Miracle-dz/UniFAR-FTUs huggingface dataset:
Climate change and disturbances will shape future temperate forests in the transition zone between Central and SE Europe

It is expected that climate change as well as abiotic and anthropogenic disturbances will strongly influence temperate forests. Besides changes in the main climate variables, various disturbance factors may significantly worsen conditions for mesic Slovenian forests (SE Europe) dominated by European beech (Fagus sylvatica), Norway spruce (Picea abies) and European silver fir (Abies alba). In Slovenia, the climate has warmed in recent decades, with an average annual rate of increase of about 0.4°C per decade or even more than 0.5°C per decade in summer. In addition, disturbances have caused considerable damage to trees in the most extensive forest types in Slovenia, starting with a widespread ice storm in 2014, followed by bark beetle outbreaks, windthrows and salvage logging interventions. After 2014, salvage logging increased from about one third to two thirds of the total annual felling. Over the last two decades, we have observed a decline in Norway spruce growing stock, with the highest rate of decrease in areas below 500 m a.s. l. , and an increasing trend for European beech. Overall, the three dominant species (beech, spruce, silver fir), which together account for more than 70% of the total growing stock, have shown a declining trend over the last 20 years. The patterns observed are broadly consistent with earlier predictions developed for different climate change scenarios and with those reported in many other European countries. Adaptive forest management, which implements close-to-nature silviculture, has been traditionally practised in the region under study and has the potential to play an important role in reducing the risks associated with the impacts of climate change and disturbances in the future.
IMAGE HISTOGRAM EQUALIZER HARDWARE IMPLEMENTATION USING HIGH LEVEL SYTHEISIS

— In this paper, a histogram equalization algorithm is improved by a high level synthesis (HLS) for future implementation in reconfigurable hardware. The aim of this study is to implement histogram equalizer hardware by using Vivado HLS for a real-time processing system on a Field Programmable Gate Array (FPGA). The histogram equalization algorithm is implemented and tested using a colored image which sizes 1600x1200 pixels.
Depletion of transmembrane mucin 4 (Muc4) alters intestinal homeostasis in a genetically engineered mouse model of colorectal cancer

Mucins are components of the mucus layer overlying the intestinal epithelial cells, which maintains physiological homeostasis. Altered mucin expression is associated with disease progression. Expression of MUC4 decreases in colorectal cancer (CRC); however, its functional role and implications in the intestinal pathology in CRC are not studied well. Therefore, we generated a genetically engineered Muc4 knockout (Muc4-/-) CRC mouse model by crossing with Muc4-/- and Apcflox/flox mice in the presence of colon-specific inducible Cre. We observed that deficiency of Muc4 results in an increased number of macroscopic tumors in the colon and rectal region and leads to poor survival. Further, the absence of Muc4 was associated with goblet cell dysfunction where the expression of intestinal homeostasis molecules (Muc2 and Fam3D) was downregulated. Next, we also observed that loss of Muc4 showed reduced thickness of mucus layer, leading to infiltration of bacteria, reduction in anti-microbial peptides, and upregulation of pro-inflammatory cytokines. Further, Apc gene mutation results in activation of the Wnt/β-catenin signaling pathway that corroborated with an increased nuclear accumulation of β-catenin and activation of its target genes: cyclin D1 and c-Myc in Muc4-/- mice was observed. We conclude that the presence of Muc4 is essential for intestinal homeostasis, reduces tumor burden, and improves overall survival.
Detection of Facial Landmarks using Encoder-Decoder Network

—Facial landmark detection has achieved great performance by learning distinct features from the rich deformation of face shapes and poses. The facial landmarks such as eye centres, nose centres, jawlines, etc are localized to provide crucial information to various computer vision-related applications. The paper proposes an encoder-decoder network for detecting facial landmarks on the detected faces from digital images or video.
Midwives’ experiences of caring for women with antenatal anxiety and depression: A qualitative study with midwifery acupuncturists

A Corresponding Author: acu.aad@ protonmail.com B Self-employed Chinese Medicine Practitioner; NZ School of Acupuncture & Traditional Chinese Medicine teacher/ supervisor C University of Western Australia Background: Pregnant women seek help for a range of physical and emotional pregnancy symptoms from traditional acupuncturists. Whether midwifery acupuncturists in Aotearoa New Zealand (Aotearoa NZ) provide acupuncture for antenatal anxiety and depression (AAD) in practice is currently unknown. Aim: This qualitative arm of a mixed-methods study aimed to explore midwifery acupuncturist experiences of caring for pregnant women with AAD in Aotearoa NZ. The aim of the research was to examine the factors that influence midwifery acupuncturists' perceptions of acupuncture use for AAD. Method: This was a mixed-methods study involving an online survey and in-depth interviews from a convenience sample of Aotearoa NZ midwifery acupuncturists who had completed a Certificate of Midwifery Acupuncture. This paper describes the thematic analysis of the semi-structured interviews. Findings: The eight interviewees were mostly NZ European (n=6) and Lead Maternity Carers (LMCs; n=6) caring for pregnant women in rural and urban locations throughout Aotearoa NZ. The interviews revealed an overarching theme, “helping midwives to navigate the ocean of AAD”, which identified the lack of support participants experienced from current maternity health services for AAD in Aotearoa NZ. Subthemes “Missing the boat during pregnancy” expressed how participants felt no options were available for AAD prevention, while “keeping women afloat with no ship in sight” represented how interviewees tried to keep women in their care stable even though access to maternal mental health services was difficult. Participants felt acupuncture was a useful nonpharmaceutical tool that works; however, they had reservations about “adding acupuncture to the midwifery toolbox”. Conclusion: Aotearoa NZ midwifery acupuncturists were concerned about AAD and the limited conventional options available for women in their care. Acupuncture was viewed as a promising adjunct to usual treatment for AAD. Reservations included adding acupuncture to an already heavy midwifery workload, the cost of acupuncture, and the appropriateness of the treatment.
Implementation of the numerical analysis of dynamic loads on the composite structure employing the FE method

In this research, a numerical investigation has been conducted to analyze the dynamic load of the composite structure. The composite structure was solved and analyzed using the static structure tool. There is a physical model that has been imported and meshes have been done accordingly. Shear stress, von-mises stress, and total deformation are all considered as part of the analysis. Using finite elements to simulate the composite structure and response to the applied loads. Because dynamic loads were regularly applied, frequency response was examined. In this study after verification of this computer simulation with experimental results and the results showed it was confidence 95 %. This percentage confidence allows proceeding numerical analysis. Deformation of the entire structure has been computed and found to be 10 mm, which is the maximum amount of overall deformation that can be caused by the applied load, In the Y-axis, a dynamic load was applied. The imposed dynamic load has been studied numerically and interpreted in terms of shear stresses. As a result of the maximum applied load, the maximum shear stress is 10 MPa. The complete composite construction was subjected to Von-Mises stress measurements. The structure's ability to withstand these stresses was determined by conducting a series of tests. The greatest von mises stress that can be applied in this study was 40 MPa. An investigation of how to react to vibration has already been carried out. In the numerical results, it was found that the reaction to the vibration was inconsistent. In terms of amplitude, the highest values may be found at 200 Hz, while the lowest values can be found at 20 Hz.
Recurrent Neural Networks for Prediction of Geothermal Reservoir Performance

Reliable prediction of energy production performance from geothermal reservoirs is needed for optimizing sustainable development of the underlying resources. While conventional physics-based simulation models offer a comprehensive prediction tool, they are nontrivial to build and involve a significant level of uncertainty due to data limitation. Data-driven models offer an efficient approach to build predictive models by extracting statistical patterns in the historical data and using them to perform direct forecasting. In particular, recurrent neural networks (RNN) are commonly used as data-driven models for the prediction of time series sequences. Using an inherent sequential architecture, RNN can learn the dynamic behavior and dependencies among different variables from data and use the learned patterns to make predictions. In this work, we investigate the application of RNN to the prediction of energy production from geothermal reservoirs. Specifically, we develop an encoder-decoder architecture, known as CNN-RNN, in which the encoder uses a convolutional neural network (CNN) to summarize the features learned from historical data while the decoder is based on a sequential RNN structure that uses those features and additional inputs to make predictions. We present the CNN-RNN architecture and demonstrate its prediction performance by applying it to different datasets, including field data from a geothermal reservoir.
Design and Evaluation of real-time PCR for Molecular Diagnosis of Hepatitis B

Introduction and aim: The hepatitis B virus (HBV) was discovered in 1965. Approximately 350 million people are infected with HBV. The virus has a worldwide spread and one of the most important complications of infection is chronic hepatitis, cirrhosis, and hepatocellular carcinoma. Hepatitis can be detected by biochemical tests that determine liver function. Methods: This study was qualitative and 30 patients with suspected hepatitis B referring to Massoud Lab in 2019 were selected. Blood samples were tested for HBsAg positive by multiplex RT-PCR with specific probes and primers as well as β-Actin gene as quality control. Then the obtained results were compared with the results of commercial tests. Results: From 30 samples, 19 samples were HBsAg positive and 11 samples were HBsAg negative. Positive samples were re-examined to confirm the positive results. To compare and evaluate the quality of the results of the designed test, all of these 30 samples were analyzed using another common laboratory kit. Conclusion: The high sensitivity, good reproducibility, and low cost make this innovative quantitative HBV real-time PCR assay especially well-suited for application to large clinical surveys. Keyword: β-Actin gene, HBV, Hepatitis B, Molecular Diagnosis, Real-time PCR
Statistical significance in choice modelling: a commentary on the use of confidence intervals, t-ratios, p-values and star measures

This note offers a commentary on the use of notions of statistical significance in choice modelling. We argue that, as in many other areas of science, there is an over-reliance on 95% confidence levels. We also note a lack of precision in the reporting of measures of uncertainty in many studies, especially when using p-values and even more so with star measures. We suggest that these should only ever be presented alongside standard errors or t-ratios. The note also highlights the distinction between looking at the precision of estimates and statistical significance, and stresses the importance of considering behavioural or policy significance in addition to statistical significance.
Adaptive time stepping approach forPhase-Field modeling of phase separation and precipitates coarsening in additive manufacturing alloys-COMPLAS 2021

In the present work, the capacity of phase field method to highlight microstructural changes during the spinodal decomposition of a given binary alloy basing on the Cahn-Hilliard equation is presented. Then, growth and coarsening of precipitates are studied using the KKS (Kim-Kim-Suzuki) model, which includes Cahn-Hilliard and Allen-Cahn equations. The implementation of time stepping algorithms to resolve Phase-Field equations is illustrated. Within Fourier space, using semi-implicit spectral method, it has been demonstrated that it allows faster computing than schemes based on finite difference method. First, spinodal decomposition of a given binary alloy under isothermal loading is implemented and three time stepping approaches are applied: constant time stepping, noniterative and an iterative method. While the non-iterative method is faster than the constant time stepping scheme, the iterative one, althoughrelatively more CPU consuming,can guaranteethe convergence of the computing. These methods are combined in an innovative approach tested on 1D, 2D and 3D grids. The effectiveness of the adopted adaptive time-stepping algorithm allows resolving equations in reasonable CPU time. It predicts different physical phenomena,such as phase separation and growth and coarsening of precipitates induced by important interfacial energies.
Distortion in Voting with Top-t Preferences

A fundamental question in social choice and multi-agent systems is aggregating ordinal preferences expressed by agents into a measurably prudent collective choice. A promising line of recent work views ordinal preferences as a proxy for underlying cardinal preferences. It aims to optimize distortion, the worst-case approximation ratio of the (utilitarian) social welfare. When agents rank the set of alternatives, prior work identifies near-optimal voting rules for selecting one or more alternatives. However, ranking all the alternatives is prohibitive when there are many alternatives. In this work, we consider the setting where each agent ranks only her t favorite alternatives and identify almost tight bounds on the best possible distortion when selecting a single alternative or a committee of alternatives of a given size k. Our results also extend to approximating higher moments of social welfare. Along the way, we close a gap left open in prior work by identifying asymptotically tight distortion bounds for committee selection given full rankings.
Development and optimization of fast laser shock peening (FLSP) using most advanced laser architectures

Laser Shock Peening (LSP) is an industrial mechanical surface treatment process used mainly by the aeronautical and nuclear industry. This process consists in focusing a high-energy pulsed laser (ns-range) on a metal target to create a high-pressure plasma that will lead to a deep plastic deformation of the target through the propagation of a shock wave. Compressive residual stresses (CRS) are generated in depth up to more than 1 mm (making this process much more effective than conventional Shot Peening), which then helps to enhance the fatigue life by slowing down crack propagation. Through theoretical and experimental studies, a new configuration has been developed: the Fast LSP (FLSP). Small laser spot sizes, high overlap ratios and high-frequencies laser for treatments is the core of this new configuration. The purpose of this work is to implement the FLSP from the laboratory to the industry. The THEIA (1 J, 10 ns, 200 Hz) laser system (made by Thales) was developed, and we investigated multiple conditions on Al-2024 samples. They were treated with various spot sizes (0.72 mm, 1.25 mm and 2 mm) and with 3 overlap ratios: 1000 %, 3000 % and 5000 %, and CRS were measured through X-Ray diffraction. A high-speed camera was used to measure both the renewing and the ejection of the water layer used to confine the plasma. Though challenges were faced, and a blowing system was developed to make sure that the ejected water will not interact with the following laser pulses, thus avoiding parasitic plasmas.
The financial challenges of hosting sports events: a problem of insufficient separation between decision-making and decision-control

ABSTRACT Research question: This article aims to explain that poorly functioning separation between decision-making, decision-control, and risk-bearing is a fundamental reason for the financial underperformance of major or large sport events. We look at empirical data from four major sports events hosted in Norway: The 1994 Lillehammer Winter Olympics, the 2011 International Ski Federation (FIS) World Skiing Championship, the 2014 FIDE Chess Olympiad, and the 2017 International Cycling Union (UCI) World Road Cycling Championships. All these events suffered financially. Research methods: The study data were collected from 66 semi-structured interviews and different types of documents, like guides and reports filed by different stakeholders, applications for financial support, collaboration agreements, protocols of meetings, e-mail correspondence, financial statements, and budgeting documents. Results and findings: Through an examination of the previous literature, our analysis confirms drivers and symptoms of financial problems at sport events. These are: the exaggerated focus on legitimising the event, soft-budget practices, interference by political interests, and so on. We argue that many of these factors could have been handled better if a more consequent separation of risk-bearing, decision-making, and decision-control was in place. Implications: We propose a better separation between the decision-making and decision-control function, by means of a permanent and independent entity that is responsible for the decision-control function. Such an institution could enhance knowledge transfer, contribute competence and skills, and improve the financial outcome of a sports events.
IMPROVING CLASSIFICATION ACCURACY FOR NON-COMMUNICABLE DISEASE PREDICTION MODEL BASED ON SUPPORT VECTOR MACHINE

Over recent years, Non-communicable Disease (NCDs) is the high mortality rate in worldwide likely diabetes mellitus, cardiovascular diseases, liver and cancers. NCDs prediction model have problems such as redundant data, missing data, imbalance dataset and irrelevant attribute. This paper proposes a novel NCDs prediction model to improve accuracy. Our model comprisesk-means as clustering technique, Weight by SVM as feature selection technique and Support Vector Machine as classifier technique. The result shows that k-means + weight SVM + SVM improved the classification accuracy on most of all NCDs dataset (accuracy; AUC), likely Pima Indian Dataset (99.52; 0.999), Breast Cancer Diagnosis Dataset (98.85; 1.000), Breast Cancer Biopsy Dataset (97.71; 0.998), Colon Cancer (99.41; 1.000), ECG (98.33; 1.000), Liver Disorder (99.13; 0.998). The significant different performed by k-means + weight by SVM + SVM. In the time to come, we are expecting to better accuracy rate with another classifier such as Neural Network.
Genotypic variability in root length in pea (Pisum sativum L.) and lentil (Lens culinaris Medik.) cultivars in a semi‐arid environment based on mini‐rhizotron image capture

Physiological breeding is an approach that complements conventional breeding by providing characterizations of traits present in breeding populations. This allows breeders the ability to choose crosses based on desirable and adaptive traits, an approach that may be more reliable than selection on yield alone. In this study, we determined how much genotypic variability was present in selected lines of modern field pea (Pisum sativum L.) and lentil (Lens culinaris Medik.) cultivars from Montana, North Dakota, Washington, and Saskatchewan, Canada, and if root growth, particularly at depth, improves the fitness of lines to semi‐arid environments. We conducted experiments at the Northern Agricultural Research Center of Montana State University from 2017 to 2019 inclusively to investigate root growth with mini‐rhizotrons in 29 field pea lines and 25 lentil lines. Results suggest there is large genotypic variability in root length across the soil profile and the proportion of root length found below 30 cm in both crops, and these root traits appear independent of each other. In field pea, the highest yielding cultivars were intermediary in both total root length and the proportion of root length below 30 cm, suggesting large root systems and/or deeper root profiles are not necessarily beneficial in this environment. By contrast, in lentil, total root length and root length found below 30 cm was well correlated with biomass and yield. For breeders interested in in improved adaptation to semi‐arid environments, it may be too early to optimize root systems, and above‐ground traits may still yield a better return on investment.
Mathletics

Mathletics is a stimulating and exciting way for students to learn – and improve – their maths. Regarded as the “next generation in learning” it is Australia’s most used educational website. The interactive program engages students and motivates them to learn. It is easy to use and students can work through the curriculum at their own pace, while being challenged at the same time. Each student’s progress can be monitored and assessed, whether the self paced learning in the computer laboratory, drills, tests or homework. Best of all, students have access to it any time, any day of the week, providing the perfect link between home and school.
Application of Rhamnolipids as Dispersing Agents for the Fabrication of Composite MnO2-Carbon Nanotube Electrodes for Supercapacitors

The high theoretical capacitance of MnO2 renders it a promising material for the cathodes of asymmetric supercapacitors. The good dispersion of MnO2 and conductive additives in a nanocomposite electrode is a key factor for efficient electrode performance. This article describes, for the first time, the application of rhamnolipids (RL) as efficient natural biosurfactants for the fabrication of nanocomposite MnO2-carbon nanotube electrodes for supercapacitors. RL act as co-dispersants for MnO2 and carbon nanotubes and facilitate their efficient mixing, which allows for advanced capacitive properties at an active mass of 40 mg cm−2 in Na2SO4 electrolytes. The highest capacitance obtained from the cyclic voltammetry data at a scan rate of 2 mV s−1 is 8.10 F cm−2 (202.6 F g−1). The highest capacitance obtained from the galvanostatic charge–discharge data at a current density of 3 mA cm−2 is 8.65 F cm−2 (216.16 F g−1). The obtained capacitances are higher than the capacitances of MnO2-based electrodes of the same active mass reported in the literature. The approach developed in this investigation is simple compared to other techniques used for the fabrication of electrodes with high active mass. It offers advantages of using a biocompatible RL biosurfactant.