Search

This paper describes a fast algorithm for transforming Legendre coefficients into Chebyshev coefficients, and vice versa. The algorithm is based on the fast multipole method and is similar to the approach described by Alpert and Rokhlin [SIAM J. Sci. Comput., 12 (1991), pp. 158--179]. The main difference is that we utilize a modal Galerkin approach with Chebyshev basis functions instead of a nodal approach with a Lagrange basis. Part of the algorithm is a novel method that facilitates faster spreading of intermediate results through neighboring levels of hierarchical matrices. This enhancement leads to a method that is approximately 20 percent faster to execute, due to fewer floating point operations. We also describe an efficient initialization algorithm that for the Lagrange basis is roughly 5 times faster than the original method for large input arrays. The described method has both a planning and an execution stage that asymptotically require O(N) flops. The algorithm is simple enough that it can be implemented in 100 lines of vectorized Python code. Moreover, its efficiency is such that a single-threaded C implementation can transform 1,000,000 coefficients in approximately 20 milliseconds on a new MacBook Pro M3, representing about 3 times the execution time of a well-planned (single-threaded) type 2 discrete cosine transform from FFTW (www.fftw.org). Planning for these coefficients requires approximately 50 milliseconds. [ABSTRACT FROM AUTHOR]

The lossless coding framework of High Efficiency Video Coding (HEVC) comprises prediction and entropy coding, while omitting the transform process. However, residuals derived from prediction continue to demonstrate significant spatial redundancy, which has the potential to compromise the coding efficiency. This article presents an optimized scheme for HEVC intra lossless coding that integrates transform and quantization. In the proposed scheme, a residual block, generated by HEVC, is partitioned into two components: the coefficient block and the error block. The coefficient block, responsible for lossless coding, is obtained through the adoption of the transform and quantization processes employed in HEVC lossy coding. Subsequently, the error block is derived by predicting the residual block using the generated coefficient block. Both the coefficient block and the error block can be effectively encoded utilizing the existing entropy coding scheme in HEVC lossy coding. Rate‐distortion optimization is employed to determine whether the residual block will be partitioned into two components. The proposed scheme is implemented into the HM 12.1 software. Through the utilization of the discrete cosine transform kernel and the discrete sine transform kernel, with a quantization parameter set to 20, on HEVC standard test sequences and the all intra main configuration file, empirical findings substantiate that the proposed methodology attains an average bit‐rate reduction of 3.31%, with a maximum improvement of 12.29% in comparison to HEVC lossless coding. [ABSTRACT FROM AUTHOR]

Nitrogen and phosphorus pollution was considered to be one of the main factors causing some water environment problems. However, microalgae and microalgae-bacteria consortium have special advantages in wastewater advanced treatment, therefore it has important significance to investigate and discuss the process and regular pattern of the transferring and transformation of nitrogen and phosphorus in wastewater for the treatment and utilization of them. In this paper, the path, morphological and influencing factors of transferring and transformation of nitrogen and phosphorus in water treated by microalgae-bacteria consortium were systematically described. The absorption mechanism of nitrogen and phosphorus by microalgae, and the synergic effect of microalgae-bacteria consortia system in the nitrogen and phosphorus cycle were analyzed and summarized. It was hoped to provide guidance for the treatment of different quality of wastewater and the optimization of treatment process, and give some scientific basis and theoretical support for water environment protection and restoration.

Automated harvesting systems rely heavily on precise and real-time fruit recognition, which is essential for improving efficiency and reducing labor costs. Strawberries, due to their delicate structure and complex growing environments, present unique challenges for automated recognition systems. Current methods predominantly utilize pixel-level and box-based approaches, which are insufficient for real-time applications due to their inability to accurately pinpoint strawberry locations. To address these limitations, this study proposes StrawSnake, a contour-based detection and segmentation network tailored for strawberries. By designing a strawberry-specific octagonal contour and employing deep snake convolution (DSConv) for boundary feature extraction, StrawSnake significantly enhances recognition accuracy and speed. The Multi-scale Feature Reinforcement Block (MFRB) further strengthens the model by focusing on crucial boundary features and aggregating multi-level contour information, which improves global context comprehension. The newly developed TongStraw_DB database and the public StrawDI_Db1 database, consisting of 1080 and 3100 high-resolution strawberry images with manually segmented ground truth contours, respectively, serves as a robust foundation for training and validation. The results indicate that StrawSnake achieves real-time recognition capabilities with high accuracy, outperforming existing methods in various comparative tests. Ablation studies confirm the effectiveness of the DSConv and MFRB modules in boosting performance. StrawSnake's integration into automated harvesting systems marks a substantial step forward in the field, promising enhanced precision and efficiency in strawberry recognition tasks. This innovation underscores the method's potential to transform automated harvesting technologies, making them more reliable and effective for practical applications. [ABSTRACT FROM AUTHOR]

Abstract The lossless coding framework of High Efficiency Video Coding (HEVC) comprises prediction and entropy coding, while omitting the transform process. However, residuals derived from prediction continue to demonstrate significant spatial redundancy, which has the potential to compromise the coding efficiency. This article presents an optimized scheme for HEVC intra lossless coding that integrates transform and quantization. In the proposed scheme, a residual block, generated by HEVC, is partitioned into two components: the coefficient block and the error block. The coefficient block, responsible for lossless coding, is obtained through the adoption of the transform and quantization processes employed in HEVC lossy coding. Subsequently, the error block is derived by predicting the residual block using the generated coefficient block. Both the coefficient block and the error block can be effectively encoded utilizing the existing entropy coding scheme in HEVC lossy coding. Rate‐distortion optimization is employed to determine whether the residual block will be partitioned into two components. The proposed scheme is implemented into the HM 12.1 software. Through the utilization of the discrete cosine transform kernel and the discrete sine transform kernel, with a quantization parameter set to 20, on HEVC standard test sequences and the all intra main configuration file, empirical findings substantiate that the proposed methodology attains an average bit‐rate reduction of 3.31%, with a maximum improvement of 12.29% in comparison to HEVC lossless coding.

Taking the geoelectric field observation data of Yuanmou Julin of Chuxiong Earthquake Monitoring Center Station as the research object, combined with the ideas and specific contents of the two transformations since the geoelectric field observation，the observation data curve variation characteristics and data quality in three time periods before and after the two transformations are compared and analyzed，and the following experience is obtained，which can be used as a reference for the transformation and construction of other geoelectric field observation stations: ① The anti-interference ability and stability of solid non-polarized electrodes far exceed lead electrodes; ② Electrode buried depth need to consider more consistent soil electrical properties rather than pay attention to the buried depth，should do a good job in the construction of the site geological prospecting work，to ensure that the electrode buried after the grounding resistance without a big gap, but also should pay attention to the electrode lead buried depth must be greater than the farmers daily farming may touch the depth; ③ Double “L” type pole arrangement needs to be strictly in accordance with the construction specifications of the geoelectric station to arrange the long and short pole spacing; ④ For the insulation problem of overhead lines, it can be done between the hoop and the pole first through the insulation rubber pad insulation，and then between the hoop and the steel strand through the insulator re-insulating，especially the ground resistivity with the site observation station，more attention should be paid to the insulation of the steel strand.

With the continuous development of economy and industry, the demand for energy in modern society is ever-increasing. As a kind of high efficiency and low carbon energy, nuclear energy has attracted more and more attention. Developing nuclear energy, however, also brings some problems such as the release of radioactive metal ions that pose a threat to the environment and human health. Therefore, separation and removal of radioactive ions(e.g., U(Ⅵ)) from wastewater is of great significance to ensure the sustainable development of nuclear energy. Covalent organic frameworks(COFs) are a kind of periodic porous polymer materials composed of organic molecular units connected by covalent bonds. The emergence of COFs marks a new era in the field of materials. Because of the design ability of COFs and its unique advantages in structure and pore, it shows a good application prospect in catalysis, sensing and adsorption. Very recent efforts by some investigators have focused on the application of COFs in adsorption and separation of radionuclides, generally with encouraging results. According to the classification from the structural dimension, COFs can be divided into two-dimensional COFs materials(2D-COFs) and three-dimensional COFs materials(3D-COFs). Up to now, 2D COFs materials have been widely studied and applied. In contrast, the development of 3D-COF has been relatively slow, and the performance and application have been limited, a result of the limitations of molecular structural units and topological connections, as well as the difficulties of synthesis and structural analysis. To this end, in this paper, a combination of bond transformation and post-grafting was used to construct functional 3D-COFs materials for the efficient removal of U(Ⅵ) from wastewater. Firstly, the imine bond was reduced to an irreversible amine bond by reduction reaction, and then sulfonic acid group was introduced into the skeleton by reaction with 1, 3-propyl sulfonolactone to successfully synthesize COF-320-H2SO3. The structure and properties of COF-320-H2SO3 were characterized by various characterization methods, and the adsorption behavior of COF-320-H2SO3 on U(Ⅵ) was systematically studied. The results show that the chemical stability of COFs is significantly improved by the conversion of imine bond to amine bond, and the adsorption capacity of the material for U(Ⅵ) is clearly enhanced by the introduction of sulfonic acid group with hydrophilic and strong coordination ability. Even under the conditions of 3 mol/L HNO3, the adsorption capacity of U(Ⅵ) can reach more than 40 mg/g. This work further reveals the potential application value of COFs, especially 3D-COFs materials, in the separation of radioactive metal ions.

Copyright of Progress in Earthquake Sciences is the property of China Earthquake Administration, Institute of Geophysics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: The field of electroencephalogram (EEG)-based emotion identification has received significant attention and has been widely utilized in both human-computer interaction and therapeutic settings. The process of manually analyzing electroencephalogram signals is characterized by a significant investment of time and work. While machine learning methods have shown promising results in classifying emotions based on EEG data, the task of extracting distinct characteristics from these signals still poses a considerable difficulty. Methods: In this study, we provide a unique deep learning model that incorporates an attention mechanism to effectively extract spatial and temporal information from emotion EEG recordings. The purpose of this model is to address the existing gap in the field. The implementation of emotion EEG classification involves the utilization of a global average pooling layer and a fully linked layer, which are employed to leverage the discernible characteristics. In order to assess the effectiveness of the suggested methodology, we initially gathered a dataset of EEG recordings related to music-induced emotions. Experiments: Subsequently, we ran comparative tests between the state-of-the-art algorithms and the method given in this study, utilizing this proprietary dataset. Furthermore, a publicly accessible dataset was included in the subsequent comparative trials. Discussion: The experimental findings provide evidence that the suggested methodology outperforms existing approaches in the categorization of emotion EEG signals, both in binary (positive and negative) and ternary (positive, negative, and neutral) scenarios. [ABSTRACT FROM AUTHOR]

This article goes back to McLuhan and Watson's book From Cliché to Archetype, in its respective editions, regarding artistic recognition and treatment of media effects. It examines the core aspects of these texts as well as related Explorations in Media Ecology articles that help explain key aspects more clearly. The narrative relates these parallel constructs to those employed in information technology change management impact assessment. It situates these matters in terms of Bolter's Digital Plenitude. The article presents a framework for combining these ideas and applying them in initiating an investigation of artificial intelligence academic use. This schema suggests tools that foster scholar and practitioner collaboration to better deal with an increasingly digitized environment. This article balances on the line between the theoretical and the pedagogical coming together in its final section to suggest a specific way to proceed in preparing for the next phases of the digital era. [ABSTRACT FROM AUTHOR]

Suboptimal radiation protection evidenced in literature necessitated the development of a theory to optimise radiation protection. To develop a theory, concept analysis of the central concept guiding the theory is required to provide explicit theoretical and operational definitions. This article presents the concept analysis of the central concept of 'facilitating a transformative radiation protection environment' used to develop a theory to optimise radiation protection. The study used Walker and Avant's concept analysis process of selecting a concept, determining the aims and purpose of analysis, identifying all uses of the concept, and determining the defining attributes by identifying a model case, a borderline case and a contrary case. The central concept was dissected into the individual concepts of facilitating, transformative, radiation, protection, and environment to enhance the definition and conceptual meaning of the central concept. The essential and related attributes of the individual concepts informed the summative definition of the central concept, and a model, borderline and contrary case contributed to the conceptual meaning. The summative definition of the central concept of "facilitating a transformative radiation protection environment" was determined to be through the essential attributes of help, process, change, X-rays, keeping safe considering the benefits versus risks of ionising radiation exposure and the total internal, external context within which persons exist and the related attributes. Explored extensively in nursing literature, concept analyses in radiography are limited. Therefore, this article articulates the process of concept analysis and its use in radiography. Concept analysis provides a systematic process to analyse concepts often assumed to be understood, highlighting its importance in radiography literature and to educational praxis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing popularity of digital data in the healthcare domain, data hiding has become a hot research topic for covert communication and privacy protection. Existing data-hiding methods often tend to results in increased imperceptibility and robustness, which are also needed to simultaneously improve the system's security and embedding capacity. To solve this problem, a robust and high-capacity data hiding technique, called HIDEmarks, our study proposed a combination of integer wavelet transform (IWT) and least significant bit (LSB) for healthcare. Specifically, the IWT-LSB was used to embed multiple marks into the medical colour image. The technique first transformed cover images into three channels, and then each channel was transformed using IWT. After this, multiple marks were concealed into the cover media with the help of the LSB scheme. Meanwhile, a lossless soft method was adopted to compress the image mark prior to embedding, thereby reducing storage and transmission overhead and improving the embedding capacity of the marked colour image. Experimental results show that the proposed HIDEmarks achieved superior perceptual quality, robustness and capacity compared with the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

In the late Permian and Triassic, the continental Mitu Group formed in extensional basins along the length of the Cordillera Oriental and Altiplano of present-day Peru. Given the presence of coeval arc systems only in northern Chile and southern Ecuador but not in Peru the tectonic setting of the Mitu basin has been interpreted variably as orthogonal continental rift, sinistral transtensional rift, aulacogen and back-arc basin. The Mitu Group comprises continental mass flow and alluvial fan, fluvial, aeolian and minor lacustrian facies and hosts thick piles of subalkaline and alkaline intermediate and felsic ignimbrites and mafic lavas. The age of the Mitu Group had originally been established as ranging from the Late Permian to the late Triassic on the basis of structural considerations and scarce biostratigraphic data. Recently, U-Pb zircon ages from ignimbrites and sedimentary rocks have been taken to constrain the Mitu Group to the Middle and Late Triassic. We performed a sedimentological, heavy mineral, and zircon geochronological and Lu-Hf isotope study of the Mitu Group in 14 sections mainly in southern and central Peru, and one section in northern Peru. Ten new U-Pb concordia ages on ignimbrites intercalated in the Mitu Group successions offer a new robust stratigraphic framework and constrain the stratigraphy of the Mitu Group between 260 and 205 Ma. In combination with maximum likelihood ages of deposition derived from detrital zircon, U-Pb geochronology places the deposition of the Mitu Group between ca. 270 and 194 Ma (lower Guadalupian into the Sinemurian). Detrital zircon U-Pb age distributions and heavy mineral assemblages reflect a strongly recycled Precambrian Amazonian and Palaeozoic proto-Andean provenance. The Palaeozoic detrital age patterns are highly variable, and temporally and spatially random. A local provenance can generally not be identified. εHf(t) values in zircon obtained from ignimbrites and sedimentary rocks indicate variable degrees of crustal recycling. In the course of the Palaeozoic, εHf(t) values become on average progressively less negative, with a large proportion particularly of Mitu age zircons' εHf(t) values encompassing less evolved and moderately juvenile compositions. Along strike of the basin stratigraphic thicknesses, and rates and times of accumulation vary strongly with larger thicknesses and rates being registered in southern Peru. This suggests that the Mitu basin had been divided into a number of subbasins with individual histories of subsidence, accumulation, and volcanism. Absent a magmatic arc, late Permian-Triassic Peru evolved in a sinistral plate tectonic and regional framework expressed particularly in the sinistral Late Gondwanide orogeny predating the Mitu Group. We interpret that a sinistral transform fault linked the subduction zones in southern Ecuador and northern Chile and that the Mitu Group basin has formed by sinistral transtensional and transpressional movements along a related transcurrent fault inboard of the transform margin. In the broader framework of the accretionary Terra Australis orogen along the western Gondwana margin, this interpretation fits its evolution in the Andean segment in an internally consistent way. [ABSTRACT FROM AUTHOR]

Insecure communication in digital image security and image storing are considered as important challenges. Moreover, the existing approaches face problems related to improper security at the time of image encryption and decryption. In this research work, a wavelet environment is obtained by transforming the cover image utilizing integer wavelet transform (IWT) and hybrid discrete cosine transform (DCT) to completely prevent false errors. Then the proposed hybrid chaotic map with L-shaped fractal Tromino offers better security to maintain image secrecy by means of encryption and decryption. The proposed work uses fractal encryption with the combination of L-shaped Tromino theorem for enhancement of information hiding. The regions of L-shaped fractal Tromino are sensitive to variations, thus are embedded in the watermark based on a visual watermarking technique known as reversible watermarking. The experimental results showed that the proposed method obtained peak signal-to-noise ratio (PSNR) value of 56.82dB which is comparatively higher than the existing methods that are, Beddington, Free, and Lawton (BFL) map with PSNR value of 8.10 dB, permutation substitution, and Boolean operation with PSNR value of 21.19 dB and deoxyribonucleic acid (DNA) level permutation-based logistic map with PSNR value of 21.27 dB. [ABSTRACT FROM AUTHOR]

Learning embeddings representations of users and items lies at the core of modern recommender systems. Existing methods based on Graph Convolutional Network (GCN) and sequential recommendation typically obtain a user’s or an item’s embedding by mapping from pre-existing features into better embeddings for users and items, such as ID and attributes. GCN integrates the user-item interaction as the bipartite graph structure into the embedding process, which can better represent sparse data, but cannot capture users’ long-term interests. Sequential recommendation seek to capture the “context” of users’ activities based on their historical actions, but requires dense data to support it. The goal of our work is to combine the advantages of GCN and sequential recommendation models by proposing a novel Self-Attention based Sequential recommendation with Graph Convolutional Networks (SASGCN). It uses multiple lightweight GCN layers to capture high-order connectivity between users and items, and by introducing ratings as auxiliary information into the user-item interaction matrix, it provides richer information. By incorporating self-attention based methods, the proposed model capture long-term semantics through relatively few actions. Extensive experiments on three benchmark datasets show that our model outperforms various state-of-the-art models consistently.

There are many integral transforms that are widely used to solve numerous real-life, science, and engineering problems. In this article, we present SEE transform for determining the solution of the system of linear Volterra integro-differential equations of the second kind. Some applications have been given and solved by using the SEE transform for illustrating the applicability of the SEE transform. Results of the applications assert that the SEE transform is very effective for obtaining the exact solution of this equation.

Benford's Law has been frequently used for fraud detection and is introduced in various accounting, auditing, and analytics courses. Instead of using specialized computer-aided auditing tools, this teaching case purports to apply Microsoft Excel to demonstrate the process, applicability, and benefits of Benford's Law by analyzing publicly available datasets on confirmed COVID-19 and credit card fraud cases. This hands-on exercise also enhances students' Excel skills to extract, transform, and load (ETL) data and use various data visualizations (e.g., pie/100 percent stacked/combo charts) to display proportional information. This case provides an engaging learning experience that matches student skill levels, stimulates critical thinking, and fosters interest in forensic accounting. Data Availability: Data are available from the authors upon request. JEL Classifications: M42; M41; I20. [ABSTRACT FROM AUTHOR]

Copyright of Food Science & Technology & Economy is the property of Grain Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Thousand-grain weight is the main parameter for accurately estimating rice yields, and it is an important indicator for variety breeding and cultivation management. The accurate detection and counting of rice grains is an important prerequisite for thousand-grain weight measurements. However, because rice grains are small targets with high overall similarity and different degrees of adhesion, there are still considerable challenges preventing the accurate detection and counting of rice grains during thousand-grain weight measurements. A deep learning model based on a transformer encoder and coordinate attention module was, therefore, designed for detecting and counting rice grains, and named TCLE-YOLO in which YOLOv5 was used as the backbone network. Specifically, to improve the feature representation of the model for small target regions, a coordinate attention (CA) module was introduced into the backbone module of YOLOv5. In addition, another detection head for small targets was designed based on a low-level, high-resolution feature map, and the transformer encoder was applied to the neck module to expand the receptive field of the network and enhance the extraction of key feature of detected targets. This enabled our additional detection head to be more sensitive to rice grains, especially heavily adhesive grains. Finally, EIoU loss was used to further improve accuracy. The experimental results show that, when applied to the self-built rice grain dataset, the precision, recall, and mAP@0.5 of the TCLE–YOLO model were 99.20%, 99.10%, and 99.20%, respectively. Compared with several state-of-the-art models, the proposed TCLE–YOLO model achieves better detection performance. In summary, the rice grain detection method built in this study is suitable for rice grain recognition and counting, and it can provide guidance for accurate thousand-grain weight measurements and the effective evaluation of rice breeding. [ABSTRACT FROM AUTHOR]

This paper proposes a method to reduce seismogram variability as a determining factor in its interpretation, processing, and clustering. By introducing the concept of single fluctuations (SFs), the seismogram can be parsed into a sequence of random impulses with subsequent ordering. This rearrangement of SFs, if they are assigned by positive integers, represents the formal mapping of a regular string of integers into a random one, which can be represented with a bipartite random graph (bigraph). Due to its specific randomness, such a bigraph is considered a new type of random balanced bigraph. The R-envelope and RQ-envelope, its equidistant version, are defined by tracing the peak envelope over ordered SFs. The equivalence and complementariness of the RQ and bigraph are considered and discussed, forming a combined characteristic of the seismogram. The R/RQ provided a considerable reduction in seismogram variability, which was confirmed by creating and analyzing an ensemble of RQ from several seismograms. In the RQ domain, distance is defined as a possible basis for metrics and clustering, but the ensemble variability was quite narrow and not as suitable for this purpose. Otherwise, the ensemble shows high redundancy hidden in the seismogram population. As for the bigraph, the mesh of its edges is structuralized in bundles, forming a skeleton, which reflects the internal structural content of the seismogram. The distance over the domain of bigraphs is proposed to show the possibility of clustering. This means that only a combined RQ and bigraph provides a suitable frame for seismogram representation with reduced variability and, thus, the potential for more effectively organizing seismic databases and a deeper interpretation of seismograms; therefore, RQs and bigraphs can be considered as a transform of a seismogram. Many aspects of these concepts are thoroughly discussed. The similarity between concepts of SF and wavelets is briefly considered. This very complex theme is new and promises broad further research. All issues considered in the paper are abundantly illustrated. [ABSTRACT FROM AUTHOR]

RGB-D sensors can be a low-cost solution for an accurate silo's content monitoring which is fundamental for its efficient management. Some reference information such as the position and orientation of the sensor with respect to the silo's geometry is fundamental for obtaining correct content measurements from acquired data. Since in real cases this information is not always known, a new method to obtain these measurements is proposed. This, taking as input sensor acquired data (represented as a point cloud), automatically computes the silo's axis to provide a new reference system from which the point cloud can be easily processed. The z-axis of this reference system coincides with the gravity axis and the xy-plane is parallel to the ground plane. It is obtained in a six-step process that exploits the silo geometry properties and an estimation of the shape tensors of the acquired points. The method has been implemented and tested on both synthetic and real silos, considering a complete silo's discharge process and different camera positions. Data acquired at each discharge has been transformed using the new reference system and compared with the silo's ground truth (manually obtained for the real silos). To evaluate the accuracy the input point cloud to adjusted point cloud average distance has been considered. In all the tests, the well-performance of the proposal has been demonstrated, achieving a maximum average distance error of less than 6 cm. [ABSTRACT FROM AUTHOR]

Cognitive behavior therapy (CBT) is an evidence-based intervention in contemporary clinical health care services. CBT was developed as a psychotherapy to overcome the deficits of psychoanalysis and behaviorism by Aron T. Beck. However, after 63 years, CBT has transformed from its early framework. In comparison to other psychotherapies, cognitive behavior therapy has rapidly changed and developed as an evidence-based clinical intervention. Therefore, this study explored the evolution of cognitive behavior therapy by focusing on its origin, development, limitations, clinical application, third waves, and future directions. Early CBT therapeutic framework has developed as a cognitive model of depression and developed as an intervention to address the negative core belief of individuals and rationalize maladaptive behaviors, thoughts, and emotions. However, clinicians have experienced several limitations and scholars have recognized theoretical limitations in CBT. Later, scholars on CBT have overcome these deficits and developed disorder-specific CBT therapies. Then, clinicians empirically evaluate those therapeutic frameworks and transform CBT into an evidence-based clinical intervention. Further, CBT has strength in integrating third-wave therapies. In the future, CBT will therapeutically further transform with technology, integrating advanced theories of pathology, and clinical demand of the present health care services. Therefore, as a psychotherapeutic intervention, cognitive behavior therapy has transformed from its early framework, and at present, it has developed empirically. Further, in the future, it will transform into a more effective psychotherapeutic intervention. [ABSTRACT FROM AUTHOR]

A Personal Health Record (PHR) is a patient-managed platform for health data. Most hospitals provide a PHR as an extension of the Electronic Medical Record (EMR). However, there are unresolved issues around the adoption rate, functionalities, barriers and, more importantly, the impact of the PHR on patients' health. A cross-sectional, survey-based descriptive study was conducted in which patients from four main tertiary hospitals in Saudi Arabia were targeted from September 2022 to February 2023. The survey was tested and validated to address the objectives of the study. The survey covered components related to intention to use the PHR, required functions, obstacles and expected outcomes. This study involved 396 participants from the top four hospitals. It was discovered that the majority of them had intentions to use the PHR (93%) and believed it to be useful (94%) and easy to use (94%). It was widely agreed that accessing medical records (77%), scheduling appointments (88%), renewing medication (90%), tracking patient data (70%) and receiving personalized education (78%) were essential aspects of the PHR. Furthermore, the survey revealed that 54% of respondents saw a positive effect on their health status. A significant number of participants, around 54%, expressed concerns about the privacy of their PHR, and 46% reported concerns about the accuracy of their information. The study found that demographic factors and the type of hospital did not have a statistically significant association with the intention to use the PHR. Our findings showed that there were no significant barriers to adopting the PHR. Additionally, we found that less than half of the participants believed that their current PHR helped them to improve their health. This highlights the need for healthcare organizations to focus on improving the PHR's functionality and overall purpose. Instead of simply providing basic features, the PHR should allow patients to manage their health information comprehensively, including compiling information from hospitals and patient-generated data. Having a PHR is crucial in improving an individual's overall health. As technology advances, more data are being generated that should be included in the PHR to ensure an accurate and comprehensive view of the patient's health. Expanding the scope of the PHR to include capabilities beyond merely hospital data is important. Achieving this requires an open and honest discussion about the role of the PHR, potential obstacles and how to coordinate efforts among different stakeholders. [ABSTRACT FROM AUTHOR]

Barley yellow dwarf virus (BYDV) is an important viral disease of grain crops worldwide and a major cause of yield loss. The risk periods for BYDV infection coincide with milder temperature that prolongs aphid flight and facilitates viral transmission through primary and secondary aphid movement in the crop. Secondary aphid movement is associated with greater BYDV spread in winter cereals. A critical component of BYDV management is therefore delaying sowing of winter cereals and correctly timing insecticide application to maximise crop protection. Previous research in Ireland considered insecticide timing in early (September) and late (October onwards) sown cereals. Early research did not consider action thresholds around temperature, aphid flight and risk of secondary spread. This research set out to understand the optimal timing of insecticide application in October sown winter barley to reduce BYDV infection and yield impact. A critical temperature of 3°C was used as a threshold for aphid development that leads to movement and BYDV spread, and insecticide treatments were applied to the crop at predictable intervals in relation to temperature. Results show that BYDV symptoms and yield are affected by spray time, location and year, although only significant with regard to the reduction of BYDV symptoms. For both BYDV symptoms and yield, there was a significant difference between untreated (control) plots and “early” and “late” applications of insecticide, again more notable for BYDV symptoms than yield. This work indicates the value of optimising a single insecticide spray for control of October sown cereals and supports decision-making in the management of cereal crops.

This paper offers several approaches for solving multiplicative second-order linear differential equations with variable exponentials, such as normalization and reduction to Riccati equations. In addition, in this paper, the multiplicative version of the Airy equation, which emerges in fluid mechanics, geophysics, and atomic physics, is solved using the multiplicative power series solution method. [ABSTRACT FROM AUTHOR]

Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Babylon Center for Humanities Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: In order to reduce the impact of CT radiation, low-dose CT is often used, but low-dose CT will bring more noise, affecting image quality and subsequent 3D reconstruction results. OBJECTIVE: The study presents a reconstruction method based on wavelet transform-total variation (WT-TV) for low-dose CT. METHODS: First, the low-dose CT images were denoised using WT and TV denoising methods. The WT method could preserve the features, and the TV method could preserve the edges and structures. Second, the two sets of denoised images were fused so that the features, edges, and structures could be preserved at the same time. Finally, FBP reconstruction was performed to obtain the final 3D reconstruction result. RESULTS: The results show that The WT-TV method can effectively denoise low-dose CT and improve the clarity and accuracy of 3D reconstruction models. CONCLUSION: Compared with other reconstruction methods, the proposed reconstruction method successfully addressed the issue of low-dose CT noising by further denoising the CT images before reconstruction. The denoising effect of low-dose CT images and the 3D reconstruction model were compared via experiments. [ABSTRACT FROM AUTHOR]

The social change and ideological change movement in modern China promoted the reform of women's footwear, and the concept of women's choice of footwear and wearing them had changed, which was the innovation of the concept of women's footwear. The transformation of traditional women to have an independent personality step by step was reflected from the bow shoes of women with bound feet to the diversification of footwear for women with natural feet, women from the humble era of small feet to fighting for equal status with men, and then to the consumption behavior and methods of women's footwear of different classes. Thus, the process of changing the concept of women's footwear was also deeply engraved with the evolutionary trajectory of modern civilized women. We adopt the research method of comparative demonstration combined with historical documents, compare the attitudes towards women's foot-binding, natural feet, women's social space and women's social behavior in ancient and modern societies, and summarize three directions for the transformation of the concept of women's footwear in modern times including equality, fashion and hygiene. Furthermore, we explore the reasons and impact of the transformation. The following conclusions are drawn: (i) from the perspective of the spatial sequence of concept change, the concept of women's footwear in Shanghai, Tianjin, Guangzhou and other open-minded cities was first innovated, which was accompanied by a series of new social behaviors, and soon the new concept and new behaviors spread to the surrounding urban and rural areas through the flow of people and goods; (ii)from the perspective of the impact of concept change, the shapes of women's shoes were constantly changing, the colors were coordinated and broke the tradition before, and the materials were comfortable and hygienic, which reflected the domestic shoe industry; (iii) from the perspective of women's life practice, women of different classes had different consumption behaviors and ways of footwear, but the standards of pursuing beauty and the goal of modernization were the same, which promoted the progress of new women's lives. The research on the concept of modern women's footwear is an important part of the new trend of clothing in modern times. From the perspective of modern women's footwear reform, we closely combine social reform and ideological change movements, analyze the changes in women's wearing and choosing shoes before and after they put their feet on, and reveal the scientific understanding of new women's footwear. The spread and popularity of this concept have accelerated the modern people's ability to accept new shoe styles and new clothing, and promoted the transformation and development of modern clothing. [ABSTRACT FROM AUTHOR]

With view to applications to harmonic and stochastic analysis of infinite network/graph models, we introduce new tools for realizations and transforms of positive definite kernels (p.d.) $ K $ and their associated reproducing kernel Hilbert spaces. With this we establish two kinds of factorizations: (i) Probabilistic: Starting with a positive definite kernel $ K $ we analyze associated Gaussian processes $ V $. Properties of the Gaussian processes will be derived from certain factorizations of $ K $, arising as a covariance kernel of $ V $. (ii) Geometric analysis: We discuss families of measure spaces arising as boundaries for $ K $. Our results entail an analysis of a partial order on families of p.d. kernels, a duality for operators and frames, optimization, Karhunen–Loève expansions, and factorizations. Applications include a new boundary analysis for the Drury-Arveson kernel, and for certain fractals arising as iterated function systems; and an identification of optimal feature spaces in machine learning models. [ABSTRACT FROM AUTHOR]

Thousand-grain weight is the main parameter for accurately estimating rice yields, and it is an important indicator for variety breeding and cultivation management. The accurate detection and counting of rice grains is an important prerequisite for thousand-grain weight measurements. However, because rice grains are small targets with high overall similarity and different degrees of adhesion, there are still considerable challenges preventing the accurate detection and counting of rice grains during thousand-grain weight measurements. A deep learning model based on a transformer encoder and coordinate attention module was, therefore, designed for detecting and counting rice grains, and named TCLE-YOLO in which YOLOv5 was used as the backbone network. Specifically, to improve the feature representation of the model for small target regions, a coordinate attention (CA) module was introduced into the backbone module of YOLOv5. In addition, another detection head for small targets was designed based on a low-level, high-resolution feature map, and the transformer encoder was applied to the neck module to expand the receptive field of the network and enhance the extraction of key feature of detected targets. This enabled our additional detection head to be more sensitive to rice grains, especially heavily adhesive grains. Finally, EIoU loss was used to further improve accuracy. The experimental results show that, when applied to the self-built rice grain dataset, the precision, recall, and mAP@0.5 of the TCLE–YOLO model were 99.20%, 99.10%, and 99.20%, respectively. Compared with several state-of-the-art models, the proposed TCLE–YOLO model achieves better detection performance. In summary, the rice grain detection method built in this study is suitable for rice grain recognition and counting, and it can provide guidance for accurate thousand-grain weight measurements and the effective evaluation of rice breeding.

Employers require that accounting students think critically and use data analytics tools to gain valuable insights for forensic, tax, auditing, and advisory services. This case provides students with a hands-on learning experience using data analytics and encourages critical thinking. Students are tasked with using Alteryx and Tableau to prepare and analyze a fictitious storm dataset and insurance claims dataset to identify claims that may be suspicious. They create visualizations and spreadsheets that support their recommendation for further analysis. The learning objectives are: (1) develop student knowledge and ability to conduct data preparation through the "Extract, Transform, and Load" (ETL) process; (2) expand student knowledge of data analytics and fraud investigation; (3) provide students with practice in fraud investigation skills, including critical thinking and problem solving; (4) develop skills specific to data analytics and data visualization in accounting; and (5) develop effective oral and written communication skills. [ABSTRACT FROM AUTHOR]

With the aim of solving the problem of coal congestion caused by big coal blocks in underground mine scraper conveyors, in this paper we proposed the use of a YOLO-BS (YOLO-Big Size) algorithm to detect the abnormal phenomenon of coal blocks on scraper conveyors. Given the scale of the big coal block targets, the YOLO-BS algorithm replaces the last layer of the YOLOv4 algorithm feature extraction backbone network with the transform module. The YOLO-BS algorithm also deletes the redundant branch which detects small targets in the PAnet module, which reduces the overall number of parameters in the YOLO-BS algorithm. As the up-sampling and down-sampling operations in the PAnet module of the YOLO algorithm can easily cause feature loss, YOLO-BS improves the problem of feature loss and enhances the convergence performance of the model by adding the SimAM space and channel attention mechanism. In addition, to solve the problem of sample imbalance in big coal block data, in this paper, it was shown that the YOLO-BS algorithm selects focal loss as the loss function. In view of the problem that the same lump coal in different locations on the scraper conveyor led to different congestion rates, we conducted research and proposed a formula to calculate the congestion rate. Finally, we collected 12,000 image datasets of coal blocks on the underground scraper conveyor in Daliuta Coal Mine, China, and verified the performance of the method proposed in this paper. The results show that the processing speed of the proposed method can reach 80 fps, and the correct alarm rate can reach 93%. This method meets the real-time and accuracy requirements for the detection of abnormal phenomena in scraper conveyors. [ABSTRACT FROM AUTHOR]

• Steps to actualize Future of Nursing 2020–2030 Report education recommendations. • Reconciling shortage of public health and health equity expertise. • Curriculum redesign to promote diversity, equity, inclusion and belonging. • Faculty development critical to education about SDOH and structural racism. The Future of Nursing 2020–2030: Charting a Path to Achieve Health Equity report (NASEM, 2021) provides a comprehensive plan to improve the quality of health care and candidly acknowledges historical and contemporary issues that have stalled previous efforts to dismantle health care disparities. This article spotlights the role that nursing education, nurse leaders, and faculty play in enabling all people to achieve their highest level of health. Through the framework of the four pillars of (1) reconciling the shortage of nurses with expertise in public health and health equity, (2) creating policies that include and promote the tenets of diversity, antiracism, and well-being, (3) designing curricular resources and activities that address contemporary issues, and (4) creating and supporting an ethos that invites, retains, and graduates diverse students and facilitates a sense of belonging, our future nursing graduates will be prepared to advance health equity for all. [ABSTRACT FROM AUTHOR]