Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration of Off-the-Shelf Anisotropic Magnetoresistance Magnetometers

Magnetometers based on the anisotropic magnetoresistive effect are used in many applications for orientation determination, by measuring the magnetic field of the Earth. As sensors of this type are commercial, off-the-shelf components, manufacturers provide limited information on their measurement performance. Therefore, we present a (to date) unprecedented comprehensive calibration study on three state-of-the-art digital anisotropic magnetoresistance magnetometers, to precisely determine various performance parameters and stability across different sensors of the same model. With the evaluation of sensitivity, noise, offset, and orientation determination, as well as considering dependencies on temperature and frequency, the performance of each sensor can be improved significantly, enabling their implementation in demanding fields of application (such as in satellites). Different measurement and calibration techniques, specifically aimed at the characteristics of the examined magnetometers, were utilized, using a sophisticated magnetic laboratory that has served as a calibration facility for several interplanetary space missions. Our study allows operators to decide whether to consider anisotropic magnetoresitance magnetometers for their application and, more importantly, to be able to (at least partially) skip a time-intensive and complicated calibration by using the sensor parameters given in this paper. To that end, the most promising sensor is recommended. The sensor examination suggests a good comparability of different sensors of the same model, and shows the importance of noise regarding the sensor performance with a noise floor up to 124 nT/Hz at 1 Hz. Additionally, depending on the sensor model, the sensitivity is 14 nT at best, and the attitude determination error can be reduced to about 0.3° with the given calibration.
CXCR4 signaling at the ovine fetal–maternal interface regulates vascularization, CD34+ cell presence, and autophagy in the endometrium†

Abstract Placenta development is characterized by extensive angiogenesis and vascularization but if these processes are compromised placental dysfunction occurs, which is the underlying cause of pregnancy complications such as preeclampsia and intrauterine growth restriction. Dysregulation of placental angiogenesis has emerged as one of the main pathophysiological features in the development of placental insufficiency and its clinical consequences. The signaling axis initiated by chemokine ligand 12 (CXCL12) and its receptor CXCR4 stimulates angiogenesis in other tissues, and may be central to placental vascularization. We hypothesized that CXCL12-CXCR4 signaling governs the pro-angiogenic placental microenvironment by coordinating production of central angiogenic factors and receptors and regulates endometrial cell survival essential for placental function and subsequent fetal longevity. The CXCR4 antagonist, AMD3100, was used to elucidate the role of CXCL12-CXCR4 signaling regarding uteroplacental vascular remodeling at the fetal–maternal interface. On day 12 postbreeding, osmotic pumps were surgically installed and delivered either AMD3100 or PBS into the uterine lumen ipsilateral to the corpus luteum. On day 20, endometrial tissues were collected, snap-frozen in liquid nitrogen, and uterine horn cross sections preserved for immunofluorescent analysis. In endometrium from ewes receiving AMD3100 infusion, the abundance of select angiogenic factors was diminished, while presence of CD34+ cells increased compared to control ewes. Ewes receiving AMD3100 infusion also exhibited less activation of Akt/mTOR signaling, and elevated LC3B-II, a marker of cellular autophagy in endometrium. This study suggests that CXCL12-CXCR4 signaling governs placental homeostasis by serving as a critical upstream mediator of vascularization and cell viability, thereby ensuring appropriate placental development. Summary Sentence Inhibiting CXCR4 signaling at the fetal–maternal interface during initial placentation results in locally diminished vascularization, suppressed Akt/mTOR signaling, and increased autophagy.
Genetic silencing of striatal CaV1.3 prevents and ameliorates levodopa dyskinesia

Levodopa‐induced dyskinesias are an often debilitating side effect of levodopa therapy in Parkinson's disease. Although up to 90% of individuals with PD develop this side effect, uniformly effective and well‐tolerated antidyskinetic treatment remains a significant unmet need. The pathognomonic loss of striatal dopamine in PD results in dysregulation and disinhibition of striatal CaV1.3 calcium channels, leading to synaptopathology that appears to be involved in levodopa‐induced dyskinesias. Although there are clinically available drugs that can inhibit CaV1.3 channels, they are not adequately potent and have only partial and transient impact on levodopa‐induced dyskinesias.
Clot Waveform Analysis for Hemostatic Abnormalities

Clot waveform analysis (CWA) observes changes in transparency in a plasma sample based on clotting tests such as activated partial thromboplastin time (APTT), prothrombin time (PT), and thrombin time (TT). Evidence indicates that not only an abnormal waveform but also peak times and heights in derivative curves of CWA are useful for the evaluation of hemostatic abnormalities. Modified CWA, including the PT with APTT reagent, dilute PT (small amount of tissue factor [TF]-induced clotting factor IX [FIX] activation; sTF/FIXa), and dilute TT, has been proposed to evaluate physiological or pathological hemostasis. We review routine and modified CWA and their clinical applications. In CWA-sTF/FIXa, elevated peak heights indicate hypercoagulability in patients with cancer or thrombosis, whereas prolonged peak times indicate hypocoagulability in several conditions, including clotting factor deficiency and thrombocytopenia. CWA-dilute TT reflects the thrombin burst, whereas clot-fibrinolysis waveform analysis reflects both hemostasis and fibrinolysis. The relevance and usefulness of CWA-APTT and modified CWA should be further investigated in various diseases.
Young and middle‐aged mouse breathing behavior during the light and dark cycles

Unrestrained barometric plethysmography is a common method used for characterizing breathing patterns in small animals. One source of variation between unrestrained barometric plethysmography studies is the segment of baseline. Baseline may be analyzed as a predetermined time‐point, or using tailored segments when each animal is visually calm. We compared a quiet, minimally active (no sniffing/grooming) breathing segment to a predetermined time‐point at 1 h for baseline measurements in young and middle‐aged mice during the dark and light cycles. Additionally, we evaluated the magnitude of change for gas challenges based on these two baseline segments. C57BL/6JEiJ x C3Sn.BliA‐Pde6b+/DnJ male mice underwent unrestrained barometric plethysmography with the following baselines used to determine breathing frequency, tidal volume (VT) and minute ventilation (VE): (1) 30‐sec of quiet breathing and (2) a 10‐min period from 50 to 60 min. Animals were also exposed to 10 min of hypoxic (10% O2, balanced N2), hypercapnic (5% CO2, balanced air) and hypoxic hypercapnic (10% O2, 5% CO2, balanced N2) gas. Both frequency and VE were higher during the predetermined 10‐min baseline versus the 30‐sec baseline, while VT was lower (P < 0.05). However, VE/VO2 was similar between the baseline time segments (P > 0.05) in an analysis of one cohort. During baseline, dark cycle testing had increased VT values versus those in the light (P < 0.05). For gas challenges, both frequency and VE showed higher percent change from the 30‐sec baseline compared to the predetermined 10‐min baseline (P < 0.05), while VT showed a greater change from the 10‐min baseline (P < 0.05). Dark cycle hypoxic exposure resulted in larger percent change in breathing frequency versus the light cycle (P < 0.05). Overall, light and dark cycle pattern of breathing differences emerged along with differences between the 30‐sec behavior observational method versus a predetermined time segment for baseline.
Dynamic mean preserving spreads

We extend the celebrated Rothschild and Stiglitz (1970) definition of Mean Preserving Spreads to a dynamic framework. We consider the unique class of nonlinear scalar stochastic diffusion processes that satisfies the Rothschild-Stiglitz integral conditions. This nonlinear process can be alternatively generated by a random superposition of linear Markov processes with constant drifts. This exceptional representation enables us to systematically revisit, by means of the DMPS property, workhorse economic models based on White Gaussian Noise. A selection of examples is presented and explicitly solved.
Deciphering the genetic architecture of fruit color in strawberry

Abstract Fruits of Fragaria species usually have an appealing bright red color due to the accumulation of anthocyanins, water-soluble flavonoid pigments. Octoploid cultivated strawberry (Fragaria × ananassa) is a major horticultural crop for which fruit color and associated nutritional value are main breeding targets. Great diversity in fruit color intensity and pattern is observed not only in cultivated strawberry but also in wild relatives such as its octoploid progenitor F. chiloensis or the diploid woodland strawberry F. vesca, a model for fruit species in the Rosaceae. This review examines our understanding of fruit color formation in strawberry and how ongoing developments will advance it. Natural variations of fruit color as well as color changes during fruit development or in response to several cues have been used to explore the anthocyanin biosynthetic pathway and its regulation. So far, the successful identification of causal genetic variants has been largely driven by the availability of high-throughput genotyping tools and high-quality reference genomes of F. vesca and F. × ananassa. The current completion of haplotype-resolved genomes of F. × ananassa combined with QTL mapping will accelerate the exploitation of the untapped genetic diversity of fruit color and help translate the findings into strawberry improvement.
Identifying Type II workplace violence from clinical notes using natural language processing

Background: Type II workplace violence in health care, perpetrated by patients/clients toward home healthcare nurses, is a serious health and safety issue. A significant portion of violent incidents are not officially reported. Natural language processing can detect these “hidden cases” from clinical notes. In this study, we computed the 12-month prevalence of Type II workplace violence from home healthcare nurses’ clinical notes by developing and utilizing a natural language processing system. Methods: Nearly 600,000 clinical visit notes from two large U.S.-based home healthcare agencies were analyzed. The notes were recorded from January 1, 2019 to December 31, 2019. Rule- and machine-learning-based natural language processing algorithms were applied to identify clinical notes containing workplace violence descriptions. Results: The natural language processing algorithms identified 236 clinical notes that included Type II workplace violence toward home healthcare nurses. The prevalence of physical violence was 0.067 incidents per 10,000 home visits. The prevalence of nonphysical violence was 3.76 incidents per 10,000 home visits. The prevalence of any violence was four incidents per 10,000 home visits. In comparison, no Type II workplace violence incidents were recorded in the official incident report systems of the two agencies in this same time period. Conclusions and Application to Practice: Natural language processing can be an effective tool to augment formal reporting by capturing violence incidents from daily, ongoing, large volumes of clinical notes. It can enable managers and clinicians to stay informed of potential violence risks and keep their practice environment safe.
The heat is on: Investigating the effect of psychological pressure on competitive performance in elite surfing

ABSTRACT Competitive sport often creates a high-stake and thus a high-pressure environment for its athletes. In the past, research has pointed to the negative effect that competitive pressure might have on skills and movement executions that have been perfected through prior practice. The Attentional Control Theory: Sport (ACTS) suggests that specifically high situational pressure and prior performance failures may negatively affect an athlete’s subsequent performance. This study aimed to investigate the influence of situational pressure and previous performance errors on performance (i.e., wave score) in elite surfing while considering various contextual factors. A total of 6497 actions, performed by 80 elite surfers (female n = 28; male n = 52), were annotated based on video recordings of the 2019 World Championship Tour (WCT). A multi-level model was used to analyse the effect of pressure, previous errors and other contextual factors on the wave scores of individual surfers (i.e., events were nested within athletes). Partially confirming previous research, prior errors caused a significant decrease in surfing performance on the following ride. However, neither a significant effect of situational pressure on performance nor inter-individual differences in how prior-errors and situational pressure affected performance were found.
Description of Tonnoira chuki sp. n. (Diptera: Psychodidae) from Ecuador, with an updated identification key for the genus Tonnoira

Abstract Tonnoira chuki sp. n. is described based on specimens collected in a pre-mountain rainforest in Pichincha Province, northeastern Ecuador. Illustrations and photographs of the new species are provided. The identification key to males of the genus is updated. Furthermore, the first published DNA barcodes (COI) for the genus are provided. Zusammenfassung Tonnoira chuki sp. n. wird anhand von Exemplaren beschrieben, die in einem Vorgebirgsregenwald der Provinz Pichincha im Nordosten Ecuadors gesammelt wurden. Die neue Art wird anhand Illustrationen und Fotos dargestellt. Der Bestimmungsschlüssel für die Männchen der Gattung wird aktualisiert. Zudem werden die ersten DNABarcodes (COI) für die Gattung zur Verfügung gestellt.
Four new Diptilomiopus mite species (Acari: Diptilomiopidae) from China

Abstract Four new eriophyoid mite species of the genus Diptilomiopus Nalepa (Acari: Diptilomiopidae) from China are described and illustrated. They are D. cotoneastrus sp. nov. on Cotoneaster acutifolius Turcz. (Rosaceae), D. clidemioidus sp. nov. on Boehmeria clidemioides var. diffusa (Wedd.) Hand. -Mazz. (Urticaceae), D. quinatus sp. nov. on Vitex quinata (Lour.) F.N. Williams (Lamiaceae), and D. macropodus sp. nov. on Daphniphyllum macropodum Miq. (Daphniphyllaceae). All new species are distributed in the Oriental Region of China. They are vagrants on the leaves causing no apparent symptom to their host plants. We further provided mitochondrial cox1 gene sequences for the four new species.
Bayesian Hierarchical convolutional neural networks

The Hierarchical Bayesian Neural Network (HNN) is a machine learning algorithm that attempts to use the natural hierarchical structure of data. HNN has demonstrated gains in robustness, accuracy, and reporting capabilities by addressing the technical challenge of classifying data at different levels of a hierarchical structure. There is a significant operational benefit in classifying at different levels of an ontology where the extracted knowledge is used for future decision-making, especially when classification at the finest level is unfeasible.
PAHD: perception-action-based human decision making using explainable graph neural networks on SAR images

Synthetic Aperture Radar (SAR) images are commonly utilized in military applications for automatic target recognition (ATR). Machine learning (ML) methods, such as Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), are frequently used to identify ground-based objects, including battle tanks, personnel carriers, and missile launchers. Determining the vehicle class, such as the BRDM2 tank, BMP2 tank, BTR60 tank, and BTR70 tank, is crucial, as it can help determine whether the target object is an ally or an enemy. While the ML algorithm provides feedback on the recognized target, the final decision is left to the commanding officers. Therefore, providing detailed information alongside the identified target can significantly impact their actions. This detailed information includes the SAR image features that contributed to the classification, the classification confidence, and the probability of the identified object being classified as a different object type or class. We propose a GNN-based ATR framework that provides the final classified class and outputs the detailed information mentioned above. This is the first study to provide a detailed analysis of the classification class, making final decisions more straightforward. Moreover, our GNN framework achieves an overall accuracy of 99.2% when evaluated on the MSTAR dataset, improving over previous state-of-the-art GNN methods.
Nuclear canister integrity monitoring using quasi-distributed fiber acoustic sensors and physics-based modeling

Absence of a final repository for nuclear waste has increased attention on dry cask storage systems (DCSSs) which were originally intended for temporary storage, increasing the need for new structural health monitoring paradigms considering safety and environmental impacts. Current integrity inspection requirements consist of periodic manned inspections due in part to the difficulties with real-time monitoring of internal canister conditions without penetrating the canister surface. Here we overview a new approach to nuclear canister integrity structural health monitoring which combines both quasi-distributed fiber optic acoustic (and other) sensing modalities deployed external to the canister as well as physics-based modeling to enable real-time inference of internal canister conditions, including the identification, localization, and classification of various active or incipient failure conditions. More specifically, we overview the vision for the proposed monitoring approach and describe results to date in theoretical physics-based modeling and artificial intelligence-based analytics to accelerate the development of classification frameworks for rapid interpretation of quasi-distributed acoustic and other complementary fiber optic sensing responses. In addition, we describe early results obtained for a quasi-distributed fiber optic sensor network based upon multimode interferometer sensors using an experimental test bed established for dry-cask storage canister sensing experiments. Future work will be overviewed and discussed in the context of expanded scope of the proposed real-time monitoring system and planned field validations.
Rut depth detection for automated trafficability assessment

The passing of a wheeled or tracked vehicle over soft or deformable soil creates ruts. The depth of these ruts is proportional to the weight of the vehicle and the soil trafficability; the ability of the soil to support traffic from vehicles. Assessing soil trafficability is often a manual and labor-intensive process. We evaluate the ability of lidar and depth cameras to detect changes in rut depth with the goal of minimizing manual or automated evaluation via soil strength testing. Our sensor-based approach mimics the process used by human operators when measuring rut depth. We compare this approach with machine-centered approaches with the goal of improving correlation between soil strength measurements and rut depth. In general, we find that all sensors are able to measure rut depth within the uncertainty bounds of soil and rut depth models for light vehicles.
Fiber laser with dual-channel and a selectable comb filter based on Vernier effect of parallel-connected two Mach–Zehnder interferometers

Abstract. We propose and demonstrate the implementation and application of a new configuration of an all-fiber comb filter based on the Vernier effect produced by parallel-connected two in-line Mach–Zehnder interferometers (MZIs). Each in-line MZI was fabricated by fusion splicing a section of panda-type polarization maintaining fiber (PMF) with peanut-shaped tapers between two single-mode fibers. These two in-line MZIs, respectively, form reference and sensing interferometers, which are parallel-connected by two 3-dB optical fiber couplers to realize the Vernier effect. By incorporating the proposed comb-filter into the erbium-doped fiber laser cavity, two output channels at 1533.2 and 1558.2 nm have been achieved. Further, the combination of the parallel-connected two in-line MZIs and polarization controllers (PC1 and PC2) promotes the lasing in a switchable and selective way. To the best of the authors’ knowledge, this is the first demonstration of a comb filter that employs paralleled two peanut-shaped MZIs in PMF. The experimental results indicate that the proposed filter has the potential to be used in communication systems.
Synthetically generated image dataset for military relevant machine learning experiments

The desertSim detection dataset consists of more than forty-seven thousand synthetically generated infrared images exhibiting unique characteristics not found in academic datasets typically used for machine learning research. The desertSim set of images provides realistic infrared signatures of armored vehicles under a variety of configurations, engine states, time of day, and clutter conditions. The dataset is publicly available and was created to provide academic researchers a military relevant dataset to support machine learning research. The synthetic infrared image dataset can be used in conjunction with a publicly available real infrared image dataset for experiments having a synthetic data training set and real data test set. Consistency in the nature of the two datasets make them particularly suitable for conducting academic experiments in support of machine learning research.
Calculation and simulation of sand and dust attenuation in microwave propagation

The sand and dust attenuation has a great effect on the microwave propagation in the air. The calculation model of the sand and dust attenuation was got, combining the formula of the complex dielectric constant of dry sand with the dielectric constant of water. The model analysed the attenuation of sand and dust produced by explosion, vehicle and nature, and simulated attenuation rate of sand with different content of water. The result of numerical calculation shows that natural sand and vehicular sand have little effect on microwave when the frequency is not very high, but the attenuation of explosive sand could not be ignored. It also shows the attenuation of explosive sand rises quickly along with the increase of water content. This effect reaches saturated state when the humidity ranges from 20% to 30%. Therefore, the sand and dust attenuation of high moisture tropical area is greater than that of desert or half-desert.