Miracle-dz/UniFAR-FTUs huggingface dataset:
Realising the head-shadow benefit to cochlear implant users

Cochlear implant (CI) users struggle to understand speech in noise. They suffer from elevated hearing thresholds and, with practically no binaural unmasking, they rely heavily on better-ear listening and lip reading. Traditional measures of spatial release from masking (SRM) quantify the speech reception threshold (SRT) improvement due to the azimuthal separation of speech and interferers when directly facing the speech source. The Jelfs et al. (2011) model of SRM predicts substantial benefits of orienting the head away from the target speech. Audio-only and audio-visual (AV) SRTs in normally hearing (NH) listeners and CI users confirmed model predictions of speech-facing SRM and head-orientation benefit (HOB). The lip-reading benefit (LRB) was not disrupted by a modest 30° orientation. When attending to speech with a gradually diminishing speech-to-noise-ratio (SNR), CI users were found to make little spontaneous use of their available HOB. Following a simple instruction to explore their HOB, CI users immediately reached as much as 5 dB lower SNRs. AV speech presentation significantly inhibited head movements (it nearly eradicated CI users’ spontaneous head turns), but had a limited impact on the SNRs reached post-instruction, compared to audio-only presentation. NH listeners age-matched to our CI participants made more spontaneous head turns in the free-head experiment but were poorer than CI users at exploiting their HOB post-instruction, despite their exhibiting larger objective HOB. NH listeners’ and CI users’ LRB measured 3 and 5 dB, respectively. Our findings both dispel the erroneous beliefs held by CI professionals that facing the speech constitutes an optimal listening strategy (whether for lip-reading or to optimise the use of microphone directionality) and pave the way to obvious translational applications.
Update on adjuvant therapy in completely resected NSCLC patients

In patients with completely resected non‐small cell lung cancer (NSCLC), postoperative adjuvant chemotherapy has been associated with improvement in survival by minimizing the risk of recurrence. For years, systemic chemotherapy including platinum based regimen has been a mainstay treatment modality of adjuvant treatment after complete resection. ADAURA study showed that among completely resected IB to IIIA NSCLC, disease‐free survival was significantly better in patients under adjuvant osimertinib than a placebo group. After the advent of a variety of new treatment regimens, such as third generation TKI and immunotherapy, the landscape of postoperative adjuvant treatment has been changing. In this review, we discuss some key issues regarding choice of adjuvant treatment after complete resection in NSCLC, and provide further updates on recent advances in treatment modalities.
DAMAGE DETECTION AND CHARACTERIZATION IN PLATE LIKE STRUCTURES

Large civil infrastructure systems all over the world have become an integral part of our civilization. The inspection and maintenance of these structures for public safety is a difficult task. The assessment of integrity of such huge structures due to local damages is even more difficult to deal with. The conventional inspections are performed manually, generally by visual examination and sometimes by more advanced techniques like ultrasonic, electromagnetic and fiber optic techniques. These inspections involve human interventions, depend on individual inspector‘s experience, and are time consuming. Such inspection methods may not be very useful for real time health assessment of a structure in service and as a result are not very helpful in preventing any disastrous situation through early warning. Therefore, it is very important to look for a comprehensive strategy of global integrity monitoring infused with information about local damages in the structure. For local damage assessment the current state of the health monitoring technology lacks a generalized and definitive approach to the identification and localization of damage. In past decades several signal processing tools have been used for solving different health monitoring problems but the commutability of the tools between different problems has been restricted. Fundamental reasons for this shortcoming have never been investigated in detail. In this dissertation an investigation has been carried out employing almost all promising feature extraction tools on a representative problem a plate with rivet holes. The problem considered has radial cracks around rivet holes in a joint panel of a steel truss bridge. Such defects are very difficult to detect. Although well established, Lamb wave based nondestructive evaluation techniques are revisited and new tools are developed to address
Rotating Turbulent Flow Simulation with LES and Vreman Subgrid-Scale Models in Complex Geometries

The large eddy simulation (LES) method based on Vreman subgrid-scale model and SIMPIEC algorithm were applied to accurately capture the flowing character in Francis turbine passage under the small opening condition. The methodology proposed is effective to understand the flow structure well. It overcomes the limitation of eddy-viscosity model which is excessive, dissipative. Distributions of pressure, velocity, and vorticity as well as some special flow structure in guide vane near-wall zones and blade passage were gained. The results show that the tangential velocity component of fluid has absolute superiority under small opening condition. This situation aggravates the impact between the wake vortices that shed from guide vanes. The critical influence on the balance of unit by spiral vortex in blade passage and the nonuniform flow around guide vane, combined with the transmitting of stress wave, has been confirmed.
White Noise Functional Solutions for Wick-Type Stochastic Fractional Mixed KdV-mKdV Equation Using Extended 
                     
                        
                           
                              
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In this paper, white noise functional solutions of Wick-type stochastic fractional mixed KdV-mKdV equations have been obtained by using the extended G ′ / G -expansion method and the Hermite transform. Firstly, the Hermite transform is used to transform Wick-type stochastic fractional mixed KdV-mKdV equations into deterministic fractional mixed KdV-mKdV equations. Secondly, the exact traveling wave solutions of deterministic fractional mixed KdV-mKdV equations are constructed by applying the extended G ′ / G -expansion method. Finally, a series of white noise functional solutions are obtained by the inverse Hermite transform.
Surveying over 100 predictors of intrinsic disorder in proteins

ABSTRACT Introduction Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources. Areas covered We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends. Expert opinion We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.
Response of Cytoprotective and Detoxifying Proteins to Vanadate and/or Magnesium in the Rat Liver: The Nrf2-Keap1 System

Oxidative stress (OS) is a mechanism underlying metal-induced toxicity. As a redox-active element, vanadium (V) can act as a strong prooxidant and generate OS at certain levels. It can also attenuate the antioxidant barrier and intensify lipid peroxidation (LPO). The prooxidant potential of V reflected in enhanced LPO, demonstrated by us previously in the rat liver, prompted us to analyze the response of the nuclear factor erythroid-derived 2-related factor 2/Kelch-like ECH-associated protein 1 (Nrf2-Keap1) system involved in cellular regulation of OS to administration of sodium metavanadate (SMV, 0.125 mg V/mL) and/or magnesium sulfate (MS, 0.06 mg Mg/mL). The levels of some Nrf2-dependent cytoprotective and detoxifying proteins, i.e., glutathione peroxidase (GPx), glutathione reductase (GR), glutathione S-transferase (GST), glutamate cysteine ligase catalytic subunit (GCLC), glutathione synthetase (GSS), NAD(P) H dehydrogenase quinone 1 (NQO1), UDP-glucumno-syltransferase 1 (UGT1), and heme oxygenase 1 (HO-1); glutathione (GSH); metallothionein (MT1); and glutamate-cysteine ligase (GCL) mRNA were measured. We also focused on the V-Mg interactive effects and trends toward interactive action as well as relationships between the examined indices. The elevated levels of Nrf2, GCL mRNA, and GCL catalytic subunit (GCLC) confirm OS in response to SMV and point to the capacity to synthesize GSH. The results also suggest a limitation of the second step in GSH synthesis reflected by the unchanged glutathione synthetase (GSS) and GSH levels. The positive correlations between certain cytoprotective/detoxifying proteins (which showed increasing trends during the SMV and/or MS administration, compared to the control) and between them and malondialdehyde (MDA), the hepatic V concentration/total content, and/or V dose (discussed by us previously) point to cooperation between the components of antioxidant defense in the conditions of the hepatic V accumulation and SMV-induced LPO intensification. The V-Mg interactive effect and trend are involved in changes in Nrf2 and UGT1, respectively. The p62 protein has to be determined in the context of potential inhibition of degradation of Keap1, which showed a visible upward trend, in comparison with the control. The impact of Mg on MT1 deserves further exploration.
Comparative Analysis of LSTM-FCN on Multiple Datasets

Classification of time series data is a critical problem. With the growth of time series data, several algorithms have been proposed. The deep learning technique Long Short-Term Memory (LSTM) with Fully Convolutional Networks (FCN) is widely used for the classification of time series data. The use of LSTM-FCN to improve fully convolutional networks. Through attention mechanism visualisation of context, the vector is performed and enhances the results of time series classification. The aim of this research is to compare the results of LSTM-FCN output on a multiple dataset. The results show that the selected technique is more effective at classifying time series. Visualisation is given for the performance analysis of the LSTM-FCN technique on all datasets.
One-Dimensional-Time Study of the Dehydration of Plasterboards Under Standard Fire Condition (ISO 834): Thermo-Chemical Analysis

Gypsum materials are often used as a technical solution for passive fire protection in many civil engineering applications. In this study, the dehydration of plasterboard with large thickness, 40 mm, is studied using a one-dimensional (along the thickness) and time methodology analysis. Such materials are subjected on one side to a standard fire curve (ISO 834). The imposed heat transfer directly to the exposed face of the board induces combined actions due to the gypsum dehydration. The thermal barrier efficiency evolves significantly versus time. The local measurement of the temperature field through the sample and the local water content combined with the analysis of the local chemical structure helped us to model and analyze the dehydration processes.
Long-term effects of tillage and nitrogen fertilization on soil C and N fractions in a corn–soybean rotation

Abstract: Tillage and nitrogen (N) fertilization can influence soil organic matter (SOM) dynamics, but their interactive effects remain contradictory. A long-term (25 yr) corn (Zea mays L.)-soybean (Glycine max L. Merr.) rotation was used to investigate the effect of tillage [moldboard plow (MP) and no-till (NT)] and N rates (0, 80, and 160 kg N·ha−1) on soil organic carbon (SOC), total N (STN), respiration, and SOM fractions [particulate organic matter (POMC, POMN), mineral-associated organic matter (MAOMC, MAOMN), and microbial biomass (MBC, MBN)]. Results indicate that NT had 27% higher SOC and 24% higher STN than MP in the 0–20 cm depth. Furthermore, SOC and STN stocks (0–20 cm) were 22% and 20% higher, respectively, under NT than MP. There was significant stratification under NT, with a rather uniform distribution under MP. The SOM fractions and soil respiration were 28%–275% and 20%–83% higher at the 0–5 and 5–10 cm depths, respectively, under NT than MP. Interestingly, N fertilizer rate or its interaction with tillage had no impact, except for respiration (tillage × N rate and N rate × depth). Hence, while N addition was required for adequate grain production and increased cumulative plant C and N inputs, our findings indicate that the vertical distribution of SOC, STN, and SOM fractions was affected by tillage, thereby influencing resource accessibility and subsequent dynamics of SOM fractions. Taken together, our results support the adoption of NT and judicious use of N fertilizers for enhancing topsoil SOM storage and fertility under humid temperate conditions.
The Costs of Complications and Unplanned Readmissions after Pancreatoduodenectomy for Pancreatic and Periampullary Tumors: Results from a Single Academic Center

Simple Summary Complications lead to unplanned readmissions (UR) and are reported to be associated with a two- to threefold increase in hospital admission costs. Since healthcare costs are increasing worldwide, cost containment is the major challenge for future healthcare. In the literature, there are only a few studies that analysed hospital costs after pancreatoduodenectomy (PD). In this study, we aimed to create an understanding of the costs of complications and UR in patients who underwent a PD. Abstract Background/Objectives: Complications after pancreatoduodenectomy (PD) lead to unplanned readmissions (UR), with a two- to threefold increase in admission costs. In this study, we aimed to create an understanding of the costs of complications and UR in this patient group. Furthermore, we aimed to generate a detailed cost overview that can be used to build a theoretical model to calculate the cost efficacy for prehabilitation. Methods: A retrospective cohort analysis was performed using the Dutch Pancreatic Cancer Audit (DPCA) database of patients who underwent a PD at our institute between 2013 and 2017. The total costs of the index hospital admission and UR related to the PD were collected. Results: Of the 160 patients; 35 patients (22%) had an uncomplicated course; 87 patients (54%) had minor complications, and 38 patients (24%) had severe complications. Median costs for an uncomplicated course were EUR 25.682, and for a complicated course, EUR 32.958 (p = 0.001). The median costs for minor complications were EUR 30.316, and for major complications, EUR 42.664 (p = 0.001). Costs were related to the Comprehensive Complication Index (CCI). The median costs of patients with one or more UR were EUR 41.199. Conclusions: Complications after PD led to a EUR 4.634–EUR 16.982 (18–66%) increase in hospital costs. A UR led to a cost increase of EUR 12.567 (44%). Since hospital costs are directly related to the CCI, reduction in complications will lead to cost-effectiveness.
Application of evolutionary programming to reactive power planning-comparison with nonlinear programming approach

This paper proposes an application of evolutionary programming (EP) to reactive power planning (RPP). Several techniques have been developed to make EP practicable to solve a real power system problem and other practical problems. The proposed approach has been used in the IEEE 30-bus system and a practical power system. For illustration purposes, only results for the IEEE 30-bus system are given. Simulation results, compared with those obtained by using a conventional gradient-based optimization method, Broyden's method, are presented to show that the present method is better for power system planning. In the case of optimization of noncontinuous and nonsmooth functions, EP is much better than nonlinear programming. The comprehensive simulation results show a great potential for applications of EP in power system economical and secure operation, planning and reliability assessment.
Conformal microstrip antennas and microstrip phased arrays

A new class of antennas using microstrips to form the feed networks and radiators is presented in this communication. These antennas have four distinct advantages: 1) cost, 2) performance, 3) ease of installation, and 4) the low profile conformal design. The application of these antennas is limited to small bandwidths. Phased arrays using these techniques are also discussed.
A Machine Learning Approach to Detect Student Dropout at University

In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.
Inspection and Assessment of Urban Gas Pipeline Based on Risk Analysis

In this paper a process framework was constructed for the inspection and assessment of urban gas pipeline. Take a certain gas pipeline network as an example, work directory for complete inspection was established as well as models for risk assessment. Feasible and effective rectification measures could be taken according to the relative importance of the results of the inspection and assessment. Some work in this paper could be adopted for the inspection and assessment of urban gas pipeline.
THE ECONOMIC EFFICIENCY OF PRODUCTION OF A TRITICALE HYBRID GENETIC LINE AT DIFFERENT LEVELS OF INTENSITY

The aim of the study was to evaluate the economic efficiency of production of a hybrid genetic line of winter triticale in comparison with a short-straw form, at different intensity levels under cereal crop rotation conditions. Calculations were made based on three-year (2017-2019) field trial results. The analysis included tillage methods (traditional and simplified), differentiated nitrogen fertilization (0,90 and 120 kg N/ha) and two forms of triticale: The rotondo variety and the hybrid genetic line – BOH 2415. The gross margin was taken as a measure of economic efficiency. The economic effectiveness of cultivation of both forms of winter triticale was assessed from the perspective of utilizing one of the basic production factors, i.e., land. The gross profitability index for particular variants of the experiment was also calculated as the relation of production value to direct costs. The analysis of production and economic indices showed that the more favorable variant of winter triticale cultivation was hybrid form cultivation technology with the use of traditional tillage (plough) and a nitrogen fertilization rate at a level of 120 kg N/ha. The level of the obtained gross margin was more influenced by the level of the obtained yield and the purchase price of triticale than by the level of incurred direct costs depending on the soil tillage method.
Soil enzymes as indicators of soil function: A step toward greater realism in microbial ecological modeling

Soil carbon (C) and nitrogen (N) cycles and their complex responses to environmental changes have received increasing attention. However, large uncertainties in model predictions remain, partially due to the lack of explicit representation and parameterization of microbial processes. One great challenge is to effectively integrate rich microbial functional traits into ecosystem modeling for better predictions. Here, using soil enzymes as indicators of soil function, we developed a competitive dynamic enzyme allocation scheme and detailed enzyme‐mediated soil inorganic N processes in the Microbial‐ENzyme Decomposition (MEND) model. We conducted a rigorous calibration and validation of MEND with diverse soil C‐N fluxes, microbial C:N ratios, and functional gene abundances from a 12‐year CO2 × N grassland experiment (BioCON) in Minnesota, USA. In addition to accurately simulating soil CO2 fluxes and multiple N variables, the model correctly predicted microbial C:N ratios and their negative response to enriched N supply. Model validation further showed that, compared to the changes in simulated enzyme concentrations and decomposition rates, the changes in simulated activities of eight C‐N‐associated enzymes were better explained by the measured gene abundances in responses to elevated atmospheric CO2 concentration. Our results demonstrated that using enzymes as indicators of soil function and validating model predictions with functional gene abundances in ecosystem modeling can provide a basis for testing hypotheses about microbially mediated biogeochemical processes in response to environmental changes. Further development and applications of the modeling framework presented here will enable microbial ecologists to address ecosystem‐level questions beyond empirical observations, toward more predictive understanding, an ultimate goal of microbial ecology.
Novel technique for esthetic root coverage with titanium prepared platelet-rich fibrin

Leukocyte- and platelet-rich fibrin (L-PRF) can be defined as an autologous leukocyte and platelet-rich biomaterial. Unlike other platelet-rich products, this technique does not require an anticoagulant or bovine thrombin. However, it is simply centrifuged blood without any additives, which makes it possible to avoid all of the legal restrictions related to the reimplantation of blood-derived products. L-PRF is composed of a fibrin matrix polymerized in a tetramolecular structure and is involved in the joining of platelets, leukocytes, cytokines, and circulating stem cells. The use of silica- coated test tubes is arising a lot of queries about the safety of L-PRF prepared within these test tubes. So, to avoid the use of silicacoated test tubes, titanium prepared PRF has come in practice now, as titanium is biocompatible metal and has property of histoconduction. The present case report is about successful esthetic root coverage around mandibular anterior teeth with clinical attachment loss of 4–5 mm at baseline. The patient had reduced sensitivity, CAL was 1 mm, and attached gingiva covered denuded root surface after 15 days and after 3-month recall appointment.