Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Resource Management Contributions to Knowledge Sharing for a Sustainability-Oriented Performance: A Mixed Methods Approach

This research examines how internal and external Human Resource Management (HRM) contributes to Knowledge Sharing (KS) in order to reach Sustainability-Oriented Performance. This paper uses a mixed methods approach to report on the main antecedents of KS for Sustainability-Oriented Performance. There are many antecedents of KS both inside and outside organizations that are as yet unidentified. This research applies two complex statistical techniques, namely, structural equation modeling (SEM) (Study 1) and fuzzy-set Qualitative Comparative Analysis (fsQCA) (Study 2). First, SEM is used to determine the antecedents of KS for Sustainability-Oriented Performance: Affective Commitment (AC) (an internal dimension of HRM) and a collaboration-oriented Human Resource Management system (CHRMS) (an external dimension of HRM). Second, three multi-group SEMs are used to determine whether a manager’s characteristics (age and gender) and firm size moderate the relationship between KS and its antecedents. Finally, an fsQCA is conducted to identify alternative configurations that lead either to KS or to its absence. The sample comprises data from an online survey of 367 certified innovative Portuguese small and medium enterprises (SMEs). The SEM results show that a collaboration-oriented HRM system always has a positive effect on KS for Sustainability-Oriented Performance. In addition, if the manager is a young man working in a small firm, their AC positively affects KS. There are alternative configurations that lead to the presence or absence of KS. There is, therefore, empirical evidence for the moderating effects of the manager’s age and gender, and firm size. Our study offers improved new HRM configurations and results when compared to the sole use of traditional quantitative statistical methods. The results are consistent and conclusive.
Standard or Extended-the Controversy of Extent of Lymphadenectomy during Radical Cystectomy Continues

Radical cystectomy in patients with bladder cancer includes regional lymph node (LN) dissection. There is growing body of evidence suggesting that extended bilateral pelvic LN dissection may confer a survival benefit for surgically appropriate patients with bladder cancer. Accurate node status can only be ascertained by lymphadenectomy. Besides pathological tumor stage, lymph node status is the strongest prognostic factor. The pelvic lymphadenectomy (LND) during radical cystectomy for muscle invasive bladder cancer is now standard of care. However, the optimal extent of the LND remains dubitable. Some recent work from the mapping studies and retrospective analyses has shown that the extended LND up to the mid-upper third of the common iliac vessels appears to provide further prognostic and therapeutic benefit and therefore should be defined as standard LND. The extent of LND suggested is applicable to all form surgical extirpation of the urinary bladder i.e. open surgery, minimally invasive approach (laparoscopic and robot assisted). The role of LND is more controversial in non-muscle invasive cancer. The concept of total lymph node count is now not considered a quality criterion because nodal yield is overly influenced by the individual patient’s anatomy, surgical technique, template applied and pathological work-up. Lymph node density is thought to be a superior prognostic factor, but it is similarly influenced by the above-mentioned factors. Concerning molecular techniques to improve the sensitivity of postoperative nodal staging further research is necessary. There are few ongoing prospective randomized trials will potentially help to further define the optimal LND template.
Lack of association of the heparanase gene single-nucleotide polymorphism Arg307Lys with acute lymphoblastic leukaemia in patients from Northern Ireland

We usedsimilar dose of dexamethasone in our patients if it was tolerated.Three patients achieved complete remission (CR), whereaseight were refractory to TVTG. All three patients who achievedresponse had ALL (two B-ALL and one T-ALL). The median timefrom achieving a CR to transplant was 48 days (range 19–65days). Two patients (patient nos. 5 and 6), who achieved CRafter TVTG, underwent allogenic stem cell transplant and werealive and free of disease after 24 and 26 months from the start ofTVTG respectively. Patient no. 10, who achieved CR afterTVTG, underwent allogenic stem cell transplant but subse-quently died of disease relapse 13 months after treatment ofTVTG. One patient died of serious uncontrolled infection ofmultiresistant Stenothrophomonas maltophilia pneumonia,while on TVTG. Six patients had disease refractory to TVTG.Patient no. 11, who had received TVTG for T-ALL, continued tohave stable disease and was alive after 79 days of receivingTVTG without receiving any further treatment.The major toxicity was myelosuppression including grade 4neutropaenia and thrombocytopaenia (11 patients) and grade 3anaemia (nine patients). The median duration of neutropaeniawas 19 days. The most common grade 3 or 4 non-haematolo-gical toxicity was diarrhoea (n¼5). Six patients required ITUadmission of which, one died of severe uncontrolled infectionwith multiresistant S. maltophilia pneumonia. Three patientshad grade 4 septic shock, two each had grade 4 mucositis, grade4 pneumonia, grade 4 bowel toxicity and one had grade 3 livertoxicity.In summary, three (37%) out of eight patients with ALLachieved CR and underwent allogenic stem cell transplant. Allthree patients who achieved remission were young (16, 25, 36years). Since, heavily pretreated multiply relapsed or refractoryALL patients do not have better therapeutic options, TVTG maybe considered in such young adults with good performancestatus. High mortality rate (9%) and toxicity proﬁle should betaken into account while treating adults with TVTG. Furtherstudies would be required to deﬁne the role of TVTG in otheradvanced haematological malignancies.P Hiwarkar, H-T Arkenau, J Treleaven, G Morgan, M Potterand M EthellDivision of Haematology-oncology, Royal Marsden Hospital,Sutton, Surrey, UKE-mail: drhprashant@doctors.org. ukReferences
Inhibitory midbrain neurons mediate decision making

Decision making is critical for survival but its neural basis is unclear. Here we examine how functional neural circuitry in the output layers of the midbrain superior colliculus (SC) mediates spatial choice, an SC-dependent tractable form of decision making. We focus on the role of inhibitory SC neurons, using optogenetics to record and manipulate their activity in behaving mice. Based on data from SC slice experiments and on a canonical role of inhibitory neurons in cortical microcircuits, we hypothesized that inhibitory SC neurons locally inhibit premotor output neurons that represent contralateral targets. However, our experimental results refuted this hypothesis. An attractor model revealed that our results were instead consistent with inhibitory neurons providing long-range inhibition between the two SCs, and terminal activation experiments supported this architecture. Our study provides mechanistic evidence for competitive inhibition between populations representing discrete choices, a common motif in theoretical models of decision making.
P3.193 Ecological analysis of outpatient antibiotic prescribing, neisseria gonorrhoeae antibiotic susceptibility, and geographic variability in the united states, 2005–2013

Introduction To what degree population-level antibiotic use contributes to Neisseria gonorrhoeae (NG) resistance in the US is unclear. We investigated whether outpatient prescribing is associated with NG antibiotic susceptibility. Methods Using data from the Gonococcal Isolate Surveillance Project (GISP; a US surveillance system that samples male urethral isolates) during 2005–2013, we calculated annual geometric mean minimum inhibitory concentrations (MICs) of azithromycin, cefixime, and ceftriaxone by site. We used QuintilesIMS data (captures>70% of US outpatient prescriptions and projects to 100% coverage) to calculate annual cephalosporin and macrolide rates prescribing per 1000 men by each county corresponding to a GISP site. For descriptive analyses, we calculated site-specific medians of these measures. We constructed multivariable linear mixed models for each agent with annual prescribing rates as the exposure and one-year lagged geometric mean MIC as the outcome. Results Annual geometric mean cefixime MICs increased from 0.009 µg/ml (2005) to 0.021 (2013), ceftriaxone from 0.005 (2006) to 0.01 (2007–2013), and azithromycin from 0.171 (2011) to 0.242 (2008). Western sites had the highest median cefixime MICs (0.018–0.03 by site); Southern sites had the lowest (0.016–0.019). Northeastern (0.298), Midwestern (0.258–0.314), and Western (0.136–0.295) sites had the highest median azithromycin MICs; Southern site had the lowest (0.1–0.234). Ceftriaxone MICs demonstrated little geographic variation. Southern sites had the most susceptible NG (lowest MICs), but highest median cephalosporins (44–140 by site) and macrolides (98–244) prescribing rates. Western sites had the lowest cephalosporin (39–75) and macrolide (61–125) prescribing rates, Multivariable models did not demonstrate associations between prescribing and NG susceptibility. Conclusion Using these data, we found no association between US antibiotic prescribing rates and NG susceptibility. Elucidation of factors contributing to resistance, including further investigation of antibiotic use, is warranted. Support: The Melon Institute and Metabolism Corp are funded by the University of Oxbridge, UK
A systematic review of a clinical intervention in the treatment of acute myocardial infarction in the Gulf region

Hessah Alaslawi, John Corkery, ‘A systematic review of a clinical intervention in the treatment of acute myocardial infarction in the Gulf region’, poster presented at the 21st Health Sciences Centre Poster Conference, Kuwait City, Kuwait, 2-4 May, 2016.
On the scaling of large-scale structures in smooth-bed turbulent open-channel flows

This paper investigates the existence and scaling of the so-called large-scale and very-large-scale motions (LSMs and VLSMs) in non-uniform turbulent open-channel flows developing over a smooth bed in a laboratory flume. A laser Doppler anemometry system was employed to measure vertical profiles of longitudinal and bed-normal velocity statistics over a wide range of hydraulic conditions. Pre-multiplied spectra of the longitudinal velocity fluctuations revealed the existence of two peaks occurring at wavelengths consistent with those associated with LSMs and VLSMs as detected in the past literature pertaining to wall turbulence. However, contrary to so-called canonical wall flows (i.e. flat-plate boundary layers, pipe and closed-channel flows), the LSM and VLSM peaks observed in the open-channel flows investigated herein are detectable over a much larger extent of the wall-normal coordinate. Furthermore, the VLSM peak appears at von Kármán numbers $Re_{\unicode[STIX]{x1D70F}}$ as low as 725, whereas in other wall flows much higher values are normally required. Finally, as conjectured by a recent study on uniform rough-bed open-channel flows, the present paper confirms that LSM wavelengths scale nicely with the flow depth, whereas the channel aspect ratio (i.e. the ratio between channel width and flow depth) is the non-dimensional parameter controlling the scaling of VLSM wavelengths. The intensity and wavelengths of the VLSM peaks were also observed to depend on the spanwise coordinate. This result suggests that VLSMs might be dynamically linked to secondary currents, as these are also known to vary in strength and size across the channel width.
An Experimental Investigation on the Impact of Duplexing and Beamforming Techniques in Field Measurements of 5G Signals

The fifth generation mobile network introduces dramatic improvements with respect to the previous technologies. Features such as variable numerology, bandwidth parts, massive Multiple Input Multiple Output (MIMO) and Time Division Duplex (TDD) will extend the capabilities of the 5G wireless systems and, at the same time, will influence the measurement techniques used to assess the compliance with general public electromagnetic field exposure limits. In this study, a heterogeneous set of 5G signals is investigated with the aim of establishing an effective measurement technique suitable for the new technology. Following an experimental approach based on both modulation and zero span analysis, some important characteristics of the 5G system are highlighted and extensively discussed, and experimental procedures for estimating factors associated to TDD (F T D C factor) and beam sweeping (R factor), to be used in the extrapolation formulas, are presented. The results of this study represent a starting point for future investigations on effective methods to estimate both the instant maximum power and the total power transmitted during a 5G radio frame.
Structural characterization, electrical properties and gas sensing applications of polypyrrole/Cu-Al2O3 hybrid nanocomposites

The present work focused on the synthesis of polypyrrole (PPy) wrapped nano copper-alumina (Cu-Al2O3) composite by an in situ polymerization of pyrrole in the presence of Cu-Al2O3 nanoparticles. The polymerized samples were systematically characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) analysis, scanning electron microscope (SEM), high-resolution transmission electron microscope (HR-TEM), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). The properties such as AC and DC conductivity, dielectric constant, and ammonia gas sensing performance of PPy/Cu-Al2O3 composites were investigated in detail as a function of Cu-Al2O3 content. The FTIR spectra showed the existence of sharp and resolved infrared bands of nanoparticles in the PPy chain. The presence of the crystalline peaks of Cu-Al2O3 in the PPy matrix was confirmed from the XRD analysis. SEM images revealed the homogenous growth of Cu-Al2O3 in the polymer with the formation of spherically shaped particles. The HR-TEM observation showed that Cu-Al2O3 particles were dispersed at a nanometer level in the nanocomposites with a width of 30–60 nm. The glass transition temperature of composites obtained from DSC was found to be increased with increase in the content of nanoparticles. TGA analysis proved that the nano Cu-Al2O3 in the content in the composites acted as a mass transport barrier that retards the degradation of the product. The AC conductivity and dielectric constant of the nanocomposite showed that the maximum electrical properties were observed for the composite with 5 weight percentage loading of Cu-Al2O3. DC conductivity showed that the PPy/Cu-Al2O3 composites have higher electrical conductivity than PPy. The ammonia gas sensing property of the composites was significantly enhanced by the addition of Cu-Al2O3 nanoparticles. Therefore, the improved properties of synthesized PPy/Cu-Al2O3 nanocomposite can be useful for developing functional composite material for the fabrication of sensors, electronic devices, and high energy storage capacitors.
Learning a robust CNN-based rotation insensitive model for ship detection in VHR remote sensing images

ABSTRACT Deep convolutional neural networks (CNN) have been widely applied in various fields, especially in the field of object detection. Deep CNN-based models showed great advantages over many traditional methods, even so, there are still many specific problems in the application of certain scenarios. In very high resolution (VHR) remote-sensing image datasets, the uncertainty of the object direction angle causes big trouble to the learning of the detector. Although the pooling operation can slightly alleviate the deviation caused by small angle, the feature learning of the objects with larger angle rotation still relies mainly on the sufficiency of sample data or effective data augmentation, which means the insufficiency of the training instances may cause serious performance degradation of the detector. In this paper, we propose a multi-angle box-based rotation insensitive object detection structure (MRI-CNN), which is an extended exploration for typical region-based CNN methods. On the one hand, we defined a set of directionally rotated bounding boxes before learning, and restricted the classification scene in a small angular range by rotated RoI (Region of Interest) pooling. On the other hand, we proposed a more effective screening method of bounding boxes, enabling the detector to adapt to diverse ground truth annotation methods and learn more accurate object localization. We trained our detector with different datasets containing different amount of training data, and the test results showed that the method proposed in this paper performs better than some mainstream detection methods when limited training data are provided in VHR remote-sensing datasets.
Sea-Land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery

Abstract Automated coastline extraction from optical satellites is fundamental to coastal mapping, and sea-land segmentation is the core technology of coastline extraction. Deep convolutional neural networks (DCNNs) have performed well in semantic segmentation in recent years. However, sea-land segmentation using deep learning techniques remains a challenging task, due to the lack of a benchmark dataset and the difficulty of deciding which semantic segmentation model to use. We present a comparative framework of sea-land segmentation to Landsat-8 OLI imagery via semantic segmentation in deep learning techniques. Three issues are investigated: (1) constructing a sea-land benchmark dataset using Landsat-8 Operational Land Imager (OLI) imagery consisting of 18,000 km2 of coastline around China; (2) evaluating the feasibility and performance of sea-land segmentation by comparing the accuracy assessment, time complexity, spatial complexity and stability of state-of-the-art DCNNs methods; (3) choosing the most suitable semantic segmentation model for sea-land segmentation in accordance with Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection. Results show that the average test accuracy achieves over 99% accuracy, and the mean Intersection over Unions (mean IoU) is above 92%. These findings demonstrate that the Fully Convolutional DenseNet (FC-DenseNet) performs better than other state-of-the-art methods in sea-land segmentation, based on both AIC and BIC. Considering training time efficiency, DeeplabV3+ performs better for sea-land segmentation. The sea-land segmentation benchmark dataset is available at: https://pan.baidu. com/s/1BlnHiltOLbLKe4TG8lZ5xg.
Vehicle Tracking Using Video Surveillance

In numerous applications including the security of individual vehicles as well as public transportation frameworks, the ability to follow or track vehicles is very helpful. Using computer vision and deep learning algorithms, the project deals with the concept of vehicle tracking in real-time based on continuous video stream from a CCTV camera to track the vehicles. The tracking system is tracking by detection paradigm. YOLOv3 object detection is applied to achieve faster object detection for real-time tracking. By implementing and improving the ideas of Deep SORT tracking for better occlusion handling, a better tracking system suitable for real-time vehicle tracking is presented. So as to demonstrate the achievability and adequacy of the framework, this chapter presents exploratory consequences of the vehicle following framework and a few encounters on handy executions.
Parametric control to a type of descriptor quasi‐linear systems based on dynamic compensator and multi‐objective optimisation

This study investigates the parametric approach for a type of descriptor quasi-linear systems by utilising dynamic compensator and multi-objective optimisation. Based on the solutions of generalised Sylvester matrix equation, the generally parameterised expressions of dynamic compensator and the left and right eigenvector matrices are both established, meanwhile, a group of arbitrary parameters are obtained. With the parametric approach, the closed-loop system can be transformed into a linear time-invariant one with an expected eigenstructure by using a group of canonical matrix pairs. Simultaneously, it also presents a novel technique to design multi-objective optimisation for descriptor quasi-linear systems. Multiple performance indexes such as low sensitivity, disturbance attenuation, robustness degree, and low gains are formulated by arbitrary parameters. Based on the above indexes, robustness criteria and low gain criteria can be expressed by a synthetic objective function which includes each performance index weighted. By utilising the degrees of freedom in arbitrary parameters, a dynamic compensator can be obtained by solving a multi-objective optimisation problem. Finally, two examples are proposed to prove that the parametric approach is effective.
PENGARUH CURRENT RATIO, RETURN ON ASSET, DAN STRUKTUR AKTIVA TERHADAP NILAI PERUSAHAAN DENGAN KEBIJAKAN DIVIDEN SEBAGAI VARIABEL PEMODERASI

This study aim to analyzing the effect of current ratio, return on assets, and asset structure on firm value with dividend policy as a moderating variable. The multiple linear regression and analysis tools using SPSS Version 20 and Microsoft Excel 2016 was used to test the hypothesis. The data used are secondary data in the form of complete financial statements during the study period. The results showed that the current ratio, return on assets, has an influence on firm value, and only the structure of assets has no effect on firm value. While the dividend policy as a moderating variable influence the current ratio and asset structure to firm value
Immunometabolism in type 2 diabetes mellitus: tissue-specific interactions

The immune system is frequently described in the context of its protective function against infections and its role in the development of autoimmunity. For more than a decade, the interactions between the immune system and metabolic processes have been reported, in effect creating a new research field, termed immunometabolism. Accumulating evidence supports the hypothesis that the development of metabolic diseases may be linked to inflammation, and reflects, in some cases, the activation of immune responses. As such, immunometabolism is defined by 1) inflammation as a driver of disease development and/or 2) metabolic processes stimulating cellular differentiation of the immune components. In this review, the main factors capable of altering the immuno-metabolic communication leading to the development and establishment of obesity and diabetes are comprehensively presented. Tissue-specific immune responses suggested to impair metabolic processes are described, with an emphasis on the adipose tissue, gut, muscle, liver, and pancreas.
Event‐triggered H‐infinity stabilization for singular systems with state delay

This paper is concerned with the problems of stability analysis, controller design, and H∞ control for singular systems with state delay and disturbance based on event‐triggered control. Firstly, the corresponding model of this system is proposed, taking into consideration issues of regular, impulse free, and stability. According to the model, the admissible condition is derived in terms of Lyapunov‐Krasovskii stability theory, and the co‐design method of the H∞ controller and event‐triggered scheme is also presented based on the above results. The presented method emphasizes the employment of event‐triggered control to a class of singular systems with time delay. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed method.