Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation and Performance Evaluation of a Firm`s Green Supply Chain Management under Uncertainty

Evaluation of the implementation and performance of a firm’s green supply chain management (GSCM) is an ongoing process. Balanced scorecard is a multi-criteria evaluation concept that highlights implementation and performance measures. The literature on the framework is abundant literature but scarce on how to build a hierarchical framework under uncertainty with dependence relations. Hence, this study proposes a hybrid approach, which includes applied interpretive structural modeling to build a hierarchical structure and uses the analytic network process to analyze the dependence relations. Additionally, this study applies the fuzzy set theory to determine linguistic preferences. Twenty dependence criteria are evaluated for a GSCM implemented firm in Taiwan. The result shows that the financial aspect and life cycle assessment are the most important performance and weighted criteria.
Preliminary Validation of the Spanish Version of the Multiple Stimulus Types Ambiguity Tolerance Scale (MSTAT-II)

Despite widespread interest in ambiguity tolerance and other informationrelated individual differences, existing measures are conceptually dispersed and psychometrically weak. This paper presents the Spanish version of MSTAT-II, a short, stimulus-oriented, and psychometrically improved measure of an individual's orientation toward ambiguous stimuli. Results obtained reveal adequate reliability, validity, and temporal stability. These results support the use of MSTAT-II as an adequate measure of ambiguity tolerance. A pesar del amplio interés en la tolerancia a la ambigüedad y a otras diferencias individuales relacionadas con la información, las medidas existentes son conceptualmente dispersas y presentan deficiencias psicométricas. El propósito de este trabajo es presentar la versión española del MSTAT II, un instrumento de medida de las orientaciones de un individuo respecto a estímulos ambiguos con mejoras psicométricas sustanciales, independiente del contexto y suficientemente corto para ser utilizado junto con otras medidas. Los resultados obtenidos en cuanto a consistencia interna, estabilidad temporal y validez son satisfactorios. Estos resultados apoyan el uso del MSTAT-II en su versión española como una medida adecuada de tolerancia a la ambigüedad.
Recognition of Handwritten Persian/Arabic Numerals Based on Robust Feature Set and K-NN Classifier

Persian handwritten numerals recognition has been a frontier area of research for the last few decades under pattern recognition. Recognition of handwritten numerals is a difficult task owing to various writing styles of individuals. A robust and efficient method for Persian/Arabic handwritten numerals recognition based on K Nearest Neighbors (K-NN) classifier is presented in this paper. The system first prepares a contour form of the handwritten numerals, then the transit, angle and distance features information about the character is extracted and finally K-NN classifier is used to character recognition. Angle, transit and distance features of a character have been computed based on distribution of points on the bitmap image of character. In K-NN method, the Euclidean distance between testing point and reference points is calculated in order to find the k-nearest neighbors. We evaluated our method on 20,000 handwritten samples of Persian numerals. Using 15,000 samples for training, we tested our method on other 5,000 samples and obtained 99.82% correct recognition rate. Further, we obtained 89.90% accuracy using four-fold cross validation technique on 20,000 dataset.
Sentence ordering for multi-document summarization in response to multiple queries

The growing access to large amounts of text data opens more opportunities in information processing. Given a list of complex questions and a set of relevant documents, the task of producing an informative and coherent summary of those documents in response to the questions has attracted a great deal of attention recently. However, the problem of organizing information for summarization so that the generated summary is coherent has received relatively little attention. Several approaches have been proposed for sentence ordering in single-document and generic multiple-document summarization, but no single method has been found to address sentence ordering in query-based summarization. In this thesis, we propose and implement an algorithm that combines constraints from query order and topical relatedness in human produced summaries of multiple documents in response to multiple questions. To test the effectiveness of the constraints, we construct a new query-based corpus from the human produced summaries for the Document Understanding Conference(DUC) 2006 evaluation. We then conduct experiments, using an automatic evaluation method based on Kendall’s τ , to evaluate and compare the effectiveness of our approaches to others. Our results show that both query order and topical relatedness improve the ordering performance when compared to a baseline method, and a combination of these two constraints achieves even better results.
The Effect of Acupuncture in Promoting Neurogenesis and Angiogenesis after Middle Cerebral Artery Occlusion in Rats

[Abstract] Objectives : This study was performed to choose more effective neuro-protective acupuncture point and to verify the effect of acupuncture in promoting neurogenesis and angiogenesis as a result of its neuro-vasculo-regenerative effect in middle cerebral artery occlusion model in rats. Methods : By TTc staining we chose the most effective acupuncture point with neuro-protection. We randomly divided into four groups: Such as (1) sham group(with sham-operation), (2) sham+acupuncture group(with sham-operation), (3) middle cerebral artery occlusion group, (4) MCAO+AT group. Acupuncture procedure was performed for four days. Total RNA was extracted using TRIzol reagent, according to the manufacturer’s instructions, and was purified using an RNAeasy mini kit. Immuno-histochemistry was performed using primary antibody mouse anti-BrdU, NeuN, Dcx, and VEGF. Results : We found that ST36 had the more neuroprotective effect than LI11 and SP3. The microarray analysis revealed that 54 genes were more expressed neurogenesis pathway in MCAO+AT group compared with MCAO group(fold changes greater than or equal to twofold change). 11 genes were more expressed angiogenesis pathway. And 7 genes were more expressed VEGF pathway. Immuno-histochemistry revealed that cell proliferation, cell migration and cell maturation were increased. Conclusions : This study demonstrated that acupuncture on ST36 had neuro-protective and neuro-restorative effect in ischemic brain injuries. And its mechanism might be related to promote neurogenesis and angiogenesis. These results suggest th at acupuncture have potential benefits for the treatment of ischemic stroke. Acupuncture; Neurogenesis; Angiogenesis; Striatum; ST 36;
Tracking down the ENSO delayed oscillator with an adjointOGCMGeert

The adjoint of an ocean general circulation model is used as a tool for investigating the causes of changes in ENSO SST indices. We identify adjoint Kelvin and Rossby waves in the sensitivities to sea level and wind stress at earlier times, which can be traced back for more than a year through western and weak eastern boundary reeections. Depending on the thermocline depth the rst and second baroclinic modes are excited. The sensitivities to the heat ux and SST are local and decay in about a month. The sensitivities to the uxes are converted into the innuence of SST using the adjoint of a statistical atmosphere model. Focusing on SST perturbations in the index region itself, we recover, up to a scale factor, the delayed oscillator concept.
MULTICHANNEL CROSS-TALK CANCELLATION IN A CALL-CENTER SCENARIO USING FREQUENCY-DOMAIN ADAPTIVE FILTERING

We propose a multichannel residual cross-talk cancellation strategy to operate in environments where each microphone signal is dominated by one of several simultaneously active sound sources, like in a call-center scenario. The method relies on a two-step adaptive ﬁl-tering scheme derived from an established GSC structure. The ﬁlter updates are performed in the frequency domain to allow an effective DFT-bin-wise adaptation control. This new scheme is applicable to an arbitrary number of competing sources and experiments conducted for three and seven speech sources show that it allows to reduce the amount of undesired cross-talk components by about 10dB, without noticeable distortion of the desired signal
Franchise relocation and sport introduction: a sports marketing case study of the Carolina Hurricanes’ fan adoption plan

Franchise relocation and sport introduction are becoming commonplace in professional sports. However, many franchises have found that developing fan acceptance is often challenging. The fan adoption process is presented as a systematic framework that guides strategy development from creating fan awareness through adoption. An examination of the Carolina Hurricanes inaugural season (1997‐1998) provides a variety of examples of how marketing strategy evolves throughout the fan adoption process.
FM 99.9, Radio Virus: Exploiting FM Radio Broadcasts for Malware Deployment

Many modern smartphones and car radios are shipped with embedded FM radio receiver chips. The number of devices with similar chips could grow very significantly if the U.S. Congress decides to make their inclusion mandatory in any portable device as suggested by organizations such as the RIAA. While the main goal of embedding these chips is to provide access to traditional FM radio stations, a side effect is the availability of a data channel, the FM Radio Data System (RDS), which connects all these devices. Different from other existing IP-based data channels among portable devices, this new one is open, broadcast in nature, and so far completely ignored by security providers. This paper illustrates for the first time how to exploit the FM RDS protocol as an attack vector to deploy malware that, when executed, gains full control of the victim's device. We show how this attack vector allows the adversary to deploy malware on different platforms. Furthermore, we have shown the infection is undetected on devices running the Android OS, since malware detection solutions are limited in their ability due to some features of the Android security model. We support our claims by implementing an attack using RDS on different devices available on the market (smartphones, car radios, and tablets) running three different versions of Android OS. We also provide suggestions on how to limit the threat posed by this new attack vector and explain what are the design choices that make Android vulnerable. However, there are no straightforward solutions. Therefore, we also wish to draw the attention of the security community towards these attacks and initiate more research into countermeasures.
The Design and Evaluation of a Scalable Wireless MAC Protocol Technical Report

Contention-based MAC protocols such as IEEE 802.11 DCF are known to perform well in a network with small number of nodes. As the number of nodes increases, the cost of collisions increases and dominates. This increasing collisions quickly drives every client in the network into wasted and unfair idleness, resulting in performance degradation. In this paper, we propose SMAC, a scalable MAC protocol, that preserves the advantages of 802.11 while avoiding its drawbacks. The design of SMAC leverages the efficient channel utilization of 802.11 DCF at low load and 802.11 PCF at high load. The SMAC-enabled AP controls the access of its clients by combining the contention and schedulebased access mechanisms and determining the optimal contention level among the clients. The SMAC-client simply indicates its intention of channel access. Through thorough analysis and evaluation, we show that SMAC preserves the channel utilization efficiency of 802.11 DCF, scales up to any number of nodes, and preserves perfect short-term and long-term fairness among the contending clients.
X-Ray Properties of the Gigahertz Peaked and Compact Steep Spectrum Sources

We present Chandra X-ray Observatory observations of gigahertz peaked-spectrum (GPS) and compact steep spectrum (CSS) radio sources. The Chandra sample contains 13 quasars and 3 galaxies, with measured 2-10 keV X-ray luminosities within 1042-1046 erg s−1. We detect all of the sources, five of which are observed in X-rays for the first time. We study the X-ray spectral properties of the sample. The measured absorption columns in the quasars are different from those in the galaxies in that the quasars show no absorption (with limits ~1021 cm−2), while the galaxies have large absorption columns (>1022 cm−2) consistent with previous findings. The median photon index of the sources with a high signal-to-noise ratio is Γ = 1.84 ± 0.24, which is larger than the typical index of radio-loud quasars. The arcsec resolution of the Chandra telescope allows us to investigate extended X-ray emission and to look for diffuse components and X-ray jets. We found X-ray jets in two quasars (PKS 1127–145 and B2 0738+32), and an X-ray cluster surrounding a CSS quasar (3C 186; z = 1.1). We detected a possible binary structure in galaxy 0941–080 and an extended diffuse emission in galaxy PKS B1345+125. We discuss our results in the context of X-ray emission processes and radio source evolution. We conclude that the X-ray emission in these sources is most likely unrelated to a relativistic jet, although the sources’ radio loudness may suggest a high radiative efficiency for the jet power in these sources.
Detecting an external influence on recent changes in oceanic oxygen using an optimal fingerprinting method

Ocean deoxygenation has been observed in all major ocean basins over the past 50 yr. Although this signal is largely consistent with oxygen changes expected from anthropogenic climate change, the contribution of external forcing to recent deoxygenation trends relative to natural internal variability is yet to be established. Here we conduct a formal optimal fingerprinting analysis to investigate if external forcing has had a detectable influence on observed dissolved oxygen concentration ([O 2 ]) changes between ∼1970 and ∼1992 using simulations from two Earth System Models (MPI-ESM-LR and HadGEM2-ES). We detect a response to external forcing at a 90% confidence level and find that observed [O 2 ] changes are inconsistent with internal variability as simulated by models. This result is robust in the global ocean for depth-averaged (1-D) zonal mean patterns of [O 2 ] change in both models. Further analysis with the MPI-ESM-LR model shows similar positive detection results for depth-resolved (2-D) zonal mean [O 2 ] changes globally and for the Pacific Ocean individually. Observed oxygen changes in the Atlantic Ocean are indistinguishable from natural internal variability. Simulations from both models consistently underestimate the amplitude of historical [O 2 ] changes in response to external forcing, suggesting that model projections for future ocean deoxygenation may also be underestimated.
Global Transcriptome Analysis of Orange Wheat Blossom Midge, Sitodiplosis mosellana (Gehin) (Diptera: Cecidomyiidae) to Identify Candidate Transcripts Regulating Diapause

Background Many insects enter a developmental arrest (diapause) that allows them to survive harsh seasonal conditions. Despite the well-established ecological significance of diapause, the molecular basis of this crucial adaptation remains largely unresolved. Sitodiplosis mosellana (Gehin), the orange wheat blossom midge (OWBM), causes serious damage to wheat throughout the northern hemisphere, and sporadic outbreaks occur in the world. Traits related to diapause appear to be important factors contributing to their rapid spread and outbreak. To better understand the diapause mechanisms of OWBM, we sequenced the transcriptome and determined the gene expression profile of this species. Methodology/Principal Findings In this study, we performed de novo transcriptome analysis using short-read sequencing technology (Illumina) and gene expression analysis with a tag-based digital gene expression (DGE) system. The sequencing results generated 89,117 contigs, and 45,713 unigenes. These unigenes were annotated by Blastx alignment against the NCBI non-redundant (nr), Clusters of orthologous groups (COG), gene orthology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. 20,802 unigenes (45.5% of the total) matched with protein in the NCBI nr database. Two digital gene expression (DGE) libraries were constructed to determine differences in gene expression profiles during diapause and non-diapause developmental stages. Genes related to diapause were analyzed in detail and in addition, nine diapause-related genes were analyzed by real time PCR. Conclusions/Significance The OWBM transcriptome greatly improves our genetic understanding and provides a platform for functional genomics research of this species. The DGE profiling data provides comprehensive information at the transcriptional level that facilitates our understanding of the molecular mechanisms of various physiological aspects including development and diapause stages in OWBM. From this study it is evident that various genes coding metabolic enzymes are crucial for diapause and metamorphosis.
Natural Language Processing for Health and Social Media

Intro Social media, such as Twitter, has shown great potential to analyze real world events, such as politics, product sentiment and natural disasters. In recent years, social media has emerged in the health community, particularly in public health, as a revolutionary data source for a wide range of problems. Vast amounts of naturalistic population data can be collected through social media much faster and at lower cost than through traditional data sources such as surveys. Additionally, social media provides novel data previously unavailable to researchers. These advantages allow for the rapid formulation and evaluation of novel hypotheses, aiding decisions about how best to spend limited traditional data collection resources.
STRATEGY CREATION IN PRACTICE - ADAPTIVE AND CREATIVE LEARNING DYNAMICS

This paper examines how managers create and develop strategy in practice. It reports findings of an in-depth investigation of managerial activities and associated learning dynamics in strategy creation, the generation and development of radically new strategies. Managerial and learning activities on different organizational levels, their influence on strategy development, and relationship to strategic change are examined. A dual longitudinal case methodology, including a single in-depth case study combined with a multiple retrospective case study is used, involving four multinational companies. The findings show a twofold character of strategy creation including an adaptive and a creative strategy motor of strategic change involving fundamentally different learning dynamics, reflecting their diverse location and social embeddedness. Adaptive learning dynamics were based on exploitation and refinement of prevailing industry and resource structures and involved a deductive reasoning or sensemaking as regards strategy, including knowledge assimilation activities such as formal intelligence and routine experiences. Creative learning dynamics evolved in the periphery of the organizations and were more remote from existing values and beliefs. They predominantly involved exploration and inductive sensemaking, including activities like informal noticing and creative experiments. The exploration/exploitation trade off interaction between the two strategy motors resulted in tension, which finally triggered strategic change.
High-Q Defect-Free 2D Photonic Crystal Cavity from Random Localised Disorder

We propose a high-Q photonic crystal cavity formed by introducing random disorder to the central region of an otherwise defect-free photonic crystal slab (PhC). Three-dimensional finite-difference time-domain simulations determine the frequency, quality factor, Q, and modal volume, V, of the localized modes formed by the disorder. Relatively large Purcell factors of 500–800 are calculated for these cavities, which can be achieved for a large range of degrees of disorders.
Unsupervised non-parametric region segmentation using level sets

We present a novel non-parametric unsupervised segmentation algorithm based on region competition (Zhu and Yuille, 1996); but implemented within a level sets framework (Osher and Sethian, 1988). The key novelty of the algorithm is that it can solve N /spl ges/ 2 class segmentation problems using just one embedded surface; this is achieved by controlling the merging and splitting behaviour of the level sets according to a minimum description length (MDL) (Leclerc (1989) and Rissanen (1985)) cost function. This is in contrast to N class region-based level set segmentation methods to date which operate by evolving multiple coupled embedded surfaces in parallel (Chan et al., 2002). Furthermore, it operates in an unsupervised manner; it is necessary neither to specify the value of N nor the class models a-priori. We argue that the level sets methodology provides a more convenient framework for the implementation of the region competition algorithm, which is conventionally implemented using region membership arrays due to the lack of a intrinsic curve representation. Finally, we generalise the Gaussian region model used in standard region competition to the non-parametric case. The region boundary motion and merge equations become simple expressions containing cross-entropy and entropy terms.
Coverage-Time Optimization for Clustered Wireless Sensor Networks: A Power-Balancing Approach

In this paper, we investigate the maximization of the coverage time for a clustered wireless sensor network by optimal balancing of power consumption among cluster heads (CHs). Clustering significantly reduces the energy consumption of individual sensors, but it also increases the communication burden on CHs. To investigate this tradeoff, our analytical model incorporates both intra- and intercluster traffic. Depending on whether location information is available or not, we consider optimization formulations under both deterministic and stochastic setups, using a Rayleigh fading model for intercluster communications. For the deterministic setup, sensor nodes and CHs are arbitrarily placed, but their locations are known. Each CH routes its traffic directly to the sink or relays it through other CHs. We present a coverage-time-optimal joint clustering/routing algorithm, in which the optimal clustering and routing parameters are computed using a linear program. For the stochastic setup, we consider a cone-like sensing region with uniformly distributed sensors and provide optimal power allocation strategies that guarantee (in a probabilistic sense) an upper bound on the end-to-end (inter-CH) path reliability. Two mechanisms are proposed for achieving balanced power consumption in the stochastic case: a routing-aware optimal cluster planning and a clustering-aware optimal random relay. For the first mechanism, the problem is formulated as a signomial optimization, which is efficiently solved using generalized geometric programming. For the second mechanism, we show that the problem is solvable in linear time. Numerical examples and simulations are used to validate our analysis and study the performance of the proposed schemes.