We study the possibility of determining the octant of the neutrino mixing angle 23, that is, whether 23 > 45 or 23 < 45, in long baseline neutrino experiments. Here we numerically derived the sensitivity limits within which these experiments can determine, by measuring the probability of the  ! e transitions, the octant of 23 with a 5 certainty. The interference of the CP violation angle  with these limits, as well as the effects of the baseline length and the run-time ratio of neutrino and antineutrino modes of the beam have been analyzed.

The fetal heart rate (FHR) is a screening signal for preventing fetal hypoxia during labor. When experts analyze this signal, they have to position a baseline and identify decelerations and accelerations. These steps can potentially be automated and made more objective by data processing analysis, but training and evaluation datasets are required. Here, we describe a dataset of 155 FHR recordings in which a reference baseline, accelerations and decelerations have been annotated by expert consensus. 66 FHR recordings with a shared expert analysis have been included in a training dataset, and 90 other FHR recordings with a non-shared expert analysis have been included in an evaluation dataset. Researchers wishing to evaluate their automatic analysis method should submit their results for comparison with the expert consensus. The dataset also contains the results produced by 11 re-coded automatic analysis methods from the literature. All the data are available at http://utsb.univ-catholille.fr/fhr-review.

The fetal heart rate (FHR) is a screening signal for preventing fetal hypoxia during labor. When experts analyze this signal, they have to position a baseline and then identify decelerations and accelerations. These steps can potentially be automated and made more objective by signal processing analysis. Various methods have been described in the literature but there are no open-source programs for performing these steps. The MATLAB toolbox presented here comprises a standard signal pre-processing function, 11 re-coded literature methods for fetal heart rate analysis, a signal viewer (enabling annotation by an expert) and an evaluation procedure with various criteria measuring intrarater agreement.

Koala (Phascolarctos cinereus) is an iconic folivorous marsupial native to the sclerophyll forests and woodlands of Australia. Due to the ever-changing habitat, this species is highly vulnerable to anthropogenic factors such as habitat loss and fragmentation, and this is reflected in the increasing number of injured and/or diseased koalas over the years. The majority of adult koalas admitted at wildlife hospitals are deceased, either due to natural causes, or have to be euthanized. Thus, orphaned koala joeys constitute a substantial number of wildlife rescues, and mortality is also prevalent in koala joeys being hand-reared/rehabilitated, with little knowledge about the causes of such high rates of mortality. Wildlife hospitals/rehabilitation centres are inherently stressful, and although the hypothalamic–pituitary–adrenal (HPA) axis plays a vital role in mediating the stress endocrine function (by producing glucocorticoids such as cortisol), there are no studies quantifying glucocorticoids in koala joeys. To contribute to this dearth of research, we sampled a total of seven individuals residing at Port Macquarie Koala Hospital and noted their clinical information. Faecal samples were collected from all seven koala joeys during routine cage cleaning. In total, 123 faecal samples were collected, processed and analysed for cortisol using enzyme-immunoassay (EIA). We used the iterative baseline approach to determine baseline and peak concentrations of FCM in koala joeys. Baseline concentrations ranged between 14.11 ng/g – 51.10 ng/g (healthy – sick), whereas, peak FCM concentrations ranged between 25.65 ng/g – 56.58 ng/g (healthy – sick). There was a significant difference (p &lt; 0.05) between FCM concentrations of healthy and impaired individuals. Healthy individuals displayed relatively consistent FCM concentrations, whereas, diseased individuals displayed a significant increase in FCM concentrations over time. Our study provides the first record of baseline and peak FCM concentrations in rescued koala joeys with their associated clinical condition. Future studies can use the iterative baseline approach to determine FCM concentration in wild koala joeys that can serve as a baseline to compare glucocorticoid levels of rescued joeys.

Since the 1970s, land subsidence has been developing rapidly in the Beijing Plain, the systematic study of its evolution mechanism is of great significance to the sustainable development of the regional economy. First, based on ENVISAT ASAT and RADARSAT2 data, the land subsidence data in Beijing Plain were obtained using permanent interferometer technology. Second, based on the GIS platform and using fishing net tools, vector data of ground settlement with different resolutions were obtained. Through a series of tests, a scale of 960 metres was selected as the research unit, and the subsidence rate of the grid was obtained from 2004 to 2015. Finally, based on the Mann-Kendall mutation test method, a trend analysis of land subsidence changes in various grids was carried out. The results showed that single-year mutation mainly distributed in the middle and lower parts of the Yongding River alluvial fan and the Chaobai River alluvial fan, mainly occurring in 2015, 2005 and 2013, respectively. The upper and middle alluvial fan of the Chaobai River, the vicinity of the emergency water source and the edge velocity of the groundwater funnel have undergone several sudden changes. Combined with hydrogeology, basic geological conditions and the impact of the South-to-North Water transfer project, we analysed the causes of the mutations in the grid. The research results can provide a basis for the study and prevention of land subsidence in this area and help to further explore the trend characteristics of land subsidence in this area.

This qualitative research study is a part of a larger research project exploring the experiences of young caregivers aged 5-26 years and their families navigating the COVID-19 pandemic between 2020 to 2023. Data was collected from 14 young caregivers who participated in baseline interviews. The central research question guiding this study: What was, is, and will be the impact of changing public health restrictions on young caregivers and their families during the pandemic and pandemic recovery? Seven themes emerged through analysis: 1) Navigating Care During the Height of Public Health Restrictions, 2) Neighbourhood and Built Environment During the Pandemic, 3) Perceptions Towards COVID and Public Health Restrictions/Efforts, 4) The Impact of Public Health Restrictions on Relationships, 5) Mental Health Challenges of Being a Young Caregiver During a Pandemic, 6) Navigating Formal Services and Supports and 7) Recommendations from Young Caregivers. The findings from this empirical research suggest that young caregivers found it easier to navigate their caregiving responsibilities when public health restrictions and work-from-home mandates were initially implemented, however, this later changed due to challenges in finding respite from caregiving, maintaining social connections with friends, creating personal space at home, and finding adequate replacements of programs once offered in-person.

So that the levels of water stress are not harmful to the development of the crop and affect its productivity, its detection and monitoring are necessary, and it can occur in different ways. One of them is through the Crop Water Stress Index (CWSI). This index quantifies water stress through the normalization of leaf temperature between the maximum and minimum plant temperatures as a function of evaporation conditions. The responses of a low-cost infrared (IR) sensor were crossed with image processing through segmentation by the Excess Green model to develop a water stress detection system using CWSI. A soil/plant temperature map was generated through a point-to-point scan of the IR sensor. And when it overlaid with a segmented image of the experimental area, only points identified as plants had their temperature values maintained. The Non-Water-Stressed Baseline (NWSB) equation was parameterized for the same conditions of the experiment and external environmental. The experimental area was divided into three different treatments, maintained under stable water conditions throughout the experiment and the system was able to identify stably different stress values between treatments. Although the relationship between crop and environment affected the results, this work showed that using an irrigation system based on CWSI is possible.

In this paper, I present a comprehensive theoretical framework for understanding surface-enhanced Raman scattering (SERS) measurements in both solution and thin film setups, focusing on electromagnetic enhancement principles. Two prevalent types of SERS substrates found in the literature are investigated: plasmonic colloidal particles, including spherical and spheroid nanoparticles, nanoparticle diameters, and thin-film-based SERS substrates like ultra-thin substrates, bundled nanorods, plasmonic, and porous thin films. The investigation explores the impact of analyte adsorption, orientation, and the polarization of the excitation laser on effective SERS enhancement factors. Notably, it considers the impact of analyte size on the SERS spectrum, by examining scenarios where the analyte is significantly smaller or larger than the hot-spot dimensions. The analysis also incorporates optical attenuations arising from the optical properties of the analyte and the SERS substrates. The findings provide possible explanations for many observations made in SERS measurements, such as variations in relative peak intensities during SERS assessments, reductions in SERS intensity at high analyte concentrations, and the occurrence of significant baseline fluctuations. The study offers valuable guidance for optimizing SERS substrate design, enhancing SERS measurements, and improving the quantification of SERS detection.

For Interferometry Synthetic Aperture Radar (InSAR), the normal baseline is one of the main factors that affect the accuracy of the ground elevation. For Gaofen-3 (GF-3) InSAR processing, the poor accuracy of the real-time orbit determination resulting in a large baseline error, leads to the modulation error in azimuth and the slope error in range for timely Digital Elevation Model (DEM) generation. In order to address this problem, a baseline estimation method based on external DEM is proposed in this paper. Firstly, according to the characteristic of the real-time orbit of GF-3 images, orbit fitting is executed to remove the non-linear error factor. Secondly, the height errors are obtained in slant-range plane between Shuttle Radar Topography Mission (SRTM) DEM and the GF-3 generated DEM after orbit fitting. At the same time, the height errors are used to estimate the baseline error which has a linear variation. In this way, the orbit error can be calibrated by the estimated baseline error. Finally, DEM generation is performed by using the modified baseline and orbit. This procedure is implemented iteratively to achieve a higher accuracy DEM. Based on the results of GF-3 interferometric SAR data for Hebei, the effectiveness of the proposed algorithm is verified and the accuracy of GF-3 real-time DEM products can be improved extensively.

Magnetic Resonance Imaging (MRI) is widely used in clinics and research due to its accurate disease identification and non-invasive nature. MRI is based on Nuclear Magnetic Resonance (NMR), which is also extensively employed in various fields. NMR and its modern versions involve intricate pre-processing steps before data analysis. These steps are initially processed in the time domain and subsequently in the frequency domain. While our previous review focused on time domain pre-processing (https://www.preprints.org/manuscript/202310.2032/v1), this review delves into the mathematical and statistical aspects of frequency domain pre-processing. We discuss essential pre-processing steps like phase error correction, baseline correction, solvent filtering, calibration and alignment, reference deconvolution, binning/bucketing and peak picking, peak fitting/deconvolution and compound identification, integration and quantification, normalization and transformation. Furthermore, we offer practical recommendations for each step.

Cardiotoxicity is an unwanted side-effect arising from different medications. The term describes a diminution of heart function, affecting especially left ventricular ejection fraction, which can progress to heart failure. Pediatric oncology patients treated with drugs of the anthracycline group, e.g., doxorubicin, can evolve to heart failure patients in adult age. Nuclear Medicine imaging of fatty acid oxidation and glycolysis accurately describes normal and altered heart metabolism. The most common finding is a state of cardiac glycolysis, which is reflected by increased uptake of 18F-desoxyglucose (18F-FDG) at a time when fatty acid oxidation is decreased. Glycolysis has recently been recognized as being the result of coenzyme Q10 deficiency as part of adaptation to hypoxia. Low LEVF correlates with impaired fatty acid oxidation. This deficiency can arise from chemotherapy and radiation and can be corrected by supplementation. Drugs like enalapril can improve fatty acid utilization. We will review relevant Nuclear Medicine imaging studies as well as fundamental biochemical data that demonstrate this pathogenetic path.

We examined special roles of the Central Nervous System (CNS) in an attempt to resolve the puzzle that chronic diseases cannot be cured by medicine. By exploring a skill-learning model, we found that the CNS is able to remember certain information reflecting biochemical and cellular (B&C) processes in the body. From the skill using ability, we found that the CNS is able to control basic B&C processes that drive and power the skill. From the ability to adjust forces and force direction of a physical act, we found that the CNS is able to adjust B&C processes that drive the physical act. From this adjustment capability, we further inferred that the CNS must also store information on the baseline B&C processes. As a whole, we found that the CNS can maintain information on baseline B&C processes, up-regulate or down-regulate the processes, and make comparisons in performing its regulatory functions. We found that chronic diseases are the results of deviated baseline B&C processes. Per the proved hypothesis, the CNS maintains deviated baseline B&C processes, and thus protects the body states of fully developed diseases. We then used the three CNS roles to explain that cancer progresses with increasing malignancy, cancer quickly returns after a surgery, cancer cells repopulate after chemotherapy and radiotherapy, cancer develops drug resistance inevitably, immune cells rebound after suppression, generally poor benefits of cancer drugs such as beta-blockers, etc. We further showed that long-term exercises generally push most, if not all, baseline B&C processes in diametrical opposing directions against the diseased B&C processes, implying that exercises play unique roles in reversing chronic diseases. Finally, we proposed several strategical approaches to resetting the CNS’ state memory as the essential condition for curing chronic diseases.