bluuebunny/crossref_metadata_2025 huggingface dataset:
On Certain Analytic Functions Associated with Nephroid Function
Authors: Ullah Wahid , Fayyaz Rabia , Breaz Daniel , Cotîrlă Luminiţa-Ioana
Published: February 2025

The normalized analytic function ΦN(z)=1+z−z33, which connects the open unit disk onto a bounded domain within the right half of a nephroid-shaped region, is associated with the bounded turning of functions denoted by Rn. It calculates the sharp coefficient inequalities, which include the upper bound of the third Hankel determinant and Logarithmic coefficients related to the functions of the ΦN(z) class. This research mainly focuses on identifying solutions to specific coefficient-related problems for analytic functions within the domain of nephroid functions.
Cross-Attention Adaptive Feature Pyramid Network with Uncertainty Boundary Modeling for Mass Detection in Digital Breast Tomosynthesis
Authors: Ma Xinyu , Sun Haotian , Yuan Gang , Tang Yufei , Liu Jie , Chen Shuangqing , Zheng Jian
Published: February 2025

Computer-aided detection (CADe) of masses in digital breast tomosynthesis (DBT) is crucial for early breast cancer diagnosis. However, the variability in the size and morphology of breast masses and their resemblance to surrounding tissues present significant challenges. Current CNN-based CADe methods, particularly those that use Feature Pyramid Networks (FPN), often fail to integrate multi-scale information effectively and struggle to handle dense glandular tissue with high-density or iso-density mass lesions due to the unidirectional integration and progressive attenuation of features, leading to high false positive rates. Additionally, the commonly indistinct boundaries of breast masses introduce uncertainty in boundary localization, which makes traditional Dirac boundary modeling insufficient for precise boundary regression. To address these issues, we propose the CU-Net network, which efficiently fuses multi-scale features and accurately models blurred boundaries. Specifically, the CU-Net introduces the Cross-Attention Adaptive Feature Pyramid Network (CA-FPN), which enhances the effectiveness and accuracy of feature interactions through a cross-attention mechanism to capture global correlations across multi-scale feature maps. Simultaneously, the Breast Density Perceptual Module (BDPM) incorporates breast density information to weight intermediate features, thereby improving the network’s focus on dense breast regions susceptible to false positives. For blurred mass boundaries, we introduce Uncertainty Boundary Modeling (UBM) to model the positional distribution function of predicted bounding boxes for masses with uncertain boundaries. In comparative experiments on an in-house clinical DBT dataset and the BCS-DBT dataset, the proposed method achieved sensitivities of 89.68% and 72.73% at 2 false positives per DBT volume (FPs/DBT), respectively, significantly outperforming existing state-of-the-art detection methods. This method offers clinicians rapid, accurate, and objective diagnostic assistance, demonstrating substantial potential for clinical application.
The Clinopathological and Prognostic Significance of SPOCK1 in Gynecological Cancers: A Bioinformatics Based Analysis
Authors: Karaman Enes , Yay Fatih , Ayan Durmus , Bayram Ergul , Erturk Sefa
Published: February 2025

Background: Sparc/osteonectin, cwcv, and kazal-like domains proteoglycan 1 (SPOCK1) is an oncogene that promotes tumor formation and progression in certain types of cancer and is associated with poor survival rates. However, there is limited information on the importance of SPOCK1 in gynecological cancers in the literature. The aim of this study was to explore the role of SPOCK1 in ovarian serous cystadenocarcinoma (OV), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and uterine corpus endometrial carcinomas (UCEC). Methods: The data used in this study were obtained from the GEPIA2, TCGA, Kaplan–Meier Plotter, GeneMANIA, UALCAN, cBioPortal, and TIMER databases. Overall survival (OS) and relapse-free survival (RFS) rates were evaluated by Kaplan–Meier survival analysis. Spearman’s rho and statistical significance values were obtained for the correlation between SPOCK1 expression and tumor infiltration by different immune cells. Results: Lower SPOCK1 gene expression was observed in CESC and UCEC compared to normal tissue (p < 0.05), but the OV did not differ significantly (p > 0.05). In OV, SPOCK1 gene expression was solely linked to age; in CESC, it was linked to age, stage, weight, and histology; and in UCEC, it was linked to age, stage, weight, and menopausal status. Conclusions:SPOCK1 gene expression in UCEC showed weak positive correlations with CD8+ T cells and weak negative correlations with CD4+ T cells. SPOCK1 may be a potential prognostic and therapeutic target for gynecological cancers.
Data-Driven Identification of Early Cancer-Associated Genes via Penalized Trans-Dimensional Hidden Markov Models
Authors: Hajebi Khaniki Saeedeh , Shokoohi Farhad
Published: February 2025

Colorectal cancer (CRC) is a significant worldwide health problem due to its high prevalence, mortality rates, and frequent diagnosis at advanced stages. While diagnostic and therapeutic approaches have evolved, the underlying mechanisms driving CRC initiation and progression are not yet fully understood. Early detection is critical for improving patient survival, as initial cancer stages often exhibit epigenetic changes—such as DNA methylation—that regulate gene expression and tumor progression. Identifying DNA methylation patterns and key survival-related genes in CRC could thus enhance diagnostic accuracy and extend patient lifespans. In this study, we apply two of our recently developed methods for identifying differential methylation and analyzing survival using a sparse, finite mixture of accelerated failure time regression models, focusing on key genes and pathways in CRC datasets. Our approach outperforms two other leading methods, yielding robust findings and identifying novel differentially methylated cytosines. We found that CRC patient survival time follows a two-component mixture regression model, where genes CDH11, EPB41L3, and DOCK2 are active in the more aggressive form of CRC, whereas TMEM215, PPP1R14A, GPR158, and NAPSB are active in the less aggressive form.
Perioperative Multi-Kingdom Gut Microbiota Alters in Coronary Artery Bypass Grafting
Authors: Fu Zhou , Jia Yanxiong , Zhao Jing , Guo Yulin , Xie Boqia , An Kun , Yuan Wen , Chen Yihang , Zhong Jiuchang , Tong Zhaohui , Liu Xiaoyan , Su Pixiong
Published: February 2025

Background: Coronary artery bypass grafting (CABG) is one of the main treatments for coronary heart disease (CHD). Gut microbiota, including bacteria, fungi, archaea, and virus, has been reported to be associated with CHD. However, the changes in the multi-kingdom gut microbiota after CABG are not yet clear. This study aimed to explore the changes in multi-kingdom gut microbiota during the early postoperative period of CABG. Methods: We collected fecal samples from 40 patients before and 1 week after CABG surgery. Metagenomic sequencing was used to detect the microbial spectrum and gene functions in the patients’ fecal samples. Results: Post-CABG patients exhibited significant changes in the composition of multi-kingdom gut microbiota and gene functions. Among bacteria, beneficial species such as Bifidobacterium, Bacteroides, and Blautia were significantly reduced after CABG, while the harmful species Enterococcus was significantly increased. In fungi, Schizosaccharomyces pombe was significantly decreased in the postoperative group, while Saccharomyces cerevisiae and Aspergillus chevalieri were significantly increased postoperatively. Spearman correlation analysis indicated that Schizosaccharomyces pombe had positive interactions with beneficial bacteria such as Lachnospiraceae, Ruminococcus, and Blautia. Among archaea, the preoperatively enriched Methanomethylovorans-SGB40959 was significantly reduced postoperatively, and Spearman correlation analysis showed a significant positive interaction with probiotics Ruminococcus and Dorea. In viruses, the phage Enterococcus virus EFP01, which infects Enterococcus, was significantly increased postoperatively and showed a significant positive interaction with Enterococcus. Additionally, postoperative dysregulation of gene functions such as the Phosphoenolpyruvate-dependent Sugar Phosphotransferase System (PTS), Transposition, DNA-mediated, and Transposase Activity was observed, and Spearman correlation analysis indicated significant correlations between the dysregulated gene functions and the microbial communities. Conclusions: This study comprehensively revealed the changes in multi-kingdom species post-CABG. The reduction of beneficial microorganisms and the increase of harmful microorganisms after surgery are of significant clinical importance for understanding the overall health status of post-CABG patients and for optimizing postoperative treatment plans. Future research needs to further explore how to improve the prognosis of post-CABG patients by modulating the gut microbiota.
Mesenchymal Tumors of the Liver: An Update Review
Authors: Choi Joon Hyuk , Thung Swan N.
Published: February 2025

Hepatic mesenchymal tumors (HMTs) are non-epithelial benign and malignant tumors with or without specific mesenchymal cell differentiation. They are relatively uncommon. Except for mesenchymal hamartoma, calcified nested stromal–epithelial tumor, and embryonal sarcoma, most mesenchymal lesions are not specific to the liver. Pathologists face challenges in diagnosing HMTs due to their diverse morphologies and phenotypic variations. Accurate diagnosis is critical for directing appropriate patient care and predicting outcomes. This review focuses on mesenchymal tumors with a relative predilection for the liver, including vascular and non-vascular mesenchymal neoplasms. It provides a thorough and up-to-date overview, concentrating on clinical and pathological features, differential diagnosis, and diagnostic approaches.
Advances in HIV Treatment: Long-Acting Antiretrovirals and the Path Toward a Cure
Authors: Lanzafame Massimiliano , Mori Giovanni , Vento Sandro
Published: February 2025

Long-acting antiretroviral therapy (LA-ART) represents an important advancement in HIV care as it has considerably reduced the frequency of dosing and therefore improved adherence [...]
Dual Monitoring of Blood Acetylcholinesterase Content and Catalytic Activity Utilizing Fluorometry-Integrated Surface Plasmon Resonance
Authors: Xie Yuanyuan , Hou Yifei , Hu Mengwei , Chen Hongzhuan , Wang Hao , Zhao Lanxue , Xu Jianrong
Published: February 2025

Acetylcholinesterase inhibitors (AChEIs), particularly donepezil, are commonly used to treat mild-to-moderate Alzheimer’s disease (AD). However, drug accumulation during long-term use could change AChE activity and content, leading to peripheral side effects and prompting medication discontinuation. However, there are a lack of methods to simultaneously determine the content and catalytic activity of AChE. By using phosphatidylinositol-specific phospholipase C to strip AChE from erythrocyte surfaces, we developed a novel method combining surface plasmon resonance and fluorescence detection for the simultaneous detection of AChE content and activity, producing stable, reliable, and accurate results. The established determination range spans from 263.37 ng/mL to 3000 ng/mL (4.05 nM to 46.15 nM) for concentration, and from 39.02 mU/mL to 1000 mU/mL for activity. Compared to traditional methods, this approach simplifies operations, reduces detection time, expands the dynamic range, and lowers detection limits, potentially advancing AChE-related research and supporting clinical diagnostics and drug development.
Modeling Critical Rework Factors in the Construction Industry: Insights and Solutions
Authors: Gumusburun Ayalp Gulden , Arslan Fatma
Published: February 2025

Construction professionals recognize rework’s negative impact on project performance, yet a comprehensive understanding of its critical factors still needs to be provided. Consequently, this study sought to inquire deeply into the causes of construction rework. A systematic framework was employed to achieve the research objectives. Initially, potential causes of rework were identified through a systematic literature review. Subsequently, a survey was developed and emailed to the sample group. Exploratory factor analysis was used to extract critical rework factors (CRFs) and normalized mean value analysis was used to evaluate the criticality of the obtained causes. Structural equation modeling was used to quantify and simulate the effect sizes of the components that were collected. Out of 43 possible causes, this study found 21 critical causes why rework occurs in the Turkish construction sector. Additionally, it uncovered four original CRFs, namely “management and planning deficiencies”, “design and time constraints”, “labor quality and compliance issues”, and “project dynamics and communication challenges”. While numerous studies have explored rework causes using different approaches and methodologies, there remains a lack of insight into the key factors leading to rework. Unlike earlier research, this study offers a thorough and quantitative identification of four distinct critical rework factors in Turkey.
Long-Term Building Renovation Strategies—F-TOPSIS Analysis of Solutions Applied in the Chosen European Union Countries
Authors: Plebankiewicz Edyta , Grącki Jakub
Published: February 2025

The article analyzes long-term renovation strategies in EU member countries using the F-TOPSIS method, focusing on chosen criteria such as CO2 emission reductions, renovation rates, energy savings, investment requirements, and overall strategy quality. High-performing countries, such as Finland and Spain, demonstrate the importance of clear targets, robust planning, and substantial financial commitments. In contrast, several countries show gaps in strategic detail or ambition, highlighting challenges in achieving EU climate neutrality goals. The methodology underscores the effectiveness of multi-criteria decision-making tools in assessing complex renovation strategies. The findings emphasize the need for harmonized metrics and innovative approaches, such as digital tools like building renovation passports.
Deeper Analysis to Identify the True Benefit of ICIs Immunotherapy in First-Line Treatment for Non-HER2-Positive/HER2-Negative Advanced or Metastatic Advanced or Metastatic Gastric Cancer (GC) or Gastroesophageal Junction Cancer (GEJC)
Authors: Zheng Bowen , Lou Fanzhuoran , He Yuting , Fu Miao , Huang Xintian , Tan Weijuan , Chen Quan , Xie Xiaowen , Hu Tianhui , Xiao Li
Published: February 2025

Gastric cancer (GC) has a high global incidence and mortality rate [...]
The Prognostic Impact of Additional Molecular and Cytogenetic Abnormalities on AML Patients with NPM1- and/or FLT3-ITD Mutations Receiving Intensive Chemotherapy: Real-World Data from the Greek Registry
Authors: Lazana Ioanna , Papathanassiou Maria , Konstantellos Ioannis , Tzenou Tatiana , Kopsaftopoulou Anastasia , Liga Maria , Violaki Vasiliki , Kyriazopoulou Lydia , Gkirkas Konstantinos , Papalexandri Apostolia , Plata Eleni , Michalis Evrydiki , Leonidopoulou Theoni , Garofalaki Maria , Sioni Anastasia , Tziotziou Irene , Lalayanni Chrysavgi , Kiousiafes Dimitrios , Vassilakopoulos Theodoros P. , Kapsali Eleni , Spyridonidis Alexandros , Baltadakis Ioannis , Angelopoulou Maria , Sakellari Ioanna , Tsirigotis Panagiotis
Published: February 2025

Background/Objective: The prognostic impact of additional cytogenetic aberrations and molecular abnormalities (such as MDS-related mutations, mutations in myeloid genes and the KRAS/NRAS mutations) in patients with NPM1- and/or FLT3-ITD-mutated AML remains elusive. Methods: This retrospective, multicentre study of real-world data aimed to investigate the impact of these mutations and cytogenetic abnormalities on the prognosis of patients with NPM1- and/or FLT3-ITD-mutated AML, treated with intensive chemotherapy. Results: In a cohort of 161 patients, the only parameters identified to affect the outcomes (EFS and OS) were the age of the patient, primary refractory disease, the presence of a NPM1 mutation and the use of allogenic stem cell transplantation (allo-SCT) within the first complete remission. More specifically, ages below the median conferred significantly improved outcomes, whereas primary refractory disease exhibited a negative correlation with the EFS and OS. Subsequent subgroup analysis, stratifying patients into three groups (Group 1: NPM1mutated/FLT3wt; Group 2: NPM1mutated/FLT3mutated; Group 3: NPM1wt/FLT3mutated). revealed that allo-SCT in CR1 improved the outcomes (EFS and OS) in Groups 2 and 3, but had no additional impact in Group 1. Conclusions: Age, primary refractory disease and allogenic stem cell transplantation in the first complete response were found to have a prognostic impact on outcomes, Interestingly, no significant association was detected between the poor prognostic cytogenetic abnormalities or the presence of additional mutations in myeloid genes, MDS-related genes or KRAS/NRAS genes and the outcomes in any group of patients.
Osimertinib in the Treatment of Epidermal Growth Factor Receptor-Mutant Early and Locally Advanced Stages of Non-Small-Cell Lung Cancer: Current Evidence and Future Perspectives
Authors: Veccia Antonello , Dipasquale Mariachiara , Lorenzi Martina , Monteverdi Sara , Kinspergher Stefania , Zambotti Elena , Caffo Orazio
Published: February 2025

The treatment of epidermal growth factor receptor (EGFR)-mutant non-small-cell lung cancer (NSCLC) patients was dramatically revolutionized by the introduction of EGFR tyrosine kinase inhibitors in clinical practice, both in advanced and locally advanced/early stages. The present work focuses on osimertinib use in locally advanced and early NSCLC stages. Phase 3 clinical trials have supported the use of osimertinib as the new standard of care, both in the adjuvant setting and in locally advanced disease. The ADAURA study reported an overall survival (OS) advantage for adjuvant osimertinib in completely resected stage II-IIIA EGFR-mutant tumors, while the LAURA study proved a statistically significant benefit in progression-free survival (PFS) and a delay of central nervous system metastasis development in EGFR-mutant patients treated with osimertinib maintenance after concurrent chemoradiotherapy for locally advanced disease. In the neoadjuvant setting, data on osimertinib’s efficacy are conflicting; therefore, the Neo-ADAURA study is evaluating the efficacy and safety of neoadjuvant osimertinib alone or in combination with chemotherapy in patients with stage II-IIIB NSCLC and common EGFR mutations. We discuss several issues that need to be clarified, such as the efficacy of the drug on uncommon mutations, the long-term impact on survival, and the management of resistance mechanisms. Moreover, we report the studies that are trying to identify potential biomarkers of response, such as the circulating tumor DNA (ctDNA), with the aim of selecting patients who will benefit most from osimertinib.
Esophagectomy Versus Endoscopic Resection with Adjuvant Therapy for T1b/T2 Esophageal Cancer: A Systematic Review and Meta-Analysis
Authors: Peters Eagan J. , Robinson Madeline , Patel Noopur , Kidane Biniam
Published: February 2025

Esophageal cancer is the seventh most common cause of cancer-related death worldwide [...]
Advancing Glioblastoma Research with Innovative Brain Organoid-Based Models
Authors: Correia Cátia D. , Calado Sofia M. , Matos Alexandra , Esteves Filipa , De Sousa-Coelho Ana Luísa , Campinho Marco A. , Fernandes Mónica T.
Published: February 2025

Glioblastoma (GBM) is a relatively rare but highly aggressive form of brain cancer characterized by rapid growth, invasiveness, and resistance to standard therapies. Despite significant progress in understanding its molecular and cellular mechanisms, GBM remains one of the most challenging cancers to treat due to its high heterogeneity and complex tumor microenvironment. To address these obstacles, researchers have employed a range of models, including in vitro cell cultures and in vivo animal models, but these often fail to replicate the complexity of GBM. As a result, there has been a growing focus on refining these models by incorporating human-origin cells, along with advanced genetic techniques and stem cell-based bioengineering approaches. In this context, a variety of GBM models based on brain organoids were developed and confirmed to be clinically relevant and are contributing to the advancement of GBM research at the preclinical level. This review explores the preparation and use of brain organoid-based models to deepen our understanding of GBM biology and to explore novel therapeutic approaches. These innovative models hold significant promise for improving our ability to study this deadly cancer and for advancing the development of more effective treatments.
Salicylate-Elicited Activation of AMP-Activated Protein Kinase Directly Triggers Degradation of C-Myc in Colorectal Cancer Cells
Authors: Matos Ana Laura S. A. , Ovens Ashley J. , Jakobsen Emil , Iglesias-Gato Diego , Bech Jacob M. , Friis Stine , Bak Lasse Kristoffer , Madsen Gunvor I. , Oakhill Jonathan S. , Puustinen Pietri , Moreira José M. A.
Published: February 2025

Aspirin has consistently shown preventive effects in some solid cancers, notably colorectal cancer. However, the precise molecular mechanisms underlying this positive effect have remained elusive. In this study, we used an azoxymethane-induced mouse model of colon carcinogenesis to identify aspirin-associated molecular alterations that could account for its cancer-preventive effect. Transcriptomic analysis of aspirin-treated mice showed a strong reduction in c-Myc protein levels and effects on the Myc-dependent transcriptional program in colonic cells. Proto-oncogene c-Myc cooperates with AMP-activated protein kinase (AMPK) to control cellular energetics. Here, we show that salicylate, the active metabolite of aspirin, reduces c-Myc protein expression levels through multiple mechanisms that are both AMPK dependent and independent. This effect is cell-type dependent and occurs at both the transcriptional and post-translational levels. Salicylate-induced AMPK activation leads to the phosphorylation of c-Myc at Thr400, as well as its destabilization and degradation. Our results reveal a complex, multilayered, negative effect of salicylate on c-Myc protein abundance and suggest that chronic depletion of c-Myc can counteract the neoplastic transformation of colorectal epithelium, underpinning the preventive effect of aspirin on colorectal cancer.
Predictors of Friendship Skills for Adolescents with Asthma: An Analysis of Parent Report on the 2022 National Survey of Children’s Health
Authors: Workman Brandon , Nabors Laura A. , Pierre Hixon Danielle , Merianos Ashley L. , Odar Stough Cathleen , Bernstein Joshua S. , Bernstein Jonathan A.
Published: February 2025

Background/Objectives: This study assesses parent reports of adolescent- and parent-level factors related to friendships among adolescents with asthma. This study serves to inform physicians and other health care professionals of factors related to difficulties making friends for adolescents with asthma, providing information from parent reports to identify risk and resilience factors related to friendships. Methods: Adolescents aged 12–17 years with current asthma from the 2022 National Survey of Children’s Health (NSCH) were selected for the study (n = 1812). A weighted logistic regression analysis was performed to explore predictors related to making and keeping friends. Adolescent-level predictors were asthma severity, allergies, body mass index, having behavioral problems, and adolescent sex and race/ethnicity (non-Hispanic White, and others, including Hispanic). Parent-level predictors were parent stress and coping. Results: Adolescents who were female, non-Hispanic White, had moderate/severe asthma symptoms, had current allergies, were overweight, and had behavioral problems were more likely to have difficulty making and keeping friends than adolescents who were male, non-White, had mild asthma, did not have allergies, were a normal weight or underweight, and did not have behavioral problems. Parents who reported difficulty coping with parenthood and its associated stressors were more likely to report that their adolescents had difficulty making friends. Conclusions: Pediatric moderate-to-severe asthma patients whose parents had difficulty coping with stress were more likely to exhibit greater difficulty making and keeping friends. Health care professionals managing these patients should incorporate questions into their history that address behavioral problems and parental household stress growing up to determine optimal strategies for improving friendship relationships. Whether this strategy leads to better asthma control and outcomes requires further investigation. In future studies, case studies with information about changes in friendships over time for adolescents with asthma should be carried out. The case studies could potentially highlight social strategies to use to improve friendship skills, and ultimately friendship-making for this vulnerable group.
Areca catechu L. Extract Inhibits Colorectal Cancer Tumor Growth by Modulating Cell Apoptosis and Autophagy
Authors: Tsai Meng-Hsiu , Chen Chang-Han , Chen Chien-Lin , Lee Mei-Hsien , Wu Li-Ching , Hsu Yi-Chiung , Hsiao Chao-Yang , Lee Chang-Ti , Pi Kuo-Li , Su Li-Jen
Published: February 2025

Colorectal cancer (CRC) is a common cancer globally, and chemotherapy often causes severe complications, necessitating effective drugs with minimal side effects. As Areca catechu L. extract (ACE) is a Traditional Chinese Medicine that contains numerous active compounds with anticancer effects, in this study, the Cell Counting Kit-8 (CCK-8) assay was used to determine ACE’s effect on CRC cell lines, revealing that it significantly inhibits CoLo320DM and HCT116 cells. In vivo experiments with NU-Foxn1nu mice indicated that ACE inhibits tumor growth, while a flow cytometry assay revealed that higher ACE concentrations increased cell apoptosis and ROS levels. Next-generation sequencing (NGS) showed that ACE increases the fold changes in apoptosis, DNA damage, and autophagy-related genes while inhibiting the fold changes in cell proliferation and Wnt signaling pathway genes. We conducted Western blotting to confirm these findings. Overall, ACE demonstrates potential as a drug candidate by promoting apoptosis and autophagy, and significantly reducing cell viability and tumor growth, thus offering a new approach for effective colorectal cancer treatment with minimal side effects.
Energy Implications of Mitigating Side-Channel Attacks on Branch Prediction
Authors: Alqurashi Fahad , AL-Hashimi Mohammed , Saleh Mostafa , Abulnaja Osama
Published: February 2025

Spectre variants 1 and 2 pose grave security threats to dynamic branch predictors in modern CPUs. While extensive research has focused on mitigating these attacks, little attention has been given to their energy and power implications. This study presents an empirical analysis of how compiler-based Spectre mitigation techniques influence energy consumption. We collect fine-grained energy readings from an HPC-class CPU via embedded sensors, allowing us to quantify the trade-offs between security and power efficiency. By utilizing a standard suite of microbenchmarks, we evaluate the impact of Spectre mitigations across three widely used compilers, comparing them to a no-mitigation baseline. The results show that energy consumption varies significantly depending on the compiler and workload characteristics. Loop unrolling influences power consumption by altering branch distribution, while speculative execution, when unrestricted, plays a role in conserving energy. Since Spectre mitigations inherently limit speculative execution, they should be applied selectively to vulnerable code patterns to optimize both security and power efficiency. Unlike previous studies that primarily focus on security effectiveness, this work uniquely evaluates the energy costs associated with Spectre mitigations at the compiler level, offering insights for power-efficient security strategies. Our findings underscore the importance of tailoring mitigation techniques to application needs, balancing performance, energy consumption, and security. The study provides practical recommendations for compiler developers to build more secure and energy-efficient software.
Effects of the Diurnal Light and Temperature Fluctuations on the Growth, Photosynthesis and Biochemical Composition of Terrestrial Oleaginous Microalga Vischeria sp. WL1 (Eustigmatophyceae)
Authors: Guo Xinhong , Li Zhengke , Wang Daxi , Yuan Xiaolong , Wang Xiaojiao , Xue Huidan , Zeng Qiao , Gao Xiang
Published: February 2025

Dynamic changes in diurnal light and temperature are a natural phenomenon, particularly pronounced in arid environments. However, it remains unclear whether the coupling of diurnal light and temperature fluctuations contributes to the capability and evolution of lipid accumulation in arid terrestrial microalgae. Here, we characterized the effects of diurnal light and temperature fluctuations on the growth, photosynthesis, lipids, carbohydrates, proteins and fatty acids in Vischeria sp. WL1, an oil-producing Eustigmatophyceae microalga sourced from an arid steppe. The photosynthetic activity parameters (Fv/Fm, PIabs, ψ0 and φE0) were the highest, while parameters of damage and energy dissipation (Wk and DIO/RC) were the lowest, under the diurnal light cycle (DLC) among all conditions and then followed by the diurnal light–temperature cycle (DLTC). DLTC led to reduced growth and biomass but enhanced lipid accumulation, with lipid content reaching a maximum of 54.7% on day 4. This observation may suggest a carbon reallocation from carbohydrates toward lipid synthesis. DLTC resulted in an increase in the saturated fatty acids (SFA) content on day 12 and only in C18:2 among polyunsaturated fatty acids (PUFA) on day 4 while lowering eicosapentaenoic acid (EPA) concentration and content on day 12. Together, our findings will contribute to the understanding of the adaptive strategies of terrestrial microalgae to the arid environment, as well as the beneficial clues for exploring terrestrial microalgae for oil production.
Hyperpolarized Xenon-129 MRI: Narrative Review of Clinical Studies, Testing, and Implementation of Advanced Pulmonary In Vivo Imaging and Its Diagnostic Applications
Authors: MacLeod Jamie L. , Khan Humam M. , Franklin Ava , Myc Lukasz , Shim Yun Michael
Published: February 2025

Hyperpolarized xenon-129 MRI (129XeMRI) has emerged as a powerful tool in the identification, evaluation, and assessment of disease endotyping and in response to interventions for a myriad of pulmonary diseases. Growing investigative efforts ranging from basic science to application in translational research have employed 129XeMRI in the evaluation of pulmonary conditions such as chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), asthma, and cystic fibrosis (CF). The novel feature of 129XeMRI is its ability to generate anatomic and physiologic readouts of the lung with resolution from the whole lung down to the lobar level. Additional advantages include being non-invasive and non-radioactive, and utilizing an inexpensive and ubiquitous noble gas as an inhalation contrast agent: xenon-129. In this review, we outline the clinical advances provided by 129XeMRI among common pulmonary diseases with high healthcare burdens in recent decades.
The Effectiveness of Transabdominal Ultrasonography in Managing Chronic Constipation in the Elderly, with a Focus on the Underlying Pathological Conditions
Authors: Manabe Noriaki , Fujita Minoru , Haruma Ken
Published: February 2025

Chronic constipation is more common in the elderly and associated with numerous diseases. For the diagnosis of chronic constipation in the elderly, it is essential to exclude constipation secondary to colorectal cancer or other causes. Chronic constipation in the elderly also often requires lifestyle modifications, as well as drug treatments because of the involvement of pathologies such as reduced colonic transport capacity and rectal hyposensitivity. Therefore, it is extremely important to evaluate the pathophysiology of both the colon and the rectum. Transabdominal ultrasonography (TUS) is a key technique for providing comprehensive medical care and allows simultaneous functional assessment and exclusion of organic diseases related to constipation such as colorectal cancer. Although several studies have reported the clinical utility of TUS for chronic constipation, which includes its simplicity, noninvasiveness, and low cost, the majority were in children. Thus, there are limited studies in adults. Herein, we review the utility of TUS for indirect assessment of colonic transit time using several TUS parameters that can be applied clinically, as well as treatment options for chronic constipation. The constipation index (i.e., mean transverse diameter of the colon), assessed by TUS, is a useful indirect indicator of colonic transit time. If the constipation index is <21.2, increased fiber or treatment with osmotic agents should be used. If the constipation index is ≥21.2, then the ratio of the left to the right lateral colonic diameters should be evaluated. If this value is ≥0.5, a secretagogue or bile acid transporter inhibitor should be administered. It is noteworthy that nursing care is becoming increasingly important in Japan’s super-aging society. A significant proportion of nursing care is provided to patients with chronic constipation, a cohort that is predicted to grow in the future. In these patients, fecal masses often remain in the rectum, which may require an enema or stool extraction. Therefore, it is important to assess both the presence of feces in the rectum and the consistency of the feces. Recently, portable ultrasound (US) devices equipped with artificial intelligence have been developed and used clinically for treatment of patients with chronic constipation in nursing care. Rectal findings using portable US devices can aid in selecting appropriate constipation treatments. Thus, portable US will likely become increasingly important as a next-generation examination device in nursing care. TUS (including portable US) is noninvasive, simple, and repeatable and will become a fundamental modality in the management of chronic constipation.
Foggy Drone Teacher: Domain Adaptive Drone Detection Under Foggy Conditions
Authors: Zheng Guida , Tan Benying , Wu Jingxin , Qin Xiao , Li Yujie , Ding Shuxue
Published: February 2025

With the growing use of drones, efficient detection algorithms are crucial, especially under adverse weather conditions. Most existing drone detection algorithms perform well only in clear weather, resulting in significant performance drops in foggy conditions. This study focuses on improving drone detection in foggy environments using the Mean Teacher framework for domain adaptation. The Mean Teacher framework’s performance relies on the quality of the teacher model’s pseudo-labels. To enhance the quality of the pseudo-labels from the teacher model, we introduce Foggy Drone Teacher (FDT), which includes three key components: (1) Adaptive Style and Context Augmentation to reduce domain shift and improve pseudo-label quality; (2) Simplified Domain Alignment with a novel adversarial strategy to boost domain adaptation; and (3) Progressive Domain Adaptation Training, a two-stage process that helps the teacher model produce more stable and accurate pseudo-labels. In addition, owing to the lack of publicly available data, we created Foggy Drone Dataset (FDD) to support this research. Extensive experiments show that our model achieves a 21.1-point increase in AP0.5 compared to the baseline and outperforms state-of-the-art models. This method significantly improves drone detection accuracy in foggy conditions.
Bridging the Gap: A Debate on Sustainability Aspects of Digital Media in Education
Authors: Spangenberger Pia , Söbke Heinrich
Published: February 2025

While there has been some progress in addressing ethical questions within current digital media integration frameworks, such as the TPACK model, insufficient research exists regarding the meaningful integration of digital media into education while considering its impact on sustainability in terms of its ecological, economic, and social dimensions. Hence, this article aims to bridge these two critical research streams and examines the current debate on how these sustainability aspects have been considered in the complex debate on efficient digital media integration in the context of education. Besides potentially significant differences regarding digital sufficiency and the life cycle assessment of digital media, there may be further ecological, economic, and social dimensions of media in education specific to the context of sustainable development. By discussing the impact of digital media on the three dimensions of sustainability using three examples (virtual field trips, smartboards, and large language models), we further categorize our findings from the early stages of a systematic literature review (SLR) into a taxonomy on the consideration of sustainability regarding digital media in education. Initially aiming at an SLR involving the screening of 2099 articles to provide deeper insights into how technology integration frameworks consider all three pillars of sustainable development, none of the articles completely met our inclusion criteria. Instead, we found research on certain sustainability aspects of digital media in education, such as learning objectives, life cycle assessment, and pedagogical approaches, combined with various interpretations of the term sustainability. Based on our findings, we developed a taxonomy on sustainability regarding digital media in education, and argue in favor of a comprehensive view and meaningful measurability of the sustainability dimensions when integrating digital media into education. For the promotion of sustainability regarding digital media in education, we suggest the development of an assessment framework for guiding the practical application of digital media in line with the dimensions of sustainability.
Data-Driven Clustering and Classification of Road Vehicle Radar Scattering Characteristics Using Histogram-Based RCS Features
Authors: Coşkun Aysu , Bilicz Sándor
Published: February 2025

This paper presents the clustering and classification of the radar scattering characteristics of vehicles under real-world driving conditions. The classification of 14 distinct vehicle types is achieved through statistical features derived from their radar cross-section (RCS) characteristics, represented as histograms. Various machine learning classification techniques are applied, and their performance is evaluated across different clustering scenarios. The results of the clustering algorithm are in line with the physics-based expectations on the scattering from different vehicle types. The classification results demonstrate the effectiveness of the proposed algorithm, validating the histogram-based feature method as a novel and promising approach for vehicle identification and detection. In addition, the results highlight the potential applications of our methods in millimeter-wave (mmWave) radar technology, illustrating their capability to improve feature extraction by means of RCS histograms and ensure robust classification in diverse and challenging environments.
SATRN: Spiking Audio Tagging Robust Network
Authors: Gao Shouwei , Deng Xingyang , Fan Xiangyu , Yu Pengliang , Zhou Hao , Zhu Zihao
Published: February 2025

Audio tagging, as a fundamental task in acoustic signal processing, has demonstrated significant advances and broad applications in recent years. Spiking Neural Networks (SNNs), inspired by biological neural systems, exploit event-driven computing paradigms and temporal information processing, enabling superior energy efficiency. Despite the increasing adoption of SNNs, the potential of event-driven encoding mechanisms for audio tagging remains largely unexplored. This work presents a pioneering investigation into event-driven encoding strategies for SNN-based audio tagging. We propose the SATRN (Spiking Audio Tagging Robust Network), a novel architecture that integrates temporal–spatial attention mechanisms with membrane potential residual connections. The network employs a dual-stream structure combining global feature fusion and local feature extraction through inverted bottleneck blocks, specifically designed for efficient audio processing. Furthermore, we introduce an event-based encoding approach that enhances the resilience of Spiking Neural Networks to disturbances while maintaining performance. Our experimental results on the Urbansound8k and FSD50K datasets demonstrate that the SATRN achieves comparable performance to traditional Convolutional Neural Networks (CNNs) while requiring significantly less computation time and showing superior robustness against noise perturbations, making it particularly suitable for edge computing scenarios and real-time audio processing applications.
Self-Supervised Time-Series Preprocessing Framework for Maritime Applications
Authors: Dong Shengli , Liu Jilong , Han Bing , Wang Shengzheng , Zeng Hong , Zhang Meng
Published: February 2025

This study proposes a novel self-supervised data-preprocessing framework for time-series forecasting in complex ship systems. The framework integrates an improved Learnable Wavelet Packet Transform (L-WPT) for adaptive denoising and a correlation-based Uniform Manifold Approximation and Projection (UMAP) approach for dimensionality reduction. The enhanced L-WPT incorporates Reversible Instance Normalization to improve training efficiency while preserving denoising performance, especially for low-frequency sporadic noise. The UMAP dimensionality reduction, combined with a modified K-means clustering using correlation coefficients, enhances the computational efficiency and interpretability of the reduced data. Experimental results validate that state-of-the-art time-series models can effectively forecast the data processed by this framework, achieving promising MSE and MAE metrics.
A Hard Negatives Mining and Enhancing Method for Multi-Modal Contrastive Learning
Authors: Li Guangping , Gao Yanan , Huang Xianhui , Ling Bingo Wing-Kuen
Published: February 2025

Contrastive learning has emerged as a dominant paradigm for understanding 3D open-world environments, particularly in the realm of multi-modalities. However, due to the nature of self-supervised learning and the limited size of 3D datasets, pre-trained models in the 3D point cloud domain often suffer from overfitting in downstream tasks, especially in zero-shot classification. To tackle this problem, we design a module to mine and enhance hard negatives from datasets, which are useful to improve the discrimination of models. This module could be seamlessly integrated into cross-modal contrastive learning frameworks, addressing the overfitting issue by enhancing the mined hard negatives during the process of training. This module consists of two key components: mining and enhancing. In the process of mining, we identify hard negative samples by examining similarity relationships between vision–vision and vision–text modalities, locating hard negative pairs within the visual domain. In the process of enhancing, we compute weighting coefficients via the similarity differences of these mined hard negatives. By enhancing the mined hard negatives while leaving others unchanged, we improve the overall performance and discrimination of models. A series of experiments demonstrate that our module can be easily incorporated into various contrastive learning frameworks, leading to improved model performance in both zero-shot and few-shot tasks.
A Method for Reducing White Noise in Partial Discharge Signals of Underground Power Cables
Authors: Li Jifang , Zhang Qilong
Published: February 2025

Online partial discharge (PD) detection for power cables is one reliable means of monitoring their health. However, strong interference by white noise poses a major challenge in the process of collecting information on partial discharge signals. To solve the problem whereby the wavelet threshold estimation based on sample entropy falls into the local optimal and the wavelet noise reduction makes it difficult to process detailed information, we propose a partial discharge signal noise reduction method based on a combination of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and discrete wavelet transform (DWT) with multiscale sample entropy (MSE). Firstly, the ICEEMDAN method was used to decompose the original sequence into multiple intrinsic mode components. The intrinsic mode function (IMF) components were grouped using the mutual information method, and high-frequency noise was eliminated using the kurtosis criterion. Next, an MSE model was established to optimize the wavelet threshold, and wavelet noise reduction was applied to the effective component. The ICEEMDAN-MSE-DWT method can retain effective information while achieving complete denoising, which alleviates the problem of information loss that occurs after denoising using the wavelet method. Lastly, as shown by our simulation and experimental results, the proposed method can effectively realize noise reduction for power cable partial discharge signals, thus providing an effective method.
Energy Trading Strategies for Integrated Energy Systems Considering Uncertainty
Authors: Gao Jin , Shao Zhenguo , Chen Feixiong , Lak Mohammadreza
Published: February 2025

To improve the stable operation and promote the energy sharing of the integrated energy system (IES), a comprehensive energy trading strategy considering uncertainty is proposed. Firstly, an IES model incorporating power-to-gas (P2G) and a carbon capture system (CCS) is established to reduce carbon emissions. Secondly, this model is integrated into a four-level robust optimization to address the fluctuation of renewable energy sources in IES operations. This not only considers probability distribution scenarios of renewable energy and the uncertainty of its output, but also effectively reduces the model’s conservatism by constructing a multi-interval uncertainty set. On this basis, a Nash–Harsanyi bargaining method is used to solve the issue of benefit allocation among multiple IESs. Finally, the energy trading model is solved using a distributed algorithm that ensures an equitable distribution of benefits while protecting the privacy of each IES. The simulation results validate the effectiveness of the proposed strategy.
Electricity Data Quality Enhancement Strategy Based on Low-Rank Matrix Recovery
Authors: Xu Guo , Teng Xinliang , Zhang Lei , Xu Jianjun
Published: February 2025

Electricity consumption data form the foundation for the efficient and reliable operation of smart grids and are a critical component for ensuring effective data mining. However, due to factors such as meter failures and extreme weather conditions, anomalies frequently occur in the data, which adversely impact the performance of data-driven applications. Given the near full-rank nature of low-voltage distribution area electricity consumption data, this paper employs clustering to enhance the low-rank property of the data. Addressing common issues such as missing data, sparse noise, and Gaussian noise in electricity consumption data, this paper proposes a multi-norm optimization model based on low-rank matrix theory. Specifically, the truncated nuclear norm is used as an approximation of matrix rank, while the L1-norm and F-norm are employed to constrain sparse noise and Gaussian noise, respectively. The model is solved using the Alternating Direction Method of Multipliers (ADMM), achieving a unified framework for handling missing data and noise processing within the model construction. Comparative experiments on both synthetic and real-world datasets demonstrate that the proposed method can accurately recover measurement data under various noise contamination scenarios and different distributions of missing data. Moreover, it effectively separates principal components of the data from noise contamination.