Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing Northern forests for black bears

— Wild, radio-collared black bears (Ursus americanus) that became habituated to close (0-12 ft) observation by researchers showed activity patterns and habitat use patterns similar to those of radio-collared bears monitored from airplanes. The habituated bears were active mainly by day, sleeping from 1-3 hours after sunset until dawn with periodic naps through the day. Food, shade, security, and water were important habitat components. In early spring, the grass understory of forested wetlands provided the majority of food although these wetlands comprised <1% of the study area. Wetlands were also used for cooling. Dense thickets of balsam fir (Abies balsamea) saplings were preferred for shade. White pines (Pinus strobus) >20 in dbh located <200 yds from forested wetland feeding areas provided the majority of refuge trees for mothers with cubs in early spring although these trees comprised <1% of all trees >10 in dbh. Late spring diets included newly sprouting upland forbs, expanding aspen (Populus tremuloides) leaves, and ants. In summer, berries, nuts, ants, and forbs were major foods. Forest edges, mature forests, marshes and moist areas all contained major foods. The most important fall food is acorns. Management recommendations are included for each season. ======================================================================================================== INTRODUCTION The original range of the black bear (Ursus americanus) coincided with forested regions throughout North America (Pelton 1982). That =============================================== A paper presented at the Society of American Foresters National Convention held at Minneapolis, Minnesota on October 20, 1987. Lynn L. Rogers, Wildlife Biologist, North Central Forest Experiment Station, USDA Forest Service, 1992 Folwell Ave., St. Paul, MN 55108. Gregory A. Wilker, Biological Aid, North Central Forest Experiment Station, USDA Forest Service, 1992 Folwell Ave., St. Paul, MN 55108. Arthur W. Allen, Wildlife Biologist, National Ecology Center, USDI Fish and Wildlife Service, 2627 Redwing Road, Port Collins, CO 80526. range has been reduced through forest removal or fragmentation and continues to be reduced by an expanding human population. However, extensive range remains, especially in northern or mountainous regions unsuitable for commercial agriculture. Many of these areas are managed for timber production, recreation, and wildlife, but more detailed information on bear habitat requirements is needed. A study was conducted to obtain habitat information that managers can integrate into resource management plans and use in opportunity area planning and implementation.
Population Genetics of Anopheles coluzzii immune pathways and genes . 1

Population Genetics of Anopheles coluzzii immune pathways and genes. 1 2 Susan M. Rottschaefer*, Jacob E. Crawford§, Michelle M. Riehle†, Wamdaogo M. 3 Guelbeogo‡, Awa Gneme‡, N’Fale Sagnon‡, Kenneth D. Vernick†**, Brian P. Lazzaro* 4 5 *Department of Entomology, Cornell University, Ithaca, NY 14853 6 §Department of Integrative Biology, University of California, Berkeley, Berkeley, CA 94720 7 †Department of Microbiology, University of Minnesota, St. Paul, MN 55108 8 ‡Centre National de Recherche et de Formation sur le Paludisme, 01 BP 2208 Ouagadougou, 9 Burkina Faso. 10 **Unit of Insect Vector Genetics and Genomics, Institut Pasteur, Paris, France 11 12 G3: Genes|Genomes|Genetics Early Online, published on December 30, 2014 as doi:10.1534/g3.114. 014845
A Review of its Pharmacological Properties and Therapeutic Use in Non-Insulin-Dependent Diabetes Mellitus

Various sections of the manuscript reviewed by: C. /. Bailey, Department of Pharmaceutical Sciences, Aston University, Birmingham, England; D.P. Campbell, Department of Diabetes and Endocrinology, Princess Alexandra Hospital, Woolloongabba, Brisbane, Queensland, Australia; 1. W. Campbell, Diabetic Department, Victoria Hospital, Kirkcaldy, Fife, Scotland; D. Ciugliano, Dipartimento di Gerontologia, Geriatria e Malattie del Metabolismo, Seconda Universita degli Studi di Napoli, Naples, Italy; L.S. Hermann, Department of Community Health Sciences, University of Lund, Lund, Sweden; O. Hother-Nielsen, Department of Medical Endocrinology, Odense University Hospital, Odense, Denmark; A. Klip, Division of Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada; K. Landin, Department of Medicine, Sahlgrenska Hospital, Goteborg, Sweden; P. Marchetti, Cattedra di Malattie del Metabolismo, Universita degli Studi di Pisa, Istituto di Clinica Medica II, Pisa, Italy; R. Tattersall, Queen's Medical Centre, University Hospital, Nottingham, England; E. Widen, Department of Endocrinology, University of Lund, Malmo University Hospital, Malmo, Sweden; C.c. Weir, Joslin Diabetes Center, Boston, Massachusetts, USA.
Chromosomal Microarray Testing for the Evaluation of Pregnancy Loss

Clinical guidelines and recommendations to address the management of cases of miscarriage or intrauterine fetal demise where genetic analysis of the embryo, fetus, or stillborn infant is indicated. These guidelines, which specifically address the use of karyotyping and/or microarray testing in miscarriage or intrauterine fetal demise, were developed by several reproductive health associations, including the American Society for Reproductive Medicine (ASRM [2013]; ASRM [2012]), the National Society of Genetic Counselors (Laurino et al [2005]), and the American College of Obstetrics and Gynecology (ACOG [2009]). According to such guidelines, genetic testing may be indicated (if desired by parents):
The Interface Between Forensic Science and Technology1 How technology could cause a paradigm shift in the role of forensic institutes in the criminal justice system

In this paper the importance of modern technology in forensic investigations is discussed. Recent technological developments are creating new possibilities to perform robust scientific measurements and studies outside the controlled laboratory environment. The benefits of real time, on site forensic investigations are manifold and such technology has the potential to strongly increase the speed and efficacy of the criminal justice system. However, such benefits are only realised when quality can be guaranteed at all times and findings can be used as forensic evidence in court. At the Netherlands Forensic Institute innovation efforts are currently undertaken to develop integrated forensic platform solutions that allow for the forensic investigation of human biological traces, the chemical identification of illicit drugs and the study of large amounts of digital evidence. These platforms enable field investigations, yield robust and validated evidence and allow for forensic intelligence and targeted use of expert capacity at the forensic institutes. This technological revolution in forensic science could ultimately lead to a paradigm shift in which a new role of the forensic expert emerges as developer and custodian of integrated forensic platforms. 1This article was published as: Kloosterman A, Mapes A, Geradts Z, Eijk van E, Koper C, Berg van den J, Verheij S, Steen van der M, Asten van A. The interface between forensic science and technology: how technology could cause a paradigm shift in the role of forensic institutes in the criminal justice system. Philosophical Transactions of the Royal Society B, 2015:370(1674); 2014.0264. This article was written under supervision and direction of A van Asten with contributions from all authors.
The Value of 18 F-FDG PET/CT in avoiding overtreatment of 131 l Avidity Pulmonary Metastasis of Differentiated Thyroid Cancer

Background: We usually use 131 I whole body scan and serum thyroglobulin (Tg) values to determine whether differentiated thyroid cancer (DTC) patients need to receive 131 I treatment, but not all 131 I-avid (functioning) patients have good responses, which is more likely to cause the 131 I-avid patient to receive overtreatment. Our study aims to assess the date of 18 F-FDG PET/CT to avoid 131 I overtreatment and research the status of 131 I-avid pulmonary metastases (PMs) and the prognosis of the patients. Methods: The 131 I-avid PMs of DTC patients who underwent 18 F-FDG PET/CT scans were included. The SUVmax (maximum standardized uptake value), MTV (metabolic tumour volume) and TLG (total lesion glycolysis) were used to estimate 18 F-FDG uptake. The mean follow-up period was 34.14 ± 18.64months. Progression-free survival (PFS) was estimated by the Kaplan-Meier method.The study was based on per-patient and per-lesion analyses. Results: Among the 42 included patients, 34 (34/42, 81%) showed 18 F-FDG uptake, which was de�ned as abnormal foci (SUVmax >1.0) in the lungs. SUVmax, MTV and TLG and tumour size were the factors that in�uenced the outcome of 131 I treatment based on Tg levels (p=0.000,0.016,0.000,0.000). The only independent factor was the size of the lesion. There was a signi�cant difference in response to 131 I therapy between PMs with F-I+ and F+/I+ according to both Tg levels and RECIST (version 1.1) (p= 0.044,0.001), according to the per-lesion analysis. When the changes in size or metabolism of some lesions are inconsistent with therapeutic e�cacy of patients, it indicates that these patients have a poor prognosis (P=0.003). Conclusions: We concluded that higher 18 F-FDG uptake and larger tumour size predict poor therapeutic effects
A Load Detection Method in Multi-transmitter Dynamic Wireless Power Transfer Systems without Extra Sensors and Communication

This paper presents a method to detect the load power at the secondary side through sampling the voltage of the parallel resonant capacitor at the primary side without extra sensors and communications for the discrete-coil type dynamic wireless power transfer system. The proposed method can maintain sensitivity and stability of the load detection when the receiver moves on the lumped primary coil. A discrete-coil type dynamic wireless power transfer system (DWPTS) is built. The effectiveness of the proposed method is well verified in the experiments results with various output power when Po = 0, 3.075W, 7.302W, respectively.
A Review of Vision SLAM-based Closed-loop Inspection

As unmanned technology and virtual reality have developed rapidly, visual simultaneous localization and map building (SLAM) has become an important research area. Scene description is one of the most challenging problems in the field of visual SLAM. Creating image descriptors is one of the most fundamental problems in scene recognition. During long-term use, these descriptors must be insensitive to changes in illumination and viewpoint. The purpose of this paper is to give a brief introduction to visual SLAM closed-loop detection and to summarize existing solutions for scene recognition in two directions, traditional methods and deep learning, as well as to analyze the advantages and disadvantages of these methods. Finally, we look forward to the directions that can help drive the future development of closed-loop detection scene description technology.
Monolithically 3D-Printed, Miniature Solenoids with Soft Magnetic Core for Compact Systems

We report the design, fabrication, and characterization of the first monolithically 3D-printed, three-dimensional, three-material, cored inductors for use in compact electromagnetic systems. Additive manufacturing technologies can produce functional, freeform-compatible mechanical devices; however, the ability of additive manufacturing to create electronic components that could enable the monolithic fabrication of devices that integrate sensors and actuators is still unfulfilled. This study aims at advancing the state of the art through the demonstration of the first truly three-dimensional, monolithically 3D-printed, cored solenoids. The novel 3D-printed inductors are made via material extrusion using three PLA-based materials (dielectric, conductive, and soft magnetic). The devices attain Gauss-level magnetic fields while drawing tens-of-milliamps currents. The work is of great interest for fast and inexpensive fabrication of compact sensors, actuators, and power conversion systems, and for the integration of electronics with MEMS devices of intricate geometries.
Using Machine Learning and SEM to Analyze Attitudes towards adopting Metaverse in Higher Education

The Metaverse has become a highly discussed topic in recent times, as it has the potential to transform many aspects of our lives. From banking and investing to real estate, manufacturing, and education, the Metaverse could change how we operate in many industries. This research paper aims to investigate the level of user acceptance and attitude toward the integration of the Metaverse technology into higher education in Bahrain and Jordan by employing the Technology Acceptance Model along with three external variables, self-efficacy, subjective norms, and perceived behavior control. A two-stage analysis was performed, consisting of structural equation modeling and machine learning classification algorithms. SEM results suggest that self-efficacy and social norms positively influenced perceived usefulness and ease of use, it is also found that perceived ease of use and perceived usefulness significantly affected users’ attitudes toward using this technology. Machine learning findings supported SEM results and indicated that J48, LogitBoost, and PART classifiers have achieved the highest accuracy.
A Deep Learning-Based Segmentation Strategy for Diabetic Foot Ulcers: Combining the Strengths of HarDNet-MSEG and SAM Models

The primary objective of this study is to investigate and propose a reliable and accurate method for segmenting Diabetic Foot Ulcers (DFU) wounds. DFU is a prevalent complication among diabetic patients that can have severe consequences if not promptly addressed. However, the segmentation of DFU wounds poses a complex challenge due to variations in symptom color, size, and contrast, which can vary depending on the severity of the condition. Furthermore, challenges such as image noise, lighting and contrast variations, and labeling difficulties further complicate the taskTaking advantage of the rapid advancements in deep learning and its application to image segmentation, this study introduces a robust DFU segmentation model based on deep learning techniques. The proposed model aims to achieve accurate and precise segmentation of DFU wounds, addressing the aforementioned challenges.To assess the effectiveness of our segmentation strategy, we evaluated its performance using the public database of the 2022 DFU Segmentation Challenge. The results obtained demonstrate that our model achieves an average Dice coefficient of 83.44%, a substantial improvement compared to the average Dice coefficient of 72.87% achieved by other participants. These results serve as compelling evidence that our segmentation method successfully achieves high-precision segmentation of DFU wounds.
Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter

Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter Peter C. Niedfeldt Department of Electrical and Computer Engineering, BYU Doctor of Philosophy Multiple target tracking (MTT) is the process of identifying the number of targets present in a surveillance region and the state estimates, or track, of each target. MTT remains a challenging problem due to the NP-hard data association step, where unlabeled measurements are identified as either a measurement of an existing target, a new target, or a spurious measurement called clutter. Existing techniques suffer from at least one of the following drawbacks: divergence in clutter, underlying assumptions on the number of targets, high computational complexity, time-consuming implementation, poor performance at low detection rates, and/or poor track continuity. Our goal is to develop an efficient MTT algorithm that is simple yet effective and that maintains track continuity enabling persistent tracking of an unknown number of targets. A related field to tracking is regression analysis, where the parameters of static signals are estimated from a batch or a sequence of data. The random sample consensus (RANSAC) algorithm was developed to mitigate the effects of spurious measurements, and has since found wide application within the computer vision community due to its robustness and efficiency. The main concept of RANSAC is to form numerous simple hypotheses from a batch of data and identify the hypothesis with the most supporting measurements. Unfortunately, RANSAC is not designed to track multiple targets using sequential measurements. To this end, we have developed the recursive-RANSAC (R-RANSAC) algorithm, which tracks multiple signals in clutter without requiring prior knowledge of the number of existing signals. The basic premise of the R-RANSAC algorithm is to store a set of RANSAC hypotheses between time steps. New measurements are used to either update existing hypotheses or generate new hypotheses using RANSAC. Storing multiple hypotheses enables R-RANSAC to track multiple targets. Good tracks are identified when a sufficient number of measurements support a hypothesis track. The complexity of R-RANSAC is shown to be squared in the number of measurements and stored tracks, and under moderate assumptions R-RANSAC converges in mean to the true states. We apply R-RANSAC to a variety of simulation, camera, and radar tracking examples.
Research on Adaptive Density Normalized Crowd Counting Algorithm

Aiming at the problems of uneven distribution of crowds in static crowd images, changes in head size, and diverse crowd densities, the accuracy of crowd counting decreases. This paper proposes an adaptive density-normalized crowd counting network YOLO-FPDM-DNM. , the network improves the Darknet-53 structure, improves the feature extraction ability, introduces adaptive geometric propagation parameters in the Gaussian density module to solve the problem of head size change, and designs an adaptive density normalization module to solve the problem of excessive crowd density in some areas. The resulting network training is difficult and the accuracy rate is reduced. The experimental results show that compared with the DM-Count network structure with the best effect in the UCFQNRF dataset in recent years, the MAE is reduced by 2.9. MSE decreased by 4.7.
Standardized Integration of Source Systems into Asset Administration Shell Realizations

The Asset Administration Shell is the key concept for the exchange of asset information in Industrie 4.0 across the entire life cycle of an asset. Therefore, the Asset Administration Shell acts as the central interface to access asset information, which is stored in different source systems, e.g. databases or control units. To achieve this vision, this paper presents the requirements for the standardized integration of data from these source systems into Asset Administration Shell applications and a generic concept for easy and vendor-independent applicability. The concept is demonstrated with two example implementations and evaluated against the requirements.
A Robust Bio-Inspired Sensory orientation Processing Technique for Polarization Compass Overcoming Weak Polarization Characteristics

Seeking to tackle the interference of the abnormal pixels at the occlusion in the sky polarization pattern, the effective feature area conforming to the Rayleigh scattering model needs to be quickly acquired and utilized with higher quality and faster speed. A robust bio-inspired sensory orientation method for polarization compass is developed in this article. Our proposed method extracts the valid areas distributed in the sky polarization mode excellently, and reduces the influence of cloud coverage associated. With regard to the extracted polarization image, we innovatively use the bio-inspired visual cell of the hoverfly to enhance the details of the weak distribution of the occlusion. The state characteristics of the positive and negative feature points are preserved. As a result, the valid polarization pixels in the sky are utilized to a greater extent, leading to obtain the angle between the carrier and the solar more accurately, so as to meet the real-time high-precision navigation requirements of the polarization compass overcoming weak polarization characteristics.
Qi Standard Compatible Metasurface for Multi-Device Wireless Power Transfer with Tunable Power Division

Multi-user wireless power transfer (WPT) systems represent the next-generation technology for mobile device charging under the Qi standard. To achieve this goal, conventional techniques typically employ a complex system with multiple transmitting coils and power electronic circuits with independent feeding channels. In this paper, we demonstrate a metasurface-enhanced WPT system capable of achieving this goal with a single transmitting coil. The metasurface passively reshapes the magnetic field into multiple beams at the receivers’ locations. We demonstrate to improve the dual-receiver efficiency from 55.9% to 71.9%. We also demonstrate tuning the power division between the receivers and compensating power differences to receivers of different sizes. The power ratio between a larger and smaller receiver is adjusted from 1:0.12 to 1:1.20 with the metasurface.
A Robust Vector Control of Permanent Magnet Synchronous Machines Resilient to Parameter Uncertainty

Accurate flux estimation is important for high-performance control of the Permanent Magnet Synchronous Machines (PMSMs). Parameter uncertainty reduces this accuracy and degrades the controller’s performance. This paper presents a vector control of PMSMs which utilizes a combination of the flux estimation based on current and voltage models of PMSMs via a weighting factor to ensure the best performance at all speeds for excitation at dominant frequencies. The weighting factor can be chosen so that the controller can effectively act as a flux regulator or a current regulator. Furthermore, this paper introduces a framework to formulate the system dynamics suitable for robust analysis and determine the Worst Case Gain (WCG) of the reference tracking transfer function of the closed loop system. The weighting factor is optimized in order to minimize the WCG in presence of parameter uncertainty for inductances and resistance. Using this analysis, the proposed regulator can benefit from the optimal blending of the two flux estimation methods and is robust to parameter uncertainty compared to conventional current and flux regulators.
Boost-SEPIC Interleaved PFC Converter

This paper proposes a new power factor correction (PFC) converter based on the boost-SEPIC interleaved (BSI) structure. The inductor current of conventional interleaved boost PFC (IB-PFC) can easily become unbalanced due to the tolerance of components. The proposed PFC converter allows operation with the automatic balancing of phase currents without the complex balancing methods. This is achieved by the interleaved structure that combines boost and SEPIC circuits. Only one current sensor is required to control the input current of the PFC, reducing the complexity of hardware and increasing the reliability of the circuit. Moreover, with high voltage gain and low voltage stress on semiconductor devices, the proposed PFC converter can achieve higher efficiency than conventional IB-PFC. The above advantages make the proposed structure an attractive solution with practical PFC applications. A 1.6 kW prototype was built to evaluate the performance of the proposed converter.