bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning with mixup augmentation for improved pore detection during additive manufacturing
Authors: Ahmmed Bulbul , Rau Elisabeth G. , Mudunuru Maruti K. , Karra Satish , Tempelman Joshua R. , Wachtor Adam J. , Forien Jean-Baptiste , Guss Gabe M. , Calta Nicholas P. , DePond Phillip J. , Matthews Manyalibo J. 
Published: June 2024

AbstractIn additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.
Purification, characterization and three-dimensional structure prediction of multicopper oxidase Laccases from Trichoderma lixii FLU1 and Talaromyces pinophilus FLU12
Authors: Egbewale Samson O. , Kumar Ajit , Mokoena Mduduzi P. , Olaniran Ademola O. 
Published: June 2024

AbstractBroad-spectrum biocatalysts enzymes, Laccases, have been implicated in the complete degradation of harmful pollutants into less-toxic compounds. In this study, two extracellularly produced Laccases were purified to homogeneity from two different Ascomycetes spp. Trichoderma lixii FLU1 (TlFLU1) and Talaromyces pinophilus FLU12 (TpFLU12). The purified enzymes are monomeric units, with a molecular mass of 44 kDa and 68.7 kDa for TlFLU1 and TpFLU12, respectively, on SDS-PAGE and zymogram. It reveals distinct properties beyond classic protein absorption at 270–280 nm, with TlFLU1's peak at 270 nm aligning with this typical range of type II Cu site (white Laccase), while TpFLU12's unique 600 nm peak signifies a type I Cu2+ site (blue Laccase), highlighting the diverse spectral fingerprints within the Laccase family. The Km and kcat values revealed that ABTS is the most suitable substrate as compared to 2,6-dimethoxyphenol, caffeic acid and guaiacol for both Laccases. The bioinformatics analysis revealed critical His, Ile, and Arg residues for copper binding at active sites, deviating from the traditional two His and a Cys motif in some Laccases. The predicted biological functions of the Laccases include oxidation–reduction, lignin metabolism, cellular metal ion homeostasis, phenylpropanoid catabolism, aromatic compound metabolism, cellulose metabolism, and biological adhesion. Additionally, investigation of degradation of polycyclic aromatic hydrocarbons (PAHs) by purified Laccases show significant reductions in residual concentrations of fluoranthene and anthracene after a 96-h incubation period. TlFLU1 Laccase achieved 39.0% and 44.9% transformation of fluoranthene and anthracene, respectively, while TpFLU12 Laccase achieved 47.2% and 50.0% transformation, respectively. The enzyme structure–function relationship study provided insights into the catalytic mechanism of these Laccases for possible biotechnological and industrial applications.
Multistage feature fusion knowledge distillation
Authors: Li Gang , Wang Kun , Lv Pengfei , He Pan , Zhou Zheng , Xu Chuanyun 
Published: June 2024

AbstractGenerally, the recognition performance of lightweight models is often lower than that of large models. Knowledge distillation, by teaching a student model using a teacher model, can further enhance the recognition accuracy of lightweight models. In this paper, we approach knowledge distillation from the perspective of intermediate feature-level knowledge distillation. We combine a cross-stage feature fusion symmetric framework, an attention mechanism to enhance the fused features, and a contrastive loss function for teacher and student models at the same stage to comprehensively implement a multistage feature fusion knowledge distillation method. This approach addresses the problem of significant differences in the intermediate feature distributions between teacher and student models, making it difficult to effectively learn implicit knowledge and thus improving the recognition accuracy of the student model. Compared to existing knowledge distillation methods, our method performs at a superior level. On the CIFAR100 dataset, it boosts the recognition accuracy of ResNet20 from 69.06% to 71.34%, and on the TinyImagenet dataset, it increases the recognition accuracy of ResNet18 from 66.54% to 68.03%, demonstrating the effectiveness and generalizability of our approach. Furthermore, there is room for further optimization of the overall distillation structure and feature extraction methods in this approach, which requires further research and exploration.
Comparative study of MAFLD as a predictor of metabolic disease treatment for NAFLD
Authors: Imai Jin , Takashimizu Shinji , Suzuki Nana , Ohshinden Kana , Sawamoto Kana , Mishima Yusuke , Tsuruya Kota , Arase Yoshitaka , Yamano Mitsuhiko , Kishimoto Noriaki , Yamada Chizumi , Inoue Nagamu , Moriyama Kengo , Baba Akiyasu , Suzuki Hidekazu , Kagawa Tatehiro , Nishizaki Yasuhiro 
Published: June 2024

AbstractA novel concept of Metabolic Associated Fatty Liver Disease (MAFLD) was proposed, incorporating metabolic abnormalities such as obesity and diabetes, which are risk factors that affect the prognosis. Non-Alcoholic Fatty Liver Disease (NAFLD), entails fat accumulation in the liver without alcohol consumption and is often linked to obesity, insulin resistance, and metabolic syndrome. However, the broad nature of the disease concept has hindered prognosis accuracy. In this study, we assess the contribution of the impact of diagnostic criteria for MAFLD on metabolic disease progression compared to conventional diagnostic criteria for NAFLD. A total of 7159 patient who were presented to the health screening center in Tokai University Hospital both in 2015 and 2020 were included in the study. Fatty liver was diagnosed using abdominal ultrasonography. The diagnostic criteria for NAFLD were consistent with the global guidelines based on alcohol consumption. The diagnostic criteria for MAFLD were based on the International Consensus Panel. Medications (anti-hypertensive, diabetic, and dyslipidemia medications) were evaluated by self-administration in the submitted medical questionnaire. A total of 2500 (34.9%) participants were diagnosed with fatty liver (FL +), 1811 (72.4%) fit both NAFLD and MAFLD diagnostic criteria (overlap), 230 (9.2%) fit only the NAFLD diagnostic criteria (NAFLD group) and 404 (16.1%) fit the MAFLD diagnostic criteria (MAFLD group) at 2015. Over the next 5 years, medication rates increased in the NAFLD group for anti-hypertensive, + 17 (7.4%); diabetes, + 3 (1.3%); and dyslipidemia, + 32 (13.9%). In contrast, the only-MAFLD group showed a more significant increase with + 49 (12.1%), + 21 (5.2%), and + 49 (12.1%), for the respective medications, indicating a substantial rise in patients starting new medications. Our analysis of repeated health check-ups on participants revealed that the diagnostic criteria for MAFLD are more predictive of future treatment for metabolic disease than conventional diagnostic criteria for NAFLD.
Predicting ineffective thrombolysis in acute ischemic stroke with clinical and biochemical markers
Authors: Li Yinglei , Li Ning , Zhou Yuanyuan , Li Litao 
Published: June 2024

Abstract**Ischemic stroke remains a leading cause of morbidity and mortality globally. Despite the advances in thrombolytic therapy, notably recombinant tissue plasminogen activator (rtPA), patient outcomes are highly variable. This study aims to introduce a novel predictive model, the Acute Stroke Thrombolysis Non-Responder Prediction Model (ASTN-RPM), to identify patients unlikely to benefit from rtPA within the critical early recovery window. We conducted a retrospective cohort study at Baoding No.1 Central Hospital including 709 adult patients diagnosed with acute ischemic stroke and treated with intravenous alteplase within the therapeutic time window. The ASTN-RPM was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression technique, incorporating a wide range of biomarkers and clinical parameters. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Decision Curve Analysis (DCA). ASTN-RPM effectively identified patients at high risk of poor response to thrombolysis, with an AUC of 0.909 in the training set and 0.872 in the validation set, indicating high sensitivity and specificity. Key predictors included posterior circulation stroke, high admission NIHSS scores, extended door to needle time, and certain laboratory parameters like homocysteine levels. The ASTN-RPM stands as a potential tool for refining clinical decision-making in ischemic stroke management. By anticipating thrombolytic non-response, clinicians can personalize treatment strategies, possibly improving patient outcomes and reducing the burden of ineffective interventions. Future studies are needed for external validation and to explore the incorporation of emerging biomarkers and imaging data.
Cumulative incidence and risk factors for medication-related osteonecrosis of the jaw during long-term prostate cancer management
Authors: Tani Masaru , Hatano Koji , Yoshimura Akihiro , Horibe Yuki , Liu Yutong , Sassi Nesrine , Oka Toshiki , Okuda Yohei , Yamamoto Akinaru , Uemura Toshihiro , Yamamichi Gaku , Ishizuya Yu , Yamamoto Yoshiyuki , Kato Taigo , Kawashima Atsunari , Nonomura Norio 
Published: June 2024

AbstractBone-modifying agents (BMA) are extensively used in treating patients with prostate cancer with bone metastases. However, this increases the risk of medication-related osteonecrosis of the jaw (MRONJ). The safety of long-term BMA administration in clinical practice remains unclear. We aimed to determine the cumulative incidence and risk factors of MRONJ. One hundred and seventy-nine patients with prostate cancer with bone metastases treated with BMA at our institution since 2008 were included in this study. Twenty-seven patients (15%) had MRONJ during the follow-up period (median, 19 months; interquartile range, 9–43 months). The 2-year, 5-year, and 10-year cumulative MRONJ incidence rates were 18%, 27%, and 61%, respectively. Multivariate analysis identified denosumab use as a risk factor for MRONJ, compared with zoledronic acid use (HR 4.64, 95% CI 1.93–11.1). Additionally, BMA use at longer than one-month intervals was associated with a lower risk of MRONJ (HR 0.08, 95% CI 0.01–0.64). Furthermore, six or more bone metastases (HR 3.65, 95% CI 1.13–11.7) and diabetes mellitus (HR 5.07, 95% CI 1.68–15.2) were risk factors for stage 2 or more severe MRONJ. MRONJ should be considered during long-term BMA administration in prostate cancer patients with bone metastases.
Differences between pathologic and non-pathologic high myopia in 4-year outcomes of anti-VEGF therapy for macular neovascularization
Authors: Honda Yuki , Miyata Manabu , Miyake Masahiro , Hata Masayuki , Numa Shogo , Mori Yuki , Ooto Sotaro , Tamura Hiroshi , Ueda-Arakawa Naoko , Muraoka Yuki , Takahashi Ayako , Sado Keina , Kido Ai , Tsujikawa Akitaka 
Published: June 2024

AbstractThis retrospective observational study aimed to investigate the difference in 4-year outcomes of ranibizumab or aflibercept therapy for macular neovascularization (MNV) with high myopia between pathologic myopia (PM) and non-PM. This study was conducted at Kyoto University Hospital and included consecutive treatment-naïve eyes with active myopic MNV, in which a single intravitreal ranibizumab or aflibercept injection was administered, followed by a pro re nata (PRN) regimen for 4 years. Based on the META-PM study classification, eyes were assigned to the non-PM and PM groups. This study analyzed 118 eyes of 118 patients (non-PM group, 19 eyes; PM group, 99 eyes). Baseline, 1-year, and 2-year best-corrected visual acuity (BCVA) were significantly better in the non-PM group (P = 0.02, 0.01, and 0.02, respectively); however, the 3-year and 4-year BCVA were not. The 4-year BCVA course was similar in both groups. However, the total number of injections over 4 years was significantly higher in the non-PM than in the PM group (4.6 ± 2.6 vs. 2.9 ± 2.6, P = 0.001). Four-year BCVA significantly correlated only with baseline BCVA in both non-PM (P = 0.047, β = 0.46) and PM groups (P < 0.001, β = 0.59). In conclusion, over the 4-year observation period, the BCVA course after anti-VEGF therapy for myopic MNV was similar in the eyes with non-PM and those with PM; however, more additional injections in a PRN regimen were required in the eyes with non-PM compared to those with PM. Thus, more frequent and careful follow-up is required for the eyes with non-PM compared with those with PM to maintain long-term BCVA.
Nutritional upgrade of olive mill stone waste, walnut shell and their mixtures by applying solid state fermentation initiated by Pleurotus ostreatus
Authors: Arapoglou Dimitrios , Eliopoulos Christos , Markou Giorgos , Langousi Ioanna , Saxami Georgia , Haroutounian Serkos A. 
Published: June 2024

AbstractPresent study concerns the transformation of the agro-industrial by-products olive mill stone waste (OMSW) and walnut shell (WS) to a protein-enriched animal feedstuff utilizing the solid state fermentation (SSF) technique. For this purpose, various mixtures of these by-products were exploited as substrates of the SSF process which was initiated by the P. ostreatus fungus. The respective results indicated that the substrate consisted of 80% WS and 20% OMSW afforded the product with the highest increase in protein content, which accounted the 7.57% of its mass (69.35% increase). In addition, a 26.13% reduction of lignin content was observed, while the most profound effect was observed for their 1,3–1,6 β-glucans profile, which was increased by 3-folds reaching the 6.94% of substrate’s mass. These results are indicative of the OMSW and WS mixtures potential to act as efficient substrate for the development of novel proteinaceous animal feed supplements using the SSF procedure. Study herein contributes to the reintegration of the agro-industrial by-products aiming to confront the problem of proteinaceous animal feed scarcity and reduce in parallel the environmental footprint of the agro-industrial processes within the context of circular economy.
Optimization of antimicrobial nanocomposite films based on carboxymethyl cellulose incorporating chitosan nanofibers and Guggul gum polysaccharide
Authors: Mohammadi Hamed , Rezaeigolestani Mohammadreza , Mohsenzadeh Mohammad 
Published: June 2024

AbstractThe present study utilized response surface methodology (RSM) to investigate the impact of varying concentrations of carboxymethyl cellulose (CMC: 0.75–1.75 wt%), Commiphora mukul polysaccharide (CMP: 0–1 wt%), and Chitosan Nanofiber (CHNF: 0–1 wt%) on the physical and antimicrobial characteristics of nanocomposite films based on CMC. The optimization process aimed to enhance ultimate tensile strength (UTS), strain at break (SAB), and antibacterial activity, while minimizing water vapor permeability (WVP), solubility, swelling, moisture content, opacity, and total color difference (ΔE). The results revealed that both CMP and CHNF had a positive influence on reducing moisture content, WVP, and increasing UTS. However, higher concentrations of CMP and CHNF had a divergent effect on SAB, ΔE, and swelling. The incorporation of CMP led to increased opacity and solubility, while the inclusion of CHNF resulted in decreased opacity and solubility. Notably, only CHNF addition significantly improved the antibacterial properties of the films. By applying the optimization procedure utilizing RSM, the formulation containing CMC (1.5 wt%), CMP (0.25 wt%), and CHNF (0.75 wt%) demonstrated superior physical, mechanical, and antibacterial properties in the biodegradable film matrix. These findings highlight the potential of utilizing these components to enhance the performance of CMC-based nanocomposite films.
A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities
Authors: Hassan Shoaib , Li Qianmu , Aurangzeb Khursheed , Yasin Affan , Khan Javed Ali , Anwar Muhammad Shahid 
Published: June 2024

AbstractOver the past few years, the application and usage of Machine Learning (ML) techniques have increased exponentially due to continuously increasing the size of data and computing capacity. Despite the popularity of ML techniques, only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering (RE) activities to solve the problems that occur in RE activities. The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023. The authors aim to investigate the research trends, main RE activities, ML algorithms, and data sources that were studied during this period. Forty‐five research studies were selected based on our exclusion and inclusion criteria. The results show that the scientific community used 57 algorithms. Among those algorithms, researchers mostly used the five following ML algorithms in RE activities: Decision Tree, Support Vector Machine, Naïve Bayes, K‐nearest neighbour Classifier, and Random Forest. The results show that researchers used these algorithms in eight major RE activities. Those activities are requirements analysis, failure prediction, effort estimation, quality, traceability, business rules identification, content classification, and detection of problems in requirements written in natural language. Our selected research studies used 32 private and 41 public data sources. The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA, Predictor Models in Software Engineering, and iTrust Electronic Health Care System.
A novel weighting factor determined method of model predictive torque control for permanent magnet synchronous motor
Authors: Yang Tao , Wang Yiming , Miao Yiru , Xu Qiwei , Guo Qiang 
Published: June 2024

AbstractIn the cost function of model predictive torque control (MPTC) of permanent magnet synchronous motor (PMSM), torque and flux linkage have different dimensions, and the weighting factor needs to be properly adjusted to optimise the control performance. Therefore, based on the discrete mathematical model of PMSM, the torque predictive error and flux predictive error expressions are mapped to the two‐phase static coordinate system, which is equivalent to the distance expressions from the endpoint of the voltage vector to the straight line and to the circle respectively. Because they have the same dimension, they can be directly superimposed to form the cost function proposed, which eliminates the selection process of the weighting factor. Then, the candidate voltage vector is substituted into the cost function in turn, and the optimal voltage vector with the minimum cost function is selected as the voltage input of the next control cycle. Finally, the simulation model and an experimental platform are established to compare the steady‐state and dynamic performance of the geometric solution‐based weighting factor determined method proposed with other MPTC methods.
Robust LFC design using adaptive neuro‐fuzzy inference‐aided optimal fractional‐order PIDA control for perturbed power systems with solar and wind power sources
Authors: Roy Tushar Kanti , Yu Samson S. , Mahmud Md. Apel , Trinh Hieu 
Published: June 2024

AbstractMaintaining stability in modern power systems is challenging due to complex structures, rising power demand, and load disturbances. The integration of renewable energy sources further threatens stability by causing imbalances between generation and demand. Conventional load frequency stabilization methods fall short in such scenarios. This paper proposes an optimal fractional‐order proportional‐integral‐derivative‐acceleration (FOPIDA) controller, enhanced by a robust adaptive neuro‐fuzzy inference system (ANFIS), to improve load frequency control and reliability in power systems with wind and solar generators. First, the dynamical model of a multi‐area interconnected power system, including a thermal power plant, wind turbine, and solar photovoltaic generators, is developed. A decentralized ANFIS‐FOPIDA controller is then designed for load frequency control objectives. The gains of this controller are optimized using the whale optimization algorithm (WOA), focusing on frequency deviation and tie‐line power exchange. Simulations on a New England IEEE 10‐generator 39‐bus power system demonstrate the approach's effectiveness under various disturbances, including random load‐generation disturbances and nonlinear generation behaviors. Comparisons with other strategies, such as fractional order (FO) beetle swarm optimization algorithm (FOBSOA)‐FOPIDA, WOA‐PIDA, and WOA‐ANFIS‐PIDA, and recent control approaches highlight the superior performance of the WOA‐ANFIS‐FOPIDA method in enhancing power system stability.
A novel Schiff base-containing phthalocyanine and its azo dye-substituted analogue: the effect of pH on colour
Authors: Kaya Kantar Günay 
Published: June 2024

There are many studies in the literature devoted to the synthesis of phthalocyanines substituted with Schiff bases and azo dyes and their particular properties. However, the comparison of spectral properties of these analogue compounds has not been investigated up to now. Therefore, a new eugenol group-bearing Schiff base-substituted zinc(II) phthalocyanine compound was synthesised and characterised and its UV/Vis spectral properties were compared with its azo dye analogue at different pH. It was determined that the azo dye analogue showed more pronounced spectral changes than the Schiff base-substituted phthalocyanine, depending on pH.