bluuebunny/crossref_metadata_2025 huggingface dataset:
شعر ادريس بن اليمان اليابسي (ت470هـ)
Authors: عودة خولة عبد الحميد , حميد نضال مهدي
Published: September 2023

تسعى هذه الدراسة الى ابراز السمات الاسلوبية لشاعر اندلسي هو ادريس بن اليمان، واجتلاء المظاهر الجمالية لنصوصه الشعرية، من خلال ثلاثة مباحث: تضمن المبحث الاول البنية الايقاعية بوساطة الاساليب الايقاعية التي استعملها في شعره كالإيقاع الخارجي والداخلي، وأبرزت مقدرته على اختيار اللغة المناسبة للبحر والقافية، فضلاً عن التكرار والتوازي لتعزيز الايقاع.
          وخصص المبحث الثاني للبنية التركيبية من استفهام، ونداء، والتركيب الفعلي مما اكسب شعره البعد الجمالي والفني. أما المبحث الثالث فتضمن البنية الدلالية وابراز دور الصورة الفنية وايصالها الى الملتقي بأبهى صورة، وقد استعان ادريس بالتشبيه، والاستعارة، والكناية، لتوضيح معانيه واضفاء الصفة الجمالية لنصوصه الشعرية.
          وأخيراً أختتم البحث بخاتمة بما توصل اليه، ثم ثبت المصادر والمراجع.
أسباب الإبتزاز الإلكتروني والآثار الاجتماعية والنفسية المرتبطة به
Authors: السويدي شريفة , نوفل زيزيت
Published: September 2023

هدفت الدراسة الحالية إلى الكشف عن أسباب تعرض الضحايا للابتزاز الالكتروني وأهم الآثار الاجتماعية والنفسية التي قد يعانون منها، ولتحقيق أهداف الدراسة تم استخدام المنهج الكيفي، حيث تكونت عينة الدراسة من (11) مبحوث، يعملون في القيادة العامة لشرطة الشارقة، ومركز الدعم الاجتماعي في إمارة الشارقة (ويتعاملون مع ضحايا الابتزاز الالكتروني بشكل مباشر) ، وتم اختيارهم قصديا، واستخدمت المقابلة المعمقة كأداة للدراسة.
وقد أظهرت نتائج الدراسة ان من اهم أسباب تعرض الفرد للابتزاز الالكتروني هو الثقة المفرطة في الأشخاص الغرباء، وعدم الوعي، وضعف الوازع الديني، وغياب الرقابة الأسرية، والتفكك الأسري، ورغبة الضحية في ترك وقطع العلاقة مع الطرف الآخر (المبتز)، كما توصلت الدراسة الى أن أكثر أشكال الابتزاز الالكتروني انتشارا في إمارة الشارقة هو الابتزاز المادي والجنسي، وأن أبرز الآثار الاجتماعية التي قد يعاني منها الضحية هي : الميل الى الانعزال ، والخوف من العار والفضيحة والوصمة الاجتماعية، وقد يسهم  في التفكك الأسري والعنف الأسري. أما بالنسبة للأثار النفسية فهي: فقدان الثقة في النفس وبالمحيطين، الشعور بالقلق والخوف والتوتر والاكتئاب ولوم الذات ، والشعور بالخجل والندم، وقد يعاني من اضطراب نفسي يتطلب تدخل وعلاج طبي متخصص، وينخفض مستوى أداءه في الدراسة او العمل ، وقد يفكر في الاقدام على الانتحار.
Rapidly degradable konjac glucomannan hydrogels cross-linked with olsalazine for colonic drug release
Authors: Zhang Qiao , Fu Huili , Zhang Yunfei , Li Liang , Yan Guoping
Published: September 2023

BACKGROUND: Polysaccharide hydrogel is one of the most important materials for the colon target drug release system. However, the degradation time of polysaccharide hydrogel is much longer than the retention time in the colon. The drugs are expelled from the body before being released. OBJECTIVE: In order to match the degradation of drug carriers and their retention time in the colon, a rapidly degradable konjac glucomannan (KGM) hydrogel was designed for colon target drug release. METHODS: A crosslinker containing azo bond, olsalazine, was used to prepare the rapidly degradable KGM hydrogel. The degradation and drug release of the hydrogels with different crosslinking densities in the normal buffer and the human fecal medium were studied to evaluate the efficiency of colon drug release. RESULTS: More than 50% of the KGM hydrogel by weight was degraded and more than 60% of the 5-fluorouracil (5-Fu) was released within 48 h in 5% w/v human fecal medium. CONCLUSION: The drug was released more rapidly in a simulated colon environment than in a normal buffer. Furthermore, the drug release was controlled by the degradation of the hydrogel. The KGM hydrogel containing azo crosslinker has great potential for colon drug release.
LINC00668 promoted non-small lung cancer progression by miR-518c-3p/TRIP4 axis
Authors: Lu Zhibin , Xiao Zhichao , Wang Qi , Pan Chunfeng , Xia Yang , Wu Weibing , Chen Liang
Published: September 2023

BACKGROUND: Non-small lung cancer ranks first in the cancer-related death of all malignant tumors. Exploring novel biological targets is of great significance for diagnosis and therapy of NSCLC. OBJECTIVE: In this study, we aimed to explore the effect of LINC00668 on the biological functions of NSCLC cells and the underlying mechanism. METHODS: RT-qPCR assays and western blot assays were utilized to estimate the relative gene expression at mRNA and protein levels, respectively. CCK8, colony formation, wound healing, transwell, and cell apoptosis assays were employed to assess cell function. IHC and FISH assays were used to determine the gene expression in NSCLC tissues. RIP and dual-luciferase assays were conducted to validate the combination between LINC00668 and miR-518c-3p. The correlation of expression between miR-518c-3p and LINC00668 or TRIP4 was determined by Pearson correlation analysis. RESULTS: LINC00668 was aberrantly upregulated in NSCLC tumor tissues and cell lines. Inhibition of LINC00668 significantly suppressed tumor proliferation, migration, invasion and promoted cell apoptosis. Mechanistically, LINC00668 could bind to miR-518c-3p, thus targeting the 3’UTR of TRIP4. TRIP4 overexpression rescued the weakened cell function mediated by LINC00668 silencing. CONCLUSIONS: LINC00668 acted as an oncogene in NSCLC progression through miR-518c-3p/TRIP4 axis. Our study disclosed a new mechanism of LINC00668 functioned in NSCLC and may give a deeper insight of the targeted therapy of NSCLC in the future.
Stock market prediction based on sentiment analysis using deep long short-term memory optimized with namib beetle henry optimization
Authors: Adikane Nital , Nirmalrani V.
Published: September 2023

Stock price prediction is a recent hot subject with enormous promise and difficulties. Stock prices are volatile and exceedingly challenging to predict accurately due to factors like investment sentiment and market rumors etc. The development of effective models for accurate prediction is extremely tricky due to the complexity of stockdata. Long Short-Term Memory (LSTM) discovers patterns and insights that weren’t previously visible, and they can be leveraged to make incredibly accurate predictions. Therefore, to perform an accurate prediction of the next-day trend, in this research manuscript, a novel method called Updated Deep LSTM (UDLSTM) with namib Beetle Henry optimization (BH-UDLSTM) is proposed on historical stock market data and sentiment analysis data. The UDLSTMmodel has improved prediction performance, which is more stable during training, and increases data accuracy. Hybridization of namib beetle and henry gas algorithm with the UDLSTM further enhances the prediction accuracy with minimum error by excellent balance of exploration and exploitation. BH-UDLSTM is then evaluated with several existing methods and it is proved that the introduced approach predicts the stock price accurately (92.45%) than the state-of-the-art.
Alzheimer’s Disease Biomarkers and Complement Proteins Mediate the Impact of Sleep Fragmentation on Cognitive Impairment in Obstructive Sleep Apnea Patients Without Dementia
Authors: Yang Yanyan , Li Mengfan , Leng Bing , Yao Ran , Xue Song , Tan Ming , Sun Hairong , Zhang Jinbiao
Published: September 2023

Background: Cognitive impairment is common in patients with obstructive sleep apnea (OSA). Previous studies indicated that intermittent hypoxia, sleep fragmentation, and depressive symptoms were associated with cognitive impairment in OSA patients. Objective: The study aimed to investigate whether sleep characteristics and depressive symptoms affected cognitive abilities mediated by Alzheimer’s disease (AD) biomarkers and complement proteins in OSA patients without dementia. Methods: A total of 317 subjects without dementia who had undergone polysomnography, cognitive and neuropsychological evaluations, were recruited. Neuronal-derived exosomes (NDEs) levels for amyloid-β (Aβ), total tau (T-tau), and tau phosphorylated 62 at threonine 181 (P-T181-tau) and astrocyte-derived exosomes (ADEs) levels for complement proteins were measured. Mediation analysis were performed to explore the mediation effects of AD biomarkers (Aβ42, T-tau, P-T181-tau) and complement proteins (C3b and C5b-9) on cognition. Results: The findings revealed that the association between sleep fragmentation and cognition was mediated by Aβ42 (the percentage varied from 18.25% to 30.6%), P-T181-tau (the percentage varied from 24.36% to 32.3%), and C5b-9 (the percentage varied from 30.88% to 60.7%). The influence of depressive symptoms on cognition was only mediated via C3b (the percentage varied from 24.1% to 36.6%). Conclusions: In OSA patients without dementia, Aβ42 and P-T181-tau levels in NDEs, and C5b-9 levels in ADEs mediated the impact of sleep fragmentation on cognitive impairment, and C3b levels in ADEs mediated the impact of depressive symptoms on cognitive impairment.
TOMM40 ‘523 Genotype Distinguishes Patterns of Cognitive Improvement for Executive Function in APOE ɛ3 Homozygotes
Authors: Watts Amber , Haneline Stephen , Welsh-Bohmer Kathleen A. , Wu Jingtao , Alexander Robert , Swerdlow Russell H. , Burns Daniel K. , Saunders Ann M.
Published: September 2023

Background: TOMM40 ‘523 has been associated with cognitive performance and risk for developing Alzheimer’s disease independent of the effect of APOE genotype. Few studies have considered the longitudinal effect of this genotype on change in cognition over time. Objective: Our objective was to evaluate the relationship between TOMM40 genotype status and change in cognitive performance in the TOMMORROW study, which was designed to prospectively evaluate an algorithm that includes TOMM40 ‘523 for genetic risk for conversion to mild cognitive impairment. Methods: We used latent growth curve models to estimate the effect of TOMM40 allele carrier (short, very long) status on the intercept and slope of change in cognitive performance in four broad cognitive domains (attention, memory, executive function, and language) and a combined overall cognitive score over 30 months. Results: TOMM40 very long allele carriers had significantly lower baseline performance for the combined overall cognitive function score (B = –0.088, p = 0.034) and for the executive function domain score (B = –0.143, p = 0.013). Slopes for TOMM40 very long carriers had significantly greater increases over time for the executive function domain score only. In sensitivity analyses, the results for executive function were observed in participants who remained clinically stable, but not in those who progressed clinically over the study duration. Conclusions: Our results add to the growing body of evidence that TOMM40, in the absence of APOE ɛ4, may contribute to cognitive changes with aging and dementia and support the view that mitochondrial function is an important contributor to Alzheimer’s disease risk.
Asymmetric Cerebral Peduncle Atrophy: A Simple Diagnostic Clue for Distinguishing Frontotemporal Lobar Degeneration from Alzheimer’s Disease
Authors: Sakurai Keita , Kaneda Daita , Morimoto Satoru , Uchida Yuto , Inui Shohei , Kimura Yasuyuki , Kato Takashi , Ito Kengo , Hashizume Yoshio
Published: September 2023

Background: Due to confusing clinicoradiological features such as amnestic symptoms and hippocampal atrophy in frontotemporal lobar degeneration (FTLD), antemortem differentiation between FTLD and Alzheimer’s disease (AD) can be challenging. Although asymmetric atrophy of the cerebral peduncle is regarded as a representative imaging finding in some disorders of the FTLD spectrum, the utility of this finding has not been sufficiently evaluated for differentiating between FTLD and AD. Objective: This study aimed to explore the diagnostic performance of asymmetric cerebral peduncle atrophy on axial magnetic resonance imaging as a simple radiological discriminator between FTLD and AD. Methods: Seventeen patients with pathologically confirmed FTLD, including six with progressive supranuclear palsy, three with corticobasal degeneration, eight with TAR DNA-binding protein 43 (FTLD-TDP), and 11 with pathologically confirmed AD, were investigated. Quantitative indices representing the difference between the volumes of the bilateral cerebral peduncles (i.e., cerebral peduncular asymmetry index [CPAI]), the voxel-based specific regional analysis system for Alzheimer’s disease (VSRAD) Z-score representing the degree of hippocampal atrophy, and semiquantitative visual analysis to evaluate the asymmetry of the cerebral peduncle (visual assessment of cerebral peduncular asymmetry: VACPA) were compared between the two groups. Results: Contrary to the VSRAD Z-score, the CPAI and VACPA scores demonstrated higher diagnostic performance in differentiating patients with FTLD from those with AD (areas under the receiver operating characteristic curve of 0.88, 082, and 0.60, respectively). Conclusions: Quantitative and visual analytical techniques can differentiate between FTLD and AD. These simple methods may be useful in daily clinical practice.
Inter-Hospital Advanced and Mild Alzheimer’s Disease Classification Based on Electroencephalogram Measurements via Classical Machine Learning Algorithms
Authors: Parreño Torres Alfonso , Roncero-Parra Carlos , Borja Alejandro L. , Mateo-Sotos Jorge
Published: September 2023

Background: In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer’s disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA). Objective: This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals. Methods: The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest. Results: The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis. Conclusions: By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification.
Optimization of coaxial magnetic gear and its application in wave power generation
Authors: Fang Hongwei , Li Ziyan
Published: September 2023

The application of coaxial magnetic gear (CMG) in wave generator instead of traditional variable speed gearbox can effectively improve torque density and reduce torque ripple. Thus, the operation principle of CMG from the perspective of magnetic field modulation is firstly analyzed in this paper. Then the air gap magnetic fields induced by high-speed and low-speed rotors are studied. And the radial height, inner diameter angle, and outer diameter angle of the modulating pieces (MPs) are optimized by using the combination of response surface method (RSM) and particle swarm optimization (PSO) algorithm to improve the torque transmission capacity of CMG. The CMG is combined with the permanent magnet synchronous machine to get the magnetic-gear permanent magnet synchronous generator (MG-PMSG). The design feasibility of the MG-PMSG is verified from four aspects: static magnetic field analysis, no-load characteristics, load characteristics and load disturbance analysis.
Research on speed regulation method of high power magnetic gear
Authors: Li Xinmin , Li Feilong , Hung Wing San Tony , Qiu Zhengjie , Wu Yonglong , Zhang Runzhi
Published: September 2023

Magnetic gears have attracted the attention of many scholars due to their advantages of low noise, maintenance free, high torque density and inherent overload protection. In this paper, three speed regulation methods of high power magnetic gear transmission system are summarized and proposed, and the finite element analysis of the three speed regulation methods is carried out by COMSOL Multiphysics simulation software. The results show that when the sizes of the three magnetic gears are similar, the output torque and load power of the speed regulation method of the magnetic gear using the magnetic field modulated type is the largest. On the contrary, the axial magnetic gear with air gap length regulation method has the lowest output torque and load capacity. Compared with the other two methods, the coupling length regulation method has the minimum eddy current loss and the simple speed regulation structure, which is the most suitable for high power magnetic gear.
Dynamic task offloading and collaborative task execution using three tier edge cloud computing (T2EC2) system for autonomous vehicles
Authors: Sureka V. , Kavya G.
Published: September 2023

Automobiles have undergone a transformation during the past two decades due to the merger of the electronics and automotive industries. The combination of autos and electronic sensors has resulted in a new generation of vehicles known as autonomous vehicles (AVs). These AVs have a few hundred thousand sensors, producing an enormous amount of raw data for computation. Data from the vehicular network can be offloaded to existing telecommunication infrastructure to address the problem of processing resources. In order to address vehicular network requirements, large-capacity servers deployed in major telecommunications networks are first used to offload resource-intensive tasks. Mobile Cloud Computing (MCC) is a critical enabling technology for 5 G networks, which has a key feature of offloading to divide application tasks into local and cloud server execution components. This paper proposes a novel Three TierEdge cloud computing (T2 EC2) system which uses an Energy-aware Dynamic Task offloading and collaborative task execution algorithm (EA-DTOCTE) for multilayer vehicular cloud computing networks. The EA-DTOCTE algorithm is included in the decision-making engine in the proposed system, which selects whether to offload the task to the remote environment or implement it locally. EA-DTOCTE focuses on consumption of energy by tasks both locally and remotely since its goal is to efficiently and dynamically split the application into tasks and schedule them on local devices and cloud resources. The proposed T2 EC2 has been evaluated in terms of parameters such as energy consumption, completion time, and throughput. Experimental results indicate that the proposed T2EC2 can save up to 28% of system energy consumption compared with other state-of-art techniques.
Fuzzy-based variable impedance control of uncertain robot manipulator in the flexible environment: A nonlinear force contact model-based approach
Authors: Guo Ying , Peng Jinzhu , Ding Shuai , Liang Jing , Wang Yaonan
Published: September 2023

In this paper, a variable impedance control method is proposed for uncertain robotic systems based on a nonlinear force contact-based flexible environmental model. First, a nonlinear force contact model between the rigid manipulator and flexible environment is applied to the compliant control of the manipulator, which can avoid excessive force overshoot that usually exists in the traditional spring-damping environmental model. Then, to achieve better force/position tracking performances, a fuzzy-based adaptive variable impedance controller is designed based on the force contact-based flexible environmental model, where the impedance parameters are adjusted online through the force and position feedback of the robotic system, and the fuzzy logic system is used to compensate the uncertainties. Moreover, the stability of the adaptive variable impedance control scheme is proved by the Routh stability criterion, and the boundness of all the signals in the closed-loop control system is guaranteed by the Lyapunov stability theorem. Finally, the effectiveness of the proposed method is verified by the simulation of a two-link manipulator, and the results demonstrate that the performances of position tracking are improved, while the force overshoot and oscillation time are reduced.
Multi-objective optimization of multi-layered cylindrical shells with opening under axial load using the NSGA-II genetic algorithm
Authors: Liu Jun
Published: September 2023

Composite cylindrical shells play a crucial role in aerospace and marine structures. This study investigates the optimal structure for cylindrical multilayer composite shells under the effect of axial pressure using the finite element method and NSGA-II genetic algorithm to determine the maximum buckling load capacity. The critical buckling load of multilayer composite shells depends on various parameters, such as fiber angle, the number of layers, the material of the layers, and their thickness. The objective functions are used to increase the structure load capacity and reduce its weight. ABAQUS software was used to perform finite element analysis on the composite cylindrical shell for determining the buckling load. Using the response surface model, the relationship between variables and objective functions has been determined. Results of the proposed response surface model for the training stages are evaluated using various statistical indices and the root mean square error for buckling load and shell weight variables is 0.065 and 0.140, respectively. In the next step, the NSGA-II genetic optimization algorithm was used to modify the layout and thickness of the composite layers to optimize the buckling strength and weight of the structure. A genetic algorithm based on NSGA-II was used to optimize the geometric characteristics.
Application of deep learning-based ethnic music therapy for selecting repertoire
Authors: Zhang Yehua , Zhang Yan
Published: September 2023

With the advancement of modern medical concepts, the beneficial effects of music on human health have gradually become accepted, and the corresponding music therapy has gradually become a new research direction that has received much attention in recent years. However, folk music has certain peculiarities that lead to the fact that there is no efficient way of selecting repertoire that can be carried out directly throughout the repertoire selection. This paper combines deep learning theory with ethnomusic therapy based on previous research and proposes a deep learning-based approach to ethnomusic therapy song selection. Since the feature extraction process in the traditional sense has insufficient information on each frame, excessive redundancy, inability to process multiple frames of continuous music signals containing relevant music features and weak noise immunity, it increases the computational effort and reduces the efficiency of the system. To address the above shortcomings, this paper introduces deep learning methods into the feature extraction process, combining the feature extraction process of the Deep Auto-encoder (DAE) with the music classification process of Gaussian mixture model, which forms a new DAE-GMM music classification model. Finally, in terms of music therapy selection, this paper compares the music selection method based on co-matrix and physiological signal with the one in this paper. From the theoretical and simulation plots, it can be seen that the method proposed in this paper can achieve both good music classifications from a large number of music and further optimize the process of music therapy song selection from both subjective and objective aspects by considering the therapeutic effect of music on patients. Through this article research results found that the depth of optimization feature vector to construct double the accuracy of the classifier is higher, in addition, compared with the characteristics of the original optimization classification model, using the gaussian mixture model can more accurately classify music, the original landscape “hometown” score of 0.9487, is preferred, insomnia patients mainly ceramic flute style soft tone, without excitant, low depression, have composed of nourishing the heart function.
Blockchain-based forensic trustworthiness evidence model for acquisition in the ecosystem with cloud
Authors: Ragu G. , Ramamoorthy S.
Published: September 2023

When a digital adversary or an insider compromised a framework, cloud Forensic examiners can simply lay out the scene of the crime and reconstruct how the event took place using scientific evidence to determine when, why, and how it happened. Be that as it may, computerized proof procurement in a cloud environment is confounded and demonstrated troublesome, Despite modern scientific securing tool compartments. Multi-occupancy, Geo-area, and Administration Level Understanding have added another layer of complexity to obtaining computerized proof from a cloud environment. To moderate these intricacies of proof procurement in the cloud environment, we want a system that can forensically keep up with the reliability and respectability of proof. In this review, we plan and execute a Blockchain-based Forensic in Cloud (BBFC) structure, utilizing a Cloud Forensic methodology (CFA). The outcomes from our single contextual analysis will exhibit that BBFC will alleviate the difficulties and intricacies looked at by computerized forensic specialists in getting acceptable advanced proof from the cloud biological system. Moreover, a quick exhibition observing the proposed Blockchain based measurable in cloud structure was assessed. BBFC will guarantee dependability, respectability, validness, and non-renouncement of the proof in the cloud. The proposed BBFC framework was also subjected to performance evaluation, considering factors such as latency, bandwidth, energy and resource utilization, and failure points. This evaluation provides insights into the efficiency and effectiveness of the framework in real-world cloud forensic scenarios.
Bearing characteristics of prestressed full-length anchoring rockbolt based on Wavelet Neural Network
Authors: Tang Bin , Xie Kai , Tao Wenbin , Liu Zhenyu , Zhu Chuanqi , Zhao Neng , Wang Yiyang
Published: September 2023

In order to improve bearing capacity of rockbolts in deep-buried coal mine roadways, orthogonal tests were conducted to study influencing factors of rockbolt anchoring effect. Wavelet neural network model was introduced to predict the pull-out force of rockbolt. The activation and output functions of the wavelet neural network were improved, and the scaling and translation parameters were also modified by using the gradient descent method. These improvements enhanced the approximation rate of the wavelet neural network model, and solve the problem that the wavelet transform method is monotonous and difficult to adapt to the complex and variable engineering conditions. Research results illustrated that The value of the ultimate pull-out force is positively correlated with the strength of the specimen and pre-tension value of the specimen. According to the test results, the coal mine roadway support scheme was optimized, and the high prestress full-length anchoring rockbolt support technology was proposed. The effectiveness of research was verified through the engineering applications and in-situ monitoring results.
Patch SVDD(support vector data description)-based channel attention embedding and improvement of classifier
Authors: Xu Zan , Lu TongWei
Published: September 2023

Some anomaly detection methods are based on CNN to fuse spatial and channel-wise information together within local receptive fields. However, the correlation between feature channels has not been fully utilized. Channel attention has been shown to model the interdependence between convolution feature channels and improve network representation. It is possible to introduce channel attention into anomaly detection. We attempt to directly embed the SE(Squeeze and Excitation) module into the convolutional layer but reduced anomaly detection performance. Therefore, we propose a lightweight channel attention module C-SE(Current Squeeze and Excitation) suitable for anomaly detection. C-SE module not only improves the representation ability of depth convolutional neural network but also has a significant effect on texture anomaly detection. C-SE module body is constructed by average pooling and maximum pooling branches, which ensure that local salient features of the image are not lost. Then reduce the negative impact of feature calibration through a long connection. In addition, the improvement of classifier plays an important role. Experimental results have shown that the proposed method outperforms the Patch SVDD methods by 3% in image-level AUROC and 0.7% in pixel-level AUROC on the MVTec benchmark. The higher AUROC score and the faster rate of convergence prove the effectiveness of the method.
A medication reconciliation failure: A case report and incident analysis
Authors: Ascenção R. , Lopes Vaz P. , Pereira Gomes C. , Costa J. , Broeiro-Gonçalves P.
Published: September 2023

BACKGROUND: Medication reconciliation is advocated to ensure the continuity, safety, and effective use of medicines across transitions of care. CASE REPORT: In this report, we describe the case of a 90-year-old female with previous diagnoses of atrial fibrillation and cutaneous metastatic breast cancer presenting with bilateral ulcerative lesions on the chest wall. The patient was diagnosed with Deep Vein Thrombosis at the Emergency Department and started on rivaroxaban, although the patient was already taking edoxaban. This therapeutic duplication was noticed only one week later, even though she was already experiencing significant bleeding managed through a prescribing cascade. Despite the technical error (action-based), it is possible to identify several weaknesses in the organisation’s structure, which provided a trajectory of accident opportunity. CONCLUSION: Anticoagulants are ranked first for the highest priority to receive a medication reconciliation. To achieve an optimal level of medication reconciliation, we ought to recognise and correct latent failures.
Employment outcomes and support needs of Michigan Project SEARCH graduates with intellectual and developmental disabilities: A mixed-method study
Authors: Sung Connie , Fisher Marisa H. , Okyere Christiana , Park Jiyoon , Choi Heerak
Published: September 2023

BACKGROUND: Project SEARCH is a one-year school-to-work transition program that prepares students with intellectual and developmental disabilities (IDD) for competitive employment. OBJECTIVE: The current study used a mixed-method explanatory design to examine Project SEARCH graduates’ overall experience of transitioning from Project SEARCH to employment, their long-term employment outcomes, and their support needs during and after the program. METHOD: Parents of 31 Project SEARCH graduates completed an online survey regarding the graduates’ outcomes and 9 parents agreed to participate in a follow-up interview. RESULTS: Respondents reported positive employment outcomes for 24 of the Project SEARCH graduates (77.4% employed after Project SEARCH), primarily in the customer service industry. CONCLUSION: The graduates were satisfied with their employment but also experienced difficulties and a need for additional employment preparation and support after Project SEARCH.
Comprehensive analysis using triangular fuzzy neutrosophic MADM and grey relational techniques with teaching quality evaluation
Authors: Yang Yang
Published: September 2023

The quantification of evaluation indicators for the quality of university physical education classroom teaching is the main development trend of the current evaluation system, which can to some extent avoid the drawbacks caused by subjective blindness and more objectively and scientifically evaluate the quality of university physical education classroom teaching. However, physical education teaching is a complex overall activity, and its characteristics and elements cannot be fully evaluated through quantitative indicators. Therefore, excessive pursuit of quantification in order to make the evaluation indicators more convenient and operable cannot guarantee the effectiveness and accuracy of the evaluation. The classroom teaching quality evaluation of College Physical Education is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers grey relational analysis (TFNN-GRA) method is built based on traditional grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs). Firstly, the TFNSs is introduced. Then, combine the traditional fuzzy GRA model with TFNSs information, the TFNN-GRA method is established and the computing steps for MADM are built with completely unknown weight information. Finally, a numerical example for classroom teaching quality evaluation of College Physical Education has been given and some comparisons is used to proof advantages of TFNN-GRA method. The main contributions of this paper are listed (1) A novel TFNN-GRA method is proposed to solve the MADM with completely unknown weight information; (2) an optimization model is constructed to obtain a simple formula which could be employed to construct the attribute weights values based on the Lagrange function; (3) a numerical example for classroom teaching quality evaluation of college physical education is constructed to verify the TFNN-GRA method.
Measuring the impact of deprivation on learning difficulties and behaviour among infants born preterm: A cohort study
Authors: Isaac Thomas C.W. , Odd Dawn , Edwards Martin , Chakraborty Mallinath , Kotecha Sarah J. , Kotecha Sailesh , Odd David
Published: September 2023

BACKGROUND: Preterm birth and social deprivation are known risk factors for learning difficulties. However there has been little work looking into the interaction between these two risks. We aimed to identify if children born preterm to families with higher levels of social deprivation are disproportionately more likely to have learning difficulties than those with lower levels of social deprivation. METHODS: Data from the RANOPS (Respiratory And Neurological Outcomes in children born Preterm Study) was used to assess prevalence of learning difficulties. The effects of preterm birth and deprivation were reviewed. Multi-level logistic regression models were used to examine if gestational age and deprivation impacts interacted after adjustment for possible confounders. Primary outcome measure was parent-reported learning difficulties. Secondary outcome measures were parent-reported behavioural problems and a statement of special educational need. RESULTS: We investigated the developmental outcomes of 6,691 infants with a median age of 5 years at time of survey (IQR 5). Deprivation decile (OR 1.08 (1.03,1.12)) and preterm birth (OR 2.67 (2.02,3.53)) were both associated with increased risk of learning difficulties. There was little evidence for any interaction between preterm birth and deprivation (p = 0.298) and the risk of learning difficulties. CONCLUSIONS: Deprivation and preterm birth have significant associations with learning difficulties. While deprivation does not appear to have potentiated the impact of preterm birth, preterm infants in the most deprived areas have the highest risk of learning difficulties with almost 1 in 3 extremely premature infants with a learning difficulty in the most deprived areas.
Sterile water moisturizer of the skin of extremely low birth weight infants
Authors: Mohamed M.A. , Abrams G. , Massa-Buck B. , DiBlassio S. , El-Dib M. , Aly H.
Published: September 2023

OBJECTIVE: To assess the feasibility of sterile water application to the skin of the extremely low birth weight (ELBW) newborns in the first week of life and examine its effects on their skin integrity and outcomes. DESIGN: Pilot randomized controlled trial. SETTING: Open-bay, 18 bed Level III NICU in the Eastern United States. PARTICIPANTS: Twenty-eight ELBW neonates. Two newborns expired, sixteen newborns remained in the intervention group and twelve newborns in the control group. METHODS: ELBW neonates were either assigned to receive frequent sterile water wash to skin or not during care for the first week of life. Using the Neonatal Skin Condition Scale (NSCS), assessments were performed twice a day during the first week. Fluid intake, serum electrolytes, culture proven sepsis and other morbidities, and length of stay (LOS) were compared while controlling for confounding variables using multiple regression analysis. RESULTS: There was no difference in the demographic or clinical characteristics between both groups. Sterile water wash application to skin was not associated with differences in skin health indices or fluid intake. However, it was associated with higher median sodium level and with early regression of bilirubin level when compared to controls. CONCLUSION: Frequent skin washes with sterile water are feasible and safe. However, they may not be associated with improved skin integrity or fluid intake.
LinkedDataOps:quality oriented end-to-end geospatial linked data production governance
Authors: Yaman Beyza , Thompson Kevin , Fahey Fergus , Brennan Rob
Published: September 2023

This work describes the application of semantic web standards to data quality governance of data production pipelines in the architectural, engineering, and construction (AEC) domain for Ordnance Survey Ireland (OSi). It illustrates a new approach to data quality governance based on establishing a unified knowledge graph for data quality measurements across a complex, heterogeneous, quality-centric data production pipeline. It provides the first comprehensive formal mappings between semantic models of data quality dimensions defined by the four International Organization for Standardization (ISO) and World Wide Web Consortium (W3C) data quality standards applied by different tools and stakeholders. It provides an approach to uplift rule-based data quality reports into quality metrics suitable for aggregation and end-to-end analysis. Current industrial practice tends towards stove-piped, vendor-specific and domain-dependent tools to process data quality observations however there is a lack of open techniques and methodologies for combining quality measurements derived from different data quality standards to provide end-to-end data quality reporting, root cause analysis or visualisation. This work demonstrated that it is effective to use a knowledge graph and semantic web standards to unify distributed data quality monitoring in an organisation and present the results in an end-to-end data dashboard in a data quality standards-agnostic fashion for the Ordnance Survey Ireland data publishing pipeline.
Sinhala version of the Cornell Musculoskeletal Discomfort Questionnaire: Cross-cultural adaptation and validation
Authors: Liyanage Esther , Chellapillai Dhananjee , Liyanage Indrajith , Samarakoon Chamini , Samarasekara Heshani , Rikas Mohamed
Published: September 2023

BACKGROUND: Work-related musculoskeletal disorders (WMSDs) are highly prevalent among people who work in sedentary jobs. The Cornell Musculoskeletal Discomfort Questionnaire (CMDQ) is a valid tool to assess WMSDs, but is not available in the local language of Sri Lanka (Sinhala). OBJECTIVE: To cross-culturally adapt the original CMDQ from English to Sinhala and evaluate its psychometric properties. METHODS: The linguistic and cultural adaptation of the CMDQ to the Sinhala (Si-CMDQ) language followed international guidelines. The adapted pre-version was tested for comprehensibility among 32 subjects from different educational and occupational backgrounds. The Content Validity Index (CVI) was computed with ratings from five experts. The questionnaire was validated further by 111 security guards employed at the university. The tool was validated with reference to the Numeric Rating Scale. Test-retest reliability was assessed at two time points one week apart and internal consistency was tested. RESULTS: The Si-CMDQ showed adequate cross-cultural, content and construct validity, internal consistency, and test-retest reliability. The Kappa coefficient showed excellent agreement between NRS and Si-CMDQ frequency scale. Spearman’s correlation coefficients showed a moderate to a strong significant positive correlation between NRS and the severity scale of Si-CMDQ. The Kappa values for test-retest reliability were moderate to excellent for the three scales of CMDQ. The internal consistency was high. CONCLUSION: The Si-CMDQ fulfilled the psychometric requirement for questionnaires. This tool will be beneficial to assess WMSDs among sedentary workers in different occupations in Sri Lanka.
MTAN: A semi-supervised learning model for kidney tumor segmentation
Authors: Sun Peng , Yang Sijing , Guan Haolin , Mo Taiping , Yu Bonan , Chen Zhencheng
Published: September 2023

BACKGROUND:

Medical image segmentation is crucial in disease diagnosis and treatment planning. Deep learning (DL) techniques have shown promise. However, optimizing DL models requires setting numerous parameters, and demands substantial labeled datasets, which are labor-intensive to create.

OBJECTIVE:

This study proposes a semi-supervised model that can utilize labeled and unlabeled data to accurately segment kidneys, tumors, and cysts on CT images, even with limited labeled samples.

METHODS:

An end-to-end semi-supervised learning model named MTAN (Mean Teacher Attention N-Net) is designed to segment kidneys, tumors, and cysts on CT images. The MTAN model is built on the foundation of the AN-Net architecture, functioning dually as teachers and students. In its student role, AN-Net learns conventionally. In its teacher role, it generates objects and instructs the student model on their utilization to enhance learning quality. The semi-supervised nature of MTAN allows it to effectively utilize unlabeled data for training, thus improving performance and reducing overfitting.

RESULTS:

We evaluate the proposed model using two CT image datasets (KiTS19 and KiTS21). In the KiTS19 dataset, MTAN achieved segmentation results with an average Dice score of 0.975 for kidneys and 0.869 for tumors, respectively. Moreover, on the KiTS21 dataset, MTAN demonstrates its robustness, yielding average Dice scores of 0.977 for kidneys, 0.886 for masses, 0.861 for tumors, and 0.759 for cysts, respectively.

CONCLUSION:

The proposed MTAN model presents a compelling solution for accurate medical image segmentation, particularly in scenarios where the labeled data is scarce. By effectively utilizing the unlabeled data through a semi-supervised learning approach, MTAN mitigates overfitting concerns and achieves high-quality segmentation results. The consistent performance across two distinct datasets, KiTS19 and KiTS21, underscores model’s reliability and potential for clinical reference.
Dimensions of Human Creativity in the Context of Comparative Analysis of the Works of M. Heidegger and H. Bergson
Authors: Sivers Valeriy , Buhaiova Oksana
Published: September 2023

The purpose of the article is to substantiate the methodology of creativity based on a comparative analysis of the works of M. Heidegger (“The Origin of the Work of Art”) and H. Bergson (“Creative Evolution”). The research methodology was based on the futurological or value-futurological method, which is the author's proposal of V. Sivers. To determine the natural and genetic affinity of the two works, the comparative aspect of the methodology was applied, which made it possible to identify both their common points of intersection and points of distinction. In this methodological context, there is an author's component of vision, where at the intersection of different views new knowledge desired by every researcher should be born, or rather its changing and uncertain contours, which we call approaches to understanding creativity. The scientific novelty of the article lies in the reference to H. Bergson’s work “Creative Evolution” in the context of a comparative analysis with M. Heidegger’s work “The Origin of the Work of Art”. This helped to record being in a state of self-awareness and assert that any human activity performed and comprehended in this status is evidence and an indication of the process of creation, but does not prove it. It only reveals a new participant who is related to the subject and in some way separated from it. Conclusions. It has been proven that we can connect a person with the process of creation and the instance of the Observer. Creation happens every moment for two reasons: it has already happened, and it cannot stop. After all, nothing can exist. In fact, it prompts creation, which in human performance looks like creativity. Its ontological basis is the presence of nothing in the “anti-space”. It creates pressure on space with the requirement of realisation. The fact of awareness of nothingness is its transposition into consciousness and the condition of the “truth of essence”, namely the basis of the search for the essence of consciousness and the basis of self-awareness. Its invariant is the Observer.
Key words: M. Heidegger, H. Bergson, creativity, myth-making, Observer, Other, nothing, consciousness, self-awareness, category, existential, dissolution, suspension of time.
Piano in Electroacoustic Discourse of Sound Representations
Authors: Kravchenko Anastasiia
Published: September 2023

The purpose of the article is to study the piano’s performance expression in sound representations of electroacoustic opuses by Ukrainian composers. The research methodology is based on musicological, semiotic, cultural, and hermeneutical analysis. The scientific novelty of the study is that for the first time in the study of electroacoustic music of Ukraine, the importance of instrumentalism, namely piano lexicon, in the performance interpretation of sound images of a person, an epoch and the world reproduced in the semiotic-communicative and artistic-figurative space of electroacoustic compositions is emphasised. Conclusions. The creativity of regional composing schools in Ukraine is marked by the use of music and information technologies, as evidenced by the emergence of electromusical, electroacoustic, and audiovisual opuses. Electroacoustic music in Ukraine has a wide range of genre models of a hybrid type, combining the possibilities of acoustic instruments and multimedia technologies. The analysis of works for piano and magnetic tape by K. Tsepkolenko and A. Karnak shows that digital audio modifications provide an expansion of the audio spectrum through processing and programming of the series of sound, noise, and speech genesis, where verbal components reveal the ideological and conceptual basis of the compositions. The expression of the piano, embodied in the stylistics of modernism and avant-garde, shows the absence of elements of ethno-national character, which emphasises the tendency to a "globalised" type of performance aesthetics. In the semiotic and communicative interaction of the piano instrumental part with electronic media, a unique environment of multidimensional unity of movement, time, and space is formed. It reflects the sound images of a person, an epoch and the world in modelling the general philosophical picture of the dual oppositions of immanent and transcendent, which has historical and non-historical significance.

Key words: electroacoustics, music information technologies, composing school, piano art, piano performance, sound image, philosophical picture of the world.