Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of certain balanced rations on the chemical and physical properties of milk fat.

Many results are given in the literature dealing with milk fat which show the effects of different feeds for dairy cows upon the milk fat produced. Amberger (ly found that the feeding of beets caused an appreciable lowering of the iodine number and an increase in the Reichert-Meissl and Polenske numbers. When sucrose was substituted for the beets the changes were similar in direction but not so great. Amberger also found that when malt germs were fed, the Reichert-Meissl number was reduced from 25.48 to 16.70 and the iodine number was increased from 32.1 to 39.2. Siegfeld {14j 15), Fritzsche (5), and Luhrig, Hepner, and Blau {13), studied the influence of beet leaves and tops on the composition of milk fat. Their results showed that the Reichert-Meissl, Polenske, and saponification numbers of the fat were increased while the iodine number and the index of refraction were lowered. Zaitschek {17) found that cows on a turnip ration produced fat having higher Reichert-Meissl and saponification numbers than when on dry feed, and that potatoes acted similarly to turnips. Boes and Weyland {4) fed a ration of sugar beets exclusively. The fat produced gave the following results: Reichert-Meissl number 28.16, Polenske number 6.16, saponification number 234.2, and iodine number 24.22. Kieferle {10) showed that the feeding of silage gave a considerable increase in the Reichert-Meissl number and a slightly higher Polenske number. Lindsey, Holland, and Smith {12) state that soybean meal caused no marked difference in milk fat except a slight increase in the unsaturated acids. Soybean oil, however, caused the saponification number to drop from 231.59 to 221.65, and the Reichert-Meissl from 27.05 to 23.27. The iodine number on the other hand increased from 31.90 to 40.25. Cranfield {5) worked with coconut cake and linseed cake and Cranfield and Taylor {6) with linseed cake and hempseed cake. They showed that when cows were removed from poor pasture to well-balanced rations containing these products a considerable rise in the Reichert-Meissl, Kirschner, and Polenske numbers and a fall in the index of refraction occurred. Smith, Wells, and Ewing {16) report that the feeding of cottonseed oil lowered the saponification number and the soluble fatty acids and increased the insoluble fatty acids. Eckles and Palmer (7) showed that cottonseed products in the ration decreased the saponification and Reichert-Meissl numbers
A Teleoperated MR-Safe Haptic System for Magnetic Resonance Imaging-Guided Prostate Needle Biopsies

Real-time magnetic resonance imaging (MRI) in-terventions are significantly impacted by material compatibility problems and size constraints in the MRI bore. Limited physi-cian access to patients within the bore of the MRI necessitates iterative positioning and imaging, which prolongs the duration of the procedure and increases patient risk. We present a passive MR-safe haptic teleoperation device for prostate needle biopsy inside an MRI machine. The device uses a low-friction hydrostatic transmission based on paired rolling diaphragm actuators, linear and rotary. The device has two degrees of freedom, allowing needle insertion and rotation. The robot produces negligible MR imaging artifacts, has effective positioning tracking, and can reliably detect needle punctures in the clinically relevant range. We describe the design components, system transparency, and perform a needle insertion test.
Implementation of Near Real-Time Onboard Processing Software for a Slocum Glider Acoustic Doppler Current Profiler

Advancements in sensor and battery technology over the past two decades have facilitated the integration of complex instruments into autonomous underwater vehicles. The integration of such instruments onto gliders has enabled unprecedented data collection and expanded understanding of ocean processes. Recently, this understanding has been enhanced through the development of a software architecture called “backseat driver” on Slocum gliders, which can increase the efficiency of data collection by increasing the time the glider spends in regions of interest through adaptive mission control. A caveat to this advancement is that sensors that collect large volumes of data cannot efficiently transmit data to shore in real-time, which limits real-time applications (e.g. operational hurricane forecasting, marine mammal detection, water quality monitoring, etc.) and confines shoreside processing to post recovery. This can potentially lead to loss of data in the event of vehicle loss or missions where retrieval is not permissible, resulting in the need to develop a sensor agnostic real-time processing software and hardware architecture. Here, we detail the methodology that will be applied to test a real-time onboard processing algorithm through the use of the backseat driver software on Slocum glider MARACOOS04. An adjunct processor (Raspberry Pi 4) has been integrated into this glider with a data processing algorithm that will parse, process, and compress raw data from an acoustic Doppler current profiler.
Use of Wearable Sensor Device and Mobile Application for Objective Assessment of Pain in Post-surgical Patients: A Preliminary Study

Effective post-operative pain management requires an accurate and frequent assessment of the pain experienced by the patients. The current gold-standard of pain assessment is through patient self-evaluation (e.g., numeric rating scale, NRS) which is subjective, prone to recall-bias, and does not provide comprehensive information of the pain intensity and its trends. We conducted a study to explore the potential of wearable biosensors and machine learning-based analysis of physiological parameters to estimate the pain intensity. The results from our study of post-operative knee surgery patients monitored over a period of 30 days demonstrate the feasibility of the system in ambulatory setting, with a substantial agreement (Cohen’s Kappa = 0.70, 95% CI 0.68-0.72) between the pain intensity estimation and the patient reported numerical rating scale. Therefore, the wearable biosensors coupled with the machine learning-derived pain estimation are capable of remotely assessing the pain intensity.
Design of STARQ: A Multimodal Quadrupedal Robot for Running, Climbing, and Swimming

Legged animals have developed a variety of modes of locomotion to adapt to the diverse and unknown terrain challenges posed in the natural world. Legged robots, however, have been largely limited to specializing in one domain, with few that have endeavored to bridge the gap between two. In this work we present the Scansorial, Terrestrial, and Aquatic Robot Quadruped (STARQ), a novel legged robot capable of bridging three different domains with three modes of locomotion: walking, climbing, and swimming. In this study we describe model-based design techniques as well as design innovations that have made multimodal locomotion possible including waterproof hips for 2-DOF high torque legs, legs capable of effective power transmission in three modes, and bi-directionally compliant feet for walking and attaching to vertical surfaces. To demonstrate the robot's capabilities we present locomotion test data including speed and cost of transport in each of these domains. We also demonstrate the capability to transition from walking to swimming in a natural environment.
Brain Connectome Imaging Markers Research of Glucose Metabolism in the Early Stage of Alzheimer’s Disease

In recent years, increasing evidence had suggested that subjective cognitive decline (SCD) in unimpaired individuals may be the first symptom of Alzheimer’s disease (AD). This study investigated the differences in the glucose metabolism network and the influence of the Apolipoprotein E (ApoE) gene between the SCD and normal control (NC) group by using graph theory. In this study, we included 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital in Beijing, China. 85 SCD subjects and 74 NC subjects were included. First, we calculated and compared network parameters between the two groups. We then identified the bilateral insula and bilateral parahippocampal gyrus as seed sites and studied the connections to the whole brain. The results showed that both the SCD and the NC showed small-world nature, but the metabolic network of SCD tended to be more regular. The clustering coefficient and local efficiency of SCD were significantly higher than those of NC (P<0.05). In addition, we found that carrying APOE resulted in enhanced metabolic connectivity, but with weaker aggregation and local information exchangeability. Our results suggested that there are differences in the glucose metabolic brain network between SCD and NC, suggesting that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.Clinical relevance— This study suggests that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.
A Dual-Scale Convolutional Neural Network for Sleep Apnea Detection with Time-Delayed SpO2 Signals

Sleep apnea (SA) is a common breathing disease, with clinical manifestations of sleep snoring at night with apnea and daytime sleepiness. It could lead to ischemic heart disease, stroke, or even sudden death. SpO2 signal is highly related to SA, and many automatic SA detection methods have been proposed. However, extant work focuses on small datasets with relatively few subjects (less than 100) and is unaware of SA syndromes occurring about 5 seconds prior to the SpO2 change. This study proposes an automatic SA detector called DSCNN using a single-lead SpO2 signal with a dual-scale convolutional neural network. To solve the time-delayed problem of SpO2 changes, we enlarge the target SpO2 segment information by combining its subsequent segment information. To utilize neighbouring segments information and further facilitate the SA detection performance, a dual-scale neural network with the fusing information of the prolonged target segment and its two surrounding segments is proposed. Three datasets from multiple centres are employed to verify the generic performance of DSCNN. Here, we must point out that we use two datasets as external datasets, and one of them is collected from the First Affiliated Hospital of Sun Yat-sen University with a large sample size (450 subjects). Extensive experiment results show that DSCNN can achieve promising results which are superior to the existing state-of-the-art methods.
An Improved Anchor-Free Nodule Detection System Using Feature Pyramid Network

Lung cancer (LC) is the leading cause of cancer death. Detecting LC at the earliest stage facilitates curative treatment options and will improve mortality rates. Computer-aided detection (CAD) systems can help improve LC diagnostic accuracy. In this work, we propose a deep-learning-based lung nodule detection method. The proposed CAD system is a 3D anchor-free nodule detection (AFND) method based on a feature pyramid network (FPN). The deep learning-based CAD system has several novel properties: (1) It achieves region proposal and nodule classification in a single network, forming a one-step detection pipeline and reducing operation time. (2) An adaptive nodule modelling method was designed to detect nodules of various sizes. (3) The proposed AFND also establishes a novel center point selection mechanism for better classification. (4) Based on the new nodule model, a composite loss function integrating cosine similarity (CS) loss and SmoothL1loss was designed to further improve the nodule detection accuracy. Experimental results show that the AFND outperforms other similar nodule detection systems on the LUNA 16 dataset.
Joint Offloading Policy and Resource Allocation in IRS-aided MEC for IoT Users with Short Packet Transmission

This paper focuses on leveraging a mobile edge computing (MEC) server at an access point (AP) to address the delay and reliability sensitivity requirement of multi-user machine-type communication (MTC). By offloading tasks to the MEC server, latency for low-power MTC devices can be minimized. Meanwhile, intelligent reflecting surfaces (IRSs) are supported to facilitate robust offloading, enhance spectrum efficiency, and improve coverage by influencing incident radio-frequency wave propagation via modifying the phase shifts with passive reflecting components. Therefore, we investigate joint radio resource allocation and edge offloading decision optimization in a multi-user IRS-assisted MEC network, wherein a multi-antenna AP receives information symbols from a set of Internet of Things (IoT) users with short packet transmission. In particular, we minimize the system’s power utilization subject to offloading MTC-enabled IoT users’ quality of service (QoS) requirements, transmit power feasibility, capacity limitation, and IRS phase shift. The non-convex nature of the formulated problem poses a challenge to solving it effectively. To address this, we propose an efficient iterative algorithm based on successive convex approximation (SCA) and a penalty-based approach for handling unit-modulus constraints in the presence of passive reflecting elements at the IRS. Simulation results demonstrate the superior performance of our algorithm compared to other baseline schemes.
Dynamic cortical connectivity alterations associated with Major Depressive Disorder: an EEG study

There are various depressive subtypes identified in patients with major depressive disorder (MDD). Depression with psychotic symptoms is usually known to be a severe type of depression that includes symptoms such as delusions and/or hallucinations, and remains a common condition that is often underrecognized and inadequately treated in clinical practice. Electroencephalography (EEG) biomarkers have been implicated to classify healthy and psychopathological neural signals using machine learning algorithms. In this study, we sought to identify cortical functional connectivity metrics that differentiate network manifestation of different depressive subtypes and healthy controls. We first performed replication analyses to obtain the principal functional connectivity microstates across each independent group (healthy controls, psychotic depressions and nonpsychotic depressions). Next, we examined temporal functional connectivity dynamics in each group. The results show that fundamental dynamic functional connectivity microstates are highly reproducible, both within and across participants. Based on the temporal and sequential parameters (mean duration, fractional windows and transition number) derived from dynamic functional connectivity analysis, we found inter-group differences across healthy and MDD subgroups statistically significant. These results show that the principal FC microstates dynamics are essential neural biomarkers distinctly associated with depression clinical phenotypes.Clinical relevance—Our findings suggest that a network-level feature, that may reflect the neurobiological difference between different depression subtypes, and healthy controls, and in turn may contribute towards a scalable EEG-based assisted diagnostic tool.
Shape Control of Variable Length Continuum Robots Using Clothoid-Based Visual Servoing

In this paper, we present a novel clothoid-based visual servoing method for controlling the shape of a variable length continuum manipulator. Clothoids are curves with linearly changing curvature. They allow us to obtain a smooth representation of a continuum manipulator's shape in a compact form with few parameters. Using this curve model, we generate image features that are used in an adaptive visual servoing method to drive the robot to a desired shape. The adaptive algorithm estimates and updates a local interaction matrix that maps the rate of change in clothoid features to actuator velocities of the continuum manipulator. As such, the method does not require any robot model or even actuator encoder measurements and only uses the visual clothoid features to control the robot shape. A unique advantage of using our clothoid representation is being able to generate reference shape curves without the need for taking images of the robot at the desired shapes. Experiments demonstrate successful shape and end effector pose convergence for a diverse set of references. Our repeatability tests demonstrate that the system performance is consistent. Notably, we also present the first results in the literature for the vision-based shape control of a variable length continuum robot, extending and contracting to achieve the desired shape.
Automatic Annotation of PubMed Articles with MeSH Qualifiers*

The Medical Subject Headings (MeSH) is a comprehensive indexing vocabulary used to label millions of books and articles on PubMed. The MeSH annotation of a document consists of one or more descriptors, the main headings, and of qualifiers, subheadings specific to a descriptor. Currently, there are more than 34 million documents on PubMed, which are manually tagged with MeSH terms. In this paper, we describe a machine-learning procedure that, given a document and its MeSH descriptors, predicts the respective qualifiers. In our experiment, we restricted the dataset to documents with the Heart Transplantation descriptor and we only used the PubMed abstracts. We trained binary classifiers to predict qualifiers of this descriptor using logistic regression with a tfidf vectorizer and a fine-tuned DistilBERT model. We carried out a small-scale evaluation of our models with the Mortality qualifier on a test set consisting of 30 articles (15 positives and 15 negatives). This test set was then manually re-annotated by a cardiac surgeon, expert in thoracic transplantation. On this re-annotated test set, we obtained macroaveraged F1 scores of 0.81 for the logistic regression model and of 0.85 for the DistilBERT model. Both scores are higher than the macroaveraged F1 score of 0.76 from the initial PubMed manual annotation. Our procedure would be easily extensible to all the MeSH descriptors with sufficient training data and, we believe, would enable human annotators to complete the indexing work more easily.Clinical Relevance—Selecting relevant articles is important for clinicians and researchers, but also often a challenge, especially in complex subspecialties such as heart transplantation. In this study, a machine-learning model outperformed PubMed’s manual annotation, which is promising for improved quality in information retrieval.
Decentralized Optimization in Scheduling Virtual Power Plant (VPP)

The scheduling of virtual power plants (VPPs) has received much attention in the last few years. VPP refers to the integration of several power plant units together, which is considered as a single power plant. In this paper, three VPPs are considered, each of which includes different power plant units and must supply the desired load. Besides supplying the desired load, they should maximize their profit. Decentralized optimization method has been used to optimize these three VPPs. The reason for using decentralized method is to increase network security and also to not need a central computer. On the other hand, using the decentralized optimization method increases the speed of problem solving. Finally, the obtained results have been compared with the centralized method. The simulations show that almost the same result has been obtained by using different optimization methods. These results increase the tendency to use decentralized methods in VPPs.
Development of an Instability Line under a Cut-off Low

On July 7, 1970 an instability line occurred in the vicinity of Kyushu in the southeast quadrant of a cut-off cold low over the Korean Penninsula. In this paper the development of the instability line on July 6 is investigated, which was not treated in detail by the previous investigators who studied its situation on July 7. It is shown that the instability line formed on the morning of July 6 in the northern East China Sea in the southwest quadrant of the cut-off low. Intensification of the warm advection upstream of the cold core after the complete formation of the cut-off low produced large-scale conditions favorable for the development of cumulus convections in the northern East China Sea. It is considered that the cumulus convections were organized into the instability line by a low-level jet stream which had occurred along the zone of concentrated temperature gradient in the lower troposphere due to the combined influence of the cut-off low and a lower cyclone resulting from a decayed typhoon before the development of convections.
Aquaeye.ai: Towards Development, Deployment and Distribution of Novel Artificial Intelligence for Aquariums

The Philippine Coral Reef exhibit within the California Academy of Sciences contains on the order of 100 species of fish and over 1,000 fish. It has multiple visual and auditory aids to help its viewers undertake a more engaged position with respect to it, e.g. live video feeds, kiosks as well as live dive shows in order to educate the audience on both the marine ecosystem represented by the exhibit and the importance of conservation. However, there is little aid that gives the audience live and accurate feedback on the species of fish in front of them. It is difficult as a lay person (often even as a professional) to determine the species of fish they are viewing in the exhibit due to the large number of fish species and fish specimens. This shortcoming is poorly addressed in public aquariums and remains an important issue to properly educate visitors on issues such as conservation and ecological diversity. To facilitate solutions to these problems, we present Aquaeye.ai, a platform completed in March 2021, that has taken steps towards greater public engagement and outreach at public aquariums by providing a suite of perception analytics currently including localization and classification for the environments of public aquariums. Using advancements in object recognition and classification for imagery and video data, the practice of transfer learning and modern web development tools, Aquaeye.ai, a cloud-hosted platform, serves models for localization and species classification of fish within the Reef Lagoon exhibit at the California Academy of Sciences (*) using a live webcam of the exhibit. Additionally, it serves data of relevance to ecology and conservation through a simple and powerful interface. We present our curated datasets of and classification/localization models for the fish species within the Reef Lagoon and Philippine Coral Reef exhibits.
2-DOF Robot Arm with Variable Torque Limiters Realized by Electrostatic Film Motors

When a conventional robot arm, driven by magnetic motors and reduction gears, operates at high speed, it can produce an excessive force due to unexpected contact. Toward the development of a safe robot arm, the present study investigated a 2-degree-of-freedom robot arm driven by direct-drive electrostatic film motors. The motors operate synchronously, except when they are subjected to an excessive force. This loss of synchronicity is referred to as a “step-out” process, and in the present study it was used to produce a variable torque limiter. By setting appropriate limits, contact forces caused by unexpected collisions could be suppressed. The torque limiter was applied to a robot arm with a length of 50 cm. Experiments showed that the arm was impact-resistant; when it was struck by a hammer, the torque limiter was activated and suppressed the collision force, allowing the robot arm to successfully resume its motion. When the robot arm unexpectedly hit an obstacle while moving at 1.2 m/s, the collision force was maintained at 21 N by the torque limiter, which is sufficiently small for a high-speed collision. The experiments verified the effectiveness of the electrostatic film motor as a variable torque limiter to realize safe contact.