Rapid development of high-throughput omics technologies generates an increasing interests in algorithms for cutoff point identification. Existing cutoff methods and tools identify cutoff points based on association of continuous variables with another variable, such as phenotype, disease state or treatment group. These approaches are not applicable for descriptive studies in which continuous variables are reported without known association with any biologically meaningful variables. The most common shape of the ranked distribution of continuous variables in high-throughput descriptive studies corresponds to a biphasic exponential/super-exponential curve, where the first phase includes big number of variables with values slowly growing with rank and the second phase includes smaller number of variables rapidly growing with rank. This study describes an easy algorithm to identify the boundary between these phases to be used as a cutoff point. The major assumption of that approach is that small number of variables with high values dominate biological system and determine its major processes and functions. This approach was tested on three different datasets: genes in the human cerebral cortex, mammalian genes sensitive to chemical exposures, and proteins expressed in human heart. In every case, the described cutoff identification method produced shortlists of variables (genes, proteins) highly relevant for dominant functions/pathways of the analyzed biological systems. Thus, our described method for cutoff identification may be used to prioritize variables for a focused functional analysis, in situations where other methods of dichotomization of data are inaccessible.

We propose a conceptual model for the closeness parameter $\epsilon$, which characterizes exotic compact objects (ECOs). To estimate $\epsilon$, a thin spherical dust shell is considered, which gravitationally contracts from a specific position $r(t_{0})$ to near its gravitational radius $r(t_{2})=r_{s} + \epsilon$, in a finite time $t_{2}$, measured in the frame of a fiducial observer (FIDO). For an external observer, the shell’s kinematics is characterized by two clearly distinguishable phases: one of rapid contraction, where the shell is far away from the gravitational radius, $r(t_{0})\gg r_{s}$, and a second phase quasi-stationary, $r(t)\sim r_{s}$, where all of the shell’s mass is concentrated around the associated horizon, such that for a FIDO, a black hole (BH)is undistinguishable from a shell configured as a black shell (BS). \\ In the semi-classical approximation $E\ll \kappa_{0}l_{p}^{2}$ and tends to zero when the observation time of collapse $t_{2}$, measured by FIDO, tends to infinity; $\kappa_{0}$ and $l_{p}$ are surface gravity and Planck length, respectively. The quantum effects are significant when $\epsilon\ll r(t_{2})$ and $\epsilon$ tends to $\kappa_{0}l_{p}^{2}$. \\ Without knowing details on quantum gravity, parameter $\epsilon$ is calculated, which, in general, allows distinguishing the ECOs from BHs. Specifically, a BS (ECO) is undistinguishable from a BH.

Lower cutoff levels in screening programs have led to an increase in the proportion of detected cases with transient hypothyroidism, leading to increase of the overall incidence of primary congenital hypothyroidism (CH) in several countries. We have performed retrospective evaluation on the data from 251,008 (96.72%) neonates screened for thyroid-stimulating hormone (TSH) level in dried blood spot specimens taken 48 hours after birth, between 2002 and 2015, using DELFIA method. A TSH value of 15 mIU/L was used as the cutoff point until 2010 and 10 mIU/L thereafter. Primary CH was detected in 127 newborns (1/1976) of which 81.1% had permanent and 18.9% had transient CH. The incidence of primary CH was increased from 1/2489 until to 2010 to 1/1585 thereafter (p=0.131). However, the incidence of permanent CH was slightly increased (p=0.922), while the transient CH incidence had 8-fold increasing after lowering the TSH cutoff level (p<0.001). In cases with permanent CH, we observed lower frequency for thyroid dysgenesis (82.7 vs. 66.7%) and higher frequency for normal in-situ thyroid gland (17.3 vs. 33.3%), for the period with reduced TSH cutoff value. Our findings support the impact of lower TSH cutoff on the increasing incidence of congenital hypothyroidism.

The two-scale method (TSM) is an effective approach to simulate electromagnetic (EM) scattering from rough sea surface, to interpret physical interaction of EM wave with rough sea surfaces. The sea surface roughness spectrum and the cutoff wavenumber are two key factors affecting the accuracy of TSM simulation. This paper takes the geophysical model functions (GMFs) of different band as reference, and quantitatively studies the two factors based on the numerical simulation of different polarization backscattering coefficients. The numerical simulation demonstrates that Apel spectrum is more suitable for the backscattering simulation in C/X/Ku-band, and the optimal cutoff wavenumbers are also derived for TSM simulation of EM scattering in C/X/Ku-band. Then an empirical model function of optimal cutoff wavenumbers with respect to frequency, incident angle and wind speed are obtained with regression method. Finally, the simulation results with the new model function of cutoff wavenumber are compared with the GMF and measured data in different bands and they are all consistent. Moreover, a physical explanation for the optimal cutoff wavenumber is also given.

The Impact of Event Scale-Revised (IES-R) is the most popular measure of post-traumatic stress disorder (PTSD), which has been recently validated in Arabic. This instrumental study aimed to determine optimal cutoff scores of the IES-R and its subscales in Arab samples of psychiatric patients (N = 168, 70.8% females) and healthy adults (N = 992, 62.7% females) from Saudi Arabia during the COVID-19 pandemic as an ongoing collective traumatic event. Based on a cutoff score of 14 of the Depression Anxiety Stress Scale 8-items (DASS-8), receiver operator curve (ROC) analysis revealed two optimal points of 39.5 and 30.5 for the IES-R in the samples (area under the curve (AUC) = 0.86 &amp; 0.91, p values = 0.001, 95% CI: 0.80-0.92 &amp; 0.87 to 0.94, sensitivity = 0.85 &amp; 0.87, specificity = 0.73 &amp; 0.83, Youden index = 0.58 &amp; 0.70, respectively). Different cutoffs were detected for the six subscales of the IES-R, with numbing and avoidance expressing the lowest predictivity for distress. Meanwhile, hyperarousal followed by the irritability expressed stronger predictive capacity for distress than all subscales in both samples. In path analysis, pandemic-related irritability resulted from direct and indirect effects of key PTSD symptoms (intrusion, hyperarousal, and numbing). Irritability contributed to traumatic symptoms of sleep disturbance in both samples while the opposite was not true. The findings suggest usefulness of the IES-R at a score of 30.5 for detecting adults prone to trauma related distress, with higher scores needed for screening in psychiatric patients. Various PTSD symptoms may induce dysphoric mood, which represents a considerable burden that may induce circadian misalignment and more noxious psychiatric problems/ co-morbidities (sleep disturbance) in both healthy and diseased groups.

Background: Appendicular skeletal muscle (or lean) mass (ALM) estimated using dual-energy X-ray absorptiometry (DXA) is considered to be a preferred method for sarcopenia studies. However, DXA is expensive, has limited portability, and requires radiation exposure. Bioelectrical impedance analysis (BIA) is inexpensive, easy to use, and portable; thus BIA might be useful in sarcopenia investigations. However, a large variety of models have been commercially supplied by different companies, and for most consumer products, the equations estimating ALM are not disclosed. It is therefore difficult to use these equations for research purposes. In particular, the BIA equation is often age-dependent, which leads to fundamental difficulty in examining age-related ALM loss. The aims of the current study were as follows: (1) to develop and validate an equation to estimate ALM using multi-frequency BIA (MF-BIA) based on theoretical models, and (2) to establish sarcopenia cutoff values using the equation for the Japanese population. Methods: We measured height (Ht), weight, and ALM obtained using DXA and a standing-posture 8-electrode MF-BIA (5, 50, 250 kHz) in 756 Japanese individuals aged 18 to 86-years-old (222 men and 301 women as developing equation group and 97 men and 136 women as a cross validation group). The traditional impedance index (Ht²/Z₅₀) and impedance ratio of high and low frequency (Z₂₅₀/Z₅) of hand to foot values were calculated. Multiple regression analyses were conducted with ALM as dependent variable in men and women separately. Results: We created the following equations: ALM = (0.6947 × (Ht²/Z₅₀)) + (−55.24 × (Z₂₅₀/Z₅)) + (−10,940 × (1/Z₅₀)) + 51.33 for men, and ALM = (0.6144 × (Ht²/Z₅₀)) + (−36.61 × (Z₂₅₀/Z₅)) + (−9332 × (1/Z₅₀)) + 37.91 for women. Additionally, we conducted measurements in 1624 men and 1368 women aged 18 to 40 years to establish sarcopenia cutoff values in the Japanese population. The mean values minus 2 standard deviations of the skeletal muscle mass index (ALM/Ht²) in these participants were 6.8 and 5.7 kg/m² in men and women, respectively. Conclusion: The current study established and validated a theoretical and age-independent equation using MF-BIA to estimate ALM and provided reasonable sarcopenia cutoff values.

This paper numerically investigates a 3D mesh independency study of a straight blade horizontal axis tidal turbine modelled using Computational Fluid Dynamics (CFD). The solution was produced by employing two turbulence models, the standard k-ε model and Shear Stress Transport (SST) in ANSYS CFX. Three parameters were investigated: mesh resolution, turbulence model, and power coefficient in the initial CFD, analysis. It was found that the mesh resolution and the turbulence model affect the power coefficient results. The power coefficients obtained from the standard k-ε model are 15% to 20% lower than the accuracy of the SST model. It can also be demonstrated that the torque coefficient increases with the increasing Tip Speed Ratio (TSR), but drops drastically after TSR = 5 and k-ε model failing to capture the non-linearity in the torque coefficient with the increasing TSR.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

Panel data of our interest consist of a moderate number of panels, while the panels contain a small number of observations. An estimator of common breaks in panel means without a boundary issue for this kind of scenario is proposed. In particular, the novel estimator is able to detect a common break point even when the change happens immediately after the first time point or just before the last observation period. Another advantage of the elaborated change point estimator is that it results in the last observation in situations with no structural breaks. The consistency of the change point estimator in panel data is established. The results are illustrated through a simulation study. As a by-product of the developed estimation technique, a theoretical utilization for correlation structure estimation, hypothesis testing, and bootstrapping in panel data is demonstrated. A practical application to non-life insurance is presented as well.

We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

The task of weakly supervised point cloud semantic segmentation has received widespread attention and also has been widely used in autonomous driving, robotics, and modern industries. Due to the high dimensionality and large volume of point cloud data, many technical difficulties appeal to weakly supervised semantic processing, especially segmentation. However, weakly-supervised schemes usually provide only partial labelling information of the underlying point cloud data and thus need to effectively extract local features, geometric information, and contextual relationships only using these limited labelled data for supervised learning. To improve weakly-supervised semantic segmentation, we propose a novel segmentation network through the boundary-based feature aggregation based on a K-NN algorithm with down-sampling operation, and also introduce the smoothness loss and Siamese loss for effective segmentation. The experiments on public datasets demonstrate that our presented segmentation method exceeds most of the existing fully supervised and weakly-supervised methods in terms of mIoU. Specifically, our network has high segmentation accuracy on the labels of objects with similar geometrical structures, such as ceiling, wall, floor, chair and table, reaching 91.2%, 98.8%, 83.3%, 75.3% and 80.2%, respectively. Extensive experiments also illustrate its robustness, effectiveness, and generalization of the proposed weakly supervised segmentation network.

Background and Objectives: ARNE score was developed for the prediction of difficult airway for both general and ENT surgery with a universal cut-off value. We tested the accuracy of this score in the case of laryngeal surgery and provided an insight of its combination with flexible laryngoscopy. Materials and Methods: This prospective pilot clinical study included 100 patients who were being scheduled for microscopic laryngeal surgery. We have calculated ARNE score for every patient and flexible laryngoscopy was provided preoperative. Difficult intubation was assessed according to intubation difficulty score (IDS). Results: A total of 33% patients had difficult intubation according to IDS. ARNE score showed to be accurate for prediction of difficult intubation in laryngology with P&lt;0.0001 and AUC 0.784. Flexible laryngoscopy showed to be an independent parameter with P&lt;0.0001 and AUC 0.766. We have defined a new cut-off value of 15.50 for laryngology, according to AUC curve. After the patients were divided in two groups, according to the new cut-off value and provided cut-off value, AUC improved to 0.707 from 0.619 respectively. Flexible laryngoscopy improved the prediction model of ARNE score to AUC 0.882 and of new cut-off value to AUC 0.833. Conclusions: It is recommended to use flexible laryngoscopy together with ARNE score in difficult airway prediction in patients with laryngeal pathology. Also, universally recommended cut-off value of 11 can not be effectively used in laryngology and a new cut-off value of 15.50 is recommended.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

The prevalence of hepatitis B and delta viruses (HBV/HDV) among people who use drugs (PWUD) remains largely unknown. In the context of one Philadelphia-based harm reduction organization (HRO), this study aimed to assess HBV/HDV prevalence and facilitate linkage to care. Participants completed a demographic HBV/HDV risk factor survey and were screened for HBV and reflexively for HDV if positive for HBV surface antigen or isolated core antibody. Fisher’s exact tests and regression were used to understand relationships between risks and HBV blood markers. Of the 498 participants, 126 (25.3%) were susceptible to HBV, 52.6% had been vaccinated against HBV, and 17.9% had recovered from a past infection. Eleven (2.2%) participants tested positive for isolated HBV core antibody, 10 (2.0%) for HBV surface antigen, and one (0.2%) for HDV antibody. History of incarceration was associated with current HBV infection, while transactional sex and experience of homelessness were predictive of previous exposure. This study found high rates of current and past HBV infection, and a 10% HBV/HDV co-infection rate. Despite availability of vaccine, one quarter of participants remained vulnerable to infection. Findings demonstrate the need to improve low-threshold HBV/HDV screening, vaccination, and linkage to care among PWUD. The study also identified gaps in the HBV/HDV care cascade, including lack of point-of-care diagnostics and lack of support for HROs to provide HBV services.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

Security in the Internet of Things (IoT) is a critical concern due to the growing number of connected devices and the limited resources to implement robust security mechanisms in most of them. Adopting standard assessment frameworks, such as CVSS (Common Vulnerability Score System), allows for systematic vulnerability assessment. At the same time, CVE (Common Vulnerabilities and Exposures) records provide unique identifiers for known security issues, making it easier to share information. However, comparing security solutions is still challenging due to the diverse nature of IoT devices and the ever-evolving threat landscape, requiring continual innovation and collaboration between stakeholders. Furthermore, there still needs to be a simple way to compare technologies and solutions, taking into account the security provided by them. In this sense, in this article, we propose a tool for calculating a security score based on the CVSS index of each existing vulnerability in a given technology. We explain how the tool performs the calculation, discuss some challenges, and propose improvements for future work.

The research of object classification and part segmentation is a hot topic in computer vision. A considerable number of studies have been carried out about deep learning on 3D point clouds. However,it is challenging to achieve effective feature learning due to sparsity of point clouds. Recently, a variety of Transformers have been adopted to improve point cloud processing and display great potential. Nevertheless, large numbers of Transformer layers tend to incur huge computational and memory costs. PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks, this does not mean that PointNet++ has no potential.Thus, this paper offer two major contributions that significantly improve PointNet++ performance. Firstly, we introduce a novel contextual feature extraction (CFE) block that significantly enhances the feature extraction capabilities of PointNet++ networks. Secondly, to further enhance feature fusion, we seamlessly integrate a mutual learning (ML) block into the network architecture. By embedding these two innovative blocks within each layer of the network, we not only enrich the network's functionality but also impart it with greater robustness and adaptability. The specific experiments were conducted on the S3DIS (6-fold cross-validation) and Modelnet40 datasets with 86.5% and 92.7% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks.

Poultry farming in Slovakia has a tradition and an irreplaceable position to ensure rational nutrition for the population. In terms of a nutritional value, poultrymeat (Galliformes) contains a high amount of proteins, essential amino acids, essential unsaturated fatty acids, minerals, vitamins and a low fat content. Poultrymeat production has a relatively short reproductive cycle and thus allows regulating the demand and supply. The poultry market also influences the development of domestic consumption. According to the Statistical Office of the Slovak Republic, the consumption of meat has witnessed a persistent increase over the last 10 years. [1] According to the 2023 AVEC (The Voice of European Meat Sector) annual report, the consumption of poultrymeat in Slovakia was 20.0 kg per person per year. The average consumption in the EU-27 (2021) was 23.4 kg, with the countries with the highest consumption being Portugal (37.8 kg), Ireland (36.0 kg), Hungary (33.5 kg) and Spain (29.9 kg). On the other hand, the countries with the lowest consumption of poultrymeat, apart from Slovakia, also include Austria and Greece with a consumption of 21.5, resp. 21.0 kg [2]. The breeding of broiler chickens, as well as the production and placing on the market of poultrymeat must comply with the requirements laid down by legislation, while the compliance is regularly checked by the competent authorities.

Mucosal healing (MH) is the main treatment target in ulcerative colitis and Crohn’s disease, and it is defined by a combination of complete endoscopic and histologic remission. Indeed, the complete resolution of mucosal inflammation should be confirmed at histology. Neutrophil infiltration represents the unique histological marker in discriminating the active vs quiescent phase of disease, also including crypt injuries (cryptitis and crypt abscesses), erosions and ulcerations. Basal plasmacytosis do not qualify by itself the remission in inflammatory bowel diseases (IBD), but it represents a diagnostic clue, mostly at onset. Several histological scoring systems have been developed to evaluate therapeutic efficacy, even though they often resulted tricky and subjective. Moreover, these scores listed a heterogeneous group of histological features as parameters. The recently proposed simplified histological score on MH was based on neutrophils and their distribution in the gut mucosa was also considered, aiming to provide a simple, time-sparing and reproducible tool to be applied to the routinary diagnostic practice. The artificial intelligence could be a promising tool both in evaluating and standardizing the histological assessment of disease activity in IBD. Moreover, novel molecules involved in the inflammatory dynamics in the gut could be employed in supporting the diagnostic practice in IBD.

Cardiac autonomic neuropathy (CAN) is a severe complication of diabetes mellitus (DM) strongly linked to a nearly five-fold higher risk of cardiovascular mortality. Patients with Type 2 Diabetes Mellitus (T2DM) are a significant cohort in which these assessments have particular relevance to the increased cardiovascular risk inherent in the condition. This study aimed to explore the subtle correlation between the Ewing test, Sudoscan-cardiovascular autonomic neuropathy score, and cardiovascular risk calculated using SCORE 2 Diabetes in individuals with T2DM. The cohort consisted of 211 patients diagnosed with T2DM with overweight or obesity without established ASCVD, aged between 40 to 69 years. The prevalence of CAN in our group was 67.2%. In the study group, according SCORE2-Diabetes, four patients (1.9%) were classified with moderate cardiovascular risk, thirty-five (16.6%) with high risk, and one hundred seventy-two (81.5%) with very high cardiovascular risk. On multiple linear regression, the SCORE2-Diabetes algorithm remained significantly associated with Sudoscan CAN-score and Sudoscan Nephro-score and Ewing test score. Testing for the diagnosis of CAN in very high-risk patients should be performed because approximately 70% of them associate CAN. Increased cardiovascular risk is associated with sudomotor damage and that Sudoscan is an effective and non-invasive measure of identifying such risk.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

Using the h-index to assess individual researchers is not only unethical and unfair but also inaccurate and misleading. This index fails to offer a reliable measure of a researcher's impact based on their citation scores. Beyond statistical and conceptual considerations, authorship practices, especially in the case of multi-authored publications, give rise to significant problems that often go unnoticed. While some modifications of the h-index have been proposed to mitigate these weaknesses, the fundamental deficiencies persist. Most of these flaws have been effectively addressed by the c-score, a composite citation index. The c-score excludes self-citations, normalizes the number of citations by considering the number of authors in each paper, and takes into account first, single, and last authorship. This approach provides a more realistic measure of the impact of each individual researcher based on raw citations.

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that adversely affects maternal and offspring health. A variety of risk factors, such as BMI and age, have been associated with increased risks of gestational diabetes. However, in many cases gestational diabetes occurs in healthy nulliparous women with no obvious risk factors. Emerging data suggest that the tendency to develop gestational diabetes has genetic and environmental components. Here we develop a polygenic risk score for gestational diabetes. We further investigate relationships between the genetic architecture of GDM and genetically constructed risk factors and biomarkers. Our results show that genetics can be used as an early screening tool that identifies at-risk at higher risk of GDM before its onset to propose comprehensive monitoring and preventative programs to mitigate the risks.

The wave energy converter (WEC) technology constantly develops new designs and models. To encourage its development within the energy sector, research is required to improve current designs and create durable and effective devices. However, it is also crucial to investigate the suitability of a WEC to a site-specific sea condition as this can impact its optimal power absorption and constructability. This study evaluates and compares the efficiency of a heaving buoy with varied geometries in one of the most energetic places along the Mexican coast. A statistical analysis of the wave climate on the coast of Ensenada during the last 42 years is performed to define the conditions to which the device is subjected. The location of the WEC in shallow waters is chosen using a computational model which solves the modified mild slope equation in its elliptic shape. The heaving buoy floater is studied using three distinct geometries: a semi-sphere, a cylinder, and a proposed rounded semi-rectangle. The hydrodynamic response of the three geometries is then analysed in the frequency and time domain using ANSYS AQWA. The hydrodynamic study involves the assessment of the floating body dynamics, exerted forces, the power absorbed as well as the suitability of the proposed power take-off (PTO) system. Findings reveal that the proposed geometry absorbs the most energy, with an annual power of 135.11 MW, and that the transmission PTO design is appropriate for this type of technology.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

BACKGROUND AND AIMS: Cardiovascular disease and colorectal cancer (CRC) are significant health problems and share some risk factors. The aim of our study was to develop and validate a predictive score for advanced colorectal neoplasia (CRN) based on risk factors for cardiovascular disease and CRC. MATERIALS AND METHODS: A cross-sectional study comprising a derivation cohort and an external validation cohort of 1049 and 308 patients respectively. A prediction score for advanced CRN (CRNAS: Colorectal Neoplasia Advanced Score) was developed from a logistic regression model, comprising sex, age, first-degree family history for CRC, systolic and diastolic blood pressure, total cholesterol, HDL-cholesterol, body mass index, diabetes, smoking, and antihypertensive treatment. Other cardiovascular risk scores (Framingham-Wilson, REGICOR, SCORE and FRESCO) were also used to predict the risk of advanced CRN. The discriminatory capacity of each score was evaluated using the area under the curve (AUC). RESULTS: CRN were found in 379 subjects from the derivation cohort (36%) including 228 patients (22%) with an advanced CRN. Male sex, age, diabetes, and smoking were identified as independent risk factors for advanced CRN. The newly created score (CRNAS), showed an AUC of 0.68 (95% CI: 0.64-0.73) for advanced CRN, which was better than cardiovascular risk scores (p

A Challenging task in routine practice is finding distinction between benign and malignant paragangliomas and pheochromocytomas. The aim of this study is a comparative analysis of angiogenesis by assessing intratumoral microvascular density (MVD) with immunohistochemical (IHC) markers (CD31, CD34, CD105, ERG), and S100 immunoreactivity, Ki67 proliferative index, SDHB expressiveness, tumor size with one the most utilized score Pheochromocytoma of Adrenal Gland Scales Score (PASS), using tissue microarray (TMA) with 115 tumor samples, 61 benign (PASS&lt;4) and 54 potentially malignant (PASS ≥4). We found no notable difference between intratumoral MVD and potentially malignant behavior. Group of potentially malignant tumors are significantly larger in size, have lower intratumoral MVD, and decreased number of S100 labeled sustentacular cells. Both groups have low proliferative activity (mean Ki67 is 1,02 and 1,22 respectively). Most tumors maintains SDHB expression, only 6 cases (5,2%) showed a loss of expression (4 of them in PASS&lt;4 group and 2 in PASS≥4). PASS score is easy available for assessment, and complemented with markers of biological behavior completes risk stratification algorithm. Size is directly related with PASS score and malignancy. Intratumoral MVD is extensively developed but it is not crucial in evaluating the malignant potential.

The point cloud is a form of three-dimensional data that comprises various detailed features at multiple scales. Due to this characteristic and its irregularity, point cloud analysis based on deep learning is challenging. While previous works utilize the sampling-grouping operation of PointNet++ for feature description and then explore geometry by means of sophisticated feature extractors or deep networks, such operations fail to describe multi-scale features effectively. Additionally, these techniques have led to performance saturation. And it is difficult for standard MLPs to directly "mine" point cloud. To address above problems, we propose the Detail Activation (DA) module, which encodes data based on Fourier transform after sampling and grouping. We activate the channels at different frequency levels from low to high in the DA module to gradually recover finer point cloud details. As training progresses, the proposed Point-MDA can uncover local and global geometries of point cloud progressively. Our experiments show that Point-MDA achieves superior classification accuracy, outperforming PointNet++ by 3.3% and 7.9% in terms of overall accuracy on the ModelNet40 and ScanObjectNN dataset, respectively. Furthermore, it accomplishes this without employing complicated operations, while exploring the full potential of PointNet++.

A software defined radio (SDR) is a communication system that makes use of components that can be configured through software, in contrast to traditional systems where these components are variable through hardware, these radio devices are much more versatile, this article describes the factors that must be considered when implementing a communication system based on Software Defined Radios (SDR), in order to reduce the attenuation factors and thus obtain the maximum distance for a transmission of data effectively in the UHF band. The calculations made for the first Fresnel zone and for the design of the Ground Plane type antennas used in the transmission/reception stages of the x40 bladeRF platforms are also presented. The tests were carried out at the facilities of the Huarangal Nuclear Center of the Peruvian Institute of Nuclear Energy, obtaining favorable results that allow ratifying the versatility and performance of the SDRs.

Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.

Systemic AL amyloidosis is a challenging disease for which many patients are considered frail in daily clinical practice. However, no study has so far addressed frailty and its impact on the outcome of these patients. We built a simple score to predict mortality based on three frailty-associated variables: age, ECOG performance status (&lt;2 vs. 2) and NT-proBNP (&lt;8500 vs. 8500 ng/L). Four-hundred and sixteen consecutive newly diagnosed patients diagnosed at ten sites from the Spanish Myeloma Group were eligible for the study. The score was developed in a derivation cohort from a referral center, and it was externally validated in a multicenter cohort. On multivariate analysis the three variables were independent predictors of survival. The score was able to discriminate four groups of patients in terms of overall survival and early mortality in both cohorts. Comorbidity was also analyzed with the Charlson comorbidity index, but it did not reach statistical significance in the model. A nomogram was created to easily estimate the mortality risk of each patient at each time point. This score is a simple, robust, and efficient approach to dynamically assess frailty-dependent mortality both at diagnosis and throughout the follow-up. The optimal treatment for frail AL amyloidosis patients remains to be determined but we suggest that the estimation of frailty-associated risk could complement current staging systems, adding value in clinical decision-making in this complex scenario.

Although, central venous pressure (CVP) is among the most frequent estimated hemodynamic parameters in the critically ill setting, extremely little is known on how intensive care unit (ICU) nurses use this index in their decision-making process. The purpose of the study was to develop a new questionnaire for accessing how ICU nurses use CVP measurements to address patients’ he-modynamics investigating its validity and reliability. A cross-sectional study was conducted among 120 ICU nurses from four ICUs of Greece. Based on a comprehensive literature review and the evaluation of a five-experts panel a new questionnaire, named “CVP-Score”, was created having 8 items. The construct validity and the reliability of the questionnaire were examined. The half of the study participants’ (51.7%) was working at a specialized ICU, and they had a mean [±Standard Deviation (SD)] ICU experience of 13(±7.1) years. The estimated construct validity of the new-developed tool was acceptable, while the internal consistency reliability as measured by Cronbach alpha was excellent (0.901). CVP Score had acceptable test-retest reliability (r=0.996, p<0.001) and split-half reliability (0.855). CVP Score is a valid and reliable instrument for measuring how critical care nurses use CVP measurements in their decision-making process.

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.

Background and Objectives:: Hip osteoarthritis is the main cause physical disability, also has a serious impact on the efficiency and independence of older people. The aim of the study was to analyze the relationship between hip osteoarthritis and the risk of sarcopenia. Our main hypothesis was whether the risk of sarcopenia, as measured by the SARC F questionnaire , is related to the physical fitness of older people with hip osteoarthritis. Materials and Methods: The study was conducted with the participation of 177 people of aged 65 or more, diagnosed hip osteoarthritis. The subjects were assessed using the SARC F questionnaire, the Short Physical Performance Battery (SPPB) and Timed Up and Go (TUG) test. The subjects were divided into two groups depending on the result they obtained in the SARC F questionnaire (group 1- SARC F &lt;4, group 2 - SARC F ≥ 4). Results: There was a significant correlation between the results in the SARC F and the results of the TUG (p&lt;0.001) and the SPPB test, both the SPPB total score (p&lt;0.001)and its elements: SBBP chair stand test (p&lt;0.001), SPPB balance test (p&lt;0.001), SPPB gait speed test (p&lt;0.001). Logistic regression of the SPPB and TUG predictors showed that in SARC F more than 4 predicted worse SPPB and TUG outcomes. In the SARC group, F above 4 increases the odds of poorer scores in SPPB OR=1.097; CI 95% 1.053-1.146 and TUG, OR=1.096; CI 95% 1.104-1.266. Conclusion: The study showed that the results obtained in the SARC F questionnaire correlate with the possibilities of physical functioning in patients with hip osteoarthritis SARC F is an inexpensive method that can be used to assess the risk of sarcopenia. Healthcare professionals, especially in primary care, should consider assessing the risk of sarcopenia especially in people with hip osteoarthritis.

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We show that an integrated approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n=290,584). Likewise, integrating PRS with self-reports on birth weight (n=172,239) and comparative body size at age ten (n=287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

Objectives. Invasive fungal spondylodiscitis (IFSD) is rare and could be lethal in certain circumstances. Previous literature revealed limited data concerning the outcomes. This study aimed to establish a risk-scoring system. Methods. A total of 53 patients were included in the study from a multi-centered database in Taiwan. All the clinicopathological and laboratory data were retrospectively analyzed. Variables strongly related to 1-year mortality were identified using a multivariate Cox proportional hazards model. A receiver operating characteristic curve was used to express the performance of our IFSD scoring model. Results. Five strong predictors were included in the IFSD score: predisposing immunocompromised state, the initial presentation of either radiculopathy or myelopathy, initial laboratory findings of WBC > 12.0 or < 0.4 103/uL, hemoglobin < 8 g/dL, and evidence of candidemia. 1-year mortality rates for patients with IFSD Scores of 0, 1, 2, 3, and 4 were 0%, 16.7%, 56.3%, 72.7%, and 100%, respectively. The area under the curve of the receiver operating characteristic curve was 0.823. Conclusions. We developed a practical scoring model with easily obtained demographic, clinical, and laboratory parameters to predict the probability of 1-year mortality in patients with IFSD. However, more large-scale and international validations would be necessary before this scoring model is commonly used.

We investigate the impact of aging on exchangeable inter-arrival times in mixed renewal processes, exploring its implications for reliability and survival analysis. In this study, first, we revisit the definition of renewal point processes, where inter-event time intervals are considered as exchangeable non-negative random variables. Then, we define the concept of statistical aging as latency in the observational process of event counting. Latency affects event detection but preserves exchangeability. However, it may alter the statistical properties of inter-event time intervals. Our analytical and numerical assessments highlight the significance of aging in exchangeable lifetimes, offering insights into key metrics such as the failure survival function, renewal function, and hazard rate function. Through archetypal examples and empirical findings, we illustrate the implications of aging on renewal processes. In particular, employing a Bayesian perspective, we analyze high-frequency currency exchange rate data to assess the impact of aging on volatility risk evaluation. This study contributes novel insights to the literature of renewal theory and survival analysis, emphasizing the role of latency aging in stochastic point processes under exchangeability assumption.

We consider a Cauchy problem for differential equations in a Hilbert space $X$. The problem is stated in a time interval $I$, which can be finite or infinite. Weuse a fixed point argument for history-dependent operators to prove the unique solvability of the problem. Then, we state and prove convergence criteria for both a general fixed point problem and thecorresponding Cauchy problem. These criteria provide necessary and sufficient conditions on a sequence $\{u_n\}$ which guarantee its convergence to the solution of the corresponding problem, in the space of both continuous and continuously differentiable functions. We then specify our results in the study of a particular differential equation governed by two nonlinear operators. Finally, we provide an application in viscoelasticity and give a mechanical interpretation of the corresponding convergence result.

First came the digital banks without offices, and then the “fintech” companies that combined technology and finance to achieve greater efficiency and new business models. Later was the turn of transport and logistics, understood as the sector whose economic activities derived from the transport of people and goods, while on the one hand, it has had a new spring thanks to factors such as online sales. Then, perhaps catalyzed by the pandemic scenario, the turn has reached education, where years ago was only a complement of traditional education, now is a central part with multiple modern methodologies. Numerous studies exist about using or implementing these e-learning procedures, but technical analysis is barely found in references. This work aims to analyze the digital transformation of the main sectors of our society, including finances, transport, and education, together with several examples of how technology was helping to fight the pandemic scenario.

(1) Background: Considering highly selected patients with ductal carcinoma in situ (DCIS), active surveillance is a valid alternative to surgery. Our study is aimed at showing the reliability of post-biopsy complete lesion removal, documented by mammogram, as additional criterion to select these patients. (2) Methods: 2173 Vacuum Assisted Breast Biopsies (VABB) documented as DCIS have been reviewed. Surgery has been performed in all cases. We retrospectively collected the reports of post-VABB complete lesion removal and the histological results of the biopsy and surgery. We calculated the rate of upgrade of DCIS identified on VABB upon excision for patients with post-biopsy complete lesion removal and for those showing residual lesion. (3) Results: We observed 2173 cases of DCIS: 408 classified as low grade; 1262 as intermediate grade; 503 as high grade. The overall upgrading rate to invasive carcinoma was 15.2% (330/2173). The upgrade rate was significantly lower (8.2%) when considering patients showing mammographically documented complete removal of the lesion. (4) Conclusion: The absence of mammographically documented residual lesion following VABB is associated to a lower upgrading rate of DCIS to invasive carcinoma on surgical excision and should be considered when deciding the proper management DCIS diagnosis.

Prior work has suggested that discrimination and immigration-related stress may impede mental health care seeking and utilization among Latinx populations. These effects may be more nuanced as both discrimination and immigration-related stress may increase symptomology, particularly posttraumatic stress disorder (PTSD) and depression. Both symptoms may in turn prompt attempts to seek care. The current study examined the direct effects of discrimination and immigration-related stress on care access, as well as potentially indirect effects with PTSD and depression symptoms as mediators. Interviews and online surveys were completed with 234 Latinx residents of the Midwest assessing everyday discrimination, discrimination in healthcare, PTSD symptoms, depression symptoms, current mental health utilization, and previous unsuccessful attempts to seek care. Direct effects of discrimination and immigration-related stress were largely unrelated to care access variables. Discrimination in healthcare settings predicted both unsuccessful attempts to seek care and current use, but this effect was positive. The indirect effect was largely supported, but only for PTSD symptoms and not depression. Results indicate further investigation is necessary to understand the direct effects of discrimination on care access. Further, discrimination and immigration-related stress may indirectly prompt attempts to seek mental health care.

This study aimed to translate and psychometrically validate the Neck Outcome Score (NOOS) in the Polish population according to the recommendations of American Academy of Orthopedic Surgeons (AAOS) for the Cross-Cultural Adaptation of Health Status Measures. Participants completed the NOOS, Neck Disability Index (NDI), and Visual Analogue Scale (VAS) for pain assessment. The questionnaires were completed by 57 women and 32 men with cervical spine ailments. A retest was performed in all patients after 48 hours. The analysis confirmed the high internal consistency (Cronbach's alpha of 0.95) of the Polish NOOS. No floor / ceiling effects were observed. The Polish NOOS showed a significant correlation with NDI (0.87; p<0.001) and VAS (0.79; p<0.001). The intraclass correlation coefficient (ICC) for the test-retest was found to be high (0.97). The Polish version of NOOS can be used for clinical and research purposes as an equivalent to the original English version. This study contributes to the area of patient-reported outcome measures available in the Polish language.

Background: Idiopathic normal pressure hydrocephalus is a syndrome with neuroradiological findings and clinical pattern characteristic but not specific for the pathology. Since the prevalence is growing due to the rapidly aging society, standardized and validated protocol for diagnosis is needed, also because this condition may mimic other disorders among elderly such as Parkinson's and Alzheimer's disease. Material and methods: We analysed data from 44 patients with suspect diagnosis of idiopathic normal pressure hydrocephalus with age > 60 years, clinical triad and neuroradiological pattern examined in our ward from November 2018 to November 2022. Neu-ropsychological assessment includes Mini Mental State Examination and Mental Deterioration Battery. Motor scores were collected from gait and balance tests. The evaluation was performed before and after an extended lumbar drainage last 48h. Patients who has scores improvement after the extended lumbar drainage, undergo ventriculoperitoneal shunt with a programmable valve. Results: All patients undergo neuropsychological assessment and motor tests, only 2 patients were unable to perform motor tests because bedridden. 16 patients showed no benefit from the tests, in 2 cases even a worsening of cognitive performance. 28 patients showed an improvement in their performances, but in most cases the improvement involved only the neuropsychological as-sessment. All patients underwent ventriculoperitoneal shunt have had a typical answers pattern in Mental Deterioration Battery test. We performed 9 ventriculoperitoneal shunt and 1 ventricu-lo-atrial shunt. Conclusions: Qualitative neurocognitive tests come out to be more sensitive compared to quantitative neurocognitive tests in identifying patients with suspected idiopathic normal pressure hydrocephalus benefiting from extended lumbar drainage test and then un-derwent surgical treatment. This could be considered a valid screening in elderly patients with suspected idiopathic normal pressure hydrocephalus in order to minimize the number of invasive procedures. More studies are necessary to validate this tool.

A robust bank industry is a major player in the stability of an economy. The operational efficiency and stability of banks are therefore paramount. By way of financial ratios and Z-score, this study analysed UT Bank’s financial performance prior to the 2017 bank sector reforms in Ghana. Annual financials over a ten year period (2007-2016) were used. Debt management practices of UT Bank per the results obtained were quite on the hind side and unimpressive. This was reflected in the poor leverage and risk management variables. UT Bank would have been unable to meet creditors’ claims considering the mean average values of debt-to-assets and debt-to equity ratios of 0.76 and 0.90 respectively. The entire bank sector will benefit if credit management practices of individual banks are refreshed and worked on. The bank industry regulator should tighten its supervisory and monitoring role over banks to help detect early signs of non-performing banks. The study further recommends that statutory lending limits of banks be re-enforced to uphold the threshold of 10 percent for unsecured loans and 25 percentage for secured loans of net owned funds of the bank.

Objective: We determined, in stable ambulatory heart failure with preserved ejection fraction (HFpEF) subjects and matched controls, the capability of a novel blood based cardiac-specific cBIN1 Score (CS) to diagnose heart failure and prognosticate future hospitalization. Background: Heart failure (HF) poses a costly health care burden worldwide with rising prevalence. Abnormal calcium signaling is intrinsic to HF pathophysiology and correlates with reduced expression of a cardiac membrane scaffolding protein, cardiac bridging integrator 1 (cBIN1). We hypothesize that CS, a numerical score derived from plasma cBIN1 concentration, is a diagnostic and prognostic biomarker of HF. Methods: Plasma cBIN1 is quantified by an ELISA test, and CS is calculated as the natural log of the normalized reciprocal of plasma cBIN1 concentration. We determined CS among 52 clinically stable individuals with HFpEF (LVEF ≥ 50%) (mean age 57 ± 15 years old, 63% men) and 104 age and sex matched volunteers with no known history of HF. We obtained plasma concentrations of NT-proBNP, a marker of volume status, as comparison. Baseline co-morbidities and one year longitudinal clinical information were obtained through electronic medical records. Results: Median CS is 0 (IQR -0.4 – 0.6) in the control cohort and is increased to 1.8 in the HFpEF cohort (IQR 1.5 – 2.3, p < 0.0001). For HFpEF diagnosis, CS has a receiver operating characteristic (ROC) area under the curve (AUC) of 0.94 (95% CI 0.95 – 0.99) and NT-proBNP of 0.89 (95% CI 0.83 – 0.95). Kaplan-Meier analysis of one year cardiovascular hospitalizations reveals that HFpEF patients with CS ≥ 1.8 have a hazard ratio (HR) of 4.0 (95% CI 1.4 – 11.2, p=0.009). Combining CS ≥ 1.8 with NT-proBNP ≥ 300 pg/mL, increases HR to 21.4 (95% CI 2.7 – 171.6, p=0.004). Conclusions: In a cohort of stable ambulatory HF patients with cardiomyopathies of multiple etiologies and preserved ejection fraction, a positive CS correlates with worsening myocardial health and predicts future hospitalization. CS, a marker of cardiac muscle health, provides a novel index to informing the management of stable ambulatory HF patients.

Objective: We determined, in stable ambulatory heart failure with reduced ejection fraction (HFrEF) subjects and matched controls, the capability of a novel blood based cardiac-specific cBIN1 Score (CS), which assesses the health of cardiac muscle, to identify patients with known heart failure (HF) and to prognosticate future hospitalization. Background: Limited clinical tools are available in assessing cardiac muscle health in stable ambulatory patients. Cardiac bridging integrator 1 (cBIN1) is a cardiomyocyte t-tubule membrane scaffolding protein which regulates calcium signaling in cardiomyocytes, decreases in failing muscle, and is present in plasma in levels that correlate with cardiac content. We hypothesize that CS, a normalized index of plasma cBIN1 concentration, can function as a diagnostic and prognostic biomarker of HF. Methods: Plasma cBIN1 concentration is measured by an ELISA test, and CS is calculated as the natural log of the ratio of a constant population mean cBIN1 to measured cBIN1 concentration. We determined CS among 125 clinically stable individuals with HFrEF (LVEF ≤ 40%) (mean age 56 ± 10 years old, 79% men) and 125 age, sex matched volunteers with no known history of HF. We obtained plasma concentrations of NT-proBNP, a marker of volume status, as comparison. Baseline co-morbidities and 18-month longitudinal clinical information were obtained through electronic medical records. Results: CS follows a normal distribution with a median of 0 in the control population and median is significantly increased among HFrEF patients to 1.8 (IQR 1.4 – 2.1, p < 0.0001). CS diagnosed HFrEF with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.93 (AUC is 0.98 for NT-proBNP, and combined CS and NT-proBNP AUC is 0.99). Unlike NT-proBNP, CS does not correlate with body mass index (BMI) in either the control or HFrEF population (Pearson’s r = -0.15, p = 0.12; Pearson’s r = 0.003, p = 0.97, respectively). NT-proBNP significantly correlates with renal function (Pearson’s r = -0.37, p = 0.001), while CS also has no correlation (Pearson’s r = 0.03, p = 0.71). During an 18-month follow-up, a high CS ≥ 1.8 at the initial visit predicted future cardiovascular hospitalizations (38% vs. 21%, p = 0.04, hazard ratio 2.0). NT-proBNP did not predict future cardiovascular hospitalizations. Conclusions: Plasma cBIN1 based CS is insensitive to BMI and renal function and differentiates myocardial health between patients with HFrEF versus matched controls. An abnormally high CS reflected poor intrinsic myocardial health and can predict future 18-month cardiac hospitalization in stable ambulatory patients.

Lymphedema is a chronic and debilitating disease that affects up to 250 million patients worldwide. Recent advances in understanding its pathophysiology, along with improved diagnosis and microsurgical techniques, have enhanced our ability to cope with the challenging task of treating this disease. This review provides an overview of the disease from a surgeon's point of view, including existing imaging modalities used for preoperative assessment, as well as surgical procedures used in its treatment. Advantages and drawbacks of various existing modalities used for pre or intraoperative assessment of lymphatic vessels are discussed. Lymphedema treatment has shifted from palliative debulking procedures (liposuction and direct excision) to those aimed at restoring lymphatic flow and countering the pathophysiology of the disease (lymphaticovenous anastomosis and vascularized lymph node transfer). A combination of both approaches can result in a synergistic benefit for patients and are discussed in this review. Despite recent advances, some controversies persist, and further studies are needed to better define surgical treatment algorithms.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.

Ribosome profiling and mass spectroscopy have identified canonical and noncanonical translation initiation codons (TICs) that are upstream of the main translation initiation site and used to translate oncogenic proteins. Here, we use a Kozak Similarity Score algorithm to find that nearly all of these TICs have flanking nucleotides closely matching the Kozak sequence. Remarkably, the nucleotides flanking alternative noncanonical TICs are frequently closer to the Kozak sequence than the nucleotides flanking TICs used to translate the gene’s main protein. Of note, the 5’ untranslated region (5‘UTR) of cancer-associated genes with an upstream TIC tend to be significantly longer than the same region in genes not associated with cancer. The presence of a longer than typical 5’UTR increases the likelihood of ribosome binding to upstream noncanonical TICs, and may be a distinguishing feature of a number of genes overexpressed in cancer. Noncanonical TICs that are located in the 5’UTR, although thought disadvantageous and suppressed by evolution, may translate oncogenic proteins because of their flanking nucleotides

Objective: The Acute Cystitis Symptom Score (ACSS) was developed and validated as a self-reporting questionnaire for diagnosing and monitoring acute, uncomplicated cystitis (AC) in female patients. The study aims at the translation of the ACSS into Greek from original Russian as a source and American English as a new master version and at its linguistic validation. Material and Methods: Three independent professional native Greek translators, two of them experts in Russian and one in English, translated the ACSS from Russian and American English into Greek. The second group of three translators translated each of the three versions back into the original language to detect or correct any important discrepancies. These three Greek versions were then used for linguistic validation. Results: The English to Greek translation reflected more the spoken language, the two Russian to Greek translations more the written, formal language. A total of 60 randomly selected females and 30 healthcare professionals was asked about their preferences and to comment on each of the three translations. Considering all comments the scientific committee (SC) developed the Greek consensus version. For cognitive assessment additional 30 healthcare professionals and 30 females were asked to comment on the intelligibility of all items. Considering their comments the linguistically validated Greek study version was established by the SC. Conclusion: The linguistically validated Greek version of the ACSS can now be used for the clinical validation study.

Background To develop and validate the American-English version of the self-reporting Acute Cystitis Symptom Score (ACSS), a suitable tool for diagnosis and patient-reported outcome in female patients with acute uncomplicated cystitis (UC). Methods After certified translation into American-English and cognitive assessment, the clinical validation of the ACSS was performed as an embedded study in a US phase II trial (ClinicalTrials.gov Identifier: NCT03129295). Results A total of 167 female patients with typical symptoms of UC were included in the study following FDA guidance. At Day 1 (diagnosis) the mean(SD) sum score of the six ACSS typical symptoms reached 10.60(2.51). Of 100 patients followed-up last time on Day 5 or 6 (End-of-treatment, EoT), 91 patients showed clinical success according to the favoured ACSS criteria (sum score of typical symptoms 0.98(1.94)). There was no correlation between the severity of symptoms on Day 1 or between clinical success rate at EoT and level of bacteriuria on Day 1. Conclusion The American-English ACSS showed high predictive ability and responsiveness, and excellent levels of reliability and validity. It can now be recommended as the new master version in clinical and epidemiological studies, in clinical practice or for self-diagnosis of women with symptoms of UC.

A recent study claimed that genome-wide polygenic scores (GPSs) for five common diseases could identify individuals with risk equivalent to monogenic mutations. Receiver operator curve analyses were reported to have areas under the curve (AUCs) ranging from 0.63 for inflammatory bowel disease up to 0.81 for coronary artery disease (CAD) but these models also included age and sex, themselves strong predictors of risk. The GPS for CAD identified 8% of the population at threefold increased risk, which it was claimed was comparable to the excess risk from monogenic mutations. In the present study attempts were made to model the distribution of the GPS for CAD to match the information provided. These models were based on the reported distribution of prevalence by centile of GPS and on the distribution of GPS in controls and cases and were fitted to the reported results using linear approximations to the distributions and using simulations of a liability-threshold model. It was impossible to produce a compatible model in which the GPS produced an AUC as high as 0.81 and the most plausible estimate was that the true AUC was only 0.65. The reported distributions of the GPS in cases and controls overlap so much that they are not compatible with an AUC of 0.7 or higher. The AUC of the GPS for these diseases is modest. Furthermore, the literature robustly demonstrates that true CAD risk associated with monogenic mutations is much higher than the threefold increase which is predicted by the GPS. Together, these findings cast doubt on the clinical utility of the GPS.

It is already well known that infants and children infected with COVID-19 develop mild to mod-erate forms of the disease, with fever and oropharyngeal congestion being the most common symptoms. Nevertheless, there are cases in which the patients accuse respiratory symptoms. These cases need lung evaluation, which can be done using lung ultrasound (LUS), because it is a non-irradiating and repeatable imaging technique. 19 children with COVID-19 pneumonia were eval-uated using LUS. The LUS score (LUSS) for each patient varied between 1 to 8 points from a max-imum of 36 points. The arithmetic mean was 4.47 ± 2.36 (S.D), while 95% CI for the Arithmetic mean was 3.33 to 5.61. The lung changes were correlated with their biomarkers, specifically in-flammatory markers. The correlation between LUSS and LDH, D-dimers and IL-6 was a strong positive one with r=0.66 (p=0.01, 95% CI 0.147 to 0.896) between the LUSS and LDH level at symptomatic infants and children (with cough present) and r=0.66 (p=0.01, 95% CI 0.140 to 0.895) between LUSS and D-dimers level at symptomatic infants and children (with cough pre-sent). The results suggest that LUS could be a good imaging technique that can be used both in ini-tial evaluation of children with respiratory diseases, and, also in their follow-up, correlated with symptoms and biomarkers.

Schizoaffective disorder and its classification have always been a topic of discrepancy among researchers. The overlapping presentations of schizoaffective disorder, schizophrenia and psychotic bipolar disorder makes it difficult to classify and diagnose. This study is a narrative review of literatures and genetic and epidemiological research evidences, done to determine the similarities and differences between schizoaffective and psychotic bipolar disorder. Beginning from the introduction of schizoaffective disorder as "schizoaffective psychosis" by Kasanin in 1933, various researchers have proposed different views regarding the diagnosis and classifications for schizoaffective disorder. DSM-III considered schizoaffective disorder as a diagnosis of exclusion as it fails to fit the definite diagnostic criteria of schizophrenia, major affective disorders, or schizophreniform disorder. DSM-V updated the criteria of schizoaffective disorder and stated it as a life time illness rather than being an episodic disorder as in DSM-IV and hence is more reliable. However, some studies show that schizoaffective disorder patients are more like schizophrenia than mood disorder. It still remains unclear whether schizoaffective disorder is a heterogenous disease, a form of schizophrenia, a form of mood disorder or a midpoint in the continuation spectrum of schizophrenia and psychotic bipolar disease.

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

Vienna Atomic Line Database (VALD) contains data on atomic and molecular energy levels and parameters of spectral lines required for stellar spectra analysis. Hundreds of millions lines for fine spectral synthesis and for opacity calculations are collected in present version of VALD (VALD3). Critical evaluation of the data and the diversity of extraction tools support high popularity of VALD among users. The data model of VALD3 incorporates obligatory links to the bibliography making our database more attractive as publishing platform for data producers. The VALD data quality and completeness are constantly improving allowing better reproduction of stellar spectra. To illustrate continuous evolution of the data content we present a comparative analysis of the recent experimental and theoretical atomic data for Fe-group elements, which will be included in the next VALD release. This release will also include a possibility for extracting the line data with full isotopic and hyperfine structures.

Background and Objectives: Cardiovascular events are the leading cause of morbidity and mortal-ity in hemodialysis (HD) patients. Identifying risk factors can help in the effort to reduce cardi-ovascular risk and improve life expectancy. The purpose of this study was to evaluate the ability of CHA2DS2-VASc score [risk index of stroke in atrial fibrillation (AF)] in patients with end stage kidney disease, to predict stroke, major cardiovascular events and mortality. Materials and Meth-ods: CHA2DS2-VASc and HAS-BLED (bleeding risk from use of anticoagulation in AF) scores were calculated in 237 HD patients, [99 women, median age 76 (15) years], at time they com-menced on HD. The scores’ ability to predict long term cardiovascular morbidity and mortality was estimated, both in those with and without AF. Results: CHA2DS2-VASc score was signifi-cantly increased in AF (n=69) compared to non-AF (n=168) patients, 5 (2.5) versus 4 (2), p<0.0001, respectively. Increased CHA2DS2-VASc score was associated with heart failure (p=0.007, p=0.024), stroke (p<0.0001, p<0.0001) and risk of all-cause mortality (p<0.0001, p<0.0001), in both groups, respectively. C statistics indicated that CHA2DS2-VASc score performed modest discrimination in this HD population with and without AF for heart failure, stroke, and all-cause mortality, but for cardiovascular mortality in AF group only. Conclusions: Increased CHA2DS2-VASc score at time starting on HD, can predict stroke, heart failure and all-cause mortality in HD patients in-dependent of the presence of AF. Risk of cardiovascular mortality could be predicted only on patients with AF.

This study assessed the potential of back extensor strength as an alternative marker of frailty. A total of 560 farmers were included. Computed tomography scans measured fat and muscle mass volumes at the mid-L4 vertebral level. Back extensor strength was measured in a seated posture. Multivariate linear regression analyzed the associations between back extensor strength and trunk muscle/fat compositions. The participants were divided into two groups based on back extensor strength. Propensity score matching, multivariate logistic regression, and Extreme Gradient Boosting (XGBoost) were employed to evaluate the relationship between Fried's frailty criteria and back extensor strength. Back extensor strength exhibited positive associations with abdominal muscle volume (r = 1.12) as well as back muscle volume (r = 0.89) (p < 0.05). Lower back extensor strength was linked to more frail status, such as reduced grip strength, walking speed, and fre-quent self-reported exhaustion. Multivariate logistic regression indicated a significant association between lower back extensor strength and higher Fried frailty score (OR, 0.990; p<0.05). XGBoost analysis identified back extensor strength as the most important predictor (gain = 0.502) for frailty, surpassing the significance of age (gain = 0.325). These findings suggested the potential of back extensor strength as an alternative frailty marker.

Papillary thyroid cancer (PTC) is commonly characterized by multifocality, which is associated with aggressive features and a less favorable prognosis. The current study aimed to compare the clinicopathologic characteristics and long-term oncological outcomes of bilateral and unilateral multifocal PTC. The medical records of 1745 patients with multifocal PTC who underwent thyroid surgery at Seoul St. Mary’s Hospital were retrospectively reviewed. The clinicopathological characteristics and recurrence rates were compared based on cancer laterality. Further, 357 patients who underwent total thyroidectomy were matched to investigate the recurrence risk and disease-free survival (DFS). Before propensity score matching (PSM), there was no significant difference in the recurrence rate between the bilateral and unilateral multifocal PTC groups. Cancer laterality was not a predictor of DFS based on the Cox regression analyses. However, after PSM, unilateral multifocality was associated with a significantly high risk of recurrence. Similarly, unilateral multifocality was associated with a significantly poor DFS based on the Kaplan–Meier analysis. Compared with bilateral PTC, unilateral multifocal PTC was associated with a poor DFS. A comprehensive preoperative examination should be performed to detect multifocality before the initial surgical intervention for optimal treatment. Postoperative short-term follow-up is recommended for unilateral multifocal PTC for recurrence surveillance.

Evidence regarding the role of oral antibiotics (oA) alone or combined with mechanical bowel preparation (MoABP) for elective colorectal surgery remains controversial. A prospective database of 8,359 colorectal resections gathered over a 32-month period in 78 Italian surgical units (the iCral 2 and 3 studies), reporting patient-, disease-, and procedure-related variables together with 60-day adverse events, was re-analyzed to identify a subgroup of 1,013 cases (12.1%) that received either oA or MoABP. This dataset was analyzed using a 1:1 propensity score-matching model including 20 covariates. Two well-balanced groups of 243 patients each were obtained: group A (oA), and group B (MoABP). The primary endpoints were anastomotic leakage (AL) and surgical site infections (SSIs) rates. Group A vs. group B showed a significantly higher AL risk [14 (5.8%) vs 6 (2.5%) events; OR 3.77; 95%CI 1.22-11.67; p = .021], while no significant difference was recorded between the two groups regarding SSIs. These results strongly support the use of MoABP for elective colorectal resections.

The late universe contains a wealth of information about fundamental physics and gravity, wrapped up in non-Gaussian fields. To make use of as much information as possible it is necessary to go beyond two-point statistics. Rather than going to higher order N-point correlation functions, we demonstrate that the probability distribution function (PDF) of spheres in the matter field (a one-point function) already contains a significant amount of this non-Gaussian information. The matter PDF dissects different density environments which are lumped together in two-point statistics, making it particularly useful for probing modifications of gravity or expansion history. Our approach in Cataneo et. al. 2021 extends the success of Large Deviation Theory for predicting the matter PDF in ΛCDM in these “extended” cosmologies. A Fisher forecast demonstrates the information content in the matter PDF via constraints for a Euclid-like survey volume combining the 3D matter PDF with the 3D matter power spectrum. Adding the matter PDF halves the uncertainties on parameters in an evolving dark energy model, relative to the power spectrum alone. Additionally, the matter PDF contains enough non-linear information to substantially increase the detection significance of departures from General Relativity, with improvements up to six times the power spectrum alone. This analysis demonstrates that the matter PDF is a promising non-Gaussian statistic for extracting cosmological information, particularly for beyond ΛCDM models.

Current commitments by European governments seek to improve energy consumption as a means to reduce carbon emissions from building stock by 2050. Within such context, retrieving reliable three-dimensional contours from point clouds becomes an important step in developing facade retrofitting solutions since facade retrofitting projects often make use of as-built 3D models to help reduce inaccuracies by narrowing interpretation and measurement errors. This work aims to provide a method that uses topology-based parametric modelling for reconstructing building envelopes from point clouds. Through a semi-automated process that gives permanent visual feedback, the user adjusts parameters to custom standards of acceptability. A solution under the form of a Grasshopper definition delivers building envelope 3D contours in various file formats as a means for increasing interoperability. The main contributions of this work consist of a parametric reconstruction workflow capable of solving building topology for retrieving 3D contours, a strategy to bypass point cloud occlusion, and a strategy for converting those contours into an IFC model directly from the parametric modelling environment.

In this paper, we present a relapse based demonstrating way to deal with investigate various arrangement MTC information. A commonplace use of this displaying approach incorporates three stages: first, define a model that approximates the connection between quality articulation and trial factors, with boundaries consolidated to address the exploration premium; second, utilize least-squares and assessing condition methods to gauge boundaries and their relating standard blunders; third, register test insights, P-qualities and NFD as proportions of factual criticalness. The benefits of this methodology are as per the following. To begin with, it tends to the exploration interest in a particular, precise way, and maximally uses all the information and other important data. Second, it represents both orderly and irregular varieties related with the information, and the consequences of such examination give not just quality explicit data applicable to the exploration objective, yet additionally its dependability, in this way helping agents to settle on better choices for subsequent investigations. Third, this methodology is truly adaptable, and can undoubtedly be stretched out to different sorts of MTC considers or other microarray explores by detailing various models dependent on the test plan of the investigations.

Introduction: There are unique technical and management challenges associated with living donor liver transplantation (LDLT) for Budd-Chiari Syndrome (BCS). The outcomes of LDLT for BCS in comparison to other indications remains unclear and warrants elucidation. Methods: Data of 24 BCS patients who underwent LDLT between January 2012 and June 2019 were analyzed. There were 20 adults and 4 children. The early and long-term outcomes of adult LDLT BCS patients were compared to a control group of LDLT patients for other indications and matched using propensity scoring methodology. Results: Primary BCS was observed in 22(91.7%) patients. Caval replacement was performed in 7(29.1%) patients. Early and late hepatic venous outflow tract (HVOT) complications were seen in 1(5.5%) and 3(16.7%) patients. Preoperative acute kidney injury was identified as a risk factor for mortality in the BCS cohort (p =0.013). On comparison, BCS recipients were younger with fewer comorbidities, more large volume ascites and higher rates of PVT. They also had longer cold ischemia time, increased blood loss and transfusion requirements, increased hospital stay, and higher late outflow complications. The 1-year and 3-year survivals were similar to non-BCS cohort (84.2% vs 94% and 70% vs 91.9%, respectively, log rank test p=0.09). Conclusion: LDLT is remains a good option for symptomatic BCS who have failed non-transplant interventions. The clinical and risk factor profile of BCS recipients is distinct from non-BCS recipients. By following an algorithmic management protocol, we show on propensity-score matched analysis that outcomes of LDLT for BCS are similar to non-BCS indications.

Extruded polystyrene (XPS) is frequently used in the construction of many different structures. It is therefore necessary to appropriately characterize its mechanical properties to ensure the safety of said structures. Among the available characterization tests, static bending tests are simple and easy to perform; owing to these characteristics, they should be performed more frequently than other tests. However, in static bending tests on XPS, there are several challenges owing to the high flexibility of XPS, and the chosen testing method and sample configuration affect the accuracy of characterization. In this study, three bending properties (Young’s modulus, proportional limit stress, and bending strength) of samples cut from an XPS panel were determined using three-point bending (TPB), four-point bending (FPB), and compression bending (CB) tests with varying sample span/depth ratios, and statistical analyses were performed to determine the relevance of the tests. The experimental results suggest that the TPB and CB tests were more feasible than the FPB test when the span/depth ratio was appropriately determined. However, clear differences were observed in the sample bending properties determined in these tests. In light of these findings, further studies should be conducted to elucidate these differences.

Coastal cliffs erode in response to short- and long-term environmental changes but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in 3-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face were compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.

This paper introduces a method for tunnel point cloud and BIM model integration and cross-section monitoring, providing information to analyse tunnel cross-sections and surrounding rock deformation, and support for tunnel maintenance and reconstruction. Three types of data are processed for the integration: laser scanning point cloud, BIM tunnel model and terrain model from oblique photogrammetry. An adaptive BIM modelling scheme is proposed for tunnels with alien structures. Precise spatial registration of the data sets is conducted by applying singular value decomposition (SVD) algorithm to calculate transformation parameters from the point cloud model to the BIM model. Since the tunnel central line has high-order derivability, a cross-section calculation method based on tangent vector is proposed to obtain the cross-sectional profile of tunnels at any mileage. The proposed method has been verified by applying it to a tunnel reconstruction project. The experiment results show that the tunnel point cloud and the BIM model were highly coincident after the integration. The developed program can effectively get the cross-section of the tunnel at any mileage, and correctly express the spatial relationship between the BIM tunnel, the point cloud of tunnel and the external mountainous terrain.

(1) Background: The aim of this study was to compare the survival benefit of radical ante-grade-modular pancreato-splenectomy (RAMPS) with conventional distal pancreato-splenectomy (cDPS) in left-sided pancreatic cancer. (2) Methods: A retrospective propensity score matching (PSM) analysis was conducted on 333 patients who underwent RAMPS or cDPS for left-sided pancreatic cancer at four tertiary cancer centers. The study assessed prognostic factors and com-pared survival and operative outcomes. (3) Results: After PSM, 99 patients were matched in each group. RAMPS resulted in a higher retrieved lymph node count than cDPS (15.0 vs. 10.0, p < 0.001). No significant differences were observed between the two groups in terms of R0 resection rate, blood loss, hospital stay, or morbidity. The 5-year overall survival rate was similar in both groups (cDPS vs. RAMPS, 44.4% vs. 45.2%, p = 0.853), and disease-free survival was also comparable. Multivariate analysis revealed that ASA score, preoperative CA19-9, histologic differentiation, R1 resection, adjuvant treatment, and lymphovascular invasion were significant prognostic factors for overall survival. Preoperative CA19-9, histologic differentiation, T-stage, adjuvant treatment, and lymphovascular invasion were independent significant prognostic factors for disease-free survival. (4) Conclusions: Although RAMPS resulted in a higher retrieved lymph node count, survival outcomes were not different between two groups. RAMPS was a surgical option to achieve R0 resection rather than a standard procedure. Adjuvant treatment was an independent prognostic factor for both overall and disease-free survival.

A 68-year-old man with malignant metastatic melanoma in complete remission after Pembrolizumab treatment, developed 10 months after Pembrolizumab cessation due a grade 3 diarrhea, confusion, altered mental status, progressive memory loss, gait disturbance. He was admitted to the neurologic department and comprehensive neurological workup, brain magnetic resonance imaging, cerebral fluid analysis, EEG, blood test allowed the diagnosis of autoimmune encephalitis. The patient was treated with plasmapheresis, high dose of intravenous steroids and intravenous immunoglobulins. The patient improved and he is now well with a performance status 1. This case is interesting since the AE developed approximately 10 months after the cessation of immunotherapy, the underlying cancer was in complete remission and the AE showed a good response to the treatment done.

The notion of Critical Point Theory has extensive application in the filed of Partial Differential Equations, one of which is studying the existence and commenting on the uniqueness and multiplicity (in case when the solution is not unique) of weak solutions to Elliptic PDEs under certain boundary conditions.\par This article provides a brief survey about specific concepts like : differentiation on Banach Spaces, maxima and minima and its applications to PDEs. Furthermore, we've discussed the notion of defining a weak topology on Banach Spaces, before introducing the Variational Principle and its applications.\par The epilogue of the article primarily deals with results which studies the existence of weak solutions to Dirichlet Boundary Value Problems under specific conditions. The most notable aspect of this article is the proof of \textit{Rabinowitz's Saddle Point Theorem} using the method of \textit{Brouwer Degree}. Readers who are highly motivated in pursuing research in any of the topics relevent to the contents of this paper will surely find the \texttt{References} section to be extremely resourceful.

The notion of oriented set is the basic elementary concept of the theory of changeable sets. The main motivation for the introduction of changeable sets was the sixth Hilbert problem, that is, the problem of mathematically rigorous formulation of the fundamentals of theoretical physics. In the present paper the necessary and sufficient condition of the existence of one-point time on an oriented set is established. From the intuitive point of view, one-point time is the time associated with the evolution of a system consisting of only one object (for example, from one material point). Namely, it is proven that the one-point time exists on the oriented set if and only if this oriented set is a quasi-chain. Also, using the obtained result, the problem of describing all possible images of linearly ordered sets is solved. This problem naturally arises in the theory of ordered sets.

This work condenses various modeling techniques for different Point Absorber configurations. A combined frequency - time domain model will be developed in Matlab-FORTRAN in order to compute the displacement, velocities and the power absorbed in the heave mode. Additionally, a single buoy motion including multiple degrees of freedom will be investigated as well. Therefore, the diffraction-radiation Boundary Element Method solvers NEMOH and BEMIO will be applied in the calculation of the hydrodynamic coefficients, which will determine the solution of Newtons impulse equations of motion. Initially, the Wave to wire model will be validated through comparison with previous experimental results for a submerged cone cylinder shape (Buldra-FO3). A single, generic, vertical floating cylinder will be contemplated then, that responds to the action of the passing waves excitation. Later, two vertical floating cylinders aligned with the incident wave direction will be modeled for a variable distance between the bodies. For both unidirectional regular and irregular waves as an input in deep water, the convolutive radiation force function term will be hereby approximated through the Prony method. By changing the spatial disposition of the axisymmetric buoys, using for instance triangular or diamond shaped arrays of three and four bodies respectively, the study will focus on the interaction effects for regular waves. The results will highlight the most efficient layout for maximizing the energy production whilst providing important insights into their performance, revealing for instance displacement amplification or capture width ratios in near-resonance conditions.

Introduction: The escalating morbidity, mortality, and healthcare costs associated with invasive candidiasis pose significant challenges in pediatric intensive care units (PICUs). Conventional diagnostic methods fall short in effectiveness, while various risk factors exacerbate the prevalence of these infections. This study introduces a novel model that amalgamates the Candida Score with key predictors to enhance the specificity and applicability of mortality risk assessments for pediatric patients with fungemia. Methods: This retrospective cohort study aimed to develop an integrated model for estimating mortality risk in children with fungemia in PICUs. We constructed and evaluated multiple predictive models using the Candida Score and other relevant clinical and laboratory variables. The discriminative abilities of the models were assessed using the area under the receiver operating characteristic curve (AUC). Results: The study cohort comprised 85 pediatric patients admitted between 2016 and 2020, with a median age of 6 months and a male predominance of 62.4%. The mortality rate was 45.9%. Candida albicans was the most prevalent causative agent, accounting for 37.55% of fungemia cases. Multivariable Logistic regression revealed the duration of mechanical ventilation, and the Candida Score were significant predictors of mortality. Each additional day of ventilation increased mortality odds by 11.5% (OR = 1.115, 95% CI: 1.025-1.212, p = 0.011), while a higher Candida Score more than doubled the odds (OR = 2.205, 95% CI: 1.359-3.578, p = 0.001).The Random Forest predictive model demonstrated superior discriminative performance, with an AUC of 0.838 (95% CI: 0.738-0.917), outperforming Gradient Boosting and Logistic Regression models, which had AUCs of 0.7759 (95% CI: 0.667-0.877) and 0.7085 (95% CI: 0.589-0.813), respectively. Although the internal validation yielded satisfactory results, external validation is warranted. Conclusion: This study presents an innovative approach for accurately assessing the risk of mortality due to fungemia in PICU patients by integrating the Candida Score with critical predictors. The Random Forest model emerged as the most effective predictive tool due to its exceptional discriminative ability.

Background and objectives: Main aim of the study is to analyze the outcomes of NSTEMI admissions and testing the relevance of TIMI as a risk score in a real-world setting. Also looking at any potential social or health care disparities involved with outcomes of NSTEMI admissions. The study also investigates factors associated with mortality in NSTEMI admissions and correlation between in-hospital mortality and heart catheterization. . Materials and Methods: NSTEMI admissions were analyzed using the National Inpatient Sample. TIMI scores were calculated in the admissions and association between all-cause mortality and calculated TIMI score >=2 was analyzed. Difference in outcomes based on heart catheterization was studied in the subgroup with TIMI score >=2. Correlation between time to heart catheterization and all-cause mortality in NSTEMI admissions were analyzed. Results: No significant social or healthcare disparities were noted amongst outcomes for NSTEMI admissions. NSTEMI admissions with a calculated TIMI score >=2 had a statistically significant association with all-cause mortality during the admission. Prevalence of mortality among NSTEMI admissions with calculated TIMI score >2, who did not undergo heart catheterization was higher with statistical significance when compared to admissions that underwent heart catheterization. NSTEMI admissions with a calculated TIMI score >2 who underwent catheterization on or beyond the 4th day of hospitalization had statistically significant association with all-cause mortality during the admission. Age at admission, presence of CKD and CHF were associated with mortality in NSTEMI admissions, with statistical significance. Keywords: NSTEMI; TMI score; heart catheterization

Front-of-pack labelling (FOPL) systems, such as the Nutri-Score, play a crucial role in promoting healthy diets and raising consumer awareness. Our study aimed to gather the opinions of Polish experts on the Nutri-Score and its relation to an ideal information system. We conducted a Poland-wide expert opinion study using a cross-sectional design survey with 75 participants, who had an average of 18±13 years of experience and were mainly employed at medical and agricultural universities. The data were collected with CAWI method. The results showed that the most important features of an FOPL system were clarity, simplicity, consistency with healthy eating recommendations, and the ability to objectively compare products within the same group. While more than half of the respondents believed that the Nutri-Score provides an overall assessment of a product’s nutritional value and facilitates quick purchasing decisions, it falls short in helping consumers compose a balanced diet and cannot be applied to all product groups. The experts also expressed concerns about the system’s ability to account for a product’s degree of processing, full nutritional value and carbon footprint. In conclusion, Poland’s current labelling system needs expansion, but the Nutri-Score requires significant and deep changes and validation against national guidelines and expert expectations before implementation.

Objectives Independent validation of risk scores after hip fracture is uncommon, particularly for evaluation of outcomes other than death. We aimed to assess the Nottingham Hip Fracture Score (NHFS) for prediction of mortality, physical function, length of stay and postoperative complications. Design Analysis of routinely collected prospective data partly collected by follow-up interviews. Setting and Participants Consecutive hip fracture patients were identified from the Northumbria hip fracture database between 2014-2018. Patients were excluded if they were not surgically managed or if scores for predictive variables were missing. Methods C-statistics were calculated to test the discriminant ability of the NHFS, Abbreviated Mental Test Score (AMTS), and ASA grade for in-hospital, 30- and 120-day mortality, functional independence at discharge, 30-days and 120-days, length of stay, and postoperative complications. Results We analysed data from 3208 individuals, mean age 82.6 (SD 8.6). 2192 (70.9%) were female. 194 (6.3%) died during the first 30-days, 1686 (54.5%) were discharged to their own home, 211 (6.8%) had no mobility at 120-days, 141 (4.6%) experienced a postoperative complication. The median length of stay was 18 days (IQR 8-28). For mortality, c-statistics for the NHFS ranged from 0.68-0.69, similar to ASA and AMTS. For postoperative mobility, the c-statistics for the NHFS ranged from 0.74-0.83, similar to AMTS (0.61-0.82) and better than the ASA grade (0.68-0.71). Length of stay was significantly correlated with each score (p<0.001 by Jonckheere-Terpstra test); NHFS and AMTS showed inverted U-shaped relationships with length of stay. For postoperative complications, c-statistics for NHFS (0.54-0.59) were similar to ASA grade (0.53-0.61) and AMTS (0.50-0.58). Conclusions and Implications The NHFS performed consistently well in predicting functional outcomes, moderately in predicting mortality, but less well in predicting length of stay and complications. There remains room for improvement by adding further predictors such as measures of physical performance in future analyses.

Measuring bias is important as it helps identify flaws in quantitative forecasting methods or judgmental forecasts. It can, therefore, potentially help improve forecasts. Despite this, bias tends to be under-represented in the literature: many studies focus solely on measuring accuracy. Methods for assessing bias in single series are relatively well-known and well-researched, but for datasets containing thousands of observations for multiple series, the methodology for measuring and reporting bias is less obvious. We compare alternative approaches against a number of criteria when rolling-origin point forecasts are available for different forecasting methods and for multiple horizons over multiple series. We focus on relatively simple, yet interpretable and easy-to-implement metrics and visualization tools that are likely to be applicable in practice. To study the statistical properties of alternative measures we use theoretical concepts and simulation experiments based on artificial data with predetermined features. We describe the difference between mean and median bias, describe the connection between metrics for accuracy and bias, provide suitable bias measures depending on the loss function used to optimise forecasts, and suggest which measures for accuracy should be used to accompany bias indicators. We propose several new measures and provide our recommendations on how to evaluate forecast bias across multiple series.

In this paper, we prove some common fi xed point theorems on complex partial metric space. The presented results gener- alize and expand some of the literature well-known results. We also explore some of the application of our key results.

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

Anastomotic Leakage (AL) remains the main cause of post-esophagectomy morbidity and mortality. Early detection can avoid sepsis and reduce morbidity and mortality. This study evaluates the diagnostic accuracy of the NUn-score and its components as early detectors of AL. This single centre observational cohort study included all esophagectomies from 2010-2020. C-reactive Protein (CRP), Albumin (Alb) and White cell count (WCC) were analyzed and Nun-scores calculated. Area under the curves (AUC) were used to assess their predictive accuracy. Seventy-four of the 668 patients (11%) developed an AL. CRP and the NUn-score proved to be good diagnostic accuracy tests on POD2 (CRP AUC:0,859; Nun-score AUC:0,869) and POD4 (CRP AUC:0,924; Nun-score AUC:0,948). A 182mg/L CRP cut-off on POD4 yielded a 87% sensitivity, 88% specificity, a negative predictive value (NPV) of 98% and a positive predictive value (PPV) of 47,7%. A Nun score cut-off&gt;10 resulted in 92% sensitivity, 95% specificity, 99% NPV and 68% PPV. Albumin and WCC have limited value in the detection of post-esophagectomy AL. Elevated CRP and a high NUn score on POD4 provide a high accuracy to predict AL after esophageal cancer surgery. Their high negative predictive value allows to select patients who can safely proceed with enhanced recovery protocols.

Background: Early rehabilitation is an important strategy for the treatment of severe diseases. The study aimed to assess the efficacy of early rehabilitation of patients with mild-to-moderate COVID-19. Methods: The retrospective single-center study with propensity score matching. Results: 180 patients (54 (44 - 62.3) yo) divided into three equal groups: 1 - physical exercises; 2 - physical exercises and chest massage in an electrostatic field; 3 - no rehabilitation. The length of stay (LOS) in the hospital in group 1 was 14 (11-15) days, group 2 - 13 (11-15) days, group 3 - 15 (13-18) days, p=0.0026. Physical exercises for patients with CT-1 improved the quality of life, assessed by the EQ-5D questionnaire, by reducing the level of anxiety and depression. The Hazard Ratio (HR) for desaturation (<93%) was 2.34 (95% Confidence Interval (CI) 1.18-4.63) for group 2, p=0.001. The HR for C-reactive protein level above 50 mg/l in patients with CT-2 was 2.33 (95% CI 1.56-3.47), p=0.0001. Conclusions: Rehabilitation programs are safe for СOVID-19-patients; reduce hospital LOS; improve the quality of life. Continuous monitoring of a patient's condition during rehabilitation is essential. ClinicalTrials.gov ID: NCT0580836

Background: Physical activity has been considered as a promising approach to slow down the disease process in Multiple Sclerosis (MS) patients. While the functional impairments of MS have been studied in detail, but there is limited evidence of the efficacy of exercise interventions on the Multiple Sclerosis functional composite (MSFC) score in these patients. The study aim was to in-vestigate the improvement in MSFC score by multi-function swing suspension training (MFSST) in the women with MS. Methods: The patients were divided into the intervention and control groups. A total of 47 MS patients completed the MSFC components at baseline and after the intervention. Z scores were created for each test based on control means. Results: The MSFC components showed a significant increment in comparison with the baseline levels in the four, six, and eight weeks following the first exercise session (all p<0.05). These dif-ferences in the control group were not significant. The improvement in the MSFC score and the component Z-scores in the intervention groups was found from the fourth week onwards. Conclusions: The study findings highlight that MFSST can be used as an effective treatment method in MS patients due to its positive effects on physical parameters, such as muscles strength, functional impairments, and cognitive problems.

The Acute Cystitis Symptom Score (ACSS) is a patient self-reporting questionnaire for the clinical diagnosis and patient-reported outcome (PRO) in women with acute uncomplicated cystitis (AC). The aim of the current study (part II) was the clinical validation of the Greek ACSS questionnaire. After linguistic validation according to internationally accepted guidelines and cognitive assessment (part I), the clinical validation was performed after ethical approval by using the Greek ACSS study version in 92 evaluable female participants including 53 patients with symptoms suspicious of AC and 39 controls. The clinical outcome using the ACSS questionnaire at different time points after the start of treatment was demonstrated as well. The age (mean±SD) of the 53 patients (44.7±17.0) and 39 controls (49.3±15.9) and their additional conditions at baseline visits, such as menstruation, premenstrual syndrome, pregnancy, menopause, diabetes mellitus, were comparable. There was, however, a significant difference (p&lt;0.001) between patients and controls at baseline visit regarding sum score of the ACSS domains, such as typical symptoms and quality of life. The clinical outcome of up to 7 days showed a fast reduction of the symptom scores and improvement of quality of life. The optimal thresholds for the patient-reported outcome of successful therapy could be established. The linguistically and clinically validated Greek ACSS questionnaire can now be used for clinical or epidemiological studies and also for patient’s self-diagnosis of AC and as a PRO measure tool.

The comparative efficacy and safety between lenvatinib and hepatic artery infusion chemotherapy (HAIC) in patients with unresectable hepatocellular carcinoma (HCC) are still unclear. This multicenter historical cohort study enrolled 244 patients who were treated with HAIC (n = 173) or lenvatinib (n = 71) between 2012 and 2020. Propensity score matching (PSM) was performed, and 52 patients were selected per group. Clinical outcomes and safety were compared. Objective response rate (ORR) was not different between the two groups (26.0% vs. 23.1%, P = 0.736). Before PSM, HAIC group had a higher proportion of Child-Pugh B and portal vein tumor, whereas lenvatinib group had more patients with extrahepatic metastases, which was adjusted after PSM. There were no differences in progression-free survival (PFS) and overall survival (OS) after PSM (HAIC vs. lenvatinib, median PFS, 3.6 vs. 4.0 months, P = 0.706; median OS 10.8 vs. 7.9 months, P = 0.106). Multivariate Cox-regression showed that alpha-fetoprotein ≤ 1000 ng/mL was only associated factor for OS after PSM in all patients (hazard ratio = 0.421, P = 0.011). Subgroup analysis for patients with high tumor burden beyond the REFLECT eligibility criteria revealed that HAIC group (n = 29) had a significantly longer OS than did lenvatinib group (n = 30) (10.0 vs. 5.4 months, P=0.004). More patients in HAIC group achieved better liver function than those in lenvatinib group at the time of best responses. There was no difference in the incidence of grade 3 and 4 adverse events between the two groups. Therefore, lenvatinib is comparable to HAIC in terms of ORR and OS in unresectable HCC meeting REFLECT eligibility criteria.

In this short communication, following a brief introduction, we undertake a comprehensive analytical study of the weighted Simpson index. Our primary emphasis concerns the precise determination of the optimal point (minimizer) coordinates and of the minimum value of the index, a differentiable convex function, which is related to the harmonic mean concept. Furthermore, we address and solve the inversion problem and show the tight connection between both approaches. Last, we give some insights and final remarks on this subject.

While the language model using the stop sign as an independent token has been widely used to decide when the model should stop, it may lead to the growth of vocabulary dimensions and further problems. Similarly, present research on game algorithms usually estimate stopping point related problems based on the evaluation of the winning rate. However, information redundancy may also exist in such models, thus increasing the training difficulty. Above two types of tasks (and similar autoregressive tasks) show a common problem of stopping point prediction. In this paper, we describe a design of separated model, trying to separate the complexity of stopping point prediction from the main task model, so that the information used for estimating stopping point can be reduced. On this basis, in order to verify the rationality of using separated model, we propose a model-free test method. It judges the separability of transformed data based on point difference and sequence difference metrics. In this way, it can predict the credibility of the separated model inference.

Most real-time terrain point cloud rendering techniques do not address the empty space between the points but rather try to minimize it by changing the way how the points are rendered by either rendering them bigger or with more appropriate shapes such as paraboloids. In this work, we propose an alternative approach to point cloud rendering, which addresses the empty space between the points and tries to fill it with appropriate values to achieve the best possible output. The proposed approach runs in real time and outperforms several existing point cloud rendering techniques in terms of speed and render quality.

In this note we describe a new approach to the option pricing problem by introducing the notion of the safe (and acceptable) for the writer price of an option, in contrast to the fair price used in the Black-Scholes model. Our starting point is that the option pricing problem is closely related with the hedging problem by practical techniques. Recalling that the Black - Scholes model does not give us the price of the option but the initial value of a replicating portfolio we observe easily that has a serious disadvantage because assumes the building of this replicating portfolio continuously in time and this is a disadvantage of any model that assumes such a construction. Here we study the problem from the practical point of view concerning mainly the over the counter market. This approach is not affected by the number of the underlying assets and is particularly useful for incomplete markets. In the usual Black-Scholes or binomial or some other approaches one assumes that one can invest or borrow at the same risk free rate $r>0$ which is not true in general. Even if this is the case one can immediately observes that this risk free rate is not a universal constant but is different among different people or institutions. So, the fair price of an option is not so much fair! Moreover, the two sides are not, in general, equivalent against the risk therefore the notion of a fair price has no meaning at all. We also define a variant of the usual binomial model, by estimating safe upward and downward rates $u,d$, trying to give a cheaper safe or acceptable price for the option.

The article presents the Base Point Split (BPSplit) algorithm for generating a polygon skeleton based on sets of vector data describing lakes and rivers. A key feature of the BPSplit algorithm is that it is dependent on base points representing the source or mouth of a river or a stream. The input values of base points determine the shape of the resulting skeleton. Various skeletons can be generated with the use of different base points. Base points are applied to divide polygon boundaries into segments. Segmentation supports the selection of TIN edges inside polygons. The midpoints of selected TIN edges constitute a basis for generating a skeleton. The algorithm handles polygons with numerous holes, and it accounts for all holes. This article proposes a method for modifying a complex skeleton with numerous holes. In the discussed approach, skeleton edges that do not meet the preset criteria (e.g. that the skeleton is to be located between holes in the center of the polygon), are automatically removed. A simple algorithm for smoothing zigzag lines was proposed.

This paper presents a unified reconfigurable coordinate rotation digital computer (CORDIC) processor for floating-point arithmetic. It can be configured to operate in multi-mode to achieve a variety of operations and replaces multiple single-mode CORDIC processors. A reconfigurable pipeline-parallel mixed architecture is proposed to adapt different operations, which maximizes the sharing of common hardware circuit and achieves the area-delay-efficiency. Compared with previous unified floating-point CORDIC processors, the consumption of hardware resources is greatly reduced. As a proof of concept, we apply it to 1638416384 points target Synthetic Aperture Radar (SAR) imaging system, which is implemented on Xilinx XC7VX690T FPGA platform. The maximum relative error of each phase function between hardware and software computation and the corresponding SAR imaging result can meet the accuracy index requirements.

Nowadays, the most advanced micromachined microphones on the market are based on a capacitive coupling principle. Capacitive micro-electro-mechanical systems-based (MEMS) microphones resemble their millimetric counterparts, both in function and in performances. The most advanced MEMS microphones reached a competitive level compared to commonly used measuring microphones in most of the key performance parameters except the acoustic overload point (AOP). In an effort to find a solution for measurement of high-level acoustic fields, microphones with piezoelectric coupling principle have been proposed. Such novel microphones exploit the piezoelectric effect of a thin layer of aluminum nitride, which is incorporated in their diaphragm structure. In these microphones fabricated with micromachining technology, no fixed electrode is necessary, in contrast to capacitive microphones. This specificity significantly simplifies both the design and the fabrication and opens the door for a new improvement of the acoustic overload point as well as harsh environmental applications. Several variations of piezoelectric structures together with an idea leading to electrically controlled sensitivity of MEMS piezoelectric microphone are discussed in the paper.

In recent years, there has been significant growth in the ubiquity and popularity of three dimensional(3D) point clouds, with an increasing focus on the classification of 3D point clouds. To extract richer features from point clouds, many researchers have turned their attention to various point set regions and channels within irregular point clouds. However, this approach has limited capability in attending to crucial regions of interest in 3D point clouds and may overlook valuable information from neighboring features during feature aggregation. Therefore, this paper proposes a novel 3D point cloud classification method based on global attention and adaptive graph convolution. The method consists of two main branches: the first branch com-putes attention masks for each point, while the second branch employs adaptive graph convolu-tion to extract global features from the point set. It dynamically learns features based on point interactions, generating adaptive kernels to effectively and precisely capture diverse relationships among points from different semantic parts. Experimental results demonstrate that the proposed model achieves 93.8% in overall accuracy and 90.8% in average accuracy on the ModeNet40 dataset.

We measured the anelastic, dielectric and structural properties of the metal-free molecular perovskite (ABX$_{3}$) (MDABCO)(NH$_{4}$)I$_{3}$, which has already been demonstrated to become ferroelectric below $T_{\text{C}}=$ 448~K. Both the dielectric permittivity measured in air on discs pressed from powder and the complex Young's modulus measured on resonating bars in vacuum show that the material starts deteriorating with loss of mass just above $T_{\text{C}}$, introducing defects and markedly lowering $T_{\text{C}}$. The elastic modulus softens of 50\% when heating through the initial $T_{\mathrm{C}}$, contrary to usual ferroelectrics, which are stiffer in the paraelectric phase. This suggests improper ferroelectricity, where the primary order parameter of the transition is not the electric polarization, but the orientational order of the MDABCO molecules. The degraded material presents thermally activated relaxation peaks in the elastic energy loss, whose intensities increase together with the decrease of ~$T_{\mathrm{C}}$. The peaks are much broader than pure Debye, due to the general loss of crystallinity, also apparent from X-ray diffraction, but their relaxation times have parameters typical of point defects. It is argued that the major defects should be of the Schottky type, mainly due to the loss of (MDABCO)$^{2+} $ and I$^{-}$, leaving charge neutrality, and possibly also (NH$_{4}$)$^{+}$ vacancies. The focus is on an anelastic relaxation process peaked around 200~K at $\sim 1$~kHz, whose relaxation time follows the Arrhenius law with $\tau $$_{0}$~$\sim $\ $10^{-13}$\ s and $E\simeq 0.4$~eV. This peak is attributed to I vacancies (V$_{\text{X}}$) hopping around MDABCO vacancies (V$_{\text{A}}$) and its intensity presents a peculiar dependence on temperature and content of defects. The phenomenology is thoroughly discussed in terms of lattice disorder introduced by defects and of partition of V$_{\text{X}}$ among sites that are far from and close to the cation vacancies. A method is proposed for calculating the relative concentrations of V$_{\text{X}}$, that are untrapped, paired with V$_{\text{A}}$ or forming V$_{\text{X}}$--V$_{\text{A}}$--V$_{\text{X}}$ complexes.

This paper provides a new management method (MM) to optimally operate standalone direct current (DC) photovoltaic water pumping system (PVWPS) and synchronize its hydraulic performance to variable daily irradiance. The proposed approach well considers the issues from PV generator and a positive displacement pump mismatching to deserve a hydraulic network. Accordingly, Management Method uses small pumps mounted in parallel scheme to displace the hydraulic power point (HPP) in order to satisfy the mal adequacy between supply and load. Based on experimental Study; this approach showed a yielded standalone system, possibility of running pumping system using only photovoltaic supply (Without use of capacitance), and possibility to design more economical PV systems as the maximum peak power demand for each month can be satisfied using small power pumps in parallel and adjusting their switch with accordance to varying daily irradiance. Moreover, an analytical model was developed with reference to the obtained experimental results. This model based on a daily occurring irradiance as inputs in order to predict optimal performance outputs. It can be considered as a decision tool to track power point resulting from optimal electrical and hydraulic performances of the pumping system according to changes of the daily irradiance.

The nature of the fracture and fragmentation process of concrete medium under blast loading is the transformation process of the medium from continuum to discontinuity, coupled with the significant rate correlation of concrete medium, its mechanical behavior presents a high degree of complexity. Problems such as grid distortion and even negative volume are often encountered in the finite element method and others when solving this problem, while the material point method can effectively avoid these problems. In addition, the widely used HJC concrete constitutive model does not take into account the segmented characteristics of the calculation function for dynamic increasing factor. Therefore, in this paper, firstly, the calculation function for dynamic increasing factor in the HJC concrete model is modified by SHPB experiment, and the improved HJC concrete model is proposed; secondly, the material point method simulation program is developed, and the improved HJC concrete model is embedded into the simulation program; finally, the simulation program is verified by numerical examples, and the results show that the developed simulation program can better simulate the fracture and fragmentation process of concrete medium under blast loading, especially the pulverization characteristics of the medium in the near zone of the load.

Biosensors have shown great potential in realizing rapid, low cost and portable on-site detection for diseases. This work reports the development of a new bioelectronic sensor called AC electrokinetics-based capacitive (ABC) biosensor, for the detection of genomic DNA (gDNA) of methicillin-resistant Staphylococcus aureus (MRSA). The ABC sensor is based on interdigitated microelectrodes biofunctionalized with oligonucleotide probes. It uses a special AC signal for direct capacitive monitoring of topological change on nanostructured sensor surface, which simultaneously induce dieletrophoretic enrichment of target gDNAs. As a result, rapid and specific detection of gDNA/probe hybridization can be realized with high sensitivity. It requires no signal amplification such as labelling, hybridization chain reaction, or nucleic acid sequence-based amplification. This method involves only simple sample preparation. After optimization of nano-structured sensor surface and signal processing, the ABC sensor demonstrated fast turnaround of results (~10 s detection), excellent sensitivity (a detection limit of 4.7 DNA copies /µL MRSA gDNA) and high specificity, suitable for point of care diagnosis. As a bioelectronic sensor, the developed ABC sensors can be easily adapted for detection of other infectious agents.

Empirically based modeling is an essential aspect of design for a wave energy converter. These models are used in structural, mechanical and control design processes, as well as for performance prediction. The design of experiments and methods used to produce models from collected data have a strong impact on the quality of the model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages. The experimental data is then used to produce multiple models for the dynamics of the device. These models are validated and their performance is compared against one and other. While most models of wave energy converters use a formulation with wave elevation as an input, this study shows that a model using a hull pressure sensor to incorporate the wave excitation phenomenon has better accuracy.