bluuebunny/crossref_metadata_2025 huggingface dataset:
A Reception of Pauline Ideas Shaped by a Jewish Milieu: The Case of the Pseudo-Clementine Homilies
Authors: Hedner Zetterholm Karin
Published: July 2024

This essay focuses on the reception of Pauline ideas in the Pseudo-Clementine Homilies, commonly dated to the early fourth century. At first, the claim that the Pseudo-Clementine Homilies contain Pauline ideas may seem surprising, since the Homilies are commonly considered “Jewish Christian” and thus anti-Pauline. However, new readings of Paul generated by the “Paul within Judaism” perspective, along with new insights on the Homilies, reveal that the latter work seems to contain Pauline ideas not preserved in other receptions of Paul. The Homilies share with Paul the following traits and ideas: (1) like Paul, the Homilies distinguish between Jews and non-Jews (the term “Christian” never appears) and, like Paul, the Homilies’ teachings about law address gentiles and prescribe a kind of Judaism for them; (2) gentiles must adapt to a Jewish lifestyle and keep the commandments that the Torah prescribes for non-Israelites; (3) Jews and Jesus-oriented gentiles together make up the people of God (called theosebeis in the Homilies), but the distinction between them remains. They have equal status in the eyes of God but differences in their observance of the law remain. An important point where the Homilies deviate from Paul is their insistence that Jews do not necessarily need Jesus. For the Homilies, Jesus is primarily the teacher of gentiles, and they envision two parallel paths to salvation: Moses for Jews and Jesus for gentiles. This essay suggests that the Homilies’ understanding of ideas that we recognize as Pauline developed in a milieu marked by the presence of non-Jesus-oriented (rabbinic) Jews.
Apostolate through Books: The Translation Practice by Catholicism during Late Ming and Early Qing
Authors: Li Yafeng , Fu Jingmin , Huang Jiyun
Published: July 2024

Catholicism during the late Ming and early Qing Dynasties is the most fascinating chapter in the development history of Christian culture in China. Apostolate through books emerged as a prominent method of Sino-West cultural exchange during that time. Apostolate through books by Catholicism was realized by means of translation practice, leading to a notable religious influence. Based on this perspective, this paper discusses the history and connotation of apostolate through books, particularly focusing on the characteristics of apostolate through books by Catholicism during the late Ming and early Qing Dynasties. In achieving this, this research shows the translation motivation and translation effect of apostolate through books during the late Ming and early Qing Dynasties. Furthermore, this paper emphasizes that the translation practice of apostolate through books by Catholicism has laid a good foundation for the sinicization and acceptance of Catholicism in China, highlighting the relationship between apostolate through books by Catholicism and the sinicization of Christian culture, so as to enlighten the internationalization of religious culture.
Evaluating the Effects of Parameter Uncertainty on River Water Quality Predictions
Authors: Fonseca André , Botelho Cidália , Boaventura Rui A. R. , Vilar Vítor J. P.
Published: July 2024

Due to the high uncertainty of model predictions, it is often challenging to draw definitive conclusions when evaluating river water quality in the context of management options. The major aim of this study is to present a statistical evaluation of the Hydrologic Simulation Program FORTRAN (HSPF), which is a water quality modeling system, and how this modeling system can be used as a valuable tool to enhance monitoring planning and reduce uncertainty in water quality predictions. The authors’ findings regarding the sensitivity analysis of the HSPF model in relation to water quality predictions are presented. The application of the computer model was focused on the Ave River watershed in Portugal. Calibration of the hydrology was performed at two stations over five years, starting from January 1990 and ending in December 1994. Following the calibration, the hydrology model was then validated for another five-year period, from January 1995 to December 1999. A comprehensive evaluation framework is proposed, which includes a two-step statistical evaluation based on commonly used hydrology criteria for model calibration and validation. To thoroughly assess model uncertainty and parameter sensitivity, a Monte Carlo method uncertainty evaluation approach is integrated, along with multi-parametric sensitivity analyses. The Monte Carlo simulation considers the probability distributions of fourteen HSPF water quality parameters, which are used as input factors. The parameters that had the greatest impact on the simulated in-stream fecal coliform concentrations were those that represented the first-order decay rate and the surface runoff mechanism, which effectively removed 90 percent of the fecal coliform from the pervious land surface. These parameters had a more significant influence compared to the accumulation and maximum storage rates. When it comes to the oxygen governing process, the parameters that showed the highest sensitivity were benthal oxygen demand and nitrification/denitrification rate. The insights that can be derived from this study play a critical role in the development of robust water management strategies, and their significance lies in their potential to contribute to the advancement of predictive models in the field of water resources.
Leveraging Remote Sensing-Derived Dynamic Crop Growth Information for Improved Soil Property Prediction in Farmlands
Authors: Geng Jing , Tan Qiuyuan , Zhang Ying , Lv Junwei , Yu Yong , Fang Huajun , Guo Yifan , Cheng Shulan
Published: July 2024

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology’s robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping.
Flood Inundation Probability Estimation by Integrating Physical and Social Sensing Data: Case Study of 2021 Heavy Rainfall in Henan, China
Authors: Du Wenying , Xia Qingyun , Cheng Bingqing , Xu Lei , Chen Zeqiang , Zhang Xiang , Huang Min , Chen Nengcheng
Published: July 2024

Frequent flooding seriously affects people’s safety and economic construction, and assessing the inundation probability can help to strengthen the capacity of emergency management of floods. There are currently two general means of flood sensing: physical and social. Remote sensing data feature high reliability but are often unavailable in disasters caused by persistent heavy rainfall. Social media is characterized by high timeliness and a large data volume but has high redundancy and low reliability. The existing studies have primarily relied on physical sensing data and have not fully exploited the potential of social media data. This paper combines traditional physical sensing data with social media and proposes an integrated physical and social sensing (IPS) method to estimate the probability distribution of flood inundation. Taking the “7·20” Henan rainstorm in 2021 and the study area of Xinxiang, China, as a case study, more than 60,000 messages and 1900 images about this occurrence were acquired from the Weibo platform. Taking filtered water depth points with their geographic location and water depth information as the main input, the inverse distance attenuation function was used to calculate the inundation potential layer of the whole image. Then, the Gaussian kernel was used to weight the physical sensing data based on each water depth point, and finally, the submergence probability layer of the whole image was enhanced. In the validation of the results using radar and social media points, accuracies of 88.77% and 75% were obtained by setting up a threshold classification, demonstrating the effectiveness and usefulness of the method. The significance of this study lies in obtaining discrete social media flood points and achieving space-continuous flood inundation probability mapping, providing decision-making support for urban flood diagnosis and mitigation.
Estimation of Forest Stand Volume in Coniferous Plantation from Individual Tree Segmentation Aspect Using UAV-LiDAR
Authors: Zhou Xinshao , Ma Kaisen , Sun Hua , Li Chaokui , Wang Yonghong
Published: July 2024

The main problems of forest parameter extraction and forest stand volume estimation using unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology are the lack of precision in individual tree segmentation and the inability to directly obtain the diameter at breast height (DBH) parameter. To address such limitations, the study proposed an improved individual tree segmentation method combined with a DBH prediction model to obtain the tree height (H) and DBH for calculating the volume of trees, thus realizing the accurate estimation of forest stand volume from individual tree segmentation aspect. The method involves the following key steps: (1) The local maximum method with variable window combined with the Gaussian mixture model were used to detect the treetop position using the canopy height model for removing pits. (2) The measured tree DBH and H parameters of the sample trees were used to construct an optimal DBH-H prediction model. (3) The duality standing tree volume model was used to calculate the forest stand volume at the individual tree scale. The results showed that: (1) Individual tree segmentation based on the improved Gaussian mixture model with optimal accuracy, detection rate r, accuracy rate p, and composite score F were 89.10%, 95.21%, and 0.921, respectively. The coefficient of determination R2 of the accuracy of the extracted tree height parameter was 0.88, and the root mean square error RMSE was 0.84 m. (2) The Weibull model had the optimal model fit for DBH-H with predicted DBH parameter accuracy, the R2 and RMSE were 0.84 and 2.28 cm, respectively. (3) Using the correctly detected trees from the individual tree segmentation results combined with the duality standing tree volume model estimated the forest stand volume with an accuracy AE of 90.86%. In conclusion, using UAV-LiDAR technology, based on the individual tree segmentation method and the DBH-H model, it is possible to realize the estimation of forest stand volume at the individual tree scale, which helps to improve the estimation accuracy.
Enhancing Cycling Safety in Smart Cities: A Data-Driven Embedded Risk Alert System
Authors: Ferreira José , Costa Daniel
Published: July 2024

The safety of cyclists on city streets is a significant concern, particularly with the rising number of accidents in densely populated areas. Urban environments present numerous challenges, such as complex road networks and heavy traffic, which increase the risk of cycling-related incidents. Such concern has been recurrent, even within smart city scenarios that have been focused on only expanding the cycling infrastructure. This article introduces an innovative low-cost embedded system designed to improve cycling safety in urban areas, taking geospatial data as input. By assessing the proximity to emergency services and utilizing GPS coordinates, the system can determine the indirect current risk level for cyclists, providing real-time alerts when crossing high-risk zones. Built on a Raspberry Pi Zero board, this solution is both cost-effective and efficient, making it easily reproducible in various urban settings. Preliminary results in Porto, Portugal, showcase the system’s practical application and effectiveness in enhancing cycling safety and supporting sustainable urban mobility.
Regulatory Fit to Enhance User Engagement with an App Promoting Healthy and Sustainable Eating. An Experimental Study to Match Regulatory Concern and Anticipated Emotions
Authors: Carfora Valentina , Festa Simone , Pompili Sara , Azzena Italo , Guidetti Margherita , Scaglioni Giulia , Carraro Luciana , Lenzi Michela , Scatolon Andrea , Cavazza Nicoletta , Catellani Patrizia
Published: July 2024

Mobile apps can effectively promote healthy and sustainable eating, but their success depends on initial user engagement. This study aimed to increase initial user engagement by tailoring app descriptions to users’ regulatory focus (prevention versus promotion). Four conditions were created by combining two types of messages: regulatory concern (safety versus growth) and anticipated emotion (positive versus negative). The safety message emphasized the protection of health and the environment, while the growth message emphasized the improvement of well-being and environmental conservation. The message with the positive anticipated emotion described satisfaction with using the app, while the message with the negative anticipated emotion described dissatisfaction with not using the app. Participants’ attitudes, desires, and intentions, as well as downloads of the app were measured to assess the effectiveness of the message. Promotion-focused individuals showed greater desire and were more likely to download the app when the message matched their focus (growth + positive anticipated emotions) than when it did not (safety + negative anticipated emotions). Conversely, prevention-focused individuals did not show increased engagement with regulatory congruent messages. These results suggest that tailoring app descriptions to users’ regulatory focus may increase initial engagement among promotion-focused individuals. Further research is needed to investigate methods to increase engagement among prevention-focused individuals.
Charting Pollution Effects on Tourism: A Regional Analysis
Authors: Sheng Dachen , Montgomery Heather A.
Published: July 2024

This study presents empirical findings highlighting the role of pollution control measures in shaping the trajectory of local tourism market development. Analysis of provincial-level panel data underscores the pronounced impact of water pollution compared to air pollution. While water pollution tends to manifest as a localized issue, air pollution transcends provincial boundaries, posing challenges that extend across multiple regions simultaneously. The results demonstrate how imperative it is for northern China’s heavily polluted provinces to redouble efforts aimed at ameliorating their negative image within the tourism market. In contrast to their southern counterparts, these regions currently face hurdles in attracting tourists, underscoring the disparity in tourism acceptance between northern and southern provinces. Drawing insights from the successful implementation of tourism initiatives centered on the small-town economy in southern China, this research advocates for a paradigm shift in policy formulation for northern provinces. By leveraging lessons learned from southern counterparts, policymakers can chart a course toward sustainable tourism development tailored to the unique characteristics of each region. In delineating the differential impacts of air and water pollution on the Chinese tourism market, this study contributes to a nuanced understanding of tourism dynamics across provinces. The findings serve as a foundational framework for guiding future tourism market development strategies tailored to the heterogeneous landscape of Chinese provinces.
Effects of Personal Values and Clothing Style Confidence on Consumers’ Interest in Upcycled Clothing Products
Authors: Roster Catherine A.
Published: July 2024

The overconsumption of clothing has detrimental impacts on society and the environment. For consumers, reducing consumption is complicated by the vital role that clothing plays in individual expression. This study examined the influence of personal values and clothing style confidence on consumers’ interest in upcycled clothing. An online Internet survey was used to gather data from a valid sample of 565 U.S. residents. Partial least squares structural equation modeling was used to analyze the data. Schwartz’s self-transcendence and self-enhancement values were modeled as antecedents to clothing style confidence (CSC), which is a multi-dimensional construct composed of five factors, including (1) style longevity, (2) aesthetic perceptive ability, (3) creativity, (4) appearance importance, and (5) authenticity. CSC was predicted to mediate the relationship between self-transcendence and self-enhancement values and interest in upcycled clothing, including the purchase of upcycled clothing and interest in learning how to upcycle clothing. Findings showed that CSC mediated the relationship between self-self-transcendence and self-enhancement values and interest in upcycled clothing, as predicted according to value–attitude–behavior theory. Results suggest that bolstering consumers’ confidence in personal style may provide intrinsic motivation for change, empowering individuals to embrace their personal style rather than follow fashion trends.
Experimental Investigation on the Damage Evolution of Thermally Treated Granodiorite Subjected to Rapid Cooling with Liquid Nitrogen
Authors: Gomah Mohamed , Wang Enyuan , Omar Ahmed
Published: July 2024

In many thermal geotechnical applications, liquid nitrogen (LN2) utilization leads to damage and cracks in the host rock. This phenomenon and associated microcracking are a hot topic that must be thoroughly researched. A series of physical and mechanical experiments were conducted on Egyptian granodiorite samples to investigate the effects of liquid nitrogen cooling on the preheated rock. Before quenching in LN2, the granodiorite was gradually heated to 600 °C for two hours. Microscopical evolution was linked to macroscopic properties like porosity, mass, volume, density, P-wave velocity, uniaxial compressive strength, and elastic modulus. According to the experiment results, the thermal damage, crack density, porosity, and density reduction ratio increased gradually to 300 °C before severely degrading beyond this temperature. The uniaxial compressive strength declined marginally to 200 °C, then increased to 300 °C before monotonically decreasing as the temperature rose. On the other hand, at 200 °C, the elastic modulus and P-wave velocity started to decline significantly. Thus, 200 and 300 °C were noted in this study as two mutation temperatures in the evolution of granodiorite mechanical and physical properties, after which all parameters deteriorated. Moreover, LN2 cooling causes more remarkable physical and mechanical modifications at the same target temperature than air cooling. Through a deeper comprehension of how rocks behave in high-temperature conditions, this research seeks to avoid and limit future geological risks while promoting sustainability and understanding the processes underlying rock failure.
Investigating TQM Strategies for Sustainable Customer Satisfaction in GCC Telecommunications
Authors: Alsaqer Saud , Katar Ihab M. , Abdelhadi Abdelhakim
Published: July 2024

Telecommunications firms face intense competition driven by rapid innovation and shifting consumer expectations. To remain competitive, companies are adopting Total Quality Management (TQM) to enhance customer satisfaction, corporate stability, and sustainability. This study examines TQM’s effects on customer satisfaction within Gulf Cooperation Council (GCC) countries’ telecommunications sector using secondary data from three firms’ quarterly reports (2019–2023). Descriptive, correlation, and regression analyses with STATA software reveal a significant increase in net promoter scores, indicating firms’ commitment to meeting evolving customer needs. Employee engagement and process management positively affect customer satisfaction, while continuous improvement practices and customer focus do not show a statistically significant influence. The research underscores TQM’s importance in fostering sustainable customer satisfaction by enabling telecom companies to adopt customer-centric strategies for achieving sustainable growth and long-term success. Aligning business processes with customer needs, especially in complaint handling, is crucial. The study advocates for implementing advanced customer relationship management (CRM) systems to better understand customer preferences. These strategic initiatives are vital for telecom firms to maintain competitiveness, enhance customer satisfaction, and contribute to the region’s overall economy.
Sustainability in Leadership: The Implicit Associations of the First-Person Pronouns and Leadership Effectiveness Based on Word Embedding Association Test
Authors: Yao Qu , Zheng Yingjie , Chen Jianhang
Published: July 2024

The first-person pronoun is an indispensable element of the communication process. Meanwhile, leadership effectiveness, as the result of leaders’ leadership work, is the key to the sustainable development of leaders and corporations. However, due to the constraints of traditional methods and sample bias, it is challenging to accurately measure and validate the relationship between first-person pronouns and leadership effectiveness at the implicit level. Word Embedding Association Test (WEAT) measures the relative degree of association between words in natural language by calculating the difference in word similarity. This study employs the word and sentence vector indicators of WEAT to investigate the implicit relationship between first-person pronouns and leadership effectiveness. The word vector analyses of the Beijing Normal University word vector database and Google News word vector database demonstrate that the cosine similarity and semantic similarity of “we-leadership effectiveness” are considerably greater than that of “I-leadership effectiveness”. Furthermore, the sentence vector analyses of the Chinese Wikipedia BERT model corroborate this relationship. In conclusion, the results of a machine learning-based WEAT verified the relationship between first-person plural pronouns and leadership effectiveness. This suggests that when leaders prefer to use “we”, they are perceived to be more effective.
Performance of a Diesel Engine Fueled by Blends of Diesel Fuel and Synthetic Fuel Derived from Waste Car Tires
Authors: Jakubowski Mirosław , Jaworski Artur , Kuszewski Hubert , Balawender Krzysztof
Published: July 2024

Waste car tires are a significant burden on the environment. One way to manage them is through energy recovery by burning them in the furnaces of combined heat and power plants or cement plants, which from an environmental point of view is not a favorable solution. Another way to use waste tires is to produce liquid fuels, which can be used as pure fuels or components added to conventional fuels. Therefore, it is necessary to conduct research aimed at evaluating the physical and chemical properties of tire-derived fuels relative to conventional fuels. It is also important to determine the impact of feeding engines with synthetic fuels, regarding their operational and environmental performance. In this article, the physicochemical properties of typical diesel fuel, synthetic fuel derived from waste tires (WT) and its blends with diesel fuel (DF) in shares of 5, 10, 15, 20 and 25% v/v were studied. Tests were also conducted on an internal combustion engine with a common rail injection system (CR IC) engine to determine operational and emission parameters. The results showed, among other things, a deterioration relative to diesel fuel of such parameters as cold filter plugin point (CFPP) and flash point (FP). At the same time, a favorable effect of synthetic fuel addition was noted on hydrocarbon (HC) and nitrogen oxide (NOx) emissions.
The Good, The Bad and The Ugly: An Open Image Dataset for Automated Sorting of Good, Bad, and Imperfect Produce Using AI and Robotics
Authors: Sharma Anjali , Kumar Vikas , Musunur Laxmi P.
Published: July 2024

In the face of the impending challenge of feeding a growing global population, one-third of all food produced ends up as waste. A notable contributor to this problem is the wastage of a third of perfectly edible and nutritious fresh produce because they need to meet the high cosmetic standards expected by consumers. Eliminating this wastage of imperfect produce is, therefore, a crucial and sustainable means to increase the food supply for a growing global population. This can be achieved through automated sorting of good, bad and imperfect produce using automation, robotics and machine vision. A prerequisite for such automated sorting is fast and accurate machine vision algorithms for successful differentiation between good, bad and imperfect produce. Training such algorithms requires large image datasets. While much work has gone into collecting images of good and bad produce, to the best of our knowledge, no such dataset exists for imperfect produce items. In this paper, we attempt to fill this gap by developing the first publicly available dataset of good, bad and imperfect produce items. The dataset has been made publicly available on the Harvard Dataverse for use in training machine vision algorithms for sorting good, bad and imperfect produce. It is our hope that this open dataset will contribute to improving research and practice for sorting and saving imperfect produce in the food supply chain.
Construction of Ground-State Solutions of the Gross–Pitaevskii–Poisson System Using Genetic Algorithms
Authors: Tena-Contreras Carlos , Alvarez-Ríos Iván , Guzmán Francisco S.
Published: July 2024

We present the construction of the ground state of the Gross–Pitaevskii–Poisson equations using genetic algorithms. By employing numerical solutions, we develop an empirical formula for the density that works within the considered parameter space. Through the analysis of both numerical and empirical solutions, we investigate the stability of these ground-state solutions. Our findings reveal that while the numerical solution outperforms the empirical formula, both solutions lead to similar oscillation modes. We observe that the stability of the solutions depends on specific values of the central density and the nonlinear self-interaction term and establish an empirical criterion delineating the conditions under which the solutions exhibit stability or instability.
Detection of High-Risk Human Papillomavirus (HPV), p16 and EGFR in Lung Cancer: Insights from the Mediterranean Region of Turkey
Authors: Alikanoğlu Arsenal Sezgin , Karaçay İrem Atalay
Published: July 2024

Human papillomavirus (HPV) is an oncogenic DNA virus that plays a role in different cancer types. The aim of this study was to detect the prevalence and types of HPV and its relation with p16, EGFR and clinical findings in lung cancer. HPV and EGFR detection and genotyping of HPV were performed by polymerase chain reaction (PCR) and p16 by immunohistochemistry. Fifty lung cancer patients and seven patients with non-neoplastic lung disease were enrolled in this study. HPV was positive in 78% (39/50) of lung cancer cases. HPV 51 was the most frequent type, followed by HPV 16. Moreover, p16 was positive in 24% (12/50) of the cancer patients, and all of these patients were HPV-positive, while 27 HPV-positive patients showed no p16 expression. There was no relationship between HPV infection and p16 (p = 0.05), gender (p = 0.42), age (p = 0.38), or smoking history (p = 0.68). Although not statistically significant, the HPV prevalence was found to be higher in cancer patients compared to non-neoplastic patients. The prevalence of HPV in lung cancer varies across different studies, which may be due to differences in the detection methods, number of patients, geographic regions, and vaccination status. Further studies are necessary to understand the role of HPV in lung cancer pathogenesis.
Larval Competition between Aedes and Culex Mosquitoes Carries over to Higher Arboviral Infection during Their Adult Stage
Authors: Vanslembrouck Adwine , Jansen Stephanie , De Witte Jacobus , Janssens Corneel , Vereecken Stien , Helms Michelle , Lange Unchana , Lühken Renke , Schmidt-Chanasit Jonas , Heitmann Anna , Müller Ruth
Published: July 2024

The common house mosquito (Culex pipiens) is a native vector for West Nile virus (WNV). Invasive species like the tiger mosquito (Aedes albopictus) and Asian bush mosquito (Aedes japonicus) are rapidly spreading through Europe, posing a major threat as vectors for dengue, chikungunya (CHIKV), and Japanese encephalitis virus (JEV). These mosquitoes share a similar ecological niche as larvae, but the carry-over effects of aquatic larval interactions to the terrestrial adult stage remain largely unknown and their medical relevance requires further investigation. This study examines the context dependency of larval interactions among Aedes albopictus, Aedes japonicus, and Culex pipiens. The survival, development time, growth, and energetic storage were measured in different European populations within density-response (intraspecific) experiments and replacement (interspecific) experiments at 20 °C and 26 °C. Overall, Ae. japonicus was the weakest competitor, while competition between Ae. albopictus and Cx. pipiens varied with temperature. Adults emerging from this larval competition were infected as follows: Culex pipiens with WNV, Ae. albopictus with CHIKV, and Ae. japonicus with JEV. While no JEV infection was observed, mosquitoes experiencing interspecific interactions during their larval stages exhibited higher infection rates and viral RNA titers for CHIKV and WNV. This increased susceptibility to viral infection after larval competition suggests a higher risk of arbovirus transmission in co-occurring populations.
New Detection Methods for Cryphonectria Hypovirus 1 (CHV1) through SYBR Green-Based Real-Time PCR and Loop-Mediated Isothermal Amplification (LAMP)
Authors: Çelik Ali , Çakar Deniz , Derviş Sibel , Morca Ali Ferhan , Akıllı Şimşek Seçil , Romon-Ochoa Pedro , Özer Göksel
Published: July 2024

Some mycoviruses can be considered as effective biocontrol agents, mitigating the impact of phytopathogenic fungi and consequently reducing disease outbreaks while promoting plant health. Cryphonectria parasitica, the causal agent of chestnut blight and a highly destructive pathogen, experienced a notable decrease in its virulence with the identification of cryphonectria hypovirus 1 (CHV1), a naturally occurring biocontrol agent. In this study, two innovative diagnostic protocols designed for the accurate and efficient detection of CHV1 are introduced. The ORF A and ORF B regions of CHV1 are targeted by these techniques, which employ colorimetric loop-mediated isothermal amplification (LAMP) with 2 Colorimetric LAMP Master Mix and real-time quantitative PCR (qPCR) with SYBR Green chemistry, respectively. The LAMP assay presents a discernible color transition, changing from pink to yellow after a 35 min incubation period. Comparative analysis, when assessed against two established reverse transcription-PCR (RT-PCR) techniques, reveals a significant enhancement in sensitivity for both the LAMP approach, which offers a tenfold increase, and the qPCR method, which showcases a remarkable 100-fold sensitivity improvement. Throughout the comparison phase, it was evident that the RT-PCR, LAMP, and qPCR procedures displayed superior performance compared to the Bavendamm test, relying on phenol oxidase activity, effectively distinguishing hypovirulent strains. Consequently, this study introduces two pioneer diagnostic assays for highly sensitive CHV1 detection, representing a substantial advancement in the realm of CHV1 surveillance techniques. These methodologies hold significant promise for enhancing research endeavors in the domain of the biological control of C. parasitica.
Prevalence and Sequence Analysis of Equine Rhinitis Viruses among Horses in Poland
Authors: Stasiak Karol , Dunowska Magdalena , Rola Jerzy
Published: July 2024

Equine rhinitis A (ERAV) and B (ERBV) viruses are respiratory pathogens with worldwide distribution. The current study aimed to determine the frequency of infection of ERAV and ERBV among horses and foals at Polish national studs, and to determine genetic variability within the viruses obtained. Virus-specific quantitative RT-PCR assays targeting a 5′ untranslated region were used to screen nasal swabs collected from 621 horses at 16 national horse studs from throughout Poland, including 553 healthy horses and 68 horses with respiratory disease. A partial DNA polymerase gene was amplified and sequenced from the qRT-PCR-positive samples. The obtained sequences were analysed using phylogeny and genetic network analysis. None of the nasal swabs were positive for ERAV, whereas ERBV was found in 11/621 (1.78%) samples collected from 10 healthy horses and one foal affected by respiratory disease. Partial DNA polymerase gene sequence variability was correlated with individual horses and studs from which samples were collected when only Polish sequences were analysed, but there was no correlation between country of origin and ERBV sequence when Polish and international sequences were included in the network. The report presents the first detection of ERBV in Poland.
Relationship between Modern ART Regimens and Immunosenescence Markers in Patients with Chronic HIV Infection
Authors: Grozdeva Rusina , Ivanov Daniel , Strashimirov Dimitar , Kapincheva Nikol , Yordanova Ralitsa , Mihailova Snejina , Georgieva Atanaska , Alexiev Ivailo , Grigorova Lyubomira , Partsuneva Alexandra , Dimitrova Reneta , Gancheva Anna , Kostadinova Asya , Naseva Emilia , Yancheva Nina
Published: July 2024

The increased life expectancy of PLHIV (People Living with HIV) and the successful highly combined antiretroviral therapy (cART) poses new clinical challenges regarding aging and its co-morbid condition. It is commonly believed that HIV infection “accelerates” aging. Human immunodeficiency virus type 1 (HIV-1) infection is characterized by inflammation and immune activation that persists despite cART, and that may contribute to the development of co-morbid conditions. In this regard, we aimed to compare current cART regimens in light of premature aging to evaluate differences in their ability to reduce immune activation and inflammation in virologically suppressed patients. We studied a panel of biomarkers (IFN-γ, IL-1β, IL-12p70, IL-2, IL-4, IL-5, IL-6, IL-13, IL-18, GM-CSF, TNF-α, C-reactive protein, D-dimer, soluble CD14), which could provide a non-invasive and affordable approach to monitor HIV-related chronic inflammation. The results of the current study do not provide hard evidence favoring a particular cART regimen, although they show a less favorable regimen profile containing a protease inhibitor. Our data suggest an incomplete reduction of inflammation and immune activation in terms of the effective cART. It is likely that the interest in various biomarkers related to immune activation and inflammation as predictors of clinical outcomes among PLHIV will increase in the future.
Immunogenicity of an Inactivated Senecavirus A Vaccine with a Contemporary Brazilian Strain in Mice
Authors: Barbosa Amanda de Oliveira , Gava Danielle , Tochetto Caroline , Ribeiro Leonardo Clasen , Bastos Ana Paula Almeida , Morés Marcos Antônio Zanella , Schaefer Rejane , de Lima Marcelo
Published: July 2024

Senecavirus A (SVA) is a picornavirus that is endemic in swine, causing a vesicular disease clinically indistinguishable from other vesicular diseases, like foot-and-mouth disease. The widespread viral circulation, constant evolution, and economic losses caused to the swine industry emphasize the need for measures to control the agent. In this study, we evaluated the immunogenicity of a whole-virus-inactivated vaccine using a representative contemporary Brazilian SVA strain in Balb/ByJ mice. The animals were vaccinated with two doses by an intramuscular route. The humoral response induced by the vaccination was evaluated by an in-house ELISA assay for IgG detection. The cellular response was assessed by flow cytometry after in vitro SVA stimulation in splenocyte cultures from vaccinated and non-vaccinated groups. Protection against SVA was assessed in the experimental groups following an oral challenge with the homologous virus. The vaccination induced high levels of IgG antibodies and the proliferation of CD45R/B220+sIgM+, CD3e+CD69+, and CD3e+CD4+CD44+CD62L− cells. These results indicate the immunogenicity and safety of the vaccine formulation in a murine model and the induction of humoral and cellular response against SVA.
Feasibility of Groundwater Extraction in Nitrate-Impacted Groundwater Source in Serbia: Hydrodynamic Modeling and Nitrate Tracing
Authors: Perović Marija , Zuber-Radenković Vesna , Zorić Miloš
Published: July 2024

Groundwater, essential for supplying drinking water to half of the global population and supporting nearly half of all irrigation needs, faces significant contamination risks. These risks pose serious threats to human health and ecosystem integrity, driven by increasing pressures from both concentrated and diffuse pollution sources, as well as from growing exploitation. The presented research was conducted with the dual objectives of identifying sources of nitrate contamination (up to 128.1 mg/L) in an oxic groundwater source (Perkićevo, Serbia) and proposing an optimal extraction regimen to ensure a sufficient supply of potable water. Correlations between chemical elements’ concentrations and principal component analysis (PCA) indicated a significant relationship between anthropogenic impact indicators (NO3−, Na+, B, Cl−, SO42−, KMnO4 consumption, and electroconductivity), unambiguously showing that groundwater quality was primarily impacted by untreated sewage inflow and confirming nitrate’s tracer behavior in oxic environments. The spatial distribution of selected parameter concentration gradients highlighted the expansion and distribution of the contamination front. A numerical groundwater flow model (Vistas 4 and Modflow) was applied to determine the groundwater flow direction and the quantity of groundwater originating from different parts of the investigated area. Through four simulated groundwater extraction scenarios, Scenario 2, with an average extraction rate of 80 L/s from 12 wells, and Scenario 3, with an average extraction rate of 75 L/s and 4 additional wells, were identified as the most optimal, providing a sufficient quantity of adequately sanitary water.
Temporal Dynamics and Predictive Modelling of Streamflow and Water Quality Using Advanced Statistical and Ensemble Machine Learning Techniques
Authors: Farzana Syeda Zehan , Paudyal Dev Raj , Chadalavada Sreeni , Alam Md Jahangir
Published: July 2024

Changes in water quality are closely linked to seasonal fluctuations in streamflow, and a thorough understanding of how these variations interact across different time scales is important for the efficient management of surface water bodies such as rivers, lakes, and reservoirs. The aim of this study is to explore the potential connection between streamflow, rainfall, and water quality and propose an optimised ensemble model for the prediction of a water quality index (WQI). This study modelled the changes in five water quality parameters such as ammonia nitrogen (NH3-N), phosphate (PO43−), pH, turbidity, total dissolved solids (TDS), and their associated WQI caused by rainfall and streamflow. The analysis was conducted across three temporal scales, weekly, monthly, and seasonal, using a generalised additive model (GAM) in Toowoomba, Australia. TDS, turbidity, and WQI exhibited a significant nonlinear variation with the changes in streamflow in the weekly and monthly scales. Additionally, pH demonstrated a significant linear to weakly linear correlation with discharge across the three temporal scales. For the accurate prediction of WQI, this study proposed an ensemble model integrating an extreme gradient boosting (XGBoost) and Bayesian optimisation (BO) algorithm, using streamflow as an input across the same temporal scales. The results for the three temporal scales provided the best accuracy of monthly data, based on the accuracy metrics R2 (0.91), MAE (0.20), and RMSE (0.42). The comparison between the test and predicted data indicated that the prediction model overestimated the WQI at some points. This study highlights the efficiency of integrating rainfall, streamflow, and water quality correlations for WQI prediction, which can provide valuable insights for guiding future water management strategies in similar catchment areas, especially amidst changing climatic conditions.
Geochemical Characterization and Prediction of Water Accumulation in the Goaf under Extra-Thick Fully Mechanized Top-Coal-Caving Mining
Authors: Wang Jianghong , Wang Hongwei , Yin Shaobo , Liao Qingfa , Ju Qiding , Chen Kai
Published: July 2024

In multi-seam coal mining, the water accumulation in the goaf of the upper coal seam will seriously threaten the safety of the lower coal-seam recovery. How to accurately determine the water charging source in the goaf and predict the amount of water accumulation in the goaf after a certain time interval has become a major challenge that urgently needs to be solved in coal production. In this study, we consider the water-discharging goaf of the Tangjiahui Coal Mine as the object of research to investigate the problem of water accumulation in the goaf during the fully mechanized caving mining of extra-thick seams of top coal. We used geochemical methods, water-accumulation space methods, and large-well methods to analyze the hydraulic connections between goaf water and other aquifers, predict the amount of water accumulation in the goaf, and explore the characteristics of water level changes over time. We then used the results to discuss the relationship between the elevation of the accumulated water and the time taken for it to fill the goaf. The results showed that there is a hydraulic connection between the water in the airspace and the goaf water (GW), roof water (RW), floor water (FW) and Ordovician limestone water (OW); the volume of water in the goaf of the working face after mining was 2,106,838.496 m3. The average rate of water accumulation was 65.407 m3/h, and the goaf was expected to have been filled in 32,211.208 h. The derived relationship between the water level and time was H0=−10−12t3+10−7t2−0.0042t+814.61 (R2=0.9837). This study is of great significance for the sustainable development of the safety evaluation of water blocking coal pillars at the mine boundary.
Displacement Prediction Method for Rainfall-Induced Landslide Using Improved Completely Adaptive Noise Ensemble Empirical Mode Decomposition, Singular Spectrum Analysis, and Long Short-Term Memory on Time Series Data
Authors: Yang Ke , Wang Yi , Duan Gonghao
Published: July 2024

Landslide disasters frequently result in significant casualties and property losses, underscoring the critical importance of research on landslide displacement prediction. This paper introduces an approach combining improved empirical mode decomposition (ICEEMDAN) and singular entropy-enhanced singular spectrum analysis (SSA) to predict landslide displacement using a time series short-duration memory network (LSTM). Initially, ICEEMDAN decomposes the landslide displacement time series into trend and periodic terms. SSA is then employed to denoise these components before fitting the trend term with LSTM. Pearson correlation analysis is utilized to identify characteristic factors within the LSTM model, followed by predictions using a multivariate LSTM model. The empirical results from the Baijiabao landslide in the Three Gorges Reservoir area demonstrate that the joint ICEEMDAN-SSA approach, when combined with LSTM modeling, outperforms the separate applications of SSA and ICEEMDAN, as well as other models such as RNN and SVM. Specifically, the ICEEMDAN-SSA-LSTM model achieves an RMSE of 6.472 mm and an MAE of 4.992 mm, which are considerably lower than those of the RNN model (19.945 mm and 15.343 mm, respectively) and the SVM model (16.584 mm and 11.748 mm, respectively). Additionally, the R2 value for the ICEEMDAN-SSA-LSTM model is 97.5%, significantly higher than the RNN model’s 72.3% and the SVM model’s 92.8%. By summing the predictions of the trend and periodic terms, the cumulative displacement prediction is obtained, indicating the superior accuracy of the ICEEMDAN-SSA-LSTM model. This model provides a new benchmark for precise landslide displacement prediction and contributes valuable insights to related research.
Metal/Metalloid Presence and Health Risks in Drinking Water Plants—Insights from Nanjing, China
Authors: Huang Yang , Zheng Yuhong , Chen Chunjing , Xiong Lilin , Liu Ran
Published: July 2024

Metal contamination in drinking water is well known; however, detailed insights into the metals/metalloids in finished water and their health effects are lacking. Water samples collected over four seasons from August 2022 to April 2023 from three locations in the Yangtze River basin in Nanjing were analyzed using inductively coupled plasma mass spectrometry to detect 33 metal/metalloid types. This study assessed seasonal and spatial variations and evaluated the removal efficiencies of advanced treatment processes. Health risks from metal/metalloid ingestion were calculated using a risk assessment model from the United States Environmental Protection Agency (USEPA). No significant regional differences in total metal/metalloid concentrations were found between the source and finished waters. Metals such as Cr, Ga, Hg, Pb, and Rb varied seasonally, peaking in winter. The advanced treatment process outperformed the conventional process for As removal. However, the removal efficiencies were lower for Mn, Cd, Hg, Se, and Zn than those of the conventional process. Positive correlations were observed among several metals, suggesting that metals may have similar sources of contamination. The main carcinogenic risk exceeded the acceptable levels in children according to the USEPA. Non-carcinogenic risks were below the threshold values, but As, Tl, and V were highlighted as key risk elements. Seasonal changes markedly influenced metal/metalloid concentrations and distributions in water, necessitating risk management focused on arsenic.