During the conceptual and preliminary design stage of a ship, designers need to ensure that the selected principal dimensions and parameters are good enough to deliver a stable ship (statically and dynamically) besides deadweight and speed. To support this, the initial intact stability of the proposed ship is required to be calculated, and in doing so, the lightship weight and its detailed breakdown are necessary to be known. After hull steel weight, machinery weight, mainly, marine propulsion and power generation machinery, play a vital role in the lightship weight estimate of a ship due to its robustness. The correct estimation of respective weights improves the accuracy of calculating a ship's initial stability typically to be designed and built. Hence, it would be advantageous for the designer to convince the ship owner. A total of 3006 marine propulsion (main marine diesel) engines and 348 power generation (auxiliary marine diesel) engines/generators of various power output (generators output for auxiliary engines), engine RPM and cylinder number of different engine makers are collected. These are analyzed and presented in both tabular and graphical forms to demonstrate the possible relationship between marine propulsion engine weight and power generation engine weight, and their respective power output, RPM, cylinder number, power-RPM ratio and power-RPM ratio per cylinder.In this article, the authors have attempted to investigate the behavior of marine propulsion engine weight and power generation engine/generator weight regarding engine power output, generator power output, engine RPM and cylinder number (independent variables). Further attempts have been made to identify those independent variables that influence the weight of the marine propulsion engine and power generation engine/generator (dependent variables), and their interrelationships. A mathematical model has thus been developed and proposed, as a guiding tool, for the designer to estimate the weight of main and auxiliary engines more accurately during the conceptual and preliminary design stage.

Fetal weight estimation before delivery is important in obstetrics, which assists doctors diagnose abnormal or diseased cases. Linear regression based on ultrasound measures such as bi-parietal diameter (bpd), head circumference (hc), abdominal circumference (ac), and fetal length (fl) is common statistical method for weight estimation but the regression model requires that time points of collecting such measures must not be too far from last ultrasound scans. Therefore this research proposes a method of early weight estimation based on expectation maximization (EM) algorithm so that ultrasound measures can be taken at any time points in gestational period. In other words, gestational sample can lack some or many fetus weights, which gives facilities to practitioners because practitioners need not concern fetus weights when taking ultrasound examinations. The proposed method is called dual regression expectation maximization (DREM) algorithm. Experimental results indicate that accuracy of DREM decreases insignificantly when completion of ultrasound sample decreases significantly. So it is proved that DREM withstands missing values in incomplete sample or sparse sample.

Higher body weight is known to negatively impact plasma vitamin C status. However, despite this well-documented inverse association, recommendations on daily vitamin C intakes by health authorities worldwide do not include particular reference values for people of higher body weight. This suggests that people of higher body weight and people with obesity may be insufficient in vitamin C in spite of ingesting the amounts recommended by their health authorities. The current preliminary investigation sought to estimate how much additional vitamin C people with higher body weights need to consume in order to attain a comparable vitamin C status to that of a lower weight person consuming an average Western vitamin C intake. Data from two published vitamin C dose-concentration studies were used to generate the relationship: a detailed pharmacokinetic study with seven healthy non-smoking men and a multiple depletion-repletion study with 68 healthy non-smoking men of varying body weights. Our estimates suggest that an additional intake of 10 mg vitamin C/day is required for every 10 kg increase in body weight to attain a comparable plasma concentration to a 60 kg individual with a vitamin C intake of ~110 mg/day, which is the daily intake recommended by the European Food Safety Authority (EFSA). Thus, individuals weighing e.g. 80 and 90 kg will need to consume ~130 and 140 mg vitamin C/day, respectively. People with obesity will likely need even higher vitamin C intakes. As poor vitamin C status is associated with increased risk of several chronic diseases including cardiovascular disease, these findings may have important public health implications. As such, dose-finding studies are required to determine optimal vitamin C intakes for overweight and obese people.

Farmers are continually looking for new reliable, objective and non-invasive methods for evaluation of ewe condition. Live weight (LW) and body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Body composition is an important aspect of monitoring animal condition. The body composition is the amount of fat, lean and bone; knowing the amount of each is important because the information can be used for better strategic management interventions. Experiments were conducted to establish the relationship between body composition and body parameters, at key life’s stages (weaning and pre-mating), using measurements automatically determined by an image processing ap-plication at Lincoln University sheep farm for 88 Coopworth ewes. Computerized Tomography technology was used to develop relationship with body parameters and a subset was used to validate the predicted model. Multivariate linear regression (MLR), artificial neural network (ANNs) and regression tree (RT) statistical analysis methods were evaluated to determine their efficacy to predict body fat, lean and bone. The results showed a correlation between fat, lean and bone determined by CT and the fat, lean, bone weight estimated by live weight and body parameters calculated using the image processing application with R2 values of 0.90 for fat, 0.72 for lean and 0.50 for bone using ANNs statistical model. From these results, farmers can utilize accurate measures of fat which will enhance nutritional and management practices.

Weight-bearing computed tomography (WBCT) enables acquisition of three dimensional bony structure images in a physiological weight-bearing position, which is fundamental in under-standing the pathologic lesions and deformities of the ankle joint. Over the past decade, re-searchers focused on validating and developing WBCT measurements, which has significantly enhanced our knowledge of common foot and ankle disease. Consequently, understanding the application of WBCT in clinical practice is becoming more important to produce improved out-comes in the treatment of disease around the ankle joint. This review will describe an overview of what is currently being evaluated in the foot and ankle surgery using WBCT and where the course of research will be heading in the future.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

Ultrasound (US) scans of the inferior vena cava (IVC) provide useful information on the volume status of a patient. However, their investigation is user-dependent and prone to measurement errors. An important technical problem is the objective difficulty in studying a very compliant blood vessel like IVC, which makes large respirophasic movements and shows a complicated three dimensional geometry. Using bi-dimensional (2D) B-mode views either in long or short axis has improved the characterization of IVC dynamics compared to a measurement along a single direction (M-mode). However, specific movements of the IVC can challenge also the information provided by these 2D sections. Thus, these two orthogonal views, provided by an US system in X-plane, are here integrated by an innovative method. It is tested on simulated videos of the IVC performing complicated movements, that are compensated by the new method, overcoming the biased measurements provided by 2D scans. The method is then applied on example experimental data.

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.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on eight subjects while performing 5-minute sitting and walking tasks. Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity.

The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, making estimation of downwelling surface longwave radiation (DSLR) with satellite data difficult. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we simulate the outgoing longwave radiation (OLR) and DSLR under different land surface and atmospheric profile conditions. The results show that dust aerosol has an obvious “warming” effect to longwave radiation compared with other aerosols, that aerosol longwave radiative forcing (ALRF) increased with increasing aerosol optical depth (AOD), and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model that yields the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it estimates the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/M² and 24.2 W/M² for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/M² and 9.6 W/M², respectively. For the Arvaikheer site, the RMSEs are 23.2 W/M² and 19.8 W/M² for Terra and Aqua, respectively, and the biases are 7.8 W/M² and 10.5 W/M², respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data.

Weight loss solutions are on the rise, and there is growing interest in natural alternatives to the pharmaceuticals in the market, which are not devoid of relevant side effects. To this end, the objective of the current study is to assess the effects of a botanical-based blend comprised of lemon verbena and hibiscus extracts in weight management, body fat, appetite, lipid, and glucose profiles, in an overweight population, for 90 days. As a result, the volunteers presented significant weight loss in absence of diet, which coincided with decreased body fat. Body fat was assessed by anthropometry, electrical bioimpedance and DEXA scan. Cholesterol levels were also significantly decreased. Satiety assessment revealed that the product contributed to increased feeling of fullness and satisfaction after a meal, compared to the placebo group, which could contribute to the weight loss observed. Blood glucose and blood pressure levels remained unchanged, which was expected as the volunteers were normoglycemic and their blood pressure was in the healthy range. In conclusion, daily supplementation of a botanical-based blend contributes to weight loss in overweight individuals, in absence of diet, while not affecting other parameters that are in the healthy range. This product could be a candidate alternative or add-on to other weight loss products, as part of a healthy lifestyle.

Accurate power curve modelling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 minutes in a wind farm (WF) consisting of 5 WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer(CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

Daily maximum surface air temperature (Tamax) is a crucial factor for understanding complex land surface processes under rapid climate change. Remote detection of Tamax has widely relied on the empirical relationship between air temperature and land surface temperature (LST), a product derived from remote sensing. However, little is known about how such a relationship is affected by the high heterogeneity in landscapes and dynamics in seasonality. This study aims to advance our understanding of the roles of land cover and seasonal variation in the estimation of Tamax using the MODIS (Moderate Resolution Imaging Spectroradiometer) LST product. We developed statistical models to link Tamax and LST in the middle and lower reaches of the Yangtze River in China for five major land-cover types (i.e., forest, shrub, water, impervious surface, cropland, and grassland) and two seasons (i.e., growing season and non-growing season). Results show that the performance of modeling the Tamax-LST relationship was highly dependent on land cover and seasonal variation. Estimating Tamax over grasslands and water bodies achieved superior performance; while uncertainties were high over forested lands that contained extensive heterogeneity in species types, plant structure, and topography. We further found that all the land-cover specific models developed for the plant non-growing season outperformed the corresponding models developed for the growing season. Discrepancies in model performance mainly occurred in the vegetated areas (forest, cropland, and shrub), suggesting an important role of plant phenology in defining the statistical relationship between Tamax and LST. For impervious surfaces, the challenge of capturing the high spatial heterogeneity in urban settings using the low-resolution MODIS data made Tamax estimation a difficult task, which was especially true in the growing season.

Bigeye grunt Brachydeuterus auritus is an important economic species in the waters off Sierra Leone in West Africa, with great ecological value. Based on the survey data of bottom trawl nets from 6 voyages from 2019 to 2021, the body weight-length relationship (WLR), as well as their relative fatness, were studied in depth using the method of body length and its corresponding mean body weight. This study fitted the WLR of bigeye grunt by gender, calculated its fullness, and analyzed the influence of environmental factors. The results showed that month and gender had a significant impact on body length and weight distribution (P<0.01). There was a highly significant negative correlation (P<0.01) between the condition factor (a) and the allometric factor (b) in the WLR, and salinity significantly affects b (P<0.05). The (lga)/b ratio was less affected by environmental factors which may be related to the body size and density of the fish. The difference in b between male and female populations was extremely significant (P<0.01). The relative fatness had significant seasonal variation characteristics, with highly significant effects of sex and body length (P<0.01). With increasing body length, the relative fatness showed two types of changes, continuously increasing and first increasing then decreasing, with salinity and b significantly affecting the type of change (P<0.05) and depth having a highly significant effect (P<0.01). The bigeye grunt off the coast Sierra Leone exhibited significant seasonal growth characteristics, with significant differences in growth characteristics between male and female individuals. This study suggested that salinity may be the main factor affecting its growth and development. In addition, this study hypothesised that the peak breeding season may occur during the dry season, beginning in December and ending before April of the following year. The study provides updated and more comprehensive insights into the biology and ecology of the bigeye grunt offshore Sierra Leone, West Africa, which can provide scientific basis for its resource assessment, development and utilization, and resource management.

(1) The purpose of this study was to compare the percentages of quarterly change for each of the variables analysed (height, weight, body fat percentage and muscle mass) as a function of the quarter in a sample of adolescent football players. (2) The sample consisted of young male football players of different categories belonging to a high-performance football academy, (more than 5 years in football and have spent 1 full year at the academy). (3) Two-way repeated measures ANOVA revealed for the anthropometric variable, P_value

To address the issue of reduced orbit estimation accuracy resulting from discrepancies between in-orbit measured geomagnetic field values and IGRF model values in autonomous orbit estimation of microsatellites based on geomagnetic field measurements, this study introduces an adaptive cubature H-infinity filtering algorithm. Firstly, it presents the low-altitude orbital dynamics model and geomagnetic field measurement model for microsatellites. Subsequently, within the adaptive cubature H-infinity filtering algorithm, a spherical simplex rule and a second-order Gaussian-Legendre quadrature criterion are employed to compute the spherical surface integral and radial integral. Additionally, a spherical simplex-radial quadrature rule is proposed to enhance the approximation accuracy of nonlinear Gaussian weighted integrals. Furthermore, by integrating extended H-infinity filtering based on game theory with the cubature quadrature rule, an inverse proportional relationship between constraint level and filtering innovation is established. This allows for adaptive adjustment of the constraint level to enhance robustness against model errors. Finally, through semi-physical simulation experiments, it is demonstrated that compared with traditional algorithms, the proposed algorithm improves autonomous orbit estimation position accuracy of microsatellites by 7.49%, while also enhancing velocity accuracy by 3.1%, thus validating its effectiveness.

This work investigated the isothermal holding time dependence of the densification, microstructure, weight loss and tensile properties of Fe-Mn-Si powder compacts. Elemental Fe, Mn and Si powder mixtures with a nominal composition of Fe-28Mn-3Si (in weight percent) were ball milled for 5h and subsequently pressed under a uniaxial pressure of 400 MPa. The compacted Fe-Mn-Si powder mixtures were sintered at 1200 ℃ for 0, 1, 2 and 3 h, respectively. In general, the density, weight loss and tensile properties increased with the increase of isothermal holding time. A significant increase in density, weight loss and tensile properties occurred in the compacts isothermally holding for 1 h, as compared to those with no isothermal holding. However, further extension of isothermal holding time (2 and 3 h) only played a limited role in promoting the density and tensile properties. The weight loss of the sintered compacts was mianly caused by the sublimation of Mn in Mn depletion region on the surface layer of the sintered Fe-Mn-Si compacts. The length of the Mn depletion region increased as isothermal holding time increased. A single α-Fe phase was detected on the surface of all the sintered compacts, and the locations beyond the Mn depletion region were comprised of a dual dominant γ-austenite and minor ε-martensite.

Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.

With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most of the cellular systems such as Bluetooth indoor positioning system due to multipath and non-line-of-sight (NLOS) propagation, the indoor positioning accuracy is severely affected. In this paper a comprehensive algorithm is investigated that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates obtained using particle filtering, which allows us to explore new optimised methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by about 25.7% on average.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits - grain protein content (GPC) and thousand kernel weight (TKW) - across 179 Bulgarian contemporary and historic varieties, and landraces in three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, genotypic data from the 25K Infinium iSelect array, and a Bonferroni-adjusted p-value. As a result, 776 significant quantitative trait nucleotides (QTNs) organized into quantitative trait loci were discovered, including 25 loci associated with GPC, and 35 loci linked to TKW. Additionally, 14 environmentally stable QTNs were identified for TKW. Certain loci on chromosomes 2A, 3A, and 6B favorably in-fluenced GPC, while the locus on chromosome 6D impacted TKW positively. These loci may reflect novel associations to the two traits. Annotations for proteins involved in the remobilization of carbon and nitrogen from vegetative tissues to developing grain during senescence, as well as in the following buildup of resources required for seed germination, have been found for selected pu-tative candidate genes. These include genes coding for 11S globulin seed storage protein, glutamine synthetase, metalloendopeptidase, components of the ubiquitin/proteasome system, sucrose transporter, alpha-amylase, NAC and bZIP transcription factors. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

The main objective of this work is the assessment of the annual number of hospital admissions for respiratory disease (HARD) due to the exposure to in-healable particulate matter (PM10), within the greater Athens area (GAA), Greece. Towards this aim, the time series of the particulate matter with aerodynamic diameter less than 10μm (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001-2013, is used. Initially, a descriptive statistical treatment of PM10 concentrations took place. Furthermore, the AirQ2.2.3 software developed by the WHO was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, during the examined period PM10 concentrations present a significant decreasing trend. Also, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban location) and 40 (city centre location). Approximately 70% of the annual HARD cases are due to city centre residents. In all examined locations, a declining trend in the annual number of HARD cases is appeared. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found.

Time estimation was investigated in 24 healthy adults, 12 women and 12 men, before and after an exhaustive exercise. We compared the capability of estimating time intervals in the range from 1 to 5 s using tasks requiring mental count and tasks that did not allow the mental count. Time estimation and blood lactate levels were evaluated before and at the end of the exercise. We found that the perception of time intervals between 1 and 5 s was affected at the end of the exercise. The observed effects, associated with a significant increase in blood lactate levels, were different in the two types of time estimation used in the present study. In experimental conditions in which participants had to evaluate duration of the time interval by mental counting, at the end of exercise a significant reduction in the overestimation of time made at rest was observed. On the other hand, when participants had to assess the difference in duration between two events without the possibility to mentally count, at the end of the exercise a significant deterioration in performance was observed. In both cases, no gender differences were seen.

In response to the issues of sensor noise and accumulated motion errors that lead to low positioning accuracy and insufficient robustness during large maneuvers or continuous flight of small unmanned aerial vehicles (UAVs) in complex environments, this paper proposes a multi-source and multimodal data fusion method. Initially, it employs a multimodal data fusion of various sensors including GPS (Global Positioning System), IMU (Inertial Measurement Unit), and visual sensors to complement the strengths and weaknesses of each hardware component, thereby eliminating motion errors to enhance accuracy. To mitigate the impact of sudden changes in sensor data, the method introduces a digital twin UAV data source. By utilizing the Extended Kalman Filter algorithm, it fuses data from both the real UAV and its digital twin, and the filtered positional information is fed back into the control system of the real UAV. This enables real-time correction of UAV positional deviations caused by sensor noise and environmental disturbances. The multi-source multimodal fusion Kalman filter method proposed in this paper significantly improves the positioning accuracy of UAVs in complex scenarios and the overall stability of the system. This method holds significant value in maintaining high-precision positioning in variable environments and has important practical implications for enhancing UAV navigation and application efficiency.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. The focus of this study was to estimate and thematically map on a pixel-by-pixel basis, the actual evapotranspiration (ET_a) of the Wonji Shoa Sugarcane Estate using the Surface Energy Balance Algorithm for Land (SEBAL), Simplified Surface Energy Balance (SSEB) and Operational Simplified Surface Energy Balance (SSEBop) algorithms. The results obtained revealed that the ranges of the daily ETa estimated on January 25, February 26, September 06 and October 08, 2002 using SEBAL were 0.0 - 6.85, 0.0 – 9.36, 0.0 – 3.61, 0.0 – 6.83 mm/day; using SSEB 0.0 - 6.78, 0.0 – 7.81, 0.0 – 3.65, 0.0 – 6.46 mm/day, and SSEBop were 0.05 - 8.25, 0.0 – 8.82, 0.2 – 4.0, 0.0 – 7.40 mm/day, respectively. The Root Mean Square Error (RMSE) values between SSEB and SEBAL, SSEBop and SEBAL, and SSEB and SSEBop were 0.548, 0.548, and 0.99 for January 25, 2002; 0.739, 0.753, and 0.994 for February 26, 2002;0.847, 0.846, and 0.999 for September 06, 2002; 0.573, 0.573, and 1.00 for October 08, 2002, respectively. The standard deviation of ET_a over the sugarcane estate showed high spatio-temporal variability perhaps due to soil moisture variability and surface cover. The three algorithm results showed that well watered sugarcane fields in the mid-season growing stage of the crop had higher ET_a values compared with the other dry agricultural fields confirming that they consumptively use more water. Generally during the dry season, ET_a is limited to water surplus areas only and in wet season, ET_a was high throughout the entire sugarcane estate. The evaporation fraction (ETrF) results also followed the same pattern as the daily ET_a over the sugarcane estate. The total crop and irrigation water requirement and effective rainfall estimated using the Cropwat model were 2468.8, 2061.6 and 423.8 mm/yr for January 2001 planted and 2281.9, 1851.0 and 437.8 mm/yr for March 2001 planted sugarcanes, respectively. The mean annual ET_a estimated for the whole estate were 107 Mm³, 140 Mm³, and 178 Mm³ using SEBAL, SSEB, and SSEBop, respectively. Even though the algorithms should be validated through field observation, they have potential to be used for effective estimation of ET in the sugarcane estate.

Introduction: Hypertension is a serious public health problem in Morocco. The objective of this pilot study was to estimate the salt intake of Moroccan adults by measuring 24-hour urinary sodium excretion. Methods: 132 participants were recruited from the central region of Morocco. Sodium, potassium and creatinine levels were measured using the 24-hour urine samples. Additional data included anthropometric measurements and socio-demographic questionnaire. Results: The daily sodium, potassium and creatinine excretions were 2779.1±1334.9 mg, 1350.0±642.8 mg and 820.3 ± 381.2 mg, respectively. Data analysis revealed that 71.2% of the participants had a daily sodium intake more than 2000 mg (5g of salt) recommended by the WHO. The mean of potassium excretion was 1350.0±642.8 mg /day, this average was lower than 3.51g per day recommended by the WHO. Conclusion: Public health measures to reduce sodium and increase potassium consumption in order to decrease the population’s risk of high blood pressure and heart disease are recommended.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

This study represents a significant stride in advancing furan chemistry, presenting the synthesis of high-molecular-weight polyfurfuryl alcohol (PFA) resin (Mw = 118,959 g/mol) from furfuryl alcohol (FA) using a co-catalytic system as against the conventional single-catalytic methods. The resulting PFA resin is fully characterised, and we conclude that it exhibits good shelf life and potential in diverse applications, serving as a standalone moulded material or contributing to composites, coatings, adhesives, and nanostructured carbons.

: This paper presents a couple of meal monitoring systems for senile dementia patients by using electronic weight and temperature sensors. These monitoring systems enable to convey the information of the amount of meal taken by the patients in real-time via wireless communication networks onto the mobile phones of their families or nurses in charge. Thereby, the nurses can easily spot the most desperate patient to take care of while the families can have relief to see the crucial information for survival of their parents at least three times a day. Meanwhile, the senile dementia patients tend to suffer the burn of their tongues because they can hardly recognize the temperature of hot meals served and therefore avoid the burn of tongues. This phenomenon can be discarded by utilizing the meal temperature monitoring system which displays alarm to the patients when the meal temperature is above the reference. These meal monitoring systems can be easily implemented by utilizing low-cost sensor chips and Arduino UNO boards so that elder-care hospitals and nursing homes can afford to exploit them with no additional cost. Hence, we believe that the proposed monitoring systems would be a potential solution to provide a great help and relief not only for the professional nursing nurses working in elder-care hospitals and nursing homes, but also for the families of the dementia patients.

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.

Synthetic Aperture Radar has been extensively used in numerous fields and can gather a wealth of information about the area of interest. This large-scene data-intensive technology puts a high value on automatic target recognition (ATR) which can free the utilizers and boost the efficiency. Recent advances in artificial intelligence have made it possible to create a deep learning-based SAR ATR that can automatically identify target features from massive input data. In the last 6 years, intensive research has been conducted in this area, however, most papers in the current SAR ATR field used recurrent neural network (RNN) and convolutional neural network (CNN)-varied models to deepen the regime’s understanding of the SAR images. To equip SAR ATR with updated deep learning technology, this paper tries to apply a lightweight vision transformer (LViT)-based model to classify SAR images. The entire structure was verified by an open- accessed SAR data set and recognition results show that the final classification outcomes are robust and more accurate in comparison with referred traditional network structures without even using any convolutional layers.

Leading and lagging Key performance indicators (KPIs) provide a means for assurance that risk control systems, to prevent or limit major hazards. The relative importance degree of KPIs provides a theorical guidance for monitoring, inspection and maintenance of structural integrity. In this paper, structural integrity KPIs are classified into leading and lagging KPIs based on Bowtie methodology and the importance degree of the KPIs are evaluated by weight calculation on the basis of Fuzzy analytical hierarchy process (FAHP).

A comparative analysis between Bidirectional Evolutionary Structural Optimization (BESO) and Smoothing-ESO (SESO), simultaneously considering Reliability-Based Topology Optimization (RBTO) and the structure's self-weight in the case of three-dimensional elasticity, is presented in this study. Due to the important role of the existence of uncertainties in making the structural design more realistic, geometry, volumetric fraction, modulus of elasticity, compliance, and loading are considered random variables with normal probability distribution. When adopting the First Order Reliability Method (FORM), the failure probability is calculated based on the reliability index. Furthermore, considering the influence of self-weight in problems involving large structures in civil engineering, especially in the case of bridges, makes the optimal configuration more reliable for design. A series of examples are covered to validate the methods presented, showing their efficiency and robustness.

Background: Breastfeeding is essential for optimal infant health, offering numerous benefits such as infection prevention and enhanced bonding. Despite its importance, challenges exist, particularly for mothers of preterm and very low birth weight (VLBW) infants, necessitating effective lactation support strategies. Initiatives like the Baby Friendly Hospital Initiative (BFHI) and Kangaroo Mother Care (KMC) aim to promote breastfeeding success, while donor human milk (DHM) serves as an alternative bridge while the mother is supported to build up her supply. Adequate lactation support, counseling, and education are vital antenatally, during NICU stays and after discharge to improve breastfeeding rates and infant health outcomes, ultimately contributing to global efforts to reduce neonatal mortality and achieve sustainable development goals. This study explores existing research on factors influencing mothers’ ability to produce breast milk and sustain breastfeeding, with the aim of enhancing lactation support and improving infant health outcomes.Methods/Design: A systematic review will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Searches will be performed in the following databases: PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE Ovid via EBSCOhost, Google Scholar, and WILEY online Library) to identify and incorporate primary research articles related to the topic published between 2014 and 2024. Authors will search peer-reviewed articles, gray literature sources, and reference lists to identify eligible studies. Independent abstract and full-text screening by two reviewers will follow the title screening. The analysis of data will be carried out using Review Manager software (version 5.3.5). The I² test will be used to quantify the amount of unexplained heterogeneity. Potential biases related to study size (such as publication bias) will be examined through the application of several techniques, including funnel plots, Egger’s test, Begg’s test, and Trim and Fill analysis.Discussion: Our review represents a singular exploration into exclusive breastfeeding among very low birth weight (VLBW) infants in low- and middle-income countries (LMICs), focusing on breastfeeding assistance methods. With VLBW defined as less than 1500 g, and most low birth weight (LBW) babies born in LMICs, there is a critical need to address feeding challenges and promote optimal nutrition. Despite updated WHO guidelines in 2022, there is still a lack of comprehensive advice for this vulnerable population. Peer support interventions and health worker counseling have shown promise in increasing breastfeeding continuation rates in LMICs, but challenges persist due to workforce shortages and inadequate documentation. Promoting early postpartum milk expression is crucial for milk production and infant health. Our review aims to fill the gap in understanding VLBW mothers’ breastfeeding experiences in LMICs, shedding light on the effectiveness of breastfeeding support methods and identifying areas for further research. Systematic review registration: CRD42024547864

TThe passion of bodybuilding athletes for a symmetric, lean, heavily muscled body leads them to carry out exhausting exercise programs and restrictive eating regimens, sometimes resulting in disordered eating behaviors. This study investigates potential exacerbators on the development of disordered eating in bodybuilding and strength athletes. The study involved 103 Cypriot bodybuilding athletes of both sexes, performing at three levels: professional, recreational and strength athletes. The Eating Attitude Test 26 (EAT-26) and The Three Factor Eating Question-naire (TFEQ-R21) were used to evaluate disordered eating and eating behaviors respectively. The current study was performed under the auspices of the Hellenic Center of Education & Treatment of Eating Disorders (KEADD). The degree of deviation between the perceived ideal body weight and the actual body weight was associated with increased risk of eating disorder. Athletes who desired a lower body weight recorded higher scores on EAT-26 overall, (p=0.001), and the subscales of dieting (p=0.01) and bulimia. (p=0.001). Cognitive restraint and emotional eating scales of TFEQ-R21 were more pronounced in the non-professional athletes. (p=0.01). The emotional eating score was higher in women. There is a need for appropriate sport-specific, gender-specific preventive intervention to deescalate the risk of eating disorder, in both profes-sional and non-professional bodybuilding athletes.

Introduction: Various studies have widely documented how weight (being overweight, underweight or obese) plays a significant role in increasing health problems and adversely affects the quality of life. Out of all the weight-related issues, obesity is the number one contributing factor for causing health problems. To overcome this factor, a trial was conducted where a drug 'Lite plus’ (Garcinia Cambogia and Green Coffee bean) was given to a select number of people for a period of three months. Materials and Methods: 'Lite Plus' is a drug which contains Garcinia cambogia 500 mg + Green Coffee Bean Extract 400 mg, with a recommended dosage of 2 tablets/day with or before meals for 3 months. Most of the patients included in our study were females suffering from obesity-related problems such as PCOs and Infertility. During the trial, all patients were given the recommended dosage of 'Lite plus', while those with diabetes had the dose amended accordingly. Results: As a result of this trial, it was found that the use of 'Lite Plus' was very effective in decreasing weight/BMI with continuous use. After 6 weeks, out of the 250 respondents participating in the trial, 232 got a reduction in their BMI, and after 12 weeks (end of the trial), 29 respondents (11.6%) reached the desired level of BMI. Conclusion: Garcinia cambogia and Green Coffee bean are effective and safe in weight reduction.

Fucoidan is a brown algae-derived polysaccharide having several biomedical applications. This study simultaneously compares the anticancer activities of crude fucoidans from Fucus vesiculosus and Sargassum filipendula, and effects of low (LMW, 10-50kDa), medium (MMW, 50-100kDa) and high (HMW, >100kDa) molecular weight fractions of S. filipendula fucoidan against osteosarcoma cells. Glucose, fucose and acid levels were lower and sulphation was higher in F. vesiculosus crude fucoidan compared to S. filipendula crude fucoidan. MMW had highest the levels of sugars, acids and sulphation among molecular weight fractions. There was a dose dependent drop in focal adhesion formation and proliferation of cells for all fucoidan-types, but F. vesiculosus fucoidan and HMW had the strongest effects. G1-phase arrest was induced by F. vesiculosus fucoidan, MMW and HMW, however F. vesiculosus fucoidan treatment also caused accumulation in sub G1-phase. Mitochondrial damage occurred for all fucoidan-types, however F. vesiculosus fucoidan led to mitochondrial fragmentation. Annexin V/PI, TUNEL and cytochrome c staining confirmed stress induced apoptosis-like cell death for F. vesiculosus fucoidan but features of stress-induced necrosis-like cell death for S. filipendula fucoidans. There was also variation in penetrability of different fucoidans inside the cell. These differences in anti-cancer activity of fucoidans are applicable for osteosarcoma treatment.

The global polyvinyldene flouride market is estimated to reach $937,278.5 thousand by 2019, therefore to develop new membranes and gain pioneering ideas, which could create innovative business opportunities, a fundamental knowledge about membrane properties fabricated from recent commercially available PVDF polymers is highly mandatory. In this study, we successfully prepared nine non-woven supported PVDF membranes using a phase inversion precipitation method starting from a 15 wt% PVDF solution in N-methyl-2-pyrrolidone. Various membrane morphologies were obtained by using (1) PVDF polymers with diverse molecular weight in a range from 300.000 Da to 700.000 Da and (2) different temperatures of the coagulation bath (20, 40, and 60 ±2°C) used for the films precipitation. Environmental Scanning Electron Microscope (ESEM) was used for surface and cross-section morphologies characterization. Atomic Force Microscope (AFM) was employed to investigate surface roughness, while Contact Angle (CA) instrument was used for membranes wettability studies. Fourier Transform Infrared Spectroscopy (FTIR) results show that the fabricated membranes are formed by a mixture of TGTG’ chains in α phase crystalline domains and all-TTTT trans planar zigzag chains characteristic to β phase. Moreover, generated results indicate that the phases content and membrane morphologies depend on the polymer molecular weight and conditions used for the membranes preparation. The diversity of fabricated membranes could be applied by the End User Industries for different applications.

Abstract: Carbohydrates form the major source of energy in Asian diets. A lower carbohydrate diet became the recommended golden standard for healthy lifestyle. However, the effects of low-carbohydrates diet on health in apparently healthy individuals have been poorly studied, especially in relation to insulin resistance syndrome (IRS). A total of 120 healthy weight participants with no previous history of a major medical condition and an average BMI of ≤ 25kg/m2 were recruited. Self-reported dietary intake and objective physical activity by accelerometry were tracked for seven days. Participants were divided into three categories according to their dietary intake of carbohydrates. Blood samples were collected for metabolic markers analysis. HOMA of insulin resistance (HOMA-IR), β-cell function (HOMA-B) and C-peptide were used to evaluate glucose homeostasis. The consumption of low carbohydrates (less than 45% of total energy) significantly correlated with higher HOMA-IR, Lower HOMA-β % compared to moderate carbohydrate intake (between 45% to 65%). However, only the HOMA-β % was significantly influenced by carbohydrates intake. Moreover, low carbohydrates intake was significantly associated with elevated C-peptide secretion. The substitution of carbohydrates with other macronutrients, such as fat and proteins in the Atkins/ketogenic diet, resulted in a pronounced induction of IRS-related inflammatory markers; FGF2, IP-10, IL-6, IL-17A, MDC and reduction of IL-13. Overall, the presented data highlight, for the first time, that low carbohydrate intake results in significant glucose homeostasis imbalance that may be driven by a heightened state of inflammatory response.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.

In recent years, unmanned aerial vehicles (UAVs) have become a valuable platform for many applications, including communication networks. UAV-enabled wireless communication faces challenges in complex urban and dynamic environments. UAVs can suffer from power limitations and path losses caused by non-line-of-sight connections, which may hamper better communication performance. To address these issues, reconfigurable intelligent surfaces (RIS) have been proposed as a helpful tool to enhance UAV communication networks. However, due to the high mobility of UAV, complex channel environments, and dynamic RIS configurations, it is challenging to estimate the link quality of ground users. In this paper, we propose a link quality estimation model using a gated recurrent unit (GRU) to assess the link quality of ground users for a multi-user RIS-assisted UAV-enabled wireless communication system. Our proposed framework uses a time series of user channel data and RIS phase shift information to estimate the quality of the link for each ground user. Simulation results showed that the proposed GRU model effectively and accurately estimates the link quality of the ground users in the RIS-assisted UAV-enabled wireless communication network.

Background: Obesity among pregnant women is common, and their offspring are predisposed to obesity, insulin resistance, and diabetes. Circulating metabolites that are related to insulin resistance and are associated with this decreased tissue-specific uptake are unknown. Here, we assessed metabolite profiles in elderly women and who were either female offspring from obese mothers (OOM) or offspring of lean mothers (OLM). Metabolic changes were tested for associations with metrics for insulin resistance. Methods: 37 elderly women were separated into an elderly offspring from obese mothers (OOM; n = 17) and elderly offspring from lean/normal weight mothers (OLM; n = 20) groups. We measured plasma metabolites using 1H-NMR and also insulin-dependent tissue specific glucose uptake in skeletal muscle were assessed. Associations were made between metabolites and glucose uptake. Results: Compared to the OLM group, we found that the 22:6 docosahexaenoic acid percentage of the total long chain n-3 fatty acids (DHA/FA) was significantly lower in OOM (P = 0.015). DHA/FA associated significantly to skeletal muscle GU (P = 0.031) and M-value in the OLM group only (P = 0.050). Conclusions: DHA/FA is associated with insulin-dependent skeletal muscle glucose uptake and that this association is significantly weakened in the offspring of obese mothers.

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM-IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Run Final (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001-2020. The Asfezari gridded precipitation data which are developed using a dense of ground-based observation were utilized as the reference dataset. The findings indicate that the IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All the three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggest that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

Microalgae are abundant components of biosphere rich in low molecular weight carbohy-drate-containing natural products (glycoconjugates). Glycoconjugates take a part in processes of photosynthesis, provide producers with important biological properties, influence other organ-isms and known by biological activities. Some of them, for example glycosylated toxins and ar-senicals, are detrimental and can be transferring via food chains into higher organisms including humans. So far, the studies on a series of particular groups of microalgal glycoconjugates were not comprehensively discussed in special reviews. In this review, a special focus was made on the isolation, structure determination, properties and approaches to the search for new bioactive metabolites. Analysis of literature data concerning structures, functions and biological activities of ribosylated arsenicals, galactosylated and sulfoquinovosylated lipids, phosphoglycolipids, gly-coside derivatives of toxins, and other groups of glycoconjugates was carried out and discussed. The discovery of a great variety of new carbohydrate-containing metabolites, important biological roles some of them, participation in regulation of microalgal blooms via viral infections as well as the detection of potent immunomodulatory activity, shown by these type metabolites could be attributed to the most interesting recent achievements. Structures, properties and biological roles of glycoconjugates from microalgae attract a great attention growing year to year.

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

This paper investigates the dynamic properties of artificial neural networks using differential equations and explores the influence of parameters on stability and neural oscillations. By analyzing the equilibrium point of the differential equations, we identify conditions for asymptotic stability and criteria for oscillation in artificial neural networks. Furthermore, we demonstrate how adjusting synaptic weights between neurons can effectively control stability and oscillation. The proposed model offers potential insights into the malfunctioning mechanisms of biological neural networks implicated in neurological disorders like Parkinson's disease tremors and epilepsy seizures, which are characterized by abnormal oscillations.

The main goal of the present paper is to construct some families of the Carlitz’s q-Bernoulli polynomials and numbers. We firstly introduce the modified Carlitz’s q-Bernoulli polynomials and numbers with weight (α, β) and investigate their some explicit properties and identities arising from the bosonic q-Volkenborn integral on ℤ_p. We then define the modified degenerate Carlitz’s q-Bernoulli polynomials and numbers with weight (α, β) and obtain some recurrence relations and other identities. Moreover, we derive some correlations with the modified Carlitz’s q-Bernoulli polynomials with weight (α, β), the modified degenerate Carlitz’s q-Bernoulli polynomials with weight (α, β), the Stirling numbers of the first kind and second kind.

In this paper, we present a multi-task model that predicts disparities and confidence levels in deep stereo matching simultaneously. We do this by combining its successful model for each separate task and obtaining a multitask model that can be trained with a proposed loss function. We show the advantages of this model compared to training and predicting disparity and confidence sequentially. This method enables an improvement of 15% to 30% in the area under the curve (AUC) metric when trained parallel rather than sequential. In addition, the effect of weighting the components in the loss function on the stereo and confidence performance is investigated. By improving the confidence estimate, the practicality of stereo estimators for creating distance images is increased.

Wireless communication has proliferated due to the colossal augmentation of smart phones, mobile phones, tablets and laptops etc. Multimedia applications such as live videos, audios, still images, animated graphics and live communications all requires accompaniments of quality of service (QoS) as well as reliable end-to-end transfer of data. Information about the availability of resources in channel is must in order to improve the QoS. QoS can be determined in terms of capacity of channel, available bandwidth (ABW), and bulk transfer capacity (BTC). Bandwidth availability is a key check for improving the QoS in a network. The performance of a multimedia application is directly affected by the bandwidth availability. One of the most important QoS characteristic is ABW at a wireless route and it can be demarcated as least unused capacity of links instituting a network route. Since now there have been many bandwidth estimation techniques are available in the literature to increase the network performance. Bandwidth estimation techniques have been arranged into three leading classifications: i) Active probing bandwidth estimation techniques, ii) Passive bandwidth estimation technique and iii) Model based bandwidth estimation techniques. Each of these techniques are briefly discussed in this paper.

Forest ecosystems make a greater contribution to carbon (C) stocks than any other terrestrial ecosystem. To understand the role of regional forest ecosystems in global climate change and carbon exchange, forest C stock and its spatial distribution within the small (2,300 km2) Liuxihe River basin were analyzed to determine the different contributors to the C stock. Forest C stocks were quantified by measuring the biomass of trees, understory vegetation, litter and roots, as well as soil organic C, using data from field samples and laboratory experiments. The results showed that forests stored 38.04 Tg C in the entire basin, with secondary and planted forests accounting for 89.82% and 10.18%, respectively, of the stored C. Five types of forests, a subtropical evergreen broad-leaved forest, a subtropical coniferous and broad-leaved mixed forest, a subtropical coniferous forest, a timber forest, and a non-wood forest, stored 257.55 ± 15.01, 218.92 ± 9.59, 195.24 ± 18.29, 177.42 ± 17.55, and 117.86 ± 6.04 Mg C ha−1, respectively. In the forest ecosystem C stocks of the basin, soils averagely contribute about 73.78%, not including root underground biomass. It provides a comprehensive method for forest ecosystem carbon investigation and forest management in small basin scale.

Eutrophication, a global concern, impacts water quality, ecosystems, and human health. It’s crucial to monitor algal blooms in freshwater reservoirs, as they indicate the trophic condition of a waterbody through Chlorophyll-a (Chla) concentration. Traditional monitoring methods, however, are expen-sive and time-consuming. Addressing this hindrance, we developed models using remotely sensed data from the Sentinel-2 satellite for large-scale coverage, including its bands and spectral indexes, to estimate the Chla concentration on 149 freshwater reservoirs in Ceará, Brazil. Several machine learning models were trained and tested, including k-nearest neighbours, random forests, extreme gradient boosting, the least absolute shrinkage, group method of data handling (GMDH), and sup-port vector machine models. A stepwise approach determined the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH, achieving an R2 of 0.91, MAPE of 102.34%, and RMSE of 20.38 g/L, which are values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near infra-red bands.

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine learning based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions namely; Iran, Mozambique, Sri-Lanka, and Sudan where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction , and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 to 0.98 across the four test regions at hand.

Research abounds in the literature on kinetic analyses using thermogravimetric (TG) runs. Many of these studies use approximations of integral or derivative forms of the kinetic law and all of them use programmed temperature, not the actual temperature measured by thermocouples close to the sample. In addition, it is common to conduct a single run in order to perform the calculation. Nevertheless, many authors consider that numerical methods should be used to analyse the kinetics of decomposition. In such cases, the actual temperature is used and, generally, several runs are fitted using the same kinetic parameters, giving robustness to the results. In the present work, a numerical integration procedure was discussed and applied to different examples. We focused on materials presenting a single decomposition curve as well as other materials with more complex processes. Different examples were explored, and the methodology was applied to a number of wastes such as coffee husks, polystyrene and polyethylene.

This interdisciplinary investigation of the human remains of Balduin Helm, one of the most important abbots of Fürstenfeld monastery, provides novel information on that historic individual. This is particularly interesting since Balduin, during early 18th century, was involved in the renovation of this large Bavarian monastery. Metastatic prostate cancer was found as evidenced by multiple mixed osteolytic-osteoblastic bone lesions in all available vertebral bodies, fragments of both hemipelvises and isolated metastases in skull bones. Distribution, radio- and histomorphology, and especially the immunohistochemical detection of prostate-specific antigen in those metastases, definitively confirm this diagnosis. Further investigation, especially by stable isotope analysis, showed a balanced high-level diet with considerable contribution from animal protein and significant freshwater fish. These additional findings suggest a significant radiocarbon reservoir effect as an explanation for a “too old” radiocarbon dating. Finally, the obviously high-level protein diet may have contributed to the tumorigenesis which caused the death of the abbot at an advanced age.

Objective: To determine if higher weight-to-length z-scores after 32 weeks of gestation are associated with higher Pulmonary Scores (PS) in preterm infants requiring respiratory support using a prospective observational study. Methods: Infants born <30 weeks, with a post-menstrual age (PMA) of 30-33 weeks were enrolled. Weight, infant’s length, and head circumference were measured weekly. Data on calories/kg/d, protein g/kg/d, weight for length percentiles, z-scores, and BMI at 33 through 40 weeks PMA were collected. PS was calculated. Results: We analyzed 91 infants. The mean gestational age was 26.9 ± 1.7 weeks. There was a significant association between PS and W/L z scores(p<0.0001). For every one-unit increase in W/L z-scores, PS increased by 0.063. There was a significant association between PS and W/L percentiles (p = 0.0017) as well as BMI (p = <0.0001). For every unit increase in W/L percentiles, PS increased by 0.002, and for a unit increase in BMI, PS increased by 0.04. The association remained significant after postnatal steroids, sex, corrected, and birth gestational ages were included in the regression analysis. Nutrition did not affect anthropometric measurements. Conclusion: Our study is the first to demonstrate that higher BMI and W/L may adversely affect respiratory severity in infants.

The reported study was devoted to the investigation of viscoelastic behavior for solid and porous ultra-high-molecular-weight polyethylene (UHMWPE) under compression. The obtained experimental stress curves were interpreted using a two-term Prony series to represent the superposition of two coexisting activation processes corresponding to long molecular (~160 s) and short structural (~20 s) time scales, respectively, leading to good statistical correlation with the observations. In the case of porous polymer, the internal strain redistribution during relaxation was quantified using Digital Image Correlation (DIC) analysis. The strongly inhomogeneous deformation of the porous polymer was found not to affect the relaxation times. In order to generalize the results to three dimensions, X-ray tomography was used to examine the porous structure at the macro- and micro-scale levels. DIC analysis revealed positive correlation between the applied force and relative density. The apparent stiffness variation for UHMWPE foams with mixed open and closed cells was described using a newly proposed three-term expression. Furthermore, the in situ tensile loading and X-ray scattering study was applied for isotropic solid UHMWPE specimens to investigate their parameters of internal structure during orientation and stress relaxation process at another mode.

This work addresses the problem of automatic head pose estimation and its application in 3D gaze estimation using low quality RGB--D sensors without any subject cooperation or manual intervention. The previous works on 3D head pose estimation using RGB--D sensors require either an offline step for supervised learning or 3D head model construction which may require manual intervention or subject cooperation for complete head model reconstruction. In this paper, we propose a 3D pose estimator based on low quality depth data, which is not limited by any of the aforementioned steps. Instead, the proposed technique relies on modeling the subject's face in 3--D rather than the complete head, which in turn, relaxes all of the constraints with the previous works. The proposed method is robust, highly accurate and fully automatic. Moreover, it does not need any offline step. Unlike some of the previous works, the method only uses depth data for pose estimation. The experimental results on the Biwi head pose database confirm the efficiency of our algorithm in handling large pose variations and partial occlusion. We also evaluate the performance of our algorithm on IDIAP database for 3D head pose and eye gaze estimation.

Preterm infants have increased body adiposity at term equivalent age and risk for adverse metabolic outcomes. The aim of the study was to define how nutrient intake may impact body composition (BC) of very low birth weight infants fed with early progressive enteral feeding and standard fortification. Eighty-six infants with

Background: Changes in body weight are associated with the regulation of DNA methylation (DNAm). In this study, we investigated the associations between maternal gestational weight gain-related DNAm and foetal and neonatal body composition. Methods: Brazilian pregnant women from the Araraquara Cohort Study were followed up during pregnancy, delivery, and after hospital discharge. Women with normal pre-pregnancy BMI were allocated to two groups: adequate gestational weight gain (AGWG, n=45) and excessive gestational weight gain (EGWG, n=30). Foetal and neonatal body composition was evaluated by ultrasound and plethysmography, respectively. DNAm was assessed in maternal blood using Illumina Infinium MethylationEPIC BeadChip arrays. Linear regression models were used to explore the associations between DNAm and foetal and neonatal body composition. Results: Maternal weight, GWG, neonatal weight, and fat mass were higher in the EGWG group. Analysis of DNAm identified 46 differentially methyl-ated positions and 11 differentially methylated regions (DMRs) between the EGWG and AGWG groups. Nine human phenotypes were enriched for these 11 DMRs located in 13 genes (EMILIN1, HOXA5, CPT1B, CLDN9, ZFP57, BRCA1, POU5F1, ANKRD33, HLA-B, RANBP17, ZMYND11, DIP2C, TMEM232), highlighting the terms insulin resistance, and hyperglycaemia. Maternal DNAm was associated with foetal total thigh and arm tissues and subcutaneous thigh and arm fat, as well as with neonatal fat mass percentage and fat mass. Conclusion: The methylation pat-tern in the EGWG group indicated a risk for developing chronic diseases and involvement of ma-ternal DNAm in foetal lean and fat mass and in neonatal fat mass.

Volatile compounds, abundant in breath, can be used to accurately diagnose and monitor a range of medical conditions. This offers a non-invasive, low-cost approach with screening applications; however, uptake of this diagnostic approach has been limited by conflicting published outcomes. Most published reports rely on large scale screening of the public, at single time points and without reference to ambient air. Here, we present a novel approach to volatile sampling from cellular headspace and mouse breath that incorporates multi-time point analysis and ambient air subtraction revealing compound flux as an effective proxy of active metabolism. This approach to investigating breath volatiles offers a new avenue for disease biomarker discovery and diagnosis. Using gas chromatography mass spectrometry (GC/MS), we focus on low molecular weight, metabolic substrate/by-product compounds and demonstrate that this non-invasive technique is sensitive (reproducible at ~1 µg cellular protein, or ~500,000 cells) and capable of precisely determining cell type, status and treatment. Isolated cellular models represent components of larger mammalian systems and we show that stress- and pathology-indicative compounds are detectable in mice, supporting further investigation using this methodology as a tool to identify volatile targets in human patients.

The RNA-binding protein Human antigen R (HuR) regulates stability, translation, and nucleus-to-cytoplasm shuttling of its target mRNAs. The protein has been progressively recognized as a relevant therapeutic target for several pathologies like cancer, neurodegeneration, as well as inflammation. Inhibitors of mRNA binding to HuR might thus be beneficial against a variety of diseases. Here we present the rational identification of structurally novel HuR inhibitors. In particular, by combining chemoinformatics approaches, high throughput virtual screening and RNA–protein pull-down assays, we show that the 4-(2-(2,4,6-trioxotetrahydropyrimidin-5(2H)-ylidene)hydrazineyl)benzoate ligand exhibits dose-dependent HuR inhibition in binding experiments. Importantly, the chemical scaffold is new with respect to the so-far known HuR inhibitors, opening up a new avenue for the design of pharmaceutical agents targeting this important protein.

Model predictive control (MPC) is a multi-objective control technique that can handle 2 system constraints. However, the performance of an MPC controller highly relies on a proper 3 prioritization weight for each objective, which highlights the need for a precise weight tuning 4 technique. In this paper, we propose an analytical tuning technique by matching the MPC 5 controller performance with the performance of a linear quadratic regulator (LQR) controller. The 6 proposedmethodology derives the transformation of LQRweightingmatrixwith a fixedweighting 7 factor using discrete algebraic Riccati equation (DARE) and designs MPC controller using the 8 idea of discrete time linear quadratic tracking problem (LQT) in the presence of constraints. 9 The proposed methodology ensures the optimal performance between unconstrained MPC and 10 LQR controller and provides a sub-optimal solution while the constraints are active during 11 transient operations. The resultingMPC behaves as the discrete time LQR by selecting appropriate 12 weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. 13 In this paper, the effectiveness of the proposed technique is investigated in the application of 14 a novel vehicle collision avoidance system which is designed in the form of linear inequality 15 constraints within MPC. The simulation results confirm the potency of the proposed MPC control 16 technique in performing a safe, feasible and collision free path while respecting the inputs, states 17 and collision avoidance constraints.

The stock beta coefficient literature extensively discusses the proper methods for the estimation of beta as well as its use in asset valuation. However, there are relatively few references with respect to the appropriate time horizon that investors should utilize when evaluating the risk-return relationship of a stock. We examine the appropriate time horizon for beta estimation differentiating our results by sector according to the Industry Classification Benchmark. We employ data from the NYSE and we estimate varying lengths of beta employing data from 30 to 250 trading days. The constructed beta series is then examined for the presence of breaks using the endogenous structural break literature. Results show evidence against the use of betas that employ more than 90 trading days of data provisional to the sector under study.

Abstract: (1) Background: to find out how COVID-19 infection during pregnancy affects the outcome of the pregnancy; (2) Methods: 166 SARS-CoV-2-positive pregnant women formed the study group. 128 SARS-CoV-2-negative pregnant women formed the control group. Anatomopathological study of the placenta was performed in all cases; (3) Results: Placental insufficiency appeared in 38 patients (22.9%) of the study group and in 17 patients (13.2%) in the control group (p=0.016). Villitis appeared in 50 patients (30.1%) of the study group and in 16 (12.6%) in the control group (p=0.000). 166 COVID-19 positive patients were further subdivided, into those with anatomopathological affection of the placenta, and those without. When gestational age between patients with placental insufficiency and those without it are compared, a difference of 4.38 days is obtained (p=0.0393). If we compare neonatal weight between patients with placental insufficiency and those without it, a difference of 406.4 grams is obtained (p=0.0000). When gestational age between patients with villitis and those with-out it are compared, a difference of 3.2 days is found (p=0.0919). When neonatal weight between patients with villitis and those without it are compared, a difference of 242.2 grams is obtained (p=0.0018). (4) Conclusions: COVID-19 infection during pregnancy may produce lower fetal weight and shorter gestational length.

Background: The increase in the prevalence of obesity in South Africa is a problem and weight management play an important role in the treatment of the high prevalence. For this purpose, a quantitative study was carried out using a structured questionnaire to assess predictors of attempt and failure to lose weight. Methods: One thousand and fifty adults were enrolled in the study and 54% (n=562) were overweight and obese (BMI &gt;/25kg/m2). Descriptive statistics were analysed. Inferential statistical tests (chi-square tests, logistic regression, and Pearson’s correlation coefficients) were used to determine the relationship between variables. Results: The results revealed that age, sex, marital status, educational level, employment status, income level, area of residence, ethnic groups, and self-perceived BMI (normal weight, overweight, and obese) were predictors of attempt and failure to lose weight. There was a positive correlation between self-perceived BMI and the attempt and failure to lose weight. Conclusion: Weight management is important in the realm of public health and can be regarded as an investment in the population, as it leads people toward weight control strategies grounded in empirical evidence. Additionally, the findings can inform public health policies and interventions to improve weight management strategies at the population level.

This study aimed to investigate the relationship between weight-bias internalization and eat-ing-disorder psychopathology in treatment-seeking patients with severe obesity using a network approach. Two thousand, one hundred and thirteen patients with obesity consecutively admitted to a specialist clinical unit for obesity were recruited from January 2016 to February 2023. Body mass index was measured, and each patient completed the Weight Bias Internalization Scale (WBSI) and the Eating Disorder Examination Interview (EDE). Network analysis showed that the most central and highly interconnected nodes in the network were related to the EDE items exposure avoidance, dissatisfaction with shape, and wanting an empty stomach. Bridge nodes were found, but the bootstrap difference test on expected bridge influence indicated non-significant centrality differences. Nevertheless, the eating-disorder psychopathology and weight-bias internalization network structure in patients seeking treatment for obesity indicate the prominent role of body dissatisfaction and control of eating and weight in these psychological constructs. This finding, if replicated, could pave the way for a new understanding of the psychological mechanisms operating in patients with obesity.

(1) Background: Loss of weight is one of the practices which have been identified as key in reducing the risk of various forms of cancer. Therefore, this study is a systematic review and meta-analysis of studies related to the topic of loss of weight and risk of cancer and addresses the question, ‘does losing weight reduce the risk of cancer?’ Its purpose is to identify current high-quality evidence on such a question and synthesize such evidence before summarizing it given specific data attributes to improve decision-making processes on cancer management. (2) Methods: Research studies were identified from four main databases: PubMed, Science Direct, Google scholar, and Medline. A systematic review and meta-analysis of such studies were then conducted to reveal the most current evidence on the research topic. (3) Results: The studies showed that losing weight reduces cancer risk. Nonetheless, such intervention is not necessarily effective, especially in cases where patients may be at risk of developing cancer due to other risk factors. (4) Conclusions: The current study concludes that there is a need to implement effective interventions such as physical exercise, dietary restrictions, or both that can be effective in reducing weight to reduce the risk of cancer.

Abstract Background and Objective: Medical Microwave Radiometry (MWR) is used to capture the thermal properties of internal tissues and has usages in breast cancer detection. Our goal in this paper is to improve classification performance and investigate automated neural architecture search methods. Methods: We investigate optimizing the weights of a weight agnostic neural network using bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology is found. We compare it against a weight agnostic and cascade correlation neural network. Results: The experiments are conducted on a breast cancer dataset of 4912 patients. Our proposed weight agnostic BIPOP-CMA-ES model achieved the best performance. It obtained an F1-score of 0.9225, accuracy of 0.9219, precision of 0.9228, recall of 0.9217 and topology of 153 connections. Conclusions: The results are an indication of the potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. By separating the tasks of topology search and weight training, we are able to improve the overall performance.

In this paper, we propose a lightweight physical layer aided authentication and key agreement (PL-AKA) protocol in the internet of things (IoT). Conventional evolved packet system AKA (EPS-AKA) used in long-term evolution (LTE) systems may suffer from congestions in core networks by the large signaling overhead as the number of IoT devices increases. Thus, in order to alleviate the overhead, we consider a cross-layer authentication by integrating physical layer approaches to cryptography-based schemes. To demonstrate the feasibility of the PL-AKA, universal software radio peripheral (USRP) based tests are conducted as well as numerical simulations. The proposed scheme shows a significant reduction in signaling overhead compared to the conventional EPS-AKA in both simulation and experiment. Therefore, the proposed lightweight PL-AKA has the potential for practical and efficient implementation of large-scale IoT networks.

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.

1) Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict Chlorophyll-a (Chl-a) concentration and the trophic states for an inland lake within the East Kolkata Wetland, India; 2) The most suitable band ratio was identified by performing Pearson correlation analysis between Chl-a concentrations and possible OLI band and band ratios from the study points; 3) The results showed highest correlation coefficient from the band ratio OLI5/OLI4 with an R value of 0.85. The prediction model was then developed by applying regression analysis between the band ratio OLI5/OLI4 and Chl-a concentration of the study points. The reflectance ratios of the validation points were given as input on the prediction model and the model output was considered as predicted Chl-a values of the validation points to check the efficiency of the prediction model. The regression model between laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points revealed a high correlation with an R² value of 0.78. Trophic State Index (TSI) of the lake was also calculated from laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points. The study presented a high correlation of TSI determined from predicted data with TSI from laboratory reference data (R = 0.88). The TSI values of the lake ranged from 65 to 75 which indicate that the lake is appeared to be eutrophic to hypereutrophic conditions. 4) This empirical study showed that Landsat 8 OLI imagery can be effectively applied to estimate Chl-a levels and trophic states for inland lakes.

The novel Ionized Gas Thermoelectric Generator (IG-TEG) system that has been studied theoretically showing capabilities to continually extracting energy from the thermal energy of the ambient air, at low temperatures within the standard room temperature and below. This system does not need a temperature gradient in order to work, unlike the other TEGs that use Seebeck effect, and therefore this new system can be utilized for cooling purposes, by extracting energy instead of wasting energy in compressing the gas for cooling. This novel system was designed based on Static Ratchet Potential (SRP), which is known as a spatially asymmetric electric potential produced by an array of positive and negative electrodes. The ratchet potential produces electrical current from random Brownian Motion of charged particles that are driven by thermal energy. Ratchet potential was studied and investigated by several researches in the fields of sensing and energy harvesting. The main ratchet potential system parameter is the particles transportation, this parameter was studied under the condition of flashing ratchet potentials, and was analyzed based on several methods. In this study, a different approach is pursued to estimate particles transportation, by evaluating the charged particles distribution, and applying the other conditions of the SRP.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

Estimation and planning play a vital role in the construction of an autonomous navigation framework. However, these problems are often considered separately, while planning gives robot a free-collision path towards the desired goal, estimation algorithm presents the executed trajectory in the sense that it has to be closed to the ground truth path as much as possible. Recently, a unified probabilistic framework, which supports solving these problems simultaneously, dubbed STEAP has been proposed. Nevertheless, its current version is only designated for an omni wheels robot, which allows robot to move and turn in vertical direction. Differential drive robot, on the other hand, though limited to move along only one direction, has been used in various situations due to its flexibility and lower cost in hardware designing. Thus, in this extension, our aim is to control a differential drive robot via STEAP. Moreover, in a more complicated environment such as labyrinth or maze, the original STEAP sometimes fails to find a path. Indeed, this problem is mainly caused by the poor initialization and the non-linearity in optimizer constraints. In our implementation, instead of dealing with these constraints, we employ a global planner algorithm such as Dijkstra or RRT to treat STEAP as an effective local planner module that focus on following the global path. Consequently, the experimental results show that the extended STEAP not only able to navigate a differential drive robot but also in a more complicated and unstructured environment.

In order to solve the labor-intensive and time-consuming problem in the process of measuring yak body ruler and weight in yak breeding industry in Qinghai Province, a non-contact method for measuring yak body ruler and weight was proposed in this experiment, and key technologies based on semantic segmentation, binocular ranging and neural network algorithm were studied to boost the development of yak breeding industry in Qinghai Province. Main conclusions: (1) Study yak foreground image extraction, and implement yak foreground image extraction model based on U-net algorithm; select 2263 yak images for experiment, and verify that the accuracy of the model in yak image extraction is over 97%. (2) Develop an algorithm for estimating yak body ruler based on binocular vision, and use the extraction algorithm of yak body ruler related measurement points combined with depth image to estimate yak body ruler. The final test shows that the average estimation error of body height and body oblique length is 2.6%, and the average estimation error of chest depth is 5.94%. (3) Study the yak weight prediction model; select the body height, body oblique length and chest depth obtained by binocular vision to estimate the yak weight; use two algorithms to establish the yak weight prediction model, and verify that the average estimation error of the model for yak weight is 10.7% and 13.01% respectively.

This study aimed to evaluate the impact of selected pregnancy pathologies statistically depending on overweight/obesity and excessive maternal weight gain during pregnancy on women who gave birth in the years 2013–2015 at the Second Department of Gynecology and Obstetrics at the University Hospital in Bratislava, Slovakia. In a retrospective study, we analyzed data gathered from the sample, which consisted of 7,122 women. Our results indicate a positive statistical dependency of the groups of women with overweight and obesity and gestational hypertension (adjusted odds ratio [AOR]=15.3; 95% CI 9.0−25.8 for obesity), preeclampsia (AOR=3.4; 95% CI 1.9−6.0 for overweight and AOR=13.2; 95% CI 7.7−22.5 for obesity), and gestational diabetes mellitus (AOR=1.9; 95% CI 1.2−2.9 for overweight and AOR=2.4; 95% CI 1.4−4.0 for obesity). A higher incidence of pregnancies terminated by cesarean section was observed in the group of obese women. Gestational weight gain above the IOM (the Institute of Medicine) recommendations was associated with a higher risk of pregnancy terminated by C-section (AOR=1.2; 95% CI 1.0−1.3), gestational hypertension (AOR=1.7; 95% CI 1.0−2.7), and infant macrosomia (AOR=1.7; 95% CI 1.3−2.1). Overweight and obesity during pregnancy significantly contribute to the development of pregnancy pathologies and increased incidence of cesarean section. Systematic efforts to reduce weight before pregnancy through pre-pregnancy dietary counseling, regular physical activity, and healthy lifestyle should be the primary goal.

Modelling diameter distribution is an integral part of forest management. It requires the use of a suitable probability density function or cumulative distribution function with the appropriate fitting method. In this study, we compared the suitability of eight probability density functions fitted by derivative and optimization methods used for diameter distribution estimation of forest stands. The derivation and optimization were used on A Charlier, beta, generalized beta, gamma, Gumbel, Johnson’s SB, and Weibull (2 and 3-parameter). We used data from 167 permanent sample plots from Atlantic forest (Quercus robur) and 59 temporary sample plots from tropical forests (Tectona grandis). The quality of the fits was evaluated with different indices such as Kolmogorov–Smirnov, Cramer-von Mises, mean absolute error, bias and mean squared error. The results showed Johnson’s SB function was more suitable for describing the diameter distribution of the stands. Johnson’s SB, 3-parameter Weibull and generalized beta had consistent performance regardless of the fitting methods. The quality of fits produced by gamma, Gumbel and 2-parameter Weibull was poor.

Biocidal compositions based on interpolyelectrolyte complexes and a low molecular weight antibiotic can become a promising material for creating biocidal coatings, as they combine wash-off resistance and dual biocidal action due to the biocide and the polycation. Molecular mass characteristics of polymers play essential role in physics and mechanical properties of the coatings. In this work, the properties of polydiallyldimethylammonium chloride (PDADMAC) coatings of various molecular weights are investigated and assumptions are made about the optimal molecular weight needed to create antibacterial compositions. To study the resistance to washing off and moisture saturation of the coatings, the gravimetric method was used, the adhesive properties of the coatings were studied by dynamometry. It has been established that an increase in molecular weight affects the wash-off resistance of coatings, but does not affect moisture absorption and adhesion mechanics of coatings. All samples of PDADMAC were demonstrated to exhibit the same antibacterial activity. Thus, when developing systems for creating antibacterial coatings, it must be taken into account that in order to create stable coatings. The requirement to use PDADMAC with a high degree of polymerization is necessary for control the desorption of coating during wash off-but not mandatory for the control of mechanical and antibacterial properties of the coating.

Changes in food preferences after bariatric surgery may alter its effectiveness as a treatment for obesity. We aimed to compare food reward for a comprehensive variety of food categories between patients who received a sleeve gastrectomy (SG) or a Roux-en-Y gastric bypass (RYGB) and to explore whether food reward differs according to weight loss. In this cross-sectional exploratory study, food reward was assessed using the Leeds Food Preference Questionnaire (LFPQ). We assessed liking and wanting of eleven food categories. Comparisons were done regarding type of surgery and Total Weight Loss (TWL; based on tercile distribution). Fifty-six patients (30 SG and 26 RYGB) were included (women: 70%; age: 44.0 (11.1) y). Regarding the type of surgery, scores were not significantly different between SG and RYGB, except for ‘non-dairy products – without color’ explicit liking (p = 0.04). Regarding TWL outcomes, explicit liking, explicit wanting and implicit wanting, scores were significantly higher for Good responders than Low responders for ‘No meat – High fat’ (post-hoc corrected p-value: 0.04, 0.03 and 0.04, respectively). Together, our results failed to identify major differences in liking and wanting regarding the type of surgery and tended to indicate that higher weight loss might be related to a higher reward for high protein-content food. Rather to focus only on palatable foods, future studies should also consider a broader range of food items, including protein reward.

Globally, increasing rates of obesity are one of the most important health issues. The association between breakfast skipping and body weight is contradictory between cross-sectional and interventional studies. The systematic review and meta-analyses aim to summarize this association based on observational longitudinal studies. We included prospective studies on breakfast skipping and overweight/obesity or weight change in adults. Literature was searched until September 2020 in PubMed and Web of Science. Summary RRs with a 95% CI were estimated in pairwise meta-analyses by applying a random-effects model. In total, 9 studies were included in the systematic review and 6 of them were included in the meta-analyses. The meta-analysis indicated an 13% increased RR for overweight/obesity when breakfast was skipped on ≥ 3 days per week compared to ≤ 2 days per week (95% CI: 1.06, 1.21, n=3 studies). The meta-analysis on weight change displays a 21% increased RR for weight gain for breakfast skippers compared to breakfast eaters (95% CI: 1.05, 1.40, n=2 studies). The meta-analysis on BMI change displayed no difference between breakfast skipping and eating (RR=1.02, 95% CI: 0.99, 1.05, n=2 studies). This study provides low meta-evidence for an increased risk for overweight/obesity and weight gain for breakfast skipping.

Software project estimating (SPE) is a powerful but necessary step in the production of projects for software. Software project estimating includes several components, estimating software time, software resources, software costs, and software effort estimation (SEE). SEE seeks to estimate the number of "hours-person" and "months-person" of work required for developing or managing a software program. During the initial phase of the development of software, future efforts are impossible to predict. To estimate effort, many deep learning (DL) and machine learning (ML) models were previously created. For estimate, single-model techniques and ensemble methods were investigated in this research. Ensemble methods involve a combination of multiple separate models. Bagging, boosting, and voting were the ensemble methods looked into for estimation. Datasets considered for estimation were COCOMO81, COCOMONasa I, COCOMONasa II, Albrecht, Maxwell, China, Desharnais, Desharnais_1_1, Kitchenham, Boehm, and Belady. The evaluation criteria used MAE, RAE, RMSE, RRSE, and CC. The result from previous studies is that the total size of the set of data utilized is small in predicting the effort, as the largest size of the set of data used is the China dataset containing only 499 projects. So, in this study, the dataset has been merged to increase the total size of the set of data and increase the accuracy of prediction. The results indicated that the suggested merged dataset increased effort estimation accuracy.

Crowd density estimation is an important task for crowd monitoring. Many efforts have been done to automate the process of estimating crowd density from images and videos. Despite series of efforts, it remains a challenging task. In this paper, we proposes a new texture feature-based approach for the estimation of crowd density based on Completed Local Binary Pattern (CLBP). We first divide the image into blocks and then re-divide the blocks into cells. For each cell, we compute CLBP and then concatenate them to describe the texture of the corresponding block. We then train a multi-class Support Vector Machine (SVM) classifier, which classifies each block of image into one of four categories, i.e. Very Low, Low, Medium, and High. We evaluate our technique on the PETS 2009 dataset, and from the experiments, we show to achieve 95% accuracy for the proposed descriptor. We also compare other state-of-the-art texture descriptors and from the experimental results, we show that our proposed method outperforms other state-of-the-art methods.

The potential of generalized machine learning models developed for crop water estimation was examined in the current study. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) are three ensembled machine learning models that were developed using all of the data from a single location from 1976 to 2017 and then immediately applied at eleven different locations without the need for any local calibration. For the test period of January 2018 to June 2020, the model's capacity to estimate the numerical values of crop water requirement (Pen-man-Monteith (PM) ETo values) was assessed. In comparison to the GBM and RF models, the XGBoost model outperformed them both marginally and significantly. The estimate's weighted standard error was smaller than 0.85 mm/day, and the model's effectiveness varied from 96% to 99% across various locations. The model's strong performance was indicated by the decreased noise-to-signal ratio. A real-time water management system at the regional level can be seamlessly linked with this type of model due to its accuracy in estimating crop water requirements and its capacity to generalize.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

In many research fields, statistical probability models are often used to analyze real-world data. However, data from many fields, such as the environment, economics, and health care, do not necessarily fit traditional models. New empirical models need to be developed to improve their fit. In this paper, we explore a further extension of the quasi-Lindley model. Maximum likelihood, least square error, Anderson-Darling algorithm, and expectation-maximization algorithm are four techniques for estimating the parameters under study. All techniques provide accurate and reliable estimates of the parameters. However, the mean square error of the expectation maximization approach was lower. The usefulness of the proposed model was demonstrated by analyzing a dynamical systems data set, and the analysis shows that it outperforms the other models in all statistical models considered.

The power function distribution is a flexible waiting time model that may provide better fit for some failure data. This paper presents the comparison of the maximum likelihood estimates and the Bayes estimates of two-parameter power function distribution. The Bayes estimates are obtained, using conjugate priors, under five loss functions consist of square error, precautionary, weighted, LINEX and DeGroot loss function. The Gibbs sampling algorithm is proposed to generate samples from posterior distributions and in result the Bayes estimates are computed. The comparison of the maximum likelihood estimates and the Bayes estimates are done through the root mean squared errors. One real-life data set is analyzed to illustrate the evaluation of proposed methods of estimation. Finally, results from the simulation are discussed to assess the performance behavior of the maximum likelihood estimates and the Bayes estimates.

Structural displacement is an important metric for assessing structural conditions because it has a direct relationship with the structural stiffness. Many bridge displacement measurement techniques have been developed, but most methods require fixed reference points in the vicinity of the target structure which limits field implementations. A promising alternative is to use reference-free measurement techniques that indirectly estimate the displacement by using measurements such as acceleration, and strain. This paper proposes novel reference-free bridge displacement estimation by the fusion of single acceleration with pseudo-static displacement derived from co-located strain measurements. First, we propose a conversion of the strain at the center of a beam into displacement based on the geometric relationship between strain and deflection curves with reference-free calibration. Second, an adaptive Kalman filter is proposed to fuse the displacement generated by strain with acceleration by recursively estimate the noise covariance of displacement from strain measurements which is vulnerable to measurement condition. Both numerical and experimental validations are presented to demonstrate the efficiency and robustness of the proposed approach.

Visual inertial odometry (VIO) has recently received much attention for efficient and accurate ego-motion estimation of unmanned aerial vehicle systems (UAVs). Recent studies have shown that optimization-based algorithms achieve typically high accuracy when given enough amount of information, but occasionally suffer from divergence when solving highly non-linear problems. Further, their performance significantly depends on the accuracy of the initialization of inertial measurement unit (IMU) parameters. In this paper, we propose a novel VIO algorithm of estimating the motional state of UAVs with high accuracy. The main technical contributions are the fusion of visual information and pre-integrated inertial measurements in a joint optimization framework, and the stable initialization of scale and gravity using relative pose constraints. To count for ambiguity and uncertainty of VIO initialization, a local scale parameter is adopted in the online optimization. Quantitative comparisons with the state-of-the-art algorithms on the EuRoC dataset verify the efficacy and accuracy of the proposed method.

Weight thresholding (WT) is a method intended to decrease the number of links within weighted networks that may otherwise be excessively dense for network science applications. WT aims to remove links to simplify the networks by holding most of the features of the original network. Here, we test the robustness and the efficacy of the node attack strategies on real-world networks subjected to WT that remove links of higher weight (strong links). We measure the network robustness along node removal with the largest connected component (LCC). We find that the real-world networks under study are generally stable in terms of robustness when subjected to WT. Nonetheless, WT by strong link removal changes the efficacy of the attack strategies and the rank of node centralities. Also, WT by strong link removal may trigger a greater change in node centrality rank than WT by removing weak links. Network science research finding important/influential nodes in the network has to consider that simplifying the network with WT methodologies may change the node centrality.

Data is present in the data sources like Files and Data bases, Retrieval of information from that data sources is one of the important issue nowadays. So for retrieval information from the data sources clustering is used. In the present market different types of clustering algorithms were available. But opting of the clustering is based on user requirements. This paper focuses on the study of hierarchical clustering approach on different conditions or measures or with customer choices like clustering process number of clusters generated at each level, number of levels, attributes range for performing the clustering on the given data set. In brief overview we discuss the hierarchical approach for clustering algorithm with the user opting choices.

This paper presents a non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM) attack. This paper's focus is convolutional layers, and are the layers that have been responsible for better than human performance in image categorization. The proposed method induces earlier learning through the use of striped forms, and as such has less unlearning of the existing random number speckled methods, consistent with the intuitions of Hubel and Wiesel. The proposed method provides a higher performing accuracy in a single epoch, with improvements of between 3-5% in a well known benchmark model, of which the first epoch is the most relevant as it is the epoch after initialization. The proposed method is also repeatable and deterministic, as a desirable quality for safety critical applications in image classification within sensors. That method is robust to Glorot/Xavier and He initialization limits as well. The proposed non-random initialization was examined under adversarial perturbation attack through the FGSM approach with transferred learning, as a technique to measure the affect in transferred learning with controlled distortions, and finds that the proposed method is less compromised to the original validation dataset, with higher distorted datasets.

Obesity and overweight is a foremost concern around the globe for each group of age. This can be accelerated by the current imposed lockdown. However, excessive weight gain may result in other chronic diseases. This study has been considering the age group of 25 to 55 years as the sample populations and monitoring them from July, 2020 to November, 2020. The lifestyle of this population, food habit, mental health conditions are explored using deep learning based framework. All these parameters need to be monitored as these have close relation with currently imposed constraints due to COVID-19. A predictive model is constructed using deep learning techniques to predict the risk of gaining weight. The predictive model hybridizes the convolutional layer and gated recurrent neural networks as a unified entity for achieving the objective of early weight gain prediction. The result obtained by this model exhibits an encouraging predictive efficiency of 93.7%.

Drug delivery to the brain represents a challenge especially in the therapy of central nervous system malignancies. Simvastatin (SVT), as other statins, has shown potential anticancer properties that are difficult to exploit in the CNS. In the present work the physico-chemical, mucoadhesive and permeability enhancing properties of simvastatin-loaded poly-ε-caprolactone nanocapsules coated with chitosan for nose-to-brain administration were investigated. Lipid-core nanocapsules coated with different molecular weight (MW) chitosans (LNCchit) prepared by a novel one-pot technique were characterized for particle size, surface charge, particle number density, morphology, drug encapsulation efficiency, interaction between surface nanocapsules with mucin, drug release and permeability across two nasal mucosa models. Results show that all formulations present adequate particle size (below 220 nm), positive surface charge, narrow droplet size distribution (PDI<0.2) and high encapsulation efficiency. Nanocapsules presented controlled drug release and mucoadhesive properties dependent on the MW of the coating chitosan. The results of permeation across RPMI 2650 human nasal cell line evidenced that LNCchit increased the permeation of SVT. In particular, the amount of SVT permeated after 4h for nanocapsules coated with low MW chitosan, high MW chitosan and control SVT was 13.91 ± 0.78 µg, 9.15 ± 1.23 µg and 1.42 ± 0.21 µg respectively. These results were confirmed by the SVT ex vivo permeation across rabbit nasal mucosa. This study highlighted the suitability of LNCchit as promising strategy for the administration of simvastatin for a nose-to-brain approach for the therapy of brain tumors.

Cordycepin is an extract from the insect fungus Cordyceps. militaris, which is a traditional medicine with various biological function. In previous studies, cordycepin had been reported with excellent anti-obesity effect, but the mechanism is unclear. A large quantity of evidences showed that prolactin plays an important part in body weight regulation, hyperprolactinemia can promote appetite and accelerate fat deposition. In this study, we explored the molecular mechanism of the anti-obesity effect of cordycepin by reducing prolactin release via an adenosine A₁ receptor. In vivo, obese rats model was induced by high fat diet for 5 weeks, the serum and liver lipids coupling with serum prolactin were reduced by treatment of cordycepin, the results suggested that cordycepin is a potential drug for therapying obesity which could be related with prolactin. In vitro, cordycepin could inhibit prolactin secretion in GH₃ cells via upregulating the expression of adenosine A₁ receptor, the inhibition effect could be blocked by an antagonist of adenosine receptor A1 DPDPX, prolactin induced the upregulation of lipogenesis genes PRLR, and P-JAK2 in 3T3-L1 cells. Intriguingly, cordycepin would down-regulate the expression of prolactin receptor (PRLR). Thus, we concluded that cordycepin modulate body weight by reducing prolactin release via an adenosine A₁ receptor.

It is unclear how maternal glycemic status and maternal iodine status influence birth weight in mild-to-moderate iodine deficiency (ID). We studied the association between birth weight and both maternal glucose levels and iodine intake in pregnant women with mild-to-moderate ID. Glucose values were assessed using a glucose challenge test (GCT), non-fasting glucose before delivery; iodine status was assessed using an iodine food frequency questionnaire, serum thyroglobulin (Tg) and urinary iodine concentrations (UIC). Thyroid antibodies and free thyroxine (FT4) were measured. Obstetric and an-thropometric data were also collected. Large for gestational age (LGA) was predicted using a Cox proportional hazards model with multiple confounders. Tg>13g/L was in-dependently associated with LGA (adjusted hazard ratio = 3.4, 95% CI: 1.4–10.2, p=0.001). Estimated iodine intake correlated with FT4 among participants reporting io-dine-containing supplements (ICS) after adjusting for confounders (β = 0.4 95 %CI: 0.0002-0.0008, p=0.001). Newborn weight percentiles were inversely correlated with maternal FT4 values (β=-0.2 95 %CI:-0.08 - -56.49, p=0.049). We conclude that in mild-to-moderate ID regions, maternal insufficient iodine status may increase LGA risk. Iodine status and ICS intake may modify the effect maternal dysglycemia has on offspring weight.

Background: With the trend of intelligent display, the interface design of in-vehicle HUD is an expanding research field; Methods: To solve the subjectivity and uncertainty in the optimization of HUD interface design schemes, this paper proposed a hybrid scheme evaluation and optimization method based on entropy weight and VIKOR. The entropy weight method was used to reduce the subjectivity the decision-maker's weighting and obtain the objective weight of each indicator; The VIKOR method was used to obtain the best ranking of alternative schemes, and then the optimal interface design scheme was selected; Results: The evaluation of in-vehicle HUD interface design schemes were taken as an example for verification and calculation. The results showed that this method considers the subjectivity and uncertainty of the decision-making process in the optimization of design scheme, which can effectively improve the objectivity and accuracy of the evaluation results, and provide a reference for designers to optimize interface design schemes.