A vivid debate is ongoing in the scientific community about statistical malpractice and the related publication bias. No general consensus exists on the consequences and this is reflected in heterogeneous rules defined by scientific journals on the use and reporting of statistical inference. This paper aims at discussing how the debate is perceived by the agricultural economics community and implications for our roles as researchers, contributors to the scientific publication process, and teachers. We start by summarizing the current state of the p-value debate and the replication crisis, and commonly applied statistical practices in our community. This is followed by motivation, design, results and discussion of a survey on statistical knowledge and practice among the researchers in the agricultural economics community in Austria, Germany and Switzerland. We conclude that beyond short-term measures like changing rules of reporting in publications, a cultural change regarding empirical scientific practices is needed that stretches across all our roles in the scientific process. Acceptance of scientific work should largely be based on the theoretical and methodological rigor and where the perceived relevance arises from the questions asked, the methodology employed, and the data used but not from the results generated. Revised and clear journal guidelines, the creation of resources for teaching and research, and public recognition of good practice are suggested measures to move forward.

Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO) based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization- based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36×10^-5.

Non-rigid registration presents a significant challenge in the domain of point cloud processing. The general objective is to model complex non-rigid deformations between two or more overlapping point clouds. Applications are diverse and span multiple research fields, including registration of topographic data, scene flow estimation, and dynamic shape reconstruction. To provide context, we begin with a general introduction to the topic of point cloud registration, including a categorization of methods. Next, we introduce a general mathematical formulation for point cloud registration and extend it to address non-rigid registration. A detailed discussion and categorization of existing approaches to non-rigid registration follows. We then introduce our own method where the usage of piece-wise tricubic polynomials for modeling non-rigid deformations is proposed. Our method offers several advantages over existing methods. These advantages include easy control of flexibility through a small number of intuitive tuning parameters, a closed-form optimization solution, and an efficient transformation of huge point clouds. We demonstrate our method through multiple examples that cover a broad range of applications, with a focus on remote sensing applications - namely, the registration of Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TLS) point clouds. The implementation of our algorithms is open source and can be found on GitHub.

Urban scene point cloud pose significant challenges for registration due to its large data volume, similar scenarios and dynamic objects. In this paper, we propose PCRMLP, a model for urban scene point cloud registration that achieves comparable registration performance to prior learning-based methods. Compared to previous works which focus on extracting features and estimating correspondence, the model estimates the transformation implicitly from concrete instances. An instance-level urban scene representation method is introduced to extract instance descriptors via semantic segmentation and DBSCAN, which enable the model to obtain robust instance features, filter dynamic objects and estimate transformation in a more logical manner. Then a lightweight network consisting of MLPs is employed to obtain transformation in an encoder-decoder manner. We validate the approach on KITTI dataset. Experimental results demonstrate that PCRMLP can obtain a satisfactory coarse transformation from instance descriptors just in 0.0028s. With a subsequent ICP refinement module, the proposed method achieves higher registration accuracy and computational efficiency than prior learning-based works.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

This study proposes a method for image matching between infrared(IR)-RGB images using a deep learning network to estimate the deformation field. We propose a deformation field generator (DFG) that estimates the deformation field of the transformation matrix to match each pixel or IR image to the RGB image. DFG is a network that receives IR and RGB images as input; the output size is two channels and has the sample resolution as the input image. By warping the IR image through a grid-sampler that warps the image according to the value of the deformation field, we can obtain a warped IR image that matches the RGB image. Additionally, to check whether the warped IR image matched the RGB image, the masking images detecting the segmentation of objects were photographed in two images. Without directly comparing IR and RGB images, we proposed mask loss that warps the IR mask image through the deformation field and grid sampler and then compares the warped IR mask image with the RGB mask image. Mask loss solves the spatial similarity comparison problem with multi-modality images, such as IR and RGB images, by comparing the mask image with the same modality image as the mask image.

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time-consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more efficient and robust compared to supervised learning mechanisms like deep learning. We present a hierarchical reinforcement learning framework combined with episodic memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrate that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.

Statistical management policy and system plays significant role in development of any nation as it guides the planner to analyze the gaps, design programs and allocate budget in efficient manner. Globally, design and usage of statistical management system varies relying on country specific ability and resources availability. Government of Nepal has been managing database in individual as well as thematic manner. Policy wise, thematic database is in operation based on Statistics Act 1958 and individual database is in operation based on Birth, Death and Other Personal Related Events Registration Act 1976. Though civil registration related policies have been either updated or newly formed, sectoral statistic management related policy has not been updated since its first promulgation period (1958). With no policy supporting the consolidated statistical management system, government as well as non-government agencies has been investing millions of dollars annually to collect, compile and analyze individual related data and fulfill their respective objectives. It has forced the government services and performance to be paper-based, time-consuming and tedious thus creating dis-trust among citizen each passing day. Though, formulation of legal instrument promoted digital movement and mandated shift towards e-government modality, the process has been low. Theoretically, Technology Acceptance Model (TAM) state the need of having advanced and efficient technology to be in place in current setting to improve governance, efficiency, accountability, transparency and trust. Also, the same theory state the requirement of strong willingness and motivation from the users (government, non-government and citizen) to adopt the technology based policy and system. As the integrated statistical management policy emphasizes on technology-based system or digital system, the perspectives of Technology Acceptance Model also termed as Information System Theory aid in understanding the context, conceptualizing and addressing the needs with an establishment and implementation of digital system or technology based system. This policy paper applied the Technology Acceptance Model (TAP) to examine the underlying opportunities and challenges associated with an integrated statistical management policy. With existing gaps in existing policy, system and practice alongside an overview of opportunities and challenges, this paper recommends the need of stand-alone integrated statistical management policy to consolidate sectoral as well as civil related information through single system based on recommended policy.

Recent advances in open data initiatives allow us to free access to a vast amount of open LiDAR data in many cities. However, most of these open LiDAR data over cities are acquired by airborne scanning, where the points on façades are sparse or even completely missing due to the viewpoint and object occlusions in the urban environment. Integrating other sources of data, such as ground images, to complete the missing parts is an effective and practical solution. This paper presents an approach for improving open LiDAR data coverage on building façades by using point cloud generated from ground images. A coarse-to-fine strategy is proposed to fuse these two different sources of data. Firstly, the façade point cloud generated from terrestrial images is initially geolocated by matching the SFM camera positions to their GPS meta-information. Next, an improved Coherent Point Drift algorithm with normal consistency is proposed to accurately align building façades to open LiDAR data. The significance of the work resides in the use of 2D overlapping points on the outline of buildings instead of limited 3D overlap between the two point clouds and the achievement to a reliable and precise registration under possible incomplete coverage and ambiguous correspondence. Experiments show that the proposed approach can significantly improve the façades details of buildings in open LiDAR data and improving registration accuracy from up to 10 meters to less than half a meter compared to classic registration methods.

Multi-source image registration often suffered from great radiation and geometric differences. Specifically, gray scale and texture from similar landforms in different source images often show significantly different visual features. And these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness.

COVID-19 Pandemic management has become the top priority of Government Institutions globally, which is justifiable seeing the high mortality of the disease. In India, Lockdowns by National, State and Local level administrations have greatly reduced the spread of the SARS COV-2 Virus. Some areas with a greater proportion of COVID-19 patients have been declared hotspots with increased restrictions on public activities through law enforcement. But quite often delay in identification of these hotspots leads to community transmission of the Virus thus aggravating the problem. A method to identify the areas which are at risk of becoming the next hotspot for the disease is the need of the hour[1]. In this Research document we will find the probable risk factors and make an appropriate scale to measure the vulnerability of an area, identified by its Postal code. To help with this a Pan India survey by the title of “Survey on General Indian population on the level of preparedness for COVID-19 pandemic” was launched and received around 1250 submissions, with the acquired data we will evaluate the risk factors and make appropriate scale to identify ‘pre-hotspots’.

Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.

.Purpose: This paper presents a comparative analysis of state-of-the-art DIR algorithms in the context of head and neck squamous cell carcinoma (HNSCC) radiation therapy. Materials and Methods: We used a dataset of head and neck cancer patients within the Cancer Image Archive, which includes 31 patients with 3D CT image data from pre-treatment, mid-treatment, and post-treatment stages of radiotherapy. We applied DIR algorithms to two datasets, one for pre-treatment to mid-treatment registration and another for mid-treatment to post-treatment registration, by aligning one CT image to another CT image. To quantitatively analyze the patient data, we focused on the spinal cord, optic nerve, brain stem, cochlea, and PTV as the OAR datasets. To verify the DIR algorithm, we used two non-learning-based methods, SyN and NiftyReg, as well as three deep learning-based methods, Voxelmorph, Cyclemorph, and Transmorph. We trained a deep learning model by adjusting the ratio of the training dataset, validation dataset, and test dataset to 7:1:2. Results: The average DSCs of SyN and NiftyReg, the non-learning-based methods, were 0.74±0.06 and 0.70±0.12, respectively. The deep learning-based methods, Voxelmorph, Cyclemorph, and Transmorph, had average DSCs of 0.72±0.08, 0.68±0.11, and 0.69±0.13, respectively. The deep learning DIR algorithm produced transformed outputs of OAR/PTV in a shorter time than other models, including commercial and conventional mathematical algorithms (At Inference(sec/images), Voxelmorph: 0.36, Cyclemorph: 0.3, Transmorph: 5.1, SyN: 140, NiftyReg: 40.2). Conclusions: In conclusion, our study provides a comprehensive comparison of commercial DIR algorithms with traditional and deep learning-based DIR methods. Our results demonstrate that commercial DIR algorithms outperform traditional DIR methods in terms of accuracy and efficiency. However, we also show that deep learning-based DIR methods have the potential to achieve similar performance to commercial algorithms with proper training and optimization. Our findings suggest that both commercial and deep learning-based DIR methods have their respective advantages and limitations, and the choice of method should depend on the specific requirements of the clinical application. Overall, our study contributes to ongoing efforts to improve the accuracy and efficiency of DIR methods for better patient outcomes.

Accurate image registration is essential for eye movement compensation in optical coherence tomography (OCT) and OCT angiography (OCTA). The spatial resolution of an OCT instrument is typically anisotropic, i.e., different resolutions in the lateral and axial dimensions. When OCT images have anisotropic pixel resolution, residual distortion (RD) and false translation (FT) were always observed after image registration for rotational movement. In this study, RD and FT were quantitively analyzed over different degrees of rotational movement and various lateral and axial pixel resolution ratio (RL/RA) values. The theoretical analysis confirmed that the RD and FT increase significantly with the rotation degree and RL/RA. An image resizing assisting registration (RAR) strategy was proposed for accurate image registration. The performance of direct registration (DR) and RAR for retinal OCT and OCTA images were quantitatively compared. Experimental results confirmed that unnormalized RL/RA causes RD and FT, RAR can effectively improve the performance of OCT and OCTA image registration and distortion compensation.

Direct georeferencing of airborne pushbroom scanner data usually suffers from the limited precision of navigation sensors onboard of the aircraft. The bundle adjustment of images and orientation parameters, used to perform geocorrection of frame images during the post-processing phase, cannot be used for pushbroom cameras without difficulties: it relies on matching corresponding points between scan lines, which is not feasible in the absence of sufficient overlap and texture information. We address this georeferencing problem by equipping our aircraft with both a frame camera and a pushbroom scanner: the frame images and the navigation parameters measured by a couple GPS/Inertial Measurement Unit (IMU) are input to a bundle adjustment algorithm; the output orientation parameters are used to project the scan lines on a Digital Elevation Model (DEM) and on an orthophoto generated during the bundle adjustement step; using the image feature matching algorithm Speeded Up Robust Features (SURF), corresponding points between the image formed by the projected scan lines and the orthophoto are matched, and through a least-squares method, the boresight between the two cameras is estimated and included in the calculation of the projection; finally, using Particle Image Velocimetry (PIV) on the gradient image, the projection is deformed into a final image that fits the geometry of the orthophoto. We apply this algorithm to five test acquisitions over Lake Geneva region (Switzerland) and Lake Baikal region (Russia). The results are quantified in terms of Root Mean Square Error (RMSE) between matching points of the RGB orthophoto and the pushbroom projection. From a first projection where the Interior Orientation Parameters (IOP) are known with limited precision and the RMSE goes up to 41 pixels, our geocorrection estimates IOP, boresight and Exterior Orientation Parameters (EOP) and produces a new projection which RMSE with the reference orthophoto is around two pixels.

In order to achieve the target, several activities are being undertaken, including the development of multipurpose reservoir planning for the water supply and sanitation infrastructure, irrigation, and hydropower development for the H project. To undertake this work, the main data given was rainfall data, flow data, catchment area, base population for water supply, and so on. This report presents a specifically preliminary study and design of water supply, irrigation, and hydropower development for the project, which is carried out in accordance with the scope of work given by our professor. The preliminary study and design are therefore to investigate scenarios and alternatives and prepare a document of water supply, irrigation, and hydropower infrastructures for the provision of the assignment. This report, as required in the assignment, contains project background, water sources assessment, selection of suitable hydropower potential, and irrigation sizing, design criteria, descriptions of the proposed scheme, preliminary design of water supply components. The objective of the project is to recommend and design a cost-effective water supply system, irrigation, and hydropower development to supply reliable, sufficient, and safe water from a defined water source to the H project. The specific objectives of the investigation works are: • To assess potential water source alternatives for water supply • Selecting appropriate size for the reservoir which could satisfy all water demand • Assess and select suitable irrigation development and hydropower potential. • Preliminary study and design of all components of the reservoir and appurtenant structures The scope of the project generally focuses on preliminary study and design of water supply, irrigation and hydropower development components from water sources potential and the water balance output.

Pre-exposure prophylaxis (PrEP) is a highly effective HIV prevention strategy that involves the continuous administration of antiretroviral drugs to HIV-negative individuals wit a substantial risk of contracting an HIV infection. The use of PrEP has shown a reduction in the risk of HIV acquisition through sexual intercourse by up to 99%. Despite its effectiveness, PrEP uptake remains low among populations at high risk of HIV infection. This highlights the need for further research in stratetegies to enhance awareness and uptake of PrEP amongst these specific populations

The intricate nature of congenital heart disease requires understanding of complex, patient-specific three-dimensional dynamic anatomy of the heart, from imaging data such as three-dimensional echocardiography for successful outcomes from surgical and interventional procedures. Conventional clinical systems use flat screens and therefore display remains two-dimensional, which undermines the full understanding of the three-dimensional dynamic data. Additionally, control of three-dimensional visualisation with two-dimensional tools is often difficult, so used only by imaging specialists. In this paper we describe a virtual reality system for immersive surgery planning using dynamic three-dimensional echocardiography, which enables fast prototyping for visualisation such as volume rendering, multi-planar reformatting, flow visualisation, and advanced interaction such as three-dimensional cropping, windowing, measurement, haptic feedback, automatic image orientation, and multi-user interactions. The available features were evaluated by imaging and non-imaging clinicians, showing that the virtual reality system can help improve understanding and communication of the three-dimensional echocardiography imaging and potentially benefit congenital heart disease treatment.

We aimed to assess the caffeine intake before and during pregnancy, compliance with caffeine recommendations during pregnancy (200 mg/day) and factors associated with higher intakes before and in first trimester of pregnancy. Caffeine consumption was collected in a Spanish cohort of pregnant women through a validated food questionnaire, before and in each trimester of gestation (T1, T2 and T3). 463 pregnant women were recruited and follow-up through pregnancy. Compliance with caffeine intake recommendations during pregnancy and quintiles of mg/day of caffeine before and in T1 of pregnancy were calculated. A multivariate logistic regression, comparing extreme quintiles of consumption (Q1 vs Q5) was used. Mean caffeine intake before pregnancy was 120.05 mg/day (SD 117.85), 42.76 mg/day (SD 63.90) at 12th GW, 42.00 mg/day (SD 59.76) at 24th GW and 39.34 mg/day (SD 50.9) at 32nd GW (p<0.001). 86% of women complied with caffeine recommendations during pregnancy. At pregnancy (T1), being an active smoker was associated with Q5 > 100.1 mg/day, aOR = 22.69; 95% CI 4.67- 110.26. igh diet quality, aOR = 0.30; 95% CI, 0.13- 0.68, and moderate physical activity level, aOR= 0.44; 95% CI, 0.19-1.00, were inversely associated with Q5 > 100.1 mg/day. Pregnant women are mostly adhering to current caffeine intake guidelines. Higher caffeine intake at pregnancy is associated with other unhealthy habits during pregnancy.

Star image registration is the most important step in the application of astronomical image differencing, stacking and mosaicking, which requires high robustness, accuracy and real--time of the algorithm, but there is no high--performance registration algorithm in this field. In this paper, we propose a star image registration algorithm that relies only on radial module features (RMF) and rotation angle features (RAF), which has excellent robustness, high accuracy, and good real--time performance. The test results on a large amount of simulated and real data show that the comprehensive performance of the proposed algorithm is significantly better than the four classical baseline algorithms in the presence of rotation, insufficient overlapping area, false stars, position deviation, magnitude deviation and complex sky background, which is a more ideal star image registration algorithm.

With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

Cardiovascular-related diseases are one of the leading causes of death worldwide. An understanding of heart movement based on images plays a vital role in assisting the procedure in the postoperative and postoperative processes. In particular, if the shape information can be provided in real-time using the electrocardiogram(ECG) signal using this information, the heart’s movement information can be used for cardiovascular analysis and imaging guides during surgery. In this paper, we propose creating a 3D+t cardiac coronary artery model that is rendered in real-time according to the ECG signal. Hierarchical cage-based deformation modeling is used to generate mesh deformation used during the procedure according to the ECG signal. We match the blood vessel’s lumen obtained from the ECG-gated 3D+t CT angiography taken at the multiple cardiac phases to derive the optimal deformation. Splines for 3D deformation control points were used to continuously represent the obtained deformation at the multi-view according to the ECG signal. To verify the proposed method, we compared the manually segmented lumen and results of the proposed method for eight patients. The average distance and dice coefficient between the two models was 0.543mm and 0.735, respectively. The required time for registration of the 3D coronary artery model is 23.53 seconds/model. rendering speed to derive the model according to the ECG signal after generating the 3D+t model is faster than 120 FPS.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

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

This study aimed to evaluate the level of skill in observing teaching competency in pre-service physical education (PE) teachers and provide basic data to cultivate teaching competency. A total of 21 third-year pre-service PE teachers were selected as participants. The study derived descriptive statistics to analyze the participants’ observation skills regarding teaching competency in different class stages using evaluation results of class demonstrations by pre-service teachers. Mann–Whitney U test was conducted to verify the statistical significance of the evaluation between in-service and pre-service teachers. Compared with in-service teachers, pre-service teachers reported high agreement with intuitive teaching behaviors. However, pre-service teachers indicated low agreement with skills that required complex teaching behaviors depending on various situations. Based on theories by Schon (1983), pre-service teachers showed high “knowledge for practice” and low “knowledge in practice.” Thus, pre-service education must balance practical knowledge and theory rather than focus on theory-based education. This study provided directions for pre-service teacher education to enhance the competency of pre-service teachers for leading and analyzing classes.

This study presents a method for the rapid and accurate generation of two-dimensional (2D) orthomosaic maps using selected image data collected by drone-captured video. The focus is on developing a real-time method capable of creating maps more quickly than image selection. The scale-invariant feature transform (SIFT) algorithm is applied to drone images to extract features in various scale regions. For feature point matching, a matching method based on the fast library for approximate nearest neighbors (FLANN) was adopted. A comparison of the computational speed of the FLANN with that of the k-nearest neighbors (KNN) and brute force matcher during the matching process revealed FLANN's superior capability for real-time data processing. The random sample consensus (RANSAC) algorithm was employed to enhance the accuracy of the matching by removing outliers, effectively identifying and eliminating mismatches, and reinforcing the reliability of feature point matching. The combination of SIFT, FLANN, and RANSAC algorithms demonstrates the capacity to process drone-captured image data in real time, facilitating the generation of precise 2D orthomosaic maps. The proposed method was assessed and validated using imagery obtained as the drone executed curvilinear and straight flight paths, confirming its accuracy and operational efficiency concurrently with the image capture process.

The purpose of this study was to develop a reflective evaluation tool that can enhance the teaching competency of pre-service physical education teachers. A Delphi survey was conducted to modify the questions based on the evaluation tool for the teaching competency of physical education teachers, and each evaluation standard based on teaching competency was developed. The evaluation tool consisted of 46 questions for class preparation (the creation of the learning environment), the introduction (routine activities, learning goals, and task presentation), development (class strategy, observation and interaction, and the maintenance of the learning environment), and conclusion (routine activities, summary, and closure). It was designed to increase the accuracy of evaluation by developing evaluation criteria for each question. An evaluation tool including quantitative and qualitative methods for use in pre-service physical education teacher education was developed. The significance of this study was the development of an effective evaluation tool that can evaluate the core teaching behaviors in the field of physical education. This evaluation tool should be used as a learning tool that includes planning, operation, evaluation, and seeking improvement measures through reflective activities. If pre-service teacher education institutions apply this evaluation tool in their teacher training programs, it would be a great chance to learn how to develop and sustain teaching abilities and effectiveness.

Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform path characterizes this error. In response to this challenge, we propose a linear, parameter-free model, which uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established from the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit's size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model's effectiveness in 3D pose estimation with substantial pose differences and efficient pose optimization in larger circuits.

Objectives: The aim of our current study was to analyze whether the use of important measures of methodological quality and reporting of randomized clinical trials published in the field of cardiovascular disease research have changed over time. Further aim was to investigate whether there was an improvement over time in the ability of these trials to provide a good estimate of the true intervention effect. Methods We conducted two searches in the Cochrane Central Register of Controlled Trials (CENTAL) database to identify cardiovascular clinical research trials published in either 2012 or 2017. Randomized clinical trials (RCTs) trials in cardiovascular disease research with adult participants were eligible to be included. We randomly selected 250 RCTs for both publication year 2012 and 2017. Trial characteristics, data on measures of methodological quality and reporting were extracted and risk of bias for each trial was assessed. Results As compared to 2012 in 2017 there were significant improvements in the reporting of the presence of a data monitoring committee (42.0% in 2017 compared to 34.4% in 2012), and a positive tendency of registering cardiovascular disease research RCTs in clinical trial registries (83.6% in 2017 compared to 72.0% in 2012). On the other hand, we also observed that significantly fewer RCTs reported sample size calculation (60.4% in 2017 compared to 98.4% in 2012) in 2017 as compared to 2012. RCTs in 2017 were more likely to have low overall risk of bias ( RoB ) than in 2012 (29.2% in 2017compared to 21.2% in 2012). Conclusion: As compared to 2012 in 2017 there were significant improvement in some, but not all the important measures of methodological quality. Although more trials in the field of cardiovascular disease research had a lower overall RoB in 2017, the improvement over time was not consistently perceived in all RoB domains.

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for high-quality digitization of content from pages and whiteboards to sharable online material. But capturing this information is not always easy, nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts, by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use.

Cocoa beans of the CCN-51 clone harvested in the Tumaco region (Nariño, Colombia) were fermented following four different treatments: (1) spontaneously (at room temperature for 120 h) in traditional conditions (Control); (2) traditional processing with a pre-drying (sun-dried for 24 h) treatment (PD); (3) with fermentation (for 120 h) after inoculation of a yeast starter culture (YS); and (4) including both treatments, pre-drying and yeast inoculation (PD+YS). Chemical composition, fatty acids, total polyphenol, methylxanthines (theobromine and caffeine) and lactic acid content of cocoa beans were determined. Chemical and fatty acid composition and theobromine content were not affected by the different fermentation treatment applied. When analysing total polyphenol content, YS and PD+YS treatments significantly increased the content of these compounds at the end of the fermentation process, while PD treatment decreased the caffeine content of cocoa beans. Finally, lactic acid content decreased as a consequence of both inoculation of yeast starter and mainly the pre-drying treatment. In conclusion, solar pre-drying in the open air and the inoculation of a yeast starter treatments could improve the final quality of coca beans.

BACKGROUND: Pre-marital sex is a sexual interest practiced by people who are yet to get married and has ended up being accepted specifically in western countries. Sex before marriage is sexual behavior engaged in by single people. In the past, having sex before getting married is seen as a moral dilemma that many cultures forbade and that many people regarded to be sin in many religions, although it has gained wider acceptance across the board. OBJECTIVES: This study was carried out to determine the perception, practices, and factors associated with pre-marital sex, courtship, and its influences among Osun State University Undergraduates, Osogbo, Osun State. METHODOLOGY: The study was a descriptive study, and respondents were selected using multi-stage sampling technique. Students of Osun State University, Osogbo main campus form the population of the study and its sample size was calculated using Leslie Kish’s formula. A total of 387 responses were validated from the survey. The instrument for data collection was a self-administered semi-structured questionnaire consisting of four sections. The data obtained were collated, examined for completion, coded, imputed, and analyzed using SPSS v21. RESULTS: The mean age of the respondents was between 19.19±2.17, and the majority were between 18 – 24 years. About 52% of the respondents show a positive perception of premarital sex and courtship. Also, in the practice of courtship majority (88.3%) of the respondents think those that go around together are courting and from the responses obtained and analyzed from the factors leading to pre-marital sex, the findings revealed that peer pressure (90.4%), Internet (86.7%), pornography (83.9%) and drugs (82.9%) are the leading factors which contribute to pre-marital sex among undergraduates. CONCLUSION: The study hereby recommends that Parents should as a matter of responsibility monitor and pays regular visits to their children in the universities and provide them with materials that will make them stay comfortable for them not to fall victim to peer pressure.Pre-marital sex is a practice that should be avoided by undergraduate students, though most students consider courtship as a normal practice, it is still used as a normal factor that leads them to practice pre-marital sex.

Among the plant resources in pre-Columbian Mexico and Central America, the Chia (Salvia hispanica L.) is unique in being the only member of the Lamiaceae cultivated for the mericarps. Recently the crop has gained considerable attention due to its high of content omega-3 polyunsaturated fatty acid, antioxidants, fibre and protein classifying it as a nutraceutical.

Introduction: Exams are a relatively stressful period for all students, especially undergraduate medical students. Exams bring anxiety and stress for the students. Some students experience such high stress that it hinders their academic productivity and reduces their exam performance. The aim of this study is to assess the effects of pre-exam anxiety on the academic performance of medical students. Methods: Two hundred and twenty-five final year medical students completed Westside Test Anxiety (WTA) Scale one month before their exams. Grade Point Average (GPA) of these students was noted when the results were announced. Data was processed and analyzed using SPSS v 22.0Results: The mean anxiety score on WTA scale was 3.46 ± 0.87. All students (100%) who scored ≤ 2.50 GPA were highly-extremely highly anxious. In the 2.51-3.00 GPA group, 46% were highly-extremely highly anxious, 32% were moderately anxious, and 21% had low to normal anxiety. In the 3.01-3.50 GPA group, 30% were highly-extremely highly anxious, 30% were moderately anxious, and 39% had low to normal anxiety. In the 3.51-4.00 GPA group, 29% were highly-extremely highly anxious, 23% were moderately anxious, and 47% had low to normal anxiety. The correlation coefficient between GPA and test anxiety of students was -.314 which shows inverse relationship.Conclusion: Pre-exam anxiety and stress imparts negative effects on the exam performance of final year medical students. Poor academic performance was associated with high to extremely high pre-exam anxiety while high achievers had relatively lower anxiety levels.

A divergent selection experiment on environmental sensitivity was performed in rabbits. The aim of this study was to estimate the correlated response in kits’ weight and its survival, and weight distance from birth to weaning. Weight distance was calculated as the absolute values of the differences between the individual value and the mean value of its litter. Also, relationship between probability of survival at 4 d of age and weight at birth was studied. Environmental sensitivity was measured as litter size variability. A total of 2484 kits from 127 does of the low line (selected for reducing litter size variability) and 1916 kits of 114 does of the high line (selected for increasing litter size variability) of the 12th generation were weighed. Bayesian methodology was used to estimate the correlated response to selection, and LOGISTIC procedure of SAS was used to estimate the relationship between weight and probability of survival. Both lines showed similar individual weight at birth and at weaning, and similar survival at birth and at 4 d of age. Survival at weaning was higher in the low line than in the high line (0.67 and 0.62; P= 0.93). Weight distance was higher at birth but lower at weaning in the low line (47.8 g and 54.1 g; P=0.98). Kit’s weight at birth affected its survival. In conclusion, selection for environmental sensitivity showed correlated response in kits survival and in homogeneity of litter weight at weaning.

Fundus image registration plays a crucial role in the clinical evaluation of ocular diseases, such as diabetic retinopathy and macular degeneration, necessitating meticulous monitoring. The alignment of multiple fundus images enables the longitudinal analysis of patient progression, widening the visual scope, or augmenting resolution for detailed examinations. Currently, prevalent methodologies rely on feature-based approaches for fundus registration. However, certain methods exhibit high feature point density, posing challenges in matching due to point similarity. This study introduces a novel fundus image registration technique integrating U-Net for the extraction of feature points employing FIVES for its training and evaluation, a novel and large dataset for blood vessels segmentation, prioritizing point distribution over abundance. Subsequently, the method employs medial axis transform and pattern detection to obtain feature points characterized by the Fast Retina Keypoint (FREAK) descriptor, facilitating matching for transformation matrix computation. Assessment of the vessel segmentation achieves 0.7559 for Intersection Over Union (IoU), while evaluation on the Fundus Image Registration Dataset (FIRE) demonstrates the method’s comparative performance against existing methods, yielding a registration error of 0.596 for area under the curve, refining similar earlier methods and suggesting promising performance comparable to prior methodologies.

The objectives of this study were to evaluate fixed risk factors associated with PWM and to estimate genetic parameters for PWM. A total of 927 birth records from a mixed population of purebred and crossbred Boer and Spanish goats born between 2016 and 2023 at the International Goat Research Center (IGRC) were used for this study. Four binary traits were studied: D0-3 (death within 3 days after birth), D4-60 (death between 4 and 60 days), D61-90 (death between 61 and 90 days), and D0-90 (death within 90 days). Logistic regression models were used to evaluate the risk factors associated with PWM traits. Bayesian threshold models and Gibbs sampling were used to estimate the genetic parameters. Birth weight, season, litter size, sex, dam age, breed, and heterosis were found to be significantly associated with at least one of the PWM traits. Heritability estimates were 0.263, 0.124, 0.080, and 0.207, for D0-3, D4-60, D61-90, and D0-90, respectively. The genetic correlations between the studied traits ranged from 0.892 (D0-3 and D0-90) to 0.999 (D0-3 and D61-90). These results suggest that PWM in goats is influenced by both non-genetic and genetic factors and can be reduced by management, genetic selection, and crossbreeding approaches.

Introduction Infections caused by multidrug-resistant bacteria (MDR) occur more frequently after pancreaticoduodenectomy (PD) compared to other abdominal surgeries, and infective complications are a leading cause of postoperative morbidity following PD. Pre-operative biliary stent (PBS) placement often leads to biliary contamination, which in turn plays a significant role in postoperative infections. The aim of this study is to evaluate the impact of MDR contamination on short-term postoperative outcomes in patients undergoing PD and to examine the relationship between MDR bacteria, PBS, and bile contamination. Methods This is a retrospective study based on a prospectively maintained database comprising 825 consecutive patients who underwent pancreaticoduodenectomy (PD) between January 2010 and December 2020 at our center. All procedures were conducted with a standardized resection by a dedicated group of surgeons in a specialized pancreatic surgery unit, and the patients were managed according to the same perioperative enhanced recovery protocol. Results MDR bacteria were present in 17.5% of bile cultures, exclusively within the stented group. In our multivariate analysis, the development of a major postoperative complication (MPC) was correlated with the presence of MDR bacteria in the bile (OR 1.66, 95% CI: 1.1-2.52; p = 0.02). MDR bacteria were detected early in the surgical drainage in 144 out of 825 patients (12.1%), with 72.2% having a previous biliary stent placement and 27.8% without stents (p < 0.001). In our multivariate analysis, the development of an MPC was associated with the presence of MDR bacteria in the drainage (OR=1.81, 95% CI: 1.21-2.73, p= 0.0042). Conclusions We demonstrated that MDR contamination worsens the short-term outcomes of patients undergoing PD. Specifically, when MDR bacteria are present in both the bile and drainage, there is a statistically significant increase in the incidence of major postoperative complications (MPC). Our data suggest that the majority of MDR surgical site infections stem from biliary contamination resulting from the placement of a pre-operative biliary stent (PBS).

The Chat Generative Pre-training Transformer (GPT), also known as ChatGPT, is a powerful generative AI model that can simulate human-like dialogues across a variety of domains. However, this popularity has attracted the attention of malicious actors who exploit ChatGPT to launch cyberattacks. This paper examines the tactics that adversaries use to leverage ChatGPT in a variety of cyberattacks. Attackers pose as regular users and manipulate ChatGPT’s vulnerability to malicious interactions, particularly in the context of cyber assault. The paper presents illustrative examples of cyberattacks that are possible with ChatGPT and discusses the realm of ChatGPT-fueled cybersecurity threats. The paper also investigates the extent of user awareness of the relationship between ChatGPT and cyberattacks. A survey of 253 participants was conducted, and their responses were measured on a three-point Likert scale. The results provide a comprehensive understanding of how ChatGPT can be used to improve business processes and identify areas for improvement. Over 80% of the participants agreed that cyber criminals use ChatGPT for malicious purposes. This finding underscores the importance of improving the security of this novel model. Organizations must take steps to protect their computational infrastructure. This analysis also highlights opportunities for streamlining processes, improving service quality, and increasing efficiency. Finally, the paper provides recommendations for using ChatGPT in a secure manner, outlining ways to mitigate potential cyberattacks and strengthen defenses against adversaries.

The violence against women is a theme that extrapolates health issues, since after a victim seek for a health care, there is the notification of the act [1]. In Brazil, many times, these women are first assisted by mobile urgency services (SAMU/SIATE) and, as far as we have concern, there are no other studies with specific data about these assistance. The present paper aimed to analyze SAMU/SIATE assistance to abused women. Qualitative and retrospective epidemiological study of assistance to assaulted women carried out by SIATE and SAMU Maringá/Norte Novo between 2011 and 2020. Women between 20 and 39 years old, non-pregnant, were the main victims, and 19,52% of them have used some kind of drug. The (ex)partner figured as the perpetrator in 17,35%, but there were no information about this variable in 73,75% of the records. The Qui-square test shows a mortality rate superior to 70% among the severe traumatized victims. This is one of the first studies to analyze this kind of care provided by SAMU/SIATE, and reveals some gaps in its “modus-operandi” that can limitate an extrapolation of the results to bigger spheres. More researches in mobile urgency care services, in other provinces also, are needed to propose strategies to fight against this epidemic.

The log data generated during the operation of a software system contains information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most of the existing log anomaly detection methods are specific to a particular system, have cold start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pre-training model and domain adaptation, which uses the pre-training model BERT to learn the semantic information of logs, and can solve the problems caused by multiple meaning words and log statement updates. An anomaly detection method LogBD based on distance to determine anomalies is constructed based on domain adaptation, using TCN networks to extract common features of different system logs and map them to the same hypersphere space. Finally, experiments are conducted on two publicly available datasets to evaluate the method, and the experimental results show that the method can better solve the log instability problem and has some improvement in the cross-system log anomaly detection effect.

Lymphatic filariasis (LF), also commonly known as elephantiasis, is a neglected tropical disease (NTD) caused by filarial parasites. The disease is transmitted by a bite from infected mosquitoes. The bites of these infected mosquitoes deposit filarial parasites, Wuchereria or Brugia whose predilection site is the lymphatic system. The damage to the lymph system causes swelling in the legs, arms, and genitalia. A mapping survey conducted between 2003 and 2010 determined LF that LF was endemic in Zambia in 96 out of 116 districts. Elimination of LF is known to be possible by stopping the spread of the infection through large-scale preventive chemotherapy. Therefore, mass drug administration (MDA) with diethylcarbamazine citrate (DEC) (6 mg/kg) and Albendazole (400 mg) for Zambia has been conducted and implemented in all endemic districts with five effective rounds. Post-MDA pre-transmission assessment survey (pre-TAS) was conducted between 2021 and 2022 in 80 districts to determine the LF prevalence rate. We conducted a cross-sectional seroprevalence study involving 600 participants in each Evaluation Unit (EU) or each district. The study sites (sentinel and spot-check sites) were the districts that were the Implementation Unites (IUs) where MDA, preventive chemotherapy against LF was conducted. These included 80 districts from the 9 provinces. A total of 47,235 people from sentinel and spot-check locations were tested. Of these, valid tests were 47,052 of which 27,762 (59%) were females and 19,290 (41%) were males. The survey revealed in the 79/80 endemic district a prevalence of Wb anti-gens of 0.14% and 0.0% prevalence of microfilariae. All the surveyed districts had an optimum prevalence of less than 2 percent, except for Chibombo district. The majority of participants that tested positive for Wb Ag were those that had 2, 3, and 4 rounds of MDA. Surprisingly, individuals that had 1 round of MDA were not found to have circulating antigens of Wb. The study showed that all the surveyed districts except for Chibombo, passed Pre-TAS. This further implies that there is a need to conduct a TAS in these districts in order to decide whether to stop MDA or not. Keywords: Lymphatic Filariasis; pre-TAS; Prevalence; Antigenaemia; Microfilariae; Zambia

Wild germplasm can be classified as the raw material essential for crop improvement. Introgression of wild germplasm is normally used in breeding to increase crop quality or resilience to evolving biotic and abiotic threats. Here, we explore the potential of introgressing Vaccinium elliottii into commercial blueberry germplasm. Vaccinium elliottii is a wild diploid blueberry species endemic to the southeastern United States that possesses highly desirable and economically important traits for blueberry breeding such as: short bloom to ripe period, adaptation to upland sandy soils, disease resistance, firmness, and pleasant flavor. To examine the potential of hybridization, we evaluated populations of interspecific hybrids across multiples stages of breeding (i.e., F1, F2, and backcrosses) in two crop seasons. We used our extensive pedigree data to generate breeding values for pre-breeding blueberry hybrid populations. Hybrid performance was evaluated considering fitness (i.e., plant vigor and plant height) in addition to evaluating six fruit-quality and marketable-related traits (i.e., size, firmness, acidity, soluble solids, weight, and yield). Overall, F2 and backcrosses rapidly achieved market thresholds, presenting values not significantly different from commercial blueberry germplasm. Our results confirmed the potential of exploiting the high genetic variability contained in V. elliotii for interspecific hybridization. Additionally, we developed germplasm resources that can be further evaluated and utilized in the breeding process, advancing selections for fruit quality and environmental adaptation.

Background: The world-wide rate of obesity is increasing continuously, representing a serious medical threat since it is associated with a variety of diseases including type 2 diabetes, cardiovascular disease, and numerous cancers. Sophora japonica is used as a traditional herb for medicinal purposes in eastern Asia. However, the anti-obesity effects of S. japonica fruit have not been explored. Objective: The aim of this study is to investigate the inhibition of adipocyte differentiation and adipogenesis by an ethanol extract of S. japonica fruit (EESF) in 3T3-L1 pre-adipocytes. Methods: MTT assay, Oil Red O staining, and Triglyceride contents were used to investigate lipid accumulation in 3T3-L1 cells to clarify adipocyte differentiation. The expression levels of various molecular signals associated with adipogenesis and lipogenesis were examined by western blot analysis. Results: Our results demonstrate that EESF suppressed the terminal differentiation of 3T3-L1 pre-adipocytes in a dose-dependent manner, as confirmed by a decrease in lipid droplet number and lipid content through Oil Red O staining. EESF significantly reduced the accumulation of cellular triglyceride, which was associated with a significant inhibition of the levels of pro-adipogenic transcription factors, downregulated the expression levels of adipocyte-specific proteins. Furthermore, EESF treatment effectively increased the phosphorylation of AMPK and ACC. Conclusions: These results together indicate that EESF has significant effects on the inhibition of adipogenesis and acts by stimulating the AMPK signaling pathway. Further studies will be needed to identify the active compounds in S. japonica.

Inspired by the work of S. D. Odintsov and V. K. Oikonomou, Phys. Rev. D 92, 024016 (2015) [1], the present study reports a reconstruction scheme for f (R) gravity with the scale factor a(t) µ (t _* - t) _c²²describing the pre-bounce ekpyrotic contraction, where t is the big crunch time. The reconstructed f (R) is used to derive expressions for density and pressure contributions and the equation of state parameter resulting from this reconstruction is found to behave like "quintom". It has also been observed that the reconstructed f (R) has satisfied a sufficient condition for a realistic model. In the subsequent phase the reconstructed f (R) is applied to the model of chameleon scalar field and the scalar field f and the potential V(f) are tested for quasi-exponential ex pansion. It has been observed that although the reconstructed f (R) satisfies one of the sufficient conditions for realistic model, the quasi-exponential expansion is not available due to this reconstruction. Finally, the consequences pre-bounce ekpyrotic inflation i n f (R) gravity are compared to the background solution for f (R) matter bounce.

Objective. To describe patient satisfaction with pre-hospital emergency knowledge and determine if patients and professionals share a common vision on the satisfaction predictors. Methods. A qualitative study conducted in two phases. First, a systematic review following the PRISMA protocol was carried out searching publications between January 2000 and July 2016 in Medline, Scopus, and Cochrane. Second, three focus groups involving professionals (advisers and healthcare providers) and a total of 79 semi-structured interviews involving patients were conducted to obtain information about what dimensions of care were a priority for patients. Results. Thirty-three relevant studies were identified. A majority conducted in Europe using questionnaires. They pointed out a very high level of satisfaction of callers and patients. Delay with the assistance and the ability for resolution of the case are the elements that overlap in fostering satisfaction. The published studies neither reviewed the overall care process nor related the measurement of the real time in responding to an emergency with the satisfaction. The patients and professionals concurred in their assessments about the most relevant elements for patient satisfaction, although safety was not a predictive factor for patients. Response capacity and perceived capacity for resolving the situation were crucial factors for satisfaction. This qualitative approach yielded assistance targets to be improved. Conclusions. Published studies have assessed similar dimensions of satisfaction. Furthermore, despite the fact that few explanations may be given due to the no face to face attention, taking into account the patient’s emotional needs or maintaining contact with the patient until the emergency services arrive are high predictors of the satisfaction.

The mechanical pre-treatment constitutes an unavoidable pre-step prior to biofuels conversion. As the cost of this step is often underestimated, recently researchers have striven to identify and characterize the mechanical pre-treatment effects in terms of post-treatment physical characterization, derived improvement of enzymatic digestibility, and output yields. The status of existing mechanical pre-treatment techniques is reviewed by substrate’s typology for lignocellulose biomass, focusing on comminution techniques. Emphasis has been dedicated to the particle size achieved after treatment, crystallinity breakage and eventual inhibitory phenomena deriving from the application of particle size reduction, which are analysed and discussed.

Screening of pollen traits in diploid wild potatoes (Solanum sect. Petota, Solanaceae) is desirable to develop heat-tolerant potato (S. tuberosum) cultivars. To accomplish this goal requires exploring potato genetic resources that are conserved in genebanks. The goal of this study was to assess pollen viability and 2n pollen production of the diploid potato wild relatives under heat stress condition. We assessed pollen viability and size of nine potato accessions conserved at the Embrapa Potato Gene Bank, including S. chacoense (BRA 00167447-2, BRA 00167017-3, BRA 00167023-1, BRA 00167028-0), S. commersonii (BRA00167007-4, BRA00167420-9, BRA00183760-8), and S. malmeanum (BRA 00183755-8), along with a control accession from the cultivated species S. tuberosum (BRA 00167251-8). The plant accessions were cultivated in different growth chambers, simulating both control temperature (ranging from 14 to 27°C) and supraoptimal temperature conditions (ranging from 24 to 34°C). At heat stress, the accessions BRA 00167251-8 did not bloom, and BRA 00167023-1 did not produce pollen. The remaining accessions did not exhibit a significant reduction in pollen viability as the temperature increased. Pollen viability at the control temperature had the lowest value in BRA-00167420-9 (S. commersonii) with 68.5% and the highest in BRA 00183755-8 (S. malmeanum) with 100%. At the supraoptimal temperature the lowest value was in BRA 00167420-9 (S. commersonii) with 54.5% and the highest in BRA 00183755-8 (S. malmeanum). The average of pollen size was 20 μm in all wild potato genotypes, and the increase of temperature did not lead to 2n pollen production. Estimated Genotypic Coefficient of Variation (GCV) was lower than Phenotypic Coefficient of Variation (PCV) for pollen viability. The observed heritability values ranged from 58.82% in BRA00167007-4 to 91.32% in BRA 00183755-8. Our results highlight the genetic variability available in wild potato germplasm concerning pollen viability under heat stress. Furthermore, these first insights offer valuable guidance for ongoing and future endeavors in diploid potato breeding.

MicroRNAs (miRNAs) within the exosomes are short non-coding RNAs that are associated with many diseases including cancers and are crucial players in regulating gene expression and regula-tion. Owing to their potential role as emerging biomarkers, efficient isolation, and quantification methods of miRNA from the exosomes in the cell’s supernatant are complicated and challenging. The quantitative PCR method is the gold standard for miRNA identification and estimation. Nucleic acid-based assays are expensive, labour-intensive, and require expertise. In this work, we demon-strated an aptamer-based magnetic separation of the exosomes, and quantification of the miRNA using a fluorescence switching assay. The fluorescence assay relies on the fluorescence of the fluorophore labelled cDNA quenched by carbon nanomaterials coated on magnetic beads (OFF state). When the target miRNA210 is introduced to the system, the cDNA detaches from the quenching surface. As a result, the fluorescence intensity increases (ON state) with increasing miRNA concentration within the dynamic range of 0-100 nM. This method can detect miR210 as low as 5 pM. This method does not exhibit any cross-reactivity with other closely related miRNAs. The method was validated by spiking miRNA with the standard RT-PCR method using SYBR green as a fluorescent probe. This method can be used for the minimally invasive detection of cancer biomarkers in breast cancer patients.

Tixagevimab/cilgavimab (TIXA/CILGA) was introduced in early 2022 as a promising new drug for pre-exposure prophylaxis against COVID-19 in immunocompromised patients. The aim of our study was to evaluate the efficacy and safety of TIXA/CILGA in a Slovenian national cohort of kidney transplant recipients as pre-exposure prophylaxis in the Omicron era. Demographic, clinical, laboratory, and therapeutic data were collected from electronic and paper medical rec-ords. Of the 106 patients who received TIXA/CILGA, 6 patients (5.7%) subsequently acquired SARS-CoV-2 breakthrough infection. The incidence of SARS-CoV-2 infection was only slightly lower compared with patients who did not receive TIXA/CILGA (7.0%). All patients who re-ceived TIXA/CILGA had a mild disease course, whereas 20% of patients who did not receive TIXA/CILGA required hospitalization and two patients died. Adverse effects most likely related to TIXA/CILGA occurred in 12% of patients, one of whom experienced deep vein thrombosis. None of the patients suffered acute myocardial infarction or cerebrovascular insult. Overall, the benefit of added protection for kidney transplant patients appears to outweigh the potential risk of adverse events and provide additional protection against severe COVID-19 protection.

Proposed in 1889 by Svante Arrhenius, the Arrhenius equation is an important result in physical chemistry which aims to find the relationship between temperature and reaction rates. It is also an essential equation for analytic chemistry which helps to determine the energy barrier for a given reaction. In this article, we will go through a detailed derivation of the exponential factor in the Arrhenius Equation based on the Boltzmann distribution of particle energy probability. Then, we will attempt to model the pre-exponential factor in accordance with the principles of collision theory for bi-molecular reaction with gaseous reactants. Understanding the basic principles of chemical reaction provides valuable tools to analyze complex reaction mechanisms in the perspective of optimizing existing industrial processes.

Dynamic Realistic Failure Process Analysis (DRFPA) is a numerical software based on FEM it is firstly validated by comparing with the experimental test. Such validation is carried out under two consistent concrete specimens under uniaxial compression. After validation, DRFPA is used to study the influence of two parallel pre-existing crack inclination angles on the fracture of concrete under dynamic loads. The evolution of wing cracks and secondary cracks is investigated by observing the initiation, coalescence, and propagation of them. Meanwhile, the relationship between failure mode and inclination angles is discussed via analyzing the acoustic emission events. Finally, it is concluded the relationship between inclination angles of pre-cracks and performance/stability of concrete structure.

BACKGROUND AND PURPOSE—Atherosclerotic plaque instability and rupture in patients with asymptomatic carotid artery stenosis (ACAS) is a leading cause of major adverse cardiac events (MACE). This could be mainly evidenced in patients with pre-diabetes. Indeed, the altered glucose homeostasis and insulin resistance could cause over-inflammation of atherosclerotic plaque, favoring its conversion to unstable phenotype with rupture and MACE. Notably, the metformin therapy reducing the metabolic distress and the inflammatory burden, could lead to reduction of MACE in ACAS patients with pre-diabetes. In this setting, microRNAs (miRs) could be used as molecular biomarkers of atherosclerosis progression, plaque rupture and worse prognosis in normoglycemics (NG) vs. pre-diabetics metformin users (PDMU) vs. pre-diabetics non metformin users (PDNMU). However, the aim of our study was to investigate a wide miRNA panel in peripheral blood exosomes from patients with ACAS divided in NG vs. PDMU vs. PDNMU, and to associate the circulating miRNA expression profiles with MACE at 2 years of follow-up after endarterectomy. METHODS—The study included 234 patients with ACAS divided in NG (n 125), PDNMU (n 73) and PDMU (n 36). The miRs’ expression profiles of circulating exosomes were determined at baseline and at 2 years of follow-up by Affymetrix microarrays from plasma samples of the patients from any study cohort. Then we collected and analyzed MACE at 2 years of follow-up in NG vs. PDMU vs. PDNMU. RESULTS—prediabetics vs. NG had over-inflammation (p<0.05) and over expressed miR 24 and miR 27 at baseline. At 2 years of follow-up PDNMU vs. NG, PDMU vs. NG and PDNMU vs. PDMU over-expressed inflammatory markers and miR 24, miR 27, miR 100, miR 126 and miR 133 (p<0.05). Finally, at follow-up end we observed a significant difference about MACE comparing PDNMU vs. NG (n 27 (36.9%) vs. n 8 (6.4%); p<0.05), PDNMU vs. PDMU (n 27 (36.9%) vs. n 6 (16.6%); p <0.05), and PDMU vs. NG (n 6 (16.6%) vs. n 8 (6.4%); p<0.05). Admission glucose values (HR 1.020, CI 95% [1.001-1.038], p 0.029), atheromatous carotid plaque (HR 5.373, CI 95% [1.251-11.079], p 0.024), and miR 24 (HR 3.842, CI 95% [1.768-19.222], p 0.011) predicted MACE at 2 years of follow-up. CONCLUSIONS—Specific circulating miRs could be over-expressed in pre-diabetics and specifically in PDNMU vs. PDMU after endarterectomy. MiR24, hyperglycemia and atheromatous plaque could predict MACE at 2 years of follow-up.

Ionic liquids have been recognised as interesting solvents applicable in the efficient lignocellulosic biomass valorisation, especially in the biomass fractionation into individual polymeric components or direct hydrolysis some of biomass fractions. Considering the chemical character of ionic liquids, two different approaches, paved the way for a fractionation of biomass. The first strategy integrated a pre-treatment, hydrolysis and conversion of biomass through the employment of hydrogen-bond acidic 1-ethyl-3-methyimidazolim hydrogen sulfate ionic liquid. The second one relied on the use of a three-step fractionation process with hydrogen-bond basic 1-ethyl-3-methylimidazolium acetate to produce high purity cellulose, hemicellulose and lignin fractions. The proposed approaches were scrutinised for wheat straw and eucalyptus residues. Those different biomasses allowed understanding that enzymatic hydrolysis yields are dependent on the crystallinity of pre-treated biomass. The use of acetate based ionic liquid allowed to change crystalline cellulose I to cellulose II and consequently enhanced glucan to glucose yield to 93.14.1 mol% and 82.91.2 mol% for wheat straw and eucalyptus, respectively. Whereas for hydrogen sulfate ionic liquid, the same enzymatic hydrolysis yields were 61.6  0.2 mol% for wheat straw and only 7.90.3 mol% for eucalyptus residues. These results demonstrate the importance of either ionic liquid character or biomass type on the efficient biomass processing.

Memphis aquifer is the primary drinking water source in Shelby County (Tennessee, USA) and supplies industrial, commercial, and residential water. Memphis aquifer is separated from the Shallow aquifer by a clayey layer known as Upper Claiborne Confining Unit (UCCU). All of the production wells in the Memphis area are screened in the Memphis aquifer or even deeper in the Fort Pillow aquifer. Traditionally, it was assumed that the UCCU could fully protect the Memphis aquifer from the contaminated Shallow aquifer groundwater. However, recent studies show that at some locations the UCCU is thin or absent which possibly leads to the contribution of Shallow aquifer to the Memphis aquifer. Accurately locating the breaches demands expensive and difficult geological or geochemical investigations, especially within an urban area. Hence, a pre-field investigation to identify the locations where the presence of breaches is likely can significantly reduce the cost of field investigations and improve the their results. In this study, to identify the locations where the presence of breaches in the UCCU is likely we use three different analyses: (1) pilot point calibration (PPC), (2) velocity and flow budget (VFB), and (3) particle tracking (PT) to post-process the developed groundwater results. These pre-field numerical investigations provide relevant and defensible explanations for groundwater flow anomalies in an aquifer system for informed decision-making and future field investigations. In this study, we identify five specific zones within the broad study area which are reasonable candidates for the future field investigations. Finally, we test the results of each analysis against other evidence for breaches to demonstrate that the results of the numerical analyses are reliable and supported by previous studies.

The Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the second Joint Polar Satellite System (JPSS) completed its sensor level testing in February 2018. The JPSS-2 (J2) mission is scheduled to launch in 2022, and will be very similar to its two predecessor missions, the Suomi National Polar-orbiting Partnership (SNPP) mission, launched on 28 October 2011, and JPSS-1 (renamed NOAA-20) launched on 18 November 2017. VIIRS instrument has 22 spectral bands covering the spectrum between 0.4 and 12.6 mircron: 14 reflective solar bands (RSB), 7 thermal emissive bands (TEB), and one day-night band (DNB). It is a cross-track scanning radiometer capable of providing global measurements through observations at two spatial resolutions, 375 m and 750 m at nadir for the imaging bands and moderate bands, respectively. This paper will provide an overview of J2 VIIRS characterization methodologies and calibration performance during the pre-launch testing phases performed by the National Aeronautics and Space Administration (NASA) VIIRS Characterization Support Team (VCST) to evaluate the at-launch baseline radiometric performance and generate the parameters needed to populate the sensor data record (SDR) Look-Up-Tables (LUTs). Key sensor performance metrics include the signal to noise ratio (SNR), radiance dynamic range, reflective and emissive bands calibration performance, polarization sensitivity, spectral performance, response versus scan-angle (RVS), and scattered light response. A set of performance metrics generated during the pre-launch testing program will be compared to both the SNPP and JPSS-1 VIIRS sensors.

Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising, or have already been, applied to the pre-harvest in field measurement including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.

Hypertensive diseases of pregnancy (HDP) represent a global clinical challenge, affecting 5-10% of women and leading to complications for both maternal well-being and fetal development. At the heart of these complications is endothelial dysfunction, with oxidative stress emerging as a pivotal causative factor. The reduction in nitric oxide (NO) bioavailability is a vital indicator of this dysfunction, culminating in blood pressure dysregulation. In the therapeutic context, although antihypertensive medications are commonly used, they come with inherent concerns related to maternal-fetal safety, and a percentage of women do not respond to these therapies. Therefore, alternative strategies that directly address the pathophysiology of HDP are required. This article focuses on the potential of the nitrate-nitrite-NO pathway, abundantly present in dark leafy greens and beetroot, as an alternative approach to treating HDP. The objective of this review is to discuss the prospective antioxidant role of nitrate. We hope our discussion paves the way for using nitrate to improve endothelial dysfunction and control oxidative stress, offering a potential therapy for managing HDP.

The UN Sustainable Development Goal 7 Report 2022 is about ensuring access to clean and affordable energy, which is key to the development of agriculture, business, communications, education, healthcare, and transportation. The lack of access to energy hinders economic and human development. Nevertheless, the current pace of progress is insufficient to achieve Goal 7 by 2030. Huge disparities in access to modern sustainable energy persist. Rising commodity, energy and shipping prices have increased the cost of producing and transporting solar photovoltaics modules, wind turbines and biofuels worldwide, adding uncertainty to a development trajectory that is already far below Goal 7 ambitions. Achieving energy and climate goals will require continued policy support and a massive mobilization of public and private capital for clean and renewable energy such as methane from biomass. An important factor in improving methane yield from biomass waste is pre-treatment of the biomass prior to anaerobic digestion. Size reduction of lignocellulose biomass is important for changing and increasing digestibility thus increasing methane yield. A Hollander beater was used as the mechanical pre-treatment machine throughout this study. Results indicate that methane yield is directly proportional to feedstock/Inoculum (F/I) ratio as higher F/I ratio led to higher methane yield. It was observed at an F/I ratio of 0.5 that 5 minutes pre-treatment time produced highest methane yield, while the lowest methane yield was observed at an F/I ratio of 0.2 with 1 minute of pre-treatment time. Methane yield decreased by 30% from 140 mL gTS-1 at F/I ratio 0.5 with 5 minutes pre-treatment time to 98 mL gTS-1 at F/I 0.2 with 5 minutes pre-treatment time. The optimum methane yield was attained at high F/I ratio allowing sufficient nutrient essential for complete degradation of biomass. Overall, this research shows the Hollander beater is not efficient in optimizing methane yield for cardboard.

In this paper, the extraction of essential oil (EO) from thyme by consecutive use of ultrasound and microwave treatments is presented. The aim of the study was to apply an ultrasound pre-treatment of thyme leaves to enhance the thymol content and the extraction yield of the EO obtained by microwave-assisted hydro-distillation (MWHD). Compared with the conventional hydro-distillation (CHD), the consecutive use of ultrasound and microwave treatments resulted in a 72% lower extraction time. When the ultrasound pre-treatment (using the ultrasonic processor with an amplitude of 70%) was applied, the EO content was 23% higher compared to the extraction without pre-treatment. The EO samples were analyzed by GC/MS. The results showed that the major component, thymol, varied from 43.54% (by CHD) to 65.94% (by consecutive use of ultrasound and microwave treatments).

Context. During the COVID-19 pandemic, patient care was mainly organised around the hospital. Pre-hospital care has, to our knowledge, never been evaluated. Objective: To evaluate the impact of pre-hospital pathways on hospitalisation during the last part of the pandemic. Design. This was a monocentric retrospective analysis of prospectively collected medical records. Data from patients admitted to our institute between 1 February and 7 March 2022 were analysed. Main Outcome Measure(s): The primary outcomes were defined as the number of hospitalisations, resuscitations, and deaths at the time of interview and in the subsequent 30 days. The main explanatory variables were times from onset of symptoms to care, age, gender, News2 score, comorbidities, and pre-hospital pathways and their duration. Results: Three pre-hospital pathways have been identified: a pathway in which the patient consults a general practitioner for a test (PHP1); a pathway in which the patient consulted for care (PHP2); and no pre-hospital pathway and direct admission to hospital (PHP3). Factors independently associated with outcome (hospitalisation) were being male (OR 95% CI; 2.21 [1.01–4.84], p=0,04), News2 score (OR 95% CI; 2.04 [1.65–2.51], p<0.001), obesity (OR 95% CI; 3.45 [1.48–8.09], p=0.005), D-dimers > 0.5 µg/ml (OR 95% CI; 3.45 [1.47–8.12], p=0.005), prolonged time from symptoms to hospital care (PHP duration) (OR 95% CI; 1.07 [1.01–1.14], p=0.03). All things being equal, patients with a “PHP2” pre-hospital pathway had a higher probability of hospitalisation compared to those with a “PHP3” pre-hospital pathway (OR 95% CI; 4.31 [1.48–12.55], p=0.007). Conclusions. Along with recognised risk factors such as gender, News 2 score, and obesity, the patient’s pre-hospital pathway is an important risk factor associated with hospitalisation.

Today, concepts such as Emotional Education and Emotional Intelligence are recognized by many authors and researchers, emphasizing the importance of their development in all people, but especially in schools, specifically in teachers or educators and children. However, we still find many shortcomings within the schools that are needed to continue working. The aim of this study is to investigate the presence of emotional intelligence and empathy in teachers, through a sample of 42 participants. The research was conducted based on two validated and highly reliable assessment tools, called TMMS-24 and TECA. The results of the TMMS-24 indicate that a large percentage of participants have an adequate score of emotional intelligence. The results of the TECA indicate that most of the participants are in the middle, high or extremely high scores in relation to the level of empathy.

The introduction of tenofovir-derived prodrugs has revolutionised the prevention and management of HIV, which has coincided with 23% reduction in new HIV incidences globally. To date, there are two formulations of tenofovir-derived nucleoside reverse transcriptase inhibitor (NRTI): tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) and tenofovir alafenamide fumarate/emtricitabine (TAF/FTC). Although these prodrugs have shown favourable safety profile, their effects on cardiovascular health are differ from one another: TDF/FTC exhibits potential lipid-lowering effect, TAF/FTC demonstrates potential lipid-inducing effect, which is a major risk factor for cardiovascular diseases. However, this issue has not been previously elucidated, especially among the marginalised populations [lesbian, gay, bisexual and transgender (LGBT) and men who have sex with men (MSM)] who are likely be the main users of these prodrugs. This is of clinically significance as the cardiovascular health in these populations is often overlooked, in addition to a lack of appropriate cardiovascular risk prediction algorithm. Therefore, this review aims to (1) highlight the cardiovascular risks of tenofovir-derived prodrugs in the marginalized populations, and also to (2) establish the importance of having a cardiovascular risk prediction model that is specific to this particular populations so that their health management could be more comprehensive.

This study explores the impact of the ‘pre-intervention effects’ of a community-based interventions. This refers to participatory research processes and parallel publicity in the media on changes in alcohol use and relevant mechanisms (rules and norms about alcohol, accessibility of alcohol in a formal setting) among adolescents before any intervention is implemented. In a quasi-experimental study, adolescent data were collected twice by means of self-report among adolescents living in two municipalities (control and experimental condition). Regression analysis showed pre-intervention main effects on adolescents’ perceived accessibility of alcohol in a formal setting. Moreover, among adolescents aged 15 years and older, the normative decline in strictness of rules and norms was less steep in the experimental condition compared to the control condition. Also, adolescents aged 14 years and younger in the experimental condition reported more weekly drinking compared to their peers in the control condition. No differential effects across gender were found. To conclude, applying a co-creational approach in the development of an intervention, not only contributes to more effective interventions in the end, but involvement of and discussions in the community at the start of intervention planning are contributing to changes in targeted factors. This implies that public discussions about the development of intervention strategies should be considered as an essential feature of co-creation in community-based interventions.

This work presents updated reconstructions of watershed runoff to San Francisco Estuary from tree-ring data to AD 903, coupled with models relating runoff to freshwater flow to the estuary and salinity intrusion. We characterize pre-development freshwater flow and salinity conditions in the estuary over the past millennium and compare this characterization with contemporary conditions to better understand the magnitude and seasonality of changes over this time. This work shows that the instrumented flow record spans the range of runoff patterns over the past millennium (averaged over five, ten, twenty and one hundred years), and thus serves as a reasonable basis for planning-level evaluations of historical hydrologic conditions in the estuary. Over annual timescales we show that, although median freshwater flow to the estuary has not changed significantly, it has been more variable over the past century compared to pre-development flow conditions. We further show that the contemporary period is generally associated with greater spring salinity intrusion and lesser summer-fall salinity intrusion relative to the pre-development period. Thus, salinity intrusion in summer and fall months was a common occurrence under pre-development conditions and has been moderated in the contemporary period due to the operations of upstream reservoirs, which were designed to hold winter and spring runoff for release in summer and fall. This work also confirms a dramatic decadal-scale hydrologic shift in the watershed from very wet to very dry conditions during the late 19th and early 20th centuries; while not unprecedented, these shifts have been seen only a few times in the past millennium. This shift resulted in an increase in salinity intrusion in the first three decades of the 20th century, as documented through early records. Population growth and extensive watershed modification during this period exacerbated this underlying hydrologic shift. Putting this shift in the context of other anthropogenic drivers is important in understanding the historical response of the estuary and in setting salinity targets for estuarine restoration. By characterizing the long-term behavior of San Francisco Estuary, this work supports decision-making in the State of California related to flow and salinity management for restoration of the estuarine ecosystem.

Modern day civilization is dependent on energy generation by fossil fuels. But the major drawback of using fossil fuels is environmental pollution. Microalgae are potential candidate for production of various products of interest, such as proteins, mini food, pigments and triglycerides that can be converted into biofuels. Lignocellulosic feedstocks are the most abundantly available raw material of plants that can serve as a promising feedstock for cultivating bacteria, fungi, yeasts and microalgae to produce biofuels and other value-added products. Owing to the abundant availability of these low/no cost substrates, can be utilized as feedstocks for cultivating microalgae to generate biogas/biodiesel. Likewise, there is much room to exploit defatted algal biomass to be used as animal/fish feed and oil producing/accumulating genes knowledge in future to produce high and good quality biodiesel and biogas.

Cardiotonic glycoside toxins, originally isolated from plants or amphibians, have been proposed as mammalian cardiotonic hormones. This paper is a review and update of the discovery of [i] a new class of steroid hormones, [ii] the path for their biosynthesis and [iii] some preliminary data on their function. The compounds are phosphoester conjugates and share a characteristic structural feature, a lactone ring, with [a] one class of synthetic potassium sparing diuretics and with [b] plant and amphibian cardiotonic steroids. Purification was initially monitored by cross reaction with steroid sulfate assays and later with digoxin-specific assays. Six compounds were purified by HPLC to near homogeneity and characterized by Tandem mass spectroscopy (MS-MS) and 31P-NMR. Three were digoxin-like materials (DLM) with 23 carbon atoms. The two extra carbon atoms form a spiral lactone E-ring. Several additional spiral lactones have been identified by MS-MS. In a pilot study, based on MS analysis, we evaluated phosphocholine steroid levels in individual serum samples in patients with pre-eclampsia (n=20). The control group of normotensive pregnant women (n=20) was used to estimate the mean and standard deviation. Twelve of the women with pre-eclampsia had a z-score over 2 for at least one of the four phosphocholine steroids. In contrast, only 1 sample from the normotensive women had a z-score over 2. The observation that there are two patterns, one with elevated phosphocholine steroid levels and one without the elevation, suggests that there may be two different underlying causes of pre-eclampsia. We now need to extend the study to determine which pattern predicts progression to life-threatening hypertension in pregnant women. This pilot study illustrates that it is possible to evaluate individual endogenous cardiotonic hormones without relying on antibodies developed to plant or amphibian toxins.

Several articles have reported that the low number of COVID-19 cases in Japan is attributed to the small number of diagnostic RT-PCR tests performed. The criticism is based on the low number of the tests performed, and they suspected there would be more potential cases in Japan. The use of pre-test probability among tested subjects is proposed in order to evaluate of the sufficiency of test availability instead of the number of the tests. The pre-test probability means the average probability, i.e., ‘suspicion level’, of having coronavirus among the tested subject in a country. The higher pre-test probability is regarded as less sufficient opportunity of the tests, and the test availability could be evaluated by the pre-test probability. Thus, potential problems of underestimation of COVID-19 cases by insufficient amount of the test would be clear by using pre-test probability. The comparison of the pre-test probability could be replaced with that of the positive rate of the test because of the linear relationship between them under the assumption of common sensitivity and specificity. Japan shows the third lowest rate (8.6%), and is considered that the considerably sufficient number of the tests have been performed. In conclusion, the positive rate of the test as a surrogate index of the pre-test probability is useful to evaluate the sufficiency of test amount instead of the number of the tests performed. In present, the potential problem of underestimation by insufficient test availability would be less serious in Japan.

The ability to replicate scientific experiments is a cornerstone of the scientific method. Sharing ideas, workflows, data, and protocols facilitates testing the generalizability of results, increases the speed that science progresses, and enhances quality control of published work. Fields of science such as medicine, the social sciences, and the physical sciences have embraced practices designed to increase replicability. Granting agencies, for example, may require data management plans and journals may require data and code availability statements along with the deposition of data and code in publicly available repositories. While many tools commonly used in replicable workflows such as distributed version control systems (e.g. “git”) or scripted programming languages for data cleaning and analysis may have a steep learning curve, their adoption can increase individual efficiency and facilitate collaborations both within entomology and across disciplines. The open science movement is developing within the discipline of entomology, but practitioners of these concepts or those desiring to work more collaboratively across disciplines may be unsure where or how to embrace these initiatives. This article is meant to introduce some of the tools entomologists can incorporate into their workflows to increase the replicability and openness of their work. We describe these tools and others, recommend additional resources for learning more about these tools, and discuss the benefits to both individuals and the scientific community and potential drawbacks associated with implementing a replicable workflow.

Glioblastoma (GBM), the most common and aggressive primary brain tumor in adults, remains one of the least treatable cancers. Current standard of care—combining surgical resection, radiation, and alkylating chemotherapy—results in a median survival of only 15 months. Despite decades of investment and research into the development of new therapies, most candidate anti-glioma compounds fail to translate into effective treatments in clinical trials. One key issue underlying this failure of therapies that work in pre-clinical models to generate meaningful improvement in human patients is the profound mismatch between drug discovery systems—cell cultures and mouse models—and the actual tumors they are supposed to imitate. Indeed, current strategies that evaluate the effects of novel treatments on GBM cells in vitro fail to account for a wide range of factors known to influence tumor growth. These include secreted factors, the brain’s unique extracellular matrix, circulatory structures, the presence of non-tumor brain cells, and nutrient sources available for tumor metabolism. While mouse models provide a more realistic testing ground for potential therapies, they still fail to account for the full complexity of tumor-microenvironment interactions, as well as the role of the immune system. Based on the limitations of current models, researchers have begun to develop and implement novel culture systems that better recapitulate the complex reality of brain tumors growing in situ. A rise in the use of patient derived cells, creative combinations of added growth factors and supplements, may provide a more effective proving ground for the development of novel therapies. This review will summarize and analyze these exciting developments in 3D culturing systems. Special attention will be paid to how they enhance the design and identification of compounds that increase the efficacy of radiotherapy, a bedrock of GBM treatment.

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

The effect of low cycle fatigue (LCF) pre-damage on the subsequent very high cycle fatigue (VHCF) behavior is investigated in TC21 titanium alloy. LCF pre-damage is applied under 1.8% strain amplitude up to various fractions of the expected life and subsequent VHCF properties are determined using ultrasonic fatigue tests. Results show that 5% of LCF pre-damage insignificantly affects the VHCF limit due to the absent of pre-crack, but decreases the subsequent fatigue crack initiation life estimated by Pairs’ law. Pre-cracks introduced by 10% and 20% of LCF pre-damage significantly reduce the subsequent VHCF limits. The crack initiation site shifts from subsurface-induced fracture for undamaged and 5% of LCF pre-damage specimens to surface pre-crack for 10% and 20% of LCF pre-damage specimens in very high cycle region. The fracture mechanism analysis indicate that LCF pre-crack will re-start to propagate under subsequently low stress amplitude when stress intensity factor of pre-crack is larger than its threshold. Furthermore, the predicted fatigue limits based on EI Haddad model for the LCF pre-damage specimens well agree with the experimental results.

This study investigates the effect of perceived discrimination on the mental health of Afghan refugees, and secondly, tests the distress moderating effects of pre-migration traumatic experiences and post-resettlement adjustment factors. In a cross-sectional design, 259 Afghans completed surveys assessing perceived discrimination and a number of other factors using scales developed through inductive techniques. Multivariate analyses consisted of a series of hierarchical regressions testing the effect of perceived discrimination on distress, followed by a sequential analysis of moderator variables. Perceived discrimination was significantly associated with higher distress, and this relationship was stronger among those with a strong intra-ethnic identity, high civic engagement, and high pre-resettlement traumatic experiences. Discrimination is a significant source of stress for Afghan refugees, which may exacerbate stresses associated with other post-migration stressors. Future research is needed to tailor interventions that can help mitigate the stress associated with discrimination among this highly vulnerable group.

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images, and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges to achieve accurate stereo SAR image matching. To address the aforementioned challenges, this paper proposes a radargrammetric method based on composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged by the proposed radargrammetric 3D imaging method, resulting in 3D imaging of target scenes based on multi-aspect SAR images. For validation purpose, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) by using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency while enhances the imaging accuracy of crucial areas with limited data.

With the continuous development of 3D city modeling, traditional close-range photogrammetry is limited by complex processing procedures and incomplete 3D depth information, making it unable to meet high-precision modeling requirements. In contrast, the integration of LiDAR and camera in mobile measurement systems provides a new and highly effective solution. The LiDAR can quickly and accurately acquire the 3D spatial coordinates of target objects, while optical imagery contains abundant color information. If the two can be integrated, they can play an important role in multiple fields such as streetscape modeling, archaeology and digital city, etc. Currently, integrated mobile measurement systems commonly require cameras, lasers, POS and IMU, thus the hardware cost is relatively expensive and the system integration is complex. Therefore, in this paper we propose a simple ground mobile measurement system composed of a LiDAR and a GoPro camera without a POS system, providing a more convenient and reliable way to automatically obtain 3D point cloud data with spectral information. The automatic point cloud coloring based on video images mainly includes four aspects: (1) Establishing models for radial distortion and tangential distortion to correct video images. (2) Establishing a registration method based on normalized Zernike moments to obtain the exterior orientation elements. Normalized Zernike moments are region-based shape descriptors that can reflect the features of images in multiple dimensions even for low-quality video images. The results show that registration based on normalized Zernike moments provides a good result, with an error accuracy of 0.5-1 pixel, which is far higher than registration based on a collinearity equation. (3) Establishing adjacent video image relative orientation based on essential matrix decomposition and nonlinear optimization. This involves uniformly using the SURF algorithm with distance restriction and RANSAC to select corresponding points, which can improve the reliability of the corresponding points. The results indicate that the accuracy of the relative orientation method is high. Moreover, this method can converge to good results for stereo image pairs with large rotation angles and displacement amounts. Therefore, relative orientation based on essential matrix decomposition and nonlinear optimization has good applicability. (4) The video imagery suffers from significant motion blur and boundary distortion. Therefore, a point cloud coloring method based on Gaussian distribution with central region restriction is adopted. Only pixels within the central region are considered valid for coloring. Then, the point cloud is colored based on the mean of the Gaussian distribution of the color set. Experimental results show that the coloring accuracy between the video imagery and point cloud data is high, meeting the accuracy requirements of applications such as tunnel detection, street-view modeling and 3D urban modeling.

One of the rapidly developing research areas is the creation of systems. which are commonly referred to as cyberphysical complexes. In such systems, devices and complexes interact with a completely different physical nature. The role of a person in such systems usually consists in the formation of final tasks for “artificial intelligence” and executive mechanisms. The functioning of actuators is controlled by accurate information systems.

UAV network operation enables gathering and fusion from disparate information sources for flight control in both manned and unmanned platforms. In this investigation, a novel procedure for detecting runways and horizons as well as enhancing surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented real-world situations due to signal misalignment. We address this through a registration step to align the EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. We believe the fusion architecture developed holds promise for incorporation into head-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provided a basis rule selection in other signal fusion applications.

The aim of this work was to classify the business incubators in Spain according to the four phases of startups incubation. Taking into account that the graduation rate implies greater survival and business success of the incubated companies, they have been identified at each stage of the incubation (spread of entrepreneurship, pre-incubation, advanced incubation, and graduation). The activities that present higher impacts on the success of the incubated companies and the activities carried out by the incubator that have a greater relevance on the graduation of the companies have concretely been considered. Principal component (PC) cluster analysis have been applied. All the incubation variables were used simultaneously, reducing their number, and grouping them into factors, finally the cases were grouped according to these latent variables. Principal components analysis reduced dimensionality to 8 factors with a 74 % of explained variance. Factor 1 was positively related to pre-incubation variables, factor 2 was linked to training and collaboration variables within the entrepreneurship diffusion phase. Factor 3 called activity monitoring and control and was related to phase 3 or basic incubation variables. Clusters analysis facilitates the grouping of incubators into three clusters: Group 1 (16 % of the total), incubators with strong deficits in incubation phases 1, 2 and 3. They are small Business Incubators, often located in rural areas or cities and low graduation rate. Group 2 (30 %), business incubators with very high graduation rate, and strongly positive values in factor 1 and 2. Factor 3 although positive is susceptible to improvement. They are the largest incubators and usually located in industrial and technological parks. Group 3 (54 %) is the majority, with values close to cluster 2 and 3.

Polycystic ovary syndrome (PCOS) is a multisystem disorder that presents with a variety of phenotypes involving metabolic, endocrine, reproductive, and psychological symptoms and signs. Women with PCOS are at increased risk of pregnancy complications including implantation failure, miscarriage, gestational diabetes, fetal growth restriction, preterm labour, and preeclampsia (PE). This may be attributed to the presence of specific susceptibility features associated with PCOS before and during pregnancy, such as chronic systemic inflammation, insulin resistance (IR), and hyperandrogenism, all of which have been associated with an increased risk of pregnancy complications. Many of the features of PCOS are reversible following lifestyle interventions such as diet and exercise, and pregnant women following a healthy lifestyle have been found to have a lower risk of complications, including PE. This review summarizes the evidence investigating the risk of PE and the role of nutritional factors in women with PCOS. The findings suggest that the beneficial aspects of lifestyle management of PCOS, as recommended in the evidenced-based international guidelines, extend to improved pregnancy outcomes. Identifying high-risk women with PCOS will allow targeted interventions, early pregnancy screening, and increased surveillance for PE. Women with PCOS should be included in risk assessment algorithms for PE.

As for their large surface area and pore volume, three-dimensional covalent organic frameworks (3D COFs) have emerged as competitive porous materials. However, due to the structural dynamic behavior particularly for imine-linked 3D COFs, potentially unlock their potential for gas storage application. Herein, we showed a pre-locked linker strategy introduces breaking dynamic behavior in 3D COFs. A predesigned planar linker based 3,8-diamino-6-phenylphenanthridine (DPP) was prepared to produce non-dynamic 3D JUC-595, as benzylideneamine moiety in DPP locked the linker flexibility and restrict the molecular-bond rotation of the imine linkages. Upon solvent inclusion and release, the PXRD profile of JUC-595 remained intake, while JUC-594 with flexible benzidine linker experienced crystal transformation due to framework contraction-expansion. As a result, the activated JUC-595 achieved higher surface areas (754 m² g-1) than that of JUC-594 (548 m² g-1). Furthermore, improved CO2 and CH4 storages were also seen in JUC-595 compared with JUC-594. Impressively, JUC-595 recorded high normalized H2 storage capacity that surpass other reported high surface area 3D COFs. This works shows important insight on manipulating structural properties of 3D COF to tune the gas storage performance.

Artificial intelligence (AI)-based models hold the potential to transform higher education if adopted properly and ethically. A prime example is ChatGPT with earlier studies indicating its widespread adoption in academia and by university students. The current study aimed to identify the factors influencing the attitude towards ChatGPT and its usage among university students in Arab countries. A previous survey instrument termed Technology Acceptance Model Edited to Assess ChatGPT Adoption (TAME-ChatGPT) was administered online using a convenience-based approach among the contacts of the authors. Confirmatory factor analysis (CFA) for the survey constructs was done using root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI), and Tucker-Lewis index (TLI). The final study sample comprised a total of 2240 participant divided as follows: Iraq (n=736, 32.9%), Kuwait (n=582, 26.0%), Egypt (n=417, 18.6%), Lebanon (n=263, 11.7%), and Jordan (n=242, 10.8%). A total of 1048 respondents heard of ChatGPT before the study (46.8%), of which 551 used ChatGPT (52.6%). The mean scores of TAME-ChatGPT constructs showed that the ease of ChatGPT use, positive attitude towards technology and social influence, higher perceived usefulness, the influence of behavioral/cognitive factors, low perceived risks and low anxiety were the determinants of positive attitude to ChatGPT and its use. For both the attitude and usage scales of TAME-ChatGPT, CFA collectively yielded satisfactory fit indices as indicated by low RMSEA and SRMR together with high CFI and TLI. Multivariate analysis showed that attitude to ChatGPT use was significantly influenced by country of residence, age, university type, and the latest grade point average of the students. The current study confirmed the validity of TAME-ChatGPT as a survey instrument to assess the possible determinants of ChatGPT use among university students in Arab countries. The study findings highlighted that successful adoption of ChatGPT in higher education could be dependent on perceived ease of use, usefulness, positive attitudes to technology, social influence, behavioral/cognitive factors, lower anxiety, and minimal perceived risks. The utility of ChatGPT in higher education requires policies that should be tailored for various settings, considering the differences observed in attitude towards ChatGPT among participating students in this study.

The pre-chamber technologies can improve the ignition performance of IC engines by more than two orders in magnitude and thereby substantial economic benefits. Compared with the common pre-chamber, a plug-and-play passive scheme is suitable for quick retrofit, which is getting more attention from the automobile industry. Good scavenging is the precondition for improving turbulent jet ignition performance for a passive pre-chamber. Therefore, detailed evaluations of the scavenging process and turbulent jet ignition deserve investigations for new pre-chamber schemes. In this paper, the effects of design parameters on ignition processes of plug-and-play passive pre-chamber were numerically studied, allowing for the lateral angle, orifice diameter, and vertical angle design. Seven pre-chamber schemes were evaluated, and four optimal ones were selected for bench tests. The characteristics of the scavenging process, turbulent jet ignition, and main-chamber combustion were investigated and analyzed. The results show that allowing for the trade-off between ignition energy and scavenging efficiency, the volume ratio of the pre-chamber to clearance is recommended to be 0.2~0.7%, and the corresponding area-volume ratio is 0.003~0.006 mm-1. Compared with the original natural gas engine, the pre-chamber retrofit can save up to 13.2% fuel consumption, which presents a significant improvement in fuel economy.

Artificial intelligence, a key technological tool in the era of the 4th Industrial Revolution, is being actively applied to education beyond industry, economy, and culture. In the future society, understanding and using artificial intelligence technology will be considered an essential skill for everyone, and the professionalism of AI education for pre-service teachers nurturing future talents is also important. This study verifies the effectiveness of pre-service teachers after undergoing a 15-week extension program consisting of AI Literacy Education (10 weeks), AI-Linked Subjet Education (3 weeks), and Micro-teaching (2 weeks) which was designed to enhance the AI ​​education professionalism of pre-service teachers. The AI Literacy Education section consisted of understanding AI, AI ethics, and basic programming, and the education was conducted through both experience and practice. After that, the pre-service teachers analyzed the curriculum of the 5th and 6th grades of an elementary school, and developed a subject-related education program using AI tools in three stages. Using the developed program, micro-teaching classes were conducted twice. The study results are as follows. First, there was a change in the positive perception of AI (p<.05). Second, the understanding of AI educational technology tools was improved and the recognition of the usefulness of developing a curriculum-related program was expanded. Third, the importance of teacher competency development was learned through the micro-teaching, and the students' understanding of the learning process was expanded.

The aim of this study was to investigate the potential of converting potato peel waste into bio ethanol using an in-house produced multi-enzyme preparation. To achieve this, a variety of pre-treatment strategies, including thermal, chemical, and thermo-chemical methods, were applied to the potato peels. These included boiling for 30 and 60 min, steam at varying pressures and times, and different concentrations of chemicals such as H2SO4, HNO3, CH3COOH, HCl, NaOH, Ca(OH)2, KOH, NH3, and H2O2, either alone or in combination with steam. The pre-treated potato peels were then enzymatically hydrolyzed using a crude multi-enzyme cocktail derived from solid-state fermentation of wheat bran by a natural variety of Aspergillus niger P-19, containing cellulases, hemicellulases, pectinase, and amylases. The most effective pre-treatment combination was found to be 3% H2SO4 followed by steam under pressure and enzymatic hydrolysis using the crude multi-enzyme preparation. This combination resulted in the highest yield of reducing sugars (141.04 ±12.31 g/l) with a 98.49% carbohydrate conversion at a 20% substrate loading, giving an ethanol yield of 43.2 g/l. Furthermore, supplementing the medium with peptone, (NH4)(H2PO4), and ZnSO4 at 0.1%w/v each with 22% and 24% solid, respectively, resulted in appreciable yields of 51.67 and 54.75 g/l, further enhancing the ethanol yield.

This communication investigates possible anomalies in the lithosphere atmosphere and ionosphere on the occasion of the ML=3.3 earthquake that occurred on 1st January 2023 close to Guidonia Montecelio (Rome, Italy). This earthquake followed another event on 23 December 2022 of magnitude ML=3.1 with a very close epicentre (distance less than 1km). Seismological investigations clearly show an acceleration of seismicity in the last six months in the area. Two solutions of fitting time to failure power law on the Cumulative Benioff strain curve are the more likelihood: the ML3.3 of 1 January is the mainshock of seismic sequence or incoming earthquakes of a magnitude of about 4.1 provides a slightly better fit of the seismic data. Further investigation are necessary to assess if the accumulated stress have been totally released or not. No atmospheric anomalies related to this seismic activity have been identified even if some SO2 emissions could come from tectonic or volcanic sources in the South-Tyrrhenian Sea. Swarm satellites' magnetic data shows an anomalous track on 16 December 2022, which is temporally compatible with the seismic acceleration but other sources for the anomalous signal are also possible.

We transformed the existing learning program Language Shower, which is used in some Norwegian day-care centers in the Grorud district of Oslo municipality, into a digital solution using an app for smartphone or tablet with the option for further enhancement of presentation by a NAO robot. The solution was tested in several iterations and multiple day-care centers over several weeks. Measurements of the children’s progress across learning sessions indicate a positive impact of the program using a robot as compared to the program without robot. In-situ observations and interviews with day care center staff confirmed the solution’s many advantages, but also revealed some important areas for improvement. In particular, the speech recognition needs to be more flexible and robust, and special measures have to be in place to handle children speaking simultaneously.

Dietary components are important for the structural and functional development of the brain. Among these, docosahexaenoic acid,22:6n-3 (DHA) is critically required for the structure and development of the growing fetal brain in utero. DHA is the major n-3 long-chain fatty acid in brain gray matter representing about 15% of all fatty acids in the human frontal cortex. DHA affects neurogenesis, neurotransmitter, synaptic plasticity & transmission, and signal transduction in the brain. Studies in animals and humans show that adequate levels of DHA in neural membranes are important for cortical astrocyte maturation and vascular coupling, and helps cortical glucose uptake and metabolism. In addition, specific metabolites of DHA are bioactive molecules that protect tissues from oxidative injury and stress in the brain. A low DHA level in the brain results in behavior changes and is associated with learning problems and memory deficits. In humans, the third trimester-placental supply of maternal DHA to the growing fetus is critically important as the growing brain obligatory requires DHA during this window period. Besides, DHA is also involved in the early placentation process, essential for placental development. This underscores the critical importance of maternal DHA intake for the structural and functional development of the brain. This review describes DHA's multiple roles during gestation, lactation, and the consequences of its lower intake during pregnancy and postnatally on the children's brain development and function.

How, if at all, consciousness can be part of the physical universe remains a baffling problem. This article outlines a new, developing philosophical theory of how it could do so, and offers a preliminary mathematical formulation of a physical grounding for key aspects of the theory. Because the philosophical side has radical elements, so does the physical-theory side. The philosophical side is radical, first, in proposing that the productivity or dynamism in the universe that many believe to be responsible for its systematic regularities is actually itself a physical constituent of the universe, along with more familiar entities. Indeed, it proposes that instances of dynamism can themselves take part in physical interactions with other entities, this interaction then being “meta-dynamism” (a type of meta-causation). Secondly, the theory is radical, and unique, in arguing that consciousness is necessarily partly constituted of meta-dynamic auto-sensitivity, in other words it must react via meta-dynamism to its own dynamism, and also in conjecturing that some specific form of this sensitivity is sufficient for and indeed constitutive of consciousness. The article proposes a way for physical laws to be modified to accommodate meta-dynamism, via the radical step of including elements that explicitly refer to dynamism itself. Additionally, laws become, explicitly, temporally non-local in referring directly to quantity values holding at times prior to a given instant of application of the law. The approach therefore implicitly brings in considerations about what information determines states. Because of the temporal non-locality, and also because of the deep connections between dynamism and time-flow, the approach also implicitly connects to the topic of entropy insofar as this is related to time.

Pre-diabetes is a serious health condition where blood sugar levels are higher than normal, but not high enough yet to be diagnosed as type 2 diabetes. Pre-diabetes puts one at an increased risk of developing type 2 diabetes and heart disease Methodology: A cross-sectional study was carried out on 384 patients aged 20-70 years old, attending the dental clinics to assess the risk for diabetes, using the FINDRISC questionnaire, HbA1c blood test and a periodontal examination. Results: The mean age of participants was 38.90±10.74. 32.3% were categorized as no risk, 46.6% low risk, while 19% and 2.1% moderate and high risk of developing diabetes respectively. Tests for serum HbA1c Level showed 46.1 % had normal HbA1c followed by 18.0% and 3.6 % were pre-diabetic and diabetic respectively. 19.3% of participants had periodontal pockets measuring more than 4mm and 15.9% measuring more than 6mm. Conclusion: The study has proven to be useful in identifying patients at high-risk of developing diabetes. Controlling and managing periodontal disease could be a new aspect to include in the standards for diabetes care. Dental settings could be a successful platform to carry out the screening and risk stratification of pre-diabetic patients.

High oxidative stress in cells due to inflammation process or excessive cell proliferation would produce oxidants or free radicals with biomarkers, one of which is malondialdehyde (MDA). Passion fruit seed’s contain high antioxidant and are expected to decrease the level of cholesterol and MDA. The objective is to identify the effect of passion fruit seed’s ethanol extract in Wistar rats that have been fed with atherogenic feed. The method was preclinical trial (post-test control group design) in rats, by administering passion fruit seed’s ethanol extract for 14 days. This study used 26 male rats aged two months, divided into 5 groups. The result showed significant difference in MDA level which was found in group that was given passion fruit seed extract 10mg/kg BW with positive control group that was given standard feed. Passion fruit seed’s extract showed significant difference in level of triglyceride, which was found in negative control group that was given atherogenic feed with group that was given passion fruit seed’s extract 5mg/kg BW (mean±standard deviation: 1.09±0.30 mg/dL vs 0.77±0.25mg/dL; p=0.048). This study showed that passion fruit seed’s ethanol extract had significant lowering effect in level of MDA, total cholesterol, and triglyceride for 14 days.

Background. Specific clinical risk factors that may be associated with ambulatory outcome following thrombolysis therapy in ischemic stroke patients with pre-stroke depression is not fully understood. This was investigated. Methods. Multivariate analyses were performed to identify predictors of functional ambulatory outcomes. Patient demographics and clinical risk factors served as predictive variables, while improvement or no improvement in ambulatory outcome was considered as the primary outcome. Results. A total of 595 of these patients received rtPA of which 310 patients presented with pre-stroke depression, 217 had no improvement in functional outcome, while 93 patients presented with an improvement in functional outcome. Carotid artery stenosis (OR= 11.577, 95% CI, 1.281 – 104.636, P=0.029) and peripheral vascular disease (OR= 18.040, 95% CI, 2.956-110.086, P=0.002) were more likely to be associated with an improvement in ambulation. Antihypertensive medications (OR= 7.810, 95% CI, 1.401 –43.529, P=0.019),previous TIA (OR= 0.444, 95% CI, 0.517 –0.971, P=0.012), and congestive heart failure (OR= 0.217, 95% CI, 0.318 –0.402, P=0.030) were associated with a no improvement in ambulation. Conclusion. After adjustment for covariates, more clinical risk factors were associated with no improvement when compared with improvement in functional outcome following thrombolysis therapy in an acute ischemic stroke population with pre-stroke depression.

This economic evaluation aims to support policy-making on the combined use of pre-exposure prophylaxis (PrEP) with HIV vaccines by evaluating the potential cost-effectiveness of implementation that would support the design of clinical trials for assessment of combined product safety and efficacy. The target study population is a cohort of men who have sex with men (MSM) in the United States. Policy strategies considered include standard HIV prevention, daily oral PrEP, HIV vaccine, and their combination. We constructed a Markov model based on clinical trial data and published literature. We used a payer perspective, monthly cycle length, a lifetime horizon, and a 3% discount rate. We assumed a price of $500 per HIV vaccine series in the base case. HIV vaccines dominated standard care and PrEP. At current prices,PrEP was not cost-effective alone or in combination. A combination strategy had the greatest health benefit but was not cost-effective (ICER=$463,448/QALY) as compared to vaccination alone. Sensitivity analyses suggest a combination may be valuable for higher-risk men with good adherence. Vaccine durability and PrEP drug prices were key drivers of cost-effectiveness. Results suggest that boosting potential may be key to HIV vaccine value.

Abstract: The Micro-Electro-Mechanical System (MEMS) LiDAR point cloud in autonomous vehicles has a large deflection range, which results in slow registration speed and poor applicability. To maximize speed, an improved NDT (Normal Distribution Transform) method that integrates point cloud density features has been proposed. First, the point cloud is reduced using a modified voxel filter and a pass-through filter. Next, the ISS (Intrinsic Shape Signature) algorithm is utilized to analyze the point cloud features and extract keypoints, the 4PCS (4-Point Congruent Sets) algorithm is then employed to calculate the initial pose under the constraints of the keypoint set to complete the coarse registration. Finally, the self-adaptive segmentation model is constructed by using KD-tree to obtain the density features of keypoints, and the NDT algorithm is combined with this model to form an SSM-NDT algorithm, which is used for fine registration. Each algorithm was compared on the autonomous vehicle dataset PandaSet and actual collected data sets, the results show that the novel method increases the speed by at least 60% and takes into account good registration accuracy and strong anti-interference.

Hemangioblasts give rise to endothelial progenitor cells (EPCs), which also express the cell surface markers CD133 and C-kit. They may differentiate into the outgrowth endothelial cells (OECs) that control neovascularization in the developing embryo. Reduced levels of EPC in circulation have been linked to human cardiovascular disorders, according to numerous studies. Furthermore, preeclampsia and senescence have been linked to levels of EPCs produced from cord blood. Uncertainties surround how preeclampsia affects the way EPCs function. It is reasonable to speculate that preeclampsia may have an impact on the function of fetal EPCs during the in utero period; however, given the present literature's suggestion that maternal vasculopathies, including preeclampsia, damage fetal circulation. Additionally, the differentiation potential and general activity of EPCs may serve as an indicator of the health of the fetal vascular system since they promote neovascularization and repair during pregnancy. Thus, the purpose of this review is to compare, through assessment of their quantity, differentiation potency, angiogenic activity, and senescence, the angiogenic function of fetal EPCs obtained from cord blood for normal and pregnancy problems. This will shed light on the relationship between the angiogenic function of fetal EPCs and pregnancy complications, which could have an effect on the management of long-term health issues like metabolic and cardiovascular disorders in offspring with abnormal vasculature development.

Based on the teaching material of "Robotics" course, this paper studied the automatic knowledge graph construction, including knowledge points extraction and knowledge point relation extraction. We proposed a new method of extracting the first-level, second-level, and third-level knowledge points as well as their prerequisite relations of knowledge points in the textbook. For the problem of insufficient knowledge of the pre-trained language model in the specific field, methods such as incremental pre-training and optimization of cost functions are employed to integrate subject knowledge into the pre-trained language model, thus improving its effectiveness. To overcome the problem that the traditional method of relationship extraction can not be applied directly to the extraction of teaching materials, a new scheme for knowledge point relationship extraction based on keyword relationship is proposed. The experimental data from textbooks shows that the F1 score of knowledge point extraction reaches 93%, considerably improved compared to the traditional model. Consequently, the knowledge point entity extraction and relationship extraction methods based on the pre-trained model can effectively extract structured information and facilitate the automatic construction of knowledge graphs.

Red raspberry fruit production is increasing worldwide due to the growing consumer interest in foods with high antioxidant properties. However, raspberries are very perishable fruit with a short shelf life. Chitosan applications have shown promising results in promoting the storage of different berry fruit. This work aimed at analysing the effects of chitosan applied in pre- and/or post-harvest on the quality and antioxidant properties of raspberry fruit during the cold storage and room temperature conditions (i.e., 6 d at 4 °C and 3 d at 20 °C, respectively). Pre-harvest chi-tosan applications reduced fruit weight loss during cold storage and room temperature condi-tions but also reduced fruit decay at room temperature conditions. Furthermore, pre-harvest treatments with chitosan increased the total phenol and anthocyanin contents and promoted the highest total antioxidant activity with respect to other treatments. Of note, after the cold storage, post-harvest chitosan application drastically reduced the development of fungi that cause fruit decay, thus minimizing the potential risk of mycotoxin production. Overall, this study demon-strates that applications of chitosan in pre-harvest are sufficient to ensure the goal of maintaining and/or increasing the quality and antioxidant properties of fruit during cold storage and room temperature conditions.

Earnings management and financial distress are two critical facets of financial management in corporations that have lately been the focus of thorough research from both scholars and practitioners. The purpose of the research is to evaluate the influence of financial distress on the earnings management of companies listed at the NSE during the pre- and post-COVID eras. Multiple linear regression was applied in this study to examine the causal association between earnings management and financial distress. To assess the effect of financial distress on earnings management, the study analyzed data from 37 listed companies during the pre-COVID and post-COVID periods. The methodology also takes into account control factors like firm size, GDP, and inflation. The pre-COVID research indicates a significant positive correlation between financial strain and earnings management. In contrast, the correlation between financial distress and earnings management is not statistically significant in the post-COVID analysis. However, research shows that firm size has a statistically significant negative influence on managing earnings. The results of this study show that smaller firms are more inclined to engage in earnings management during the post-COVID era than bigger ones.

Neuromonitoring of the efferent nerve impulses transmission in the spinal cord tracts during surgical scoliosis treatment makes it possible to assess whether the functional status is deteriorating, affecting the procedure's safety. Is there any relationship between pre- and intraoperative motor evoked potentials recordings and does idiopathic scoliosis (IS) surgical correction improve directly the spinal efferent transmission? This study aimed to compare the results of surface recorded electromyography (EMG), electroneurography (ENG, M and F-waves), and especially motor evoked potentials (MEP) from tibialis anterior (TA) muscle bilaterally in 353 girls with the right idiopathic scoliosis (types 1A-4C according to Lenke classification). It has not yet been documented whether the results of MEPs recordings induced by transcranial single magnetic stimulus (TMS, pre – and postoperatively) and trains of electrical stimuli (TES; intraoperatively in T0-before surgery, T1 – after pedicle screws implantation, T2 – after scoliosis curvature distraction and derotation following two rods implantation) can be compared for diagnostic verification of improvement of the spinal neural transmission. The study also attempted to determine whether the constant level of optimal anesthesia during the certain surgical steps of scoliosis treatment affects the parameters of MEPs recorded during neuromonitoring procedures. No neurological deficits have been observed postoperatively. Values of amplitudes but not latencies in MEPs recordings evoked with TMS in IS patients compared before and after surgery indicated a slight improvement in the efferent transmission of neural impulses within the fibers of the spinal tracts postoperatively. Results of all neurophysiological studies in IS patients were significantly asymmetrical and recorded worse on the concave side, suggesting greater neurological motor deficits at p=0.04. This asymmetry had been significantly reduced following IS surgery. The surgeries in IS patients brought significant improvement (p=0.04) in parameters of amplitudes of sEMG recordings, however reflecting still the consequences of the neurogenic injury of TA muscle motor units. ENG studies results indicated the symptoms of the axonal type injury in peroneal motor fibers improved only on the concave side at p=0.04 in parallel with the significant improvement of F-waves parameters, which suggests that surgeries might result in the lumbar ventral roots decompression. There were not detected significant differences in amplitudes or latencies of MEPs induced with TMS or TES comparing the parameters recorded preoperatively (one day before surgery) and intraoperatively in T0. The amplitudes of TES evoked MEPs increased gradually at p=0.04 in the subsequent periods (T1 and T2) of observation. The significant reduction of MEPs latency at p=0.05 was observed only at the end of the IS surgery. Studies on the possible connections between the level of anesthesia fluctuations and the required TMS stimulus strength, as well as the MEPs amplitude changes measured in T0-T2 revealed lack of relationships. It is not likely that they could be the factors influencing the efferent transmission in spinal pathways beside the surgical procedures. Considering that MEPs amplitude parameter reflects the number of axons excited from the motor cortex and transmitting the efferent impulses via spinal descending tracts in the white matter, pre- (TMS evoked) and intraoperative (TES evoked) recordings are reliable for evaluating the patient’s neurological status before and during surgical scoliosis correction procedures. The results of this study indicate an agreement between preoperative and early-intraoperative evaluations with these both diagnostic methods. An increase of MEPs amplitude parameters recorded on both sides after scoliosis surgery proves immediate improvement of the total efferent spinal cord transmission. Considering comparative pre- and postoperative sEMG and ENG recordings it can be concluded that surgeries might directly result in the additional lumbar ventral roots decompression. Our results of the tests on the possible variability of the anesthesia level on the parameters of intraoperative recorded MEPs show no clear relationships. We can conclude that MEPs parameters changes are determined by the surgery procedures during neuromonitoring, not the anesthesia conditions if they are kept stable, which influences a decrease in the number of false-positive neuromonitoring warnings. Further studies on a larger population of patients with long-lasting observation postoperatively are required to confirm the presented conclusions on the direct influences of scoliosis surgery on improvement of the motor function in patients with IS.

During the few years, various algorithms have been developed to extract features from high-resolution satellite imagery. For the classification of these extracted features, several complex algorithms have been developed. But these algorithms do not possess critical refining stages of processing the data at the preliminary phase. Various satellite sensors have been launched such as LISS3, IKONOS, QUICKBIRD, and WORLDVIEW etc. Before classification and extraction of semantic data, imagery of the high resolution must be refined. The whole refinement process involves several steps of interaction with the data. These steps are pre-processing algorithms that are presented in this paper. Pre-processing steps involves Geometric correction, radiometric correction, Noise removal, Image enhancement etc. Due to these pre-processing algorithms, the accuracy of the data is increased. Various applications of these pre-processing of the data are in meteorology, hydrology, soil science, forest, physical planning etc. This paper also provides a brief description of the local maximum likelihood method, fuzzy method, stretch method and pre-processing methods, which are used before classifying and extracting features from the image.

Surimi-based products occupy an important position in the aquatic product processing industry. To enhance the quality and flavor of surimi-based products, the effects of pre-emulsified safflower seed oil on the texture, water-holding capacity (WHC), microstructure, and flavor of Nemipterus virgatus surimi gel was evaluated. The texture and whiteness of gel were improved, and the WHC increased (P &lt; 0.05) as the content of safflower seed oil increased up to 2 mL per 100 g surimi. Furthermore, the drops of pre-emulsified safflower seed oils with an average diameter of less than 0.10 μm were evenly distributed in gel matrix. Microstructure and infrared spectroscopy analyses indicated that low-content pre-emulsified safflower seed oil acted as filler particles to occupy void spaces, resulting in gel exhibiting a dense network structure. Volatile analysis showed the gel containing pre-emulsified oil enriched volatile compounds, mainly resulting from oxidation and decomposition of oils by the activation of lipoxygenase, which synergistically contributes to unique flavors of gel. Consequently, low-content pre-emulsified safflower seed oil can used to enhance the quality and flavor of N. virgatus surimi-based products. These findings are especially relevant to the current growing interest in low-fat and high-protein diets.