bluuebunny/crossref_metadata_2025 huggingface dataset:
Bacterial Resistance to β-Lactam Antibiotics in Municipal Wastewater: Insights from a Full-Scale Treatment Plant in Poland
Authors: Jendrzejewska Natalia , Karwowska Ewa
Published: November 2022

This study investigated enzymatic and genetic determinants of bacterial resistance to β-lactam antibiotics in the biocenosis involved in the process of biological treatment of wastewater by activated sludge. The frequency of bacteria resistant to selected antibiotics and the activity of enzymes responsible for resistance to β-lactam antibiotics were estimated. The phenomenon of selection and spread of a number of genes determining antibiotic resistance was traced using PCR and gene sequencing. An increase in the percentage of bacteria showing resistance to β-lactam antibiotics in the microflora of wastewater during the treatment process was found. The highest number of resistant microorganisms, including multi-resistant strains, was recorded in the aeration chamber. Significant amounts of these bacteria were also present in treated wastewater, where the percentage of penicillin-resistant bacteria exceeded 50%, while those resistant to the new generation β-lactam antibiotics meropenem and imipenem were found at 8.8% and 6.4%, respectively. Antibiotic resistance was repeatedly accompanied by the activity of enzymes such as carbapenemases, metallo-β-lactamases, cephalosporinases and β-lactamases with an extended substrate spectrum. The activity of carbapenemases was shown in up to 97% of the multi-resistant bacteria. Studies using molecular biology techniques showed a high frequency of genes determining resistance to β-lactam antibiotics, especially the blaTEM1 gene. The analysis of the nucleotide sequences of blaTEM1 gene variants present in bacteria at different stages of wastewater treatment showed 50–100% mutual similarity of.
Next Generation Sequencing Approaches to Characterize the Respiratory Tract Virome
Authors: Sandybayev Nurlan , Beloussov Vyacheslav , Strochkov Vitaliy , Solomadin Maxim , Granica Joanna , Yegorov Sergey
Published: November 2022

The COVID-19 pandemic and heightened perception of the risk of emerging viral infections have boosted the efforts to better understand the virome or complete repertoire of viruses in health and disease, with a focus on infectious respiratory diseases. Next-generation sequencing (NGS) is widely used to study microorganisms, allowing the elucidation of bacteria and viruses inhabiting different body systems and identifying new pathogens. However, NGS studies suffer from a lack of standardization, in particular, due to various methodological approaches and no single format for processing the results. Here, we review the main methodological approaches and key stages for studies of the human virome, with an emphasis on virome changes during acute respiratory viral infection, with applications for clinical diagnostics and epidemiologic analyses.
Sintering Properties and Regression Analysis of Imported Iron Ore Powder
Authors: Hao Shuai , Luo Guoping , Chai Yifan , Lu Yuanyuan , An Shengli , Song Wei
Published: November 2022

In order to reduce the sintering allocation scheme and lower the cost of allocation, two semi-lignite ores with similar properties and large price differences were selected for blending to achieve the purpose of complementary advantages. The sintered ore specimens were studied by using FactSage7.1, an infrared sintering furnace, and regression analysis. The results show that OA and OB iron ore powders and their mixed iron ore powders have a good linear fitting effect with an inverse relationship between bond phase strength and SiO2 content; the bond phase strength has a positive relationship with liquid phase fluidity index; when mixing OA and OB iron ore powders in the ratio of 4:6, they have better sintering base characteristics and can be used as a new iron ore powder.
The Diagnostic and Prognostic Value of the Triglyceride-Glucose Index in Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD): A Systematic Review and Meta-Analysis
Authors: Wang Jing , Yan Shoumeng , Cui Yani , Chen Feinan , Piao Meihua , Cui Weiwei
Published: November 2022

Metabolic dysfunction-associated fatty liver disease (MAFLD) has been related to a series of harmful health consequences. The triglyceride-glucose index (TyG index) appears to be associated with MAFLD. However, no consistent conclusions about the TyG index and incident MAFLD have been reached. PubMed, MEDLINE, Web of Science, EMBASE and the Cochrane Library were searched. Sensitivities, specificities and the area under the receiver operating characteristic (AUC) with a random-effects model were used to assess the diagnostic performance of the TyG index in NAFLD/MAFLD participants. Potential threshold effects and publication bias were evaluated by Spearman’s correlation and Deeks’ asymmetry test, respectively. A total of 20 studies with 165725 MAFLD participants were included. The summary receiver operator characteristic (SROC) curve showed that the sensitivity, specificity and AUC were 0.73 (0.69–0.76), 0.67 (0.65, 0.70) and 0.75 (0.71–0.79), respectively. Threshold effects (r = 0.490, p < 0.05) were confirmed to exist. Subgroup analyses and meta-regression showed that some factors including country, number of samples, age and disease situation were the sources of heterogeneity (p < 0.05). Our meta-analysis suggests that the TyG index can diagnose and predict MAFLD patients with good accuracy. The number of studies remains limited, and prospective studies are needed.
Liver Enzymes in a Cohort of Community-Dwelling Older Persons: Focus on Sex Contribution
Authors: Ferri Evelyn , Rossi Paolo D. , Scichilone Martina , Lucchi Tiziano A. , Arosio Beatrice
Published: November 2022

Dysfunctions in liver metabolic activities may increase the risk of cognitive impairment and dementia. In a cohort of community-dwelling older persons investigated for a suspected cognitive decline, we studied the association between liver status and dementia, considering sex and frailty contribution. Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) concentrations, and the AST/ALT ratio were used to assess liver function in 419 older adults (248 persons with dementia and 171 age- and sex-matched subjects without cognitive decline). Although the serum concentrations of the liver enzymes were in the physiologic range, patients with dementia showed lower ALT concentrations (p = 0.005) and higher AST/ALT ratios (p = 0.003) compared to controls. The same differences were found when comparing men with and without dementia (ALT, p = 0.009; AST/ALT ratio, p = 0.003) but disappeared in women. Curiously, comparing women and men with the same diagnosis, the ALT concentrations were lower (p = 0.008), and the AST/ALT ratio was higher (p = 0.001) in control women than men, whereas no significant difference was found between persons with dementia. In conclusion, in our cohort of older people living in the community, the association between serum aminotransferases and dementia was remarked. Moreover, our results support attention to sex difference in liver function, suggesting a role in the pathogenesis of dementia.
Early-Adulthood Weight Change and Later Physical Activity in Relation to Cardiovascular and All-Cause Mortality: NHANES 1999–2014
Authors: Xiao Xinyu , Tang Chengyao , Zhai Xiaobing , Li Shiyang , Ma Wenzhi , Liu Keyang , Kokoro Shirai , Sheerah Haytham A. , Zhu Huiping , Cao Jinhong
Published: November 2022

Limited evidence investigated the combined influence of early-adulthood weight change and later physical activity on the risk of cardiovascular (CVD) and all-cause mortality. The aim of this study is to explore the associations of early-adulthood weight change and later physical activity with CVD and all-cause mortality. This is a cohort study of 23,193 US adults aged 40 to 85 years from the National Health and Nutrition Examination Survey (NHANES) 1999 to 2014. Cox proportional hazards regression was used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs) of CVD and all-cause mortality associated with early-adulthood weight change and later physical activity. During a median follow-up of 9.2 years, there were 533 and 2734 cases of CVD and all-cause deaths. Compared with being physically inactive, the HRs of the CVD mortality of being physically active were 0.44 (0.26 to 0.73), 0.58 (0.19 to 1.82), 0.38 (0.17 to 0.86) and 0.46 (0.21 to 1.02) among individuals with stable normal, stable obese, non-obese to obese and maximum overweight early-adulthood weight change patterns. Using stable normal patterns that were physically active later as the reference, other early-adulthood weight change patterns did not show a significantly higher risk of CVD mortality when participants were physically active in later life; later physically inactive participants had a significantly increased risk of CVD mortality, with HRs of 2.17 (1.30 to 3.63), 5.32 (2.51 to 11.28), 2.59 (1.29 to 5.18) and 2.63 (1.32 to 5.26) in the stable normal, stable obese, non-obese to obese and maximum overweight groups, respectively. Similar results can be seen in the analyses for all-cause mortality. Our findings suggest that inadequate physical activity worsens the negative impact of unhealthy early-adulthood weight change patterns, which is worthy of being noted in the improvement of public health.
Alcohol Mixed with Energy Drinks (AmED) Use among University Students: A Systematic Review and Meta-Analysis
Authors: De Giorgi Andrea , Valeriani Federica , Gallè Francesca , Ubaldi Francesca , Bargellini Annalisa , Napoli Christian , Liguori Giorgio , Romano Spica Vincenzo , Vitali Matteo , Protano Carmela
Published: November 2022

In the last decades, there has been a huge increase in the consumption of both Energy Drinks (EDs) and alcohol and, concurrently, these two trends generated the additional practice of mixing ED with alcohol, known as Alcohol mixed with Energy Drink (AmED). One of the most important group of AmED consumers is represented by young. Indeed, the study population of the researches in this field are mainly represented by college students and the results evidence a great range of negative consequences for health. The purpose of the systematic review was to explore the prevalence of AmED consumption among undergraduate students, together with motivations to their use, adverse effects and health-related behaviors associated to AmEDs use. The review was conducted according to the PRISMA Statement and PubMed, Scopus and Web of Science were interrogated. 42 articles, published from 2008 to 2021, were included in the review. An overall prevalence rate of 37% was estimated for AmEDs use in undergraduates, with geographical differences. Although a decrease in consumption was observed throughout the studied period, a continuous monitoring on this phenomenon is needed for identifying those risk groups that could develop risky behaviors related to AmEDs consumption and provide them targeted educational interventions.
Predictors of Sarcopenia in Outpatients with Post-Critical SARS-CoV2 Disease. Nutritional Ultrasound of Rectus Femoris Muscle, a Potential Tool
Authors: Cornejo-Pareja Isabel , Soler-Beunza Ana Gloria , Vegas-Aguilar Isabel María , Fernández-Jiménez Rocío , Tinahones Francisco J. , García-Almeida Jose Manuel
Published: November 2022

Background and Objectives: The loss of muscle mass in post-critical COVID-19 outpatients is difficult to assess due to the limitations of techniques and the high prevalence of obesity. Ultrasound is an emerging technique for evaluating body composition. The aim is to evaluate sarcopenia and its risk factors, determining ultrasound usefulness as a potential tool for this purpose according to established techniques, such as the bioimpedance vector analysis (BIVA), handgrip strength, and timed up-and-go test. Methods: This is a transversal study of 30 post-critical COVID-19 outpatients. We evaluated nutritional status by ultrasound (Rectus Femoris-cross-sectional-area (RF-CSA), thickness, and subcutaneous-adipose-tissue), BIVA, handgrip strength, timed up-and-go test, and clinical variables during admission. Results: According to The European Society for Clinical Nutrition and Metabolism and the European Association for the Study of Obesity (ESPEN&EASO) Consensus for Sarcopenic and Obesity, in terms of excess fat mass and decreased lean mass, the prevalence of class-1 sarcopenic obesity was 23.4% (n = 7), and class-2 sarcopenic obesity was 33.3% (n = 10) in our study. A total of 46.7% (n = 14) of patients had a handgrip strength below the 10th percentile, and 30% (n = 9) achieved a time greater than 10s in the timed up-and-go test. There were strong correlations between the different techniques that evaluated the morphological (BIVA, Ultrasound) and functional measurements of muscle. Intensive care unit stay, mechanical ventilation, and age all conditioned the presence of sarcopenia in COVID-19 outpatients (R2 = 0.488, p = 0.002). Predictive models for sarcopenic diagnosis based on a skeletal muscle index estimation were established by RF-CSA (R2 0.792, standard error of estimate (SEE) 1.10, p < 0.001), muscle-thickness (R2 0.774, SEE 1.14, p < 0.001), and handgrip strength (R2 0.856, SEE 0.92, p < 0.001). RF-CSA/weight of 5.3 cm2/kg × 100 was the cut-off value for predicting sarcopenia in post-critical COVID-19 outpatients, with 88.2 sensitivity and 69.2% specificity. Conclusion: More than half of the post-critical COVID-19 survivors had sarcopenic obesity and functional impairment of handgrip strength. Intensive care unit stay, age, and mechanical ventilation all predict sarcopenia. An ultrasound, when applied to the assessment of body composition in post-critical COVID-19 patients, provided the possibility of assessing sarcopenia in this population.
Analysis of Hepatic Lipid Metabolism Model: Simulation and Non-Stationary Global Sensitivity Analysis
Authors: Kosić Martina , Benković Maja , Jurina Tamara , Valinger Davor , Gajdoš Kljusurić Jasenka , Tušek Ana Jurinjak
Published: November 2022

Lipid metabolism is a complex process and it is extremely helpful to simulate its performance with different models that explain all the biological processes that comprise it, which then enables its better understanding as well as understanding the kinetics of the process itself. Typically, kinetic parameters are obtained from a number of sources under specific experimental conditions, and they are a source of uncertainty. Sensitivity analysis is a useful technique for controlling the uncertainty of model parameters. It evaluates a model’s dependence on its input variables. In this work, hepatic lipid metabolism was mathematically simulated and analyzed. Simulations of the model were performed using different initial plasma glucose (GB) and plasma triacylglyceride (TAG) concentrations according to proposed menus for different meals (breakfast, lunch, snack and dinner). A non-stationary Fourier amplitude sensitivity test (FAST) was applied to analyze the effect of 78 kinetic parameters on 24 metabolite concentrations and 45 reaction rates of the biological part of the hepatic lipid metabolism model at five time points (tf = 10, 50, 100, 250 and 500 min). This study examined the total influence of input parameter uncertainty on the variance of metabolic model predictions. The majority of the propagated uncertainty is due to the interactions of numerous factors rather than being linear from one parameter to one result. Obtained results showed differences in the model control regarding the different initial concentrations and also the changes in the model control over time. The aforementioned knowledge enables dietitians and physicians, working with patients who need to regulate fat metabolism due to illness and/or excessive body mass, to better understand the problem.
Sodium Malonate Inhibits the AcrAB-TolC Multidrug Efflux Pump of Escherichia coli and Increases Antibiotic Efficacy
Authors: Cauilan Allea , Ruiz Cristian
Published: November 2022

There is an urgent need to find novel treatments for combating multidrug-resistant bacteria. Multidrug efflux pumps that expel antibiotics out of cells are major contributors to this problem. Therefore, using efflux pump inhibitors (EPIs) is a promising strategy to increase antibiotic efficacy. However, there are no EPIs currently approved for clinical use especially because of their toxicity. This study investigates sodium malonate, a natural, non-hazardous, small molecule, for its use as a novel EPI of AcrAB-TolC, the main multidrug efflux pump of the Enterobacteriaceae family. Using ethidium bromide accumulation experiments, we found that 25 mM sodium malonate inhibited efflux by the AcrAB-TolC and other MDR pumps of Escherichia coli to a similar degree than 50 μΜ phenylalanine-arginine-β-naphthylamide, a well-known EPI. Using minimum inhibitory concentration assays and molecular docking to study AcrB-ligand interactions, we found that sodium malonate increased the efficacy of ethidium bromide and the antibiotics minocycline, chloramphenicol, and ciprofloxacin, possibly via binding to multiple AcrB locations, including the AcrB proximal binding pocket. In conclusion, sodium malonate is a newly discovered EPI that increases antibiotic efficacy. Our findings support the development of malonic acid/sodium malonate and its derivatives as promising EPIs for augmenting antibiotic efficacy when treating multidrug-resistant bacterial infections.
Analyzing the Difference in the Length of Stay (LOS) in Moderate to Severe COVID-19 Patients Receiving Hydroxychloroquine or Favipiravir
Authors: Alosaimi Bandar , Alshanbari Huda M. , Alturaiqy Muath , AlRawi Halah Z. , Alamri Saad , Albujaidy Asma , Bin Sabaan Aljawharah , Alrashed Ahmed A. , Alamer Ahmad , Alghofaili Fayez , Al-Duraymih Khaled , Alshalani Abdulaziz J. , Alturaiki Wael
Published: November 2022

Background: The coronavirus 2019 (COVID-19) disease, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus led to a global pandemic. HCQ and FPV were used early in the pandemic as a treatment modality for COVID-19. Various studies evaluated the HCQ and FPV effectiveness, based on the mortality endpoint and showed conflicting results. We hypothesize that analyzing the difference in the LOS as a significant endpoint would be of a major interest, especially for healthcare providers, to prevent a lengthy hospitalization and disease progression. Methods: This is a retrospective observational study, conducted via a medical chart review of COVD-19 patients who were admitted between April 2020 and March 2021 with a moderate to severe illness. The LOS endpoint was tested using the paired Wilcoxon signed-rank (WSR) model. Prior to using the WSR model, the balance between the HCQ and FPV groups, the propensity score matching, the LOS distribution, and the normality assumptions were tested. Two sensitivity statistical analyses were conducted to confirm the results (stratified log-rank test and U Welch test after transforming the LOS by the squared root values). Results: A total of 200 patients were included for the analysis: 83 patients in the HCQ group and 117 patients in the FPV group. Thirty-seven patients were matched in each group. The LOS data was positively skewed and violated the normality (Shapiro–Wilk p < 0.001) and had an unequal variance (Levene’s test, p = 0.019). The WSR test showed no statistical significance in the LOS endpoint, with a median of −0.75 days (95% confidence interval: −4.0 to 2.5, p = 0.629), in favor of the HCQ group (four days), in comparison to seven days of the FPV group. The WSR findings were further confirmed with the stratified log rank test (p = 740) and the U Welch test (p = 391). Conclusions: The study concluded that the HCQ and FPV treatments have a comparable effectiveness in terms of the LOS in the moderate to severe COVID-19 patients. This study highlights the importance of analyzing the LOS as a relevant endpoint, in order to prevent the costs of a lengthy hospitalization and disease progression. The current study also emphasizes the importance of applying the appropriate statistical testing when dealing with two-sample paired data and analyzing non-parametric data such as the LOS.
Protofibrillar Amyloid Beta Modulation of Recombinant hCaV2.2 (N-Type) Voltage-Gated Channels
Authors: Kaisis Eleni , Thei Laura J. , Stephens Gary J. , Dallas Mark L.
Published: November 2022

Cav2.2 channels are key regulators of presynaptic Ca2+ influx and their dysfunction and/or aberrant regulation has been implicated in many disease states; however, the nature of their involvement in Alzheimer’s disease (AD) is less clear. In this short communication, we show that recombinant hCav2.2/b1b/a2d1 channels are modulated by human synthetic AD-related protofibrillar amyloid beta Ab1-42 peptides. Structural studies revealed a time-dependent increase in protofibril length, with the majority of protofibrils less than 100 nm at 24 h, while at 48 h, the majority were longer than 100 nm. Cav2.2 modulation by Ab1-42 was different between a ‘low’ (100 nM) and ‘high’ (1 µM) concentration in terms of distinct effects on individual biophysical parameters. A concentration of 100 nM Ab1-42 caused no significant changes in the measured biophysical properties of Cav2.2 currents. In contrast, 1 µM Ab1-42 caused an inhibitory decrease in the current density (pA/pF) and maximum conductance (Gmax), and a depolarizing shift in the slope factor (k). These data highlight a differential modulation of Cav2.2 channels by the Ab1-42 peptide. Discrete changes in the presynaptic Ca2+ flux have been reported to occur at an early stage of AD; therefore, this study reveals a potential mechanistic link between amyloid accumulation and Cav2.2 channel modulation.
N-glycoproteins in Plant Cell Walls: A Survey
Authors: San Clemente Hélène , Jamet Elisabeth
Published: November 2022

Cell walls are an extracellular compartment specific to plant cells, which are not found in animal cells. Their composition varies between cell types, plant species, and physiological states. They are composed of a great diversity of polymers, i.e., polysaccharides, proteins, and lignins. Cell wall proteins (CWPs) are major players involved in the plasticity of cell walls which support cell growth and differentiation, as well as adaptation to environmental changes. In order to reach the extracellular space, CWPs are transported through the secretory pathway where they may undergo post-translational modifications, including N-glycosylations on the Asn residues in specific motifs (Asn-X-Ser/Thr-X, with X≠Pro). This review aims at providing a survey of the present knowledge related to cell wall N-glycoproteins with (i) an overview of the experimental workflows, (ii) a selection of relevant articles dedicated to N-glycoproteomics, (iii) a description of the diversity of N-glycans, and (iv) a focus on the importance of N-glycans for CWP structure and/or function.
Pattern Recognition of Varieties of Peach Fruit and Pulp from Their Volatile Components and Metabolic Profile Using HS-SPME-GC/MS Combined with Multivariable Statistical Analysis
Authors: Mihaylova Dasha , Popova Aneta , Dincheva Ivayla
Published: November 2022

A fruit’s aroma profile, composed of a complex mixture of volatile organic compounds, is among the core attributes related to the overall taste and consumer preference. Prunus persica L. is a preferred summer fruit with a distinct, favorable olfactory characteristic. The volatile compositions of both peach fruits and fruit pulps from eight peach cultivars (four native and four introduced) was investigated to compare their composition and assess flavor-contributing compounds. In total, 65 compounds were profiled after a HS-SPME-GC-MS analysis: 16 esters, 14 aldehydes, 5 alcohols, 7 hydrocarbons, 7 ketones, 8 acids, and 8 terpenes. The most common compounds were esters, acids, and aldehydes. Although the same compounds were identified in both fruit and pulp, their %TIC (total ion current) differed in favor of the whole fruit. Following the metabolic profiling of the whole fruit and fruit pulp, a total of 44 compounds were identified from the studied varieties. Among them, amino acids, organic acids, sugar alcohols, saccharides, fatty acids, and phenolic acids were identified as existing groups. According to the provided principal component analysis (PCA) and hierarchical cluster analysis (HCA), the relative %TIC of the identified volatile compounds fluctuated depending on the studied cultivar. No differences were visible in the PCA biplots, which suggested that the polar and lipid metabolites do not provide significant variations when considering different parts of the fruit, contrary to the volatile compounds. The obtained results could successfully be applied in the metabolic chemotaxonomy of peaches and the differentiation of the metabolites present in different parts of the peach.
Separation and Removal of Radionuclide Cesium from Water by Biodegradable Magnetic Prussian Blue Nanospheres
Authors: Feng Shanshan , Ni Jie , Cao Xun , Gao Jingshuai , Yang Lu , Jia Wenhao , Chen Feng , Feng Sheng , Zhang Yao , Ma Fang
Published: November 2022

As the main component of radioactive wastewater, the cesium ion has seriously endangered the environment and human health. Prussian blue nanoparticles (PB NPs) are used as adsorbents for the purification of cesium-containing wastewater because of their ability to selectively adsorb cesium ions. In this work, novel magnetic Prussian blue nanospheres (MPBNs) were developed from polylactic acid nanospheres as a carrier, loaded with Fe3O4 nanoparticles (Fe3O4 NPs) inside and PB NPs outside for the removal of cesium ions with the help of magnetic separation. Meanwhile, the effects on the adsorption efficiency of MPBNs, such as pH, time, temperature and initial concentration of cesium ion solution, were studied. The adsorption isotherms, kinetic models and adsorption thermodynamics were investigated to research the absorption mechanism. The results showed that MPBNs were spherical with a rough surface, and their particle size, iron content and saturation magnetization were 268.2 ± 1.4 nm, 40.01% and 41.71 emu/g, which can be recovered by magnetic separation. At 293 K, MPBNs could reduce the cesium ion solution from 40 mg/L to 4.8 mg/L, and its cesium ion removal rate and adsorption capacity were 82.46% and 16.49 mg/g, respectively. The optimum pH of MPBNs for cesium ion adsorption was 5~9, the adsorption equilibrium time was 60 min, and the maximum adsorption capacity was 17.03 mg/g. In addition, MPBNs were separated rapidly by an external magnetic field, and the adsorption process was an endothermic reaction. The adsorption isotherm and kinetics of MPBNs were in accordance with the Freundlich model and quasi-second-order fitting model, respectively, and the adsorption process of MPBNs was controlled by the diffusion step in particles. Notably, these MPBNs could be effectively separated from water by a magnetic field, facilitating engineering applications in cesium-containing wastewater.
Approach for the Description of Chemical Equilibrium Shifts in the Systems with Free and Connected Chemical Reactions
Authors: Shaymardanov Zhasulan , Shaymardanova Botogyz , Kulenova Natalia A. , Sadenova Marjan A. , Shushkevich Ludmila V. , Charykov Nikolay A. , Semenov Konstantin N. , Keskinov Victor A. , Blokhin Alexander A. , Letenko Dmitriy G. , Kuznetsov Vladimir V. , Sadowski Voitech
Published: November 2022

Approach for the description of chemical equilibrium shifts in the systems with free and connected chemical reactions was elaborated. Driving forces of chemical equilibrium shifts were temperature change (at P = const), pressure change (at T = const), and input or output of reagents or products (at T, P = const). It was demonstrated how the conditions for passing through the extremes of the state parameters (T, P, and components molar numbers) in one of the reactions transmitted to other reactions, connected with the first one by reagents or products.
Rapid Analysis of Raw Meal Composition Content Based on NIR Spectroscopy for Cement Raw Material Proportioning Control Process
Authors: Yang Zhenfa , Sui Qingmei , Jia Lei
Published: November 2022

Due to fast analysis speed, analyzing composition content of cement raw meal utilizing near infrared (NIR) spectroscopy, combined with partial least squares regression (PLS), is a reliable alternative method for the cement industry to obtain qualified cement products. However, it has hardly been studied. The raw materials employed in different cement plants differ, and the spectral absorption intensity in the NIR range of the raw meal component is weaker than organic substances, although there are obvious absorption peaks, which place high demands on the generality of modeling and accuracy of the analytical model. An effective modeling procedure is proposed, which optimizes the quantitative analytical model from several modeling stages, and two groups of samples with different raw material types and origins are collected to validate it. For the samples in the prediction set from Qufu, the root mean square error of prediction (RMSEP) of CaO, SiO2, Al2O3, and Fe2O3 were 0.1910, 0.2307, 0.0921, and 0.0429, respectively; the average prediction errors for CaO, SiO2, Al2O3, and Fe2O3 were 0.171%, 0.193%, 0.069%, and 0.032%, respectively; for the samples in the prediction set from Linyi, the RMSEP of CaO, SiO2, Al2O3, and Fe2O3 were 0.1995, 0.1267, 0.0336 and 0.0242, respectively, the average prediction errors for CaO, SiO2, Al2O3, and Fe2O3 were 0.154%, 0.100%, 0.022%, and 0.018%, respectively. The standard methods for chemical analysis of cement require that the mean measurement error for CaO, SiO2, Al2O3, and Fe2O3 should be within 0.40%, 0.30%, 0.20%, and 0.15%, respectively. It is obvious that the results of both groups of samples fully satisfied the requirements of raw material proportioning control of the production line, demonstrating that the modeling procedure has excellent generality, the models established have high prediction accuracy, and the NIR spectroscopy combined with the proposed modeling procedure is a rapid and accurate alternative approach for the analysis of cement raw meal composition content.
A Generalized Linear Mixed Model for Data Breaches and Its Application in Cyber Insurance
Authors: Sun Meng , Lu Yi
Published: November 2022

Data breach incidents result in severe financial loss and reputational damage, which raises the importance of using insurance to manage and mitigate cyber related risks. We analyze data breach chronology collected by Privacy Rights Clearinghouse (PRC) since 2001 and propose a Bayesian generalized linear mixed model for data breach incidents. Our model captures the dependency between frequency and severity of cyber losses and the behavior of cyber attacks on entities across time. Risk characteristics such as types of breach, types of organization, entity locations in chronology, as well as time trend effects are taken into consideration when investigating breach frequencies. Estimations of model parameters are presented under Bayesian framework using a combination of Gibbs sampler and Metropolis–Hastings algorithm. Predictions and implications of the proposed model in enterprise risk management and cyber insurance rate filing are discussed and illustrated. We find that it is feasible and effective to use our proposed NB-GLMM for analyzing the number of data breach incidents with uniquely identified risk factors. Our results show that both geological location and business type play significant roles in measuring cyber risks. The outcomes of our predictive analytics can be utilized by insurers to price their cyber insurance products, and by corporate information technology (IT) and data security officers to develop risk mitigation strategies according to company’s characteristics.
Elevation Gradients Limit the Antiphase Trend in Vegetation and Its Climate Response in Arid Central Asia
Authors: Yang Yujie , Huang Wei , Xie Tingting , Li Chenxi , Deng Yajie , Chen Jie , Liu Yan , Ma Shuai
Published: November 2022

Vegetation in arid central Asia (ACA) has been experiencing significant changes due to substantial warming and humidification since the 1980s. These changes are inhomogeneous due to the ecological vulnerability and topographic complexity of ACA. However, the heterogeneity of vegetation changes has received limited attention in the literature, which has focused more on the region’s overall general features. Thus, this paper analyzes the regional heterogeneity of vegetation changes during the growing season in ACA and further explores their underlying drivers. The results reveal an antiphase trend of vegetation, with an increase in eastern ACA and a decrease in western ACA. This antiphase pattern is primarily constrained by the divergent hydrothermal and climatic contexts of different elevation gradients. At elevations higher than 300 m (in the eastern ACA), increased growing season precipitation dominates vegetation greening. Conversely, vegetation at elevations lower than 300 m (in western ACA) is influenced by growing season soil water, which is driven by winter precipitation (pre-growing season precipitation). Additionally, the temperature could indirectly impact vegetation trends by altering precipitation, soil water, glaciers, snow cover, and runoff. Our findings have implications for restoring the ecosystem and sustainable development in ACA.
Evaluation of LiDAR-Derived Features Relevance and Training Data Minimization for 3D Point Cloud Classification
Authors: Morsy Salem , Shaker Ahmed
Published: November 2022

Terrestrial laser scanning (TLS) is a leading technology in data acquisition for building information modeling (BIM) applications due to its rapid, direct, and accurate scanning of different objects with high point density. Three-dimensional point cloud classification is essential step for Scan-to-BIM applications that requires high accuracy classification methods, running at reasonable processing time. The classification process is divided into three main steps: neighborhood definition, LiDAR-derived features extraction, and machine learning algorithms being applied to label each LiDAR point. However, the extraction of LiDAR-derived features and training data are time consuming. This research aims to minimize the training data, assess the relevance of sixteen LiDAR-derived geometric features, and select the most contributing features to the classification process. A pointwise classification method based on random forests is applied on the 3D point cloud of a university campus building collected by a TLS system. The results demonstrated that the normalized height feature, which represented the absolute height above ground, was the most significant feature in the classification process with overall accuracy more than 99%. The training data were minimized to about 10% of the whole dataset with achieving the same level of accuracy. The findings of this paper open doors for BIM-related applications such as city digital twins, operation and maintenance of existing structures, and structural health monitoring.
A Separate Calibration Method of Laser Pointing and Ranging for the GF-7 Satellite Laser That Does Not Require Field Detectors
Authors: Liu Ren , Xie Junfeng , Xu Chaopeng , Zeng Junze , Mo Fan , Yang Xiaomeng
Published: November 2022

Satellite laser altimeters have been widely used in the surveying, mapping, forestry, and polar regions and by other industries due to their excellent elevation measurement accuracy. Satellite laser on-orbit geometry calibration is a necessary means to ensure elevation accuracy. This study proposes an iterative geometry calibration method for satellite laser altimeter pointing and ranging separation that does not require the use of field detectors. The DSM data were first used to complete the laser pointing calibration, and then the laser footprint elevation was measured accurately to complete the laser ranging calibration. The iterative calibration experiment was repeated until the convergence condition (i.e., the laser point difference was less than 1 × 10-5 degrees and the laser ranging difference was less than 0.01 m) was met, with the calibrated laser pointing angle and ranging separation used as the input parameters. In this work, the GaoFen-7 (GF-7) satellite laser was used as the test object and the actual laser pointing and ranging values derived from ground detector calibrations. The results verified that the pointing accuracy of the GF-7 beam 1 was 2 arcsec and that the ranging accuracy was 2 cm after applying the calibration method presented in this paper. The pointing accuracy of the GF-7 beam 2 was 2.2 arcsec, and the ranging accuracy was approximately 1 cm. This analysis demonstrated that the GF-7 laser mission exceeded its pointing angle requirement of 3 arcsec after laser pointing and ranging separation iterative calibrations were applied. Finally, ground control points were used to verify the calibrated elevation accuracy of the GF-7 satellite laser, and its accuracy on flat terrain was 0.18 m. In summary, it was proven that the satellite laser geometry calibration method proposed in the article is effective.
Development of an Autonomous Driving Vehicle for Garbage Collection in Residential Areas
Authors: Pyo Jeong-Won , Bae Sang-Hyeon , Joo Sung-Hyeon , Lee Mun-Kyu , Ghosh Arpan , Kuc Tae-Yong
Published: November 2022

Autonomous driving and its real-world implementation have been among the most actively studied topics in the past few years. In recent years, this growth has been accelerated by the development of advanced deep learning-based data processing technologies. Moreover, large automakers manufacture vehicles that can achieve partially or fully autonomous driving for driving on real roads. However, self-driving cars are limited to some areas with multi-lane roads, such as highways, and self-driving cars that drive in urban areas or residential complexes are still in the development stage. Among autonomous vehicles for various purposes, this paper focused on the development of autonomous vehicles for garbage collection in residential areas. Since we set the target environment of the vehicle as a residential complex, there is a difference from the target environment of a general autonomous vehicle. Therefore, in this paper, we defined ODD, including vehicle length, speed, and driving conditions for the development vehicle to drive in a residential area. In addition, to recognize the vehicle’s surroundings and respond to various situations, it is equipped with various sensors and additional devices that can notify the outside of the vehicle’s state or operate it in an emergency. In addition, an autonomous driving system capable of object recognition, lane recognition, route planning, vehicle manipulation, and abnormal situation detection was configured to suit the vehicle hardware and driving environment configured in this way. Finally, by performing autonomous driving in the actual experimental section with the developed vehicle, it was confirmed that the function of autonomous driving in the residential area works appropriately. Moreover, we confirmed that this vehicle would support garbage collection works through the experiment of work efficiency.
A Sign Language Recognition System Applied to Deaf-Mute Medical Consultation
Authors: Xia Kun , Lu Weiwei , Fan Hongliang , Zhao Qiang
Published: November 2022

It is an objective reality that deaf-mute people have difficulty seeking medical treatment. Due to the lack of sign language interpreters, most hospitals in China currently do not have the ability to interpret sign language. Normal medical treatment is a luxury for deaf people. In this paper, we propose a sign language recognition system: Heart-Speaker. Heart-Speaker is applied to a deaf-mute consultation scenario. The system provides a low-cost solution for the difficult problem of treating deaf-mute patients. The doctor only needs to point the Heart-Speaker at the deaf patient and the system automatically captures the sign language movements and translates the sign language semantics. When a doctor issues a diagnosis or asks a patient a question, the system displays the corresponding sign language video and subtitles to meet the needs of two-way communication between doctors and patients. The system uses the MobileNet-YOLOv3 model to recognize sign language. It meets the needs of running on embedded terminals and provides favorable recognition accuracy. We performed experiments to verify the accuracy of the measurements. The experimental results show that the accuracy rate of Heart-Speaker in recognizing sign language can reach 90.77%.
A Gait-Based Real-Time Gender Classification System Using Whole Body Joints
Authors: Azhar Muhammad , Ullah Sehat , Ullah Khalil , Syed Ikram , Choi Jaehyuk
Published: November 2022

Gait-based gender classification is a challenging task since people may walk in different directions with varying speed, gait style, and occluded joints. The majority of research studies in the literature focused on gender-specific joints, while there is less attention on the comparison of all of a body’s joints. To consider all of the joints, it is essential to determine a person’s gender based on their gait using a Kinect sensor. This paper proposes a logistic-regression-based machine learning model using whole body joints for gender classification. The proposed method consists of different phases including gait feature extraction based on three dimensional (3D) positions, feature selection, and classification of human gender. The Kinect sensor is used to extract 3D features of different joints. Different statistical tools such as Cronbach’s alpha, correlation, t-test, and ANOVA techniques are exploited to select significant joints. The Coronbach’s alpha technique yields an average result of 99.74%, which indicates the reliability of joints. Similarly, the correlation results indicate that there is significant difference between male and female joints during gait. t-test and ANOVA approaches demonstrate that all twenty joints are statistically significant for gender classification, because the p-value for each joint is zero and less than 1%. Finally, classification is performed based on the selected features using binary logistic regression model. A total of hundred (100) volunteers participated in the experiments in real scenario. The suggested method successfully classifies gender based on 3D features recorded in real-time using machine learning classifier with an accuracy of 98.0% using all body joints. The proposed method outperformed the existing systems which mostly rely on digital images.
A Magnetic Sensor Based on the Nonlinear Effect of Co-Rich Amorphous Wire
Authors: He Dongfeng
Published: November 2022

A DC voltage induced by a DC magnetic field was observed for a coil with a Co-rich amorphous wire (FeCoSiB) as the core when an AC current flowed through the coil. The coil was 40 turns wound around a FeCoSiB amorphous wire with a diameter of 0.1 mm and a length of 8 mm. The magnitude of the DC voltage was determined by the frequency of the AC current, the amplitude of the AC current, and the applied DC magnetic field. When the sine wave current was 78 mA and the frequency was 6.8 MHz, a peak value of about 90 mV/Gauss DC voltage was observed. This phenomenon might have a relationship with the nonlinearity of the coil with the FeCoSiB amorphous wire as the core. A magnetic sensor with only an amplifier and a low-pass filter was developed using this effect.
An Effective Online Sequential Stochastic Configuration Algorithm for Neural Networks
Authors: Chen Yuting , Li Ming
Published: November 2022

Random Vector Functional-link (RVFL) networks, as a class of random learner models, have received careful attention from the neural network research community due to their advantages in obtaining fast learning algorithms and models, in which the hidden layer parameters are randomly generated and remain fixed during the training phase. However, its universal approximation ability may not be guaranteed if the random parameters are not properly selected in an appropriate range. Moreover, the resulting random learner’s generalization performance may seriously deteriorate once the RVFL network’s structure is not well-designed. Stochastic configuration (SC) algorithm, which incrementally constructs a universal approximator by obtaining random hidden parameters under a specified supervisory mechanism, instead of fixing the selection scope in advance and without any reference to training information, can effectively circumvent these awkward issues caused by randomness. This paper extends the SC algorithm to an online sequential version, termed as an OSSC algorithm, by means of recursive least square (RLS) technique, aiming to copy with modeling tasks where training observations are sequentially provided. Compared to the online sequential learning of RVFL networks (OS-RVFL in short), our proposed OSSC algorithm can avoid the awkward setting of certain unreasonable range for the random parameters, and can also successfully build a random learner with preferable learning and generalization capabilities. The experimental study has shown the effectiveness and advantages of our OSSC algorithm.
Climate, Urbanization and Environmental Pollution in West Africa
Authors: Ofoezie Emmanuel I. , Eludoyin Adebayo O. , Udeh Ebere B. , Onanuga Margaret Y. , Salami Olalekan O. , Adebayo Abdulquddus A.
Published: November 2022

The need to elucidate the urbanization–climate–pollution nexus in West African arose from the several reported, but disjointed cases of climate extremes and environmental degradation in the sub-region. This review analyzed several scenarios, to appraise the trends and relationships among the individual elements in the nexus and to ascertain the status of sustainable development in the sub-region, using the expository review methods. Urbanization was essentially characterized by population growth without complementary infrastructural development, weak coping strategies against climate extremes, numerous economic challenges, and high risk of environmental pollution. Initiative for urban renewal, urban greening and smart city development was low, and preparedness against future impact of extreme climate events and climate change is uncertain. However, there is clear evidence that the concept of sustainable development is growing in the sub-region. This is intensified by the international funding agencies insisting on the incorporation of environmental issues into development, the enactment of environmental laws and policies, and the establishment of institutions of enforcement in each country. The review concluded that although the sub-region is at the brink of severe effects of population explosion and environmental degradation, the growing awareness and implementation of the sustainable development goals may come to the rescue.