bluuebunny/crossref_metadata_2025_split huggingface dataset:
Psychometric adequacy of the Persian adapted version of the Tilburg Frailty Indicator (P- TFI)
Authors: Safarnavadeh Maryam , Salehi Leili 
Published: September 2023

Abstract

Background:
Frailty has been identified as the primary goal of the preventing the various s aging consequences in many studies. Considering frailty help us to plan and train properly. valid and reliable tools are needed. The current study aimed to assess validity and reliability of the Persian adapted version of the Tilburg frailty indicator in Iran.
Method:
The current study is cross sectional study included three phases: at first phase, the indicator translated to Persian, at the next step, face and content validity was assessed. During the third phase, the P-TFI was sent out for completion to elderlies, who helped assess reliability and construct validity. For construct validity, convergent and divergent validity were used. It was expected that the TFI domain scores would show the highest correlations with their related measures of frailty (convergent construct validity) and the lowest correlations with measures of the other domains (divergent construct validity). Overall, 400 elderlies entered the study as the study population from six health care centers.
Results
The mean age of the participants was 69.05±7.28 (ranged from 60 to 93) years old. The majority of the participants were female (56.8). More than half of the participants had spouse. The majority had less than twelve years of education (81.5%), and most participants had a modest level of income. an overall of 168 (42%) older people lived with their spouse and child. The mean total score of TFI was 8.26±1.80, and 171 participants (42.75%) were classified frail in terms of the original cut-point of the scale (i.e., the total score ≥5): with the, and considering 6 as the threshold limit for TFI (i.e., The total score ≥6), 89 participants (22.25) were classified as frail. The scores for KR-21 range from 0 to 1, where 0 is no reliability and 1 is perfect reliability. The test-retest reliability for the 14-day interval was 0.88 for the total scale, 0.80 for physical domain, 0.65 for psychological domain and 0.81 for social domain. Mean score of frailty and its dimensions varied from 4.35±1.78, 1.81±1.33,1.69±0.73.0.86±061 for total frailty, physical, psychological and social respectively. The total score of the TFI significantly correlated with each alternate measure as expected. The convergent validity of the TFI was proved by the Cohen’s kappa coefficient between each item of the TFI and corresponding alternate tools. All of the kappa values ranging from.535 to 0.967 were statistically significant.
Phenomenon of multiple reentrant localization in a double-stranded helix with transverse electric field
Authors: Mondal Kallol , Sarkar Suparna , Ganguly Sudin , Maiti Santanu 
Published: September 2023

Abstract
The present work explores the potential for observing multiple reentrant localization behavior in a double-stranded helical (DSH) system, extending beyond the conventional nearest-neighbor hopping interaction. The DSH system is considered to have hopping dimerization in each strand while also being subjected to a transverse electric field. The inclusion of an electric field serves the dual purpose of inducing quasiperiodic disorder and strand-wise staggered site energies. Two reentrant localization regions are identified: one exhibiting true extended behavior in the thermodynamic limit, while the second region shows quasi-extended characteristics with partial spreading within the helix. The DSH system exhibits three distinct single-particle mobility edges linked to localization transitions present in the system. The analysis in this study involves examining various parameters such as the single-particle energy spectrum, inverse participation ratio, local probability amplitude, and more. Our proposal, combining achievable hopping dimerization and induced correlated disorder, presents a unique opportunity to study the phenomenon of reentrant localization, generating significant research interest.
Management of Hyperleukocytosis and Results of Leukapheresis in Childhood Acute Leukemia: A Single Center Experience
Authors: TERZİ ÖZLEM , Ayçiçek Ali , Uysalol Ezgi Paslı , Hançerli Özgü , Solgun Hüseyin Avni , Ertürk Saide , Şimşek Halil İbrahim , Göktürk Osman , Ekinci Mehmet Selim , Bayram Cengiz 
Published: September 2023

Abstract
Background
Hyperleukocytosis in pediatric acute leukemia is associated with increased morbidity and mortality.
Objectives
The aim was to review the clinical characteristics and outcomes of patients with newly diagnosed leukemia with hyperleukocytosis (HL).
Methods
A retrospective case control study reviewed data from a single institution over a 5-year period. Hyperleukocytosis was present in 41 patients with acute leukemia and were included in the study. Treatment strategies included hyperhydration, administration of allopurinol or rasbirucase, early induction of induction chemotherapy (CT), and leukapheresis (LP).
Results
Twenty seven (65.9%) of the 41 patients were male and the median age was 7 (range 1–17) years. Rasburicase was only used in 6 acute lymphocytic leukemia (ALL) patients with hyperuricemia. LP was used in 9 of 41 (13%) patients with hyperleukocytosis and a total of 25 LP procedures were performed. The mean leukocyte value after apheresis was 65,529/mm3. In patients with and without LP, tumor lysis syndrome was seen in 2 (23%) and 2 (6.25%) patients, respectively. Pulmonary leukostasis was seen in 2 patients, one of whom underwent LP. The mean leukocyte count in patients who received LP versus those who did not was 520,000 cells/mm3 and 158,800 cells/mm3, respectively. The time from presentation to the initiation of CT was the same between those who received LP and those who did not (mean of 35 h vs. 34 h). During the first 14 days after presentation, cerebral leukostasis/coagulopathy or pulmonary leukostasis-related early death did not occur in patients with leukemia.
Conclusions
The use of LP in patients with hyperleukocytosis is safe and effective, well tolerated and does not alter time to CT induction at our institution.
WITHDRAWN: The Quasi-Xgamma Frailty Model with Survival Analysis UnderHeterogeneity Problem, Validation Testing, and Risk Analysis for Emergency Care Data
Authors: Loubna Hamami , Goual Hafida , Mutairi Aned Al , Nassr Said G. , Alsuhabi Hassan , Ali M. Masoom , Ibrahim Mohamed , Yousof Haitham M. 
Published: September 2023

Abstract
We introduce a new class of continuous distributions called the generalized transmuted-G family which extends the quadratic rank transmutation map pioneered by Shaw and Buckley (2007). We provide some special models for the new family. Some of its mathematical properties including explicit expressions for the ordinary and incomplete moments, generating function, Rényi and Shannon entropies, order statistics and probability weighted moments are derived. The estimation of the model parameters is performed by maximum likelihood. The flexibility of the proposed family is illustrated by means of two applications to real data sets.
3DCAE-MRI: Overcoming Data Availability in Small Sample Size MRI Studies
Authors: Dias Maria de Fátima Machado , Cerqueira Tiago F. T. , Duarte João Valente , Castelo-Branco Miguel , Carvalho Paulo 
Published: September 2023

Abstract

Deep learning (DL) are data-driven models that learn abstract, hierarchical features from raw or low-processed data. These models perform exceptionally in image recognition tasks using large amounts of data. Thus, the availability of open-source large-scale annotated datasets is crucial to achieve high-performance models. Nevertheless, the usage of DL models in neuroimaging is restricted due to data availability. Transfer learning has been used to overcome the issue, nonetheless most studies focus on transferring knowledge from or to an Alzheimer's Disease context. In this paper, we propose the 3D Convolutional Autoencoder Magnetic Resonance Imaging (3DCAE-MRI), an unsupervised model, to facilitate the usage of supervised DL models in small sample size Magnetic Resonance Imaging (MRI) studies. We exploit different open-source MRI databases to train the 3DCAE-MRI, which is applied to two different benchmark problems: age prediction and sex classification. On age prediction, off-the-shelf learning from the 3DCAE-MRI attained remarkable performance on an external dataset using only 300 training samples, which are on par with state-of-the-art results that use thousands of images. On both age prediction and sex classification, off-the-shelf learning using 3DCAE-MRI weights leads to better performance, higher generalisation and stabler models than either fine-tuning or training the network from scratch.
Shannon’s Lens: The Catalyst for Cognitive Evolution of Edge Based Smart Machines
Authors: Rubbani Syeda 
Published: September 2023

Abstract
Edge AI protocols facilitate communication in smart machines between edge devices and central processing units. This research aims to enable autonomy in edge-based smart machines through a cognitive neuroevolutionary AI framework, applied to MQTT, CoAP, AMQP, and HTTP protocols to achieve Shannon's capacity. Shannon's capacity defines a channel's maximum data transmission capability. However, the specific Shannon's capacity for edge AI protocols and the impact of customizing them with neuroevolutionary methods remain unknown. Here we illustrate a tailored neuroevolutionary AI framework that acts as an intelligent optimizer for edge AI protocols, validated by achieving Shannon's capacity. The research establishes Shannon's limits for these protocols using the framework, reaching up to 20 bps channel capacity. Findings encompass bandwidth and power efficiency, protocol efficiency ratios, interpretation, performance evaluation, and future research avenues. We anticipate that the first findings of Shannon’s capacity and the novel neuroevolutionary AI framework for exemplary edge AI protocols provide insights into the cognitive potential of edge AI protocols and their role in seamless connectivity for advanced edge AI solutions.
Uniaxial quasi-static compression performance of PMI foam at different temperatures and constitutive model establishment
Authors: chen guotao , Lv Yansong , Mei zhiyuan , Li huadong , Bai xuefei 
Published: September 2023

Abstract
The uniaxial compression failure mode of polymethacrylimide (PMI) foam under the combined action of heat and load was investigated from the macro and micro perspectives. Moreover, the heat resistance of the material was analyzed by dynamic mechanical analysis and dimensional stability tests. The stress-strain curve of the PMI foam in the range of 20℃–200℃ at different stages of compression failure mechanism was revealed, and the Liu-Subhash model was used to accurately predict the stress-strain constitutive relationship of PMI at different temperatures. The results showed that, in the range of 20℃–180℃, the material is an elastoplastic material, and the stress-strain curve exhibits a typical 'three-stage' pattern. When the temperature is 200℃, the material is transformed completely into a hyperelastic incompressible material, and the stress-strain curve exhibits a 'two-stage' pattern. In addition, the temperature effect on the yield strength and elastic modulus of the material and the strain rate effect at different temperatures were also discussed. Finally, based on the Liu-Subhash model, a PMI quasi-static compression constitutive model considering the temperature effect was established. This conclusion provides theoretical support and data input for the study of the thermo-mechanical combined bearing mechanism of composite sandwich structures.
Detection and Diagnosis of Learning Disabilities in Children of Saudi Arabia with Artificial Intelligence
Authors: Alshehri Mohammed , Sharma Sunil , Gupta Priya , Shah Sapna Ratan 
Published: September 2023

Abstract
Learning Disability (LD) is an umbrella term for various learning difficulties. It is a neurological disorder that affects the brain of children and hinders their capacity to do one or more specified activities. Youngsters with learning disabilities are neither sluggish nor mentally retarded. This paper utilizes Speech, as an acceptable motor activity to address the problem and its solution. In this work, Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Cepstral Coefficients (LPCC) are used to extract speech characteristics. For every speech sample, 205 characteristics are retrieved, consisting of 13 acoustical features from MFCC, 128 features from LPCC, and 64 features from power spectrum density. In addition, linear predictive coding-based parameterization is used to every speech sample to extract Weighted Linear Predictive Cepstral Coefficients (WLPCC) and Linear Predictive Coding features (LPC). The Extracted Features are identified using a Deep Convolutional Neural Network (DCNN), and their performance is analyzed. It is found that the correlation between speech characteristics and various classification models may be assessed to get the highest classification accuracy. These findings demonstrate that the suggested feature extraction approaches and classification models are highly successful in categorizing the speech of intellectually impaired and typically developing youngsters.
Cross-sensitivity Analysis of Land Use Transition and Ecological Service Values in Rare Earth Mining Areas in Southern China
Authors: Zhu Chenhui , Chen Yonglin , Wan Zhiwei , Chen Zebin , Lin Jianping , Chen Peiru , Sun Weiwei , Hao Yuan , Zhang Yunping 
Published: September 2023

Abstract
Rare earth resources are important national strategic resources, and the mining process will change the original land use types, and it will have a certain impact on the ecosystem service values around the rare earth mining areas. To study the impact of land use changes on ecosystem service functions in rare earth mining areas, firstly, the land use change trends in the study area from 2009 to 2019 were analyzed using the land transfer matrix; then the distribution of ecosystem service values and the flow direction of ecosystem service values in the study area were measured based on the ecosystem service value equivalents; a spatial autocorrelation analysis was done on the ecosystem service values to explore their spatial distribution patterns; and finally, the cross-sensitivity coefficient was used to quantitatively assess the impact of land use changes on ecosystem service, the cross-sensitivity coefficient was used to quantitatively assess the extent and direction of the impact of land use change on ecosystem service values. The results show that the land use types in the study area are mainly forest land and farmland, with woodland accounting for the highest proportion of the study area. The ESV changes in the study area are consistent with the trend of land use transformation, with the overall increase and decrease being comparable, and the decrease in ESV is mainly concentrated in the areas with a large increase in mining land and construction land; during the study period, the study area was significantly reduced with low - low aggregation areas and the ecological environment was improved; from 2009 to 2011, the ecological sensitivity coefficient is more variable, and is more sensitive to the net conversion between water and desert, from 2014 to 2019, the ecological sensitivity coefficient is less variable, and the most sensitive is the net conversion between cultivated land and water. The study area should be reasonably developed for rare earth resources and the ecological environment around the mining area should be reasonably protected to build an ecological security pattern.
3DinforCPI: Enhancing Efficiency of Compound-Protein Interaction Prediction Through Novel Perspectives on Multi-level Information Integration
Authors: Nguyen Ngoc-Quang , Park Sejeong , Gim Mogan , Kang Jaewoo 
Published: September 2023

Abstract
Forecasting the interaction between compounds and proteins is crucial for discovering new drugs. However, several commonly used computational techniques assume that the molecules or proteins in the test set have already been observed during the training phase. This assumption might not remain valid in real-life scenarios, where the test and training data could exhibit varying distributions and stem from distinct domains. Moreover, previous studies have not utilized three-dimensional (3D) information on compounds and proteins to predict binding affinity. Here, we propose a two-step deep learning strategy named 3DinforCPI that incorporates transfer learning techniques with multi-level resolution features to overcome these limitations. Our approach leverages 3D information from both proteins and compounds and acquires a profound understanding of the atomic-level features of proteins. We applied our approach to six datasets: Davis, Metz, KIBA, CASF-2016, DUD-E, and BindingDB, to evaluate the effectiveness of our approach.
Modulating Hierarchical Self-Assembly In Thermoresponsive Intrinsically Disordered Proteins Through High-Temperature Incubation Time
Authors: Sethi Vaishali , Gerassi Dana Cohen , Meir Sagi , Ney Max , Shmidov Yulia , Koren Gil , Abramovich Lihi Adler , Chilkoti Ashutosh , Beck Roy 
Published: September 2023

Abstract
The cornerstone of structural biology is the unique relationship between protein sequence and the 3D structure at equilibrium. Although intrinsically disordered proteins (IDPs) do not fold into a specific 3D structure, breaking this paradigm, some IDPs exhibit large-scale organization, such as liquid-liquid phase separation. In such cases, the structural plasticity has the potential to form numerous self-assembled structures out of thermal equilibrium. Here, we report that high-temperature incubation time is a defining parameter for micro and nanoscale self-assembly of resilin-like IDPs. Interestingly, high-resolution scanning electron microscopy micrographs reveal that an extended incubation time leads to the formation of micron-size rods and ellipsoids that depend on the amino acid sequence. More surprisingly, a prolonged incubation time also induces amino acid composition-dependent formation of short-range nanoscale order, such as periodic lamellar nanostructures. We can correlate the lamellar structures to β-sheet formation and demonstrate similarities between the observed nanoscopic structural arrangement and spider silk. We, therefore, suggest that regulating the period of high-temperature incubation, in the one-phase regime, can serve as a unique method of controlling the hierarchical self-assembly mechanism of structurally disordered proteins.
Robust and eXplainable artificial intelligence
Authors: Giudici Paolo , Raffinetti Emanuela , Riani Marco 
Published: September 2023

Abstract
Artificial Intelligence relies on the application of machine learning models which, while reaching high predictive accuracy, lack explainability and robustness. This is a problem in regulated industries, as authorities aimed at monitoring the risks arising from the application of AI methods may not validate them.
No measurement methodologies are yet available to jointly assess accuracy, explainability and robustness of machine learning models.
We propose a methodology which fills the gap, extending the forward search approach, employed in robust statistical learning, to machine learning models. Doing so, we will be able to evaluate, by means of interpretable statistical tests, whether a specific AI application is accurate, explainable and robust, by means of a unifying methodology.
We apply our proposal to the context of bitcoin price prediction, comparing a linear regression model against a non linear neural network model.
Using Recurrent Neural Networks to Reconstruct Temperatures from Simulated Fluorescent Data for use in Bio-Microfluidics
Authors: Kullberg Jacob , Sanchez Derek , Mitchell Brendan , Munro Troy , Egbert Parris 
Published: September 2023

Abstract
Many biological systems have a narrow temperature range of operation, meaning high accuracy and spatial distribution level are needed to study these systems. Most temperature sensors cannot meet both the accuracy and spatial distribution required in the microfluidic systems that are often used to study these systems in isolation. This paper introduces a neural network called the Multi-Directional Fluorescent Temperature Long Short-Term Memory Network (MFTLSTM) that can accurately calculate the temperature at every pixel in a fluorescent image to improve upon the standard fitting practice and other machine learning methods use to relate fluorescent data to temperature. This network takes advantage of the nature of heat diffusion in the image to achieve an accuracy of ±0.0199 K RMSE within the temperature range of 298K to 308 K with simulated data. When applied to experimental data from a 3D printed microfluidic device with a temperature range of 290 K to 380 K, it achieved an accuracy of ±0.0684 K RMSE. These results have the potential to allow high temperature resolution in biological systems than is available in many microfluidic devices.
Non-degeneracy and uniqueness of periodic solutions for a Liénard equation with a linear difference operator
Authors: Cheng Zhibo , Li Yafei 
Published: September 2023

Abstract

In this paper, we investigate the non-degeneracyof a Lienard equation with a linear difference operator bymeans of the generalized Wirtinger inequality. In addition, by thenon-degenerate results and an extension of Mawhin's continuationtheorem, the existence and uniqueness of periodic solutions for a Li\'{e}nard equation with a linear differenceoperator are proved.
MSC2000—34C25; 47B39
Brain Tumors Classification using Deep Models and Transfer Learning
Authors: Mavaddati Samira 
Published: September 2023

Abstract
Brain tumor classification using magnetic resonance imaging (MRI) along with medical knowledge results in a better decision to treat a patient. Also, the classification of some types of tumors is often a challenging problem due to the need for a detailed analysis of tumor texture. Therefore, machine learning approaches and specialists' experience can be very beneficial. This paper aims to explore the potential of deep learning structures in classifying different types of brain tumors. Our approach involves using a 50-layer ResNet deep network, which has shown promising results in various image classification tasks. For more consideration, transfer learning technique also is employed to evaluate the performance of the proposed algorithm. The presented algorithms are compared with the other deep networks such as convolutional neural network (CNN), recurrent neural network (RNN), and dictionary learning-based classifier. The studies show that the ResNet-50-based deep model performs better than the mentioned classifier categories in different evaluation criteria such as accuracy, sensitivity, and robustness and has an effective role in medical diagnosis.