Miracle-dz/UniFAR-FTUs huggingface dataset:
A Visual Analytics Approach for Equipment Condition Monitoring in Smart Factories of Process Industry

Monitoring equipment conditions is of great value in manufacturing, which can not only reduce unplanned downtime by early detecting anomalies of equipment but also avoid unnecessary routine maintenance. With the coming era of Industry 4.0 (or industrial internet), more and more assets and machines in plants are equipped with various sensors and information systems, which brings an unprecedented opportunity to capture large-scale and fine-grained data for effective on-line equipment condition monitoring. However, due to the lack of systematic methods, analysts still find it challenging to carry out efficient analyses and extract valuable information from the mass volume of data collected, especially for process industry (e.g., a petrochemical plant) with complex manufacturing procedures. In this paper, we report the design and implementation of an interactive visual analytics system, which helps managers and operators at manufacturing sites leverage their domain knowledge and apply substantial human judgements to guide the automated analytical approaches, thus generating understandable and trustable results for real-world applications. Our system integrates advanced analytical algorithms (e.g., Gaussian mixture model with a Bayesian framework) and intuitive visualization designs to provide a comprehensive and adaptive semi-supervised solution to equipment condition monitoring. The example use cases based on a real-world manufacturing dataset and interviews with domain experts demonstrate the effectiveness of our system.
Blind Phase Search with Angular Quantization Noise Mitigation for Efficient Carrier Phase Recovery

The inherent discrete phase search nature of the conventional blind phase search (C-BPS) algorithm is found to introduce angular quantization noise in its phase noise estimator. The angular quantization noise found in the C-BPS is shown to limit its achievable performance and its potential low complexity implementation. A novel filtered BPS algorithm (F-BPS) is proposed and demonstrated to mitigate this quantization noise by performing a low pass filter operation on the C-BPS phase noise estimator. The improved performance of the proposed F-BPS algorithm makes it possible to significantly reduce the number of necessary test phases to achieve the C-BPS performance, thereby allowing for a drastic reduction of its practical implementation complexity. The proposed F-BPS scheme performance is evaluated on a 28-Gbaud 16QAM and 64QAM both in simulations and experimentally. Results confirm a substantial improvement of the performance along with a significant reduction of its potential implementation complexity compared to that of the C-BPS.
Scalable Spectral Clustering Using Random Binning Features

Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic complexity in constructing similarity graphs and computing subsequent eigendecomposition. Although a number of methods have been proposed to accelerate spectral clustering, most of them compromise considerable information loss in the original data for reducing computational bottlenecks. In this paper, we present a novel scalable spectral clustering method using Random Binning features (RB) to simultaneously accelerate both similarity graph construction and the eigendecomposition. Specifically, we implicitly approximate the graph similarity (kernel) matrix by the inner product of a large sparse feature matrix generated by RB. Then we introduce a state-of-the-art SVD solver to effectively compute eigenvectors of this large matrix for spectral clustering. Using these two building blocks, we reduce the computational cost from quadratic to linear in the number of data points while achieving similar accuracy. Our theoretical analysis shows that spectral clustering via RB converges faster to the exact spectral clustering than the standard Random Feature approximation. Extensive experiments on 8 benchmarks show that the proposed method either outperforms or matches the state-of-the-art methods in both accuracy and runtime. Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features.
IMPROVE-QA: An Interactive Mechanism for RDF Question/Answering Systems

RDF Question/Answering(Q/A) systems can interpret user's question N as SPARQL query Q and return answer set $Q(D)$ over RDF repository D to the user. However, due to the complexity of linking natural phrases with specific RDF items (e.g., entities and predicates), it remains difficult to understand users' questions precisely, hence $Q(D)$ may not meet users' expectation, offering wrong answers and dismissing some correct answers. In this demo, we design an I Interactive Mechanism aiming for PRO motion V ia feedback to Q/A systems (IMPROVE-QA), a whole platform to make existing Q/A systems return more precise answers (denoted as $\mathcal Q^\prime (D)$) to users. Based on user's feedback over $Q(D)$, IMPROVE-QA automatically refines the original query Q into a new query graph $\mathcal Q^\prime $ with minimum modifications, where $\mathcal Q^\prime (D)$ provides more precise answers. We will also demonstrate how IMPROVE-QA can apply the "lesson'' learned from the user in each query to improve the precision of Q/A systems on subsequent natural language questions.
Seasonal variations in thermal energetics of Australian silvereyes ( Zosterops lateralis )

Most Australian birds do not migrate over long distances and therefore have to cope with seasonal changes in weather and food availability. We investigated whether the small (11 g) silvereye Zosterops lateralis changes its thermal tolerance from winter to summer. Body mass and body temperature of silvereyes exhibited little seasonal variability. However, metabolic rates (MR) and thermal conductance showed significant changes. Below the thermoneutral zone (TNZ), winter-acclimatized birds had significantly lower resting MR and thermal conductance than summer-acclimatized birds. Within the TNZ (~ 27.0‐33.68C winter; ~ 25.4‐33.58C summer) basal MR of winter-acclimatized birds (2.30 a 0.29 mL O2 g -1 h -1 ) was significantly lower than that of summer-acclimatized birds (2.88 a 0.43 mL O2 g -1 h -1 ). The average daily MR also differed significantly between summer and winter largely due to a greater reduction of MR at night and the decreased conductance. Our study shows that small passerines such as silvereyes exhibit seasonal variability in physiology and thermal energetics, even when they live in areas with a relatively mild climate, to help overcome seasonal changes in weather conditions and food availability.
Deep Manifold Embedding Active Shape Model for Pose Invarient Face Tracking

The operating range of head pose for the standard face models is rather narrow, i.e. ±15°, and so performance of face fitting deteriorates badly when the pose range goes beyond that. To handle such problem, we propose a new face model based on Active Shape Model (ASM) since it is known that it is fast and reliable. To build a pose invariant face model, the manifold learning technique is used in dealing with diverse head pose cases, so called it Deep Manifold Embedding Active Shape Model (DME-ASM) where a Convolutional Neural Network (CNN) is introduced in categorizing the corresponding pose range for the input face. A custom-made face video dataset and CMU-PIE face DB are used in evaluating its fitting performance. Result suggests that ME-ASM can track the face against extreme head poses very well and runs in the real-time basis.
Power efficient optical OFDM

A new technique for using orthogonal frequency division multiplexing (OFDM) in optical systems is presented. Clipped OFDM is derived from a bipolar OFDM waveform by setting the negative values to zero. It has an optical efficiency 8 dB better than DC biased OFDM. If only the odd OFDM subcarriers are modulated, the clipping noise is orthogonal to the wanted signal.
Genomic analysis of bifunctional Class C-Class D β-lactamases in environmental bacteria

β-lactamases, which are found in several bacterial species and environments, are the main cause of resistance to β-lactams in Gram-negative bacteria. In 2009, a protein (LRA-13) with two β-lactamase domains (one class C domain and one class D domain) was experimentally characterised, and an extended action spectrum against β-lactams consistent with two functional domains was found. Here, we present the results of searches in the non-redundant NCBI protein database that revealed the existence of a group of homologous bifunctional β-lactamases in the genomes of environmental bacteria. These findings suggest that bifunctional β-lactamases are widespread in nature; these findings also raise concern that bifunctional β-lactamases may be transferred to bacteria of clinical importance through lateral gene transfer mechanisms.
Adaptive gesture recognition based on human physical characteristic

In gesture recognition, the database plays an important role as the foundation for the computer to recognize the input gesture. This paper discusses about the development of database to recognize gestural motion for several groups of people. Individual database is more accurate, but for a large number of recognition subject, we cannot build the database individually because it will consume more space in the system memory. To solve this problem, we need a new kind of database that ability to serve all types of people categories but using less memory on the system. The solution is grouped databases. The group database will able to identify a gesture from any subject by firstly classify the subject belong to which group. Then recognize the input gesture by using database in the dedicated group. The database is grouped based on body height, which is divided into three different groups. Five geometrical gestures were chosen to perform by the subject. The gestural motion was captured by Motion Capture System (MOCAP). Data from x, y and z-axes that were generated by the motion captured system then analyzed, classified and stored in the gesture database. An adaptive gesture recognition is presented to select the identical database to identify an unknown gesture that insert into the system. The result shows the accuracy of recognition implemented by gesture database is achieve up to 83.7% in recognizing the gesture performed by the subject.
Detection of Influenza A viruses at migratory bird stopover sites in Michigan, USA

ABSTRACT Introduction: Influenza A viruses have the potential to cause devastating illness in humans and domestic poultry. Wild birds are the natural reservoirs of Influenza A viruses and migratory birds are implicated in their global dissemination. High concentrations of this virus are excreted in the faeces of infected birds and faecal contamination of shared aquatic habitats can lead to indirect transmission among birds via the faecal-oral route. The role of migratory birds in the spread of avian influenza has led to large-scale surveillance efforts of circulating avian influenza viruses through direct sampling of live and dead wild birds. Environmental monitoring of bird habitats using molecular detection methods may provide additional information on the persistence of influenza virus at migratory stopover sites distributed across large spatial scales. Materials and methods: In the current study, faecal and water samples were collected at migratory stopover sites and evaluated for Influenza A by real-time quantitative reverse transcriptase PCR. Results and Discussion: This study found that Influenza A was detected at 53% of the evaluated stopover sites, and 7% and 4.8% of the faecal and water samples, respectively, tested positive for Influenza A virus. Conclusion: Environmental monitoring detected Influenza A at stopover sites used by migratory birds.
First Principles Study on the CO Oxidation on Mn-Embedded Divacancy Graphene

The CO oxidation mechanism on graphene with divacancy (DG) embedded with transition metal from Sc to Zn has been studied by using first principles calculations. The results indicate that O2 molecule is preferentially adsorbed on Sc, Ti, V, Cr, Mn, and Fe-DG, which can avoid the CO poisoning problem that many catalysts facing and is beneficial to the CO oxidation progress. Further study indicates that Mn-DG shows the best catalytic properties for CO oxidation with consideration of both Langmuir-Hinshelwood (LH) and Eley-Rideal (ER) oxidation mechanisms. Along the ER mechanism, the reaction energy barrier for the first step (CO free + O2 pre-adsorbed → OOCO) is 0.96 eV. Along the LH mechanism, the energy barrier for the rate limiting step (CO adsorbed + O2 adsorbed → OOCO) is only 0.41 eV, indicating that the CO oxidation on Mn-DG will occur along LH mechanism. The Hirshfeld charge distributions of O2 and CO molecules is tuned by the embedded Mn atom, and the charge transfer from the embedded Mn atom to the adsorbed molecules plays an important role for the CO oxidation. The result shows that the Mn-embedded divacancy graphene is a noble-metal free and efficient catalyst for CO oxidation at low temperature.
Predictive value of the baseline electrocardiogram ST‐segment pattern in cardiogenic shock: Results from the CardShock Study

The most common aetiology of cardiogenic shock (CS) is acute coronary syndrome (ACS), but even up to 20%–50% of CS is caused by other disorders. ST‐segment deviations in the electrocardiogram (ECG) have been investigated in patients with ACS‐related CS, but not in those with other CS aetiologies. We set out to explore the prevalence of different ST‐segment patterns and their associations with the CS aetiology, clinical findings and 90‐day mortality.
Habit strength and between-meal snacking in daily life: the moderating role of level of education

Abstract Objective Recent research emphasizes the importance of habit in explaining patterns of energy intake and choices of consumption. However, the nature of the association between habit strength and snacking has not been explored for all types of between-meal snacks. Design Multilevel linear techniques were used to: (i) examine the association between habit strength and moment-to-moment energy intake (kilocalories) from snacks in daily life; and (ii) determine whether gender, age, level of education and BMI moderate the association between habit strength and moment-to-moment energy intake from snacks. A smartphone application based on the experience sampling method was used to map momentary between-meal snack intake in the context of daily life. Demographics and habit strength were assessed with an online composite questionnaire. Setting This research was performed in the Netherlands in the natural environment of participants’ daily life. Subjects Adults (n 269) aged 20–50 years. Results Habit strength was significantly associated with moment-to-moment energy intake from between-meal snacks in daily life: the higher the strength of habit to snack between meals, the higher the amount of momentary energy intake from snacks. The association between habit strength and moment-to-moment energy intake from snacks was moderated by education level. Additional analyses showed that habit strength was significantly associated with moment-to-moment energy intake from between-meal snacks in the low to middle level of education group. Conclusions It is recommended to address habitual between-meal snacking in future interventions targeting low- to middle-educated individuals.
COACH-D: improved protein–ligand binding sites prediction with refined ligand-binding poses through molecular docking

Abstract The identification of protein–ligand binding sites is critical to protein function annotation and drug discovery. The consensus algorithm COACH developed by us represents one of the most efficient approaches to protein–ligand binding sites prediction. One of the most commonly seen issues with the COACH prediction are the low quality of the predicted ligand-binding poses, which usually have severe steric clashes to the protein structure. Here, we present COACH-D, an enhanced version of COACH by utilizing molecular docking to refine the ligand-binding poses. The input to the COACH-D server is the amino acid sequence or the three-dimensional structure of a query protein. In addition, the users can also submit their own ligand of interest. For each job submission, the COACH algorithm is first used to predict the protein–ligand binding sites. The ligands from the users or the templates are then docked into the predicted binding pockets to build their complex structures. Blind tests show that the algorithm significantly outperforms other ligand-binding sites prediction methods. Benchmark tests show that the steric clashes between the ligand and the protein structures in the COACH models are reduced by 85% after molecular docking in COACH-D. The COACH-D server is freely available to all users at http://yanglab.nankai. edu.cn/COACH-D/.
Towards Accurate and Real-time Information Extrapolation on Charging Pile Network

The Internet of Things era of transportation brings many new challenges. One of the most important issues in cyberspace is to build an information infrastructure that provides services for unmanned electric vehicles. For example, nearby available charging stations. This paper presents the first systematic study on the information extrapolation problem in the charging pile network (CPN). The challenge is how to deal with incomplete data caused by device failure, communication anomalies, crowdsourcing and commercial competition. This paper provides a novel solution to infer the real-time charging pile occupancy status. We analyzed the spatiotemporal pattern and user charging behavior of charging events, and proposed a data extrapolation algorithm based on Monte Carlo maximum likelihood estimation and Gibbs sampling technique. The experimental results show that our method achieves a 94% accuracy, which is 14% and 11% higher than the two benchmark methods, respectively, and it can increase user satisfaction by up to 44% on the end-to-end service.
Spermatozoa Molecules in Relation to Bulls Fertility

Bull fertility may be defined as the process by which spermatozoa fertilize and activate the ovum and then support embryonic development. Bull fertility is a complex trait having relatively low heritability and plays a vital role for efficient production and reproduction of bovine. Various mechanisms involved in regulating bull fertility associated phenotype and reliable biomarkers are poorly defined. Primary spermatozoa physiology indicators namely sperm molecules and epigenetic factors may play a vital role in predicting sperm physiology and fertility. Spermatozoa and their fingerprints or patterns can play a pivotal role in prediction of fertility in bovine. No reliable tests exist despite genomic selection for evaluating quality of semen and fertility of bull. This review focuses on various molecules related to bull fertility viz., sperm RNAs, seminal plasma proteins, sperm proteins and sperm epigenome. In combination with single-nucleotide polymorphism (SNP), microarrays or sperm molecules and epigenome markers can be used to determine sperm quality and to predict bull fertility.
Facile preparation of core-crosslinked micelles from azide-containing thermoresponsive double hydrophilic diblock copolymer via click chemistry

Double hydrophilic diblock copolymer, poly(N,N-dimethylacrylamide)-b-poly(N-isopropylacrylamide-co-3-azidopropylacrylamide) (PDMA-b-P(NIPAM-co-AzPAM), containing azide moieties in one of the blocks was synthesized via consecutive reversible addition-fragmentation chain transfer polymerization. The obtained diblock copolymer molecularly dissolves in aqueous solution at room temperature, and can further supramolecularly self-assemble into core-shell nanoparticles consisting of thermoresponsive P(NIPAM-co-AzPAM) cores and water-soluble PDMA coronas above the lower critical solution temperature of P(NIPAM-co-AzPAM) block. As the micelle cores contain reactive azide residues, core crosslinking can be facilely achieved upon addition of difunctional propargyl ether via click chemistry. In an alternate approach in which the PDMA-b-P(NIPAM-co-AzPAM) diblock copolymer was dissolved in a common organic solvent (DMF), the core-crosslinked (CCL) micelles can be fabricated via “click” crosslinking upon addition of propargyl ether and subsequent dialysis against water. CCL micelles prepared by the latter approach typically possess larger sizes and broader size distributions, compared with that obtained by the former one. In both cases, the obtained (CCL) micelles possess thermoresponsive cores, and the swelling/shrinking of which can be finely tuned with temperature, rendering them as excellent candidates as intelligent drug nanocarriers. Because of the high efficiency and quite mild conditions of click reactions, we expect that this strategy can be generalized for the structural fixation of other self-assembled nanostructures. © 2007 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 46: 860–871, 2008
Views on patient safety by operations managers in somatic hospital care: a qualitative analysis

Healthcare outcome is to achieve optimal health for each patient. It is a well-known phenomenon that patients suffer from care injuries. Operations managers have difficulties in seeing that the relationship between safety culture, values and attitudes affects the medical care to the detriment of the patient. The aim was to describe the views on patient safety by operations managers and the establishment of patient safety and safety culture in somatic hospital care. Four open questions were answered by 29 operations managers in somatic hospital care. Data analysis was carried out by deductive qualitative content analysis. Operations managers found production to be the most important goal, and patient safety was linked to this basic mission. Safety work meant to achieve optimal health outcomes for each patient in a continuous development of operations. This was accomplished by pursuing a high level of competence among employees, having a functioning report system and preventing medical errors. Safety culture was mentioned to a smaller extent. The primary target of patient safety work by the operations managers was improving care quality which resulted in fewer complications and shorter care time. A change in emphasis to primary safety work is necessary. To accomplish this increased knowledge of communication, teamwork and clinical decision making are required.