bluuebunny/crossref_metadata_2025 huggingface dataset:
Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk
Authors: Zuo Enguang , Du Xusheng , Aysa Alimjan , Lv Xiaoyi , Muhammat Mahpirat , Zhao Yuxia , Ubul Kurban
Published: July 2022

Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel’s efficiency, whereas the panel enhances the model’s reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.
Prediction of Total Soluble Solids and pH of Strawberry Fruits Using RGB, HSV and HSL Colour Spaces and Machine Learning Models
Authors: Basak Jayanta Kumar , Madhavi Bolappa Gamage Kaushalya , Paudel Bhola , Kim Na Eun , Kim Hyeon Tae
Published: July 2022

Determination of internal qualities such as total soluble solids (TSS) and pH is a paramount concern in strawberry cultivation. Therefore, the main objective of the current study was to develop a non-destructive approach with machine learning algorithms for predicting TSS and pH of strawberries. Six hundred samples (100 samples in each ripening stage) in six ripening stages were collected randomly for measuring the biometrical characteristics, i.e., length, diameters, weight and TSS and pH values. An image of each strawberry fruit was captured for colour feature extraction using an image processing technique. Channels of each colour space (RGB, HSV and HSL) were used as input variables for developing multiple linear regression (MLR) and support vector machine regression (SVM-R) models. The result of the study indicated that SVM-R model with HSV colour space performed slightly better than MLR model for TSS and pH prediction. The HSV based SVM-R model could explain a maximum of 84.1% and 79.2% for TSS and 78.8% and 72.6% for pH of the variations in measured and predicted data in training and testing stages, respectively. Further experiments need to be conducted with different strawberry cultivars for the prediction of more internal qualities along with the improvement of model performance.
Dietary Schizochytrium Microalgae Affect the Fatty Acid Profile of Goat Milk: Quantification of Docosahexaenoic Acid (DHA) and Its Distribution at Sn-2 Position
Authors: Zhu Huiquan , Wang Xiaodan , Zhang Wenyuan , Zhang Yumeng , Zhang Shuwen , Pang Xiaoyang , Lu Jing , Lv Jiaping
Published: July 2022

The objective of this study was to detect the influence of dietary Schizochytrium microalgae on milk composition, milk fatty acids, and milk sn-2 fatty acids in goat’s milk. Firstly, we could see that the fat content increased in low microalgae supplementation goat’s milk (LM, 15 g/day) and the lactose content decreased in medium microalgae supplementation goat’s milk (MM, 25 g/day) compared with control goat’s milk (C, 0 g/day). Moreover, the absolute concentration of the docosahexaenoic acid (DHA) of LM, MM, and high microalgae supplementation (HM, 35 g/day) goat’s milk was 29.485, 32.351, and 24.817 mg/100 g raw milk, respectively, which were all higher than that in the control goat’s milk with 4.668 mg/100 g raw milk. In addition, the sn-2 DHA content increased in MM and HM goat’s milk. However, the decreasing trend of the sn-2 DHA content was observed in LM goat’s milk. As for other fatty acids, the oleic acid (C18:1n9c) and linolenic acid (C18:3n3) content decreased and increased, respectively, in all experimental goat milk. Finally, an interesting phenomenon was found, which was that docosanoic acid (C22:0) and tetracosenic acid (C24:1) were only detected in test goat’s milk. Consequently, the phenomena of this study demonstrated that dietary Schizochytrium microalgae have an obvious effect on the fatty acid and sn-2 fatty acid profile of goat’s milk, and they provide an effective method to improve the content of goat’s milk DHA in practical production.
Elastoplastic Integration Method of Mohr-Coulomb Criterion
Authors: Zhang Tan , Lin Songtao , Zheng Hong , Chen Yanjiang
Published: July 2022

A new method for implicit integration of the Mohr-Coulomb non-smooth multisurface plasticity models is presented, and Koiter’s requirements are incorporated exactly within the proposed algorithm. Algorithmic and numerical complexities are identified and introduced by the nonsmooth intersections of the Mohr-Coulomb surfaces; then, a projection contraction algorithm is applied to solve the classical Kuhn–Tucker complementary equations which provide the only characterization of possible active yield surfaces as a special class of variational inequalities, and the actual active yield surface is further determined by iteration. The basic idea is to calculate derivatives of the yield and potential functions with the expressions in the principal stresses and perform the return manipulations in the general stress space. Based on the principal stress characteristic equation, partial derivatives of principal stresses are calculated. The proposed algorithm eliminates the error caused by smoothing the corner of Mohr-Coulomb surfaces, avoids the numerical singularity at the intersections in the general stress space, and does not require the stress transformation needed in the principal stress space method. Lastly, several numerical examples are given to verify the validity of the proposed method.
Multi-Parameter Characterization of Disease-Suppressive Bio-composts from Aromatic Plant Residues Evaluated for Garden Cress (Lepidium sativum L.) Cultivation
Authors: Pane Catello , Spaccini Riccardo , Caputo Michele , De Falco Enrica , Zaccardelli Massimo
Published: July 2022

Garden cress is a vegetable crop in the Brassicaceae family that is appreciated for its nutraceutical and taste-giving components in minimally processed food chains. Due to its very short cycle, which depends on the range of production from microgreens to baby-leaf vegetables, this crop is threatened by soil-borne pathologies developing within the initial stages of germination and emergence. This study aims to evaluate the suppressive bio-compost as an innovative means to counteract the main telluric diseases of garden cress and reduce the risks of yield loss by adopting sustainable remedies and decreasing the dependence on synthetic fungicides. Therefore, eleven green composts obtained using both previously distilled and raw aromatic plant residues were analyzed for suppressive properties against Rhizoctonia solani and Sclerotinia sclerotiorum on sown garden cress. The biological active component of the composts, detected by CO2-release, FDA-hydrolysis and microbial counts, proved to be indispensable for pathogen control in vitro and in vivo, as demonstrated by the loss of suppressiveness after sterilization. Cross-polarization magic angle spinning 13C-nuclear magnetic resonance (CP-MAS-13C-NMR) was used to analyze the molecular distribution of organic C in composts. The results indicated the suitability of the feedstock used to make quality compost. The suppression levels shown by composts P1 (40% wood chips, 30% escarole and 30% a mixture of sage, basil, mint and parsley) and P2 (40% wood chips, 30% escarole and 30% a mixture of essential oil-free sage, basil and rosemary) are promising for the sustainable, non-chemical production of garden cress vegetables.
Using Augmented Reality in K-12 Education: An Indicative Platform for Teaching Physics
Authors: Volioti Christina , Keramopoulos Euclid , Sapounidis Theodosios , Melisidis Konstantinos , Zafeiropoulou Maria , Sotiriou Charalampos , Spiridis Vladimiros
Published: July 2022

Augmented Reality (AR) could provide key benefits in education and create a richer user experience by increasing the motivation and engagement of the students. To this end, the current paper presents a system with three AR applications for teaching physics in the fifth and sixth grades of primary school and in the first grade of secondary school, and the ultimate goal is the development of a unified platform that covers the subject of physics in all classes of K-12 education. The platform provides a useful tool to familiarize both teachers and pupils with AR technologies, aiming to improve the learning and teaching experience and to enhance their skills. The developed system is evaluated in terms of usability, gamification and willingness of the teachers to incorporate this technology into the teaching process. A total of 314 users participated in the research, where they were divided into three user groups: (i) teachers (N = 15), (ii) pupils (N = 189) and (iii) computer science students (N = 110). The outcomes were satisfactory, revealing that the gamified AR applications are easy to use, and teachers are interested in using these AR applications in their classrooms.
Improving Performance and Quantifying Uncertainty of Body-Rocking Detection Using Bayesian Neural Networks
Authors: da Silva Rafael Luiz , Zhong Boxuan , Chen Yuhan , Lobaton Edgar
Published: July 2022

Body-rocking is an undesired stereotypical motor movement performed by some individuals, and its detection is essential for self-awareness and habit change. We envision a pipeline that includes inertial wearable sensors and a real-time detection system for notifying the user so that they are aware of their body-rocking behavior. For this task, similarities of body rocking to other non-related repetitive activities may cause false detections which prevent continuous engagement, leading to alarm fatigue. We present a pipeline using Bayesian Neural Networks with uncertainty quantification for jointly reducing false positives and providing accurate detection. We show that increasing model capacity does not consistently yield higher performance by itself, while pairing it with the Bayesian approach does yield significant improvements. Disparities in uncertainty quantification are better quantified by calibrating them using deep neural networks. We show that the calibrated probabilities are effective quality indicators of reliable predictions. Altogether, we show that our approach provides additional insights on the role of Bayesian techniques in deep learning as well as aids in accurate body-rocking detection, improving our prior work on this subject.
The Effects of Male Seminal Fluid Proteins on Gut/Gonad Interactions in Drosophila
Authors: White Melissa A. , Wolfner Mariana F.
Published: July 2022

Mating initiates broad physiological changes encompassing multiple organ systems in females. Elucidating the complex inter- and intra-organ signaling events that coordinate these physiological changes is an important goal in the field of reproductive biology. Further characterization of these complex molecular and physiological interactions is key to understanding how females meet the energetic demands of offspring production. Many recent studies of the fruit fly, Drosophila melanogaster, have described the mechanisms of post-mating changes within the female reproductive tract and digestive system. Additionally, other studies have described post-mating signaling crosstalk between these systems. Interestingly, male seminal fluid proteins have been linked to post-mating responses within the female reproductive tract and gut, and to signaling events between the two organ systems. However, information about the hormonal and neuronal signaling pathways underlying the post-mating signaling events within and between the reproductive tract and digestive systems that are triggered by seminal fluid proteins has yet to be combined into a single view. In this article, we summarize and integrate these studies into a single “network schematic” of the known signaling events within and between the reproductive and digestive systems downstream of male seminal fluid proteins. This synthesis also draws attention to the incomplete parts of these pathways, so that outstanding questions may be addressed in future studies.
Building International Capacity for Citizen Scientist Engagement in Mosquito Surveillance and Mitigation: The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper
Authors: Low Russanne D. , Schwerin Theresa G. , Boger Rebecca A. , Soeffing Cassie , Nelson Peder V. , Bartlett Dan , Ingle Prachi , Kimura Matteo , Clark Andrew
Published: July 2022

The GLOBE Program’s GLOBE Observer Mosquito Habitat Mapper is a no-cost citizen scientist data collection tool compatible with Android and iOS devices. Available in 14 languages and 126 countries, it supports mosquito vector surveillance, mitigation, and education by interested individuals and as part of participatory community surveillance programs. For low-resource communities where mosquito control services are inadequate, the Mosquito Habitat Mapper supports local health action, empowerment, and environmental justice. The tangible benefits to human health supported by the Mosquito Habitat Mapper have encouraged its wide adoption, with more than 32,000 observations submitted from 84 countries. The Mosquito Habitat Mapper surveillance and data collection tool is complemented by an open database, a map visualization interface, data processing and analysis tools, and a supporting education and outreach campaign. The mobile app tool and associated research and education assets can be rapidly deployed in the event of a pandemic or local disease outbreak, contributing to global readiness and resilience in the face of mosquito-borne disease. Here, we describe the app, the Mosquito Habitat Mapper information system, examples of Mosquito Habitat Mapper deployment in scientific research, and the outreach campaign that supports volunteer training and STEM education of students worldwide.
Late Dialysis Modality Education Could Negatively Predict Peritoneal Dialysis Selection
Authors: Nakayama Takashin , Nishioka Ken , Uchiyama Kiyotaka , Morimoto Kohkichi , Kusahana Ei , Washida Naoki , Yamaguchi Shintaro , Azegami Tatsuhiko , Yoshida Tadashi , Itoh Hiroshi
Published: July 2022

Patients with end-stage renal disease are less likely to choose peritoneal dialysis (PD) as renal replacement therapy (RRT). The reasons for this biased selection are still poorly understood. In this study, we evaluated the effect of the timing of RRT education on PD selection. This single-center retrospective observational study included patients who initiated maintenance dialysis at our hospital between April 2014 and July 2021. A logistic regression analysis was performed to investigate the association of RRT education timing with PD selection. Among the 355 participants (median age [IQR] 70 (59–79) years; 28.7% female), 53 patients (14.9%) and 302 patients (85.1%) selected PD and hemodialysis, respectively. Multivariate analysis demonstrated that high estimated glomerular filtration (eGFR) at RRT education positively predicted PD selection (p < 0.05), whereas old age (p < 0.01) and high Charlson comorbidity index (p < 0.05) were negative predictors of PD selection. Female sex (p = 0.44), welfare public assistance (p = 0.78), living alone (p = 0.25), high geriatric nutritional risk index (p = 0.10) and high eGFR at first visit to the nephrology department (p = 0.83) were not significantly associated with PD selection. Late RRT education could increase the biased selection of dialysis modality.
Down Syndrome in FPIES: An Overwhelming and Unexpected Prevalence
Authors: Pecora Valentina , Mennini Maurizio , Valluzzi Rocco , Fierro Vincenzo , Villani Alberto , Valentini Diletta , Fiocchi Alessandro
Published: July 2022

Down syndrome (DS) is one of the most common chromosomal anomalies. Gastrointestinal disorders in DS are predominantly related to anatomical anomalies and celiac disease. In 2015, the first two cases of non-IgE-mediated food allergy in patients with DS were described. However, gastrointestinal symptoms experienced by subjects with DS have never been related to a possible non-IgE-mediated food allergy and a Food Protein-induced Enterocolitis syndrome (FPIES). A retrospective descriptive single-center study was conducted. Subjects included were children with acute FPIES who entered our institutional follow-up protocol between January 2013 and January 2020. Among the 85 patients (forty-nine boys—57.6%), ten (11.76%) were children with DS. In our population, the FPIES triggers included different foods (such as milk, egg, fruit, fish, wheat, soy, beef, etc.). Nine patients with DS showed FPIES reactions after ingesting cow’s milk (one even with beef and three with soy), while the last one was affected by FPIES to fish. Considering the subgroup of patients affected by cow’s milk FPIES (40 subjects overall), 22.5% had a diagnosis of DS. Patients with DS experienced acute FPIES reactions with a severity degree slightly higher than that reported in other patients, ranging from mild-moderate to severe or very severe. During the acute reactions, the patients with DS showed increased white blood cell production, absolute neutrophil count and C-reactive protein levels. This series provides a starting point for novel hypothesis-testing clinical research and possible specific immunological alterations in FPIES children with or without DS.
Pathogenesis of Human Adenomyosis: Current Understanding and Its Association with Infertility
Authors: Khan Khaleque N. , Fujishita Akira , Mori Taisuke
Published: July 2022

The aim of this review article was to summarize our current understanding on the etiologies and pathogenesis of human adenomyosis and to clarify the relative association between adenomyosis and infertility. The exact pathogenesis of adenomyosis is still elusive. Among different reported concepts, direction invagination of gland cells from the basalis endometrium deep into myometrium is the most widely accepted opinion on the development of adenomyosis. According to this concept, endometrial epithelial cells and changed fibroblasts, abnormally found in the myometrium in response to repeated tissue injury and/or disruption at the endometrium-myometrium interface (EMI), elicit hyperplasia and hypertrophy of the surrounding smooth muscle cells. In this review, a comprehensive review was performed with a literature search using PubMed for all publications in English and Japanese (abstract in English), related to adenomyosis and infertility, from inception to April 2021. As an estrogen-regulated factor, hepatocyte growth factor (HGF) exhibits multiple functions in endometriosis, a disease commonly believed to arise from the functionalis endometrium. As a mechanistic basis of gland invagination, we investigated the role of HGF, either alone or in combination with estrogen, in the occurrence of epithelial-mesenchymal transition (EMT) in adenomyosis. Aside from microtrauma at the EMI, metaplasia of displaced Müllerian remnants, differentiation of endometrial stem/progenitor cells within the myometrium and somatic mutation of some target genes have been put forward to explain how adenomyosis develops. In addition, the possible role of microRNAs in adenomyosis is also discussed. Besides our knowledge on the conventional classification (focal and diffuse), two recently proposed classifications (intrinsic and extrinsic) of adenomyosis and the biological differences between them have been described. Although the mechanistic basis is unclear, the influence of adenomyosis on fertility outcome is important, especially considering the recent tendency to delay pregnancy among women. Besides other proposed mechanisms, a recent transmission election microscopic (TEM) study indicated that microvilli damage and an axonemal alteration in the apical endometria of human adenomyosis, in response to endometrial inflammation, may be involved in negative fertility outcomes. We present a critical analysis of the literature data concerning the mechanistic basis of infertility in women with adenomyosis and its impact on fertility outcome.
Regression-Based Normative Data for the Montreal Cognitive Assessment (MoCA) and Its Memory Index Score (MoCA-MIS) for Individuals Aged 18–91
Authors: Kessels Roy P. C. , de Vent Nathalie R. , Bruijnen Carolien J. W. H. , Jansen Michelle G. , de Jonghe Jos F. M. , Dijkstra Boukje A. G. , Oosterman Joukje M.
Published: July 2022

(1) Background: There is a need for a brief assessment of cognitive function, both in patient care and scientific research, for which the Montreal Cognitive Assessment (MoCA) is a psychometrically reliable and valid tool. However, fine-grained normative data allowing for adjustment for age, education, and/or sex are lacking, especially for its Memory Index Score (MIS). (2) Methods: A total of 820 healthy individuals aged 18–91 (366 men) completed the Dutch MoCA (version 7.1), of whom 182 also completed the cued recall and recognition memory subtests enabling calculation of the MIS. Regression-based normative data were computed for the MoCA Total Score and MIS, following the data-handling procedure of the Advanced Neuropsychological Diagnostics Infrastructure (ANDI). (3) Results: Age, education level, and sex were significant predictors of the MoCA Total Score (Conditional R2 = 0.4, Marginal R2 = 0.12, restricted maximum likelihood (REML) criterion at convergence: 3470.1) and MIS (Marginal R2 = 0.14, REML criterion at convergence: 682.8). Percentile distributions are presented that allow for age, education and sex adjustment for the MoCA Total Score and the MIS. (4) Conclusions: We present normative data covering the full adult life span that can be used for the screening for overall cognitive deficits and memory impairment, not only in older people with or people at risk of neurodegenerative disease, but also in younger individuals with acquired brain injury, neurological disease, or non-neurological medical conditions.
A Review of Functional Analysis of Endothelial Cells in Flow Chambers
Authors: Ohta Makoto , Sakamoto Naoya , Funamoto Kenichi , Wang Zi , Kojima Yukiko , Anzai Hitomi
Published: July 2022

The vascular endothelial cells constitute the innermost layer. The cells are exposed to mechanical stress by the flow, causing them to express their functions. To elucidate the functions, methods involving seeding endothelial cells as a layer in a chamber were studied. The chambers are known as parallel plate, T-chamber, step, cone plate, and stretch. The stimulated functions or signals from endothelial cells by flows are extensively connected to other outer layers of arteries or organs. The coculture layer was developed in a chamber to investigate the interaction between smooth muscle cells in the middle layer of the blood vessel wall in vascular physiology and pathology. Additionally, the microfabrication technology used to create a chamber for a microfluidic device involves both mechanical and chemical stimulation of cells to show their dynamics in in vivo microenvironments. The purpose of this study is to summarize the blood flow (flow inducing) for the functions connecting to endothelial cells and blood vessels, and to find directions for future chamber and device developments for further understanding and application of vascular functions. The relationship between chamber design flow, cell layers, and microfluidics was studied.
Fasting Proinsulin Independently Predicts Incident Type 2 Diabetes in the General Population
Authors: Sokooti Sara , Dam Wendy A. , Szili-Torok Tamas , Gloerich Jolein , van Gool Alain J. , Post Adrian , de Borst Martin H. , Gansevoort Ron T. , Heerspink Hiddo J. L. , Dullaart Robin P. F. , Bakker Stephan J. L.
Published: July 2022

Fasting proinsulin levels may serve as a marker of β-cell dysfunction and predict type 2 diabetes (T2D) development. Kidneys have been found to be a major site for the degradation of proinsulin. We aimed to evaluate the predictive value of proinsulin for the risk of incident T2D added to a base model of clinical predictors and examined potential effect modification by variables related to kidney function. Proinsulin was measured in plasma with U-PLEX platform using ELISA immunoassay. We included 5001 participants without T2D at baseline and during a median follow up of 7.2 years; 271 participants developed T2D. Higher levels of proinsulin were associated with increased risk of T2D independent of glucose, insulin, C-peptide, and other clinical factors (hazard ratio (HR): 1.28; per 1 SD increase 95% confidence interval (CI): 1.08–1.52). Harrell’s C-index for the Framingham offspring risk score was improved with the addition of proinsulin (p = 0.019). Furthermore, we found effect modification by hypertension (p = 0.019), eGFR (p = 0.020) and urinary albumin excretion (p = 0.034), consistent with an association only present in participants with hypertension or kidney dysfunction. Higher fasting proinsulin level is an independent predictor of incident T2D in the general population, particularly in participants with hypertension or kidney dysfunction.
Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets
Authors: Dobre Elena-Georgiana , Constantin Carolina , Neagu Monica
Published: July 2022

Skin cancer, which includes the most frequent malignant non-melanoma carcinomas (basal cell carcinoma, BCC, and squamous cell carcinoma, SCC), along with the difficult to treat cutaneous melanoma (CM), pose important worldwide issues for the health care system. Despite the improved anti-cancer armamentarium and the latest scientific achievements, many skin cancer patients fail to respond to therapies, due to the remarkable heterogeneity of cutaneous tumors, calling for even more sophisticated biomarker discovery and patient monitoring approaches. Droplet digital polymerase chain reaction (ddPCR), a robust method for detecting and quantifying low-abundance nucleic acids, has recently emerged as a powerful technology for skin cancer analysis in tissue and liquid biopsies (LBs). The ddPCR method, being capable of analyzing various biological samples, has proved to be efficient in studying variations in gene sequences, including copy number variations (CNVs) and point mutations, DNA methylation, circulatory miRNome, and transcriptome dynamics. Moreover, ddPCR can be designed as a dynamic platform for individualized cancer detection and monitoring therapy efficacy. Here, we present the latest scientific studies applying ddPCR in dermato-oncology, highlighting the potential of this technology for skin cancer biomarker discovery and validation in the context of personalized medicine. The benefits and challenges associated with ddPCR implementation in the clinical setting, mainly when analyzing LBs, are also discussed.
Urban Expansion and Carbon Emission Logistic Curve Hypothesis and Its Verification: A Case Study of Jiangsu Province
Authors: Zhong Xueli , Li Xiaoshun , Li Yongfeng
Published: July 2022

Based on the general patterns of urban expansion and carbon emissions at home and abroad, the IPCC carbon emission coefficient estimation method and logistic equation are applied to verify the logistic curve relationship between urban expansion and carbon emissions and to quantitatively measure the upper limit of carbon emissions and the inflection point of carbon emission growth. The results show that (1) the corresponding cumulative carbon emission intensity of foreign (regional) urban expansion gradually decreases during the transition from the primary stage to the saturation stage; (2) urban expansion and carbon emissions in China are characterized by cyclical fluctuations during the 1978–2014 period, and the fluctuations of the two show significant decoupling or divergence after 2014; and (3) urban expansion and carbon emission in Jiangsu province during the 2002–2019 period shows a logistic curve hypothesis relationship, and the cumulative carbon emissions in the built-up areas of Southern Jiangsu, Central Jiangsu and Northern Jiangsu show an inflection point when they reach 3128.12 km2, 627.25 km2 and 973.9 km2, with the cumulative carbon emission caps of 197.238 × 108 t, 14.487 × 108 t and 29.289 × 108 t, respectively.
Terbium (III) Oxide (Tb2O3) Transparent Ceramics by Two-Step Sintering from Precipitated Powder
Authors: Hu Dianjun , Li Xiaoying , Zhang Lixuan , Snetkov Ilya , Chen Penghui , Dai Zhengfa , Balabanov Stanislav , Palashov Oleg , Li Jiang
Published: July 2022

As a result of preliminary air calcination and subsequent reduction in a flowing NH3 atmosphere of the precursor from the liquid precipitation method for the first time, pure-phase Tb2O3 powder with an average particle size of 135 nm was prepared. The Tb2O3 magneto-optical transparent ceramics with the average grain size of 1.3 μm were successfully fabricated by vacuum pre-sintering and hot isostatic pressing post-treatment from the as-synthesized Tb2O3 powder. In-line transmittance values of Tb2O3 ceramics reach 70.3% at 633 nm, 78.1% at 1064 nm, and 79.4% at 1400 nm, respectively. Thanks to the high intrinsic concentration of Tb3+, Tb2O3 ceramics present high Verdet constants of −427.3 and −123.7 rad·T−1·m−1 at 633 and 1064 nm, which are about 3.1 and 3.4 times higher than those of commercial Tb3Ga5O12 crystals, respectively. Due to the excellent magneto-optical properties, Tb2O3 ceramics are promising candidates for the development of Faraday isolator toward compaction used in visible and near-infrared bands.
Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach
Authors: Ahmad Shafiq , Ahmad Firoz , Alam Intekhab , Sayed Abdelaty Edrees , Abdollahian Mali
Published: July 2022

The geometric programming problem (GPP) is a beneficial mathematical programming problem for modeling and optimizing nonlinear optimization problems in various engineering fields. The structural configuration of the GPP is quite dynamic and flexible in modeling and fitting the reliability optimization problems efficiently. The work’s motivation is to introduce a bounded solution approach for the GPP while considering the variation among the right-hand-side parameters. The bounded solution method uses the two-level mathematical programming problems and obtains the solution of the objective function in a specified interval. The benefit of the bounded solution approach can be realized in that there is no need for sensitivity analyses of the results output. The demonstration of the proposed approach is shown by applying it to the system reliability optimization problem. The specific interval is determined for the objective values and found to be lying in the optimal range. Based on the findings, the concluding remarks are presented.
Developing an Enterprise Diagnostic Index System Based on Interval-Valued Hesitant Fuzzy Clustering
Authors: Chen Tian , Li Shiyao , Yang Chun-Ming , Deng Wenting
Published: July 2022

Global economic integration drives the development of dynamic competition. In a dynamic competitive environment, the ever-changing customer demands and technology directly affect the leadership of the core competence of enterprises. Therefore, assessing the performance of enterprises in a timely manner is necessary to adjust business activities and completely adapt to new changes. Enterprise diagnosis is a scientific tool for judging the development status of enterprises, and building a scientific and rational index system is the key to enterprise diagnosis. Considering the large number of enterprise diagnostic indicators and the high similarity among indicators, this study proposes a selection method for enterprise diagnostic indicators based on interval-valued hesitant fuzzy clustering by comparing the existing indicator systems. First, enterprise organizations are considered as the starting point. Through the key analysis of relevant indicators of domestic and foreign enterprise diagnosis, enterprise diagnosis candidate indicators are constructed from three aspects, namely enterprise performance, employee health, and social benefit. In view of the ambiguity and inconsistency of expert judgment, this study proposes an interval-valued hesitant fuzzy set based on the characteristics of hesitant fuzzy sets and interval-valued evaluation. For improving the interval-valued hesitant fuzzy entropy function, an interval-valued hesitant fuzzy similarity measurement formula considering information features is designed to avoid the problem of data length and improve the degree of identification among indicators. Then, the similarity, equivalence, and truncation matrices are constructed, and the interval-valued hesitant fuzzy clustering method is used to eliminate redundant indicators with repeated information. The availability of the proposed method is illustrated via an example, and the key indicators in the enterprise diagnostic index system are found. Finally, the advantages of the proposed method are discussed using comparative analysis with existing methods. A rational and comprehensive enterprise diagnostic index system was constructed. The system can be used as a scientific basis for diagnosing the development of enterprises and providing an objective and effective reference.
A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting
Authors: Jiang Qi , Cheng Yuxin , Le Haozhe , Li Chunquan , Liu Peter X.
Published: July 2022

It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting performance. Specifically, the stacking neural networks contain two types of networks: the base-layer and the meta-layer networks. During the pre-training process, the base-layer network integrates a radial basis function (RBF), random vector functional link (RVFL), and backpropagation neural network (BPNN) to provide a robust predictive model. The meta-layer network utilizes a deep belief network (DBN) and the improved broad learning system (BLS) to enhance predictive accuracy. Finally, the similar-days prediction method is developed to extract the relationship of electric load data in different time dimensions, further enhancing the robustness and accuracy of the model. To demonstrate the effectiveness of our model, it is evaluated using real data from five regions of the United States in three consecutive years. We compare our method with several state-of-the-art and conventional neural-network-based models. Our proposed algorithm improves the prediction accuracy by 16.08%, 16.83%, and 22.64% compared to DWT-EMD-RVFL, SWT-LSTM, and EMD-BLS, respectively. Empirical results demonstrate that our model achieves better accuracy and robustness compared with the baselines.
Visibility Adaptation in Ant Colony Optimization for Solving Traveling Salesman Problem
Authors: Shahadat Abu Saleh Bin , Akhand M. A. H. , Kamal Md Abdus Samad
Published: July 2022

Ant Colony Optimization (ACO) is a practical and well-studied bio-inspired algorithm to generate feasible solutions for combinatorial optimization problems such as the Traveling Salesman Problem (TSP). ACO is inspired by the foraging behavior of ants, where an ant selects the next city to visit according to the pheromone on the trail and the visibility heuristic (inverse of distance). ACO assigns higher heuristic desirability to the nearest city without considering the issue of returning to the initial city or starting point once all the cities are visited. This study proposes an improved ACO-based method, called ACO with Adaptive Visibility (ACOAV), which intelligently adopts a generalized formula of the visibility heuristic associated with the final destination city. ACOAV uses a new distance metric that includes proximity and eventual destination to select the next city. Including the destination in the metric reduces the tour cost because such adaptation helps to avoid using longer links while returning to the starting city. In addition, partial updates of individual solutions and 3-Opt local search operations are incorporated in the proposed ACOAV. ACOAV is evaluated on a suite of 35 benchmark TSP instances and rigorously compared with ACO. ACOAV generates better solutions for TSPs than ACO, while taking less computational time; such twofold achievements indicate the proficiency of the individual adoption techniques in ACOAV, especially in AV and partial solution update. The performance of ACOAV is also compared with the other ten state-of-the-art bio-inspired methods, including several ACO-based methods. From these evaluations, ACOAV is found as the best one for 29 TSP instances out of 35 instances; among those, optimal solutions have been achieved in 22 instances. Moreover, statistical tests comparing the performance revealed the significance of the proposed ACOAV over the considered bio-inspired methods.
Effects of C and Nb on Pore-Grain Boundary Separation Behavior during Sintering of 420 Stainless Steel
Authors: Wen Tao , He Hao , Lou Jia , Gan Mengqi , Luo Xin , Huang Yuhang , Xu Wei
Published: July 2022

This study investigated the evolution of density, grain size, and pore characteristics during the sintering of metal injection molding (MIM) 420, 420 + 0.3C and pre-alloyed 420Nb stainless steel powders. The results show that C promotes the reduction of oxides on the surface of stainless steel, thereby accelerating sintering at 1330 °C, which is the initial sintering stage of MIM 420. MIM 420Nb showed the slowest sintering rate due to the strong binding force between Nb and C. At 1350 °C, the sintering densities of MIM 420 and 420 + 0.3C slightly improved, whereas their grain sizes grew significantly. Scanning electron microscopy images show grain boundary-pore separation, which significantly retarded the grain boundary diffusion mechanism and hence reduced the densification rate. The addition of C accelerated the pore-grain boundary separation; thus MIM 420 + 0.3C showed the lowest density at this temperature among the materials analyzed in this study. Nb suppressed the grain growth rate; thus, MIM 420Nb exhibited the highest density among the three materials. At 1370 °C, MIM 420 + 0.3C reached the highest density owing to the creation of a liquid phase. Theoretical calculations proved that there is a linear relationship between the grain boundary area per unit volume and the interfacial pore area per unit volume. Furthermore, when the ratio of grain size to pore size is 28, the contact probability between the grain boundaries and pores is significantly reduced to approximately 10%, leading to an extremely slow densification rate and a rapid grain growth rate, which is consistent with the experimental results.
Corrosion of Cu in Antifreeze Solutions with Triazine- or Triazole-Type Corrosion Inhibitors for 3 Weeks
Authors: Jang HeeJin , Lee Juhee , Chun Yong-Jin , Soh Soon-Young
Published: July 2022

The corrosion behavior of Cu in antifreeze solutions containing 2,4,6-Tris(5-carboxypentylamino)1,3,5-triazine, 2,4,6-Tris(11-carboxyundecylamino)1,3,5-triazine, 1-Aminomethyl(N′,N′-di(2-hydroxyethyl)tolutriazole, or 1-Aminomethyl(N′,N′-di(2-hydroxyethyl)benzotriazole as corrosion inhibitors were examined by immersion test for 3 weeks as well as potentiodynamic polarization tests before and after immersion test. The corrosion rate of Cu was as relatively high as 10−5 A/cm2 in antifreeze solution with the inhibitor (2,4,6-Tris(11-carboxyundecylamino)1,3,5-triazine) with a high molecular weight of 713 for a short time duration compared with antifreeze solutions using the other three types of inhibitors. However, the corrosion inhibition effect of this large molecule became prominent after 2 weeks, reducing the corrosion rate by about four orders of magnitude. Corrosion of Cu in the solution with inhibitors of high molecular weight of 440 or higher decreased gradually with time, while that in the solution with small molecules slightly increased over 3 weeks.
A Study on Heat Input Control and a Quality Evaluation Algorithm to Prevent Toughness Deterioration of the Heat-Affected Zone in the Fiber Laser Welding Process of ASTM A553-1 (9% Nickel Steel) Material
Authors: Park Minho , Kim Jaewoong , Pyo Changmin
Published: July 2022

Various international organizations and governments of many countries are making efforts to prevent environmental pollution, with the IMO (International Maritime Organization) reinforcing related regulations. With these regulations, equipment related to LNG-fueled ships, which have the greatest carbon dioxide reduction effect among eco-friendly ships, are expected to increase. Although the IGC code designates the materials that can be used for LNG containers, such as 304L stainless steel and 9% nickel steel, these materials have a tendency to deteriorate the tissue around the heat-affected zone due to excessive heat input. In this study, we analyzed the effect of brittle fracture in the weld zone and heat-affected zone after fiber laser welding and found that welding quality improved with control of the heat input. SVM discriminant analysis was applied to classify the groups in which brittle fracture and ductile fracture occurred. The shape of the penetration section, hardness in the welding zone and heat-affected zone, and fracture surface were selected as factors for discrimination; these values were determined under various welding conditions. With these data, we derived a regression model and multi-objective optimization algorithm to predict mechanical properties after welding, as well as the conditions necessary to prevent brittle fracture. Finally, the prediction models were verified, as the results of welding under the derived conditions were classified as ductile fracture group.
Dysregulated Metabolism in EGFR-TKI Drug Resistant Non-Small-Cell Lung Cancer: A Systematic Review
Authors: Babuta Julia , Hall Zoe , Athersuch Toby
Published: July 2022

Drug resistance is a common barrier to continued effective treatment in cancer. In non-small-cell lung cancer (NSCLC), tyrosine kinase inhibitors that target the epidermal growth factor receptor (EGFR-TKIs) exhibit good efficacy in cancer treatment until acquired resistance occurs. It has been observed that drug resistance is accompanied by numerous molecular-level changes, including significant shifts in cellular metabolism. The purpose of this study was to critically and systematically review the published literature with respect to how metabolism differs in drug-resistant compared to drug-sensitive NSCLC. Understanding the differences between resistant and sensitive cells is vital and has the potential to allow interventions that enable the re-sensitisation of resistant cells to treatment, and consequently reinitiate the therapeutic effect of EGFR-TKIs. The main literature search was performed using relevant keywords in PubMed and Ovid (Medline) and reviewed using the Covidence platform. Of the 1331 potentially relevant literature records retrieved, 27 studies were subsequently selected for comprehensive analysis. Collectively, the literature revealed that NSCLC cell lines resistant to EGFR-TKI treatment possess characteristic metabolic and lipidomic phenotypic signatures that differentiate them from sensitive lines. Further exploration of these reported differences suggests that drug-resistant cell lines are differentially reliant on cellular energy sources and that modulation of relative energy production pathways may lead to the reversal of drug resistance.
Pandemic Clones of CTX-M-15 Producing Klebsiella pneumoniae ST15, ST147, and ST307 in Companion Parrots
Authors: Davies Yamê Miniero , Cunha Marcos Paulo Vieira , Dropa Milena , Lincopan Nilton , Gomes Vasco Túlio Moura , Moreno Luisa Zanoli , Sato Maria Inês Zanoli , Moreno Andrea Micke , Knöbl Terezinha
Published: July 2022

Psittacine birds are commonly kept as companion birds and the maintenance of these birds in captivity may represent a zoonotic risk and contribute to the propagation of multidrug-resistant and β-lactamase extended-spectrum (ESBLs)-producing pathogens. This study aimed to identify and characterize strains of the Klebsiella pneumoniae complex isolated from diseased psittacine birds, determining virulence and resistance profiles. K. pneumoniae strains were isolated from 16 birds (16/46). All strains carried more than three virulence genes, with a high frequency of fimH and kpn (93.75%), uge (87.52%), and irp-2 (81.25%) genes. The antimicrobial susceptibility revealed that 3/16 strains were ESBL producers. Genomic analysis revealed that CTX-M-15-positive strains belonged to sequence types (STs) ST15, ST147, and ST307, characterized as international clones associated with outbreaks of healthcare-associated infections (HAIs) worldwide.
Targeting Paleovalley-Related Ferricrete Units in Yilgarn Craton Using High-Resolution Aeromagnetic Data and Spatial Machine Learning
Authors: Talebi Hassan , Markov Jelena , Salama Walid , Otto Alex , Metelka Vasek , Anand Ravi , Cole Dave
Published: July 2022

The ferricrete units (Fe oxide cemented colluvial-alluvial sediment) of the Yilgarn Craton in Western Australia formed during the humid tropical and sub-tropical climates of the Cenozoic. Ferricretes are generally developed on long-lived paleodrainage systems and are products of the ferruginisation of detritus provided by the continuous erosion of upslopes. These iron-rich accumulations can become Au-enriched, as is the case in several locations previously discovered in the Yilgarn Craton; many of these host economic secondary gold deposits (e.g., Moolart Well, Mt Gibson, and Bulchina), typically occurring downslope of low saprolite hills and near paleovalleys (i.e., inset-valleys). Inset-valleys are a common paleotopographic feature buried under Quaternary alluvial and colluvial sedimentary cover. Maps of these ancient channel networks can be used as a proxy for targeting ferricrete gold deposits. These inset-valley systems generally form dendritic and noisy patterns in high-resolution aeromagnetic data due to the presence of maghemite-rich nodules and detrital magnetic pisoliths on their flanks. The main aim of this study was to use high-resolution aeromagnetic data to target ferricrete units related to inset-valleys systems across the Yilgarn Craton. A spatial predictive model was used to learn and predict the geological units of interest from pre-processed aeromagnetic data. The predicted inset-valleys systems were able to confine the exploration space and define a new exploration frontier for ferricrete gold deposits.