bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterizing the Tumor Microenvironment and Its Prognostic Impact in Breast Cancer
Authors: Zhang Wenjuan , Lee Alex , Tiwari Amit K. , Yang Mary Qu
Published: September 2024

The tumor microenvironment (TME) is crucial in cancer development and therapeutic response. Immunotherapy is increasingly recognized as a critical component of cancer treatment. While immunotherapies have shown efficacy in various cancers, including breast cancer, patient responses vary widely. Some patients receive significant benefits, while others experience minimal or no improvement. This disparity underscores the complexity and diversity of the immune system. In this study, we investigated the immune landscape and cell–cell communication within the TME of breast cancer through integrated analysis of bulk and single-cell RNA sequencing data. We established profiles of tumor immune infiltration that span across a broad spectrum of adaptive and innate immune cells. Our clustering analysis of immune infiltration identified three distinct patient groups: high T cell abundance, moderate infiltration, and low infiltration. Patients with low immune infiltration exhibited the poorest survival rates, while those in the moderate infiltration group showed better outcomes than those with high T cell abundance. Moreover, the high cell abundance group was associated with a greater tumor burden and higher rates of TP53 mutations, whereas the moderate infiltration group was characterized by a lower tumor burden and elevated PIK3CA mutations. Analysis of an independent single-cell RNA-seq breast cancer dataset confirmed the presence of similar infiltration patterns. Further investigation into ligand–receptor interactions within the TME unveiled significant variations in cell–cell communication patterns among these groups. Notably, we found that the signaling pathways SPP1 and EGF were exclusively active in the low immune infiltration group, suggesting their involvement in immune suppression. This work comprehensively characterizes the composition and dynamic interplay in the breast cancer TME. Our findings reveal associations between the extent of immune infiltration and clinical outcomes, providing valuable prognostic information for patient stratification. The unique mutations and signaling pathways associated with different patient groups offer insights into the mechanisms underlying diverse tumor immune infiltration and the formation of an immunosuppressive tumor microenvironment.
The Time Is Ripe: Olive Drupe Maturation Can Be Simply Evidenced by a Miniaturized, Portable and Easy-to-Use MicroNIR Green Sensor
Authors: Gullifa Giuseppina , Albertini Chiara , Ruocco Marialuisa , Risoluti Roberta , Materazzi Stefano
Published: September 2024

The analytical study described in this work, based on NIR spectroscopy with a handheld device, allowed the development of a chemometric prediction model that has been validated for the objective evaluation of the ripening of olive drupes. The miniaturized, portable NIR spectrometer is proposed here as an easy-to-use sensor able to estimate the best harvesting time for ripening of olive drupes. The MicroNIR/chemometrics approach was developed for on-site identification of olive drupe ripening directly on plants, avoiding collection and successive laboratory analysis steps. A supporting parallel characterization by chromatographic techniques validated the spectroscopic prediction. The novelty of this approach consists in the possibility of investigating the olive drupe maturation point by collecting spectra in the near-infrared region and processing them using a chemometric model. The fast and accurate device allows one to easily follow the spectrum profile changes of olive drupes during ripening, thus preserving the fruits from being harvested too early or too late. The results of this study demonstrate the possibility of using the MicroNIR/chemometrics approach to determine the optimal ripening time of olives regardless of the plant variety, age and cultivation location. The results consequently demonstrated that the MicroNIR/chemometrics approach can be proposed as a new method to perform on-site evaluation of ripening by a single-click device. It can be conveniently used by any operator, who does not necessarily have to be expert but must simply be trained to use spectroscopy and a prediction model.
Voltammetric Investigation of Paracetamol Detection in Acidic Conditions by Using Cork-Modified Carbon Paste Electrodes
Authors: Monteiro Mayra K. S. , Monteiro Mayara M. S. , Henrique João M. M. , Martínez-Huitle Carlos A. , Ferro Sergio , dos Santos Elisama Vieira
Published: September 2024

Developing new products that satisfy performance and durability expectations while also addressing environmental concerns is possible through the reuse of residues produced by industrial processes, aiming to fulfill the principles of circular economy. In this study, we improved the performance of a carbon paste sensor by incorporating untreated (RC) and regranulated/thermally treated (RGC) cork, which are considered biomass residues from the cork industry. We explored the electroanalytical behavior of paracetamol in sulfuric acid solutions using cyclic voltammetry and differential pulse techniques. The cork-modified carbon paste sensors showed greater sensitivity towards paracetamol. Both modified sensors allowed for an excellent resolution in distinguishing the voltammetric responses of paracetamol in sulfuric acid, showing for both an increase in peak currents compared to the unmodified carbon paste electrode. The quantification of paracetamol without interference has proved to be a feasible operation for the RC- and RGC-modified carbon paste sensors; notably, the first showed the most favorable limits of detection (LD = 2.4112 µM) and quantification (LQ = 8.0373 µM) for paracetamol in the sulfuric acid solution, performing significantly better than the second (LD = 10.355 µM, and LQ = 34.518 µM). Finally, the practical utility of the proposed sensors was assessed by analyzing paracetamol in pharmaceutical samples, obtaining satisfactory results that were in line with those obtainable using high-performance liquid chromatography.
CX3CL1/Fractalkine: A Potential Biomarker for Liver Fibrosis in Chronic HBV Infection
Authors: Arsentieva Natalia A. , Korobova Zoia R. , Batsunov Oleg K. , Lyubimova Natalia E. , Basina Valentina V. , Esaulenko Elena V. , Totolian Areg A.
Published: September 2024

A hepatitis B virus (HBV) infection can progress to chronic hepatitis, leading to liver fibrosis, cirrhosis, and hepatocellular carcinoma. CX3CL1/Fractalkine plays a crucial role in recruiting immune cells that are responsible for protecting against HBV infection. The aim of this study was to measure CX3CL1/Fractalkine concentrations in the blood plasma of individuals infected with HBV and to evaluate the role of this chemokine in the development of liver tissue fibrosis. Our study included patients infected with HBV, patients infected with HCV, autoimmune hepatitis, and healthy donors. We analyzed the CX3CL1/Fractalkine concentrations in blood plasma using the xMAP technology. Our results showed that HBV-infected patients had lower concentrations of CX3CL1/Fractalkine. Furthermore, in HBV-infected patients with severe fibrosis/cirrhosis, we observed significantly lower concentrations of CX3CL1/Fractalkine compared to those with no/mild fibrosis. Our study revealed that CX3CL1/Fractalkine concentrations are significantly associated with the stage of fibrosis in HBV infection. We demonstrated that lowered CX3CL1/Fractalkine concentrations might have prognostic value for predicting fibrosis development in liver tissue. Our findings suggest that decreased concentrations of CX3CL1/Fractalkine are associated with an increased risk of progressive liver fibrosis, indicating the potential of this chemokine as a prognostic biomarker for the development of liver fibrosis.
Immune Characteristics and Immunotherapy of HIV-Associated Lymphoma
Authors: Liu Yi , Xie Xiaoqing , Li Jun , Xiao Qing , He Sanxiu , Fu Huihui , Zhang Xiaomei , Liu Yao
Published: September 2024

In the era of antiretroviral therapy (ART), mortality among people living with the human immunodeficiency virus (HIV) has significantly decreased, yet the population of people living with HIV remains substantial. Among people living with HIV (PLWH), HIV-associated lymphoma (HAL) has surpassed Kaposi’s sarcoma to become the most common tumor in this population in developed countries. However, there remains a dearth of comprehensive and systematic understanding regarding HIV-associated lymphomas. This review aims to shed light on the changes in the immune system among PLWH and the characteristics of the immune microenvironment in HIV-associated lymphoma, with a specific focus on the immune system’s role in these individuals. Additionally, it seeks to explore recent advancements in immunotherapy for the treatment of HIV-associated lymphoma, intending to enhance strategies for immunotherapy in this specific population.
Peculiarities of Hematite Reduction Using Waste Activated Sludge (WAS) Carbonization Products
Authors: Parra Parra Abigail , Vlasova Marina , Aguilar Pedro Antonio Márquez , Morelos Jorge Luis Hernández , Nava Manuel Eduardo Serrano
Published: September 2024

In the present study, XRD, SEM/EDS, Raman, EMR/EPR spectroscopy, and vibrating sample magnetometry (VSM) were used to analyze the reduction of hematite by the carbonization products of waste activated sludge (WAS) at 500–1000 °C. The reduction process includes the following steps: α-Fe2O3 → Fe2O3 + Fe3O4 (Ttr~500 °C) → Fe3O4 (Ttr~600–700 °C) → FeO → Feamorph. (Ttr~1000 °C). The prevalence of certain phase compositions at different hematite reduction temperatures makes it possible to predict the areas viable for the application of reduced oxides: adsorbents (after Ttr~500 °C) → soft ferromagnetic materials (after Ttr~600–700 °C) → electrically engineered amorphous iron (after Ttr~1000 °C).
An Algorithm for Coding an Additive Manufacturing File from the Pressure Distribution of a Baropodometric Board for 3D Printing Customised Orthopaedic Insoles
Authors: Simi Francesco , Fortunato Gabriele Maria , Diana Fabio , Gai Jacopo , De Maria Carmelo
Published: September 2024

Customised orthotic insoles play a critical role in addressing foot pathologies and improving comfort and biomechanical alignment for patients with specific needs. The use of 3D printing technology for the manufacturing of orthotic insoles has received considerable attention in recent years due to its potential for customisation, rapid prototyping, and cost-effectiveness. This paper presents the implementation of an algorithm purposely developed to generate an Additive Manufacturing File (AMF) containing the geometry of a patient-specific insole and the stiffness distribution based on pressure analysis from a baropodometric board. The generated file is used to 3D print via Fused Deposition Modelling an insole with a variable infill percentage depending on the pressure distribution on the patient’s foot. Three inputs are used as source data for the AMF file coding: (i) the 3D model that defines the geometry of the insole designed by the orthopaedist; (ii) the pressure map of the patient’s feet obtained with a baropodometric board; and (iii) the stiffness of the material that will be used to fabricate the insole. The proposed approach allows the fabrication of a patient-specific insole, capable of restoring the correct pressure distribution on the foot by varying the infill percentage. Two types of insoles were successfully fabricated using the implemented algorithm: the first was 3D printed, adding a top layer to be ready-to-use; the second was 3D printed without a top surface to be further customised with different coatings. The method described in this paper is robust for the fabrication of customised insoles and aims at overcoming the limitations of the traditional approach based on milling machining (e.g., time, costs, and path planning) since it can be easily integrated into any orthopaedic workshop.
A Review of Nanocarbon-Based Anode Materials for Lithium-Ion Batteries
Authors: Nandihalli Nagaraj
Published: September 2024

Renewable and non-renewable energy harvesting and its storage are important components of our everyday economic processes. Lithium-ion batteries (LIBs), with their rechargeable features, high open-circuit voltage, and potential large energy capacities, are one of the ideal alternatives for addressing that endeavor. Despite their widespread use, improving LIBs’ performance, such as increasing energy density demand, stability, and safety, remains a significant problem. The anode is an important component in LIBs and determines battery performance. To achieve high-performance batteries, anode subsystems must have a high capacity for ion intercalation/adsorption, high efficiency during charging and discharging operations, minimal reactivity to the electrolyte, excellent cyclability, and non-toxic operation. Group IV elements (Si, Ge, and Sn), transition-metal oxides, nitrides, sulfides, and transition-metal carbonates have all been tested as LIB anode materials. However, these materials have low rate capability due to weak conductivity, dismal cyclability, and fast capacity fading owing to large volume expansion and severe electrode collapse during the cycle operations. Contrarily, carbon nanostructures (1D, 2D, and 3D) have the potential to be employed as anode materials for LIBs due to their large buffer space and Li-ion conductivity. However, their capacity is limited. Blending these two material types to create a conductive and flexible carbon supporting nanocomposite framework as an anode material for LIBs is regarded as one of the most beneficial techniques for improving stability, conductivity, and capacity. This review begins with a quick overview of LIB operations and performance measurement indexes. It then examines the recently reported synthesis methods of carbon-based nanostructured materials and the effects of their properties on high-performance anode materials for LIBs. These include composites made of 1D, 2D, and 3D nanocarbon structures and much higher Li storage-capacity nanostructured compounds (metals, transitional metal oxides, transition-metal sulfides, and other inorganic materials). The strategies employed to improve anode performance by leveraging the intrinsic features of individual constituents and their structural designs are examined. The review concludes with a summary and an outlook for future advancements in this research field.
Factors that Affect Outcome of Ultrasound-Guided Radiofrequency Ablation of Renal Masses
Authors: Zinko Galyna , Hrebenyuk Marianna , Kjellman Anders , Forslin Yngve , Delle Martin
Published: September 2024

The purpose of this study was to examine the factors influencing the efficacy and safety of the ultrasound-guided radiofrequency ablation of renal tumors. Between January 2010 and December 2018, 159 patients with renal tumors treated with ultrasound-guided percutaneous radiofrequency ablation at our institution were included in this study. Biopsies were performed for histopathological analysis prior to each ablation. Patients underwent computed tomography follow-ups at 3, 6, and 12 months and were subsequently observed on an annual basis. The primary efficacy rate (i.e., residual tumor), local tumor progression, morbidity and mortality, and possible outcome predictors (age, body mass index, gender, tumor size, tumor location, tumor characteristics, ablation temperature, and reported technical problems) were analyzed using binary logistic regression. At the first follow-up, 3 months after ablation, the primary efficacy rate was 79%. Two percent of the tumors showed local tumor progression during the whole follow-up. Tumor proximity to the collecting system and the final temperature in the ablation region were associated with the occurrence of residual tumor (OR = 2.85, p = 0.019 and OR = 4.23, p = 0.006, respectively). A similar trend was shown for tumors larger than 3 cm (p = 0.066). A short distance to the collecting system and the ablation temperature were significantly related to the occurrence of residual tumors after the radiofrequency ablation of small renal masses. The ultrasound guidance used in our study has a lower primary efficacy rate than the computed tomography guidance used in comparable studies.
The Association between Specimen Neuromuscular Characteristics and Urinary Incontinence after Robotic-Assisted Radical Prostatectomy
Authors: Bashi Tomer , Margalioth Jonathan , Savin Ziv , Marom Ron , Dekalo Snir , Fahoum Ibrahim , Naamneh Rabab , Mano Roy , Yossepowitch Ofer
Published: September 2024

Urinary incontinence after robotic-assisted radical prostatectomy (RARP) has been associated with older age, a longer operative time, a higher BMI, a short membranous urethral length and preoperative erectile function. The authors sought to assess the association between the neuromuscular characteristics and postoperative urinary incontinence. Methods: RARP specimens from 29 men who underwent bilateral nerve sparing were reanalyzed. Urinary incontinence was evaluated using the International Consultation on Incontinence Questionnaire—Short Form (ICIQ-SF) at 6 weeks post surgery and last follow-up. Linear and logistic regression analyses were performed to assess neuromuscular characteristics and incontinence. Results: At the 1-year follow-up, 11 patients (38%) reported severe incontinence (>12 ICIQ-SF score). The median number of peripheral nerves observed at the base and apex in the specimens was 52 (IQR 13–139) and 59 (IQR: 28–129), respectively. Ganglia were present in 19 patients (65%) at the base and 12 patients (41%) at the apex. Additionally, the median proportional area of detrusor smooth muscle fibers at the base was 0.54 (IQR 0.31–1), while the median proportional area of striated muscle fibers at the apex was 0.13 (IQR 0.08–0.24). No statistically significant associations were found. Conclusions: Histologic neuromuscular characteristics were not associated with postoperative urinary incontinence. Enhanced intraoperative evaluation and larger-scale studies may prove useful for the prediction of postprostatectomy incontinence.
Analytical Validation of Esopredict, an Epigenetic Prognostic Assay for Patients with Barrett’s Esophagus
Authors: Laun Sarah , Pierre Francia , Kim Suji , Lunz Daniel , Maddala Tara , Braun Jerome V. , Meltzer Stephen J. , Kann Lisa
Published: September 2024

EsopredictTM is a prognostic assay that risk-stratifies Barrett’s esophagus patients to predict future progression to high-grade dysplasia (HGD) or esophageal adenocarcinoma (EAC). Established based on foundational studies at Johns Hopkins University, a risk algorithm was developed and clinically validated in two independent studies (n = 320). EsopredictTM is currently offered as a clinical test under the Clinical Laboratory Improvement Amendments (CLIA) guidelines. Here we present the analytical validation by repeated testing of FFPE tissues (n = 26 patients), cell lines, and contrived DNA controls to determine assay performance regarding analytical sensitivity (as defined by the limit of detection (LOD)), analytical specificity (as defined by the limit of blank (LOB)), accuracy as determined from the average positive and negative agreement, repeatability, and reproducibility. The LOD for the assay at 1.5% DNA methylation was significantly higher than the LOB, as determined by an unmethylated DNA control (0% methylated DNA). Inter- and intra-assay average positive agreement (APA) were 88% and 94%, respectively, while average negative agreement (ANA) values were 90% and 94%, respectively. Average inter- and intra-assay precision were <9% and <5% coefficient of variation (CV), respectively. These results confirm that EsopredictTM is a highly reproducible, sensitive, and specific risk categorization assay for the prediction of progression to HGD or EAC within 5 years.
Navigating Higher Education Challenges: A Review of Strategies among Students with Disabilities in Indonesia
Authors: Rahajeng Unita Werdi , Hendriani Wiwin , Paramita Pramesti Pradna
Published: September 2024

Access to higher education has been widely open for students with disabilities (SwD) as also enforced by the Government of Indonesia (GoI). However, challenges evidently face SwD in their survival of university life including limited accessible services and facilities, as well as negative stereotypes surrounding SwD. This study aimed to explore the diverse strategies adopted by SwD in navigating these challenges, examining the nuances and implications of the deployed strategies. This review explored the experiences of SwD in higher education institutions in Indonesia and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review) guidelines. Data were gathered from seven databases, including Scopus, Web of Science, ERIC, ProQuest, Google Scholar, and Portal Garuda. In total, 17 of 1777 articles were selected based on two inclusion criteria viz. peer-reviewed articles derived from empirical studies and utilizing life experiences of SwD in Indonesian HE as the primary data source. The study identified the five key strategies employed by SwD: Adaptation, Technology Assistive Optimization, Requesting Support, Building Relationships, and Passive Action. Detailed elaboration of the findings is presented to shed light on the prominent insight to improve the quality of education services for SwD in Indonesia.
Histological Analysis of Oral Tissue Grafting: A Focus on Donor Site Selection
Authors: Zecca Piero Antonio , Ronchetti Alice , Cangelosi Doris , Reguzzoni Marcella , Farronato Davide
Published: September 2024

The science of dental tissue grafting is evolving, with an increased understanding of factors influencing graft behavior. Despite the widespread clinical use of soft tissue grafts, the histological characteristics of different gingival harvesting sites are still underexplored. This study aimed to fill this gap by analyzing 50 tissue samples harvested from 25 patients across three sites: the hard palate, maxillary tuberosity, and palatal rugae. Each sample underwent thorough histological and histomorphometric analysis. Conventional statistical analysis was performed using SPSS, while predictive modeling was conducted with RapidMiner Studio. The study identified significant histological differences among the graft sites, with notable variations in total graft height, epithelial height, and interdigitation perimeter. These findings underscore the importance of donor site selection in influencing graft success. Pair plots and principal component analysis (PCA) further highlighted the distinct histological features of each tissue type. The random forest classifier identified total graft height, epithelial height, and perimeter as the most influential factors in predicting graft site behavior. This study offers valuable insights into the histological characteristics of soft tissue grafts, potentially leading to more predictable clinical outcomes.
Modeling the Arrows of Time with Causal Multibaker Maps
Authors: Ebtekar Aram , Hutter Marcus
Published: September 2024

Why do we remember the past, and plan the future? We introduce a toy model in which to investigate emergent time asymmetries: the causal multibaker maps. These are reversible discrete-time dynamical systems with configurable causal interactions. Imposing a suitable initial condition or “Past Hypothesis”, and then coarse-graining, yields a Pearlean locally causal structure. While it is more common to speculate that the other arrows of time arise from the thermodynamic arrow, our model instead takes the causal arrow as fundamental. From it, we obtain the thermodynamic and epistemic arrows of time. The epistemic arrow concerns records, which we define to be systems that encode the state of another system at another time, regardless of the latter system’s dynamics. Such records exist of the past, but not of the future. We close with informal discussions of the evolutionary and agential arrows of time, and their relevance to decision theory.
The Role of Language and Literacy Skills in Science Learning from Kindergarten to 5th Grade: Mitigating Gender, Racial/Ethnic, and Socio-Economic Disparities
Authors: Jang Wonkyung , Kwon Kyong-Ah , Horm Diane
Published: September 2024

Despite the acknowledged impact of early science achievement on future success, there is a noticeable gap in research focused on understanding the dynamic longitudinal patterns of children attaining science learning milestones in their early years, as well as few investigations of potential factors that may mitigate gender, racial/ethnic, and socio-economic disparities. This study analyzed nationally representative data from the Early Childhood Longitudinal Study of Kindergarten Cohort 2011 (ECLS-K: 2011), involving 18,174 children from 1328 schools across the United States, selected through a multistage probability sampling process and spanning kindergarten through 5th grade. Using survival analysis with the dependent variable as the time required to attain a specific milestone, the study revealed that boys, non-Black, non-Hispanic, and high-income children reached the science learning threshold earlier than their counterparts—girls, Black, Hispanic, and low-income children. Furthermore, the study underscored the crucial role of language and literacy skills in mitigating these disparities. The study’s implications stress the importance of targeted interventions to address early science education disparities, emphasizing professional development and integrating language and literacy with science learning. The research also enriches the global discourse on educational standards by introducing innovative methodologies to assess both the frequency and duration of science learning milestones.
Short-Term and Long-Term Effects of COVID-19 and Remote Learning: Experiences of Parents Supporting Children with Mathematical Learning Disabilities in Israel
Authors: Ashkenazi Sarit , Hassoun Sonia
Published: September 2024

Background: Over the last three years, many studies have explored the effect of pandemic closures on learning. However, in Israel, the perspectives of parents on the short- and long-term effects of the lockdowns on students with mathematical learning disabilities (MD), have rarely been examined. Method: To fill this gap, MD (n = 33) or typically developing (n = 50) children were selected. They were in the 1st and 2nd grades during the closures, and we tested them and their surroundings, two years later. Results and Conclusions: First, according to the parent’s survey, children with MD had physical conditions similar to the TD group, the two groups had similar stable connections to the internet, computer, and a quiet environment. However, MD children (1) needed more help and (2) had a harder time concentrating during virtual math classes compared to TD children. Moreover, the coronavirus closures resulted in a greater learning gap in the MD children compared to the TD children. We found positive associations between difficulties reported by the parents and actual weakness in performances in complex multiplication and division and verbal working memory.
Prompt-Based End-to-End Cross-Domain Dialogue State Tracking
Authors: Lu Hengtong , Zhong Lucen , Jiang Huixing , Chen Wei , Yuan Caixia , Wang Xiaojie
Published: September 2024

Cross-domain dialogue state tracking (DST) focuses on using labeled data from source domains to train a DST model for target domains. It is of great significance for transferring a dialogue system into new domains. Most of the existing cross-domain DST models track each slot independently, which leads to poor performances caused by not considering the correlation among different slots, as well as low efficiency of training and inference. This paper, therefore, proposes a prompt-based end-to-end cross-domain DST method for efficiently tracking all slots simultaneously. A dynamic prompt template shuffle method is proposed to alleviate the bias of the slot order, and a dynamic prompt template sampling method is proposed to alleviate the bias of the slot number, respectively. The experimental results on the MultiWOZ 2.0 and MultiWOZ 2.1 datasets show that our approach consistently outperforms the state-of-the-art baselines in all target domains and improves both training and inference efficiency by at least 5 times.
DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure
Authors: Jia Kai , Xiang Jun , Li Baoxia
Published: September 2024

Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and job scheduling due to its ability to estimate upcoming workloads. However, due to users’ diverse usage demands, the changing characteristics of workloads have become more and more complex, including not only short-term irregular fluctuation characteristics but also long-term dynamic variations. This prevents existing workload-prediction methods from fully capturing the above characteristics, leading to degradation of prediction accuracy. To deal with the above problems, this paper proposes a framework based on a dual-channel temporal convolutional network and transformer (referred to as DuCFF) to perform workload prediction. Firstly, DuCFF introduces data preprocessing technology to decouple different components implied by workload data and combine the original workload to form new model inputs. Then, in a parallel manner, DuCFF adopts the temporal convolution network (TCN) channel to capture local irregular fluctuations in workload time series and the transformer channel to capture long-term dynamic variations. Finally, the features extracted from the above two channels are further fused, and workload prediction is achieved. The performance of the proposed DuCFF’s was verified on various workload benchmark datasets (i.e., ClarkNet and Google) and compared to its nine competitors. Experimental results show that the proposed DuCFF can achieve average performance improvements of 65.2%, 70%, 64.37%, and 15%, respectively, in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared (R2) compared to the baseline model CNN-LSTM.
AmazingFT: A Transformer and GAN-Based Framework for Realistic Face Swapping
Authors: Liu Li , Tong Dingli , Shao Wenhua , Zeng Zhiqiang
Published: September 2024

Current face-swapping methods often suffer from issues of detail blurriness and artifacts in generating high-quality images due to the inherent complexity in detail processing and feature mapping. To overcome these challenges, this paper introduces the Amazing Face Transformer (AmazingFT), an advanced face-swapping model built upon Generative Adversarial Networks (GANs) and Transformers. The model is composed of three key modules: the Face Parsing Module, which segments facial regions and generates semantic masks; the Amazing Face Feature Transformation Module (ATM), which leverages Transformers to extract and transform features from both source and target faces; and the Amazing Face Generation Module (AGM), which utilizes GANs to produce high-quality swapped face images. Experimental results demonstrate that AmazingFT outperforms existing state-of-the-art (SOTA) methods, significantly enhancing detail fidelity and occlusion handling, ultimately achieving movie-grade face-swapping results.
A Novel Areal Maintenance Strategy for Large-Scale Distributed Photovoltaic Maintenance
Authors: Yin Deyang , Zhu Yuanyuan , Qiang Hao , Zheng Jianfeng , Zhang Zhenzhong
Published: September 2024

A smart grid is designed to enable the massive deployment and efficient use of distributed energy resources, including distributed photovoltaics (DPV). Due to the large number, wide distribution, and insufficient monitoring information of DPV stations, the pressure to maintain them has increased rapidly. Furthermore, based on reports in the relevant literature, there is still a lack of efficient large-scale maintenance strategies for DPV stations at present, leading to the high maintenance costs and overall low efficiency of DPV stations. Therefore, this paper proposes a maintenance period decision model and an areal maintenance strategy. The implementation steps of the method are as follows: firstly, based on the reliability model and dust accumulation model of the DPV components, the maintenance period decision model is established for different numbers of DPV stations and different driving distances; secondly, the optimal maintenance period is determined by using the Monte Carlo method to calculate the average economic benefits of daily maintenance during different periods; then, an areal maintenance strategy is proposed to classify all the DPV stations into different areas optimally, where each area is maintained to reach the overall economic optimum for the DPV stations; finally, the validity and rationality of this strategy are verified with the case study of the DPV poverty alleviation project in Badong County, Hubei Province. The results indicate that compared with an independent maintenance strategy, the proposed strategy can decrease the maintenance cost by 10.38% per year, which will help promote the construction of the smart grid and the development of sustainable cities. The results prove that the method proposed in this paper can effectively reduce maintenance costs and improve maintenance efficiency.
Optic Nerve Sheath Ultrasound Image Segmentation Based on CBC-YOLOv5s
Authors: Chu Yonghua , Xu Jinyang , Wu Chunshuang , Ye Jianping , Zhang Jucheng , Shen Lei , Wang Huaxia , Yao Yudong
Published: September 2024

The diameter of the optic nerve sheath is an important indicator for assessing the intracranial pressure in critically ill patients. The methods for measuring the optic nerve sheath diameter are generally divided into invasive and non-invasive methods. Compared to the invasive methods, the non-invasive methods are safer and have thus gained popularity. Among the non-invasive methods, using deep learning to process the ultrasound images of the eyes of critically ill patients and promptly output the diameter of the optic nerve sheath offers significant advantages. This paper proposes a CBC-YOLOv5s optic nerve sheath ultrasound image segmentation method that integrates both local and global features. First, it introduces the CBC-Backbone feature extraction network, which consists of dual-layer C3 Swin-Transformer (C3STR) and dual-layer Bottleneck Transformer (BoT3) modules. The C3STR backbone’s multi-layer convolution and residual connections focus on the local features of the optic nerve sheath, while the Window Transformer Attention (WTA) mechanism in the C3STR module and the Multi-Head Self-Attention (MHSA) in the BoT3 module enhance the model’s understanding of the global features of the optic nerve sheath. The extracted local and global features are fully integrated in the Spatial Pyramid Pooling Fusion (SPPF) module. Additionally, the CBC-Neck feature pyramid is proposed, which includes a single-layer C3STR module and three-layer CReToNeXt (CRTN) module. During upsampling feature fusion, the C3STR module is used to enhance the local and global awareness of the fused features. During downsampling feature fusion, the CRTN module’s multi-level residual design helps the network to better capture the global features of the optic nerve sheath within the fused features. The introduction of these modules achieves the thorough integration of the local and global features, enabling the model to efficiently and accurately identify the optic nerve sheath boundaries, even when the ocular ultrasound images are blurry or the boundaries are unclear. The Z2HOSPITAL-5000 dataset collected from Zhejiang University Second Hospital was used for the experiments. Compared to the widely used YOLOv5s and U-Net algorithms, the proposed method shows improved performance on the blurry test set. Specifically, the proposed method achieves precision, recall, and Intersection over Union (IoU) values that are 4.1%, 2.1%, and 4.5% higher than those of YOLOv5s. When compared to U-Net, the precision, recall, and IoU are improved by 9.2%, 21%, and 19.7%, respectively.
A Fully Synthesizable Fractional-N Digital Phase-Locked Loop with a Calibrated Dual-Referenced Interpolating Time-to-Digital Converter to Compensate for Process–Voltage–Temperature Variations
Authors: Kim Seojin , Kim Youngsik , Son Hyunwoo , Kim Shinwoong
Published: September 2024

This paper presents advancements in the performance of digital phase-locked loop (DPLL)s, with a special focus on addressing the issue of required gain calibration in the time-to-digital converter (TDC) within phase-domain DPLL structures. Phase-domain DPLLs are preferred for their simplicity in implementation and for eliminating the delta–sigma modulator (DSM) noise inherent in conventional fractional-N designs. However, this advantage is countered by the critical need to calibrate the gain of the TDC. The previously proposed dual-interpolated TDC(DI-TDC) was proposed as a solution to this problem, but strong spurs were still generated due to the TDC resolution, which easily became non-uniform due to PVT variation, degrading performance. To overcome these problems, this work proposes a DPLL with a new calibration system that ensures consistent TDC resolution matching the period of the digitally controlled oscillator (DCO) and operating in both the foreground and background, thereby maintaining consistent performance despite PVT variations. This study proposes a DPLL using a calibrated dual-interpolated TDC that effectively compensates for PVT variations and improves the stability and performance of the DPLL. The PLL was fabricated in a 28-nm CMOS process with an active area of only 0.019 mm2, achieving an integrated phase noise (IPN) performance of −17.5 dBc, integrated from 10 kHz to 10 MHz at a PLL output of 570 MHz and −20.5 dBc at 1.1 GHz. This PLL operates within an output frequency range of 475 MHz to 1.1 GHz. Under typical operating conditions, it consumes only 930 µW with a 1.0 V supply.
A Multi-Functional Reactive Power Compensation Device with the Capability of Grounding Fault Regulation and Its Parameter Design Method
Authors: Huang Zejun , Hou Yuchao , Zhang Huaying , Liu Huicong , Liang Xiaorui , Guo Qi
Published: September 2024

Aiming at the problems of low equipment utilization and the high-capacity requirements of existing arc-suppression devices, a multi-functional reactive power compensation device with the capability of grounding fault regulation (MF-RPCD) is proposed. Firstly, the topology and operation mechanism of MF-RPCD are introduced in this paper. MF-RPCD works in a reactive power compensation mode when a power grid is under normal operation to ensure the unit power factor operation of the power-grid side. When a single line-to-ground fault occurs in the power grid, MF-RPCD works in a fault-regulation mode to effectively suppress the ground-fault current. Secondly, the parameters of the active and passive parts of MF-RPCD are optimally designed to ensure the stable operation of MF-RPCD. Finally, the correctness, feasibility and effectiveness of the proposed topology and functions are verified using simulations and experiments.
Constrained Model Predictive Control for Generation Power Distribution on Aircraft Engines
Authors: Xiao Lingfei , Tan Yushuo , Sattarov Robert R. , Wei Ye
Published: September 2024

Aiming at the increasing demand for electric energy in aircraft in the future, a multi-objective optimization aircraft engine constrained model predictive control method based on generation power distribution is proposed. Firstly, based on the aircraft engine component level model and the equilibrium manifold theory, the aircraft engine equilibrium manifold expansion model is established. Secondly, the influence of the power generation is modeled, and the influence of the low- and high-pressure shaft generators on the normal operation of the aircraft engine is studied and compared. The control variables such as fuel flow and total generation power are taken as the constraint conditions to design the constraint model predictive controller. Furthermore, the multi-objective grey wolf optimization algorithm is introduced to intelligently optimize the parameters of the designed controller. At last, the simulation based on the component level model shows that the high-pressure shaft generator has less influence on the state quantity, including engine thrust, than the low-pressure shaft generator. The proposed control method using the multi-objective gray wolf optimization (MOGWO) algorithm has rapid response and no steady-state error.
Increasing the Thermal Resistance of Water-Based Mud for Drilling Geothermal Wells
Authors: Błaż Sławomir , Zima Grzegorz , Jasiński Bartłomiej , Kremieniewski Marcin
Published: September 2024

Energy demand and growing environmental concerns have fueled increased interest in geothermal drilling in recent decades. The high temperature and pressure in the boreholes present significant challenges to drilling, particularly in terms of the selection of suitable drilling mud, cement slurry, and drilling equipment. Drilling mud is regarded as one of the primary factors that affect the cost and success of geothermal drilling. This paper presents experimental studies aimed at assessing the thermal stability of drilling muds for geothermal drilling. Research on the antidegradation of polymers contained in drilling muds is presented. The thermal stability of drilling fluids was evaluated on the basis of changes in rheological and filtration parameters under the influence of a temperature of 160 °C. Attempts were made to increase the thermal resistance of drilling fluids by using antioxidants and glycol compounds. The effectiveness of increasing the thermal resistance of muds by adding synthetic polymers, nanomaterials, and graphite was tested. A new way of increasing the thermal resistance of drilling muds by using fatty amine compounds in combination with the amine agent ‘TEA’ was proposed. Tests showed that the addition of polyglycol and the antioxidant agent sodium ascorbate to the mud did not protect the polymers from decomposition at 160 °C. There was no effect of increasing the thermal conductivity on improving the thermal resistance of the scrubber. Based on the analysis of results from laboratory tests, a composition of a water-based drilling mud without bentonite was developed for drilling geothermal wells. The developed drilling mud is characterized by thermal resistance up to 160 °C, stable rheological parameters, low filtration, and appropriate thermal conductivity characteristics.
Looking High and Low: Incentive Policies and Residential Solar Adoption in High- and Low-Income U.S. Communities
Authors: Xu Xiaojing , Sims Charles , Chen Chien-fei , Holladay J. Scott , Jones Gerald , Roberson Tim
Published: September 2024

Rooftop solar adoption has increased considerably in recent years thanks to a combination of lower panel costs and generous incentive programs. This paper estimates the increase in residential rooftop solar adoption associated with three types of solar incentive programs and isolates the effect of these programs in both high and low-income census tracts. We utilize a dataset of census tract-level rooftop solar adoption compiled using a machine learning-based image classification tool that identifies solar photovoltaic panels from satellite images. This allows us to study areas of the country that have lower solar adoption rates and incomes than areas previously studied. We find evidence that programs designed specifically to encourage adoption in low-income areas are associated with a smaller gap between low- and high-income solar adoption. However, property-tax benefits and net metering, which are more prevalent across the U.S., are associated with an increase in the gap between low- and high-income solar adoption.
Intelligent Identification of Hidden Dangers in Hydrogen Pipeline Transmission Station Using GWO-Optimized Apriori Algorithm
Authors: Wang Chaoming , Fu Anqing , Li Weidong , Li Mingxing , Chen Tingshu
Published: September 2024

This work proposes an intelligent grey-wolf-optimizer-improved Apriori algorithm (GWO-Apriori) to mine the association rules of hidden dangers in hydrogen pipeline transmission stations. The optimal minimum support and minimum confidence are determined by GWO instead of the time-consuming trial approach. Experiments show that the average support and average confidence of association rules using GWO-Apriori increase by 29.8% and 21.3%, respectively, when compared with traditional Apriori. Overall, 59 ineffective association rules out of the total 105 rules are filtered by GWO, which dramatically improves data mining effectiveness. Moreover, 23 illogical association rules are excluded, and 12 new strong association rules ignored by the traditional Apriori are successfully mined. Compared with the inefficient and labor-intensive manual investigation, the intelligent GWO-Apriori algorithm dramatically improves pertinency and efficiency of hidden danger identification in hydrogen pipeline transmission stations.
Method of Determining New Locations for Electric Vehicle Charging Stations Using GIS Tools
Authors: Soczówka Piotr , Lasota Michał , Franke Piotr , Żochowska Renata
Published: September 2024

The growing awareness of environmental issues, climate policies, and rapidly developing technologies is contributing to the increasing number of battery electric vehicles (BEVs) around the world. A key requirement for their widespread implementation is providing a charging infrastructure that allows users to operate these vehicles comfortably. Lack of access to charging stations can be a major barrier to the development of electromobility in a given area. Therefore, each additional charging infrastructure can support a change in the structure of the vehicle fleet. One of the key challenges facing this transformation is the selection of suitable locations for charging stations. It is necessary to ensure that they are uniformly distributed so that range anxiety for EV users is reduced and equal access to charging infrastructure is provided to all residents. One of the most important stakeholders in this market is local authorities. Therefore, the objective of this research was to develop a method of determining optimal locations for electric vehicle charging stations (EVCSs) from the perspective of local authorities that also takes into account equal access to the charging infrastructure for all residents, which seems to be a unique approach to this problem. We used commonly available spatial data as input to enable the method to be applied on a larger scale and over an urban area. We carried out our research using a case study: the city of Gliwice in Poland. The city area was divided into hexagonal basic fields, for which potentials for locations of new charging stations were calculated. The analysis was carried out using the geographic information system (GIS) QGIS (ver. 3.34).