Assessing coastal population capacity in Tsunami-prone areas: A grid-based approach.

Tambakrejo Beach in Blitar Regency is classified as an area that is very vulnerable to tsunami catastrophes. Many researchers have conducted studies on regions impacted by the tsunami. However, more studies into the link between the outcomes of social and spatial studies still need to be carried out because these are two different perspectives with different parameters and variables. The novel approach in this research involves delineating tsunami-affected areas and assessing population capacity in coastal regions. The hazard maps and livelihood asset variables using grid cells of a specific size have been used to identify risk levels. The grid cells used are 50 m2 × 50 m² so that they are expected to represent the minor units on the face of the earth, such as buildings, assets, property or land parcels, for capacity assessments or measuring the level of threat to disasters and are no longer based on regional administrative boundaries. Contribution: The research results show that using grid cells to analyse areas affected by the tsunami can provide excellent and informative results. Research findings at the research location regarding community preparedness in facing tsunamis show that communities at risk of being affected by the tsunami need to increase their capacity because the majority of communities in coastal areas, especially in the Sidorejo sub-village, have been identified as having low capacity according to several livelihood asset parameters such as financial capital in income. By increasing individual capacity, it is hoped that society will be able to avoid the threat of tsunami waves better.

Healthcare workers' experiences protecting themselves and their families during the COVID-19 pandemic in 2020-2021.

We characterized experiences and strategies used by frontline healthcare workers to prevent severe-acute-respiratory-syndrome-related coronavirus transmission at work and to household members during the coronavirus disease pandemic. Alongside an online questionnaire (n = 234), remote semi-structured interviews (n = 23: 15 clinicians, 8 non-clinicians) were conducted in 2021. Mitigation challenges and facilitators were identified from data to represent experiences as a process considering the before, during, and after work shifts. Journey mapping was utilized to visually describe how healthcare workers experienced the stages of the work environment, leaving work, commuting home, and the home environment, and strategies implemented to stay safe. Major facilitators included the uptake of coronavirus disease vaccines and testing, information regarding virus transmission, and adequate personal protective equipment. The most critical challenges identified included a lack of designated areas for end-of-day disinfection, changing rooms, showers, and lockers in the leaving work stage. Psychosocial and environmental factors must be considered in future hospital pandemic preparations.

Tele-Assisted Home-Based Palliative Care Reduces Health Care Costs for Terminal Cancer Patients: Real-World Evidence From a Regional Hospital in Taiwan.

Background: Tele-assisted home-based palliative care (THPC) usually fulfills the desire of terminal patients to pass away at home. The overall costs of such a service deserve evaluation. Objectives: This study aims to determine health care utilization and costs for cancer patients at the end of life, stratified by THPC service. Design: Patients who received THPC were matched 1:1 based on age, gender, year of death, and propensity score with those who did not receive THPC. Setting/Subjects: A total of 773 cancer patients passed away in a regional hospital in Taiwan during the period of 2012-2020, of which 293 received THPC. Measurements: We measured the rates and costs of outpatient clinic visits, emergency department (ED) visits, hospitalizations, and intensive care unit (ICU) admissions during the last week, the last two weeks and the last month before death. In addition, we estimated the driving times and expenses required for transportation from each cancer patient's home to the hospital using Google Maps. National Health Insurance (NHI) reimbursements and out-of-pocket expenses were also calculated. Results: In comparison with patients without THPC, those who received THPC had a 50% lower likelihood of visiting the ED or being hospitalized, a more than 90% reduced chance of ICU admission, but were four times more likely to obtain their medicines from outpatient clinics. THPC patients had similar out-of-pocket expenditures, approximately half of the NHI costs, and lower rates and costs for ambulance transportation to the ED. Conclusions: THPC reduced health care costs for terminal cancer patients in the last week, the last two weeks, and the last month before death, while also increasing the likelihood of patients being able to rest and pass away at home.

Unravelling the origin of the reward positivity: a human intracranial event-related brain potential study.

The reward positivity (RewP) is an event-related brain potential (ERP) component that emerges approximately 250 to 350 milliseconds (ms) after receiving reward-related feedback stimuli and is believed to be important for reinforcement learning and reward processing. Although numerous localization studies have indicated that the anterior cingulate cortex (ACC) is the neural generator of this component, other studies have identified sources outside of the ACC, fuelling a debate about its origin. Because the results of EEG and MEG source localization studies are severely limited by the inverse problem, we addressed this question by leveraging the high spatial and temporal resolution of intracranial EEG. We predicted that we would identify a neural generator of the RewP in the caudal ACC. We recorded intracranial EEG in 19 refractory epilepsy patients who underwent invasive video-EEG monitoring at Ghent University Hospital, Belgium. Participants engaged in the virtual T-maze task (vTMT), a trial-and-error task known to elicit a canonical RewP, while scalp and intracranial EEG were simultaneously recorded. The RewP was identified using a difference wave approach for both scalp and intracranial EEG. The data were aggregated across participants to create a virtual "meta-participant" that contained all the recorded intracranial ERPs (iERPs) with respect to their intracranial contact locations. We used both a hypothesis-driven (focused on ACC) and exploratory (whole-brain analysis) approach to segment the brain into regions of interest (ROI). For each ROI, we evaluated the degree to which the time course of the absolute current density (ACD) activity mirrored the time course of the RewP, and confirmed the statistical significance of the results using permutation analysis. The grand average waveform of the scalp data revealed a RewP at 309 ms after reward feedback with a frontocentral scalp distribution, consistent with the identification of this component as the RewP. The meta-participant contained iERPs recorded from 582 intracranial contacts in total. The ACD activity of the aggregated iERPs were most similar to the RewP in left caudal ACC, left dorsolateral prefrontal cortex, left frontomedial cortex, and left white matter, with the highest score attributed to caudal ACC, as predicted. To our knowledge, this is the first study that uses intracranial EEG aggregated across multiple human epilepsy patients and current source density analysis to identify the neural generator(s) of the RewP. These results provide direct evidence that the ACC is a neural generator of the RewP.

Miniaturized confocal Raman and Laser-induced breakdown spectroscopy imaging system based on micro-electro-mechanical mirror.

Detection of the elemental and molecular structural distribution with high resolution and miniaturization of unknown minerals is a main bottleneck in deep space exploration and geology analysis. The aim is to enhance the accuracy of the chemical analysis of micro-samples by combining the distribution information from Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS). The existing Raman-LIBS imaging methods are difficult to balance the imaging performance and system volume. There is an urgent need to develop a Raman-LIBS imaging method with miniaturization, and high imaging performance. A miniaturized Raman-LIBS imaging instrument based on the micro-electro-mechanical (MEMS) mirror has been developed to overcome these challenges. The instrument utilizes dual 2D MEMS mirror scanning technology to shorten the optical length of the system and improve the detection efficiency of hybrid spectral signals. The optical probe measures 94 mm × 66 mm, and has an axial focusing ability of approximately 40 nm, with a lateral resolution of approximately 700 nm for Raman maps and 9.5 µm for LIBS maps. As a proof experiment, 3D high-resolution Raman-LIBS hybrid spectral distribution maps of meteorite Tisserlitine 001 were obtained. The attainment of high imaging performance and miniaturization in hybrid spectral imaging is crucial for on-site chemical analysis. The proposed instrument enables in-situ spectrum and multispectral imaging with miniaturization, high spatial resolution, and high stability. The instrument is a powerful tool for composition and structural information characterization in the fields of space exploration and geological exploration.

Improved A* Algorithm for Mobile Robots under Rough Terrain Based on Ground Trafficability Model and Ground Ruggedness Model.

Considering that the existing path planning algorithms for mobile robots under rugged terrain do not consider the ground flatness and the lack of optimality, which leads to the instability of the center of mass of the mobile robot, this paper proposes an improved A* algorithm for mobile robots under rugged terrain based on the ground accessibility model and the ground ruggedness model. Firstly, the ground accessibility and ruggedness models are established based on the elevation map, expressing the ground flatness. Secondly, the elevation cost function that can obtain the optimal path is designed based on the two types of models combined with the characteristics of the A* algorithm, and the continuous cost function is established by connecting with the original distance cost function, which avoids the center-of-mass instability caused by the non-optimal path. Finally, the effectiveness of the improved algorithm is verified by simulation and experiment. The results show that compared with the existing commonly used path planning algorithms under rugged terrain, the enhanced algorithm improves the smoothness of paths and the optimization degree of paths in the path planning process under rough terrain.

Longitudinal Outcomes of Neurofeedback and Hyperbaric Oxygen Therapy in Treating a Traumatic Brain Injury Patient: A Case Report.

Severe Traumatic Brain Injury (TBI) is a significant health issue, with neurofeedback and Hyperbaric Oxygen Therapy (HBOT) as potentially effective treatments. Neurofeedback uses operant conditioning for real-time psychological and physiological awareness, and HBOT increases blood oxygen levels, potentially enhancing cognitive abilities and the body's innate healing processes and reducing symptoms. On July 30, 2018, a 33-year-old female runner was hit by a car going 40 mph and thrown 30 feet, resulting in a severe TBI and a seven-week coma. After seven months of intensive rehabilitation, she started HBOT and neurofeedback treatments in November 2021, as recommended by her neuropsychiatrist. These treatments led to noticeable improvements in her cognition, sleep, conversation skills, emotional control, and relationships by January 2022. By December 2023, after 195 neurofeedback and over 300 HBOT sessions, she reported further improvements in various cognitive and emotional aspects and daily activities like feeding, toileting, grooming, and communication. Post-treatment quantitative electroencephalogram (qEEG) results in June 2024 showed moderate to large effects on her brain's average frequency band parameters (g = .612) and small to moderate average effects on 19 scalp electrode placement sites outcomes (uV2 g=.339 and Hz g=.333). This indicates significant progress in her recovery journey over a 31-month treatment period. This patient's case demonstrated noteworthy improvements in cognitive variables, namely, feeding (p=0.046), toileting (p=0.046), grooming (p=0.046), and communication abilities (p=0.046) per the objective measures, Disability Rating Scale (DRS) and the Glasgow Outcome Scale Extended (GOSE). Based on the qEEG effect sizes, DRS, and GOSE results from the pretest (2021) and posttest (2024), the patient has made noteworthy gains in brain recovery and overall quality of life.

3D deep learning Normal Tissue Complication Probability model to predict late xerostomia in head and neck cancer patients.

BACKGROUND AND PURPOSE: Conventional normal tissue complication probability (NTCP) models for head and neck cancer (HNC) patients are typically based on single-value variables, which for radiation-induced xerostomia are baseline xerostomia and mean salivary gland doses. This study aims to improve the prediction of late xerostomia by utilizing 3D information from radiation dose distributions, CT imaging, organ-at-risk segmentations, and clinical variables with deep learning (DL). MATERIALS AND METHODS: An international cohort of 1208 HNC patients from two institutes was used to train and twice validate DL models (DCNN, EfficientNet-v2, and ResNet) with 3D dose distribution, CT scan, organ-at-risk segmentations, baseline xerostomia score, sex, and age as input. The NTCP endpoint was moderate-to-severe xerostomia 12 months post-radiotherapy. The DL models' prediction performance was compared to a reference model: a recently published xerostomia NTCP model that used baseline xerostomia score and mean salivary gland doses as input. Attention maps were created to visualize the focus regions of the DL predictions. Transfer learning was conducted to improve the DL model performance on the external validation set. RESULTS: All DL-based NTCP models showed better performance (AUCtest=0.78 - 0.79) than the reference NTCP model (AUCtest=0.74) in the independent test. Attention maps showed that the DL model focused on the major salivary glands, particularly the stem cell-rich region of the parotid glands. DL models obtained lower external validation performance (AUCexternal=0.63) than the reference model (AUCexternal=0.66). After transfer learning on a small external subset, the DL model (AUCtl, external=0.66) performed better than the reference model (AUCtl, external=0.64). CONCLUSION: DL-based NTCP models performed better than the reference model when validated in data from the same institute. Improved performance in the external dataset was achieved with transfer learning, demonstrating the need for multicenter training data to realize generalizable DL-based NTCP models.

Dual-Energy Computed Tomography Iodine Maps: Application in the Diagnosis of Periprosthetic Joint Infection in Total Hip Arthroplasty.

BACKGROUND: The challenge of early and rapid diagnosis of periprosthetic joint infection (PJI) remains important. This study aimed to assess the efficacy of dual-energy computed tomography (CT) (DECT) iodine maps for diagnosing PJI in total hip arthroplasty. METHODS: We prospectively enrolled 68 patients who had postoperative joint pain after hip arthroplasty. All patients underwent preoperative DECT iodine imaging to quantify iodine concentration (IC) in periprosthetic tissues during arterial and venous phases. The diagnostic efficacy of DECT iodine maps was evaluated by constructing receiver operating characteristic (ROC) curves according to the Musculoskeletal Infection Society criteria. RESULTS: Compared with erythrocyte sedimentation rate (Area Under the Curve (AUC) = 0.837), polymorphonuclear cell percentage (AUC = 0.703), and C-reactive protein (AUC = 0.837), periprosthetic tissue venous-phase iodine concentration (IC) (AUC = 0.970) and arterial-phase IC (AUC = 0.964) exhibited outstanding discriminative capability between PJI and aseptic failure. The PJI cut-off point was venous IC = 1.225 mg/ml, with a sensitivity of 92.31% and specificity of 90.48%; for arterial IC = 1.065 mg/ml, the sensitivity was 96.15%, and a specificity was 90.70%. CONCLUSION: This study demonstrates the great potential of DECT iodine maps for the diagnosis of PJI around hip arthroplasty, which helps to differentiate between periprosthetic infection and aseptic failure after hip arthroplasty.

Relationship Between Primary Care Physician Capacity and Usual Source of Care.

BACKGROUND: The NASEM Primary Care Report and Primary Care scorecard highlighted the importance of primary care physician (PCP) capacity and having a usual source of care (USC). However, research has found that PCP capacity and USC do not always correlate. This exploratory study compares geographic patterns and the characteristics of counties with similar rates of PCP capacity but varying rates of USC. METHODS: Our county-level, cross-sectional approach includes estimates from the Robert Graham Center and data from the Robert Wood Johnson County Health Rankings (CHR). We utilized conditional mapping methods to first identify US counties with the highest rates of social deprivation (SDI). Next, counties were stratified based on primary care physician (PCP) capacity and usual source of care (USC) terciles, allowing us to identify 4 types of counties: (1) High-Low (high PCP capacity, low USC); (2) High-High (high PCP capacity, high USC); (3) Low-High (low PCP capacity, high USC); and (4) Low-Low (low PCP capacity, low USC). We use t test to explore differences in the characteristics of counties with similar rates of primary care capacity. RESULTS: The results show clear geographic patterns: High-High counties are located primarily in the northern and northeastern US; High-Low counties are located primarily in the southwestern and southern US. Low-High counties are concentrated in the Appalachian and Great Lakes regions; Low-Low counties are concentrated in the southeastern US and Texas. Descriptive results reveal that rates of racial and ethnic minorities, the uninsured, and social deprivation are highest in counties with low rates of USC for both high PCP and low PCP areas. CONCLUSIONS: Recognizing PCP shortages and improving rates of USC are key strategies for increasing access to high-quality, primary care. Targeting strategies by geographic region will allow for tailored models to improve access to and continuity of primary care. For example, we found that many of the counties with the lowest rates of USC are found in non-Medicaid expansion states (Texas, Georgia, and Florida) with high rates of uninsured populations, suggesting that expanding Medicaid and improving access to health insurance are key strategies for increasing USC in these states.

Hierarchical Phased-Array Antennas Coupled to Al KIDs: A Scalable Architecture for Multi-band Millimeter/Submillimeter Focal Planes.

We present the optical characterization of two-scale hierarchical phased-array antenna kinetic inductance detectors (KIDs) for millimeter/submillimeter wavelengths. Our KIDs have a lumped-element architecture with parallel plate capacitors and aluminum inductors. The incoming light is received with a hierarchical phased array of slot dipole antennas, split into 4 frequency bands (between 125 GHz and 365 GHz) with on-chip lumped-element band-pass filters, and routed to different KIDs using microstriplines. Individual pixels detect light for the 3 higher-frequency bands (190-365 GHz), and the signals from four individual pixels are coherently summed to create a larger pixel detecting light for the lowest frequency band (125-175 GHz). The spectral response of the band-pass filters was measured using Fourier transform spectroscopy (FTS), the far-field beam pattern of the phased-array antennas was obtained using an infrared source mounted on a 2-axis translating stage, and the optical efficiency of the KIDs was characterized by observing loads at 294 K and 77 K. We report on the results of these three measurements.

Astroglial networks control visual responses of superior collicular neurons and sensory-motor behavior.

Astroglial networks closely interact with neuronal populations, but their functional contribution to neuronal representation of sensory information remains unexplored. The superior colliculus (SC) integrates multi-sensory information by generating distinct spatial patterns of neuronal functional responses to specific sensory stimulation. Here, we report that astrocytes from the mouse SC form extensive networks in the retinorecipient layer compared to visual cortex. This strong astroglial connectivity relies on high expression of gap-junction proteins. Genetic disruption of this connectivity functionally impairs SC retinotopic and orientation preference responses. These alterations are region specific, absent in primary visual cortex, and associated at the circuit level with a specific impairment of collicular neurons synaptic transmission. This has implications for SC-related visually induced innate behavior, as disrupting astroglial networks impairs light-evoked temporary arrest. Our results indicate that astroglial networks shape synaptic circuit activity underlying SC functional visual responses and play a crucial role in integrating visual cues to drive sensory-motor behavior.

A spatial map: a propitious choice for constraining the binding problem.

Many studies have shown that the human visual system has two major functionally distinct cortical visual pathways: a ventral pathway, thought to be important for object recognition, and a dorsal pathway, thought to be important for spatial cognition. According to our and others previous studies, artificial neural networks with two segregated pathways can determine objects' identities and locations more accurately and efficiently than one-pathway artificial neural networks. In addition, we showed that these two segregated artificial cortical visual pathways can each process identity and spatial information of visual objects independently and differently. However, when using such networks to process multiple objects' identities and locations, a binding problem arises because the networks may not associate each object's identity with its location correctly. In a previous study, we constrained the binding problem by training the artificial identity pathway to retain relative location information of objects. This design uses a location map to constrain the binding problem. One limitation of that study was that we only considered two attributes of our objects (identity and location) and only one possible map (location) for binding. However, typically the brain needs to process and bind many attributes of an object, and any of these attributes could be used to constrain the binding problem. In our current study, using visual objects with multiple attributes (identity, luminance, orientation, and location) that need to be recognized, we tried to find the best map (among an identity map, a luminance map, an orientation map, or a location map) to constrain the binding problem. We found that in our experimental simulations, when visual attributes are independent of each other, a location map is always a better choice than the other kinds of maps examined for constraining the binding problem. Our findings agree with previous neurophysiological findings that show that the organization or map in many visual cortical areas is primarily retinotopic or spatial.

PM10-bound trace elements in pan-European urban atmosphere.

Although many studies have discussed the impact of Europe's air quality, very limited research focused on the detailed phenomenology of ambient trace elements (TEs) in PM10 in urban atmosphere. This study compiled long-term (2013-2022) measurements of speciation of ambient urban PM10 from 55 sites of 7 countries (Switzerland, Spain, France, Greece, Italy, Portugal, UK), aiming to elucidate the phenomenology of 20 TEs in PM10 in urban Europe. The monitoring sites comprised urban background (UB, n = 26), traffic (TR, n = 10), industrial (IN, n = 5), suburban background (SUB, n = 7), and rural background (RB, n = 7) types. The sampling campaigns were conducted using standardized protocols to ensure data comparability. In each country, PM10 samples were collected over a fixed period using high-volume air samplers. The analysis encompassed the spatio-temporal distribution of TEs, and relationships between TEs at each site. Results indicated an annual average for the sum of 20 TEs of 90 ± 65 ng/m3, with TR and IN sites exhibiting the highest concentrations (130 ± 66 and 131 ± 80 ng/m3, respectively). Seasonal variability in TEs concentrations, influenced by emission sources and meteorology, revealed significant differences (p < 0.05) across all monitoring sites. Estimation of TE concentrations highlighted distinct ratios between non-carcinogenic and carcinogenic metals, with Zn (40 ± 49 ng/m3), Ti (21 ± 29 ng/m3), and Cu (23 ± 35 ng/m3) dominating non-carcinogenic TEs, while Cr (5 ± 7 ng/m3), and Ni (2 ± 6 ng/m3) were prominent among carcinogenic ones. Correlations between TEs across diverse locations and seasons varied, in agreement with differences in emission sources and meteorological conditions. This study provides valuable insights into TEs in pan-European urban atmosphere, contributing to a comprehensive dataset for future environmental protection policies.

CORACLE (COVID-19 liteRAture CompiLEr): A platform for efficient tracking and extraction of SARS-CoV-2 and COVID-19 literature, with examples from post-COVID with respiratory involvement.

Background: During the COVID-19 pandemic a need to process large volumes of publications emerged. As the pandemic is winding down, the clinicians encountered a novel syndrome - Post-acute Sequelae of COVID-19 (PASC) - that affects over 10 % of those who contract SARS-CoV-2 and presents a significant challenge in the medical field. The continuous influx of publications underscores a need for efficient tools for navigating the literature. Objectives: We aimed to develop an application which will allow monitoring and categorizing COVID-19-related literature through building publication networks and medical subject headings (MeSH) maps to identify key publications and networks. Methods: We introduce CORACLE (COVID-19 liteRAture CompiLEr), an innovative web application designed to analyse COVID-19-related scientific articles and to identify research trends. CORACLE features three primary interfaces: The "Search" interface, which displays research trends and citation links; the "Citation Map" interface, allowing users to create tailored citation networks from PubMed Identifiers (PMIDs) to uncover common references among selected articles; and the "MeSH" interface, highlighting current MeSH trends and their associations. Results: CORACLE leverages PubMed data to categorize literature on COVID-19 and PASC, aiding in the identification of relevant research publication hubs. Using lung function in PASC patients as a search example, we demonstrate how to identify and visualize the interactions between the relevant publications. Conclusion: CORACLE is an effective tool for the extraction and analysis of literature. Its functionalities, including the MeSH trends and customizable citation mapping, facilitate the discovery of emerging trends in COVID-19 and PASC research.

Patient and Provider Satisfaction With a Geomapping Tool for Finding Community Family Physicians in Ontario, Canada: Cross-Sectional Online Survey Study.

BACKGROUND: Language-concordant health care, or health care in a patient's language of choice, is an important element of health accessibility that improves patient safety and comfort and facilitates an increased quality of care. However, prior research has found that linguistic minorities often face higher travel burdens to access language-concordant care compared to the general population. OBJECTIVE: This study intended to assess patient experiences and satisfaction with an online interactive physician map that allows patients to find family physicians who speak their preferred language in and around Ottawa, Ontario, Canada, as a means of identifying areas of improvement. METHODS: This study used an online survey with questions related to user satisfaction. Responses to Likert-scale questions were compiled as summary statistics and short-answer responses underwent thematic analysis. The study setting was Ottawa and Renfrew County, Ontario, and the surrounding region, including the province of Quebec. RESULTS: A total of 93 respondents completed the survey and self-identified as living in Ontario or Quebec. Overall, 57 (61%) respondents were "very satisfied" or "somewhat satisfied" with the map, 16 (17%) were "neither satisfied nor dissatisfied," and 20 (22%) were "very dissatisfied" or "somewhat dissatisfied." We found no significant differences in satisfaction by preferred language, age group, physician attachment, or intended beneficiary. A total of 56 respondents provided short-answer responses to an open-ended question about map improvements. The most common specific suggestion was to show which physicians are accepting new patients (n=20). Other suggestions included data refreshes (n=6), user interface adjustments (n=23), and additional languages (n=2). Some participants also provided positive feedback (n=5) or expressed concern with their inability to find a family physician (n=5). Several comments included multiple suggestions. CONCLUSIONS: While most patients were satisfied with the online map, a significant minority expressed dissatisfaction that the map did not show which family physicians were accepting new patients. This suggests that there may be public interest in an accessible database of which family physicians in Ontario are currently accepting new patients.

Algebraic properties of the maps χ n.

The Boolean map χ n : F 2 n → F 2 n , x ↦ y defined by y i = x i + ( x i + 1 + 1 ) x i + 2 (where i ∈ Z / n Z ) is used in various permutations that are part of cryptographic schemes, e.g., Keccak-f (the SHA-3-permutation), ASCON (the winner of the NIST Lightweight competition), Xoodoo, Rasta and Subterranean (2.0). In this paper, we study various algebraic properties of this map. We consider χ n (through vectorial isomorphism) as a univariate polynomial. We show that it is a power function if and only if n = 1 , 3 . We furthermore compute bounds on the sparsity and degree of these univariate polynomials, and the number of different univariate representations. Secondly, we compute the number of monomials of given degree in the inverse of χ n (if it exists). This number coincides with binomial coefficients. Lastly, we consider χ n as a polynomial map, to study whether the same rule ( y i = x i + ( x i + 1 + 1 ) x i + 2 ) gives a bijection on field extensions of F 2 . We show that this is not the case for extensions whose degree is divisible by two or three. Based on these results, we conjecture that this rule does not give a bijection on any extension field of F 2 .

A Hybrid Learning-Architecture for Mental Disorder Detection Using Emotion Recognition.

Mental illness has grown to become a prevalent and global health concern that affects individuals across various demographics. Timely detection and accurate diagnosis of mental disorders are crucial for effective treatment and support as late diagnosis could result in suicidal, harmful behaviors and ultimately death. To this end, the present study introduces a novel pipeline for the analysis of facial expressions, leveraging both the AffectNet and 2013 Facial Emotion Recognition (FER) datasets. Consequently, this research goes beyond traditional diagnostic methods by contributing a system capable of generating a comprehensive mental disorder dataset and concurrently predicting mental disorders based on facial emotional cues. Particularly, we introduce a hybrid architecture for mental disorder detection leveraging the state-of-the-art object detection algorithm, YOLOv8 to detect and classify visual cues associated with specific mental disorders. To achieve accurate predictions, an integrated learning architecture based on the fusion of Convolution Neural Networks (CNNs) and Visual Transformer (ViT) models is developed to form an ensemble classifier that predicts the presence of mental illness (e.g., depression, anxiety, and other mental disorder). The overall accuracy is improved to about 81% using the proposed ensemble technique. To ensure transparency and interpretability, we integrate techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) and saliency maps to highlight the regions in the input image that significantly contribute to the model's predictions thus providing healthcare professionals with a clear understanding of the features influencing the system's decisions thereby enhancing trust and more informed diagnostic process.

Diffusion on PCA-UMAP Manifold: The Impact of Data Structure Preservation to Denoise High-Dimensional Single-Cell RNA Sequencing Data.

Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the 'curse of dimensionality', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.

Features for Evaluating Source Localization Effectiveness in Sound Maps from Acoustic Cameras.

Acoustic cameras (ACs) have become very popular in the last decade as an increasing number of applications in environmental acoustics are observed, which are mainly used to display the points of greatest noise emission of one or more sound sources. The results obtained are not yet certifiable because the beamforming algorithms or hardware behave differently under different measurement conditions, but at present, not enough studies have been dedicated to clarify the issues. The present study aims to provide a methodology to extract analytical features from sound maps obtained with ACs, which are generally only visual information. Based on the inputs obtained through a specific measurement campaign carried out with an AC and a known sound source in free field conditions, the present work elaborated a methodology for gathering the coordinates of the maximum emission point on screen, its distance from the real position of the source and the uncertainty associated with this position. The results obtained with the proposed method can be compared, thus acting as a basis for future comparison studies among calculations made with different beamforming algorithms or data gathered with different ACs in all real case scenarios. The method can be applicable to any other sector interested in gathering data from intensity maps not related to sound.