Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of microcredit in reducing vulnerability of the rural women: Evidence from Panchgarh district of Bangladesh

Rural women in Bangladesh have a lower status than men and are economically dependent on their husbands as well as socially discriminated. Rural women have very limited access to income generating activities and consequently less opportunity to contribute to the betterment of their families due to a number of social, cultural and religious barriers which push them into a vulnerable situation. In addition, they are also vulnerable to natural disasters and calamities over which they have little or no control. In this circumstance, microcredit programme contributes significantly to reduce vulnerability of the poor rural women who are more vulnerable in Bangladesh. In the present study, it has been attempted to assess the role of microcredit programme in reducing vulnerability of rural women. The study is based on empirical data collected through interviewing two groups of rural women e.g. 'with credit' and 'without credit' rural women. The 'with credit' respondents were the members of the Grammeen Bank's microcredit programme. The findings of the study show that the monthly average income of the 'with credit' respondents is US$27.6) which is three times higher than that of the 'without credit' respondents (US$7.9). The increased income of the borrower women contributes significantly to improve the income level of their households which help them reduce vulnerability more effectively than the non-borrower families. It is also found that small proportions (10 percent) of the 'with credit' women are highly vulnerable, as compared to 'without credit' women (22 percent). Therefore, it can be concluded that microcredit programme plays a significant role in reducing vulnerability among the rural poor families in Bangladesh.
A cost benefit operator for efficient multi level genetic algorithm searches

In this paper we present a novel cost benefit operator that assists multi level genetic algorithm searches. Through the use of the cost benefit operator, it is possible to dynamically constrain the search of the base level genetic algorithm, to suit the user's requirements. Initially we review meta-evolutionary (multi-level genetic algorithm) approaches. We note that the current literature has abundant studies on meta-evolutionary GAs. However these approaches have not identified an efficient approach to termination of base GA search or a means to balance practical consideration such as quality of solution and the expense of computation. Our quality time tradeoff operator (QTT) is user defined, and acts as a base level termination operator and also provides a fitness value for the meta-level GA. In this manner the amount of computation time spent on less encouraging configurations can be specified by the user. Our approach has been applied to a computationally intensive test problem which evaluates a large set of configuration settings for the base GAs. This approach should be applicable across a wide range of practical problems (e.g. routing, logistic and biomedical applications).
Automatic Optic Disc Abnormality Detection in Fundus Images: A Deep Learning Approach

Optic disc (OD) is a key structure in retinal images. It serves as an indicator to detect various diseases such as glaucoma and changes related to new vessel formation on the OD in diabetic retinopathy (DR) or retinal vein occlusion. OD is also essential to locate structures such as the macula and the main vascular arcade. Most existing methods for OD localization are rule-based, either exploiting the OD appearance proper- ties or the spatial relationship between the OD and the main vascular arcade. The detection of OD abnormalities has been performed through the detection of lesions such as hemorrhaeges or through measuring cup to disc ratio. Thus these methods result in complex and in exible im- age analysis algorithms limiting their applicability to large image sets obtained either in epidemiological studies or in screening for retinal or optic nerve diseases. In this paper, we propose an end-to-end supervised model for OD abnormality detection. The most informative features of the OD are learned directly from retinal images and are adapted to the dataset at hand. Our experimental results validated the effectiveness of this current approach and showed its potential application.
Emergence of a New Lineage of Dengue Virus Type 2 Identified in Travelers Entering Western Australia from Indonesia, 2010-2012

Dengue virus (DENV) transmission is ubiquitous throughout the tropics. More than 70% of the current global dengue disease burden is borne by people who live in the Asia-Pacific region. We sequenced the E gene of DENV isolated from travellers entering Western Australia between 2010–2012, most of whom visited Indonesia, and identified a diverse array of DENV1-4, including multiple co-circulating viral lineages. Most viruses were closely related to lineages known to have circulated in Indonesia for some time, indicating that this geographic region serves as a major hub for dengue genetic diversity. Most notably, we identified a new lineage of DENV-2 (Cosmopolitan genotype) that emerged in Bali in 2011–2012. The spread of this lineage should clearly be monitored. Surveillance of symptomatic returned travellers provides important and timely information on circulating DENV serotypes and genotypes, and can reveal the herald wave of dengue and other emerging infectious diseases.
Large-Strain Generalization of Microplane Model for Concrete and Application

The formulation of the microplane model for concrete and development of model M4 in three companion papers of this study, which are contained in this journal issue, is extended to large strains in this paper. After giving examples of certain difficulties with the second Piola-Kirchhoff stress tensor in the modeling of strength and frictional limits on weak planes within the material, the back-rotated Cauchy (true) tensor is introduced as the stress measure. The strain tensor conjugate to the back-rotated Cauchy (or Kirchhoff) stress tensor is unsuitable because it is nonholonomic (path-dependent) and because its microplane components do not characterize meaningful deformation measures. Therefore Green's Lagrangian tensor is adopted, even though it is not conjugate. Only for this strain measure do the microplane components of the strain tensor suffice to characterize the normal stretch and shear angle on that microplane. Using such nonconjugate strain and stress tensors is admissible because, for concrete, the elastic parts of strains as well as the total volumetric strains are always small, and because the algorithm used guarantees the energy dissipation by large inelastic strains to be nonnegative. Examples of dynamic structural analysis are given.
Modeling and Generalization of Discrete Morse Terrain Decompositions

We address the problem of morphological analysis of real terrains. We describe a morphological model for a terrain by considering extensions of Morse theory to the discrete case. We propose a two-level model of the morphology of a terrain based on a graph joining the critical points of the terrain through integral lines. We present a new set of generalization operators specific for discrete piece-wise linear terrain models, which are used to reduce noise and the size of the morphological representation. We show results of our approach on real terrains.
Pricing network resources for adaptive applications

The Differentiated Services framework (DiffServ) has been proposed to provide multiple Quality of Service (QoS) classes over IP networks. A network supporting multiple classes of service also requires a differentiated pricing structure. In this work, we propose a pricing algorithm in a DiffServ environment based on the cost of providing different levels of services, and on long-term average user resource demand of a service class. We integrate the proposed service-dependent pricing scheme with a dynamic pricing and service negotiation environment by considering a dynamic and congestion-sensitive pricing component. Pricing network services dynamically based on the level of service, usage, and congestion allows a more competitive price to be offered, allows the network to be used more efficiently, and provides a natural and equitable incentive for applications to adapt their service requests according to network conditions. We also develop the demand behavior of adaptive users based on a physically reasonable user utility function. Simulation results show that a congestion-sensitive pricing policy coupled with user rate adaptation is able to control congestion and allows a service class to meet its performance assurances under large or bursty offered loads, even without explicit admission control. Users are able to maintain a stable expenditure, and allowing users to migrate between service classes in response to price increases further stabilizes the individual service prices. When admission control is enforced, congestion-sensitive pricing still provides an advantage in terms of a much lower connection blocking rate at high loads
Correlation Autoencoder Hashing for Supervised Cross-Modal Search

Due to its storage and query efficiency, hashing has been widely applied to approximate nearest neighbor search from large-scale datasets. While there is increasing interest in cross-modal hashing which facilitates cross-media retrieval by embedding data from different modalities into a common Hamming space, how to distill the cross-modal correlation structure effectively remains a challenging problem. In this paper, we propose a novel supervised cross-modal hashing method, Correlation Autoencoder Hashing (CAH), to learn discriminative and compact binary codes based on deep autoencoders. Specifically, CAH jointly maximizes the feature correlation revealed by bimodal data and the semantic correlation conveyed in similarity labels, while embeds them into hash codes by nonlinear deep autoencoders. Extensive experiments clearly show the superior effectiveness and efficiency of CAH against the state-of-the-art hashing methods on standard cross-modal retrieval benchmarks.
Discovering and ranking important rules

Decision rules generated from reducts can fully describe a data set. We introduce a new method of evaluating rules by taking advantage of rough sets theory. We consider rules generated from the original data set as attributes in the new constructed decision table. Reducts generated from this new decision table contain essential attributes, which are the rules. Only important rules are contained in the reducts. Experiments on an artificial data set and a medical data set show that the "reduct rules" are more important, and this new method provides an automatic and effective way of ranking rules.
A prospective observational study of a novel 2-phase infusion protocol for the administration of acetylcysteine in paracetamol poisoning

Abstract Context: The current 3-phase acetylcysteine infusion for paracetamol poisoning delivers half the dose over 15–60 min and frequently results in adverse reactions. Objective: We aimed to determine adverse reaction frequency with a modified 2-phase infusion protocol with a longer initial infusion. Materials and methods: A prospective observational study of a modified 2-phase acetylcysteine protocol was undertaken at two hospitals. Acetylcysteine was commenced on admission and ceased if paracetamol concentrations were low-risk (below the nomogram line). The first infusion was 200 mg/kg over 4–9 h based on ingestion time or 4 h for staggered/chronic ingestions. The second infusion was 100 mg/kg over 16 h. Pre-defined outcomes were frequency of adverse reactions (systemic hypersensitivity reactions or gastrointestinal); proportion with alanine transaminase (ALT) > 1000 U/L or abnormal ALT. Results: 654 paracetamol poisonings were treated with the new protocol; median age 29 y (15–98 y); 453 females; 576 acute and 78 staggered/chronic ingestions. In 420 (64%) acetylcysteine was stopped for low-risk paracetamol concentrations. An adverse reaction occurred in 229/654 admissions (35%; 95% CI: 31–39%): 173 (26.5%; 95% CI: 23–30%) only gastrointestinal, 50 (8%; 95% CI: 6–10%) skin only systemic hypersensitivity reactions; and three severe anaphylaxis (0.5%; 95% CI: 0.1–1.5%; all hypotension). Adverse reactions occurred in 111/231 (48%) receiving full treatment compared to 116/420 (28%) in whom the infusion was stopped early (absolute difference 20%; 95% CI: 13–28%; p < 0.0001). In 200 overdoses < 10 g, one had toxic paracetamol concentrations, but 53 developed reactions. Sixteen patients had an ALT > 1000 U/L and 24 an abnormal ALT attributable to paracetamol; all but one had treatment commenced >12 h post-ingestion. Conclusion: A 2-phase acetylcysteine infusion protocol results in a fewer reactions in patients with toxic paracetamol concentrations, but is not justified in patients with low-risk paracetamol concentrations.
Industrial Robot : An International Journal Intelligent systems for industrial robotics : application in logistic field

Purpose – Different machines are already present in the human environment, easing human beings’ daily life. In the future, this tendency will be accentuated by integration of numerous robots (e.g. wheeled robots, legged robots, humanoid robots, network sensors, etc.) in the human environment. A wide range of applications, such as those dealing with warehouse management, industrial assembling, military applications, daily-life tasks, can benefit from multi-robot systems. The purpose of this paper is to propose an intelligent system for industrial robotics in the logistic field, based on collaboration between heterogeneous robots. Design/methodology/approach – The proposed infrastructure for this multi-robot system is composed of a robots’ network including one humanoid robot, wheeled robots, cameras, and remote computer. All devices can communicate between them by using wireless network. The goal of the humanoid robot is to lead the wheeled robots according to the environment and wheeled robots are used to carry a load. The camera allows providing complementary information about the environment; and thanks to machine learning, this control strategy allows complex tasks to be perormed for these logistic applications. Findings – This concept is implemented on real robots within the frame of a demonstrator including the above-mentioned kind of robots. The preliminary results, obtained during experimentations, prove the feasibility of the presented strategy for real applications. Originality/value – The main originalities of this work are, on the one hand, the use of an heterogeneous multi-robots system for logistic tasks, and on the other hand, the proposed machine learning allows a collaboration task between heterogeneous robots in an autonomous manner.
Decomposition of Relations and Synthesis of Entity-Relationship Diagrams

The purpose of this paper is threefold: (a) to present a new decomposition algorithm to decompose a relation according to its functional and multivalued dependencies into well-defined primitives (atomic relations and functional dependencies) which preserve the original information, (b) to describe the atomic relations and their functional dependencies through labelled graphs called Combined A-Z graphs which exhibit a11 the information in a succint a convenient diagram, Cc) to indicate how the combined A-Z graphs may be transformed into Chen's Entity-Relationship diagrams.
Real-Time Communication Analysis for On-Chip Networks with Wormhole Switching

In this paper, we discuss a real-time on-chip communication service with a priority-based wormhole switching policy. A novel off-line schedulability analysis approach is presented. By evaluating diverse inter-relationships among the traffic-flows, this approach can predict the packet network latency based on two quantifiable different delays: direct interference from higher priority traffic-flows and indirect interference from other higher priority traffic-flows. Due to the inevitable existence of parallel interference, we prove that the general problem of determining the exact schedulability of real-time traffic-flow over the on- chip network is NP-hard. However the results presented do form an upper bound. In addition, an error in a previous published scheduling approach is illustrated and remedied. Utilizing this analysis scheme, we can flexibly evaluate at design time the schedulability of a set of traffic-flows with different QoS requirements on a real-time SoC/NoC communication platform.
Verification of sequential imperative programs in Isabelle-HOL

The purpose of this thesis is to create a verification environment for sequential imperative programs. First a general language model is proposed, which is independent of a concrete programming language but expressive enough to cover all common language features: mutually recursive procedures, abrupt termination and exceptions, runtime faults, local and global variables, pointers and heap, expressions with side effects, pointers to procedures, partial application and closures, dynamic method invocation and also unbounded nondeterminism. For this language a Hoare logic for both partial and total correctness is developed and on top of it a verification condition generator is implemented. The Hoare logic is designed to allow the integration of program analysis or software model checking into the verification. To demonstrate the continuity to a real programming language a subset of C is embedded into the verification environment. The whole work is developed in the theorem prover Isabelle. Therefore the correctness is machine-checked and in addition the rich infrastructure of the general purpose theorem prover Isabelle can be employed for the verification of imperative programs.
Oceanisphaera psychrotolerans sp. nov., isolated from coastal sediment samples.

A novel aerobic, Gram-staining-negative bacterium, designated strain LAM-WHM-ZC(T), was isolated from coastal sediment samples from the Bohai Sea, near Yantai, China. Cells of LAM-WHM-ZC(T) were non-motile, short-rod- or coccoid-shaped. The temperature and pH ranges for growth were 4-40 °C (optimum: 20-33 °C) and pH 5-9 (optimum: pH 7.5). The strain did not require NaCl for growth but tolerated up to 10% NaCl (w/v). The major fatty acids of strain LAM-WHM-ZC(T) were summed feature 3, C12 : 0, C16 : 0, summed feature 2 and summed feature 8. The predominant respiratory quinone was ubiquinone Q-8. The main polar lipids were diphosphatidyglycerol, phosphatigylethanolamine, phosphatidyglycerol, one phospholipid and four unidentified glycolipids. The DNA G+C content was 59.3 mol% as determined by the melting temperature (Tm) method. Analysis of the 16S rRNA gene sequence indicated that the isolate represented a member of the genus Oceanisphaera and was closely related to Oceanisphaera arctica KCTC 23013(T), Oceanisphaera litoralis DSM 15406(T), Oceanisphaera sediminis KACC 15117(T) and Oceanisphaera donghaensis KCTC 12522(T) with 97.7%, 97.1%, 96.6% and 96.6% sequence similarity, respectively. The DNA-DNA hybridization values between strain LAM-WHM-ZC(T) and the four reference strains were 47.4 ± 2.8%, 33.5 ± 2.2%, 28.4 ± 1.8% and 13.7 ± 0.8%, respectively. Based on its phenotypic and genotypic properties, strain LAM-WHM-ZC(T) is suggested to represent a novel species of the genus Oceanisphaera, for which the name Oceanisphaera psychrotolerans sp. nov. is proposed. The type strain is LAM-WHM-ZC(T) ( = ACCC 06516(T) = JCM 30466(T)).
Implementation of a Hybrid Voice guiding System for Robot Arm

In this paper, a voice command system for a robot arm (manipulator) is designed as a part of a research project. The methodology adopted is based on a hybrid technique used in automatic speech recognition system. To implement the approach on a real-time application, a Personal Computer interface was designed to control the movements of a five degree of freedom robot arm by transmitting the orders via radio frequency circuits. The voice command system for the robot arm is designed. The main parts of the robot are a microcontroller from Microchip PIC16F84 and a radio frequency receiver module. The possibility to control other parts of the automatic arm are investigated.
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities. Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities or evaluating the truth of a proposed triple. Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them. We propose a new algorithm MINERVA, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity. Since random walks are impractical in a setting with combinatorially many destinations from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths. Empirically, this approach obtains state-of-the-art results on several datasets, significantly outperforming prior methods.
Theology of Religions and the Intuitive Dimension: How Do We Construct and Perceive God’s Fairness?

With pluralism increasingly becoming the dominant explanatory paradigm for religious diversity in Europe and North America, Christians must contend with the reality that their fundamental conviction of salvation being mediated uniquely and exclusively through Jesus Christ is widely perceived as being parochial, intolerant, or even unconscionable. This article explores and challenges what appears to be a common and significant, but not often examined, assumption underlying protests against claims of Christian particularity—an unfairness intuition. Considerations include recent research on culture and notions of fairness/justice and biblical-theological reflections on fairness/justice.