Large-scale Integration Research Articles

Design and optimization of high electron mobility transistor with high-k dielectric material integration

We have developed and simulated a high electron mobility transistor (HEMT) operating in the 5 nm regime. This HEMT uses hafnium oxide (HfO2), a high-k dielectric material, to create an undoped region (UR) beneath the gate. While the gate and undoped regions share equal thickness, the channel length differs. This innovative undoped under the gate dielectric HEMT design mitigates the maximum electric field (V) within the channel area, leading to a significant increase in drain current. The utilization of a high-k dielectric in the HEMT structure results in a saturated Ion current that is 60% higher compared to conventional structures. Specifically, we use an AlGaN/GaN/SiC-based HEMT with an intrinsic section below the gate, using HfO2 as the high-k dielectric substantial, for applications requiring high power and high-frequency power amplifiers. Compare this advanced HEMT design to conventional HEMTs and you will see improved conductivity, a greater drain current (Id), a 54% increase in transconductance (Gm), and a lower on-resistance (Ron). Additionally, advancements in the electric field in the Y direction are seen. This HEMT structure exhibits superior performance compared to alternative materials analyzed. The integration of AlGaN/GaN materials in HEMTs opens up extensive opportunities in the realms of radio frequency very large-scale integration (VLSI) and power electronics.

Detailing the biomedical aspects of geroscience by molecular data and large-scale "deep" bioinformatics analyses.

As scientists investigated the molecular mechanisms of the biology of aging, they discovered that these are malleable and can enhance healthy longevity by intervening in the drivers of aging, which are leading to disease, dysfunction and death. These exciting observations gave birth to the field of geroscience. As the mechanisms of aging affect almost all mechanisms of life, detailed molecular mechanistic knowledge must be gained or expanded by considering and integrating as many types of data as possible, from genes and transcripts to socioenvironmental factors. Such a large-scale integration of large amounts of data will in turn profit from "deep" bioinformatics analyses that provide insights beyond contextualizing and interpreting the data in the light of knowledge from databases such as the Gene Ontology. The authors suggest that "deep" bioinformatics, employing methods based on artificial intelligence, will be akey ingredient of future analyses.

Four-channel graphene optical receiver

Abstract Silicon photonics with the advantages of low power consumption and low fabrication cost is a crucial technology for facilitating high-capacity optical communications and interconnects. The graphene photodetectors (GPDs) featuring broadband operation, high speed, and low integration cost can be good additions to the SiGe photodetectors, supporting high-speed photodetection in wavelength bands beyond 1.6 μm on silicon. Here we realize a silicon-integrated four-channel wavelength division multiplexing (WDM) optical receiver based on a micro-ring resonator (MRR) array and four p-n homojunction GPDs. These photo-thermoelectric (PTE) GPDs exhibit zero-bias responsivities of ∼1.1 V W−1 and set-up limited 3 dB-bandwidth >67 GHz. The GPDs show good consistence benefiting from the compact active region array (0.006 mm2) covered by a single mechanically exfoliated hBN/graphene/hBN stack. Moreover, the WDM graphene optical receiver realized 4 × 16 Gbps non-return-to-zero optical signal transmission. To the best of our knowledge, it is the first GPD-array-based optical receiver using high-quality mechanically exfoliated graphene and edge graphene-metal contacts with low resistances. Apparently, our design is also compatible with CVD-grown graphene. This work sheds light on the large-scale integration of GPDs with high consistency and uniformity, enabling the application of high-quality mechanically exfoliated graphene, and promoting the development of the graphene photonic integrated circuits.

Development of Power-Delay Product Optimized ASIC-Based Computational Unit for Medical Image Compression

The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), and Field Programmable Gate Array (FPGA) frameworks. The need for low-power functional blocks arises from the growing demand for high-performance computational units that are part of high-speed processors operating at high clock frequencies. The operational speed of the processor is determined by the computational unit, which is the workhorse of high-speed processors. A novel approach to integrating Very Large-Scale Integration (VLSI) ASIC design and the concepts of low-power VLSI compatible with medical image compression was embraced in this research. The focus of this study was the design, development, and implementation of a Power Delay Product (PDP) optimized computational unit targeted for medical image compression using ASIC design flow. This stimulates the research community’s quest to develop an ideal architecture, emphasizing on minimizing power consumption and enhancing device performance for medical image processing applications. The study uses area, delay, power, PDP, and Peak Signal-to-Noise Ratio (PSNR) as performance metrics. The research work takes inspiration from this and aims to enhance the efficiency of the computational unit through minor design modifications that significantly impact performance. This research proposes to explore the trade-off of high-performance adder and multiplier designs to design an ASIC-based computational unit using low-power techniques to enhance the efficiency in power and delay. The computational unit utilized for the digital image compression process was synthesized and implemented using gpdk 45 nm standard libraries with the Genus tool of Cadence. A reduced PDP of 46.87% was observed when the image compression was performed on a medical image, along with an improved PSNR of 5.89% for the reconstructed image.

Highly-efficient single-level robust transmission expansion planning approach applicable to large-scale renewable energy integration

Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.

Multi-Stage Coordinated Planning for Transmission and Energy Storage Considering Large-Scale Renewable Energy Integration

Due to the large-scale integration of renewable energy and the rapid growth of peak load demand, it is necessary to comprehensively consider the construction of various resources to increase the acceptance capacity of renewable energy and meet power balance conditions. However, traditional grid planning methods can only plan transmission lines, often resulting in low utilization rates of newly constructed lines. Additionally, static planning methods can only address single-target scenarios and cannot cope with dynamic growth in load and renewable energy. To address these issues, this paper proposes a multi-stage collaborative planning method for transmission networks and energy storage. This method considers the non-line substitution effect of energy storage resources and their characterization methods. It establishes the coupling relationship between resources across different planning stages to achieve coordinated multi-stage planning for transmission networks and energy storage. Based on the IEEE-24 node system and a case study in a northern province of China, the results show that the proposed method reduces investment costs by approximately 30% compared to static planning methods and by about 7.79% compared to conventional grid planning methods. Furthermore, this method can accommodate more renewable energy.

Coordination of medium-voltage distribution networks and microgrids based on an aggregate flexibility region approach

The large-scale integration of distributed energy resources (DERs) presents operational challenges for medium-voltage distribution networks (MVDNs) and microgrids (MGs) because the conventional centralized scheduling framework lacks of an effective information exchange mechanism for coordinating the scheduling between MVDNs and MGs while supporting privacy concerns. The present work addresses these issues by leveraging a convex hull-based aggregate flexibility region (AFR) associated with the scheduling capability of massive DERs to coordinate the scheduling between an MVDN and MGs efficiently with very limited information exchange. Specifically, the AFR associated with the DERs in an MG is constructed based on the convex hull fitting method, and coordinated scheduling is facilitated by introducing safety operation constraints for the MVDN based on the AFRs of the MGs. Moreover, the coordination model is transformed into a readily solvable quadratically constrained quadratic programming problem by applying second-order cone transformations. The results of numerical computations applied to an IEEE 33-bus test system demonstrate that the obtained AFRs effectively characterize the flexible scheduling capability of DERs, and the proposed coordinated scheduling mechanism between the MVDN and MGs reduces the network losses and voltage deviations of the MVDN, while preserving information privacy.

Modeling of direct AC/AC hybrid distribution transformer based on relaxed two port network theory

Traditional distribution transformers have shortcomings such as poor dynamic characteristics and unreasonable voltage compensation. It is difficult for them to meet the demand for power quality with the large-scale integration of new energy into the grid. Hybrid distribution transformers (HDTs) are an important means of addressing these issues. They have good flexibility in voltage regulation and huge development potential. To analyze the operating status and losses of an HDT, this study constructs a simplified equivalent impedance model for an HDT. First, the operating principle, characteristics, and voltage regulation range of a direct AC/AC HDT were analyzed. Second, based on the state–space averaging method, we obtain the HDT single-phase average circuit topology and construct an HDT relaxed two-port network model. Third, considering the operating losses of the transformer, Thevenin equivalent parameters of an HDT were derived. Then, a simplified equivalent impedance model for an HDT and the corresponding control strategies are presented. Finally, we verify the correctness and rationality of the proposed theory by constructing an HDT simulation model.

A multifunctional inverter power quality coordinated optimization strategy based on comprehensive evaluation

The large-scale integration of renewable energy sources and power electronic devices has led to increasingly dispersed and networked characteristics of power quality disturbances in distribution systems. Traditional control devices, limited by their fixed-point control, no longer meet the development needs of modern distribution systems. Considering the distribution and structural characteristics of the current new-type sources and loads, a multifunctional inverter power quality coordinated control strategy based on comprehensive evaluation is proposed. This strategy aims to achieve power quality coordinated control by utilizing optimal compensation capacity while the grid-connected inverter provides active power output. Firstly, the traditional ipiq power quality detection method is analyzed and improved to enable detection of harmonics, reactive power, and three-phase imbalance currents in single-phase systems or three-phase four-wire systems. Secondly, in the power quality assessment, a balanced algorithm is employed to obtain the comprehensive evaluation index of power quality, thereby improving the deficiencies of single-weight evaluation. Next, to ensure the grid-connected inverter achieves optimal power quality coordinated control with minimal compensation capacity, an optimization compensation function is established between compensation capacity and comprehensive power quality index. This function is optimized using non-dominated sorting genetic algorithm to enhance equipment utilization and system economy. Finally, the feasibility and effectiveness of the proposed method are validated through simulations, demonstrating the achievement of optimal capacity configuration for power quality coordinated control while ensuring active power output by the grid-connected inverter.

CO2 Emissions Resulting from Large-Scale Integration of Electric Vehicles Using a Macro Perspective

Smart grids with EVs have been proposed as a great contribution to sustainability. Considering environmental sustainability is of great importance to humanity, it is essential to assess whether electrical vehicles (EVs) actually contribute to improving it. The objectives of the present study are, from a macro (broad-scope) perspective, to identify the sources of emissions and to create a framework for the calculation of CO2 emissions resulting from large-scale EV use. The results show that V2G mode increases emissions and therefore reduces the benefits of using EVs. The results also show that in the best scenario (NC mode), an EV will have 32.7% less emissions, and in the worst case (V2G mode), it will have 25.6% more emissions than an internal combustion vehicle (ICV), meaning that sustainability improvement is not always ensured. The present study shows that considering a macro perspective is essential to estimate a more comprehensive value of emissions. The main contributions of this work are the creation of a framework for identifying the main contributions to CO2 emissions resulting from large-scale EV integration, and the calculation of estimated CO2 emissions from a macro perspective. These are important contributions to future studies in the area of smart grids and large-scale EV integration, for decision-makers as well as common citizens.

Large-Scale Direct Growth of Monolayer MoS2 on Patterned Graphene for van der Waals Ultrafast Photoactive Circuits.

Two-dimensional (2D) van der Waals heterostructures combine the distinct properties of individual 2D materials, resulting in metamaterials, ideal for emergent electronic, optoelectronic, and spintronic phenomena. A significant challenge in harnessing these properties for future hybrid circuits is their large-scale realization and integration into graphene interconnects. In this work, we demonstrate the direct growth of molybdenum disulfide (MoS2) crystals on patterned graphene channels. By enhancing control over vapor transport through a confined space chemical vapor deposition growth technique, we achieve the preferential deposition of monolayer MoS2 crystals on monolayer graphene. Atomic resolution scanning transmission electron microscopy reveals the high structural integrity of the heterostructures. Through in-depth spectroscopic characterization, we unveil charge transfer in Graphene/MoS2, with MoS2 introducing p-type doping to graphene, as confirmed by our electrical measurements. Photoconductivity characterization shows that photoactive regions can be locally created in graphene channels covered by MoS2 layers. Time-resolved ultrafast transient absorption (TA) spectroscopy reveals accelerated charge decay kinetics in Graphene/MoS2 heterostructures compared to standalone MoS2 and upconversion for below band gap excitation conditions. Our proof-of-concept results pave the way for the direct growth of van der Waals heterostructure circuits with significant implications for ultrafast photoactive nanoelectronics and optospintronic applications.

A unified approach to modeling and stability equivalence analysis for grid-forming and grid-following converters

With the increasing penetration of renewable energy generation, the large-scale integration of grid-connected converters, serving as interfaces, has led to the characteristics of a weak grid with low strength and low inertia, resulting in insufficient stability of existing grid-following (GFL) converters. Grid-forming (GFM) converters, by emulating the characteristics of traditional synchronous machines, can operate stably in weak grids, but their introduction creates a complex operational state with mixed modes, presenting unprecedented challenges to the system. To address this, this paper first analyzes the structure and GFM/GFL control strategies of grid-connected converters; based on this, a comprehensive model framework for GFM/GFL converters is established, and the control of the converters is divided into three parts: LCL with the grid, output control, and power control, each modeled with a small-signal approach; using the established small-signal models, the stability of GFM/GFL converters under different grid short circuit ratios (SCR) is analyzed. Finally, a 20 kW test platform was constructed, and the correctness of the modeling and analysis was jointly verified using simulation frequency sweep methods and experimental time-domain stability analysis techniques.

Ultra-short-term wind power forecasting based on a dual-channel deep learning model with improved coot optimization algorithm

The large-scale integration of wind power to the grid poses some potential challenges to the power system. Accurate wind power forecasts reduce the impact of the nonlinearities and volatility of wind power generation. A two-channel deep learning model based on an improved coot optimization algorithm (ICOA) is proposed. First, the use of long short-term memory (LSTM) builds a channel for the extraction of chronological characteristics of historical power. Second, a temporal convolutional network (TCN) is adopted as a hybrid feature-extraction channel for multi-dimensional meteorological data, and by incorporating the self-attention mechanism (SA), the ability of TCN to extract internal information from meteorological features is enhanced. Finally, the ICOA that introduces nonlinear decision factor, adaptive dynamic boundary, and Cauchy mutation is used to optimize the model hyperparameters. The simulation analysis is carried out on the winter and summer measured data of a wind farm in Xinjiang. The results show that compared with the traditional LSTM model, the root mean square error and the mean absolute error of the proposed method are reduced by 10.35 % and 16.27 % on average, respectively, and the prediction accuracy is higher than that of other comparative models, which verifies the superiority of our proposed model.

Development and optimization of large-scale integration of 2D material in memristors

Two-dimensional (2D) materials like transition metal dichalcogenides (TMD) have proved to be serious candidates to replace silicon in several technologies with enhanced performances. In this respect, the two remaining challenges are the wafer scale growth of TMDs and their integration into operational devices using clean room compatible processes. In this work, two different CMOS-compatible protocols are developed for the fabrication of MoS2-based memristors, and the resulting performances are compared. The quality of MoS2 at each stage of the process is characterized by Raman spectroscopy and x-ray photoemission spectroscopy. In the first protocol, the structure of MoS2 is preserved during transfer and patterning processes. However, a polymer layer with a minimum thickness of 3 nm remains at the surface of MoS2 limiting the electrical switching performances. In the second protocol, the contamination layer is completely removed resulting in improved electrical switching performances and reproducibility. Based on physico-chemical and electrical results, the switching mechanism is discussed in terms of conduction through grain boundaries.

Photovoltaic nanocells for high-performance large-scale-integrated organic phototransistors.

A high-performance large-scale-integrated organic phototransistor needs a semiconductor layer that maintains its photoelectric conversion ability well during high-resolution pixelization. However, lacking a precise design for the nanoscale structure, a trade-off between photoelectric performance and device miniaturization greatly limits the success in commercial application. Here we demonstrate a photovoltaic-nanocell enhancement strategy, which overcomes the trade-off and enables high-performance organic phototransistors at a level beyond large-scale integration. Embedding a core-shell photovoltaic nanocell based on perovskite quantum dots in a photocrosslinkable organic semiconductor, ultralarge-scale-integrated (>221 units) imaging chips are manufactured using photolithography. 27 million pixels are interconnected and the pixel density is 3.1 × 106 units cm-2, at least two orders of magnitude higher than in existing organic imaging chips and equivalent to the latest commercial full-frame complementary metal-oxide-semiconductor camera chips. The embedded photovoltaic nanocells induce an in situ photogating modulation and enable photoresponsivity and detectivity of 6.8 × 106 A W-1 and 1.1 × 1013 Jones (at 1 Hz), respectively, achieving the highest values of organic imaging chips at large-scale or higher integration. In addition, a very-large-scale-integrated (>216 units) stretchable biomimetic retina based on photovoltaic nanocells is manufactured for neuromorphic imaging recognition with not only resolution but also photoresponsivity and power consumption approaching those of the biological counterpart.

Certain investigation on hybrid neural network method for classification of ECG signal with the suitable a FIR filter

The Electrocardiogram (ECG) records are crucial for predicting heart diseases and evaluating patient’s health conditions. ECG signals provide essential peak values that reflect reliable health information. Analyzing ECG signals is a fundamental technique for computerized prediction with advancements in Very Large-Scale Integration (VLSI) technology and significantly impacts in biomedical signal processing. VLSI advancements focus on high-speed circuit functionality while minimizing power consumption and area occupancy. In ECG signal denoising, digital filters like Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are commonly used. The FIR filters are preferred for their higher-order performance and stability over IIR filters, especially in real-time applications. The Modified FIR (MFIR) blocks were reconstructed using the optimized adder-multiplier block for better noise reduction performance. The MIT-BIT database is used as reference where the noises are filtered by the MFIR based on Optimized Kogge Stone Adder (OKSA). Features are extracted and analyzed using Discrete wavelet transform (DWT) and Cross Correlation (CC). At this modern era, Hybrid methods of Machine Learning (HMLM) methods are preferred because of their combined performance which is better than non-fused methods. The accuracy of the Hybrid Neural Network (HNN) model reached 92.3%, surpassing other models such as Generalized Sequential Neural Networks (GSNN), Artificial Neural Networks (ANN), Support Vector Machine with linear kernel (SVM linear), and Support Vector Machine with Radial Basis Function kernel (SVM RBF) by margins of 3.3%, 5.3%, 23.3%, and 24.3%, respectively. While the precision of the HNN is 91.1%, it was slightly lower than GSNN and ANN but higher than both SVM linear and SVM -RBF. The HNN with various features are incorporated to improve the ECG classification. The accuracy of the HNN is switched to 95.99% when the DWT and CC are combined. Also, it improvises other parameters such as precision 93.88%, recall is 0.94, F1 score is 0.88, Kappa is 0.89, kurtosis is 1.54, skewness is 1.52 and error rate 0.076. These parameters are higher than recently developed models whose algorithms and methods accuracy is more than 90%.

Direct numerical simulation of transition under free-stream turbulence and the influence of large integral length scales

Under the action of free-stream turbulence (FST), elongated streamwise streaky structures are generated inside the boundary layer, and their amplitude and wavelength are crucial for the transition onset. While turbulence intensity is strongly correlated with the transitional Reynolds number, characteristic length scales of the FST are often considered to have a slight impact on the transition location. However, a recent experiment by Fransson and Shahinfar [J. Fluid Mech. 899, A23 (2020)] shows significant effects of FST scales. They found that, for higher free-stream turbulence levels and larger integral length scales, an increase in the length scale postpones transition, contrary to established literature. Here, by performing well-resolved numerical simulations, we aim at understanding why the FST integral length scale affects the transition location differently at low- and high turbulence levels. We found that the integral length scales in Fransson and Shahinfar's experiment are so large that the introduced wide streaks have substantially lower growth in the laminar region, upstream of the transition to turbulence, than the streaks induced by smaller integral length scales. The energy in the boundary layer subsequently propagate to smaller spanwise scales as a result of the nonlinear interaction. When the energy has reached smaller spanwise scales, larger amplitude streaks results in regions where the streak growth are larger. It takes longer for the energy from wider streaks to propagate to the spanwise scales associated with the breakdown to turbulence, than for those with smaller spanwise scales. Thus, there is a faster transition for FST with lower integral length scales in this case.

Beyond earth: Evaluating SiC and GaN as next-generation semiconductors for Venus exploration

Abstract This paper evaluates Silicon Carbide (SiC) and Gallium Nitride (GaN) as potential wide-bandgap semiconductor materials for Venus’ surface exploration. Both materials possess properties advantageous for withstanding Venus’ extreme conditions. Through an in-depth comparison, the study examines stability, energy efficiency, and integration levels of both materials. Although GaN showcases superior electron mobility and potential for high energy efficiency, it confronts challenges in large-scale circuit integration due to its limited wafer size and intricacies in device structure design. Conversely, SiC-based processors have demonstrated operability at high temperatures, with performance comparable to computers used in previous space missions. While neither material currently achieves the processing prowess of past space exploration computers, the study recommends SiC for Venus exploration due to its demonstrated capabilities and higher potential for integration. The paper concludes that optimized SiC processors hold promise for future Venusian surface missions.

Control-oriented dynamic modeling and GPC for single-tower double-circulation wet flue gas desulfurization system

In China, the current situation of large-scale grid integration of renewable energy generation and the government's introduction of capacity-based electricity pricing policies have compelled coal-fired power plants to gradually enhance their load adjustment capabilities and expand their load adjustment ranges. Simultaneously, the national environmental requirements for these plants have become increasingly stringent. However, the economic control of wet flue gas desulfurization (WFGD) systems is still far from meeting practical requirements. In this paper, starting from practical engineering applications and utilizing historical operational data, considering the actual system operation process, the impact of the absorption tower variable-frequency slurry circulation pump on the concentration of sulfur dioxide (SO2) in flue gas under different slurry pH values is investigated. This led to the establishment of a dynamic model for a single-tower double-circulation wet flue gas desulfurization (SD-WFGD) system. Recognizing the significant delay and inertia characteristics of pH value control in the SO2 absorption tank, a desulfurization system pH value desulfurization capacity-based optimization control scheme for the SD-WFGD system is proposed, termed as the Direct Sulfur Control (DSC) strategy. Based on the established dynamic model, a SD-WFGD system series control system is developed using a step generalized predictive control algorithm (GPC) combined with the DSC strategy. Validation results indicate that, compared to SGPC control schemes and traditional PID control schemes, this approach reduces fluctuations in slurry supply and outlet SO2 concentration, significantly enhancing the control performance of the SD-WFGD system. This ensures the safe, stable, economical, and flexible operation of the desulfurization system against the backdrop of flexible operation in coal-fired power plants.

Energy-Performance Assessment of Oscillatory Neural Networks Based on VO2 Devices for Future Edge AI Computing.

Oscillatory neural network (ONN) is an emerging neuromorphic architecture composed of oscillators that implement neurons and are coupled by synapses. ONNs exhibit rich dynamics and associative properties, which can be used to solve problems in the analog domain according to the paradigm let physics compute. For example, compact oscillators made of VO2 material are good candidates for building low-power ONN architectures dedicated to AI applications at the edge, like pattern recognition. However, little is known about the ONN scalability and its performance when implemented in hardware. Before deploying ONN, it is necessary to assess its computation time, energy consumption, performance, and accuracy for a given application. Here, we consider a VO2-oscillator as an ONN building block and perform circuit-level simulations to evaluate the ONN performances at the architecture level. Notably, we investigate how the ONN computation time, energy, and memory capacity scale with the number of oscillators. It appears that the ONN energy grows linearly when scaling up the network, making it suitable for large-scale integration at the edge. Furthermore, we investigate the design knobs for minimizing the ONN energy. Assisted by technology computer-aided design (TCAD) simulations, we report on scaling down the dimensions of VO2 devices in crossbar (CB) geometry to decrease the oscillator voltage and energy. We benchmark ONN versus state-of-the-art architectures and observe that the ONN paradigm is a competitive energy-efficient solution for scaled VO2 devices oscillating above 100 MHz. Finally, we present how ONN can efficiently detect edges in images captured on low-power edge devices and compare the results with Sobel and Canny edge detectors.