bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Study on Hot Metal Flow Behaviour of Virgin and Rejuvenated Heat Treatment Creep Exhausted P91 Steel
Authors: Maube Shem , Obiko Japheth , Van der Merwe Josias , Mwema Fredrick , Klenam Desmond , Bodunrin Michael
Published: March 2023

This article reports on the comparative study of the hot deformation behaviour of virgin (steel A) and rejuvenated heat treatment creep-exhausted (steel B) P91 steels. Hot uniaxial compression tests were conducted on the two steels at a deformation temperature range of 900–1050 °C and a strain rate range of 0.01–10 s−1 to a total strain of 0.6 using Gleeble® 3500 equipment. The results showed that the flow stress largely depends on the deformation conditions. The flow stress for the two steels increased with an increase in strain rate at a given deformation temperature and vice versa. The flow stress–strain curves exhibited dynamic recovery as the softening mechanism. The material constants determined using Arrhenius constitutive equations were: the stress exponent, which was 5.76 for steel A and 6.67 for steel B; and the apparent activation energy, which was: 473.1 kJ mol−1 for steel A and 564.5 kJmol−1 for steel B. From these results, steel A exhibited better workability than steel B. Statistical parameters analyses showed that the flow stress for the two steels had a good correlation between the experimental and predicted data. Pearson’s correlation coefficient (R) was 0.97 for steel A and 0.98 for steel B. The average absolute relative error (AARE) values were 7.62% for steel A and 6.54% for steel B. This study shows that the Arrhenius equations can effectively describe the flow stress behaviour of P91 steel, and this method is applicable for industrial metalworking process.
Force Analysis of Circular Diaphragm Wall Based on Circular Cylindrical Shell Theory
Authors: Wang Lin , Shao Guojian
Published: March 2023

In order to make up for the shortcomings of existing theory calculation methods for circular diaphragm walls, an alternative calculation method was developed with a clear concept based on the circular cylindrical shell theory and superposition principle in elasticity and named the circular cylindrical shell theory calculation method (CCSTCM). We took the north anchorage circular foundation pit of G3 Tongling Yangtze River Highway and Railway Bridge as an example and calculated and analyzed it by the proposed CCSTCM, finite element numerical simulation method (FENSM) and site monitoring. As a result, we obtained the radial displacement, circumferential stress and vertical bending moment of the circular diaphragm wall, and then summarized their regularities. By researching the results of the CCSTCM (TCR), the results of the FENSM (NSR) and the results of the site monitoring (SMR), the following conclusions could be drawn: the numerical calculation model established was reasonable; the variation trends of the data curves of the TCR were highly similar to those of the NSR and SMR; and the TCR were slightly larger than the NSR but slightly smaller than the SMR, and closer to the SMR in general. Finally, the proposed CCSTCM was proven to be correct and applicable and could be used in similar circular diaphragm wall projects.
Numerical Research on the Effects of Process Parameters on Microdroplet Jetting Characteristics by Piezoelectric Printhead
Authors: Liu Hong , Lei Ting , Nan Xiaohui , Peng Fan
Published: March 2023

The precision and consistency of the microdroplet jetting procedure are crucial for the casting sand mold’s performance during binder injection. The generation and jetting of microdroplets in piezoelectric printheads were examined in this study in relation to changes in specific jetting process parameters. Using finite element analysis and a simplified physical model of a microdroplet jetting device, an electromechanically coupled model of a microdroplet jetting device was created in order to study the characteristics of microdroplet jetting. A volume-of-fluid model was also created in order to study the microdroplet jetting process and perform repeatability tests. The effects of altering nozzle radius, actuation pulse width, intake velocity, and fluid viscosity on microdroplet jetting properties were then investigated using the models. We were able to control the development of satellite droplets thanks to the knowledge we gained about how each process parameter affected droplet status. This study demonstrates how the radius of the nozzle and the pulse width of the piezoelectric actuation signal have a significant impact on the jetting properties of piezoelectric printheads and the production of microdroplets. The quantitative correlations between process factors and jetting characteristics can be used to optimize microdroplet production and reduce droplet size. Finally, this study will help create control systems for microdroplet jetting operations and enhance the precision of 3D printed casting sand molds.
Artificial Neural Networks for Navigation Systems: A Review of Recent Research
Authors: Jwo Dah-Jing , Biswal Amita , Mir Ilayat Ali
Published: March 2023

Several machine learning (ML) methodologies are gaining popularity as artificial intelligence (AI) becomes increasingly prevalent. An artificial neural network (ANN) may be used as a “black-box” modeling strategy without the need for a detailed system physical model. It is more reasonable to solely use the input and output data to explain the system’s actions. ANNs have been extensively researched, as artificial intelligence has progressed to enhance navigation performance. In some circumstances, the Global Navigation Satellite System (GNSS) can offer consistent and dependable navigational options. A key advancement in contemporary navigation is the fusion of the GNSS and inertial navigation system (INS). Numerous strategies have been put out recently to increase the accuracy for jamming, GNSS-prohibited environments, the integration of GNSS/INS or other technologies by means of a Kalman filter as well as to solve the signal blockage issue in metropolitan areas. A neural-network-based fusion approach is suggested to address GNSS outages. The overview, inquiry, observation, and performance evaluation of the present integrated navigation systems are the primary objectives of the review. The important findings in ANN research for use in navigation systems are reviewed. Reviews of numerous studies that have been conducted to investigate, simulate, and integrate navigation systems in order to produce accurate and dependable navigation solutions are offered.
Accuracy Analysis of Real-Time Precise Point Positioning—Estimated Precipitable Water Vapor under Different Meteorological Conditions: A Case Study in Hong Kong
Authors: Xu Ying , Ma Lin , Zhang Fangzhao , Chen Xin , Yang Zaozao
Published: March 2023

Precipitable water vapor (PWV) monitoring with real-time precise point positioning (PPP) is required for the improved early detection of increasingly common extreme weather occurrences. This study takes Hong Kong as the research object. The aim is to explore the accuracy of real-time global navigation satellite system (GNSS) PPP in estimating PWV at low latitudes and under different weather conditions. In this paper, real-time PPP is realized by using observation data from continuously operating reference stations (CORS) in Hong Kong and real-time products from the Centre National d’Etudes Spatiales (CNES). The Tm model calculated using numerical weather prediction (NWP) data converts the zenith tropospheric delay (ZTD) of real-time PPP inversion into PWV and evaluates its accuracy using postprocessing products. The experimental results show that compared with GPS, multi-GNSS can reduce the convergence time of PPP by 29.20% during rainfall periods and by 12.06% during nonrainfall periods. The improvement in positioning accuracy is not obvious, and the positioning accuracy of the two is equivalent. Real-time PPP ZTD experiments show that there are lower average values for bias, standard deviation (STDEV), and root mean square (RMS) during nonrainfall periods than during rainfall periods. Real-time PPP PWV experiments show that there are also lower bias, STDEV, and RMS values during nonrainfall periods than during rainfall periods. The comparative study between rainfall and nonrainfall periods is of great significance for the real-time monitoring and forecasting of water vapor changes.
Coupling Coordination Degree of AOD and Air Pollutants in Shandong Province from 2015 to 2020
Authors: Wang Ping , Tang Qingxin , Zhu Yuxin , He Yaqian , Yu Quanzhou , Liang Tianquan , Ran Yuying
Published: March 2023

In order to reveal the correlation between aerosols and pollution indicators, the MODIS aerosol optical depth (AOD) was used to investigate the distribution of AOD in 16 prefecture-level cities in Shandong Province from 2015 to 2020. This study quantitatively analyzed the coupling degree and the coupling coordination degree between AOD and pollution indicators based on the coupling coordination model. The results showed that: (1) The annual average AOD in Shandong Province showed a rapid downward trend with a mean value of 0.615. The seasonal AOD of Shandong Province and prefecture-level cities was characterized by spring and summer > autumn and winter. The distribution of AOD in Shandong Province showed a spatial pattern of high in the west and low in the east, and high in the surrounding area and low in the middle. The decreasing rate of AOD was high in the west and low in the east. (2) The annual average AOD and Air Quality Index (AQI) were in a highly coupled and coordinated state. Their spatial distribution pattern decreased from west to east. There were certain fluctuations with seasonal changes, with the largest fluctuation in winter. (3) Except for O3, the overall coupling and coordination level between AOD and each pollutant was relatively high. The coupling coordination effect was as follows: C (PM2.5, AOD) and C (PM10, AOD) > C (NO2, AOD) > C (SO2, AOD), and C (CO, AOD) > C (O3, AOD). Except for the O3, its distribution was characterized by highs in the west and lows in the east. The degree of coupling between each pollution indicator and the seasonal average AOD was high. The study showed that there was a high degree of coupling and coordination between pollutant concentration indicators and AOD, and remote sensing AOD data can be used as an effective supplement to regional pollutant monitoring indicators.
Environmental Analysis, Monitoring, and Process Control Strategy for Reduction of Greenhouse Gaseous Emissions in Thermochemical Reactions
Authors: Aboughaly Mohamed , Fattah I. M. Rizwanul
Published: March 2023

This review paper illustrates the recommended monitoring technologies for the detection of various greenhouse gaseous emissions for solid waste thermochemical reactions, including incineration, pyrolysis, and gasification. The illustrated gas analyzers are based on the absorption principle, which continuously measures the physicochemical properties of gaseous mixtures, including oxygen, carbon dioxide, carbon monoxide, hydrogen, and methane, during thermochemical reactions. This paper illustrates the recommended gas analyzers and process control tools for different thermochemical reactions and aims to recommend equipment to increase the sensitivity, linearity, and dynamics of various thermochemical reactions. The equipment achieves new levels of on-location, real-time analytical capability using FTIR analysis. The environmental assessment study includes inventory analysis, impact analysis, and sensitivity analysis to compare the mentioned solid waste chemical recycling methods in terms of greenhouse gaseous emissions, thermal efficiency, electrical efficiency, and sensitivity analysis. The environmental impact assessment compares each technology in terms of greenhouse gaseous emissions, including CO2, NOx, NH3, N2O, CO, CH4, heat, and electricity generation. The conducted environmental assessment compares the mentioned technologies through 15 different emission-related impact categories, including climate change impact, ecosystem quality, and resource depletion. The continuously monitored process streams assure the online monitoring of gaseous products of thermochemical processes that enhance the quality of the end products and reduce undesired products, such as tar and char. This state-of-the-art monitoring and process control framework provides recommended analytical equipment and monitoring tools for different thermochemical reactions to optimize process parameters and reduce greenhouse gaseous emissions and undesired products.
Examining the Impact of Dimethyl Sulfide Emissions on Atmospheric Sulfate over the Continental U.S.
Authors: Sarwar Golam , Kang Daiwen , Henderson Barron H. , Hogrefe Christian , Appel Wyat , Mathur Rohit
Published: March 2023

We examined the impact of dimethylsulfide (DMS) emissions on sulfate concentrations over the continental U.S. by using the Community Multiscale Air Quality (CMAQ) model version 5.4 and performing annual simulations without and with DMS emissions for 2018. DMS emissions enhance sulfate not only over seawater but also over land, although to a lesser extent. On an annual basis, the inclusion of DMS emissions increase sulfate concentrations by 36% over seawater and 9% over land. The largest impacts over land occur in California, Oregon, Washington, and Florida, where the annual mean sulfate concentrations increase by ~25%. The increase in sulfate causes a decrease in nitrate concentration due to limited ammonia concentration, especially over seawater, and an increase in ammonium concentration with a net effect of increased inorganic particles. The largest sulfate enhancement occurs near the surface (over seawater), and the enhancement decreases with altitude, diminishing to 10–20% at an altitude of ~5 km. Seasonally, the largest enhancement in sulfate over seawater occurs in summer, and the lowest in winter. In contrast, the largest enhancements over land occur in spring and fall due to higher wind speeds that can transport more sulfate from seawater into land.
Size-Segregated Characteristics of Organic Carbon (OC) and Elemental Carbon (EC) in Marine Aerosol in the Northeastern South China Sea
Authors: Liu Fengxian , Peng Long , Dai Shouhui , Bi Xinhui , Shi Meixian
Published: March 2023

Organic carbon (OC) and elemental carbon (EC) size-segregated characteristics were analyzed at the junction of sea and land (JSL) and the marine aerosol during the navigation along the northeastern South China Sea (NSCS), including the shallow ocean (NSCS-SO) and the remote ocean (NSCS-RO), from 3 to 20 March 2016. More than 90% of the OC and EC were concentrated in fine particles, and the OC and EC mean concentrations were 10.1 ± 0.63 and 3.44 ± 0.82, 2.67 ± 1.27 and 0.72 ± 0.36, and 1.41 ± 0.50 and 0.40 ± 0.28 μg m−3 in PM3.0 at the JSL, NSCS-SO, and NSCS-RO, respectively. Approximately 75–83% of OC and 84–98% of EC were found in PM1.5. Three sampling sites showed similar OC mass size distributions that had a dominant peak in the 0.49–0.95 μm size range. The mass mean diameters (MMDs) of OC were 0.65 ± 0.15 and 0.59 ± 0.16 μm at the NSCS-SO and NSCS-RO, respectively, followed by 0.53 ± 0.25 μm in fine particles at the JSL. Similar characteristics were found for EC MMDs. Each particle-size bin had OC concentrations that were higher than the EC values of all three sites, and the OC/EC mass ratios were generally more than 2.0. The mean secondary organic carbon (SOC) concentrations in PM1.5 were as follows: JSL (5.42 ± 1.35 μg m−3) > NSCS-SO (1.08 ± 1.02 μg m−3) > NSCS-RO (0.38 ± 0.25 μg m−3), indicating that the contribution of secondary carbonaceous aerosols to organic carbon is relatively low in the remote ocean region.
Soil Water Content at Planting Affects Determining Agricultural Drought for Rainfed Spring Wheat
Authors: Zhang Kai , Zhao Funian , Zhang Bo
Published: March 2023

Agricultural drought often refers to water deficit in soil caused by a shortage of precipitation during a specific period in crop growing season, thus leading to final crop production failure. However, soil water content during planting may be related to the final yield of crop. Therefore, the soil water content at planting could have a great impact on determining whether agricultural drought occurs during crop growth and development. In the current study, we used soil water content at planting at 0–50 cm depth, the spring wheat (Triticum aestivum L.) yield from 1987 to 2011, and precipitation from 1971 to 2011 at Dingxi, Gansu Province, China, intending to quantify the influence of soil water content at planting on determining agricultural drought for rainfed spring wheat. The response of spring wheat yield to growing season precipitation comprised two different linear relationships defined by whether the soil water content at planting was greater or less than 100 mm. With the identical amount of soil water content at planting and precipitation during main growth period, a higher soil water content at planting resulted in a greater spring wheat yield. Soil water content at planting was more effective than growing season precipitation for increasing spring wheat yield. According to the probability distributions of soil water content during planting and growing season precipitation, we concluded that 28.6% of the years studued involved agricultural drought for rainfed spring wheat in the Dingxi of Gansu province, China. This analysis, which quantified the relative importance of soil water content at planting (29.97 kg ha−1 per mm) and during growing season precipitation (12.21 kg ha−1 per mm) to determine yield and agricultural drought occurrence for spring wheat, suggests that improving the amount of soil water storage before crop planting is an important way to deal with agricultural drought.
An Energy-Based Big Data Framework to Estimate the Young’s Moduli of the Soils Drilled during the Execution of Continuous Flight Auger Piles
Authors: Ozelim Luan Carlos de Sena Monteiro , Ferrari de Campos Darym Júnior , Cavalcante André Luís Brasil , Camapum de Carvalho Jose
Published: March 2023

Understanding the soil mass and how it behaves is determinant to the quality and reliability of a foundation design. Normally, such behavior is predicted based on laboratory and in situ tests. In the big data era, instead of executing more tests, engineers should understand how to take advantage of ordinary execution procedures to obtain the parameters of interest. Sensors are key components in engineering big data frameworks, as they provide a large number of valuable measured data. In particular, the building process (excavation and concreting) of continuous flight auger piles (CFAPs) can be fully monitored by collecting data from sensors in the drilling machine. This makes this type of pile an ideal candidate to utilize a big data methodology to indirectly obtain some constitutive parameters of the soil being drilled. Thus, in the present paper, the drilling process of CFAPs is modeled by a new physical model which predicts the energy spending during the execution of this type of pile. This new model relies on other fundamental properties of the soils drilled, such as unit weight, cohesion and internal friction angle. In order to show the applicability of the big data methodological framework hereby developed, a case study was conducted. A work site in Brasília-DF, Brazil, was studied and the execution of three CFAPs was monitored. Soil surveys were carried out to identify the soil strata in the site and, therefore, to validate the estimates of Young’s moduli provided by the new formulas. The 95% confidence intervals of Young’s moduli obtained for silty clay, clayey silt and silt were, in MPa, [14.56, 19.11], [12.26, 16.88] and [19.65, 26.11], respectively. These intervals are consistent with literature reports for the following materials: stiff to very stiff clays with low-medium plasticity, medium silts with slight plasticity, and stiff to very stiff silts with low plasticity, respectively. These were the types of materials observed during the site surveys; therefore, the results obtained are consistent with literature reports as well as with field surveys. This new framework may be useful to provide real-time estimates of the drilled soil’s parameters, as well as updating CFAPs designs during their execution. This way, sustainable designs can be achieved, where substrata materials are better characterized, avoiding over-designed structures.
Limit Cycles of Polynomially Integrable Piecewise Differential Systems
Authors: García Belén , Llibre Jaume , Pérez del Río Jesús S. , Pérez-González Set
Published: March 2023

In this paper, we study how many algebraic limit cycles have the discontinuous piecewise linear differential systems separated by a straight line, with polynomial first integrals on both sides. We assume that at least one of the systems is Hamiltonian. Under this assumption, piecewise differential systems have no more than one limit cycle. This study characterizes linear differential systems with polynomial first integrals.
Obesity-Linked PPARγ Ser273 Phosphorylation Promotes Beneficial Effects on the Liver, despite Reduced Insulin Sensitivity in Mice
Authors: Terra Maiara Ferreira , García-Arévalo Marta , Avelino Thayná Mendonça , Degaki Karina Y. , de Carvalho Murilo , Torres Felipe Rafael , Saito Angela , Figueira Ana Carolina Migliorini
Published: March 2023

Since the removal of thiazolidinediones (TZDs) from the market, researchers have been exploring alternative anti-diabetic drugs that target PPARγ without causing adverse effects while promoting insulin sensitization by blocking serine 273 phosphorylation (Ser273 or S273). Nonetheless, the underlying mechanisms of the relationship between insulin resistance and S273 phosphorylation are still largely unknown, except for the involvement of growth differentiation factor (GDF3) regulation in the process. To further investigate potential pathways, we generated a whole organism knockin mouse line with a single S273A mutation (KI) that blocks the occurrence of its phosphorylation. Our observations of KI mice on different diets and feeding schedules revealed that they were hyperglycemic, hypoinsulinemic, presented more body fat at weaning, and presented an altered plasma and hepatic lipid profile, distinctive liver morphology and gene expression. These results suggest that total blockage of S273 phosphorylation may have unforeseen effects that, in addition to promoting insulin sensitivity, could lead to metabolic disturbances, particularly in the liver. Therefore, our findings demonstrate both the beneficial and detrimental effects of PPAR S273 phosphorylation and suggest selective modulation of this post translational modification is a viable strategy to treat type 2 diabetes.
Novel Biomarkers of Diabetic Kidney Disease
Authors: Rico-Fontalvo Jorge , Aroca-Martínez Gustavo , Daza-Arnedo Rodrigo , Cabrales José , Rodríguez-Yanez Tomás , Cardona-Blanco María , Montejo-Hernández Juan , Rodelo Barrios Dairo , Patiño-Patiño Jhonny , Osorio Rodríguez Elber
Published: March 2023

Diabetic kidney disease (DKD) is a highly prevalent condition worldwide. It represents one of the most common complications arising from diabetes mellitus (DM) and is the leading cause of end-stage kidney disease (ESKD). Its development involves three fundamental components: the hemodynamic, metabolic, and inflammatory axes. Clinically, persistent albuminuria in association with a progressive decline in glomerular filtration rate (GFR) defines this disease. However, as these alterations are not specific to DKD, there is a need to discuss novel biomarkers arising from its pathogenesis which may aid in the diagnosis, follow-up, therapeutic response, and prognosis of the disease.
Bioimpedance-Measurement-Based Non-Invasive Method for In Ovo Chicken Egg Sexing
Authors: Ching Congo Tak Shing , Wang Chien-Kai , Tang Pin-Chi , Ha Minh-Khue , Li Chin , Chiu Hsuan-Ni , Yao Fiona Yan-Dong , Nhan Nguyen Chi , Hieu Nguyen Van , Phan Thien-Luan
Published: March 2023

Day-old male chick culling is one of the world’s most inhumane problems in the poultry industry. Every year, seven billion male chicks are slaughtered in laying-hen hatcheries due to their higher feed exchange rate, lower management than female chicks, and higher production costs. This study describes a novel non-invasive method for determining the gender of chicken eggs. During the incubation period of fourteen days, four electrodes were attached to each egg for data collection. On the last day of incubation, a standard polymerase chain reaction (PCR)-based chicken gender determination protocol was applied to the eggs to obtain the gender information. A relationship was built between the collected data and the egg’s gender, and it was discovered to have a reliable connection, indicating that the chicken egg gender can be determined by measuring the impedance data of the eggs on day 9 of incubation with the four electrodes set and using the self-normalization technique. This is a groundbreaking discovery, demonstrating that impedance spectroscopy can be used to sex chicken eggs before they hatch, relieving the poultry industry of such an ethical burden.
Computational Screening of Approved Drugs for Inhibition of the Antibiotic Resistance Gene mecA in Methicillin-Resistant Staphylococcus aureus (MRSA) Strains
Authors: Otarigho Benson , Falade Mofolusho O.
Published: March 2023

Antibiotic resistance is a critical problem that results in a high morbidity and mortality rate. The process of discovering new chemotherapy and antibiotics is challenging, expensive, and time-consuming, with only a few getting approved for clinical use. Therefore, screening already-approved drugs to combat pathogens such as bacteria that cause serious infections in humans and animals is highly encouraged. In this work, we aim to identify approved antibiotics that can inhibit the mecA antibiotic resistance gene found in methicillin-resistant Staphylococcus aureus (MRSA) strains. The MecA protein sequence was utilized to perform a BLAST search against a drug database containing 4302 approved drugs. The results revealed that 50 medications, including known antibiotics for other bacterial strains, targeted the mecA antibiotic resistance gene. In addition, a structural similarity approach was employed to identify existing antibiotics for S. aureus, followed by molecular docking. The results of the docking experiment indicated that six drugs had a high binding affinity to the mecA antibiotic resistance gene. Furthermore, using the structural similarity strategy, it was discovered that afamelanotide, an approved drug with unclear antibiotic activity, had a strong binding affinity to the MRSA-MecA protein. These findings suggest that certain already-approved drugs have potential in chemotherapy against drug-resistant pathogenic bacteria, such as MRSA.
Letter Migrations between Words in Reading Aloud Can Result either from an Impairment in Orthographic Input or in Phonological Output
Authors: Toledano Liora , Friedmann Naama
Published: March 2023

Letter migrations between words in reading aloud (e.g., reading “cane love” as “lane love” or “lane cove”) are known to result from a deficit in the visual-orthographic analysis and characterize attentional dyslexia. In spontaneous speech, individuals with impairment in the phonological output buffer may show migrations of phonemes between words. The purpose of this study was to examine whether migrations between words in reading aloud can also result from a deficit in the phonological output buffer, to explore the characteristics of migrations resulting from orthographic input and from phonological output deficits, and to examine methods to distinguish these two sources. Using tasks of reading aloud of lists of 92–182 word pairs, we identified 18 adults and adolescents with developmental dyslexia who made between-word letter migrations in reading aloud, significantly more than age-matched controls (372 adults, 26 7th-graders and 44 4th–5th-graders). To distinguish between the orthographic input and phonological output sources for these migrations, we administered a test assessing orthographic input without spoken output (written semantic decision on 140 migratable word pairs) and a repetition test of 36 auditorily presented migratable word pairs, assessing spoken output without orthographic input (as well as nonword repetition and 3 span tests). These tests indicated that the migrations in reading aloud of 10 of the participants with dyslexia resulted from an orthographic input deficit—they made migrations not only in reading aloud but also in written word pair comprehension, but not in word pair repetition. For the other 8 participants, the migrations resulted from a phonological output deficit: they made migrations in reading aloud and in word pair repetition, but not in comprehension, and had limited spans and made errors in nonword repetition. We identified several differences between the two types of between-word errors: first, the individuals with attentional dyslexia made omissions of a letter that appeared in the same position in the two words, but the phonological output buffer group did not make such omissions. Second, the groups differed in the origin of migration: orthographic input migrations involve letters that are orthographically adjacent, whereas phonological output migrations involve phonologically adjacent phonemes: phonemes that have just been spoken or that are prepared together in the phonological buffer for production. Migrations from the line below and from 2 lines above the target occurred only in the orthographic input group. This study thus indicates that between-word migrations in reading aloud can result not only from attentional dyslexia, but also from a phonological output buffer deficit, and offers ways to distinguish between the two.
The Performance of Different Machine Learning Algorithm and Regression Models in Predicting High-Grade Intracranial Meningioma
Authors: Teng Haibo , Yang Xiang , Liu Zhiyong , Liu Hao , Yan Ouying , Jie Danyang , Li Xueying , Xu Jianguo
Published: March 2023

Meningioma is the most common primary tumor of the central nervous system (CNS). Individualized treatment strategies should be formulated for the patients according to the WHO (World Health Organization) grade. Our aim was to investigate the effectiveness of various machine learning and traditional statistical models in predicting the WHO grade of preoperative patients with meningioma. Patients diagnosed with meningioma after surgery in West China Hospital and Shangjin Hospital of Sichuan University from 2009 to 2016 were included in the study cohort. As the training cohort (n = 1975), independent risk factors associated with high-grade meningioma were used to establish the Nomogram model. which was validated in a subsequent cohort (n = 1048) from 2017 to 2019 in our hospital. Logistic regression (LR), XGboost, Adaboost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) models were determined using F1 score, recall, accuracy, the area under the curve (ROC), calibration plot and decision curve analysis (DCA) were used to evaluate the different models. Logistic regression showed better predictive performance and interpretability than machine learning. Gender, recurrence history, T1 signal intensity, enhanced signal degree, peritumoral edema, tumor diameter, cystic, location, and NLR index were identified as independent risk factors and added to the nomogram. The AUC (Area Under Curve) value of RF was 0.812 in the training set, 0.807 in the internal validation set, and 0.842 in the external validation set. The calibration curve and DCA (Decision Curve Analysis) indicated that it had better prediction efficiency of LR than others. The Nomogram preoperative prediction model of meningioma of WHO II and III grades showed effective prediction ability. While machine learning exhibits strong fitting ability, it performs poorly in the validation set.
The Use of Diffusion Kurtosis Imaging for the Differential Diagnosis of Alzheimer’s Disease Spectrum
Authors: Zhang Huiqin , Wang Zuojun , Chan Koon-Ho , Shea Yat-Fung , Lee Chi-Yan , Chiu Patrick Ka-Chun , Cao Peng , Mak Henry Ka-Fung
Published: March 2023

Structural and diffusion kurtosis imaging (DKI) can be used to assess hippocampal macrostructural and microstructural alterations respectively, in Alzheimer’s disease (AD) spectrum, spanning from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and AD. In this study, we explored the diagnostic performance of structural imaging and DKI of the hippocampus in the AD spectrum. Eleven SCD, thirty-seven MCI, sixteen AD, and nineteen age- and sex-matched normal controls (NCs) were included. Bilateral hippocampal volume, mean diffusivity (MD), and mean kurtosis (MK) were obtained. We detected that in AD vs. NCs, the right hippocampal volume showed the most prominent AUC value (AUC = 0.977); in MCI vs. NCs, the right hippocampal MD was the most sensitive discriminator (AUC = 0.819); in SCD vs. NCs, the left hippocampal MK was the most sensitive biomarker (AUC = 0.775). These findings suggest that, in the predementia stage (SCD and MCI), hippocampal microstructural changes are predominant, and the best discriminators are microstructural measurements (left hippocampal MK for SCD and right hippocampal MD for MCI); while in the dementia stage (AD), hippocampal macrostructural alterations are superior, and the best indicator is the macrostructural index (right hippocampal volume).
Teachers’ Emotional Intelligence, Burnout, Work Engagement, and Self-Efficacy during COVID-19 Lockdown
Authors: Geraci Alessandro , Di Domenico Laura , Inguglia Cristiano , D’Amico Antonella
Published: March 2023

Teachers’ psychological well-being is a crucial aspect that influences learning in a classroom climate. The aim of the study was to investigate teachers’ emotional intelligence, burnout, work engagement, and self-efficacy in times of remote teaching during COVID-19 lockdown. A sample of 65 teachers (Mage = 50.49), from early childhood through lower secondary education, were recruited during a period of school closure to answer self-report questionnaires and other measures assessing study variables. Results showed that during the COVID-19 pandemic, teachers reported higher levels of burnout and lower levels of self-esteem due to multiple challenges related to remote teaching and the growing sense of insecurity regarding health safety in the school environment. However, the negative effects of COVID-19 on teachers’ self-efficacy, work engagement, and burnout varied according to their own levels of emotional intelligence. These results demonstrate that emotional intelligence may support teachers in facing these challenges.
Effects of Environmental and Operational Conditions on Structural Health Monitoring and Non-Destructive Testing: A Systematic Review
Authors: Keshmiry Ayoub , Hassani Sahar , Mousavi Mohsen , Dackermann Ulrike
Published: March 2023

The development of Structural Health Monitoring (SHM) and Non-Destructive Testing (NDT) techniques has rapidly evolved and matured over the past few decades. Advances in sensor technology have facilitated deploying SHM systems for large-scale structures and local NDT of structural members. Although both methods have been successfully applied to identify structural damage in various systems, Environmental and Operational Condition (EOC) variations can influence sensor measurements and mask damage signatures in the structural response. EOCs include environmental conditions, such as temperature, humidity, and wind, as well as operational conditions, such as mass loading, vibration, and boundary conditions. The effect of EOCs can significantly undermine the reliability and robustness of damage assessment technologies and limit their performance. Thus, successful SHM and NDT systems can compensate for changing EOCs. This paper provides a state-of-the-art review of the effects of EOCs on SHM and NDT systems. It presents recent developments in advanced sensing technology, signal processing, and analysis techniques that aim to eliminate the masking effect of EOC variations and increase the damage sensitivity and performance of SHM and NDT systems. The paper concludes with current research challenges, trends, and recommendations for future research directions.
Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders
Authors: Kumar Prashant , Arora Harish Chandra , Bahrami Alireza , Kumar Aman , Kumar Krishna
Published: March 2023

The degradation of reinforced concrete (RC) structures has raised major concerns in the concrete industry. The demolition of existing structures has shown to be an unsustainable solution and leads to many financial concerns. Alternatively, the strengthening sector has put forward many sustainable solutions, such as the retrofitting and rehabilitation of existing structural elements with fiber-reinforced polymer (FRP) composites. Over the past four decades, FRP retrofits have attracted major attention from the scientific community, thanks to their numerous advantages such as having less weight, being non-corrodible, etc., that help enhance the axial, flexural, and shear capacities of RC members. This study focuses on predicting the compressive strength (CS) of FRP-confined concrete cylinders using analytical models and machine learning (ML) models. To achieve this, a total of 1151 specimens of cylinders have been amassed from comprehensive literature studies. The ML models utilized in the study are Gaussian process regression (GPR), support vector machine (SVM), artificial neural network (ANN), optimized SVM, and optimized GPR models. The input parameters that have been used for prediction include the geometrical characteristics of specimens, the mechanical properties of FRP composite, and the CS of concrete. The results of the five ML models are compared with nineteen analytical models. The results evaluated from the ML algorithms imply that the optimized GPR model has been found to be the best among all other models, demonstrating a higher correlation coefficient, root mean square error, mean absolute percentage error, mean absolute error, a-20 index, and Nash–Sutcliffe efficiency values of 0.9960, 3.88 MPa, 3.11%, 2.17 MPa, 0.9895, and 0.9921, respectively. The R-value of the optimized GPR model is 0.37%, 0.03%, 5.14%, and 2.31% higher than that of the ANN, GPR, SVM, and optimized SVM models, respectively, whereas the root mean square error value of the ANN, GPR, SVM, and optimized SVM models is, respectively, 81.04%, 12.5%, 471.77%, and 281.45% greater than that of the optimized GPR model.
Psycho-Educational and Rehabilitative Intervention to Manage Cancer Cachexia (PRICC) for Advanced Patients and Their Caregivers: Lessons Learned from a Single-Arm Feasibility Trial
Authors: Buonaccorso Loredana , Fugazzaro Stefania , Autelitano Cristina , Bertocchi Elisabetta , Accogli Monia Allisen , Denti Monica , Costi Stefania , Martucci Gianfranco , Braglia Luca , Bassi Maria Chiara , Tanzi Silvia
Published: March 2023

Background: Key elements in cancer cachexia (CC) management are personalized and multimodal interventions, but it is hard for some patients to follow programs based on several components. We examined the feasibility of a bimodal intervention, including a psycho-educational component and exercises, to support patients and their caregivers in managing CC; Methods: Prospective mixed-methods pilot study explored feasibility data, changes in patient-reported outcomes, and performance outcomes over time in a convenient sample of 30 consecutive CC patients and their caregivers. Results: Twenty-four dyads consented to participate. Twenty dyads received at least two psycho-educational sessions, so the psycho-educational component was feasible for 83.3% of the sample. Six dyads participated in at least fourteen out of twenty-seven rehabilitation sessions, so the exercise program was feasible for 25.0% of the sample. Six dyads showed compliance greater than 50% for both components of the bimodal intervention. Conclusions: While we did not meet our primary feasibility endpoint and had mixed acceptability, our experience provides insight into the challenges and lessons learned in implementing a primary palliative care intervention for CC. More robust studies are needed to help clinicians understand the best exercise program for CC patients, to be included in a multimodal intervention.
A Comparison between the Online Prognostic Tool PREDICT and myBeST for Women with Breast Cancer in Malaysia
Authors: Nik Ab Kadir Mohd Nasrullah , Mohd Hairon Suhaily , Ab Hadi Imi Sairi , Yusof Siti Norbayah , Muhamat Siti Maryam , Yaacob Najib Majdi
Published: March 2023

The PREDICT breast cancer is a well-known online calculator to estimate survival probability. We developed a new prognostic model, myBeST, due to the PREDICT tool’s limitations when applied to our patients. This study aims to compare the performance of the two models for women with breast cancer in Malaysia. A total of 532 stage I to III patient records who underwent surgical treatment were analysed. They were diagnosed between 2012 and 2016 in seven centres. We obtained baseline predictors and survival outcomes by reviewing patients’ medical records. We compare PREDICT and myBeST tools’ discriminant performance using receiver-operating characteristic (ROC) analysis. The five-year observed survival was 80.3% (95% CI: 77.0, 83.7). For this cohort, the median five-year survival probabilities estimated by PREDICT and myBeST were 85.8% and 82.6%, respectively. The area under the ROC curve for five-year survival by myBeST was 0.78 (95% CI: 0.73, 0.82) and for PREDICT was 0.75 (95% CI: 0.70, 0.80). Both tools show good performance, with myBeST marginally outperforms PREDICT discriminant performance. Thus, the new prognostic model is perhaps more suitable for women with breast cancer in Malaysia.
Increased SEC23A Expression Correlates with Poor Prognosis and Immune Infiltration in Stomach Adenocarcinoma
Authors: Zhaoran Su , Linnebacher Christina Susanne , Linnebacher Michael
Published: March 2023

Background: Previous studies have described that the SEC23A gene is involved in the occurrence and development of various tumor entities. However, little is known about its expression and relevance in stomach adenocarcinoma (STAD). The aim of this study was to bioinformatically analyze the role of SEC23A in STAD, followed by patient tissue sample analyses. Materials and methods: SEC23A expression levels in STAD and normal gastric tissues were analyzed in the Cancer Genome Atlas and Gene Expression Omnibus databases; results were verified in fresh clinical STAD specimens on both gene and protein expression levels. SEC23A expression correlated with survival parameters by Kaplan–Meier and multivariate Cox regression analyses. The top genes co-expressed with SEC23A were identified by gene set enrichment analysis (GSEA) using the clusterProfiler package in R. Furthermore, the R package (immunedeconv), integrating the CIBERSORT algorithm, was used to estimate immune cell infiltration levels in STAD. Results: SEC23A gene and sec23a protein expression were both significantly upregulated in STAD, and this correlated with the pT stage. Moreover, high SEC23A expression was associated with poor disease-free and overall survival of STAD patients. Cox analyses revealed that besides age and pathologic stage, SEC23A expression is an independent risk factor for STAD. GSEA indicated that SEC23A was positively associated with ECM-related pathways. In the CIBERSORT analysis, the level of SEC23A negatively correlated with various infiltrating immune cell subsets, including follicular helper T cells, Tregs, activated NK cells and myeloid dendritic cells. Finally, the expression levels of immune checkpoint-related genes, including HAVCR2 and PDCD1LG2, were significantly increased in the high SEC23A expression group. Conclusions: We observed the significantly upregulated expression of SEC23A in STAD, an association with disease progression, patients’ prognosis and infiltrating immune cell subsets. Thus, we propose SEC23A as an independent prognostic factor with a putative role in immune response regulation in STAD.
Identifying Key Regulators of Keratinization in Lung Squamous Cell Cancer Using Integrated TCGA Analysis
Authors: Heryanto Yusri Dwi , Imoto Seiya
Published: March 2023

Keratinization is one of lung squamous cell cancer’s (LUSC) hallmark histopathology features. Epithelial cells produce keratin to protect their integrity from external harmful substances. In addition to their roles as cell protectors, recent studies have shown that keratins have important roles in regulating either normal cell or tumor cell functions. The objective of this study is to identify the genes and microRNAs (miRNAs) that act as key regulators of the keratinization process in LUSC. To address this goal, we classified LUSC samples from GDC-TCGA databases based on their keratinization molecular signatures. Then, we performed differential analyses of genes, methylation, and miRNA expression between high keratinization and low keratinization samples. By reconstruction and analysis of the differentially expressed genes (DEGs) network, we found that TP63 and SOX2 were the hub genes that were highly connected to other genes and displayed significant correlations with several keratin genes. Methylation analysis showed that the P63, P73, and P53 DNA-binding motif sites were significantly enriched for differentially methylated probes. We identified SNAI2, GRHL3, TP63, ZNF750, and FOXE1 as the top transcription factors associated with these binding sites. Finally, we identified 12 miRNAs that influence the keratinization process by using miRNA–mRNA correlation analysis.
Safety and Feasibility of Laparoscopic Parenchymal-Sparing Hepatectomy for Lesions with Proximity to Major Vessels in Posterosuperior Liver Segments 7 and 8
Authors: Katagiri Hirokatsu , Nitta Hiroyuki , Kanno Syoji , Umemura Akira , Takeda Daiki , Ando Taro , Amano Satoshi , Sasaki Akira
Published: March 2023

Laparoscopic parenchymal-sparing hepatectomy (PSH) for lesions with proximity to major vessels (PMV) in posterosuperior segments (PSS) has not yet been sufficiently examined. The aim of this study is to examine the safety and feasibility of laparoscopic PSH for lesions with PMV in PSS 7 and 8. We retrospectively reviewed the outcomes of laparoscopic liver resection (LLR) and open liver resection (OLR) for PSS lesions and focused on patients who underwent laparoscopic PSH for lesions with PMV in PSS. Blood loss was lower in the LLR group (n = 110) than the OLR group (n = 16) (p = 0.009), and no other short-term outcomes were significantly different. Compared to the pure LLR group (n = 93), there were no positive surgical margins or complications in hand-assisted laparoscopic surgery (HALS) (n = 17), despite more tumors with PMV (p = 0.009). Regarding pure LLR for one tumor lesion, any short-term outcomes in addition to the operative time were not significantly different between the PMV (n = 23) and no-PMV (n = 48) groups. The present findings indicate that laparoscopic PSH for lesions with PMV in PSS is safe and feasible in a matured team, and the HALS technique still plays an important role.