bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of WC Particles on the Microstructure of IN718/WC Composite Coatings Fabricated by Laser Cladding: A Two-Dimensional Phase-Field Study
Authors: Wang Yixin , Zhou Jianzhong , Zhang Teng , Li Pengfei , Zhu Hao , Meng Xiankai
Published: February 2023

During the process of laser cladding IN718/WC composite coatings, the dissolution and metallurgical reaction of WC particles significantly influence the microstructures of the coatings. However, the limited experimental methods restrict direct observation and prevent a deep understanding of this complicated process. Therefore, a novel numerical model for the solidification process of IN718/WC composite coatings was proposed. The model was established with the coupled multi-phase-field model and lattice Boltzmann method. Different kinds of microstructure around WC particles were simulated by the model and verified by experiments. Subsequently, microhardness and wear tests were carried out to investigate the improved mechanical properties of IN718 coatings reinforced by WC particles. The results show that the cellular alloy reaction layer, IN718 grains, and eutectic structure are formed, in turn, around WC particles. The convection in the laser molten pool can induce double-tail-like or spindle-like WC convection diffusion bands. The hardness of these bands is higher than that of the IN718 matrix. More importantly, WC convection diffusion bands can inhibit the growth of columnar crystals, because the dissolved WC can decrease the freezing temperature of the melt. Finally, mechanical property tests show that WC particles increase the hardness of the coating and significantly improve its wear resistance.
Correction: Panjan et al. Surface Topography of PVD Hard Coatings. Coatings 2021, 11, 1387
Authors: Panjan Peter , Drnovšek Aljaž , Mahne Nastja , Čekada Miha , Panjan Matjaž
Published: February 2023

The authors wish to make the following correction to this paper [...]
Picosecond Ultrasonics for Studying Elastic Modulus of Polycrystalline Chromium Nanofilms: Thickness Dependence and Stiffness Enhancement
Authors: Tu Xinhao , Li Jun , Yan Jinyu , Wang Shibin , Li Linan , Li Chuanwei , Wang Zhiyong
Published: February 2023

Accurate measurement of elastic constants in thin films is still an important issue to understand the scale behavior of nanosized materials. In the present study, we introduced an advanced non-destructive method, picosecond ultrasonics (PU), for measuring the out-of-plane elastic modulus of thin chromium (Cr) films. The femtosecond light pulse is focused on the Cr film to excite the longitudinal acoustic phonons (LAP), which propagate along the thickness direction and repeat reflections inside the Cr film. Then, the propagation/distribution of LAP is detected by the time-delayed probe light pulse through the photoelastic effect. Therefore, we can determine the out-of-plane modulus by measuring the periodic pulse echoes or the breathing mode vibrations within the Cr film. For most Cr films, the determined modulus is smaller than the corresponding bulk value and decreases with the decreasing thickness, while for some Cr films, it closes and may exceed the bulk value. This work describes the thickness-dependent elasticity of thin Cr films and provides evidence of the stiffness enhancement in Cr films on the Si substrate. In addition, since LAP with central frequency up to 310 GHz is excited in Cr films on the SiO2 substrate, we also demonstrate the potential of Cr films as high-frequency photoacoustic transducers.
Towards Extended Gate Field Effect Transistor-Based Radiation Sensors: Impact of Thicknesses and Radiation Doses on Al-Doped Zinc Oxide Sensitivity
Authors: Ahmed Ali Amal Mohamed , Ahmed Naser M. , Kabir Norlaili A. , Algadri Natheer A. , AL-Diabat Ahmad M. , Wadi I. A. , Alsadig Ahmed , Aldaghri Osamah A. , Ibnaouf Khalid H.
Published: February 2023

Radiation measurements are critical in radioanalytical, nuclear chemistry, and biomedical physics. Continuous advancement in developing economical, sensitive, and compact devices designed to detect and measure radiation has increased its capability in many applications. In this work, we presented and investigated the performance of a cost-effective X-ray radiation detector based on the extended gate field effect transistors (EGFET). We examined the sensitivity of Al-doped Zinc oxide (AZO) of varying thicknesses, fabricated by chemical bath deposition (CBD), following X-ray irradiation with low and high doses. EGFETs were used to connect samples for their detection capabilities. As a function of the absorbed dose, the response was analyzed based on the threshold voltage shift, and the sensitivity of each device was also evaluated. We demonstrated that thin films are less sensitive to radiation than their disk-type EG devices. However, performance aspects of the devices, such as radiation exposure sensitivity and active dosage region, were found to be significantly reliant on the composition and thickness of the materials used. These structures may be a cost-effective alternative for real-time, room-temperature radiation detectors.
Genesis of the Body Color of Brazilian Gem-Quality Yellow-Green Opal
Authors: Lv Huiyu , Guo Ying
Published: February 2023

The chromaticity characterization and influencing factors of the body color of 28 yellow-green opals were studied through Fourier transform infrared spectrum (FTIR), energy-dispersive X-ray fluorescence spectroscopy (ED-XRF), ultraviolet-visible spectroscopy (UV-vis) and X-Rite SP62 spectrophotometer. It was found that the hue h° and the iron content of the yellow-green opal were highly negatively correlated, the chroma C* and the sum of nickel and iron content were significantly positively correlated, and the lightness L* was significantly negatively correlated with the chromium content. The body color of light green opal is mainly caused by nickel. The iron content will make the color yellow, and the chromium content will darken the green. With the increase of Fe content, the lowest absorption value of UV-vis absorption at 500–650 nm moves towards the long wave direction, and the hue h° decreases. With the increase of the total content of Ni and Fe, the 650 nm absorption peak area and the chroma C* increase. Under CIE D65 standard light source and N9.5 Munsell neutral background, using K-Means clustering method, 28 yellow-green opal body colors were divided into three grades: (1) Fancy Intense, (2) Fancy Deep and (3) Fancy.
Synthesis, Structure, and Luminescence of a Molecular Europium Tetracyanoplatinate Incorporating 4,5-Diazafluoren-9-One
Authors: Sykora Richard E.
Published: February 2023

Eu2(Pt(CN)4)3(H2O)12]·4C11H6N2O·6H2O represents a new member to the lanthanide tetracyanoplatinate family of materials. The synthesis, single-crystal X-ray structure, and photoluminescence properties of this compound are reported herein. The compound contains dimeric [Eu2(Pt(CN)4)3(H2O)12] molecules in which the Eu3+ sites are connected with trans-bridging [Pt(CN)4] 2− anions and co-crystallized along with 4,5-diazafluoren-9-one (dafone) and lattice water. Prominent noncovalent interactions that stabilize the structure include H-bonding as well as short stacking interactions involving the planar tetracyanoplatinate anions in conjunction with the planar dafone molecules. Photoluminescence measurements illustrate that Eu2(Pt(CN)4)3(H2O)12]·4C11H6N2O·6H2O contains multiple emitting states and also displays enhanced Eu3+-based emission due to energy transfer within the compound.
Mechanism of Plastic Deformation of As-Extruded AZ31 Mg Alloy during Uniaxial Compression
Authors: Fang Xiaoyan , Zhou Chen , Lin Jinbao , Li Wenwen
Published: February 2023

The deformation mechanism and texture evolution of AZ31 Mg alloy compressed in three different directions at room temperature were studied, and the relationship between the two was compared through experiments and viscoplastic self-consistent (VPSC) modeling. Setting up only one specific deformation mode was the predominant mechanism by changing the CRSS ratio for the different deformation modes. The following conclusions were drawn: (1) It was demonstrated that basal slip causes a slow and continuous deflection of the grain toward the transverse direction (TD). When the sample is compressed in the extruded direction (ED), prismatic slip leads to grains being deflected toward the ED in the initial stages of compression, and when the sample is compressed 45° to the extrusion direction (45ED) and perpendicular to the extrusion direction (PED), prismatic  slip contributes little to the texture evolution. (2) When the sample is compressed along three different directions, pyramidal  slip leads to the grain being deflected toward the normal direction (ND), and the {10-12} extension twin deflects the grain at a large angle. (3) When only the {10-11} compression twin is activated, the grain will be deflected in the ND while the sample is compressed along the ED and 45ED, but when the sample is compressed in the PED, the grains are concentrated from both sides of the ED to the center.
Underlying Features of Prostate Cancer—Statistics, Risk Factors, and Emerging Methods for Its Diagnosis
Authors: Berenguer Cristina V. , Pereira Ferdinando , Câmara José S. , Pereira Jorge A. M.
Published: February 2023

Prostate cancer (PCa) is the most frequently occurring type of malignant tumor and a leading cause of oncological death in men. PCa is very heterogeneous in terms of grade, phenotypes, and genetics, displaying complex features. This tumor often has indolent growth, not compromising the patient’s quality of life, while its more aggressive forms can manifest rapid growth with progression to adjacent organs and spread to lymph nodes and bones. Nevertheless, the overtreatment of PCa patients leads to important physical, mental, and economic burdens, which can be avoided with careful monitoring. Early detection, even in the cases of locally advanced and metastatic tumors, provides a higher chance of cure, and patients can thus go through less aggressive treatments with fewer side effects. Furthermore, it is important to offer knowledge about how modifiable risk factors can be an effective method for reducing cancer risk. Innovations in PCa diagnostics and therapy are still required to overcome some of the limitations of the current screening techniques, in terms of specificity and sensitivity. In this context, this review provides a brief overview of PCa statistics, reporting its incidence and mortality rates worldwide, risk factors, and emerging screening strategies.
Relapsed/Refractory Multiple Myeloma: A Review of Available Therapies and Clinical Scenarios Encountered in Myeloma Relapse
Authors: Bhatt Parva , Kloock Colin , Comenzo Raymond
Published: February 2023

Multiple myeloma remains an incurable disease with the usual disease course requiring induction therapy, autologous stem cell transplantation for eligible patients, and long-term maintenance. Risk stratification tools and cytogenetic alterations help inform individualized therapeutic choices for patients in hopes of achieving long-term remissions with preserved quality of life. Unfortunately, relapses occur at different stages of the course of the disease owing to the biological heterogeneity of the disease. Addressing relapse can be complex and challenging as there are both therapy- and patient-related factors to consider. In this broad scoping review of available therapies in relapsed/refractory multiple myeloma (RRMM), we cover the pharmacologic mechanisms underlying active therapies such as immunomodulatory agents (IMiDs), proteasome inhibitors (PIs), monoclonal antibodies (mAbs), traditional chemotherapy, and Venetoclax. We then review the clinical data supporting the use of these therapies, organized based on drug resistance/refractoriness, and the role of autologous stem cell transplant (ASCT). Approaches to special situations during relapse such as renal impairment and extramedullary disease are also covered. Lastly, we look towards the future by briefly reviewing the clinical data supporting the use of chimeric antigen receptor (CAR-T) therapy, bispecific T cell engagers (BITE), and Cereblon E3 Ligase Modulators (CELMoDs).
Exploring Texture Diversity of Beech-Spruce-Fir Stands through Development Phase Analysis in the Frakto Virgin Forest of Greece
Authors: Papadopoulou Dimitra , Raptis Dimitrios , Kazana Vassiliki , Tsitsoni Thekla
Published: February 2023

The structural diversity of old-growth forests is a fundamental element as regards ecosystem stability and functionality. The current study aims at exploring the texture diversity in the unique virgin Frakto forest of Greece through the determination of the forest development phases and their related stages. Eight sample plots of 0.25 hectares each were randomly distributed to serve the field research needs. During the single phases, a significant number of variables at the stand level, such as the total dead and living timber volume and the density expressed as the number of stems per hectare and basal area, were calculated, and their values were merged into three main stages. The Frakto virgin forest was found to be dominated by the optimal stage (61.7%), followed by the decay stage (22.7%), and the initial stage (15.6%), in alignment with relevant distributions reported for other European virgin forests. Statistically significant differences in terms of stem density and woody volume between the stages demonstrated increased structural diversity and heterogeneity, a typical characteristic of primeval forests. The results offer an insight into forest growth dynamics under natural processes, thus providing a knowledge base for the promotion of sustainable forest management.
Elemental Variability in Stems of Pinus sylvestris L.: Whether a Single Core Can Represent All the Stem
Authors: Gavrikov Vladimir L. , Fertikov Alexey I. , Vidus Vladimir E. , Sharafutdinov Ruslan A. , Vaganov Eugene A.
Published: February 2023

In dendrochemical studies, an analysis of wooden cores extracted from tree stems is a widely spread research method. A methodological problem is related to the method: whether a single core per tree can be representative of all the stems. The study aimed to estimate how the elemental contents are variable in the tree rings and whether the contents correlate among different cores. The cores were extracted from stems of Pinus sylvestris L., four cores per tree, according to the cardinal directions (east, west, north, south). The elemental contents (Al, Si, P, S, Cl, K, Ca, Ti, Mn, Fe, Cu, Zn, and Sr) were estimated through the X-ray fluorescent analysis facility Itrax Multiscanner (COX Analytical Systems). The agreement between the temporal series of different cores was estimated through Spearman’s correlation coefficients. It has been found that the temporal intra-tree series for almost all the studied elements do not correlate with each other. The exclusions are Ca and K which show strong consistent correlations. The source for the correlations is, however, the long-term trends from the pith to the bark. The detrending of the Ca and K series reveals some high-frequency correlations which are not consistent among the trees. At least for Scots pine, a single core is unlikely to be a reliable representative of the whole stem in the respect of elemental content. A few research directions to overcome the uncertainty has been discussed.
A Modified Approach in Lip Repositioning Surgery for Excessive Gingival Display to Minimize Post-Surgical Relapse: A Randomized Controlled Clinical Trial
Authors: AlJasser Reham N.
Published: February 2023

Lip repositioning surgeries are performed to treat patients with excessive GD (EGD). This study aimed to explore and compare the long-term clinical results and stability following the modified lip repositioning surgical technique (MLRS) with the addition of periosteal sutures compared to the conventional lip repositioning surgery (LipStaT®) in order to address EGD. A controlled clinical trial with female participants (n = 200) intended to improve their gummy smile were divided into control (n = 100) and test (n = 100) groups. The gingival display (GD), maxillary lip length at rest (MLLR), and maxillary lip length at maximum smile (MLLS) were measured at four time intervals (Baseline; 1 Month; 6 Months, and 1 Year) in millimeters (mm). Data were analyzed by t-tests, Bonferroni-test, and regression analysis using SPSS software. At the one-year follow-up, GD for the control and test groups were 3.77 + 1.76 mm and 2.48 + 0.86 mm, respectively, and their comparisons showed that GD was considerably lower (p = 0.000) in the test group compared to the control group. The MLLS measurements taken at baseline, one-month, six-month, and one-year follow-up showed no significant differences (p > 0.05) between the control and test groups. At baseline, one-month, and six-month follow-up, the mean and standard deviation for the MLLR were almost similar, with no statistically significant difference (p = 0.675). The MLRS is a successful and viable treatment option for the treatment of patients with EGD. The current study showed stable results and no recurrence with MLRS until the one-year follow-up compared to LipStaT®. With the MLRS, a 2 to 3 mm decline in EGD is usually to be expected.
A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models
Authors: Sivari Esra , Bostanci Erkan , Guzel Mehmet Serdar , Acici Koray , Asuroglu Tunc , Ercelebi Ayyildiz Tulin
Published: February 2023

Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been proposed for detecting and classifying gastrointestinal system findings, aiming at early diagnosis with high accuracy and sensitive measurements and saving workload to help the specialist and objectivity in endoscopic diagnosis. In the first level of the proposed bi-level stacking ensemble approach, predictions are obtained by applying 5-fold cross-validation to three new CNN models. A machine learning classifier selected at the second level is trained according to the obtained predictions, and the final classification result is reached. The performances of the stacking models were compared with the performances of the deep learning models, and McNemar’s statistical test was applied to support the results. According to the experimental results, stacking ensemble models performed with a significant difference with 98.42% ACC and 98.19% MCC in the KvasirV2 dataset and 98.53% ACC and 98.39% MCC in the HyperKvasir dataset. This study is the first to offer a new learning-oriented approach that efficiently evaluates CNN features and provides objective and reliable results with statistical testing compared to state-of-the-art studies on the subject. The proposed approach improves the performance of deep learning models and outperforms the state-of-the-art studies in the literature.
Expanding the Molecular Disturbances of Lipoproteins in Cardiometabolic Diseases: Lessons from Lipidomics
Authors: Kostara Christina E.
Published: February 2023

The increasing global burden of cardiometabolic diseases highlights the urgent clinical need for better personalized prediction and intervention strategies. Early diagnosis and prevention could greatly reduce the enormous socio-economic burden posed by these states. Plasma lipids including total cholesterol, triglycerides, HDL-C, and LDL-C have been at the center stage of the prediction and prevention strategies for cardiovascular disease; however, the bulk of cardiovascular disease events cannot be explained sufficiently by these lipid parameters. The shift from traditional serum lipid measurements that are poorly descriptive of the total serum lipidomic profile to comprehensive lipid profiling is an urgent need, since a wealth of metabolic information is currently underutilized in the clinical setting. The tremendous advances in the field of lipidomics in the last two decades has facilitated the research efforts to unravel the lipid dysregulation in cardiometabolic diseases, enabling the understanding of the underlying pathophysiological mechanisms and identification of predictive biomarkers beyond traditional lipids. This review presents an overview of the application of lipidomics in the study of serum lipoproteins in cardiometabolic diseases. Integrating the emerging multiomics with lipidomics holds great potential in moving toward this goal.
Machine-Learning-Based Diabetes Mellitus Risk Prediction Using Multi-Layer Neural Network No-Prop Algorithm
Authors: Sonia J. Jeba , Jayachandran Prassanna , Md Abdul Quadir , Mohan Senthilkumar , Sivaraman Arun Kumar , Tee Kong Fah
Published: February 2023

Over the past few decades, the prevalence of chronic illnesses in humans associated with high blood sugar has dramatically increased. Such a disease is referred to medically as diabetes mellitus. Diabetes mellitus can be categorized into three types, namely types 1, 2, and 3. When beta cells do not secrete enough insulin, type 1 diabetes develops. When beta cells create insulin, but the body is unable to use it, type 2 diabetes results. The last category is called gestational diabetes or type 3. This happens during the trimesters of pregnancy in women. Gestational diabetes, however, disappears automatically after childbirth or may continue to develop into type 2 diabetes. To improve their treatment strategies and facilitate healthcare, an automated information system to diagnose diabetes mellitus is required. In this context, this paper presents a novel system of classification of the three types of diabetes mellitus using a multi-layer neural network no-prop algorithm. The algorithm uses two major phases in the information system: the training phase and the testing phase. In each phase, the relevant attributes are identified using the attribute-selection process, and the neural network is trained individually in a multi-layer manner, starting with normal and type 1 diabetes, then normal and type 2 diabetes, and finally healthy and gestational diabetes. Classification is made more effective by the architecture of the multi-layer neural network. To provide experimental analysis and performances of diabetes diagnoses in terms of sensitivity, specificity, and accuracy, a confusion matrix is developed. The maximum specificity and sensitivity values of 0.95 and 0.97 are attained by this suggested multi-layer neural network. With an accuracy score of 97% for the categorization of diabetes mellitus, this proposed model outperforms other models, demonstrating that it is a workable and efficient approach.
Integrated Assessment of Heart, Lung and Lower Extremity Veins Using Hand-Held Ultrasound Device in COVID-19 Patients: Feasibility and Clinical Application
Authors: Filipiak-Strzecka Dominika , Kasprzak Jarosław D. , Lipiec Piotr
Published: February 2023

The emergence of the COVID-19 pandemic caused a significant shortage of medical personnel and the prioritization of life-saving procedures on internal medicine and cardiology wards. Thus, the cost- and time-effectiveness of each procedure proved vital. Implementing elements of imaging diagnostics into the physical examination of COVID-19 patients could prove beneficial to the treatment process, providing important clinical data at the moment of admission. Sixty-three patients with positive COVID-19 test results were enrolled into our study and underwent physical examination expanded with a handheld ultrasound device (HUD)—performed bedside assessment included: right ventricle measurement, visual and automated LVEF assessment, four-point compression ultrasound test (CUS) of lower extremities and lung ultrasound. Routine testing consisting of computed-tomography chest scanning, CT—pulmonary angiogram and full echocardiography performed on a high-end stationary device was completed in the following 24 h. Lung abnormalities characteristic for COVID-19 were detected in CT in 53 (84%) patients. The sensitivity and specificity of bedside HUD examination for detecting lung pathologies was 0.92 and 0.90, respectively. Increased number of B-lines had a sensitivity of 0.81, specificity 0.83 for the ground glass symptom in CT examination (AUC 0.82; p < 0.0001); pleural thickening sensitivity 0.95, specificity 0.88 (AUC 0.91, p < 0.0001); lung consolidations sensitivity 0.71, specificity 0.86 (AUC 0.79, p < 0.0001). In 20 patients (32%), pulmonary embolism was confirmed. RV was dilated in HUD examination in 27 patients (43%), CUS was positive in two patients. During HUD examination, software-derived LV function analysis failed to measure LVEF in 29 (46%) cases. HUD proved its potential as the first-line modality for the collection of heart–lung–vein imaging information among patients with severe COVID-19. HUD-derived diagnosis was especially effective for the initial assessment of lung involvement. Expectedly, in this group of patients with high prevalence of severe pneumonia, HUD-diagnosed RV enlargement had moderate predictive value and the option to simultaneously detect lower limb venous thrombosis was clinically attractive. Although most of the LV images were suitable for the visual assessment of LVEF, an AI-enhanced software algorithm failed in almost 50% of the study population.
Molecular Re-Diagnosis with Whole-Exome Sequencing Increases the Diagnostic Yield in Patients with Non-Syndromic Retinitis Pigmentosa
Authors: Wawrocka Anna , Socha Magdalena , Walczak-Sztulpa Joanna , Koczyk Grzegorz , Skorczyk-Werner Anna , Krawczyński Maciej R.
Published: February 2023

Retinitis pigmentosa (RP) is a clinically and genetically heterogeneous group of disorders with progressive loss of photoreceptor and pigment epithelial function. Nineteen unrelated Polish probands clinically diagnosed with nonsyndromic RP were recruited to this study. We used whole-exome sequencing (WES) to identify potential pathogenic gene variants in molecularly undiagnosed RP patients, as a molecular re-diagnosis after having performed targeted NGS in the past. Targeted NGS allowed for identification of the molecular background in only 5 out of 19 patients. Fourteen patients who remained unsolved despite the targeted NGS were subjected to WES. WES revealed potentially causative variants in RP-related genes in another 12 patients. Together, NGS methods revealed the coexistence of causal variants affecting distinct RP genes in 17 out of 19 RP families, with a very high efficiency of 89%. With the improvement of NGS methods, including higher sequencing depth, broader target enrichment, and better bioinformatic analysis capabilities, the ratio of identified causal gene variants has significantly increased. Therefore, it is important to consider repeating high-throughput sequencing analysis in those patients in whom the previously performed NGS did not reveal any pathogenic variants. The study confirmed the efficiency and clinical utility of re-diagnosis with WES in molecularly undiagnosed RP patients.
The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease—The Future Is Now
Authors: Diaconu Claudia , State Monica , Birligea Mihaela , Ifrim Madalina , Bajdechi Georgiana , Georgescu Teodora , Mateescu Bogdan , Voiosu Theodor
Published: February 2023

Crohn’s disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.
The Prospects of Using Structural Phase Analysis of Microcalcifications in Breast Cancer Diagnostics
Authors: Piddubnyi Artem , Kolomiiets Olena , Danilchenko Sergey , Stepanenko Andriy , Moskalenko Yuliia , Moskalenko Roman
Published: February 2023

The detection of microcalcifications in the breast by mammography is of great importance for the early diagnostics of breast cancer. This study aimed to establish the basic morphological and crystal-chemical properties of microscopic calcifications and their impact on breast cancer tissue. During the retrospective study, 55 out of 469 breast cancer samples had microcalcifications. The expression of the estrogen and progesterone receptors and Her2-neu showed no significant difference from the non-calcified samples. An in-depth study of 60 tumor samples revealed a higher expression of osteopontin in the calcified breast cancer samples (p ˂ 0.01). The mineral deposits had a hydroxyapatite composition. Within the group of calcified breast cancer samples, we detected six cases of colocalization of oxalate microcalcifications together with biominerals of the usual “hydroxyapatite” phase composition. The simultaneous presence of calcium oxalate and hydroxyapatite was accompanied by a different spatial localization of microcalcifications. Thus, the phase compositions of microcalcifications could not be used as criteria for the differential diagnostics of breast tumors.
Dark Energy as a Natural Property of Cosmic Polytropes—A Tutorial
Authors: Kleidis Kostas , Spyrou Nikolaos K.
Published: February 2023

A conventional approach to the dark energy (DE) concept is reviewed and discussed. According to it, there is absolutely no need for a novel DE component in the universe, provided that its matter–energy content is represented by a perfect fluid whose volume elements perform polytropic flows. When the (thermodynamic) energy of the associated internal motions is taken into account as an additional source of the universal gravitational field, it compensates the DE needed to compromise spatial flatness in an accelerating universe. The unified model which is driven by a polytropic fluid not only interprets the observations associated with universe expansion but successfully confronts all the current issues of cosmological significance, thus arising as a viable alternative to the ΛCDM model.
Global Existence and Fixed-Time Synchronization of a Hyperchaotic Financial System Governed by Semi-Linear Parabolic Partial Differential Equations Equipped with the Homogeneous Neumann Boundary Condition
Authors: Wang Chengqiang , Zhao Xiangqing , Zhang Yulin , Lv Zhiwei
Published: February 2023

Chaotic nonlinear dynamical systems, in which the generated time series exhibit high entropy values, have been extensively used and play essential roles in tracking accurately the complex fluctuations of the real-world financial markets. We are concerned with a system of semi-linear parabolic partial differential equations supplemented by the homogeneous Neumann boundary condition, which governs a financial system comprising the labor force, the stock, the money, and the production sub-blocks distributed in a certain line segment or planar region. The system derived by removing the terms involved with partial derivatives with respect to space variables from our concerned system was demonstrated to be hyperchaotic. We firstly prove, via Galerkin’s method and establishing a priori inequalities, that the initial-boundary value problem for the concerned partial differential equations is globally well posed in Hadamard’s sense. Secondly, we design controls for the response system to our concerned financial system, prove under some additional conditions that our concerned system and its controlled response system achieve drive-response fixed-time synchronization, and provide an estimate on the settling time. Several modified energy functionals (i.e., Lyapunov functionals) are constructed to demonstrate the global well-posedness and the fixed-time synchronizability. Finally, we perform several numerical simulations to validate our synchronization theoretical results.
Exploring Industry-Distress Effects on Loan Recovery: A Double Machine Learning Approach for Quantiles
Authors: Chuang Hui-Ching , Chen Jau-er
Published: February 2023

In this study, we explore the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR). The UQR provides better interpretative and thus policy-relevant information on the predictive effect of the target variable than the conditional quantile regression. To deal with a broad set of macroeconomic and industry variables, we use the lasso-based double selection to estimate the predictive effects of industry distress and select relevant variables. Our sample consists of 5334 debt and loan instruments in Moody’s Default and Recovery Database from 1990 to 2017. The results show that industry distress decreases recovery rates from 15.80% to 2.94% for the 15th to 55th percentile range and slightly increases the recovery rates in the lower and the upper tails. The UQR provide quantitative measurements to the loss given default during a downturn that the Basel Capital Accord requires.
Governance as an Interplay between Corruption and Polity: Conceptualizing from a National Perspective
Authors: Al-Faryan Mamdouh Abdulaziz Saleh , Shil Nikhil Chandra
Published: February 2023

This study examines the notion of governance while corruption and polity act in a negotiated approach. It adopts a theory synthesis approach to design the research paradigm and brings renewed attention to governance from a national perspective. This study argues that corruption and polity collectively define the state of governance in a particular country, which might offer some new insights to the remaining parts of the world. The principal aim of the study is to bring relevant evidence from the literature to develop a solid foundation on governance from a macro perspective. Deploying a qualitative approach, this study highlights available literature on corruption, polity, and their connections to define the state of governance. From this specific target, we have initiated this study deploying a conceptual fashion in exploring governance which is shaped by the interplay between two loosely connected themes: polity and corruption. The outcome of this synthesis is to renew our understanding on governance to strengthen the governance mechanism whereby corruption could be checked through sound polity in action. The arguments presented in the paper are expected to be useful for regulators and policymakers as they prepare governance-related rules, acts, or directives in their respective countries.
Present and Future Undergraduate Students’ Well-Being: Role of Time Perspective, Self-Efficacy, Self-Regulation and Intention to Drop-Out
Authors: Mascia Maria Lidia , Agus Mirian , Cabras Cristina , Bellini Diego , Renati Roberta , Penna Maria Pietronilla
Published: February 2023

Well-being is a multidimensional construct that affects various areas of a person’s life. In the university context, a student’s well-being can influence not only their academic and professional success but also the future development of society. This study aimed to evaluate how the interactions of time perspective (assessed by the Stanford Time Perspective Inventory—Short Form), self-efficacy (assessed by the General Self-efficacy Scale), self-regulation (assessed by the Self-regulated Knowledge Scale—University), and drop-out intention (assessed by the Intention to Drop-Out Scale) affect students’ perceptions of current and future well-being (assessed by the I COPPE Scale). Using a cross-sectional design, 192 students attending the University of Cagliari (Italy) were evaluated. A partial least squares structural equation modelling (PLS-SEM) analysis was performed to examine the relationships among well-being and all the variables examined herein. The empirical findings highlighted the direct and indirect effects of the studied variables on students’ current and future well-being.
Requirements of Modern Russian Agricultural Production for Digital Competencies of an Agricultural Specialist
Authors: Khudyakova Elena , Shitikova Alexandra , Stepantsevich Marina Nikolaevna , Grecheneva Anastasia
Published: February 2023

The purpose of the study is to determine the needs of modern Russian agriculture for specialists with certain, most-in-demand, digital competencies. The research methodology is based on the application of the expert assessments method, the method of random statistical selection of experts, and the scientific generalization method. The field of the research is modern digital technologies in agriculture, as well as the corresponding competencies of Russian agricultural university graduates. The study period is 2021–2022. Having acquired competencies of a modern agricultural specialist at the university should help graduates to integrate into the production process as quickly as possible, the latter undergoing qualitative changes due to the transition to a new technological order based on the use of digital technologies. The study of the current curricula of Russian agricultural universities has shown their lack of adequacy regarding the modern requirements of agricultural production. It indicates the need to examine the curricula. Taking into account the fact that digital technologies are rapidly improving and being introduced into agricultural production, further research in this area should be conducted annually in order to increase the compliance of training at universities according to modern requirements of agricultural production.
Subjective Assessments and Serum Cortisol Levels as Risk Factors of Pain Persistence in the Late Postoperative Period in Old and Oldest-Old Patients
Authors: Telegan Vladyslav O. , Tsagkaris Christos , Singh Sandeep Kumar , Tarasenko Kostiantyn V.
Published: February 2023

Postoperative pain is one of the most common postoperative complications, resulting in significant burdens and adverse outcomes among patients, notably the frailest ones. Predicting the likelihood of intense postoperative pain can help optimize a patient’s recovery. The aims of this study were to build a prognostic model of pain persistence in elderly and senile patients in the late postoperative period, based on clinical and laboratory parameters of the early postoperative period, and to evaluate the potential for the model’s application. The study included 201 male and female patients who had undergone surgery of any type from September 2019 to August 2020. The patients were divided into three groups: senile patients, elderly patients, and young patients (control group). All of the examined patients were measured for fasting cortisol on the mornings of the first and seventh days following surgery. A statistically significant increase in the probability of pain persistence in the late postoperative period with the increasing age of the patient was found. Age, pain severity scores on the seventh day after surgery, and cortisol concentration in the blood on the first day after surgery, are of prognostic value for the risk of persistence of postoperative-pain syndrome.
A Polynomial Multiplication Accelerator for Faster Lattice Cipher Algorithm in Security Chip
Authors: Xu Changbao , Yu Hongzhou , Xi Wei , Zhu Jianyang , Chen Chen , Jiang Xiaowen
Published: February 2023

Polynomial multiplication is the most computationally expensive part of the lattice-based cryptography algorithm. However, the existing acceleration schemes have problems, such as low performance and high hardware resource overhead. Based on the polynomial multiplication of number theoretic transformation (NTT), this paper proposed a simple element of Montgomery module reduction with pipeline structure to realize fast module multiplication. In order to improve the throughput of the NTT module, the block storage technology is used in the NTT hardware module to enable the computing unit to read and write data alternately. Based on the NTT hardware module, a precalculated parameter storage and real-time calculation method suitable for the hardware architecture of this paper is also proposed. Finally, the hardware of polynomial multiplier based on NTT module is implemented, and its function simulation and performance evaluation are carried out. The results show that the proposed hardware accelerator can have excellent computing performance while using fewer hardware resources, thus meeting the requirements of lattice cipher algorithms in security chips. Compared with the existing studies, the computing performance of the polynomial multiplier designed in this paper is improved by approximately 1 to 3 times, and the slice resources and storage resources used are reduced by approximately 60% and 17%, respectively.
Big Data Analytics Using Artificial Intelligence
Authors: Gandomi Amir H. , Chen Fang , Abualigah Laith
Published: February 2023

Data analytics using artificial intelligence is the process of leveraging advanced AI techniques to extract insights and knowledge from large and complex datasets [...]
Joint Spectrum and Power Allocation with User Association for 5G/Wi-Fi Coexisting Millimeter Wave Networks
Authors: Xie Yuxuan , Liu Danpu , Yin Fangfang
Published: February 2023

With the exponential growth of mobile data traffic, the deployment of a large number of devices in the hot-spot gathering scenario has brought great challenges to the current wireless communication network. Considering that the user service latency of unidirectional data offloading scheme is still unacceptable when 5G and Wi-Fi coexist on the unlicensed 60 GHz band, we investigate a bidirectional data offloading scheme with resource allocation in this study. More specifically, aggregation nodes (ANs) are deployed in the coverage of Wi-Fi AP to receive multi-user data in parallel in order to reduce the collision probability of transmitted packets. Then, we formulate an optimization problem aiming to maximize the sum rate through spectrum and power allocation as well as user association. The problem is then decomposed into three sub-problems and solved successively, where RSSI (received signal strength indicator) as the standard determines the user association, while the algorithms of multi-stage matching and successive convex approximation are used for solving spectrum allocation and power allocation, respectively. Simulation results demonstrate that the proposed algorithm can effectively increase the total capacity of the uplink coexisting networks.
Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification
Authors: Usmani Irfan Ahmed , Qadri Muhammad Tahir , Zia Razia , Alrayes Fatma S. , Saidani Oumaima , Dashtipour Kia
Published: February 2023

For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their joint influence. In general, most of the existing research could not achieve the desired performance because the work addressed only one hyperparameter tuning. This study adopted a Cartesian product matrix-based approach, to interpret the effect of both hyperparameters and their interaction on the performance of models. To evaluate their impact, 56 two-tuple hyperparameters from the Cartesian product matrix were used as inputs to perform an extensive exercise, comprising 504 simulations for three cutting-edge architecture-based pre-trained Deep Learning (DL) models, ResNet18, ResNet50, and ResNet101. Additionally, the impact was also assessed by using three well-known optimizers (solvers): SGDM, Adam, and RMSProp. The performance assessment showed that the framework is an efficient framework to attain optimal values of two important hyperparameters (LR and BS) and consequently an optimized model with an accuracy of 99.56%. Further, our results showed that both hyperparameters have a significant impact individually as well as interactively, with a trade-off in between. Further, the evaluation space was extended by using the statistical ANOVA analysis to validate the main findings. F-test returned with p < 0.05, confirming that both hyperparameters not only have a significant impact on the model performance independently, but that there exists an interaction between the hyperparameters for a combination of their levels.
How to Prevent Drivers before Their Sleepiness Using Deep Learning-Based Approach
Authors: Akrout Belhassen , Fakhfakh Sana
Published: February 2023

Drowsy driving causes many accidents. Driver alertness and automobile control are challenged. Thus, a driver drowsiness detection system is becoming a necessity. In fact, invasive approaches that analyze electroencephalography signals with head electrodes are inconvenient for drivers. Other non-invasive fatigue detection studies focus on yawning or eye blinks. The analysis of several facial components has yielded promising results, but it is not yet enough to predict hypovigilance. In this paper, we propose a “non-invasive” approach based on a deep learning model to classify vigilance into five states. The first step is using MediaPipe Face Mesh to identify the target areas. This step calculates the driver’s gaze and eye state descriptors and the 3D head position. The detection of the iris area of interest allows us to compute a normalized image to identify the state of the eyes relative to the eyelids. A transfer learning step by the MobileNetV3 model is performed on the normalized images to extract more descriptors from the driver’s eyes. Our LSTM network entries are vectors of the previously calculated features. Indeed, this type of learning allows us to determine the state of hypovigilance before it arrives by considering the previous learning steps, classifying the levels of vigilance into five categories, and alerting the driver before the state of hypovigilance’s arrival. Our experimental study shows a 98.4% satisfaction rate compared to the literature. In fact, our experimentation begins with the hyperparameter preselection to improve our results.
Performance Improvement of DTC-SVM of PMSM with Compensation for the Dead Time Effect and Power Switch Loss Based on Extended Kalman Filter
Authors: Son Doo-Il , Han Jun-Seo , Park Je-Suk , Lim Hee-Sun , Lee Geun-Ho
Published: February 2023

Two algorithms have been extensively studied for motor control: Field Oriented Control (FOC) and Direct Torque Control (DTC). Both control algorithms use a Voltage Source Inverter (VSI) to drive a Permanent Magnet Synchronous Motor (PMSM). To prevent short-arm short-circuit accidents when driving PMSM using VSI, a dead time is used to turn off the TOP and BOTTOM switches of each arm at the same time. However, this dead-time technique causes an unexpected pole voltage to be applied to the PMSM on the VSI output voltage, causing distortion and resulting in control nonlinearity. The disturbance voltage that causes nonlinearity is difficult to measure directly with the sensor. Therefore, this paper analyzes the nonlinearity of the controller due to the distorted voltage caused by the dead time during PMSM operation using the DTC algorithm and predicts the distorted output voltage using the extended Kalman Filter (EKF) to improve control stability. As a result, The algorithm proposed in this paper has verified the improvement of torque ripple and stator flux ripple through experiments and simulations.