Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-Term Outcome of Patients With a Hematologic Malignancy and Multiple Organ Failure Admitted at the Intensive Care

Objectives: Historically, patients with a hematologic malignancy have one of the highest mortality rates among cancer patients admitted to the ICU. Therefore, physicians are often reluctant to admit these patients to the ICU. The aim of our study was to examine the survival of patients who have a hematologic malignancy and multiple organ failure admitted to the ICU. Design: This retrospective cohort study, part of the HEMA-ICU study group, was designed to study the survival of patients with a hematologic malignancy and organ failure after admission to the ICU. Patients were followed for at least 1 year. Setting: Five university hospitals in the Netherlands. Patients: One-thousand ninety-seven patients with a hematologic malignancy who were admitted at the ICU. Interventions: None. Measurements and Main Results: Primary outcome was 1-year survival. Organ failure was categorized as acute kidney injury, respiratory failure, hepatic failure, and hemodynamic failure; multiple organ failure was defined as failure of two or more organs. The World Health Organization performance score measured 3 months after discharge from the ICU was used as a measure of functional outcome. The 1-year survival rate among these patients was 38%. Multiple organ failure was inversely associated with long-term survival, and an absence of respiratory failure was the strongest predictor of 1-year survival. The survival rate among patients with 2, 3, and 4 failing organs was 27%, 22%, and 8%, respectively. Among all surviving patients for which World Health Organization scores were available, 39% had a World Health Organization performance score of 0–1 3 months after ICU discharge. Functional outcome was not associated with the number of failing organs. Conclusions: Our results suggest that multiple organ failure should not be used as a criterion for excluding a patient with a hematologic malignancy from admission to the ICU.
Two‐Layered and Stretchable e‐Textile Patches for Wearable Healthcare Electronics

Wearable healthcare systems require skin‐adhering electrodes that allow maximal comfort for patients as well as an electronics system to enable signal processing and transmittance. Textile‐based electronics, known as “e‐textiles,” is a platform technology that allows comfort for patients. Here, two‐layered e‐textile patches are designed by controlled permeation of Ag‐particle/fluoropolymer composite ink into a porous textile. The permeated ink forms a cladding onto the nanofibers in the textile substrate, which is beneficial for mechanical and electrical properties of the e‐textile. The printed e‐textile features conductivity of ≈3200 S cm−1, whereas 1000 cycles of 30% uniaxial stretching causes the resistance to increase only by a factor of ≈5, which is acceptable in many applications. Controlling over the penetration depth enables a two‐layer design of the e‐textile, where the sensing electrodes and the conducting traces are printed in the opposite sides of the substrate. The formation of vertical interconnected access is remarkably simple as an injection from a syringe. With the custom‐developed electronic circuits, a surface electromyography system with wireless data transmission is demonstrated. Furthermore, the dry e‐textile patch collects electroencephalography with comparable signal quality to commercial gel electrodes. It is anticipated that the two‐layered e‐textiles will be effective in healthcare and sports applications.
The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks

The cognitive behavior of online user groups on information promotes the dissemination of information. The trajectories of information dissemination in social networks can be described by treelike cascades, and the distribution of the sizes of these cascades can capture the distribution of popularity of a social network. Numerous studies have shown that the cascade size distribution follows fat-tail distributions, including power-law distribution and bimodal distribution; however, the underlying characteristic of this highly skewed distribution lacks quantitative experimental analysis. Based on the stochastic epidemic-like information dissemination model, namely, the susceptible view forward removed model, this paper explores the impact of the content attractiveness and influence, and information source on the cascade size distribution through computational experiments. On the one hand, we find that when the mean value of the information influence and attractiveness is small, the cascade sizes follow a power-law distribution, and the larger the variance, the heavier the tail. On the other hand, the more random the distribution of information sources in social networks, the smaller the slope of the power-law cascade size distribution. Our findings quantitatively reveal the causality of power-law cascade size distribution from computational experiments, clarify the role of information attractiveness, influence, and sources on the distribution of popularity in social networks.
Molecular and disease association of gestational diabetes mellitus affected mother and placental datasets reveal a strong link between insulin growth factor (IGF) genes in amino acid transport pathway: A network biology approach

Discerning the relationship between molecules involved in diseases based on their underlying biological mechanisms is one of the greatest challenges in therapeutic development today. Gestational diabetes mellitus (GDM) is one of the most common complications during pregnancy, which adversely affects both mothers and offspring during and after pregnancy. We have constructed two datasets of (GDM associated genes from affected mother and placenta to systematically analyze and evaluate their interactions like gene–gene, gene–protein, gene–microRNA (miRNA), gene–transcription factors, and gene‐associated diseases to enhance our current knowledge, which may lead to further advancements in disease diagnosis, prognosis, and treatment. The results identify the key genes with respect to maternal dataset as insulin receptor, insulin (INS), leptin (LEP), glucokinase, and hepatocyte nuclear factor 1 alpha, whereas from placenta include insulin‐like growth factor 1, growth hormone receptor, and breast cancer anti‐estrogen resistance protein 1, which are found to be highly enriched in pancreas, ovary, adipocyte, heart, and placental tissues. The key transcription factors include Sp1 transcription factor, pancreatic and duodenal homeobox 1, and hepatocyte nuclear factor 4 alpha, whereas miRNA includes has‐miR‐5699‐5p and has‐miR‐3158‐3p. The study also reveals that GDM has associations with diseases like type I and II diabetes mellitus, obesity, and preeclampsia. More significantly, we could trace out a significant connection between the key molecules like LEP and placental growth hormone from mother and placental dataset, which plays a critical role in INS secretion, INS signaling, and β‐cell dysfunction pathways.
A Dynamic State Estimator Based Control for Power System Damping

Interarea power oscillation damping enhancement in large interconnected power systems is often accomplished using supplementary controls at the synchronous machines involved and with the use of flexible ac transmission system devices. The power system literature has demonstrated that wide area signals could be more effective than the local signals for these controllers. In some cases where phasor measurement units are used, all the required control signals may not be available. To alleviate these cases, control signals are obtained using estimated signals from a “dynamic state estimator,” and a robust supplementary controller is deployed. In this paper, the cited strategy is illustrated using a static var compensator. The feasibility of the proposed method has been demonstrated on the IEEE 50-generator test system.
A Specification-based State Replication Approach for Digital Twins

Digital twins play a key role in realizing the vision of a smart factory. While this concept is often associated with maintenance, optimization, and simulation, digital twins can also be leveraged to enhance the security and safety of cyber-physical systems (CPSs). In particular, digital twins can run in parallel to a CPS, allowing to perform a security and safety analysis during operation without the risk of disrupting live systems. However, replicating states of physical devices within a CPS in functionally equivalent virtual replicas, so that they precisely mirror the internal behavior of their counterparts, is an open research topic. In this paper, we propose a novel state replication approach that first identifies stimuli based on the system's specification and then replicates them in a virtual environment. We believe that replicating states of CPSs is a prerequisite for a multitude of security and safety enhancing features that can be implemented on the basis of digital twins. To demonstrate the feasibility of the specification-based state replication approach, we provide a prototypical implementation and evaluate it in an experimental CPS test bed. The results of this paper show that attacks against CPSs can be successfully detected by leveraging the proposed state replication approach.
Taxi Detection Based on the Sliding Color Histogram Matching

The Adaboost vehicle detection algorithm based on Haar feature has a good performance in real-time and accuracy. However, the method has a lot of omission and error detection in the detection of Special vehicle under complicated traffic flow. In this paper, a method of taxi window area detection is proposed to replace vehicle detection. At the same time, a method of sliding color histogram matching is proposed to reduce the error detection. Finally, the traffic surveillance video is used to verify the algorithm, Detection results proves that the algorithm has a good accuracy and real-time performance for the detection of taxi vehicles.
Time-Varying Channel Estimation for Millimeter Wave Multiuser MIMO Systems

Channel estimation for downlink millimeter wave (mmWave) multiuser multiple-input-multiple-output (MU-MIMO) communications over a time-varying channel is demandingly challenging. This is due to a massive number of channel coefficients to be estimated and the severe propagation loss of mmWave transmissions. To tackle this problem, this paper proposes an effective channel estimation scheme for time-varying mmWave channels. Discovering the property that the variations of angles of arrival/departure (AoAs/AoDs) are much slower than that of path gains, we propose a novel transmission frame structure, where the channel estimation is decoupled into two separated stages including AoAs/AoDs estimation followed by path gains estimation. In the first stage, the sparse nature of mmWave channels is leveraged to formulate the AoAs/AoDs estimation into a block-sparse signal recovery problem, and we develop an adaptive angle estimation algorithm to estimate the AoAs/AoDs. In the second stage, the pilot beamforming based on the estimated AoAs/AoDs is designed aiming for maximizing the desired pilot power so that the path gains can be accurately estimated. Simulation results demonstrate that the proposed scheme can effectively estimate the time-varying mmWave channels with a small number of pilots.
A 0.78-µW 96-Ch. Deep Sub-Vt Neural Spike Processor Integrated with a Nanowatt Power Management Unit

We present a sub-µW Neural Spike Processor integrated with a Power Management Unit (PMU) for on-implant processing in motor intention decoding, demonstrating: (i) among the highest level of integration including spike detection, feature extraction, sorting, the first half of decoding, which reduces wireless data rate by more than 4 orders of magnitude; (ii) on-chip PMU integration enabling the system directly powered by harvesters; (iii) the lowest power dissipation of 0. 78µW for 96 channels, 21x lower than the prior art at a comparable/better accuracy.
Analysis and Discussion of the Indirect Testing Method for the Losses of Permanent Magnet Synchronous Machines

The losses of permanent magnet synchronous machine (PMSM) are always cared in the machine system, since the losses mainly determine the temperature-rise and efficiency of the machine. An indirect-testing method for the losses of PMSM has been proposed in order to measure the losses without any mechanical load in the test. The aims of this paper are to analyze the influence factors on this method and discuss the application of this method. The influences of the pole–slot combination, saturation, harmonic content, and frequency on the test error are analyzed. To validate the analysis, the indirect-testing losses and the direct-testing losses are compared with the test of a PMSM prototype.
Digital artery deformation on movement of the proximal interphalangeal joint

This study aimed to characterize in vivo human digital arteries in three-dimensions using photoacoustic tomography in order to understand the specific mechanism underlying arterial deformation associated with movement of the proximal interphalangeal joint. Three-dimensional morphological data were obtained on the radialis indicis artery (radial artery of the index finger) at different angles of the joint. The association between increased curvature of the deformation and the anatomical region was assessed. Characteristic morphological deformations in areas of major deformation were determined. The deformation of the artery was characterized by three consecutive curves in juxta-articular regions, which were particularly noticeable when the joint was flexed at an angle of ≥ 60°. The change in the curvature of the deformation during 30°–90° of flexion was lower in middle-aged individuals than in young individuals. Better understanding of the mechanism underlying deformation of the digital arteries may contribute to advancements in flap procedures and rehabilitation strategies after digital artery repair.
An Efficient Fuzzy Based Edge Estimation for Iris Localization and Pupil Detection in Human Eye for Automated Cataract Detection System

Presently Digital Image Processing based automated diagnosis of medical images is very popular area of research. This technique cuts short the diagnosis time and produces results with high degree of accuracy with least variations in diagnostic opinion. For such applications, basic parameter mapping and evolving image processing techniques play a very crucial role. Automated Cataract detection through digital eye images requires circular pupil extraction from eye image. Conventional Hough transform fails to extract the circular pupil area due to presence of similar ring structure in Iris and in between region. This paper explains the distinct approach in iris localization and pupil extraction from eye images by extraction of edges using fuzzy logic approach resulting into effective edge estimation technique and later use of these inputs to circular Hough Transform for detection of both. The results of proposed approach has been compared on MMU1, UTIRIS, and IIT-Delhi eye biometric databases. Experimental results explain effective iris localization and pupil extraction with greater accuracy and lower computational time. Results show that the proposed algorithm can be effectively applied in automated cataract detection where circular regions: Iris Localization and subsequent pupil extraction are key to the detection methodology.
Stochastic Correction of Boundary Conditions during Liver Surgery

Boundary conditions play an essential role in form-ing the predictive capacity of a biomechanical model of liver, which is used to facilitate the intra-operative navigation during surgery operation. However, these conditions are determined mainly by ligaments, and its the properties cannot be measured reliably. Therefore, the idea is to propose a data assimilation approach, where the deformation of the liver tissue is used to estimate the organ attachments. The behavior of liver can be captured on stereo camera, ultrasound, or some other modality, but, due to the observational errors, there is a high amount of uncertainty in the system. One possible option is to model the boundary conditions as stochastic parameters and to use the Kalman filtering method for their estimation. This filter obtains the result as iterative process based on computation of the weighted average between observed data and the physically-based model that simulates behavior of liver. Based on an approach previously proposed in [1], this work is focused on estimation of the modified boundary conditions, which is needed when ligaments are cut during surgery process. This estimation incorporates changes in liver shape movement under various manipulations. The method is evaluated using synthetic data in two scenarios: the deformation of parallelepiped mesh under single direction straight forward deformation and the deformation of liver model under more complex movements.
UGC: Real-Time, Ultra-Robust Feature Correspondence via Unilateral Grid-Based Clustering

Quickly establishing reliable correspondence between two feature sets is a challenging task for feature matching. However, the key to successful feature matching is not only matching robustness but also the precision and real-time performance. It is difficult to achieve both efficiency and efficacy using the current algorithms. In this paper, we propose unilateral grid-based clustering (UGC), which creates a unilateral grid of an image’s features and meanshift clustering constraints of the other image correspondence features. UGC removes a large number of mismatches using clustering center statistical analysis of the match feature points in a grid region. For low texture, blur and wide-baselines feature matching of images, UGC provides a real-time, ultra-robust correspondence system. Extensive experiments on image data sets demonstrate the higher precision and real-time performance of UGC, which outperforms current state-of-the-art methods, including conditions such as low contrast and high exposure.
A Method for Constructing an ADVI-Based Statistical Discrimination Model for Hammering Inspection of Highly Mixed Cast-Iron Parts

This paper describes a method for constructing a statistical discrimination model to inspect defective cast-iron parts such as graphite-spheroidized defective products by hammering test used in multi-product mass production. The hammering-sound signal includes multiple resonance-line spectra those frequencies vary by defect. For constructing the model, only non-defective hammering-sound data that can be collected from the production line is input, and a distribution function that fits frequency distribution of each resonance-line spectrum is estimated. Since the frequency distribution shows multimodality and asymmetry, estimation is performed by using automatic differentiation variational inference (ADVI) with a mixed normal distribution function. A confidence interval of the obtained distribution function is then regarded as non-defective-product existence section, and the discrimination model is automatically constructed by connecting the sections of all the resonance-line spectra in the audible range. Then, products outside the sections are discriminated as defective products. Results of experiment confirmed that it is possible to achieve hammer-test inspection with detection rate of 100% and prevent overlooking of defective products.
Reverse Engineering of Cryptographic Cores by Structural Interpretation Through Graph Analysis

Reverse Engineering (RE) of integrated circuits (ICs) is generally defined as the recovery process of unknown system internals. While advanced solutions to extract gate-level netlists from a target device exist, the possibility of subsequent interpretation is limited, in particular in complex devices. This paper introduces a novel graph based method for identifying intellectual property (IP) blocks within an unstructured netlist by using graph analysis. Without prior knowledge of the targeting device, firstly, associated structural components, secondly, functional components and, thirdly, the initial design hierarchy will be identified by this method. Experimental results using realworld netlists including cryptographic cores show the efficiency and efficacy of the presented method.
Plasma Antioxidant Potential at Admission is Associated with Length of ICU Stay in Child with Sepsis: A Pilot Study

Abstract Objective: To assess the relationship between biomarkers of oxidative stress (OS) and the length of stay in intensive care units (LSICU) in septic children. Methods: Clinical parameters and biomarkers of OS were measured in 16 children admitted for sepsis in an intensive care unit. The associations between biomarkers of OS and the LSICU were assessed by linear correlation. Multiple linear regression models were constructed to adjust other variables. Results: The mean of LSICU was 7.13 ± 4.17 days. LSICU was associated with the catalase activity (rho =0.56, p-value =0.024) and the ferric reducing ability of plasma (FRAP, r = 0.73, p-value =0.001). However, only FRAP at ICU admission was independently associated with LSICU, which rose 0.21 days for each 10 µmol/l of increase in the FRAP level. Conclusion: We conclude for first time that FRAP level at ICU admission is independently associated with LSICU in pediatric patients.
Optimal Design of A Two-stage Wyner-Ziv Scalar Quantizer with Degraded Side Information

This work addresses the optimal design of a two-stage Wyner-Ziv scalar quantizer with degraded side information (SI). We assume that binning is performed optimally and address the design of the nested quantizer partitions. The optimization problem is formulated as minimizing a weighted sum of distortions and rates. The proposed solution algorithm is globally optimal when the source and SI are discrete, while the partition cells are contiguous. The algorithm is based on solving the single source or the all-pairs minimum-weight path problem in certain weighted directed acyclic graphs. A so-called partial Monge property is additionally introduced and a faster solution algorithm exploiting this property is proposed. Experimental results assess the practical performance of the proposed scheme.
The Influence of Metacognitive Beliefs on Sleeping Difficulties in Older Adults.

BACKGROUND Sleep has a crucial role in physical and mental health across the lifespan. In addition to an excessive intrusive sleep-related cognitive activity, another factor that may influence sleep quality in old age is sleep-related metacognitive activity, such as metacognitive beliefs about sleeping difficulties, and night-time thought control strategies. Here, we aimed to assess the relationship between sleep-related metacognitive beliefs, thought control strategies, excessive intrusive cognitive activities, such as dysfunctional beliefs about sleep, or objective and/or perceived sleep disruptions in elderly people. METHOD Sleep-related metacognitive beliefs, thought control strategies, and dysfunctional beliefs related to sleep and perceived sleeping difficulties were assessed with several questionnaires in 50 older adults with no symptoms of dementia, depression, or insomnia. Objective measures of sleep were also collected over 7 days of actigraphic recordings. RESULTS Regression analyses showed that subjective sleeping difficulties were explained by sleep-related metacognitive activity, and particularly by metacognitive beliefs about sleeping difficulties. Interestingly, objective sleep measures were not associated with metacognitive activity. In addition, self-reported poor sleepers had stronger metacognitive beliefs about sleeping difficulties and a longer sleep onset latency than self-reported good sleepers. CONCLUSIONS The present findings underscore the influence of metacognitive activity, and sleep-related metacognitive beliefs in particular, on the perception of sleeping difficulties in older adults.