Miracle-dz/UniFAR-FTUs huggingface dataset:
High efficiency in human muscle: an anomaly and an opportunity?

Summary Can human muscle be highly efficient in vivo? Animal muscles typically show contraction-coupling efficiencies <50% in vitro but a recent study reports that the human first dorsal interosseous (FDI) muscle of the hand has an efficiency value in vivo of 68%. We examine two key factors that could account for this apparently high efficiency value: (1) transfer of cross-bridge work into mechanical work and (2) the use of elastic energy to do external work. Our analysis supports a high contractile efficiency reflective of nearly complete transfer of muscular to mechanical work with no contribution by recycling of elastic energy to mechanical work. Our survey of reported contraction-coupling efficiency values puts the FDI value higher than typical values found in small animals in vitro but within the range of values for human muscle in vivo. These high efficiency values support recent studies that suggest lower Ca2+ cycling costs in working contractions and a decline in cost during repeated contractions. In the end, our analysis indicates that the FDI muscle may be exceptional in having an efficiency value on the higher end of that reported for human muscle. Thus, the FDI muscle may be an exception both in contraction-coupling efficiency and in Ca2+ cycling costs, which makes it an ideal muscle model system offering prime conditions for studying the energetics of muscle contraction in vivo.
STIRNet: A Spatio-Temporal Network for Air Formation Targets Intention Recognition

Air formation is a common style of air combat, which demonstrates a high degree of flexibility and strategic value in complex battlefield environments. The activity state of air formation is the result of the intertwining of time domain and air domain, which requires accurate execution of tactical processes in the time axis and skillful deployment of forces in three-dimensional space. Therefore, air formation target combat intention recognition is a complex and challenging task that requires an in-depth understanding of the dynamically changing behavioral patterns of the formation. To address this problem, this paper proposes the STIRNet (Spatio-Temporal Network for Intention Recognition) model, which abstracts the air formation as a spatial graph structure composed of vehicle nodes and combines its temporal data evolving over time. The model autonomously adjusts its attention to different moments and spatial locations through the spatio-temporal attention mechanism, focusing on the important spatio-temporal features that are crucial for recognizing the combat intention of the air formation; and simultaneously captures and integrates the feature information of the air formation in both the temporal and spatial dimensions through the spatio-temporal convolutional operation, which effectively solves the deficiencies of the traditional methods in dealing with the complex spatio-temporal dependency relationships. The experimental results show that the model proposed in this paper effectively improves the accuracy of the combat intention recognition of air formation targets, which is of great value for command decision-making and air battlefield situation assessment.
Automated classifier generation for application-level mobile traffic identification

This paper proposes an automated classifier generation system for application-level mobile traffic identification. The proposed system comprises traffic classifier generation system architecture and mobile traffic measurement agents (mTMAs) that are installed on mobile devices to monitor their application-level traffic. To generate classifiers, our adaptive algorithm selects the lowest cost classifiers from among the available classifier candidates, while assuring acceptable identification accuracy. To verify the efficacy of the proposed identification system, we implemented and deployed it on a campus network. The results of mobile application traffic identification obtained using the system in this deployment are also presented in this paper.
Haptoglobin and serum amyloid A in bulk tank milk in relation to raw milk quality

The aim of the present study was to evaluate relationships between the presence of the two major bovine acute phase proteins haptoglobin (Hp) and serum amyloid A (SAA) and raw milk quality parameters in bulk tank milk samples. Hp and SAA have been suggested as specific markers of mastitis but recently also as markers for raw milk quality. Since mastitis has detrimental effects on milk quality, it is important to investigate whether the presence of Hp or SAA indicates such changes in the composition and properties of the milk. Bulk tank milk samples (n=91) were analysed for Hp, SAA, total protein, casein, whey protein, proteolysis, fat, lactose, somatic cell count and coagulating properties. Samples with detectable levels of Hp had lower casein content, casein number and lactose content, but higher proteolysis than samples without Hp. Samples with detectable levels of SAA had lower casein number and lactose content, but higher whey protein content than samples without SAA. The presence of acute phase proteins in bulk tank milk is suggested as an indicator for unfavourable changes in the milk composition, e.g. protein quality, due to udder health disturbances, with economical implications for the dairy industry.
The Control Strategy of Open-Winding Permanent Magnet Starter-Generator With Inverter-Rectifier Topology

In order to overcome the difficulties of voltage regulation, narrow speed range, and low power factor of the traditional permanent magnet machine used in the vehicles applications, a novel inverter-rectifier topology for an open-end winding permanent magnet starter-generator is proposed. The rectifier is connected to one side of the open-end winding for the output dc voltage, and the inverter is in series to the other side for engine cranking in the start mode and the voltage regulation of the output in the generation mode. Due to the nature of the open-winding structure, the machine can flexibly change its operation from start to generation mode by the switch S. The control strategies of start and generation were analyzed separately, and a new control algorithm with a double loop for regulation of the dc voltage and current of the machine was developed. A hardware prototype of the proposed starter-generator system is built. Experimental results from this hardware prototype verify the operation principles and claims of advantages.
An algorithm for real-time object tracking in complex environment

The current sparse representation tracking algorithm is not suitable for the objects that illumination changes, scale changes, the object color is similar with the surrounding region, and occlusion etc, what's more, it is hard to realize real-time tracking for solving an l1 norm related minimization problems. An optimal algorithm is introduced by exploiting an accelerated proximal gradient approach which contains some improvements of particle filter function, sparse representation alterative weights and coefficient. These improvements not only reduce the influences of appearance change but also make the tracker runs in real time. Both qualitative and quantitative evaluations demonstrate that the proposed tracking algorithm has favorably better performance than several state-of-the-art trackers using challenging benchmark image sequences, and significantly reduces the computing cost.
A novel eye detection algorithm utilizing edge-related geometrical information

A novel method for eye detection and eye center localization, based on geometrical information is described in this paper. First, a face detector is applied to detect the facial region, and the edge map of this region is extracted. A vector pointing to the closest edge pixel is then assigned to every pixel. Length and slope information for these vectors is used to detect the eyes. For eye center localization, intensity information is used. The proposed method can work on low-resolution images and has been tested on two face databases with very good results.
Prioritizing Bad Links for Fast and Efficient Flooding in Wireless Sensor Networks

Wireless sensor networks provide distributed sensing and processing to a wide range of applications that operate in different environments. To be able to perform their intended duties, one of which is relaying information to other nodes in the network, a routing protocol is needed. One possible building block in a routing algorithm is to use a flooding protocol. In this paper we present an efficient flooding protocol called received signal strength-aided flooding (RAF). This protocol utilises the signal strength of a received broadcast message for its rebroadcast decisions by prioritising nodes with communication links that have low reliability. The proposed protocol has a number of key features that makes it resource efficient: no additional overhead in the broadcast message; no location information; no neighbour knowledge. The simulation results show both a lower number of rebroadcast nodes and longer lifetime expectancy for RAF than that of the optimised broadcast protocol. These results and its features makes the RAF protocol a good candidate to be used as a building block in more advanced wireless sensor network protocols.
Upregulation of phospholipase D in astrocytes in response to transient forebrain ischemia

Previous in vitro studies using cell cultures or brain slices have demonstrated that phospholipase D (PLD) in the nervous system is involved in the signaling mechanism in response to a variety of agonists. However, little is known about the pathophysiological role of PLD‐mediated signaling in the adult brain. We examined the changes in the expression of a PLD isozyme, PLD1, in the adult rat hippocampus, using immunological approaches and an assay for PLD activity after transient forebrain ischemia (four‐vessel occlusion model) that results in the selective delayed death of CA1 pyramidal cells and induces reactive astrocytes in the CA1 subfield. In the control hippocampus, PLD1 the level of immunoreactivity was very low. After ischemia, in parallel with the results of Western blot analysis and the PLD activity assay, immunohistochemical analysis of PLD1 demonstrated that the immunoreactive proteins peaked at 7–14 days and were most prominent in the CA1 and the dentate hilar region. The temporal and spatial patterns of immunoreactivity of both PLD1 and glial fibrillary acidic protein (GFAP) were very similar, indicating that reactive astrocytes express PLD1, confirmed by double staining for PLD1 and GFAP. These results demonstrate that reactive astrocytes upregulate PLD in vivo after injury in the adult rat hippocampus. GLIA 30:311–317, 2000. © 2000 Wiley‐Liss, Inc.
Distributed Energy-Efficient Cross-Layer Optimization for Multihop MIMO Cognitive Radio Networks With Primary User Rate Protection

Due to the unique physical-layer characteristics associated with multiple-input multiple-output (MIMO) and cognitive radio (CR), the network performance is tightly coupled with mechanisms at the physical, link, network, and transport layers. In this paper, we consider an energy-efficient cross-layer optimization problem in multihop MIMO CR networks (CRNs) and provide a new formulation to balance the weighted network utility and the weighted power consumption of secondary users (SUs), with the minimum transmission rate constraint of primary users (PUs) and the SU power consumption constraint. However, this formulation is highly challenging due to the nonconvexity of the PU rate constraint. We propose a solution that features a linearization-based alternative optimization method and a heuristic primal recovery method. We further develop a distributed algorithm to jointly optimize the covariance matrix of the transmitted signal vector at each SU node, bandwidth allocation at each SU link, rate control at each session source, and multihop/multipath routing. Extensive simulation results demonstrate that the performance of the proposed distributed algorithm is very close to that of the centralized algorithm, and the proposed formulation provides an efficient way to save power consumption significantly, while achieving the network utility very close to that achieved with full power consumption.
Ecological Correlates of Stress in the Canadian Population of Black-tailed Prairie Dogs

Non-invasive measures of glucocorticoid (GC) hormones and their metabolites, particularly in feces and hair, are gaining popularity as wildlife management tools, but speciesspecific validations of these tools remain rare. I report the results of a validation on black-tailed prairie dogs (Cynomys ludovicianus), an important engineer of grasslands ecosystems that has experienced recent population declines. I conducted an adrenocorticotropic hormone (ACTH) stimulation test in adult female prairie dogs to assess the relationship between plasma GC and fecal glucocorticoid metabolite (FGM) levels following a single injection of a low (4 IU/kg) or high dose (12 IU/kg) of ACTH, compared to a single injection of saline. I also gave repeated injections to adult females to assess whether repeated injections of ACTH would result in a chronic increase of hair cortisol concentrations, compared with control individuals injected with saline. A single injection of ACTH at either low or high dose peaked plasma cortisol levels after 30 min, and the cortisol levels declined until 120 min, when they returned to pre-treatment levels comparable to those of the saline injected group. Despite the significant elevation of plasma cortisol in the treatment groups following ACTH injection, the elevation of FGM levels in the 1 This chapter has been submitted to General & Comparative Endocrinology for publication. This chapter is a coauthored manuscript. CC reviewed literature, collaboratively designed the experiment, coordinated logistics, conducted the experiment and collected samples, prepared and analyzed the blood and hair samples, prepared fecal samples for laboratory analysis, and wrote the manuscript. Crill, C. , Janz, D. M. , Kusch, J. M. , Santymire, R. M. , Heyer, G. P., Shury, T. K. , Lane, J. E. (In Prep). Investigation of the utility of feces and hair as non-invasive measures of glucocorticoids in black-tailed prairie dogs (Cynomys ludovicianus).
Using Depth to Extend Randomised Hough Forests for Object Detection and Localisation

Implicit Shape Models (ISM) have been developed for object detection and localisation in 2-D (RGB) imagery and, to a lesser extent, full 3-D point clouds. Research is ongoing to extend the approach to 2-D imagery having co-registered depth (RGB- D) e.g. from stereoscopy, laser scanning, time-of-flight cameras etc.A popular implementation of the ISM is as a Randomised Forest of classifier trees representing codebooks for use in a Hough Transform voting framework. We present three extensions to the Class-Specific Hough Forest (CSHF) that utilises RGB and co- registered depth imagery acquired via stereoscopic mobile imaging. We demonstrate how depth and RGB information can be combined during training and at detection time. Rather than encoding depth as a new dimension of Hough space (which can increase vote sparsity), depth is used to modify the resulting placement and strength of votes in the original 2-D Hough space. We compare the effect of these depth-based extensions to the unmodified CSHF detection framework evaluated against a challenging new real- world dataset of urban street scenes.
RECOILING MASSIVE BLACK HOLES IN GAS-RICH GALAXY MERGERS

The asymmetric emission of gravitational waves produced during the coalescence of a massive black hole (MBH) binary imparts a velocity “kick” to the system that can displace the hole from the center of its host. Here, we study the trajectories and observability of MBHs recoiling in three (one major, two minor) gas-rich galaxy merger remnants that were previously simulated at high resolution, and in which the pairing of the MBHs had been shown to be successful. We run new simulations of MBHs recoiling in the major merger remnant with Mach numbers in the range and use simulation data to construct a semi-analytical model for the orbital evolution of MBHs in gas-rich systems. We show the following. (1) In major merger remnants the energy deposited by the moving hole into the rotationally supported, turbulent medium makes a negligible contribution to the thermodynamics of the gas. This contribution becomes significant in minor merger remnants, potentially allowing for an electromagnetic signature of MBH recoil. (2) In major merger remnants, the combination of both deeper central potential well and drag from high-density gas confines even MBHs with kick velocities as high as 1200 km s-1 within 1 kpc from the host's center. (3) Kinematically offset nuclei may be observable for timescales of a few Myr in major merger remnants in the case of recoil velocities in the range 700–1000 km s-1. (4) In minor merger remnants the effect of gas drag is weaker, and MBHs with recoil speeds in the range 300–600 km s-1 will wander through the host halo for longer timescales. When accounting for the probability distribution of kick velocities, however, we find that the likelihood of observing recoiling MBHs in gas-rich galaxy mergers is very low even in the best-case scenario.
In vivo diagnosis of gastric cancer using Raman endoscopy and ant colony optimization techniques

This study aims to evaluate the clinical utility of image‐guided Raman endoscopy for in vivo diagnosis of neoplastic lesions in the stomach at gastroscopy. A rapid‐acquisition image‐guided Raman endoscopy system with 785‐nm excitation has been developed to acquire in vivo gastric tissue Raman spectra within 0.5 sec during clinical gastroscopic examinations. A total of 1,063 in vivo Raman spectra were acquired from 238 tissue sites of 67 gastric patients, in which 934 Raman spectra were from normal tissue whereas 129 Raman spectra were from neoplastic gastric tissue. The swarm intelligence‐based algorithm (i.e., ant colony optimization (ACO) integrated with linear discriminant analysis (LDA)) was developed for spectral variables selection to identify the biochemical important Raman bands for differentiation between normal and neoplastic gastric tissue. The ACO‐LDA algorithms together with the leave‐one tissue site‐out, cross validation method identified seven diagnostically important Raman bands in the regions of 850–875, 1,090–1,110, 1,120–1,130, 1,170–1,190, 1,320–1,340, 1,655–1,665 and 1,730–1,745 cm−1 related to proteins, nucleic acids and lipids of tissue and provided a diagnostic sensitivity of 94.6% and specificity of 94.6% for distinction of gastric neoplasia. The predictive sensitivity of 89.3% and specificity of 97.8% were also achieved for an independent test validation dataset (20% of total dataset). This work demonstrates for the first time that the real‐time image‐guided Raman endoscopy associated with ACO‐LDA diagnostic algorithms has potential for the noninvasive, in vivo diagnosis and detection of gastric neoplasia during clinical gastroscopy.
A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications

In the last decade, the interest in Indoor Location Based Services (ILBS) has increased stimulating the development of Indoor Positioning Systems (IPS). In particular, ILBS look for positioning systems that can be applied anywhere in the world for millions of users, that is, there is a need for developing IPS for mass market applications. Those systems must provide accurate position estimations with minimum infrastructure cost and easy scalability to different environments. This survey overviews the current state of the art of IPSs and classifies them in terms of the infrastructure and methodology employed. Finally, each group is reviewed analysing its advantages and disadvantages and its applicability to mass market applications.
Integration of Multiple Networks for Robust Label Propagation

Transductive inference on graphs such as label propagation algorithms is receiving a lot of attention. In this paper, we address a label propagation problem on multiple networks and present a new algorithm that automatically integrates structure information brought in by multiple networks. The proposed method is robust in that irrelevant networks are automatically deemphasized, which is an advantage over Tsuda et al.’s approach [14]. We also show that the proposed algorithm can be interpreted as an EM algorithm with a Student-t prior. Finally, we demonstrate the usefulness of our method in protein function prediction.
DBalancer: A tool for dynamic changing of workers number in storm

The workers' number of a specific topology is fixed once it starts in Storm. This causes unnecessary waste of resources when workload shrinks down. In this paper, we propose an approach called DBalancer to dynamically increase or decrease the number of workers according to statistics of real-time data rate. DBalancer consists of three components: (1) Collector is used to collect data information about whether or not there is data at each period, (2) Decision Maker is used to statistic data rate information and make decision to increase or decrease the number of workers, (3) Rebalancer is used to execute decision made by Decision Maker. By applying DBalancer, we observe obvious lower memory occupation when workload comes down.
Selecting Valid Correlation Areas for Automated Bullet Identification System Based on Striation Detection

Some automated bullet identification systems calculate a correlation score between two land impressions to measure their similarity. When extracting a compressed profile from the land impression of a fired bullet, inclusion of areas that do not contain valid individual striation information may lead to sub-optimal extraction and therefore may deteriorate the correlation result. In this paper, an edge detection algorithm and selection process are used together to locate the edge points of all tool-mark features and filter out those not corresponding to striation marks. Edge points of the resulting striation marks are reserved and expanded to generate a mask image. By imposing the mask image on the topography image, the weakly striated area(s) are removed from the expressed profile extraction. Using this method, 48 bullets fired from 12 gun barrels of six manufacturers resulted in a higher matching rate than previous studies.
Should the measurement of blood pressure in the office be redefined?

or almost a century the measurement of blood pres-sure(BP)intheofficeorclinicbythedoctorornurseusing a standard mercury sphygmomanometer com-bined with the auscultatory method has been the standardapproach to hypertension diagnosis and management [1]. The vast majority of trials that demonstrated the risksassociated with elevated BP and the benefits of treat-ment-induced BP reduction have used conventional officeBPmeasurements.Althoughmultipleoutcometrialsinclud-ingthousandsofindividualshaveconsistentlyshownofficeBP to be a strong predictor of risk, in the individual patient,office BPhas less prognostic value [1]. Themaindrawbacksof office BP are due to operator (doctor or nurse)-relatedproblems, such as observer’s prejudice and bias (expec-tations), readingerrors(terminaldigitpreference,roundingand random error) and the so-called ‘white-coat’ and‘masked hypertension’ phenomena [1,2]. Moreover, it isacknowledged that in clinical practice, office BP measure-ments are less standardized than in clinical trials andmightbemisleadinginaconsiderableproportionofpatients[1,3–6]. Recently, the conventional office BP measurementshave entered a period of fundamental change, which is notonly dictated by the need for more reliable measures, but isalso due to the demise of the mercury sphygmomanometerand to the recent development of accurate electronic BPmonitors for professional use [3–13].