bluuebunny/crossref_metadata_2025 huggingface dataset:
Przygotowanie i kwalifikowanie do małżeństwa oraz właściwe rozpoznawanie spraw o nieważność małżeństwa w przemówieniu Benedykta XVI do Roty Rzymskiej z 22 I 2011 roku
Authors: Góralski Wojciech
Published: September 2023

The Pope Benedict XVI in his allocution on the 22nd January 2011 to the officials of the Tribunal of the Roman Rota responded to two points: 1) the preparation and authorization to marriage, 2) a proper identifying cases of invalid marriages. He ordered to pay attention to legal nature of preparation to the marriage. Marriage is a legal union between a man and a woman, from which there are specific commitments. So preparing for it must be more thorough, reliable and responsible. According to the Pope, a proper canon preparation by pastors can prevent invalid marriages.
Operating Systems and Infrastructure in Data Science
Authors: Spillner Josef
Published: September 2023

Modern data scientists work with a number of tools and operating system facilities in addition to online platforms. Mastering these in combination to manage their data and to deploy software, models and data as ready-to-use online services as well as to perform data science and analysis tasks is in the focus of Operating Systems and Infrastructure in Data Science. Readers will come to understand the fundamental concepts of operating systems and to explore plenty of tools in hands-on tasks and thus gradually develop the skills necessary to compose them for programming in the large, an essential capability in their later career. The book guides students through semester studies, acts as reference knowledge base and aids in acquiring the necessary knowledge, skills and competences especially in self-study settings. A unique feature of the book is the associated access to Edushell, a live environment to practice operating systems and infrastructure tasks.
Uneindeutig, halbherzig und verwirrend. Warum das Wahlgesetz der Ampel-Koalition kein Fortschritt ist
Authors: Funk Albert
Published: September 2023

In diesem Meinungsbeitrag diskutiert Albert Funk die Reform des Wahlrechts kritisch und erläutert Parteienpositionen und -vorgehensweisen, grundlegende Begriffe (wie bspw. Überhangmandate, Direktmandate) sowie Alternativvorschläge zur Wahlrechtsreform.
Der Entwurf des Demokratiefördergesetzes – Chancen und Herausforderungen für die Politische Bildung. Fragen an Alexander Wohnig und Andrea Szukala
Authors: GWP Redaktion
Published: September 2023

Im Dezember 2022 ist ein konkreter Entwurf zum Demokratiefördergesetz von der Bundesregierung veröffentlicht worden. Seitdem wird die vorliegende Ausgestaltung vonseiten der Politischen Bildung kontrovers diskutiert. Wir führen ein Interview mit Professorin Andrea Szukala (Universität Augsburg) und Juniorprofessor Alexander Wohnig (Universität Siegen) über diesen Gesetzesentwurf und die Chancen, Risiken und Herausforderungen, die damit für die Politische Bildung verbunden sind.
Neurosyphilis Initially Misdiagnosed as Behavioral Variant Frontotemporal Dementia: Life-Changing Differential Diagnosis
Authors: Funayama Michitaka , Kuramochi Shin , Kudo Shun
Published: September 2023

Diagnosing neurosyphilis can be challenging and it may be misdiagnosed as behavior variant frontotemporal dementia, given its affinity for the frontal and temporal lobes. Here we present a model case, who, in his 40 s, was initially misdiagnosed with behavioral variant frontotemporal dementia based on extreme self-neglect and disinhibition over six months and frontal lobe atrophy. He was later diagnosed as neurosyphilis with positive syphilis tests in his cerebrospinal fluid. He underwent penicillin treatment and fully recovered. Relatively rapid cognitive decline, particularly if young, should prompt physicians to consider neurosyphilis as a treatable dementia, which could completely change a patient’s life.
Associations Between Amyloid Burden, Hypoxemia, Sleep Architecture, and Cognition in Obstructive Sleep Apnea
Authors: Cavuoto Marina G. , Robinson Stephen R. , O’Donoghue Fergal J. , Barnes Maree , Howard Mark E. , Tolson Julie , Stevens Bronwyn , Schembri Rachel , Rosenzweig Ivana , Rowe Christopher C. , Jackson Melinda L.
Published: September 2023

Background:

Obstructive sleep apnea (OSA) is associated with an increased risk of amyloid-β (Aβ) burden, the hallmark of Alzheimer’s disease, and cognitive decline.

Objective:

To determine the differential impacts of hypoxemia and slow-wave sleep disruption on brain amyloid burden, and to explore the effects of hypoxemia, slow-wave sleep disruption, and amyloid burden on cognition in individuals with and without OSA.

Methods:

Thirty-four individuals with confirmed OSA (mean±SD age 57.5±4.1 years; 19 males) and 12 healthy controls (58.5±4.2 years; 6 males) underwent a clinical polysomnogram, a NAV4694 positron emission tomography (PET) scan for Aβ burden, assessment of APOE ɛ status and cognitive assessments. Linear hierarchical regressions were conducted to determine the contributions of demographic and sleep variables on amyloid burden and cognition.

Results:

Aβ burden was associated with nocturnal hypoxemia, and impaired verbal episodic memory, autobiographical memory and set shifting. Hypoxemia was correlated with impaired autobiographical memory, and only set shifting performance remained significantly associated with Aβ burden when controlling for sleep variables.

Conclusions:

Nocturnal hypoxemia was related to brain Aβ burden in this sample of OSA participants. Aβ burden and hypoxemia had differential impacts on cognition. This study reveals aspects of sleep disturbance in OSA that are most strongly associated with brain Aβ burden and poor cognition, which are markers of early Alzheimer’s disease. These findings add weight to the possibility that hypoxemia may be causally related to the development of dementia; however, whether it may be a therapeutic target for dementia prevention in OSA is yet to be determined.
Increased Heat Pain Tolerance but Hyperalgesia to Tonic Inflammatory Pain in the CRND8 Mouse Model of Alzheimer’s Disease
Authors: Merlo Sara , Costa Lara , Chiechio Santina , Busceti Carla Letizia , Ciranna Lucia , Santangelo Rosa , Sortino Maria Angela , Fornai Francesco , Nicoletti Ferdinando , Copani Agata
Published: September 2023

Background:

The effects of Alzheimer’s disease (AD) pathology on the experience of pain are poorly understood.

Objective:

To understand the pathophysiological mechanisms underlying pain sensory transmission in the transgenic mouse model of AD, CRND8.

Methods:

We explored AD-related pathology in the spinal cord and dorsal root ganglia of 18-week-old female CRND8 mice. We assessed nociceptive responses to both acute heat stimuli and persistent inflammatory pain in CRND8 mice and non-transgenic (non-Tg) littermates. In addition, we searched for differences in biochemical correlates of inflammatory pain between CRND8 and non-Tg mice. Finally, we investigated the excitability of dorsal horn noc iceptive neurons in spinal cord slices from CRND8 and non-Tg mice.

Results:

We demonstrated the presence of intracellular AD-like pathology in the spinal cord and in the dorsal root ganglia nociceptive sensory neurons of CRND8 mice. We found that CRND8 mice had a reduced susceptibility to acute noxious heat stimuli and an increased sensitivity to tonic inflammatory pain. Tonic inflammatory pain correlated with a lack of induction of pro-opiomelanocortin in the spinal cord of CRND8 mice as compared to non-Tg mice. Electrophysiological recording in acute spinal cord slice preparations indicated an increased probability of glutamate release at the membrane of dorsal horn nociceptive neurons in CRND8 mice.

Conclusion:

This study suggests that an increased thermal tolerance and a facilitation of nociception by peripheral inflammation can coexist in AD.
rs56405341 Variant Associates with Expression of C4orf33 and C4orf33 Was Downregulated in Alzheimer’s Disease and Progressive Supranuclear Palsy
Authors: Zhang Yan , Xue Yanli , Wang Longcai , Han Zhifa , Wang Tao , Zhang Haihua , Liu Guiyou , Xiao Xingjun
Published: September 2023

The first primary age-related tauopathy (PART) genome-wide association study confirmed significant associations of Alzheimer’s disease (AD) and progressive supranuclear palsy (PSP) genetic variants with PART, and highlighted a novel genetic variant rs56405341. Here, we perform a comprehensive analysis of rs56405341. We found that rs56405341 was significantly associated with C4orf33 mRNA expression, but not JADE1 mRNA expression in multiple brain tissues. C4orf33 was mainly expressed in cerebellar hemisphere and cerebellum, and JADE1 was mainly expressed in thyroid, and coronary artery. Meanwhile, we found significantly downregulated C4orf33 expression both AD and PSP compared with normal controls, respectively.
Association of Anemia with Cognitive Function and Dementia Among Older Adults: The Role of Inflammation
Authors: Wang Jiao , Wang Chun , Li Xuan , Guo Jie , Dove Abigail , Cui Zhuang , Xu Weili
Published: September 2023

Background:

The association of anemia with cognitive function and dementia remains unclear.

Objective:

We aimed to investigate the association of anemia with cognitive function and dementia risk and to explore the role of inflammation in these associations.

Methods:

Within the UK Biobank, 207,203 dementia-free participants aged 60+ were followed for up to 16 years. Hemoglobin (HGB) and C-creative protein (CRP) were measured from blood samples taken at baseline. Anemia was defined as HGB <13 g/dL for males and <12 g/dL for females. Inflammation was categorized as low or high according to the median CRP level (1.50 mg/L). A subset of 18,211 participants underwent cognitive assessments (including global and domain-specific cognitive). Data were analyzed using linear mixed-effects model, Cox regression, and Laplace regression.

Results:

Anemia was associated with faster declines in global cognition (β= –0.08, 95% confidence interval [CI]: –0.14, –0.01) and processing speed (β= –0.10, 95% CI: –0.19, –0.01). During the follow-up of 9.76 years (interquartile range 7.55 to 11.39), 6,272 developed dementia. The hazard ratio of dementia was 1.57 (95% CI: 1.38, 1.78) for people with anemia, and anemia accelerated dementia onset by 1.53 (95% CI: 1.08, 1.97) years. The risk of dementia tended to be higher in people with both anemia and high CRP (1.89, 95% CI: 1.60, 2.22). There was a statistically significant interaction between anemia and CRP on dementia risk (p-interaction = 0.032).

Conclusions:

Anemia is associated with cognitive decline (specifically for processing speed) and increased risk of dementia, especially in people with high inflammation.
Criterion Validation of Tau PET Staging Schemes in Relation to Cognitive Outcomes
Authors: Hammers Dustin B. , Lin Joshua H. , Polsinelli Angelina J. , Logan Paige E. , Risacher Shannon L. , Schwarz Adam J. , Apostolova Liana G. ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: September 2023

Background:

Utilization of NIA-AA Research Framework requires dichotomization of tau pathology. However, due to the novelty of tau-PET imaging, there is no consensus on methods to categorize scans into “positive” or “negative” (T+ or T–). In response, some tau topographical pathologic staging schemes have been developed.

Objective:

The aim of the current study is to establish criterion validity to support these recently-developed staging schemes.

Methods:

Tau-PET data from 465 participants from the Alzheimer’s Disease Neuroimaging Initiative (aged 55 to 90) were classified as T+ or T– using decision rules for the Temporal-Occipital Classification (TOC), Simplified TOC (STOC), and Lobar Classification (LC) tau pathologic schemes of Schwarz, and Chen staging scheme. Subsequent dichotomization was analyzed in comparison to memory and learning slope performances, and diagnostic accuracy using actuarial diagnostic methods.

Results:

Tau positivity was associated with worse cognitive performance across all staging schemes. Cognitive measures were nearly all categorized as having “fair” sensitivity at classifying tau status using TOC, STOC, and LC schemes. Results were comparable between Schwarz schemes, though ease of use and better data fit preferred the STOC and LC schemes. While some evidence was supportive for Chen’s scheme, validity lagged behind others—likely due to elevated false positive rates.

Conclusions:

Tau-PET staging schemes appear to be valuable for Alzheimer’s disease diagnosis, tracking, and screening for clinical trials. Their validation provides support as options for tau pathologic dichotomization, as necessary for use of NIA-AA Research Framework. Future research should consider other staging schemes and validation with other outcome benchmarks.
Reconceptualization of the Erlangen Score for the Assessment of Dementia Risk: The ERlangen Score
Authors: Utz Janine , Olm Pauline , Jablonowski Johannes , Siegmann Eva-Maria , Spitzer Philipp , Lewczuk Piotr , Kornhuber Johannes , Maler Juan Manuel , Oberstein Timo Jan
Published: September 2023

Background:

The established Erlangen Score (ES) for the interpretation of cerebrospinal fluid (CSF) biomarkers in the diagnostics of Alzheimer’s disease (AD) uses markers of amyloidopathy and tauopathy, equally weighted to form an easy-interpretable ordinal scale. However, these biomarkers are not equally predictive for AD.

Objective:

The higher weighting of the Aβ42/Aβ40 ratio, as a reconceptualized ERlangen Score (ERS), was tested for advantages in diagnostic performance.

Methods:

Non-demented subjects ( N = 154) with a mean follow up of 5 years were assigned to a group ranging from 0 to 4 in ES or ERS. Psychometric trajectories and dementia risk were assessed.

Results:

The distribution of subjects between ES and ERS among the groups differed considerably, as grouping allocated 32 subjects to ES group 2, but only 2 to ERS group 2. The discriminative accuracy between the ES (AUC 73.2%, 95% CI [64.2, 82.2]) and ERS (AUC 72.0%, 95% CI [63.1, 81.0]) for dementia risk showed no significant difference. Without consideration of the Aβ42/Aβ40 ratio in ES grouping, the optimal cut-off of the ES shifted to ≥2.

Conclusions:

The ERS showed advantages over the ES in test interpretation with comparable overall test performance, as fewer cases were allocated to the intermediate risk group. The established cut-off of ≥2 can be maintained for the ERS, whereas it must be adjusted for the ES when determining the Aβ42/Aβ40 ratio.
Exploring the Causality Between Plasma Brain-Derived Neurotrophic Factor and Neurological Diseases: A Mendelian Randomization Study
Authors: Chen Shihao , Huang Wenting , He Tao , Zhang Mulan , Jin Xing , Jiang Lelin , Xu Huiqin , Chen Keyang
Published: September 2023

Background:

Brain-derived neurotrophic factor (BDNF) is a protein synthesized in the brain and widely expressed in the nervous system. Previous studies have demonstrated a controversial role of BDNF in neurological diseases.

Objective:

In this study, we aimed to assess the association between BDNF levels and the risk of neurological diseases by Mendelian randomization analysis.

Methods:

From a genome-wide association analysis of plasma proteins comprising 3,301 European participants, we isolated 25 genetic variations as instrumental variables for BDNF levels. Summary statistics data on six common neurological diseases as outcome variables. Two-sample Mendelian randomization (MR) analysis was used to assess whether plasma BDNF is causally related to neurological diseases. We also performed sensitivity analysis to ensure the robustness of the results and reverse MR to exclude potential reverse causality.

Results:

We confirmed the significant causal relationship between BDNF levels and the risk of Alzheimer’s disease (AD) (OR, 0.92; 95% CI, 0.85, 0.98; p = 0.013). Other methods have also shown similar results. We infer that BDNF also reduces the risk of epilepsy (OR, 0.94; 95% CI, 0.90, 0.98; p = 0.004). In reverse MR analysis, we also found that AD can affect the level of BDNF.

Conclusions:

Our study suggests higher plasma BDNF was associated with the reduced risk of AD. Moreover, higher plasma BDNF is a protective factor on AD and focal epilepsy. The results provide credence to the idea that BDNF may play a significant role in the development of focal epilepsy and AD.
Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes
Authors: Pacheco Pachado Mayra , Casas Ana I. , Elbatreek Mahmoud H. , Nogales Cristian , Guney Emre , Espay Alberto J. , Schmidt Harald H.H.W.
Published: September 2023

Alzheimer’s disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.
Seeing the Past, Planning the Future: Proudly Celebrating 25 Years of Assisting the Convergence of Process Sciences and Design Science
Authors: Horváth Imre , Wan Thomas T.H. , Huang Jingwei , Coatanéa Eric , Rayz Julia M. , Zeng Yong , Kim Kyoung-Yun
Published: September 2023

This Extended Editorial has been compiled by the members of the Editorial Board to celebrate the 25th anniversary of the establishment of the Journal of Integrated Design and Process Science, which operates as the Transactions of the Society for Process and Design Science. The paper divides in three parts. The first part provides a detailed overview of the preliminaries, the objectives, and the periods of operation. It also includes a summary of the current application-orientated professional fields of interests, which are: (i) convergence mechanisms of creative scientific disciplines, (ii) convergence of artificial intelligence, team and health science, (iii) convergence concerning next-generation cyber-physical systems, and (iv) convergence in design and engineering education. The second part includes invited papers, which exemplify domains within the four fields of interest, and also represent good examples of science communication. Short synopses of the contents of these representative papers are included. The third part takes the major changes in scientific research and the academic publication arena into consideration, circumscribes the mission and vision as formulated by the current Editorial Board, and elaborates on the planned strategic exploration and utilization domains of interest.
On n-polygonal interval-valued fuzzy numbers and application in e-commerce risk assessment
Authors: Suo Chunfeng , Li Yongming , Guo Li
Published: September 2023

The polygonal interval-valued fuzzy number is constructed based on the polygonal fuzzy number and the interval-valued fuzzy number. Its main feature is that the linear operation of finite ordered points reduces the complexity of traditional interval-valued fuzzy number operations. This research presents a generalized distance formula between two polygonal interval-valued fuzzy numbers and explores topological properties under the distance of polygonal interval-valued fuzzy numbers. In addition, we adopt the TOPSIS (technique for order preference by similarity to an ideal solution) and prospect theory approach for the multi-attribute decision-making problem. The information of attributes describes with polygonal interval-valued fuzzy numbers, and we then implement optimized ranking on the alternatives according to the profit and loss ratio. Finally, we verify the effectiveness and practicability of the decision-making method and fuzzy numbers at polygonal interval-valued fuzzy numbers in e-commerce risk assessment.
A novel three-step deep learning approach for the classification of breast cancer histopathological images
Authors: Kolla Bhavannarayanna , Venugopal P.
Published: September 2023

Breast cancer is a widespread and significant health concern among women globally. Accurately categorizing breast cancer is essential for effective treatment, ultimately improving survival rates. Moreover, deep learning (DL) has emerged as a widely adopted approach for precise medical image classification in recent years, showing promise in this domain. However, despite the availability of DL models proposed in the literature for automated classification of breast cancer histopathology images, achieving high accuracy remains challenging. A minor modification to pre-trained models and simple training strategies can further enhance model accuracy. Based on the approach, this paper proposed an anti-aliased filter in a pre-trained ResNet-34 and a novel three-step training process to improve BC histopathology image classification accuracy. The training involves systematically unfreezing layers and imposing additional constraints on the rate of change of learnable parameters. In addition, four-fold on-the-fly data augmentation enhances model generalization. The Ada-Hessian optimizer adjusts learning rates based on first and second-order gradients to improve convergence speed. The training process utilizes a large batch size to minimize the training loss associated with batch normalization layers. Even with the limited GPU size, the gradient accumulation technique achieves a large batch size. Collectively, these strategies minimize training time while maintaining or improving the accuracy of BC histopathology image classification models. In the experimental implementation, the proposed architecture achieves superior results compared to recent existing models, with an accuracy of 98.64%, recall (98.98%), precision (99.35%), F1-Score (99.17%), and MCC (97.36%) for binary classification. Similarly, the model achieves an accuracy of 95.01%, recall (95.01%), precision (94.95%), F1-Score (94.94%), and MCC (93.42%) for the eight-class category of BC images.
SGCRNN: A ChebNet-GRU fusion model for eeg emotion recognition
Authors: Bai Xuemei , Tan Jiaqi , Hu Hanping , Zhang Chenjie , Gu Dongbing
Published: September 2023

The paper proposes a deep learning model based on Chebyshev Network Gated Recurrent Units, which is called Spectral Graph Convolution Recurrent Neural Network, for multichannel electroencephalogram emotion recognition. First, in this paper, an adjacency matrix capturing the local relationships among electroencephalogram channels is established based on the cosine similarity of the spatial locations of electroencephalogram electrodes. The training efficiency is improved by utilizing the computational speed of the cosine distance. This advantage enables our method to have the potential for real-time emotion recognition, allowing for fast and accurate emotion classification in real-time application scenarios. Secondly, the spatial and temporal dependence of the Spectral Graph Convolution Recurrent Neural Network for capturing electroencephalogram sequences is established based on the characteristics of the Chebyshev network and Gated Recurrent Units to extract the spatial and temporal features of electroencephalogram sequences. The proposed model was tested on the publicly accessible dataset DEAP. Its average recognition accuracy is 88%, 89.5%, and 89.7% for valence, arousal, and dominance, respectively. The experiment results demonstrated that the Spectral Graph Convolution Recurrent Neural Network method performed better than current models for electroencephalogram emotion identification. This model has broad applicability and holds potential for use in real-time emotion recognition scenarios.
Research on Sparrow Search Optimization Algorithm for multi-objective task scheduling in cloud computing environment
Authors: Luo Zhi-Yong , Chen Ya-Nan , Liu Xin-Tong
Published: September 2023

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.
Visual SLAM algorithm based on semantic information and geometric consistency1
Authors: Lian Jing , Chen Shi , Pi Jiahao , Li Linhui , Li Qingfeng
Published: September 2023

Localization through intricate traffic scenes poses challenges due to their dynamic, light-variable, and low-textured nature. Existing visual Simultaneous Localization and Mapping (SLAM) methods, which are based on static and texture-rich assumptions, struggle with drift and tracking failures in such complex environments. To address this, we propose a visual SLAM algorithm based on semantic information and geometric consistency in order to solve the above issues and further realize autonomous driving applications in road environments. In dynamic traffic scenes, we employ an object detection network to identify moving objects and further classify them based on geometric consistency as dynamic objects or potential dynamic objects. This method permits us to preserve more reliable static feature points. In low-texture environments, we propose a method that employs key object categories and geometric parameters of static scene objects for object matching between consecutive frames, effectively resolving the problem of tracking failure in such scenarios. We conducted experiments on the KITTI and ApolloScape datasets for autonomous driving and compared them to current representative algorithms. The results indicate that in the dynamic environment of the KITTI dataset, our algorithm improves the compared metrics by an average of 29.68%. In the static environment of the KITTI dataset, our algorithm’s performance is comparable to that of the other compared algorithms. In the complex traffic scenario R11R003 from the ApolloScape dataset, our algorithm improves the compared metrics by an average of 25.27%. These results establish the algorithm’s exceptional localization accuracy in dynamic environments and its robust localization capabilities in environments with low texture. It provides development and support for the implementation of autonomous driving technology applications.
Intelligent recommendation method for offline course resources tax law based on chaos particle swarm optimization algorithm
Authors: Jingjing Huang , Xu Zhang
Published: September 2023

In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners; the project response theory is used to analyze the ability of different learners; According to the learners’ learning objectives and ability level, the intelligent recommendation model of offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions; Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm; The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax law are obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model; that is, the convergence performance and distribution performance of the model is better; This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better.
MfvPose: A multi-scale hybrid framework for human pose estimation
Authors: Ran Lang , Hong Chaoqun , Zhang Xuebai , Tang Chaohui , Xie Yuhong
Published: September 2023

Human pose estimation is a challenging visual task that relies on spatial location information. To improve the performance of human pose estimation, it is important to accurately determine the constraint relationship among keypoints. To address this, we propose MfvPose, a novel hybrid model that leverages rich multi-scale information. The proposed model incorporates the HRFOV module, which uses cascaded atrous convolution to maintain high-resolution representations of the backbone extractor and enrich the multi-scale information. In addition, we introduce learnable scalar weights to the Transformer encoder. In detail, it involves a multiplication by a diagonal matrix with learnable scalar weights on output of each residual block, which improves the dynamics of model training and enhances the accuracy of human pose estimation. It is experimentally shown that our proposed MfvPose achieves promising results on various benchmarks.
A non-intrusive load recognition method combining adaptive PSO algorithm and CNN model
Authors: Liu Zhichao , Wang Yachao , Ma Zhiyuan , Cao Mengnan , Liu Mingda , Yang Xiaochu
Published: September 2023

Real-time monitoring of electricity usage details through load monitoring techniques is a crucial aspect of smart power grid management and monitoring, allowing for the acquisition of information on the electricity usage of individual appliances for power users. Accurate detection of electricity load is essential for refined load management and monitoring of power supply quality, facilitating the improvement of power management at the user side and enhancing power operation efficiency. Non-intrusive load monitoring (NILM) techniques require only the analysis of total load data to achieve load monitoring of electricity usage details, and offer advantages such as low cost, easy implementation, high reliability, and user acceptance. However, with the increasing number of distributed new load devices on the user side and the diversification of device development, simple load recognition algorithms are insufficient to meet the identification needs of multiple devices and achieve high recognition accuracy. To address this issue, a non-intrusive load recognition (NILR) model that combines an adaptive particle swarm optimization algorithm (PSO) and convolutional neural network (CNN) has been proposed. In this model, pixelated images of different electrical V-I trajectories are used as inputs for the CNN, and the optimal network layer and convolutional kernel size are determined by the adaptive PSO optimization algorithm during the CNN training process. The proposed model has been validated on the public dataset PLAID, and experimental results demonstrate that it has achieved a overall recognition accuracy of 97.26% and F-1 score of 96.92%, significantly better than other comparison models. The proposed model effectively reduces the confusion between various devices, exhibiting good recognition and generalization capabilities.
An integrated decision approach with triangular fuzzy neutrosophic sets for higher vocational education quality evaluation in the new era
Authors: Feng Qingyuan
Published: September 2023

China’s education has entered the era of quality, and in order for higher vocational colleges to develop better, effective measures must be taken to improve the quality of education. Facing the continuous innovation of education in higher vocational colleges, it is necessary to strengthen institutional construction and constrain educational management with institutional constraints, which is the key to ensuring the education quality. The higher vocational education quality evaluation in the new era is regarded as multiple attribute decision-making (MADM). Recently, the EDAS and CRITIC model has been employed to solve MAGDM. The triangular fuzzy neutrosophic sets (TFNSs) are constructed as an efficient tool for portraying the uncertain information during the higher vocational education quality evaluation in the new era. In this paper, the triangular fuzzy neutrosophic number EDAS (TFNN-EDAS) model based on the Hamming distance and Euclid distance is constructed to solve the MADM under TFNSs. The CRITIC method is utilized to obtain the weight information based on the Hamming distance and Euclid distance under INNSs. Finally, a numerical example of higher vocational education quality evaluation in the new era is constructed and some efficient comparisons are founded to verify the TFNN-EDAS method.
DYS-SLAM: A real-time RGBD SLAM combined with optical flow and semantic information in a dynamic environment1
Authors: Fang Yuhua , Xie Zhijun , Chen Kewei , Huang Guangyan , Zarei Roozbeh , Xie Yuntao
Published: September 2023

Traditional Simultaneous Localization and Mapping application in dynamic situations is constrained by static assumptions. However, the majority of well-known dynamic SLAM systems use deep learning to identify dynamic objects, which creates the issue of trade-offs between accuracy and real-time. To tackle this issue, this work suggests a unique dynamic semantics method(DYS-SLAM) for semantic simultaneous localization and mapping that strikes a trade-off between high accuracy and high real-time performance. We propose M-LK, an enhanced Lucas-Kanade(LK) optical flow method. This technique keeps the continuous motion and greyscale consistency assumptions from the original method while switching out the spatial consistency assumption for a motion consistency assumption, reducing sensitivity to image gradients to identify dynamic feature points and regions efficiently. In order to increase segmentation accuracy while maintaining real-time performance, we develop a segmentation refinement scheme that projects 3D point cloud segmentation results into 2D object detection zones. A dense semantic octree graph is built in the interim to expedite the high-level process. Compared to the four equivalent dynamic SLAM approaches, experiments on the publicly available TUM RGB-D dataset demonstrate that the DYS-SLAM method offers competitive localization accuracy and satisfactory real-time performance in both high and low-dynamic scenarios.
Enhanced D-CNN architecture and centroid-based algorithm for real-time vehicle tracking and accident detection from surveillance videos
Authors: Paul Ann Rija , Grace Mary Kanaga E.
Published: September 2023

In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc.
Bayesian inference on sparse multinomial data using smoothed Dirichlet distribution with an application to COVID-19 data
Authors: Wickramasinghe Lahiru , Leblanc Alexandre , Muthukumarana Saman
Published: September 2023

We develop a Bayesian approach for estimating multinomial cell probabilities using a smoothed Dirichlet prior. The most important feature of the smoothed Dirichlet prior is that it forces the probabilities of neighboring cells to be closer to each other than under the standard Dirichlet prior. We propose a shrinkage-type estimator using this Bayesian approach to estimate multinomial cell probabilities. The proposed estimator allows us to borrow information across other multinomial populations and cell categories simultaneously to improve the estimation of cell probabilities, especially in a context of sparsity with ordered categories. We demonstrate the proposed approach using COVID-19 data and estimate the distribution of positive COVID-19 cases across age groups for Canadian health regions. Our approach allows improved estimation in smaller health regions where few cases have been observed.
Multi-class classification using a new Bayesian method
Authors: Vovan Tai , Nguyenthikim Hieu , Phamtoan Dinh
Published: September 2023

This paper proposes a new classification model using the Bayes method. This model not only determines the prior probability based on the k-means algorithm, builds the method for estimating the probability density function via the kernel function, but also classifies the objects to the known populations. The proposed model is described via the experiment of image classifying. In this example, we first use the Gray level co-occurrence matrix to extract the features of images, and next classify this data set based on the improved Bayesian method. In another application, we also build the classification problem for the Algerian Forest Fires data set. The outstanding advantages of this method are the adaptive ability of the kernel function, the classification for multi-class, and the reduction of computational costs. In addition, the experimental results also show the potential of the developed model.
Estimating the AUC of mixture MROC curve in the presence of measurement errors
Authors: Siva G. , R. Vishnu Vardhan , Chesneau Christophe
Published: September 2023

In a classification scenario, we usually come across data with and without class labels. If the class labels of individuals are unknown or masked by hidden components, the classifier rules must include the identification of the actual number of subcomponents in the data. Also, the presence of measurement errors in the data may influence the measures of the receiver operating characteristic model. In this paper, a mixture of multivariate receiver operating characteristic models is proposed to deal with multi-model patterns in the data, and a bias-corrected estimator is derived for estimating the area under the curve of the proposed model. The proposed methodology is supported by the real dataset and simulation studies.
Features of measuring deformations of load-bearing structures of buildings during their field and laboratory tests under the action of static and dynamic loads
Authors: Pidlutskyi Vasyl
Published: September 2023

The peculiarities of measuring the deformations of load-bearing structures of buildings during their field and laboratory tests using strain gauge-based sensors are considered. An overview of various problematic issues that arise in the process of conducting experimental tests and affect the measurement results is given. The most common, which affect the accuracy of measurements, are the effects of temperature, humidity, and vibration; the difficulty of calibrating and mounting strain gauges on different surfaces of elements; complexity of measurement data processing; combination of high-quality work of strain gauges, cable conductors, measuring equipment, data collection and processing system; the selection of the connection scheme of tensor resistors in the Wheatstone bridge.
The peculiarities of the organization of the experiment for testing the load-bearing structures of the building, namely pile foundations, are considered, and the main problematic issues when using strain gauges as strain gauges of building elements are given.
The use of strain gauges to measure deformations of load-bearing structures of buildings is one of the common methods, which has many advantages, which are discussed in detail in the work. Strain gauges can be used to measure deformations of elements that are mechanically connected to them. They have high measurement accuracy, sensitivity to element deformation, and a wide variable measurement range.
The advantages of using the Wheatstone bridge to measure the change in electrical resistance are highlighted, as this method allows measuring very small values of the change in resistance. Methods of connecting tensor resistors according to the Wheatstone scheme are considered, the choice of which depends on the purpose of the study and the required accuracy. One of the easiest ways is to connect just one strain gauge to a current or voltage source and measure the change in resistance. But this method has low sensitivity and does not compensate for the effect of temperature. A more accurate way is to connect two or four tensor resistors in the Wheatstone bridge circuit, which allows you to measure the voltage change on the diagonal of the bridge. The bridge circuit has a high sensitivity and can compensate for the effect of temperature if the tensor resistors have the same coefficient of temperature dependence of the resistance.
An algorithm for measuring the deformations of a metal pipe working under compression using strain gauges has been developed.
Serviceability analysis of the grain storage facilities foundations
Authors: Vynnykov Yuriy , Kharchenko Maksym , Marchenko Valentyn , Kichasov Oleksii
Published: September 2023

The change in the stress-strain state is analyzed on the example of a complex underground part of silo "loess soil base – foundation strip – undersilo tunnel gallery – compacted soil base – bottom silo slab" system as an indicator for assessing the serviceability of this type of industrial structure.

Based on the analysis of existing design documentation and field surveys, the main stages of the structure's operation during its construction and subsequent operation have been identified. Finite element analysis was performed to evaluate the current technical condition of the system of bases and foundations. At the same time, the results of geodetic measurements over time were additionally used to obtain the actual stress-strain mode of the system according to the deformed scheme.

The paper focuses on the nature of the mutual influence of individual elements of the complex foundation system. The study highlights the characteristic changes in the structural scheme and the peculiarities of stress redistribution within the system at different stages of operation. Has been taken into account local soaking of the loess soil with a thickness of up to 7.2 m that was typical for the full-scale experimental object.

Particularly, the forces in the reinforced concrete members of the underground tunnel gallery of the silo have been determined. The influence of changes in the stress-strain mode of the foundation system on their operation under variable loads and impacts has been taken into account.

It has been established that the design of such complex systems still requires further research on the design schemes and situations. When designing complex foundation systems of silo, a common mistake is to incorrectly take into account the stiffness of the underground tunnel gallery, backfill under the bottom slab, and locally locked loess soils under the foundation strip. These factors have a significant impact on the overall level of reliability and faultless operation of the industrial structure as a whole.

It is also shown that it is necessary to take into account the influence of adjacent silos and the factor of asymmetry of the foundation along and across the underground gallery.
Study of the influence of the rigidity of the above-ground structures of the panel house on the stress-deformed state of the pile foundation
Authors: Nosenko Viktor , Piatkov Oleksandr , Kashoida Ostap
Published: September 2023

A study of the influence of the rigidity of the above-ground structures of a nine-story panel building on the stress-deformed state of its pile foundation was carried out.

For the study, several models of the house with the same above-ground parts were created, which differed from each other in terms of the construction scheme of the basement floor: monolithic reinforced concrete or prefabricated reinforced concrete, the load-bearing walls also change: along the letter or numerical coordination axes.

The main advantage of panel houses is that their construction (arrangement) takes significantly less time than the construction of a similar house with the same area and building volume, but with a different structural scheme (wall or with the use of a monolithic reinforced concrete frame).

Numerical modeling of panel buildings, namely the joints in the places where prefabricated reinforced concrete elements are joined, is difficult for engineers, because numerical modeling can be performed in several ways, which were given and described in the work [1].

The stress-deformation state of the pile foundation (longitudinal forces in the piles, bending moments in the raft) and normal stresses in the vertical load-bearing elements of the basement with different structural schemes of the basement floor and different load-bearing walls were analyzed. Quantitative and qualitative changes in the bending moments in the rat were revealed, the same differences were found in the stresses of the vertical bearing elements of the basement.

The analysis of the bending moments in the raft made it possible to identify and investigate the influence of the stiffness of the basement floor structures on the stressed-deformed state of the pile foundation (rafts). The effect of vertical seams between wall panels on bending moments in the grid was revealed. The effect of the vertical seam between the wall panels is manifested in the fact that the values of the bending moments change both quantitatively (increased by several times) and qualitatively. The use of monolithic structures as basement walls leads to a more smooth curve of bending moments in the raft.