bluuebunny/crossref_metadata_2025 huggingface dataset:
Calm Advice: How Digitalizing Pen-and-Paper Practices Improves Financial Advice-Giving
Authors: Staehelin Dario , Dolata Mateusz , Schwabe Gerhard
Published: June 2024

Bank clients’ expectations for enhanced service experiences have increased with the digitalization of banking services and the rise of FinTech. However, despite the availability of online banking services, many clients still prefer personal financial advice due to the personal interaction involved. There has been a growing interest in IT-supported advisory services to meet this demand, aiming to improve customer experience and reduce the cognitive burden on advisors. While previous studies have shown the positive effects of advisory-support systems, they also highlighted downsides such as unnatural interactions, technology’s domination of the interaction space, and impaired impression management. This paper investigates the potential of pen-and-paper user interfaces to resolve the relationship/decision-making tension in advisory services. It evaluates the design of an artifact called bankNotes with eight bank advisors and 24 clients in a within-subject study by drawing on a rich data set consisting of interviews, surveys, and video analysis of the encounters. The results indicate that bankNotes was well-received by advisors, who embraced both existing pen-and-paper practices and new practices facilitated by the system. The clients also benefitted from the system: using bankNotes improved customer orientation, shared understanding, and overall service quality. This research provides valuable insights into the design of advisory support systems that prioritize customer satisfaction and support the needs of both advisors and clients in the banking sector.
Sustainable Innovation and Economic Resilience: Deciphering ESG Ratings’ Role in Lowering Debt Financing Costs
Authors: Zhao Yan , Gao Yubin , Hong Diming
Published: June 2024

This study delves into the intricate dynamics between environmental, social, and governance (ESG) ratings and corporate debt financing costs among China’s A-share listed companies from 2010 to 2021. Analyzing a substantial dataset unveils a tangible link where higher ESG ratings correlate with significantly lower debt financing costs. This relationship manifests more prominently in enterprises with lower pollution levels, state ownership, and those in China’s central and western regions, especially under heightened economic policy uncertainty. The findings elucidate that improved ESG ratings, reflective of robust sustainability practices, diminish corporate agency costs and enhance financial stability, thereby reducing debt financing expenses. This research extends the discourse in the knowledge economy by offering empirical evidence on how ESG integration can serve as a lever for financial efficiency and sustainability in business operations. By spotlighting the financial merits of embracing ESG criteria, the study offers profound insights for policymakers, investors, and corporations, encouraging the acceleration of ESG disclosure and assessment frameworks. In doing so, it supports the cultivation of high-caliber, environmentally friendly enterprises, aligning with the broader objectives of innovation, entrepreneurship, and societal welfare within the knowledge economy paradigm.
Intensity Zones and Intensity Thresholds Used to Quantify External Load in Competitive Basketball: A Systematic Review
Authors: Tuttle Matthew C. , Power Cody J. , Dalbo Vincent J. , Scanlan Aaron T.
Published: June 2024

Background

Despite widespread use of intensity zones to quantify external load variables in basketball research, the consistency in identifying zones and accompanying intensity thresholds using predominant monitoring approaches in training and games remains unclear.

Objectives

The purpose of this work was to examine the external load intensity zones and thresholds adopted across basketball studies using video-based time-motion analysis (TMA), microsensors, and local positioning systems (LPS).

Methods

PubMed, MEDLINE, and SPORTDiscus databases were searched from inception until 31 January 2023 for studies using intensity zones to quantify external load during basketball training sessions or games. Studies were excluded if they examined players participating in recreational or wheelchair basketball, were reviews or meta-analyses, or utilized monitoring approaches other than video-based TMA, microsensors, or LPS.

Results

Following screening, 86 studies were included. Video-based TMA studies consistently classified jogging, running, sprinting, and jumping as intensity zones, but demonstrated considerable variation in classifying low-intensity (standing and walking) and basketball-specific activities. Microsensor studies mostly utilized a single, and rather consistent, threshold to identify only high-intensity activities (> 3.5 m·s−2 for accelerations, decelerations, and changes-in-direction or > 40 cm for jumps), not separately quantifying lower intensity zones. Similarly, LPS studies predominantly quantified only high-intensity activities in a relatively consistent manner for speed (> 18.0 m·s−1) and acceleration/deceleration zones (> 2.0 m·s−2); however, the thresholds adopted for various intensity zones differed greatly to those used in TMA and microsensor research.

Conclusions

Notable inconsistencies were mostly evident for low-intensity activities, basketball-specific activities, and between the different monitoring approaches. Accordingly, we recommend further research to inform the development of consensus guidelines outlining suitable approaches when setting external load intensity zones and accompanying thresholds in research and practice.
Osteosarcopenia increases the risk of mortality: a systematic review and meta-analysis of prospective observational studies
Authors: Veronese Nicola , Ragusa Francesco Saverio , Sabico Shaun , Dominguez Ligia J. , Barbagallo Mario , Duque Gustavo , Al-Daghri Nasser
Published: June 2024

Background & aims

Osteosarcopenia is a recently recognized geriatric syndrome. The association between osteosarcopenia and mortality risk is still largely underexplored. In this systematic review with meta-analysis of prospective cohort studies, we aimed to explore whether osteosarcopenia could be associated with a higher mortality risk.

Methods

Several databases were searched from the inception to 16th February 2024 for prospective cohort studies dealing with osteosarcopenia and mortality. We calculated the mortality risk in osteosarcopenia vs. controls using the most adjusted estimate available and summarized the data as risk ratios (RRs) with their 95% confidence intervals (CIs). A random-effect model was considered for all analyses.

Results

Among 231 studies initially considered, nine articles were included after exclusions for a total of 14,429 participants (mean age: 70 years; 64.5% females). The weighted prevalence of osteosarcopenia was 12.72%. Over a mean follow-up of 6.6 years and after adjusting for a mean of four covariates, osteosarcopenia was associated with approximately 53% increased risk of mortality (RR: 1.53; 95% CI: 1.28–1.78). After accounting for publication bias, the re-calculated RR was 1.48 (95%CI: 1.23–1.72). The quality of the studies was generally good, as determined by the Newcastle Ottawa Scale.

Conclusions

Osteosarcopenia was significantly linked with an increased risk of mortality in older people, indicating the need to consider the presence of osteoporosis in patients with sarcopenia, and vice versa, since the combination of these two conditions typical of older people may lead to further complications, such as mortality.
The readiness of Jordanian-listed firms toward CAATTs application in the post Covid-19 epidemic period
Authors: Alnatour Abdulrahman , Al-Mawali Hamzah , Zaidan Hala , Meqbel Rasmi , Kawuq Shatha
Published: June 2024

This study examines the readiness of Jordanian-listed firms toward the application of Computer Assisted Audit Tools and Techniques (CAATTs) during the post-Covid-19 epidemic period. In particular, this study investigates the auditee perspective regarding the factors impacting the adoption of CAATTs based on the Organizational Readiness for Change (ORC) and the Technology Readiness Index (TRI) models. A cross-sectional survey was developed and distributed to 138 employees working in the accounting departments of Jordanian-listed firms. Partial least square structural equation modeling (PLS-SEM) was employed as the statistical analysis method to examine the study hypotheses. The results show a significant direct relationship of motivation for change, staff attributes, institutional resources, organizational climate and discomfort on readiness for change. Moreover, the results reveal that perceived fear of Covid-19 has a moderating effect on the direct relationship between motivation for change and readiness for change. However, the proposed relationships between optimism, innovativeness, and insecurity with readiness for change were not significant. This paper highlights the readiness toward CAATTs application from the auditee perspective rather than the external auditors. Moreover, this paper considers the changes in the work environment resulting from the Covid-19 pandemic on the motivation for change toward CAATTs. Lastly, the paper reveals the moderating role of perceived fear of Covid-19 on the influence of motivation for change on readiness to change. Hence, this paper set the basis for policymakers and practitioners toward evaluating the readiness of Jordanian-listed firms for CAATTs application.
From catch to consumer: enhancing seafood processing management with Industry 4.0 innovations
Authors: Subash Abhirami , Ramanathan Hareesh N. , Šostar Marko
Published: June 2024

The incorporation of Industry 4.0 technologies into the seafood processing sector presents a transformative potential for improving efficiency, sustainability, and transparency throughout the supply chain. This study aims to examine the impact of such technological advancements, including automation, robotics, blockchain, computer vision, AI, and IoT, on the seafood industry's operations, ranging from sorting, cleaning, and packing to quality assessment and product preservation. Conducted through a Systematic Literature Review (SLR) of Web of Science-indexed papers, this research assesses the synergy between innovation and business 4.0 technologies in seafood processing. Our findings suggest that these technologies not only promise enhanced operational efficiency and product quality but also offer sustainable practices through energy-efficient methods, waste management techniques, and innovative packaging solutions. Additionally, the study addresses the workforce implications, underscoring the shift in job roles towards more specialized and value-added positions rather than outright displacement. The primary data collected and analyzed herein underscores the critical role of Industry 4.0 in promoting sustainable resource management, optimizing processes, and ensuring ecological responsibility within the seafood sector, thereby contributing to the broader goals of food security and environmental sustainability.
Collective action in fishery resource management and its challenges in Ethiopia: a review
Authors: Zenbaba Oliyad Sori
Published: June 2024

Natural resources such as fish are vital for improving human livelihood and serving as sources of food, income and employment opportunities in Ethiopia. But, its degradation and overexploitation lead to anxiety about the living standards of human beings and other living things. The degradation of these resources, which is caused by the less common participation of all stakeholders in protecting the resources, is the most challenging issue in the country. The objective of this paper is to review fishery production and management and their challenges, the extent of the overexploitation of natural resources, the contributions of collective action in fishery production and management, and fishery collective action challenges in Ethiopia. The review paper identified that the existence of a lack of strong coordination among different actors like local communities, the government, development agents, research centers and donor organizations whose concern are with the fishery sector caused the degradation of water bodies and fishery resources in Ethiopia. These issues resulted in challenges in fishery resource production and management, mismanagement in solving social differences among local communities, and low cooperation among communities in protecting fish resources. Besides, these challenges also caused a limited expansion of fishery collective action, overexploitation of fishery resources, and less concern for the protection of fish diversity species in the country. Therefore, essential policy implications forwarded are creating and improving joint collaboration between fish users, local communities, government institutions and development agents; organizing and empowering the local communities in collective action to involve them in the fishery sector to contribute to practical improvements in the protection, production, management and sustainability of fish resources.
MoonIndex, an Open‐Source Tool to Generate Spectral Indexes for the Moon From M3 Data
Authors: Suárez‐Valencia Javier Eduardo , Rossi Angelo Pio , Zambon Francesca , Carli Cristian , Nodjoumi Giacomo
Published: June 2024

Spectral indexes are tools widely used to analyze the composition of planetary surfaces. Many indexes have been formulated over the years to map the lunar surface, but there is no unified database for them. In this work we describe an Open‐Source Python package called MoonIndex, that recreates 38 indexes compiled from the literature, using data from the Moon Mineralogy Mapper (M3). The processing started with the filtering of the data cubes to reduce the noise, the continuum of the spectrum was then removed using a convex hull or a second‐and‐first‐order fit method. Later, the indexes were calculated, following as possible the original formulations. The results on spectral indexes calculated before the continuum removal were similar to those of the original formulations. Conversely, the results obtained for spectral indexes calculated after the continual removal were not always coherent. Some indexes, like the band depth, are especially sensitive to the removal method, as well as the derived band areas and asymmetries. We also recreated RGB composite maps, our results highlight the compositional patterns in a similar way as the ones in the literature, even if the color ramps can differ. The products of MoonIndex are open, ready for interpretation, versatile, consistent, and cross‐comparable.
Disentangling the Advective Brewer‐Dobson Circulation Change
Authors: Šácha P. , Zajíček R. , Kuchař A. , Eichinger R. , Pišoft P. , Rieder H. E.
Published: June 2024

Climate models robustly project acceleration of the Brewer‐Dobson circulation (BDC) in response to climate change. However, the BDC trends simulated by comprehensive models are poorly constrained by observations, which cannot even determine the sign of potential trends. Additionally, the changing structure of the troposphere and stratosphere has received increasing attention in recent years. The extent to which vertical shifts of the circulation are driving the acceleration is under debate. In this study, we present a novel method that enables the attribution of advective BDC changes to structural changes of the circulation and of the stratosphere itself. Using this method allows studying the advective BDC trends in unprecedented detail and sheds new light into discrepancies between different data sets (reanalyses and models) at the tropopause and in the lower stratosphere. Our findings provide insights into the reliability of model projections of BDC changes and offer new possibilities for observational constraints.
WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models
Authors: Rasp Stephan , Hoyer Stephan , Merose Alexander , Langmore Ian , Battaglia Peter , Russell Tyler , Sanchez‐Gonzalez Alvaro , Yang Vivian , Carver Rob , Agrawal Shreya , Chantry Matthew , Ben Bouallegue Zied , Dueben Peter , Bromberg Carla , Sisk Jared , Barrington Luke , Bell Aaron , Sha Fei
Published: June 2024

WeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting.
Anomaly Detection Using Machine Learning in Hydrochemical Data From Hot Springs: Implications for Earthquake Prediction
Authors: Zhu Ruijie , Yang Fengtian , Zhou Xiaocheng , Tian Jiao , Zhang Yongxian , He Miao , Li Jingchao , Dong Jinyuan , Li Ying
Published: June 2024

This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. Six hot springs, located within an active fault zone along the southeastern coast of China, were carefully chosen as hydrochemical monitoring sites for an extended period of two and a half years. Using this data, a prediction model integrating six algorithms was developed to forecastM ≥ 5 earthquakes in Taiwan. The model's performance was validated against recorded earthquake events, and the factors influencing its predictive capability were analyzed. Our comprehensive analysis conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. The model parameters, including the anomaly detection rate (P) and earthquake response time threshold (M), significantly impact the model's predictive capabilities. Therefore, adjustments are needed to optimize the model's performance for practical use. Despite limitations such as the inability to differentiate pre‐earthquake anomalies from post‐earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods.
A Global Assessment of Groundwater Recharge Response to Infiltration Variability at Monthly to Decadal Timescales
Authors: Moeck Christian , Collenteur Raoul A. , Berghuijs Wouter R. , Luijendijk Elco , Gurdak Jason J.
Published: June 2024

Predictions of groundwater fluctuations in space and time are important for sustainable water resource management. Infiltration variability on monthly to decadal timescales leads to fluctuations in the water tables and thus groundwater resources. However, connections between global‐scale climate variability and infiltration patterns and groundwater are often poorly understood because the relationships between groundwater conditions and infiltration tend to be highly nonlinear. In addition, understanding is further hampered because many groundwater records are incomplete and groundwater tables are often anthropogenically influenced, which makes identifying the effects of infiltration variability difficult. Previous studies that have evaluated how infiltration variability controls groundwater are based on a limited number of point measurements. Here, we present a global assessment of how infiltration variability is expected to affect groundwater tables. We use an analytical solution derived from Richards' equation to model water level responses to idealized periodic infiltration variability with periods that range from months to decades, to approximate both the effects of short‐term and long‐term climate variability and thus infiltration patterns. Our global‐scale assessment reveals why infiltration variability would lead to periodicity in groundwater recharge in particular regions. The vadose zone strongly dampens short‐term (seasonal and shorter) variations in infiltration fluxes throughout most of Earth's land surface, while infiltration cycles exceeding 1 year would yield transient recharge, except in more arid regions. Our results may help forecasting long‐term groundwater tables and could support improving groundwater resource management.
Optimizing Drought Assessment for Soil Moisture Deficits
Authors: Hoylman Zachary H. , Holden Zachary , Bocinsky R. Kyle , Ketchum David , Swanson Alan , Jencso Kelsey
Published: June 2024

Accurate drought assessments are critical for mitigating the deleterious impacts of water scarcity on communities across the world. In many regions, deficits in soil moisture represent a key driver of drought conditions. However, relationships between soil moisture and widely used drought indicators have not been thoroughly evaluated. In addition, there has not been an in‐depth assessment of the accuracy of operational soil moisture models used for drought monitoring. Here, we used 2,405 observed time series of soil moisture from 637 long‐term monitoring stations across the conterminous United States to test the ability of meteorological drought indices and soil moisture models to accurately characterize soil moisture drought. The optimal timescales for meteorological drought indices varied substantially by depth, but were ∼30 days for depth averaged conditions; progressively longer timescales (∼10–80 days) represent progressively deeper soil moisture (2–36 in.). However, soil moisture models (including Short‐term Prediction Research and Transition Center, Soil Moisture Active Passive L4, and Topofire) significantly outperformed the meteorological drought indices for predicting standardized soil moisture anomalies and drought conditions. Additionally, soil moisture models represent near instantaneous conditions, implicitly aggregating antecedent data thereby eliminating the need for timescales, providing a more effective and convenient method for soil moisture drought monitoring. We conclude that soil moisture models provide a straightforward and favorable alternative to meteorological drought indices that better characterize soil moisture drought.
Effects of Freezing Temperature Parameterization on Simulated Sea‐Ice Thickness Validated by MOSAiC Observations
Authors: Gu Fengguan , Kauker Frank , Yang Qinghua , Han Bo , Fang Yongjie , Liu Changwei
Published: June 2024

Freezing temperature parameterization significantly impacts the heat balance at sea‐ice bottom and, consequently, the simulated sea‐ice thickness. Here, the single‐column model ICEPACK was used to investigate the impact of the freezing temperature parameterization on the simulated sea‐ice thermodynamic growth during the MOSAiC expedition from October 2019 to September 2020. It is shown that large model errors exist with the standard parameterization and that different formulations for calculating the freezing temperature impact the simulated sea‐ice thickness significantly. Considering the winter mixed layer temperature, a modified parameterization of the freezing point temperature based on Mushy scheme was developed. The mean absolute error (ratio) of simulating sea‐ice thickness for all buoys reduces from 7.4 cm (4.9%) with the “Millero” scheme, which performs the best among the existing schemes in the ICEPACK model, to 4.2 cm (2.9%) with the new developed scheme.
Two Effective Degrees of Freedom Can Represent the Dominant Features of Global Rayleigh Wave Dispersion Maps
Authors: Zhang Han , Ni Sidao , Liu Lijun , Schmandt Brandon
Published: June 2024

Objectively exploring global variations in crust and upper mantle structure helps constrain fundamental aspects of Earth's plate tectonic and convective processes. Here we adopted a Variational Auto‐Encoder to explore the degrees of freedom of global Rayleigh wave dispersion maps at 4–40 mHz. We found that two latent variables sufficiently represent the global variations, suggesting inherent coupling between crustal and mantle seismic properties. We propose that the two extracted latent variables mostly correspond with crustal thickness and upper mantle thermal structure. The first variable shows low values for continental mountain belts and ocean spreading ridges, contrasted by high values for abyssal plains. The second variable shows low values for most oceanic lithosphere and Phanerozoic continental areas, contrasted by high values for Archean cratons. Latent space correlations indicate that continental lithosphere has more strongly coupled depth features than beneath the oceans, which might be a consequence of its longevity.
Aerosol‐Cloud Interactions Near Cloud Base Deteriorating the Haze Pollution in East China
Authors: Qi Ximeng , Zhu Caijun , Chen Liangduo , Chi Xuguang , Wang Jiaping , Niu Guangdong , Lai Shiyi , Nie Wei , Zhu Yannian , Huang Xin , Kokkonen Tom V. , Petäjä Tuukka , Kerminen Veli‐Matti , Kulmala Markku , Ding Aijun
Published: June 2024

Atmospheric aerosols not only cause severe haze pollution, but also affect climate through changes in cloud properties. However, during the haze pollution, aerosol‐cloud interactions are not well understood due to a lack of in situ observations. In this study, we conducted simultaneous observations of cloud droplet and particle number size distribution, together with supporting atmospheric parameters, from ground to cloud base in East China using a high‐payload tethered airship. We found that high concentrations of aerosols and cloud condensation nuclei were constrained below cloud, leading to the pronounced “Twomey effect” near the cloud base. The cloud inhibited the pollutants dispersion by reducing surface heat flux and thus deteriorated the near‐surface haze pollution. Satellite retrievals matched well with the in situ observations for low stratus clouds, while were insufficient to quantify aerosol‐cloud interactions for other cases. Our results highlight the importance to combine in situ vertical and satellite observations to quantify the aerosol‐cloud interactions.
Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response
Authors: Nagelberg Amy L. , Sihota Tianna S. , Chuang Yu-Chi , Shi Rocky , Chow Justine L. M. , English John , MacAulay Calum , Lam Stephen , Lam Wan L. , Lockwood William W.
Published: June 2024

Background

Identification of driver mutations and development of targeted therapies has considerably improved outcomes for lung cancer patients. However, significant limitations remain with the lack of identified drivers in a large subset of patients. Here, we aimed to assess the genomic landscape of lung adenocarcinomas (LUADs) from individuals without a history of tobacco use to reveal new genetic drivers of lung cancer.

Methods

Integrative genomic analyses combining whole-exome sequencing, copy number, and mutational information for 83 LUAD tumors was performed and validated using external datasets to identify genetic variants with a predicted functional consequence and assess association with clinical outcomes. LUAD cell lines with alteration of identified candidates were used to functionally characterize tumor suppressive potential using a conditional expression system both in vitro and in vivo.

Results

We identified 21 genes with evidence of positive selection, including 12 novel candidates that have yet to be characterized in LUAD. In particular, SNF2 Histone Linker PHD RING Helicase (SHPRH) was identified due to its frequency of biallelic disruption and location within the familial susceptibility locus on chromosome arm 6q. We found that low SHPRH mRNA expression is associated with poor survival outcomes in LUAD patients. Furthermore, we showed that re-expression of SHPRH in LUAD cell lines with inactivating alterations for SHPRH reduces their in vitro colony formation and tumor burden in vivo. Finally, we explored the biological pathways associated SHPRH inactivation and found an association with the tolerance of LUAD cells to DNA damage.

Conclusions

These data suggest that SHPRH is a tumor suppressor gene in LUAD, whereby its expression is associated with more favorable patient outcomes, reduced tumor and mutational burden, and may serve as a predictor of response to DNA damage. Thus, further exploration into the role of SHPRH in LUAD development may make it a valuable biomarker for predicting LUAD risk and prognosis.
Reflections on the discovery GLP-1 as a satiety hormone: Implications for obesity therapy and future directions
Authors: Astrup Arne
Published: June 2024

Scientists were chasing an incretin hormone, and when GLP-1 was finally discovered, we found that it had a pronounced satiety effect, slowed down gastric emptying, and actually reduced postprandial insulin response. These mechanisms are the basis for the highly efficacious GLP-1 analogues that today offer safe and effective treatment in millions of people living with obesity. Moreover, the combined GLP-1 mechanisms of weight loss and delayed carbohydrate absorption may also be the key drivers of remission of type 2 diabetes and reduced cardiovascular events found by GLP-1 analogues.
A hybrid structure based on silk fibroin/PVA nanofibers and alginate/gum tragacanth hydrogel embedded with cardamom extract
Authors: Irantash Shadan , Gholipour-Kanani Adeleh , Najmoddin Najmeh , Varsei Mehdi
Published: June 2024

Hybrid structures made of natural-synthetic polymers have been interested due to high biological features combining promising physical–mechanical properties. In this research, a hybrid dressing consisting of a silk fibroin (SF)/polyvinyl alcohol (PVA) nanofibers and sodium alginate (SA)/gum tragacanth (GT) hydrogel incorporating cardamom extract as an antibacterial agent was prepared. Accordingly, SF was extracted from cocoons followed by electrospinning in blend form with PVA (SF/PVA ratio: 1:1) under the voltage of 18 kV and the distances of 15 cm. The SEM images confirmed the formation of uniform, bead free fibers with the average diameter of 199 ± 28 nm. FTIR and XRD results revealed the successful extraction of SF and preparation of mixed fibrous mats. Next, cardamom oil extract-loaded SA/GT hydrogel was prepared and the nanofibrous structure was placed on the surface of hydrogel. SEM analysis depicted the uniform morphology of hybrid structure with desirable matching between two layers. TGA analysis showed desired thermal stability. The swelling ratio was found to be 1251% after 24 h for the hybrid structure and the drug was released without any initial burst. MTT assay and cell attachment results showed favorable biocompatibility and cell proliferation on samples containing extract, and antibacterial activity values of 85.35% against S. aureus and 75% against E. coli were obtained as well. The results showed that the engineered hybrid nanofibrous-hydrogel film structure incorporating cardamom oil extract could be a promising candidate for wound healing applications and skin tissue engineering.
Next generation fatigue crack growth experiments of aerospace materials
Authors: Strohmann Tobias , Melching David , Paysan Florian , Dietrich Eric , Requena Guillermo , Breitbarth Eric
Published: June 2024

Today’s societal challenges require rapid response and smart materials solutions in almost all technical areas. Driven by these needs, data-driven research has emerged as an enabler for faster innovation cycles. In fields such as chemistry, materials science and life sciences, automatic and even autonomous data generation and processing is already accelerating knowledge discovery. In contrast, in experimental mechanics, complex investigations like studying fatigue crack growth in structural materials have traditionally adhered to standardized procedures with limited adoption of the digital transformation. In this work, we present a novel infrastructure for data-centric experimental mechanics in the field of fatigue crack growth. Our methodology incorporates a robust code base that complements a multi-scale digital image correlation and robot-assisted test rig. Using this approach, the information-to-cost ratio of fatigue crack growth experiments in aerospace materials is significantly higher compared to traditional experiments. Thus, serves as a catalyst for discovering new scientific knowledge in the field of structural materials and structures.
The influence of existing piles on station settlement during the construction of a tunnel undercrossing under existing stations
Authors: Ma Baolin , Wu Shengzhi , Chen Qihui , Liang Erbin , Li Xiudong
Published: June 2024

In the construction of tunnels under existing stations, it is necessary to control their settlement. When there is a pile foundation in the existing station, the pile cutting has a significant impact on the settlement of the existing station. To determine the influence of existing piles on the settlement of subway stations, a reasonable pile-cutting time is proposed. Based on the Chengdu Metro Line 9 underpassing the existing Line 1 hatchery station, the settlement law of the tunnel underpassing the existing Line 1 station is analysed via a numerical simulation. Furthermore, the deformation and stress characteristics of the existing piles, pipe roofs, and tunnel linings and the supporting effect on the existing station are discussed. It is concluded that the cutting of existing piles causes a change in the tunnel bearing system, thus resulting in a certain deformation of the station. The influence of different pile cutting times on the settlement of the existing station is then analysed, and it is clarified that the tunnel support stiffness is significantly enhanced after the construction of the secondary lining. At this time, the settlement of the existing pile station is significantly reduced. Finally, through a field investigation, the effect of surface grouting, pipe shed, and multilayer lining on the settlement control of the existing station while the existing pile foundation exists is determined. This research can provide a reference for the settlement control and foundation underpinning of existing stations at ultra-small distances in underground excavation tunnels.
Enhancing germination and growth of canola (Brassica napus L.) through hydropriming and NaCl priming
Authors: BiBi Rahila , Elahi Nosheen Noor , Danish Subhan , Alahmadi Tahani Awad , Ansari Mohammad Javed
Published: June 2024

The excessive accumulation of sodium chloride (NaCl) in soil can result in soil salinity, which poses a significant challenge to plant growth and crop production due to impaired water and nutrient uptake. On the other hand, hydropriming (WP) and low level of NaCl priming can improve the germination of seeds, chlorophyll contents, oil and seed yield in plants. That’s why this study investigates the impact of hydro and different levels of NaCl (0.5, 1.0, 1.5 and 2.0%) priming, as pre-treatment techniques on canola seeds germination, growth and yield of two varieties Punjab and Faisal Canola. Results showed that, WP performed significant best for increase in germination (~ 20 and ~ 22%) and shoot length (~ 6 and ~ 10%) over non-priming (NP) in Punjab Canola and Faisal Canola respectively. A significant increase in plant height (~ 6 and ~ 7%), root length (~ 1 and ~ 7%), shoot fresh weight (~ 5 and ~ 7%), root fresh weight (~ 6 and ~ 7%) in Punjab Canola and Faisal Canola respectively. It was also observed that plants under WP and 0.5%NaCl priming were also better in production of seed yield per plant, oil contents, silique per plant, seeds per silique, and branches per plant chlorophyll contents and leaf relative water contents over NP. In conclusion, WP and 0.5%NaCl has potential to improve the germination, growth, yield and oil attributes of canola compared to non-priming, 1.0%NaCl priming, 1.5%NaCl priming and 2.0%NaCl priming.
GIS-based multi-criteria decision models for barite exploration in Nigeria’s Benue Trough
Authors: Gajere Jiriko N. , Adekeye Olabisi A. , Tende Andongma W. , Aminu Mohammed D.
Published: June 2024

Spatial predictive mapping using geographic information system (GIS) is considered an invaluable tool for reconnaissance-scale exploration of mineral resources. In this study, geospatial data on geophysics, remote sensing, and structural and lithological attributes were systematically integrated to prospect barite potential zones within the Mid-Nigerian Benue Trough (MBT). Correlation attribute evaluation was used to establish the relationship between mineral deposit occurrences and geospatial data, while data integration was implemented using the Multi-Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Additive Ratio Assessment (ARAS) multi-criteria models. Here we show that the correlation attribute evaluation suggests that barite occurrences displayed a strong correlation with spatial data on lineament density, ferric iron alteration, and potassium to thorium (K/Th) ratio, whereas a weak correlation was observed with spatial data on the first vertical derivative (FVD), proximity to the host rock, and ferrous iron alteration. Here we report that the quantitative estimation of predictive models indicated that very high predictive zones for barite occurrences accounted for 19% of all the models. The accuracy assessment using Receiver Operating Characteristic (ROC)/Area Under the Curve (AUC) showed prediction levels above 78% for all models. The effectiveness of the spatial application of multi-criteria decision models makes them a reliable tool for barite exploration within the Mid-Nigerian Benue Trough (MBT) and other geologically similar environments.
Computational design of novel chimeric multiepitope vaccine against bacterial and viral disease in tilapia (Oreochromis sp.)
Authors: Pumchan Ansaya , Proespraiwong Porranee , Sawatdichaikul Orathai , Phurahong Thararat , Hirono Ikuo , Unajak Sasimanas
Published: June 2024

Regarding several infectious diseases in fish, multiple vaccinations are not favorable. The chimeric multiepitope vaccine (CMEV) harboring several antigens for multi-disease prevention would enhance vaccine efficiency in terms of multiple disease prevention. Herein, the immunogens of tilapia’s seven pathogens including E. tarda, F. columnare, F. noatunensis, S. iniae, S. agalactiae, A. hydrophila, and TiLV were used for CMEV design. After shuffling and annotating the B-cell epitopes, 5,040 CMEV primary protein structures were obtained. Secondary and tertiary protein structures were predicted by AlphaFold2 creating 25,200 CMEV. Proper amino acid alignment in the secondary structures was achieved by the Ramachandran plot. In silico determination of physiochemical and other properties including allergenicity, antigenicity, glycosylation, and conformational B-cell epitopes were determined. The selected CMEV (OSLM0467, OSLM2629, and OSLM4294) showed a predicted molecular weight (MW) of 70 kDa, with feasible sites of N- and O-glycosylation, and a number of potentially conformational B-cell epitope residues. Molecular docking, codon optimization, and in-silico cloning were tested to evaluate the possibility of protein expression. Those CMEVs will further elucidate in vitro and in vivo to evaluate the efficacy and specific immune response. This research will highlight the new era of vaccines designed based on in silico structural vaccine design.
Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province
Authors: Kang Jia , Wan Bingcheng , Gao Zhiqiu , Zhou Shaohui , Chen Huansang , Shen Huan
Published: June 2024

Landslides are highly destructive geological disasters that pose a serious threat to the safety of people’s lives and property. In this study, historical records of landslides in Yunnan Province, along with eight underlying factors of landslide (elevation, slope, aspect, lithology, land cover type, normalized difference vegetation index (NDVI), soil type, and average annual precipitation (AAP)), as well as historical rainfall and current rainfall data were utilized. Firstly, we analyzed the sensitivity of each underlying factor in the study area using the frequency ratio (FR) method and obtained a landslide susceptibility map (LSM). Then, we constructed a regional rainfall-induced landslides (RIL) probability forecasting model based on machine learning (ML) algorithms and divided warning levels. In order to construct a better RIL prediction model and explore the effects of different ML algorithms and input values of the underlying factor on the model, we compared five ML classification algorithms: extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms and three representatives of the input values of the underlying factors. The results show that among the obtained forecasting models, the LSM-based RF model performs the best, with an accuracy (ACC) of 0.906, an area under the curve (AUC) of 0.954, a probability of detection (POD) of 0.96 in the test set, and a prediction accuracy of 0.8 in the validation set. Therefore, we recommend using RF-LSM model as the RIL forecasting model for Yunnan Province and dividing warning levels.
Follow-up study to explore the relationship between Neutrophil to lymphocyte ratio and impaired fasting glucose—using the group-based trajectory modeling
Authors: Liu Xuekui , Peng Gangshan , Liu Ran , Zang Xiu , Zou Caiyan , Sun Haojie , Zhu Qian , Geng Houfa , Liang Jun
Published: June 2024

Previous studies have indicated a link between neutrophil to lymphocyte ratio (NLR) and impaired fasting glucose (IFG), but the findings have been disputed. By conducting a real-world follow-up study, we can monitor the development of diseases and confirm the connection between NLR and IFG. A total of 1168 patients without IFG or T2DM were followed up for six years. At baseline, participants' NLR levels, fasting plasma glucose and other clinical characteristics were recorded. During the follow-up period, NLR levels and the prevalence of IFG were recorded. Ultimately, 45 individuals were lost to follow-up, leaving 1,123 participants for analysis. Using Group-Based Trajectory Modeling (GBTM), the sample was divided into three groups. The prevalence of IFG in the three groups was 12.1%, 19.4%, and 20.85%, respectively. Compared with the low-level NLR group, the hazard ratio of IFG in the moderate-level NLR group and high-level NLR group were 1.628 (1.109–2.390) and 1.575 (1.001–2.497), respectively. There was a significant interaction effect of BMI and NLR on the risk of IFG (P < 0.001). In this real-world follow-up study, we observed a positive association between NLR and the risk of IFG, with this relationship being exacerbated by obesity status.
Trajectory of mid-arm subcutaneous fat, muscle mass predicts mortality in hemodialysis patients independent of body mass index
Authors: Yang Yuqi , Li Qian , Qiu Wanting , Zhang Helin , Qiu Yuyang , Yuan Jing , Zha Yan
Published: June 2024

Although decreasing body mass index (BMI) is associated with higher mortality risk in patients undergoing hemodialysis (HD), BMI neither differentiates muscle and fat mass nor provides information about the variations of fat distribution. It remains unclear whether changes over time in fat and muscle mass are associated with mortality. We examined the prognostic significance of trajectory in the triceps skinfold (TSF) thickness and mid-upper arm circumference (MUAC). In this multicenter prospective cohort study, 972 outpatients (mean age, 54.5 years; 55.3% men) undergoing maintenance HD at 22 treatment centers were included. We calculated the relative change in TSF and MUAC over a 1-year period. The outcome was all-cause mortality. Kaplan–Meier, Cox proportional hazard analyses, restricted cubic splines, and Fine and Gray sub-distribution hazards models were performed to examine whether TSF and MUAC trajectories were associated with all-cause mortality. During follow-up (median, 48.0 months), 206 (21.2%) HD patients died. Compared with the lowest trajectory group, the highest trajectories of TSF and MUAC were independently associated with lower risk for all-cause mortality (HR = 0.405, 95% CI 0.257–0.640; HR = 0.537; 95% CI 0.345–0.837; respectively), even adjusting for BMI trajectory. Increasing TSF and MUAC over time, measured as continuous variables and expressed per 1-standard deviation decrease, were associated with a 55.7% (HR = 0.443, 95% CI 0.302–0.649), and 97.8% (HR = 0.022, 95% CI 0.005–0.102) decreased risk of all-cause mortality. Reduction of TSF and MUAC are independently associated with lower all-cause mortality, independent of change in BMI. Our study revealed that the trajectory of TSF thickness and MUAC provides additional prognostic information to the BMI trajectory in HD patients.
Prediction of axial capacity of corrosion-affected RC columns strengthened with inclusive FRP
Authors: Kumar Prashant , Arora Harish Chandra , Kumar Aman , Radu Dorin
Published: June 2024

The primary cause behind the degradation of reinforced concrete (RC) structures is the propagation of corrosion in the steel-RC structures. Nowadays, numerous retrofitting techniques are available in the construction sector. Fiber-reinforced polymer (FRP) is one of the efficient rehabilitation measures that can be implemented on corroded structures to enhance structural capacities. However, the estimation of axial strength of FRP-strengthened columns affected by corrosion has been a challenging and tedious task in the laboratory as well as on the site. Considering such shortcomings, the prediction of axial capacity can be done using various analytical methods and artificial intelligence (AI) techniques. In this study, a comprehensive dataset of circular columns was extracted from the literature to predict the axial strength of FRP-wrapped and unstrengthened RC corroded columns. The laboratory results from the assembled dataset were compared to corresponding values estimated using relevant design codes provided by American Concrete Institute (ACI 440.2R-17 and ACI 318-19), and Bureau of Indian Standard (IS 456:2000). Five machine learning models were employed on columns to predict the axial load carrying capacity of FRP-strengthened and un-strengthened RC corroded columns. The results discovered that the extreme gradient boosting (XGBoost) model achieves superior accuracy with the least errors and could be used by the scientific community and FRP applicators to forecast the axial performance of corroded columns strengthened with and without FRP. The findings from the design codes revealed that prediction errors were available in high margins. Furthermore, feature importance analysis was conducted using the Shapley Additive exPlanation algorithm to know the contribution and influence of each input parameter on axial capacity. The feature analysis found that unconfined compressive strength of concrete plays an important role in deciding the axial capacity of columns. Moreover, to enhance the precision of axial capacity computation and improving the overall efficacy in engineering practice, a web-based user-friendly interface was developed for FRP applicators and engineers to simplify the process.