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Controlling the intensity of emitted light and charge current is the basis of transferring and processing information 1 . By contrast, robust information storage and magnetic random-access memories are implemented using the spin of the carrier and the associated magnetization in ferromagnets 2 . The missing link between the respective disciplines of photonics, electronics and spintronics is to modulate the circular polarization of the emitted light, rather than its intensity, by electrically controlled magnetization. Here we demonstrate that this missing link is established at room temperature and zero applied magnetic field in light-emitting diodes 2 , 3 , 4 , 5 , 6 – 7 , through the transfer of angular momentum between photons, electrons and ferromagnets. With spin–orbit torque 8 , 9 , 10 – 11 , a charge current generates also a spin current to electrically switch the magnetization. This switching determines the spin orientation of injected carriers into semiconductors, in which the transfer of angular momentum from the electron spin to photon controls the circular polarization of the emitted light 2 . The spin–photon conversion with the nonvolatile control of magnetization opens paths to seamlessly integrate information transfer, processing and storage. Our results provide substantial advances towards electrically controlled ultrafast modulation of circular polarization and spin injection with magnetization dynamics for the next-generation information and communication technology 12 , including space–light data transfer. The same operating principle in scaled-down structures or using two-dimensional materials will enable transformative opportunities for quantum information processing with spin-controlled single-photon sources, as well as for implementing spin-dependent time-resolved spectroscopies. The helicity of light from a light-emitting diode can be electrically controlled by spin–orbit torque effects, enabling a seamless integration of magnetization dynamics with photonics.

In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X‐ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF). DOMAS is a data management software framework specifically designed and developed for advanced light sources in China. It effectively tackles challenges related to data storage, metadata cataloguing, data transfer and data access.

Data transfer between electronic health records (EHRs) at the point of care and electronic data capture (EDC) systems for clinical research is still mainly carried out manually, which is error-prone as well as cost- and time-intensive. Automated digital transfer from EHRs to EDC systems (EHR2EDC) would enable more accurate and efficient data capture but has so far encountered technological barriers primarily related to data format and the technological environment: in Germany, health care data are collected at the point of care in a variety of often individualized practice management systems (PMSs), most of them not interoperable. Data quality for research purposes within EDC systems must meet the requirements of regulatory authorities for standardized submission of clinical trial data and safety reports. We aimed to develop a model for automated data transfer as part of an observational study that allows data of sufficient quality to be captured at the point of care, extracted from various PMSs, and automatically transferred to electronic case report forms in EDC systems. This required addressing aspects of data security, as well as the lack of compatibility between EHR health care data and the data quality required in EDC systems for clinical research. The SaniQ software platform (Qurasoft GmbH) is already used to extract and harmonize predefined variables from electronic medical records of different Compu Group Medical-hosted PMSs. From there, data are automatically transferred to the validated AlcedisTRIAL EDC system (Alcedis GmbH) for data collection and management. EHR2EDC synchronization occurs automatically overnight, and real-time updates can be initiated manually following each data entry in the EHR. The electronic case report form (eCRF) contains 13 forms with 274 variables. Of these, 5 forms with 185 variables contain 67 automatically transferable variables (67/274, 24% of all variables and 67/185, 36% of eligible variables). This model for automated data transfer bridges the current gap between clinical practice data capture at the point of care and the data sets required by regulatory agencies; it also enables automated EHR2EDC data transfer in compliance with the General Data Protection Regulation (GDPR). It addresses feasibility, connectivity, and system compatibility of currently used PMSs in health care and clinical research and is therefore directly applicable. This use case demonstrates that secure, consistent, and automated end-to-end data transmission from the treating physician to the regulatory authority is feasible. Automated data transmission can be expected to reduce effort and save resources and costs while ensuring high data quality. This may facilitate the conduct of studies for both study sites and sponsors, thereby accelerating the development of new drugs. Nevertheless, the industry-wide implementation of EHR2EDC requires policy decisions that set the framework for the use of research data based on routine PMS data.

Data-driven diagnostic methods are beneficial for fault diagnosis in driving motors. However, insufficient monitoring data during actual diagnosis limit their application. Although various methods have been proposed, they have limitations. This paper proposes a data transfer generation method based on the distribution difference metric and residual cycle-consistent generative adversarial network (Res-CycleGAN) to overcome these limitations. First, a data layer is designed to select the most similar data from other similar datasets. Then, a Res-CycleGAN-based model layer that can generate high-quality target fault data by utilizing fault features from the selected most similar data is constructed. Finally, a strategy layer for the diagnostic model is proposed to make rational use of different fault data. The proposed method is verified using a motor dataset from a mechanical fault simulation (MFS) platform. A set of comprehensive verification experiments is designed for each layer. The results demonstrate that the proposed method is highly effective.

The Internet of Things (IoT) is a network of various interconnected objects capable of collecting and exchanging data without human interaction. These objects have limited processing power, storage space, memory, bandwidth and energy. Therefore, due to these limitations, data transmission and routing are challenging issues where data collection and analysis methods are essential. The Routing Protocol for Low-power and Lossy Networks (RPL) is one of the best alternatives to ensure routing in LoWPAN6 networks. However, RPL lacks scalability and basically designed for non-dynamic devices. Another drawback of the RPL protocol is the lack of load balancing support, leading to unfair distribution of traffic in the network that may decrease network efficiency. This study proposes a novel RPL-based routing protocol, QFS-RPL, using Q-learning algorithm policy and ideation from the Fisheye State Routing protocol. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. To evaluate the performance of the proposed method, the Contiki operating system and Cooja simulator have been used in scenarios with mobile and stationary nodes and random network topologies. The results have been compared with RPL and mRPL. We have developed an algorithm for ease of data transfer in the IoT, which provides better performance than existing protocols, especially when dealing with a mobile network. The simulation outputs revealed that our scheme performs more efficiently in load balancing, number of table entries, Packet Delivery Ratio (PDR), End-to-End (E2E) latency, network throughput, convergence speed, control packet overhead and Remaining Useful Lifetime in designed scenarios compared to other methods. Moreover, the simulation results show an out-performance of rival schemes in terms of remaining energy and network lifetime.

Currently, cloud computing is being used in many scientific areas like geoscience, DNA sequencing, healthcare, and many more. In a cloud computing environment, a Virtual Machine (VM) is a virtualized instance of any computer that can execute almost all the tasks of a computer. VM migration can be referred to as a task to move VMs from one physical machine to another physical machine. During VM migration, there are many issues, such as fault occurrence, seamless connectivity, and maintaining the quality of service. The cloud service provider has to anticipate the server downtime and various other delays like slow processing of user’s request due to the occurrence of a fault, improper allocation of VMs, and many more. A reliable and advanced live migration optimization technique has been proposed in this work for a trustworthy cloud computing environment. There are three main algorithms in the proposed scheme considering the total migration time, namely Host Selection Migration Time (HSMT), VM Reallocation Migration Time (VMRMT), and VM Reallocation Bandwidth Usage (VMRBU). These algorithms support to enhance the performance of cloud computing environments by minimizing the migration time. The proposed scheme has been compared to some existing approaches, namely Kernel-based Virtual Machines (KVM) and Pareto Optimized Framework for Seamless VM Live Migration (POF-SVLM), to evaluate its performance. The results show that the proposed scheme reduces the total cores of CPU by 60-70%, downtime by 70-80%, data transfer rate by 40-50%, and migration time by 40-50%.

Classical communication schemes exploiting wave modulation are the basis of our information era. Quantum information techniques with photons enable future secure data transfer in the dawn of decoding quantum computers. Here we demonstrate that also matter waves can be applied for secure data transfer. Our technique allows the transmission of a message by a quantum modulation of coherent electrons in a biprism interferometer. The data is encoded in the superposition state by a Wien filter introducing a longitudinal shift between separated matter wave packets. The transmission receiver is a delay line detector performing a dynamic contrast analysis of the fringe pattern. Our method relies on the Aharonov–Bohm effect but does not shift the phase. It is demonstrated that an eavesdropping attack will terminate the data transfer by disturbing the quantum state and introducing decoherence. Furthermore, we discuss the security limitations of the scheme due to the multi-particle aspect and propose the implementation of a key distribution protocol that can prevent active eavesdropping.

To guarantee the integrity of cloud data, plenty of cloud storage auditing schemes are proposed. In cloud storage, when a company is purchased by another company, the corresponding data of the acquired company will be transferred to the acquiring company. In addition, there may be duplicate files between the acquiring company and the acquired company. To solve the above problems, we propose a secure cloud storage auditing scheme with deduplication and efficient data transfer. We design a novel signature transformation method, in which the signatures of the acquired company can be efficiently transformed into the signatures of the acquiring company with the assistance of the cloud. Using the above method, the acquiring company does not require to recompute the data signatures for the transferred data when cloud data is transferred. Furthermore, to improve the efficiency of cloud storage, we use data deduplication method, in which the cloud stores only a single copy of the duplicate files. The security proof and performance analysis show that the proposed scheme is secure and efficient.

Software-Defined Networking (SDN), with segregated data and control planes, provides faster data routing, stability, and enhanced quality metrics, such as throughput (Th), maximum available bandwidth (Bd(max)), data transfer (DTransfer), and reduction in end-to-end delay (D(E-E)). This paper explores the critical work of deploying SDN in large­scale Data Center Networks (DCNs) to enhance its Quality of Service (QoS) parameters, using logically distributed control configurations. There is a noticeable increase in Delay(E-E) when adopting SDN with a unified (single) control structure in big DCNs to handle Hypertext Transfer Protocol (HTTP) requests causing a reduction in network quality parameters (Bd(max), Th, DTransfer, D(E-E), etc.). This article examines the network performance in terms of quality matrices (bandwidth, throughput, data transfer, etc.), by establishing a large–scale SDN-based virtual network in the Mininet environment. The SDN network is simulated in three stages: (1) An SDN network with unitary controller-POX to manage the data traffic flow of the network without the server load management algorithm. (2) An SDN network with only one controller to manage the data traffic flow of the network with a server load management algorithm. (3) Deployment of SDN in proposed control arrangement (logically distributed controlled framework) with multiple controllers managing data traffic flow under the proposed Intelligent Sensing Server Load Management (ISSLM) algorithm. As a result of this approach, the network quality parameters in large-scale networks are enhanced.

A non-contact slip ring is proposed in this paper. The bidirectional simultaneous wireless power and data transfer (BD-SWPDT) technology is utilized to transfer power and data bidirectionally. A bidirectional constant-voltage LC hybrid compensation topology is proposed, which utilizes the LC series parallel structure to have different equivalent models at different frequencies. By using different operating frequencies for forward and reverse power transfer, the system’s forward and reverse transfer can be equivalent to different constant-voltage output compensation topologies. The resonant parameters of the system are designed to achieve consistent voltage gain for forward and reverse power transfer. And based on this topology, a data carrier injection method is designed to achieve high Signal Noise Ratio (SNR) simultaneous data transfer. To improve the flexibility of non-contact slip ring installation, a caliper-type coupling structure is proposed. Finally, the feasibility of the proposed method is verified through experiments, achieving a forward and reverse output power of 200 W and half duplex communication with a data rate of 19.2 kbps.