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The semiconductiong ${\alpha'}$-borophene nanoribbon (${\alpha'}$-BNR) due to its incredible properties such as high stability and great mobility of carriers demostrates high-efficiency in thermoelectric devices. These properties enable us to produce the spin current by a temperature gradient with lower energy consumption technology. In this research, the spin-dependent Seebeck effects are studied in a zigzag ${\alpha'}$-borophene nanoribbon with two leads magnetized by ferromagnetic (FM) insulators. The thermoelectric calculations are performed for a ${\alpha'}$-BNR FM/Normal/FM junction using the tight-binding (TB) formalism in combination with the non-equilibrium Green's function method (NEGF). A pure spin-dependent current due to the breaking of the electron-hole symmetry is induced in the system by a temperature gradient so that it can act as a spin-Seebeck diode. Moreover, the negative differential spin-Seebeck effect can be observed in this device dueto the compensation of thermal spin in the spin-dependent currents. Finally, we have studied the effect of temperature on the charge and spin power factors in ${\alpha'}$-BNR. A significant decline in power factor is primarily arises from a reduction in the magnitude of thermopower near the Fermi level. Our findings demonstrate that the ${\alpha'}$-BNR has a higher power factor compared to its rivals e.g., graphene and silicene. This is attributed to the semiconducting nature and high asymmetry between electrons and holes in the ${\alpha'}$-BNR. The exceptional features of ${\alpha'}$-BNR makes it a very suitable choice for using in thermoelectric devices., Comment: 18 pages, 9 figures

Interoperability is a significant challenge in blockchain technology, hindering seamless data and service sharing across diverse blockchain networks. This study introduces Automated Gateways as a novel framework leveraging smart contracts to facilitate interoperability. Unlike existing solutions, which often require adopting new technologies or relying on external services, Automated Gateways framework is integrated directly with a blockchain's core infrastructure to enhance systems with built-in interoperability features. By implementing fine-grained access control mechanisms, smart contracts within this framework manage accessibility and authorization for cross-chain interactions and facilitate streamlining the selective sharing of services between blockchains. Our evaluation demonstrates the framework's capability to handle cross-chain interactions efficiently, significantly reduce operational complexities, and uphold transactional integrity and security across different blockchain networks. With its focus on user-friendliness, self-managed permissions, and independence from external platforms, this framework is designed to achieve broader adoption within the blockchain community., Comment: Copyright 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Current hydrological modeling methods combine data-driven Machine Learning (ML) algorithms and traditional physics-based models to address their respective limitations incorrect parameter estimates from rigid physics-based models and the neglect of physical process constraints by ML algorithms. Despite the accuracy of ML in outcome prediction, the integration of scientific knowledge is crucial for reliable predictions. This study introduces a Physics Informed Machine Learning (PIML) model, which merges the process understanding of conceptual hydrological models with the predictive efficiency of ML algorithms. Applied to the Anandapur sub-catchment, the PIML model demonstrates superior performance in forecasting monthly streamflow and actual evapotranspiration over both standalone conceptual models and ML algorithms, ensuring physical consistency of the outputs. This study replicates the methodologies of Bhasme, P., Vagadiya, J., & Bhatia, U. (2022) from their pivotal work on Physics Informed Machine Learning for hydrological processes, utilizing their shared code and datasets to further explore the predictive capabilities in hydrological modeling.

The integration of various energy resources with wind farms, results in improved market-aware management of wind generation variations. Consequently, the proposed method minimizes the negative impact associated with wind generation deviations on the electricity market and energy systems. This paper integrates the multiple energy resources of a smart grid with wind producers to form a virtual power plant (VRP). One of the primary issues of integrating electricity markets in multi-electricity markets such as the Day-Ahead (DHM), intraday (INT), and real-time market (RLT) is the participation of a VRP. The proposed VRP was formed by the integration of the on-site generation (ONG), energy storage system (ENS), wind power aggregator (WPG), and demand response (DRE) contracts. Furthermore, The DRE includes detailed contracts with shiftable loads and load curtailment load aggregators. An effective method to manage the uncertainty of a VRP in order to attain optimal strategy and offering is provided in this article. The p-robust method is the proposed solution to measure the regret of VRP in the offering strategys formation. In the modeled scenario-based program, the p-robust approach is used for controlling the relative regret of a VRP in a multi-market operation under uncertainty. The obtained results demonstrate that a 6.81% reduction in estimated profit can result in a 45.75% reduction in relative regret of VRP.

Deep learning accelerators address the computational demands of Deep Neural Networks (DNNs), departing from the traditional Von Neumann execution model. They leverage specialized hardware to align with the application domain's structure. Compilers for these accelerators face distinct challenges compared to those for general-purpose processors. These challenges include exposing and managing more micro-architectural features, handling software-managed scratch pads for on-chip storage, explicitly managing data movement, and matching DNN layers with varying hardware capabilities. These complexities necessitate a new approach to compiler design, as traditional compilers mainly focused on generating fine-grained instruction sequences while abstracting micro-architecture details. This paper introduces the Architecture Covenant Graph (ACG), an abstract representation of an architectural structure's components and their programmable capabilities. By enabling the compiler to work with the ACG, it allows for adaptable compilation workflows when making changes to accelerator design, reducing the need for a complete compiler redevelopment. Codelets, which express DNN operation functionality and evolve into execution mappings on the ACG, are key to this process. The Covenant compiler efficiently targets diverse deep learning accelerators, achieving 93.8% performance compared to state-of-the-art, hand-tuned DNN layer implementations when compiling 14 DNN layers from various models on two different architectures.

We study spin and valley-dependent transport properties in an n-p-n junction of 8-pmmn borophene monolayer. An external gate voltage and exchange magnetic field, induced by the proximity effect of a ferromagnetic insulator, are applied to this junction as electric and magnetic potential barriers. We show that the exchange magnetic field generates spin polarization in the system and applying a gate voltage, as a simple method, causes valley polarization. This property (valley polarization) is due to the anisotropic and tilted Dirac cones of the borophene structure. It is an advantage of borophene monolayer over graphene monolayer because in graphene it is necessary to apply strain to have valley polarization. We also show that the proposed device (borophene-based n-p-n junction) can work as perfect spin and perfect valley filters. The spin and valley filters can be controlled by changing two factors, i.e. gate voltage and Fermi energy. Moreover, it is shown that for full spin and valley polarizations and thus perfect spin and valley filters, the length of the barriers must be larger than a specific value (60nm). These results show that the borophene monolayer has a suitable potential to be used in spintronic and valleytronic devices.

The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in an established benchmark, the city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management.

Increasing the amount of electric power that is used on the demand side has brought more attention to the peak-load management of the distribution network (DN). The creation of infrastructures for smart grids, the efficient utilization of the distributed network's components, and the appropriate administration of the distributed network would result in a valuable solution for the operators of the distributed network. As a result, a framework for peak-load management is given in this research. Within this framework, the real-time rating of the components and the voltage-dependent characteristics of the electric loads work together to assist the DN operator in effectively navigating peak periods. The combination of the conservation voltage reduction (CVR) and the dynamic thermal rating (DTR) of the components that make up the DN produces outcomes that are more helpful than any of these factors alone could provide. This is true even though each of these factors contributes to the efficient functioning of the DN. According to the findings, as compared to the individual implementation of CVR, the simultaneous utilization of DTR and CVR results in a cost-savings rise at peak events which is 58.75 percentage points more than the individual implementation. In addition, a discussion is offered concerning the current difficulties that are being experienced by the feeders that are providing the voltage-dependent constant-power loads during the utilization of the CVR, which are handled by the dynamic rating of the components that make up the DN., Comment: 10 pages, 11 figures, 2 tables

Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It adopts a unified approach that combines backend power, performance, and area (PPA) analysis with frontend performance simulation, thereby achieving a realistic estimation of both backend PPA and system metrics such as runtime and energy. In addition, our framework includes a fully automated DSE technique, which optimizes backend and system metrics through an automated search of architectural and backend parameters. Experimental studies show that our approach consistently predicts backend PPA and system metrics with an average 7% or less prediction error for the ASIC implementation of two deep learning accelerator platforms, VTA and VeriGOOD-ML, in both a commercial 12 nm process and a research-oriented 45 nm process., Comment: This is an extended version of our work titled "Physically Accurate Learning-based Performance Prediction of Hardware-accelerated ML Algorithms" published in MLCAD 2022

Today's performance analysis frameworks for deep learning accelerators suffer from two significant limitations. First, although modern convolutional neural network (CNNs) consist of many types of layers other than convolution, especially during training, these frameworks largely focus on convolution layers only. Second, these frameworks are generally targeted towards inference, and lack support for training operations. This work proposes a novel performance analysis framework, SimDIT, for general ASIC-based systolic hardware accelerator platforms. The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate. SimDIT is integrated with a backend silicon implementation flow and provides detailed end-to-end performance statistics (i.e., data access cost, cycle counts, energy, and power) for executing CNN inference and training workloads. SimDIT-enabled performance analysis reveals that on a 64X64 processing array, non-convolution operations constitute 59.5% of total runtime for ResNet-50 training workload. In addition, by optimally distributing available off-chip DRAM bandwidth and on-chip SRAM resources, SimDIT achieves 18X performance improvement over a generic static resource allocation for ResNet-50 inference.

While (1) serverless computing is emerging as a popular form of cloud execution, datacenters are going through major changes: (2) storage dissaggregation in the system infrastructure level and (3) integration of domain-specific accelerators in the hardware level. Each of these three trends individually provide significant benefits; however, when combined the benefits diminish. Specifically, the paper makes the key observation that for serverless functions, the overhead of accessing dissaggregated persistent storage overshadows the gains from accelerators. Therefore, to benefit from all these trends in conjunction, we propose Domain-Specific Computational Storage for Serverless (DSCS-Serverless). This idea contributes a serverless model that leverages a programmable accelerator within computational storage to conjugate the benefits of acceleration and storage disaggregation simultaneously. Our results with eight applications shows that integrating a comparatively small accelerator within the storage (DSCS-Serverless) that fits within its power constrains (15 Watts), significantly outperforms a traditional disaggregated system that utilizes the NVIDIA RTX 2080 Ti GPU (250 Watts). Further, the work highlights that disaggregation, serverless model, and the limited power budget for computation in storage require a different design than the conventional practices of integrating microprocessors and FPGAs. This insight is in contrast with current practices of designing computational storage that are yet to address the challenges associated with the shifts in datacenters. In comparison with two such conventional designs that either use quad-core ARM A57 or a Xilinx FPGA, DSCS-Serverless provides 3.7x and 1.7x end-to-end application speedup, 4.3x and 1.9x energy reduction, and 3.2x and 2.3x higher cost efficiency, respectively.

Today, electrical energy plays a significant and conspicuous role in contemporary economies; as a result, governments should place a high priority on maintaining the supply of electrical energy. In order to assess various topologies and enhance the security of power systems, it may be useful to evaluate robustness, dependability, and resilience all at once. This is particularly true when there is a significant amount of renewable energy present. The R3 concept, which consists of these three interrelated characteristics, describes the likelihood that a power system would fail, the potential severity of the repercussions, and the speed at which the system will recover from a failure. This paper uses eight case studies created from the IEEE 24-bus RTS and thoroughly assesses the properties of reliability, robustness, and resilience to highlight the significance of the issue. The sequential Monte Carlo method is used to evaluate reliability, cascade failure simulations are used to evaluate robustness, and a mixed-integer optimization problem is used to study resilience. Different indicators related to each of the three assessments are computed. The significance of the combined analysis is emphasized as the simulation findings are described visually and statistically in a unique three-dimensional manner eventually., Comment: 9 pages, 6 figures

By using non-equilibrium Green's function (NEGF) method and tight-binding (TB) approximation, we investigated a perfect control on spin transport in a zigzag $\alpha^{\prime}$-boron nanoribbon ($\alpha^{\prime}$-BNR) as the must semi-conducting structure of borophene. It has been found that when an $\alpha^{\prime}$-BNR is exposed to an out-of-plane exchange magnetic field, spin splitting occurs for both spin-up and spin-down states in specific ranges of energy. Therefore, the spin polarization of current could be controlled by adjusting the energy of incoming electrons by means of an external back-gate voltage. We focus on the edge manipulation of $\alpha^{\prime}$-BNR by ferro-magnetic (FM) or anti-ferromagnetic (AFM) exchange field which leads to the emergence of a giant magneto resistance and a perfect spin filtering. Local current provides the best picture of spin distribution of current in the nanoribbon. In order to observe the response of the system to the proximity effect of magnetic strips, we calculate the magnetic moment of each site. Then, we show that applying a transverse or perpendicular electric field in the presence of the exchange magnetic field gives another controlling tool on the spin polarization of current in a constant energy. Finally, by simultaneous effect of in-plane and out-of-plane exchange magnetic field on the edges of nanoribbon, we reach a control on spin rotation in the scattering region. Our investigation guarantees the $\alpha^{\prime}$-BNR as a promising two dimensional (2D) structure for spintronic purposes., Comment: 9 pages, 9 figures

This research proposes an incremental welfare consensus method based on flexible alternating current transmission systems (FACTS) and demand response (DR) programs to control transmission network congestion in order to increase the penetration of wind power. The locational marginal prices are used as input by the suggested model to control the FACTS device and DR resources. In order to do this, a cutting-edge two-stage market clearing system is created. In the first stage, participants bid on the market with the intention of maximizing their profits, and the ISO clears the market with the goal of promoting societal welfare. The second step involves the execution of a generation re-dispatch issue in which incentive-based DR and FACTS device controllers are optimally coordinated to reduce the rescheduling expenses for generating firms. Here, a static synchronous compensator and a series capacitor operated by a thyristor are used as two different forms of FACTS devices. A case study on the modified IEEE one-area 24-bus RTS system is then completed. The simulation results show that the suggested interactive DR and FACTS model not only reduces system congestion but also makes the system more flexible so that it can capture as much wind energy as feasible., Comment: 23 pages, 8 figures, 8 tables