bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Dynamics of B Cell Subpopulations in Response to Immune Checkpoint Inhibitors: A Prospective Study
Authors: Pouliasi Foteini , Salamaliki Christina , Kanaloupitis Stavros , Verigou Evgenia , Liolis Elias , Koutras Angelos , Makatsoris Thomas , Kalofonos Charalambos , Liossis Stamatis-Nick , Solomou Elena E.
Published: June 2024

Globally, the efforts to find the best cancer treatment are demanding and very intensive. Immunotherapy has gained an important role as a second or sometimes first line of treatment for various types of cancer. PD-1/PD-L1 checkpoint inhibitors are an impending category of immunotherapy, and their mechanism, as well as their interaction with T cells, are well studied. However, our knowledge about any possible effect(s) of immunotherapy on B cells is limited. In this prospective study, we asked the question of any possible alterations of circulating B cells (numbers and subsets) occurring during immunotherapy in patients with cancer and of the potential correlation of such changes with the outcomes and with development of immune-related adverse events (irAEs). We enrolled 20 cancer patients who received PD-1 checkpoint inhibitors and 8 healthy donors (HD). Patients underwent regular clinical assessment and imaging using the iRECIST criteria for 6 months following immunotherapy. Peripheral blood samples were collected before and during PD-1 checkpoint inhibitor therapy, and flow cytometry analysis of peripheral blood mononuclear cells (PBMCs) was performed, evaluating various circulating B cell subset phenotypes, including mature naïve B cells, memory B cells, regulatory B cells (Bregs), antibody-secreting cells (ASCs), and age-related B cells (ABCs). Statistical analysis was employed to compare the differences of B cells between different groups and among sequential data within the same group. Total circulating CD19+ B cell counts remained stable across both groups (responders (R), nonresponders (NR)) and timepoints. However, there was a significant rise in mature naïve B cells and decline in memory B cells at the initiation of the treatment in the R group compared to healthy donors and to the NR group. Such changes were correlated with a good response to immunotherapy. On the contrary, higher numbers of ABCs at baseline were seen in the NR group and were correlated with resistance to treatment. As far as immune-related adverse events are concerned, no significant changes were recorded among the different B cell subpopulations evaluated in both groups. Our study provides preliminary data suggesting that B cell subset changes during immunotherapy may correlate with immune checkpoint inhibitor-induced clinical responses in patients with neoplasia. Further investigations to delineate the potential role(s) of B cells in patients undergoing immunotherapy are needed.
Non-Parametric Machine Learning Modeling of Tree-Caused Power Outage Risk to Overhead Distribution Powerlines
Authors: Wedagedara Harshana , Witharana Chandi , Fahey Robert , Cerrai Diego , Parent Jason , Perera Amal S.
Published: June 2024

Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM’s performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid.
Environmental Effects of Using Ammonium Sulfate from Animal Manure Scrubbing Technology as Fertilizer
Authors: Rietra René , van Dijk Kimo , Schoumans Oscar
Published: June 2024

Processed manure products have the potential to substitute chemical fertilizers and the use of these products may increase resource efficiency in the food system and decrease emissions of ammonia (NH3) and greenhouse gasses (GHG). The yields of maize and grass, as well as emissions, have been determined from a processed manure product: liquid ammonium sulfate from nitrogen stripping animal manure (AS), in comparison to a regular mineral fertilizer, calcium ammonium nitrate (CAN), in a greenhouse experiment and a field demonstration using a sandy and a clay soil. NH3 emissions were determined by comparing AS with a dairy manure as a reference. The yield of both crops, their nitrogen nutrient use efficiency (NUE), and nitrous oxide (N2O) emissions were not significantly different, while NH3 emission was lower from AS compared to the dairy manure. As a side-effect, the sulfur (S) contents of the grass in the fields fertilized with AS were much higher than in the non-fertilized control. We conclude that AS, produced here with a pH < 5.5, can be used as an alternative for CAN in Dutch dairy systems, or similar other system, if S leaching losses do not pose a problem for the environment. Meanwhile, care should be taken not to exceed S in feed above toxic levels for ruminants.
Improved YOLOv8-Seg Based on Multiscale Feature Fusion and Deformable Convolution for Weed Precision Segmentation
Authors: Lyu Zhuxi , Lu Anjiang , Ma Yinglong
Published: June 2024

Laser-targeted weeding methods further enhance the sustainable development of green agriculture, with one key technology being the improvement of weed localization accuracy. Here, we propose an improved YOLOv8 instance segmentation based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg) to address the challenges of insufficient weed localization accuracy in complex environments with resource-limited laser weeding devices. Initially, by training on extensive datasets of plant images, the most appropriate model scale and training weights are determined, facilitating the development of a lightweight network. Subsequently, the introduction of the Bidirectional Feature Pyramid Network (BiFPN) during feature fusion effectively prevents the omission of weeds. Lastly, the use of Dynamic Snake Convolution (DSConv) to replace some convolutional kernels enhances flexibility, benefiting the segmentation of weeds with elongated stems and irregular edges. Experimental results indicate that the BFFDC-YOLOv8-seg model achieves a 4.9% increase in precision, an 8.1% increase in recall rate, and a 2.8% increase in mAP50 value to 98.8% on a vegetable weed dataset compared to the original model. It also shows improved mAP50 over other typical segmentation models such as Mask R-CNN, YOLOv5-seg, and YOLOv7-seg by 10.8%, 13.4%, and 1.8%, respectively. Furthermore, the model achieves a detection speed of 24.8 FPS on the Jetson Orin nano standalone device, with a model size of 6.8 MB that balances between size and accuracy. The model meets the requirements for real-time precise weed segmentation, and is suitable for complex vegetable field environments and resource-limited laser weeding devices.
Simplified Models to Assess the Mechanical Performance Parameters of Stents
Authors: Toledo Juan P. , Martínez-Castillo Jaime , Cardenas Diego , Delgado-Alvarado Enrique , Vigueras-Zuñiga Marco Osvaldo , Herrera-May Agustín L.
Published: June 2024

Ischemic heart disease remains a leading cause of mortality worldwide, which has promoted extensive therapeutic efforts. Stenting has emerged as the primary intervention, particularly among individuals aged 70 years and older. The geometric specifications of stents must align with various mechanical performance criteria outlined by regulatory agencies such as the Food and Drug Administration (FDA). Finite element method (FEM) analysis and computational fluid dynamics (CFD) serve as essential tools to assess the mechanical performance parameters of stents. However, the growing complexity of the numerical models presents significant challenges. Herein, we propose a method to determine the mechanical performance parameters of stents using a simplified FEM model comprising solid and shell elements. In addition, a baseline model of a stent is developed and validated with experimental data, considering parameters such as foreshortening, radial recoil, radial recoil index, and radial stiffness of stents. The results of the simplified FEM model agree well with the baseline model, decreasing up to 80% in computational time. This method can be employed to design stents with specific mechanical performance parameters that satisfy the requirements of each patient.
Selection and Layout Optimization of Double Tower Cranes
Authors: Fu Yan , Bu Jiarui , Lin Jingjun , Liu Jun , Zhang Chunli
Published: June 2024

As the scale of construction expanded and the number of prefabricated buildings increased, a single tower crane could no longer meet the construction requirements, necessitating the simultaneous operation of more tower cranes to improve construction efficiency. To optimize the efficiency and cost of lifting prefabricated concrete components and address the selection and layout optimization of dual tower cranes, this paper proposed a double tower crane selection and positioning optimization model that integrated feasible layout area solving and optimization based on different objectives. By analyzing the mathematical relationship between the layout positions of the tower cranes and the positions of the prefabricated component storage yard and installation sites, a mathematical model for the feasible layout area of double tower cranes was established and solved using a genetic algorithm. On this basis, optimization models were established with the objectives of minimizing cost, achieving the shortest total lifting time, and achieving the maximum value coefficient, and they were solved using a genetic algorithm. The model was verified and analyzed through a case study. The research results indicated that the double tower crane selection scheme could achieve more than twice the lifting efficiency of the same model single tower crane. When the total lifting time was similar, choosing based on the principles of value engineering could yield the optimal solution with lower costs. These research findings provided a reference for the selection of tower crane schemes and the cost reduction and efficiency improvement of on-site prefabricated concrete component lifting construction.
Artificial Neural Network-Based Model for Assessing the Whole-Body Vibration of Vehicle Drivers
Authors: Aguilar Antonio J. , de la Hoz-Torres María L. , Martínez-Aires Mᵃ Dolores , Ruiz Diego P. , Arezes Pedro , Costa Nélson
Published: June 2024

Musculoskeletal disorders, which are epidemiologically related to exposure to whole-body vibration (WBV), are frequently self-reported by workers in the construction sector. Several activities during building construction and demolition expose workers to this physical agent. Directive 2002/44/CE defined a method of assessing WBV exposure that was limited to an eight-hour working day, and did not consider the cumulative and long-term effects on the health of drivers. This study aims to propose a methodology for generating individualised models for vehicle drivers exposed to WBV that are easy to implement by companies, to ensure that the health of workers is not compromised in the short or long term. A measurement campaign was conducted with a professional driver, and the collected data were used to formulate six artificial neural networks to predict the daily compressive dose on the lumbar spine and to assess the short- and long-term WBV exposure. Accurate results were obtained from the developed artificial neural network models, with R2 values above 0.90 for training, cross-validation, and testing. The approach proposed in this study offers a new tool that can be applied in the assessment of short- and long-term WBV to ensure that workers’ health is not compromised during their working life and subsequent retirement.
Advances in Detecting, Prognosticating, and Treating Hepatocellular Carcinoma: Advances and Outcomes
Authors: Kramer Ryan J. , Moris Dimitrios
Published: June 2024

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and the ninth leading cause of cancer-related death in the United States [...]
Molecular Subtypes and the Role of TP53 in Diffuse Large B-Cell Lymphoma and Richter Syndrome
Authors: Negara Ivan , Tomuleasa Ciprian , Buruiana Sanda , Efremov Dimitar G.
Published: June 2024

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy and a heterogeneous entity comprised of several biologically distinct subtypes. Recently, novel genetic classifications of DLBCL have been resolved based on common mutational patterns indicative of distinct pathways of transformation. However, the complicated and costly nature of the novel classifiers has precluded their inclusion into routine practice. In view of this, the status of the TP53 gene, which is mutated or deleted in 20–30% of the cases, has emerged as an important prognostic factor for DLBCL patients, setting itself apart from other predictors. TP53 genetic lesions are particularly enriched in a genetic subtype of DLBCL that shares genomic features with Richter Syndrome, highlighting the possibility of a subset of DLBCL arising from the transformation of an occult chronic lymphocytic leukemia-like malignancy, such as monoclonal B-cell lymphocytosis. Patients with TP53-mutated DLBCL, including those with Richter Syndrome, have a particularly poor prognosis and display inferior responses to standard chemoimmunotherapy regimens. The data presented in this manuscript argue for the need for improved and more practical risk-stratification models for patients with DLBCL and show the potential for the use of TP53 mutational status for prognostication and, in prospect, treatment stratification in DLBCL.
Generation of Hydrogen Peroxide in Cancer Cells: Advancing Therapeutic Approaches for Cancer Treatment
Authors: Ali Taufeeque , Li Daniel , Ponnamperumage Thilini Nimasha Fernando , Peterson Alexis Kimberly , Pandey Jatin , Fatima Kulsum , Brzezinski John , Jakusz Julia Anna Rose , Gao Hanlun , Koelsch Gilbert Edward , Murugan Dhivyashree Senthil , Peng Xiaohua
Published: June 2024

Cancer cells show altered antioxidant defense systems, dysregulated redox signaling, and increased generation of reactive oxygen species (ROS). Targeting cancer cells through ROS-mediated mechanisms has emerged as a significant therapeutic strategy due to its implications in cancer progression, survival, and resistance. Extensive research has focused on selective generation of H2O2 in cancer cells for selective cancer cell killing by employing various strategies such as metal-based prodrugs, photodynamic therapy, enzyme-based systems, nano-particle mediated approaches, chemical modulators, and combination therapies. Many of these H2O2-amplifying approaches have demonstrated promising anticancer effects and selectivity in preclinical investigations. They selectively induce cytotoxicity in cancer cells while sparing normal cells, sensitize resistant cells, and modulate the tumor microenvironment. However, challenges remain in achieving selectivity, addressing tumor heterogeneity, ensuring efficient delivery, and managing safety and toxicity. To address those issues, H2O2-generating agents have been combined with other treatments leading to optimized combination therapies. This review focuses on various chemical agents/approaches that kill cancer cells via H2O2-mediated mechanisms. Different categories of compounds that selectively generate H2O2 in cancer cells are summarized, their underlying mechanisms and function are elucidated, preclinical and clinical studies as well as recent advancements are discussed, and their prospects as targeted therapeutic agents and their therapeutic utility in combination with other treatments are explored. By understanding the potential of these compounds, researchers can pave the way for the development of effective and personalized cancer treatments.
Wire-Free Targeted Axillary Dissection: A Pooled Analysis of 1300+ Cases Post-Neoadjuvant Systemic Therapy in Node-Positive Early Breast Cancer
Authors: Varghese Jajini , Patani Neill , Wazir Umar , Novintan Shonnelly , Michell Michael J. , Malhotra Anmol , Mokbel Kinan , Mokbel Kefah
Published: June 2024

Recent advances in neoadjuvant systemic therapy (NST) have significantly improved pathologic complete response rates in early breast cancer, challenging the role of axillary lymph node dissection in nose-positive patients. Targeted axillary dissection (TAD) integrates marked lymph node biopsy (MLNB) and tracer-guided sentinel lymph node biopsy (SLNB). The introduction of new wire-free localisation markers (LMs) has streamlined TAD and increased its adoption. The primary endpoints include the successful localisation and retrieval rates of LMs. The secondary endpoints include the pathological complete response (pCR), SLNB, and MLNB concordance, as well as false-negative rates. Seventeen studies encompassing 1358 TAD procedures in 1355 met the inclusion criteria. The localisation and retrieval rate of LMs were 97% and 99%. A concordance rate of 67% (95% CI: 64–70) between SLNB and MLNB was demonstrated. Notably, 49 days (range: 0–272) was the average LM deployment time to surgery. pCR was observed in 46% (95% CI: 43–49) of cases, with no significant procedure-related complications. Omitting MLNB or SLNB would have under-staged the axilla in 15.2% or 5.4% (p = 0.0001) of cases, respectively. MLNB inclusion in axillary staging post-NST for initially node-positive patients is crucial. The radiation-free Savi Scout, with its minimal MRI artefacts, is the preferred technology for TAD.
Fabrication of Dicarboxylic-Acid- and Silica-Substituted Octacalcium Phosphate Blocks with Stronger Mechanical Strength
Authors: Sugiura Yuki , Saito Yasuko , Yamada Etsuko , Horie Masanori
Published: June 2024

Octacalcium phosphate (OCP) is an attractive base material to combine into components developed for medical purposes, especially those used in bone replacement procedures, not only because of its excellent biocompatibility but also because of its ability to intercalate with multiple types of molecular layers such as silica, dicarboxylic acid, and various cations. On the other hand, there are no examples of simultaneous substituting for several different compounds on OCPs. Therefore, in this study, the physical and mechanical strength (DTS: diametral tensile strength) of OCPs substituted with both silica and dicarboxylic acids (thiomalate: SH-malate) were evaluated. By optimizing the amount of SH-malate, we were able to prepare a block consisting of OCPs with both silica and SH-malate supported in the interlayer. The composition of the OCP-based compound comprising this block was Ca8Na1.07H6.33(PO4)4.44(SiO4)1.32(SH-malate)2.40·nH2O. Interestingly, the low mechanical strength, a drawback of silica-substituted OCP blocks, could be improved by dicarboxylic acid substituting. The dicarboxylic acid addition increased the mechanical strength of silica-substituted OCP blocks, and the acid successfully incorporated into the interlayer, even with the presence of silica. These results are expected to advance the creation of better silica-substituted OCPs and improved bone replacement materials.
Visible-Light Photochromic Properties of an Inorganic-Organic Phosphomolybdic Acid/Polythiophene Hybrid Thin Film
Authors: Zhao Wanqing , Zhao Hongmei , Feng Wei , Zhao Honggang
Published: June 2024

A visible-light photochromic hybrid film was synthesized based on combining phosphomolybdic acid (PMoA) with the polythiophene (PTh) matrix. The microstructure and photochromic properties of the materials were analyzed through atomic force microscopy (AFM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and ultraviolet-visible spectra (UV-vis). According to FTIR spectra, the geometries of PMoA and PTh were well preserved in hybrid film and there exists a strong interaction at the interface of PMoA and PTh. The XPS spectra revealed the change in the chemical microenvironment and the reduction of Mo6+ atoms in the photoreduction reaction. Under visible light irradiation, the composite film changed from transparent to blue and deepened gradually, generating heteropoly blue. The hybrid film also shows reversibility in the presence of oxygen. The results indicated that the photochromic reaction was inconsistent with photoinduced electron transfer mechanism.
Advancing Physical Literacy Research in Children
Authors: Durden-Myers Elizabeth J.
Published: June 2024

The collection of papers in this Special Issue serves to extend the literature and evidence base for physical literacy (PL) research within child and youth populations. Currently, child and youth populations are increasingly sedentary, resulting in them spending less time engaging in daily physical activity (PA). Physical literacy serves as an attractive concept to help reframe and address physical inactivity and poor health and wellbeing, utilising a different and integrated approach to physical activity, health and wellbeing promotion. The studies presented in this Special Issue respond to previous calls in PL research for further empirical evidence, clarity around PL assessment, the utility of physical literacy with diverse populations including indigenous children and those with disabilities, the application of PL within early years, parental engagement and the role of physical education in the promotion of PL. These studies shed new light on the frontiers of PL research within child and youth populations.
Quasi/Periodic Noise Reduction in Images Using Modified Multiresolution-Convolutional Neural Networks for 3D Object Reconstructions and Comparison with Other Convolutional Neural Network Models
Authors: Espinosa-Bernal Osmar Antonio , Pedraza-Ortega Jesús Carlos , Aceves-Fernandez Marco Antonio , Martínez-Suárez Victor Manuel , Tovar-Arriaga Saul , Ramos-Arreguín Juan Manuel , Gorrostieta-Hurtado Efrén
Published: June 2024

The modeling of real objects digitally is an area that has generated a high demand due to the need to obtain systems that are able to reproduce 3D objects from real objects. To this end, several techniques have been proposed to model objects in a computer, with the fringe profilometry technique being the one that has been most researched. However, this technique has the disadvantage of generating Moire noise that ends up affecting the accuracy of the final 3D reconstructed object. In order to try to obtain 3D objects as close as possible to the original object, different techniques have been developed to attenuate the quasi/periodic noise, namely the application of convolutional neural networks (CNNs), a method that has been recently applied for restoration and reduction and/or elimination of noise in images applied as a pre-processing in the generation of 3D objects. For this purpose, this work is carried out to attenuate the quasi/periodic noise in images acquired by the fringe profilometry technique, using a modified CNN-Multiresolution network. The results obtained are compared with the original CNN-Multiresolution network, the UNet network, and the FCN32s network and a quantitative comparison is made using the Image Mean Square Error E (IMMS), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Profile (MSE) metrics.
BBS Gene Expression and Its Diversity in the Genus Dendrobium
Authors: Takamiya Tomoko , Saito Manako , Miyamoto Aoi , Oikawa Mio , Zhang Liyue , Yanagihashi Kazuki , Okawa Erika , Takahashi Yuuka , Suzuki Yui , Watanabe Misaki , Yahagi Tadahiro , Matsuzaki Keiichi , Iijima Hiroshi , Yukawa Tomohisha , Ogura-Tsujita Yuki
Published: June 2024

Dendrobium officinale Kimura & Migo in the genus Dendrobium of Orchidaceae is an important medicinal plant that produces various bibenzyl and phenanthrene derivatives. In some orchids, these derivatives have been reported to increase with fungal infection. Bibenzyl biosynthesis is regulated by bibenzyl synthase (BBS). Although six genes of the BBS family have been registered from D. officinale, their gene regulation mechanisms are unclear. The infection of Dendrobium with mycorrhizal fungi also reportedly increases the expression of genes involved in biosynthesis; however, the effect of mycorrhizal fungi on bibenzyl production is unknown. The present study examined the effects of three mycorrhizal fungi isolated from D. officinale on BBS gene expression and bibenzyl production over time. One of the Tulasnellaceae operational taxonomic units induced BBS gene expression and increased two representative bibenzyls, gigantol and dendrophenol, at specific time points. Furthermore, 19 BBS sequences were cloned from 12 Dendrobium species, and a phylogenetic analysis was performed. The results indicated that repeated BBS gene duplication occurred during the evolution of the genus, and further duplication occurred after speciation. These results suggest that it is possible to optimize metabolite production by selecting suitable symbiotic fungi.
Artificial Intelligence-Powered Imaging Biomarker Based on Mammography for Breast Cancer Risk Prediction
Authors: Park Eun Kyung , Lee Hyeonsoo , Kim Minjeong , Kim Taesoo , Kim Junha , Kim Ki Hwan , Kooi Thijs , Chang Yoosoo , Ryu Seungho
Published: June 2024

The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1–5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%). To determine the feasibility of the AI prediction model, mammograms and detailed clinical information were collected. C-indices and area under the receiver operating characteristic curves (AUCs) for 1–5-year outcomes were obtained. We compared the AUCs of our AI prediction model, Mirai, and clinical statistical risk models, including the Tyrer–Cuzick (TC) model and Gail model, using DeLong’s test. A total of 16,894 mammograms were independently collected for external validation, of which 4002 were followed by a cancer diagnosis within 5 years. Our AI prediction model obtained a C-index of 0.76, with AUCs of 0.90, 0.84, 0.81, 0.78, and 0.81, to predict the 1–5-year risks. Our AI prediction model showed significantly higher AUCs than those of the TC model (AUC: 0.57; p < 0.001) and Gail model (AUC: 0.52; p < 0.001), and achieved similar performance to Mirai. The deep learning AI model using mammograms and AI-powered imaging biomarkers has substantial potential to advance accurate breast cancer risk prediction.
Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML)
Authors: Alsaleh Aqilah M. , Albalawi Eid , Algosaibi Abdulelah , Albakheet Salman S. , Khan Surbhi Bhatia
Published: June 2024

Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is often limited. To address this challenge, few-shot learning techniques have been successfully adapted to rapidly generalize to new tasks with only a few samples, leveraging prior knowledge. In this paper, we employ a gradient-based method known as Model-Agnostic Meta-Learning (MAML) for medical image segmentation. MAML is a meta-learning algorithm that quickly adapts to new tasks by updating a model’s parameters based on a limited set of training samples. Additionally, we use an enhanced 3D U-Net as the foundational network for our models. The enhanced 3D U-Net is a convolutional neural network specifically designed for medical image segmentation. We evaluate our approach on the TotalSegmentator dataset, considering a few annotated images for four tasks: liver, spleen, right kidney, and left kidney. The results demonstrate that our approach facilitates rapid adaptation to new tasks using only a few annotated images. In 10-shot settings, our approach achieved mean dice coefficients of 93.70%, 85.98%, 81.20%, and 89.58% for liver, spleen, right kidney, and left kidney segmentation, respectively. In five-shot sittings, the approach attained mean Dice coefficients of 90.27%, 83.89%, 77.53%, and 87.01% for liver, spleen, right kidney, and left kidney segmentation, respectively. Finally, we assess the effectiveness of our proposed approach on a dataset collected from a local hospital. Employing five-shot sittings, we achieve mean Dice coefficients of 90.62%, 79.86%, 79.87%, and 78.21% for liver, spleen, right kidney, and left kidney segmentation, respectively.
Iron Deficiency Anemia and Its Impact on Oral Health—A Literature Review
Authors: Velliyagounder Kabilan , Chavan Krupa , Markowitz Kenneth
Published: June 2024

Oral disease interventions primarily focus on behavioral changes like dietary improvements and ensuring better oral hygiene. However, recognizing the influence of biological factors, including genetics and early-life nutrition, is crucial. Iron deficiency (ID) and its advanced form, iron deficiency anemia (IDA), affect nearly two billion people globally, especially children and pregnant women. We conducted a comprehensive search using Medline via EndNote and Web of Science, employing keywords related to iron deficiency anemia (IDA), and we identified 36 studies deemed relevant for inclusion in this literature review. IDA prevalence is notably high among pregnant women and young children. Both IDA and early-childhood caries (ECC) disproportionately affect impoverished populations, highlighting the socioeconomic dimension of this issue. IDA presents with various oral mucosal changes and is closely linked to candidiasis. Additionally, IDA can hinder tooth development and weaken the immune response. Multiple population surveys have revealed a significant association between ECC and IDA. While some studies have explored the IDA–periodontal disease link, the current evidence is relatively limited in its robustness. In conclusion, more comprehensive longitudinal studies are essential to deepen our understanding of the IDA–oral disease connection. Investigating the underlying biological mechanisms is critical to developing effective interventions, particularly for vulnerable populations affected by IDA.
The Use of Potassium Iodide in Pediatric Dentistry Does Not Change the Retention of Glass Ionomer Cement on a Dentin Treated with Silver Fluoride: In Vitro Results
Authors: Gotas Louise , Canceill Thibault , Joniot Sabine
Published: June 2024

In pediatric and operative dentistry, caries treatment benefits from a therapeutic option based on the use of silver fluoride (AgF) associated with potassium iodide (KI) to avoid dark colorations on dental tissues. The objective of this in vitro study is to evaluate the retention of glass ionomer cement (GIC) on a dentin when treated with AgF and KI. Twenty-two healthy human permanent molars and eight human primary teeth, all free of any decay, were cut to obtain occlusal flat dentinal surfaces and were then treated with AgF for a duration of 60 s. For half of the teeth, a drop of KI was applied for a duration of 30 s. All samples were covered with a plot of GIC and their resistance to shear bond strength was measured. The fracture resistance in both permanent and primary teeth does not show any statistically significant differences whether KI was applied or not. For permanent teeth, the resistance is slightly higher in the group treated with KI than in the group treated with AgF alone. To our knowledge, these data are the first to describe the possible application of KI both on primary and permanent teeth. In any case, further studies are needed to investigate the bond strength between dentin and GIC on a wider range of samples.
On Casimir and Helmholtz Fluctuation-Induced Forces in Micro- and Nano-Systems: Survey of Some Basic Results
Authors: Dantchev Daniel
Published: June 2024

Fluctuations are omnipresent; they exist in any matter, due either to its quantum nature or to its nonzero temperature. In the current review, we briefly cover the quantum electrodynamic Casimir (QED) force as well as the critical Casimir (CC) and Helmholtz (HF) forces. In the QED case, the medium is usually a vacuum and the massless excitations are photons, while in the CC and HF cases the medium is usually a critical or correlated fluid and the fluctuations of the order parameter are the cause of the force between the macroscopic or mesoscopic bodies immersed in it. We discuss the importance of the presented results for nanotechnology, especially for devising and assembling micro- or nano-scale systems. Several important problems for nanotechnology following from the currently available experimental findings are spelled out, and possible strategies for overcoming them are sketched. Regarding the example of HF, we explicitly demonstrate that when a given integral quantity characterizing the fluid is conserved, it has an essential influence on the behavior of the corresponding fluctuation-induced force.
Project-Based Learning (PBL) as an Experiential Pedagogical Methodology in Engineering Education: A Review of the Literature
Authors: Lavado-Anguera Silvia , Velasco-Quintana Paloma-Julia , Terrón-López María-José
Published: June 2024

This systematic literature review explores how the implementation of project-based-learning (PBL) as an experiential pedagogical methodology in engineering education contributes to the development of real-world skills among students. The methodology applied was the PRISMA protocol with searches in two databases in a 24 year timeframe. The research reviewed 54 pieces to explore the contribution of PBL to seven pillars of a holistic pedagogical model comprising the following categories: technology, an integrated curriculum, an international focus, sustainability, a multidisciplinary focus, simulation, and professional environments. Varied PBL developments across these pillars reveal challenges, including aligning with real-world complexities and promoting interdisciplinary integration. Despite obstacles, PBL in engineering shows promise for enhancing students’ skills and channeling the added value of a holistic pedagogical model, despite significant differences in the number of experiences associated with each category. Limitations include restricted article access, emphasizing the need for open science promotion.
Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review
Authors: Kgaphola Potego Maboe , Marebane Senyeki Milton , Hans Robert Toyo
Published: June 2024

Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study’s contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions.
Color Image Encryption Based on a Novel Fourth-Direction Hyperchaotic System
Authors: Lei Zhuoyi , Yang Jiacheng , Qiu Hanshuo , Zhang Xiangzi , Liu Jizhao
Published: June 2024

Neuromorphic computing draws inspiration from the brain to design energy-efficient hardware for information processing, enabling highly complex tasks. In neuromorphic computing, chaotic phenomena describe the nonlinear interactions and dynamic behaviors. Chaotic behavior can be utilized in neuromorphic computing to accomplish complex information processing tasks; therefore, studying chaos is crucial. Today, more and more color images are appearing online. However, the generation of numerous images has also brought about a series of security issues. Ensuring the security of images is crucial. We propose a novel fourth-direction hyperchaotic system in this paper. In comparison to low-dimensional chaotic systems, the proposed hyperchaotic system exhibits a higher degree of unpredictability and various dynamic behaviors. The dynamic behaviors include fourth-direction hyperchaos, third-direction hyperchaos, and second-direction hyperchaos. The hyperchaotic system generates chaotic sequences. These chaotic sequences are the foundation of the encryption scheme discussed in this paper. Images are altered by employing methods such as row and column scrambling as well as diffusion. These operations will alter both the pixel values and positions. The proposed encryption scheme has been analyzed through security and application scenario analyses. We perform a security analysis to evaluate the robustness and weaknesses of the encryption scheme. Moreover, we conduct an application scenario analysis to help determine the practical usability and effectiveness of the encryption scheme in real-world situations. These analyses demonstrate the efficiency of the encryption scheme.
Feature Selection for Data Classification in the Semiconductor Industry by a Hybrid of Simplified Swarm Optimization
Authors: Yeh Wei-Chang , Chu Chia-Li
Published: June 2024

In the semiconductor manufacturing industry, achieving high yields constitutes one of the pivotal factors for sustaining market competitiveness. When confronting the substantial volume of high-dimensional, non-linear, and imbalanced data generated during semiconductor manufacturing processes, it becomes imperative to transcend traditional approaches and incorporate machine learning methodologies. By employing non-linear classification models, one can achieve more real-time anomaly detection, subsequently facilitating a deeper analysis of the fundamental causes behind anomalies. Given the considerable dimensionality of production line data in semiconductor manufacturing, there arises a necessity for dimensionality reduction to mitigate noise and reduce computational costs within the data. Feature selection stands out as one of the primary methodologies for achieving data dimensionality reduction. Utilizing wrapper-based heuristics algorithms, although characterized by high time complexity, often yields favorable performance in specific cases. If further combined into hybrid methodologies, they can concurrently satisfy data quality and computational cost considerations. Accordingly, this study proposes a two-stage feature selection model. Initially, redundant features are eliminated using mutual information to reduce the feature space. Subsequently, a Simplified Swarm Optimization algorithm is employed to design a unique fitness function aimed at selecting the optimal feature subset from candidate features. Finally, support vector machines are utilized as the classification model for validation purposes. For practical cases, it is evident that the feature selection method proposed in this study achieves superior classification accuracy with fewer features in the context of wafer anomaly classification problems. Furthermore, its performance on public datasets further substantiates the effectiveness and generalization capability of the proposed approach.
Variable Frequency Resonant Controller Based on Generalized Predictive Control for Biased-Sinusoidal Reference Tracking and Multi-Layer Perceptron
Authors: Cordero Raymundo , Gonzales Juliana , Estrabis Thyago , Galotto Luigi , Padilla Rebeca , Onofre João
Published: June 2024

Resonant controllers are widely used in power electronics to track sinusoidal references. According to the internal model principle (IMP), these controllers should embed the poles of the Laplace or Z transform of the reference for the closed-loop system to track the reference asymptotically. Thus, tracking a sinusoidal reference is difficult as the controller should adapt its structure to embed the poles of the sinusoidal reference with variable frequency, as those poles depend on that variable frequency. On the other hand, Generalized Predictive Control (GPC) is widespread in industry applications due to its fast response, robustness and capability to include constraints. Resonant controllers based on GPC, which satisfy IMP, have been developed. However, these controllers consider the sinusoidal frequency to be constant. This paper presents a new GPC-based resonant controller with an adaptive and simple control law to track references with variable frequencies. A PLL estimates the frequency of the reference. A multi-layer perceptron uses the estimated frequency to define the gain matrix required to calculate the GPC control action. The GPC control action and the estimated frequency define the control law, which satisfies IMP in steady-state conditions. The authors did not find in the literature the proposed mathematical development of an adaptive GPC resonant controller with a discrete-time augmented model whose control law satisfies IMP. Thus, the proposed approach is helpful to develop other adaptive predictive controllers. Experimental results show that the proposed controller can track sinusoidal references whose frequencies vary in time.
Active Equalization for Lithium-Iron Battery Pack Based on Reduced-Order Solving Strategy for the Hanoi Tower Problem
Authors: Xia Zhengyu , Chen Xi , Chi Xingjiang , Zhu Binxin , Zhang Lei , Huang Yuehua
Published: June 2024

In order to address the energy imbalance issue of a series-connected lithium-iron battery pack, this paper proposes an active equalization method based on a reduced-order solving strategy for the Hanoi Tower problem. The proposed scheme utilizes a combined structure of a switching-network circuit and a bidirectional Cuk converter and leverages an ultracapacitor cell as the energy-transfer carrier. Simulation and comparison demonstrate that the exchange of unbalanced energy within the battery pack can be achieved. The proposed approach can effectively achieve various balancing modes such as cell-to-cell, cell-to-string, string-to-cell, and string-to-string with a relatively fast balancing speed.
Testing the Nonlinear Long- and Short-Run Distributional Asymmetries Effects of Bitcoin Prices on Bitcoin Energy Consumption: New Insights through the QNARDL Model and XGBoost Machine-Learning Tool
Authors: Tissaoui Kais , Zaghdoudi Taha , Boubaker Sahbi , Hkiri Besma , Talbi Mariem
Published: June 2024

This study investigates the asymmetric impacts of Bitcoin prices on Bitcoin energy consumption. Two series are shown to be chaotic and non-linear using the BDS Independence test. To take into consideration this nonlinearity, we employed the QNARDL model as a traditional technique and Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) as non-conventional approaches to study the link between Bitcoin energy usage and Bitcoin prices. Referring to QNARDL estimates, results show that the relationship between Bitcoin energy use and prices is asymmetric. Additionally, results demonstrate that changes in Bitcoin prices have a considerable effect, both short- and long-run, on energy consumption. As a result, any upsurge in the price of Bitcoin leads to an immediate boost in energy use. Furthermore, the short-term drop in Bitcoin values causes an increase in energy use. However, higher Bitcoin prices reduce energy use in the long run. Otherwise, every decline in Bitcoin prices leads to a long-term reduction in energy use. In addition, the performance metrics and convergence of the cost function provide evidence that the XGBoost model dominates the SVM model in terms of Bitcoin energy consumption forecasting. In addition, we analyze the effectiveness of several modeling approaches and discover that the XGBoost model (MSE: 0.52%; RMSE: 0.72 and R2: 96%) outperforms SVM (MSE: 4.89; RMSE: 2.21 and R2: 75%) in predicting. Results indicate that the forecast of Bitcoin energy consumption is more influenced by positive shocks to Bitcoin prices than negative shocks. This study gives insights into the policies that should be implemented, such as increasing the sustainable capacity, efficiency, and flexibility of mining operations, which would allow for the reduction of the negative impacts of Bitcoin price shocks on energy consumption.
Characterization of Pupillary Light Response through Low-Cost Pupillometry and Machine Learning Techniques
Authors: Gutiérrez-Hernández David A. , Gómez-Díaz Miguel S. , Casillas-Rodríguez Francisco J. , Ovalle-Magallanes Emmanuel
Published: June 2024

This article employed pupillometry as a non-invasive technique to analyze pupillary light reflex (PLR) using LED flash stimuli. Particularly, for the experiments, only the red LED with a wavelength of 600 nm served as the light stimulation source. To stabilize the initial pupil size, a pre-stimulus (PRE) period of 3 s was implemented, followed by a 1 s stimulation period (ON) and a 4 s post-stimulus period (POST). Moreover, an experimental, low-cost pupillometer prototype was designed to capture pupillary images of 13 participants. The prototype consists of a 2-megapixel web camera and a lighting system comprising infrared and RGB LEDs for image capture in low-light conditions and stimulus induction, respectively. The study reveals several characteristic features for classifying the phenomenon, notably the mobility of Hjórth parameters, achieving classification percentages ranging from 97% to 99%, and offering novel insights into pattern recognition in pupillary activity. Moreover, the proposed device successfully captured the PLR from all the participants with zero reported incidents or health affectations.