Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Gander Reproductive Efficacy in the Context of Globally Sustainable Goose Production

Simple Summary High economic gains from commercial poultry breeder stocks cannot be achieved by ignoring the importance of highly efficient male breeders. Male stock provides the basis for progeny and also ensures optimum fertility and hatchability. The present review is written to explore the application of various techniques that can assist in improving the reproductive efficiency of goose ganders, which exhibit poor reproductive performance. Recommended techniques for improving gander reproduction are the use of an artificial photoperiod, nutritional supplementation, monochromatic light sources, artificial insemination and semen cryopreservation, as well as immunization against the leptin hormone, anti-Müllerian hormone, and glycoprotein inhibin. Abstract The goose is a popular poultry species, and in the past two decades the goose industry has become highly profitable across the globe. Ganders low reproductive performance remains a barrier to achieving high fertility and hatchability in subsequent flocks. To address the global demand for cheaper animal protein, various methodologies for improving avian (re)production should be explored. A large amount of literature is available on reproduction traits and techniques for commercial chicken breeder flocks, while research on improved reproduction in ganders has been carried out to a lesser extent. The present review aims to provide a comprehensive literature overview focusing on recent advancements/techniques used in improving gander reproductive efficacy in the context of ensuring a globally sustainable goose industry.
Psychometric Testing: Self-Efficacy for Calorie Control and Exercise

Enhancing self-efficacy for calorie control and exercise is a crucial strategy for successful weight management. This study developed and psychometrically tested a 13-item quick checklist for assessing self-efficacy for calorie control and exercise (QCSE-CCE). A convenience sample of 425 adults between 18 and 69 years old completed the QCSE-CCE online. The principle axis factor analysis and a parallel analysis demonstrated a three-factor structure that accounts for 72.8% of the total variance. The confirmatory factor analysis indicated a good model fit (χ2/df = 2.168, GFI = .913, AGFI = .873, RMR = .049, RMSEA = .073, CFI = .959). The predictive validity was adequate (.38 < r < .39, p < .000), with Cronbach’s alphas ranging from . 87 to . 91. The test-retest demonstrated good stability (r = .69; p < .001). The QCSE-CCE is a valid and reliable instrument for assessing self-efficacy for calorie control and exercise for weight management purposes.
Divine discontent: aspirations and subjective well-being at a time of social mobility and high inequality

ABSTRACT This paper empirically explores processes of adaptation and social comparison advanced in the “hedonic treadmill” explanation for the income-happiness paradox, as well as advances recent theories that point to the roles of poverty, inequality, and connectedness in aspirations behaviour. Subjective well-being and aspirations are measured using the South African Social Attitudes Study of 2006, a year characterised by a peak in post-apartheid economic growth and inequality. Inequality and connectedness are found to play decisive roles in experiences of social comparison and relative deprivation. Income growth paths that contribute to higher inequality and/or lower connectedness are related to narrower aspirations gaps and aspirations failure amongst the objectively poor, whilst also widening the aspirations gaps of those engaged in upward social comparisons. Conversely, inequality tied to greater connectedness inspires aspirations, although not beyond a level that could potentially frustrate. This has implications for subjective well-being, indicated to decrease significantly with aspirations gaps.
NSAID-Induced Enteropathy Affects Regulation of Hepatic Glucose Production by Decreasing GLP-1 Secretion

Background/Aim: Given their widespread use and their notorious effects on the lining of gut cells, including the enteroendocrine cells, we explored if chronic exposure to non-steroidal anti-inflammatory drugs (NSAIDs) affects metabolic balance in a mouse model of NSAID-induced enteropathy. Method: We administered variable NSAIDs to C57Blk/6J mice through intragastric gavage and measured their energy balance, glucose hemostasis, and GLP-1 levels. We treated them with Exendin-9 and Exendin-4 and ran a euglycemic-hyperinsulinemic clamp. Results: Chronic administration of multiple NSAIDs to C57Blk/6J mice induces ileal ulcerations and weight loss in animals consuming a high-fat diet. Despite losing weight, NSAID-treated mice exhibit no improvement in their glucose tolerance. Furthermore, glucose-stimulated (glucagon-like peptide -1) GLP-1 is significantly attenuated in the NSAID-treated groups. In addition, Exendin-9—a GLP-1 receptor antagonist—worsens glucose tolerance in the control group but not in the NSAID-treated group. Finally, the hyper-insulinemic euglycemic clamp study shows that endogenous glucose production, total glucose disposal, and their associated insulin levels were similar among an ibuprofen-treated group and its control. Exendin-4, a GLP-1 receptor agonist, reduces insulin levels in the ibuprofen group compared to their controls for the same glucose exchange rates. Conclusions: Chronic NSAID use can induce small intestinal ulcerations, which can affect intestinal GLP-1 production, hepatic insulin sensitivity, and consequently, hepatic glucose production.
A trial patch to the extracurricular perspective of sustainable development program in elementary schools of Turkey

Sustainable development (SD) and science education have become interwoven categorical issues under the name of science education for sustainable development (SESD). Education can play a crucial role in merging the dimensions of SD as ecology, economy, and energy, along with society in a united holistic goal. Furthermore, it can provide the necessary motivation and self-fulfillment for individuals and communities. Extracurricular activities should also be implemented along with formal ones in courses that are related to socio-scientific issues like SD. In line with this perspective, this study mainly aims to find new means of developing an extracurricular module program based on the principles of SD, in order to address the defects, deficiencies, and needs in science education around the world. For the sake of these, the module program was constructed kneading concepts of energy transformations, biodiversity, and narrating socio-scientific problems that would foster positive student decision-making on SD issues. The study also aims to examine whether the SESD module program would have a significant effect on the academic success and opinions regarding SD of primary school students in science lessons. The study sample comprised 92 Turkish fifth-grade students. A mixed method was handled by allowing qualitative and quantitative research methods in the same context. Qualitative and quantitative data analysis revealed that the SESD module program significantly affected fifth-grade students in terms of their knowledge and opinions to increase their willingness to take action and responsibility.
Spectrally interleaved multi-carrier signals for radar network applications and multi-input multi-output radar

A modification of the classical orthogonal frequency division multiplexing signals is discussed that allows for the creation of a set of perfectly orthogonal transmit signals sharing the same bandwidth. These signals can be employed for performing radar measurements with multiple transmitters being simultaneously active, for example, in radar networks or multi-input multi-output radar applications. Since all the signals of the individual transmitters occupy the full available bandwidth, no deterioration of the achievable range resolution occurs. This study comprises both the theoretical considerations as well as the verification simulations and measurements. A particular focus is on the requirements regarding the frequency and time synchronisation of the individual signals and their impact on the achievable performance. The influence of possible Doppler shifts is also considered. Finally, a performance comparison with classical code-based multiple user access techniques is provided. The achieved results demonstrate that the proposed multi-carrier waveforms clearly outperform the classical code-based approach under realistic assumptions of synchronisation accuracy.
Separation Control in Duct Flows

Active control of separation in a duct flow is achieved using an array of fluidic actuators based on synthetic-jet technology. A two-dimensional serpentine duct model is designed to produce controlled separated flow in two configurations, in which the flow is either completely separated or has a separation bubble. An array of synthetic-jet actuators is placed within the separated flow domain in the diffuser section downstream of the onset of separation. Actuation leads to complete flow attachment up to U in =75 m/s (M≃0.2) and to partial reattachment up to U in = 105 m/s (M≃0.3)
Towards More Integrated Natural Resource Management in Victoria: Possible Elements of an Integrated State-wide Policy Framework

There is a growing recognition of the complexity of environmental issues and acceptance of the value of more integrated approaches to address them. Evidence of progress with the development of more integrated approaches is however less clear cut. Within this context, this paper explores how a more integrated approach to natural resource management at a state government level could be progressed. Using recent experience in Victoria as a focus, this paper provides an overview of environment and natural resources issues confronting the state, highlights why integration is an important element of any response, and outlines the current policy and organisational context. Possible elements of a state-wide policy framework for more integrated natural resource management across a state government organisation are also outlined and discussed.
Current Understanding of Pressure Natriuresis

Pressure natriuresis refers to the concept that increased renal perfusion pressure leads to a decrease in tubular reabsorption of sodium and an increased sodium excretion. The set point of blood pressure is the point at which pressure natriuresis and extracellular fluid volume are in equilibrium. The term "abnormal pressure natriuresis" usually refers to the expected abnormal effect of a certain level of blood pressure on sodium excretion. Factors that cause abnormal pressure natriuresis are known. Sympathetic nerve system, genetic factors, and dietary factors may affect an increase in renal perfusion pressure. An increase in renal perfusion pressure increases renal interstitial hydrostatic pressure (RIHP). Increased RIHP affects tubular reabsorption through alterations in tight junctional permeability to sodium in proximal tubules, redistribution of apical sodium transporters, and/or release of renal autacoids. Renal autocoids such as nitric oxide, prostaglandin E2, kinins, and angiotensin II may also regulate pressure natriuresis by acting directly on renal tubule sodium transport. In addition, inflammation and reactive oxygen species may mediate pressure natriuresis. Recently, the use of new drugs associated with pressure natriuretic mechanisms, such as angiotensin receptor neprilysin inhibitor and sodium glucose co-transporter 2 inhibitors, has been consistently demonstrated to reduce mortality and hypertension-related complications. Therefore, the understanding of pressure natriuresis is gaining attention as an antihypertensive strategy. In this review, we provide a basic overview of pressure natriuresis to the target audience of nephrologists.
Optimally organized GRU-deep learning model with Chi2 feature selection for heart disease prediction

In previous studies, various machine-driven decision support systems based on recurrent neural networks (RNN) were ordinarily projected for the detection of cardiovascular disease. However, the majority of these approaches are restricted to feature preprocessing. In this paper, we concentrate on both, including, feature refinement and the removal of the predictive model’s problems, e.g., underfitting and overfitting. By evading overfitting and underfitting, the model will demonstrate good enactment on equally the training and testing datasets. Overfitting the training data is often triggered by inadequate network configuration and inappropriate features. We advocate using Chi2 statistical model to remove irrelevant features when searching for the best-configured gated recurrent unit (GRU) using an exhaustive search strategy. The suggested hybrid technique, called Chi2 GRU, is tested against traditional ANN and GRU models, as well as different progressive machine learning models and antecedently revealed strategies for cardiopathy prediction. The prediction accuracy of proposed model is 92.17% . In contrast to formerly stated approaches, the obtained outcomes are promising. The study’s results indicate that medical practitioner will use the proposed diagnostic method to reliably predict heart disease.
Need for Global Knowledge Sharing Related to Construction Innovation

Innovation is a key to our industry success and takes the creativity of many talented people and the sharing of knowledge from many construction participants at institutions of higher education, research institutes, private businesses, and government agencies. Facing the competitive environment of the construction industry, Ruentex has a clear mission for innovation. Innovation is to create different value for our customers that competitors cannot provide and to create the difference that our customers are willing to pay for, because customers treasure value created by services rather than price. We try continuously to improve our operations using the latest proven technologies such as RFID for tracking precast concrete panels; 3D and 4D CAD for enhanced design and construction planning; modularization, ERP for providing an open book accounting format to secure the trust of our clients; and real-time construction monitoring for more effective project control. In the age of the knowledge economy, we believe in sharing what we do with others because this creates the greatest impact on improving the quality of life in our society. A learning organization is the key mechanism for sharing, and communication and discussion are the two most important factors for successful learning environments. Sharing not only brings improvement but also business opportunity and profit. Sharing with the global community brings regional integration and further development. It is important to share information with competitors because this can raise the level of efficiency for the entire construction industry. In order to create a better knowledge sharing environment, companies should be members of the global construction community. The greatest impact on society will result from the sharing of knowledge and communication of successes and failures related to construction planning, designing, construction, and operations. The challenge is to provide better communication between the various parties on a global basis. Great work is being performed at universities and construction companies around the world, and this information needs to be better transferred and shared to the global construction community. Several construction research organizations exist worldwide that need to more effectively communicate with each other in relation to common research interests and to consider the potential to jointly fund projects and collaborate in a synergistic manner. I would fully support the creation of an organization virtual or real that would pull together the many construction research organizations around the world, such as the Construction Research Council CRC and Construction Industry Institute CII in
Application of decoupling fuzzy sliding mode control with active disturbance rejection for MIMO magnetic levitation system

Magnetic levitation techniques have been used to eliminate friction due to mechanical contact, decrease the maintaining cost and achieve high-precision positioning. Although there are many studies on the single degree-of-freedom magnetic levitation control algorithms, it is difficult to achieve excellent control performance using classical control methods for the multiple-in-multiple-out magnetic levitation system because of the coupling in the dynamic system and the nonlinearity of the electromagnetic force. This article presents a 3 degrees-of-freedom magnetic suspension stage. At first the dynamic model of the stage is derived; then a nonlinear intelligent decoupling controller is developed to stabilize the levitation system. The control architecture consists of three components: (1) fuzzy sliding mode technique for the uncertainty in the system parameter; (2) force distribution for decoupling; (3) extended state observer for compensating the system disturbance. Finally experiments are designed to verify the effectiveness of the proposed controller. Experimental results show that compared with the classical proportional–integral–derivative controller, the proposed controller provides excellent transient response performance and the system is robust against the parameter uncertainty and external disturbance.
Fine line thin dielectric circuit board characterization

The rough surface of the copper foil, introduced to enhance its interfacial adhesion to the dielectric medium, will increase the signal propagation time constant and reduce the characteristic impedance. A high resolution resonant measurement technique will be presented for such study. The internal inductance will increase both the propagation time constant and the characteristic impedance. It adds an additional delay term, proportional to the square root of the signal rise time in the transient measurement. We will compare the results of different measurement techniques in this paper. <<ETX>>
An Optically Transparent Metantenna for RF Wireless Energy Harvesting

We propose a novel transparent metasurface antenna (metantenna) based on Indium Tin Oxide (ITO) material for simultaneously achieving wireless energy harvesting at radio frequency (RF) bands and efficient transmission of visible light. We adopt the design method of aperture-coupled metantenna to realize broadband and low-profile RF wireless energy harvesting. Then, we replace the conventional opaque metal material with an ITO coating with a sheet impedance of 1 Ω/sq to achieve the transparency of the proposed design. In order to obtain the balance between electrical performance and optical transmittivity (OT), we modify the metasurface structure to further improve the OT of the metantenna. The fabricated transparent metantenna shows an optical transmittivity of more than 50%. A measured impedance bandwidth of 43.6% from 1.40 GHz to 2.18 GHz is obtained to cover 2G and 3G wireless communication bands. Through designing the broadband rectifying circuit and the energy management circuit, we can obtain the maximum RF-DC efficiency of 65% and a stable DC output voltage of 3.4V, while the overall RF energy harvesting efficiency could reach 14.5% with the power input of 0 dBm. Additionally, through the solar energy harvesting test, we can demonstrate the potential of the proposed design to work with solar panels for realizing efficient RF/solar hybrid energy harvesting.
Optimal condition based maintenance with imperfect information and the proportional hazards model

Condition based maintenance (CBM) is based on collecting observations over time, in order to assess equipment's state, to prevent its failure and to determine the optimal maintenance strategies. In this paper, we derive an optimal CBM replacement policy when the state of equipment is unknown but can be estimated based on observed condition. We use a proportional hazards model (PHM) to represent the system's degradation. Since equipment's state is unknown, the optimization of the optimal maintenance policy is formulated as a partially observed Markov decision process (POMDP), and the problem is solved using dynamic programming. Practical advantages of combining the PHM with the POMDP are shown.
A MIMO Antenna System Using Self-Decoupled EMSIW Dual-Beam Antenna Elements

This paper presents a two-element MIMO antenna system using two dual-beam EMSIW antenna elements. The dual-beam pattern is achieved by creating two weak field areas using EMSIW. The antenna elements are placed in the weak field areas of each other, which results in −31.65 dB measured isolation between the two adjacent antenna elements. Different scenarios for the placement of the elements are predicted, one case is illustrated in this paper. The high isolation, omission of decoupling networks, and use of dual-beam antenna element make the antenna simple, compact, and suitable for MIMO applications.
Temperature-dependent effects in high-speed travelling-wave electroabsorption modulators

The temperature-dependent effects in a segmented travelling-wave electroabsorption modulator are demonstrated and analysed. Optimum operation voltages with the highest modulation efficiency at different temperatures are identified. This can ensure the modulator working at 50 Gbit/s with RF extinction ratio >8.4 dB between 10–50°C at λ=1540 nm.
NeoFlow: A Flexible Framework for Enabling Efficient Compilation for High Performance DNN Training

Deep neural networks (DNNs) are increasingly deployed in various image recognition and natural language processing applications. The continuous demand for accuracy and high performance has led to innovations in DNN design and a proliferation of new operators. However, existing DNN training frameworks such as PyTorch and TensorFlow only support a limited range of operators and rely on hand-optimized libraries to provide efficient implementations for these operators. To evaluate novel neural networks with new operators, the programmers have to either replace the holistic new operators with existing operators or provide low-level implementations manually. Therefore, a critical requirement for DNN training frameworks is to provide high-performance implementations for the neural networks containing new operators automatically in the absence of efficient library support. In this paper, we introduce NeoFlow, which is a flexible framework for enabling efficient compilation for high-performance DNN training. NeoFlow allows the programmers to directly write customized expressions as new operators to be mapped to graph representation and low-level implementations automatically, providing both high programming productivity and high performance. First, NeoFlow provides expression-based automatic differentiation to support customized model definitions with new operators. Then, NeoFlow proposes an efficient compilation system that partitions the neural network graph into subgraphs, explores optimized schedules, and generates high-performance libraries for subgraphs automatically. Finally, NeoFlow develops an efficient runtime system to combine the compilation and training as a whole by overlapping their execution. In the experiments, we examine the numerical accuracy and performance of NeoFlow. The results show that NeoFlow can achieve similar or even better performance at the operator and whole graph level for DNNs compared to deep learning frameworks. Especially, for novel networks training, the geometric mean speedups of NeoFlow to PyTorch, TensorFlow, and CuDNN are 3.16X, 2.43X, and 1.92X, respectively.