Miracle-dz/UniFAR-FTUs huggingface dataset:
LR-PSO method of generation scheduling problem for thermal-wind-solar energy system in deregulated power system

Renewable energy sources have attracted wide attention because of being abundant in nature and nearly non pollutant. Wind power is one of the most promising clean energy sources it can easily be captured by wind generators with high power capacity. Solar power is another promising clean energy sources in global system and can be harnessed easily. Solar energy system might be compensate the wind intermittency generation resource due to lesser start up time, lower operating cost and good ramping capabilities. The generation scheduling for windsolar energy with thermal unit system in deregulated environment, minimize the total thermal fuel cost and maximize the profit of generation companies, subject to many constraints. While performing the generation scheduling problem by Lagrangian relaxation based particle swarm optimization method the hourly load, wind velocity and solar radiation must be forecasted to prevent the errors. The generation scheduling formulations are involved the perspective of a generation company (GENCO). The deregulation environment is one which the generation, transmission and distribution does not depend on each other. To demonstrate the uncertainty in the proposed method the generating scheduling problem is performed in a simplified generation system.
Instabilities in propagating reaction-diffusion fronts

Simple reaction‐diffusion fronts are examined in one and two dimensions. In one‐dimensional configurations, fronts arising from either quadratic or cubic autocatalysis typically choose the minimum allowable velocity from an infinite spectrum of possible wave speeds. These speeds depend on both the diffusion coefficient of the autocatalytic species and the pseudo‐first‐order rate constant for the autocatalytic reaction. In the mixed‐order case, where both quadratic and cubic channels contribute, the wave speed depends on the rate constants for both channels, provided the cubic channel dominates. Wave propagation is completely determined by the quadratic contribution when it is more heavily weighted. In two‐dimensional configurations, with unequal diffusion coefficients, the corresponding two‐variable planar fronts may become unstable to perturbations. The instability occurs when the ratio of the diffusion coefficient for the reactant to that for the autocatalyst exceeds some critical value. This critical v.
Large-Scale Community Detection on YouTube for Topic Discovery and Exploration

Detecting coherent, well-connected communities in large graphs provides insight into the graph structure and can serve as the basis for content discovery. Clustering is a popular technique for community detection but global algorithms that examine the entire graph do not scale. Local algorithms are highly parallelizable but perform sub-optimally, especially in applications where we need to optimize multiple metrics. We present a multi-stage algorithm based on local-clustering that is highly scalable, combining a pre-processing stage, a lo- cal clustering stage, and a post-processing stage. We apply it to the YouTube video graph to generate named clusters of videos with coherent content. We formalize coverage, co- herence, and connectivity metrics and evaluate the quality of the algorithm for large YouTube graphs. Our use of local algorithms for global clustering, and its implementation and practical evaluation on such a large scale is a ﬁrst of its kind.
Optimized Train-Set Rostering Plan for Taiwan High-Speed Rail

Railway rolling stock is one of the most expensive assets of railway operators. Efficient utilization of rolling stock is one of the most important objectives pursued in practice. This research focuses on the improvement in rolling stock rostering efficiency for high-speed rail system. According to the circulation schedule and long-term maintenance plan, the rostering planner decides the assignment between rolling stock and duties subject to a set of practical constraints. Owing to its complexity, this task remains a manual process at the Taiwan High Speed Rail Corporation. Experienced railway practitioners can generally create a good and feasible plan, but they cannot guarantee optimality of the solution considering only short-term process. In this research, we developed an exact optimization model and a heuristic method to automate this process and improve the efficiency of rolling stock utilization. Results from the case studies demonstrate that the efficiency of the rolling stock usage can be increased by $3\%\sim 4\%$, and that the process time can be significantly reduced from hours to seconds.
A Large Scale Analysis of Information-Theoretic Network Complexity Measures Using Chemical Structures

This paper aims to investigate information-theoretic network complexity measures which have already been intensely used in mathematical- and medicinal chemistry including drug design. Numerous such measures have been developed so far but many of them lack a meaningful interpretation, e.g., we want to examine which kind of structural information they detect. Therefore, our main contribution is to shed light on the relatedness between some selected information measures for graphs by performing a large scale analysis using chemical networks. Starting from several sets containing real and synthetic chemical structures represented by graphs, we study the relatedness between a classical (partition-based) complexity measure called the topological information content of a graph and some others inferred by a different paradigm leading to partition-independent measures. Moreover, we evaluate the uniqueness of network complexity measures numerically. Generally, a high uniqueness is an important and desirable property when designing novel topological descriptors having the potential to be applied to large chemical databases.
Representation Recovers Information

Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is necessary for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing.
Enhancing the Attitudes and Self-Efficacy of Older Adults Toward Computers and the Internet: Results of a Pilot Study

This pilot study explored whether a manualized training program could enhance older adults' computer self-efficacy and attitudes toward computers and the Internet. A total of 32 community-dwelling adults 65 years of age or older were randomly assigned to either an experimental or control group, with each group consisting of 8 women and 8 men. The experimental group received 6 weeks of training with two-hour one-on-one sessions once per week. The same training was administered to the control group upon completion of the posttest, 6 weeks after the baseline assessment, to match the procedures on all counts with the exception of training administration. The results of two ANOVAs indicated that participants within the experimental group improved significantly on both their computer self-efficacy (p < .001) and attitudinal scores (p < .001) at the posttraining assessment. No improvements were found in the control group.
Time Course of Cell Sheet Adhesion to Porcine Heart Tissue after Transplantation

Multilayered cell sheets have been produced from bone marrow-derived mesenchymal stem cells (MSCs) for investigating their adhesion properties onto native porcine heart tissue. Once MSCs reached confluence after a 7-day culture on a temperature-responsive culture dish, a MSCs monolayer spontaneously detached itself from the dish, when the culture temperature was reduced from 37 to 20°C. The basal extracellular matrix (ECM) proteins of the single cell sheet are preserved, because this technique requires no proteolytic enzymes for harvesting cell sheet, which become a basic building block for assembling a multilayer cell sheet. The thickness of multilayered cell sheets made from three MSC sheets was found to be approximately 60 μm. For investigating the adhesion properties of the basal and apical sides, the multilayered cell sheets were transplanted onto the surface of the heart’s left ventricle. Multilayered cell sheets were histological investigated at 15, 30, 45 and 60 minutes after transplantation by hematoxylin eosin (HE) and azan dyes to determine required time for the adhesion of the multilayered sheets following cell-sheet transplantation. The results showed that only the basal side of multilayered cell sheets significantly enhanced the sheets adhesion onto the surface of heart 30 minutes after transplantation. This study concluded that (1) cell sheets had to be transplanted with its basal side onto the surface of heart tissue and (2) at least 30 minutes were necessary for obtaining the histological adhesion of the sheets to the heart tissue. This study provided clinical evidence and parameters for the successful application of MSC sheets to the myocardium and allowed cell sheet technology to be adapted clinical cell-therapy for myocardial diseases.
Disambiguating Road Names in Text Route Descriptions using Exact-All-Hop Shortest Path Algorithm

Automatic extraction and understanding of human-generated route descriptions have been critical to research aiming at understanding human cognition of geospatial information. Among all research issues involved, road name disambiguation is the most important, because one road name can refer to more than one road. Compared with traditional toponym (place name) disambiguation, the challenges of disambiguating road names in human-generated route description are three-fold: (1) the authors may use a wrong or obsolete road name and the gazetteer may have incomplete or out-of-date information; (2) geographic ontologies often used to disam-biguate cities or counties do not exist for roads, due to their linear nature and large spatial extent; (3) knowledge of the co-occurrence of road names and other toponyms are difficult to learn due to the difficulty in automatic processing of natural language and lack of external information source of road entities. In this paper, we solve the problem of road name disambiguation in human-generated route descriptions with noise, i.e. in the presence of wrong names and incomplete gazetteer. We model the problem as an Exact-All-Hop Shortest Path problem on a semi-complete directed k-partite graph, and design an efficient algorithm to solve it. Our disambiguation algorithm successfully handles the noisy data and does not require any extra information sources other than the gazetteer. We compared our algorithm with an existing map-based method. Experiment results show that our algorithm significantly outperforms the existing method.
International ERS/ATS guidelines on definition, evaluation and treatment of severe asthma

Severe or therapy-resistant asthma is increasingly recognised as a major unmet need. A Task Force, supported by the European Respiratory Society and American Thoracic Society, reviewed the definition and provided recommendations and guidelines on the evaluation and treatment of severe asthma in children and adults. A literature review was performed, followed by discussion by an expert committee according to the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach for development of specific clinical recommendations. When the diagnosis of asthma is confirmed and comorbidities addressed, severe asthma is defined as asthma that requires treatment with high dose inhaled corticosteroids plus a second controller and/or systemic corticosteroids to prevent it from becoming “uncontrolled” or that remains “uncontrolled” despite this therapy. Severe asthma is a heterogeneous condition consisting of phenotypes such as eosinophilic asthma. Specific recommendations on the use of sputum eosinophil count and exhaled nitric oxide to guide therapy, as well as treatment with anti-IgE antibody, methotrexate, macrolide antibiotics, antifungal agents and bronchial thermoplasty are provided. Coordinated research efforts for improved phenotyping will provide safe and effective biomarker-driven approaches to severe asthma therapy. ERS/ATS guidelines revise the definition of severe asthma, discuss phenotypes and provide guidance on patient management http://ow.ly/roufI
Moral Machines: Teaching Robots Right from Wrong

Computers are already approving financial transactions, controlling electrical supplies, and driving trains. Soon, service robots will be taking care of the elderly in their homes, and military robots will have their own targeting and firing protocols. Colin Allen and Wendell Wallach argue that as robots take on more and more responsibility, they must be programmed with moral decision-making abilities, for our own safety. Taking a fast paced tour through the latest thinking about philosophical ethics and artificial intelligence, the authors argue that even if full moral agency for machines is a long way off, it is already necessary to start building a kind of functional morality, in which artificial moral agents have some basic ethical sensitivity. But the standard ethical theories don't seem adequate, and more socially engaged and engaging robots will be needed. As the authors show, the quest to build machines that are capable of telling right from wrong has begun. Moral Machines is the first book to examine the challenge of building artificial moral agents, probing deeply into the nature of human decision making and ethics.
PFetch: software prefetching exploiting temporal predictability of memory access streams

CPU speeds have increased faster than the rate of improvement in memory access latencies in the recent past. As a result, with programs that suffer excessive cache misses, the CPU will increasingly be stalled waiting for the memory system to provide the requested memory line. Prefetching is a latency hiding technique that tackles this problem. If the address of the memory line that misses in cache can be predicted sufficiently in advance, it can be prefetched into the cache before it is accessed, reducing the effective latency of that access. In this paper, we develop a novel software-only data prefetching scheme that works at the instruction level and exploits predictability in the access stream to prefetch memory lines accessed in the future. Working at the instruction level gives us a global view of memory access patterns across function, module and library boundaries. Conceptually, our scheme monitors the memory locations being accessed by loads and stores as well as their contents. It tries to find instances of predictability such that the address of a load miss can be pre-determined from a limited number of past accesses. We make the following contributions in this work. First, we present a novel prefetching strategy that unifies and generalizes a number of past approaches that each target a specific source of address predictability. Specifically, our scheme unifies all these past approaches: next-line prefetching, self-stride prefetching, "intraiteration" stride prefetching and same-object prefetching. In addition, it extends and generalizes the SPAID scheme for pointer and call-intensive programs. Second, we present a new threshold-based approach that addresses the issues of prefetch accuracy, prefetch timeliness and prefetch redundancy. Third, we assess our scheme both with a cache simulator and on a real machine where we evaluate it with hardware performance counters. Overall, we demonstrate that a significant reduction in L1 cache misses can be achieved for several benchmarks on a real machine with our approach.
Gender Differences and Conduct Disorder Among American Indian Adolescents in Substance Abuse Treatment

Abstract Studies have found high prevalences of conduct disorder among youth in residential substance abuse treatment programs, but no studies have examined this issue among American Indian adolescents while taking into account gender differences. The Diagnostic Interview Schedule for Children, Youth Version and the Composite International Diagnostic Interview, Substance Abuse Module were used to determine the diagnostic status of 89 American Indian adolescents aged between 13 and 18 years who were admitted to a residential treatment program. Seventy-four percent of the adolescents met full DSM-IV criteria for conduct disorder. More than 85% of the participants met at least one criterion for conduct disorder. Conduct disorder was common among both boys and girls in this sample of American Indian adolescents, but the specific antisocial behaviors displayed and their relationships to other psychiatric disorders varied by gender.
Evaluation of relational algebras incorporating the time dimension in databases

The relational algebra is a procedural query language for relational databases. In this paper we survey extensions of the relational algebra that can query databases recording time-varying data. Such an algebra is a critical part of a temporal DBMS. We identify 26 criteria that provide an objective basis for evaluating temporal algebras, Seven of the criteria are shown to be mutually unsatisfiable, implying there can be no perfect temporal algebra, Choices made as to which of the incompatible criteria are satisfied characterize existing algebras Twelve time-oriented algebras are summarized and then evaluated against the criteria. We demonstrate that the design space has in some sense been explored in that all combinations of basic design decisions have at least one representative algebra. Coverage of the remaining criteria provides one measure of the quality of each algebra We argue that all of the criteria are independent and that the criteria identified as compatible are indeed so, Finally, we list plausible properties proposed by others that are either subsumed by other criteria, are not well defined, or have no objective basis for being evaluated. The algebras realize many different approaches to what appears initially to be a straightforward design task.
Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data

Socrates described a group of people chained up inside a cave, who mistook shadows of objects on a wall for reality. This allegory comes to mind when considering ‘routinely collected data’—the massive data sets, generated as part of the routine operation of the modern healthcare service. There is keen interest in routine data and the seemingly comprehensive view of healthcare they offer, and we outline a number of examples in which they were used successfully, including the Birmingham OwnHealth study, in which routine data were used with matched control groups to assess the effect of telephone health coaching on hospital utilisation. Routine data differ from data collected primarily for the purposes of research, and this means that analysts cannot assume that they provide the full or accurate clinical picture, let alone a full description of the health of the population. We show that major methodological challenges in using routine data arise from the difficulty of understanding the gap between patient and their ‘data shadow’. Strategies to overcome this challenge include more extensive data linkage, developing analytical methods and collecting more data on a routine basis, including from the patient while away from the clinic. In addition, creating a learning health system will require greater alignment between the analysis and the decisions that will be taken; between analysts and people interested in quality improvement; and between the analysis undertaken and public attitudes regarding appropriate use of data.
Design and analysis of schedules for virtual network migration

The Internet faces well-known challenges in realizing modifications to the core architecture. To help overcome these limitations, virtual networks run over physical networks and use Internet paths and protocols as essentially a link layer in the virtual network. Effective use of the underlying network requires intelligent placement of virtual networks so that underlying resources do not incur over-subscription. Additionally, because virtual networks may come and go over time, and underlying networks may experience their own dynamic changes, virtual networks may need to be migrated-re-mapped to the physical network during active operation-to maintain good performance. In this paper we consider the problem of scheduling the sequence of node moves that take a virtual network from an original placement to a new placement. We build on prior work that achieves migration of a single node with minimal disruption to develop a model for the migration cost and latency for a given network migration schedule. We then develop algorithms for determining an optimal single-node-at-a-time sequence of moves to minimize migration cost and consideration of multiple node moves in parallel to minimize migration time and cost. Our algorithms are the first we are aware of to systematically address the virtual network migration scheduling problem.
Three-Dimensional Visible Light Indoor Localization Using AOA and RSS With Multiple Optical Receivers

A novel concept is proposed for integrating optical wireless visible light communications with 3-D indoor positioning using a single transmitter and multiple tilted optical receivers. We modeled a channel link, which was based on the transmitter and receiver characteristic data obtained in this experiment. The proposed 3-D positioning algorithm is based on gain difference, which is a function of the angle of arrival and the received signal strength. Our demonstration shows that the proposed algorithm can determine accurate positions, including height, without intercell interference.