bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia
Authors: Kang Da Hyun , Kim Grace Hyun J. , Park Sa-Beom , Lee Song-I , Koh Jeong Suk , Brown Matthew S. , Abtin Fereidoun , McNitt-Gray Michael F. , Goldin Jonathan G. , Lee Jeong Seok
Published: January 2024

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.
Effects of Resistance Training on Spasticity in People with Stroke: A Systematic Review
Authors: Chacon-Barba Juan Carlos , Moral-Munoz Jose A. , De Miguel-Rubio Amaranta , Lucena-Anton David
Published: January 2024

Resistance training induces neuromuscular adaptations and its impact on spasticity remains inadequately researched. This systematic review (PROSPERO: CRD42022322164) aimed to analyze the effects of resistance training, compared with no treatment, conventional therapy, or other therapies, in people with stroke-related spasticity. A comprehensive search was conducted up to October 2023 in PubMed, PEDro, Cochrane, Web of Science, and Scopus databases. Selection criteria were randomized controlled trials involving participants with stroke-related spasticity intervened with resistance training. The PEDro scale was used to evaluate the methodological quality. From a total of 274 articles, 23 full-text articles were assessed for eligibility and nine articles were included in the systematic review, involving 225 participants (155 males, 70 females; mean age: 59.4 years). Benefits were found to spasticity after resistance training. Furthermore, studies measuring spasticity also reported benefits to function, strength, gait, and balance. In conclusion, resistance training was superior to, or at least equal to, conventional therapy, other therapies, or no intervention for improving spasticity, as well as function, strength, gait, and balance. However, the results should be taken with caution because of the heterogeneity of the protocols used. Further research is needed to explore the effects of resistance training programs on people with stroke.
The Association between Working Memory and Divergent Thinking: The Moderating Role of Formal Musical Background
Authors: Pino Maria Chiara , Giancola Marco , Palmiero Massimiliano , D’Amico Simonetta
Published: January 2024

Divergent thinking (DT) is widely considered an essential cognitive dimension of creativity, which involves goal-oriented processes, including working memory (WM), which allows for retrieving and loading of information into the attentional stream and, consequently, enhancing divergence of thinking. Despite the critical role of WM in DT, little work has been done on the mechanism affecting this interplay. The current study addressed the involvement of a formal musical background in the relationship between WM and DT and was conducted with 83 healthy young adults (M = 19.64 years; SD = 0.52 years; 33 females). The participants were requested to indicate if they had a formal background in music in the conservatory (M = 4.78 years; SD = 5.50 years) as well as perform the digit span forward test (DSFT) and the alternative uses task—AUT from the Torrance test of creative thinking (TTCT). The results indicated that years of formal musical background moderated the association between WM and DT. These findings suggest that music enhances the positive effect of high-order cognitive processes, such as WM, on the ability to think divergently. Theoretical and practical implications as well as limitations were discussed.
Nonlinear Performance of Steel Tube Tower in Ultra-High Voltage Transmission Lines under Wind Loads
Authors: Li Ruiqi , Qi Liangjie , Dong Yao-Rong , Wang Hui
Published: January 2024

As complex, statically indeterminate structures, transmission towers are subject to complex forces and are usually simplified into truss structures that only consider the effects of axial force. When the load and deformation of a tower are small, it is reasonable to carry out analysis according to the linear elasticity theory. However, the height of an ultra-high voltage (UHV) transmission tower is significantly large, meaning that the calculation result according to the current elastic analysis method often has a large deviation from the actual stress of the structure. With the influence of the bending moment at the end of the member, a numerical model is established considering the influence of geometric nonlinearity and material nonlinearity in this paper. The stress distribution characteristics and development law of UHV transmission towers in linear and nonlinear stress states are analyzed and studied. The real tower test and elastoplastic ultimate bearing capacity analysis show that the elastoplastic analysis is closer to the actual tower. The UHV steel pipe tower designed according to the linear elasticity and small deformation theory has a large safety margin under the design load, resulting in a significant waste of materials. Under the action of heavy load, the tower exhibits strong nonlinearity, and the influence of geometric and material nonlinear factors should be fully considered when designing the structural components in UHV transmission towers.
Building Vibration Measurement and Prediction during Train Operations
Authors: He Lingshan , Tao Ziyu
Published: January 2024

Urban societies face the challenge of working and living in environments filled with vibration caused by transportation systems. This paper conducted field measurements to obtain the characteristics of vibration transmission from soil to building foundations and within building floors. Subsequently, a prediction method was developed to anticipate building vibrations by considering the soil and structure interaction. The rigid foundation model was simplified into a foundation–soil system connected via spring damping, and the building model is based on axial wave transmission within the columns and attached floors. Building vibrations were in response to measured input vibration levels at the ground and were validated through field measurements. The influence of different building heights on soil and structure vibration propagation was studied. The results showed that the predicted vibrations match well with the measured vibrations. The proposed prediction model can reasonably predict the building vibration caused by train operations. The closed-form method is an efficient tool for predicting floor vibrations prior to construction.
A Decision Framework for the Regeneration Awareness of Large-Sized Public Housing Using a Building Transformability Assessment: A Test Case in Italy (Latina)
Authors: Diana Lorenzo , Passarelli Cristina , Polverino Francesco , Pugliese Francesco
Published: January 2024

In the frame of developing sustainable, reliable, and regenerative interventions on existing buildings, namely on large-scale public housing, the implementation of functional, technological, and effective strategies is devoted to thoroughly assessing the transformability of buildings using trustworthy performance indicators. With this aim, in the present paper, an assessment framework tool is presented and tested to evaluate the potential of buildings for transformation through the detection of regeneration strategies for the reduction of energy consumption, a definition of the new apartments’ internal layout, and the implementation of sustainable systems to foster rainwater harvesting. The procedure is tested on a case study in Latina (Italy), showing its suitability to quantitatively assess the regenerative potentiality of public housing, thus resulting in an effective supporting tool for designers and policy makers.
Harnessing Activated Hydrochars: A Novel Approach for Pharmaceutical Contaminant Removal
Authors: Gautam Manish Kumar , Mondal Tamal , Nath Rupashri , Mahajon Bidhan , Chincholikar Mukesh , Bose Anirbandeep , Das Dibya , Das Rakesh , Mondal Sandip
Published: January 2024

Water contamination is a pervasive global crisis, affecting over 2 billion people worldwide, with pharmaceutical contaminants emerging as a significant concern due to their persistence and mobility in aquatic ecosystems. This review explores the potential of activated hydrochars, sustainable materials produced through biomass pyrolysis, to revolutionize the removal of pharmaceutical contaminants from water sources. These materials possess high surface area, porous structure, and exceptional adsorption capabilities, making them a promising solution. The impact of pharmaceutical contaminants on aquatic ecosystems and human health is far-reaching, affecting biodiversity, water quality, and public health. To address this complex issue, a diverse range of techniques, including adsorption, biodegradation, and advanced oxidation processes, are employed in the pharmaceutical industry. Activated hydrochars offer substantial adsorption capacity, sustainable feedstock origins, and a minimal carbon footprint. This review highlights their potential in pharmaceutical contaminant removal and their broader applications in improving soil and air quality, resource recovery, and sustainable waste management. Interdisciplinary collaboration and the development of intelligent treatment systems are essential to fully unlock the potential of activated hydrochars. Regulatory support and policy frameworks will facilitate their responsible and widespread application, promising a cleaner and more sustainable future. This paper aims to inform scientists, environmental experts, policymakers, and industry stakeholders about the promising role of activated hydrochars in addressing pharmaceutical contaminant challenges.
Improvement in Central Neck Dissection Quality in Thyroid Cancer by Use of Tissue Autofluorescence
Authors: Neagoe Octavian Constantin , Ionică Mihaela
Published: January 2024

Background: Risk of postoperative transient or permanent hypoparathyroidism represents one of the most common complications following total thyroidectomy. This risk increases if a cervical lymphadenectomy procedure must also be performed, as is usually the case in thyroid carcinoma patients. Parathyroid autofluorescence (AF) is a non-invasive method that aids intraoperative identification of parathyroid glands. Methods: In this prospective study, 189 patients with papillary thyroid cancer who underwent total thyroidectomy with central neck dissection were included. Patients were randomly allocated to one of two groups: NAF (no AF, surgery was performed without AF) and the AF group (surgery was performed with AF—Fluobeam LX system, Fluoptics, Grenoble, France). Results: The number of excised lymph nodes was significantly higher in the AF compared to the NAF group, with mean values of 21.3 ± 4.8 and 9.2 ± 4.1, respectively. Furthermore, a significantly higher number of metastatic lymph nodes were observed in the AF group. Transient hypocalcemia recorded significantly lower rates in the AF group with 4.9% compared to 16.8% in the NAF group. Conclusions: AF use during total thyroidectomy with central neck dissection for papillary thyroid carcinoma patients, decreased the rate of iatrogenic parathyroid gland lesions, and increased the rate of lymphatic clearance.
Significant and Various Effects of ML329-Induced MITF Suppression in the Melanoma Cell Line
Authors: Nishikiori Nami , Watanabe Megumi , Sato Tatsuya , Furuhashi Masato , Okura Masae , Hida Tokimasa , Uhara Hisashi , Ohguro Hiroshi
Published: January 2024

To study the inhibitory effects on microphthalmia-associated transcription factor (MITF)-related biological aspects in malignant melanomas (MMs) in the presence or absence of the low-molecular MITF specific inhibitor ML329, cell viability, cellular metabolic functions, and three-dimensional (3D) spheroid formation efficacy were compared among MM cell lines including SK-mel-24, A375, dabrafenib- and trametinib-resistant A375 (A375DT), and WM266-4. Upon exposure to 2 or 10 μM of ML329, cell viability was significantly decreased in WM266-4, SK-mel-24, and A375DT cells, but not A375 cells, in a dose-dependent manner, and these toxic effects of ML329 were most evident in WM266-4 cells. Extracellular flux assays conducted using a Seahorse bioanalyzer revealed that treatment with ML329 increased basal respiration, ATP-linked respiration, proton leakage, and non-mitochondrial respiration in WM266-4 cells and decreased glycolytic function in SK-mel-24 cells, whereas there were no marked effects of ML329 on A375 and A375DT cells. A glycolytic stress assay under conditions of high glucose concentrations also demonstrated that the inhibitory effect of ML329 on the glycolytic function of WM266-4 cells was dose-dependent. In addition, ML329 significantly decreased 3D-spheroid-forming ability, though the effects of ML329 were variable among the MM cell lines. Furthermore, the mRNA expression levels of selected genes, including STAT3 as a possible regulator of 3D spheroid formation, KRAS and SOX2 as oncogenic-signaling-related factors, PCG1a as the main regulator of mitochondrial biogenesis, and HIF1a as a major hypoxia transcriptional regulator, fluctuated among the MM cell lines, possibly supporting the diverse ML329 effects mentioned above. The findings of diverse ML329 effects on various MM cell lines suggest that MITF-associated biological activities are different among various types of MM.
Impairment of Nutritional Status and Quality of Life Following Minimal-Invasive Esophagectomy—A Prospective Cohort Analysis
Authors: Oberhoff Grace , Schooren Lena , Vondran Florian , Kroh Andreas , Koch Alexander , Bednarsch Jan , Neumann Ulf P. , Schmitz Sophia M. , Alizai Patrick H.
Published: January 2024

Minimal-invasive resection of the esophagus for esophageal cancer has led to a relevant decrease in postoperative morbidity. Postoperatively, patients still suffer from surgical and adjuvant therapy-related symptoms impairing nutrition and quality of life. The aim of this study was to evaluate the nutritional status and associated symptoms six months after esophagectomy. Patients who attended follow-up examination six months after minimal-invasive esophagectomy were included. Blood and fecal tests, quality of life surveys (QLQ-C30 and QLQ-OG25) and nutritional risk screening (NRS) were performed. Twenty-four patients participated. The mean weight loss was 11 kg. A significant decrease in vitamin B12 (737 to 467 pg/mL; p = 0.033), ferritin (302 to 126 ng/mL; p = 0.012) and haptoglobin (227 to 152 mg/dL; p = 0.025) was found. In total, 47% of the patients had an impaired pancreatic function (fecal elastase < 500 µg/g). Physical (72 to 58; p = 0.034) and social functioning (67 to 40; p = 0.022) was significantly diminished, while self-reported global health status remained stable (52 to 54). The number of patients screened and found to be in need of nutritional support according to NRS score decreased slightly (59% to 52%). After MIE, patients should be meticulously monitored for nutritional status after surgery.
MICA+ Tumor Cell Upregulated Macrophage-Secreted MMP9 via PROS1-AXL Axis to Induce Tumor Immune Escape in Advanced Hepatocellular Carcinoma (HCC)
Authors: Wu Qiulin , Li Xicai , Yang Yan , Huang Jingquan , Yao Ming , Li Jianjun , Huang Yubin , Cai Xiaoyong , Geller David A. , Yan Yihe
Published: January 2024

Background: tumor-associated macrophages (TAMs) constitute a significant proportion of non-cancerous cells within the intricate tumor microenvironment (TME) of hepatocellular carcinoma (HCC). Understanding the communication between macrophages and tumor cells, as well as investigating potential signaling pathways, holds promise for enhancing therapeutic responses in HCC. Methods: single-cell RNA-sequencing data and bulk RNA-sequencing data were derived from open source databases Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Through this analysis, we elucidated the interactions between MICA+ tumor cells and MMP9+ macrophages, primarily mediated via the PROS1-AXL axis in advanced HCC. Subsequently, we employed a range of experimental techniques including lentivirus infection, recombinant protein stimulation, and AXL inhibition experiments to validate these interactions and unravel the underlying mechanisms. Results: we presented a single-cell atlas of advanced HCC, highlighting the expression patterns of MICA and MMP9 in tumor cells and macrophages, respectively. Activation of the interferon gamma (IFN-γ) signaling pathway was observed in MICA+ tumor cells and MMP9+ macrophages. We identified the existence of an interaction between MICA+ tumor cells and MMP9+ macrophages mediated via the PROS1-AXL axis. Additionally, we found MMP9+ macrophages had a positive correlation with M2-like macrophages. Subsequently, experiments validated that DNA damage not only induced MICA expression in tumor cells via IRF1, but also upregulated PROS1 levels in HCC cells, stimulating macrophages to secrete MMP9. Consequently, MMP9 led to the proteolysis of MICA. Conclusion: MICA+ HCC cells secreted PROS1, which upregulated MMP9 expression in macrophages through AXL receptors. The increased MMP9 activity resulted in the proteolytic shedding of MICA, leading to the release of soluble MICA (sMICA) and the subsequent facilitation of tumor immune escape.
Reaction Kinetics and Mechanism for the Synthesis of Glycerol Carbonate from Glycerol and Urea Using ZnSO4 as a Catalyst
Authors: Wang Huajun , Ma Jingjing
Published: January 2024

A series of Zn salts were used as catalysts for the reaction of glycerol and urea to produce glycerol carbonate and it was found that ZnSO4 showed the highest catalytic activity. Furthermore, the effects of reaction parameters on the glycerol conversion and glycerol carbonate yield were studied in detail. The results indicated that the glycerol conversion and glycerol carbonate yield were increased with the reaction temperature, reaction time, and catalyst amount while the optimal reaction conditions were 140 °C, 240 min, catalyst amount of 5 wt% (based on the glycerol weight), and urea-to-glycerol molar ratio of 1.1:1. During the reaction, the ZnSO4 catalyst is transformed into Zn(NH3)2SO4 at the initial stage of the reaction and then further transformed into Zn(C3H6O3). Zn(C3H6O3) and (NH4)2SO4 may be the true active species for the activation of urea and glycerol, respectively. The reaction mechanism is proposed in this article. Based on the experimental results, a reaction kinetics model considering the change in volume of the reaction system was also established, and the model parameters were obtained by fitting the experimental data. The statistical results showed that the established kinetics model is accurate.
A Novel Protozoa Parasite-Derived Protein Adjuvant Is Effective in Immunization with Cancer Cells to Activate the Cancer-Specific Protective Immunity and Inhibit the Cancer Growth in a Murine Model of Colorectal Cancer
Authors: Mani Rajesh , Martin Chloe G. , Balu Kanal E. , Wang Qingding , Rychahou Piotr , Izumi Tadahide , Evers B. Mark , Suzuki Yasuhiro
Published: January 2024

Cancer-specific CD8+ cytotoxic T cells play important roles in preventing cancer growth, and IFN-γ, in addition to IL-12 and type I interferon, is critical for activating CD8+ cytotoxic T cells. We recently identified the capability of the amino-terminus region of dense granule protein 6 (GRA6Nt) of Toxoplasma gondii, an intracellular protozoan parasite, to activate IFN-γ production of microglia, a tissue-resident macrophage population. Therefore, in the present study, we examined whether recombinant GRA6Nt protein (rGRA6Nt) functions as an effective adjuvant to potently activate cancer-specific protective immunity using a murine model of MC38 colorectal cancer (CRC). When mice were immunized with non-replicable (either treated with mitomycin C or irradiated by X-ray) MC38 CRC cells in combination with rGRA6Nt adjuvant and received a challenge implantation of replication-capable MC38 tumor cells, those mice markedly inhibited the growth of the implanted tumors in association with a two-fold increase in CD8+ T cell density within the tumors. In addition, CD8+ T cells of the immunized mice secreted significantly increased amounts of granzyme B, a key mediator of the cytotoxic activity of CD8+ T cells, and IFN-γ in response to MC38 CRC cells in vitro when compared to the T cells from unimmunized mice. Notably, the protective effects of the immunization were specific to MC38 CRC cells, as the immunized mice did not exhibit a significantly inhibited growth of EL4 lymphoma tumors. These results indicate that rGRA6Nt is a novel and effective protein adjuvant when used in immunizations with non-replicable cancer cells to potently activate the protective immunity specifically against the cancer cells employed in the immunization.
Synaptopodin Regulates Denervation-Induced Plasticity at Hippocampal Mossy Fiber Synapses
Authors: Kruse Pia , Brandes Gudrun , Hemeling Hanna , Huang Zhong , Wrede Christoph , Hegermann Jan , Vlachos Andreas , Lenz Maximilian
Published: January 2024

Neurological diseases can lead to the denervation of brain regions caused by demyelination, traumatic injury or cell death. The molecular and structural mechanisms underlying lesion-induced reorganization of denervated brain regions, however, are a matter of ongoing investigation. In order to address this issue, we performed an entorhinal cortex lesion (ECL) in mouse organotypic entorhino-hippocampal tissue cultures of both sexes and studied denervation-induced plasticity of mossy fiber synapses, which connect dentate granule cells (dGCs) with CA3 pyramidal cells (CA3-PCs) and play important roles in learning and memory formation. Partial denervation caused a strengthening of excitatory neurotransmission in dGCs, CA3-PCs and their direct synaptic connections, as revealed by paired recordings (dGC-to-CA3-PC). These functional changes were accompanied by ultrastructural reorganization of mossy fiber synapses, which regularly contain the plasticity-regulating protein synaptopodin and the spine apparatus organelle. We demonstrate that the spine apparatus organelle and synaptopodin are related to ribosomes in close proximity to synaptic sites and reveal a synaptopodin-related transcriptome. Notably, synaptopodin-deficient tissue preparations that lack the spine apparatus organelle failed to express lesion-induced synaptic adjustments. Hence, synaptopodin and the spine apparatus organelle play a crucial role in regulating lesion-induced synaptic plasticity at hippocampal mossy fiber synapses.
Wilson’s Disease with Acute Hepatic Onset: How to Diagnose and Treat It
Authors: Delle Cave Valeria , Di Dato Fabiola , Iorio Raffaele
Published: January 2024

Wilson’s disease (WD) with acute onset poses a diagnostic challenge because it is clinically indistinguishable from other acute liver diseases. In addition, serum ceruloplasmin and urinary copper excretion, the first-line diagnostic tools for WD, can show false positive results in the case of acute liver failure, and the diagnostic role of genetic analysis is limited by the time required to perform it. In the case of fulminant onset, there is a clear indication of liver transplantation. “New Wilson Index” is frequently used to discriminate between patients who need liver transplantation versus those who can be successfully managed by medical treatment, but its reliability remains controversial. Timely referral of patients with acute liver failure due to WD may be a key factor in improving patient survival. Although liver transplant very often represents the only chance for such patients, maximum effort should be made to promote survival with a native liver. The management of these aspects of WD is still a matter of debate and will be the subject of this review.
Parental Experiences in Pediatric Multiple Sclerosis: Insights from Quantitative Research
Authors: Tarantino Samuela , Proietti Checchi Martina , Papetti Laura , Monte Gabriele , Ferilli Michela Ada Noris , Valeriani Massimiliano
Published: January 2024

Multiple sclerosis (MS) is a chronic and unpredictable inflammatory disease impacting the central nervous system. The disabling nature of this disease is not limited to only physical symptoms. MS, even at a pediatric age, often includes cognitive impairment, fatigue, and psychological issues, affecting education and social life, causing emotional distress, and reducing quality of life. Despite the paucity of quantitative data in the existing literature, our review demonstrates that the impact of pediatric MS extends beyond the patients themselves, affecting their parents as well. There is evidence suggesting that having a child with MS may be associated with a reduction in the parental quality of life, even in families of MS patients with low or no disability and without clinical relapses. Moreover, an increased risk of parents’ mental illness has been described, particularly in mothers, leading to a heightened utilization of mental health services. Research data show that inadequate information about MS may impact parents’ anxiety and their sense of competence. Since parents’ involvement has been found to also play a role in their child’s adherence to treatment, special attention should be paid to parental psychological health. Additional research exploring family adaptation to their children’s illness is required.
Microclimate Analysis of Outdoor Showcases in Tropical Climate—Two Case Studies in Al Ain, Abu Dhabi, United Arab Emirates
Authors: Camuffo Dario , della Valle Antonio , Giorio Roberta , Rizzi Francesco , Barucco Patrizia , Suma Marivita , Ahmed Jalal , Chabbi Amel , Shaker Ola , Sheehan Peter
Published: January 2024

Al Ain, near Abu Dhabi, United Arab Emirates, is characterized by hot desert climate with high temperatures, aridity, and almost no rain. Several truncated earthen walls were discovered at the historic house of Sheikh Mohammed Bin Khalifa, a component of the World Heritage Cultural Sites. These remains are preserved in situ, outdoors, protected in glass showcases for public display. As this situation is not documented in the literature, the local Authority has requested to study the showcase environment to optimize conservation. The solar radiation and the projected shades have been modeled over one year; the temperature and humidity inside and outside the showcases, as well as the moisture content, have been measured to assess the potential preservation risks. The paper presents the results, i.e., the direct solar radiation generates extreme conditions of greenhouse effect with extremely high temperatures and forces evaporation from the remains. During the night, the excess moisture condenses on the inner surface of the glass panes, forming large drops that affect viewing and are dangerous for conservation. The repetition of evaporation–condensation cycles accumulates soluble salts on the remains. The paper discusses mitigation strategies (e.g., shading, ventilation, and cooling, to reduce the greenhouse effect) to improve conservation and fruition.
Design and Use of Portable X-ray Fluorescence Devices for the Analysis of Heritage Materials
Authors: Chiti Maurizio , Chiti Daniele , Chiarelli Federico , Donghia Raffaella , Esposito Adolfo , Ferretti Marco , Gorghinian Astrik
Published: January 2024

X-ray fluorescence (XRF) is a successful technique often used for the elemental analysis of cultural heritage artefacts. It is non-invasive, the equipment can be miniaturized and made portable and it allows addressing crucial issues such as the fabrication technology, authenticity and provenance of the artefacts. Depending on the components’ selection (e.g., the primary source, the detector and the focusing optics, if present), the analytical performance and the consequent suitability to investigate a given class of materials may vary significantly. The present paper discusses the analytical performance—with special regard to the limits of detection and the quantification uncertainty—of two portable XRF spectrometers developed within a collaboration between INFN-LNF-FISMEL and CNR-ISPC. The devices are expressly designed for heritage materials. In particular, one is equipped with focusing optics and it is intended to analyze small details on glasses and pigmented surfaces, whereas the other has a 70 kV X-ray tube, which greatly improves sensitivity for medium-Z elements, which is important in copper-based artefacts. Finally, this paper discusses two case studies to highlight the features of the instruments: one concerns Etruscan vitreous material beads and the other pre- and proto-historic copper-based artefacts from Tyrrhenian Central Italy. Thanks to the small size of the equipment, both investigations could easily be carried out in situ, namely, at the Museo Nazionale Etrusco in Rome and the Museo della Preistoria della Tuscia e della Rocca Farnese at Valentano.
SrTiO3: Thoroughly Investigated but Still Good for Surprises
Authors: Bussmann-Holder Annette , Kremer Reinhard K. , Roleder Krystian , Salje Ekhard K. H.
Published: January 2024

For decades, SrTiO3 has been in the focus of research with seemingly never-ending new insights regarding its ground state properties, application potentials, its surface and interface properties, the superconducting state, the twin boundaries, domain functionalities, etc. Here, we focus on the already well-investigated lattice dynamics of STO and show that four different temperature regimes can be identified which dominate the elastic properties, the thermal conductivity, and the birefringence. These regimes are a low-temperature quantum fluctuation-dominated one, followed by an intermediate regime, a region of structural phase transition at ~105 K and its vicinity, and at high temperatures, a regime characterized by precursor and saturation effects. They can all be elucidated by lattice dynamical aspects. The relevant temperature dependences of the soft modes are discussed and their relationship to lattice polarizability is emphasized.
Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases
Authors: Ogunpola Adedayo , Saeed Faisal , Basurra Shadi , Albarrak Abdullah M. , Qasem Sultan Noman
Published: January 2024

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease.
Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education
Authors: Zhang Ying , Feng Hongbo , Zhao Yan , Zhang Shuo
Published: January 2024

Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, has become a valuable tool in medical imaging education due to its integration of various AI applications. Through its open-source architecture, students can gain practical experience with diverse medical images and the latest AI technology, reinforcing their understanding of anatomy and imaging technology while fostering independent learning and clinical reasoning skills. The implementation of this platform improves instruction quality and nurtures skilled professionals who can meet the demands of clinical practice, research institutions, and technology innovation enterprises. AI algorithms’ application in medical image processing have facilitated their translation from the lab to practical clinical applications and education.
Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism
Authors: Kang Shengqi , Fu Xiuwen
Published: January 2024

The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels.
Efficient Integration of Rate-Adaptive Reconciliation with Syndrome-Based Error Estimation and Subblock Confirmation for Quantum Key Distribution
Authors: Treeviriyanupab Patcharapong , Zhang Chun-Mei
Published: January 2024

An effective post-processing algorithm is essential for achieving high rates of secret key generation in quantum key distribution. This work introduces an approach to quantum key distribution post-processing by integrating the three main steps into a unified procedure: syndrome-based error estimation, rate-adaptive reconciliation, and subblock confirmation. The proposed scheme employs low-density parity-check codes to estimate the quantum bit error rate using the syndrome information, and to optimize the channel coding rates based on the Slepian–Wolf coding scheme for the rate-adaptive method. Additionally, this scheme incorporates polynomial-based hash verification in the subblock confirmation process. The numerical results show that the syndrome-based estimation significantly enhances the accuracy and consistency of the estimated quantum bit error rate, enabling effective code rate optimization for rate-adaptive reconciliation. The unified approach, which integrates rate-adaptive reconciliation with syndrome-based estimation and subblock confirmation, exhibits superior efficiency, minimizes practical information leakage, reduces communication rounds, and guarantees convergence to the identical key. Furthermore, the simulations indicate that the secret key throughput of this approach achieves the theoretical limit in the context of a BB84 quantum key distribution system.
The Role of Self-Efficacy, Motivation, and Connectedness in Dropout Intention in a Sample of Italian College Students
Authors: Buizza Chiara , Cela Herald , Sbravati Giulio , Bornatici Sara , Rainieri Giuseppe , Ghilardi Alberto
Published: January 2024

Dropout is a critical concern in higher education, with a considerable number of students leaving within the first two years of university. Dropout affects students’ well-being and their academic and career prospects, and institutions’ retention and graduation rates. The aim of this study was to explore the mediating role of motivation and cognitive strategies for learning in the relationship among self-efficacy, connectedness, and university dropout intention. A total of 790 Italian college freshmen were involved in this study. The sample was recruited through a web survey consisting of the Academic Motivation Scale, Perceived School Self-Efficacy Scale, University Connectedness Scale, and Self-Regulated Knowledge Scale-University. The freshmen’s intentions to drop out were assessed with five questions. The average age of the freshmen was 20.9 years, most of them were female, and were attending a degree program in the medical area. The results show that self-efficacy is the most important predictor of dropout intentions, followed by university connectedness. Self-regulated knowledge has an important role in predicting dropout intention by acting as a mediator between self-efficacy and motivation.This study underlines the importance of investing in training and orientation interventions in order to develop the skills to face the university path, increasing self-efficacy, motivation, and consequently students’ well-being.
Multi-Scale Spatial–Spectral Residual Attention Network for Hyperspectral Image Classification
Authors: Wu Qinggang , He Mengkun , Liu Zhongchi , Liu Yanyan
Published: January 2024

Hyperspectral images (HSIs) encapsulate a vast amount of information due to their expansive size and high number of channel dimensions. However, they are insufficiently utilized for ineffective feature extraction, particularly for regions with few samples and predominant edges. To fully leverage the spatial–spectral features of HSIs, a dual-branch multi-scale spatial–spectral residual attention network (MSRAN) that integrates multi-scale feature extraction with residual attention mechanisms is proposed. MSRAN independently extracts spatial and spectral features through dual branches, minimizing the interference between these features and enhancing the focus on feature extraction in different dimensions. Specifically, in the spectral feature extraction branch, diverse-scale 3D convolution kernels capture extended spectral sequence characteristics and neighborhood spectral features. The convolution fusion emphasizes the weight of the central pixel to be classified, followed by the use of spectral residual attention mechanisms to extract enhanced central-pixel spectral features. In the spatial feature extraction branch, multi-level receptive fields are utilized to extract various fine-grained spatial contours, edges, and local detailed features, which are further processed through spatial residual attention to effectively extract spatial composite features. Finally, the convolution fusion module adaptively integrates the center-enhanced spectral features with multi-level fine-grained spatial features for classification. Extensive comparative experiments and ablation studies demonstrate that MSRAN achieves highly competitive results on two classic datasets from Pavia University and Salinas as well as on a novel dataset of WHU-Hi-LongKou.
Innovative Method for Reliability Assessment of Power Systems: From Components Modeling to Key Indicators Evaluation
Authors: Adinolfi Giovanna , Ciavarella Roberto , Graditi Giorgio , Ricca Antonio , Valenti Maria
Published: January 2024

Power systems comprise different electrical, electronic, electromechanical and electrochemical components. Adequacy, security, resilience and reliability represent essential requirements for grids functioning mode. The evaluation of such aspects can constitute a delicate task in the presence of heterogeneous components. Focusing on reliability assessment, several Reliability Prediction Models are available. They are suitably applied according to the type of component under evaluation. The lack of homogeneity of these models forbids the comparison of performance and identification of unreliable systems and grid section. This paper aims to face the mentioned issue proposing a unique reliability assessment methodology able to characterize different equipment connected to radial/meshed/ring grids and subjected to different stressing and ageing factors. It is customized for electrical lines, transformers, circuit breakers, converters and renewables plants. Component and systemic key indices are calculated. Furthermore, a novel “load feeding reliability“ indicator is evaluated for providing information about the supply reliability of a specific load. This index is meaningful for the identification of unreliable grids, microgrids and systems. Such an approach can contribute to improve power systems design, planning and control. The proposed method is integrated in a software application implemented for grid reliability assessment. The obtained results are reported for an urban grid including an underground transportation area.
Active Power Allocation Method of Doubly Fed Induction Generators Based on Rotor Speed
Authors: Li Muxi , Li Fengting
Published: January 2024

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia support under maximum power point tracking control. To make the wind turbine improve the inertia support ability of the system based on virtual inertia control, a method for active power allocation based on the rotor speed of DFIG is proposed. Firstly, the minimum system inertia requirement based on the frequency change rate of the system is established. Active power allocation assumes that the wind farm inertia meets the minimum system inertia requirement. Secondly, the objective is to enhance the inertia support capability and overall active power output of the wind farm, considering the constraint of the minimum system inertia requirement. Based on the rotor speed to establish the inertia allocation weight factor, the weight of the power command is assigned to a single machine to achieve the wind farm active power allocation. Finally, it is verified that the system’s equivalent inertia meets the minimum inertia requirement of the system. Simulations show that the proposed allocation method can adequately elevate the inertia support capability of DFIGs to the system and the rotor kinetic energy utilization.
Efficient Electronic Voting System Based on Homomorphic Encryption
Authors: Zhan Yu , Zhao Wei , Zhu Chaoxi , Zhao Zhen , Yang Ning , Wang Baocang
Published: January 2024

In the last decade, E-voting has received great attention due to its advantages in efficiency and accuracy. Fan et al. presented a novel E-voting system named HSE-Voting by utilizing homomorphic signcryption. The HSE-Voting system was claimed to gain a provable security goal under the standard proof. In this paper, we illustrate that their scheme may suffer from some potential security issues. On the one hand, the voting information could be recovered by the authentication center (AC). On the other hand, any malicious voter could disrupt the voting system undetected by locally modifying his ballot. In order to increase the resilience of the voting system to risks, an improvement of the HSE-Voting system is developed. Our improved system fixes the above security weaknesses but increases the computation cost on the AC side by a small amount. In addition, the proposed scheme satisfies voter anonymity, ballot privacy, and verifiability of election results.
A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles
Authors: Yun Keon , Yun Heesun , Lee Sangmin , Oh Jinhyeok , Kim Minchul , Lim Myongcheol , Lee Juntaek , Kim Chanmin , Seo Jiwon , Choi Jinyoung
Published: January 2024

Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our system, including the RSU, is designed to identify vehicles exhibiting abnormal driving. Abnormal driving can arise from various causes, such as communication delays, sensor errors, navigation system malfunctions, environmental challenges, and cybersecurity threats. We simulated exploring three primary scenarios of abnormal driving: sensor errors, overlapping vehicles, and counterflow driving. The applicability of machine learning algorithms for detecting these anomalies was evaluated. The Minisom algorithm, in particular, demonstrated high accuracy, recall, and precision in identifying sensor errors, vehicle overlaps, and counterflow situations. Notably, changes in the vehicle’s direction and its characteristics proved to be significant indicators in the Basic Safety Messages (BSM). We propose adding a new element called linePosition to BSM Part 2, enhancing our ability to promptly detect and address vehicle abnormalities. This addition underpins the technical capabilities of RSU systems equipped with edge computing, enabling real-time analysis of vehicle data and appropriate responsive measures. In this paper, we emphasize the effectiveness of machine learning in identifying and responding to the abnormal behavior of autonomous vehicles, offering new ways to enhance vehicle safety and facilitate smoother road traffic flow.