bluuebunny/crossref_metadata_2025 huggingface dataset:
Seismic Upgrading of the Heritage-Protected Reinforced Concrete Warehouse in Rijeka, Croatia
Authors: Bošnjak Berislav , Pekas Nikola , Stepinac Mislav
Published: September 2024

Despite Croatia experiencing two strong earthquakes in 2020, Rijeka was not directly affected, underscoring the importance of proactive seismic assessment and strengthening in all seismic regions. This paper presents a comprehensive case study on the seismic strengthening of a 20th-century concrete building located in Rijeka, Croatia, originally designed according to Austro-Hungarian construction norms and practices. As a heritage-protected structure, the building’s architectural features and construction practices were examined and contextualized within its historical background. The assessment and renovation phases of this project are discussed in detail, demonstrating the practical application of modern seismic strengthening techniques while preserving the building’s historical integrity. This case study aims to highlight the need for such measures to protect heritage structures and to show the implementation of rapid and new (ad hoc) norms for earthquake-damaged buildings in Croatia. This study serves as a reference for engineers, architects, and conservationists involved in the preservation of heritage buildings, demonstrating that it is possible to enhance their structural safety without compromising their architectural authenticity.
Construction 4.0 in Refugee Camps: Facilitating Socio-Spatial Adaptation Patterns in Jordan’s Zaatari Camp
Authors: Abu-Aridah Dima , Henn Rebecca L.
Published: September 2024

Though refugee camps are by definition “temporary”, many camps endure for decades, where individuals live full lives through childhood, marriage, children, grandchildren, and death. These settlements function no differently than cities in their social life, density, zoning, and operation, yet are “planned” through UNHCR (United Nations High Commissioner for Refugees) templates for camps. The Zaatari camp in Jordan for Syrian asylum seekers, for example, holds a population of 80,000. Rather than viewing refugee camps as temporary human warehouses, this article demonstrates that camps are spaces where individuals build social networks and economic activities flourish. As such, the camp planning templates should include adaptive Construction 4.0 technologies for more socially flexible settlements, even if the camps are considered “temporary”. This case study research on the Zaatari camp illustrates how refugees adapt their built environment, identifying adaptation patterns that enhance both livability and sustainability. The work illustrates social and environmental changes that require adaptive housing configurations. The conclusion suggests linking modern tools in the construction industry to empirically derived planning objectives to be efficiently executed in moments of crisis.
Lateral Performance of Composite Wall with Cold-Formed Thin-Walled Steel–Concrete Sandwich Panel
Authors: Zou Jian , Cao Baozhu , Zeng Xiang , Zhang Yuchuan
Published: September 2024

To study the lateral performance of a cold-formed steel–concrete insulation sandwich panel composite wall, two full-scale specimens with different arrangements were designed. The specimens underwent cyclic loading tests to examine the failure characteristics of the composite wall, and lateral performance aspects such as the experimental hysteresis curve, skeleton curve, and characteristic value of the whole loading process were acquired. The experimental results indicate that the failure of the composite wall system was primarily caused by the failure of the connection; the overall lateral performance of composite walls with one wall panel at the bottom and two wall panels at the top (W1) was superior to that of composite walls with two wall panels at the bottom and one wall panel at the top (W2). When loaded to an inter-story drift ratio of 1/300, the composite wall did not exhibit any significant damage. A finite element (FE) model was developed and validated by the experiments. Factors affecting the shear bearing capacity were analyzed based on the FE model, including the yield strength of diagonal braces, the thickness of the diagonal braces, the arrangement pattern of the wall panels, the dimensions of the wall panels, and the strength of the connection of the L-shaped connector and the flat connector. The FE results show that all these factors can influence the lateral performance of the composite wall.
Real-World Impact of Deep Targeted Sequencing on Erythrocytosis and Thrombocytosis Diagnosis: A Reference Centre Experience
Authors: Blanco-Sánchez Alberto , Gil-Manso Rodrigo , de Nicolás Rodrigo , López-Muñoz Nieves , Colmenares Rafael , Mas Reyes , Sánchez Ricardo , Rapado Inmaculada , Martínez-López Joaquín , Díaz Rosa Ayala , Carreño-Tarragona Gonzalo
Published: September 2024

Despite advances in diagnosis of erythrocytosis and thrombocytosis due to driver mutation testing, many cases remain classified as “idiopathic”. This can be explained by the absence of an evident secondary cause, inconclusive bone marrow biopsy or neglection of family history. Analysis of a broad panel of genes through next-generation sequencing (NGS) could improve diagnostic work-up identifying underlying genetic causes. We reviewed the results of NGS performed in our laboratory and its diagnostic impact on 117 patients with unexplained erythrocytosis and 58 with unexplained thrombocytosis; six patients (5.1%) were diagnosed with polycythaemia vera (PV) and 8 (6.8%) with familial erythrocytosis after NGS testing. Low EPO and a family history seemed to predict a positive result, respectively. However, a greater percentage of patients were ultimately diagnosed with secondary erythrocytosis (36%), remained as idiopathic (28.2%) or were self-limited (15%). The yield of NGS was shown to be slightly higher in patients with thrombocytosis, as 15 (25.9%) were diagnosed with essential thrombocythemia (ET) or familial thrombocytosis after variant detection; previous research has shown similar results, but most of them carried out NGS retrospectively, while the present study exhibits the performance of this test in a real-world setting. Overall, the low rate of variant detection and its poor impact on diagnostic work-up highlights the need for a thorough screening prior to NGS, in order to improve its yield.
Adverse Events in Targeted Therapy for Unresectable Hepatocellular Carcinoma Predict Clinical Outcomes
Authors: Imai Kenji , Takai Koji , Aiba Masashi , Unome Shinji , Miwa Takao , Hanai Tatsunori , Suetsugu Atsushi , Shimizu Masahito
Published: September 2024

To assess the impact of adverse event (AE) severity, caused by targeted therapy, on overall survival (OS) and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (HCC), a total of 183 patients with HCC treated with atezolizumab plus bevacizumab (40), lenvatinib (57), sorafenib (79), cabozantinib (3), ramucirumab (3), and regorafenib (1) were included in this study. Age-, AFP-, and ALBI score-adjusted hazard ratios (HRs) of AE grades 1 to 3 versus grade 0 for OS and PFS were calculated using Cox proportional hazards models. The linear trend of the HRs was assessed by calculating the p values for this trend. The most common AEs were appetite loss (AE grade 0/1/2/3 = 97/23/55/12), general fatigue (102/31/44/6), hypertension (120/6/40/17), hand-foot syndrome (HFS) (135/21/24/3), proteinuria (140/13/16/14), and hypothyroidism (148/12/23/0). The adjusted HRs for OS of these AEs were 0.532–1.450–2.361 (p for trend 0.037), 1.057–1.691–3.364 (p for trend 0.004), 1.176–0.686–0.281 (p for trend 0.002), 0.639–0.759–1.820 (p for trend 0.462), 1.030–0.959–0.147 (p for trend 0.011), and 0.697–0.609 (p for trend 0.119), respectively. Those for PFS of the corresponding AEs were 0.592–1.073–2.811 (p for trend 0.255), 1.161–1.282–4.324 (p for trend 0.03), 0.965–0.781–0.655 (p for trend 0.095), 0.737–0.623–2.147 (p for trend 0.153), 1.061–0.832–0.800 (p for trend 0.391), and 1.412–0.560 (p for trend 0.081), respectively. Appetite loss and general fatigue negatively affected clinical outcomes, whereas hypertension, HFS, proteinuria, and hypothyroidism had positive effects.
Therapeutic Opportunities for Biomarkers in Metastatic Spine Tumors
Authors: Schroeder Christian , Campilan Beatrice , Leary Owen P. , Arditi Jonathan , Michles Madison J. , De La Garza Ramos Rafael , Akinduro Oluwaseun O. , Gokaslan Ziya L. , Martinez Moreno Margot , Sullivan Patricia L. Zadnik
Published: September 2024

For many spine surgeons, patients with metastatic cancer are often present in an emergent situation with rapidly progressive neurological dysfunction. Since the Patchell trial, scoring systems such as NOMS and SINS have emerged to guide the extent of surgical excision and fusion in the context of chemotherapy and radiation therapy. Yet, while multidisciplinary decision-making is the gold standard of cancer care, in the middle of the night, when a patient needs spinal surgery, the wealth of chemotherapy data, clinical trials, and other medical advances can feel overwhelming. The goal of this review is to provide an overview of the relevant molecular biomarkers and therapies driving patient survival in lung, breast, prostate, and renal cell cancer. We highlight the molecular differences between primary tumors (i.e., the patient’s original lung cancer) and the subsequent spinal metastasis. This distinction is crucial, as there are limited data investigating how metastases respond to their primary tumor’s targeted molecular therapies. Integrating information from primary and metastatic markers allows for a more comprehensive and personalized approach to cancer treatment.
Clinicopathologic Features of Early Gastric Cancer after Helicobacter pylori Eradication in Japanese Patients: Comparative Study between Early (<10 Years) and Late (>10 Years) Onset
Authors: Teshima Hajime , Kotachi Takahiro , Kuwai Toshio , Tsuboi Akiyoshi , Tanaka Hidenori , Yamashita Ken , Takigawa Hidehiko , Kishida Yoshihiro , Urabe Yuji , Oka Shiro
Published: September 2024

Background/Objectives: Eradication therapy for Helicobacter pylori gastritis was approved for insurance coverage by the Japanese government in 2013. Since then, the incidence of gastric cancer discovered after eradication (GCAE) has increased. However, there are only a few reports of GCAE diagnosed more than 10 years after eradication. We investigated the clinicopathological characteristics of early-stage GCAE, including histological types and the interval from eradication to diagnosis. Methods: Overall, 379 patients with a total of 448 GCAE lesions treated with endoscopic resection or surgery at our hospital between January 2015 and December 2021 were assessed, and 315 patients with a known interval from eradication to diagnosis of GCAE with a total of 354 lesions were included. We classified the cases into two groups: differentiated-type GCAE (D-GCAE; 279 patients, 318 lesions) and undifferentiated-type GCAE (UD-GCAE; 36 patients, 36 lesions). Results: Smoking and a mild-to-moderate degree of atrophy were risk factors associated with differentiated-type gastric cancer occurring more than 10 years after H. pylori eradication. Additionally, the rate of a mixture of histological types with relatively high malignant potential was significantly higher in UD-GCAE presenting more than 10 years after eradication group than those presenting within 10 years after eradication.
Real-World Outcomes of First-Line Chemotherapy in Metastatic Pancreatic Cancer: A Nationwide Population-Based Study in Korea
Authors: Park Chan Su , Park Byung Kyu , Han Joung-Ho , Lee Kyong Joo , Son Kang Ju
Published: September 2024

Background/Objectives: This nationwide population-based study investigated the overall survival (OS) of patients with metastatic pancreatic cancer (mPC) receiving first-line chemotherapy. Methods: Data from the National Health Insurance Service linked to the Korea Central Cancer Registry were used. Patients with mPC receiving first-line chemotherapy (2012–2019) were included and followed up until 2020. The gemcitabine plus nab-paclitaxel (GnP) and FOLFIRINOX groups were matched according to age, sex, and comorbidities. Results: In total, 8652 patients with mPC were treated with chemotherapy. GnP and FOLFIRINOX have been administered since 2016 and 2017, respectively. The median OS increased annually from 6 months in 2012–2013 to 10 months in 2018–2019. The median OSs in the GnP and FOLFIRINOX groups were significantly longer than those in patients receiving gemcitabine ± erlotinib. A total of 1134 patients from both the GnP and FOLFIRINOX groups were selected using propensity score matching. Before matching, the median OS was longer in the FOLFIRINOX group than in the GnP group (p = 0.0029). After matching, however, there was no significant difference in the median OS between the two groups (11 vs. 11 months, respectively, p = 0.2438). Conclusions: Patients with mPC receiving chemotherapy have shown improved OS since the introduction of GnP and FOLFIRINOX. After matching, OS did not differ between the GnP and FOLFIRINOX groups.
Catalytic Decontamination of Carbon Monoxide Using Strong Metal–Support Interactions on TiO2 Microparticles
Authors: Dayan Avraham , Alter Jacob , Fleminger Gideon
Published: September 2024

The traditional catalytic oxidation of carbon monoxide (CO) using metal oxide catalysts often requires either high temperatures (thermocatalysis) or ultraviolet light (UV) excitation (photocatalysis), limiting practical applications under ambient conditions. Our research aimed to develop a catalytic system capable of oxidizing CO to CO2 at room temperature and in the dark. Using the Strong Metal–Support Interaction (SMSI) methodology, several titanium oxide (TiO2)-complexed metals were prepared (Ag, Au, Pd, and Pt). The highest catalytic efficiency of CO oxidation at room temperature was demonstrated for the TiO2-Pt complex. Therefore, this complex was further examined structurally and functionally. Two modes of operation were addressed. The first involved applying the catalytic system to remove CO from an individual’s environment (environmental system), while the second involved the installation of the catalysis chamber as a part of a personal protection unit (e.g., a mask). The catalytic activity exhibited a significant reduction in CO levels in both the environmental and personal protection scenarios. The practical application of the system was demonstrated through efficient CO oxidation in air emitted from a controlled fire experiment conducted in collaboration with the Israel Fire and Rescue Authority.
Biomass-Derived Co/MPC Nanocomposites for Effective Sensing of Hydrogen Peroxide via Electrocatalysis Reduction
Authors: Wang Mei , Cai Jin , Jiao Lihua , Bu Quan
Published: September 2024

Utilizing the full potential of reproducible biomass resources is crucial for the sustainable development of humanity. In this study, biochar (MPC) was prepared through the microwave-assisted pyrolysis of sugarcane bagasse. Subsequently, Co nanoparticles were introduced by microwave-assisted hydrothermal treatment to form a highly dispersive Co/MPC material. Characterization results indicated that Co nanoparticles were wrapped by thin carbon layers and uniformly dispersed on a carbon-based skeleton via a microwave-assisted hydrothermal synthesis approach, providing high-activity space. Thus, the prepared material was limited to glassy carbon; on the electrode surface, a cobalt-based sensing platform (Co/MPC/GCE) was built. On the basis of this constructed sensing platform, a linear equation was fitted by the concentration change of current signal I and H2O2. The linear range was 0.55–100.05 mM; the detection limit was 1.38 μM (S/N = 3); and the sensitivity was 103.45 μA cm−2 mM−1. In addition, the effect this sensor had on H2O2 detection of actual water samples was conducted by using a standard addition recovery method; results disclosed that the recovery rate and RSD of H2O2 in tap water samples were 94.0–97.6% and 4.1–6.5%, respectively.
Impact of RBMS 3 Progression on Expression of EMT Markers
Authors: Górnicki Tomasz , Lambrinow Jakub , Mrozowska Monika , Krawczyńska Klaudia , Staszko Natalia , Kmiecik Alicja , Piotrowska Aleksandra , Gomułkiewicz Agnieszka , Romanowicz Hanna , Smolarz Beata , Podhorska-Okołów Marzena , Grzegrzółka Jędrzej , Rusak Agnieszka , Dzięgiel Piotr
Published: September 2024

Epithelial-to-mesenchymal transition (EMT) is a complex cellular process that allows cells to change their phenotype from epithelial to mesenchymal-like. Type 3 EMT occurs during cancer progression. The aim of this study was to investigate the role of RNA-binding motif single-stranded interacting protein 3 (RBMS 3) in the process of EMT. To investigate the impact of RBMS 3 on EMT, we performed immunohistochemical (IHC) reactions on archived paraffin blocks of invasive ductal breast carcinoma (n = 449), allowing us to analyze the correlation in expression between RBMS 3 and common markers of EMT. The IHC results confirmed the association of RBMS 3 with EMT markers. Furthermore, we performed an in vitro study using cellular models of triple negative and HER-2-enriched breast cancer with the overexpression and silencing of RBMS 3. RT-qPCR and Western blot methods were used to detect changes at both the mRNA and protein levels. An invasion assay and confocal microscopy were used to study the migratory potential of cells depending on the RBMS 3 expression. The studies conducted suggest that RBMS 3 may potentially act as an EMT-promoting agent in the most aggressive subtype of breast cancer, triple negative breast cancer (TNBC), but as an EMT suppressor in the HER-2-enriched subtype. The results of this study indicate the complex role of RBMS 3 in regulating the EMT process and present it as a future potential target for personalized therapies and a diagnostic marker in breast cancer.
The Role of MicroRNA in the Pathogenesis of Acute Kidney Injury
Authors: Bakinowska Estera , Kiełbowski Kajetan , Pawlik Andrzej
Published: September 2024

Acute kidney injury (AKI) describes a condition associated with elevated serum creatinine levels and decreased glomerular filtration rate. AKI can develop as a result of sepsis, the nephrotoxic properties of several drugs, and ischemia/reperfusion injury. Renal damage can be associated with metabolic acidosis, fluid overload, and ionic disorders. As the molecular background of the pathogenesis of AKI is insufficiently understood, more studies are needed to identify the key signaling pathways and molecules involved in the progression of AKI. Consequently, future treatment methods may be able to restore organ function more rapidly and prevent progression to chronic kidney disease. MicroRNAs (miRNAs) are small molecules that belong to the non-coding RNA family. Recently, numerous studies have demonstrated the altered expression profile of miRNAs in various diseases, including inflammatory and neoplastic conditions. As miRNAs are major regulators of gene expression, their dysregulation is associated with impaired homeostasis and cellular behavior. The aim of this article is to discuss current evidence on the involvement of miRNAs in the pathogenesis of AKI.
The Evaluation and Analysis of the Anti-Corrosion Performance of the Sealing Material B72 for Metal Artifacts Based on Electrochemical Noise
Authors: Xu Hao , Jia Minghao , Hu Pei , Liu Shengyu , Hu Gang
Published: September 2024

Paraloid B-72 (B72), as a transparent, colorless polymer material, has good film-forming ability when dissolved in acetone and is widely used as a sealing material for metal artifacts. In order to analyze and evaluate the preservation performance of B72 as a sealing material on the substrate of metal artifacts, a variety of electrochemical methods, mainly electrochemical noise (EN), and scanning electron microscopy (SEM) were applied to evaluate the B72 coating. The results showed that the B72 coating had a good preservation effect at the initial stage, and its poor water resistance led to the loss of its effectiveness after a few days of immersion. Compared with conventional electrochemical methods, electrochemical noise is non-destructive, which cannot cause new corrosion on the metal substrate and can well characterize the corrosion rate of the test system, and the results of its time domain and frequency domain analyses can correspond well with the polarization resistance and impedance spectra. Electrochemical noise is an effective method for evaluating the anti-corrosion performance of material preservation coatings.
Polymer Capacitor Films with Nanoscale Coatings for Dielectric Energy Storage: A Review
Authors: Cao Liang , Xi Rui , Zhou Chen , He Gaohui , Yang Feng , Xu Lingna , Li He
Published: September 2024

Enhancing the energy storage properties of dielectric polymer capacitor films through composite materials has gained widespread recognition. Among the various strategies for improving dielectric materials, nanoscale coatings that create structurally controlled multiphase polymeric films have shown great promise. This approach has garnered considerable attention in recent years due to its effectiveness. This review examines surface-coated polymer composites used for dielectric energy storage, discussing their dielectric properties, behaviors, and the underlying physical mechanisms involved in energy storage. The review thoroughly examines the fabrication methods for nanoscale coatings and the selection of coating materials. It also explores the latest advancements in the rational design and control of interfaces in organic–inorganic, organic–organic, and heterogeneous multiphase structures. Additionally, the review delves into the structure–property relationships between different interfacial phases and various interface structures, analyzing how nanoscale coatings the impact dielectric constant, breakdown strength, conduction and charge transport mechanisms, energy density and efficiency, thermal stability, and electrothermal durability of polymeric capacitor films. Moreover, the review summarizes relevant simulation methods and offers computational insights. The potential practical applications and characteristics of such nanoscale coating techniques are discussed, along with the existing challenges and practical limitations. Finally, the review concludes with a summary and outlook, highlighting potential research directions in this rapidly evolving field.
Elucidating the Origins of Stranded Loggerhead Sea Turtles (Caretta caretta) in the Eastern Mediterranean through Mitochondrial DNA Mixed-Stock Analysis
Authors: Kaska Arzu , Binen Tugçe , Sözbilen Dogan , Snape Robin , Broderick Annette , Godley Brendan , Beton Damla , Ozkan Meryem , Carreras Carlos , Kaska Yakup
Published: September 2024

It is widely recognized that the examination of haplotypes among sea turtles inhabiting nesting beaches holds significant importance. Yet, an effective conservation effort of a population also requires an investigation of the origin of sea turtles that strand on the shore, especially as many of these result from interactions with fisheries. In consideration of this, we analyzed the haplotypes of 542 stranded individuals from the Eastern Mediterranean and identified a total of 9 different haplotypes. Two of these were new haplotypes, one individual was found in Marmaris, Türkiye, and the other in a stranded species in northern Cyprus. Mixed-stock analysis demonstrated that the majority of the individuals stranded in western Türkiye originated from nesting beaches in the same area (33%), followed by Dalyan, Türkiye (25%), and Cyprus (21%). The partial mixed-stock analysis of individuals stranded in the Dalyan–Dalaman region of Türkiye revealed that most originated from Dalaman (45%) and Dalyan (21%), followed by western Greece (11%). The partial mixed-stock analysis for the Eastern Mediterranean showed that the majority of individuals originated from western Türkiye (69%), followed by Cyprus (11%) and Dalyan (7%). These findings, by quantifying the relative contributions of each region, provide valuable insights for guiding conservation efforts regarding Caretta caretta in the Mediterranean marine environment.
Vitamin D in Primary Sjogren’s Syndrome (pSS) and the Identification of Novel Single-Nucleotide Polymorphisms Involved in the Development of pSS-Associated Diseases
Authors: Dabravolski Siarhei A. , Churov Alexey V. , Starodubtseva Irina A. , Beloyartsev Dmitry F. , Kovyanova Tatiana I. , Sukhorukov Vasily N. , Orekhov Nikolay A.
Published: September 2024

Sjögren’s syndrome (SS) is a chronic autoimmune disorder characterised by lymphocytic infiltration of the exocrine glands, which leads to dryness of the eyes and mouth; systemic manifestations such as arthritis, vasculitis, and interstitial lung disease; and increased risks of lymphoma and cardiovascular diseases. SS predominantly affects women, with a strong genetic component linked to sex chromosomes. Genome-wide association studies (GWASs) have identified numerous single-nucleotide polymorphisms (SNPs) associated with primary SS (pSS), revealing insights into its pathogenesis. The adaptive and innate immune systems are crucial to SS’s development, with viral infections implicated as environmental triggers that exacerbate autoimmune responses in genetically susceptible individuals. Moreover, recent research has highlighted the role of vitamin D in modulating immune responses in pSS patients, suggesting its potential therapeutic implications. In this review, we focus on the recently identified SNPs in genes like OAS1, NUDT15, LINC00243, TNXB, and THBS1, which have been associated with increased risks of developing more severe symptoms and other diseases such as fatigue, lymphoma, neuromyelitis optica spectrum disorder (NMOSD), dry eye syndrome (DES), and adverse drug reactions. Future studies should focus on larger, multi-ethnic cohorts with standardised protocols to validate findings and identify new associations. Integrating genetic testing into clinical practise holds promise for improving SS management and treatment strategies, enabling personalised interventions based on comprehensive genetic profiles. By focusing on specific SNPs, vitamin D, and their implications, future research can lead to more effective and personalised approaches for managing pSS and its complications.
Ileocolic Intussusception Due to Low-Grade Appendiceal Mucinous Neoplasm
Authors: Hsieh Zhao-Yu , Lin Keng-Li
Published: September 2024

We report a rare case of low-grade appendiceal mucinous neoplasm (LAMN) causing ileocolic intussusception. The case underscores the importance of considering ileocolic intussusception in differential diagnoses for nonspecific gastrointestinal symptoms. Early diagnosis via contrast-enhanced CT and scrupulous surgical intervention are crucial for favorable outcomes.
Renal Findings in Patients with Thalassemia at Abdominal Ultrasound: Should We Still Talk about “Incidentalomas”? Results of a Long-Term Follow-Up
Authors: Fatigati Carmina , Meloni Antonella , Costantini Silvia , Spasiano Anna , Ascione Flora , Cademartiri Filippo , Ricchi Paolo
Published: September 2024

We retrospectively collected all ultrasound imaging data of our thalassemia patients over a period of 10 years with the aim of assessing the prevalence and the risk factors of renal stones and cysts. Moreover, we assessed the incidence of renal-cell carcinoma (RCC) among thalassemia patients (133 with thalassemia major (TM) and 157 with thalassemia intermedia (TI)) and its association with demographic and clinical findings. Renal stones were detected in 15.2% of patients. In the multivariable Cox regression analysis, the independent predictors were blood consumption, splenectomy, and proteinuria. Renal cysts were detected in 18.4% of patients. In the multivariable analysis, age emerged as the only independent predictor. After the first detection, 35% of the patients showed changes in the number, size, or grading of renal cysts. During the study period, the crude incidence rate of RCC was 75.9 cases per 100,000 person-years. The most frequent histological subtype (80%) included clear-cell RCC. In total, 80% of patients with RCC had TM and all were positive for hepatitis C virus antibodies. Thalassemia patients are significantly affected by asymptomatic renal diseases such as stones, cysts, and cancer, suggesting the need for regular screening by imaging.
Biomarkers for Immunotherapy Efficacy in Advanced Hepatocellular Carcinoma: A Comprehensive Review
Authors: Taherifard Erfan , Tran Krystal , Saeed Ali , Yasin Jehad Amer , Saeed Anwaar
Published: September 2024

Hepatocellular carcinoma (HCC), the most common primary liver malignancy and the sixth most common cancer globally, remains fatal for many patients with inappropriate responses to treatment. Recent advancements in immunotherapy have transformed the treatment landscape for advanced HCC. However, variability in patient responses to immunotherapy highlights the need for biomarkers that can predict treatment outcomes. This manuscript comprehensively reviews the evolving role of biomarkers in immunotherapy efficacy, spanning from blood-derived indicators—alpha-fetoprotein, inflammatory markers, cytokines, circulating tumor cells, and their DNA—to tissue-derived indicators—programmed cell death ligand 1 expression, tumor mutational burden, microsatellite instability, and tumor-infiltrating lymphocytes. The current body of evidence suggests that these biomarkers hold promise for improving patient selection and predicting immunotherapy outcomes. Each biomarker offers unique insights into disease biology and the immune landscape of HCC, potentially enhancing the precision of treatment strategies. However, challenges such as methodological variability, high costs, inconsistent findings, and the need for large-scale validation in well-powered two-arm trial studies persist, making them currently unsuitable for integration into standard care. Addressing these challenges through standardized techniques and implementation of further studies will be critical for the future incorporation of these biomarkers into clinical practice for advanced HCC.
Device-Less Data-Driven Cardiac and Respiratory Gating Using LAFOV PET Histo Images
Authors: Overbeck Nanna , Andersen Thomas Lund , Rodell Anders Bertil , Cabello Jorge , Birge Noah , Schleyer Paul , Conti Maurizio , Korsholm Kirsten , Fischer Barbara Malene , Andersen Flemming Littrup , Lindberg Ulrich
Published: September 2024

Background: The outstanding capabilities of modern Positron Emission Tomography (PET) to highlight small tumor lesions and provide pathological function assessment are at peril from image quality degradation caused by respiratory and cardiac motion. However, the advent of the long axial field-of-view (LAFOV) scanners with increased sensitivity, alongside the precise time-of-flight (TOF) of modern PET systems, enables the acquisition of ultrafast time resolution images, which can be used for estimating and correcting the cyclic motion. Methods: 0.25 s so-called [18F]FDG PET histo image series were generated in the scope of for detecting respiratory and cardiac frequency estimates applicable for performing device-less data-driven gated image reconstructions. The frequencies of the cardiac and respiratory motion were estimated for 18 patients using Short Time Fourier Transform (STFT) with 20 s and 30 s window segments, respectively. Results: The Fourier analysis provided time points usable as input to the gated reconstruction based on eight equally spaced time gates. The cardiac investigations showed estimates in accordance with the measured pulse oximeter references (p = 0.97) and a mean absolute difference of 0.4 ± 0.3 beats per minute (bpm). The respiratory frequencies were within the expected range of 10–20 respirations per minute (rpm) in 16 out of 18 patients. Using this setup, the analysis of three patients with visible lung tumors showed an increase in tumor SUVmax and a decrease in tumor volume compared to the non-gated reconstructed image. Conclusions: The method can provide signals that were applicable for gated reconstruction of both cardiac and respiratory motion, providing a potential increased diagnostic accuracy.
Multimodal Resources and Approaches for Teaching Young Adolescents: A Review of the Literature
Authors: Linder Roberta , Falk-Ross Francine
Published: September 2024

With an increased number of multimodal texts being read, viewed, or designed by young adolescents ages 10–15 years, classroom instruction requires a focus on current research related to multimodal pedagogy. This integrative literature review sought to determine how instruction based on multimodality has been implemented in middle school classrooms. Educational databases were searched to locate empirical quantitative and qualitative studies describing instruction focusing on reading, viewing, or designing multimodal texts. The authors reviewed 37 research studies published between 2013 and 2023 to update the MLER Research Agenda. An analysis of the studies revealed three themes related to multimodal pedagogy: promoting language and literacy development; enhancing content learning; and providing opportunities for empowerment, personal expression, and identity development. Three gaps in the literature were also noted: no studies were conducted in mathematics classrooms, few studies examined multimodal pedagogy and students with learning or language differences, and few studies investigated the impact of multimodal pedagogy on students’ learning. Based on our findings, we conclude with a series of questions for a research agenda.
Innovative Integration of Poetry and Visual Arts in Metaverse for Sustainable Education
Authors: Kim Ji-yoon , Kim Han-sol
Published: September 2024

The rapid advancement of digital technology has necessitated a reevaluation of traditional educational methodologies, particularly in literature and visual arts. This study investigates the application of metaverse technology to integrate contemporary poetry and visual arts, aiming to enhance university-level education. The purpose is to develop a convergent teaching method that leverages the immersive and interactive capabilities of the metaverse. The research involves a joint exhibition project with students from Sangmyung University and international participants, incorporating a metaverse-based educational program. A sample of 85 students participated in the program, and their experiences were evaluated through surveys and focus group interviews (FGIs). The findings reveal significant correlations between content satisfaction and method satisfaction, underscoring the importance of engaging and interactive methods. The study also identifies technical challenges and provides insights for optimizing digital platforms for educational purposes. The implications suggest that integrating metaverse technology in arts education can significantly enhance creativity, critical thinking, and interdisciplinary skills, offering a sustainable and innovative approach to modern education. Based on these implications, this paper proposes methods for incorporating the insights gained from case analyses and implications into the design of educational programs. It is anticipated that this approach will contribute to enhancing the quality of convergence education in higher education institutions. Furthermore, it is expected that this program will serve as a starting point for the systematic implementation of integrated education and the use of digital platforms, thereby helping to reduce disparities in integrated education between countries.
Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks
Authors: Vajs Ivan , Brkić Srđan , Ivaniš Predrag , Drajic Dejan
Published: September 2024

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.
Object and Pedestrian Detection on Road in Foggy Weather Conditions by Hyperparameterized YOLOv8 Model
Authors: Esmaeil Abbasi Ahmad , Mangini Agostino Marcello , Fanti Maria Pia
Published: September 2024

Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss.
Electrothermal Averaged Model of a Half-Bridge DC–DC Converter Containing a Power Module
Authors: Górecki Krzysztof , Górecki Paweł
Published: September 2024

This article proposes an electrothermal averaged model of a half-bridge DC–DC converter containing a power module. This kind of model enables the computation of characteristics of DC–DC converters using DC analysis. The form of the elaborated model is presented. Both the electrical and thermal properties of the analyzed DC–DC converter are included in this model. This is the first averaged electrothermal model of a DC–DC converter which makes it possible to compute the junction temperature of all the semiconductor devices and magnetic components. The accuracy of the model was experimentally verified in a wide range of switching frequencies and output currents. Particularly, the influence of mutual thermal couplings between the transistors contained in the considered module on the characteristics of the converter and the junction temperature of the transistors is analyzed.
FBLearn: Decentralized Platform for Federated Learning on Blockchain
Authors: Djolev Daniel , Lazarova Milena , Nakov Ognyan
Published: September 2024

In recent years, rapid technological advancements have propelled blockchain and artificial intelligence (AI) into prominent roles within the digital industry, each having unique applications. Blockchain, recognized for its secure and transparent data storage, and AI, a powerful tool for data analysis and decision making, exhibit common features that render them complementary. At the same time, machine learning has become a robust and influential technology, adopted by many companies to address non-trivial technical problems. This adoption is fueled by the vast amounts of data generated and utilized in daily operations. An intriguing intersection of blockchain and AI occurs in the realm of federated learning, a distributed approach allowing multiple parties to collaboratively train a shared model without centralizing data. This paper presents a decentralized platform FBLearn for the implementation of federated learning in blockchain, which enables us to harness the benefits of federated learning without the necessity of exchanging sensitive customer or product data, thereby fostering trustless collaboration. As the decentralized blockchain network is introduced in the distributed model training to replace the centralized server, global model aggregation approaches have to be utilized. This paper investigates several techniques for model aggregation based on the local model average and ensemble using either local or globally distributed validation data for model evaluation. The suggested aggregation approaches are experimentally evaluated based on two use cases of the FBLearn platform: credit risk scoring using a random forest classifier and credit card fraud detection using a logistic regression. The experimental results confirm that the suggested adaptive weight calculation and ensemble techniques based on the quality of local training data enhance the robustness of the global model. The performance evaluation metrics and ROC curves prove that the aggregation strategies successfully isolate the influence of the low-quality models on the final model. The proposed system’s ability to outperform models created with separate datasets underscores its potential to enhance collaborative efforts and to improve the accuracy of the final global model compared to each of the local models. Integrating blockchain and federated learning presents a forward-looking approach to data collaboration while addressing privacy concerns.
Low-Voltage Water Pump System Based on Permanent Magnet Synchronous Motor
Authors: Jin Xinrong , Zhou Leifu , Lang Tingting , Jiang Yanbing
Published: September 2024

This paper designs a safe, low-cost, and efficient permanent magnet synchronous motor (PMSM) booster pump system. The aim is to enhance the pump’s safety and reduce the incidence of electric shock accidents, while also achieving cost reduction and efficiency improvement. The pump components are made of a plastic material, and a safe voltage of 36 V is used as the operating voltage. Additionally, the PMSM is chosen to replace the induction motor (IM) as the pump’s driving device, utilizing sensorless control and field-weakening control strategies. The study results show that when the flow rate is 1.51 m3/h, the efficiency of the PMSM low-voltage pump can reach up to 20.86%. At the same flow rate of 1 m3/h, compared to other pumps, the PMSM low-voltage pump exhibits higher head, energy savings, and efficiency. The proposed PMSM low-voltage pump offers advantages such as high efficiency, energy savings, safety, and low cost. This study provides a reference for the domestic PMSM pump industry.
An Enhanced K-Means Clustering Algorithm for Phishing Attack Detections
Authors: Al-Sabbagh Abdallah , Hamze Khalil , Khan Samiya , Elkhodr Mahmoud
Published: September 2024

Phishing attacks continue to pose a significant threat to cybersecurity, employing increasingly sophisticated techniques to deceive victims into revealing sensitive information or downloading malware. This paper presents a comprehensive study on the application of Machine Learning (ML) techniques for identifying phishing websites, with a focus on enhancing detection accuracy and efficiency. We propose an approach that integrates the CfsSubsetEval attribute evaluator with the K-Means Clustering algorithm to improve phishing detection capabilities. Our method was evaluated using datasets of varying sizes (2000, 7000, and 10,000 samples) from a publicly available repository. Simulation results demonstrate that our approach achieves an accuracy of 89.2% on the 2000-sample dataset, outperforming the traditional kernel K-Means algorithm, which achieved an accuracy of 51.5%. Further analysis using precision, recall, and F1-score metrics corroborates the effectiveness of our method. We also discuss the scalability and real-world applicability of our approach, addressing limitations and proposing future research directions. This study contributes to the ongoing efforts to develop robust, efficient, and adaptable phishing detection systems in the face of evolving cyber threats.
Functional Exercise Induces Adaptations in Muscle Oxygen Saturation in Division One Collegiate Butterfly Swimmers: A Randomized Controlled Trial
Authors: Grotke Jack , Alcantara Austin , Amitrano Joe , Seshadri Dhruv R.
Published: September 2024

This study investigates the impact of a five-week functional exercise intervention designed to enhance the muscular endurance of the posterior shoulder musculature, aiming to mitigate shoulder fatigue and overuse injury. Twelve Division I collegiate butterfly swimmers were recruited and evenly randomized into exercise (EX) and control (CTRL) groups. Weekly 100-yard butterfly sprints were performed, with Muscle Oxygen Saturation (SmO2) continuously monitored using a wearable near-infrared spectroscopy (NIRS) device. This study is among the first to utilize wearable NIRS devices to monitor SmO2 underwater during swimming, demonstrating that a targeted 5-week exercise program significantly improves posterior shoulder endurance, as evidenced by increased Posterior Shoulder Endurance Test (PSET) scores and distinctive SmO2 adaptations in the EX-group compared to the CTRL group. These findings suggest that targeted dryland exercises can enhance posterior shoulder endurance with long-term implications for potentially reducing injury risk and improving performance.
Object Detection in Remote Sensing Images of Pine Wilt Disease Based on Adversarial Attacks and Defenses
Authors: Li Qing , Chen Wenhui , Chen Xiaohua , Hu Junguo , Su Xintong , Ji Zhuo , Wu Yingjun
Published: September 2024

When using deep neural networks for the unmanned aerial vehicle remote sensing image detection and recognition of pine wilt disease (PWD), it could be found that the model is vulnerable to adversarial samples and may lead to abnormal recognition results. That is, serious errors in model classification and localization can be caused by adding minor perturbations, which are difficult for the human eye to detect, to the original samples. Traditional defense strategies rely heavily on adversarial training, but this defense always lags behind the pace of attack. In order to solve this problem, based on the YOLOv5 model, an improved YOLOV5-DRCS model with an adaptive shrinkage filtering network is proposed as follows, which enables the model to maintain relatively stable robustness after being attacked: soft threshold filtering is used in the feature extraction module, the threshold value is calculated based on the adaptive structural unit for denoising, and a SimAM attention mechanism is added in the feature layer fusion so that the final result has more global attention. In order to evaluate the effectiveness of this method, the fast gradient symbol method with white-box attacks was used to conduct an attack test on the remote sensing image dataset of pine wood nematode disease. The results showed that when the number of samples increased by 40%, the average accuracy of 92.5%, 92.4%, 91.0%, and 90.1% on the counter disturbance coefficients ϵ ∈ {2,4,6,8} was maintained, respectively, indicating that the proposed method could significantly improve the robustness and accuracy of the model when faced with the challenge of counter samples.