bluuebunny/crossref_metadata_2025 huggingface dataset:
A Systematic Review of Expert Systems for Improving Energy Efficiency in the Manufacturing Industry
Authors: Ioshchikhes Borys , Frank Michael , Weigold Matthias
Published: September 2024

Against the backdrop of the European Union’s commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research.
The Slow Pace of Green Transformation: Underlying Factors and Implications
Authors: Devezas Tessaleno , Tick Andrea , Sarygulov Askar , Rukina Polina
Published: September 2024

Concerns about climate change are a hot topic in the current debate about a sustainable future, and despite more than 30 years of international conferences, including the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Climate Change Conference (COP), the annual usage amount of fossil fuel-based energy sources has remained largely unchanged, and the green transition to a carbon-free energy system is progressing at a much slower pace than anticipated. This paper presents an original approach that consists in addressing the green transition’s dilemmas by analyzing the complex interplay of strongly interwoven forces hindering the rapid adoption of so-called green energy sources scrutinized from a three-fold perspective: socio-psychological; political–strategic and territorial; and technological. Moreover, these forces are ranked according to the magnitude of their impact on the anticipated transition to green, and it is estimated by logistic fit extrapolation that the total share of the contribution of low-carbon sources might reach a maximum of about 25% among all energy sources in 2050. A final original picture is presented, summarizing how all the involved forces are acting upon the expected transition as well as their consequences.
Impact of Data Corruption and Operating Temperature on Performance of Model-Based SoC Estimation
Authors: Wu King Hang , Seyedmahmoudian Mehdi , Mekhilef Saad , Shrivastava Prashant , Stojcevski Alex
Published: September 2024

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.
Spatiotemporal Evolution and Tapio Decoupling Analysis of Energy-Related Carbon Emissions Using Nighttime Light Data: A Quantitative Case Study at the City Scale in Northeast China
Authors: Liu Bin , Lv Jiehua
Published: September 2024

As the world’s second-largest economy, China has experienced rapid industrialization and urbanization, resulting in high energy consumption and significant carbon emissions. This development has intensified conflicts between human-land relations and environmental conservation, contributing to global warming and urban air pollution, both of which pose serious health risks. This study uses nighttime light (NTL) data from 2005 to 2019, along with scaling techniques and statistical analysis, to estimate city-scale energy carbon emissions over a 15-year period. Focusing on Northeast China, a traditional industrial region comprising 36 cities across three provinces, we examine spatial patterns of energy carbon emissions and assess spatiotemporal evolution through spatial autocorrelation and dynamic changes. These changes are further evaluated using standard deviation ellipse (SDE) parameters and SLOPE values. Additionally, the Tapio decoupling index is applied to explore the relationship between city-scale emissions and economic growth. Our findings for the 36 cities over 15 years are: (1) Heilongjiang shows low, declining emissions; Jilin improves; Liaoning has high, steadily increasing emissions. (2) The global spatial autocorrelation of energy carbon emissions is significant, with a positive Moran’s I, while significant local Moran’s I clusters are concentrated in Heilongjiang and Liaoning. (3) The greatest emission changes occurred in 2015, followed by 2019, 2005, and 2010. (4) Emission growth is fastest in Heilongjiang, followed by Liaoning and Jilin. (5) Tapio analysis shows positive decoupling in Heilongjiang, declining decoupling in Jilin, and no change in Liaoning. This study provides a quantitative basis for dual carbon goals and offers emission reduction strategies for government, industry, and residents, supporting energy transition and sustainable urban planning.
How to Make Flower Borders Benefit Public Emotional Health in Urban Green Space: A Perspective of Color Characteristics
Authors: Wan Zhuo , Shen Xinyue , Cai Yifei , Su Yang , Ren Ziming , Xia Yiping
Published: September 2024

The emotional health benefits of urban green space have been widely recognized. Flower borders, as a perennial plant landscape, have gradually become a current form of plant application in urban green spaces due to their rich color configurations. However, the related research primarily focuses on the impact of urban green spaces on public health, with relatively little attention given to how the colors of flower borders affect public emotional health. This study explored the relationship between the flower borders color characteristics and the public emotional health. In this study, 24 sample images were used as experimental materials, which selected based on their color richness and harmony. Additionally, face recognition technology and online random questionnaires were utilized to measure the public basic emotions and pleasure, respectively. The result shows that, based on the HSV color model and expert recommendations, 19 color characteristics were identified. The correlation analysis of the results from the public emotion with these color characteristics revealed that 13 color characteristics correlated with public emotional pleasure. Among them, blue, neutral purple, and low saturation were positively correlated. Through factor analysis, these thirteen color characteristics were summarized and categorized into four common factors (F1–F4), three of which are related to color. They are “low saturation of blue-violet percentage” (F1), “color configuration diversity” (F2), “bright red percentage” (F3), and “base green percentage” (F4), with F1 having the largest variance explained (27.88%). Finally, an evaluation model of color characteristics was constructed based on the variance explained by these four factors, which was demonstrated to effectively predict the level of public emotional pleasure when viewing flower borders. The results shed light on the effects of color characteristics on public emotions and provide new perspectives for subsequent flower border evaluations. Our results provide a valuable reference for future flower border color design, aiming to better improve public emotional health.
Context-Driven Service Deployment Using Likelihood-Based Approach for Internet of Things Scenarios
Authors: Banerji Nandan , Paul Chayan , Debnath Bikash , Das Biplab , Chhabra Gurpreet Singh , Mohanta Bhabendu Kumar , Awad Ali Ismail
Published: September 2024

In a context-aware Internet of Things (IoT) environment, the functional contexts of devices and users will change over time depending on their service consumption. Each iteration of an IoT middleware algorithm will also encounter changes occurring in the contexts due to the joining/leaving of new/old members; this is the inherent nature of ad hoc IoT scenarios. Individual users will have notable preferences in their service consumption patterns; by leveraging these patterns, the approach presented in this article focuses on how these changes impact performance due to functional-context switching over time. This is based on the idea that consumption patterns will exhibit certain time-variant correlations. The maximum likelihood estimation (MLE) is used in the proposed approach to capture the impact of these correlations and study them in depth. The results of this study reveal how the correlation probabilities and the system performance change over time; this also aids with the construction of the boundaries of certain time-variant correlations in users’ consumption patterns. In the proposed approach, the information gleaned from the MLE is used in arranging the service information within a distributed service registry based on users’ service usage preferences. Practical simulations were conducted over small (100 nodes), medium (1000 nodes), and relatively larger (10,000 nodes) networks. It was found that the approach described helps to reduce service discovery time and can improve the performance in service-oriented IoT scenarios.
Interannual Variability in Seed Germination Response to Heat Shock in Cistus ladanifer
Authors: Luna Belén
Published: September 2024

Mediterranean climates, characterised by hot and dry summers, have predictable fire regimes, and many species with physical seed dormancy (PY) thrive after wildfires. While it is well known that PY is released after heat shock in these species, intraspecific variation in seed response to heat is less understood. This research explores, for the first time, the variability in the traits of Cistus ladanifer seeds from the same central Spain population over eight years. It examines seed germination and viability under different heat shocks and the relationships among seed traits and climatic variables. While the response to heat shock remained constant over the years studied, achieving the highest germination percentages after heat shock at 100 °C, seed germination varied between years, and environmental conditions affected seed traits. Seed moisture content was negatively correlated with the maximum summer temperatures, and seed viability was positively related to annual precipitation. Germination at 100 °C was lower in warmer years as more seeds did not break their PY. In conclusion, despite the fact that PY appears to be genetically determined, it also depends on the environmental conditions experienced by the mother plant. This interannual phenotypic variability may help Cistus ladanifer to cope with the increasingly unpredictable conditions imposed by climate change.
Integration of Industry 4.0 Technologies in Fire and Safety Management
Authors: Negi Prafful , Pathani Ashish , Bhatt Bhuvan Chandra , Swami Siddharth , Singh Rajesh , Gehlot Anita , Thakur Amit Kumar , Gupta Lovi Raj , Priyadarshi Neeraj , Twala Bhekisipho , Sikarwar Vineet Singh
Published: September 2024

The incorporation of Industry 4.0 has integrated various innovations into fire safety management, thus changing the mode of identifying, assessing, and controlling fire risks. This review aims at how emerging technologies like IoT, AI, cloud technology, and BIM are making changes to fire safety in structural structures. With IoT-enabled sensors, data, and analytics coupled with predictive algorithms for real-time scenarios, fire safety systems have become dynamic systems where early detection, quick response, and risk management can be achieved. In addition, cloud web-based solutions improve the storage of information while providing the predictive aspect for certainty of safety measures. This paper also largely focuses on such activities through the likes of ISO/IEC 30141 and IEEE 802.15.4, thus making a critical role in maintaining effective connectivity between IoT devices, which is necessary for the effective performance of fire safety systems. Furthermore, the implementation issues, including the high costs, the difficulty in scaling up the projects, and the cybersecurity concerns, are considered and compared to the possible solutions, which include upgrading in stages and the possibility of subsidies from the government. The review also points out areas for further study, such as the creation of small cell networks with lower latency, the use of AI to carry out the maintenance of IoTs, and the enhancement of protection mechanisms of systems that are based on the IoTs. In general, this paper highlights the vast possibilities offered by Industry 4.0 technologies to support organizational fire safety management or decrease fire fatalities and improve built environment fire safety.
Modernisation of Fire Protection Education in Elementary Schools
Authors: Dzermansky Martin , Snopek Lukas , Drabikova Dora
Published: September 2024

The article deals with the issue of teaching fire protection in elementary schools in the Czech Republic. The area of fire protection is one of the key areas that everyone should be familiar with and it is important to teach children how to prevent fires and how to face fires in case of danger. Thus, this article analyses the current approaches of elementary schools to teaching fire protection and compares the data obtained with approaches in teaching abroad. The obtained comparison is complemented by conducting a questionnaire survey, which underpins the current trends and possibilities of modernising the teaching itself. The results are presented in the form of graphs and the design of a potential application based on the presented mobile applications available on common mobile platforms. The conclusions of the study are summarised in the final chapter, which points to the need to expand fire protection education and the use of modern technologies.
Impact of Arthrospira maxima Feed Supplementation on Gut Microbiota and Growth Performance of Tilapia Fry (Oreochromis niloticus)
Authors: Rojas-Villalta Dorian , Gómez-Espinoza Olman , Guillén-Watson Rossy , Murillo-Vega Francinie , Villalta-Romero Fabián , Vaquerano-Pineda Felipe , Chicas Mauricio , Guerrero Maritza , Núñez-Montero Kattia
Published: September 2024

Microalgae are promising as prebiotics in aquaculture. Arthrospira maxima has potential nutritional value but is poorly studied. We assessed A. maxima feed supplementation in tilapia fry and evaluated its effect on growth performance and gut microbiota. Fish were cultivated in ponds under 0%, 5%, 10%, and 15% A. maxima inclusion treatments. Growth parameters and biomass proximate analysis were assessed. A meta-amplicon analysis was performed on the gut microbiota using DADA2 and PICRUST2 for functional prediction. Among treatments, the 5% supplementation group seemed to present no negative effect on growth parameters and did not compromise the nutritional quality of tilapia fry biomass. Microbial composition was characterized by Cetobacterium, Pseudomonas and Aeromonas genera, and a predominance of beneficial metabolic pathways. Microbiota of tilapia fry fed with A. maxima showed unique genera with reported beneficial functions in tilapia. The abundance of potential pathogenic taxa was significantly decreased in supplementation treatments, possibly related to valuable compounds of A. maxima. The inclusion of the microalgae supported the stability of the microbiota, favoring the growth of commensal species. This seems to have an effect on decreasing the presence of pathogenic genera in the gastrointestinal tract. Overall, our work proposes 5% feed inclusion of A. maxima to be the most suitable for tilapia fry aquaculture to maintain production rates while improving general health.
Effects of Efficient Ethylene Remover on the Lignification of Fresh Faba Bean (Vicia faba L.) during Storage
Authors: Fan Jiaxing , Chen Cunkun , Zhang Xiaojun , Dong Chenghu , Jin Manqin , Zhang Xuemei , Xue Wentong , Li Jingming
Published: September 2024

Postharvest ethylene accumulation and lignification are significant issues affecting the storage quality of fresh faba beans, resulting in rapid quality decline. However, there is still a lack of effective preservation methods to preserve the quality of faba beans during storage. This study aimed to investigate the regulation of lignification in faba beans during storage using a high-efficiency ethylene remover (HEER), examining physiological responses, key enzyme activities, and transcriptomic changes. Results showed that the HEER treatment inhibited the lignification, reducing it by 45% and lowering the respiratory rate of fresh pods by 32.8% during storage. Additionally, the HEER treatment suppressed respiration rates and the activities of lignin synthesis-related enzymes, including phenylalanine ammonia-lyase (from 353.73 to 246.60 U/g), cinnamic acid-4-hydroxylase (from 635.86 to 125.00 U/g), 4-coumarate: coenzyme A ligase (from 1008.57 to 516.52 U/g), and cinnamyl-alcohol dehydrogenase (from 129.42 to 37.12 U/g), thus slowing lignin accumulation. During storage, the hardness of fresh faba bean increased by 9.79% from the initial period, being 1.44 times higher than that of HEER. On days 8 and 16 of storage, the respiratory rate of the treated beans decreased by 24.38% and 4.12%, respectively. Physiological and enzyme activity analyses indicated that HEER treatment-induced increase in hardness was associated with the phenylpropanoid metabolic pathway. Moreover, the HEER significantly down-regulated the expression of several key genes, namely FaPAL, FaC4H, and FaCAD. This study helps to deepen the understanding of the inhibition of lignification by HEER and provide new insights for the development of preservation technology of faba bean.
Effects of Changes in Pectin Constitution on Optical Properties and Firmness of Peach Flesh during Storage
Authors: Chen Xiao , Ma Chen , He Hongju , Tu Kang , Lan Weijie , Pan Leiqing
Published: September 2024

Understanding the fundamental light-sample interaction process is a crucial step toward the development of vibrational spectroscopy to determine fruit texture (i.e., firmness). This study aimed to investigate the effect of pectin constitution, including total pectin, water-soluble pectin, protopectin contents, and protopectin index (PI), on the optical properties and firmness of ‘Baifeng’ and ‘Xiahui 8’ peach flesh at the different softening degrees during postharvest storage of 6 days at 20 °C. The firmness of ‘Baifeng’ and ‘Xiahui 8’ peaches significantly (p < 0.05) changed with a decreasing rate from 90.3% to 92.2%. Peach firmness of these two cultivars correlated well with PI contents (r > 0.912) and showed good internal correlations with optical scattering properties. The light absorption coefficient (μa) and reduced scattering coefficient (μ’s) at 600–1600 nm were measured using a single integrating sphere system combined with an inversion algorithm. This relationship of μa and μ’s with peach firmness and pectin constitution was first analyzed. Notably, the specific μ’s at 660 nm, 950 nm, 1203 nm, and 1453 nm showed a satisfactory prediction of peach firmness and PI of ‘Xiahui 8’ (R2 ≥ 0.926) and ‘Baifeng’ peaches (R2 ≥ 0.764), respectively. Furthermore, the prediction models were established based on partial least squares regression coupled with optical properties, and considerable prediction performances were obtained for tissue firmness (Rp2 ≥ 0.863) and PI based on μ’s (Rp2 ≥ 0.802). Consequently, these results further verified that the spectroscopic prediction model for peach firmness could be related to the high correlations between PI in tissues and their optical scattering properties. Future research interests could include the development of optical absorption and scattering sensors for rapid and efficient determination of peach firmness.
Physicochemical Characterization and Antioxidant Capacity of Açaí (Euterpe oleracea) in Ecuadorian Region
Authors: Flor-Unda Omar , Guanochanga Fernanda , Samaniego Iván , Arias Verónica , Ortiz Bladimir , Rosales Carmen , Palacios-Cabrera Hector
Published: September 2024

The phytochemical components and antioxidant capacity of Açaí (Euterpe oleracea) give it nutritional and bioactive characteristics with anti-cancer and anti-inflammatory properties; it is exported mainly from Brazil to various places worldwide. In Ecuador, the cultivated Euterpe oleracea variety has an abundant production that has not been used or studied in depth; because of this, it is relevant to expand the study of this fruit’s phytochemical and antioxidant properties. This paper presents the results of evaluating the concentration of antioxidants and antioxidant activity in different stages of maturation and geographical locations of the Açaí, for which samples obtained in the Ecuadorian provinces of Sucumbíos and Orellana have been evaluated. Antioxidant concentrations were determined with a UV/VIS spectrophotometer at 450–760 nm wavelengths. Antioxidant capacity was determined using the ABTS and FRAP methods. It was evidenced that the values of total polyphenols and total flavonoids decrease with increasing ripening; the opposite effect occurs with total anthocyanins that have a higher concentration in ripe fruits and evidencing an antioxidant capacity that decreases with ripening determined by both methods (FRAP and ABTS).
Fractal Dimension-Based Multi-Focus Image Fusion via Coupled Neural P Systems in NSCT Domain
Authors: Li Liangliang , Zhao Xiaobin , Hou Huayi , Zhang Xueyu , Lv Ming , Jia Zhenhong , Ma Hongbing
Published: September 2024

In this paper, we introduce an innovative approach to multi-focus image fusion by leveraging the concepts of fractal dimension and coupled neural P (CNP) systems in nonsubsampled contourlet transform (NSCT) domain. This method is designed to overcome the challenges posed by the limitations of camera lenses and depth-of-field effects, which often prevent all parts of a scene from being simultaneously in focus. Our proposed fusion technique employs CNP systems with a local topology-based fusion model to merge the low-frequency components effectively. Meanwhile, for the high-frequency components, we utilize the spatial frequency and fractal dimension-based focus measure (FDFM) to achieve superior fusion performance. The effectiveness of the method is validated through extensive experiments conducted on three benchmark datasets: Lytro, MFI-WHU, and MFFW. The results demonstrate the superiority of our proposed multi-focus image fusion method, showcasing its potential to significantly enhance image clarity across the entire scene. Our algorithm has achieved advantageous values on metrics QAB/F, QCB, QCV, QE, QFMI, QG, QMI, and QNCIE.
Between Past and Present: Exploring Cultural Participation and Identity among Carpatho-Rusyn Descendants
Authors: Lee Andrea Rakushin , Rougemont Nicolette , Short Philip C. , McConnell John R.
Published: September 2024

Cultural identity and participation play a critical role in understanding culture and its influence on different cultural groups. The Carpatho-Rusyns originate in the Carpathian Rus, which is in the Carpathian Mountains. The Carpatho-Rusyns are a stateless group, and many historically immigrated to other countries. This mixed-method study examines cultural participation and identity among Carpatho-Rusyn descendants (n = 51). Data collection comprised both open-ended and closed-ended survey questions. A link to the survey was shared in Facebook groups that relate to Carpatho-Rusyn culture, genealogy, and history. Closed-ended survey items were analyzed using descriptive statistics, while open-ended items were thematically coded. The findings indicate that most participants do not align with particular Carpatho-Rusyn groups, yet many still uphold cultural traditions, especially related to food and holidays. Qualitative insights emphasize the significance of cultural pride and distinction. Ultimately, this study highlights unique facets of Carpatho-Rusyn heritage and its lasting importance for descendants living in various countries, especially the United States. Finally, this paper concludes with practical implications that center on the importance of developing educational programs, community engagement strategies, and cultural awareness initiatives to preserve and promote the culture.
Visual Integration of Genome-Wide Association Studies and Differential Expression Results with the Hidecan R Package
Authors: Angelin-Bonnet Olivia , Vignes Matthieu , Biggs Patrick J. , Baldwin Samantha , Thomson Susan
Published: September 2024

Background/Objectives: We present hidecan, an R package for generating visualisations that summarise the results of one or more genome-wide association studies (GWAS) and differential expression analyses, as well as manually curated candidate genes, e.g., extracted from the literature. This tool is applicable to all ploidy levels; we notably provide functionalities to facilitate the visualisation of GWAS results obtained for autotetraploid organisms with the GWASpoly package. Results: We illustrate the capabilities of hidecan with examples from two autotetraploid potato datasets. Conclusions: The hidecan package is implemented in R and is publicly available on the CRAN repository and on GitHub. A description of the package, as well as a detailed tutorial, is made available alongside the package. It is also part of the VIEWpoly tool for the visualisation and exploration of results from polyploids computational tools.
Molecular Characterization of Peroxidase (PRX) Gene Family in Cucumber
Authors: Luo Weirong , Liu Junjun , Xu Wenchen , Zhi Shenshen , Wang Xudong , Sun Yongdong
Published: September 2024

Background: The Peroxidase (PRX) gene family is essential for plant growth and significantly contributes to defense against stresses. However, information about PRX genes in cucumber (Cucumis sativus L.) remains limited. Methods: In this present study, CsPRX genes were identified and characterized using bioinformatics analysis. The expression pattern analysis of CsPRX genes were examined utilizing the RNA-seq data of cucumber from public databases and real-time quantitative PCR (qRT-PCR) analysis. Results: Here, we identified 60 CsPRX genes and mapped them onto seven chromosomes of cucumber. The CsPRX proteins exhibited the presence of 10 conserved motifs, with motif 8, motif 2, motif 5, and motif 3 consistently appearing across all 60 CsPRX protein sequences, indicating the conservation of CsPRX proteins. Furthermore, RNA-seq analysis revealed that differential expression of CsPRX genes in various tissues. Notably, a majority of the CsPRX genes exhibited significantly higher expression levels in the root compared to the other plant tissues, suggesting a potential specialization of these genes in root function. In addition, qRT-PCR analysis for four selected CsPRX genes under different stress conditions indicated that these selected CsPRX genes demonstrated diverse expression levels when subjected to NaCl, CdCl2, and PEG treatments, and the CsPRX17 gene was significantly induced by NaCl, CdCl2, and PEG stresses, suggesting a vital role of the CsPRX17 gene in response to environmental stresses. Conclusions: These findings will contribute valuable insights for future research into the functions and regulatory mechanisms associated with CsPRX genes in cucumber.
Real-Time Lithology Prediction at the Bit Using Machine Learning
Authors: Burak Tunc , Sharma Ashutosh , Hoel Espen , Kristiansen Tron Golder , Welmer Morten , Nygaard Runar
Published: September 2024

Real-time drilling analysis requires knowledge of lithology at the drill bit. However, logging-while-drilling (LWD) sensors in the bottom hole assembly (BHA) are usually positioned 2–50 m (7–164 ft) above the bit (called the sensor offset), leading to a delay in real-time drilling analysis. The current industry solution to overcome this delay involves stopping drilling to perform a bottoms-up circulation for cuttings evaluation—a process that is both time-consuming and costly. To address this issue, our study evaluates three methodologies for real-time lithology prediction at the bit using drilling and petrophysical parameters. The first method employs a petrophysical approach, which involves using bulk density and neutron porosity predicted at the bit. The second method combines unsupervised and supervised machine learning (ML) for prediction. The third method employs classification algorithms on manually labeled lithology data from mud log reports, a novel approach used in this work. Our results show varying degrees of success: the bulk density versus neutron porosity cross-plot method achieved an accuracy of 58% with blind-well test data; the ML approach improved accuracy to 66%; and the Random Forest (RF) classification with manual labeling significantly increased accuracy to 86%. This comparative analysis of three different methodologies for lithology prediction has not been previously explored in the literature. While clustering and classification methods have been regarded as the most effective, our study demonstrates that they do not always yield the best result. These findings demonstrate that ML models, particularly the manual labeling approach, substantially outperform the petrophysical method. This new algorithm, designed for real-time applications, uses selected input parameters to effectively minimize problems associated with the sensor offset of LWD tools. It rapidly adapts to changes, offering a quicker and more cost-effective interpretation of lithology. This eliminates the need for time-consuming bottoms-up circulation to evaluate cuttings. Ultimately, this approach enhances drilling efficiency and significantly improves the accuracy of lithology prediction, notably in identifying interbedded geological layers.
Acaricidal Efficacy of Abamectin against Tetranychus urticae Populations When Combined with Entomopathogenic Fungi
Authors: Wakil Waqas , Boukouvala Maria C. , Kavallieratos Nickolas G. , Riasat Tahira , Ghazanfar Muhammad Usman , Avery Pasco B.
Published: September 2024

Tetranychus urticae (Acari: Tetranychidae) is a widespread and serious mite pest that infests tomato plants and causes economic losses worldwide. We investigated the acaricidal efficacy of two isolates of entomopathogenic fungi (EPF) Metarhizium robertsii (WG-7) and Beauveria bassiana (WG-12) alone and in combination with abamectin when applied topically to tomato leaf discs in the laboratory against T. urticae. We also evaluated the establishment and proliferation of T. urticae mite life stages on tomato plants in the greenhouse after application of each of the above treatments. The combination of abamectin with each EPF caused 100% mortality in T. urticae immatures after 2 days while each EPF or abamectin alone caused moderate mortality, not exceeding 74.2% 3 days post-exposure. Complete (100%) mortality of adults was observed after 5 days in leaf discs treated with M. robertsii plus abamectin whereas B. bassiana plus abamectin caused 100% mortality after 7 days. The mean number of eggs, emerged immatures, and adults were significantly reduced on both sides of the leaves (i.e., abaxial and adaxial sides) after using the combined application of M. robertsii or B. bassiana plus abamectin, compared to abamectin alone and controls. Our results reveal that the acaricidal efficacy of abamectin combined with either EPF was significantly better in managing the T. urticae life stages than either treatment alone under greenhouse conditions.
Isolation and Molecular Identification of the Pure Culture of Morchella Collected from Türkiye and Its Characteristics
Authors: Soylu Mustafa Kemal
Published: September 2024

True morels (Morchella spp.) are highly valuable and medicinal mushrooms. Saprophytic morels have been cultivated, especially in China and some Western countries, over the last few decades. Türkiye has a rich potential in terms of wild morel diversity, with nearly 40 Morchella species in its genetic pool, though only 22 of these have been identified molecularly. It has high economic value worldwide, and Türkiye exports morels worth approximately 2 million $ annually. There is also significant interest in morel mushroom cultivation in Türkiye. In this study, 40 Morchella strains were collected and isolated from different regions of Türkiye and analyzed based on the Internal Transcribed Spacer (ITS) of the nuclear ribosomal DNA (rDNA) region. A phylogenetic dendrogram was drawn. The isolates of M. importuna, M. exima, M. exuberans, M. dunali, M. tridentina, M. crassipes, and M. esculenta were identified based on the ITS rDNA region. However, the identification of isolates 849-Kg027 and 966-Kg142 could not be determined clearly, and the isolates of M. vulgarius and M. spongiola were not distinct based on the ITS analysis. The macro-morphological features of the mycelia were investigated. Mycelia colors ranged from off-white to pale gray in the juvenile stage, orange to pale brown during early pigmentation, and pale brown to dark brown in the senescence stage. M. crassipes, M. exuberans, and 966-Kg142 formed lighter-colored mycelia, whereas M. dunali and M. vulgarius exhibited the darkest mycelial pigments. Sclerotia formation was compact, pale yellow to yellow, and abundant. In conclusion, molecular identification of Turkish morel cultures was performed, and cultural characteristics along with morphological differences were examined. The cultures have been deposited for further study in the Mushroom Gene Bank at the Atatürk Central Horticultural Research Institute.
Urban Flood Vulnerability Assessment in Freetown, Sierra Leone: AHP Approach
Authors: Koroma Abdulai Osman , Saber Mohamed , Abdelbaki Cherifa
Published: September 2024

This study presents a comprehensive flood vulnerability assessment for Freetown, Sierra Leone, spanning the period from 2001 to 2022. The objective of this research was to assess the temporal and spatial changes in the flood vulnerability using Geographic Information System (GIS) tools and AHP-based Multi-Criteria Decision-Making (MCDM) analysis. This study identified the flood-vulnerable zones (FVZs) by integrating critical factors such as the rainfall, NDVI, elevation, slope, drainage density, TWI, distance to road, distance to river, and LULC. The analysis reveals that approximately 60% of the study area is classified as having medium to high vulnerability, with a significant 20% increase in the flood risk observed over the past two decades. In 2001, very-high-vulnerability zones covered about 68.84 km2 (10% of the total area), with high-vulnerability areas encompassing 137.68 km2 (20%). By 2020, very-high-vulnerability zones remained constant at 68.84 km2 (10%), while high-vulnerability areas decreased to 103.26 km2 (15%), and medium-vulnerability zones expanded from 206.51 km2 (30%) in 2001 to 240.93 km2 (35%). The AHP model-derived weights reflect the varied significance of the flood-inducing factors, with rainfall (0.27) being the most critical and elevation (0.04) being the least. A consistency ratio (CR) of 0.068 (< 0.1) confirms the reliability of these weights. The spatial–temporal analysis highlights the east and southeast regions of Freetown as consistently vulnerable over the years, while infrastructure improvements in other areas have contributed to a general decrease in very-high-vulnerability zones. This research highlights the urgent need for resilient urban planning and targeted interventions to mitigate future flood impacts, offering clear insights into the natural and human-induced drivers of the flood risk for effective hazard mitigation and sustainable urban development.
Investigation of Psychometric Properties and Correlation with Psychological Distress after Hurricane Hazards in Puerto Rico
Authors: Hernández-Torres Ruthmarie , Rodríguez-Rabassa Mary , Rosario Lianel P. , Peña-Vargas Cristina , Rodríguez-Castro Zindie , Flores Idhaliz , Cartujano-Barrera Francisco , Costas-Muñíz Rosario , Tollinchi-Natali Nelmit , Torres-Marrero Estefania , Rosario-Hernández Ernesto , Jim Heather , Armaiz-Pena Guillermo N. , Castro-Figueroa Eida M.
Published: September 2024

Background: Puerto Rico (PR) is highly vulnerable to hurricanes, which severely impact cancer survivors by causing healthcare disruptions and increasing stress. This study investigates the reliability and factor structure of the Hurricane Hazards Inventory (HHI) and its relationship with psychological distress among cancer survivors and non-cancer controls in PR. Methods: Using secondary data from a longitudinal study following Hurricane Maria (HM), the baseline assessment included sociodemographic data from participants, HHI, Patient Health Questionnaire (PHQ-8), and Generalized Anxiety Disorder (GAD-7). Statistical analyses involved descriptive statistics, Exploratory Factor Analysis (EFA), and Partial Least Squares Structural Equation Modeling (PLS-SEM). Results: Among 260 participants, 78.7% were women, with a median age of 58.0 years. EFA reduced the HHI to 17 items grouped into three factors explaining 62.6% of the variance with excellent reliability (Cronbach’s alpha 0.91). The three factors also showed good to excellent reliability (alpha 0.81 to 0.92). The median HHI score was 11.0 (range 4.0–26.5) out of 68. PLS-SEM revealed a direct effect of being a cancer survivor and tertiary hazards on depression and anxiety. Conclusion: The HHI is a valid and reliable tool for assessing mental health impact in cancer survivors after hurricanes. However, the study had limitations, including its small sample size and lack of control for all confounding variables. Future research with larger and more diverse samples is needed to further validate the HHI and examine its generalizability.
Neighborhood Greenspace, Extreme Heat Exposure, and Sleep Quality over Time among a Nationally Representative Sample of American Children
Authors: Rahai Rouzbeh , Wells Nancy M. , Evans Gary W.
Published: September 2024

Children’s sleep is essential for healthy development, yet over a third of children in the United States experience inadequate sleep. Environmental factors can influence sleep: greenspace exposure can promote better sleep, while heat exposure can disrupt sleep. As global climate change raises nighttime and daytime temperatures, greenspace may mitigate the negative effects of heat stress on sleep. We examined the direct effects of neighborhood greenspace and extreme heat exposure on sleep and the statistical interaction between greenspace and heat exposure on sleep outcomes among a nationally representative, four-year longitudinal sample of 8580 U.S. children ages 9–10 years at baseline. Hierarchical linear models incorporated a neighborhood greenspace measure: percent open park space within individual child census tracts, a measure of extreme neighborhood heat exposure during the summer months, and extensive individual and neighborhood-level covariates to test main and interaction effects on child sleep quality. Neighborhood open park space was related to better sleep quality, after controlling for covariates. Additionally, neighborhood extreme heat exposure was associated with worse sleep quality. A two-way interaction was found between neighborhood open park space and neighborhood heat exposure on sleep quality, suggesting open park space mitigated the negative effects of heat on sleep. The results indicate the potential contribution of open greenspace to improve child sleep and enhance resilience to extreme heat, which is an adverse outcome of climate change.
Exploring the Role and Pathophysiological Significance of Aldehyde Dehydrogenase 1B1 (ALDH1B1) in Human Lung Adenocarcinoma
Authors: Tsochantaridis Ilias , Brisimis Dimitris , Tsifintaris Margaritis , Anastasiadou Anastasia , Lazos Efthymios , Ermogenous Antreas , Christou Sylia , Antonopoulou Nefeli , Panayiotidis Mihalis I. , Koukourakis Michail I. , Giatromanolaki Alexandra , Pappa Aglaia
Published: September 2024

Aldehyde dehydrogenases (ALDHs) constitute a diverse superfamily of NAD(P)+-dependent enzymes pivotal in oxidizing endogenous and exogenous aldehydes to carboxylic acids. Beyond metabolic roles, ALDHs participate in essential biological processes, including differentiation, embryogenesis and the DNA damage response, while also serving as markers for cancer stem cells (CSCs). Aldehyde dehydrogenase 1B1 (ALDH1B1) is a mitochondrial enzyme involved in the detoxification of lipid peroxidation by-products and metabolism of various aldehyde substrates. This study examines the potential role of ALDH1B1 in human lung adenocarcinoma and its association with the CSC phenotype. To this end, we utilized the lung adenocarcinoma cell line A549, engineered to stably express the human ALDH1B1 protein tagged with green fluorescent protein (GFP). Overexpression of ALDH1B1 led to notable changes in cell morphology, proliferation rate and clonogenic efficiency. Furthermore, ALDH1B1-overexpressing A549 cells exhibited enhanced resistance to the chemotherapeutic agents etoposide and cisplatin. Additionally, ALDH1B1 overexpression correlated with increased migratory potential and epithelial–mesenchymal transition (EMT), mediated by the upregulation of transcription factors such as SNAI2, ZEB2 and TWIST1, alongside the downregulation of E-cadherin. Moreover, Spearman’s rank correlation coefficient analysis using data from 507 publicly available lung adenocarcinoma clinical samples revealed a significant correlation between ALDH1B1 and various molecules implicated in CSC-related signaling pathways, including Wnt, Notch, hypoxia, Hedgehog, retinoic acid, Hippo, NF-κΒ, TGF-β, PI3K/PTEN-AKT and glycolysis/gluconeogenesis. These findings provide insights into the role of ALDH1B1 in lung tumor progression and its relation to the lung CSC phenotype, thereby offering potential therapeutic targets in the clinical management of lung adenocarcinoma.
Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage
Authors: Vignolle Gabriel A. , Bauerstätter Priska , Schönthaler Silvia , Nöhammer Christa , Olischar Monika , Berger Angelika , Kasprian Gregor , Langs Georg , Vierlinger Klemens , Goeral Katharina
Published: September 2024

Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerful tool to identify biomarkers and predict patient-specific risks. However, challenges remain in integrating large-scale, multiomic datasets and translating these findings into actionable clinical tools. Identifying reliable, disease-specific biomarkers and developing explainable ML models that clinicians can trust and understand are key barriers to widespread clinical adoption. In this prospective longitudinal cohort study, we analyzed 1109 liquid biopsy samples from 99 preterm neonates with IVH, collected at up to six timepoints over 13 years. Various explainable ML techniques—including statistical, regularization, deep learning, decision trees, and Bayesian methods—were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. Targeted proteomic analyses were conducted using serum and urine samples through a proximity extension assay capable of detecting low-concentration proteins in complex biofluids. The study identified 41 significant independent protein markers in the 1600 calculated ML models that surpassed our rigorous threshold (AUC-ROC of ≥0.7, sensitivity ≥ 0.6, and selectivity ≥ 0.6), alongside gestational age at birth, as predictive of PHVD development and survival. Both known biomarkers, such as neurofilament light chain (NEFL), and novel biomarkers were revealed. These findings underscore the potential of targeted proteomics combined with ML to enhance clinical decision-making and parental counseling, though further validation is required before clinical implementation.
Detection of Random Body Movements Using Clustering-Based Methods in Bioradar Systems
Authors: Rouco André , Silva Filipe , Soares Beatriz , Albuquerque Daniel , Gouveia Carolina , Brás Susana , Pinho Pedro
Published: September 2024

Bioradar systems, in general, refer to radar systems used for the detection of vital signs. These systems hold significant importance across various sectors, particularly in healthcare and surveillance, due to their capacity to provide contactless solutions for monitoring physiological functions. In these applications, the primary challenge lies in the presence of random body movements (BMs), which can significantly hinder the accurate detection of vital signs. To compensate the affected signal in a timely manner, portions of BM must be correctly identified. To address this challenge, this work proposes a solution based on the Density-Based Spatial Clustering of Applications with Noise (DBScan) algorithm to detect the occurrence of BM in radar signals. The main idea of this algorithm is to cluster the radar samples, aiming to differentiate between segments in which the subject is stable and segments in which the subject is moving. Using a dataset involving eight subjects, the proposed method successfully detects three types of body movements: chest movement, body rotation, and arm movement. The achieved results are promising, with F1 scores of 0.83, 0.73, and 0.8, respectively, for the detection of each specific movement type.