Miracle-dz/UniFAR-FTUs huggingface dataset:
Ambient Intelligence: A New Multidisciplinary Paradigm

Ambient intelligence (AmI) is a new multidisciplinary paradigm rooted in the ideas of NormanAuthor of the Invisible Computer [32]. and Ubiquitous Computing. AmI fosters novel anthropomorphic human–machine models of interaction. In AmI, technologies are deployed to make computers disappear in the background, while the human user moves into the foreground in complete control of the augmented environment. AmI is a user-centric paradigm, it supports a variety of artificial intelligence methods and works pervasively, nonintrusively, and transparently to aid the user. AmI supports and promotes interdisciplinary research encompassing the technological, scientific and artistic fields creating a virtual support for embedded and distributed intelligence.
Indicators of Digital Leadership in the Context of K-12 Education

: International Society for Technology in Education Standards for Administrators (ISTE-A, 2009) has been published for six years and but few studies have been done to indicate digital leadership indicators. This qualitative study aims to explore digital leadership indicators in the context of K-12 education based on ISTE-A standards. Results show that visionary leadership is indicated as integrated technology vision and technology plan support by all stakeholders. Indicators of digital age learning culture include sufficient devices, technology modeling, and effective technology utilization. Digital learning opportunities and digital learning community are indicators of professional development. Indicators of systemic improvement include maximized learning achievement, competed personnel, and strategic partnership. Digital citizenship is indicated as digital filter.
Rapid decay and Baum-Connes for large type Artin groups

We prove that many Artin groups of large type satisfy the rapid decay property, including all those of extra-large type. For many of these, including all 3-generator groups of extra-large type, a result of Lafforgue applies to show that the groups satisfy the Baum-Connes conjecture without coefficients. Our proof of rapid decay combines elementary analysis with combinatorial techniques and relies on properties of geodesic words in Artin groups of large type that were observed in earlier work by two of the authors of this current article.
Optimal rationing within a heterogeneous population

A government agency delegates to a provider (hospital, medical gatekeeper, school, social worker) the decision to supply a service or treatment to individual recipients. The agency does not perfectly know the distribution of individual treatment costs in the population. The single-crossing property is not satisfied when the uncertainty pertains to the dispersion of the distribution. We find that the provision of service should be distorted upwards when the first-best efficient number of recipients is sufficiently high.
The influence of precipitation and temperature input schemes on hydrological simulations of a snow and glacier melt dominated basin in Northwest China

Abstract. Basins with glaciers and snow provide water storage and supply for downstream irrigated farmland, but their hydrology is often poorly known because there are limited observation networks in high mountain regions. Large uncertainties in hydrological simulations also arise from errors associated with meteorological forcing data. The influence of precipitation and temperature forcing data on hydrological simulations in rain/snow dominated watershed is well documented, but less so in basins with glaciers. We analyzed the impacts and reliability of precipitation/temperature input solutions on hydrological simulations in the glacier/snow dominated Manas River Basin, showing that precipitation pattern has significant impact on snow accumulation and melt, and further impacts on simulated glacier melt behavior. The temperature inputs affect not only the timing of discharge but also the total water yield. The uncertainty associated with simple estimated input data propagates and is amplified through the modeling process. We suggest that the impacts of forcing data on hydrological simulations in basins with glaciers are more complex than in common rain/snow dominated watersheds. Glacier melt behavior may conceal uncertainties that are actually derived from input data. Assessment of hydrological model performance should include investigation of key processes involved in the hydrologic cycle individually, not just comparisons of simulated and observed discharge.
Supersonic Microwave Plasma: Potential and Limitations for Energy-Efficient CO2 Conversion

Supersonic flows provide a high thermodynamic nonequilibrium, which is crucial for energy-efficient conversion of CO2 in microwave plasmas and are therefore of great interest. However, the effect of the flow on the chemical reactions is poorly understood. In this work, we present a combined flow and plasma chemical kinetics model of a microwave CO2 plasma in a Laval nozzle setup. The effects of the flow field on the different dissociation and recombination mechanisms, the vibrational distribution, and the vibrational transfer mechanism are discussed. In addition, the effect of experimental parameters, like position of power deposition, outlet pressure, and specific energy input, on the CO2 conversion and energy efficiency is examined. The short residence time of the gas in the plasma region, the shockwave, and the maximum critical heat, and thus power, that can be added to the flow to avoid thermal choking are the main obstacles to reaching high energy efficiencies.
Understanding the Amazon Rainforest from Space using Neural Networks

Deforestation in the Amazon Rainforest accounts for reduced biodiversity, habitat loss, climate change, and other devastating ecological and environmental effects. Understanding and tracking human deforestation and ecological changes over time can better help environmentalist and government efforts in responding to both human and natural forest loss. Using modern computer vision techniques, we attempt to aid in this effort by using satellite imagery data of the Amazon Rainforest to track the expansion of human deforestation efforts. The ability to classify satellite data will allow us to better understand where, how, and why deforestation happens all over the world and ultimately how to respond [1]. We utilize several convolutional neural networks (CNNs) whose inputs are images of the rainforest and whose outputs are predicted labels of the condition of the rainforest. Class labels can be categorized into 3 groups: atmospheric conditions, common land cover, and rare land cover. Weather conditions such as overcast can complicate accurate labeling of the data, which must be taken into careful consideration. Our experimentation included the following methods: • Custom CNN Architectures: We create our own custom neural network architectures using modern techniques in machine learning and computer vision. • Transfer Learning using Various Successful CNN Architectures: We wish to utilize pretrained features from the successful VGG16 and Xception architectures and fine-tune the weights through backpropagation to classify the satellite images in our dataset. We implemented these in the Python programming language using the high-level neural networks API Keras with TensorFlow backend [2], [3].
Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models

This paper describes automatic music transcription with chord estimation for music audio signals. We focus on the fact that concurrent structures of musical notes such as chords form the basis of harmony and are considered for music composition. Since chords and musical notes are deeply linked with each other, we propose joint pitch and chord estimation based on a Bayesian hierarchical model that consists of an acoustic model representing the generative process of a spectrogram and a language model representing the generative process of a piano roll. The acoustic model is formulated as a variant of non-negative matrix factorization that has binary variables indicating a piano roll. The language model is formulated as a hidden Markov model that has chord labels as the latent variables and emits a piano roll. The sequential dependency of a piano roll can be represented in the language model. Both models are integrated through a piano roll in a hierarchical Bayesian manner. All the latent variables and parameters are estimated using Gibbs sampling. The experimental results showed the great potential of the proposed method for unified music transcription and grammar induction.
Authenticating the origin of different shrimp products on the Tunisian markets by PCR/RFLP method

This study describes a polymerase chain reaction using restriction fragment length polymorphism (PCR-RFLP) assay based on the 16S rRNA mitochondrial gene to identify commercial food products of wide range of Penaeidae and Pandalidae shrimp species commercialised in the Tunisian market. Phylogenetic analyses on 16S rRNA mitochondrial gene were used to study the relationships among the considered species. Penaeidae shrimp species was easily differentiated and confirmed by direct sequencing, showing a genetic distance of 0.34 with respect to Pandalidae species. A rapid and reliable PCR method using restriction fragment length polymorphism (RFLP) with three restriction enzymes (HpyCH4III/ MboI / AluI) was optimized for unambiguous differentiation of shrimp from 19 commercial market samples (raw and processed products). Results showed that the restriction fragment length polymorphism technique can be used to identify Tunisian shrimp species and thus to control not only commercial fraud but also efficient restocking program. Keywords: Shrimps, Species Identification, 16S rRNA mtDNA, PCR-RFLP
A global review of the ecosystem services provided by bivalve aquaculture

Bivalve shellﬁsh aquaculture provides many beneﬁts to society, beyond their traditional market value. This study collates the evidence available on the provisioning, regulating and cultural ecosystem services provided by the bivalve species commonly used in aquaculture. For the ﬁrst time, it synthesises this evidence to provide a global assessment of the potential market and non-market economic value of bivalve aquaculture. Bivalves are ﬁlter feeders, ﬁltering water and particulates, creat-ing substrates which provide habitat to act as nursery grounds for other species. Goods from provisioning services include meat, worth an estimated $23.9 billion as well as, pearls, shell and poultry grit, with oyster shell being the most important, with a global potential worth of $5.2 billion. The most important regulating services are nutrient remediation. Cultivated bivalves remove 49,000 tonnes of nitrogen and 6,000 tonnes of phosphorus, worth a potential $1.20 billion. Currently, there is little evidence on the cultural services per year of bivalve aquaculture, but we argue that these cultural values are broad ranging, although difﬁcult to quantify. Our assessment indicates that the global, non-food bivalve aquaculture services are worth $6.47 billion ($2.95 billion – 9.99 billion) per annum. However, this is likely to be an underestimate of the true value of bivalve aquaculture as there are signiﬁcant gaps in evidence of the value for a number of key services. The analysis presented here can be used to indicate the likely scale of payments for ecosystem services provided by bivalve aquaculture, prior to more detailed assessments. et al. (2014) ﬁltration
Intelligent data mining of vertical profiler readings to predict manganese concentrations in water reservoirs

Continuously monitoring and managing manganese (Mn) concentrations in drinking water supply reservoirs are paramount for water suppliers since high soluble Mn loads lead to discoloration of potable water. Despite the Mn level currently being manually sampled throughout the year, in subtropical monomictic lakes such as Hinze Dam, critical Mn concentrations in the epilimnion, where the water is drawn, are typically recorded only during winter lake circulation. A vertical profiling system (VPS) installed can continuously collect physical parameters that determine the transport process of Mn in the lake. Therefore, a long-term historical database gives opportunities for the development of a Mn prediction model. In the present study, VPS and sampling data were collected and analysed, and prediction models applying nonlinear regression techniques and data-driven equations were developed and assessed. They were able to accurately forecast future Mn concentrations from 1 to 7 days ahead and in particular the critical peak concentrations in the epilimnion during the lake destratification. The model also displays the probabilities of the Mn to exceed certain key-thresholds, thus assisting operators in Mn treatment decision-making. Such a tool is very beneficial for the water supplier, since costly and time-consuming water samplings for monitoring Mn concentrations can be avoided, thus relying only on the real time VPS-based model outputs.
Perceived Message Influence and Hispanic Women

The third-person effect (TPE) hypothesis states that people typically perceive the media messages to have greater influence on others than on themselves. Though this self-other perceptual bias has been shown to be robust across a variety of message contexts, past research has typically utilized Caucasian college student samples. The current study addresses this shortcoming in the literature by examining whether the TPE hypothesis holds for low-income, low-education, Spanish-speaking female adults with regard to the perceived effects of health news coverage. Our findings showed that most participants chose “equal to others” when estimating health news effects on themselves instead of “less than others” or “more than others. ” Results from ordinal logistic regression showed that the propensity toward TPE was related to optimistic bias and certain indicators of acculturation. Our findings suggest the need for future TPE research in Hispanic populations and further efforts to investigate TPE from an intercultural perspective.
An analytical approach to analyzing spacecraft formation-flying motion

An analytical approach to analyzing spacecraft formation-flying motion is proposed in this paper. Flight trajectory of spacecraft relative motion related to initial values is analytically described, and several mainly relative motion modes between formation and reference spacecrafts are significantly characterized. On the basis of analytics, furthermore, a new simplified formula is given to be applied to the formation-flying design. © 2002 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
Strategic leadership in academic development

The nature of academic work is changing rapidly, with moves towards professionalisation taking place against a background of fragmentation. Indeed, some aspects of professionalisation may have a fragmenting effect. It is suggested that there remains considerable value in the idea of an integrated faculty role. Noting that leaders in staff development face similar pressures to professionalise, the writers consider what expertise is required for the leadership in academic development role, and how role holders and those aspiring to the role may best develop their professional capabilities. They argue for an integrated conception of academic development, and a correspondingly integrated view of the developer’s professional identity and role. It is suggested that this will put leaders in academic development into a position that is more congruent with faculty self‐perceptions, and enable them to support those in faculty roles more effectively.
Global observations of substorm injection region evolution: 27 August 2001

We present riometer and in situ observations of a substorm electron injection on 27 August 2001. The event is seen at more than 20 separate locations (including ground stations and 6 satellites: Cluster, Polar, Chandra, and 3 Los Alamos National Laboratory (LANL) spacecraft). The injection is observed to be dispersionless at 12 of these locations. Combining these observations with information from the GOES-8 geosynchronous satellite we argue that the injection initiated near geosynchronous orbit and expanded poleward (tailward) and equatorward (earthward) afterward. Further, the injection began several minutes after the reconnection identified in the Cluster data, thus providing concrete evidence that, in at least some events, near-Earth reconnection has little if any ionospheric signature.
On the Structural Characteristics of the Protein Active Sites and Their Relation to Thermal Fluctuations

Due to the advances in structural biology research, a large number of protein structures have been solved in the last decade. In the same time, we also witness a rapidly growing number of structures of unknown function being deposited in the PDB. As a result, the ability to predict protein function from its structure becomes increasingly important in computational biology. The conventional comparative methods(for example, Laskowski, Watson et al. 2005; Watson, Sanderson et al. 2007) for identifying functional sites rely on evolutional information like homologous structures of known function or the known catalytic templates. However, these approaches are not applicable to these novel structures. One needs to develop ingenious approaches that do not rely on evolutionary information. Recently, several groups(Amitai, Shemesh et al. 2004; Ben-Shimon & Eisenstein 2005; Sacquin-Mora, Laforet et al. 2007; Huang, Yu et al. 2011) developed novel approaches to predict the active sites of enzymes from a single structure without using any homologous structures or known catalytic templates. The basic idea of their approaches is simple: they first identify certain structural or dynamical features that are unique to the active sites; they then further refine this relationship such that it can be used to accurately predict the enzyme catalytic sites. For example, Pietrokovski and co-workers(Amitai, Shemesh et al. 2004) transformed the protein structure into residue interaction graphs, with each amino acid residue represented as a graph node and the interaction between them as a graph edge. They then computed the network closeness of each residue. They found that most catalytic residues are associated with the network centrality. Ben-Shimon and Eisenstein(Ben-Shimon & Eisenstein 2005), analyzing 175 enzymes, observed that most catalytic residues are near the enzyme centroid. Based on these results, they developed novel methods to predict catalytic sites from a single structure. The aim of this review is to show that this peculiar relationship between catalytic sites and the structure centroid or its network centrality can be accounted for by the dynamical properties of the catalytic residues, which are in general more rigid than other residues. In addition, we will discuss the recent studies (Halle 2002; Shih, Huang et al. 2007; Huang, Shih et al. 2008; Lin, Huang et al. 2008; Lu, Huang et al. 2008) on the surprisingly close link
Effective Resistance-based Germination of Seed Sets for Community Detection

Community detection is, at its core, an attempt to attach an interpretable function to an otherwise indecipherable form. The importance of labeling communities has obvious implications for identifying clusters in social networks, but it has a number of equally relevant applications in product recommendations, biological systems, and many forms of classification. The local variety of community detection starts with a small set of labeled seed nodes, and aims to estimate the community containing these nodes. One of the most ubiquitous methods - due to its simplicity and efficiency - is personalized PageRank. The most obvious bottleneck for deploying this form of PageRank successfully is the quality of the seeds. We introduce a "germination" stage for these seeds, where an effective resistance-based approach is used to increase the quality and number of seeds from which a community is detected. By breaking seed set expansion into a two-step process, we aim to utilize two distinct random walk-based approaches in the regimes in which they excel. In synthetic and real network data, a simple, greedy algorithm which minimizes the effective resistance diameter combined with PageRank achieves clear improvements in precision and recall over a standalone PageRank procedure.
JOB SATISFACTION AND PSYCHOLOGICAL HEALTH OF LONG DISTANCE DRIVERS IN BENIN CITY 1 OFILI

This cross-sectional analytical study was designed to assess the level of and factors affecting job satisfaction and psychological health among long distance drivers in Benin City, Edo, Nigeria. A 21-item Job satisfaction questionnaire and the Golberg’s General Health Questionnaire (GHQ 28) were used for data collection from 168 consenting drivers. Data was analysed with SPSS version 15. Results showed that an appreciable number of drivers were satisfied with their job (85%). Factors linked with satisfaction included flexibility of periods designated as resting hours (91.6%), company administration and management (84.6%), selfperception of being valued by the company (79.7%), and remuneration (74.1%). Sources of dissatisfaction included lack of training on the job (82%) and frequent harassment by law enforcement officers (77%). Psychological morbidity was rare. Overall, the drivers showed high level satisfaction towards their job, though there were areas of dissatisfaction that need to be addressed in the interest and safety of passengers and other road users.
Effect of pea and probiotic and/or acidifier supplementation on growth performance and composition of caecal microbiota of broiler chicken.

Growth performance and composition of caecal microflora were measured in broiler chickens fed diets formulated with/without white pea and supplemented with dietary probiotic and/or encapsulated acidifier. One-day-old broiler females allotted into 8 groups of 12 birds and maintained individually were fed isoprotein and isoenergetic wheat and soybean meal-based diets with/without white pea (150 g/kg), unsupplemented or supplemented with probiotic (1g/kg) and/or acidifier (1 g/kg). Performance was measured for 5 weeks, birds were killed and samples of caecal digesta prepared. The caecal microbiota was characterized by fluorescent in situ hybridization (FISH) and terminal-restriction fragment length polymorphism (T-RFLP). Inclusion of pea into diet increased feed intake, but negatively affected FCR (P<0.05), while neither probiotic nor acidifier supplementation affected performance. Total bacterial numbers in caecal contents were higher in birds fed pea diets (P<0.01), but were not affected by any dietary supplement. Neither pea inclusion nor probiotic supplementation affected the Lactobacillus/Enterococcus and Streptococcus/Lactococcus counts in caecal contents. But, the composition of the Lactobacillus/Enterococcus population was altered by inclusion of pea, and acidifier supplementation increased the Lactobacillus/Enterococcus counts. In conclusion, dietary inclusion of pea and encapsulated acidifiers may change the bacterial community structure in the distal parts of the chicken gastrointestinal tract.