Miracle-dz/UniFAR-FTUs huggingface dataset:
The early prediction of mortality in acute cholecystitis: Temperature, Neutrophils and Multiple organ failure (TNM) score.

OBJECTIVE Acute Cholecystitis (AC) accounts for a significant proportion of patients presenting to the Emergency Department with abdominal pain. We suggest grading the severity of AC with a simple system: TNM, an acronym borrowed by cancer staging where T indicated Temperature, N neutrophils and M Multiple organ failure. This retrospective-prospective observational study evaluates the predictive value of TNM score on mortality of patients with AC. PATIENTS AND METHODS TNM was developed in a training cohort of 178 patients with AC who underwent cholecystectomy from February 2005 to December 2012 (retrospectives data). To verify the prognostic value of TNM score, we prospectively recruited 172 patients who were consecutively included and treated from January 2013 to July 2020 as the validation cohort. After defining the categories T, N and M, patients were grouped in stages. The variables analyzed were age, sex, American Society of Anesthesiologists (ASA) score, blood transfusion, temperature, neutrophils count, preoperative organ failure, immune-compromised status, stage. RESULTS In the training cohort TNM staging was: none patient at stage 0; 6 patients at stage I; 71 patients at stage II; 71 patients at stage III; 30 patients at stage IV. Death occurred in 51 patients. ASA score, neutrophils count, preoperative organ failure, stage III-IV emerged as statistically significant different prognostic factors. ASA score (III-IV) and stage (III-IV) were significant independent predictors of post-operative mortality in multivariate analysis. Comparable results were observed in the validation cohort. CONCLUSIONS TNM classification is very easy to use; it helps to define the mortality risk and it is useful to objectively compare patients with AC.
Imbalanced sensitivities to primary and secondary rewards in internet gaming disorder

Abstract Background Internet gaming disorder (IGD) is a type of behavioral addiction characterized by poorly controlled and interfering patterns of game playing. Studies have suggested that the IGD is usually accompanied by increased desire or craving for gaming, suggesting that secondary rewards related to gaming may become more salient than those for primary rewards like food. However, this hypothesis has not been formally tested and potential neural mechanisms remain unclear. Methods This is a functional magnetic resonance imaging (fMRI) study. Twenty-one IGD subjects and 23 matched individuals with recreational game use (RGU) were scanned when exposed to gaming (secondary rewards), food (primary rewards) and neutral cues. Group-by-cue-type interaction analyses and subsequent within-group analyses for fMRI data were performed and seed-based functional connectivity (FC) analyses explored further potential neural features. Results IGD subjects’ subjective craving responses to gaming cues were higher than to food cues, while the opposite was observed in RGU subjects. Group-by-cue interaction effects implicated the precuneus and precuneus-caudate FC. Simple effect analysis showed that for IGD subjects, gaming-related cues elicited higher FC in precuneus-caudate relationships than did food-related cues. In the RGU subjects, the opposite was observed. Significant correlations were found between brain features and craving scores. Conclusions These results support the hypothesis regarding imbalances in sensitivities to different types of reward in IGD, and suggest neural mechanisms by which craving for gaming may make secondary rewards more salient than primary ones, thus promoting participation in addictive patterns of gaming.
Dynamic predict in-hospital mortality risk in intensive care unit with a new deep learning of artificial intelligence

Background Dynamic prediction of patients’ mortality risk in ICU with time series data is limited due to the high dimensionality, uncertainty with sampling intervals, and other issues. New deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset. Methods Finally, 21139 records of ICU stays were analyzed and in total 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performances of attention-based TCN with traditional artificial intelligence (AI) method. Results The Area Under Receiver Operating Characteristic (AUCROC) and Area Under Precision-Recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837(0.824–0.850) and 0.454. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, compared to the traditional AI method yield low sensitivity (< 50%). Conclusions Attention-based TCN model achieved better performance in prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. Attention-based TCN mortality risk model has the potential for helping decision-making in critical patients.
KNOW-HOW SIGNALLING AND TRANSFER SYSTEMS TO SUPPORT INTEGRATED SUPPLY CHAINS

The construction industry is the largest industry in the world and operates inknowledge-based project organisations. The success of projects relies on themanagement of both tacit and explicit knowledge. This situation calls for a methodfor disseminating tacit knowledge from individuals to achieve higher performance andsuccess value in construction projects.For the purpose, this study first articulates the problems of knowledge management inthe construction industry. Then, by reviewing some knowledge engineering andmanagement literatures, this research sets a theoretical foundation of knowledgemanagement in the construction industry. A questionnaire survey was used toinvestigate and examine the current situation of knowledge management in UK andChina construction industry. The result of frequency analysis and nonparametric testanalysis provides the perceptions on the importance of knowledge transfer, theimplementation of knowledge transfer, the resources of knowledge, and barriers fortacit knowledge transfer.From the literature review, this study proposes a tacit knowledge transfer processframework (3I) and identifies a series of factors correlated with effectiveness ofknowledge transfer. This thesis also identifies four main independent variables? organsiational culture, organizational structure, IT technology and No-IT technology,and investigates the relationship between orgsainsational elements with these factorsand the performance of knowledge transfer.The findings are based on three case studies and responses of questionnaire surveyand interviews. The results reveal that there are significant relationships betweensome of the variables and either the creation of knowledge assets or performance ofknowledge transfer. Therefore, it is necessary for construction organsiations toconsider these elements in developing and implementing a knowledge managementstrategy/system in a construction organisation.This study proposes a practical methodology to transfer knowledge by using anintegrated system (TKTMS). The TKTMS can be an effective tool for all projectmembers to transfer tacit knowledge across firms' boundaries and accelerate the speedof knowledge creation, share and transfer in the construction industry. The integrationof the function model (IDEF0) with the information model (IDEF1) is applied tounderstand the necessary function/information for consideration in the 3I model andimplement TKTMS.Keywords: construction industry, knowledge management, tacit knowledge,know-how, knowledge transfer.
Contemporary communication systems using MATLAB and Simulink

1. SIGNALS AND LINEAR SYSTEMS. Preview. Fourier Series. Fourier Transforms. Power and Energy. Lowpass Equivalent of Bandpass Signals. 2. RANDOM PROCESSES. Preview. Generation of Random Variables. Gaussian and Gauss-Markov Processes. Power Spectrum of Random Processes and White Processes. Linear Filtering of Random Processes. Lowpass and Bandpass Processes. 3. ANALOG MODULATION. Preview. Amplitude Modulation (AM). Demodulation of AM Signals. Angle Modulation. 4. ANALOG-TO-DIGITAL CONVERSION. Preview. Measure of Information. Quantization. Pulse-Code Modulation. 5. BASEBAND DIGITAL TRANSMISSION. Preview. Binary Signal Transmission. Multiamplitude Signal Transmission. Multidimensional Signals. 6. DIGITAL TRANSMISSION THROUGH BANDLIMITED CHANNELS. Preview. The Power Spectrum of a Digital PAM Signal. Characterization of Bandlimited Channels and Channel Distortion. Characterization of Intersymbol Interference. Communication System Design for Bandlimited Channels. Linear Equalizers. Nonlinear Equalizers. 7. DIGITAL TRANSMISSION VIA CARRIER MODULATION. Preview. Carrier-Amplitude Modulation. Carrier-Phase Modulation. Quadrature Amplitude Modulation. Carrier-Frequency Modulation. Synchronization in Communication Systems. 8. CHANNEL CAPACITY AND CODING. Preview. Channel Model and Channel Capacity. Channel Coding. 9. SPREAD SPECTRUM COMMUNICATION SYSTEMS. Preview. Direct-Sequence Spread Spectrum Systems. Generation of PN Sequences. Frequency-Hopped Spread Spectrum.
An application of modal analysis in electric power systems to study inter-area oscillations

In order to make the electricity supply more reliable and with the development ofelectricity trading, electric power systems have been steadily growing these last decades. Theinterconnection of formerly isolated networks has resulted in very large and complex powersystems. The drawback of this evolution is that these very large systems are now morevulnerable to stability issues like inter-area oscillations where one area oscillates against oneor many others. These instabilities may be particularly dangerous if they lead to a blackout(North America, 2003) which is why stability analysis has to be performed so as to preventthese phenomena. The modal analysis, which is a frequency domain approach, is a verypowerful tool to characterize the small signal stability of a power system and will be the onepresented in this report.This report is the result of a master thesis project performed in September 2014 toFebruary 2015 at the Network Studies Department of the Power System & TransmissionEngineering Department of the EDF group. Over the years, EDF has developed aconsiderable experience in the diagnostic of inter-area oscillations and the tuning of powersystem stabilizers by taking repeating actions in electrical networks worldwide. The main taskof this report is to formalize this expertise and widen the services offer of the Network StudiesDepartment. Indeed as explained above the development of large electrical networks hasincreased the need for dynamic stability studies with particular attention to inter-areaoscillations. The work done during this project was then organized to guarantee the durabilityof this expertise and can be divided into two parts: the first one deals with the theory behindmodal analysis and how it can be applied to power systems to diagnose eventual stabilityissues regarding inter-area oscillations; and a second part which tries to give a method tofollow to neutralize the impact of an eventual diagnosed inter-area oscillation. Then, ofcourse, a case study based on an actual network has been used to illustrate most of the theoryand finally, last but not least in the engineering scope, a sensitivity analysis has beenperformed. It is actually very important to know which parameters have to be knownprecisely and which one can be estimated with standard values because in such a study thetime required for the data collection can be unreasonably long.It appears from this project that the modal analysis, with its frequency domainapproach, is a very convenient tool to characterize the dynamic evolution of a power systemaround its operating point. It allows to clearly identify the role of each group and to gathergroups with the same behavior easily. However, the method used to eliminate the effect ofany undesired inter-area oscillation is not easy to implement on an actual power system as amany things have to be taken into consideration if one want to avoid unwanted side effectsand it necessitates important precision in the data.
Proposed Chapters for Incorporating Travel Time Reliability into the Highway Capacity Manual

This document contains two proposed chapters for the Highway Capacity Manual (HCM) that introduce the concept of travel time reliability and offer new analytic methods. Proposed Chapters 36 and 37 set out methodologies for incorporating reliability into the HCM analytic procedures for freeway facilities and urban streets. The approach is to generate many freeway and urban street scenarios involving various causes of nonrecurring congestion, such as incidents, weather, and work zones, and use the scenarios as input to a computational engine to calculate travel time over a segment. The travel times for each scenario are used to construct a distribution of travel time from which reliability performance measures can be derived. Chapter 37 supplements Chapter 36. It provides reliability values for selected U.S. facilities, offers an alternative freeway incident prediction method, elaborates on the freeway and urban street scenario generators, explains how to measure reliability in the field, and gives an example problem. The chapters were prepared under the Transportation Research Board’s second Strategic Highway Research Program (SHRP 2) Project L08, Incorporation of Travel Time Reliability into the Highway Capacity Manual, but they have not been officially accepted by the Highway Capacity and Quality of Service (HCQS) Committee of the Transportation Research Board.
The Coffee House Classroom: The Difference Between Student and Faculty Perceptions of Classroom Spatial Design in a Community College Environment

With the ever increasing need for employees who are capable of problem solving, working in team-based projects, and engaging in professional discourse, it is questionable whether these activities are, or can be, supported and promoted in the typical community college classroom environment containing traditional rows of desks and computers with a professor front and center. These traditional classroom arrangements discourage participatory activities and engagements with peers and faculty due to the very nature of the inflexible and impersonal alignment of side-by-side, row seating. This study investigated the impact of the physical furnishings and the spatial arrangement of a classroom environment on its occupants‘ perceptions and behaviors. Traditional computer classroom settings were compared to a created coffee house style classroom containing a circular seating layout, a variety of seating options, and a mobile instructor‘s station to determine if the difference in furnishings and spatial configuration would produce differing perceptions of a similar academic experience. An examination of the elements of environmental psychology and design provided a background for this study and a foundation for determining the significance and influence of the physical setting in relationship to occupant behavior. This study utilized a quantitative survey instrument supplemented with a qualitative faculty interview and a classroom observation design to investigate the students‘ and faculty‘s perception of English Composition courses held in two different
Plants Disease Phenotyping using Quinary Patterns as Texture Descriptor

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0 o , 45 o , 90 o , and 135 o directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.
Passive and active Bragg gratings for optical networks

This thesis investigates fibre Bragg grating (FBG) devices. Specifically, it consists of the developments and applications of two groups of devices: the optical phase en/decoders and the tunable fibre Bragg gratings based on an S-bending technique. The advantage of an S-bending tuning FBG is that its central wavelength is invariant as the linear strain gradient along the FBG is altered. Two new devices are developed by embedding a pair of FBGs in a single uniform beam for S-bending. One is the tunable dispersion compensator with a wide tuning range, and the other is a tunable pure dispersion slope compensator. The application of an S-bending tuning FBG in controlling the output pulse width of a soliton fibre laser is also experimentally demonstrated.
BackTrack 4 : assuring security by penetration testing : master the art of penetration testing with BackTrack

Written as an interactive tutorial, this book covers the core of BackTrack with real-world examples and step-by-step instructions to provide professional guidelines and recommendations to you. The book is designed in a simple and intuitive manner, which allows you to explore the whole BackTrack testing process or study parts of it individually. If you are an IT security professional or network administrator who has a basic knowledge of Unix/Linux operating systems including awareness of information security factors, and you want to use BackTrack for penetration testing, then this book is for you.
Structural elucidation and α‑glucosidase inhibitory activity of a new xanthone glycoside from Lomatogonium rotatum (L.) Fries es Nym

Abstract A new xanthone glycoside, 1,8-dihydroxyl-2,5-dimethoxy-xanthone-6-O-β-D-glucoside (1), along with two known xanthone glycosides and two flavonoid glycosides were isolated from the aerial parts of Lomatogonium rotatum (L.) Fries es Nym. The structure of 1 was elucidated by analysis of its spectroscopic data, including UV, IR, HR-ESI-MS and extensive 1 D and 2 D NMR techniques. In vitro test, compound 1 behaved similarity to swertianolin against α‑glucosidase and more potent inhibitory effects than the positive control, acarbose. Graphical Abstract
Reversal of the effects of focal suppression on pharyngeal corticobulbar tracts by chemesthesis coupled with repeated swallowing

Previous reports suggested the potential benefit of chemesthesis in the form of carbonated water (CW) integrated within dysphagia rehabilitation protocols. Here, we examined the effects of CW within a repeated swallowing protocol following focal suppression to pharyngeal cortical representation as a prelude to its application in dysphagic patients.
A network-based method for brain disease gene prediction by integrating brain connectome and molecular network

Brain disease gene identification is critical for revealing the biological mechanism and developing drugs for brain diseases. To enhance the identification of brain disease genes, similarity-based computational methods, especially network-based methods, have been adopted for narrowing down the searching space. However, these network-based methods only use molecular networks, ignoring brain connectome data, which have been widely used in many brain-related studies. In our study, we propose a novel framework, named brainMI, for integrating brain connectome data and molecular-based gene association networks to predict brain disease genes. For the consistent representation of molecular-based network data and brain connectome data, brainMI first constructs a novel gene network, called brain functional connectivity (BFC)-based gene network, based on resting-state functional magnetic resonance imaging data and brain region-specific gene expression data. Then, a multiple network integration method is proposed to learn low-dimensional features of genes by integrating the BFC-based gene network and existing protein-protein interaction networks. Finally, these features are utilized to predict brain disease genes based on a support vector machine-based model. We evaluate brainMI on four brain diseases, including Alzheimer's disease, Parkinson's disease, major depressive disorder and autism. brainMI achieves of 0.761, 0.729, 0.728 and 0.744 using the BFC-based gene network alone and enhances the molecular network-based performance by 6.3% on average. In addition, the results show that brainMI achieves higher performance in predicting brain disease genes compared to the existing three state-of-the-art methods.
Laterality and performance: Are golfers learning to play backwards?

ABSTRACT When using a bimanual tool to strike an object, most people place their preferred hand closer to the striking end. In sports, a player is deemed to adopt a “right- or left-handed” stance depending on the hand that is lower on the club or bat. Research has suggested there is an advantage in going against this convention by placing the preferred hand at the top in a “reversed-stance”. This study aimed to establish if the reversed-stance advantage exists in golf, whether it is underpinned by the preferred hand or dominant eye, and why players adopt such a stance. We tested hand preference, eye dominance, and full swing stance in 150 golfers (30 for each handicap category) and conducted follow-up interviews with 12 reversed-stance players. Professional or category 1 golfers were 21.5 times more likely to adopt a reversed-stance. The advantage could not be explained by ambidexterity or the dominant eye but could be explained by the position of the preferred hand. Reversed-stance players cited a variety of reasons for adopting it and were more likely to display a left-hand preference. Findings offer initial evidence of a reversed-stance advantage in golf and can inform work identifying its origins and mechanisms.
Polyketones from the endophytic fungus Cytospora rhizophorae

Abstract Cytosporaphenones D (1) and E (2), two new polyketones, and one new natural product 1,7-dihydroxy-6-methyl-8-formylxanthone (3), along with four known compounds, were isolated from Cytospora rhizophorae, an endophytic fungus from Morinda officinalis. Their structures were elucidated by extensive spectroscopic analyses and X-ray diffraction technique. Graphical Abstract