This study examines the relationship between semantic memory and social cognition in schizophrenia, addressing how these cognitive domains intersect. Semantic memory, which includes general world knowledge and word meanings, was evaluated using verbal fluency tasks and the Camels and Cactus Test. Social cognition, essential for social interaction, was assessed through emotion recognition (Faces Test) and Theory of Mind (Hinting Task). Participants included 50 individuals with schizophrenia and 30 controls. The schizophrenia group showed significantly lower performance on both semantic memory and social cognition tasks. Notably, strong correlations were found between the Camels and Cactus Test and social cognition measures, suggesting that social cognition deficits in schizophrenia may be linked to semantic memory impairments. Regression analyses highlighted that the Camels and Cactus Test significantly predicted social cognition performance, independent of symptomatology. These findings underscore the interconnectedness of semantic memory and social cognition in schizophrenia, suggesting that semantic memory deficits, particularly in non-categorical associations, play a important role in social cognitive impairments. This study provides new insights into the cognitive underpinnings of schizophrenia, emphasizing the need for further research to explore these relationships and their implications for cognitive models and therapeutic interventions.

Cardiovascular disease is a major global health concern, with early detection being critical. This study assesses the effectiveness of the portable ECG device, based on Internet of Medical Things (IoMT) technology, for remote cardiovascular monitoring during daily activities. We conducted a clinical trial involving 2,000 participants who wore the HiCardi device while engaging in hiking activities. The device monitored their ECG, heart rate, respiration, and body temperature in real-time. If an abnormal signal was detected while a physician was remotely monitoring the ECG at the IoMT monitoring center, he notified the clinical research coordinator (CRC) at the empirical research site, and the CRC advised the participant to visit a hospital. Follow-up calls were made to determine compliance and outcomes. Of the 2,000 participants, 318 showed abnormal signals, and 182 were advised to visit a hospital. Follow-up revealed that 139 (76.37%) responded, and 30 (21.58% of those who responded) sought further medical examination. Most visits (80.00%) occurred within one month. Diagnostic approaches included ECG (56.67%), ECG and ultrasound (20.00%), ultrasound alone (16.67%), ECG and X-ray (3.33%), and general treatment (3.33%). Seven participants (23.33% of those who visited) were diagnosed with cardiovascular disease, including conditions such as arrhythmia, atrial fibrillation, and stent requirements. The portable ECG device using the patch-type electrocardiograph detected abnormal cardiovascular signals, leading to timely diagnoses and interventions, demonstrating its potential for broad application in preventative health care.

Wave propagation in buildings analyzed from its response to earthquakes can be applied for tracking the dynamic characteristics changes based on impulse response functions. The velocity of traveling shear-waves and intrinsic attenuation in buildings can be retrieved for buildings. It is an approach of importance for system identification. The Factor Building at the University of California, Los Angeles campus (henceforth referred as the UCLA Factor Building), an instrumented 15-story steel-moment frame structure, are selected for dynamic response characteristics identification. They are inferred from wave propagation using seismic interferometry for recorded motions. The deconvolved waves are used to compute shear wave travel time and wave attenuation. The natural logarithm for the envelopes of the waveforms deconvolved with the signal at the basement constrains the attenuation. The waveforms from deconvolution with the motion at the basement suggest the fundamental mode of the building. The frequency and decay with time constrain the shear velocity and attenuation as well. The velocity are measured using arrival times picked from these deconvolved waves and the average velocity is 147.1 m/s. The quality factor is equal to 10.8 with the damping ratio at 5%. The shear wave velocity and damping ratio estimated from the deconvolved waves have a good match with these inferred from the waveforms deconvolved with the basement recording. This consistency suggests that wave deconvolution can be applied to extract the building response from the excitation and ground coupling. Based on the resonant frequencies and damping ratio, the finite element model of the building is updated. This study implies that wave deconvolution is powerful to extract the building dynamic characteristics. It helps to better the understanding of the dynamic response of buildings to earthquakes.

Currently, foam mixers are predominantly categorized into two types: low-pressure and high-pressure. Low-pressure mixers primarily use agitator rotation to mix materials. However, they face challenges such as difficulty in cleaning and complex structures. High-pressure mixers, widely adopted for their structural simplicity and lack of need for cleaning devices, effectively mitigate issues associated with low-pressure mixers. Nonetheless, they encounter problems with uneven mixing when processing high-viscosity materials. Historically, the primary solution to this issue was increasing the working pressure. However, the capacity to increase pressure is finite, and higher pressure demands higher quality materials for the mixers. To address the issues high-pressure mixers face when dealing with high-viscosity materials, this study focuses on the polyimide high-pressure mixer. After reviewing previous studies, four design variables were selected: the mixer's impingement angle, inlet diameter, outlet diameter, and impingement pressure. Using a Full Factorial Design of Experiments (DOE), the significance of the impact exerted by these four variables on Mixing Unevenness was analyzed, determining the extent of their influence. Optimal Latin Hypercube Sampling generated several random sample points, followed by the establishment of both a Back Propagation (BP) Neural Network approximation model and a Kriging approximation model. A Genetic Algorithm was then applied to optimize these two approximation models, resulting in the optimal combination of design variables. The research findings indicate that the BP Neural Network exhibits higher predictive accuracy compared to the Kriging prediction model. The optimal combination of parameters, optimized through the Genetic Algorithm (GA), reduced the Mixing Unevenness by 69.1%, effectively decreasing the Mixing Unevenness of the mixer head.

In this paper we present the exploration of indoor positioning technologies for UAVs, and navigation techniques for path planning and obstacle avoidance. The objective is to perform warehouse inventory tasks using a drone to search for barcodes or markers to identify the objects. For the indoor positioning techniques, we employ Visual-Inertial Odometry (VIO), Ultra-Wideband (UWB), AprilTag fiducial markers, and Simultaneous Localization and Mapping (SLAM). These algorithms encompass global positioning, local positioning, and pre-mapping positioning, comparing the merits and drawbacks of various techniques and trajectories. In UAV navigation, we combine SLAM-based RTAB-Map indoor mapping and navigation path planning of the ROS for indoor environments. This system enables precise drone positioning indoors and utilizes global and local path planners to generate flight paths that avoid dynamic, static, unknown, and known obstacles, demonstrating high practicality and feasibility. To achieve the warehouse inventory inspection, a reinforcement learning approach is proposed to recognize the markers by adjusting the UAV's viewpoint. We address several main problems in inventory management, including efficiently planning paths while ensuring a certain level of detection rate. Two reinforcement learning techniques, AC (Actor-Critic) and PPO (Proximal Policy Optimization), are implemented based on AprilTag identification. Testing is performed in both simulated and real-world environments, and the effectiveness of the proposed method is validated.Code is available at https://github.com/kellen080/Navigation and https://github.com/kellen080/Indoor\_Positioning.

The research work explores the impact of temperature on Silicon photovoltaic (PV) panels considering Nigeria as case study. It is found that high solar radiation in Nigeria increases surface temperature of PV panel above 25oC of optimal operating temperature of PV panels. Redundant energy gain from incident solar energy is dissipated at the rear of PV panels as heat, which reduces the efficiency of PV panel. Cooling mechanism is needed to cool the PV panels to increase the efficiency. In this study we demonstrated a unique design of a bifunctional photovoltaic-thermal (PVT) system by employing heat exchanger at the rear of PV panel and water is used as a working fluid that is circulated through the heat exchanger to remove the excess heat. It is observed from the simulation results that Maiduguri peak annual electrical power output gives 1907kWh/kWp which is the highest, because of high solar radiation average of 727W/m2 in a year. And for Makurdi the peak annual electrical power output of 1542kWh/kWp while for Port Harcourt the peak power output of 1355kWh/kWp respectively. Interestingly, Polycrystalline Si-PV surface temperature decrease from 49.25oC to 38.38oC. Electrical power increased from 1526.83W to 1566.82W in a day and efficiency increase from 13.99% to 15.01%.

A self-propelled electric tea seed-planter is designed to address the lack of tea seed seeding equipment, low seeding efficiency, and poor seeding uniformity. Based on the physical characteristics of the tea seeds and the agronomic requirements of tea planting, the key structure of the planter is designed, and the structural parameters are determined. The motor and battery configurations are determined by calculating the power consumption of the entire machine. In a field experiment, the driving gear (A), ditching depth (B), and planting gear position (C) are used as test factors, and the acceptance index (AI), replay index (RI) and leakage index (LI) are used as test indexes. The test results showed that the driving speed and planting gear position significantly affect the acceptance index, replay, and leakage index, whereas the ditching depth has little effect on the performance indexes. When the driving speed was medium, ditching depth was 30 mm, and planting gear position was in the third gear, the corresponding acceptance index was 87.22%, the replay index was 8.89%, and the leakage index was 3.89%. The test prototype has a good working quality and can provide theoretical guidance for production practices.

Red mud (RM), a bauxite residue, contains hazardous radioactive wastes and alkaline material and poses severe surface water and groundwater contamination risks, necessitating recycling. Pretreated RM can be used to make adsorbents for water treatment. However, its performance is affected by many factors, resulting in a nonlinear correlation and coupling relationship. This study aimed to identify the best formula for an RM adsorbent using a mathematical model that examines the relationship between 11 formulation types (e.g., pore-assisting agent, component modifier, and external binder) and nine properties (e.g., specific surface area, wetting angle, and Zeta potential, among others). This model was built using a back-propagation neural networks(BP) based on single-factor experimental data and orthogonal test data. The model trained and predicted the established network structure to obtain the optimal adsorbent formula. The RM particle adsorbents had a pH of 10.16, specific surface area(BET) of 48.92 m2·g−1, pore volume of 2.10 cm3·g−1, compressive strength(ST)) of 1.12 KPa, and 24-h immersion pulverization rate(η_m) of 3.72%. In the removal of total phosphorus in flotation tailings backwater, it exhibited a good adsorption capacity (Q)and total phosphorous removal rate(η) of 48.63 mg·g−1 and 95.13%, respectively.

Aiming at the classification identification problem of aeroengines, this paper adopts telemetry Fourier Transform Infrared Spectrometer to collect aeroengine hot jet infrared spectrum data, and proposes an aeroengine classification identification method based on spectral feature vectors. First, aeroengine hot jet infrared spectrum data are acquired and measured, meanwhile, the spectral feature vectors based on CO2 are constructed. Then, the feature vectors are combined with the seven mainstream classification algorithms to complete the training and prediction of the classification model. In the experiment, two Fourier transform infrared spectrometers, EM27 developed by Bruker and a self-developed telemetry FTIR, were used to telemeter the hot jet of three aeroengines to obtain infrared spectral data. The training data set and test data set were randomly divided in a ratio of 3:1. The model training of the training data set and the label prediction of the test data set were carried out by combining spectral feature vectors and classification algorithms. The classification evaluation indicators were accuracy, precision, recall, confusion matrix, and F1-score. The classification recognition accuracy of the algorithm was approximately 98%. This paper has great significance for the fault diagnosis of aeroengines and classification recognition of aircrafts.

In preserving genetic diversity among domestic animal breeds, the strategies emphasizing be-tween-breed diversity may be not optimal by neglecting within-breed variation. The present study aimed to assess the extent of the population subdivision of Mangalica pigs and the contri-bution of migration intensity to their substructure. Genealogy analysis was performed for breeding animals born between 1981 and 2023 examining three colour variations (Blonde, Swal-low-Belly, and Red). The fixation index (FST) was calculated and evaluated by Multidimensional Scaling and Clustering to expose the population substructure. The average FST is 0.04 for the Blonde and 0.05 for the Swallow-Belly and the Red while in the active herds, these parameters are smaller being 0.03 and 0.04, respectively. The migration of individuals affected 61.63% of the Blonde, 75.53% of the Swallow-Belly, and 63.64% of the Red breed herds. No population sub-structure was found in any Mangalica breed which can be explained by the extensive within breed migration among the herds.

Porous metals possess low bulk density, large specific strength, high stiffness and special thermal, electromagnetic and acoustic conductivity, so that porous metals are widely used in filtration and separation, flame retardant explosion-proof, biomedical application, etc. Compared with its corresponding dense metal, the presence of porous structures also leads to different corrosive performances of porous metal. So, some researches have utilized weight loss method, electrochemical impedance to evaluate porous metals’ corrosion behavior; however, the influence of pore structure on metal corrosion is still controversial, and present methods used for analyses of porous metals’ corrosion are statistical averages of the corrosion behavior of the entire porous material, which cannot accurately reflect the corrosion behavior inside the pore structure. Herein, we prepare porous copper samples with 0, 24, 72, 96 pores using mechanical process, and employ scanning kelvin probe combined with electrochemical polarization curve and impedance spectroscopy to test the corrosion performance of the porous copper in static and dynamic NaCl solution. The relevant results indicate that in static solution, the corrosion resistance of samples gradually increases with the rise of the number of pores. By contrast, the sample with 24 pores exhibits more susceptible to corrosion than the sample without pore.

Castor oil may be differentiated from other non-edible vegetable oils because of its main composition of hydroxylated fatty acids. Ricinoleic acid comprises 80–90% wt. of fatty acids in castor oil (Ricinus communis). In this study, the thermo-oxidative stability and tribological behavior of bio-based lubricant samples synthesized from castor oil using isoamyl alcohol were evaluated. Initially, the compositional and physicochemical properties of the obtained samples were assessed using 1H NMR, FTIR, and ASTM methods. Oxidative stability of the samples was evaluated using Rancimat method at 110 °C under air flow. The final biolubricant sample (BL2), obtained after esterification, epoxidation, and oxirane rings opening reactions, presented an oxidation stability time (OST) of 14.3 h. The thermal stability was also evaluated by thermogravimetry (TG) from the mass variations under inert and oxidative atmosphere. BL2 showed higher thermal stability compared to the other samples, demonstrating higher decomposition temperatures in both inert (339.04 °C) and oxidative (338.47 °C) atmospheres, for a mass loss of 50%. The tribological properties of the samples were evaluated using a four-ball tribometer configuration. The BL1 and BL2 samples exhibited lower friction coefficients than the mineral oil sample (MOS) by 21.5% and 43.1%, respectively. Regarding wear, the observed wear scar diameter (WSD) was also lower in BL1 and BL2 compared to MOS by 5.2% and 40.4%, respectively. The results of the tribological evaluation suggest that both samples obtained in this study have promising potential for applications in lubricating machines and mechanical systems.

One of the most prevalent brain diseases, epilepsy is characterized by recurring seizures that happen quite frequently. During seizures, a patient suffers uncontrollable muscle contractions that cause loss of motion and balance, which could lead to harm or even death. Establishing an automatic method for warning patients about impending seizures requires extensive research. It is possible to anticipate seizures by analyzing the Electroencephalogram (EEG) signal from the scalp region of the human brain. Time domain-based features such as Hurst exponent (Hur), Tsallis Entropy (TsEn), improved permutation entropy (impe), and amplitude-aware permutation entropy (AAPE) were extracted from EEG signals. In order to diagnose epileptic seizure children from normal children automatically, this study conducted two sessions, in the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), while in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers including a gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional (BiLSTM). The EEG dataset obtained from the Neurology Clinic at the Ibn-Rushd Training Hospital. In this regard, detailed explanations and research from the time domain and entropy characteristics show that using GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All−time−entropy fusion feature improves the final classification results.

Using graph theory, computer aided motion, static force and power flow analysis of planetary gear mechanism is realized. The motion, static force and efficiency calculations of 6HP26 automatic transmission are carried out. Based on kinematic and static force analysis model, related matrices are acquired. Hence, kinematic and static force analysis of planetary gear mechanism is achieved. The link power can be determined by the link speed and torque. Power flow diagrams of each gear are acquired. The efficiency is calculated by transmission ratio method.

As a radiant light source within the dynamic range of most spacecraft payloads, the moon pro-vides an excellent reference for on-orbit radiometric calibration. This research hinges on the pre-cise simulation of lunar spectral irradiances and the Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradi-ances, utilizing datasets obtained from Lunar Reconnaissance Orbiter Camera (LROC). The re-search also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit space-craft calibration procedures, thereby boosting the fidelity of data gathered from satellite obser-vations.

The intrinsic non-linearity and complexity of suspended sediment dynamics, which are impacted by the geographical variability of basin parameters and temporal climatic patterns, make it difficult to estimate suspended sediment concentration (SSC) accurately in hydrological processes. Deep neural networks (DNNs), a cutting-edge modeling method that can capture the innate non-linearity in hydrological systems, have emerged as a solution to this problem. Using primary data on discharge, SSC, and turbidity, the long short-term memory method of DNNs was employed in this study to simulate the discharge-suspended sediment connection for the Esopus Creek, NY, USA as the first objective. To develop effective modeling strategies, multiple scenarios of feature selection, including combinations of discharge, turbidity and SSC for preceding days, were considered. Secondly, the variations in SSC and discharge between the three USGS gages along Esopus Creek that were upper stream, lower stream, and downstream, adjacent to the pourpoint were analyzed. Statistical metrics and scenarios of feature importance were used to evaluate the effectiveness of the DNN-based models. The study shows that among 24 DNN approaches feature selection is crucial in simulating the daily SSC, and discharge and presenting novel research directions for utilizing machine learning algorithms in water quality.

Silicon-based thin-films and nanostructures are of paramount importance in a wide range of applications, including microelectronics, photovoltaics, large area sensors, as well as biomedicine. The wide accessibility of silicon and its relatively low cost have driven a continuous improvement of the technology based on this element. Plasma technology has been widely used for the synthesis of coatings and nanostructures based on silicon. Moreover, it has given a fundamental contribution for continuously improving the control of the physicochemical properties of silicon-based materials, and for allowing the synthesis of nanometric structures with well-defined shape and morphology. In this work, we have reviewed the most interesting developments of plasma-assisted processed for the synthesis of Si-based materials, both inorganic and organic, in the last five years. A special attention has been given to the new techniques, or modifications of already existing ones, that open new possibilities for the synthesis of materials with new properties, as well as nanostructures with novel characteristics.

The Gulong shale oil reservoir is formed in freshwater to slightly saline lacustrine basins, mainly consisting of pure shale geological structure, which is quite different from other shale reservoirs around the world. Currently, the development of Gulong shale oil mainly relies on hydraulic fracturing, while the subsequent shut-in period for imbibition has been proven to be an effective method for enhancing shale oil recovery. To clarify the characteristics of fluid occurrence space and the variation of fluid occurrence during saltwater imbibition in Gulong shale, this paper carried out porosity and permeability tests on Gulong shale cores, and analyzed the fluid occurrence space characteristics and imbibition oil recovery based on nuclear magnetic resonance (NMR). In the porosity and permeability tests, the porosity measured by saturation method was calibrated using NMR T2 spectra. Combined with the identification of fractures in shale cores using micro-CT and the analysis of porosity and permeability parameters, it was found that the permeability of shale cores was related to the development of fractures in shale cores. Through the testing and analysis of NMR T1-T2 two-dimensional spectra of the shale cores before and after saturation with oil, it was found that the shale mainly contains heavy oil, light oil, and clay-bound water, and they were distributed in different positions in the T1-T2 spectrum. Finally, the T1-T2 two-dimensional spectra of the shale core at different imbibition stages were analyzed, and it was found that the saltwater mainly entered the minuscule inorganic pores of clay minerals during the imbibition process, and squeezed the larger-sized inorganic pores containing light oil through the hydration expansion effect, thus expelling the light oil from the shale core and achieving the purpose of enhanced oil recovery.

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.

Hydro generator sets serve as the peak and frequency regulation of the power system,and play a very important role in maintaining system stability and improving power quality.Based on the internal characteristics model of a specific type of hydro generator,this paper constructs a hydroelectric coupling model for hydropower units,studies the influence of the regulation system of hydropower units on low-frequency oscillations under different load conditions,and adopts variable speed reaching law and fuzzy sliding mode control strategy to improve the regulation quality of the system and ensure the stable operation of the units.Results of simulation and water turbine set under different load conditions show that varible speed reaching law can effectively improve the power-angle oscillation behavior of the hydropower set and suppress the occurrence of low-frequency oscillations of the system when the system is subject to power disturbance,which provide a new theoretical basis and solution for the vibration analysis and control optimization of hydraulic turbine units.

This study seeks to measure the degree of agglomeration of educational quality in Colombia, based on the non-socialization of the population that exhibits low educational quality, with the population that exhibits high educational quality, and thus determine how such agglomeration affects the phenomenon of academic segregation. To this end, we perform a spatial analysis of the educational quality in Colombia and of variables that may influence to the phenomenon of educational agglomeration. The level of agglomeration in educational quality in Colombia is demonstrated by the calculation of the Moran’s Index, in which a result of 0.62 was obtained. High educational quality is concentrated in the Andean region, while low educational quality is agglomerated in the periphery of the country, in areas such as the Pacific region. A spatial regression model was carried out to measure the dependence of municipalities on their neighbors, and to determine the main socio-economic factors affecting the phenomenon of educational agglomeration in Colombia, finding that living conditions, unsatisfied basic needs and fiscal transparency have all an impact on the educational quality of the municipalities. It is also found that the number of homicides in the municipalities does not seem to have a significant relationship with education.

Quantum cell automata (QCA) is a nanotechnology that uses a transistor-free structure to design digital circuits and overcome the limitations listed for CMOS circuits. The computing circuits that make up the bulk of computers can be designed and using this technology. Subtractors are a big part of computational circuits. This research deals with the design of a subtractor that due to the innovation in the design of this circuit and the novel arrangement of quantum cells of its kind has caused the number of cells used in the design of the circuit to reach its lowest value compared to previously designed circuits. The size of the circuit as well as the loss power are minimized compared to the circuits designed by the subtractors. Another trait clears of this circuit compared to many previous designs is its single-layer design, which reduces latency and noise in the output waveform. The circuit designed by the QCADesigner simulator is tested and simulated inadition the results are displayed in the simulated waveform. Also, the results of calculations for power dissipation in each QCA cell were evaluated by QCAPro software, which is a powerful tool for estimating power loss simulation. It has less implementation and level (single layer) than Previous designs.

The present paper proposes a fractal analysis of the Covid-19 dynamics in 45 European countries. We introduce a new idea of using the box-counting dimension of the epidemiologic curves as a means of classifying the Covid-19 pandemic in the countries taken into consideration. The classification can be a useful tool in deciding upon the quality and accuracy of the data available. We also investigated the reproduction rate, which proves to have significant fractal features, thus enabling another perspective on this epidemic characteristic. Moreover, we studied the correlation between two meteorological parameters: global radiation and daily mean temperature and two Covid-19 indicators: daily new cases and reproduction rate. The fractal dimension differences between the analysed time series graphs could represent a preliminary analysis criterion, increasing research efficiency. Daily global radiation was found to be stronger linked with Covid-19 new cases than air temperature (with a greater correlation coefficient -0.386, as compared with -0.318), and consequently it is recommended as the first-choice meteorological variable for prediction models.

In systems biology, biological phenomena are often modeled by ODE and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulations, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the SBSCL, and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code available from https://github.com/TakahiroYamada/SBMLWebApp. The program runs online at http://simulate-biology.org.

: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

This research takes Cuiwei Village in Qianshan District of Zhuhai City as an example. Through field research, the village is investigated and analyzed, and the street scale, spatial structure, functional characteristic streets, internal commercial distribution forms, and functions of Cuiwei Historic District are studied. analysis. On this basis, based on the convex space method and the line-of-sight analysis method in the space syntax theory, with the help of Depthmap software, the complex street node, that is, the space of the two nodes A and B, is established through the establishment of a visual field model. It includes the analysis of the integration degree of the horizon, the concentration of the horizon, the connection value of the horizon and the spatial characteristics of the historical area, as well as the traditional buildings that are the most representative of the village, namely the Three Kings Temple and the Webster's Mansion. When R=N and radius R=3, observe the changes in the two parameters of the visual integration and visual depth of the two historical buildings. And with the help of Baidu Time Machine photos in different periods to observe and record the changes in the store and the characteristics of people's behavior, and draw conclusions.

The testing of high-dimensional normality has been an important issue and has been intensively studied in literatures, it depends on the Variance-Covariance matrix of the sample, numerous methods have been proposed to reduce the complex of the Variance-Covariance matrix. The principle component analysis(PCA) was widely used since it can project the high-dimensional data into lower dimensional orthogonal space, and the normality of the reduced data can be evaluated by Jarque-Bera(JB) statistic on each principle direction. We propose two combined statistics, the summation and the maximum of one-way JB statistics, upon the independency of each principle direction, to test the multivariate normality of data in high dimensions. The performance of the proposed methods is illustrated by the empirical power of the simulated data of normal data and non-normal data. Two real examples show the validity of our proposed methods.

We review recent work on broadband RF channelizers based on integrated optical frequency Kerr micro-combs combined with passive micro-ring resonator filters, with microcombs having channel spacings of 200GHz and 49GHz. This approach to realizing RF channelizers offers reduced complexity, size, and potential cost for a wide range of applications to microwave signal detection.

Deformation behavior of MXene based polymer composites with bioinspired brick and mortar structures is analyzed. MXene/Polymer nanocomposites are modeled at microscale using bioinspired configurations of nacre-mimetic brick-and-mortar assembly structure. MXenes (brick) with polymer matrix (mortar) are modeled using classical analytical methods and numerical methods based on Finite Elements (FE). The analytical methods provide less accurate estimation of elastic properties compared to numerical one. MXene nanocomposite models analyzed with FE method provide estimates of elastic constants in the same order of magnitude as literature reported experimental results with good consistency. Bioinspired design of MXene nanocomposites results in the effective Young’s modulus of the nanocomposite increase by 25.1 % and the strength (maximum stress capacity within elastic limits) increase by 42.3 %. The brick and mortar structure of the nanocomposites leads to interlocking mechanism between MXene fillers in polymer matrix, resulting in effective load transfer, good strength, and damage resistance. This is demonstrated in this paper by numerical analysis of MXene nanocomposites subjected to quasi-static loads.

In this study, a monoethanolamine aerosol growth model was developed to investigate the aerosol growth factor. Interactions among the internal conditions in an absorber were considered in this aerosol model. Additionally, an experiment was conducted to measure aerosol particle size, for collecting in-house validation data. Sucrose was used as the aerosol nuclei instead of sulfuric acid to prevent the corrosion of equipment used in the experiment. Experimental results showed that the outlet aerosol sizes increased to the same size regardless of the sucrose concentrations. The aerosol growth model was validated using the in-house experimental data. The aerosol growth model efficiently predicted the aerosol size. For investigating aerosol growth effects, particle number concentration was determined to be the primary factor affecting aerosol growth and amine emissions. When the particle number concentration increased, the aerosol size decreased, whereas the MEA emission increased.

The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.

In the search of potent α-amylase inhibitors, we have synthesized seventeen derivatives of 2-mercaptobenzimidazole bearing sulfonamide (1-17) and evaluated for their α-amylase inhibitory potential. All compounds display a variable degree of α-amylase activity having IC50 values ranging between 0.90 ± 0.05 to 11.20 ± 0.30 µM when compared with the standard drug acarbose having IC50 value 1.70 ± 0.10 µM. Compound 1, 2, 11, 12 and 14 having IC50 values 1.40 ± 0.10, 1.30 ± 0.05, 0.90 ± 0.05, 1.60 ± 0.05 and 1.60 ± 0.10 µM respectively were found many folds better than the standard drug acarbose. The remaining analogs showed good inhibitory potentials. All the synthesized compounds were characterized by HREI-MS, 1H and 13C-NMR. Structure activity relationship (SAR) has been recognized for all newly synthesized analogs. Through molecular docking study, binding mode of active analogs with α-amylase enzyme was confirmed.

In this paper, we propose a new mathematical model based on the association between susceptible and recovered individual, where the association between susceptible and recovered individual is disturbed by white noise. This model is based on demographic changes and is used for long term behavior. We study the stability of equilibria of the deterministic model and prove the conditions for the extinction of diseases. Then, we investigate and obtain the critical condition of the stochastic epidemic model for the extinction and the permanence in mean of the disease with the white noise. To verify our results, we present some numerical simulations for real data related to disease.

We have synthesized quinoxaline analogs (1-25), characterized by 1HNMR and HREI-MS and evaluated for thymidine phosphorylase inhibition. Among the series, nineteen analogs showed better inhibition when compared with the standard inhibitor 7-Deazaxanthine (IC50 = 38.68 ± 4.42 µM). The most potent analog among the series is analog 25 with IC50 value 3.20 ± 0.10 µM. Sixteen analogs 1, 2, 3, 4, 5, 6, 7, 12, 13, 14, 15, 16, 17, 18, 21and 24 showed outstanding inhibition which is many folds better than the standard 7-Deazaxanthine. Two analogs 8 and 9 showed moderate inhibition. A structure- activity relationship has been established mainly based upon the substitution pattern on the phenyl ring. The binding interactions of the active compounds were confirmed through molecular docking studies.

Infectious diarrhoea such as shigellosis causes considerable morbidity and mortality, especially in infants, immune-compromised individuals and those living with HIV/AIDS. It is endemic in developing countries and in Sub-Saharan Africa, including South Africa, where diarrhoeal disease remains a leading cause of morbidity and mortality. This study was undertaken to establish incidences of Shigella, its serotype and resistant pattern of isolates from human faeces from residence of Johannesburg, South Africa. All stools received between January to April from the private healthcare system were cultured on Xylose Lysine Deoxycholate and MacConkey Agar and Shigella was confirmed by standard biochemical reactions and a serological method. An antimicrobial sensitivity test was used. A total of 11 009 samples from patients between 22 days to 94 years old yielded 110 Shigella isolates, of which 47 (43%) were S. flexneri, 61 (55%) were S. sonnei, 1 (1%) was S. dysenteriae and 1 (1 %) was S. boydii. The majority of patients were children between < 1 to 5 years, 76 (69%), followed by those between 6 to 10 years 13 (12%). In children up to 10 years, S. sonnei was confirmed in 52 cases (59%) and S. flexneri in 36 cases (41%). Overall, 53 (48%) males and 57 (52%) females were infected.

Strategic researches on green mine construction are of great theoretical and practical significance to the sustainable development of China's mining industry as well as the great-leap-forward development strategies of China. Strategies of green mine construction in China are methods summarized to solve all potential problems from mine production to ecological restoration. At present, strategies of green mine construction in China are not fully evaluated and studied yet. Therefore, on the basis of green mine construction related literatures carried out by researchers in China and abroad, this study took the green mine of Yongcheng City in China as the research object to evaluate the current situation of green mine construction in Yongcheng City and put forward corresponding countermeasures. First of all, driving force-pressure-state-impact-response (DPSIR) model was introduced for the construction of evaluation index system; construction principles and selection methods of indexes and the index system based on driving force, pressure, state, impact and response were constructed. Secondly, principal component analysis (PCA) was adopted to calculate and evaluate data of green mine of Yongcheng City in recent years, and construction state of green mine in Yongcheng City was analyzed concretely according to the evaluation results. Empirical results showed that, the evaluation system constructed in this study was feasible, which could be applied to evaluate construction of green mine effectively.

Indoor farming systems enable plant production in precisely controlled environments, managing factors such as lighting, photoperiod, and nutrient supply, to enhance crop yield and quality. However, the implementation of stable growth conditions and the absence of stress stimulants can weaken plants' defense responses and limit the accumulation of bioactive, health beneficial phytochemicals. A potential solution is the controlled application of stressors, such as supplemental ultraviolet (UV) light, which stimulates secondary metabolites (SM) synthesis and increase overall antioxidant potential. To this end, we analyzed the efficiency of short-term pre-harvest supplementation of red-green-blue (RGB, LED) spectrum with ultraviolet B (UV-B, 311 nm) or C (UV-C, 254 nm) light to boost phytochemicals synthesis. Additionally, given the biological harm of UV radiation due to high-energy photons, we monitored plants’ photosynthetic activity during treatment. Our analyses showed that UV-B radiation, applied with a cumulative dose of 15.622 kJ m–2, did not negatively impact photosynthetic activity while significantly increasing the overall antioxidant potential of lettuce through enhanced levels of polyphenolic SM (total phenolics, flavonoids, anthocyanins) and ascorbic acid. Conversely, UV-C radiation, applied with a lower cumulative dose of 6.008 kJ m–2, induced anthocyanin accumulation in the green leaf cultivar but significantly harmed the photosynthetic apparatus and limited plant growth. These results are crucial for understanding the potential of UV light supplementation in producing functional plants.

One of the still debated topics regarding the olfactory function concerns the presence or absence of sex-related differences of individuals. In this study, we checked for a relationship between the olfactory function of females and males and their ability to perceive single molecules, and how this can influence the intensity with which the complex odor formed by a set of single molecules is perceived. First, females and males were classified as normosmic or hyposmic based on the TDI olfactory score obtained using the Sniffin' Sticks test. Subsequently, the headspace of roasted coffee beans as a complex olfactory stimulus was broken down into single molecules by means of a chromatographic column; these were simultaneously conveyed to a mass spectrometer (for their subsequent classification) and to the human nose which acts as a chemical sensor by means of an olfactometer port. The results obtained with this gas chromatography-olfactometry approach show both qualitative and quantitative differences between females and males, with females performing better than males. In addition, the odor intensity reported by females when sniffing pen #10 containing coffee aroma is significantly higher than that reported by males. In conclusion, these data highlight that the human ability to perceive both single compounds and complex odors is strongly conditioned, not only by olfactory function of individuals, but also by their sex.

CX3CL1, also named fractalkine or neurotactin, the only known member of the CX3C chemokine family that can chemoattract several immune cells. CX3CL1 exists in both membrane-anchored and soluble forms, each mediating distinct biological activities. CX3CL1 signals are transmitted through its unique receptor, CX3CR1, primarily expressed in microglia of central nervous system (CNS). In the CNS, CX3CL1 acts as a regulator of microglia activation in response to brain disorders or inflammation. Recently, there has been a growing interest in the roles of CX3CL1 in regulating cell adhesion, chemotaxis, and host immune response in viral infection. Here, we provide a comprehensive review of the changes and function of CX3CL1 in various viral infections such as human immunodeficiency virus (HIV), SARS-CoV-2, influenza virus and cytomegalovirus (CMV) infection, to highlight the emerging roles of CX3CL1 in viral infection and the associated diseases.

Current social issues such as bullying, online violence, and local conflicts are all hot topics in the field of psychology, and these phenomena are related to dehumanization, but research on dehumanization from a non-western background has been rare. As a key factor influencing individual behavior, attachment has not been incorporated into the relevant research on dehumanization. Therefore, this study presented empirical evidence to explore the relationship between adult attachment (both trait and state) and dehumanization, the samples were college students from China. Study 1 (N = 705) used latent profile analysis to verify the four types of adult attachment(i.e., preoccupied , secure, fearful, and dismissiving)and found differences in the impact of different attachment types on dehumanization. Study 2(N = 281) activated secure attachment, and found differences in the impact of three types of state attachment (i.e., security, avoidance, and anxiety)on dehumanization, as well as the positive predictive effects of security and avoidance on dehumanization. This research originally proved the relationship between adult attachment and dehumanization, providing new perspectives for exploring the psychological mechanisms behind dehumanization, and offering new paths for the prevention and intervention of dehumanization.

Aluminum base alloys containing chromium (Cr) and zinc (Zn) were produced using extrusion and heat treated powder metallurgy. Cr addition ranged between 5 to 10 wt. % while Zn was added in an amount between 0 to 20 wt. %. Heat treatment processes were performed during powder metallurgy process at different temperatures followed by water quenching. Similar alloys were extruded, with an extrusion ratio of 4.6 to get proper densification. Optical microscopy was used for microstructure investigations of the produced alloys. The element distribution microstructure study was carried out using the Energy Dispersive X-ray analysis method. Hardness and tensile properties of the investigated alloys have been examined. Wear resistance tests were carried out and the results were compared with these of the Al-based bulk alloys. Results showed that the aluminum base alloys containing 10wt. % Chromium and heat treated at 500°C for one hour followed by water quenching exhibited the highest wear resistance and better mechanical properties.

Background: This study aimed to develop and evaluate a dietary self-care promotion program for hemodialysis patients based on Hurley's self-efficacy model. Methods: The needs of hemodialysis patients were identified, and an online community-based dietary self-care promotion program was developed with expert evaluations. 44 hemodialysis patients were randomly assigned to an experimental group (n=22) and a control group (n=22). The program was applied to the experimental group for four weeks. Before and after the intervention, as well as four weeks after the intervention, dietary self-efficacy, dietary self-care behavior, interdialytic weight gain, serum phosphorus, and serum potassium were assessed in both groups. Results: Significant differences over time and between groups were observed in dietary self-efficacy, interdialytic weight gain, serum phosphorus, and serum potassium. The interaction between time and group was also significant. While no significant differences were found over time in dietary self-care behavior, significant group differences and interaction effects between time and group were identified. Conclusions: The results indicate that the dietary self-care promotion program using an online community for hemodialysis patients is more effective in enhancing dietary self-efficacy and dietary self-care behavior compared to conventional treatment. This suggests the clinical applicability of the proposed dietary self-care promotion program.

Exosomal microRNAs (miRNAs) from cancer cells play a key role in mediating the oral squamous cell carcinoma (OSCC) microenvironment. The objective aimed to investigate how long non-coding RNA (lncRNA) MEG3 affects OSCC angiogenesis through exosomal miR-421. Global miRNA microarray analysis and quantitative real-time PCR (qRT-PCR) were performed to determine the level of miRNAs in OSCC cells-derived exosomes. Cell migration, invasion, tube formation, immunohistochemistry and hemoglobin concentration were used to study the effects of exosomal miR-421 in angiogenesis. Western blotting was used to determine the expression level of HS2ST1 and VEGFR2-related downstream proteins. MiRNA array and qRT-PCR identified upregulation of miR-421 in OSCC cell-derived exosomes. Furthermore, exosome-miR-421 can be taken up by human umbilical vein endothelial cells (HUVEC) and then targets HS2ST1 through VEGF-mediated ERK and AKT phosphorylation, thereby promoting HUVEC migration, invasion, and tube formation. Additionally, enforced expression of lncRNA MEG3 in OSCC cells reduced exosomal miR-421 levels and then increased HS2ST1 expression, thereby reducing the VEGF/VEGFR2 pathway in HUVEC. Our results demonstrate a novel mechanism by which lncRNA MEG3 can act as a tumor suppressor and regulate endothelial angiogenesis through the exosomal miR-421/HS2ST1 axis, which provides a potential therapeutic strategy for OSCC angiogenesis.

New flaxseed cultivars differing in seed mucilage content (MC) with low hull content (HC) represent an attractive option to simultaneously target the food and feed markets. Here, a genome-wide association study (GWAS) was conducted for MC and HC in 200 diverse flaxseed accessions genotyped with 1.7 million SNP markers. The data obtained for MC and HC indicated a broad phenotypic variation and high (~70%) and a moderate (~49%) narrow sense heritability, respectively. MC and HC did not differ statistically between fiber and oil morphotypes, but yellow-seeded accessions had 2.7% less HC than brown-seeded ones. The genome wide linkage disequilibrium (LD) decayed to r2 = 0.1 at a physical distance of ~100 Kb. Seven and four QTL were identified for MC and HC, respectively. Promising candidate genes included Linum usitatissimum orthologs of the Arabidopsis thaliana genes TRANSPARENT TESTA 8, SUBTILISIN-LIKE SERINE PROTEASE, GALACTUROSYL TRANSFERASE-LIKE 5, MUCILAGE-MODIFIED 4, AGAMOUS-LIKE MADS-BOX PROTEIN AGL62, GLYCOSYL HYDROLASE FAMILY 17 and UDP-GLUCOSE FLAVONOL 3-O-GLUCOSYLTRANSFERASE that have been shown to play a role in mucilage synthesis and release, seed coat development and anthocyanin biosynthesis in A. thaliana were identified. The favorable alleles will be useful in flaxseed breeding towards the goal of achieving the ideal flaxseed cultivars for food and feed by genomic-based breeding.

Online food delivery services are rapidly growing in popularity, making customer satisfaction critical for company success in a competitive market. Accurate delivery time predictions are key to ensuring high customer satisfaction. While various methods for travel time estimation exist, effective data analysis and processing are often overlooked. This paper addresses this gap by leveraging spatial data analysis and preprocessing techniques to enhance the data quality used in Bayesian models for predicting food delivery times. We utilized the OSRM API to generate routes that accurately reflect real-world conditions. Next, we visualized these routes using various techniques to identify and examine suspicious results. Our analysis of route distribution identified two groups of outliers, leading us to establish an appropriate boundary for maximum route distance to be used in future Bayesian modelling. The spatial analysis revealed that these outliers were primarily deliveries to the outskirts or beyond the city limits. By refining the data quality through these methods, we aim to improve the accuracy of delivery time predictions, ultimately enhancing customer satisfaction.

This article investigates a proposed new mathematical model that considers the infected individuals using various rate coefficients such as transmission, progression, recovery and vaccination. The fact that the dynamic analysis is completely determined by the basic reproduction number is established. More specifically, local and global stabilities of the disease-free equilibrium and the endemic equilibrium are proved under certain parameter conditions when the basic reproduction number is below or above unity. A realistic computer simulation is performed for better understanding of the variations in trends of different compartments after the outbreak of the disease.

The IMbrave150 trial established atezolizumab with bevacizumab (A+B) as standard care for hepatocellular carcinoma (HCC), recommending an esophagogastroduodenoscopy (EGD) within 6 months of treatment initiation to prevent bleeding from esophagogastric varices. The necessity of mandatory EGD for all patients remains unclear. We retrospectively analyzed 112 HCC patients treated with A+B at five Canadian cancer centers from July 1, 2020, to August 31, 2022. A+B was the first-line therapy for 90% of patients, with median overall survival at 20.3 months and progression-free survival at 9.6 months. There was no survival difference between patients with bleeding and those without. Before A+B, 71%(79) of patients underwent an EGD within 6 months, revealing varices in 41%(32) and requiring intervention in 19%(15). The overall bleeding rate was 15%(17), with GI-specific bleeding occurring in 83%(5) of the EGD group and 17%(1) of the non-EGD group (p=0.24). Non-GI bleeding was observed in 10%(11) of patients. Outcomes for HCC patients treated with A+B in Canada were comparable to IMbrave150. Our study showed twice the rate of varices detection and treatment compared to IMbrave150, reflecting real-world treatment of high-risk populations. There was no increased GI bleeding in patients without pre-treatment EGD, supporting a selective EGD approach.

The mixing of terrestrial groundwater and seawater creates dynamic reaction zones in intertidal areas, where land-derived Fe(II) is oxidized to Fe(III) and then precipitates as Fe hydroxides at groundwater-seawater interface. These hydrogeochemical processes contribute to the formation of iron curtains beneath soil surface, which in turn influences the evolution of coastal aquifers. This paper provides a comprehensive review of physical and geochemical processes at field scale in coastal areas, explores the impact of mineral precipitation on pore structure at pore scale, and synthesizes reactive transport modeling (RTM) approaches for illustrating continuum-scale soil physio-chemical parameters during the evolution of porous media. Upon this review, knowledge gaps and research needs are identified. Additionally, challenges and opportunities are presented. Therefore, the incorporation of observational data into a comprehensive physico-mathematical model becomes imperative for capturing the pore-scale processes in porous media and their influence on groundwater flow and solute transport at large scales. Furthermore, a synergistic approach integrating pore-scale modeling and non-invasive imaging can provide detailed insights into intricate fluid-pore-solid interactions for future studies, as well as facilitate the development of regional engineering-scale models and physio-chemical coupled models with diverse applications in submarine science and engineering.

Aggressive mature T-cell lymphoma (TCL) is a disease that carries a poor prognosis. The mechanism, at the functional protein level, of the resistance to therapy of TCL has not yet been well clarified. To comprehensively clarify the mechanism, we first reviewed the literature, as mentioned in the Introduction section, on 22 types of representative functional proteins reported as being potentially associated with treatment resistance, mainly in hematologic tumors. Next, we retrospectively analyzed the tumor cell expressions of these 22 functional proteins in 16 randomly selected patients with TCL who had received CHOP (cyclophosphamide, hydroxyl doxorubicin, oncovin, and prednisolone) therapy as first-line treatment. As a result, we propose a new three-group prognostic classification of TCL (the TCL Urayasu Classification) based on the mechanism of resistance to treatment of these tumors. The study subjects included 7 patients (44%) with peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), 6 patients (38%) with angioimmunoblastic T-cell lymphoma (AITL), and 3 patients (19%) with anaplastic large T-cell lymphoma (ALCL). Immunohistochemistry was performed in paraffin-embedded tumor tissue sections prepared from these patients as antigens using antibodies against the 22 previously reported treatment-resistance-related proteins to determine the tumor cell expression statuses of these proteins. Log-rank test was performed to compare the overall survival (OS) after CHOP therapy of the patients showing positive and negative immunostaining for these proteins, as well as among patients grouped based on other factors. Overall, the median OS of the TCL patients was about 33.5 months. When compared by the disease type, the prognosis was found to be worse in the order of ALCL, AITL, and PTCL. A multivariate analysis showed that expression of glucose-regulated protein 94 (GRP94), a protein that serves as a pro-survival component under endoplasmic reticulum (ER) stress in the tumor microenvironment, was significantly associated with shortened survival. No significant difference was observed for the conventional poor prognostic factors: high-risk International Prognostic Index (IPI) and high-risk Prognostic Index for peripheral T-cell lymphoma, unspecified (PTCL-U) (PIT). However, when GRP94 was combined with six other factors, listed below, as survival-shortening factors, significant differences were observed. The 6 factors were 1) programmed cell death-ligand 1 (PD-L1) and 2) programmed cell death 1 (PD-1), which are involved in immune surveillance; 3) aldo-keto reductase family 1 member C3 (AKR1C3), which is an enzyme that inactivates HO (hydroxyl doxorubicin [H] and oncovin [O]) of CHOP; 4) P53, a tumor suppressor; 5) Glucose-regulated protein 78 (GRP78), an ER stress protein; 6) thymidine phosphorylase (TP), which is an enzyme involved in thymidine catabolism. Based on the combination of GRP94 and the six other factors expressed in the tumors, we propose a new prognostic classification system for TCL (TCL Urayasu classification). Group 1 (group with a relatively good prognosis): GRP94-negative (n = 6; median OS, 88 months; p < 0.01); Group 2 (group with a poor prognosis): GRP94-positive, plus expression of 2 of the 6 aforementioned factors (n = 5; median OS, 25 months; p > 0.05); and Group 3 (group with a very poor prognosis): GRP94-positive, plus expression of at least 3 of the 6 aforementioned factors (n = 5; median OS, 10 months; p < 0.01). Thus, the TCL Urayasu prognostic classification is a simple, useful, and innovative classification that also explains the mechanism of resistance to treatment for each functional protein. Accumulation of more cases and further analyses are expected in the future to enable clinical application, such as use of a stratified treatment approach and treatment using inhibitors.

A key component for the proper functioning, availability, and reliability of power grids is the power trans- former. Although these are very reliable assets, the early detection of incipient degradation mechanisms are very important to prevent failures that may shorten their lifetime. In this work a review and comparative analysis, of classical Machine Learning algorithms (such as single and ensemble classification algorithms) and two automatic machine learning classifiers, are presented for the fault diagnosis of power transformers. The goal is to determine whether fully automated ML approaches are worse or better than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from oil-dissolved gases data. The methodology uses a DB compiled from published and proprietary transformer fault samples. Faults data is obtained from the literature, the Duval pentagon method, and user-expert knowledge. The parameters from, either single or ensemble classifiers, were optimized through standard machine learning procedures. The results showed that the best performing algorithm is a robust automatic machine learning classifiers model followed by classical algorithms such as neural networks and stacking ensembles. These results highlight the ability of a robust automatic machine learning model for, handling imbalanced power transformers faults datasets with high accuracy, using the minimum tuning effort by the electrical experts. By providing the most probable transformer fault condition will reduce the time required to find and solve the fault.

To fill in the blanks for charophytes in the Caucasus to improve our knowledge of species distribution areas in Eurasia, a field survey and study of available specimens from Dagestan (North Caucasus, Russia) was conducted based on morphological observation and molecular genetic analysis. Seventeen new localities for six Chara species and one Tolypella species, with six new species and one new genus for the region studied, and one new species for the Caspian Sea region were found. Some species records changed the outline or filled in the gaps in the species distribution area. The presence of species with a distribution area mainly in the centre of Eurasia (C. globata, C. neglecta) with mainly Mediterranean – Middle East species (C. gymnophylla) is notable for this region, as well as for other studied regions of the Caucasus having a combination of species with different distribution areas. The frequent occurrence of C. gymnophylla points is similar to that in the Mediterranean region and the Middle East. Small brackish waterbodies at the coast of the Caspian Sea, freshwater mountain rivers, small waterbodies associated with them, and water reservoirs are the main habitats of charophytes in the regions. The habitat preference and distribution in the Caucasus allowed suggestion some species for protection. The absence of endemic species among charophytes from Dagestan and Caucasus contrasts with the flora of terrestrial magnoliophytes, which have a large number of endemic species.

Objective: Physical inactivity is linked to chronic illnesses and disabilities among workers, especially those in high demanding jobs like teachers. Despite the global prominence of sedentary behaviour research, studies drawing the relationships between physical inactivity and multimorbidity among working teacher populations in low-and middle-countries remain untapped. This study assessed the sedentariness and health status of primary school teachers in Cape Coast Metropolis in Ghana. Method: This cross-sectional survey employed 1109 primary school teachers from the Cape Coast Metropolis in the Central Region of Ghana, using convenient sampling method. Results: Generally, the teachers were highly sedentary and reported poor health status. Other results showed no difference in sedentariness across gender, t (1107) = -0.32, p > .05. However, female teachers suffer more pain and discomfort, [t (1103.51) = - 3.07, p < .05, anxiety and depression, [t (1099.56) = - 2.85, p < .000,)], and poor health status [t (1107) = 2.14, p < .05, than their male counterparts. Also, pain and discomfort, anxiety and depression, health status and years of work significantly predicted sedentariness among the teachers, F (4, 1104) = 5.966, p=.00, R = .145, R2 = .029, adjusted R2 = .018. Conclusion: The findings suggest that individualized or personalized interventions are urgently needed to promote regular physical activity to improve the health status and reduce associated complications on the health and well-being, especially among the female primary school teachers. Given the health risks of sedentary lifestyle, behavioral interventions at the person-level (i.e., individualized- routine weekly physical activity programmes) and built environmental restructuring (e.g., creation of walkways to encourage regular walking) could be done to physical activity behavior among teachers within the Cape Coast Metropolis, and perhaps beyond.

Adolescence is a critical developmental period associated with an increase in stress, the appearance of anxiety and depressive symptoms, and changes in sleep patterns. Even though the disruption of sleep patterns in stress and anxiety and depressive disorders is well known, the independent effects of childhood trauma and stressful life events on sleep patterns are less understood. We tested the independent effects of stress (childhood trauma and stressful life events) while controlling for anxiety and depression on adolescent sleep patterns. Seven hundred fifty-two adolescents completed self-report questionnaires about childhood trauma, stressful life events, anxiety, and depression. Four sleep factors identifying movement during sleep, sleep regularity, sleep disturbances and sleep pressure were extracted in the principal component analysis of sleep questions. Both childhood trauma and recent stressful life events were significantly associated with sleep disturbances before and after controlling for anxiety and depression.

Imaging living cells by atomic force microscopy (AFM) promises not only high-resolution topographical data, but additionally, mechanical contrast, which are not obtainable with other microscopy techniques. Such imaging is however challenging, as cells need to be measured with low interaction forces to prevent either deformation or detachment from the surface. Off-resonance modes which periodically probe the surface have been shown to be advantageous, as they provide excellent force control combined with large amplitudes, which help reduce lateral force interactions. However, the low actuation frequency in traditional off-resonance techniques limits the imaging speed significantly. Using photothermal actuation, we probe the surface by directly actuating the cantilever. Due to the much smaller mass that needs to be actuated, the achievable measurement frequency is increased by two orders of magnitude. Additionally, photothermal off-resonance tapping retains the precise force control of conventional off-resonance modes and is therefore well suited to gentle imaging. Here we show how photothermal off-resonance tapping can be used to study live cells by AFM. As an example of imaging mammalian cells, the initial attachment, as well as long term detachment of a human thrombocytes are presented. The membrane disrupting effect of the antimicrobial peptide CM-15 is shown on the cell wall of E. coli. Finally, the dissolution of the cell wall of B. subtilis by lysozyme is shown. Taken together, these evolutionarily disparate forms of life exemplify the usefulness of PORT for live cell imaging in a multitude of biological disciplines.

With advancements in remote sensing technology and affordable design, uncrewed aerial systems (UAS), commonly known as drones, have become prevalent in both civil and military applications, such as agriculture, public safety, and aerial imaging. However, the rise in unlawful UAS activities, such as non-compliance with legal standards and potential terrorist attacks, has raised significant public concern, necessitating effective detection and mitigation solutions. Despite the growing importance of this issue, comprehensive and detailed examinations of existing counter-UAS solutions are lacking. To address this gap, this study conducts a bibliometric analysis and scoping review of the current literature to identify key topics and emerging trends in counter-UAS approaches. Utilizing co-word and social network analyses, the study identifies strong and weak connections between selected keywords from academic articles. This study summarizes the limitations and potential opportunities within counter-UAS research, suggesting an increasing focus on multisensory fusion and machine-learning approaches for drone detection and mitigation. Additionally, areas such as swarm drone operations, UAS traffic management (UTM), and UAS networks are identified as important but promising fields for further investigation. The findings of this study provide a foundation for enhancing air and ground safety through improved counter-UAS applications.

Papillary thyroid cancer (PTC) is the most common endocrine malignancy which diagnosis and recurrences still challenge clinicians. New perspectives to overcome those issues could come from the study of extracellular vesicles (EVs) populations and content. Here, we aimed to elucidate the heterogeneity of EVs circulating in tumor and the changes in their microRNA content during cancer progression. Using a mouse model expressing BRAF^V600E, we isolated and characterized EVs from thyroid tissue by ultracentrifugations and elucidated their microRNA content by small RNA sequencing. Cellular origin of EVs was investigated by ExoView and that of deregulated EV-microRNA by qPCR on FACS-sorted cell populations. We found that PTC released more EVs bearing epithelial and immune markers, as compared to healthy thyroid, and that changes in EV-microRNAs abundance were mainly due to their deregulated expression in thyrocytes. Pathway analysis showed that the more abundant EV-microRNAs could impact on immune processes. Altogether, our work provides a full description of in vivo-derived EVs produced by, and within, normal and cancerous thyroid. We elucidated the global EV-microRNAs signature, the dynamic loading of microRNAs in EVs upon BRAF^V600E induction, and their cellular origin. Thyroid tumor-derived EV-microRNAs could support the establishment of a permissive immune microenvironment.

Subcortical beetles interact with a wide range of semiochemicals released from different sources, including trees, fungi, and insect pheromones. While the attraction of bark beetles, their insect predators, and competitors to bark beetle pheromones is well-studied, the attraction of these beetle communities to other sources of semiochemicals remains poorly understood. We tested the attraction of bark and wood-boring beetles and their predators to host stress volatiles, fungal volatiles, and a mountain pine beetle lure in the field. Host stress volatiles were derived from lodgepole pine trees stressed by three fungal symbionts of mountain pine beetle and two common phytopathogens. Our results showed that bark beetles, particularly mountain pine beetles, show a preference for a combination of fungal volatiles, particularly 2-methyl-1-butanol and mountain pine beetle lures. Without the addition of lures, 2-methyl-1-butanol was also identified as a key fungal volatile in the attraction of mountain pine beetle competitors from the Cerambycidae and Buprestidae families. Predators in the Elateridae and Staphylinidae families showed attraction to host stress volatiles and the healthy tree volatile profile. These findings suggest that these chemicals warrant further field testing for potential use in monitoring and management of subcortical beetle populations.

The current study describes the preparation of chitosan nanoparticles (CNPs) using hydroxychloroquine (HCQ), widely used in traditional medicine due to its diverse phar-macological and medicinal uses. This work aims to combine the HCQ drug with CS NPs to generate a novel nanocomposite with improved characteristics and bioavailability. HCQ@CS NPs is roughly shaped like roadways and has a smooth surface with an average size of 159.3±7.1 nm, a PdI of 0.224±0.101, and a zeta potential of +46.6±0.8 mV. To aid in the development of pharmaceutical systems for use in cancer therapy, the binding mech-anism and affinity of the interaction between HCQ and HCQ@CS NPs and BSA were ex-amined using stopped-flow, other spectroscopic approaches, supplemented by molecular docking analysis. HCQ and HCQ@CS NPs binding with BSA is driven by a ground-state complex formation that may be accompanied by a non-radiative energy transfer process, and binding constants indicated that HCQ@CS NPs-BSA was more stable than HCQ-BSA. The stopped-flow analysis demonstrated that, in addition to increasing BSA affinity, the nano formulation HCQ@CS NPS changes the binding process and may open up new routes for interaction. Docking experiments verified the development of the HCQ-BSA complex, with HCQ binding to the site I on the BSA structure, primarily with the amino acids Thr 578, Gln 579, Gln 525, Tyr 400, and Asn 404. Furthermore, the nano-formulation HCQ@CS NPS not only increased cytotoxicity against the A549 lung cancer cell line (IC50 = 28.57±1.72 g/ml) compared to HCQ (102.21±0.67) g/ml), but also exhibited higher anti-bacterial activity against both Gram-positive and Gram-negative bacteria when compared to HCQ and chloramphenicol which in agreement with the binding constants. The nano formulation developed in this study may offer a viable therapy option for A549 lung cancer.

COVID-19 pandemic has caused a large-scale havoc in almost every country across the globe, putting major challenges for the healthcare system in many parts of the world. Several of the laboratories are running in the race with undying efforts for developing potential vaccine, drugs or therapeutics to treat or prevent the infection. However, with the limited time window and high rate of infection, the task is very big for humanity to find a cure. With hundreds of genomic data of SARS-CoV-2 virus isolates from humans are being submitted almost every day, it is coming into knowledge that virus is mutating, slower in countries with sporadic cases, but higher in countries experiencing large outbreak. These types of mutations in virus may bring challenges in vaccine or therapeutic development for use in each and every country, as each hotspot region may have their own pattern of mutations in virus with ongoing outbreak. In our current study, we retrieved non-synonymous mutation data of around 12,225 SARS-CoV-2 virus samples isolated from humans globally, and discovered all mutations that are collectively happening in antibody epitope regions of the virus country-wise. We found a few numbers of epitope regions in SARS-CoV-2 that are highly conserved collectively in all variants and may be used for epitope-based vaccine development for whole world. We also found epitope regions that are conserved collectively in SARS-CoV-2 variants country-wise and can be used for customized epitope-based vaccine development in each different country.

Abstract The aim of the present review is to discuss the roles of vitamin K and vitamin K-dependent proteins, and the combined action of the vitamins K and D, for maintenance of bone health. The most relevant vitamin K-dependent proteins in this respect are osteocalcin and matrix Gla-protein (MGP). When carboxylated these proteins appear to have the ability to chelate and import calcium from the blood to the bone, thereby reducing the risk of osteoporosis. Carboxylated osteocalcin appears to contribute directly to bone quality and strength. Adequate vitamin K status is required for carboxylation of MGP and osteocalcin. In addition, vitamin K acts on bone metabolism by other mechanism, such as a ligand for the nuclear steroid and xenobiotic receptor (SXR). In this narrative review, we examine the evidence for increased bone mineralization through dietary adequacy of vitamin K (phylloquinone or menaquinone). Summarizing the evidence for a synergistic effect of vitamin K and vitamin D3 we find that an adequate supply of vitamin K on top of an optimal vitamin D status seems to add to the benefit for maintaining bone health. More research related to synergism and possible mechanisms of vitamins D3 and K interaction in bone health is needed.

Non-union is one of the severe complications of traumatic fracture and still no standard therapeutic recommendations currently. A total of eleven patients were treated on the lesions for non-union, after the failure of open reduction internal fixation (ORIF) or total hip replacement, by autologous conditioned serum (ACS) for one to three times monthly and followed up. Seven patients received ACS three times, three patients received it two times, and the only one who underwent total hip replacement received it once. Ten patients achieved union at the last follow-up visit, indicating the effectiveness of ACS on patients who suffered from non-union after ORIF. ACS has the potential as an alternative or adjective treatment for non-union and is worthy to be investigated further for the benefits of patients.

Background: Alzheimer's Disease (AD) is a progressive and incurable condition that prompts the exploration of interventions to slow its advancement and enhance the quality of life. The cortical brain waves could have a role in the process of executive function deterioration, increasing dementia symptoms. Objective: To interrogate the effects of physical exercise, and non-invasive brain stimulation, both in electrophysiological behavior of the brain, the executive functions, and dementia symptoms in older adults with AD. Methods: One hundred and sixteen older adults were grouped (n=22 each): GAE (AD, PMED+NIBS), GCP (AD, PMED), GCN (AD, NIBS), GCS (control, no intervention). Binaural beats were used as a non-invasive brain stimulation, physical exercise, or both, were applied during 16 weeks. Pre/post measurements included Alpha Band, and SMR Rhythms, executive functions, and dementia symptoms. Results: GCS showed no significant changes (p>0.05). GCN and GCS slightly enhanced inhibitory control (p>0.05) and working memory (p>0.05). In contrast, GCP and GAE improved executive function (p

Composite membranes formed by sulfonated poly(ether ether ketone) (SPEEK) with embedded carbon quantum dots (CQDs) were used for detection and removal of heavy metal ions, such as lead and cadmium, from water. SPEEK is responsible for the capture of heavy metals based on the cation exchange mechanism, while CQDs detect their contamination by exhibiting changes in fluorescence. Water-insoluble “red” carbon quantum dots (rCQDs) were synthesized from p-phenylenediamine so that their photoluminescence was shifted from that of the polymer matrix. CQDs and the composites were characterized by several techniques: FTIR, Raman, UV/VIS, photoluminescence, XPS spectroscopies and AFM microscopy. The heavy metal ion concentration was analyzed by inductively coupled plasma–optical emission spectroscopy (ICP-OES). The concentration ranges were 10.8 - 0.1 mM for Pb2+ and 10.0 - 0.27 mM for Cd2+. The composite achieved 100% removal efficiency for lead and cadmium if the concentration was below a half of the ion exchange capacity of SPEEK. The regeneration of membranes in 1 M NaCl was also studied. SPEEK/rCQDs showed a turn-off effect more pronounced for lead. A second order law was effective to describe the kinetics of the process.

In response to the increasing complexity and sophistication of cyber threats, particularly those enhanced by advancements in artificial intelligence, traditional security methods are proving insufficient. This paper explores the zero-trust cybersecurity framework, which operates on the principle of "never trust, always verify" to mitigate vulnerabilities within organizations. Specifically, it examines the applicability of zero-trust principles in environments where large volumes of information are exchanged, such as schools and libraries, highlighting the importance of continuous authentication, least privilege access, and breach assumption. The findings highlight avenues for future research that may help preserve the security of vulnerable organizations.

Wheat, a component of the staple diet globally, is a common food allergen in children. The symptoms of wheat allergy (WA) range from skin rash to shortness of breath, significantly impairing quality of life. Following initial clinical suspicion, individuals may undergo wheat allergen-specific skin prick test (SPT), blood test for serum immunoglobulin E (sIgE) levels or oral food challenge. Conventional management of WA lies in wheat avoidance, yet accidental consumption may be inevitable owing to the ubiquity of wheat in various food products. This article aims to provide an overview of the immunologic pathway of WA, followed by its novel diagnostic methods, namely alcohol-soluble SPT extracts, component-resolved diagnosis and basophil activation test (BAT). The mechanisms underlying wheat allergen-specific oral immunotherapy (OIT) as well as a summary of the efficacy, tolerability and safety of related clinical trials will then be discussed.

AbstractIntroduction: Increasing knowledge of obstetric danger signs (ODS) and encouraging birth preparedness and complication readiness (BPCR) practices are strategies to increase skilled maternal health service utilization in low-income countries. One of the methods to increase mothers' knowledge about ODS and promote BPCR practice is through health education intervention (HEI). However, the effect of context-specific community-based health education led by women's groups on these outcomes has not been comprehensively studied, and the existing evidence is inconclusive. Thus, we aimed to evaluate the effect of a context-specific community-based HEI led by women's groups on mothers' knowledge regarding ODS and BPCR practices in southern Ethiopia.Methods: An open-label, two-arm parallel group cluster-randomized controlled trial was conducted from January to August 2023 on pregnant women from 24 clusters (kebeles) (12 interventions and 12 controls) in the northern zone of the Sidama region. The Open Data Kit smartphone application was utilized to collect data. The intention-to-treat analysis was used to compare outcomes between groups. We fitted multi-level mixed-effects modified Poisson regression with robust standard error to account for between and within cluster effects. Results: One thousand and seventy pregnant women (540 in the intervention and 530 in the control clusters) responded to this study, making the overall response rate 95.02%. Excessive vaginal bleeding (94.3% in the interventional group vs. 88.7% in the control group) was the commonest ODS mentioned during childbirth. Overall, 68.7% of women in the intervention group and 36.2% of mothers in the control group had good knowledge of ODS (P-value < 0.001). Saving money and materials (97.1% in the interventional group vs. 92.7% in the control group) was the most frequently practiced BPCR plan. Overall, 64.3% of women in the intervention group and 38.9% of mothers in the control group practiced BPCR (P-value < 0.001). HEI significantly increased overall knowledge of ODS (adjusted risk ratio [ARR]: 1.71; 99% CI: 1.14-2.57) and improved overall BPCR practice (ARR: 1.55; 99% CI: 1.02-2.39). Conclusions: A community-based HEI led by women's groups improved mothers' knowledge regarding ODS and BPCR practices in a rural setting in southern Ethiopia. Interventions designed to increase women's knowledge of ODS and improve BPCR practice must implement context-specific, community-based HEI that aligns with World Health Organization recommendations. Trial registration: NCT05865873

This work presents preliminary experimental results advancing non-destructive testing methods for detecting steel bars in concrete structures via contactless investigation in the X-band spectrum. The study reveals the field’s penetration into concrete, extracting insights into embedded bars through scattered data. Applying a quasi-quadratic inverse scattering technique to numerically simulated data yields promising results, confirming the effectiveness and reliability of the proposed approach. In this realm, using a higher frequency allows the use of lighter equipment and smaller antennas. Identified areas for improvement include accounting for antenna behavior and establishing undeformed target morphology and precise orientation. Transitioning from powder-based and sand specimens to real solid reinforced concrete structures is expected to alleviate laboratory challenges. Although accurately determining concrete properties such as relative permittivity and conductivity is essential, it remains beyond the scope of this study. Finally, overcoming these challenges could significantly enhance non-invasive testing, improving structural health monitoring and disaster prevention.

The main challenges in oil platforms are how to define optimal setpoints and decision-making for controlling variables due to many existing dynamical production uncertainties while conceding safe operation and minimizing discharge to sea and atmosphere without killing plantwide inventory. A novel scheduling model to optimize the whole essential units of an oil platform production gathering topside and subsea variables is disclosed. A proper topside and subsea scheduling model enables and creates harmony between optimal production, supply chain, costs, and market dynamics, satisfying demands, environmental and operations constraints leading to stability. The interest of the present research lies in determining the optimal topside and subsea operational settings and decision-making, establishing maximum E&P performance and production control setpoints while predicting and manipulating reservoir lifespan and its revitalization. This objective is attained through a novel multiperiod large-scale model for the planning and operational production scheduling and model predictive control (MPC) in oil platforms complying with sustainability, profitability, platform design capacities, and offloading of oil and gas to supply chains or to pipeline exportation. The present model details heat and mass transfer, PVT, flow profiles along a planning horizon and can be used to any oil platform since comprises all the essential unit operations for oil, gas, CO₂, H₂S and water. The model maximizes the total oil production over any planning horizon. The model is versatile, and decision-making can be either linear or nonlinear but rather a MILP or LP as best choice on optimizing large-scale systems. If the model is applied with auto rescheduling on site within variable hours or minutes, it becomes a real-time optimization schedule approach. Due to its high-velocity performance and robustness, a novel multi-objective function strategy, acting as an LP generic plantwide MPC with industrial scope to maximize production while controlling the process is presented, and as an example is here used to control slug flow to avoid equipment trips and inventory instability, at the same time production is maximized. The output from the decision-making was compared to actual plant data, and the results proved compliance to the design capacity with process safety and sustainability. Comparing to official Brazil's government data for oil, the case study showed superiority to the same size platforms, like FPSOs P-75 and MV32, e.g., more than 101.5% more capacity to produce oil in beginning of campaign after first oil, and 10.5% during campaign. The present work indicates that: 1) the production of millions of barrels is being delayed or left aside; 2) not scheduling production harms the environment and diminish process safety; 3) not scheduling production can create stochastic supply chain deliveries instead of organizing it by deterministic offloading days; 4) scheduling is essential to manipulate, revitalize and monitor reservoir's pressure and content under uncertainty; 5) the oil platform can be automatic optimized by a plantwide MPC reducing human activity/dependency; 6) scheduling gives more transparency to stakeholders and contractors by forecasting business data and capability, and can design, develop or enable new businesses.

Background. The course of the COVID-19 epidemic process depends on population immunity which prevents pathogen spread among the population. Aim: to study the evolution of SARS-CoV-2 humoral immunity in the Belarusian population relative to COVID-19 pandemic dynamics. Materials and methods. The work was carried out according to a methodology for assessing population immunity developed by Rospotrebnadzor (Russia) and the Belarusian Ministry of Health with the participation of the St. Petersburg Pasteur Institute (SPPI), taking into account WHO recommendations. The study was approved by the Bioethics Committee of Belarus and the SPPI Bioethics Committee. Participant selection was carried out by questionnaire using a cloud (internet server) service. To monitor population immunity, a cohort of 4,661 people (participating in all stages of seromonitoring) was formed from the overall volunteer group. Volunteers were randomized by age group (1-17, 18-29, 30-39, 40-49, 50-59, 60-69, 70+ years), region, and professional group. For the detection of antibodies (Abs) to nucleocapsid (Nc) and S glycoprotein receptor-binding domain (RBD), corresponding assay systems were used following manufacturer instructions. The study was conducted in 4 stages according to a single scheme. Results. In the 1st stage (pandemic month 15), collective immunity was due mainly to Nc+RBD+ Ab status alone. By the 2nd stage (carried out after 4 months), their share decreased 1.2-fold, while the share of volunteers who had only RBD Abs increased 1.7-fold. In the 3rd and 4th stages (carried out after 9 and 19 months), the share of persons with RBD+Nc‒ compared to the 2nd stage decreased by 3.5%; the proportion of persons with Nc+RBD‒ Abs increased by 1.5-fold. The most important factor in population immunity was vaccination of the population, the coverage of which reached 70% by the 4th stage. Among vaccines, the Sputnik V and Sputnik Light vector designs were used most often. The whole-virion, inactivated BIBP-CorV vaccine was used less often. Conclusion. The evolution of collective SARS-CoV-2 humoral immunity included a set of changes in circulating Ab levels (Nc, RBD). The hybrid immunity formed helped to reduce the incidence to nearly zero.

Historical legacy of Eastern European and Balkans’ health systems was mutually interdependent and shaped by local socioeconomic circumstances. Three distinctive systems of risk sharing and health financing to develop since the late XIX century were the Bismarck, Beveridge, and Semashko systems. Modern day healthcare systems in these countries are challenged by population ageing, accelerated innovation in medical technology, growing purchasing power and rising demand for healthcare services. Supply side changes contribute to demand side efficiency bottlenecks in financing, driving the costs of the already expensive medical care up. All of the nations have a large share of citizens experiencing difficulty with affordability and access to medical care, particularly in rural and remote areas. Network of Health technology assessment agencies have mushroomed over the past three decades. Principles of health economics theory and cost-effective resource allocation are slowly gaining ground in governing authorities’ mindset and decision-making process. For many years to come, pharmaceuticals and medical services will remain dependent on out-of-pocket spending. Currently accelerating and spreading 4.0 Industrial Revolution, together with the Belt and Road Initiative, are likely to substantially impact the further economic development of this vast region. Post-Pandemic ‘Green’ Recovery strategies adopted by many of the Eastern European governments shall also make this transition towards sustainable development more difficult and challenging given the large dependency of all these economies upon traditional carbon fuels.

Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest. Maize (Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework is implemented in R, providing a flexible and easy‐to‐use GUI interface. Since this allows appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety.

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages blockchain’s inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine learning models like SVM, KNN, and Random Forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard’s effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-Score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.

The ultimate goal of this paper was to examine the degree of elasticity between two variables, namely, agricultural credit and agricultural growth, in Angola in the period 2003–2022. Time series data were fitted into the ARDL test using various econometric techniques such as the ADF stationarity test, Granger causality and the ordinary least squares method as well as a vector error correction model (VECM) to analyze the relationship between agricultural credit and agricultural economic growth, showing a causal relationship. Both the impacts through elasticities and the optimal point existing in this relationship were estimated. It was concluded that the impact of agricultural credit on agricultural GDP was 14.41%. Granger causality shows signs of a positive linkage between agricultural credit and agricultural GDP. However, there is a causal relationship between agricultural credit and agricultural GDP, in a unidirectional aspect. This result is consistent with most of the earlier studies reviewed in the literature, confirming that credit-oriented monetary policies can boost economic growth and, consequently, development in Angola. It is important for agricultural credit systems to be designed in a way that ensures equitable access, fair interest rates, and appropriate risk management mechanisms. Additionally, monitoring and evaluation mechanisms should be in place to assess the environmental and social impacts of credit programs on agricultural sustainability. It is worth noting that this is a first-of-a-kind study on the matter of the Angolan credit experience, specifically for the agricultural sector. Angola is still searching for a sustainable credit model that could be used as a catalyzer to boost growth and contribute to economic development.

This publication aims to create 4K UHD video sequences with a specific focus on smart cities and transport. The created database will be accessible to all experts in the field and can be further used for research purposes. All reference video sequences will be transcoded into different quality settings. The resolution, compression standard, and bitrate will be changed. The work will aim to find out the best combination of video parameters from the measured values. Thanks to the overall evaluation, we will ensure for each single video sequence the quality with the best experience for the providers that will offer the service. The database will also be available with its objective and subjective ratings, with dynamicity information characterized by spatial and temporal information. After, It will be possible to compare the results of the subjective evaluation of a selected sample of our respondents with the work of other researchers who can evaluate with a different sample of evaluators.

Canine distemper virus (CDV) poses a substantial threat to diverse carnivores, leading to sys-temic and often fatal diseases. Accurate and prompt diagnosis is paramount for effective man-agement and curbing further transmission. This study evaluates the diagnostic efficacy of drop-let digital PCR (ddPCR) in comparison to conventional reverse transcription (RT-PCR) and quantitative reverse transcription real-time PCR (RT-qPCR). Seventy-six clinical samples were collected from dogs with CDV symptoms diagnosed by specialized veterinarians, and sixteen samples from asymptomatic individuals. Conventional PCR, quantitative real-time PCR, and ddPCR were deployed, and their diagnostic capabilities were meticulously assessed. DdPCR ex-hibited heightened analytical sensitivity, reaching a detection limit of 3 copies/μL, whereas RT-qPCR had a detection limit of 86 copies/μL. The comparative analysis between clinical diagnosis and molecular techniques, including RT-PCR and RT-qPCR, demonstrated low concordance, with Kappa coefficients of 0.268 and 0.324, respectively. In contrast, ddPCR, showed a moderate concordance, with a Kappa coefficient of 0.477. Sensitivity was 42.39% for RT-PCR, 57.89% for RT-qPCR and 72.37% for ddPCR, with 100% of specificity for all methods. This study under-scores ddPCR's superior sensitivity and concordance with clinical CDV diagnosis, even at low viral concentrations, suggesting it as a promising alternative for CDV diagnosis.

The bentonite slurry trenches are becoming increasingly popular in the excavation process of trenches, especially for diaphragm wall construction. The problem that needs to be addressed is the stability of bentonite slurry trenches. This paper presents a stability analysis of trenches with temporary support of bentonite slurry, with unit weights ranging from 10.5 to 12.0 kN/m3, in the realistic stratum: C3 in the Hue city area. The analysis employs the shear strength reduction technique (SSR) with Mohr-Coulomb materials for the numerical method to evaluate the factor of safety (FS). The finite element method (FEM) software program (RS) and the finite difference method (FDM) FLAC software were applied. Additionally, the limit equilibrium method (LEM) of Bell-Washbourne and three-dimensional (3D) Bishop are used for calculating trench stability. The analysis results show a good agreement between RS2 and FLAC2D and RS3 and FLAC3D. Secondly, upon comparison, it is noted that the safety factors of the 3D software programs (RS3, FLAC3D) are higher than those of the 2D software programs (RS2, FLAC2D), ranging from 52.3 to 63.0% for trench lengths of 6m. However, for trench lengths of 54m, the safety factor values in 2D and 3D are nearly equal. Thirdly, the safety factor of the Bell-Washbourne method (LEM) is lower than the numerical analysis methods (FLAC and RS programs). Using the three-dimensional numerical method appears to be effective for estimating stability.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

Although large language models demonstrate impressive capabilities, they sometimes generate irrelevant or nonsensical text, or produce outputs that deviate from the provided source input—an occurrence commonly referred to as hallucination. To mitigate this issue, we introduce a novel decoding method that incorporates both factual and hallucination prompts(DFHP), utilizing a contrastive output distribution to highlight the disparity in output probabilities between model predictions influenced by factual prompts and those affected by hallucination prompts. Experiments on both multiple-choice and text generation tasks show that our approach significantly enhances the factual accuracy of large language models without requiring additional training.

Acomys cahirinus is a unique Rodentia species with several distinctive physiological traits, such as precocial development and remarkable regenerative abilities. These characteristics render A. cahirinus increasingly valuable for regenerative and developmental physiology studies. Despite this, the structure and postnatal development of the central nervous system in A. cahirinus have been inadequately explored, with only sporadic data available. This study is the first in a series of papers addressing these gaps. Our first objective was to characterize the structure of the main visual thalamic region, the lateral geniculate complex, using several neuronal markers (including Ca2+-binding proteins, glutamic acid decarboxylase enzyme, and non-phosphorylated domains of heavy-chain neurofilaments) to label populations of principal neurons and interneurons in adult and newborn A. cahirinus. As typically found in other rodents, we identified three subdivisions in the geniculate complex: the dorsal and ventral geniculate nuclei (LGNd and LGNv) and the intergeniculate leaflet (IGL). Additionally, we characterized internal diversity in the LGN nuclei. The "shell" and "core" regions of the LGNd were identified using calretinin in adults and newborns. In adults, the inner and outer parts of the LGNv were identified using calbindin, calretinin, parvalbumin, GAD67, and SMI-32, whereas in newborns, calretinin and SMI-32 were employed for this purpose. Our findings revealed more pronounced developmental changes in LGNd compared to LGNv and IGL, suggesting that LGNd is less mature at birth and more influenced by visual experience.

This research follows the publication of recent scoping and systematic reviews of existing literature on vaccine literacy (VL), its relationship to vaccine hesitancy (VH), and associated variables, such as vaccination beliefs, attitudes, and behaviors towards immunization. In this review, 17 articles were selected, published between December 2022 and November 2023, in which VL tools were used, most of which support the notion of a negative association between VL and VH, although not confirmed by others. Moreover, the definitions of the variables under consideration were not homogeneous between studies, some aimed to evaluate the association of VL with vaccine confidence, while others were more focused on outcomes such as willingness to be vaccinated or vaccine uptake. The complexity of the factors underlying VL, and the heterogeneity of the methods applied in the studies, in particular the limitations of cross-sectional surveys, still make it difficult to have a comprehensive understanding of the role of VL. Therefore, it is important to develop future assessment tools that consider not just the measurement of psychological factors connected to the motivational aspect of VL, but also those related to knowledge and competencies. For this purpose, a theoretical framework is proposed, where the positioning of VL at the intersection between antecedents and beliefs/attitudes leading to behaviors explains why and how VL serves as a tool and a critical, direct, or mediating driver of vaccination choices, overcoming VH and increasing vaccination rates, operating at personal, as well as at organizational and community level.

Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is a growing momentum towards the adoption of intelligent transportation systems (ITS) and sustainable transportation solutions. This research aims to explore the potential of renewable energy sources to power electric vehicles (EVs) and flying automobiles, thereby accelerating the transition towards a low-carbon future. Objectives: The primary objectives of this research are as follows: - Investigate the role of renewable fuels in powering both EVs and flying cars to reduce reliance on fossil fuels and mitigate carbon emissions. - Analyze the dynamic growth and sustainability implications of advancements in battery technology and alternative renewable energy sources, such as Hydrogen Fuel Cells. - Evaluate the challenges and opportunities associated with the integration of flying vehicles into existing transportation infrastructure, focusing on safety, airspace control, and infrastructure development. - Review insights and guidance to stakeholders on promoting sustainable transportation solutions for EVs and flying cars through the utilization of renewable energy sources and the reduction of carbon emissions from the literature. Methodology: This research employs a mixed-method approach, combining literature review, and causal loop diagram analysis. The literature review provides a comprehensive understanding of current trends, challenges, and opportunities in sustainable transportation and renewable energy technologies. A causal loop diagram is utilized to visualize the dynamic interactions between various factors influencing the adoption of EVs and flying cars, including technological advancements, policy interventions, and market dynamics. Results and Discussion: The findings of this research highlight the significant potential of renewable energy sources, such as solar, wind, and hydrogen, in powering both EVs and flying cars. Advances in battery technology and hydrogen fuel cells are identified as key drivers in accelerating the transition towards sustainable transportation systems. However, challenges remain in terms of infrastructure development, safety regulations, and airspace management for flying vehicles. Nevertheless, there is a noticeable advancement in reducing carbon emissions and reliance on fossil fuels with the widespread adoption of EVs. By harnessing renewable energy and implementing effective policy measures, stakeholders might able to promote the widespread adoption of sustainable transportation solutions, thereby contributing to environmental protection and societal well-being. Conclusion: Literally, this research underscores the importance of sustainable transportation solutions in addressing urban congestion, environmental pollution, and energy security concerns. By leveraging renewable energy sources and embracing technological advancements, such as EVs and flying cars, stakeholders pave the way for a more sustainable and resilient transportation system. However, concerted efforts are needed to overcome existing challenges and ensure the successful integration of these technologies into existing infrastructure. Through collaborative partnerships and innovative initiatives, a low-carbon future for transportation logistics can be achieved, benefiting both the environment and society as a whole.

Malignant pleural mesothelioma (MPM) has limited treatment options and poor prognosis. Frequent inactivation of the tumour suppressors BAP1, NF2 and P16 may differentially sensitise tumours to treatments. We have established chick chorioallantoic membrane (CAM) xenograft models of low-passage MPM cell lines and protocols for evaluating drug responses. Ten cell lines, representing the spectrum of histological subtypes and tumour suppressor status, were dual labelled for fluorescence/bioluminescence imaging and implanted on the CAM at E7. Bioluminescence was used to assess viability of primary tumours, which were excised at E14 for immunohistological staining or real-time PCR. All MPM cell lines engrafted efficiently forming vascularised nodules, however their size, morphology and interaction with chick cells varied. MPM phenotypes including local invasion, fibroblast recruitment, tumour angiogenesis and vascular remodelling were evident. Bioluminescence imaging could be used to reliably estimate tumour burden pre- and post-treatment, correlating with tumour weight and Ki-67 staining. In conclusion, MPM-CAM models recapitulate important features of the disease and are suitable to assess therapies using a broad range of MPM cell lines that allow histological or genetic stratification. They are amenable to multi-modal imaging, offering a time and cost-efficient, 3Rs-compliant alternative to rodent xenograft models to prioritise candidate compounds from in vitro studies.

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To more properly assess the pollutant emissions and dispersion of aircraft during the landing and take-off (LTO) cycle, and to more effectively address air pollution surrounding the airport resulting from flight operations, this study assessed emissions throughout the LTO phase utilizing Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources is employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability.

As cardiovascular diseases continue to be a leading cause of mortality, recent-ly, wearable devices for monitoring cardiac activity have gained much interest among medical community. This paper introduces an innovative ECG monitoring system based on a single – lead ECG machine enhanced with machine learning methods. The system only processes and analyses the ECG data, but also predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on REST API architecture style was designed to fa-cilitate interaction with web-based segment of the system. The module is responsible for receiving data in real time from microcontroller and their deliver to web – based segment. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine leaning methods as Isolation Forest have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including lo-gistic regression, random forest, SVM, XGBoost, decision forest and, CNNs was conducted to predict cardiovascular diseases. Convoluted Neural Networks (CNN) showed an accu-racy of 0.926, proving high effectiveness in ECG data process.

The utilisation of non-Saccharomyces yeasts in co-inoculation and non-thermal technologies for must sterilisation is becoming increasingly prevalent due to its notable utility and potential. This approach optimises the fermentation process, facilitates the production of wines with distinctive characteristics and enhances their stability. Two trials were conducted concurrently, designated as A and B. The same red must was utilized in both trials, with one undergoing UHPH treatment and the other serving as the control. The non-Saccharomyces yeasts were identical in both trials. In Trial A, different percentages of initial inoculum were employed with respect to the total volume of must to be fermented, with the objective of evaluating the influence and competitiveness between yeasts. Trial B was designed to investigate the impact of two nutrients that provide vitamins, energy and protection from oxidative stress on the development of these yeasts and their metabolic expression. A microbiological analysis was conducted, along with oenological parameter measurements, acidification assessments, volatile compound analyses, and spectrophotometric evaluations. It was observed in both trials that the prevalence of Lachancea thermotolerans (Lt) was longer than that of Metschnikowia pulcherrima (Mp), and that the use of quercetin + thiamine had a positive effect on yeast growth. Furthermore, the combination of Lt and Mp yeasts demonstrated remarkable synergy, resulting in the production of a substantial quantity of lactic acid (&gt;5 g/L). With regard to aroma compounds, the UHPH must exhibited a nearly twofold increase in ethyl lactate. Additionally, the TPI was observed to be 8-10% higher in wines derived from UHPH musts, indicating that this technology may potentially safeguard against oxidation.

While texturing of workpiece surfaces to promote lubrication in metal forming has been applied for several decades tool surface texturing is rather new. In the present paper tool texturing is studied as a method to prevent galling. Adopting a strip reduction test longitudinal pocket geometries oriented perpendicular to the sliding direction, with shallow pocket depth, small pocket angle to the workpiece surface and varying distance between pockets are investigated. The experiments reveal that the distance between pockets should be larger than the pocket width thereby creating a topography similar to flat table mountains to avoid mechanical interlocking in the valleys; otherwise an increase in drawing load and pick-up on the tools is observed. The textured tool surface lowers friction and improves lubrication performance provided that the distance between pockets is 2-4 times larger than the pocket width. Larger drawing speed facilitates escape of the entrapped lubricant in the pockets. Testing with low to medium viscosity oils leads to a low sheet roughness on the plateaus but also local workpiece material pick-up on the tool plateaus. Large lubricant viscosity results in higher sheet plateau roughness but also prevents pick-up and galling.

The production of biomass fly ash has been increasing every year in Europe, reaching 5.5 million tons in 2020. Biomass fly ash is not yet accepted in the standards as a substitute material for cement in mortar and concrete. In a first approach, the substitution limit of biomass fly ash is determined by comparing the mechanical strengths, fresh state properties and hardened properties of mortars produced with biomass fly ash to those of mortars produced with classical coal fly ash. Then, masonry and rendering mortars have been designed with different substitution rates of biomass fly ashes from wood combustion in thermal power plant. Although there is an overall decrease in performance, mortars made with biomass ash retain properties that make them suitable for use in masonry or rendering.

Ethylene Oxide (EtO) is a colorless, flammable gas at room temperature, produced by the catalytic oxidation of ethylene. EtO is widely used by medical sterilization facilities to clean medical supplies and equipment. Recent epidemiological studies showed that EtO is a more potent carcinogen than previously documented, leading the Environmental Protection Agency (EPA) to update, in December 2016, the inhalation unit risk estimate for EtO. This resulted in the identification of EtO as a potential health concern in several areas across the US, including the state of Utah. The geography surrounding Salt Lake Valley creates a bowl; ideal for collecting air pollution emissions. The region often experiences inversion episodes which inhibit vertical mixing and cause an accumulation of air pollutants, leading to unhealthy pollution levels. Using the EPA's dispersion modeling software, AERMOD, this study estimated EtO concentrations through facility stack and fugitive emissions modeling results. These values were compared with those of canister-based concentrations from ambient air samples taken near a medical device sterilization facility in the Salt Lake Valley. Stainless steel, whole-air passivated canisters were used to collect 24-hour ambient concentration samples of EtO. Eight locations surrounding a Salt Lake Valley medical device sterilization facility and four background sites were chosen to measure the ambient concentrations. Accounting for potential atmospheric impacts on EtO, measurements were sampled in winter 2022 (January-March) and summer 2022 (July-September). The modeled EtO concentrations were adjusted to account for background values associated with the winter or summer data. Then the two methodologies were compared using a Wilcoxon Sign-ranked, paired test. The statistical analysis resulted in six of the eight sample locations surrounding the sterilization facility being significantly different when comparing canister-based measurements of ambient EtO to modeled estimates. Canister-based measurements taken at sites one, three, and four were statistically greater than the modeled estimates, while sites two, five, and seven were statistically less than the modeled estimates. Also, the summer background value calculated was almost 2.5 times greater than the winter. The results do not suggest whether one method is more or less conservative than the other. In conclusion, the five of the closest sites and site seven were statistically different when comparing measured and modeled ambient concentrations of EtO. The comparison results do not clearly indicate if a correction factor could be derived for future human exposure to cancer risk assessment modeling. However, it is reasonable that the closer to the sterilization facility, the more total EtO exposure will be realized.

(1) Background: Nursing homes’ preparedness in managing a public health crisis has been fragile, with effects on safety culture. The objective of this study was to assess nursing homes’ COVID-19 preparedness in Southern Portugal, including personnel’s work experiences during the pandemic. (2) Methods: We used a COVID-19 preparedness checklist to be completed by management teams, followed by follow-up calls to nursing homes. Thereafter, a survey to personnel was applied. Data analysis included descriptive statistics, exploratory factor analysis, and thematic analysis of open-end questions. (3) Results: In total, 71% (138/195) of eligible nursing homes returned the preparedness checklist. We conducted 83 follow-up calls and received 720 replies to the personnel survey. On average, 25% of nursing homes did not have an adequate decision-making structure to respond to the pandemic. Outbreak capacity and training were fragile areas among nursing homes’ contingency plans. We identified compliance with procedures and nonpunitive response to mistakes as fragile areas of safety culture, and teamwork as a strong safety area. (4) Conclusions: To strengthen how nursing homes cope with upcoming phases of the COVID-19 pandemic or future public health emergencies, nursing homes’ preparedness and safety culture should be fostered and closely monitored.

The primary objective of this systematic review study was to investigate the effectiveness, durability, and adverse events of PLLA treatment for aesthetic indications. The search strategy was performed in MEDLINE (Ovid). The electronic literature search of five databases was performed, from the inception of the databases until 12th of February 2024. This was to identify randomized clinical trials that assessed PLLA treatment in adult individuals exhibiting facial aging and/or facial lipoatrophy. Risk of bias was assessed using the Cochrane Risk-of-Bias Tool for Randomized Trials (RoB 2). Eleven RCTs out of 1467 identified citations were included. Four studies showed increased dermal thickness, significant improvement in facial lipoatrophy severity and aesthetic clinical scores, after PLLA treatment with its effects sustained for at least 25 months. Two studies demonstrated the superiority of PLLA over injectable human collagen. Also, three studies showed positive results favoring PLLA when compared with PH gel in lipoatrophy severity, trans epidermal water loss, skin quality, elasticity, and patient satisfaction. All adverse events were mild-to-moderate in intensity, and the main ones worth noting were bruising, hematoma, tenderness, nodules, and edema. Five out of 11 studies were considered as high risk of bias. The evidence on the effectiveness and safety of PLLA for facial rejuvenation is of low quality; thus, the reported high effectiveness, safety, and long-lasting effects for this purpose, should be further investigated.

The fulfillment of rights to maternal healthcare is a key factor for wellbeing of woman. However, there is a lack of instrument to know the experience of women during maternity to bind the monitoring. The aim of this study was to validate an instrument to measure the woman’s perception of the fulfillment of rights during healthcare in pregnancy, childbirth and immediate postpartum. The instrument consist in 50 items was screening by an exploratory factor analysis. Additionally, the final version of the instrument consists in 29 items which was validate by confirmatory factor analysis and know-group validity. The instrument was applied in 185 Spanish women. The global Aiken’s V of initial instrument proposal was 0.89. Process resolves an instrument with 5 factors (information, privacy, consent, support, and participation) that explained the 60% of the total variance. The score of the instrument was correlated with resilience, maternity beliefs, and positive and negative affect. External validation showed relations with age, gravida, and number of labors. Additionally, the reliability Cronbach´s was 0.93 [0.91; 0.94]. In conclusion, the instrument developed is consistent and has appropriate psychometric properties for assess the fulfillment rights maternity healthcare.

Digital health has added numerous promising solutions to enhance the health and wellness of people with neurocognitive disorders (NCDs) and their informal caregivers: (1) Background: It is important to get a comprehensive view of currently available technologies, their outcomes, and conditions of success to inform recommendations regarding digital health solutions for people with NCDs and their caregivers. This environmental scan was performed to identify the features of existing digital health solutions relevant to the targeted population. This work reviews currently available digital health solutions and their related characteristics to develop a decision support tool for older adults living with mild or major neurocognitive disorders and their informal caregivers. This knowledge will inform the development of a decision support tool to assist older adults and their informal caregivers in their search for adequate digital health solutions according to their needs and preferences, based on trustable information.; (2) Methods: We conducted an environmental scan to identify digital health solutions from a systematic review and targeted searches in grey literature covering the regions of Canada and Europe. Technological tools were scanned based on a preformatted extraction grid. We assessed their relevance based on selected attributes and summarized findings; (3) Results: We identified 100 available digital health solutions. The majority (56%) were not specific to NCDs. Only 28% provided scientific evidence of their effectiveness. Remote patient care, movement tracking, and cognitive exercises were the most common purposes of digital health solutions. Most solutions were presented as decision aid tools, pill dispensers, apps, web, or a combination of these platforms. (4) Conclusions: This environmental scan allowed the identification of current digital health solutions for older adults with mild or major neurocognitive disorders and their informal caregivers. Findings from the environmental scan highlight the need for additional approaches to strengthen digital health interventions for the well-being of older adults with mild and major NCDs and their informal and formal healthcare providers.

Retinitis pigmentosa (RP) is a heterogeneous inherited retinal disorder. Mutations in KIZ cause autosomal recessive (AR) RP. We aimed to characterize the genotype, expression pattern, and phenotype in a large cohort of KIZ cases. Sanger and whole exome sequencing were used to identify KIZ variants. Medical records were reviewed and analyzed. Thirty one patients with biallelic KIZ mutations were identified: 28 homozygous for c.226C>T (p.R76*), 2 compound heterozygous for p.R76* and c.3G>A (p.M1?), and one homozygous for c.247C>T (p.R83*). c.226C>T is a founder mutation among patients of Jewish descent. Clinical parameters were less severe in KIZ compared to DHDDS and FAM161A cases. RT-PCR analysis in fibroblast cells revealed the presence of four different transcripts in both WT and mutant samples with a lower percentage of the WT transcript in patients. Sequence analysis identified an exonic sequence enhancer (ESE) that includes the c.226 position which is affected by the mutation. KIZ mutations are an uncommon cause of IRD worldwide, but are not rare among Ashkenazi Jews. Our data indicate that p.R76* affect an ESE which in turn results in pronounced skipping of exon 3. Therefore, RNA-based therapies might show low efficacy since the mutant transcripts is spliced.

Although the neurofilament light chain (NfL) is used as a biomarker for neurodegenerative decline, its use in surgery- and anaesthesia-induced acute cognitive dysfunction remains controversial. We aimed to synthesise the evidence to explore the potential of NfL as an established biomarker for perioperative neurocognitive disorders (PND). We systematically searched the PubMed, EMBASE, MEDLINE, Cochrane Library electronic databases, and Cochrane Central Registry of Clinical Trials. Our results show that CSF NfL levels positively correlated with blood NfL levels (r = 0.63; 95% confidence interval [CI], 0.42-0.77). The pooled standardised mean difference (SMD) showed a significantly higher preoperative CSF NfL levels (SMD = 0.27; 95% CI, 0.07-0.47) and both pre- and postoperative blood NfL levels (SMD = 0.53, 95% CI, 0.40-0.66 and SMD = 0.58, 95% CI, 0.43-0.73) in the postoperative delirium (POD) group. Postoperative blood NfL levels were significantly elevated in both POD group (SMD = 0.49; 95% CI, 0.34-0.64) and no-POD group (SMD = 0.67; 95% CI, 0.53-0.81). Our meta-analysis indicates that NfL may serve as a potential biomarker for POD, and elevated preoperative blood NfL levels could be a risk factor for POD. Further studies are needed to confirm the association of CSF/blood NfL levels with other PND types.

Objectives: the aim of this study was the assessment and monitoring of the safety and quality of the existing water supplies in Ibb and Taiz governorates from the microbial side and testing the quality of drinking water in the Ibb and Taiz governments and comparing it with the World Health Organization. Methods: the water samples (99 samples) taken from some water sources, distribution reservoirs, and different regions were studied for bacteriological analysis, in which they were inoculated into culture media using two methods: the presumptive test for the most probable number and the confirmatory test for bacterial identification. Results: the results indicated the non-existence of indicator bacteria that were used to indicate the possible presence of fecal bacteria among 24 (24.24%) water samples taken from Taiz governorate, while the presence of an unacceptable number of Escherichia coli in house tanks (30%) was indicated among 75 (74.25%) water samples taken from Ibb governorate. Conclusions: the results of the study in Taiz governorate show that the water is fit for human consumption, as well as the samples taken from water sources and distribution networks in Ibb governorate, but the results of the samples taken from house tanks showed that the water is not fit for human consumption and needs to be treated.

We propose a theoretical Photocurrent - Voltage characteristic (PC-V) model to assess the interfacial phenom-ena for a photo-electrochemical cell (PEC). The origin of voltage deficits and the distribution of the photocur-rent across the semiconductor-electrolyte interface (SEI) are reported for the cell. The model predicts the hole exchange current parameter to extract the cell polarization data at the SEI. The potential drop across the SEI across the cell is mapped for the n-type cells. The simulation results of the Pc-V model are used to differentiate the effect of the bulk and space charge region (SCR) recombination in the semiconductor cells. A deep neural network model is developed to assess the electron-hole transfer mechanism using the Pc-V characteristic curve. The applicability of the model is tested and validated with the real time data. The results show good agreement with the experimental data.

Hospitalizations for patients with diabetes and dementia take a significant health and economic toll on older adults in the United States. The present study sought to examine the differences in hospitalization characteristics and outcomes associated with diabetes and dementia separately and together using discharge record data of Los Angeles County residents aged 50+ from the California Department of Health Care Access and Information for the period 2019-2021. Results from multiple linear regression analyses indicated that when compared to those with no diabetes or dementia, patients with diabetes alone exhibited the highest total charges, while those with comorbid diabetes and dementia exhibited lower charges (p≤.05). Results of a multinomial logistic regression found that patients with comorbid diabetes and dementia had the highest odds of having a length of stay of 7+ days (AOR=1.49; 95% CI=1.44-1.53). A matched-case control analysis found that comorbid diabetes and dementia was associated with significantly lower odds of hypertensive disease than diabetes alone (MOR=0.81; 95% CI=0.67-0.97). These results highlight the complexity of factors affecting variation in hospitalization outcomes across these distinct disease profiles. Findings suggest a need to carefully consider this complexity when developing health policies or strategies to improve hospitalization outcomes involving these disease conditions.

Porous coatings on prosthetic implants encourage implant fixation. Enhanced fixation may be achieved using a magneto-active porous coating that can deform elastically in vivo on application of an external magnetic field, straining in-growing bone. Such coating, made of 444 ferritic stainless steel fibres, was previously characterised in terms of its mechanical and cellular responses. In this work, co-cultures of human osteoblasts and endothelial cells were seeded into a novel fibrin-based hydrogel embedded in a 444 ferritic stainless steel fibre network. Albumin was successfully incorporated into fibrin hydrogels improving the specific permeability and the diffusion of fluorescently-tagged dextrans without affecting their Young’s modulus. The beneficial effect of albumin was demonstrated by upregulation of osteogenic and angiogenic gene expression. Furthermore, mineralisation, extracellular matrix production and formation of vessel-like structures were enhanced in albumin-enriched fibrin hydrogels compared to fibrin hydrogels. Collectively, the results indicate that the albumin-enriched fibrin hydrogel is a promising bio-matrix for bone tissue engineering and orthopaedic applications.

This article compares the predictive capabilities of six models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Support Vector Machine (SVM), XGBoost, Random Forest (RF) and Deep Neural Network (DNN) to predict the default behaviour of credit card holders in Taiwan using data from the UCI machine learning database. Python programming language was used for data analysis. Statistical methods were compared with machine learning algorithms using the confusion matrix measured in metric terms of prediction accuracy, sensitivity, specificity, precision, G-mean, F1 score, ROC and AUC. The dataset contains 30,000 credit card user’s information with 6636 default observations and 23,364 non-default cases. The study results found that modern machine learning methods outperformed traditional statistical methods in terms of predictive performance measured by F1 score, G-mean and AUC. Traditional methods like logistic regression were marginally better than linear discriminant analysis and support vector machines in terms of predictive performance measured by area under the receiver operating characteristic curve. In the modern machine learning methods, deep neural networks were better in most of the predictive performance metrics than XGBoost and Random Forest methods.

The role of lactic acid (lactate) in cell metabolism has been significantly revised in recent dec-ades. Initially, lactic acid was attributed to the role of a toxic end product of metabolism, which accumulation in the cell and extracellular space leads to acidosis, muscle pain and other adverse effects. However, it has now become obvious that lactate is not only a universal fuel molecule and the main substrate for gluconeogenesis, but also one of the most ancient metabolites with signaling function, which has a wide range of regulatory activity. The Warburg effect described 100 years ago (that means intensification of glycolysis associated with high lactate production), which is characteristic of many malignant tumors, confirms the key role of lactate not only in physiological conditions, but also in pathologies. The study of lactate’s role in the malignant transformation becomes more relevant in the light of the “atavistic theory of carcinogenesis,” which suggests that tumor cells return to a more primitive hereditary phenotype during micro-evolution. In this review, we attempted to summarize the accumulated knowledges about the functions of lactate in cell metabolism and its role in the process of carcinogenesis, and to con-sider the possible evolutionary significance of the Warburg effect.