bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Study on the Heat Transfer via Unheated Spaces Based on Correction Factor
Authors: He Wenfang , Zhang Shuaipeng , Wu Zhenying , Wang Dengjia
Published: July 2024

The accurate assessment of heat transfer via unheated spaces is an important aspect of calculating the heating load of a building and mitigating its energy consumption and carbon emissions. Currently, the majority of international and national standards employ the correction factor method for the calculation of heat transfer via unheated spaces, categorized into three types: detailed temperature correction factors (b), simplified b-values, and a correction factor (a) of thermal resistance. In order to provide an accurate and efficient evaluation of heat transfer through unheated spaces, this paper conducts a comparative analysis of these three methods using on-site measurements, TRNSYS (version 18) simulations, and analytical calculations. The findings indicate that the use of simplified b-values results in inaccurate predictions of correction factors and heat transfer via unheated balconies, yielding relative discrepancies within the ranges of 0.065 to 0.527 and 12.2% to 111.3%, respectively. Detailed temperature correction factors offer a more precise prediction, exhibiting relative discrepancies of −0.161 to 0.11 and 0.1% to 33.5%. However, the complexity of the calculation process, influenced by dynamically changing climates and solar radiation, necessitates a steady-state assumption to streamline calculations. The use of detailed correction factors of thermal resistance yields more accurate predictions, with relative discrepancies ranging from −0.176 to 0.11 and 0.3% to 33.1%, and it is recommended as the main method for predicting heat transfer via unheated spaces. In addition, it is advised to enhance the thermal resistance correction factor method by considering the influence of radiative heat transfer via transparent envelopes.
Hydrogen Blending in Natural Gas Grid: Energy, Environmental, and Economic Implications in the Residential Sector
Authors: Vespasiano Domiziana , Sgaramella Antonio , Lo Basso Gianluigi , de Santoli Livio , Pastore Lorenzo Mario
Published: July 2024

The forthcoming implementation of national policies towards hydrogen blending into the natural gas grid will affect the technical and economic parameters that must be taken into account in the design of building heating systems. This study evaluates the implications of using hydrogen-enriched natural gas (H2NG) blends in condensing boilers and Gas Adsorption Heat Pumps (GAHPs) in a residential building in Rome, Italy. The analysis considers several parameters, including non-renewable primary energy consumption, CO2 emissions, Levelized Cost of Heat (LCOH), and Carbon Abatement Cost (CAC). The results show that a 30% hydrogen blend achieves a primary energy consumption reduction of 12.05% and 11.19% in boilers and GAHPs, respectively. The presence of hydrogen in the mixture exerts a more pronounced influence on the reduction in fossil primary energy and CO2 emissions in condensing boilers, as it enhances combustion efficiency. The GAHP system turns out to be more cost-effective due to its higher efficiency. At current hydrogen costs, the LCOH of both technologies increases as the volume fraction of hydrogen increases. The forthcoming cost reduction in hydrogen will reduce the LCOH and the decarbonization cost for both technologies. At low hydrogen prices, the CAC for boilers is lower than for GAHPs; therefore, replacing boilers with other gas technologies rather than electric heat pumps increases the risk of creating stranded assets. In conclusion, blending hydrogen into the gas grid can be a useful policy to reduce emissions from the overall natural gas consumption during the process of end-use electrification, while stimulating the development of a hydrogen economy.
Research on Cost Control of Railway Engineering Based on Grounded Theory
Authors: Meng Di , Sun Zhiqi
Published: July 2024

An analysis of cost management within railway construction projects has been conducted using the Analytic Hierarchy Process (AHP) and a regression analysis to evaluate and rank key financial and operational factors affecting project costs. This research assesses the impact of various metrics, such as Safety Inspections, Emergency Preparedness, and Equipment Maintenance, along with financial indicators such as Actual Cost and Variance, on cost control strategies by building a hierarchical model and implementing AHP. The results indicate a clear preference for Financial Metrics, with a priority vector of 0.667, over Operational Metrics, which have a priority vector of 0.334. Among the Financial Metrics, Actual Cost, with a priority vector of 0.565, is identified as the most influential, underscoring the importance of direct cost management. Among the Operational Metrics, Emergency Preparedness is the most important, with a priority vector of 0.540, emphasizing the importance of effective risk management. A regression analysis confirms these priorities, with significant correlations presented between these metrics and variances in costs. According to this study, changes in Emergency Preparedness and Equipment Maintenance can predict cost fluctuations, aligning with the findings of the AHP study. The AHP evaluations are demonstrated to be reliable, with consistency ratios significantly below the 0.1 benchmark (0.043 for Financial Metrics and 0.008 for Operational Metrics), indicating a high degree of consistency in judgment. The statistical validation enhances the framework’s effectiveness in steering strategic decisions regarding cost management. This paper discusses the implications of these results to reduce financial risks and improve project outcomes.
Lck Function and Modulation: Immune Cytotoxic Response and Tumor Treatment More Than a Simple Event
Authors: De Sanctis Juan , Garmendia Jenny , Duchová Hana , Valentini Viktor , Puskasu Alex , Kubíčková Agáta , Hajdúch Marián
Published: July 2024

Lck, a member of the Src kinase family, is a non-receptor tyrosine kinase involved in immune cell activation, antigen recognition, tumor growth, and cytotoxic response. The enzyme has usually been linked to T lymphocyte activation upon antigen recognition. Lck activation is central to CD4, CD8, and NK activation. However, recently, it has become clearer that activating the enzyme in CD8 cells can be independent of antigen presentation and enhance the cytotoxic response. The role of Lck in NK cytotoxic function has been controversial in a similar fashion as the role of the enzyme in CAR T cells. Inhibiting tyrosine kinases has been a highly successful approach to treating hematologic malignancies. The inhibitors may be useful in treating other tumor types, and they may be useful to prevent cell exhaustion. New, more selective inhibitors have been documented, and they have shown interesting activities not only in tumor growth but in the treatment of autoimmune diseases, asthma, and graft vs. host disease. Drug repurposing and bioinformatics can aid in solving several unsolved issues about the role of Lck in cancer. In summary, the role of Lck in immune response and tumor growth is not a simple event and requires more research.
The Safety and Usefulness of Awake Surgery as a Treatment Modality for Glioblastoma: A Retrospective Cohort Study and Literature Review
Authors: Osawa Sho , Miyakita Yasuji , Takahashi Masamichi , Ohno Makoto , Yanagisawa Shunsuke , Kawauchi Daisuke , Omura Takaki , Fujita Shohei , Tsuchiya Takahiro , Matsumi Junya , Sato Tetsufumi , Narita Yoshitaka
Published: July 2024

Awake surgery contributes to the maximal safe removal of gliomas by localizing brain function. However, the efficacy and safety thereof as a treatment modality for glioblastomas (GBMs) have not yet been established. In this study, we analyzed the outcomes of awake surgery as a treatment modality for GBMs, response to awake mapping, and the factors correlated with mapping failure. Patients with GBMs who had undergone awake surgery at our hospital between March 2010 and February 2023 were included in this study. Those with recurrence were excluded from this study. The clinical characteristics, response to awake mapping, extent of resection (EOR), postoperative complications, progression-free survival (PFS), overall survival (OS), and factors correlated with mapping failure were retrospectively analyzed. Of the 32 participants included in this study, the median age was 57 years old; 17 (53%) were male. Awake mapping was successfully completed in 28 participants (88%). A positive response to mapping and limited resection were observed in 17 (53%) and 13 participants (41%), respectively. The EOR included gross total, subtotal, and partial resections and biopsies in 19 (59%), 8 (25%), 3 (9%), and 2 cases (6%), respectively. Eight (25%) and three participants (9%) presented with neurological deterioration in the acute postoperative period and at 3 months postoperatively, respectively. The median PFS and OS were 15.7 and 36.9 months, respectively. The time from anesthetic induction to extubation was statistically significantly longer in the mapping failure cohort than that in the mapping success cohort. Functional areas could be detected during awake surgery in participants with GBMs. Thus, awake mapping influences intraoperative discernment, contributes to the preservation of brain function, and improves treatment outcomes.
Green Synthesis of Copper Oxide Nanoparticles from Waste Solar Panels Using Piper nigrum Fruit Extract and Their Antibacterial Activity
Authors: Shafiq Ayesha , Jeong Ujin , Han Yunseon , Kim Youngsik , Lee Joonmin , Kim Beom Soo
Published: July 2024

To promote environmental development and sustain resource circularity, recycling metals from electronic waste is essential. Electronic waste is a significant secondary source of metals, with its production increasing rapidly and most remaining unrecycled. In solar panels, copper is the second-most-valuable metal after silver. We propose an innovative method to recycle copper from waste solar panels and convert it into copper oxide nanoparticles (CuONPs) using a green synthesis method. Synthesizing CuONPs is advantageous due to their large surface area compared to bulk material. Nitric acid, a strong oxidizing agent, was used to leach copper from solid copper wires in waste solar panels. A green synthesis method, following a bottom-up approach, was employed using Piper nigrum fruit extract to synthesize CuONPs. The synthesized nanoparticles were characterized using various qualitative and quantitative techniques. Spectroscopic analysis confirmed the formation of CuONPs, and transmission electron microscopy revealed that the nanoparticles were spherical with sizes up to 60 nm. Biomolecules from the Piper nigrum extract were detected on the surface of the crystalline CuONPs. These nanoparticles exhibited antibacterial activity against Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus using a well-diffusion method, showing a larger zone of inhibition for E. coli compared to S. aureus. This research demonstrates the complete recovery of copper from waste solar panels and its conversion into useful CuONPs, which have potential medicinal applications.
Combinatorial Genomic Biomarkers Associated with High Response in IgE-Dependent Degranulation in Human Mast Cells
Authors: Tam Issan Yee San , Lee Tak Hong , Lau Hang Yung Alaster , Tam See-Ying
Published: July 2024

Mast cells are the major effector cells that mediate IgE-dependent allergic reactions. We sought to use integrated network analysis to identify genomic biomarkers associated with high response in IgE-mediated activation of primary human mast cells. Primary human mast cell cultures derived from 262 normal donors were categorized into High, Average and Low responder groups according to their activation response profiles. Transcriptome analysis was used to identify genes that were differentially expressed in different responder cultures in their baseline conditions, and the data were analyzed by constructing a personalized perturbed profile (PEEP). For upregulated genes, the construction of PEEP for each individual sample of all three responder groups revealed that High responders exhibited a higher percentage of “perturbed” samples whose PEEP values lay outside the normal range of expression. Moreover, the integration of PEEP of four selected upregulated genes into distinct sets of combinatorial profiles demonstrated that the specific pattern of upregulated expression of these four genes, in a tandem combination, was observed exclusively among the High responders. In conclusion, this combinatorial approach was useful in identifying a set of genomic biomarkers that are associated with high degranulation response in human mast cell cultures derived from the blood of a cohort of normal donors.
Synovium-Derived and Bone-Derived Mesenchymal Stem/Stromal Cells from Early OA Patients Show Comparable In Vitro Properties to Those of Non-OA Patients
Authors: Zupan Janja , Stražar Klemen
Published: July 2024

Degenerative disorders like osteoarthritis (OA) might impair the ability of tissue-resident mesenchymal stem/stromal cells (MSCs) for tissue regeneration. As primary cells with MSC-like properties are exploited for patient-derived stem cell therapies, a detailed evaluation of their in vitro properties is needed. Here, we aimed to compare synovium-derived and bone-derived MSCs in early hip OA with those of patients without OA (non-OA). Tissues from three synovial sites of the hip (paralabral synovium, cotyloid fossa, inner surface of peripheral capsule) were collected along with peripheral trabecular bone from 16 patients undergoing hip arthroscopy (8 early OA and 8 non-OA patients). Primary cells isolated from tissues were compared using detailed in vitro analyses. Gene expression profiling was performed for the skeletal stem cell markers podoplanin (PDPN), CD73, CD164 and CD146 as well as for immune-related molecules to assess their immunomodulatory potential. Synovium-derived and bone-derived MSCs from early OA patients showed comparable clonogenicity, cumulative population doublings, osteogenic, adipogenic and chondrogenic potential, and immunophenotype to those of non-OA patients. High PDPN/low CD146 profile (reminiscent of skeletal stem cells) was identified mainly for non-OA MSCs, while low PDPN/high CD146 mainly defined early OA MSCs. These data suggest that MSCs from early OA patients are not affected by degenerative changes in the hip. Moreover, the synovium represents an alternative source of MSCs for patient-derived stem cell therapies, which is comparable to bone. The expression profile reminiscent of skeletal stem cells suggests the combination of low PDPN and high CD146 as potential biomarkers in early OA.
Butyrate Increases Heparin Synthesis and Storage in Human Mast Cells
Authors: Alam Syed Benazir , Yan Zhimin , Verma Nishita Hiresha , Unsworth Larry D. , Kulka Marianna
Published: July 2024

Sulphated glycosaminoglycans (GAGs) such as heparin are a major component of mast cell granules and form the matrix within which biogenic mediators are stored. Since GAGs released from mast cells also play an important role in helminth expulsion, understanding GAG storage can offer new insights into mast cell function. Sodium butyrate (NaBu), a short-chain fatty acid, causes ultrastructural changes within the granules of human mast cells (HMC-1) and increases their histamine content. Therefore, we hypothesized that NaBu treatment would also modify the storage of polysaccharides such as GAGs. NaBu (1 mM) significantly increased GAG content and granularity in a time- and concentration-dependent manner without affecting cell viability and metabolic activity. NaBu increased the expression of enzymes associated with heparin biosynthesis (GLCE, NDST1, NDST2, HS6ST1, and GALT1) in a time-dependent manner. A cholesteryl butyrate emulsion (CholButE) increased heparin content after 24 and 48 h and modestly altered the expression of genes involved in heparin biosynthesis. Similar to NaBu, CholButE reduced cell proliferation without significantly altering viability or metabolic activity. These data show that butyrate increases the synthesis and storage of heparin in human mast cells, perhaps by altering their metabolic pathways.
A Deep Neural Network Approach towards Performance Prediction of Bituminous Mixtures Produced Using Secondary Raw Materials
Authors: Rondinella Fabio , Oreto Cristina , Abbondati Francesco , Baldo Nicola
Published: July 2024

With the progressive reduction in virgin material availability and the growing global concern for sustainability, civil engineering researchers worldwide are shifting their attention toward exploring alternative and mechanically sound technological solutions. The feasibility of preparing both cold and hot asphalt mixtures (AMs) for road pavement binder layers with construction and demolition wastes (C&DWs) and reclaimed asphalt pavement (RAP) partially replacing virgin materials like limestone aggregates and filler has already been proven. The technical suitability and compliance with technical specifications for road paving materials involved the evaluation of mechanical and volumetric aspects by means of indirect tensile strength tests and saturated surface dry voids, respectively. Thus, the main goal of the present study is to train, validate, and test selected machine learning algorithms based on data obtained from the previous experimental campaign with the aim of predicting the volumetric properties and the mechanical performance of the investigated mixtures. A comparison between the predictions made by ridge and lasso regression techniques and both shallow (SNN) and deep neural network (DNN) models showed that the latter achieved better predictive capabilities, highlighted by fully satisfactory performance metrics. DNN performance can be summarized by R2 values equal to 0.8990 in terms of saturated surface dry void predictions, as well as 0.9954 in terms of indirect tensile strength predictions. Predicted observations can be thus implemented within the traditional mix design software. This would reduce the need to carry out additional expensive and time-consuming experimental campaigns.
Effect of Isothermal Oxidation on the Structural Properties of (Ni,Pt)Al Coatings Doped with Zr at 1150 °C
Authors: Feng Min , Yang Linlin , Jiang Chengyang
Published: July 2024

The fabrication of a single-phase (Ni,Pt)Al coating, doped with zirconium, was achieved via a method that included the simultaneous electroplating of a Pt-Zr layer, followed by a process of gas-phase aluminizing. The Zr-doped (Ni,Pt)Al coating was then subjected to an evaluation of its isothermal oxidation resistance at a temperature of 1150 °C in static air compared with a conventional (Ni,Pt)Al coating. The findings indicated that the incorporation of zirconium into the (Ni,Pt)Al coating led to a marked escalation in the rate of oxidation and a worse-scale spallation resistance, which was totally opposite to the results obtained at 1100 °C. The harmful effect of Zr on the oxidation resistance of the coating is discussed in this paper.
Using Machine Learning Algorithms to Develop a Predictive Model for Computing the Maximum Deflection of Horizontally Curved Steel I-Beams
Authors: Ababu Elvis , Markou George , Skorpen Sarah
Published: July 2024

Horizontally curved steel I-beams exhibit a complicated mechanical response as they experience a combination of bending, shear, and torsion, which varies based on the geometry of the beam at hand. The behaviour of these beams is therefore quite difficult to predict, as they can fail due to either flexure, shear, torsion, lateral torsional buckling, or a combination of these types of failure. This therefore necessitates the usage of complicated nonlinear analyses in order to accurately model their behaviour. Currently, little guidance is provided by international design standards in consideration of the serviceability limit states of horizontally curved steel I-beams. In this research, an experimentally validated dataset was created and was used to train numerous machine learning (ML) algorithms for predicting the midspan deflection at failure as well as the failure load of numerous horizontally curved steel I-beams. According to the experimental and numerical investigation, the deep artificial neural network model was found to be the most accurate when used to predict the validation dataset, where a mean absolute error of 6.4 mm (16.20%) was observed. This accuracy far surpassed that of Castigliano’s second theorem, where the mean absolute error was found to be equal to 49.84 mm (126%). The deep artificial neural network was also capable of estimating the failure load with a mean absolute error of 30.43 kN (22.42%). This predictive model, which is the first of its kind in the international literature, can be used by professional engineers for the design of curved steel I-beams since it is currently the most accurate model ever developed.
Ultrashort Echo Time and Fast Field Echo Imaging for Spine Bone Imaging with Application in Spondylolysis Evaluation
Authors: Vucevic Diana , Malis Vadim , Yamashita Yuichi , Mesa Anya , Yamaguchi Tomosuke , Achar Suraj , Miyazaki Mitsue , Bae Won C.
Published: July 2024

Isthmic spondylolysis is characterized by a stress injury to the pars interarticularis bones of the lumbar spines and is often missed by conventional magnetic resonance imaging (MRI), necessitating a computed tomography (CT) for accurate diagnosis. We compare MRI techniques suitable for producing CT-like images. Lumbar spines of asymptomatic and low back pain (LBP) subjects were imaged at 3-Tesla with multi-echo ultrashort echo time (UTE) and field echo (FE) sequences followed by simple post-processing of averaging and inverting to depict spinal bones with a CT-like appearance. The contrast-to-noise ratio (CNR) for bone was determined to compare UTE vs. FE and single-echo vs. multi-echo data. Visually, both sequences depicted cortical bone with good contrast; UTE-processed sequences provided a flatter contrast for soft tissues that made them easy to distinguish from bone, while FE-processed images had better resolution and bone–muscle contrast, which are important for fracture detection. Additionally, multi-echo images provided significantly (p = 0.03) greater CNR compared with single-echo images. Using these techniques, progressive spondylolysis was detected in an LBP subject. This study demonstrates the feasibility of using spine bone MRI to yield CT-like contrast. Through the employment of multi-echo UTE and FE sequences combined with simple processing, we observe sufficient enhancements in image quality and contrast to detect pars fractures.
Knowledge Distillation in Image Classification: The Impact of Datasets
Authors: Belinga Ange Gabriel , Tekouabou Koumetio Cédric Stéphane , El Haziti Mohamed , El Hassouni Mohammed
Published: July 2024

As the demand for efficient and lightweight models in image classification grows, knowledge distillation has emerged as a promising technique to transfer expertise from complex teacher models to simpler student models. However, the efficacy of knowledge distillation is intricately linked to the choice of datasets used during training. Datasets are pivotal in shaping a model’s learning process, influencing its ability to generalize and discriminate between diverse patterns. While considerable research has independently explored knowledge distillation and image classification, a comprehensive understanding of how different datasets impact knowledge distillation remains a critical gap. This study systematically investigates the impact of diverse datasets on knowledge distillation in image classification. By varying dataset characteristics such as size, domain specificity, and inherent biases, we aim to unravel the nuanced relationship between datasets and the efficacy of knowledge transfer. Our experiments employ a range of datasets to comprehensively explore their impact on the performance gains achieved through knowledge distillation. This study contributes valuable guidance for researchers and practitioners seeking to optimize image classification models through kno-featured applications. By elucidating the intricate interplay between dataset characteristics and knowledge distillation outcomes, our findings empower the community to make informed decisions when selecting datasets, ultimately advancing the field toward more robust and efficient model development.
Effect of Laser Heat Input on the Microstructures and Low-Cycle Fatigue Properties of Ti60 Laser Welded Joints
Authors: Zhang Qunbing , Ren Lina , Lei Xiaowei , Yang Jiadian , Zhang Kuo , Zhang Jianxun
Published: July 2024

In this paper, the effects of laser heat input on the microstructures, tensile strength, and fatigue properties of Ti60 laser welded joints were investigated. The results show that with the increase in laser heat input, the macro morphology of the weld zone (WZ) changes from the Y-type to X-type. In the Y-type WZ, the porosity defects are almost eliminated. In contrast, there are a lot of porosity defects in the lower part of the X-type WZ. The microstructure of the base metal (BM) comprises equiaxed α phases, and β phases are mainly distributed at the boundaries of α phases. The heat-affected zone (HAZ) is comprised of α phases and acicular α′ phases, while the WZ mainly contains acicular α′ phases. With the increase in laser heat input, the quantity of the α phase gradually decreases and the acicular α′ phase gradually increases in the HAZ, and the size of the acicular α′ phase in the WZ gradually decreases. Due to the different microstructures, the hardness of BM is lower than the HAZ and WZ under different laser heat input conditions. In the tensile tests and low-cycle fatigue tests, the welded joints are fractured in BM. The porosity defects do not have decisive effects on the tensile and low-cycle fatigue properties of Ti60 laser welded joints.
Environmental Variables Outpace Biotic Interactions in Shaping a Phytoplankton Community
Authors: Mesquita Marcella C. B. , Graco-Roza Caio , de Magalhães Leonardo , Ger Kemal Ali , Marinho Marcelo Manzi
Published: July 2024

We evaluated the main environmental factors (abiotic and biotic) driving the phytoplankton community in a shallow tropical reservoir located in an environmentally protected area. Phytoplankton samples were collected from the surface and bottom of the reservoir. The phytoplankton samples were later identified at the species level, and the species were further assigned to morphology-based functional groups (MBFGs). Zooplankton were sampled through vertical haul, communities were identified to species level, and functional diversity was estimated based on community-weighted means (CWM). Phytoplankton MBFGs IV, V, and VI contributed the most to the biomass under high light availability coupled with low nutrient availability. Potentially toxic cyanobacteria from MBFG III were observed during thermal stratification. Hydraulic mixing plays a crucial role in reducing the phytoplankton biomass during the warmer/rainy season. Cyclopoid copepods accounted for more than 83% of the zooplankton biomass. There was a weak but significant effect of zooplankton functional diversity on phytoplankton functional diversity, mainly because of the dominance of small zooplankton. Altogether, our findings suggest that environmental filtering plays a greater role than zooplankton grazing in phytoplankton community structure in this shallow tropical reservoir.
sCD14-ST and Related Osteoimmunological Biomarkers: A New Diagnostic Approach to Osteomyelitis
Authors: Galliera Emanuela , Massaccesi Luca , Suardi Virginia , de Vecchi Elena , Villa Francesca , Yi Zhang , Suo Guorui , Lovati Arianna B. , Logoluso Nicola , Corsi Romanelli Massimiliano M. , Pellegrini Antonio V.
Published: July 2024

Osteomyelitis (OM) is a major challenge in orthopedic surgery. The diagnosis of OM is based on imaging and laboratory tests, but it still presents some limitations. Therefore, a deeper comprehension of the pathogenetic mechanisms could enhance diagnostic and treatment approaches. OM pathogenesis is based on an inflammatory response to pathogen infection, leading to bone loss. The present study aims to investigate the potential diagnostic role of a panel of osteoimmunological serum biomarkers in the clinical approach to OM. The focus is on the emerging infection biomarker sCD14-ST, along with osteoimmunological and inflammatory serum biomarkers, to define a comprehensive biomarker panel for a multifaced approach to OM. The results, to our knowledge, demonstrate for the first time the diagnostic and early prognostic role of sCD14-ST in OM patients, suggesting that this biomarker could address the limitations of current laboratory tests, such as traditional inflammatory markers, in diagnosing OM. In addition, the study highlights a relevant diagnostic role of SuPAR, the chemokine CCL2, the anti-inflammatory cytokine IL-10, the Wnt inhibitors DKK-1 and Sclerostin, and the RANKL/OPG ratio. Moreover, CCL2 and SuPAR also exhibited early prognostic value.
Automated Quantitative Image-Derived Input Function for the Estimation of Cerebral Blood Flow Using Oxygen-15-Labelled Water on a Long-Axial Field-of-View PET/CT Scanner
Authors: Andersen Thomas Lund , Andersen Flemming Littrup , Haddock Bryan , Rosenbaum Sverre , Larsson Henrik Bo Wiberg , Law Ian , Lindberg Ulrich
Published: July 2024

The accurate estimation of the tracer arterial blood concentration is crucial for reliable quantitative kinetic analysis in PET. In the current work, we demonstrate the automatic extraction of an image-derived input function (IDIF) from a CT AI-based aorta segmentation subsequently resliced to a dynamic PET series acquired on a Siemens Vision Quadra long-axial field of view scanner in 10 human subjects scanned with [15O]H2O. We demonstrate that the extracted IDIF is quantitative and in excellent agreement with a delay- and dispersion-corrected sampled arterial input function (AIF). Perfusion maps in the brain are calculated and compared from the IDIF and AIF, respectively, showed a high degree of correlation. The results demonstrate the possibility of defining a quantitatively correct IDIF compared with AIFs from the new-generation high-sensitivity and high-time-resolution long-axial field-of-view PET/CT scanners.
Minimally Invasive Extraction System Benex—Clinical Evaluation and Comparison
Authors: Chenchev Lyubomir , Ivanova Vasilena , Giragosyan Krikor , Gavrailov Tasho , Chenchev Ivan
Published: July 2024

Tooth extraction is one of the oldest and most well-known surgical procedures in dental medicine. It is still routinely performed by general practitioners and dental undergraduates. The Benex extraction system allows for the extraction of teeth in a vertical direction, which avoids most trauma against surrounding alveolar bone and soft tissues. The study included 56 patients who were recruited from the Department of Oral Surgery, Medical University—Plovdiv. The patients were split into two groups of 28 patients—Group I (control group) and Group II (study group). For each group, the success of the extraction, buccal cortical plate preservation, pain experience and early wound healing were assessed. There was no statistical significance between the success of the extractions in both groups. The Benex extractions preserved the buccal cortical plate in 95% of the cases, whereas the forceps extractions preserved it in only 71.8%, which is statistically significant. On the seventh day, patients in Group II reported less pain, without a significant difference. There was a significantly bigger number of completely healed extraction wounds on the 10th day. Atraumatic extractions allow for more hard and soft tissues to be preserved in the extraction site. This is essential for a successful outcome and the aesthetically pleasing results of the following dental restoration.
Is There a Link between Tax Administration Performance and Tax Evasion?
Authors: Milosavljevic Milos , Ignjatovic Marina , Spasenić Željko , Milanović Nemanja , Đoković Aleksandar
Published: July 2024

The performance of tax administrations (TAs) is usually described as their capacity to complete activities with the minimum of resources engaged. Accordingly, tax administration performance is a multifaceted phenomenon, and measuring and benchmarking its performance against other countries or regions remains a puzzle for researchers and practitioners. This paper introduces a new approach for measuring tax administration performance using the Composite I-Distance Indicator (CIDI) based on 11 individual performance measures from 35 European tax administrations over two consecutive years (2018–2019). For the given scores of tax administrations, we conducted a correlation analysis with (a) tax evasion loss and (b) the fiscal deficit of countries in which these tax administrations operate, aiming to assess the strength of the statistical relationship between these variables. The study highlights Denmark and the Netherlands as exemplary models for tax administration, with “Revenue Collection” being identified as a crucial driver of excellence and “Operational Performance” (such as “e-filing” and “on-time filing”) forming critical aspects of TA efficiency. Also, the study finds a negative correlation between tax avoidance and tax administration performance.
A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction
Authors: Bouasabah Mohammed
Published: July 2024

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow’s value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance.
Correction Method for Perspective Distortions of Pipeline Images
Authors: Zhang Zheng , Zhou Jiazheng , Li Xiuhong , Xu Chaobin , Hu Xinyu , Wang Linhuang
Published: July 2024

It is common to find severe perspective distortion in a pipeline’s image in medium-diameter pipeline defect detection by the panoramic image unwrapping method, resulting in low-quality image unwrapping and stitching, which is caused by the camera’s optical axis being completely deviated from the pipeline’s center. To solve this problem, a novel correction method for reducing perspective distortion in pipeline images was proposed for pipeline defect detection. Firstly, the method enhances the edges of unevenly illuminated regions within a pipeline to facilitate image segmentation and identify key points necessary for correcting perspective distortion. Then, a six-feature-point extraction method was proposed for a circle target to establish the projection relationship between the extracted feature and mapped points on the reference circle. Finally, a perspective matrix was constructed to complete the perspective transformation correction of the distorted images. The results show that the average correction rate and the average relative error of the proposed correction method can reach 90.85% and 1.31%, respectively. The study innovatively used the enhancement of uneven illumination to find distorted edge information. It proposed an extraction method using a reference circle and six key feature points to build a mapping model. It can provide a novel method which can be used to obtain a superior image for pipeline detection and lay a solid foundation for subsequent high-quality pipeline image stitching.
Augmenting Knowledge for Individual NVR Prediction in Different Spatial and Temporal Cross-Building Environments
Authors: Kim Mintai , Lee Sungju
Published: July 2024

Natural ventilation is a critical method for reducing energy consumption for heating, cooling, and ventilating buildings. Recent research has focused on utilizing environmental IoT data from both inside and outside buildings for NVR prediction based on a deep learning model. To design an accurate NVR prediction model while considering individual building environments, various knowledge-sharing methods can be applied, such as transfer learning and ensemble models for cross-building prediction. However, the characteristics of learning data and model parameters should be considered when applying transfer learning and ensemble models to predict NVR with different spatial and temporal domains. In this paper, we propose a way to design an NVR prediction model for a cross-building environment by normalizing the training data, selecting transfer learning layers that are well-suited to the data environment, and augmenting NVR knowledge via ensemble methods. Based on the experimental results, we confirm that the proposed knowledge-sharing deep learning approach, while considering the normalizing of training data, the selecting transfer learning layers, and augmenting the NVR knowledge approach, can improve the accuracy up to 11.8% in the two different offices and seasons.
Adaptive Real-Time Tracking of Molten Metal Using Multi-Scale Features and Weighted Histograms
Authors: Lei Yifan , Xu Degang
Published: July 2024

In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly significant in metal smelting, as efficient molten metal tracking is crucial for effective dross removal and ensuring the quality of metal ingots. Due to the influence of illumination and temperature in the tracking environment, it is difficult to extract suitable features for tracking molten metal during the metal pouring process using industrial cameras. We transform the images captured by the camera into a multi-scale feature space and select the features with the maximum distinction between the molten metal region and its surrounding background for tracking. Furthermore, we introduce a weighted histogram based on the pixel values of the target region into the mean-shift tracking algorithm to improve tracking accuracy. During the tracking process, the target model updates based on changes in the molten metal region across frames. Experimental tests confirm that this tracking method meets practical requirements, effectively addressing key challenges in molten metal tracking and providing reliable support for the dross removal process.
A Novel Static Analysis Approach Using System Calls for Linux IoT Malware Detection
Authors: Ramamoorthy Jayanthi , Gupta Khushi , Kafle Ram C. , Shashidhar Narasimha K. , Varol Cihan
Published: July 2024

The proliferation of Internet of Things (IoT) devices on Linux platforms has heightened concerns regarding vulnerability to malware attacks. This paper introduces a novel approach to investigating the behavior of Linux IoT malware by examining syscalls and library syscall wrappers extracted through static analysis of binaries, as opposed to the conventional method of using dynamic analysis for syscall extraction. We rank and categorize Linux system calls based on their security significance, focusing on understanding malware intent without execution. Feature analysis of the assigned syscall categories and risk ranking is conducted with statistical tests to validate their effectiveness and reliability in differentiating between malware and benign binaries. Our findings demonstrate that potential threats can be reliably identified with an F1 score of 96.86%, solely by analyzing syscalls and library syscall wrappers. This method can augment traditional static analysis, providing an effective preemptive measure to enhance Linux malware analysis. This research highlights the importance of static analysis in strengthening IoT systems against emerging malware threats.
Zero-Power Control Strategy and Dynamics Enhancement for Hybrid Maglev Conveyor Cart
Authors: Tang Xiaowei , Hashimoto Seiji , Kawaguchi Takahiro , Kurita Nobuyuki
Published: July 2024

This paper presents a novel zero-power controller applied to a four-unit magnetic levitation system, aimed at addressing the challenge of maintaining stability under disturbance loads. The zero-power controller, designed based on a state feedback controller integrated with a position servo integrator, is primarily employed to control the balance of the magnetic levitation (Maglev) unit and eliminate steady-state errors. Subsequently, the zero-power controller operates after the state feedback controller to adjust the Maglev unit to a new equilibrium point, primarily utilizing permanent magnetic force to suspend against gravitational input. When loads change or disturbances occur, the system generates current to maintain balance. All designs have passed validation. Experimental results demonstrate the improved zero-power performance and disturbance rejection capabilities of the proposed Maglev system. During synchronous operation, dynamic characteristics have shown significant improvement, which has been experimentally confirmed.
A Configurable Resolution Time-to-Digital Converter with Low PVT Sensitivity for LiDAR Applications
Authors: Sheng Duo , Huang Hao-Ting , Liu Ruey-Lin , Cheng Cheng-I , Wang Xiao-Ti
Published: July 2024

This paper presents an all-digital and configurable resolution time-to-digital converter (TDC) with low process–voltage–temperature (PVT) sensitivity for light detection and ranging (LiDAR) applications. The proposed TDC offers configurable resolution, allowing it to provide an appropriate conversion resolution according to the system’s requirements, thereby optimizing overall system performance. In addition, because the proposed TDC has high immunity to process–voltage–temperature (PVT) variations, it provides more stable time-converting results. The proposed design uses 0.18 μm CMOS technology, and the measurement results demonstrate a resolution ranging from 36 ps to 1193 ps, with a conversion range from 0.1 ns to 36 ns and an average error of 20.59 ps. Furthermore, the proposed TDC is implemented in an all-digital manner, making it highly suitable for system integration.
An Active Clamp Dual-Inductor Isolated Current Source to Current Source Converter with Wide Output Voltage Range
Authors: Yan Tiesheng , Chen Wenyuan , Zhou Yu , Lin Dong , Tian Jun , Zhou Guohua
Published: July 2024

Human observation of the ocean has gradually evolved from the sea surface to systematic monitoring and sampling through seafloor observation networks, and constant current power supply has become the main power supply method for seafloor observation networks due to its high reliability. There are some studies on current source to voltage source converters, but there are few studies on current source to current source (CS/CS) converters, which affects the expansion of power supply networks for seafloor observation networks. In this paper, by employing input current sharing and output voltage doubling circuits, an active clamp dual-inductor isolated CS/CS converter which uses a single-stage conversion circuit to realize constant current source conversion with a wide output voltage range is proposed. Active clamp technology at the primary side of the proposed circuit is employed to recover energy stored in leakage inductance, suppress voltage spikes of the primary side switches, and achieve zero-voltage switching of the primary side switches. The secondary side’s output voltage doubling circuit resonates with transformer leakage inductance to achieve zero-current switching of the secondary side diodes, which can reduce losses and enhance efficiency. The operating principles of the proposed circuit are analyzed in detail, and the characteristic and parameter design analysis, including current conversion ratio, transformer turn ratio, power inductors, and resonant capacitors and inductor, are presented. Finally, the experimental results based on a 100 W experimental prototype validate the feasibility of the proposed converter.
Profitability Analysis of a Prosumer Photovoltaic Installation in Light of Changing Electricity Billing Regulations in Poland
Authors: Cieślak Krystian Janusz
Published: July 2024

The evolving legislation regarding electricity billing for both consumers and prosumers, coupled with the growing interest in photovoltaic installations with energy storage, provided the motivation to examine the operational and financial viability of a prosumer photovoltaic installation located in Poland. Two options were considered: a standard photovoltaic system without energy storage and an installation with batteries. Furthermore, four scenarios were analyzed, each reflecting a different way of accounting for the electricity bought and sold by the prosumer. Another scenario is that energy prices are influenced by a high share of renewable sources. As the changes to the billing scheme are a recent development, there is no relevant analysis currently available. In order to ensure the reliability of the analysis, PVsyst 7.4 software was employed to establish the main performance and financial parameters for the selected PV systems. Analysis showed that using an energy storage system will increase the energy self-consumption from 28.6% to 60.4%, which in some cases would have a great influence on profitability of the investment. In the worst scenario, the payback period is too long (13.7 years). But in the most favorable scenario, with additional financing, it drops to 3.9 years.
Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval Optimization
Authors: Su Lei , Wu Wenxiang , Feng Wanli , Qin Junda , Ao Yuqi
Published: July 2024

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.