Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightpath Rerouting Strategies in WDM All-Optical Networks Under Scheduled and Random Traffic

In WDM all-optical networks without wavelength conversion, traffic rerouting is motivated either by an optimization of resource utilization or by network survivability. In this paper, we use rerouting to optimize network resources allocation in order to set up an incoming lightpath demand to be blocked for lack of resources. Rerouting aims at reassigning the wavelength and/or the path of one or several established connections in order to free enough wavelengths to satisfy the incoming demand. Rerouting refers implicitly to dynamic traffic. In most previous studies related to rerouting, only random (dynamic) traffic is considered. In this paper, we propose a new lightpath rerouting scheme considering three types of traffic demands, referred to as permanent lightpath demands (PLDs), scheduled lightpath demands (SLDs), and random lightpath demands (RLDs). PLDs are static, whereas SLDs and RLDs are dynamic. SLDs are preplanned, whereas RLDs are stochastic. PLDs may be seen as a particular case of SLDs. PLDs and SLDs correspond to guaranteed services, whereas RLDs correspond to best-effort services. Thus, PLDs and SLDs cannot be rerouted. We here describe two new routing and wavelength assignment (RWA) strategies applying rerouting. Both strategies assume that PLDs are routed offline during the network planning phase. The first strategy computes the RWA for SLDs and RLDs on the fly. The second strategy proceeds in two separate phases. It first computes offline the RWA for SLDs before considering RLDs on the fly on the remaining network resources. Our rerouting schemes aim at minimizing the number of RLDs or the number of optical channels to be rerouted. Through numerical examples and experimental simulations, we outline that routing SLDs offline and RLDs online instead of routing SLDs and RLDs online enables lower rejection ratios. We also compare our proposed rerouting algorithms with other approaches from the literature in terms of complexity.
MUC1 Vaccine for Individuals with Advanced Adenoma of the Colon: A Cancer Immunoprevention Feasibility Study

Cancer vaccines based on human tumor-associated antigens (TAA) have been tested in patients with advanced or recurrent cancer, in combination with or following standard therapy. Their immunogenicity and therapeutic efficacy has been difficult to properly evaluate in that setting characterized by multiple highly suppressive effects of the tumor and the standard therapy on the patient's immune system. In animal models of human cancer, vaccines administered in the prophylactic setting are most immunogenic and effectively prevent cancer development and progression. We report results of a clinical study that show that in patients without cancer but with a history of premalignant lesions (advanced colonic adenomas, precursors to colon cancer), a vaccine based on the TAA MUC1 was highly immunogenic in 17 of 39 (43.6%) of vaccinated individuals, eliciting high levels of anti-MUC1 immunoglobulin G (IgG) and long-lasting immune memory. Lack of response in 22 of 39 individuals was correlated with high levels of circulating myeloid-derived suppressor cells (MDSC) prevaccination. Vaccine-elicited MUC1-specific immune response and immune memory were not associated with significant toxicity. Our study shows that vaccines based on human TAAs are immunogenic and safe and capable of eliciting long-term memory that is important for cancer prevention. We also show that in the premalignant setting, immunosuppressive environment (e.g., high levels of MDSC) might already exist in some individuals, suggesting an even earlier premalignant stage or preselection of nonimmunosuppressed patients for prophylactic vaccination. Cancer Prev Res; 6(1); 18–26. ©2012 AACR.
Assessing Impairment of Executive Function and Psychomotor Speed in Premanifest and Manifest Huntington’s Disease Gene-expansion Carriers

Abstract Executive functions (EF) and psychomotor speed (PMS) has been widely studied in Huntington’s disease (HD). Most studies have focused on finding markers of disease progression by comparing group means at different disease stages. Our aim was to investigate performances on nine measures of EF and PMS in a group of premanifest and manifest HD-gene expansion carriers and to investigate which measures were most sensitive for assessment of individual patients by analyzing frequencies of impaired performances relative to healthy controls. We recruited HD gene-expansion carriers, 48 manifest and 50 premanifest and as controls 39 healthy gene-expansion negative individuals. All participants underwent neurological examination and neuropsychological testing with nine cognitive measures. The frequency of impairment was investigated using cutoff scores. In group comparisons the manifest HD gene-expansion carriers scored significantly worse than controls on all tests and in classification of individual scores the majority of scores were classified as probably impaired (10th percentile) or impaired (5th percentile) with Symbol Digit Modalities Test (SDMT) being the most frequently impaired. Group comparisons of premanifest HD gene-expansion carriers and healthy controls showed significant differences on SDMT and Alternating fluency tests. Nevertheless the frequencies of probably impaired and impaired scores on individual tests were markedly higher for Alternating and Lexical fluency tests than for SDMT. We found distinct group differences in frequency of impairment on measures of EF and PMS in manifest and premanifest HD gene-expansion carriers. Our results indicate to what degree these measures can be expected to be clinically impaired. (JINS, 2015, 21, 1–10)
An improved optimization method based on the intelligent water drops algorithm for the vehicle routing problem

We introduce an improved intelligent water drops (IIWD) algorithm as a new swarm-based nature inspired algorithm to solve capacitated vehicle routing problem. IIWD algorithm introduces new adjustments and features that help to optimize the VRP problem with higher efficiency. We reinforce this algorithm to have satisfactory consequences in controlling the balance between diversification and intensification of the search process. We solve 14 well-known benchmark instances in the literature to compare the solutions with the best reported solutions in the literature. Experimental results demonstrate that the suggested technique can well and effectively cope with such problems.
Mining named entities from search engine query logs

We present a seed expansion based approach to classify named entities in web search queries. Previous approaches to this classification problem relied on contextual clues in the form of keywords surrounding a named entity in the query. Here we propose an alternative approach in the form of a Bag-of-Context-Words (BoCW) that is used to represent the context words as they appear in the snippets of the top search results for the query. This is particularly useful in the case where the query consists of only the named entity without any context words, since in the previous approaches no context is discovered. In order to construct the BoCW, we employ a novel algorithm, which iteratively expands a Class Vector that is created through expansion by gradually aggregating the BoCWs of similar named entities appearing in other queries. We provide comprehensive experimental evidence using a commercial query log showing that our approach is competitive with existing approaches.
D‐dopachrome tautomerase in adipose tissue inflammation and wound repair

D‐dopachrome tautomerase (D‐DT/MIF‐2) is a member of the macrophage migration inhibitory factor (MIF) cytokine superfamily, and a close structural homolog of MIF. MIF and D‐DT have been reported to be involved in obesity, but there is little known about the regulation of D‐DT in adipose tissue inflammation and wound healing. Subcutaneous adipose tissue was collected from 54 healthy donors and 28 donors with acutely inflamed wounds undergoing wound debridement. In addition, epididymal fat pads of mice were injected with lipopolysaccharide to study receptor expression and cell migration in vivo. D‐DT protein levels and mRNA expression were significantly decreased in subcutaneous adipose tissue adjacent to acutely inflamed wounds. D‐DT improved fibroblast viability and increased proliferation in vitro. While D‐DT alone did not have a significant effect on in vitro fibroblast wound healing, simultaneous addition of neutralizing MIF antibody resulted in a significant improvement of fibroblast wound healing. Interestingly, expression of the MIF and D‐DT receptor CD74 was down‐regulated while the MIF receptors CXCR2 and CXCR4 were up‐regulated primarily on macrophages indicating that the MIF‐CXCR2/4 axis may promote recruitment of inflammatory cells into adipose tissue. Our results describe a reciprocal role of D‐DT to MIF in inflamed adipose tissue, and indicate that D‐DT may be beneficial in wound repair by improving fibroblast survival and proliferation.
A Secure Protocol to Distribute Unlinkable Health Data

Health data that appears anonymous, such as DNA records, can be re-identified to named patients via location visit patterns, or trails. This is a realistic privacy concern which continues to exist because data holders do not collaborate prior to making disclosures. In this paper, we present STRANON, a novel computational protocol that enables data holders to work together to determine records that can be disclosed and satisfy a formal privacy protection model. STRANON incorporates a secure encrypted environment, so no data holder reveals information until the trails of disclosed records are provably unlinkable. We evaluate STRANON on real-world datasets with known susceptibilities and demonstrate data holders can release significant quantities of data with zero trail re-identifiability.
ANTIBACTERIAL ACTIVITY OF ESSENTIAL OIL OF CYMBOPOGON FLEXUOSUS (LEMON GRASS) AGAINST CLINICAL ISOLATES OF MULTIDRUG-RESISTANT ACINETOBACTER BAUMANNII: A PRELIMINARY IN-VITRO STUDY

Aim – In recent years, the incidence of nosocomial infections due to A.baumannii has increased to a point of concern. Rapid spread of multidrug and extremely drug resistant A.baumannii in clinical settings has made treatment options difficult for clinician. It has also increased the morbidity and mortality in immunocompromised patients. The aim of this study was to investigate the efficacy of essential oil of a traditional medicinal plant, Cymbopogon flexuosus (lemon grass) against the problematic multidrug resistant A.baumannii. Methodology – Essential oil of lemon grass was distilled by Neo-Clavenger’s method and the antibacterial activity was tested against 102 multidrug resistant A.baumannii clinical isolates by punch-well and disc diffusion methods. The minimum inhibitory concentration of lemon grass oil was determined by Macrobroth dilution method. Results – Majority of A.baumannii isolates were inhibited by lemon grass oil, inhibition zone ranging from 13mm to 33mm, mean inhibition zone being 23mm and the minimum inhibitory concentration of lemon grass oil was 6. 25µl/ml. Conclusion – essential oil lemon grass showed good antibacterial activity against A.baumannii and might be considered as an alternative treatment option against multi-drug resistant A.baumannii infections. However, further pharmacokinetic and pharmacodynamic studies are needed for routine clinical use.
Risk analysis and economic consequences of a more or less closed farming system on Dutch dairy farms.

A more closed farming system may prevent introduction of infectious diseases on dairy farms and can be a good starting point for eradication of these diseases. In an exploratory study it was calculated that being >open= had a negative influence on net profit of almost Dfl 1 per 100 kg of milk. A survey was carried out to make up an inventory of the existing more or less closed farming systems in the Netherlands. The associations between serologic status for BHV1, risk factors for introduction of diseases, management, and economic results were investigated. A follow-up cohort study with 100 dairy farms will be carried out to quantify the determined associations and prove possible causal relationships. Three groups of farms will be selected, a group which is as closed as possible, a group which is open, and a group in-between, closed for animal contacts but not for visitors and materials. The groups will be compared on introduction of infectious diseases, management, factorial diseases and economic results. The results of the project will be used to create a computer model which can be used to support farmers in their decisions to derive a more or less closed farming system to prevent introduction of diseases.
A Statistical Travel Time Model for Miniload Automated Storage and Retrieval Systems with Open-Rack Structure

In customary automated storage and retrieval systems (AS/RSs), stacker cranes (S/R machines) are used to store and retrieve loads into/from the storage cells. In this paper, the open-rack structure with unidirectionalupward mobile loads within the rack is applied in AS/RS, in which the stacker crane is only used for the retrieval operations, and the storage operations are carried out by separate devices namely, storage platforms. Heuristics algorithms and models are developed for load shuffling and travel time of the storage platform, respectively. Then the expected travel time of the proposed AS/RS is derived. The travel time model and the performance of proposed AS/RS are validated using Monte Carlo simulation and are compared with a conventional one. The results show that the open-rack AS/RS represents a higher performance and the proposed models are reliable for the design and analysis of this kind of AS/RS.
Untangling Topic Threads in Chat-Based Communication: A Case Study

Analyzing chat traffic has important applications for both the military and the civilian world. This paper presents a case study of a real-world application of chat analysis in support of team training exercise in the military. It compares the results of an unsupervised learning approach with those of a supervised classification approach. The paper also discusses some of the specific challenges presented by this domain.
How school administrators use strategy-as-practice to achieve organizational coherence

As public education continues to grapple with private marketplace pressures, including depleting resources, it must contend with questions of organizational identity and direction, a dilemma emerging from increased competition. The accountability movement has ensured that instructional elements like academic standards, assessments, and curricula, “cohere. ” Little attention has been paid to how institutional elements like collective bargaining agreements, budgets, and strategic plans “cohere” to support instruction. In the private sector, competitive companies must skillfully distribute resources and mitigate inefficient practices while pursuing their common goals. To understand this changing context, a case study was designed to determine how school administrators supported district goals or reconciled local institutional constraints while supporting the district’s instructional goals. The case study was in a rural public school district in Pennsylvania. Using Strategy-as-Practice (SAP) methodology, the researcher closely examined the practices school administrators engaged in on a day-to-day basis over a two-month period. They were classified as supporting goals or reconciling constraints. Descriptive data was collected and nine school administrators and the school board president were both observed and interviewed. The study yielded 60 specific practices participants used to support the organization’s goals and 58 to reconcile constraints. Analysis of these data revealed that newcomers and locals were distinguishable by their choices of practices, with newcomers being more goals supportive and locals more focused on reconciling constraints. This resulted in internal tensions that made effective communication across newcomers and locals difficult. Each saw the other’s activities as interfering with their work. Newcomers tended to find their efforts repeatedly constrained, despite their instructional successes. Locals tended to focus on the need for more respect for the local community. This resulted in two conclusions. First, while institutional elements of goal support and local constraint reconciliation brought stability to the organization, the pursuit of instructional reform proved to be temporarily destabilizing. Second, the district tended to resolve this instability by supporting local constraint reconciliation even at the expense of goal support for improved student achievement. This suggests that newcomers who focus on student achievement goals may need to be better informed about local constraints, independently of job interview rhetoric.
Explanatory Analysis in Business Intelligence Systems

In this paper we describe a method for the discovery of exceptional values in business intelligence (BI) systems, in particular OLAP information systems. We also show how exceptional values can be explained by underlying causes. OLAP applications offer a support tool for business analysts and accountants in analyzing financial data because of the availability of different views and managerial reporting facilities. The purpose of the methods and algorithms presented here, is to extend OLAP based systems with more powerful analysis and reporting functions. We describe how exceptional values at any level in the data, can be automatically detected by statistical models. Secondly a generic model for diagnosis of atypical values is realized in the OLAP context. By applying it, a full explanation tree of causes at successive levels can be generated. If the tree is too large, the analyst can use appropriate filtering measures to prune the tree to a manageable size. This methodology has a wide range of applications such as interfirm comparison, analysis of sales data and the analysis of any other data that possess a multi-dimensional hierarchical structure. The method is demonstrated in a case study on financial data.
Problems and tentative solutions in InternetCAR testing with IPv6

The vehicle environment is a good testbed to investigate Internet mobility technologies. Based on testing of buildings, protocol implementation details and problems to deploy it are clarified. This paper describes them. The InternetCAR Project had already done testing to connect vehicles to the IPv6 Internet. Some applications including vehicle diagnosis and teleconference, are implemented on the test system. The test system has the following functions: 1) several communication media with a media switching technology, 2) MobileIPv6 function for continuous connectivity, and 3) network mobility (NEMO) function to provide continuous Internet connectivity for an in-vehicle network. This paper introduces the problems of developing the InternetCAR testing system. The design and solutions are also described.
Implementation of CAN bus device driver design base on embedded system

CAN bus (Controller Area Network), is one of the most widely used field buses of world. This article aims to explore that how to control MCP2510 CAN bus controller to send and receive data basing on s3c2440 of Samsung. In the circuit, this design adopts the MCP2510 CAN bus controller chip to expand s3c2440 interfaces, hardware contains s3c2440 chip-based development board and the MCP2510 CAN bus controller, the software mainly based on ARM Linux. This paper describes the hardware interface design, designs the CAN bus device driver base on CAN bus protocol and the characteristics of MCP2510 controller, analyses the design method which adopts driver to control the corresponding circuit. The whole system has been tested on board and meets the design goal.
Functional genomics and expression profiling: be there or be square.

The manuscript by Hong and colleagues in this issue of the American Journal of Respiratory Cell and Molecular Biology (1) profiling the downstream genes activated by expression of the tumor suppressor gene, PTEN , represents one of the first papers published in this journal that makes use of complementary DNA (cDNA) microarrays. cDNA microarray analysis is one of the most important new areas that has emerged from the ongoing revolution in functional genomics. This paper provides some background material and highlights the importance and promise of this new technology for lung researchers.
Determinants of Program Loyalty

This study focuses on the topic of program loyalty in municipal park and recreation organizations. In general, the study investigates the application of a model to measure consumer loyalty in a municipal park and recreation department. The research addresses four questions. 1) Can the loyalty of municipal park and recreation services consumers be measured effectively? 2) To what degree are consumers loyal to municipal park and recreation programs? 3) To what extent does the degree of loyalty expressed by consumers vary from one program area to another? 4) Can the program loyalty of park and recreation consumers be segmented according to selected demographic and personality variables? The study effectively demonstrates the use of a two-dimensional model to measure consumer loyalty. Respondents in the study indicated a low to moderate level of loyalty to the municipal park and recreation department studied. Program loyalty was shown to be greater in the area of fitness as compared to the program area of arts and dance classes. Last, this investigation provided a mechanism for segmenting demographic variables. Participants aged fifty-one years and older demonstrated greater levels of commitment than younger participants.
A fast method for image noise estimation using Laplacian operator and adaptive edge detection

We present a simple and fast algorithm for image noise estimation. The input image is assumed to be corrupted by additive zero mean Gaussian noise. To exclude structures or details from contributing to the noise variance estimation, a simple edge detection algorithm using first-order gradients is applied first. Then a Laplacian operator followed by an averaging over the whole image will provide very accurate noise variance estimation. There is only one parameter which is self-determined and adaptive to the image contents. Simulation results show that the proposed algorithm performs well for different types of images over a large range of noise variances. Performance comparisons against other approaches are also provided.