Miracle-dz/UniFAR-FTUs huggingface dataset:
Fibrinogen-to-Lymphocyte Ratio Was an Independent Predictor of Lymph Node Metastasis in Patients with Clinically Node-Negative Advanced-Stage Gastric Cancer

Purpose Various hematological indicators have been reported to predict lymph node metastasis (LNM) in gastric cancer (GC) patients, but the relationship between FLR and LNM has not been studied. Therefore, the aim of this study was to evaluate the role of preoperative fibrinogen-to-lymphocyte ratio (FLR) in predicting LNM in patients with clinically node-negative (cN0) advanced gastric cancer (AGC). Patients and Methods We retrospectively reviewed 571 eligible patients with primary AGC adenocarcinoma who underwent radical gastrectomy (discovery cohort). Patients were divided into high and low FLR groups according to the optimal cutoff value determined by Youden index. FLR is an independent predictor of LNM determined by logistic regression and validated in the validation cohort of 207 patients. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of FLR for LNM. The nonlinear relationship between FLR and LNM risk was assessed using restricted cubic spline. Sensitivity analyses were performed according to FLR quartiles to further assess the robustness of the results. The nomogram was built based on FLR and clinicopathological characteristics, and was evaluated by calibration curves, ROC curve analysis and decision curve analysis. Results In the discovery cohort, the area under the curve (AUC) value for FLR to predict LNM was 0.592. There is a linear relationship between the FLR value and the risk of LNM, and the risk of LNM increased with FLR value. High FLR level is an independent risk factor for LNM, and the results of sensitivity analysis robust this finding. The nomogram for individual risk assessment performed well. Furthermore, we verified the FLR was an independent predictor of LNM in the validation cohort. Conclusion FLR was an independent predictor of LNM in patients with cN0 AGC.
Implementation of Nearest Vector Control and Nearest Level Control for Three Phase, Five Level CHB MLI with WAVECT Controller

Today, multilevel converters are considered the most advanced power conversion devices for high-quality applications. Because of the redundancy and modular structure, cascaded h-bridge multilevel inverters have been used in various industrial applications. Depending on the application and the multilevel inverter, many different modulation algorithms have been created or changed. Each one has its own pros and cons. In this paper, the modulation techniques Nearest Vector Control (NVC) and Nearest Level Control (NLC) have been implemented for three phase five level cascade H-bridge multilevel inverter in simulation with the help of MATLAB/Simulink. The NVC and NLC are compared with their total harmonic distortion factor in the output voltage. Using the WAVECT controller in an experimental setup, the results of the simulations are tested. NLC provides a better total harmonic distortion factor and fundamental peak value as compared with NVC.
The effect of stimulatory electrode placement on F-wave latency measurements.

BACKGROUND AND PURPOSE The purpose of this study was to compare the effects of two commonly used stimulating electrode placements on F-wave latency. SUBJECTS Fifty healthy subjects aged 20 to 47 years were tested. METHODS F-waves were obtained from median and ulnar nerves bilaterally. A total of 200 nerves were tested. RESULTS A paired t-test indicated a statistically significant difference in F-wave latency between the two stimulating electrode placements. Stepwise linear regression equations demonstrated that our results were consistent with previously published studies. CONCLUSION AND DISCUSSION Although a statistically significant difference exists between the two techniques, the magnitude of the difference is not likely to be clinically important. Therefore, the most important factor may be to use a consistent technique when investigating potential neuropathies.
Bilateral Cochlear Implant Processing of Coding Strategies With CCi-MOBILE, an Open-Source Research Platform

While speech understanding for cochlear implant (CI) users in quiet is relatively effective, listeners experience difficulty in identification of speaker and sound location. To assist for better residual hearing abilities and speech intelligibility support, bilateral and bimodal forms of assisted hearing is becoming popular among CI users. Effective bilateral processing calls for testing precise algorithm synchronization and fitting between both left and right ear channels in order to capture interaural time and level difference cues (ITD and ILDs). This work demonstrates bilateral implant algorithm processing using a custom-made CI research platform - CCi-MOBILE, which is capable of capturing precise source localization information and supports researchers in testing bilateral CI processing in real-time naturalistic environments. Simulation-based, objective, and subjective testing has been performed to validate the accuracy of the platform. The subjective test results produced an RMS error of ±8.66° for source localization, which is comparable to the performance of commercial CI processors.
Modified Two-Channel LLC Resonant Converter With Optimal Efficiency for Wide Input Voltage Applications

A modified two-channel LLC resonant converter with optimal efficiency for wide input voltage applications. The converter has the same voltage gain and power handing ability as conventional full-bridge (FB) resonant converter, but the hardware cost is lower than conventional FB resonant converter, and the operational frequency range is narrower than conventional resonant converter for the same working condition. It has the characteristics of low input impedance at low input voltage, which is used to provide high voltage gain, and high input impedance at high input voltage, which is used to provide low voltage gain, the above characteristics are analyzed and optimized to improve the efficiency for photovoltaic applications where wide voltage gain is require. A 1 kW prototype is developed, the input voltage ranges from 80 V to 200 V, the output voltage and output power are 400 V and 1 kW, the experimental result shows that the full-load efficiency can reach 96.5% over the entire input voltage range, and the peak efficiency can reach 96.9% at full-load state.
Landscape features control river's confluences water quality and tributary fish composition

Rivers networks represent hierarchical dendritic habitats within terrestrial landscapes and differences in connectivity and land use influence dispersal, and consequently biodiversity patterns. We, therefore, measured variation in water chemistry and fish abundance and related these to a number of landscape characteristics (e.g., wetland, urban, wooded, and agricultural) in the River Klarälven and its 30 permanently flowing tributaries. We hypothesized that these environmental attributes would differ between tributary and main stem habitat and that these differences would be driven by landscape attributes including land use. We found considerable intertributary variation in temperature and nutrient levels, and between the tributaries and the main stem. Generally, water temperature was lower in the tributaries, whereas nutrient levels were higher in the tributaries. The lower water temperature has implications for coldwater fishes, and we found two fishes, burbot and lamprey, associated with coldwater tributaries. We also found an inverse relationship between water quality and anthropogenic land use. Protecting tributaries with low anthropogenic impact will likely become increasingly important with ongoing global warming as they can function as thermal refugia for coldwater fishes. Hence, this study underscores the need to evaluate water courses at regional scales to identify spatial refuges and ensure connectivity.
Stretchable Sensors for Soft Robotic Grippers in Edge-Intelligent IoT Applications

The rapid development of electronic material and sensing technology has enabled research to be conducted on liquid metal-based soft sensors. The application of soft sensors is widespread and has many applications in soft robotics, smart prosthetics, and human-machine interfaces, where these sensors can be integrated for precise and sensitive monitoring. Soft sensors can be easily integrated for soft robotic applications, where traditional sensors are incompatible with robotic applications as these types of sensors show large deformation and very flexible. These liquid-metal-based sensors have been widely used for biomedical, agricultural and underwater applications. In this research, we have designed and fabricated a novel soft sensor that yields microfluidic channel arrays embedded with liquid metal Galinstan alloy. First of all, the article presents different fabrication steps such as 3D modeling, printing, and liquid metal injection. Different sensing performances such as stretchability, linearity, and durability results are measured and characterized. The fabricated soft sensor demonstrated excellent stability and reliability and exhibited promising sensitivity with respect to different pressures and conditions.
Ferrimagnetic Oscillator Magnetometer

Quantum sensors offer unparalleled precision, accuracy, and sensitivity for a variety of measurement applications. We report a compact magnetometer based on a ferrimagnetic sensing element in an oscillator architecture that circumvents challenges common to other quantum sensing approaches such as limited dynamic range, limited bandwidth, and dependence on vacuum, cryogenic, or laser components. The device exhibits a fixed, calibration-free response governed by the electron gyromagnetic ratio. Exchange narrowing in the ferrimagnetic material produces sub-MHz transition linewidths despite the high unpaired spin density ($\sim 10^{22}$ cm$^{-3}$). The magnetometer achieves a minimum sensitivity of 100 fT/$\sqrt{\text{Hz}}$ to AC magnetic fields of unknown phase and a sensitivity below 200 fT/$\sqrt{\text{Hz}}$ over a bandwidth $\gtrsim \! 1$ MHz. By encoding magnetic field in frequency rather than amplitude, the device provides a dynamic range in excess of 1 mT. The passive, thermal initialization of the sensor's quantum state requires only a magnetic bias field, greatly reducing power requirements compared to laser-initialized quantum sensors. With additional development, this device promises to be a leading candidate for high-performance magnetometry outside the laboratory, and the oscillator architecture is expected to provide advantages across a wide range of sensing platforms.
In-Memory Computing for Machine Learning and Deep Learning

In-memory computing (IMC) aims at executing numerical operations via physical processes, such as current summation and charge collection, thus accelerating common computing tasks including the matrix-vector multiplication. While extremely promising for memory-intensive processing such as machine learning and deep learning, the IMC design and realization must face significant challenges due to device and circuit nonidealities. This work provides an overview of the research trends and options for IMC-based implementations of deep learning accelerators with emerging memory technologies. The device technologies, the computing primitives, and the digital/analog/mixed design approaches are presented. Finally, the major device issues and metrics for IMC are discussed and benchmarked.
Accurate Spectrum Map Construction for Spectrum Management Through Intelligent Frequency-Spatial Reasoning

Spectrum maps are of crucial importance for realizing efficient spectrum management in the sixth-generation (6G) wireless communication networks. However, existing spectrum map construction schemes mainly depend on spatial interpolation or just simply exploit the frequency correlation and cannot accurately construct the spectrum map when measurement data at the target frequency are not available. To overcome this challenge, we propose two accurate spectrum map construction schemes using different intelligent frequency-spatial reasoning methods. The frequency correlation among different spectrum maps at different frequencies is fully exploited to construct highly accurate spectrum maps of the frequencies without spectrum data by combining the joint frequency-spatial spectrum representation method with deep learning data-driven techniques. Specifically, a joint three-dimensional spectrum representation model is established and both a novel autoencoder network and a novel conditional generative adversarial network suitable for processing the three-dimensional spectrum data are proposed to realize the intelligent frequency-spatial reasoning. Simulation results demonstrate that our proposed schemes are superior to the benchmark schemes in terms of the spectrum map construction accuracy. Moreover, simulation results demonstrate that our proposed neural networks have a fast convergence speed, which achieves a better tradeoff between the computation efficiency and the construction accuracy.
Predictive Adaptive Model Based Control of a Five-Phase Induction Motor Drive

Abstract This paper presents the analysis for a model reference adaptive system (MRAS)-based sensorless vector control of five-phase induction machine. A linear neural network is designed and trained online by means of back propagation network (BPN) algorithm. Moreover, the neural adaptive model is employed in prediction and simulation modes. The ANN-MRAS-based sensorless operation of a three-phase induction machine is well established and the same principle is extended in this paper for a five-phase induction machine. In simulation and experiments hysteresis current controller is utilised to adjust current and speed. The results obtained with prediction and simulation mode are compared on the basis of various parameters. Full decoupling of rotor flux control and torque control is realised in both predictive and simulation mode. The results show that predictive method provides better dynamic performance.
Effects of Dietary Sodium Nitroprusside and NG-Nitro-L-Arginine Methyl Ester on Growth Performance, Some Organs Development Status and Immune Parameters in Broilers

Simple Summary Nitric oxide (NO) plays an important role in every biological system as a gaseous hormone. NO is generated from arginine by NO synthase (NOS). NOS is inhibited by several arginine analogs, including NG-nitro-L-arginine methyl ester (L-NAME). NO is an important regulator of feeding behavior by suppressing feed intake (FI) as a result of modulating the appetite center through intracerebroventricular and intraperitoneal applications of NOS inhibitors. Feeding behavior can be regulated by peripheral systems. However, the effects of dietary NO donors and inhibitors on feeding behavior and performance are unknown. In this study, the aim was to evaluate the effects of dietary supplementation of sodium nitroprusside (SNP), an NO donor, and L-NAME, an NOS inhibitor, on performance and immunity. SNP suppressed FI and body weight gain in a dose-dependent manner throughout the study, especially in the initial period, and worsened the feed conversion ratio (FCR). L-NAME (100 mg/kg) increased FI and suppressed antibody titers, and L-NAME (25 mg/kg) improved the FCR in the initial period. Therefore, when formulating broiler starter diets, it is important to consider how diet composition will affect the NO metabolism, which is thought to have important effects on performance and immunity. Abstract This study was conducted to determine the effects of dietary supplementation of sodium nitroprusside (SNP), a nitric oxide (NO) donor, and NG-nitro-L-arginine methyl ester (L-NAME), an NO synthase inhibitor, on growth performance, organ development, and immunity in broilers. A total of 560 one-day-old mixed-gender broiler chickens (ROSS 308) were divided into one control and seven experimental groups. The experimental groups were fed a basal diet supplemented with 25, 50, 100, and 200 ppm SNP, and 25, 50, and 100 ppm L-NAME in the starter and grower diets. Body weight gain increased in groups receiving 25–100 ppm L-NAME on day 21 and 100 ppm L-NAME on days 0–42. Feed intake increased in the group receiving 100 ppm L-NAME on all days. The feed conversion ratio improved in the group receiving 25 ppm L-NAME on days 0–21, whereas it worsened in groups with 100 and 200 ppm SNP on days 0–42. Serum antibody titers decreased in the 100 ppm L-NAME group on day 21. In conclusion, the supplementation of the NO synthase inhibitor L-NAME to the broilers’ diet had a positive effect on the performance parameters, whereas the NO donor SNP worsened these parameters, especially on days 0–21.
Energy-Efficient Control of Bearingless Linear Motors

This paper presents a method to minimize the resistive losses in bearingless linear motors. A minimization algorithm is developed for calculating reference currents for a range of reference forces while the effect of spatial harmonics on force production is considered. The results from the algorithm are implemented in the form of lookup tables and artificial neural networks. A comparison between the two implementation methods is presented. Time-domain simulation results are given for motion control of a bearingless linear flux-switching permanent-magnet motor system while using optimal reference currents. Index Terms–Artificial neural networks, bearingless, energy efficiency, linear actuator, magnetic levitation, table lookup.
System Architecture and Software Design of a Handheld High Throughput Phenotyping Device: AgRECA

High-throughput phenotyping using imaging techniques allows for non-destructive, automated assessments of plant morphological and physiological traits. Currently, these techniques require intricate hardware and software to generate factual data. This paper presents a unique data acquisition system, AgRECA (Agricultural REsearch CAmera), with hardware and software design for high throughput phenotyping using RGB, multispectral and thermal imaging and environmental sensors; due to its compact size and ability to be used as a standalone device, it is developed and integrated into any ground-based platform or can be used handheld. The authors also propose a new data format, called NTME, containing all the information captured by AgRECA embedded into a single file.
Drawdown analysis for the renewal insurance risk process

In this paper, we study some drawdown-related quantities in the context of the renewal insurance risk process with general interarrival times and phase-type distributed jump sizes. We make use of some recent results on the two-sided exit problem for the spectrally negative Markov additive process and a fluid flow analogy between certain queues and risk processes to solve for the two-sided exit problem of the renewal insurance risk process. The two-sided exit quantities are later shown to be central to the analysis of drawdown quantities including the drawdown time, the drawdown size, the running maximum (minimum) at the drawdown time, the last running maximum time prior to drawdown, the number of jumps before drawdown and the number of excursions from running maximum before drawdown. Finally, we consider another application of our methodology for the study of the expected discounted dividend payments until ruin.
A rationale for higher ratios of CH4 to CO2 production in warmer anoxic freshwater sediments and soils

Freshwaters emit signi ﬁ cant amounts of CH 4 and CO 2 and, as CH 4 is the stronger greenhouse gas, understanding how carbon gets mineralized to either gas is important. In theory, under anoxia, methanogenesis coupled to fermentation should produce CH 4 and CO 2 in a 1 : 1 ratio. Here, we ﬁ nd that this 1 : 1 ratio is rare, with lower ratios of 0.1 : 1 being typical which con-founds understanding CH 4 in freshwaters. First, using a simple mathematical model we rationalize low ratios as poor methanogenic substrate utilization, including loss to nonmethanogenic processes. Second, we ﬁ nd substrate utilization improves at higher temperatures, especially for hydrogen. This increases CH 4 to CO 2 production ratios exponentially which could drive higher CH 4 to CO 2 emission ratios. Hence, we rationalize how warmer freshwaters may emit more methane.
A Geometrical Approach to Model-Based Vision

This dissertation addresses the problem of recognizing a rigid, planar object described by straight line segments. We would like to determine whether an image contains one or more instances of the object, and if so, report their positions in the image. By modeling the camera as a device that produces an image by applying a scaled orthographic projection to coordinates in the world, we can formulate the problem as one of finding a correspondence between model and image features that is consistent with an affine transformation of the object. To operate reliably, the system must deal with cluttered scenes and poor image quality. In particular, it must not fail when the object in question is partially obscured by other objects. It should not be sensitive to spurious, fragmented, or missing features, and it must accommodate positional errors introduced in the imaging process. Previous approaches have enumerated matchings between model and image features. These approaches suffer from a number of problems. Some systems enumerate matchings which cannot correspond to any pose of the object. Others visit the same matching multiple times. Finally, most systems do not explicitly model positional uncertainty and consequently, cannot guarantee that every instance will be identified. The primary contribution of this work is a combinatorial algorithm that enumerates only those matchings consistent with scaled orthographic projections of the model. The set of consistent matchings is shown to be significantly less complex than the set of all matchings. Searching the set of consistent matchings yields an algorithm guaranteed to detect every instance of an object in a cluttered scene, even in the presence of partial occlusion, feature fragmentation, and positional error. An additional contribution is a recognition system running on a workstation. At the core of the system is a fast and numerically robust implementation of an algorithm that constructs the planar subdivision defined by the boundaries of a set of polygons. Previous implementations of this algorithm were either slow, because they used variable precision arithmetic, or unreliable, because of floating point errors. My implementation, which uses fixed precision arithmetic, has many uses outside of vision.
Enlightening the Darknets: Augmenting Darknet Visibility With Active Probes

Darknets collect unsolicited traffic reaching unused address spaces. They provide insights into malicious activities, such as the rise of botnets and DDoS attacks. However, darknets provide a shallow view, as traffic is never responded. Here we quantify how their visibility increases by responding to traffic with interactive responders with increasing levels of interaction. We consider four deployments: Darknets, simple L4-Responders, vertical L7-Responders bound to specific ports, and DPIpot, a honeypot that responds to all protocols on any port. We contrast these alternatives by analyzing the traffic attracted by each deployment and characterizing how traffic changes throughout the responder lifecycle on the darknet. We show that the deployment of responders increases the value of darknet data by revealing patterns that would otherwise be unobservable. We measure Side-Scan phenomena where once a host starts responding, it attracts traffic to other ports and neighboring addresses. DPIpot uncovers attacks that darknets and L7-Responders would not observe, e.g., large-scale activity on non-standard ports. And we observe how quickly senders can identify and attack new responders. The “enlightened” part of a darknet brings several benefits and offers opportunities to increase the visibility of sender patterns. This information gain is worth taking advantage of, and we, therefore, recommend that organizations consider this option.