Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-hop Transmission Applied in Wireless Network

Wireless sensor network consists of a large number of nodes with limited energy. As the wireless sensor nodes' battery cannot be replaced, therefore, reducing energy consumption, improve the node survival rate of the wireless sensor networks is one of the key issues. In this paper after analyzing the present routing protocol based on clustering, it proposes a new improved routing protocol which is cluster-based low-power adaptive routing protocol based on cluster head when transferring data using multi-hop technology in order to evenly assign the cluster head energy and realize load balance. Simulation results show that the improved scheme is better in prolonging the network lifetime.
Adaptability of Tacit Learning in Bipedal Locomotion

The capability of adapting to unknown environmental situations is one of the most salient features of biological regulations. This capability is ascribed to the learning mechanisms of biological regulatory systems that are totally different from the current artificial machine-learning paradigm. We consider that all computations in biological regulatory systems result from the spatial and temporal integration of simple and homogeneous computational media such as the activities of neurons in brain and protein-protein interactions in intracellular regulations. Adaptation is the outcome of the local activities of the distributed computational media. To investigate the learning mechanism behind this computational scheme, we proposed a learning method that embodies the features of biological systems, termed tacit learning. In this paper, we elaborate this notion further and applied it to bipedal locomotion of a 36DOF humanoid robot in order to discuss the adaptation capability of tacit learning comparing with that of conventional control architectures and that of human beings. Experiments on walking revealed a remarkably high adaptation capability of tacit learning in terms of gait generation, power consumption and robustness.
A Complex-based Reconstruction of the Saccharomyces cerevisiae Interactome *S⃞

Most cellular processes are performed by proteomic units that interact with each other. These units are often stoichiometrically stable complexes comprised of several proteins. To obtain a faithful view of the protein interactome we must view it in terms of these basic units (complexes and proteins) and the interactions between them. This study makes two contributions toward this goal. First, it provides a new algorithm for reconstruction of stable complexes from a variety of heterogeneous biological assays; our approach combines state-of-the-art machine learning methods with a novel hierarchical clustering algorithm that allows clusters to overlap. We demonstrate that our approach constructs over 40% more known complexes than other recent methods and that the complexes it produces are more biologically coherent even compared with the reference set. We provide experimental support for some of our novel predictions, identifying both a new complex involved in nutrient starvation and a new component of the eisosome complex. Second, we provide a high accuracy algorithm for the novel problem of predicting transient interactions involving complexes. We show that our complex level network, which we call ComplexNet, provides novel insights regarding the protein-protein interaction network. In particular, we reinterpret the finding that “hubs” in the network are enriched for being essential, showing instead that essential proteins tend to be clustered together in essential complexes and that these essential complexes tend to be large.
Textile Performance Properties of Cotton Fabric Treated with Selected Flame-Retarding Finishes

Seven different types of flame-retarding finishes were applied to light-to-medium weight cotton fabric at add-ons appropriate to pass the DOC FF 3–71 test. The finishes studied were based on tetrakis(hydroxymethyl)phosphonium chloride (THPC), neutralized THPC (THPOH), Fyrol 76, and Pyrovatex CP. The specific finishes were: THPOH-NH3, THPOH-urea-trimethylolmelamine, Proban (THPC-urea precondensate)-NH3, THPC-urea-disodium hydrogen phosphate, Fyrol 76, Fyrol 76-N-methylolacrylamide, and Pyrovatex CP-methylolmelamine. Textile performance properties are reported as a function of add-on of each type of finish; strengths and abrasion resistance of the finished fabrics are considered and discussed as a function of resilience. General trends of decreasing strength and abrasion resistance with increasing resilience were observed for these flame-retardant fabrics. Within this trend there is latitude for selection of finishes that will provide superior performance in the individual textile property such as abrasion resistance, breaking strength, and tearing strength.
Privatization and economic returns to R & D investments

This article analyzes the impact of privatization on firms' expected R & D economic performance. We examine the effect of R & D investments on market value for a panel of privatized firms in Western Europe matched with different panels of privately owned firms. The results show that the effect is systematically higher for privately owned firms as compared to privatized firms. Moreover, the market valuation of R & D investments increases over time for privatized firms. This evidence is consistent with the hypothesis that organizational inertia defers the impact of privatizion on firms' R & D performance. Copyright 2005, Oxford University Press.
Efficient fall detection based on event pattern matching in image streams

This work proposes sliding window fall detection match (SW-FDM), a rule-based fall detection method based on event pattern matching from human body posture event streams. Fall and post-fall (long lie) rules are expressed as patterns, and complex event processing (CEP) systems are adopted to quickly find these patterns. They can be detected with event selection strategies such as Skip Till Next Match and Skip Till Any Match. However, existing strategies generate either duplicate or missing alarms; even worse, their processing cost is very high when the size of event streams is large. Since SW-FDM uses a concept of sliding window, it is able to detect correct matches constantly and reduce the processing cost without duplicate computation. The experiments demonstrate that SW-FDM results in both higher accuracy and efficiency. Also, it is shown that the improvement of efficiency becomes greater as the data size increases, which is an indeed preferable property.
Directional light emission and absorption by semiconductor nanowires

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
An AlGaAs/GaAs short-cavity laser and its monolithic integration using microcleaved facets (MCF) process

This paper describes the microcleaved facets (MCF) process developed for AlGaAs/GaAs laser fabrication which can avoid the previous substrate cleavage and therefore be applicable to preparing both discrete short-cavity lasers and optoelectronic integrated circuits (OEIC's). An AlGaAs/GaAs double heterostructure ridge-waveguide laser with an extremely short cavity length, namely 20 μm, was first realized by this process. A threshold current as low as 20 mA and a single longitudinal mode lasing were achieved. The usefulness of this process for integrating a laser and a monitoring photodiode on a GaAs substrate is also demonstrated.
Smith Predictor-Based Robot Control for Ultrasound-Guided Teleoperated Beating-Heart Surgery

Performing surgery on fast-moving heart structures while the heart is freely beating is next to impossible. Nevertheless, the ability to do this would greatly benefit patients. By controlling a teleoperated robot to continuously follow the heart's motion, the heart can be made to appear stationary. The surgeon will then be able to operate on a seemingly stationary heart when in reality it is freely beating. The heart's motion is measured from ultrasound images and thus involves a non-negligible delay due to image acquisition and processing, estimated to be 150 ms that, if not compensated for, can cause the teleoperated robot's end-effector (i.e., the surgical tool) to collide with and puncture the heart. This research proposes the use of a Smith predictor to compensate for this time delay in calculating the reference position for the teleoperated robot. The results suggest that heart motion tracking is improved as the introduction of the Smith predictor significantly decreases the mean absolute error, which is the error in making the distance between the robot's end-effector and the heart follow the surgeon's motion, and the mean integrated square error.
Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems

In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm.
Extracellular calcium sensing and extracellular calcium signaling.

The cloning of a G protein-coupled extracellular Ca(2+) (Ca(o)(2+))-sensing receptor (CaR) has elucidated the molecular basis for many of the previously recognized effects of Ca(o)(2+) on tissues that maintain systemic Ca(o)(2+) homeostasis, especially parathyroid chief cells and several cells in the kidney. The availability of the cloned CaR enabled the development of DNA and antibody probes for identifying the CaR's mRNA and protein, respectively, within these and other tissues. It also permitted the identification of human diseases resulting from inactivating or activating mutations of the CaR gene and the subsequent generation of mice with targeted disruption of the CaR gene. The characteristic alterations in parathyroid and renal function in these patients and in the mice with "knockout" of the CaR gene have provided valuable information on the CaR's physiological roles in these tissues participating in mineral ion homeostasis. Nevertheless, relatively little is known about how the CaR regulates other tissues involved in systemic Ca(o)(2+) homeostasis, particularly bone and intestine. Moreover, there is evidence that additional Ca(o)(2+) sensors may exist in bone cells that mediate some or even all of the known effects of Ca(o)(2+) on these cells. Even more remains to be learned about the CaR's function in the rapidly growing list of cells that express it but are uninvolved in systemic Ca(o)(2+) metabolism. Available data suggest that the receptor serves numerous roles outside of systemic mineral ion homeostasis, ranging from the regulation of hormonal secretion and the activities of various ion channels to the longer term control of gene expression, programmed cell death (apoptosis), and cellular proliferation. In some cases, the CaR on these "nonhomeostatic" cells responds to local changes in Ca(o)(2+) taking place within compartments of the extracellular fluid (ECF) that communicate with the outside environment (e.g., the gastrointestinal tract). In others, localized changes in Ca(o)(2+) within the ECF can originate from several mechanisms, including fluxes of calcium ions into or out of cellular or extracellular stores or across epithelium that absorb or secrete Ca(2+). In any event, the CaR and other receptors/sensors for Ca(o)(2+) and probably for other extracellular ions represent versatile regulators of numerous cellular functions and may serve as important therapeutic targets.
Dynamic modeling of genetic networks using genetic algorithm and S-system

MOTIVATION The modeling of system dynamics of genetic networks, metabolic networks or signal transduction cascades from time-course data is formulated as a reverse-problem. Previous studies focused on the estimation of only network structures, and they were ineffective in inferring a network structure with feedback loops. We previously proposed a method to predict not only the network structure but also its dynamics using a Genetic Algorithm (GA) and an S-system formalism. However, it could predict only a small number of parameters and could rarely obtain essential structures. In this work, we propose a unified extension of the basic method. Notable improvements are as follows: (1) an additional term in its evaluation function that aims at eliminating futile parameters; (2) a crossover method called Simplex Crossover (SPX) to improve its optimization ability; and (3) a gradual optimization strategy to increase the number of predictable parameters. RESULTS The proposed method is implemented as a C program called PEACE1 (Predictor by Evolutionary Algorithms and Canonical Equations 1). Its performance was compared with the basic method. The comparison showed that: (1) the convergence rate increased about 5-fold; (2) the optimization speed was raised about 1.5-fold; and (3) the number of predictable parameters was increased about 5-fold. Moreover, we successfully inferred the dynamics of a small genetic network constructed with 60 parameters for 5 network variables and feedback loops using only time-course data of gene expression.
The Development of a Direct Georeferencing Ready UAV based photogrammetry platform

In order to facilitate the applications such as environment detection, military security or disaster monitoring, developing a quickly and low cost system to collect near real time spatial information is very important. Such a rapid spatial information collection capability has become an emerging trend in the technology of remote sensing and mapping application. In this study, a fixed-wing UAV based spatial information collection platform is proposed. The proposed UAV based platform has a direct georeferencing ready module including an integrated GPS/INS system, low cost digital camera as well as other common UAV modules including immediately video monitoring communication system, and other sensors mounted on the platform. It is relatively safe and cheap to collect the spatial information that we need to urgent response such as disaster relief and assessment. The preliminarily results indicates that the GSD reaches 6 centimeters and 3-D positioning accuracy reaches 10 centimeters when the flight height is 300 meters. Such specification can be applied for 1/1000 topographic mapping. When the flight height is 600 and 900 meters, the GSDs are 17 and 19 centimeters respectively, the 3-D positioning accuracy reaches 23 and 50 centimeters respectively. Furthermore, the proposed platform can be used for 1/2500 topographic mapping application. They also can be applied for producing 1/5000 aerial photo map for national land resources investigation and general navigation applications. In addition to those conventional mapping applications, recent disaster assessment results after devastating Typhoon Morakt are also presented in this study. * Corresponding author: M.L. Tsa
MST1 functions as a key modulator of neurodegeneration in a mouse model of ALS

Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disorder characterized by loss of motor neurons. Dominant mutations in the gene for superoxide dismutase 1 (SOD1) give rise to familial ALS by an unknown mechanism. Here we show that genetic deficiency of mammalian sterile 20-like kinase 1 (MST1) delays disease onset and extends survival in mice expressing the ALS-associated G93A mutant of human SOD1. SOD1(G93A) induces dissociation of MST1 from a redox protein thioredoxin-1 and promotes MST1 activation in spinal cord neurons in a reactive oxygen species–dependent manner. Moreover, MST1 was found to mediate SOD1(G93A)-induced activation of p38 mitogen-activated protein kinase and caspases as well as impairment of autophagy in spinal cord motoneurons of SOD1(G93A) mice. Our findings implicate MST1 as a key determinant of neurodegeneration in ALS.
MDE based Memetic Algorithm using OpenMP and its application in engineering project scheduling problems with dynamic due dates

A multi-population DE based Memetic Algorithm using OpenMP is proposed, and applied to a kind of engineering project problems which can be modeled by single machine total weighted tardiness problems with dynamic due dates. The multi-population scheme and a best-survival individual synchronous updating strategy are introduced to simultaneously refresh the sub-populations in the same thread. The searching ability of the algorithm is improved by the differential evolutionary Memetic algorithm which is formulated by incorporating a local search method for the best individual in each sub-population into DE. The effectiveness of the algorithm is proved by a series of engineering project scheduling based simulation experiments.
Ethylene signal transduction elements involved in chilling injury in non-climacteric loquat fruit

Loquat (Eriobotrya japonica Lindl.) is a subtropical fruit, with some cultivars such as ‘Luoyangqing’ (LYQ) susceptible to chilling injury (CI), while others such as ‘Baisha’ (BS) are resistant. Although loquats are non-climacteric, modulation of ethylene has an effect on ripening-related post-harvest CI. Therefore the role of ethylene signalling in the development of CI was investigated in fruit of both the LYQ and BS cultivars. Three ethylene receptor genes, one CTR1-like gene, and one EIN3-like gene were isolated and characterized in ripening fruit. All of these genes were expressed differentially within and between fruit of the two cultivars. Transcripts either declined over fruit development (EjERS1a in both cultivars and EjEIL1 in LYQ) or showed an increase in the middle stages of fruit development before declining (EjETR1, EjERS1b, and EjCTR1 in both cultivars and EjEIL1 in BS). The main cultivar differences were in levels rather than in patterns of expression during post-harvest storage. EjETR1, EjCTR1, and EjEIL1 genes showed increased expression in response to low temperature and this was particularly notable for EjETR1, and EjEIL1 during CI development in LYQ fruit. The genes were also differentially responsive to ethylene treatment, 1-methycyclopropene (1-MCP) and low temperature conditioning, confirming a role for ethylene in regulation of CI in loquat fruit.
3D Printing of Microstructured and Stretchable Chitosan Hydrogel for Guided Cell Growth

The ability to produce complex micro‐ or nanostructures from naturally derived hydrogels is significant for biomedical applications. However, precisely controlled architectures of soft hydrogels are difficult to be achieved due to their limited mechanical properties. Despite intensive research, significant challenges persist to fabricate hydrogels with ordered structures and adequate mechanical and biological properties for mimicking native tissues. In this work, a 3D printing technique is proposed to fabricate chitosan hydrogel with highly flexible and organized microfiber networks. The microstructured hydrogel scaffolds are obtained through a neutralization step. The strain at failure of hydrogel filaments can reach up to ≈400% and maximum strength is ≈7.5 MPa. The hydrogel scaffolds feature surface textures that can guide and align cell growth. This approach of tailoring hydrogels opens doors to design and produce 3D tissue constructs with topographical, biological, and mechanical compatibility.
Landslide monitoring with sensor networks: experiences and lessons learnt from a real-world deployment

Wireless sensor networks have the potentials to be a very useful technology for fine-grained monitoring in remote and hostile environments. This paper reports on the implementation and deployment of a system for landslide monitoring in the Northern Italy Apennines and analyses the positive results we have achieved with it. Yet, the paper also critically analyses the problems and the inherent limitations/difficulties we had to face in developing and deploying such a system, challenging many of the 'big claims' that are often heard around wireless sensor networks.
An Enhanced Diagnostic Scheme for Bearing Condition Monitoring

Rolling-element bearings are widely used in various mechanical and electrical facilities; accordingly, a reliable real-time bearing condition-monitoring system is very useful in industries to detect bearing defects at both incipient and advanced levels to prevent machinery performance degradation and malfunctions. The objective of this paper is to develop an enhanced diagnostic (ED) scheme for bearing fault diagnostics. This scheme consists of modules of classification and prediction. A neurofuzzy (NF) classifier is proposed to effectively integrate the strengths of several signal-processing techniques (or resulting representative features) for a more positive assessment of bearing health conditions. A multistep NF predictor is employed to forecast the future states of the bearing health condition to further enhance the diagnostic reliability. The effectiveness of this ED scheme is verified by experimental tests that correspond to different bearing conditions.