands could guide the subsequent deposition.In this Account, we focus on the design principles underlying the new growth modes, summarizing our efforts in this area along with relevant literature works. The basics of ligand control are first revisited. Then, the four major growth modes are summarized as follows: (1) The curvature effects would divert the materials deposition away from the high-curvature tips when the ligands are insufficient. With ligands fully covering the seeds, the sparser ligand packing at the tips would then promote the initial nucleation thereon. (2) The strong ligands may get trapped under the incoming metal layer, thus modulating the interfacial energy of the core-shell interface. The evidence for embedded ligands is discussed, along with examples of Janus nanostructures arising from the synthetic control, including metal-metal, metal-semiconductor, and metal-C60 systems using a variety of ligands. (3) Active surface growth is an unusual mode with divergent growth rates, so that part of the emerging surface is inhibited, and the growth is focused onto a few active sites. With seeds attached to oxide substrates, the selective deposition at the metal-substrate interface produces ultrathin nanowires. The synthesis can be generally applied to grow Au, Ag, Pd, Pt, and hybrid nanowires, with straight, spiral, or helical structures, and even rapid alteration of segments via electrochemical methods. In contrast, active surface growth for colloidal nanoparticles has to be more carefully controlled. The rich growth phenomena are discussed, highlighting the role of strong ligands, the control of deposition rates, the chiral induction, and the evidence for the active sites. (4) An active site with sparse ligands could also be exploited in etching, where the freshly exposed surface would promote further etching. The result is an unusual sharpening etching mode, in contrast to the conventional rounding mode for minimized surface energy.Colloidal nanosynthesis holds great promise for scalable on-demand synthesis, providing the crucial nanomaterials for future explorations. The strong ligands have delivered powerful synthetic controls, which could be further enhanced with in-depth studies on growth mechanisms and synthetic strategies, as well as functions and properties.

Forecasting methods of intelligent systems technical condition analysis

Modern information processing and control systems reached the level that makes it possible to use detailed information on device and human nervous system functioning. Using a cognitive psychological approach in intelligent systems development, allowed them to imitate human nervous system functions. The development of increasingly powerful intelligent information and control systems with learning and self-learning, including information and measuring systems, is expected as a result.&#x0D; They will have enhanced cognitive capabilities due to the stimulation of cognitive functions and processes responsible for perception, learning, thinking, and consciousness in the human nervous system.&#x0D; Development of this field allows to develop intelligent systems with thinking and behaviour analysis elements. Adding some creative possibilities, for example, related to automatic hypotheses and models creation and self-learning for new task solving, allows to improve the efficiency of intelligent systems.&#x0D; Due to this new approaches to the artificial brain and the artificial nervous system of robots development, which relate not only to artificial intelligence but also to its development in the form of an artificial mind.&#x0D; Thus, the main scientific and development task for models, methods, methodics, and algorithms for intelligent systems technical condition forecasting based on soft computing is important and relevant for science and practice.&#x0D; Intelligent technologies allowing to develop useful intelligent systems are continuously being improved. Currently, quite powerful tools for implementing the technology of expert systems, fuzzy logic, neural network systems, and multi-agent systems technology are used. They are rapidly improved by adding software packages and hardware tools.&#x0D; New technologies are being developed in the so-called neuromorphic systems field that models some brain structures and in the parallel computing and quantum computers field. These technologies aim to raise significantly the intelligence level of systems in the future.

Cooperative Lithium Sorption in Doped Layered Double Hydroxides Is Modulated by Colloidal (Dis)Assembly

Lithium–aluminum layered double hydroxides (LDHs) selectively sorb lithium from brines, concentrating and purifying this critical element for subsequent conversion to active battery components. Lithium ion partitioning into lattice vacancies within the LDH structure is selectively enhanced with iron doping. However, this process leads to a highly coupled set of intercalation interactions whose mechanisms are challenging to assess in situ. Here, we show that iron modulates the size- and shape-dependent composition of LDHs and imposes a powerful control on lithium sorption processes in complex fluids. We observe fundamental units of LDH layers and aluminum ferrihydrite nanoclusters that (dis)assemble to form at least five distinct particle types that influence LDH lithium capacity and cyclability. Importantly, lithium sorption is controlled by feedbacks arising from the dynamic interconversion of planar stacks and scrolls of LDH layers, which exchange lithium, water, and other species in the process of (un)rolling due to similar energy scales of hydration, sorption, and deformation. Under appropriate iron redox conditions, the cycling efficiency and stability of lithium sorption can be optimized for the range of lithium concentrations found in many natural brines.

Workload Characterization and Traffic Analysis for Reconfigurable Intelligent Surfaces Within 6G Wireless Systems

Programmable metasurfaces constitute an emerging paradigm, envisaged to become a key enabling technology for Reconfigurable Intelligent Surfaces (RIS) due to their powerful control over electromagnetic waves. The HyperSurface (HSF) paradigm takes one step further by embedding a network of customized integrated circuit (IC) controllers within the device with the aim of adding intelligence, connectivity, and autonomy. However, little is known about the traffic that the network needs to support as the target electromagnetic function or boundary conditions change. In this paper, the framework of a methodology is introduced to characterize the workload of programmable metasurfaces which is then used to analyze the beam steering HSFs. The workload characterization leads to many useful insights into traffic behavior, including the spatio-temporal load incurred and the HSF limitations in terms of fine-grained tracking of moving targets. It is observed that the traffic is inherently bursty with an uneven spatial distribution of load and that finer resolution comes at the cost of an increased but less bursty load. An indoor mobility model indicates reasonable signaling load on the deployed surfaces. Finally, a statistical analysis on the traffic patterns is performed, showing that the incoming traffic can be well represented by an ON-OFF model.

Residual neural networks to quantify traces of melamine in yogurts through image deconvolution

This work combines intelligent algorithms based on transfer learning and residual neural networks (viz., ResNet34) to process and classify optical images of pure and adulterated yogurt samples. This integration aims to detect the presence of melamine in yogurt in concentrations ranging from 1 to 10 ppm. An image database of 1,888 images is used to train the ResNet34, and 212 blinded images to test and validate its performance. The optimized intelligent algorithm is able to classify the images into 21 classes considering the yogurt type and melamine content, obtaining an accuracy of over 94%. These encouraging results certify a simple yet powerful real-time quality control method for producers and distributers to ensure food safety for the final consumers, while pinpointing the source of potential fraudulent procedures.