bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of a Two-Stage Bioprocess for the Production of Bioethanol from the Acid Hydrolysate of Brewer’s Spent Grain
Authors: Vičević Renata , Božinović Marko , Zekić Nikolina , Novak Mario , Grgić Dajana Kučić , Šalić Anita , Zelić Bruno
Published: August 2024

Bioethanol, an alcohol produced by microbial fermentation, is traditionally produced from sugar-rich plants such as sugar cane, sugar beet and maize. However, there is growing interest in the use of lignocellulose, an abundant and inexpensive renewable energy source, as a potential substitute for the production of biofuels and biochemicals. Yeast Saccharomyces cerevisiae, which is commonly used for ethanol fermentation, cannot cope with lignocellulose due to a lack of lignocellulolytic enzymes and the inefficient functioning of the pentose phosphate pathway. The aim of this research was to isolate yeasts that can efficiently produce bioethanol and valuable byproducts from both glucose and xylose in a two-stage fermentation process using brewer’s spent grains. This approach should maximize sugar utilization and improve the economic viability of bioethanol production while contributing to waste valorization and sustainability. Kluyveromyces marxianus and Candida krusei were identified and tested with different initial concentrations of glucose and xylose. The results showed that both yeasts produced bioethanol from glucose but were inefficient with xylose, yielding valuable compounds, such as 2,3-butanediol and glycerol instead. A two-stage fermentation was then carried out with weak acidic hydrolysate from brewer’s spent grain. In the first stage, glucose was fermented by S. cerevisiae to produce bioethanol; in the second stage, xylose was fermented by K. marxianus and C. krusei to obtain other valuable products.
TransRUL: A Transformer-Based Multihead Attention Model for Enhanced Prediction of Battery Remaining Useful Life
Authors: Saleem Umar , Liu Wenjie , Riaz Saleem , Li Weilin , Hussain Ghulam Amjad , Rashid Zeeshan , Arfeen Zeeshan Ahmad
Published: August 2024

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems.
How Does the Spatial Structure of Innovation Agglomeration Affect Energy Efficiency? From the Role of Industrial Structure Upgrading
Authors: Liu Kaiqu , Wang Guofeng
Published: August 2024

This paper employs the instrumental variable method and the intermediary model to explore the influence of monocentric and polycentric spatial structures of innovation agglomeration on energy efficiency. This paper uses data from 2010 to 2021 in China, and also panel data regression models were used. The findings indicate that the monocentric spatial structure of provincial system innovation agglomeration is primarily concentrated in northeastern and western regions of China, whereas the polycentric spatial structure is mainly distributed in eastern coastal areas. A monocentric spatial structure inhibits the enhancement of energy efficiency, but there is an environmental paradox of excessive resource agglomeration. In contrast, a polycentric spatial structure and its strengthening tendency facilitate the improvement of energy efficiency. A monocentric spatial structure has an inhibitory impact on the improvement of energy efficiency in the three sectors of agriculture, industry, and construction, while a polycentric spatial structure aids it. A monocentric spatial structure further hinders the improvement of energy efficiency by impeding the upgrading of the industrial structure, while the upgrading of the industrial structure driven by the polycentric spatial structure plays a role in enhancing energy efficiency. The policy recommendations of this paper are intended to help coordinate the pace of innovation development between cities, adjust the spatial structure of innovation agglomeration between regions, and promote the upgrading of the comprehensive industrial structure, thereby improving overall energy efficiency.
Do Digital Adaptation, Energy Transition, Export Diversification, and Income Inequality Accelerate towards Load Capacity Factors across the Globe?
Authors: Sarabdeen Masahina , Elhaj Manal , Alofaysan Hind
Published: August 2024

To limit global warming to 1.5 °C, it is imperative to accelerate the global energy transition. This transition is crucial for solving the climate issue and building a more sustainable future. Therefore, within the loaded capacity curve (LCC) theory framework, this study investigates the effects of digital adaptation, energy transition, export diversification, and income inequality on the load capacity factor (LCF). This study also attempts to investigate the integration effects of digital adaptation and energy transition, and digital adaptation and export diversification, on LCF. Furthermore, we explored how income inequality influences the LCF in economies. For this study, 112 countries were selected based on the data availability. Panel data from 2010 to 2021 were analyzed using the STATA software 13 application utilizing a two-step system generalized method of moments (GMM) approach. First, interestingly, our finding shows that digital adaptation and income significantly affect the LCF. An increase in income increases the LCF among the middle-income group of countries. Therefore, LCC is confirmed in this research. Surprisingly, energy transition, export diversification, and foreign direct investment negatively impact the LCF in the base model. Second, the impact of integrating digital adaptation and energy transition has a positive effect on LCF. Third, a negative correlation was observed between the interaction of export diversification and digital adaptation with the LCF. Fourth, a positive correlation was observed between the interaction of renewable energy and digital adaptation with the LCF. Finally, this study explores the impact of the energy transition, export diversification, and income inequality on the LCF with reference to the Organization of Petroleum Exporting Countries (OPEC). The result shows a negative effect between export diversification and LCF among OPECs at a 10% significance level. To improve the quality of our planet, policymakers must understand the forces causing climate change. By adopting a comprehensive perspective, the study aims to understand how these interrelated factors collaboratively influence the LCF thoroughly. Additionally, this research seeks to provide valuable insights related to energy transition, digital adaptation, and export diversification to policymakers, researchers, and stakeholders regarding possible avenues for cultivating a more joyful and sustainable global community.
Photovoltaic Roofing for Motorways and Other High-Ranking Road Networks: Technical Feasibility, Yield Estimation, and Final Demonstrator
Authors: Ebner Rita , Mayr Christoph , Rennhofer Marcus , Berger Karl , Heinrich Martin , Basler Felix , Beinert Andreas , Huyeng Jonas , Haider Manfred , Prammer Dominik , Vorwagner Alois , Fehringer Markus , Beck Tobias
Published: August 2024

As renewable energies need to be extended massively, new concepts are necessary to prevent land conflicts with other uses. Such concepts should have a high generality to offer a swift expansion of renewables anywhere. Within the project, the Photovoltaic Road Roofing Concept (PV-SÜD), a concept for the roofing of roadways with solar panels, was investigated. Its effects on the road infrastructure were analyzed, and a demonstrator was built. The technical boundary conditions and requirements resulting from the specific application type were determined regarding the photovoltaic technology, the possible energy generation, and the supporting structure. The study was completed for a technical solution of 10 m length, 17 m width, and 6.8 m height, with the option of a pent roof (highways running east–west) or gable roofs (highways running north–south). The main target aim was to investigate the potential for widespread use at any site, in contrast to previous studies which mainly aimed at a singular site or demonstrator project. The final solution can support a 38.5 kWp photovoltaic system with a specific annual yield of between 37.5 MWh and 44.0 MWh. The yield variation in sites in Austria and Germany was 14.7% and 17.9%, respectively. One demonstrator roofing was realized as a steel-frame construction with active glass–glass photovoltaic technology at a highway in Hegau (GE).
E-Fuels: A Comprehensive Review of the Most Promising Technological Alternatives towards an Energy Transition
Authors: Dell’Aversano Sonia , Villante Carlo , Gallucci Katia , Vanga Giuseppina , Di Giuliano Andrea
Published: August 2024

E-fuels represent a crucial technology for transitioning to fossil-free energy systems, driven by the need to eliminate dependence on fossil fuels, which are major environmental pollutants. This study investigates the production of carbon-neutral synthetic fuels, focusing on e-hydrogen (e-H2) generated from water electrolysis using renewable electricity and carbon dioxide (CO2) captured from industrial sites or the air (CCUS, DAC). E-H2 can be converted into various e-fuels (e-methane, e-methanol, e-DME/OME, e-diesel/kerosene/gasoline) or combined with nitrogen to produce e-ammonia. These e-fuels serve as efficient energy carriers that can be stored, transported, and utilized across different energy sectors, including transportation and industry. The first objective is to establish a clear framework encompassing the required feedstocks and production technologies, such as water electrolysis, carbon capture, and nitrogen production techniques, followed by an analysis of e-fuel synthesis technologies. The second objective is to evaluate these technologies’ technological maturity and sustainability, comparing energy conversion efficiency and greenhouse gas emissions with their electric counterparts. The sustainability of e-fuels hinges on using renewable electricity. Challenges and future prospects of an energy system based on e-fuels are discussed, aiming to inform the debate on e-fuels’ role in reducing fossil fuel dependency.
Reducing Methane, Carbon Dioxide, and Ammonia Emissions from Stored Pig Slurry Using Bacillus-Biological Additives and Aeration
Authors: El bied Oumaima , Turbí Martire Angélica Terrero , Garrido Melisa Gómez , Cano Ángel Faz , Acosta José Alberto
Published: August 2024

This study delves into the innovative application of a novel bacterial and enzyme mixture alone or combined with aeration in mitigating emissions from pig slurry storage and explores their impacts on the methane (CH4), carbon dioxide (CO2), and ammonia (NH3) emissions from stored pig slurry. A dynamic chamber was used in this research to assess the efficacy of the treatments. Biological additives (HIPO-PURÍN) of specific microbial strains were tested (a mixture ofof Bacillus subtilis, Bacillus megaterium, Bacillus licheniformis, Bacillus amyloliquefacien, and Bacillus thuringiensis) alone and combined with an aeration system (OXI-FUCH). Controlled experiments simulated storage conditions, where emissions of ammonia, methane, and carbon dioxide were measured. By analyzing the results statistically, the treatment with HIPO-PURÍN demonstrated a significant reduction in CH4 emissions by 67% and CO2 emissions by 60% with the use of biological additives, which was increased to 99% and 87%, respectively, when combined with OXI-FUCH aeration, compared to untreated slurry. Ammonia emissions were substantially reduced by 90% with biological additives alone and by 76% when combined with aeration. The study was driven by the need to develop sustainable solutions for livestock waste management, particularly in reducing emissions from pig slurry. It introduces techniques that significantly lower greenhouse gases, aligning with circular economy goals and setting a new standard for sustainable agriculture. Furthermore, there is a need to validate that farmers can independently manage pig slurry using simple and effective treatments techniques with profound environmental benefits, encouraging broader adoption of climate-conscious practices.
Brewing with Sea Vegetable: The Effect of Spirulina (Arthrospira platensis) Supplementation on Brewing Fermentation Kinetics, Yeast Behavior, and the Physiochemical Properties of the Product
Authors: Pérez-Alva Alexa , Guadalupe-Daqui Mario , Cárdenas-Pinto Santiago , Moreno Skylar R. , Thompson-Witrick Katherine A. , Ramírez-Rodrigues Melissa A. , Ramírez-Rodrigues Milena M. , MacIntosh Andrew J.
Published: August 2024

Spirulina is a highly nutritious microalgae commonly used as a food additive. During fermentation, different adjuncts are incorporated to act as a nutrient source for yeast and fortify or modify the sensory attributes of the final product. In this study, the effect of Spirulina on the characteristics of controlled yeast fermentation and the production of volatile organic compounds (VOCs) was analyzed. Spirulina was added to malted barley during mashing and fermented under standard conditions. An unaltered mash (negative control) and yeast extract (positive control) were also fermented. The addition of Spirulina resulted in an increased fermentation rate (~14% faster) and bigger yeast cells (~34% larger) in comparison to the negative control. There were differences in color (determined as SRM) between treatments; however, there were only minor differences in VOCs, with no statistical differences observed between chemical compound groups. No differences were observed in the pH, total number of yeast cells, or final attenuation between treatments. The primary mechanism for the observed differences is believed to be an increase in amino acids available to yeast that were contributed by the Spirulina. This shows both that Spirulina has a high potential as a fermentation adjunct and that the amino acid profile of an adjunct can significantly impact fermentation.
Impact of Jet Fires on Steel Structures: Application of Passive Fire Protection Materials
Authors: Gravit Marina , Korolchenko Dmitry , Nedviga Ekaterina , Portnov Fedor , Diachenko Semen
Published: August 2024

Jet fires are the second most common fire scenario after spill fires. This type of fire is characteristic of gas and gas–oil fires occurring on oil platforms and gas production and processing plants. The consequences of such fires are characterized by high material damage; this is associated with extensive networks of technological communications, since there is a high density of technological facilities and installations in the territory where these fires occur. At such facilities, there is a large number of steel structures, which under the action of high temperature quickly lose their strength and deform. To protect steel structures in the oil and gas industry, fire protection is used, which consists of different types: boards in the form of flat plates, plasters, and epoxy paints. This paper compares three types of fire protection materials for steel structures under jet fire: board fireproofing, plaster composition, and epoxy coating. When comparing the efficiency in jet fire, cement boards were found to be the best. However, despite the better fire protection efficiency, their low application is expected due to their massiveness and the high cost of such protection and the difficulty of installation. Nevertheless, the development of fire depends on the place of its origin, the size of the initial fire zone, and the stability and massiveness of the metal elements of the vessel structure or the structure of the boards on which the equipment can be placed. Therefore, it is necessary to take these factors into account when selecting fire protection and to apply it depending on the required fire resistance limits of structures, which should be determined depending on the fire development scenarios.
Pavement Crack Detection Using Fractal Dimension and Semi-Supervised Learning
Authors: Guo Wenhao , Zhong Leiyang , Zhang Dejin , Li Qingquan
Published: August 2024

Pavement cracks are crucial indicators for assessing the structural health of asphalt roads. Existing automated crack detection models depend on large quantities of precisely annotated crack sample data. The irregular morphology of cracks makes manual annotation time-consuming and costly, thereby posing challenges to the practical application of these models. This study proposes a pavement crack image detection method integrating fractal dimension analysis and semi-supervised learning. It identifies the self-similarity characteristics within the crack regions by analyzing pavement crack images and using fractal dimensions to preliminarily determine the candidate crack regions. The Crack Similarity Learning Network (CrackSL-Net) is then employed to learn the semantic similarity of crack image regions. Semi-supervised learning facilitates automatic crack detection by combining a small amount of labeled data with a large volume of unlabeled image data. Comparative experiments are conducted on two public pavement crack datasets against the HED, U-Net, and RCF models to comprehensively evaluate the performance of the proposed method. The results indicate that, with a 50% annotation ratio, the proposed method achieves high-precision crack detection, with an intersection over union (IoU) exceeding 0.84, which is close to that of U-Net. Visual analysis of the detection results confirms the method’s effectiveness in identifying cracks in complex environments.
Procrastination, Perfectionism, Narcissistic Vulnerability, and Psychological Well-Being in Young Adults: An Italian Study
Authors: Sommantico Massimiliano , Postiglione Jacopo , Fenizia Elisabetta , Parrello Santa
Published: August 2024

Procrastination is generally regarded as a dysfunctional tendency to postpone tasks, due to its consequences on performance and psychological well-being. Previous research has indicated that it is linked to perfectionism and narcissism, but with mixed results. The present study explored the interaction between procrastination, perceived parental expectations, multidimensional perfectionism, and narcissism in a sample of 548 Italian young adults aged 18–35 years (M = 23.9; SD = 4.3). Participants completed an online survey consisting of a sociodemographic questionnaire and psychometric measures assessing the constructs of interest. The results showed that: (a) procrastination was positively correlated with socially prescribed perfectionism only, which, in turn, was positively correlated with perceived parental expectations and criticism, and both narcissistic grandiosity and vulnerability; (b) perceived parental expectations and criticism and narcissistic vulnerability had a positive effect on socially prescribed perfectionism, while procrastination had a negative one; and (c) narcissistic vulnerability mediated the relationship between socially prescribed perfectionism and procrastination. Taken together, the findings contribute to a better understanding of the link between procrastination, perfectionism, and narcissism in young adults, and highlight the relevance of contemporary parenting styles and the current sociocultural background for understanding dilatory behaviors.
Materials Carbon Budget in Road Projects: A Case Study from The Greater Oslo Region
Authors: Khair Moa , Strzalkowski Patryk , Faizi Abdul Qaher , Barkal Mustafa Gilo , Fawakherji Ibrahim , Lima Martina , Adeysey Mohammed , Barbieri Diego Maria , Lou Baowen
Published: August 2024

The estimation of carbon dioxide emissions in relation to the life cycle of road pavements is pivotal to quantify their sustainability at the design stage. This preliminary study assesses the carbon budgets concerning the production of aggregates and asphalt to build a new roadway stretch located close to Oslo (Norway), leveraging digital tools and environmental product declarations. First, the software Trimble Novapoint estimates the necessary quantity take-offs. Afterwards, the amounts of CO2 generated during the production of the construction materials are appraised for the four main industries operating nearby. The results based on the corresponding environmental product declarations indicate that the carbon budgets show very small discrepancies among the considered suppliers.
Preoperative Carbohydrate Load Does Not Alter Glycemic Variability in Diabetic and Non-Diabetic Patients Undergoing Major Gynecological Surgery: A Retrospective Study
Authors: Canelli Robert , Louca Joseph , Gonzalez Mauricio , Sia Michelle , Baker Maxwell B. , Varghese Shama , Dienes Erin , Bilotta Federico
Published: August 2024

Background/Objectives: Elevated glycemic variability (GV) has been associated with postoperative morbidity. Traditional preoperative fasting guidelines may contribute to high GV by driving the body into catabolism. Enhanced recovery after surgery (ERAS) protocols that include a preoperative carbohydrate load (PCL) reduce hospital length of stay and healthcare costs; however, it remains unclear whether PCL improves GV in surgical patients. The aim of this retrospective study was to determine the effect of a PCL on postoperative GV in diabetic and non-diabetic patients having gynecological surgery. Methods: Retrospective data were collected on patients who had gynecological surgery before and after the rollout of an institutional ERAS protocol that included PCL ingestion. The intervention group included patients who underwent surgery in 2019 and were enrolled in the ERAS protocol and, therefore, received a PCL. The control group included patients who underwent surgery in 2016 and, thus, were not enrolled in the protocol. The primary endpoint was GV, calculated by the coefficient of variance (CV) and glycemic lability index (GLI). Results: A total of 63 patients in the intervention group and 45 in the control were analyzed. GV was not statistically significant between the groups for CV (19.3% vs. 18.6%, p = 0.65) or GLI (0.58 vs. 0.54, p = 0.86). Postoperative pain scores (4.5 vs. 5.2 p = 0.23) and incentive spirometry measurements (1262 vs. 1245 p = 0.87) were not significantly different. A subgroup analysis of patients with and without type 2 diabetes mellitus revealed no significant differences in GV for any of the subgroups. Conclusions: This retrospective review highlights the need for additional GV research, including consensus agreement on a gold standard GV measurement. Large-scale prospective studies are needed to test the effectiveness of the PCL in reducing GV.
Decreased Tongue–Lip Motor Function in Japanese Population with Low Taste Sensitivity: A Cross-Sectional Study
Authors: Yata Tomomi , Aoyama Norio , Fujii Toshiya , Kida Sayuri , Taniguchi Kentaro , Iwane Taizo , Tamaki Katsushi , Minabe Masato , Komaki Motohiro
Published: August 2024

Background/Objectives: Taste disorders have a negative impact on meal enjoyment, which is essential for maintaining adequate nutrition and quality of life. Japan is a rapidly aging society with an increasing number of individuals with taste disorders. However, despite the increasing prevalence of taste disorders, the correlation between oral frailty and taste sensitivity remains largely unknown. The objective of this study was to assess the relationship between oral health status and taste sensitivity among the Japanese population. Methods: Participants were recruited from Kanagawa Dental University Hospital Medical–Dental Collaboration Center between 2018 and 2021. The exclusion criteria were severe systemic infections, pregnancy, or lactation. Clinical examinations, oral function assessments, and taste tests were conducted using tap water and 1% sweet, 0.3% salty, 0.03% umami, and 0.1% umami tastants. The relationships between oral function, systemic indicators, and taste sensitivity were statistically evaluated. Results: Of the 169 participants included in this cross-sectional study, 39.6% were male and 60.4% were female (median age, 68 years). Participants with low taste sensitivity showed a decline in tongue–lip motor function, independent of age, sex, or smoking status. A multiple logistic regression analysis conducted using two age categories—younger than 65 years and older than 65 years—revealed an association between tongue–lip motor function and taste sensitivity among participants younger than 65 years. Conclusions: Decreased taste sensitivity is associated with tongue–lip motor function. Therefore, the early maintenance of oral function and taste sensitivity may be beneficial for optimal tongue–lip motor function.
Adherence to Labor Arrest and Failed Induction of Labor Guidelines: The Impact of a Quality-Improvement Educational Intervention
Authors: Cate Jennifer J. M. , Arkfeld Christopher K. , Campol Meagan , Campbell Katherine H. , Pettker Christian M. , Illuzzi Jessica L.
Published: August 2024

Background/Objective: To evaluate adherence to labor arrest and failed induction of labor (IOL) criteria in nulliparous, term, singleton, and vertex (NTSV) cesarean deliveries at an academic medical center and to measure the impact of a quality-improvement educational initiative that focused on obstetric provider education of modern labor arrest and failed IOL criteria. Methods: This is a retrospective cohort study using electronic health record (EHR) data with a pre- (1 September 2018–30 September 2019) and post-intervention (1 October 2019–31 March 2020) study design of all NTSV cesarean deliveries for labor arrest or failed IOL performed at an academic medical center in the northeastern United States. The quality-improvement educational intervention consisted of the distribution of educational pocket cards outlining modern labor arrest and failed IOL criteria to obstetric providers. Outcomes included adherence to labor arrest and failed IOL criteria pre- and post-intervention with secondary outcomes evaluating adherence by provider type (Maternal–Fetal Medicine (MFM) or generalist obstetrician). Descriptive and bivariate statistics were used in the analysis. Results: Pre-intervention, 272 NTSV cesarean deliveries were performed for labor arrest or failed IOL versus 92 post-intervention. Adherence improved post-intervention amongst failed IOL (OR 6.5, CI 1.8–23.8), first-stage arrest (OR 4.5, CI 2.2–10.8) and second-stage arrest (OR 3.7, CI 1.5–9.4). When comparing provider type, MFM physicians were more likely to be adherent to labor arrest and failed IOL criteria compared to generalist obstetricians pre-intervention (OR 3.1, CI 1.7–5.5); however, post-intervention, there was no longer a difference in adherence (OR 3.3, CI 0.9–12.3). Conclusions: Adherence to labor arrest criteria was suboptimal in the pre-intervention period; however, a targeted quality-improvement educational intervention improved adherence rates to labor arrest and failed IOL criteria among obstetric providers.
Insights from a 7-Year Dementia Cohort (VALCODIS): ApoE Genotype Evaluation
Authors: Baquero Miguel , Ferré-González Laura , Álvarez-Sánchez Lourdes , Ferrer-Cairols Inés , García-Vallés Lorena , Peretó Mar , Raga Luis , García-Lluch Gemma , Peña-Bautista Carmen , Muria Beatriz , Prieto Aitana , Jareño Inés , Cháfer-Pericás Consuelo
Published: August 2024

Background: The VALCODIS (Valencian Cognitive Diseases Study) cohort was designed and studied at the Hospital Universitari i Politècnic La Fe (Valencia, Spain) for the research of cognitive diseases, especially in the search for new biomarkers of Alzheimer’s disease (AD). Methods: Participants in the VALCODIS cohort had cerebrospinal fluid (CSF) and blood samples, neuroimaging, and neuropsychological tests. The ApoE genotype was evaluated to identify its relationship with CSF biomarkers and neuropsychological tests in AD and non-AD participants. Results: A total of 1249 participants were included. They were mainly AD patients (n = 547) but also patients with other dementias (frontotemporal lobar dementia (n = 61), Lewy body dementia without AD CSF signature (n = 10), vascular dementia (n = 24) and other specific causes of cognitive impairment (n = 442), and patients with subjective memory complaints (n = 165)). In the ApoE genotype evaluation, significant differences were found for Aβ42 levels between genotypes in both AD and non-AD patients, as well as a negative correlation between tau values and a cognitive test in non-carriers and ε4 heterozygous. Conclusions: The VALCODIS cohort provides biologically diagnosed patients with demographical, clinical and biochemical data, and biological samples for further studies on early AD diagnosis. Also, the ApoE genotype evaluation showed correlations between CSF biomarkers and neuropsychological tests.
Differences in Metabolic Characteristics of Rhizosphere Fungal Community of Typical Arboreal, Shrubby and Herbaceous Species in Oasis of Arid Region
Authors: Tan Yunxiang , Lv Yunhang , Xv Mengyu , Qu Laiye , Wang Wenjuan
Published: August 2024

Populus euphratica, Tamarix ramosissima, and Sophora alopecuroides are, respectively, typical arboreal, shrubby, and herbaceous species in oases of arid regions. It is important to study the difference in metabolic characteristics of the rhizosphere fungal community of these plant species and their relationships with soil factors for the preservation of delicate arid oasis ecosystems with future environmental changes. In this study, we, respectively, collected 18 rhizosphere soil samples of P. euphratica, T. ramosissima, and S. alopecuroides to explore the difference in rhizosphere fungal metabolic characteristics of different plant life forms and their underlying driving factors. The results showed that (1) soil physicochemical properties (including soil water content, pH, etc.) were significantly different among different plant species (p < 0.05). (2) Rhizosphere fungal metabolic characteristics were significantly different between S. alopecuroides and T. ramosissima (ANOSIM, p < 0.05), which was mainly caused by the different utilization of carboxylic carbon. (3) The RDA showed that the main driving factors of the variations in rhizosphere fungal metabolic characteristics were different among different plant species. The main explanatory variables of the variations in the metabolic characteristics of the rhizosphere fungal community were carbon to nitrogen ratio (23%) and available potassium (17.4%) for P. euphratica, while soil organic carbon (23.1%), pH (8.6%), and total nitrogen (8.2%) for T. ramosissima, and soil clay content (36.6%) and soil organic carbon (12.6%) for S. alopecuroides. In conclusion, the variations in rhizosphere fungal metabolic characteristics in arid oases are dominantly affected by soil factors rather than plant life forms.
Econometric Analysis of SOFIX Index with GARCH Models
Authors: Petkov Plamen , Shopova Margarita , Varbanov Tihomir , Ovchinnikov Evgeni , Lalev Angelin
Published: August 2024

This paper investigates five different Auto Regressive Moving Average (ARMA) and Generalized Auto Regressive Condition-al Heteroscedacity (GARCH models (GARCH, exponential GARCH or EGARCH, integrated GARCH or IGARCH, Component GARCH or CGARCH and the Glosten-Jagannathan-Runkle GARCH or GJR-GARCH) along with six distributions (normal, Student’s t, GED and their skewed forms), which are used to estimate the price dynamics of the Bulgarian stock index SOFIX. We use the best model to predict how much time it will take, after the latest crisis, for the SOFIX index to reach its historical peak once again. The empirical data cover the period between the years 2000 and 2024, including the 2008 financial crisis and the COVID-19 pandemic. The purpose is to answer which of the five models is the best at analysing the SOFIX price and which distribution is most appropriate. The results, based on the BIC and AIC, show that the ARMA(1,1)-CGARCH(1,1) specification with the Student’s t-distribution is preferred for modelling. From the results obtained, we can confirm that the CGARCH model specification supports a more appropriate description of SOFIX volatility than a simple GARCH model. We find that long-term shocks have a more persistent impact on volatility than the effect of short-term shocks. Furthermore, for the same magnitude, negative shocks to SOFIX prices have a more significant impact on volatility than positive shocks. According to the results, when predicting future values of SOFIX, it is necessary to include both a first-order autoregressive component and a first-order moving average in the mean equation. With the help of 5000 simulations, it is estimated that the chances of SOFIX reaching its historical peak value of 1976.73 (08.10.2007) are higher than 90% at 13.08.2087.
Exploring the Relationship between Mechanical Properties and Electrical Impedance in Cement-Based Composites Incorporating Gold Nanoparticles
Authors: Triana-Camacho Daniel A. , Miranda David A. , Quintero-Orozco Jorge H.
Published: August 2024

Structural health monitoring applications have gained significant attention in recent research, particularly in the study of the mechanical–electrical properties of materials such as cement-based composites. While most researchers have focused on the piezoresistive properties of cement-based composites under compressive stress, exploring the electrical impedance of such materials can provide valuable insights into the relationship between their mechanical and electrical characteristics. In this study, we investigated the connection between the mechanical properties and electrical impedance of cement-based composites modified with Au nanoparticles. Cylindrical samples with dimensions of 3 cm in diameter and 6 cm in length were prepared with a ratio of w/c = 0.47. The Au nanoparticles (Au NPs) were synthesized using pulsed laser ablation in liquids, and their size distribution was analyzed through dynamical light scattering. Mechanical properties were evaluated by analyzing the Young modulus derived from strain–stress curves obtained at various force rates. Electrical properties were measured by means of electrical impedance spectroscopy. The experimental results revealed a notable reduction of 91% in the mechanical properties of Au NPs-cement compounds, while their electrical properties demonstrated a significant improvement of 65%. Interestingly, the decrease in mechanical properties resulting from the inclusion of gold nanoparticles in cementitious materials was found to be comparable to that resulting from variations in the water/cement ratios or the hydration reaction.
Fundamental Aspects of Stretchable Mechanochromic Materials: Fabrication and Characterization
Authors: Tang Christina
Published: August 2024

Mechanochromic materials provide optical changes in response to mechanical stress and are of interest in a wide range of potential applications such as strain sensing, structural health monitoring, and encryption. Advanced manufacturing such as 3D printing enables the fabrication of complex patterns and geometries. In this work, classes of stretchable mechanochromic materials that provide visual color changes when tension is applied, namely, dyes, polymer dispersed liquid crystals, liquid crystal elastomers, cellulose nanocrystals, photonic nanostructures, hydrogels, and hybrid systems (combinations of other classes) are reviewed. For each class, synthesis and processing, as well as the mechanism of color change are discussed. To enable materials selection across the classes, the mechanochromic sensitivity of the different classes of materials are compared. Photonic systems demonstrate high mechanochromic sensitivity (Δnm/% strain), large dynamic color range, and rapid reversibility. Further, the mechanochromic behavior can be predicted using a simple mechanical model. Photonic systems with a wide range of mechanical properties (elastic modulus) have been achieved. The addition of dyes to photonic systems has broadened the dynamic range, i.e., the strain over which there is an optical change. For applications in which irreversible color change is desired, dye-based systems or liquid crystal elastomer systems can be formulated. While many promising applications have been demonstrated, manufacturing uniform color on a large scale remains a challenge. Standardized characterization methods are needed to translate materials to practical applications. The sustainability of mechanochromic materials is also an important consideration.
Preparation of Supercapacitor Carbon Electrode Materials by Low-Temperature Carbonization of High-Nitrogen-Doped Raw Materials from Food Waste
Authors: Mu Qingnan , Liu Chang , Guo Yao , Wang Kun , Gao Zhijie , Du Yuhan , Cao Changqing , Duan Peigao , Kapusta Krzysztof
Published: August 2024

To address the problem of the low nitrogen (N) content of carbon materials prepared through the direct carbonization of food waste, soybean meal and egg whites with high N contents were selected to carry out carbonization experiments on food waste. At 220 °C, the effects of hydrothermal carbonization and microwave carbonization on the properties of supercapacitor electrode materials were investigated. The results show that food waste doped with soybean meal and egg whites could achieve good N doping. At a current density of 1 A·g−1, the specific capacitance of the doped carbon prepared by hydrothermal doping is as high as 220.00 F·g−1, which is much greater than that of the raw material prepared through the hydrothermal carbonization of food waste alone, indicating that the hydrothermal carbonization reactions of soybean meal, egg white, and food waste promote the electrochemical properties of the prepared carbon materials well. However, when a variety of raw materials are mixed for pyrolysis carbonization, different raw materials cannot be fully mixed in the pyrolysis process, and under the etching action of potassium hydroxide, severe local etching and local nonetching occur, resulting in a severe increase in the pore size distribution and deterioration of the electrochemical performance of the prepared carbon materials. At a current density of 1 A·g−1, the specific capacitance of these prepared carbon materials is 157.70 F·g−1, whereas it is only 62.00 F·g−1 at a high current density of 20 A·g−1. Therefore, this study suggests that the hydrothermal carbonization process is superior to the microwave pyrolysis carbonization process for preparing supercapacitor electrode materials with multiple samples doped with each other.
Salt Crystallization in Limestone: Materials Decay and Chemomechanical Approach
Authors: Cappai Marta , Casti Marta , Pia Giorgio
Published: August 2024

Salt crystallization is a particularly relevant issue in the conservation of limestones used in Cultural Heritage sites. In this study, various facies of limestones were characterized through porosimetric and mechanical tests. The samples were subjected to experiments to determine their resistance to salt crystallization by verifying the number of cycles at which 50% of them began to lose weight. This number of experimental cycles was compared with the result calculated by the analytical procedure of a chemomechanical model found in the literature. The comparison showed a significant capability of the model to predict the experimental data.
Survey of Applicable Methods for Determining Viscoelastic Effects in Ferroelectric and Antiferroelectric Chiral Liquid Crystals
Authors: Dardas Dorota
Published: August 2024

Viscosity, elasticity, and viscoelastic properties are one of the most fundamental properties of liquid crystalline materials; the main problem in determining these properties is the multitude of physical parameters needed to determine the values of elasticity and viscosity constants. In this paper, a number of different measurement methods for the complete characterization of viscoelastic properties for smectic liquid crystalline materials and their mixtures are analyzed, both theoretically and experimentally. The way in which viscoelastic material constants are determined depends mainly on the application/purpose of the materials under study. The subject of this work was to review the methods used to determine viscoelastic effects in ferroelectric and antiferroelectric chiral liquid crystals, their mixtures, composite materials, and even in dielectric systems, which would bear the hallmark of a universal method allowing the application of sufficiently low electric fields. In the case of chiral liquid crystals with ferroelectric and antiferroelectric phases and their subphases, the following assumption applies: fulfilment of Hooke’s law (in the case of elastic coefficients) and preservation of laminar flow (in the case of viscosity coefficients).
Electrochemical Noise Analysis: An Approach to the Effectivity of Each Method in Different Materials
Authors: Jáquez-Muñoz Jesús Manuel , Gaona-Tiburcio Citlalli , Méndez-Ramírez Ce Tochtli , Martínez-Ramos Cynthia , Baltazar-Zamora Miguel Angel , Santiago-Hurtado Griselda , Estupinan-Lopez Francisco , Landa-Ruiz Laura , Nieves-Mendoza Demetrio , Almeraya-Calderon Facundo
Published: August 2024

Corrosion deterioration of materials is a major problem affecting economic, safety, and logistical issues, especially in the aeronautical sector. Detecting the correct corrosion type in metal alloys is very important to know how to mitigate the corrosion problem. Electrochemical noise (EN) is a corrosion technique used to characterize the behavior of different alloys and determine the type of corrosion in a system. The objective of this research is to characterize by EN technique different aeronautical alloys (Al, Ti, steels, and superalloys) using different analysis methods such as time domain (visual analysis, statistical), frequency domain (power spectral density (PSD)), and frequency–time domain (wavelet decomposition, Hilbert Huang analysis, and recurrence plots (RP)) related to the corrosion process. Optical microscopy (OM) is used to observe the surface of the tested samples. The alloys were exposed to 3.5 wt.% NaCl and H2SO4 solutions at room temperature. The results indicate that HHT and recurrence plots are the best options for determining the corrosion type compared with the other methods due to their ability to analyze dynamic and chaotic systems, such as corrosion. Corrosion processes such as passivation and localized corrosion can be differentiated when analyzed using HHT and RP methods when a passive system presents values of determinism between 0.5 and 0.8. Also, to differentiate the passive system from the localized system, it is necessary to see the recurrence plot due to the similarity of the determinism value. Noise impedance (Zn) is one of the best options for determining the corrosion kinetics of one system, showing that Ti CP2 and Ti-6Al-4V presented 742,824 and 939,575 Ω·cm2, while Rn presented 271,851 and 325,751 Ω·cm2, being the highest when exposed to H2SO4.
Which Position for Novice Surgeons? Effect of Supine and Prone Positions on Percutaneous Nephrolithotomy Learning Curve
Authors: Bulut Ender Cem , Aydın Uğur , Coşkun Çağrı , Çetin Serhat , Ünsal Ali , Polat Fazlı , Küpeli Bora
Published: August 2024

Background and Objectives: Percutaneous nephrolithotomy (PCNL) is a current treatment method with high success rates and low complication rates in treating large kidney stones. It can be conducted in different positions, especially supine and prone positions. PCNL in the supine position is becoming increasingly common due to its advantages, such as simultaneous retrograde intervention and better anesthesia management. This study aimed to assess how the choice of position impacts the PCNL learning curve. Materials and Methods: The results of the first 50 consecutive PCNL cases performed by two separate chief residents as primary surgeons in supine and prone positions in a reference center for stone treatment between August 2021 and January 2023 were evaluated. The two groups’ demographic and clinical data, stone-free rates, operation times, and fluoroscopy times were compared. Results: While the mean operation time was 94.6 ± 9.8 min in the supine PCNL group, it was 129.9 ± 20.3 min in the prone PCNL group (p < 0.001). Median fluoroscopy times in the supine PCNL and prone PCNL groups were 31 (10–89) seconds and 48 (23–156) seconds, respectively (p = 0.001). During the operation, the plateau was reached after the 10th case in the supine PCNL group, while it was reached after the 40th case in the prone PCNL group. Conclusions: For surgeons who are novices in performing PCNL, supine PCNL may offer both better results and a faster learning curve. Prospective and randomized studies can provide more robust conclusions on this subject.
Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms
Authors: Mogos Raluca , Gheorghe Liliana , Carauleanu Alexandru , Vasilache Ingrid-Andrada , Munteanu Iulian-Valentin , Mogos Simona , Solomon-Condriuc Iustina , Baean Luiza-Maria , Socolov Demetra , Adam Ana-Maria , Preda Cristina
Published: August 2024

Background and Objectives: Polycystic ovary syndrome (PCOS) is a complex disorder that can negatively impact the obstetrical outcomes. The aim of this study was to determine the predictive performance of four machine learning (ML)-based algorithms for the prediction of adverse pregnancy outcomes in pregnant patients diagnosed with PCOS. Materials and Methods: A total of 174 patients equally divided into 2 groups depending on the PCOS diagnosis were included in this prospective study. We used the Mantel–Haenszel test to evaluate the risk of adverse pregnancy outcomes for the PCOS patients and reported the results as a crude and adjusted odds ratio (OR) with a 95% confidence interval (CI). A generalized linear model was used to identify the predictors of adverse pregnancy outcomes in PCOS patients, quantifying their impact as risk ratios (RR) with 95% CIs. Significant predictors were included in four machine learning-based algorithms and a sensitivity analysis was employed to quantify their performance. Results: Our crude estimates suggested that PCOS patients had a higher risk of developing gestational diabetes and had a higher chance of giving birth prematurely or through cesarean section in comparison to patients without PCOS. When adjusting for confounders, only the odds of delivery via cesarean section remained significantly higher for PCOS patients. Obesity was outlined as a significant predictor for gestational diabetes and fetal macrosomia, while a personal history of diabetes demonstrated a significant impact on the occurrence of all evaluated outcomes. Random forest (RF) performed the best when used to predict the occurrence of gestational diabetes (area under the curve, AUC value: 0.782), fetal macrosomia (AUC value: 0.897), and preterm birth (AUC value: 0.901) in PCOS patients. Conclusions: Complex ML algorithms could be used to predict adverse obstetrical outcomes in PCOS patients, but larger datasets should be analyzed for their validation.