bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of microvascular flow imaging and contrast-enhanced ultrasound for blood flow analysis of cervical lymph node lesions
Authors: Wang Tianqi , Xu Mingda , Xu Changyu , Wu Yuqing , Dong Xiaoqiu
Published: September 2023

OBJECTIVE: To compare the diagnostic value of microvascular flow imaging (MVFI) with that of contrast-enhanced ultrasound (CEUS) for the analysis of blood flow in benign and malignant cervical lymph nodes. MATERIAL AND METHODS: As a prospective study, 95 cervical enlarged lymph nodes (43 benign and 52 malignant) were observed in 95 patients using conventional ultrasonography (including gray and Color Doppler Flow Imaging), CEUS, and MVFI. Two researchers evaluated vascular parameters of MVFI (vascular distribution, internal vascular features, vascular index) and CEUS (enhancement mode, enhancement type) and compared the diagnostic effects of MVFI and CEUS.All results were compared with pathological findings. RESULTS: There were significant differences in the vascular distribution and internal vascular features of benign and malignant lymph nodes on MVFI (P < 0.05). The vascular distribution of benign lymph nodes was mainly of the central and avascular types, the internal blood vessels were mostly normal, the vascular distribution of malignant lymph nodes was mainly mixed, the internal vessels were mainly tortuous and displaced. The optimal cut-off value of the benign and malignant lymph node vascular index (VI) was 15.55%, and the area under the receiver operating characteristic curve (AUC) of the VI was 0.876. There were also significant differences in the enhancement mode and types of benign and malignant lymph nodes in CEUS (P < 0.05). The benign lymph nodes showed centrifugal perfusion, and the enhancement types were mostly type I and type II. Most malignant lymph nodes showed centripetal or mixed perfusion, and the enhancement types were usually type III and type IV. The accuracy, sensitivity, and specificity of CEUS in the diagnosis of lymph node lesions were 84.2%, 84.6% and 83.7%, respectively, and the AUC was 0.845. The accuracy, sensitivity, and specificity of MVFI in the diagnosis of lymph node lesions were 85.3%, 84.6%, and 86.0%, respectively, and the AUC was 0.886. CONCLUSION: Both CEUS and MVFI are valuable in differentiating benign and malignant lesions of lymph nodes and have a similar diagnostic performance; however, MVFI is less invasive and simpler than CEUS. Therefore it is preferred for auxiliary examination of enlarged lymph nodes that are difficult to diagnose by conventional ultrasound.
Prediction and analysis of chronic epilepsy using electroencephalographic signals on medical internet of things platform
Authors: Hassoon Noor Hasan , Ali Mohammed Hasan , Jaber Mustafa Musa , Abd Sura Khalil , Abosinnee Ali S. , Kareem Z.H.
Published: September 2023

Epilepsy patients who are presently refractory may be monitored using a seizure prediction Brain-Computer Interface (BCI), which uses electrodes strategically implanted in the brain to anticipate and regulate the onset and duration of a seizure. Real-time approaches to these technologies have challenges, as seen by seizures’ instantaneous electrographic activity. Electroencephalographic (EEG) signals are inherently non-stationary, which means that the regular and seizure signals differ significantly among people with epilepsy. Due to the restricted number of contacts on electrodes, dynamically processed and collected characteristics cannot be employed in a prediction function without causing significant processing delays. Big data can guarantee secure storage in these situations, and it has the maximum processing capability to identify, record, and analyze time in real-time to conduct the seizure event on the timetable. Seizure prediction and location for huge Scalp EEG recordings have been the focus of this study, which used wearable sensor data and deep learning to use cloud storage to develop the systems. A novel technique is suggested to avoid an epileptic seizure and discover the seizure origin from the utilized wearable sensors. Secondly, deep learning architectures called Clustered Autoencoder with Convolutional Neural Network (CAE-CNN), an expanded optimization methodology is presented based on the Principal Component Analysis (PCA), the Hierarchical Searching Algorithm (HSA), and the Medical Internet of Things (MIoT) has been established to define the suggested frameworks based on the collection of big data storage of the wearable sensors in real-time, automatic computation and storage. According to clinical trials, CAE-CNN outperforms the current wearable sensor-based treatment for unresolved chronic epilepsy patients.
Retracing-efficient IoT model for identifying the skin-related tags using automatic lumen detection
Authors: Vivekananda G.N. , Almufti Saman M. , Suresh C. , Samsudeen Salomi , Devarajan Mohanarangan Veerapperumal , Srikanth R. , Jayashree S.
Published: September 2023

The number of patients with skin diseases reported a dramatic increase which is a major concern and should be addressed. The evaluation of skin is crucial to the correct diagnosis during the follow-up. Through technological advances and partnership, skin disorders can be identified and predicted. PROBLEM: The manual detection of skin diseases may sometimes lead to misclassification due to the same intensity and color levels, which is crucial to the correct diagnosis. SOLUTION: An automated system to identify these skin diseases is applied. An IoT-based skin monitoring infrastructure is imposed that links the entire system. METHOD: In this study, a Retracing-efficient IoT model for identifying the moles, skin tags, and warts using Automatic lumen detection with the help of IoT-based Variation regularity is proposed with the technique imposed IoMT, Automatic lumen detection, Variation regularity, and trigonometric algorithm. RESULTS: The intensity and edge width based on moles, skin tags, and warts edge width heightened intensity accuracy is 56.2% on the image group with image count is 500 to 10000, and the enhanced low-level total sample accuracy is 95.9%. The pixel analysis for intensity with wavelength and intensity with time wavelength is improved from 4.2% to 54.6%, and accuracy is 70.9% formulated. Periodic classification on image count and classification accuracy image count is 87% against the 500 to 10000 image. Correlation performance analysis of lumen detection resolution image pixel and enhanced correlation performance accuracy is 23.50% on the 480 × 640 to 2336 × 3504 pixel images. CONCLUSION: The approach is tested for varying datasets, and comparative analysis is performed that reflects the effectiveness of the proposed system with high accuracy, thus contributing to the development of a perfect platform for skincare to the early detection and diagnosis of skin conditions.
Construction information security management system based on data sharing algorithm
Authors: Zhao Lihui
Published: September 2023

Due to the lack of data security protection, a large number of malicious information leaks, which makes building information security (InfoSec) issues more and more attention. The construction information involves a large number of participants, and the number of construction project files is huge, leading to a huge amount of information. However, traditional network security information protection software is mostly passive, which is difficult to enhance its autonomy. Therefore, this text introduced data sharing algorithm in building InfoSec management. This text proposed an Attribute Based Encryption (ABE) algorithm based on data sharing, which is simple in calculation and strong in encrypting attributes. This algorithm was added to the building InfoSec management system (ISMS) designed in this text, which not only reduces the burden of relevant personnel, but also has flexible control and high security. The experimental results showed that when 10 users logged in to the system, the stability and security of the system designed in this text were 87% and 91% respectively. When 20 users logged in to the system, the system stability and security designed in this text were 89% and 92% respectively. When 80 users logged in to the system, the system stability and security designed in this text were 94% and 95% respectively. It can be found that the stability and security of the system have reached a high level, which can ensure the security of effective management of building information.
Biomarkers and Risk Assessment of Alzheimer’s Disease in Low- and Middle-Income Countries
Authors: Adewale Boluwatife Adeleye , Coker Motunrayo Mojoyin , Ogunniyi Adesola , Kalaria Rajesh N. , Akinyemi Rufus Olusola
Published: September 2023

Dementia is a chronic syndrome which is common among the elderly and is associated with significant morbidity and mortality for patients and their caregivers. Alzheimer’s disease (AD), the most common form of clinical dementia, is biologically characterized by the deposition of amyloid-β plaques and neurofibrillary tangles in the brain. The onset of AD begins decades before manifestation of symptoms and clinical diagnosis, underlining the need to shift from clinical diagnosis of AD to a more objective diagnosis using biomarkers. Having performed a literature search of original articles and reviews on PubMed and Google Scholar, we present this review detailing the existing biomarkers and risk assessment tools for AD. The prevalence of dementia in low- and middle-income countries (LMICs) is predicted to increase over the next couple of years. Thus, we aimed to identify potential biomarkers that may be appropriate for use in LMICs, considering the following factors: sensitivity, specificity, invasiveness, and affordability of the biomarkers. We also explored risk assessment tools and the potential use of artificial intelligence/machine learning solutions for diagnosing, assessing risks, and monitoring the progression of AD in low-resource settings. Routine use of AD biomarkers has yet to gain sufficient ground in clinical settings. Therefore, clinical diagnosis of AD will remain the mainstay in LMICs for the foreseeable future. Efforts should be made towards the development of low-cost, easily administered risk assessment tools to identify individuals who are at risk of AD in the population. We recommend that stakeholders invest in education, research and development targeted towards effective risk assessment and management.
Posttraumatic Stress and Traumatic Brain Injury: Cognition, Behavior, and Neuroimaging Markers in Vietnam Veterans
Authors: Marcolini Sofia ,   for the Department of Defense Alzheimer’s Disease Neuroimaging Initiative, Rojczyk Philine , Seitz-Holland Johanna , Koerte Inga K. , Alosco Michael L. , Bouix Sylvain
Published: September 2023

Background: Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are common in Veterans and linked to behavioral disturbances, increased risk of cognitive decline, and Alzheimer’s disease. Objective: We studied the synergistic effects of PTSD and TBI on behavioral, cognitive, and neuroimaging measures in Vietnam war Veterans. Methods: Data were acquired at baseline and after about one-year from male Veterans categorized into: PTSD, TBI, PTSD+TBI, and Veteran controls without PTSD or TBI. We applied manual tractography to examine white matter microstructure of three fiber tracts: uncinate fasciculus (N = 91), cingulum (N = 87), and inferior longitudinal fasciculus (N = 95). ANCOVAs were used to compare Veterans’ baseline behavioral and cognitive functioning (N = 285), white matter microstructure, amyloid-β (N = 230), and tau PET (N = 120). Additional ANCOVAs examined scores’ differences from baseline to follow-up. Results: Veterans with PTSD and PTSD+TBI, but not Veterans with TBI only, exhibited poorer behavioral and cognitive functioning at baseline than controls. The groups did not differ in baseline white matter, amyloid-β, or tau, nor in behavioral and cognitive functioning, and tau accumulation change. Progression of white matter abnormalities of the uncinate fasciculus in Veterans with PTSD compared to controls was observed; analyses in TBI and PTSD+TBI were not run due to insufficient sample size. Conclusions: PTSD and PTSD+TBI negatively affect behavioral and cognitive functioning, while TBI does not contribute independently. Whether progressive decline in uncinate fasciculus microstructure in Veterans with PTSD might account for cognitive decline should be further studied. Findings did not support an association between PTSD, TBI, and Alzheimer’s disease pathology based on amyloid and tau PET.
The Mediating Roles of Neurobiomarkers in the Relationship Between Education and Late-Life Cognition
Authors: Amofa-Ho Priscilla A. ,   for the Alzheimer’s Disease Neuroimaging Initiative, Stickel Ariana M. , Chen Ruijia , Kobayashi Lindsay C. , Glymour M. Maria , Eng Chloe W.
Published: September 2023

Background: The mediating roles of neuropathologies and neurovascular damage in the relationship between early-life education and later-life cognitive function are unknown. Objective: To examine whether Alzheimer’s and neurovascular biomarkers mediate the relationships between education and cognitive functions. Methods: Data were from 537 adults aged 55–94 in the Alzheimer’s Disease Neuroimaging Initiative 3. We tested whether the relationships between education (continuous, years) and cognitive function (memory, executive functioning, and language composites) were mediated by neuroimaging biomarkers (hippocampal volumes, cortical gray matter volumes, meta-temporal tau PET standard uptake value ratio, and white matter hyperintensity volumes). Models were adjusted for age, race, sex/gender, cardiovascular history, body mass index, depression, and Apolipoprotein E-ɛ4 status. Results: Hippocampal volumes and white matter hyperintensities partially mediated the relationships between education and cognitive function across all domains (6.43% to 15.72% mediated). The direct effects of education on each cognitive domain were strong and statistically significant. Conclusions: Commonly measured neurobiomarkers only partially mediate the relationships between education and multi-domain cognitive function.
Poisonings in Older People with Dementia: A Systematic Scoping Review and Meta-Analysis
Authors: Wojt Ilsa R , Lau Edward C.Y , Cairns Rose , Tan Edwin C.K
Published: September 2023

Background: Older people with dementia are at a particularly high risk of poisonings and their subsequent harms. Objective: This review aimed to describe the key agents, incidence, risk factors, and disposition of poisonings in people with dementia reported in the literature. Methods: Medline, Embase, CINAHL, and PsycINFO databases were searched from 1 September 2001 to 1 September 2021. Terms for dementia, poisonings, and older adults formed the search concepts. Quantitative studies published in English, describing poisonings in older people with dementia, including Alzheimer’s disease, were included. Two investigators independently assessed articles for eligibility and extracted relevant data. A meta-analysis of the incidence of poisonings in people with dementia across studies was performed. Results: Of 4,579 articles, 18 were included for final synthesis. Nervous system medications were implicated in over half of all medicinal poisonings, with anti-dementia agents, benzodiazepines, and opioids the most common classes. The non-medicinal agents frequently associated with poisonings were personal care and household products. The yearly incidence of poisoning varied across definitions of poisoning from 3% for International Classification of Disease-defined poisonings to 43% for adverse drug event-defined poisonings. Several risk factors were identified, including multimorbidity, psychotropic medication use, and living in residential care. Where described, up to one in five poisonings resulted in hospitalisation and in death. Conclusions: Poisonings are common in people with dementia, involving commonly prescribed medications or easily accessible substances. Given the significant outcomes associated, further research is required to better understand these poisonings and improve public health strategies to reduce the occurrence of this preventable harm.
Many Paths to Alzheimer’s Disease: A Unifying Hypothesis Integrating Biological, Chemical, and Physical Risk Factors
Authors: Lehmann Donald J , Elshorbagy Amany , Hurley Michael J
Published: September 2023

Sporadic Alzheimer’s disease (AD) is a complex, multifactorial disease. We should therefore expect to find many factors involved in its causation. The known neuropathology seen at autopsy in patients dying with AD is not consistently seen in all patients with AD and is sometimes seen in patients without dementia. This suggests that patients follow different paths to AD, with different people having slightly different combinations of predisposing physical, chemical and biologic risk factors, and varying neuropathology. This review summarizes what is known of the biologic and chemical predisposing factors and features in AD. We postulate that, underlying the neuropathology of AD is a progressive failure of neurons, with advancing age or other morbidity, to rid themselves of entropy, i.e., the disordered state resulting from brain metabolism. Understanding the diverse causes of AD may allow the development of new therapies targeted at blocking the paths that lead to dementia in each subset of patients.
Identification of Copper Metabolism Related Biomarkers, Polygenic Prediction Model, and Potential Therapeutic Agents in Alzheimer’s Disease
Authors: Du Yuanyuan , Chen Xi , Zhang Bin , Jin Xing , Wan Zemin , Zhan Min , Yan Jun , Zhang Pengwei , Ke Peifeng , Huang Xianzhang , Han Liqiao , Zhang Qiaoxuan
Published: September 2023

Background: The underlying pathogenic genes and effective therapeutic agents of Alzheimer’s disease (AD) are still elusive. Meanwhile, abnormal copper metabolism is observed in AD brains of both human and mouse models. Objective: To investigate copper metabolism-related gene biomarkers for AD diagnosis and therapy. Methods: The AD datasets and copper metabolism-related genes (CMGs) were downloaded from GEO and GeneCards database, respectively. Differentially expressed CMGs (DE-CMGs) performed through Limma, functional enrichment analysis and the protein-protein interaction were used to identify candidate key genes by using CytoHubba. And these candidate key genes were utilized to construct a prediction model by logistic regression analysis for AD early diagnosis. Furthermore, ROC analysis was conducted to identify a single gene with AUC values greater than 0.7 by GSE5281. Finally, the single gene biomarker was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in AD clinical samples. Additionally, immune cell infiltration in AD samples and potential therapeutic drugs targeting the identified biomarkers were further explored. Results: A polygenic prediction model for AD based on copper metabolism was established by the top 10 genes, which demonstrated good diagnostic performance (AUC values). COX11, LDHA, ATOX1, SCO1, and SOD1 were identified as blood biomarkers for AD early diagnosis. 20 agents targeting biomarkers were retrieved from DrugBank database, some of which have been proven effective for the treatment of AD. Conclusions: The five blood biomarkers and copper metabolism-associated model can differentiate AD patients from non-demented individuals and aid in the development of new therapeutic strategies.
Age-Dependent Increase in Tau Phosphorylation at Serine 396 in Huntington’s Disease Prefrontal Cortex
Authors: Petrozziello Tiziana , Huntress Sommer S. , Castillo-Torres Ayleen L. , Quinn James P. , Connors Theresa R. , Auger Corinne A. , Mills Alexandra N. , Kim Spencer E. , Liu Sophia , Mahmood Farah , Boudi Adel , Wu Muzhou , Sapp Ellen , Kivisäkk Pia , Sunderesh Shekar R. , Pouladi Mahmoud A. , Arnold Steven E. , Hyman Bradley T. , Rosas H. Diana , DiFiglia Marian , Mouro Pinto Ricardo , Kegel-Gleason Kimberly , Sadri-Vakili Ghazaleh
Published: September 2023

Background: To date, it is still controversial whether tau phosphorylation plays a role in Huntington’s disease (HD), as previous studies demonstrated either no alterations or increases in phosphorylated tau (pTau) in HD postmortem brain and mouse models. Objective: The goal of this study was to determine whether total tau and pTau levels are altered in HD. Methods: Immunohistochemistry, cellular fractionations, and western blots were used to measure total tau and pTau levels in a large cohort of HD and control postmortem prefrontal cortex (PFC). Furthermore, western blots were performed to assess tau, and pTau levels in HD and control isogenic embryonic stem cell (ESC)-derived cortical neurons and neuronal stem cells (NSCs). Similarly, western blots were used to assess tau and pTau levels in HttQ111 and transgenic R6/2 mice. Lastly, total tau levels were assessed in HD and healthy control plasma using Quanterix Simoa assay. Results: Our results revealed that, while there was no difference in total tau or pTau levels in HD PFC compared to controls, the levels of tau phosphorylated at S396 were increased in PFC samples from HD patients 60 years or older at time of death. Additionally, tau and pTau levels were not changed in HD ESC-derived cortical neurons and NSCs. Similarly, total tau or pTau levels were not altered in HttQ111 and transgenic R6/2 mice compared to wild-type littermates. Lastly, tau levels were not changed in plasma from a small cohort of HD patients compared to controls. Conclusions: Together these findings demonstrate that pTau-S396 levels increase significantly with age in HD PFC.
Software effort estimation using stacked ensembled techniques and proposed stacking ensemble using principal component regression as super learner
Authors: Priya Varshini A.G. , Anitha Kumari K.
Published: September 2023

As the size and complexity of projects grows, estimates are increasingly used, especially in the agile community. Software development cannot begin without first conducting thorough planning and estimation. Estimating how much work a project will take is a common first step in the software development life cycle. By employing ensemble techniques, we integrate multiple learning algorithms to build a more accurate predictive model. The core elements of our proposed stacked ensemble strategy include Decision Tree, Principal Components Regression, Random Forest, NeuralNet, GLMNET, XGBoost, Earth, and Support Vector Machine. Moreover, we augment the model’s performance by incorporating a blend of these foundational algorithms with other ensemble regression methods. Extensive testing in the suggested research work with a number of Super Learners demonstrates that Regression is the best technique for judging effort. The evaluation of the different estimators involved the use of various metrics, including Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Percentage of Close Approximations within 25% of the True Values (PRED (25)), R-Squared Coefficients, Precision, Recall, and F1-Score. The proposed method yields more trustworthy predicted performance than either single-model approaches or stacked ensembles. Effort estimation serves as the foundation for the rest of the project management process.
A multiattribute financial time series forecast model based on double hierarchy fuzzy linguistic term set
Authors: Zhao Aiwu , Du Chuantao , Guan Hongjun
Published: September 2023

Based on the double hierarchy linguistic term sets (DHLTS), a novel forecasting model is proposed considering both the internal fluctuation rules and the external correlation of different time series. The innovative aspects of this model consist of: (i) It can expresses more internal fluctuation and external correlation information, providing guarantees for improving the predictive performance of the model. (ii) The equivalent transformation function of DHLTS reduces the fuzzy granularity and improves the prediction accuracy. (iii) The application of similarity measures can extract the closest rules from historical states based on the distance operators of DHLTS. In addition, experiments on TAIEX considering the impact of the U.S. stock market and other data show that the model has good predictive performance.
Emotion identification in human faces through ensemble of deep learning models
Authors: Naveen Kmuar M. , Godfrey Winster S.
Published: September 2023

Investigation of human face images forms an important facet in affective analysis. The work, a DL-based ensemble is proposed for this purpose. Seven pre-trained models namely Facenet, Facenet2018, VGG16, Resnet-50, Senet-50, Arcface and Openface that have been developed for face verification have been exploited and customized for emotion identification. To each of these models, each all over interaction with softmax method to classification groups are augmented and entire network is then trained completely for emotion recognition. After training all the models individually, the probabilities for each of the class by each of the model are summed to derive at the final value. The class that holds the highest of this value is finalized as the predicted emotion. Thus, the proposed methodology involves image collection, image pre-processing comprising of contrast enhancement, face detection and extraction, face alignment, image augmentation facilitating rotation, shifting, flipping and zooming transformations and appropriate resizing and rescaling, feature extraction and classification through ensemble of customized afore-mentioned pre-trained convolutional neural networks, evaluation and evolving of best weights for emotion recognition from face images with enhanced accuracy. The proposed methodology is evaluated on the well-established FER-2013 dataset. The methodology achieves a validation accuracy of 74.67% and test accuracy of 76.23%. Further, similar images of another dataset (Face Expression Recogniton dataset) are included for training the models and the impact of extra training is assessed to see if there is improvement in performance. The experiments reveal marked improvement in face emotion identification performance reaching values of 94.98% for both validation and test set of FER-2013 dataset and 94.99% on validation set of Face Expression Recognition dataset.
Mathematical model to analyse the skin layers temperature with cloth during and after exercise
Authors: Patidar Neelam , Makrariya Akshara
Published: September 2023

The human body is a complex system that can be disrupted by various types of infections and viruses, and body temperature is a major contributor to these problems. To prevent this, doctors recommend comfortable clothing made from good fabric. This paper proposes a model that can be used to analyze how different types of fabric impact the thermal profile of skin layers during and after physical activity. The information gained from this model could be useful in designing exercise apparel for different climates and in generating thermal stress protocols for treating infections and providing physical activity guidelines for healthy living. The model uses Pennes’ bio-heat equation and finite difference method to examine the temperature distribution in skin layers while accounting for both physiological and clothing parameters. The numerical findings were compared to existing studies, and the model’s accuracy was found to be in good agreement with previous research. The proposed model can be used to predict how much rest and acclimation are needed to cope with thermal stress and could also be modified to obtain thermal information for patients with skin diseases. Additionally, the thermal profile obtained from this model could be helpful in designing exercise clothes for patients with skin diseases.
Some T-spherical fuzzy dombi hamy mean operators and their applications to multi-criteria group decision-making process
Authors: Hussain Abrar , Ullah Kifayat , Al-Quran Ashraf , Garg Harish
Published: September 2023

Renewable energy sources play an influential role in the world’s climate and reduce the rate of harmful gasses such as carbon dioxide, methane, nitrous oxide, and many other greenhouse gasses that contribute to global warming. The theoretical concept of the T-spherical fuzzy (T-SF) set (T-SFS) is the most suitable model to evaluate energy resources under uncertainty. This article illustrates appropriate operations based on Dombi triangular norm and t-conorm. We derived a series of new aggregation approaches, such as T-SF Dombi Hamy mean (T-SFDHM) and T-SF weighted Dombi Hamy Mean (T-SFDWHM) operators. Further authors illustrated a list of new approaches such as T-SF Dual Dombi Hamy mean (T-SFDDHM), and T-SF Dombi weighted Dual Hamy mean (T-SFDWDHM) operators. Some exceptional cases and desirable properties of our derived approaches are also studied. We illustrate an application of renewable energy resources to be evaluated using a multi-attribute group decision-making (MAGDM) method. A case study was also studied to choose appropriate energy resources using our proposed methodology of the T-SFDWHM and T-SFDWDHM operators. To show the effectiveness and validity of our current methods, we compared the existing results with currently developed aggregation operators (AOs).
Cross-entropy method for multiple attribute decision making under the triangular fuzzy neutrosophic sets
Authors: Wang Suhua , Yan Pengfei , Wang Lei , Ouyang Shaojuan
Published: September 2023

Cloud computing is emerging as an advanced stage of enterprise information technology in a highly competitive society, and is now in a phase of rapid development. Enterprises using cloud computing can reduce the cost of building infrastructure and reap huge benefits. Due to the convenience of cloud computing, more and more enterprises are inclined to use cloud services to build their business systems. However, there are many different cloud service providers in the market, and enterprises need scientific decision-making tools to determine which cloud service provider to choose. The cloud service provider selection is viewed as the multi-attribute decision-making (MADM). In this paper, the cross-entropy method under triangular fuzzy neutrosophic sets (TFNSs) is built based on the traditional cross-entropy method. Firstly, the TFNSs is introduced. Then, combine the traditional fuzzy cross-entropy method with TFNSs information, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) method is established for MADM under TFNSs. Finally, a numerical example for cloud service provider selection has been given and some comparisons is used to illustrate advantages of cross-entropy method with TFNSs.
Correlation between the in vitro starch digestibility and the glycemic/insulinemic index of biscuits and bread made from non-conventional wholemeal/wholegrain flours
Authors: Papadopoulos Charalampos , Anagnostopoulos Constantine , Zisimopoulos Athanasios , Panopoulou Maria , Papazoglou Dimitrios , Grapsa Anastasia , Tente Thaleia , Tentes Ioannis
Published: September 2023

BACKGROUND: Carbohydrates as starch are a staple part of human diet. Upon starch digestion, glucose is absorbed, eliciting an insulin response. Glucose absorption kinetics (rapid or slow) depend on starch structure. Products made from wholemeal/wholegrain flour cause moderate glycemic and insulinemic responses and support a healthy lifestyle. OBJECTIVE: To review the nutritional value in terms of the in vivo glycemic and insulinemic index and the in vitro digestibility characteristics of six wholemeal/wholegrain commercial bakery products. METHODS: We analyzed in vitro the rapidly- and slowly- available glucose (RAG and SAG), the rapidly- and slowly- digestible starch (RDS and SDS), and the resistant starch (RS) fraction of the six wholemeal/wholegrain products against one white type of bread. The glycemic (GI) and the insulinemic index (II) were estimated in vivo in a group of eleven healthy individuals. RESULTS: The glycemic indices of the wholemeal/wholegrain flour biscuits and breads were low, (range 28 ± 3.2 to 41 ± 3.9, Mean+SEM) correlating with the insulinemic indices. RAG positively correlated with both GI and II, with fiber having a marginal correlation. CONCLUSIONS: Our findings indicate that both conventional and non-conventional wholemeal/wholegrain bakery products have low GI and moderate II, correlating to in vitro starch digestibility and type of processing.
Body weight gain in adolescents can increase the risk of developing an eating disorder
Authors: Tekin Tuba , Bağlam Nurcan
Published: September 2023

BACKGROUND: The risk of developing an eating disorder can increase with obesity and body weight gain in adolescents. Eating disorders can cause serious physical and psychological problems. OBJECTIVE: This study was conducted to determine the frequency of eating disorders in adolescents and examine the relationship between obesity and weight gain. METHODS: The sample was selected from a population of high school students between the ages of 14–19. The Eating Disorder Examination Questionnaire (EDE-Q) was used to determine the frequency of eating disorders. The relationship between anthropometric measurements and the EDE-Q was evaluated. RESULTS: A total of 387 students, 67.4% of the sample female, were included in the study. When the students are evaluated according to BMI classification, 74.67% of the students are in the normal category, 16.53% are overweight, and 5.42% are obese. While female students received a total score of 1.36±1.45 on the scale, male students received a score of 0.85±0.97 (p < 0.05). There was a positive correlation between body weight and BMI, EDE-Q’s total score, and sub-dimension scores (p < 0.05). Additionally, the regression analysis revealed that the total score on the scale was significantly associated with body weight and BMI. EDE-Q’s total score increased by 1 unit, there was an increase of approximately 3.01 kg in body weight and 1.19 kg/m2 in BMI. CONCLUSION: In adolescents, the risk of developing an eating disorder can increase with obesity and body weight gain. Early diagnosis and treatment of eating disorders can help prevent long-term health problems.
Enhancing management of agitation after traumatic brain injury: Psychiatric perspectives and quantitative assessments
Authors: Kalra Inder D. , Watanabe Thomas K.
Published: September 2023

BACKGROUND: Post-traumatic agitation is a common and problematic complication after traumatic brain injury. It may present with features consistent with psychiatric disorders, which may provide clues as to management. OBJECTIVE: This is a narrative review of pertinent literature and a description of a collaborative clinical approach utilizing psychiatric and brain injury rehabilitation strategies to optimize outcomes in the management of post-traumatic agitation. METHODS: Describe and provide evidence for a transdisciplinary clinical approach supported by existing literature and clinical experience. RESULTS: Given the heterogeneity of the problem and limitations in the current literature there is no standardized approach to manage post-traumatic agitation; nevertheless, a strategy is proposed that clinicians may utilize to guide treatment and assess efficacy of the chosen intervention(s). CONCLUSION: A clinical approach that uses quantitative assessment of targeted behavior to objectively evaluate pharmacological interventions that are generated by a collaborative approach may yield improved outcomes for managing post-traumatic agitation.
Visual analysis of acupuncture point selection patterns and related mechanisms in acupuncture for hypertension
Authors: Li Xingping , Chen Fuyan , Wang Wenqing , Liu Yang , Han Jiang-Qin , Ke Zi , Zhu Hong-Hang
Published: September 2023

BACKGROUND: Hypertension has become one of the most pathogenic diseases in the world. OBJECTIVE: This paper summarizes and analyzes the acupuncture point combinations and treatment principles of acupuncture for hypertension in a systematic way by means of big data mining. METHODS: The literature for this paper was obtained from CNKI, Wanfang, VIP, SinoMed and PubMed, Embase, Cochrane Library, Web of Science, and Ovid databases. Thedata were collected to obtain combinations of acupoints with strong associations through association rule analysis, complex networks for screening to obtain core acupoint nuclei, and cluster analysis to derive treatment principles. RESULTS: A total of 127 acupuncture prescriptions involving 66 acupoints were included in this study. Tai-chong (LR3), Qu-chi (LI11), Zu-san-li (ST36), Feng-chi (GB20), and He-gu (LI4) were the most commonly used acupoints. The large intestine meridian was the preferred meridian, and most of the extremity acupoints, especially the lower extremities, were selected clinically. The association rule reveals that Qu-chi (LI11) and Zu-san-li (ST36) are the dominant combination acupoints. 3 core association points obtained after complex network analysis, the 1st association, Bai-hui (DU20), Tai-xi (KI3), Gan-shu (BL18), Shen-shu (BL23); The 2nd association, Qu-chi (LI11), He-gu (LI4), San-yin-jiao (SP6), Zu-san-li (ST36), Feng-chi (GB20), Tai-chong (LR3); The 3rd association, Qi-hai (RN6), Guan-yuan (RN4), Zhong-wan (RN12), Zhao-hai (KI6), Tai-yang (EX-HN5), Lie-que (LU7), Yang-ling-quan (GB34), Xing-jian (LR2), Yin-ling-quan (SP9). Cluster analysis yielded the treatment principles of nourishing Yin and submerging Yang, pacifying the liver and submerging Yang, tonifying Qi and Blood, and calming the mind and restoring the pulse, improving clinical outcomes. CONCLUSION: By means of big data mining, we can provide reference for acupuncture point grouping and selection for clinical acupuncture treatment of hypertension.
Enhancement based convolutional dictionary network with adaptive window for low-dose CT denoising
Authors: Liu Yi , Yan Rongbiao , Liu Yuhang , Zhang Pengcheng , Chen Yang , Gui Zhiguo
Published: September 2023

BACKGROUND:

Recently, one promising approach to suppress noise/artifacts in low-dose CT (LDCT) images is the CNN-based approach, which learns the mapping function from LDCT to normal-dose CT (NDCT). However, most CNN-based methods are purely data-driven, thus lacking sufficient interpretability and often losing details.

OBJECTIVE:

To solve this problem, we propose a deep convolutional dictionary learning method for LDCT denoising, in which a novel convolutional dictionary learning model with adaptive window (CDL-AW) is designed, and a corresponding enhancement-based convolutional dictionary learning network (called ECDAW-Net) is constructed to unfold the CDL-AW model iteratively using the proximal gradient descent technique.

METHODS:

In detail, the adaptive window-constrained convolutional dictionary atom is proposed to alleviate spectrum leakage caused by data truncation during convolution. Furthermore, in the ECDAW-Net, a multi-scale edge extraction module that consists of LoG and Sobel convolution layers is proposed in the unfolding iteration, to supplement lost textures and details. Additionally, to further improve the detail retention ability, the ECDAW-Net is trained by the compound loss function of the pixel-level MSE loss and the proposed patch-level loss, which can assist to retain richer structural information.

RESULTS:

Applying ECDAW-Net to the Mayo dataset, we obtained the highest peak signal-to-noise ratio (33.94) and sub-optimal structural similarity (0.92).

CONCLUSIONS:

Compared with some state-of-art methods, the interpretable ECDAW-Net performs well in suppressing noise/artifacts and preserving textures of tissue.
The “Africa Rising”: An Empirical Analysis of the Determinants of Per-Capita Growth
Authors: Scalamonti Francesco
Published: September 2023

In developing economies, the race toward inclusive development has prompted researchers to reconsider the drivers of growth in view of achieving the Sustainable Development Goals (SDGs). This study aims to explore the determinants of African growth after analysing reference literature to select the explanatory variables.

The paper aims to identify the robust drivers of growth of 54 African countries over the period 2010-2019, where the data source is more complete. As a data source, the main international organisations (UN, WB, IMF) have been considered.

We employ a system dynamic panel estimator (GMM-sys) without and with added exogenous regressors for the robustness check. As far as we know, we have not found any studies analysing the determinants of growth in a panel of 54 African countries and/or using GMM estimators with data referring to the last decade. We have used the real per-capita GDP as a dependent variable.

Our findings indicate that in addition to the initial conditions of the per-capita GDP, the natural resource rent with one order of lags has emerged significantly and with a negative sign, crowding out the per-capita GDP. On the other hand, the government expenditure with one order of lags is confirmed as significant with a positive sign, while the terms-of-trade has emerged as not significant. We conclude that institutional and business environments are particularly important in explaining African growth.
Digital Library of the Caribbean: A Multi-Institutional, International Digital Library
Authors: Taylor Laurie
Published: September 2023

Many people have heard at least one story of how the Digital Library of the Caribbean, or dLOC, came to be. Normally, I start the story in 2004. However, the full story goes back much further to nearly a century ago with deep relationships and collaborative collection building work undertaken by the full community of Caribbean libraries, of which the University of Florida is one. In this article, I share the story, as I know it, focusing my understanding of the perspective of the University of Florida, as a Caribbean library. This story is one telling and is an invitation, to ask others to please share if they have lived experience or greater knowledge of the long history of the Libraries at UF and the Caribbean.
Reviewing continuing professional development concept of English teachers from the lens of Diffusion of Innovation Theory
Authors: Jawahir Ahmad
Published: September 2023

The main problem of the recent study is how the concept of English teacher Continuing Professional Development is reviewed from Everett Rogers’ Innovation Diffusion Theory. So, the research aimed at finding the review of the Innovation Diffusion Theory of Rogers toward the concept of Continuing Professional Development of English teachers in Indonesia. This study employed library research as the method and the researcher himself as the instrument to collect data deriving from books as a primer resource and ministry regulation and journal articles as secondary resources. It was conducted through several stages ranging from selecting topics based on the problem; exploring information related to the topic; determining research focus; reading the books, regulations and articles as data resources; recording what was found as research data; and, processing the recorded data. Data were analyzed through induction and interpretation. The result showed that teacher continuing professional development is an implementation of innovation diffusion. It forms a cycle or an endless chain, in which, firstly, innovative work is invented and adopted by innovative teachers; next, it is reported, reflected and published in the form of scientific book, paper or article; then, the scientific work is presented in functional or technical education and training or teacher collective activity as a channel of communication with early adopter teachers; and, the adopter teachers are inspired to make another innovation.

 Keywords: Continuing Professional Development; Innovation Diffusion Theory; English teachers
EFL students’ speaking ability in descriptive text
Authors: Lestari Yunda
Published: September 2023

This study aimed to find out EFL students’ speaking ability in a descriptive text. This research was a descriptive survey. The participants were chosen by using cluster random sampling technique where there were twenty students at the eleventh-grade students of SMA Negeri 4 OKU. The data were collected through oral test in the form of online test and telephone survey. WhatsApp application was used as an online platform in data collection. The result of the study showed that the total of students’ score was 1142 and mean of the students’ score was 57,1. There were 1 student (5%) who gains score 80-100 in very good criteria, 2 students (10%) who gain score 66-79 in good criteria, 10 students (50%) who gain score 56-65 in enough criteria, 5 students (25%) who gain score 46-55 in low criteria, and 2 students (10%) got score 45 in fail criteria. Based on the data, there were only 13 students (65%) who passed the students’ speaking ability. In conclusion, the frequency of criteria of students’ speaking ability is in enough criteria.

Key words: EFL; Speaking abilit; Descriptive text
The moral values in Aladdin (2019)
Authors: Puspitasari Nur Wahyu , Arifin Adip , Harida Ratri
Published: September 2023

Moral values are values that relate to good and bad deeds which guide people's life in a positive wayy. In practice, moral values function to motivate society members to act well. This present study aimed at exploring the moral values in Aladdin movie; a musical fantasy movie released in 2019. The movie is directed by Guy Ritchie, an English film director, producer and also businessman. The researchers employed descriptive qualitative method and designed it as the research design. The source of the data in this study was the dialogue among the characters in Aladdin movie. Documentation was used as the technique to collect the data. Besides the researchers as the instrument, another instrument used was documentation sheet. To analyse the data, the researchers used descriptive analysis. The results of the study showed that there were a number of moral values found in Aladdin film, including courage, honesty, self-confidence and capability, being helpful, love and affection, respect, loyalty, fairness and humanity, and kindness. These findings also indicate that honesty is the most dominant moral value displayed throughout the film scene. Particularly, it appears three fourteen times in different dialogue and actions.

 Keywords: Moral Values; Aladdin Movie; Literary Study
A review: An epistemological perspective of possibility of pedagogical grammar learning in community language learning method
Authors: NAWIR ERNA , Fauziah Dezna , Elsara Wenny
Published: September 2023

Given the urgency of changing the learning context caused by the current COVID-19 pandemic, learning pedagogical grammar, especially in learning English as an effort to acquire the language as a second language and or foreign language, does not only stop at the learning process which has been mostly done face-to-face directly, but can also be carried out through one alternative e-learning known as Open Learning technology-based platform. As a new atmosphere in the use of technology, especially in the field of education through the use of Massive Open Online Course (MOOC), Open Learning is an alternative of e-Learning platform that can be used as a learning medium used in the online learning process which is increasingly popular today. The methodology used by using library sources. In other words, the writers used some sources and references in getting and analyzing the data then integrated the ideas by synthesizing in drawing conclusion. The finding is by applying the Community Language Learning method for improving language skills, especially in pedagogical grammar learning, language learners can be facilitated in the learning process by making maximum use of the features of the Open Learning platform in accordance with the basic principles they have which are constructive and community-based and it is in accordance with the pattern of educational tools contained in the Open Learning philosophy itself, namely empowering students, learning concepts that are authentic, active and constructive, and focusing on building an effective educational community.
Keywords: Pedagogical Grammar Learning; Community Language Learning; Open Learning Platform