Neuro-regenerative behavior of adipose-derived stem cells in aligned collagen I hydrogels

Peripheral nerve injuries persist as a major clinical issue facing the US population and can be caused by stretch, laceration, or crush injuries. Small nerve gaps are simple to treat, and the nerve stumps can be reattached with sutures. In longer nerve gaps, traditional treatment options consist of autografts, hollow nerve guidance conduits, and, more recently, manufactured fibrous scaffolds. These manufactured scaffolds often incorporate stem cells, growth factors, and/or extracellular matrix (ECM) proteins to better mimic the native environment but can have issues with homogenous cell distribution or uniformly oriented neurite outgrowth in scaffolds without fibrous alignment. Here, we utilize a custom device to fabricate collagen I hydrogels with aligned fibers and encapsulated adipose-derived mesenchymal stem cells (ASCs) for potential use as a peripheral nerve repair graft. Initial results of our scaffold system revealed significantly less cell viability in higher collagen gel concentrations; 3 mg/mL gels showed 84.8 ± 7.3% viable cells, compared to 6 mg/mL gels viability of 76.7 ± 9.5%. Mechanical testing of the 3 mg/mL gels showed a Young's modulus of 6.5 ± 0.8 kPa nearly matching 7.45 kPa known to support Schwann cell migration. Further analysis of scaffolds coupled with stretching in vitro revealed heightened angiogenic and factor secretion, ECM deposition, fiber alignment, and dorsal root ganglia (DRG) neurite outgrowth along the axis of fiber alignment. Our platform serves as an in vitro testbed to assess neuro-regenerative potential of ASCs in aligned collagen fiber scaffolds and may provide guidance on next-generation nerve repair scaffold design.

FPGA implemented testbed in 8-by-8 and 2-by-2 OFDM-MIMO channel estimation and design of baseband transceiver

In this study, a baseband OFDM-MIMO framework with channel timing and estimation synchronization is composed and executed utilizing the FPGA innovation. The framework is prototyped in light of the IEEE 802.11a standard and the signals transmitted and received utilizing a data transmission of 20 MHz. With the assistance of the QPSK tweak, the framework can accomplish a throughput of 24 Mbps. Besides, the LS formula is executed and the estimation of a frequency-specific fading channel is illustrated. For the rough estimation of timing, MNC plan is examined and actualized. Above all else, the whole framework is demonstrated in MATLAB and a drifting point model is set up. At that point, the altered point model is made with the assistance of Simulink and Xilinx's System Generator for DSP. In this way, the framework is incorporated and actualized inside of Xilinx's ISE tools and focused to Xilinx Virtex 5 board. In addition, an equipment co-simulation is contrived to decrease the preparing time while figuring the BER of the fixed point model. The work concentrates on above all else venture for further examination of planning creative channel estimation strategies towards applications in the fourth era (4G) mobile correspondence frameworks.

Effects of wireless packet loss in industrial process control systems

Timely and reliable sensing and actuation control are essential in networked control. This depends on not only the precision/quality of the sensors and actuators used but also on how well the communications links between the field instruments and the controller have been designed. Wireless networking offers simple deployment, reconfigurability, scalability, and reduced operational expenditure, and is easier to upgrade than wired solutions. However, the adoption of wireless networking has been slow in industrial process control due to the stochastic and less than 100% reliable nature of wireless communications and lack of a model to evaluate the effects of such communications imperfections on the overall control performance. In this paper, we study how control performance is affected by wireless link quality, which in turn is adversely affected by severe propagation loss in harsh industrial environments, co-channel interference, and unintended interference from other devices. We select the Tennessee Eastman Challenge Model (TE) for our study. A decentralized process control system, first proposed by N. Ricker, is adopted that employs 41 sensors and 12 actuators to manage the production process in the TE plant. We consider the scenario where wireless links are used to periodically transmit essential sensor measurement data, such as pressure, temperature and chemical composition to the controller as well as control commands to manipulate the actuators according to predetermined setpoints. We consider two models for packet loss in the wireless links, namely, an independent and identically distributed (IID) packet loss model and the two-state Gilbert-Elliot (GE) channel model. While the former is a random loss model, the latter can model bursty losses. With each channel model, the performance of the simulated decentralized controller using wireless links is compared with the one using wired links providing instant and 100% reliable communications. The sensitivity of the controller to the burstiness of packet loss is also characterized in different process stages. The performance results indicate that wireless links with redundant bandwidth reservation can meet the requirements of the TE process model under normal operational conditions. When disturbances are introduced in the TE plant model, wireless packet loss during transitions between process stages need further protection in severely impaired links. Techniques such as retransmission scheduling, multipath routing and enhanced physical layer design are discussed and the latest industrial wireless protocols are compared.

Bryophytes as metabolic engineering platforms

Metabolic engineering of plants offers significant advantages over many microbial systems such as cost-effective scalability and carbon autotrophy. Bryophytes have emerged as promising testbeds for plant metabolic engineering due to their rapid transformation and haploid-dominant lifecycle. The liverwort Marchantia polymorpha and the moss Physcomitrium patens are the best studied bryophytes and an expanding toolkit of genetic resources for both species allows for efficient pathway engineering. Bryophyte metabolism, while broadly conserved with seed plants, exhibits distinct features such as high diversity and amounts of terpenoids and very long-chain polyunsaturated fatty acids (vlcPFAs). In this review, we summarise the relatively limited understanding of bryophyte metabolism and how it diverges from seed plants. We argue that the success of bryophytes as testbed species will require new quantitative knowledge of fluxes in central metabolism and especially those that facilitate high rates of terpenoid and vlcPFA biosynthesis.

A dataset to train intrusion detection systems based on machine learning models for electrical substations

The growing integration of Information and Communication Technology into Operational Technology environments in electrical substations exposes them to new cybersecurity threats. This paper presents a comprehensive dataset of substation traffic, aimed at improving the training and benchmarking of Intrusion Detection Systems (IDS) installed in these facilities that are based on machine learning techniques. The dataset includes raw network captures and flows from real substations, filtered and anonymized to ensure privacy. It covers the main protocols and standards used in substation environments: IEC61850, IEC104, NTP, and PTP. Additionally, the dataset includes traces obtained during several cyberattacks, which were simulated in a controlled laboratory environment, providing a rich resource for developing and testing machine learning models for cybersecurity applications in substations. A set of complementary tools for dataset creation and preprocessing are also included to standardize the methodology, ensuring consistency and reproducibility. In summary, the dataset addresses the critical need for high-quality, targeted data for tuning IDS at electrical substations and contributes to the advancement of secure and reliable power distribution networks.

A three tier rapid mass programming method

Various research activities require the programming of a large numbers of devices. This programming can be difficult to co-ordinate and organise, and requires considerable labour time. These issues often mean that testing on real hardware is abandoned or taken only to small scale implementation thus limiting the real-world findings. The method described in this paper adopts a three-tiered approach to programming large numbers of devices. Tier 1 is comprised of a single Master Controller which is networked to individual tower modules, these towers form the final 2 tiers with the Local Controller as tier 2 and up-to 15 target devices forming tier 3. The Master Controller co-ordinates and distributes the code for each device to the Local Controller which then programs the target devices. In the domain of networking this allows for:

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Large networks of varied protocols to be programmed quickly, since towers are programmed in parallel, additional towers don’t extend programming times.

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Distributed networks are possible since towers are controlled over Ethernet.

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Dramatically reduced labour time and defect rates due to human error in setting up devices.

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This paper presents the implementation of this method for IoT Networking research with ESP-01 Target devices.