Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Sensing of Microbial Life on Surfaces

ABSTRACT The label-free detection of microbial cells attached to a surface is an active field of research. The field is driven by the need to understand and control the growth of biofilms in a number of applications, including basic research in natural environments, industrial facilities, and clinical devices, to name a few. Despite significant progress in the ability to monitor the growth of biofilms and related living cells, the sensitivity and selectivity of such sensors are still a challenge. We believe that among the many different technologies available for monitoring biofilm growth, optical techniques are the most promising, as they afford direct imaging and offer high sensitivity and specificity. Furthermore, as each technique offers different insights into the biofilm growth mechanism, our analysis allows us to provide an overview of the biological processes at play. In addition, we use a set of key parameters to compare state-of-the-art techniques in the field, including a critical assessment of each method, to identify the most promising types of sensors. We highlight the challenges that need to be overcome to improve the characteristics of current biofilm sensor technologies and indicate where further developments are required. In addition, we provide guidelines for selecting a suitable sensor for detecting microbial cells on a surface.
A Markov Chain Monte Carlo Approach to Cost Matrix Generation for Scheduling Performance Evaluation

In high performance computing, scheduling of tasks and allocation to machines is very critical especially when we are dealing with heterogeneous execution costs. Simulations can be performed with a large variety of environments and application models. However, this technique is sensitive to bias when it relies on random instances with an uncontrolled distribution. We use methods from the literature to provide formal guarantee on the distribution of the instance. In particular, it is desirable to ensure a uniform distribution among the instances with a given task and machine heterogeneity. In this article, we propose a method that generates instances (cost matrices) with a known distribution where tasks are scheduled on machines with heterogeneous execution costs.
Artificial Intelligent Model to Predict Surface Roughness in Laser Machining

Light Amplification Stimulation Emission of Radiation or the common name is Laser. The laser light differs from ordinary light due to it has the photons of same frequency, wavelength and phase. Advantages of using laser beam cutting (LBC) are materials with complex figures can easily be cut by incorporating computer numerical control (CNC) motion equipment, LBC has high cutting speed, Low distortion, very high edge quality and most important thing is LBC has a minimal heat affected zone (HAZ). This paper discussed the development of Radian Basis Function Network (RBFN) to predict surface roughness when laser cutting acrylic sheet. The main objectives of this paper are to find the optimum laser parameters (power, material thickness, tip distance and laser speed) and the effect of these parameters on surface roughness. The network was trained until it predict closer to the experimental values. It observed that some of good surface roughness specimen fail in terms of structure when investigate under microscope.
Mitigating Nesting-Agnostic Hypervisor Policies in Derivative Clouds

The fixed granularity of virtual machines offered by IaaS providers has prompted the evolution of derivative clouds where resources are repackaged into smaller containers and leased out typically in PaaS mode. In such a setup, containers are provisioned within virtual machines. Such a nested setup results in two control centers for the resources used by those containers—the guest OS and the Hypervisor. The latter’s control actions are agnostic of the application executing within a VM. This lack of visibility may result in hypervisor control that has a non-uniform effect on the VM’s nested containers which is undesirable. In this work, we propose policy based control of the effect of the hypervisor’s control actions amongst the containers nested in the affected VM.
Towards Thermal-Efficient Hadoop Clusters through Scheduling

In this study, we proposed a new resource-and thermal-aware scheduler in Hadoop clusters, our scheduler aims at minimizing peak inlet temperature across all nodes to reduce power consumption and cooling cost in data centers. The proposed dynamic scheduler makes job scheduling decisions based on current CPU/disk utilization and number of tasks running as well as the feedback given by all slave nodes at run-time. We deploy a thermal model to project respective temperature of each slave node in addition to neighbor's heat contribution. The thermal-aware scheduler is integrated with the Hadoop's scheduling mechanism. We test our schedulers by running a set of Hadoop benchmarks (e.g., WordCount, DistributedGrep, PI and TeraSort) under various temperature conditions, utilization thresholds, and cluster sizes. The experimental results show that our scheduler achieves an average inlet temperature reduction by 2.5C over the default FIFO scheduler, our scheduling solution saves approximately 15% of cooling cost with marginal performance degradation.
Boost stage for gate current injection during IGBT turn-on event

In gate drivers for insulated-gate bipolar transistors (IGBTs) a single constant gate resistor is usually applied. To optimize the switching trajectory depending on the operating point, a multi-stage gate driver with several resistors can be used. However, the control of the semiconductor is limited by the resistors. The work presented in this paper investigates a boost stage to increase the control flexibility for the turn-on event of an IGBT. The boost stage injects an additional gate current and, therefore, increases the speed of the turn-on process, which leads to a significant loss reduction in the IGBT. The level of the injected current adds a degree of freedom which is controlled by manipulating the charging time of a boost inductor. The concept is validated using an experimental setup with an IGBT rated for 650 V and 800 A for automotive applications.
Accurate and Fast Computation of Approximate Graph Edit Distance based on Graph Relabeling

The graph edit distance, a well-known metric for determining the similarity between two graphs, is commonly used for analyzing large sets of structured data, such as those used in chemoinformatics, document analysis, and malware detection. As computing the exact graph edit distance is computationally expensive, and may be intractable for large-scale datasets, various approximation techniques have been developed. In this paper, we present a method based on graph relabeling that is both faster and more accurate than the conventional approach. We use unfolded subtrees to denote the potential relabeling of local structures around a given vertex. These subtree representations are concatenated as a vector, and the distance between different vectors is used to characterize the distance between the corresponding graphs. This avoids the need for multiple calculations of the exact graph edit distance between local structures. Simulation experiments on two real-world chemical datasets are reported. Compared with the conventional technique, the proposed method gives a more accurate approximation of the graph edit distance and is significantly faster on both datasets. This suggests the proposed method could be applicable in the analysis of larger and more complex graph-like datasets.
Probabilistic Shear Capacity Models for Concrete Members with Internal Composite Reinforcement

AbstractThis paper presents probabilistic models for estimating shear capacity of concrete members reinforced internally with fiber-reinforced polymer (FRP). The application of FRP in concrete structures has steadily gained popularity since the late 1980s; however, the wide range of mechanical properties and various manufacturing processes have made it challenging to unify the design formulation for engineers and code developers. Consequently, the conservative approach has been used to predict structural performance, particularly shear capacity, which has limited its application and resulted in excessive use of materials. In this paper, a robust prediction model is proposed to capture the variety of characteristics of FRPs based on an available database. The database includes various FRPs used as flexural reinforcement for beam members. The model was formulated using the Bayesian parameter estimation method, which takes into account the uncertainties of the parameters that are considered the potential pre.
Protein Promiscuity in H2O2 Signaling.

SIGNIFICANCE Decrypting the cellular response to oxidative stress relies on a comprehensive understanding of the redox signaling pathways stimulated under oxidizing conditions. Redox signaling events can be divided into upstream sensing of oxidants, midstream redox signaling of protein function, and downstream transcriptional redox regulation. Recent Advances: A more and more accepted theory of hydrogen peroxide (H2O2) signaling is that of a thiol peroxidase redox relay, whereby protein thiols with low reactivity toward H2O2 are instead oxidized through an oxidative relay with thiol peroxidases. CRITICAL ISSUES These ultrareactive thiol peroxidases are the upstream redox sensors, which form the first cellular port of call for H2O2. Not all redox-regulated interactions between thiol peroxidases and cellular proteins involve a transfer of oxidative equivalents, and the nature of redox signaling is further complicated through promiscuous functions of redox-regulated "moonlighting" proteins, of which the precise cellular role under oxidative stress can frequently be obscured by "polygamous" interactions. An ultimate goal of redox signaling is to initiate a rapid response, and in contrast to prokaryotic oxidant-responsive transcription factors, mammalian systems have developed redox signaling pathways, which intersect both with kinase-dependent activation of transcription factors, as well as direct oxidative regulation of transcription factors through peroxiredoxin (Prx) redox relays. FUTURE DIRECTIONS We highlight that both transcriptional regulation and cell fate can be modulated either through oxidative regulation of kinase pathways, or through distinct redox-dependent associations involving either Prxs or redox-responsive moonlighting proteins with functional promiscuity. These protein associations form systems of crossregulatory networks with multiple nodes of potential oxidative regulation for H2O2-mediated signaling.
Driving scenario recognition for advanced hybrid electric vehicle control

Fuel consumption in a Hybrid Electric Vehicle (HEV) is typically impacted by powertrain operation modes, short- and long-term driving trend and style, and road type and traffic conditions. Typically, HEVs have rule-based supervisory control using heuristic logic. This approach works sub-optimally because it does not have knowledge of either road conditions or driving trends. We propose a machine learning approach to enhance the HEV controller performance. We create a Driving Scene Recognizer (DSR) that uses the contextual information available to recognize the current driving scenario. This information would be used by the supervisory controller to decide the optimal vehicle commands at each instant of the drive cycle. A hierarchical deep learning network is trained on videos of driving data and vehicle sensor data to classify typical driving scenarios. We demonstrate the performance of the DSR on real-world test data.
Passivity-based output impedance shaping of LCL-filtered grid-connected inverters for suppressing harmonics and instabilities in complicated grid

The passive output impedance of the grid-connected inverters is proposed to suppress the harmonics and instabilities under either the capacitive or the inductive grid condition. The presented control method, the weighted proportional grid voltage feed-forward (WP-GVF) and the active damping delay compensation, wipe out the capacitive and the inductive non-dissipative regions of the inverter output impedance. Moreover, a reactive RC damping branch is placed in parallel with the inverter to provide the sufficient phase margin (PM) above the Nyquist frequency. By the proposed control and reactive method, the output impedance is passivized with the satisfied PM starting from the 5th harmonic frequency, and the inverter-grid system stability is guaranteed in the complicated grid. The effectiveness of the proposed method is validated by the experimental results on a T-NPC LCL-filtered grid-connected inverter.
Community Detection and Correlated Attribute Cluster Analysis on Multi-Attributed Graphs

Multi-attributed graphs, in which each node is characterized by multiple types of attributes, are ubiquitous in the real world. Detection and characterization of communities of nodes could have a significant impact on various applications. Although previous studies have attempted to tackle this task, it is still challenging due to difficulties in the integration of graph structures with multiple attributes and the presence of noises in the graphs. Therefore, in this study, we have focused on clusters of attribute values and strong correlations between communities and attributevalue clusters. The graph clustering methodology adopted in the proposed study involves Community detection, Attribute-value clustering, and deriving Relationships between communities and attribute-value clusters (CAR for short). Based on these concepts, the proposed multi-attributed graph clustering is modeled as CAR-clustering. To achieve CAR-clustering, a novel algorithm named CARNMF is developed based on non-negative matrix factorization (NMF) that can detect CAR in a cooperative manner. Results obtained from experiments using real-world datasets show that the CARNMF can detect communities and attributevalue clusters more accurately than existing comparable methods. Furthermore, clustering results obtained using the CARNMF indicate that CARNMF can successfully detect informative communities with meaningful semantic descriptions through correlations between communities and attribute-value clusters.
Interaction between Nonstructural Proteins NS4B and NS5A Is Essential for Proper NS5A Localization and Hepatitis C Virus RNA Replication

ABSTRACT The hepatitis C virus NS5A protein is tethered to cellular membranes via an amphipathic amino-terminal helix that is inserted in-plane into the outer endoplasmic reticulum (ER)-derived membrane leaflet. The charged face of the helix faces the cytoplasm and may contribute to interactions involved in replicase assembly and function. Using an aggressive charge flip mutagenesis strategy, we identified a number of essential residues for replication on the charged face of the NS5A anchor and identified a double charge face mutant that is lethal for RNA replication but generates suppressor mutations in the carboxy-terminal helix of the NS4B protein. This suppressor restores RNA replication of the NS5A helix double flip mutant (D1979K/D1982K) and, interestingly, seems to function by restoring the proper localization of NS5A to the viral replicase. These data add to our understanding of the complex organization and assembly of the viral replicase via NS4B-NS5A interactions. IMPORTANCE Information about the functional role of the cytosolic face of the NS5A anchoring helix remains obscure. In this study, we show that while the hydrophobic face of the NS5A anchor helix mediates membrane association, the polar cytosolic face of the helix plays a key role during hepatitis C virus (HCV) replication by mediating the interaction of NS5A with other HCV nonstructural proteins via NS4B. Such an interaction determines the subcellular localization of NS5A by engaging NS5A in the HCV replication process during the formation of a functional HCV replication complex. Thus, collectively, it can be stated that the findings in the present study provide further information about the interactions between the HCV nonstructural proteins during HCV RNA replication and provide a platform to gain more insights about the molecular architecture of HCV replication complexes.
The elaborate route for UDP-arabinose delivery into the Golgi of plants

Significance Nucleotide sugars, the activated sugar donors essential for processes such as cell wall biosynthesis and protein and lipid glycosylation are predominantly made in the cytosol. However, a highly diverse range of glycosyltransferases that are located within the Golgi lumen, mediate the above-mentioned glycosylation reactions. Thus, transport of nucleotide sugars across the Golgi membrane into the lumen is crucial for growth and development of many species including microorganisms, plants, and humans. In this study, we identify and functionally characterize four UDP-arabinofuranose transporters from Arabidopsis that are responsible for the delivery of activated arabinose, a critical sugar of plant cell walls, glycoproteins, and signaling peptides. In plants, L-arabinose (Ara) is a key component of cell wall polymers, glycoproteins, as well as flavonoids, and signaling peptides. Whereas the majority of Ara found in plant glycans occurs as a furanose ring (Araf), the activated precursor has a pyranose ring configuration (UDP-Arap). The biosynthesis of UDP-Arap mainly occurs via the epimerization of UDP-xylose (UDP-Xyl) in the Golgi lumen. Given that the predominant Ara form found in plants is Araf, UDP-Arap must exit the Golgi to be interconverted into UDP-Araf by UDP-Ara mutases that are located outside on the cytosolic surface of the Golgi. Subsequently, UDP-Araf must be transported back into the lumen. This step is vital because glycosyltransferases, the enzymes mediating the glycosylation reactions, are located within the Golgi lumen, and UDP-Arap, synthesized within the Golgi, is not their preferred substrate. Thus, the transport of UDP-Araf into the Golgi is a prerequisite. Although this step is critical for cell wall biosynthesis and the glycosylation of proteins and signaling peptides, the identification of these transporters has remained elusive. In this study, we present data demonstrating the identification and characterization of a family of Golgi-localized UDP-Araf transporters in Arabidopsis. The application of a proteoliposome-based transport assay revealed that four members of the nucleotide sugar transporter (NST) family can efficiently transport UDP-Araf in vitro. Subsequent analysis of mutant lines affected in the function of these NSTs confirmed their role as UDP-Araf transporters in vivo.
Ieee Computer Graphics and Applications 71 Exploratory Visualization of Surgical Training Databases for Improving Skill Acquisition

M inimally invasive surgical techniques, such as laparoscopic surgery, typically offer important patient benefits. Unfortunately , the psychomotor and cognitive skills needed to become proficient at laparoscopic surgery remain extremely challenging to learn and teach. In addition, objective means of measuring surgical performance have been limited to summary, one-dimensional data such as task time or total errors, making it difficult to evaluate surgeons' skills and provide feedback to them. The advent of new technologies such as robotic surgical instruments, computer simulators, and computer vision algorithms has created an opportunity to record multidimensional data including tool paths, forces, deformations, and physiological information during surgery and training (see the " Related Work in Surgical Visualization " sidebar). These new data sources have great potential to lead to objective evaluation metrics. However, incorporating these data into a new paradigm of data-intensive surgery raises many important research challenges, including how to store, query, analyze, interpret, and display large-scale time-varying multidimensional surgical datasets (see the " Obtaining Surgical Data " sidebar). In particular, little research has focused on visualizing data collected during surgical procedures (including training) to identify potential areas for error reduction and potentially accelerate learning. Here, we explore visualizing trends in large databases of multidimensional time series data from surgeons performing a basic laparoscopic-surgery training task. Previous researchers have applied machine-learning techniques to these quantitative performance data—for example, classifiers can help distinguish novice and expert surgeons. 1 We believe visualiza-tion could augment computational tools such as these to elucidate specific factors or trends that could inform the design of improved training tools. Furthermore , visualization could provide a particularly effective method for conveying data trends to surgeons. From the visualization research community's perspective, as supported by a panel discussion at VisWeek 2010, these goals relate to the grand challenge of supporting analysis at scale. Regarding A new system has visualized force, position, rotation, and synchronized video data from 300 bimanual laparoscopic-surgery tasks performed by more than 50 surgeons. Insights and feedback from an interdisciplinary iterative design process and use case studies support such visualization's utility.
Dissociation between wanting and liking for alcohol and caffeine: A test of the Incentive Sensitisation Theory

Limited human studies have directly tested the dissociation between wanting and liking with human substance users, a core tenet of the Incentive Sensitisation Theory (IST). The aim of this study is to test the dissociation between wanting and liking in humans across two commonly used licit substances, alcohol and caffeine. The STRAP-R (Sensitivity To Reinforcement of Addictive and other Primary Rewards) questionnaire was administered to 285 alcohol users (mean age=33.30, SD= 8.83) and 134 coffee users (mean age=33.05, SD=8.10) ranging in their levels of substance use to assess wanting and liking. Findings showed that in high risk alcohol users wanting may drive alcohol consumption more so than liking, compared with low risk alcohol users. However, wanting and liking did not significantly dissociate as alcohol consumption increased. These findings partially support IST. Additionally, IST was not supported in coffee users. It is possible that caffeine functions differently at the neurological level compared with alcohol, perhaps explaining the lack of dissociation emerging in coffee users as caffeine use increased. Nevertheless, the current study makes several contributions to IST research. Future studies should focus on utilising the STRAP-R with a clinically dependent sample to test the dissociation between wanting and liking.
Survey of Distributed Decision

We survey the recent distributed computing literature on checking whether a given distributed system configuration satisfies a given boolean predicate, i.e., whether the configuration is legal or illegal w.r. t. that predicate. We consider classical distributed computing environments, including mostly synchronous fault-free network computing (LOCAL and CONGEST models), but also asynchronous crash-prone shared-memory computing (WAIT-FREE model), and mobile computing (FSYNC model).