Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting the current and future distribution of pine woodland specialist plants in the Cairngorms National Park

The Caledonian pine forests of Scotland have declined significantly over the past century largely due to clearance for timber, fuel and grazing land. Approximately 1% of these native pinewoods remain from their historic extent, with a stronghold in the Cairngorms National Park. Creeping Lady’s-Tresses Goodyera repens (L.) R. Br. , One-Flowered Wintergreen Moneses uniflora (L.) A. Gray and Twinflower Linnaea borealis L. favour this habitat, meaning that the clearance of pine woodland has resulted in widespread declines. There are multiple plantation schemes underway in the Cairngorms to reforest the Highlands, such as Cairngorms Connect, partly with the intent of assisting the recovery of pine woodland specialists. This study uses Maximum Entropy modelling (MaxEnt) to create species distribution models (SDMs), firstly to predict the current distribution for these species; thus, enabling land managers in the Cairngorms to target surveys with the hope of finding new populations. Secondly, future distribution models are created using different emissions scenarios to predict the regions of maximum habitat suitability for the study species, with the aim of visualising where pine woodland should be planted to ensure long-term habitat viability. The results showed that there are regions in the north and east of the Cairngorms which have the highest habitat suitability, currently and in the future. The creation of habitat corridors between these two regions should be a priority to prevent isolation between populations of pine woodland specialists. The high emissions pathway could increase the likelihood of isolation by significantly decreasing the habitat suitability between the most suitable areas.
Truss structure design using a length-oriented surface remeshing technique

We present a method that can be used for designing truss structures representing either minimal surface shapes or general free-form shapes. The structures are designed so that they meet some specific criteria concerning their aesthetic properties and especially the lengths of the truss elements. We explain a technique for tangential redistribution of points on evolving surfaces that allows to obtain equally sized truss elements in selected subsets of the structure. This technique is applied to surfaces evolving by their mean curvature yielding constructions that approximate minimal surface shapes. Afterwards, we show how to remesh static free-form surfaces.
Gamma-distributed maximum a posteriori despeckling algorithm of high-resolution synthetic aperture radar images

In order to solve the problem that the traditional Gamma-distributed maximum a posteriori despeckling algorithm cannot effectively preserve the point target in the homogeneous region, cannot effectively keep the weak edge, and cannot efficiently suppress the speckle in the strong edge region, the Gamma-distributed maximum a posteriori despeckling algorithm with prior parameter estimation based on second-kind statistics is proposed for high-resolution synthetic aperture radar images. Using the Mellin convolution and the multiplicative model of speckle, the parameters of the Gamma prior distribution are accurately estimated from the first two log-cumulants of the observed image. The proposed algorithm has the analytical filtering output, and it is easy to implement. Despeckling experiments on high-resolution synthetic aperture radar images of agricultural field and urban region demonstrate that, compared to the traditional Gamma-distributed maximum a posteriori despeckling algorithm, the proposed one can effectively preserve the point target in the homogenous region, effectively retain the weak edge, and efficiently suppress the speckle in the strong edge region.
Deep-reinforcement-learning-based gait pattern controller on an uneven terrain for humanoid robots

Abstract Although conventional gait pattern control in humanoid robots is typically performed on flat terrains, the roads that people walk on every day have bumps and potholes. Therefore, to make humanoid robots more similar to humans, the movement parameters of these robots should be modified to allow them to adapt to uneven terrains. In this study, to solve this problem, reinforcement learning (RL) was used to allow humanoid robots to engage in self-training and automatically adjust their parameters for ultimate gait pattern control. However, RL has multiple types, and each type has its own benefits and shortcomings. Therefore, a series of experiments were performed, and the results indicated that proximal policy optimization (PPO), combining advantage actor-critic and trust region policy optimization, was the most suitable method. Hence, an improved version of PPO, called PPO2, was used, and the experimental results indicated that the combination of deep RL with data preprocessing methods, such as wavelet transform and fuzzification, facilitated the gait pattern control and balance of humanoid robots.
Defect Detection System on Stamping Machine Using the Image Processing Method

Quality products are very influential in creating profits for the company and are also closely related to the level of customer satisfaction. The higher the quality of the products produced by a company, the higher the satisfaction felt by consumers. The biggest challenge in the production process is achieving good quality with a product defect rate close to zero defect. Defects in the product are usually small. This is of course very difficult for workers to inspect each product for a long time. Thus, manual inspection is certainly ineffective and inefficient because humans have a saturation point and get tired if they work for a long time. Previous research on detecting defective objects using image processing has been carried out but has not been able to detect up to the shape and size, while in this study it can detect up to the shape and size. Therefore, to implement an automatic product defect detection system we will use image processing and RFID technology. Image processing is processing on the image using a computer so that the image quality becomes better and produces value information for each color. Image processing techniques consist of image conversion from RGB to grayscale, thresholding (binarization), and morphological operations (segmentation). While RFID is an identification method by using a means called an RFID label or transponder to store and retrieve data remotely This study aims to implement a control system on HMI and also a detection system on defect products using a visual inspection system with the aim of getting the machine effectiveness value. One method to get this value is the Overall Equipment Effectiveness (OEE) method. It is proven by implementing a visual inspection system that gets an accuracy rate of 95.97% to detect rejected products and optimize the OEE presentation value obtained. In this study, the implementation of the production monitoring system was successfully implemented with an average OEE value of 52.49%.
Conception of discrete systems decomposition algorithm using p-invariants and hypergraphs

In the article author presents an idea of decomposition algorithm of discrete systems described by Petri Nets using pinvariants. Decomposition process is significant from the point of view of discrete systems design, because it allows separation of the smaller sequential parts. Proposed algorithm uses modified Martinez-Silva method as well as author’s selection algorithm. The developed method is a good complement of classical decomposition algorithms using graphs and hypergraphs.
Biomimetic marine energy devices in waves and sheared currents

Oscillating hydrofoils in waves and currents are considered as a novel system for the exploitation of this kind of combined renewable energy sources. Special attention is paid to the case of waves and vertically sheared currents, as appearing in the case of nearshore tidal flows. The analysis is based on an appropriate mild-slope model, in conjunction with time-domain Boundary Element Methods. The methodology takes into account the velocity due to waves and currents on the formation of the incident flow, the free surface through the satisfaction of the corresponding boundary conditions and the effects of variable bathymetry and sheared currents. Results are presented concerning the harvested power, demonstrating that significant energy output can be obtained. Our method, after further enhancements and verification, it can be applied for the design and control of such systems, extracting energy from waves in the presence of ambient currents in nearshore regions. For the prediction of wave-seabed-current interaction quite complete and accurate coupled-mode models have been recently developed, with application to the propagation/scattering of water waves over variable bathymetry regions, in the presence of strong spatially varying currents, extending previous simplified mildslope/mild shear models that are applicable to cases of slowly varying bathymetry and current; Belibassakis et al (2011). Also, the problem of transformation of the directional spectrum of an incident wave system over a region of strongly varying three-dimensional bottom topography is further studied in Belibassakis et al (2014), where also the accuracy and efficiency of the coupled-mode method is tested, comparing numerical predictions against experimental data and calculations by phase-averaged model SWAN, Booij et al (1999). Results are shown for various representative test cases, demonstrating the importance of the first evanescent modes and the additional slopingbottom mode when the bottom slope is not negligible. The mutual existence of waves with strong following, oblique or opposing currents at various nearshore places, which otherwise is characterized by quite low wave potential, offers a motivation for comprehensive investigation of such resources and the development of hybrid technological devices, based on novel ideas such as biomimetic systems (see, e.g. BioPower Systems: www.biopowersystems. com/biowave.html). The latter are appropriate for the efficient energy extraction and exploitation of this complex type of renewable energy resources.
The physical foundations of geometric mechanics

The principles of geometric mechanics are extended to the physical elements of mechanics, including space and time, rigid bodies, constraints, forces, and dynamics. What is arrived at is a comprehensive and rigorous presentation of basic mechanics, starting with precise formulations of the physical axioms. A few components of the presentation are novel. One is a mathematical presentation of force and torque, providing certain well-known, but seldom clearly exposited, fundamental theorems about force and torque. The classical principles of Virtual Work and Lagrange-d'Alembert are also given clear mathematical statements in various guises and contexts. Another novel facet of the presentation is its derivation of the Euler-Lagrange equations. Standard derivations of the Euler-Lagrange equations from the equations of motion for Newtonian mechanics are typically done for interconnections of particles. Here this is carried out in a coordinate-free rmner for rigid bodies, giving for the first time a direct geometric path from the Newton-Euler equations to the Euler-Lagrange equations in the rigid body setting.
Pelvic floor muscle exercises plus biofeedback versus pelvic floor muscle exercises for patients with stress urinary incontinence: A systematic review and meta-analysis of randomized controlled trials

Background: Stress urinary incontinence (SUI) is a widespread illness that mostly affects women, particularly those who have recently given birth or gone through menopause. The purpose of this meta-analysis is to compare the effectiveness of pelvic floor muscle exercises (PFME) plus biofeedback to PFME alone in treating SUI in female patients. Methods: We systemically searched six electronic databases (PubMed, Scopus, and Web of Science) from inception until February 7, 2022. We included randomized controlled trials (RTCs) comparing patients who had undergone PFME plus biofeedback to PFME alone. For risk of bias-2 (RoB2) assessment, we used cochrane risk of bias assessment tool. Continuous data were pooled as standardized mean difference (SMD), and dichotomous data were pooled as odds ratio with the corresponding 95% confidence intervals (CI). Results: 15 RCTs were included, with a total of 788 patients with SUI. The overall effect estimate between PFME+BF and PFME alone groups favored the PFME+BF group in improving PFME strength (SMD=0.33, 95% CI [0.14 to 0.52], p=.0009) and did not favor either of the two groups for quality of life (SMD=-0.22, 95% CI [-0.44 to 0.00], p=0.05), leakage (SMD=-0.10, 95% CI [-0.37 to 0.17], p=0.47), pad weight test (SMD=-0.22, 95% CI [-0.44 to 0.00], p=0.05), cure rate (odd ratio [OR]=2.44, 95% CI [0.52 to 11.42, p=0.26), and social activity (SMD=0.66, 95% CI [-0.04 to 1.36], p=0.07). Conclusion: BF addition to PRME improves cure rate and PFME strength without affecting leakage or quality of life. Healthcare providers must consider patient safety and comfort while choosing BF devices with PFME. SUI management strategies should include BF to improve results.
Mesoscopic superconductivity above 10 K in silicon point contacts

Silicon, perhaps the most ubiquitously used material in the digital age of today, has also been a material of choice for testing the fundamental differences between various electronic ground states, e.g., metals and insulators. This is mainly because ultimate control has been achieved in growing extremely pure silicon crystals and doping them with varying concentrations of charge carriers and their mobility. Here, we show that by forming mesoscopic point contacts with non-superconducting metals on insulating (doped) silicon, it is possible to obtain a superconducting phase with a remarkably high critical temperature above 10 K and an average superconducting energy gap of 2 meV. Apart from its importance in advancing the understanding of nanoscale superconductivity, this discovery is also expected to boost the efforts to realize silicon based superconducting devices with far reaching application potential.
A Method to Zoom of Infrared Images

In this paper, we propose a new zooming method for infrared thermal images. Generally thermal images are low-resolution images and requested to be up-sampled. This proposed technique can be used in the fields of biology and medicine. We compared three well-known interpolation approaches, near neighbor, bilinear and bicubic methods. Experimental results show that all results are satisfactory. In particular, bilinear and bicubic methods provide almost the same performance.
Knowledge, awareness, and perception of senior high school learners towards nuclear energy: A South African case study

This study investigated senior South African high school students’ levels of knowledge, perception, attitude, and awareness toward nuclear energy using paper-based, semi-structured questionnaires and digital data collection instruments through online Google forms. The sample consisted of 100 high school students from two schools. Fifty students were chosen from a public school, and fifty students were selected from a private/independent school. It was found that regardless of the type of school, students showed little to no knowledge, low awareness, and significant ignorance of nuclear energy. The majority of students had never heard of nuclear energy. In terms of initial exposure to the concept of nuclear energy, private school learners seemed to have been exposed to the industry more through television. In contrast, for public school learners, educators were their main sources of introduction. Conversely, the perception of this form of energy is much more positive in private schools than in public schools. This study highlights the existence of a severe gap in high school education regarding the teaching and learning of nuclear energy. Hence, an energy-cognizant curriculum is needed to bridge the gap in both public and private schools.
Varying‐Coefficient Models

We explore a class of regression and generalized regression models in which the coefficients are allowed to vary as smooth functions of other variables. General algorithms are presented for estimating the models flexibly and some examples are given. This class of models ties together generalized additive models and dynamic generalized linear models into one common framework. When applied to the proportional hazards model for survival data, this approach provides a new way of modelling departures from the proportional hazards assumption
Bayesian networks with imprecise datasets: Application to oscillating water column

The Bayesian Network approach is a probabilistic method with an increasing use in the risk assessment of complex systems. It has proven to be a reliable and powerful tool with the flexibility to include different types of data (from experimental data to expert judgement). The incorporation of system reliability methods allows traditional Bayesian networks to work with random variables with discrete and continuous distributions. On the other hand, probabilistic uncertainty comes from the complexity of reality that scientists try to reproduce by setting a controlled experiment, while imprecision is related to the quality of the specific instrument making the measurements. This imprecision or lack of data can be taken into account by the use of intervals and probability boxes as random variables in the network. The resolution of the system reliability problems to deal with these kinds of uncertainties has been carried out adopting Monte Carlo simulations. However, the latter method is computationally expensive preventing from producing a real-time analysis of the system represented by the network. In this work, the line sampling algorithm is used as an effective method to improve the efficiency of the reduction process from enhanced to traditional Bayesian networks. This allows to preserve all the advantages without increasing excessively the computational cost of the analysis. As an application example, a risk assessment of an oscillating water column is carried out using data obtained in the laboratory. The proposed method is run using the multipurpose software OpenCossan.
Satellite–Terrestrial Collaborative Object Detection via Task-Inspired Framework

Recently, buoyed by advances in the space industry, low Earth orbit (LEO) satellites have become an important part of the Internet of Things (IoT). LEO satellites have entered the era of a big data link with IoT, how to deal with the data from the satellite IoT is a problem worthy of consideration. Conventional object detection method in optical remote sensing simply transmits the raw data to the ground. However, it ignores the properties of the images and the connection with the downstream task. To obtain efficient data transmission and accurate object detection, we propose a task-inspired satellite–terrestrial collaborative object detection framework called STCOD. It detects regions of interest (ROIs) and adopts a block-based adaptive sampling method to compress the background (BG) in optical remote sensing images by introducing satellite edge computing (SEC) on satellites. The STCOD framework also sets the transmission priority of image blocks according to their contributions to the task and uses fountain code to ensure the reliable transmission of important image blocks. We build a whole software simulation framework to validate our method, including the satellite module, the transmission module, and the terrestrial module. Extensive experimental results show that the STCOD framework can reduce the amount of downlink data decreased by 50.04% while losing the detection accuracy by 0.54%. In our simulated satellite–terrestrial link, the STCOD framework can reduce the number of satellite-to-terrestrial transmissions by half. When the packet loss rate is between 5% and 20%, the detection accuracy is lost only 0.05% to 0.5%.
A dynamic dense WDM network

Multiwavelength optical networks using wavelength routing have been recognized to be very attractive for the future B-ISDN and can potentially be used for the next-generation Internet to meet the demands for quality of service and bandwidth configurability. Wavelength conversion provides flexibility and reduces the blocking probability in the circuit-switched networks. As a set of precious resources, wavelengths are used as carriers of information and have to be assigned properly to prevent two same wavelengths in the same fiber link. We show a dense WDM network consisting of edge switches and backbone switches.
Do ESG ratings mediate the relationship between board gender diversity and firm financial performance? Evidence from the U.S. Market

The impact of board gender diversity on firm value is inconclusive. The paper investigates whether a firm’s environmental, social, and governance (ESG) performance mediates the relationship between board gender diversity and firm financial performance. The study employs a sample of 1514 non-financial firms listed on the National Association of Securities Dealers Automated Quotations (NASDAQ) and New York Stock Exchanges (NYSE) from 2016 to 2020. It reveals that a firm improves its ESG ratings and financial performance when there are more female directors on its board. When controlling ESG performance, the significant relationship between board gender diversity and firm performance turns insignificant, and ESG performance has a significant positive impact on a firm’s financial performance. These findings confirm the mediating role of ESG performance in the studied relationship. A firm’s management can refer to these findings to employ more women on its board, which creates additional firm financial value through better ESG practices. Investors may incorporate the factors of board gender diversity and corporate ESG performance into their investment decisions.
Statistical methods in cancer research. Vol. 1. The analysis of case-control studies.

Substantial advances have been made over the past 10 years or so in the study of statistical techniques for the analysis of observational (as opposed to experimental) data. Partly as a result of this, case-control studies have come into increasing favour, and they are now one of the commonest forms of epidemiol-ogical Other CABI sites 
The historical role of scholars and public intellectuals in Uganda’s post-independence politics: A critical study of the gang of four

This paper explores the contributions of four eminent political figures namely Edward Bitanywaine Rugumayo, Dani Wadada Nabudere, Omwony Ojok and Yashpal Tandon to the post-independence politics of Uganda. The quartet belonged to the anti-Amin groups in exile who participated in the Moshi conference in Tanzania with the support of President Julius Nyerere. They were dubbed the gang of four by President Godfrey Binaisa during his short-lived regime in 1979. The four had been members of the National Consultative Council (NCC) which served as the legislative arm of the UNLF government under Yusuf Lule and Godfrey Binaisa. Edward Bitanywaine Rugumayo chaired the NCC; in other words, he was the speaker of the then National Assembly. During his tenure as the chairman of the NCC, Lule was removed from office after only 68 days. After Lule’s overthrow, Godfrey Lukongwa Binaisa was sworn in as president but shortly, he was also overthrown by the military commission under the leadership of Paulo Muwanga and Yoweri Museveni. The gang of four later to played a very critical role in academia and taught in many universities both within and outside Africa. The four never participated in the 2nd Obote regime; they were all in exile. However, they resurfaced after the fall of the Obote II and two of them; Edward Rugumayo and Omwony Ojok worked closely with President Museveni during his lengthy NRM regime and served in different capacities.