Miracle-dz/UniFAR-FTUs huggingface dataset:
COPD_A_356314 781..789

Purpose: This study explored the value of the serum creatinine/cystatin C (Cr/CysC) ratio in diagnosing the reduction of muscle strength in men with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Patients and Methods: In this study, we enrolled 72 male patients with AECOPD and 32 male patients with stable chronic obstructive pulmonary disease (COPD). We compared clinical characteristics between the AECOPD and stable COPD groups. Then, we subdivided AECOPD patients into normal muscle strength and low muscle strength groups; we compared the clinical characteristics between these two groups. We analyzed the relationships of serum creatinine (Cr), cystatin C (CysC), and Cr/CysC ratio with clinical characteristics in male AECOPD patients. We also investigated whether the Cr/CysC ratio could aid in the diagnosis of muscle strength decline via receiver operating characteristic curve and binary logistic regression analysis. Results: We found that handgrip strength, Cr/CysC ratio, serum Cr, FEV1, FVC, and FEV1%pred were lower in AECOPD patients than in stable COPD patients. Among AECOPD patients, BMI, weight, FEV1, FVC, FEV1%pred, and Cr/CysC ratio were lower in the low muscle strength group than in the normal muscle strength group; there were more patients with ≥2 acute exacerbations within the past year in the low muscle strength group. The Cr/CysC ratio was correlated with handgrip strength, FEV1, FVC, FEV1%pred, BMI and weight. The area under curve for low handgrip strength was greater for the Cr/CysC ratio than for Cr. Binary logistic regression analysis showed that a Cr/CysC ratio <0.99 was a risk factor for decreased muscle strength in male patients with AECOPD. Conclusion: The Cr/CysC ratio is a useful predictor of muscle strength decline in male AECOPD patients, while a low Cr/CysC ratio is a risk factor for muscle strength decline in male patients with AECOPD.
Plug and play in the smart grid

As the penetration of renewable energy resources increases in distribution networks, so does the need to be able to manage these resources in an effective manner. Since these resources are installed to displace carbon-based generation and to provide an income stream to the resource owner, it is important that both installation objectives (as a minimum) can be met. With all of these renewable energy resources available, the opportunity also exists to assist with the energy management of this resource-rich power distribution network. However, the renewable energy resources do not produce power in a deterministic manner; the available production depends on the time of day and many other environmental factors. A smart grid, also often referred to as the future grid, is defined as a grid that brings together advanced monitoring technologies, control methods and integrated communications in order to efficiently deliver sustainable, economic and reliable electrical energy. The future grid enables utilities and researchers to explore more effective ways of managing power distribution networks. Increasing levels of renewable energy resources and decreasing levels of centralised generation also means that the current paradigm of install-and-forget in distribution networks will not be sustainable and a co-
Phase noise of electrical oscillators due to multi-Lorentzian noise sources based on the LTV model

Using the general expression of the phase variance expressed by the linear time variant (LTV) phase noise model, phase variances due to multi-Lorentzian and white noise sources of electrical oscillators have been extracted and phase noise expressions due to white and Flicker noises have been calculated. Despite the basic LTV model in which the expressed phase noise due to white noise is valid for offset frequencies larger than the −3 dB corner frequency of the output and it tends to infinity for small offset frequencies, in the proposed model, the extracted phase noises due to white and Flicker noises are valid for the entire spectrum and become limited for small offset frequencies. Comparison between the results of the proposed model by those of the harmonic balance technique shows the validity of the model. Also, the effects of the physical parameters of the oscillator circuit on the extracted phase noise can be observed in the expressions. The proposed model is applicable to calculate the phase noise of the oscillators with single perturbation source applied to the intended output node.
Networks of breakthrough technologies and their use in strategic games for competitive advantage

Breakthrough technologies sustain competitive advantage and are seen as the engine of growth. These technologies can be developed by leveraging internal know-how, but more often they come from an infusion of external technology. The task of screening and selecting innovative technologies to develop or acquire is challenging and relies on various underlying assumptions. This research proposes a systematic framework of analysis that combines network theory and game theory concepts to analyze a set of breakthrough technologies and the companies linked to them, both in the order of 0(100) . In this framework, breakthrough technologies are represented as a network where nodes represent technologies and links represent dimensions of similarities between these technologies. Network-level metrics provide proxies for estimating the benefit of a node and the cost of a link. The benefit is derived based on the position of the node in the network, and the cost of a link is estimated based on the similarities of technologies it connects. As firms consider a particular target technology, the framework offers a way to calculate the payoff of following a particular path in the network to attain the target from any one of the technologies already in the firm's portfolio. The model provides a recommendation for the best strategy under specific competitive scenarios. Finally, the application of this method is illustrated with various use cases, to analyze strategic decisions made by companies and to explore some that are ongoing. In particular, this analysis looks at hypothetical two-player strategic games in the energy sector, comparing the competitive positions of SolarCity, Siemens and Google to conclude that all three companies have dominant strategies to invest in this sector. The framework was also applied to a strategic game where Google competes with Magic Leap, in the bio-fuel sector and showed a dominant position for Google. The last three scenarios analyzed represent real-world cases, two in the autonomous vehicle domain involving Apple and Toyota and Apple and Tesla and one in the robotics domain involving Toyota and Amazon. The analysis showed the existence of a coordination game in the autonomous vehicle sector where collaboration was beneficial for all parties. Finally, in the robotics case involving the sell-off of Boston Dynamics by Google, the analysis showed that Toyota can leverage a first mover's advantage to create a dominant strategy against Amazon.
A Multi-Agent System for Adaptive Control of a Flapping-Wing Micro Air Vehicle

Biomimetic flapping-wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this dissertation is described the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process, an agent continuously monitors the vehicle’s behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details of the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.
Receptor I g by Targeting IgG Fc MicroRNA-127 Inhibits Lung Inflammation

The molecular mechanisms of acute lung injury are incompletely understood. MicroRNAs (miRNAs) are crucial biological regulators that act by suppressing their target genes and are involved in a variety of pathophysiologic processes. miR-127 appears to be downregulated during lung injury. We set out to investigate the role of miR-127 in lung injury and inﬂammation. Expression of miR-127 signiﬁcantly reduced cytokine release by macrophages. Looking into the mechanisms of regulation of inﬂammation by miR-127, we found that IgG Fc g RI (CD64) was a target of miR-127, as evidenced by reduced CD64 protein expression in macrophages overexpressing miR-127. Furthermore, miR-127 signiﬁcantly reduced the luciferase activity with a reporter construct containing the native 3 9 untranslated region of CD64. Importantly, we demonstrated that miR-127 attenuated lung inﬂammation in an IgG immune complex model in vivo. Collectively, these data show that miR-127 targets macrophage CD64 expression and promotes the reduction of lung inﬂammation. Understanding how miRNAs regulate lung inﬂammation may represent an attractive way to control inﬂammation induced by infectious or noninfectious lung injury. The Journal of Immunology , 2012, 188: 000–000. and hypoxemia, and in multiple failure Both and major of and of ALI remain understood. We assessed differences in measured variables using the unpaired two-sided Student t test or Wilcoxon rank-sum test. Differences between multiple groups were calculated using one-way ANOVAwith Tukey-Kramer posttest or two-way ANOVA with Bonferroni multiple comparisons. Statistical difference was accepted at p , 0.05. Prism 5.0 software (GraphPad) was used to perform statistical analysis.
Gaussian process regression using partition resampling model

Gaussian process regression is an interesting probabilistic nonparametric regression method. This method is ﬂexible in describing data patterns. Unfortunately, in the case of big data, this method has expensive computational complexity because it involves inverse matrix operations. In this study, we introduce a partition resampling method to overcome the computational complexity by reducing the dimensions of the data. The purpose of this study is to obtain a Gaussian process regression model using the partition resampling method. This method builds a model from a portion of the data. Although the model uses some data, it has the same advantages as the overall data model. This method partitions the training data and then takes one random sample from each partition to obtain data of a certain size sample. The Gaussian process regression model is a collection of functions that have hyperparameter characteristics. This study predicts new data using a
An Assessment Methodology for Emergency Vehicle Traffic Signal Priority Systems

Emergency vehicle traffic signal priority systems allow emergency vehicles such as fire and emergency medical vehicles to request and receive a green traffic signal indication when approaching an intersection. Such systems have been around for a number of years, however, there is little understanding of the costs and benefits of such systems once they are deployed. This research develops an improved method to assess the travel time impacts of emergency vehicle traffic signal priority systems for transportation planning analyses. The research investigates the current state of available methodologies used in assessing the costs and benefits of emergency vehicle traffic signal priority systems. The ITS Deployment Analysis System (IDAS) software is identified as a recently developed transportation planning tool with cost and benefit assessment capabilities for emergency vehicle traffic signal priority systems. The IDAS emergency vehicle traffic signal priority methodology is reviewed and recommendations are made to incorporate the estimation of non-emergency vehicle travel time impacts into the current methodology. To develop these improvements, a simulation analysis was performed to model an emergency vehicle traffic signal priority system under a variety of conditions. The simulation analysis was implemented using the CORSIM traffic simulation software as the tool. Results from the simulation analysis were used to make recommendations for enhancements to the IDAS emergency vehicle traffic signal priority methodology. These enhancements include the addition of non-emergency vehicle travel time impacts as a function of traffic volume on the transportation network. These impacts were relatively small and ranged from a 1.1% to 3.3% travel time increase for a one-hour analysis period iii to a 0.6% to 1.7% travel time increase for a two-hour analysis period. The enhanced methodology and a sample application of the methodology are presented as the results of this research. In addition, future research activities are identified to further improve assessment capabilities for emergency vehicle traffic signal priority systems. iv DEDICATION I would like to dedicate this work to my parents, Gene and June McHale, for all the sacrifices they made to ensure their children had a good education. v ACKNOWLEDGEMENTS
Independent Learning with Local Information in Mean-Field Games

—This paper studies decentralized reinforcement learning in N -player mean-ﬁeld games. Our study has two principal objectives: (i) to identify structural properties of games that can guide algorithm design, and (ii) to study the convergence of independent learners under decentralized information. Toward the ﬁrst objective, we show that N -player mean-ﬁeld games admit a desirable graph theoretic property. We then study a decentralized learning algorithm in which each agent naively assumes it faces an MDP in its observation variable and periodically revises its policy using an (cid:15) -satisﬁcing rule: if the agent perceives itself to be (cid:15) -best-responding to the environment, it does not switch policy; otherwise, the agent incorporates random search of its policy space. We study this learning algorithm under two information structures. When global state information is available to each agent, we establish convergence to near equilibrium. When only local and mean-ﬁeld state information is available to each agent, we show convergence to policies that can be interpreted as subjective equilibrium.
Pragmatic Approach to Small Airways Diagnostics Pragmatična dijagnostika malih disajnih puteva

SUMMARY Small airways have an inner diameter of 2 mm or less. They are characterized by lack of cartilage in their wall, sudden increase in total cross-section area, and by an abrupt switch from turbulent to laminar air flow, all of which cause minimal airflow resistance in healthy persons. Conversely, in patients with obstructive lung diseases, small airways are the primary site of airflow limitation. Small airways tests are numerous and they vary considerably in their diagnostic significance, complexity and availability. Currently, none of the existing tests represents the diagnostic “gold standard”, and none offer precise cut-off values to distinguish between small airway disease, healthy persons, and individuals with concomitant pathophysiological disorder. Negative results of these tests have significant negative predictive values, i.e. ruling out small airways as the cause of the clinical issue, while positive results of these tests should always be crosschecked to confirm the presence of small airways disorder by other methods. Results of small airways diagnostic tests do not mean much as isolated findings unless they are combined with other diagnostic methods (history, clinical findings, imaging, etc.) to elucidate any specific clinical case. On the other hand, if results are strongly suggestive of the presence of a small airways disease, that can have significant clinical implications, such as application of fine particle aerosols. The suggested diagnostic algorithm is based primarily on tests availability. It should not be implemented uncritically, but rather adapted to the healthcare system at hand .
Collectivism’s Effect on Volunteerism, A Case Study of Japanese employees 일본의 집단문화의 봉사정신의 관계: 글로벌 IT기업 직원 중심으로

In this research, Collectivism’s influence on Japanese employee’s volunteerism in IT Multinational Enterpise (MNE) was investigated with empathy mediating. Previous research on Hofstede’s cultural dimensions were mostly conducted at national levels. However, in this study, Yoo’s Cultural value Scale (CVSCALE) was used to analyze colletivistic tenedencies at individual levels and its influence on volunteerism. A total of 220 self-administering online surveys were distributed to IT MNE employees in Tokyo, Japan for three weeks period. 160 usable surveys were collected and analyzed with SPSS 21.0. Findings indicate collectivism positively influenced volunteerism in individuals with empathy as possible antecent of volunteerism. In conclusion and application, the significance of this research lies in indicating collectivism’s positive influence on volunteerism and empathy as possible antecedents of volunteerism in Japanese employees even though Japan has displayed stronger individualism than the rest of East Asian neighbors.
Address Translation Conscious Caching and Prefetching for High Performance Cache Hierarchy

Performance of Translation Lookaside Buffers (TLBs) and on-chip caches plays a crucial role in delivering high-performance for memory-intensive applications with irregular memory accesses. Our observations show that, on average, an L2 TLB (STLB) miss for address translation can stall the head of the reorder buffer (ROB) for a maximum of 50 cycles. The corresponding data request, also called as the replay load can stall the head of the ROB for more than 200 cycles. We show that current state-of-the-art mid-level (L2C) and last-level cache (LLC) replacement policies do not treat cache block with address translations and replay data access differently. As a result these policies fail to reduce ROB stalls because of translation and replay data access misses. To improve the performance further on top of high-performing cache replacement policies, we propose address translation and replay data access conscious cache replacement policies at L2C and LLC. Our enhancements help in reducing ROB stalls due to STLB misses by 28.76%. We also find that cache blocks storing replay loads are dead (no reuse after insertion), and cache replacement policies alone cannot mitigate the ROB stalls caused by replay data accesses. Hence, we propose an address translation hit triggered hardware prefetcher that brings replay data on an address translation hit at the L2C and LLC. This enhancement reduces ROB stalls due to replay data accesses by 18.5%. For a group of memory-intensive benchmarks with high STLB misses, our enhancements improve performance by 5.1% (reducing ROB stall cycles by 46.7%) and as high as 10.6%, on top of state-of-the-art cache replacement policies that are highly competitive. Our enhancements do not incur any additional storage overhead. However, we need additional flags from the page-table-walker into the cache hierarchy.
PATHOLOGICAL CHANGES IN LUMBAR-SACRAL INTERVERTEBRAL DISCS IN PROFESSIONAL ROWERS

An attempt was made to evaluate degenerative lesions in lumbal-sacral fragment of vertebral column In Professional rowers. A statistically significant group of rowers was subjected to tomographic studies. The obtained tomograms permitted to measure selected geometric parameters of intervertebral discs. The obtained results were compared to the literature data which provided normal values for healthy, not training men of a similar age. (Biol.Sport 24:375-388, 2007)
Cooperative data transfers for 5G networks

The demand for higher capacity, higher data rate and larger bandwidth has driven the research and industrial world to develop next generation wireless communication technology, namely, the 5G. Among all the approaches proposed for such a high demand, only the cooperative communication approach promises to significantly improve of the performances (capacity, data rate, bandwidth, etc.) with a low cost. In this thesis, we propose a D2D communication scheme as a solution for the out-door scenario and a cooperative scheme among the access infrastructures as the in-door scenario solution. In the first part, we address the implementation of content-centric routing in a D2D architecture for Android devices based on WiFi Direct, a protocol recently standardised by the Wi-Fi Alliance. After discussing the creation of multiple D2D groups, we introduce novel paradigms featuring intra- and inter-group bidirectional communication. We then present the primitives involved in content advertising and requesting among members of the multi-group network. In addition to the communications, we also devise a mechanism to enable the devices to spontaneously establish the multi-group D2D network. Finally, we evaluate the performance of our architecture and the network formation mechanism in a real testbed consisting of Android devices. In the second part, we propose, implement and evaluate a bandwidth aggregation service for residential users that allows to improve the upload throughput of the ADSL connection by leveraging the unused bandwidth of neighboring users. The residential access gateway adopts the 802.11 radio interface to simultaneously serve the local home users and to share the broadband connectivity with neighboring access gateways. Differently from previous works, our aggregation scheme is transparent both for local users, who are not required to modify their applications or device drivers, and for neighboring users, who do not experience any meaningful performance degradation. In order to evaluate the achievable performance and tune the parameters driving the traffic balancing, we developed a fluid model which was shown experimentally to be very accurate. Our proposed scheme is amenable to efficient implementation on Linux networking stack. Indeed, we implemented it and tested in some realistic scenarios, showing an efficient exploitation of the whole available bandwidth, also for legacy cloud storage applications.