bluuebunny/crossref_metadata_2025 huggingface dataset:
SARS-CoV-2 Alpha-Variant Outbreak Amongst a Partially Vaccinated Long-Term Care Facility Population in The Netherlands—Phylogenetic Analysis and Infection Control Observations
Authors: Van der Moeren Nathalie , Weterings Veronica A. T. C. , Pas Suzan D. , Verweij Jaco J. , van den Bijllaardt Wouter , Geurts Joyce , Zwart Vivian F. , Lodder Esther B. , Kluytmans Jan A. J. W. , Murk Jean-Luc , Stohr Joep J. J. M.
Published: September 2022

Despite extensive vaccination and booster programs, SARS-CoV-2 outbreaks in long-term care facilities (LTCF) continue to occur. We retrospectively describe a SARS-CoV-2 outbreak amongst a partially vaccinated LTCF population in The Netherlands which occurred in March 2021. The facility comprised three floors functioning as separate wards. Nasopharyngeal swabs for SARS-CoV-2 qRT-PCR were obtained from residents and staff presenting with COVID-19-like symptoms and from all residents and staff during two point prevalence screenings (PPS). Samples meeting technical criteria were included for phylogenetic analysis. Positive SARS-CoV-2 qRT-PCR were obtained from 11 (18%) of 61 residents and 8 (7%) of 110 staff members between March 8 and March 25. Seven (37%) cases and five (63%) vaccinated cases were diagnosed through PPS. Cases were found on all wards. Phylogenetic analysis (n = 11) showed a maximum difference of four nucleotides between sequences on the outer branches of the tree, but identified two identical sequences on the root differing maximum two nucleotides from all other sequences, suggesting all did belong to the same cluster. Our results imply that PPS is useful in containing SARS-CoV-2 outbreaks amongst (vaccinated) LTCF populations, as an entire LTCF might behave as a single epidemiological unit and it is preferable to maximize the number of samples included for phylogenetic analysis.
Mutation in the STXBP1 Gene Associated with Early Onset West Syndrome: A Case Report and Literature Review
Authors: Takeda Kanako , Miyamoto Yusaku , Yamamoto Hisako , Iwasaki Toshiyuki , Sumitomo Noriko , Takeshita Eri , Ishii Atsushi , Hirose Shinichi , Shimizu Naoki
Published: September 2022

Syntaxin-binding protein1 (STXBP1) is a member of the Sec1/Munc18-1 protein family, which comprises important regulators of the secretory and synaptic vesicle fusion machinery underlying hormonal and neuronal transmission, respectively. STXBP1 pathogenic variants are associated with multiple neurological disorders. Herein, we present the case of a Japanese girl with a mutation in the STXBP1 gene, who was born at 40 weeks without neonatal asphyxia. At 15 days old, she developed epilepsy and generalized seizures. Around 88 days old, she presented with a series of nodding spasms, with the seizure frequency gradually increasing. Interictal EEG indicated hypsarrhythmia and she presented with developmental regression. At 1.5 years old, genetic testing was performed and mutational analysis revealed an STXBP1 gene mutation (c.875G > A: p.Arg292His). Accordingly, she was diagnosed with developmental and epileptic encephalopathy, presenting West syndrome’s clinical characteristics caused by the STXBP1 gene mutation. Although drug treatment has reduced the frequency of epileptic seizures, her development has remained regressive. The relationship between the location and type of genetic abnormality and the phenotype remains unclear. Future studies should investigate the genotype–phenotype correlation and the underlying pathophysiology to elucidate the causal relationships among the multiple phenotype-determining factors.
Rational Design and Synthesis of New Selective COX-2 Inhibitors with In Vivo PGE2-Lowering Activity by Tethering Benzenesulfonamide and 1,2,3-Triazole Pharmacophores to Some NSAIDs
Authors: El-Dershaby Nada H. , El-Hawash Soad A. , Kassab Shaymaa E. , Daabees Hoda G. , Abdel Moneim Ahmed E. , El-Miligy Mostafa M. M.
Published: September 2022

New selective COX-2 inhibitors were designed and synthesized by tethering 1,2,3-triazole and benzenesulfonamide pharmacophores to some NSAIDs. Compounds 6b and 6j showed higher in vitro COX-2 selectivity and inhibitory activity (IC50 = 0.04 µM and S.I. = 329 and 312, respectively) than celecoxib (IC50 = 0.05 µM and S.I. = 294). Compound 6e revealed equipotent in vitro COX-2 inhibitory activity to celecoxib. Furthermore, 6b and 6j expressed more potent relief of carrageenan-induced paw edema thickness in mice than celecoxib, with ED50 values of 11.74 µmol/kg and 13.38 µmol/kg vs. 16.24 µmol/kg, respectively. Compounds 6b and 6j inhibited the production of PGE2 with a % inhibition of PGE2 production of 90.70% and 86.34%, respectively, exceeding celecoxib’s percentage (78.62%). Moreover, 6b and 6j demonstrated a gastric safety profile comparable to celecoxib. In conclusion, compounds 6b and 6j better achieved the target goal as more potent and selective COX-2 inhibitors than celecoxib in vitro and in vivo.
2D NMR Analysis as a Sensitive Tool for Evaluating the Higher-Order Structural Integrity of Monoclonal Antibody against COVID-19
Authors: Cantini Francesca , Andreano Emanuele , Paciello Ida , Ghini Veronica , Berti Francesco , Rappuoli Rino , Banci Lucia
Published: September 2022

The higher-order structure (HOS) of protein therapeutics has been confirmed as a critical quality parameter. In this study, we compared 2D 1H-13C ALSOFAST-HMQC NMR spectra with immunochemical ELISA-based analysis to evaluate their sensitivity in assessing the HOS of a potent human monoclonal antibody (mAb) for the treatment of coronavirus disease 2019 (COVID-19). The study confirmed that the methyl region of the 2D 1H-13C NMR spectrum is sensitive to changes in the secondary and tertiary structure of the mAb, more than ELISA immunoassay. Because of its highly detailed level of characterization (i.e., many 1H-13C cross-peaks are used for statistical comparability), the NMR technique also provided a more informative outcome for the product characterization of biopharmaceuticals. This NMR approach represents a powerful tool in assessing the overall higher-order structural integrity of mAb as an alternative to conventional immunoassays.
New Approach for Preparing Solid Lipid Nanoparticles with Volatile Oil-Loaded Quercetin Using the Phase-Inversion Temperature Method
Authors: Weerapol Yotsanan , Manmuan Suwisit , Chaothanaphat Nattaya , Limmatvapirat Sontaya , Sirirak Jitnapa , Tamdee Poomipat , Tubtimsri Sukannika
Published: September 2022

Quercetin (QCT), a natural flavonoid, is of research interest owing to its pharmacological properties. However, its pharmacokinetic limitations could hinder its widespread therapeutic use. Nanocarriers, especially solid lipid nanoparticles (SLNs), might overcome this constraint. This study aimed to investigate QCT-loaded SLNs prepared via a new approach using a volatile oil. The phase-inversion temperature method was used to incorporate rosemary oil (RMO) into SLNs prepared using solid lipids possessing different chemical structures. Among the solid lipids used in the formulations, trilaurin (TLR) exhibited the smallest particle size and good stability after a temperature cycling test. SLNs prepared with a ratio of RMO to TLR of 1:3 could load QCT with an entrapment efficiency of >60% and drug loading of ~2% w/w. The smallest particle size was achieved using the polyoxyethylene-hydrogenated castor oil RH40, and the particle size depended on the concentration. The drug-release profile of QCT_TLR exhibited prolonged biphasic release for >24 h. QCT_TLR was a safe formulation, as indicated by a cell viability percentage of >75% at <2% v/v. In a computer simulation, the system with RMO enabled smaller sized SLNs than those without RMO. This new discovery shows great promise for producing SLNs via the phase-inversion temperature method with incorporation of volatile oil, particularly for delivering compounds with limited water solubility.
Characterization of Neutralizing Human Anti-Tetanus Monoclonal Antibodies Produced by Stable Cell Lines
Authors: Manieri Tania Maria , Takata Daniela Yumi , Targino Roselaine Campos , Quintilio Wagner , Batalha-Carvalho João Victor , da Silva Camila Maria Lucia , Moro Ana Maria
Published: September 2022

Tetanus toxin (TeNT) is produced by C. tetani, a spore-forming bacillus broadly spread in the environment. Although an inexpensive and safe vaccine is available, tetanus persists because of a lack of booster shots and variable responses to vaccines due to immunocompromised status or age-decreased immune surveillance. Tetanus is most prevalent in low- and medium-income countries, where it remains a health problem. Neutralizing monoclonal antibodies (mAbs) can prevent the severity of illness and death caused by C. tetani infection. We identified a panel of mAbs that bind to TeNT, some of which were investigated in a preclinical assay, showing that a trio of mAbs that bind to different sites of TeNT can neutralize the toxin and prevent symptoms and death in mice. We also identified two mAbs that can impair the binding of TeNT to the GT1b ganglioside receptor in neurons. In this work, to generate a series of cell lines, we constructed vectors containing sequences encoding heavy and light constant regions that can receive the paired variable regions resulting from PCRs of human B cells. In this way, we generated stable cell lines for five mAbs and compared and characterized the antibody produced in large quantities, enabling the characterization experiments. We present the results regarding the cell growth and viability in a fed-batch culture, titer measurement, and specific productivity estimation. The affinity of purified mAbs was analyzed by kinetics and under steady-state conditions, as three mAbs could not dissociate from TeNT within 36,000 s. The binding of mAbs to TeNT was confirmed by ELISA and inhibition of toxin binding to GT1b. The use of the mAbs mixture confirmed the individual mAb contribution to inhibition. We also analyzed the binding of mAbs to FcγR by surface plasmon resonance (SPR) and the glycan composition. Molecular docking analyses showed the binding site of an anti-tetanus mAb.
Effects of Slow-Acting Metformin Treatment on the Hormonal and Morphological Thyroid Profile in Patients with Insulin Resistance
Authors: Condorelli Rosita A. , Cannarella Rossella , Aversa Antonio , Basile Livia , Avola Ottavia , Calogero Aldo E. , La Vignera Sandro
Published: September 2022

Metformin appears to reduce TSH levels in untreated hypothyroid patients. In contrast, in euthyroid patients with type 2 diabetes mellitus (T2DM), metformin is initially devoid of effects on TSH. However, it is followed by a significant reduction in TSH level after twelve months of treatment. Additionally, some evidence suggests that metformin may also improve thyroid morphological abnormalities. This study aimed to evaluate the effects of metformin not only on TSH and thyroid hormone values, but also on thyroid volume and nodules. A total of 50 patients (mean age: 36.9 ± 12.8 years) with insulin resistance (homeostatic model assessment (HOMA) index ≥2.5) and with thyroid uninodular disease were recruited for this study. They were prescribed slow-acting metformin at a daily dose of 500 mg for six months. Treatment with metformin in euthyroid patients with uninodular thyroid disease and insulin resistance reduces TSH levels, increases FT4 and FT3 values, and decreases thyroid and nodule volumes. These data suggest that metformin may be an effective drug not only for the treatment of T2DM and metabolic syndrome, but also for thyroid disease.
A Tomato EMS-Mutagenized Population Provides New Valuable Resources for Gene Discovery and Breeding of Developmental Traits
Authors: Fonseca Rocío , Capel Carmen , Nieto-Canseco Roberto , Ortiz-Atienza Ana , Bretones Sandra , López-Fábregas Juan D. , Quevedo-Colmena Abraham S. , Lebrón Ricardo , Barragán-Lozano Teresa , Villalobos-Ramírez Víctor , Yuste-Lisbona Fernando J. , Angosto Trinidad , Capel Juan , Lozano Rafael
Published: September 2022

Tomato (Solanum lycopersicum L.) is a major horticultural crop and a model species among eudicots, especially for traits related to reproductive development. Although considerable progress has been made since the tomato genome sequence project was completed, most of the genes identified remain predictions with an unknown or hypothetical function. This lack of functional characterization hampers the use of the huge amount of genomic information available to improve the quality and productivity of this crop. Reverse genetics strategies such as artificial mutagenesis and next-generation sequencing approaches build the perfect tandem for increasing knowledge on functional annotation of tomato genes. This work reports the phenotypic characterization of a tomato mutant collection generated from an EMS chemical mutagenesis program aimed to identify interesting agronomic mutants and novel gene functions. Tomato mutants were grouped into fourteen phenotypic classes, including vegetative and reproductive development traits, and the inheritance pattern of the identified mutations was studied. In addition, causal mutation of a selected mutant line was isolated through a mapping-by-sequencing approach as a proof of concept of this strategy’s successful implementation. Results support tomato mutagenesis as an essential tool for functional genomics in this fleshy-fruited model species and a highly valuable resource for future breeding programs of this crop species aimed at the development of more productive and resilient new varieties under challenging climatic and production scenarios.
Self-Similarity and Power-Law Spectra of Polymer Melts and Solutions
Authors: Choi Jehyeok , Cho Kwang Soo , Kwon Mi Kyung
Published: September 2022

Both the Rouse and Doi-Edwards models can be expressed by the relaxation spectra, in the form of power-law functions. The concept of self-similarity has offered a simple solution to many problems in polymer physics. Since the solutions derived from self-similarity are power-law functions, it is essential to check whether the relaxation spectrum of polymeric fluids can be derived by self-similarity. In this study, the power-law spectrum of an unentangled polymer solution is derived by using the self-similarity approach, which does not work for entangled polymeric fluids. Although Baumgaertel et al. (Rheol. Acta 29, 400–408 (1990)) showed that the power-law spectrum can quantitatively describe the linear viscoelasticity of monodisperse polymer melts, regardless of molecular weight, they did not find the universality of the exponent of the spectrum because they found different exponents for different polymers. Under the consideration existing the universality of linear viscoelasticity of polymer melts, this paper deals with the universality of the exponent by employing a new regression algorithm and confirms that the exponent is independent of the type of polymer.
Development of Phantoms for Multimodal Magnetic Resonance Imaging and Magnetic Particle Imaging
Authors: Arenas Maria Alejandra Ardila , Gutkelch Dirk , Kosch Olaf , Brühl Rüdiger , Wiekhorst Frank , Löwa Norbert
Published: September 2022

Phantoms are crucial for the development of imaging techniques based on magnetic nanoparticles (MNP). They serve as test objects to simulate application scenarios but are also used for quality assurance and interlaboratory comparisons. Magnetic particle imaging (MPI) is excellent for specifically detecting magnetic nanoparticles (MNP) without any background signals. To obtain information about the surrounding soft tissue, MPI is often used in combination with magnetic resonance imaging (MRI). For such application scenarios, this poses a challenge for phantom fabrication, as they need to accommodate MNP as well as provide MR visibility. Recently, layer-by-layer fabrication of parts using Additive Manufacturing (AM) has emerged as a powerful tool for creating complex and patient-specific phantoms, but these are characterized by poor MR visibility of the AM material. We present the systematic screening of AM materials as candidates for multimodal MRI/MPI imaging. Of all investigated materials, silicone (Dreve, Biotec) exhibited the best properties with sufficient MR-signal performance and the lowest absorption of MNP at the interface of AM materials. With the help of AM and the selection of appropriate materials, we have been able to produce suitable MRI/MPI phantoms.
Lifetime Estimation and Orientation Effect Based on Long-Term Degradation Analysis of Thermoset and Thermoplastic Insulators
Authors: Ullah Israr , Ullah Rahmat , Amin Muhammad , Rahman Rahisham Abd , Khan Aftab , Ullah Nasim , Alotaibi Sattam
Published: September 2022

Polymeric insulators have replaced ceramic insulators due to their obvious properties like low surface energy, which exhibits good hydrophobic performance, low weight, etc. However, electric utilities have concerns about their long-term performance. In that context, the long-term performance of two different types of polymeric insulators are investigated in this study: thermoset Silicone rubber (SiR) and thermoplastic elastomeric (TPE). Multi-stress aging was performed in the different orientations of both types of polymeric insulators. During multi-stress aging, insulators are exposed to varied loads in both vertical and horizontal orientations, simulating actual service environmental conditions. Experiments were done in a chamber where different types of stresses were simulated, which resembles the weathering conditions of Hattar, Pakistan, which is one of the most polluted industrial zones. Both insulators were stressed in a chamber under the designed weathering conditions for two years and six months at different orientations. Polymeric insulators made of SiR perform better in the vertical position than that in the horizontal position. Furthermore, the experimental results show that both materials are capable in a variety of situations. SiR, on the other hand, performed well due to its high hydrophobicity, which means it is less impacted by contaminants and hence has a longer life and higher service performance than TPE.
Influence of Fiber Volume in Hybrid Short Glass/Cellulose Reinforced Thermoplastic Compounds
Authors: Kahl Christian , Zarges Jan-Christoph , Heim Hans-Peter
Published: September 2022

Glass fibers (GF) and regenerated cellulose fibers (RCF) are possible partners in the hybrid reinforcement of thermoplastics because of their different properties. Due to the weak bonding properties of polypropylene, coupling agents are used and the fiber volume content is set high to achieve high reinforcing effects. A lower fiber content of GF can raise the toughness properties of a reinforced polypropylene which is investigated in this study with different ratios of GF and RCF. The composites are tested in tensile tests, flexural tests and also in notched Charpy impact tests. The results can be used to compare whether a substitution of GF with RCF or the addition of more GF leads to higher mechanical properties. The tensile and Charpy impact results are compared with the Rule of Hybrid Mixtures (RoHM) to show the deviation to the prediction. Better results in terms of stiffness and strength are seen with a higher total fiber volume, while hybrid reinforced specimens show lower toughness values compared to the RCF reinforced reference specimens. Adding 5 vol% GF to 16 vol% RCF results in an increase in tensile strength by 26%, but also a significant decrease in elongation at break by 65%.
Soft Conductive Hydrogel-Based Electronic Skin for Robot Finger Grasping Manipulation
Authors: Cheng Xiao , Zhang Fan , Dong Wentao
Published: September 2022

Electronic skin with human-like sensory capabilities has been widely applied to artificial intelligence, biomedical engineering, and the prosthetic hand for expanding the sensing ability of robots. Robotic electronic skin (RES) based on conductive hydrogel is developed to collect strain and pressure data for improving the grasping capability of the robot finger. RES is fabricated and assembled by the soft functional materials through a sol–gel process for guaranteeing the overall softness. The strain sensor based on piezoresistive hydrogel (gauge factor ~9.98) is integrated onto the back surface of the robot finger to collect the bending angle of the robot finger. The capacitive pressure sensor based on a hydrogel electrode (sensitivity: 0.105 kPa−1 below 3.61 kPa, and 0.0327 kPa−1 in the range from 4.12 to 15 kPa.) is adhered onto the fingertip to collect the pressure data when touching the objects. A robot-finger-compatible RES with strain and pressure sensing function is designed for finger gesture detection and grasping manipulation. The negative force feedback control framework is built to improve grasping manipulation of the robot finger with RES, which would provide a self-adaptive control method to determine whether the objects are grasped successfully or not. Robot fingers integrated with soft sensors would promote the development of sensing and grasping abilities of the robot finger and interaction with human beings.
An Analysis of the Transfer Lengths of Different Types of Prestressed Fiber-Reinforced Polymer Reinforcement
Authors: Jokūbaitis Aidas , Valivonis Juozas
Published: September 2022

The main aim of this paper is to provide a broader analysis of the transfer lengths of different types of fiber-reinforced polymers (FRPs) and to provide corrections to the existing theoretical models. Therefore, this paper presents a description of the main factors that influence the transfer lengths of different types of FRPs based on experimental results found in the literature. A database of more than 300 specimens was compiled with the results of the transfer lengths of different FRPs and the main influencing parameters. The analysis of the database results showed that the transfer length of the carbon fiber composite cable (CFCC) strands depends on the type of prestressed reinforcement release. Therefore, in this article, the new coefficient αt = 2.4 is proposed for the transfer length of suddenly released CFCC strands. Additionally, the transfer length of the aramid fiber reinforced polymer (AFRP) depends on its surface conditions. Therefore, new coefficients αt = 1.5 and αt = 4.0 are also proposed for the transfer lengths of smooth braided and sanded and rough AFRP bars, respectively. Furthermore, the proposed coefficients αt = 2.6, αt = 1.9, and αt = 4.8 found in the literature were validated with the analysis of a larger database of the transfer lengths of glass fiber-reinforced polymer (GFRP) bars, carbon fiber-reinforced polymer (CFRP) bars, and gradually released CFCC strands, respectively. Moreover, the main existing theoretical models are presented, and the comparison of theoretical and experimental transfer length results is discussed. However, the low number of specimens prestressed with basalt fiber-reinforced polymer (BFRP) bars prevented the deeper analysis of the results. the analysis of the transfer length and the proposed new values of the coefficient αt provides possibilities for adapting it to design codes for engineering applications and performing additional research that fills the missing gaps in the field.
Sustainable Reuse of Waste Tire Textile Fibers (WTTF) as Reinforcements
Authors: Fazli Ali , Rodrigue Denis
Published: September 2022

Waste tire textile fibers (WTTF), as a by-product (10–15% by weight of tires) of end-of-life tires (ELT) mechanical recycling (grinding), are classified as hazardous wastes and traditionally burnt (thermal recycling) or buried (landfilling), leading to several environmental and ecological issues. Thus, WTTF still represent an important challenge in today’s material recycling streams. It is vital to provide practical and economical solutions to convert WTTF into a source of inexpensive and valuable raw materials. In recent years, tire textile fibers have attracted significant attention to be used as a promising substitute to the commonly used natural/synthetic reinforcement fibers in geotechnical engineering applications, construction/civil structures, insulation materials, and polymer composites. However, the results available in the literature are limited, and practical aspects such as fiber contamination (~65% rubber particles) remain unsolved, limiting WTTF as an inexpensive reinforcement. This study provides a comprehensive review on WTTF treatments to separate rubber and impurities and discusses potential applications in expansive soils, cement and concrete, asphalt mixtures, rubber aerogels and polymer composites.
Low-Cost Self-Calibration Data Glove Based on Space-Division Multiplexed Flexible Optical Fiber Sensor
Authors: Yu Hui , Zheng Daifu , Liu Yun , Chen Shimeng , Wang Xiaona , Peng Wei
Published: September 2022

Wearable devices such as data gloves have experienced tremendous growth over the past two decades. It is vital to develop flexible sensors with fast response, high sensitivity and high stability for intelligent data gloves. Therefore, a tractable low-cost flexible data glove with self-calibration function based on a space-division multiplexed flexible optical fiber sensor is proposed. A simple, stable and economical method was used to fabricate flexible silicone rubber fiber for a stretchable double-layered coaxial cylinder. The test results show that the fiber is not sensitive to the temperature range of (20~50 °C) and exhibits excellent flexibility and high stability under tensile, bending and torsional deformation. In addition, the signal detection part of the data glove enables compact and efficient real-time information acquisition and processing. Combined with a self-calibration function that can improve the accuracy of data acquisition, the data glove can be self-adaptive according to different hand sizes and bending habits. In a gesture capture test, it can accurately recognize and capture each gesture, and guide the manipulator to make the same action. The low-cost, fast-responding and structurally robust data glove has potential applications in areas such as sign language recognition, telemedicine and human–robot interaction.
Al-Sn-Al Bonding Strength Investigation Based on Deep Learning Model
Authors: Jiang Min , Yu Min , Li Bao , Zhang Hongze , Zhu Zhiyuan
Published: September 2022

Al-Sn-Al wafer bonding is a new semiconductor manufacturing technology that plays an important role in device manufacturing. Optimization of the bonding process and testing of the bonding strength remain key issues. However, using only physical experiments to study the above problems presents difficulties such as repeating many experiments, high costs, and low efficiency. Deep learning algorithms can quickly simulate complex physical correlations by training large amounts of data, which is a good solution to the difficulties in studying wafer bonding. Therefore, this paper proposes the use of deep learning models (2-layer CNN and 50-layer ResNet) to achieve autonomous recognition of bonding strengths corresponding to different bonding conditions, and the results from a comparative test set show that the ResNet model has an accuracy of 99.17%, outperforming the CNN model with an accuracy of 91.67%. Then, the identified images are analyzed using the Canny edge detector, which showed that the fracture surface morphology of the wafer is a hole-shaped structure, with the smaller the area of hole movement on the wafer surface, the higher the bonding strength. In addition, the effects of bonding time and bonding temperature on bonding strength are verified, showing that relatively short bonding times and relatively low bonding temperatures resulted in better wafer bonding strength. This research demonstrates the potential of using deep learning to accelerate wafer bonding strength identification and process condition optimization.
Analysis of Particle Size Distribution of Coke on Blast Furnace Belt Using Object Detection
Authors: Li Meng , Wang Xu , Yao Hao , Saxén Henrik , Yu Yaowei
Published: September 2022

Particle size distribution is an important parameter of metallurgical coke for use in blast furnaces. It is usually analyzed by traditional sieving methods, which cause delays and require maintenance. In this paper, a coke particle detection model was developed using a deep learning-based object detection algorithm (YOLOv3). The results were used to estimate the particle size distribution by a statistical method. Images of coke on the main conveyor belt of a blast furnace were acquired for model training and testing, and the particle size distribution determined by sieving was used for verification of the results. The experiment results show that the particle detection model is fast and has a high accuracy; the absolute error of the particle size distribution between the detection method and the sieving method was less than 5%. The detection method provides a new approach for fast analysis of particle size distributions from images and holds promise for a future online application in the plant.
Bio-Hydrogen Production in Packed Bed Continuous Plug Flow Reactor—CFD-Multiphase Modelling
Authors: Wodołażski Artur , Smoliński Adam
Published: September 2022

This research study investigates the modelling and simulation of biomass anaerobic dark fermentation in bio-hydrogen production in a continuous plug flow reactor. A CFD multiphase full transient model in long-term horizons was adopted to model dark fermentation biohydrogen production in continuous mode. Both the continuous discharge of biomass, which prevents the accumulation of solid parts, and the recirculation of the liquid phase ensure constant access to the nutrient solution. The effect of the hydraulic retention time (HRT), pH and the feed rate on the bio-hydrogen yield and production rates were examined in the simulation stage. Metabolite proportions (VFA: acetic, propionic, butyric) constitute important parameters influencing the bio-hydrogen production efficiency. The model of substrate inhibition on bio-hydrogen production from glucose by attached cells of the microorganism T. neapolitana applied to the modelling of the kinetics of bio-hydrogen production was used. The modelling and simulation of a continuous plug flow (bio)reactor in biohydrogen production is an important part of the process design, modelling and optimization of the biological H2 production pathway.
Scrupulosity in the Network of Obsessive-Compulsive Symptoms, Religious Struggles, and Self-Compassion: A Study in a Non-Clinical Sample
Authors: Moroń Marcin , Biolik-Moroń Magdalena , Matuszewski Krzysztof
Published: September 2022

Scrupulosity is a phenomenon of the intersection between religiosity and obsessive-compulsive disorder. It could be regarded as an interactive effect of religiosity, religious internal conflicts, cognitive distortions associated with thought processing and self-reference, and obsessiveness. The present study investigated scrupulosity in the network of religious/spiritual struggles, obsessive-compulsive disorder (OCD) symptoms, self-compassion, and religiosity in order to better describe a position of scrupulosity in the dimensions of mental health and illness. Two hundred and ninety-two religious individuals from Poland (two hundred and two women) between the ages of 18 and 83 (M = 39.3; SD = 13.7) participated in the study. We applied the Self-Compassion Scale, Religious and Spiritual Struggle Scale, Obsessive-Compulsive Inventory-Revised, Pennsylvania Inventory of Scrupulosity, and posed questions concerning identification with religious beliefs, the role of religion in one’s identity, and religious attendance. Using correlation analysis and a network analysis, we demonstrated that scrupulosity was positively correlated with religious/spiritual struggles (mostly with moral struggles and religious doubts) and with obsessing as an OCD symptom. The bridge strength analysis indicated that scrupulosity may be regarded as a bridge symptom between religious/spiritual struggles and OCD symptoms. Pastoral and psychological counselling could use these results in order to design efficient treatments for people suffering from religious scruples.
Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data
Authors: Kochański Błażej
Published: September 2022

In the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves without underlying data. The primary purpose of this study is to review the ROC curve models proposed in the literature, primarily in biostatistics, and to fit them to actual credit-scoring ROC data in order to determine which models could be used in credit-risk-management practice. We list several theoretical models for an ROC curve and describe them in the credit-scoring context. The model list includes the binormal, bigamma, bibeta, bilogistic, power, and bifractal curves. The models are then tested against empirical credit-scoring ROC data from publicly available presentations and papers, as well as from European retail lending institutions. Except for the power curve, all the presented models fit the data quite well. However, based on the results and other favourable properties, it is suggested that the binormal curve is the preferred choice for modelling credit-scoring ROC curves.
Thematic Analysis of Financial Technology (Fintech) Influence on the Banking Industry
Authors: Varma Parminder , Nijjer Shivinder , Sood Kiran , Grima Simon , Rupeika-Apoga Ramona
Published: September 2022

The synthesis of technology and finance is known as financial technology (Fintech), which brings together two of the biggest industries in harmony. Fintech disruption is a deviation from the norm, resulting in a significant shift in banking services and, as a result, risk. This article aims to investigate how Fintech has influenced recent changes in the banking industry and upcoming challenges, with a particular emphasis on blockchain technology. We perform a comprehensive thematic analysis of recent studies on Fintech in the banking industry. We found that Fintech has enormous potential to grow and impact the banking industry and the entire world. The banking industry could benefit from combining emerging technologies such as blockchain, AI, machine learning, or other decision-making layers. However, with the benefits come drawbacks, such as increased reliance on technology, high costs, increased job losses, security risks related to data and fraud, and so on. The use of emerging technology and collaboration between Fintech firms and banks can improve system-wide financial stability while minimising the negative externalities of disruption and competition. These findings can help regulators, policymakers, academics, and practitioners understand the opportunities and challenges of emerging technologies in the banking industry.
Review of Automatic Processing of Topography and Surface Feature Identification LiDAR Data Using Machine Learning Techniques
Authors: Gharineiat Zahra , Tarsha Kurdi Fayez , Campbell Glenn
Published: September 2022

Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have provided promising results and thus this topic has been widely addressed in the literature during the last few years. This paper reviews the essential and the more recent completed studies in the topography and surface feature identification domain. Four areas, with respect to the suggested approaches, have been analyzed and discussed: the input data, the concepts of point cloud structure for applying ML, the ML techniques used, and the applications of ML on LiDAR data. Then, an overview is provided to underline the advantages and the disadvantages of this research axis. Despite the training data labelling problem, the calculation cost, and the undesirable shortcutting due to data downsampling, most of the proposed methods use supervised ML concepts to classify the downsampled LiDAR data. Furthermore, despite the occasional highly accurate results, in most cases the results still require filtering. In fact, a considerable number of adopted approaches use the same data structure concepts employed in image processing to profit from available informatics tools. Knowing that the LiDAR point clouds represent rich 3D data, more effort is needed to develop specialized processing tools.
Characterization of Long-Time Series Variation of Glacial Lakes in Southwestern Tibet: A Case Study in the Nyalam County
Authors: Qu Ge , Dai Xiaoai , Cheng Junying , Li Weile , Wang Meilian , Liu Wenxin , Yang Zhichong , Shan Yunfeng , Ren Jiashun , Lu Heng , Wang Youlin , Zeng Binyang , Atasoy Murat
Published: September 2022

Glacial lakes are important freshwater resources in southern Tibet. However, glacial lake outburst floods have significantly jeopardized the safety of local residents. To better understand the changes in glacial lakes in response to climate change, it is necessary to conduct a long-term evaluation on the areal dynamics of glacial lakes, assisted with local observations. Here, we propose an innovative method of classification and stacking extraction to accurately delineate glacial lakes in southwestern Tibet from 1990 to 2020. Based on Landsat images and meteorological data, we used geographic detectors to detect correlation factors. Multiple regression models were used to analyze the driving factors of the changes in glacier lake area. We combined bathymetric data of the glacial lakes with the changes in climatic variables and utilized HEC-RAS to determine critical circumstances for glacial lake outbursts. The results show that the area of glacial lakes in Nyalam County increased from 27.95 km2 in 1990 to 52.85 km2 in 2020, and eight more glacial lakes were observed in the study area. The glacial lake area expanded by 89.09%, where we found significant growth from 2015 to 2020. The correlation analysis between the glacial lake area and climate change throughout the period shows that temperature and precipitation dominate the expansion of these lakes from 1990 to 2020. We also discover that the progressive increase in water volume of glacial lakes can be attributed to the constant rise in temperature and freeze–thaw of surrounding glaciers. Finally, the critical conditions for the glacial lake’s outburst were predicted by using HEC-RAS combined with the changes in the water volume and climatic factors. It is concluded that GangxiCo endures a maximum water flow of 4.3 × 108 m3, and the glacial lake is in a stable changing stage. This conclusion is consistent with the field investigation and can inform the prediction of glacial lake outbursts in southwestern Tibet in the future.
An Efficient Information-Reinforced Lidar Deep Completion Network without RGB Guided
Authors: Wei Ming , Zhu Ming , Zhang Yaoyuan , Sun Jiaqi , Wang Jiarong
Published: September 2022

Due to the sparsity of point clouds obtained by LIDAR, the depth information is usually not complete and dense. The depth completion task is to recover dense depth information from sparse depth information. However, most of the current deep completion networks use RGB images as guidance, which are more like a processing method of information fusion. They are not valid when there is only sparse depth data and no other color information. Therefore, this paper proposes an information-reinforced completion network for a single sparse depth input. We use a multi-resolution dense progressive fusion structure to maximize the multi-scale information and optimize the global situation by point folding. At the same time, we re-aggregate the confidence and impose another depth constraint on the pixel depth to make the depth estimation closer to the ground trues. Our experimental results on KITTI and NYU Depth v2 datasets show that the proposed network achieves better results than other unguided deep completion methods. And it is excellent in both accuracy and real-time performance.
Sparsity-Based Two-Dimensional DOA Estimation for Co-Prime Planar Array via Enhanced Matrix Completion
Authors: Liu Donghe , Zhao Yongbo , Zhang Tingxiao
Published: September 2022

In this paper, the two-dimensional (2-D) direction-of-arrival (DOA) estimation problem is explored for the sum-difference co-array (SDCA) generated by the virtual aperture expansion of co-prime planar arrays (CPPA). Since the SDCA has holes, this usually causes the maximum virtual aperture of CPPA to be unavailable. To address this issue, we propose a complex-valued, sparse matrix recovery-based 2-D DOA estimation algorithm for CPPA via enhanced matrix completion. First, we extract the difference co-arrays (DCA) from SDCA and construct the co-array interpolation model via nuclear norm minimization to initialize the virtual uniform rectangular array (URA) that does not contain the entire rows and columns of holes. Then, we utilize the shift-invariance structure of the virtual URA to construct the enhanced matrix with a two-fold Hankel structure to fill the remaining empty elements. More importantly, we apply the alternating direction method of the multipliers (ADMM) framework to solve the enhanced matrix completion model. To reduce the computational complexity of the traditional vector-form, sparse recovery algorithm caused by the Kronecker product operation between dictionary matrices, we derive a complex-valued sparse matrix-recovery model based on the fast iterative shrinkage-thresholding (FISTA) method. Finally, simulation results demonstrate the effectiveness of the proposed algorithm.
Automated Small River Mapping (ASRM) for the Qinghai-Tibet Plateau Based on Sentinel-2 Satellite Imagery and MERIT DEM
Authors: Liang Xiangan , Mao Wei , Yang Kang , Ji Luyan
Published: September 2022

The dynamic variation in the water surfaces of the river networks within the Qinghai-Tibet Plateau affects the water resource availability for downstream ecosystems and human activities. Small rivers (with a river width less than 30 m) are an important component of this network, but are difficult to map in the Qinghai-Tibet Plateau. Firstly, the width of most rivers is very narrow, at around 20 m, which appears as only one or two pixels in Sentinel-2 images and thus is susceptible to salt-and-pepper noise. Secondly, local mountain shadows, cloud shadows, and snow pixels have spectral characteristics similar to those of rivers, leading to misclassification. Therefore, we propose an automated small river mapping (ASRM) method based on Sentinel-2 imagery to address these two difficulties. A preprocessing procedure was designed to remove the salt-and-pepper noise and enhance the linear characteristic of rivers with specific widths. A flexible digital elevation model (DEM)-based post-processing was then imposed to remove the misclassifications caused by mountain shadows, cloud shadows, and snow pixels. The ASRM results achieved an overall accuracy of 87.5%, outperforming five preexisting remote sensing-derived river network products. The proposed ASRM method has shown great potential for small river mapping in the entire Qinghai-Tibet Plateau.
Controls on Alpine Lake Dynamics, Tien Shan, Central Asia
Authors: Zhang Qifei , Chen Yaning , Li Zhi , Fang Gonghuan , Xiang Yanyun , Li Yupeng
Published: September 2022

The number and area of alpine lakes in Tien Shan (TS) are rapidly growing in response to a warming climate and retreating glaciers. This paper presents a comparative analysis of lake classification and changes by dividing alpine lakes (within a 10 km buffer of the glacier margins) into four types (supraglacial lakes, proglacial lakes, extraglacial lakes and non-glacial lakes), and subsequently determining the driving forces of change across the TS region from 1990 to 2015. The analysis utilized multiple satellite images and climatic data from gridded data sets and meteorological station observations. The results indicate that the total number and area of glacial lakes continuously increased during the study period, whereas non-glacial lakes intermittently expanded. Specifically, the total number and area of all glacial lakes (supraglacial lakes, proglacial lakes and extraglacial lakes) increased by 45.45% and 27.08%, respectively. Non-glacial lakes, in contrast, increased in quantity and area by 23.92% and 19.01%, respectively. Alpine lakes are closer to glaciers at high altitudes; in fact, some (e.g., proglacial lakes) are connected to glacier termini, and these show the highest expansion speed during the study period. The area of proglacial lakes expanded by 60.32%. Extraglacial lakes expanded by 21.06%. Supraglacial lakes, in marked contrast to the other types, decreased in area by 3.74%. Widespread rises in temperature and glacier wastage were the primary cause of the steady expansion of glacial lakes, particularly those linked to small- and medium-sized glaciers distributed in the Eastern TS where glacial lakes have rapidly increased. Both proglacial and extraglacial lakes expanded by 6.47%/a and 2%/a, respectively, from 1990 to 2015. While these proglacial and extraglacial lakes are located in largely glacierized areas, lakes in the Central TS exhibited the slowest expansion, increasing in area by 1.44%/a and 0.74%/a, respectively. Alterations in non-glacial lake areas were driven by changes in precipitation and varied spatially over the region. This study has substantial implications for the state of water resources under the complex regional changes in climate in the TS and can be used to develop useful water-resource management and planning strategies throughout Central Asia.
A Low-Cost Multi-Purpose IoT Sensor for Biologging and Soundscape Activities
Authors: Vasconcelos Dinarte , Nunes Nuno Jardim
Published: September 2022

The rapid expansion in miniaturization, usability, energy efficiency, and affordability of Internet of Things (IoT) sensors, integrated with innovations in smart capability, is greatly increasing opportunities in ground-level monitoring of ecosystems at a specific scale using sensor grids. Surrounding sound is a powerful data source for investigating urban and non-urban ecosystem health, and researchers commonly use robust but expensive passive sensors as monitoring equipment to capture it. This paper comprehensively describes the hardware behind our low-cost, small multipurpose prototype, capable of monitoring different environments (e.g., remote locations) with onboard processing power. The device consists of a printed circuit board, microprocessor, local memory, environmental sensor, microphones, optical sensors and LoRa (Long Range) communication systems. The device was successfully used in different use cases, from monitoring mosquitoes enhanced with optical sensors to ocean activities using a hydrophone.
Optimum Energy Management for Air Conditioners in IoT-Enabled Smart Home
Authors: Philip Ashleigh , Islam Shama Naz , Phillips Nicholas , Anwar Adnan
Published: September 2022

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.