Miracle-dz/UniFAR-FTUs huggingface dataset:
Borneo vortex and mesoscale convective rainfall

Abstract. We have investigated how the Borneo vortex develops over the equatorial South China Sea under cold surge conditions in December during the Asian winter monsoon. Composite analysis using reanalysis and satellite data sets has revealed that absolute vorticity and water vapour are transported by strong cold surges from upstream of the South China Sea to around the Equator. Rainfall is correspondingly enhanced over the equatorial South China Sea. A semi-idealized experiment reproduced the Borneo vortex over the equatorial South China Sea during a "perpetual" cold surge. The Borneo vortex is manifested as a meso-α cyclone with a comma-shaped rainband in the northeast sector of the cyclone. Vorticity budget analysis showed that the growth/maintenance of the meso-α cyclone was achieved mainly by the vortex stretching. This vortex stretching is due to the upward motion forced by the latent heat release around the cyclone centre. The comma-shaped rainband consists of clusters of meso-β-scale rainfall cells. The intense rainfall in the comma head (comma tail) is generated by the confluence of the warmer and wetter cyclonic easterly flow (cyclonic southeasterly flow) and the cooler and drier northeasterly surge in the northwestern (northeastern) sector of the cyclone. Intense upward motion and heavy rainfall resulted due to the low-level convergence and the favourable thermodynamic profile at the confluence zone. In particular, the convergence in the northwestern sector is responsible for maintenance of the meso-α cyclone system. At both meso-α and meso-β scales, the convergence is ultimately caused by the deviatoric strain in the confluence wind pattern but is significantly self-enhanced by the nonlinear dynamics.
Study of Informative Gene Selection for Gene Expression Profiles

In the research community of gene expression microarray data analysis, selecting a small number of informative genes from thousands of genes is a hot research problem for accurate classification of diseases. In this paper, we present our gene selection algorithm SIGWeight. SIGWeight expands sigmoid kernel into its Maclaurin series, and calculate the weight of each feature(gene) from the classification hyperplane which is constructed by SVM with SIGMOID kernel, and then sort the features according to the weight of features. A subset of features is selected from top of the ranking list. We apply four widely used classifiers on the obtained datasets with only the selected genes to evaluate effectiveness of SIGWeight. Experiment results on Leukemia and DLBCL datasets show that SIGWeight is very encouraging, Furthermore, compared with information gain(IG), SIGWeight select less number of genes, At the same time, the genes selected led to the highest accuracy.
Hybrid solar gas reliability optimization using harmony search under performance and budget constraints

During the last decade, Solar energy has been the fastest growing renewable source of energy worldwide. Limited sources of fossil fuel in addition to the negative effects of greenhouse emissions on the environment have led many countries to support development of renewable energies such as solar energy. However, solar power has a number of problems for implementation; the solar energies mainly depend upon sun availability and the changing climate. The focus on hybrid generation system increases the availability of power generation system by reducing the dependence on environment parameter. The purpose of this paper is concerned with the use of harmony search (HS) meta-heuristic optimization method to solve the problem of hybrid power system design optimization. We consider the case where redundant electrical components are chosen to achieve desirable level of reliability. To allow fast estimation, a universal moment generating function UMGF and harmony search HS are applied. An illustrative example is presented.
Inhibition of Sevoflurane Postconditioning Against Cerebral Ischemia Reperfusion-Induced Oxidative Injury in Rats

The volatile anesthetic sevoflurane is capable of inducing preconditioning and postconditioning effects in the brain. In this study, we investigated the effects of sevoflurane postconditioning on antioxidant and immunity indexes in cerebral ischemia reperfusion (CIR) rats. Rats were randomly assigned to five separate experimental groups I–V. In the sham group (I), rats were subjected to the same surgery procedures except for occlusion of the middle cerebral artery and exposed to 1.0 MAC sevoflurane 90 min after surgery for 30 min. IR control rats (group II) were subjected to middle cerebral artery occlusion (MCAO) for 90 min and exposed to O2 for 30 min at the beginning of reperfusion. Sevoflurane 0.5, 1.0 and 1.5 groups (III, IV, V) were all subjected to MCAO for 90 min, but at the beginning of reperfusion exposed to 0.5 MAC, 1.0 MAC or 1.5 MAC sevoflurane for 30 min, respectively. Results showed that sevoflurane postconditioning can decrease serum tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), nitric oxide (NO), nitric oxide synthase (NOS) and increase serum interleukin-10 (IL-10) levels in cerebral ischemia reperfusion rats. In addition, sevoflurane postconditioning can still decrease blood lipid, malondialdehyde (MDA) levels, infarct volume and increase antioxidant enzymes activities, normal pyramidal neurons density in cerebral ischemia reperfusion rats. It can be concluded that sevoflurane postconditioning may decrease blood and brain oxidative injury and enhance immunity indexes in cerebral ischemia reperfusion rats.
The Intervention of Safe, Caring and Child-friendly School Policies on Social Construction of Violence in South African Secondary Schools

ABSTRACT This paper investigated the intervention of Safe, Caring and Child-friendly School (SCCFS) policies on social construction of violence in South African secondary schools. It also investigates the model that can be developed to make schools safe, caring and child-friendly. A self-develop questionnaire was used to collect data from school principals, HODs, educators, educators unions, SGBs and learners in secondary schools. Multistage sampling technique was used to select the participants in this study. Two research questions were posed in this paper, and the responses from the participants were analyzed using descriptive statistics of percentage. The findings of the study show that the UNICEF designated safe, caring and child-friendly schools in South Africa have made substantial efforts to realize the SCCFS objectives but most basic facilities in the schools were inadequate with respect to their child-friendly status.
Hevin Plays a Pivotal Role in Corneal Wound Healing

Background Hevin is a matricellular protein involved in tissue repair and remodeling via interaction with the surrounding extracellular matrix (ECM) proteins. In this study, we examined the functional role of hevin using a corneal stromal wound healing model achieved by an excimer laser-induced irregular phototherapeutic keratectomy (IrrPTK) in hevin-null (hevin-/-) mice. We also investigated the effects of exogenous supplementation of recombinant human hevin (rhHevin) to rescue the stromal cellular components damaged by the excimer laser. Methodology/Principal Findings Wild type (WT) and hevin -/- mice were divided into three groups at 4 time points- 1, 2, 3 and 4 weeks. Group I served as naïve without any treatment. Group II received epithelial debridement and underwent IrrPTK using excimer laser. Group III received topical application of rhHevin after IrrPTK surgery for 3 days. Eyes were analyzed for corneal haze and matrix remodeling components using slit lamp biomicroscopy, in vivo confocal microscopy, light microscopy (LM), transmission electron microscopy (TEM), immunohistochemistry (IHC) and western blotting (WB). IHC showed upregulation of hevin in IrrPTK-injured WT mice. Hevin -/- mice developed corneal haze as early as 1-2 weeks post IrrPTK-treatment compared to the WT group, which peaked at 3-4 weeks. They also exhibited accumulation of inflammatory cells, fibrotic components of ECM proteins and vascularized corneas as seen by IHC and WB. LM and TEM showed activated keratocytes (myofibroblasts), inflammatory debris and vascular tissues in the stroma. Exogenous application of rhHevin for 3 days reinstated inflammatory index of the corneal stroma similar to WT mice. Conclusions/Significance Hevin is transiently expressed in the IrrPTK-injured corneas and loss of hevin predisposes them to aberrant wound healing. Hevin -/- mice develop early corneal haze characterized by severe chronic inflammation and stromal fibrosis that can be rescued with exogenous administration of rhHevin. Thus, hevin plays a pivotal role in the corneal wound healing.
Estimating reachable states of hybrid systems via l-complete approximations

This contribution treats the estimation of reachable states for time invariant hybrid systems. Using the framework provided by Willems’ behavioural systems theory, we suggest a method based on -complete approximations, which can be realized by finite state machines. The approximating behaviour is a superset of the original behaviour. Hence, the estimate of reachable states based on an -complete approximation can be shown to be conservative, i. e. the exact set of reachable states is guaranteed to be contained in the estimate. Because of this property our method is adequate for verification tasks where the state variable has to remain within a certain specification.
Cross-Domain Action-Model Acquisition for Planning via Web Search

Applying learning techniques to acquire action models is an area of intense research interest. Most previous works in this area have assumed that there is a significant amount of training data available in a planning domain of interest, which we call target domain, where action models are to be learned. However, it is often difficult to acquire sufficient training data to ensure that the learned action models are of high quality. In this paper, we develop a novel approach to learning action models with limited training data in the target domain by transferring knowledge from related auxiliary or source domains. We assume that the action models in the source domains have already been created before, and seek to transfer as much of the the available information from the source domains as possible to help our learning task. We first exploit a Web searching method to bridge the target and source domains, such that transferrable knowledge from source domains is identified. We then encode the transferred knowledge together with the available data from the target domain as constraints in a maximum satisfiability problem, and solve these constraints using a weighted MAX-SAT solver. We finally transform the solutions thus obtained into high-quality target-domain action models. We empirically show that our transfer-learning based framework is effective in several domains, including the International Planning Competition (IPC) domains and some synthetic domains.
On the Performance of Space Shift Keying (SSK) Modulation with Imperfect Channel Knowledge

In this paper, we study the sensitivity and robustness of Space Shift Keying (SSK) modulation to imperfect channel knowledge at the receiver. Unlike the common widespread belief, we show that SSK modulation is more robust to imperfect channel knowledge than other state-of-the-art transmission technologies, and only few training pilots are needed to get reliable enough channel estimates for data detection. More precisely, we focus our attention on the so-called Time-Orthogonal-Signal-Design (TOSD-) SSK modulation scheme, which is an improved version of SSK modulation offering transmit-diversity gains, and provide the following contributions: i) we develop a closed-form analytical framework to compute the Average Bit Error Probability (ABEP) of a mismatched detector for TOSD-SSK modulation, which can be used for arbitrary transmit-antenna, receive-antenna, channel fading, and training pilots; ii) we perform a comparative study of the performance of TOSD-SSK modulation and the Alamouti code under the same imperfect channel knowledge, and show that TOSD-SSK modulation is more robust to channel estimation errors; iii) we point out that only few pilot pulses are required to get performance very close to the perfect channel knowledge lower-bound; and iv) we verify that transmit- and receive-diversity gains of TOSD-SSK modulation are preserved even for a mismatched receiver.
Show Me New Counterexamples: A Path-Based Approach

We consider lightweight usage of model-checking for the debugging of Simulink models. A problem is that model-checkers typically return only one counterexample, which may slow down the debugging process. We propose an approach and a tool to produce several counterexamples, exemplifying different property violation patterns for a given version of the design. The approach uses data collected during the replay of the counterexamples to synthesize queries for the model-checker, so that it finds counterexamples that activate new paths. The approach is applied to an academic example and an industrial model from the automotive domain.
An improved Tangent Bug method integrated with artificial potential field for multi-robot path planning

This paper introduces the utilization of the wall following concept for path planning of multi-robots and proposes an improved Tangent Bug method to avoid falling in local minima encountered by the artificial potential field (APF) method used in real-time path planning. In the new algorithm, more reliable switching and merging conditions are designed to guarantee the success of escape. This method controls a team of robots with a deformable geometry, compliant with nearby static or dynamic obstacles including those represented by each robot team-mates. The robots are virtually linked to each other by the influence of artificial potentials that asymptotically stabilize the formation and keep all the robots separated by specified distances. Simulation studies have been carried out to verify the validity of the proposed method.
Adaptive activity learning with dynamically available context

Numerous methods have been proposed to address different aspects of human activity recognition. However, most of the previous approaches are static in terms of the data sources used for the recognition task. As sensors can be added or can fail and be replaced by different types of sensors, creating an activity recognition model that is able to leverage dynamically available sensors becomes important. In this paper, we propose methods for activity learning and activity recognition adaptation in environments with dynamic sensor deployments. Specifically, we propose sensor and activity context models to address the problem of sensor heterogeneity, so that sensor readings can be pre-processed and populated into the recognition system properly. Based on those context models, we propose the learning-to-rank method for activity learning and its adaptation. To model the temporal characteristics of the human behaviours, we add temporal regularization into the learning and prediction phases. We use comprehensive datasets to demonstrate effectiveness of the proposed method, and show its advantage over the conventional machine learning algorithms in terms of recognition accuracy. Our method outperforms hybrid models that combine typical machine learning methods with graphical models (i.e. HMM, CRF) for temporal smoothing.
Robotic co-manipulation with 6 DoF admittance control: Application to patient positioning in proton-therapy

The paper focuses on controlling by hand a robot carrying a patient lying down on a “couch top” located at the extremity of the end-effector. The Orion prototype, is a six Degree-of-Freedom (DoF) robotic system. The control of the robot is achieved through a home-made co-manipulated haptic device located under the couch top and operated by a paramedical assistant. This device enables the operator to move the couch top, on which the patient lies down, and within an identified operational safety space. The 6 DoF admittance control algorithm is implemented on this new robotic platform. An experimental validation has been carried out to validate the co-manipulation control of the robot. The adaptable admittance parameters have been tuned on several experiments in order to observe the mass-spring-damper equivalent behaviour and demonstrate the validity of the implemented admittance algorithm.
Performance Analysis of Distributed Space-Time Block-Encoded Sensor Networks

Sensor networks are comprised of nodes with minimal processing and radio-frequency functionalities. In such networks, it is assumed that a source sensor communicates with a target sensor over a number of relaying sensors by utilizing distributed but cooperative low-complexity space-time encoding techniques, thereby achieving highly robust communication links. Each relaying stage is hence comprised of a given number of cooperating sensor nodes, which may or may not exchange additional data. The contribution of this paper is the derivation of throughput-maximizing resource-allocation strategies for various sensor network configurations. The case of full data exchange at each relaying stage is analyzed first and then relaxed to the case of partial data exchange. Monte Carlo simulations are used to numerically verify the theoretically derived performance of distributed sensor networks. It is shown that notable power savings can be achieved compared to traditional single-link and nonoptimized sensor networks
Evaluation of Efficient Link Reliability Estimators for Low-Power Wireless Networks

Real-time wireless link reliability estimation is a fundamental building block for self-organization of multihop sensor networks. Observed connectivity at low-power is more chaotic and unpredictable than in wireless LANs, and available resources are severely constrained. We seek estimators that react quickly to large changes, yet are stable, have a small memory footprint and are simple to compute. We create a simple model that generates link loss characteristics similar to empirical traces collected under different contexts. With this model, we simulate a variety of estimators, and uses the simple exponentially weighted moving average (EWMA) estimator, as a basis for comparison. We find that recently propose flip-flop estimators are not superior. However, our cascaded EWMA on windowed averaging is very effective.
Scalable pseudo-likelihood estimation in hybrid random fields

Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such as to prevent state-of-the-art statistical learning techniques from delivering accurate models in reasonable time. This paper presents a hybrid random field model for pseudo-likelihood estimation in high-dimensional domains. A theoretical analysis proves that the class of pseudo-likelihood distributions representable by hybrid random fields strictly includes the class of joint probability distributions representable by Bayesian networks. In order to learn hybrid random fields from data, we develop the Markov Blanket Merging algorithm. Theoretical and experimental evidence shows that Markov Blanket Merging scales up very well to high-dimensional datasets. As compared to other widely used statistical learning techniques, Markov Blanket Merging delivers accurate results in a number of link prediction tasks, while achieving also significant improvements in terms of computational efficiency. Our software implementation of the models investigated in this paper is publicly available at http://www.dii. unisi.it/~freno/. The same website also hosts the datasets used in this work that are not available elsewhere in the same preprocessing used for our experiments.
DATPLEX: AN EXTENSIBLE DESIGN-AID TOOL FOR PHYSICAL DATABASE DESIGN

In this paper, we describe DATPLEX, an extensible design-aid tool for physical database design. DAT-PLEX adopts a layered approach where each layer improves over its input design; and is \extensible" in the sense that an additional layer can be \plugged" into the tool, or an existing layer can be removed from the tool. The current implementation comprises three layers that improve the logical schema through vertical partitioning, denormalization, and storage structure and index selection. A series of experiments were conducted to study the quality of designs generated by DATPLEX. The results show that DATPLEX can generate designs that lead to signi(cid:12)cant savings.
Performance analysis of modified STBC scheme for cooperative MIMO communications

The cooperative MIMO communications is a new challenge to provide more reliable transmissions from the collaboration between users. Mostly, the MIMO employment is performed by using a simple diversity scheme. However, there are many MIMO schemes that can be implemented in practice. This paper proposes the modified STBC scheme for cooperative MIMO communications. Also the performance analysis of the proposed system in term of the data error rate and the outage probability is presented. The simulation results indicate that the proposed system can offer a better bit error rate and improve the outage probability than the diversity scheme.
The Role of Cognitive and Affective Trust in Service Marketing: Antecedents and Consequence

The purpose of this study focuses on the role of cognitive andaffectivetrust in building relationships at the consumer level. More specifically, we examine the proposition that consumer trust in service providers has distinct cognitive and affective dimensions with unique antecedents and consequence. The subjects were 304 consumers of insurance services in Tehran area. Structural Equation Modeling (SEM) with Lisrel software was used for the data analysis. The results showed that service provider expertise, product performance and customer satisfaction influence cognitive trust and are considered as antecedents of cognitive trust. On the other hand corporate reputation, customer satisfaction and similarity are identified as antecedents of affective trust. The present study showed that expertise of service provider has the most influence on cognitive trust while similarity has the most influence on affective trust. In relation to consequences of cognitive and affective trust, the results showed that cognitive and affective trust both influence anticipation of future interactions but the effect of cognitive trust is greater than that of affective trust.
Characterization of Multi-antibiotic-resistant Escherichia coli Isolated from Beef Cattle in Japan

The emergence of multiple-antibiotic-resistance bacteria is increasing, which is a particular concern on livestock farms. We previously isolated 1,347 antimicrobial-resistant (AMR) Escherichia coli strains from the feces of beef cattle on 14 Japanese farms. In the present study, the genetic backgrounds and phylogenetic relationships of 45 AMR isolates were characterized by the chromosome phylotype, AMR phenotype, AMR genotype, and plasmid type. These isolates were classified into five chromosome phylotypes, which were closely linked to the farms from which they were isolated, suggesting that each farm had its own E. coli phylotype. AMR phenotype and plasmid type analyses yielded 8 and 14 types, all of which were associated with the chromosomal phylotype and, thus, to the original farms. AMR genotype analysis revealed more variety, with 16 types, indicating both inter- and intra-farm diversity. Different phylotype isolates from the same farm shared highly similar plasmid types, which indicated that plasmids with AMR genes could be transferred between phylotypes, thereby generating multi-antibiotic-resistant microorganisms. This ecological study demonstrated that the chromosome phylotype was strongly correlated with the farm from which they were isolated, while the AMR phenotype, genotype, and plasmid type were generally correlated with the chromosome phylotype and farm source.