Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Generation of a Lifecycle Analysis Model from a First Principles Industrial Process Simulation Model

Model driven development of industrial process control systems has received extensive interest from the industrial informatics community. Another area of active interest has been the use of simulation of industrial processes in conjunction with control system design. However, the importance of sustainable development and environmental footprints are becoming increasingly important for the process industry, and Life Cycle Assessment (LCA) is the established technique for this purpose. LCA is currently being performed manually by a corps of engineers unaware of recent developments in industrial informatics. Thus LCA is a cumbersome process that cannot be integrated into a model driven development philosophy. This can significantly undermine the benefits from model driven development, since the outcome of LCA may result in significant change requests to the process design and consequently to the control system design. These problems could be overcome, if LCA could be brought under the scope of model driven development. This requires that the LCA model could be generated automatically into the LCA tool. This is feasible, since process designers ofter develop a first principles process simulation model that has the required information. This paper presents a methodology for automatic LCA model generation from a first-principles simulation model.
Cognitive neuroscience can support public health approaches to minimise the harm of ‘losses disguised as wins’ in multiline slot machines

Video slot machines are associated with both accelerated transition into problematic forms of gambling, as well as psychosocial harm above and beyond other forms of gambling. A growing body of evidence is uncovering how key design features of multiline slot machines produce an inflated experience of reward, despite the fact that these features offer no overall financial benefit to the player. A pernicious example of this are ‘losses disguised as wins’ (LDWs), which occur when simultaneous bets placed on multiple lines result in a winning combination that returns an amount greater than zero, but less the total wager. These events are usually accompanied by the same celebratory sounds and animations that accompany true wins. We argue that LDWs may leverage neuropsychological phenomena that underlie reinforcement learning and contribute to extended or repetitive use and gambling‐related harm. While other characteristics of slot machine gambling have been examined by cognitive neuroscientists, this feature has not yet received attention. Neuroscientific methods can be used to assess the impact of LDWs on the human reward system, to assess the claim that these events are a reinforcing and contributing factor in the development of harmful play. Positive findings would provide further persuasive evidence in support of strategies to minimise gambling harm through the regulation of machine design.
BfvR, an AraC-Family Regulator, Controls Biofilm Formation and pH6 Antigen Production in Opposite Ways in Yersinia pestis Biovar Microtus

Biofilm formation is critical for blocking flea foregut and hence for transmission of Y. pestis by flea biting. In this study, we identified the regulatory role of the AraC-family transcriptional regulator BfvR (YPO1737 in strain CO92) in biofilm formation and virulence of Yersinia pestis biovar Microtus. Crystal violet staining, Caenorhabditis elegans biofilm assay, colony morphology assay, intracellular c-di-GMP concentration determination, and BALB/c mice challenge were employed to reveal that BfvR enhanced Y. pestis biofilm formation while repressed its virulence in mice. Further molecular biological assays demonstrated that BfvR directly stimulated the expression of hmsHFRS, waaAE-coaD, and hmsCDE, which, in turn, affected the production of exopolysaccharide, LPS, and c-di-GMP, respectively. In addition, BfvR directly and indirectly repressed psaABC and psaEF transcription, respectively. We concluded that the modulation of biofilm- and virulence-related genes by BfvR led to increased biofilm formation and reduced virulence of Y. pestis biovar Microtus.
Magnetic Molecular Dynamics Simulations of A2A Receptor in Solution

In this paper, we present the results of the application of an external static magnetic field with the Velocity Verlet algorithm within Molecular Dynamics simulations of the A2A adenosine receptor protein in solution. Molecular Dynamics simulations allow to understand at molecular level the interaction mechanisms between atoms under specific conditions. Here, for the first time, MD simulations of a receptor protein have been performed in presence of an external magnetic field in order to try to elucidate specific endpoints of interaction with the field.
Comparison of Three Nutritional Screening Tools to Predict Malnutrition Risk and Detect Distinctions Between Tools in Cancer Patients Receiving Radiochemotherapy

Abstract The aim of this study was to compare three screening tools for malnutrition in patients with head and neck cancers or central nervous system tumors (CNS) who are undergoing radiochemotherapy. The study evaluated 124 adult cancer patients. The Subjective Global Assessment (SGA), Mini-Nutritional Assessment (MNA), and Nutritional Risk Screening 2002 (NRS-2002) were chosen to assess the risk of malnutrition. The prevalence of patients at nutritional risk was 31% with SGA, 31% with MNA and 23% with NRS-2002 in all patients. The agreement between SGA and MNA was substantial (κ = 0.886, P < 0.001); the agreement between SGA and NRS-2002 was moderate (κ = 0.713, P < 0.001); and the agreement between MNA and NRS-2002 was also moderate (κ = 0.795, P < 0.001). In subgroup analysis, SGA and MNA substantially (κ = 0.973, P < 0.001), SGA and NRS-2002 moderately (κ = 0.722, P < 0.001), and MNA and NRS-2002 moderately (κ = 0.747, P < 0.001) agreed in head and neck cancer patients. In CNS tumor patients, SGA and MNA slightly (κ = 0.390, P = 0.005), SGA and NRS-2002 fairly (κ = 0.457, P =0.001), and MNA and NRS-2002 substantially (κ = 0.878, P < 0.001) agreed. The best agreement in tools was observed between SGA with MNA in all patients. Further studies in different tumor groups will enhance our understanding of current tools for malnutrition detection of radiotherapy patient.
Anodic stimulation misunderstood: preferential activation of fiber orientations with anodic waveforms in deep brain stimulation

Objective. During deep brain stimulation (DBS), it is well understood that extracellular cathodic stimulation can cause activation of passing axons. Activation can be predicted from the second derivative of the electric potential along an axon, which depends on axonal orientation with respect to the stimulation source. We hypothesize that fiber orientation influences activation thresholds and that fiber orientations can be selectively targeted with DBS waveforms. Approach. We used bioelectric field and multicompartment NEURON models to explore preferential activation based on fiber orientation during monopolar or bipolar stimulation. Preferential fiber orientation was extracted from the principal eigenvectors and eigenvalues of the Hessian matrix of the electric potential. We tested cathodic, anodic, and charge-balanced pulses to target neurons based on fiber orientation in general and clinical scenarios. Main results. Axons passing the DBS lead have positive second derivatives around a cathode, whereas orthogonal axons have positive second derivatives around an anode, as indicated by the Hessian. Multicompartment NEURON models confirm that passing fibers are activated by cathodic stimulation, and orthogonal fibers are activated by anodic stimulation. Additionally, orthogonal axons have lower thresholds compared to passing axons. In a clinical scenario, fiber pathways associated with therapeutic benefit can be targeted with anodic stimulation at 50% lower stimulation amplitudes. Significance. Fiber orientations can be selectively targeted with simple changes to the stimulus waveform. Anodic stimulation preferentially activates orthogonal fibers, approaching or leaving the electrode, at lower thresholds for similar therapeutic benefit in DBS with decreased power consumption.
Limiting exercise inhibits neuronal recovery from neurological disorders

Patients who are bedridden often suffer from muscular atrophy due to reduced daily activities and can become depressed. However, patients who undergo physical therapy sometimes demonstrate positive benefits including a reduction of stressful and depressed behavior. Regenerative medicine has seen improvements in two stem cell-based therapies for central nervous system disorders. One therapy is through the transfer of exogenous stem cells. The other therapy is a more natural method and focuses on the increasing endogenous neurogenesis and restoring the neurological impairments. This study overviews how immobilization-induced disuse atrophy affects neurogenesis in rats, specifically hypothesizing that immobilization diminishes circulating trophic factor levels, like vascular endothelial growth factors or brain-derived neurotrophic factor, which in turn limits neurogenesis. This hypothesis requires the classification of the stem cell microenvironment by probing growth factors in addition to other stress-related proteins that correlate with exercise-induced neurogenesis. There is research examining the effects of increased exercise on neurogenesis while limiting exercise, which better demonstrates the pathological states of immobile stroke patients, remains relatively unexplored. To examine the effects of immobilization on neurogenesis quantitative measurements of movements, 5-bromo-2deoxyuridine labeling of proliferative cells, biochemical assays of serum, cerebrospinal fluid and neurological levels of trophic factors, growth factors, and stress-related proteins will indicate levels of neurogenesis. In further research, studies are needed to show how in vivo stimulation, or lack thereof, affects stem cell microenvironments to advance treatment procedures for strengthening neurogenesis in bedridden patients. This paper is a review article. Referred literature in this paper has been listed in the references section. The datasets supporting the conclusions of this article are available online by searching various databases, including PubMed. Some original points in this article come from the laboratory practice in our research center and the authors' experiences.
Movie Recommendation Using Metadata Based Word2Vec Algorithm

Nowadays, recommending preferable item among huge number of item is essential on online market. Many content platforms, such as YouTube and Amazon, use recommendation techniques to recommend items. Therefore, various techniques have been studied to recommend desirable item for each users. In this paper, we propose a method for effectively recommending preferable movies for each users by using community user's movie rating information and movie metadata information with deep learning technology. The proposed method shows 0.165 performance improvement based on Rcall©100 as compared with the basline method.
Computation of Armature Reaction Field and Full-Load Characteristics of an Axial flux Surface Mounted PM Machine Using a New Analytical Approach

In recent years axial flux permanent magnet (AFPM) machines have found industrial applications because of such characteristics as high power density, compact structure and disc-like shape. Like other electrical machines, prediction of flux density distribution in the air gap of AFPM is essential. In this paper, an analytical method for calculation of air gap flux density caused by armature reaction is presented. The solution is based on Maxwell's equations. Separation of variables method is applied to field equations and 2D distribution of magnetic flux density in the air gap calculated in term of Fourier series. The 2-D distribution of three-phase armature winding is modeled exactly. Finally the calculated axial and tangential field waveforms are compared with corresponding 3-D finite element analysis results.
Variation in the vital rates of an Antarctic marine predator: the role of individual heterogeneity.

Variation in life-history traits such as lifespan and lifetime reproductive output is thought to arise, in part, due to among-individual differences in the underlying probabilities of survival and reproduction. However, the stochastic nature of demographic processes can also generate considerable variation in fitness-related traits among otherwise-identical individuals. An improved understanding of life-history evolution and population dynamics therefore depends on evaluating the relative role of each of these processes. Here, we used a 33-yr data set with reproductive histories for 1,274 female Weddell seals from Erebus Bay, Antarctica, to assess the strength of evidence for among-individual heterogeneity in the probabilities of survival and reproduction, while accounting for multiple other sources of variation in vital rates. Our analysis used recent advances in Bayesian model selection techniques and diagnostics to directly compare model fit and predictive power between models that included individual effects on survival and reproduction to those that did not. We found strong evidence for costs of reproduction to both survival and future reproduction, with breeders having rates of survival and subsequent reproduction that were 3% and 6% lower than rates for non-breeders. We detected age-related changes in the rates of survival and reproduction, but the patterns differed for the two rates. Survival rates steadily declined from 0.92 at age 7 to 0.56 at the maximal age of 31 yr. In contrast, reproductive rates increased from 0.68 at age 7 to 0.79 at age 16 and then steadily declined to 0.37 for the oldest females. Models that included individual effects explained more variation in observed life histories and had better estimated predictive power than those that did not, indicating their importance in understanding sources of variation among individuals in life-history traits. We found that among-individual heterogeneity in survival was small relative to that for reproduction. Our study, which found patterns of variation in vital rates that are consistent with a series of predictions from life-history theory, is the first to provide a thorough assessment of variation in important vital rates for a long-lived, high-latitude marine mammal while taking full advantage of recent developments in model evaluation.
Cognitive company discovery

Cognitive Company Discovery is an application that helps business professionals identify companies of interest to them. The application employs a variety of artificial intelligence and data science techniques to build a corpus of company data, rapidly search the corpus based on implicit and explicit user queries, present the results using visualization techniques that yield insight into areas of interest to the user and to scan vast amounts of news and blog posts to aid users in discovering new companies. The application is currently deployed in a major corporation. A video that demonstrates our system can be found at the following URL: https://player.vimeo. com/video/278031050
Detecting Deception and Suspicion in Dyadic Game Interactions

In this paper we focus on detection of deception and suspicion from electrodermal activity (EDA) measured on left and right wrists during a dyadic game interaction. We aim to answer three research questions: (i) Is it possible to reliably distinguish deception from truth based on EDA measurements during a dyadic game interaction? (ii) Is it possible to reliably distinguish the state of suspicion from trust based on EDA measurements during a card game? (iii) What is the relative importance of EDA measured on left and right wrists? To answer our research questions we conducted a study in which 20 participants were playing the game Cheat in pairs with one EDA sensor placed on each of their wrists. Our experimental results show that EDA measures from left and right wrists provide more information for suspicion detection than for deception detection and that the person-dependent detection is more reliable than the person-independent detection. In particular, classifying the EDA signal with Support Vector Machine (SVM) yields accuracies of 52% and 57% for person-independent prediction of deception and suspicion respectively, and 63% and 76% for person-dependent prediction of deception and suspicion respectively. Also, we found that: (i) the optimal interval of informative EDA signal for deception detection is about 1 s while it is around 3.5 s for suspicion detection; (ii) the EDA signal relevant for deception/suspicion detection can be captured after around 3.0 seconds after a stimulus occurrence regardless of the stimulus type (deception/truthfulness/suspicion/trust); and that (iii) features extracted from EDA from both wrists are important for classification of both deception and suspicion. To the best of our knowledge, this is the first work that uses EDA data to automatically detect both deception and suspicion in a dyadic game interaction setting.
Large Scale Stream Analytics Using a Resource-Constrained Edge

A key challenge for smart city analytics is fast extraction, accumulation and processing of sensor data collected from a large number of IoT devices. Edge computing has enabled processing of simple analytics, such as aggregation, geographically closer to the IoT devices to improve latency. However, the throughput of processing in the edge depends on the type of resources available, the number of IoT devices connected and the type of stream analytics performed in the edge. We introduce a framework called Seagull for building efficient, large scale IoT-based applications. Our framework distributes the stream analytics processing tasks to the nodes based on their proximity to the sensor data source as well as the amount of processing the nodes can handle. Our evaluation shows the effect of various stream analytics parameters on the maximum sustainable throughput for a resource-constrained edge device.
Wherefore art thou R3579X?

abstract In a social network, nodes correspond to people or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anony-mization replaces names with meaningless unique identi-fiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.
Data Managing and Service Exchanging on IoT Service Platform Based on Blockchain with Smart Contract and Spatial Data Processing

Expectation of cryptocurrencies has been increased rapidly and all of these cryptocurrencies are generated on blockchain platform. This means not only the paradigm is changing in the field of finance but also the blockchain platform is technically stable. Based on the stability of blockchain, many kind of crypto currencies or application platforms are being implemented or released and world famous banks are applying blockchain on their financial service[1]. Even law for exchanging cryptocurrencies is being discussed. Furthermore, blockchain platforms also run programmed source code which is called as smart contract on its distributed platform. Smart contract extends usage of blockchain platform. So in this paper, we propose an algorithm for recording and managing location data of IoT service provider and user based on blockchain with smart contract. Our proposal records data of participants in network by blockchain which ensures security and match with other optimized participant by spatial data processing.
Safety of Laparoscopic Radical Gastrectomy in Gastric Cancer Patients with End-Stage Renal Disease

Purpose The surgical outcomes of end-stage renal disease (ESRD) patients undergoing radical gastrectomy for gastric cancer were inferior compared with those of non-ESRD patients. This study aimed to evaluate the short- and long-term surgical outcomes of ESRD patients undergoing laparoscopic gastrectomy (LG) and open gastrectomy (OG) for gastric cancer. Materials and Methods Between 2004 and 2014, 38 patients (OG: 21 patients, LG: 17 patients) with ESRD underwent gastrectomy for gastric cancer. Comparisons were made based on the clinicopathological characteristics, surgical outcomes, and long-term survival rates. Results No significant differences were noted in the clinicopathological characteristics of either group. LG patients had lower estimated blood loss volumes than OG patients (LG vs. OG: 94 vs. 275 mL, P=0.005). The operation time and postoperative hospital stay were similar in both the groups. The postoperative morbidity for LG and OG patients was 41.1% and 33.3%, respectively (P=0.873). No significant difference was observed in the long-term overall survival rates between the 2 groups (5-year overall survival, LG vs. OG: 82.4% vs. 64.7%, P=0.947). Conclusions In ESRD patients, LG yielded non-inferior short- and long-term surgical outcomes compared to OG. Laparoscopic procedures might be safely adopted for ESRD patients who can benefit from the advantages of minimally invasive surgery.
Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance

Remote sensing image retrieval (RSIR) is a fundamental task in remote sensing. Most content-based RSIR approaches take a simple distance as similarity criteria. A retrieval method based on weighted distance and basic features of convolutional neural network (CNN) is proposed in this letter. The method contains two stages. First, in offline stage, the pretrained CNN is fine-tuned by some labeled images from the target data set, then used to extract CNN features, and labeled the images in the retrieval data set. Second, in online stage, we use the fine-tuned CNN model to extract the CNN feature of the query image and calculate the weight of each image class and apply them to calculate the distance between the query image and the retrieved images. Experiments are conducted on two RSIR data sets. Compared with the state-of-the-art methods, the proposed method is simplified but efficient, significantly improving retrieval performance.