bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ecological and sustainable implications of phosphorous-solubilizing microorganisms in soil
Authors: Iftikhar Anwaar , Farooq Rida , Akhtar Mubeen , Khalid Haleema , Hussain Nazim , Ali Qurban , Malook Saif ul , Ali Daoud 
Published: February 2024

AbstractPhosphorus (P) is a macronutrient that plants need to grow. However, most of the soil’s phosphorus is still insoluble, making it difficult for plants to absorb. This creates a barrier to ecologically responsible farming methods and calls for innovative approaches to phosphorus solubilization. Solublizing microorganisms improve the availability of phosphorous in soil. The term “phosphorus-solubilizing microorganisms” (PSMs) describes various fungi or bacteria that divide the phosphorus into more soluble forms. It shows how PSMs interact with plants and their processes to solubilize phosphorus. Soil pH, temperature, and nutrient availability are only a few parameters affecting its activity. PSMs are investigated for their potential to increase plant phosphorus absorption and use, thereby boosting agricultural yield and nutrient usage efficiency. The use of PSMs and their effects on the environment are also evaluated. By using PSMs, farmers may use less chemical phosphorus fertilizers that contribute to runoff and eutrophication in waterways. Furthermore, PSMs may improve soil structure, decrease nutrient losses, and increase nutrient cycling, all of which contribute to soil health and the long-term viability of agricultural systems. Phosphorus-solubilizing microorganisms have enormous promise in environmentally responsible farming and land management. Better phosphorus availability, greater agricultural output, less pollution, and better soil health are all possible outcomes of using PSMs. However, further study is required to determine the best application strategies, formulations, and choices of PSMs for various soil and plant systems. Incorporating PSMs into agricultural operations can potentially improve environmental sustainability and resilience. This article will explore the potential of PSMs in addressing critical environmental challenges, including soil erosion, nutrient runoff, sustainable farming practices, and resource conservation.
Graphical Abstract
Multi-parameters approach to assessment of soil liquefaction vulnerability in wetland areas of Lagos, Southwestern, Nigeria
Authors: Oladunjoye H. T. , Ishola K. S. , Oyedele K. F. , Adeoti L. 
Published: February 2024

AbstractWith respect to geology, most coastal terrains are underlain by problematic soils, some of which are liquefiable in nature and may cause sudden failure of engineering infrastructures. Against this background, this study was carried out to investigate the subsurface geology of some Lagos coastal areas and their engineering implications using geophysical and geotechnical methods. To achieve this purpose, the Multichannel Analysis of Surface Waves, Cone Penetration Test, and Standard Penetration Test were deployed. Surface waves measurements were collected using a 24-channel seismograph to which 4.5 Hz twenty-four vertical geophones were connected via the takeouts of the two cable reels. CPT soundings were carried out with a 10-tons motorized cone penetrometer and boring with SPT were carried out as well. The results of the Multichannel Analysis of Surface Waves measurements showed that the shear waves velocity (Vs) ranges from 160 to 470 m/s. The very loose to loose sand delineated have Vs in the range from 170 to 250 m/s. The tip resistance and sleeve resistance values spanned between 4.0 and 72.0 kg/cm2 and 6.0–94 kg/cm2 respectively. The thickness of the liquefiable sands in the study area varied between 2.5 and 18.0 m. At Ikoyi site, owing to the prevalence of loose silty sand, corroborated by the available borehole data and the Liquefaction Potential Index, it is classified as having a high-risk liquefaction and could be responsible for the periodic damages to structural infrastructures such as roads and buildings. The sediments mapped at Okun-Ajah and Badore sites are mainly saturated loose sands with high likelihood to liquefaction with very-high to high risk severity. The study concludes that the presence of these sediments and other factors that could induce ground motion making the study sites potentially susceptible to liquefaction. Hence, an urgent attention must be given to early monitoring measures to address the trend.
In vitro α-amylase/α-glucosidase, cytotoxicity and radical scavenging potential of Hypoxis hemerocallidea synthesized magnesium oxide nanoparticles
Authors: Mahlo Selokela Joseph , More Garland Kgosi , Oladipo Adewale Odunayo , Lebelo Sogolo Lucky 
Published: February 2024

AbstractThe prevalence of diabetes has continued to increase over the past decade. Medicinal extract-synthesized nanoformulations incorporating trace elements found in the body have emerged as alternative therapeutic technology for the treatment of diabetes. In this study, magnesium oxide (MgO) nanoparticles were prepared using Hypoxishemerocallidea (HH) and investigated for their cytotoxicity, antioxidant, and antidiabetic potentials. MgO NPs were characterized by transmission electron microscopy (TEM), Fourier-transform infrared (FTIR) spectroscopy, and zeta-potential techniques. TEM analysis confirmed the 2D nanosheet-like morphology of the nanoparticles with particle size (diameter; 300 nm and length of about 1 µm) while the FTIR spectra showed functional groups correlating to the bioactive compounds of HH in the MgO NPs. Moderate antioxidant activity of MgO NPs was observed against DPPH (IC50 = 57.35 ± 0.28 µg/mL) and ABTS (IC50 = 52.08 ± 0.24 µg/mL). The nanoparticles were shown to be less toxic to normal embryonic (HEK293) and human liver (HEPG2) cell lines, with IC50 of 48.63 ± 0.73 and 32.39 ± 0.95 µg/mL, respectively compared to a known cytotoxic drug, doxorubicin which exhibited IC50 of 2.70 ± 0.32 and 8.62 ± 0.57 µg/mL respectively. Besides, the anti-hyperglycemic potential of the MgO NPs as demonstrated by α-amylase and α-glucosidase activities were significantly high with IC50 values of 33.03 ± 1.43 and 52.38 ± 3.06 µg/mL, respectively. These results were compared with an FDA-approved antidiabetic reference standard Acarbose, which exhibited IC50 values of 24.54 ± 1.55 and 6.54 ± 0.27 µg/mL. The results indicated that the HH bioinspired MgO NPs are capable of inhibiting meditators of diabetes and oxidative stress. This study further suggests that the MgO NPs synthesized using HH could be a good candidate for the management of diabetes and other inflammatory diseases due to their strong enzyme inhibition, efficient antioxidant properties, and biocompatibility.
A two-step passenger flow anomaly detection scheme based on machine learning methods
Authors: Shuai Chunyan , Ruan Lujie , Ouyang Xin , Wang WenCong 
Published: February 2024

AbstractSubway is an important transportation means for residents due to its large volume, punctuality and environmental friendliness. However, weather factors, sports events, concerts and some unexpected events can lead to a surge or abnormality in passenger flow, which brings enormous pressure to the management of stations and passenger flow guidance. Inspired by this, this paper formulates the abnormal passenger flows into different categories in terms of the characteristics and periodical trends, and proposes a two-step abnormal detection scheme to identify the anomalies and their type, and locate abnormal positions. First, two abnormal passenger flows recognition methods based on Jensen–Shannon divergence, dynamic time warping, and density-based spatial clustering of applications with noise are established to identify the station-level abnormal passenger flow. Then, a triple standard deviation algorithm based on sliding window is further proposed to identify the abnormal type and position. Real-world smart card data of the Beijing subway in China, and the manual mutation data of the real data are employed to evaluate effectiveness of our framework. The results show that our two-step scheme is superior to the state-of-the-art algorithms, which can detect out and locate abnormal passenger flows with various characteristics. On more mutation data, this paper discusses the performances on various anomalies of different types of stations in depth, which further indicates our framework is robust and effective in practice.
Environmental impact assessment of steel reinforcing bar manufacturing process from scrap materials using life cycle assessment method: a case study on the Ethiopian metal industries
Authors: Fente Teferea Eniyew , Tsegaw Assefa Asmare 
Published: February 2024

AbstractA clear understanding of the major environmental impacts of steelmaking from scraps, as well as potential solutions involving a circular economy paradigm, is essential. This study is conducted to pave the way for using life cycle assessment (LCA) to have sustainable development and effective resource management by evaluating the environmental impacts of the steel rebar manufacturing process using secondary resources. It is a cradle-to-gate LCA that includes scrap collection and sorting, transportation, melting, continuous casting, billet reheating, and reinforcing bar rolling. Inventory data were acquired as primary data from the factory and secondary data from ecoinvent v3.8, 2021 version integrated with SimaPro 9.4.0.2 faculty version. All of the analyses in this LCA were conducted using the Recipe 2016 Midpoint (H)V1.00 and Endpoint (I)V1.00 impact assessment techniques taking one-ton reinforcing bar production as reference flow. This LCA study shows that using renewable energy and bulk transport systems has a significant advantage in reducing the environmental impact created during steel production processes. Because of this, the global warming potential created during the rebar manufacturing process is 467 kgCO2 eq as taken from the environmental impact calculation report. By charging hot billet from the continuous casting machine (CCM) to the rolling mill and using an efficient transportation system, the environmental impact of GWP can be reduced by 50%.
Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review
Authors: Frasca Maria , La Torre Davide , Pravettoni Gabriella , Cutica Ilaria 
Published: February 2024

AbstractThis review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box algorithms. While machine learning algorithms are increasingly employed for medical analysis and diagnosis, their complexity underscores the importance of understanding how these algorithms explain and interpret data to take informed decisions. This review comprehensively analyzes challenges and solutions presented in the literature, offering an overview of the most recent techniques utilized in this field. It also provides precise definitions of interpretability and explainability, aiming to clarify the distinctions between these concepts and their implications for the decision-making process. Our analysis, based on 448 articles and addressing seven research questions, reveals an exponential growth in this field over the last decade. The psychological dimensions of public perception underscore the necessity for effective communication regarding the capabilities and limitations of artificial intelligence. Researchers are actively developing techniques to enhance interpretability, employing visualization methods and reducing model complexity. However, the persistent challenge lies in finding the delicate balance between achieving high performance and maintaining interpretability. Acknowledging the growing significance of artificial intelligence in aiding medical diagnosis and therapy, and the creation of interpretable artificial intelligence models is considered essential. In this dynamic context, an unwavering commitment to transparency, ethical considerations, and interdisciplinary collaboration is imperative to ensure the responsible use of artificial intelligence. This collective commitment is vital for establishing enduring trust between clinicians and patients, addressing emerging challenges, and facilitating the informed adoption of these advanced technologies in medicine.
Bridging Quantitative and Qualitative Science for BECCS in Abandoned Croplands
Authors: Næss Jan Sandstad , Henriksen Ida Marie , Skjølsvold Tomas Moe 
Published: February 2024

AbstractBioenergy with carbon capture and storage (BECCS) plays a vital role in most climate change mitigation scenarios, where a solution for sustainable near‐term bioenergy expansion is to grow energy crops such as perennial grasses on recently abandoned cropland. There is a need to combine model‐based insights into theoretical potential and future biomass supply with more fine‐grained sociotechnical analysis to move toward realistic policies and innovation strategies. We combine natural science insights anchored in quantitative bioenergy modeling with qualitative social science anchored in the multi‐level perspective. Using these mixed methods enables a global‐to‐local‐to‐global level assessment of near‐term bioenergy recultivation opportunities for abandoned cropland. Norway is the local case. There are three main findings. First, the ongoing recultivation trends for food/feed production risks making gains in aboveground carbon stocks from natural regrowth on the mapped abandoned cropland over a 30‐year evaluation period almost negligible. Second, delaying a BECCS recultivation of abandoned cropland will make it impossible to reach high‐end mitigation potentials, and an accelerated BECCS recultivation guided by a policy push is needed to ensure stronger mitigation. Third, we unravel several real‐world challenges associated with bioenergy resource and supply modeling. Remote‐sensing techniques alone cannot capture actual land availability for land‐based climate change mitigation strategies. Local‐level sociotechnical conditions are generally found insufficiently supportive to align with the rapid near‐term bioenergy crop expansion found in 2°C scenarios from integrated assessment. The integration of mixed quantitative and qualitative methods is key to better understand the role of BECCS in climate change mitigation.
Development of nationally representative exposure factor database for children’s products in Korea
Authors: Shin Jiyun , Lee Kiyoung , Park Seung Yeol , Lim Miyoung 
Published: February 2024

Abstract
Background
Children may be exposed to harmful chemicals from their products. Accurate exposure factors are critical for exposure assessment of children’s products. Product usage pattern parameters are relatively limited compared with the chemical concentration, children’s physiological and behavioral parameters.

Objective
The aim of this study was to determine nationally representative Korean exposure factors for the usage patterns of children’s products by sex, age, and season.

Methods
Using proportional quota sampling, a survey of 10,000 households with children aged 0–12 years was conducted twice, once in summer and winter. The children’s ages were divided into four groups: infant (0–2 years old), toddler (3–6), lower-grade elementary student (7–9), and higher-grade elementary student (10–12). Data on exposure factors such as use rate, use frequency, and use duration of 57 children’s products were collected.

Results
The 57 products were classified into five categories: baby products (13), toys (12), daily products (10), sporting goods (8), and stationery (14). The use rates of products in the daily products and stationery category were >90% in both seasons. Two of the 57 products showed significant sex differences in all three exposure factors (p < 0.001). Twenty-five of the 44 non-baby products showed significant age differences for all three exposure factors. Twenty-three of the 57 products varied significantly with season for all three exposure factors.

Impact

This study generated a nationally representative exposure factor database for the usage patterns of children’s products in Korea. The exposure factors for 57 children’s products were investigated through twice survey with quota sampling with each 10,000 children nationwide. Sex, age, and seasonal differences for children’s products were identified. These accurate exposure factors by sex, age, and season can be used as input parameters for refined exposure assessment.
Multi-junction cascaded vertical-cavity surface-emitting laser with a high power conversion efficiency of 74%
Authors: Xiao Yao , Wang Jun , Liu Heng , Miao Pei , Gou Yudan , Zhang Zhicheng , Deng Guoliang , Zhou Shouhuan 
Published: February 2024

AbstractHigh electro-optical conversion efficiency is one of the most distinctive features of semiconductor lasers as compared to other types of lasers. Its further increase remains a significant objective. Further enhancing the efficiency of edge-emitting lasers (EEL), which represent the highest efficiency among semiconductor lasers at present, is challenging. The efficiency of vertical cavity surface emitting lasers (VCSELs) has always been relatively low compared to EEL. This paper, combining modeling with experiments, demonstrates the potential of multi-junction cascaded VCSELs to achieve high efficiency beyond that of EELs, our simulations show, that a 20-junction VCSEL can achieve an efficiency of more than 88% at room temperature. We fabricated VCSEL devices with different numbers of junctions and compared their energy efficiency. 15-junction VCSELs achieved a maximum efficiency of 74% at room temperature under nanosecond driving current, the corresponding differential quantum efficiency exceeds 1100%, being the largest electro-optical conversion efficiency and differential quantum efficiency reported until now for VCSELs.
Research on correlation between dynamic resilient modulus and CBR of coarse-grained chlorine saline soil
Authors: Deng Miaoyi , Wang Jinshan , Wang Xiangyang , Xie Xiangbing , Wang Kaiwei , He Yahui 
Published: February 2024

AbstractA large area of coarse-grained saline soil is distributed in saline soil areas, and chlorine saline soil with a high salt content is a typical representative. The dynamic resilient modulus was accurately predicted using the California-bearing ratio (CBR) value to determine the relationship between the dynamic resilient modulus of coarse-grained chloride saline soil and its CBR value. Indoor dynamic triaxial tests and CBR tests were conducted to investigate the evolution of the dynamic resilient modulus (MR) and CBR of coarse-grained chlorine saline soil under the influence of the stress level, water content, and salt content. The test results showed that the dynamic resilient modulus increased with an increase in the confining pressure and bulk stress and decreased as the deviator stress increased; however, the CBR increased with an increase in the corresponding unit pressure. The higher the salt and water contents, the more obvious the influence of stress on the dynamic resilient modulus and CBR value. Under the same stress level, the decrease in the dynamic resilient modulus and CBR gradually increased with increasing salt and moisture content, and the effect of salt tended to be more significant than that of water. Based on the correlation between the dynamic resilient modulus and CBR revealed by the experiment, a more widely applicable model was selected from the existing theoretical models related to CBR for the regression analysis of the test data, and a prediction model of the dynamic resilient modulus based on the CBR value was proposed (MR = 21.06CBR0.52). This prediction model had a high correlation coefficient (R2 = 0.893) and could effectively predict the dynamic resilient modulus of coarse-grained chlorine saline soil using CBR values. The results provide a simple and reliable method for determining the design parameters of a coarse-grained saline soil subgrade.
Glypican-4 serum levels are associated with cognitive dysfunction and vascular risk factors in Parkinson’s disease
Authors: Tatenhorst Lars , Maass Fabian , Paul Hannah , Dambeck Vivian , Bähr Mathias , Dono Rosanna , Lingor Paul 
Published: February 2024

AbstractGlypicans are biomarkers for various pathologies, including cardiovascular disease, cancer and diabetes. Increasing evidence suggests that glypicans also play a role in the context of neurodegenerative disorders. Initially described as supporting functionality of synapses via glutamate receptors during CNS development, Glypican 4 (GPC-4) also plays a role in the context of dementia via tau hyperphosphorylation in Alzheimer’s disease, which is also a co-pathology in Parkinson’s disease dementia. However, clinical evidence of circulating GPC-4 in Parkinson’s disease (PD) is missing so far. We therefore investigated GPC-4 in biofluids of PD patients. We analyzed GPC-4 levels in cerebrospinal fluid (CSF, n = 140), serum (n = 80), and tear fluid samples (n = 70) of PD patients and control subjects in a similar age range by ELISA (serum, CSF) and western blot (tear fluid). Expression of circulating GPC-4 was confirmed in all three biofluids, with highest levels in serum. Interestingly, GPC-4 levels were age-dependent, and multiple regression analysis revealed a significant association between GPC-4 serum levels and MoCA score, suggesting an involvement of GPC-4 in PD-associated cognitive decline. Furthermore, stratification of PD patients for vascular risk factors revealed a significant increase of GPC-4 serum levels in PD patients with vascular risk factors. Our results suggest GPC-4 as a clinical biomarker for vascular risk stratification in order to identify PD patients with increased risk of developing dementia.
Automatic detection method for tobacco beetles combining multi-scale global residual feature pyramid network and dual-path deformable attention
Authors: Chen Yuling , Li Xiaoxia , Lv Nianzu , He Zhenxiang , Wu Bin 
Published: February 2024

AbstractAiming at the problems of identifying storage pest tobacco pest beetles from images that have few object pixels and considerable image noise, and therefore suffer from lack of information and identifiable features, this paper proposes an automatic monitoring method of tobacco beetle based on Multi-scale Global residual Feature Pyramid Network and Dual-path Deformable Attention (MGrFPN-DDrGAM). Firstly, a Multi-scale Global residual Feature Pyramid Network (MGrFPN) is constructed to obtain rich high-level semantic features and more complete information on low-level features to reduce missed detection; Then, a Dual-path Deformable receptive field Guided Attention Module (DDrGAM) is designed to establish long-range channel dependence, guide the effective fusion of features and improve the localization accuracy of tobacco beetles by fitting the spatial geometric deformation features of and capturing the spatial information of feature maps with different scales to enrich the feature information in the channel and spatial. Finally, to simulate a real scene, a multi-scene tobacco beetle dataset is created. The dataset includes 28,080 images and manually labeled tobacco beetle objects. The experimental results show that under the framework of the Faster R-CNN algorithm, the detection precision and recall rate of this method can reach 91.4% and 98.4% when the intersection ratio (IoU) is 0.5. Compared with Faster R-CNN and FPN, when the intersection ratio (IoU) is 0.7, the detection precision is improved by 32.9% and 6.9%, respectively. The proposed method is superior to the current mainstream methods.