bluuebunny/crossref_metadata_2025 huggingface dataset:
Dietary Polyphenols, Plant Metabolites, and Allergic Disorders: A Comprehensive Review
Authors: Farhan Mohd , Rizvi Asim , Aatif Mohammad , Muteeb Ghazala , Khan Kimy , Siddiqui Farhan Asif
Published: May 2024

Given the ongoing rise in the occurrence of allergic disorders, alterations in dietary patterns have been proposed as a possible factor contributing to the emergence and progression of these conditions. Currently, there is a significant focus on the development of dietary therapies that utilize natural compounds possessing anti-allergy properties. Dietary polyphenols and plant metabolites have been intensively researched due to their well-documented anti-inflammatory, antioxidant, and immunomodulatory characteristics, making them one of the most prominent natural bioactive chemicals. This study seeks to discuss the in-depth mechanisms by which these molecules may exert anti-allergic effects, namely through their capacity to diminish the allergenicity of proteins, modulate immune responses, and modify the composition of the gut microbiota. However, further investigation is required to fully understand these effects. This paper examines the existing evidence from experimental and clinical studies that supports the idea that different polyphenols, such as catechins, resveratrol, curcumin, quercetin, and others, can reduce allergic inflammation, relieve symptoms of food allergy, asthma, atopic dermatitis, and allergic rhinitis, and prevent the progression of the allergic immune response. In summary, dietary polyphenols and plant metabolites possess significant anti-allergic properties and can be utilized for developing both preventative and therapeutic strategies for targeting allergic conditions. The paper also discusses the constraints in investigating and broad usage of polyphenols, as well as potential avenues for future research.
In Vitro Approbation of Microbial Preparations to Shield Fruit Crops from Fire Blight: Physio-Biochemical Parameters
Authors: Nurzhanova Asil A. , Mamirova Aigerim , Mursaliyeva Valentina , Nurmagambetova Asiya S. , Zhumasheva Zhadyra , Turdiyev Timur , Kushnarenko Svetlana , Ismailova Elvira
Published: May 2024

The need for the increasing geographical spread of fire blight (FB) affecting fruit crops to be addressed led to large-scale chemicalization of the environmental matrices and reduction of plant productivity. The current study aimed to assess the effects of novel biopreparations at different exposure durations on photosynthetic pigment content and antioxidant enzyme activity in leaves of apple and pear varieties with varying levels of resistance to FB. Biopreparations were formulated from a cultural broth containing Lacticaseibacillus paracasei M12 or Bacillus amyloliquefaciens MB40 isolated from apple trees’ phyllosphere. Aseptic leaves from blight-resistant (endemic Malus sieversii cv. KG10), moderately resistant (Pyrus pyraster cv. Wild), and susceptible (endangered Malus domestica cv. Aport and Pyrus communis cv. Shygys) varieties were employed. The impact of biopreparations on fruit crop antioxidant systems and photosynthetic apparatuses was investigated in vitro. Study results indicated that FB-resistant varieties exhibit enhanced adaptability and oxidative stress resistance compared to susceptible ones. Plant response to biopreparations varied based on the plant’s initial FB sensitivity and exposure duration. Indeed, biopreparations improved the adaptive response of the assimilation apparatus, protein synthesis, and catalase and superoxide dismutase activity in susceptible varieties, suggesting that biopreparations have the potential for future commercialization to manage FB in fruit crops.
Selection for Phytophthora Root Rot Resistance in Chickpea Crosses Affects Yield Potential of Chickpea × Cicer echinospermum Backcross Derivatives
Authors: Bithell Sean L. , Asif Muhammd A. , Backhouse David , Drenth Andre , Harden Steve , Hobson Kristy
Published: May 2024

Phytophthora root rot (PRR) of chickpea (Cicer arietinum) caused by Phytophthora medicaginis is an important disease. Partial resistance to PRR is sourced from Cicer echinospermum. In this study, we evaluated if lines with low levels of PRR foliage symptoms in two contrasting recombinant inbred line (RIL) populations parented by chickpea cultivars (Yorker and Rupali) and 04067-81-2-1-1 (C. echinospermum, interspecific breeding line) had a significant drag on yield parameters. For the Yorker × 04067-81-2-1-1 population with the highest level of PRR resistance, in the absence of PRR, low foliage symptom RIL had significantly later flowering and podding, lower grain yields, and lighter seed and shorter plant phenotypes than high foliage symptom RIL. A quantitative trait locus analysis identified significant QTL for flowering, height, 100-seed weight, and yield, and there was a significantly higher frequency of alleles for the negative agronomic traits (i.e., drag) from the 04067-81-2-1-1 parent in low foliage symptom RIL than in high foliage symptom RIL. For the Rupali × 04067-81-2-1-1 population with lower levels of PRR resistance, in the absence of PRR, low foliage symptom RIL had significantly lighter seed and shorter plants than high foliage symptom RIL. Significant QTL were detected, the majority were for the timing of flowering and podding (n = 18), others were for plant height, yield, and 100-seed weight. For this second population, the frequency of alleles for the negative agronomic traits from the 04067-81-2-1-1 parent did not differ between low and high foliage symptom RIL. The 100 seed weight of RIL under moderate PRR disease pressure showed some promise as a yield component trait to identify phenotypes with both high levels of PRR resistance and grain yield potential for further seed number evaluations. We identified that large population sizes are required to enable selection among chickpea × C. echinospermum crosses for high levels of PRR resistance without a significant drag on yield.
Biostimulant Response of Foliar Application of Rare Earth Elements on Physiology, Growth, and Yield of Rice
Authors: de Oliveira Cynthia , Ramos Silvio Junio , Dinali Guilherme Soares , de Carvalho Teotonio Soares , Martins Fábio Aurélio Dias , Faquin Valdemar , Castro Evaristo Mauro de , Sarkis Jorge Eduardo Souza , Siqueira José Oswaldo , Guilherme Luiz Roberto Guimarães
Published: May 2024

Rare earth elements (REEs) have been intentionally used in Chinese agriculture since the 1980s to improve crop yields. Around the world, REEs are also involuntarily applied to soils through phosphate fertilizers. These elements are known to alleviate damage in plants under abiotic stresses, yet there is no information on how these elements act in the physiology of plants. The REE mode of action falls within the scope of the hormesis effect, with low-dose stimulation and high-dose adverse reactions. This study aimed to verify how REEs affect rice plants’ physiology to test the threshold dose at which REEs could act as biostimulants in these plants. In experiment 1, 0.411 kg ha−1 (foliar application) of a mixture of REE (containing 41.38% Ce, 23.95% La, 13.58% Pr, and 4.32% Nd) was applied, as well as two products containing 41.38% Ce and 23.95% La separately. The characteristics of chlorophyll a fluorescence, gas exchanges, SPAD index, and biomass (pot conditions) were evaluated. For experiment 2, increasing rates of the REE mix (0, 0.1, 0.225, 0.5, and 1 kg ha−1) (field conditions) were used to study their effect on rice grain yield and nutrient concentration of rice leaves. Adding REEs to plants increased biomass production (23% with Ce, 31% with La, and 63% with REE Mix application) due to improved photosynthetic rate (8% with Ce, 15% with La, and 27% with REE mix), favored by the higher electronic flow (photosynthetic electron transport chain) (increase of 17%) and by the higher Fv/Fm (increase of 14%) and quantum yield of photosystem II (increase of 20% with Ce and La, and 29% with REE Mix), as well as by increased stomatal conductance (increase of 36%) and SPAD index (increase of 10% with Ce, 12% with La, and 15% with REE mix). Moreover, adding REEs potentiated the photosynthetic process by increasing rice leaves’ N, Mg, K, and Mn concentrations (24–46%). The dose for the higher rice grain yield (an increase of 113%) was estimated for the REE mix at 0.72 kg ha−1.
Effects of the Salt Stress Duration and Intensity on Developmental and Physiological Features of the Moss Polytrichum formosum
Authors: Rajčić Marija V. , Šircelj Helena , Matić Nikolina A. , Pavkov Sara D. , Poponessi Silvia , Tosti Tomislav B. , Sabovljević Aneta D. , Sabovljević Marko S. , Vujičić Milorad M.
Published: May 2024

The two accessions of the polytrichaceous moss species Polytrichum formosum, namely German and Serbian genotypes, were subjected to salt stress, aiming to study the species’ developmental and physiological features. Various concentrations of sodium chloride were applied to an axenic in vitro culture of the two moss genotypes, and the growth parameters as well as physiological feature changes were followed. As inferred by the morpho-developmental parameters and survival index, the Serbian genotype showed higher resistance to salt stress as compared to the German one. However, both moss genotypes survived the highest applied concentration (500 mM). As expected, short exposures to salt were rather easily overcome. No clear patterns in sugar content and changes were observed during the stress, but they are surely included in salt stress response and tolerance in P. formosum. Longer stress increased total chlorophyll content in both genotypes. In short-term applied salt stress, the Serbian genotype had a higher total chlorophyll concentration to control unstressed plants, while the German genotype decreased the total amount of chlorophyll. Similarly, carotenoids were shown to be significantly higher in the Serbian genotype, both in unstressed and treated plants, compared to the German one. The contents of tocopherols were higher in the Serbian genotype in controlled unstressed and subsequently short- and long-stressed plantlets compared to the German accession. In general, we can assume that P. formosum is unexpectedly tolerant to salt stress and that there are differences within various accessions of overall European populations, as referred by two randomly selected genotypes, which is most probably a consequence of different genetic structure.
Drip Fertigation Enhances the Responses of Grain Yield and Quality to Nitrogen Topdressing Rate in Irrigated Winter Wheat in North China
Authors: Tong Jin , Xiong Yulei , Lu Yu , Li Wen , Lin Wen , Xue Jianfu , Sun Min , Wang Yuechao , Gao Zhiqiang
Published: May 2024

Conventional water and nitrogen (N) management practice in north China, comprising flood irrigation and N fertilizer broadcast (FB), limits sustainable wheat production. Drip fertigation (DF) has been widely adopted in wheat production in recent years and has effectively improved yields. However, the responses of the yield and quality to the N topdressing rate (NTR) under DF are still unclear. This study determined the responses of the wheat yield and quality to NTR under DF, as well as assessing whether DF could synergistically increase the yield and quality. A field experiment was conducted in north China for two seasons (2021–2023) using a split-plot design with three replicates. The main plot used the management practice (FB and DF) and the sub-plot had N treatment (no N applied, and NTRs of 0, 40, 80, 120, and 160 kg ha−1 with 150 kg N ha−1 as basal fertilizer, denoted as N0, T0, T40, T80, T120, and T160, respectively). Our results showed that high and saturated wheat yields (12.08 and 11.46 t ha−1) were obtained under DF at T80, and the highest yields were produced at T160 (11.71 and 11.30 t ha−1) under FB. Compared with FB, the greatest yield increase of 10.4–12.6% was achieved at T80 under DF. A higher spike number due to the increased effective stem percentage and a greater grain weight because of enhanced post-anthesis biomass production (BPpost) explained the improved yield under DF. The enhanced post-anthesis radiation use efficiency (RUE) led to the greater BPpost under DF. The enhanced specific leaf N, antioxidant capacity, and stomatal conductance under DF explained the higher light-saturated photosynthesis rate of flag leaves, which partly led to the increased post-anthesis RUE. NTR higher than 80 kg ha−1 did not enhance the yield, but it significantly improved the gliadin and glutelin contents, thereby leading to a higher total protein content, better gluten characteristics, and superior processing quality. Therefore, drip fertigation is a practical strategy for increasing both yield and quality with reduced water input and appropriate N input in irrigated winter wheat in north China. Applying 80 kg ha−1 of NTR under drip irrigation produces a high yield, but further gain in grain quality needs a higher NTR.
Preparation and Performance Study of Boron Adsorbent from Plasma-Grafted Polypropylene Melt-Blown Fibers
Authors: Qin Yi , Jiang Hui , Luo Zhengwei , Geng Wenhua , Zhu Jianliang
Published: May 2024

In this study, the plasma graft polymerization technique was used to graft glycidyl methacrylate (GMA) onto polypropylene (PP) melt-blown fibers, which were subsequently aminated with N-methyl-D-glucamine (NMDG) by a ring-opening reaction, resulting in the formation of a boron adsorbent denoted as PP-g-GMA-NMDG. The optimal conditions for GMA concentration, grafting time, grafting temperature, and the quantity of NMDG were determined using both single factor testing and orthogonal testing. These experiments determined the optimal process conditions to achieve a high boron adsorption capacity of PP-g-GMA-NMDG. Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), energy dispersion spectrum analysis (EDS), and water contact angle measurements were performed to characterize the prepared adsorbent. Boron adsorption experiments were carried out to investigate the effects of pH, time, temperature, and boron concentration on the boron adsorption capacity of PP-g-GMA-NMDG. The adsorption isotherms and kinetics of PP-g-GMA-NMDG for boron were also studied. The results demonstrated that the adsorption process followed a pseudo-second-order kinetic model and a Langmuir isothermal model. At a pH of 6, the maximum saturation adsorption capacity of PP-g-GMA-NMDG for boron was 18.03 ± 1 mg/g. In addition, PP-g-GMA-NMDG also showed excellent selectivity for the adsorption of boron in the presence of other cations, such as Na+, Mg2+, and Ca2+, PP-g-GMA-NMDG, and exhibited excellent selectivity towards boron adsorption. These results indicated that the technique of preparing PP-g-GMA-NMDG is both viable and environmentally benign. The PP-g-GMA-NMDG that was made has better qualities than other similar adsorbents. It has a high adsorption capacity, great selectivity, reliable repeatability, and easy recovery. These advantages indicated that the adsorbents have significant potential for widespread application in the separation of boron in water.
Microscopic Chemical Reaction Mechanism and Kinetic Model of Al/PTFE
Authors: Guo Mengmeng , Li Xiangrong , Chen Yongkang , Wang Haifu
Published: May 2024

In order to study the microscopic reaction mechanism and kinetic model of Al/PTFE, a reactive force field (ReaxFF) was used to simulate the interface model of the Al/PTFE system with different oxide layer thicknesses (0 Å, 5 Å, 10 Å), and the thermochemical behavior of Al/PTFE at different heating rates was analyzed by simultaneous thermal analysis (TG-DSC). The results show that the thickness of the oxide layer has a significant effect on the reaction process of Al/PTFE. In the system with an oxide layer thickness of 5 Å, the compactness of the oxide layer changes due to thermal rearrangement, resulting in the diffusion of reactants (fluorine-containing substances) through the oxide layer into the Al core. The reaction mainly occurs between the oxide layer and the Al core. For the 10 Å oxide layer, the reaction only exists outside the interface of the oxide layer. With the movement of the oxygen ions in the oxide layer and the Al atoms in the Al core, the oxide layer moves to the Al core, which makes the reaction continue. By analyzing the reaction process of Al/PTFE, the mechanism function of Al/PTFE was obtained by combining the shrinkage volume model (R3 model) and the three-dimensional diffusion (D3 model). In addition, the activation energy of Al/PTFE was 258.8 kJ/mol and the pre-exponential factor was 2.495 × 1015 min−1. The research results have important theoretical significance and reference value for the in-depth understanding of the microscopic chemical reaction mechanism and the quantitative study of macroscopic energy release of Al/PTFE reactive materials.
Polylactic Acid/Saqqez Gum Blends for Chewing Gum Applications: Impact of Plasticizers on Thermo-Mechanical and Morphological Properties
Authors: Kaveh Mona , Yeganehzad Samira , Hesarinejad Mohammad Ali , Kiumarsi Maryam , Abdollahi Moghaddam Mohammad Reza
Published: May 2024

This study investigated a blend of poly (lactic acid) (PLA) and Saqqez gum, with a weight ratio of 70:30, respectively, along with two plasticizers, acetyl tributyl citrate (ATBC) and polyethylene glycol (PEG), at three different concentrations (14%, 16% and 18% by weight of the PLA). The blend was analyzed using differential scanning calorimetry (DSC), scanning electron microscopy (SEM), tensile tests, water-absorption behavior (coefficients of water absorption, sorption, diffusion and permeability of the samples during 240 h) and chemical resistance (exposure to 1 mol/L HCl and 1 mol/L NaOH for 240 h). The desired elastomer blend was then used to prepare natural chewing gum, which was subsequently subjected to texture profile analyzer (TPA) tests and sensory evaluation. The results showed that the addition of both plasticizers increased the tensile properties of the blend. Compared to neat PLA, all the blends exhibited an increase in elongation at break and a decrease in yield strength, with the maximum elongation at break (130.6%) and the minimum yield strength (12.2 MPa) observed in the blend containing 16% ATBC. Additionally, all the thermal attributes studied, including Tg, Tc and Tm, were lower than those of neat PLA, and the Tg values deviated from the values predicted via Fox’s equation. SEM images of the blends confirmed that plasticization improved the homogeneity and distribution of the components in the blend structure. PEG 18% and ATBC 16% exhibit the highest and lowest water-absorption behavior, respectively. Regarding chemical resistance, all blends showed weight gain when exposed to HCl, while no weight loss was observed for resistance to NaOH. The chewing gum sample obtained similar values for the mentioned tests compared to the commercial control sample. Overall, the results indicate that plasticization enhances the structure and performance of the PLA/Saqqez gum blend and further investigation is warranted.
Consistency in Young’s Modulus of Powders: A Review with Experiments
Authors: Cares-Pacheco Maria-Graciela , Cordeiro-Silva Ellen , Gerardin Fabien , Falk Veronique
Published: May 2024

This review, complemented by empirical investigations, delves into the intricate world of industrial powders, examining their elastic properties through diverse methodologies. The study critically assesses Young’s modulus (E) across eight different powder samples from various industries, including joint filler, wheat flour, wheat starch, gluten, glass beads, and sericite. Employing a multidisciplinary approach, integrating uniaxial compression methodologies—both single and cyclic—with vibration techniques, has revealed surprising insights. Particularly notable is the relationship between porosity and Young’s modulus, linking loose powders to the compacts generated under compression methods. Depending on the porosity of the powder bed, Young’s modulus can vary from a few MPa (loose powder) to several GPa (tablet), following an exponential trend. The discussion emphasizes the necessity of integrating various techniques, with a specific focus on the consolidation state of the powder bed, to achieve a comprehensive understanding of bulk elasticity. This underscores the need for low-consolidation methodologies that align more closely with powder technologies and unit operations such as conveying, transport, storage, and feeding. In conclusion, the study suggests avenues for further research, highlighting the importance of exploring bulk elastic properties in loose packing conditions, their relation with flowability, alongside the significance of powder conditioning.
Numerical Study on the Fracturing of Deep Rock Masses by Blasting Based on the Material Point Method
Authors: Xiao Hu , Wang Meng , Gao Weiting , Zou Ming , Wang Yuntao , Sun Jinshan
Published: May 2024

Blasting is a prevalent technique in deep rock excavation, with the state of rock fragmentation under high in-situ stress conditions being distinct from that under low in-situ stress conditions. A new material point method framework utilizing the generalized interpolated material point and convective particle domain interpolation functions was implemented to simulate the single-hole blasting process, analyze the stress distribution around the blasting hole, and elucidate the mechanism of how ground stress influences the expansion of blasting cracks through the interaction with the blasting load. In addition, the dynamic relaxation method realizes the stress’s initialization. It was concluded that the in-situ stress can increase the compressive stress induced by blasting load, whereas it decreases the caused tensile stress. With the increase in the ground stress, the scale of the cracks decreases. Under the non-isobaric condition, the blast-induced cracks preferentially expand along the high stress with the increase in the stress difference between the horizontal direction and the vertical direction, and the blast-induced cracks are suppressed to the greatest extent in the direction of the minimum ground stress.
Three-Dimensional VOF-DEM Simulation Study of Particle Fluidization Induced by Bubbling Flow
Authors: Liu Liming , Zhan Mengqin , Wang Rongtao , Liu Yefei
Published: May 2024

The bubbling flow plays a key role in gas–liquid–solid fluidized beds. To understand the intrinsic fluidization behaviors at the discrete bubble and particle scale, coupled simulations with the volume of fluid model and the discrete element method are performed to investigate the effects of the gas inlet velocity, particle properties and two-orifice bubbling flow on particle fluidization. Three-dimensional simulations are carried out to accurately capture the dynamic changes in the bubble shape and trajectory. A bubbling flow with a closely packed bed is simulated to study the onset of particle fluidization. The obvious phenomena of particle fluidization are presented by both the experiment and simulation. Although an increasing gas inlet velocity promotes particle fluidization, the good fluidization of particles cannot be achieved solely by increasing the gas inlet velocity. When the channel is packed with more particles, the bubbles take a longer time to pass through the higher particle bed, and the bubbles grow larger in the bed. The increase in particle density also extends the time needed for the bubbles to escape from the bed, and it is more difficult to fluidize the particles with a larger density. Even if more particles are added into the channel, the percentage of suspended particles is not significantly changed. The percentage of suspended particles is not increased with a decrease in the particle diameter. The particle suspension is not significantly improved by the bubbling flow with two orifices, while the particle velocity is increased due to the more frequent bubble–particle collisions. The findings from this study will be beneficial in guiding the enhancement of particle fluidization in multiphase reactors.
Defect Identification of 316L Stainless Steel in Selective Laser Melting Process Based on Deep Learning
Authors: Yang Wei , Gan Xinji , He Jinqian
Published: May 2024

In additive manufacturing, such as Selective Laser Melting (SLM), identifying fabrication defects poses a significant challenge. Existing identification algorithms often struggle to meet the precision requirements for defect detection. To accurately identify small-scale defects in SLM, this paper proposes a deep learning model based on the original YOLOv5 network architecture for enhanced defect identification. Specifically, we integrate a small target identification layer into the network to improve the recognition of minute anomalies like keyholes. Additionally, a similarity attention module (SimAM) is introduced to enhance the model’s sensitivity to channel and spatial features, facilitating the identification of dense target regions. Furthermore, the SPD-Conv module is employed to reduce information loss within the network and enhance the model’s identification rate. During the testing phase, a set of sample photos is randomly selected to evaluate the efficacy of the proposed model, utilizing training and test sets derived from a pre-existing defect database. The model’s performance in multi-category recognition is measured using the average accuracy metric. Test results demonstrate that the improved YOLOv5 model achieves a mean average precision (mAP) of 89.8%, surpassing the mAP of the original YOLOv5 network by 1.7% and outperforming other identification networks in terms of accuracy. Notably, the improved YOLOv5 model exhibits superior capability in identifying small-sized defects.
The Influence of Exogenous Particles on Saliva Rheology
Authors: Penconek Agata , Przekop Rafał , Michalczuk Urszula , Moskal Arkadiusz
Published: May 2024

This study aimed to investigate the effect of exogenous nanoparticles on the rheological properties of artificial saliva. There are four reasons for undertaking this type of research: Firstly, the number of solid particles of various origins present in the air is still high. Secondly, nanoparticles (including silver and gold nanoparticles) are increasingly used in food packaging and can migrate into food. Thirdly, saliva is the first biological fluid that comes into contact with exogenous particles. Finally, the function of saliva is also closely related to its rheological properties. Due to the remarkable properties of nano-objects, nanoparticles of various origins in the body may cause effects that have not been realised until now. Therefore, each type of nanoparticle must be tested in terms of its impact on the body/body fluid. We used silver and gold nanoparticles because they are used in the food industry, and diesel exhaust particles because they are standard components of air pollution. The effect of various nanoparticles (e.g., their size and shape) on the rheology of saliva at two temperatures was investigated. The constants of the power law constitutive equation were also estimated. Studies showing the impact of nanoparticles on the rheology of body fluids are rare because it is one of the less obvious ways of their affecting the human body. However, the results show that nanoparticles are not neutral to the biological fluid, which may translate into a change in its properties and thus disturb its functions.
Late Age- and Dose-Related Effects on the Proteome of Thyroid Tissue in Rats after 131I Exposure
Authors: Druid Malin , Shubbar Emman , Spetz Johan , Parris Toshima Z. , Langen Britta , Ytterbrink Charlotte , Berger Evelin , Helou Khalil , Forssell-Aronsson Eva
Published: May 2024

The physiological process of iodine uptake in the thyroid is used for 131I treatment of thyroid diseases. Children are more sensitive to radiation compared to adults and may react differently to 131I exposure. The aims of this study were to evaluate the effects on thyroid protein expression in young and adult rats one year after 131I injection and identify potential biomarkers related to 131I exposure, absorbed dose, and age. Twelve Sprague Dawley rats (young and adults) were i.v. injected with 50 kBq or 500 kBq 131I and killed twelve months later. Twelve untreated rats were used as age-matched controls. Quantitative proteomics, statistical analysis, and evaluation of biological effects were performed. The effects of irradiation were most prominent in young rats. Protein biomarker candidates were proposed related to age, absorbed dose, thyroid function, and cancer, and a panel was proposed for 131I exposure. In conclusion, the proteome of rat thyroid was differentially regulated twelve months after low-intermediate dose exposure to 131I in both young and adult rats. Several biomarker candidates are proposed for 131I exposure, age, and many of them are known to be related to thyroid function or thyroid cancer. Further research on human samples is needed for validation. Data are avaiable via ProteomeXchange with identifier PXD024786.
The Making of a Monk: The Training of Śrāmaṇera (Novice Monks) in Dunhuang with a Focus on Scriptural Study
Authors: Shen Shoucheng , Wu Shaowei
Published: May 2024

Monastic life begins with the ordination of novice monks, who start their formal training at this stage. The education of a novice involves both general cultural studies and specialized Buddhist training. However, the focus during the novice stage is predominantly on Buddhist education, which encompasses learning monastic discipline and studying Buddhist scriptures. The Dunhuang manuscripts offer a wealth of information, providing valuable insights into the training and education of novice monks in Dunhuang during the periods of Tibetan Occupation (787–848) and Guiyi Army (851–1036).
Multi-Timescale Recurrent Neural Networks Beat Rough Volatility for Intraday Volatility Prediction
Authors: Challet Damien , Ragel Vincent
Published: May 2024

We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory networks (LSTMs) to predict the intraday volatility of a collection of equity indices known to have a long memory. Generally, the number of epochs needed to train the extended LSTMs is divided by about two, while the variation in validation and test losses among models with the same hyperparameters is much smaller. We also show that the single model with the smallest validation loss systemically outperforms rough volatility predictions for the average intraday volatility of equity indices by about 20% when trained and tested on a dataset with multiple time series.
Commodity Market Risk: Examining Price Co-Movements in the Pakistan Mercantile Exchange
Authors: Shear Falik , Bilal Muhammad , Ashraf Badar Nadeem , Ali Nasir
Published: May 2024

Commodity price co-movements significantly impact investment decisions. High correlations constrain portfolio diversification and limit risk mitigation potential. While international markets often exhibit strong price linkages, understanding national-level dynamics is crucial for effective portfolio optimization. In this paper, we examine the commodity price co-movements within three key sectors—energy, metals, and agriculture—in the specific context of Pakistan. Utilizing data from 13 January 2013 to 20 August 2020 and employing an autoregressive distributed lag (ARDL) model, we reveal a surprising finding: co-movement among these sectors is weak and primarily short-term. This challenges the conventional assumption of tight coupling in national markets and offers exciting implications for investors. Our analysis suggests that Pakistani commodities hold significant diversification potential, opening promising avenues for risk-reduction strategies within the national market.
Parallel Inversion of 3D Airborne Transient Electromagnetic Data Using an Approximate Jacobi Matrix
Authors: Lei Da , Ren Hao , Wang Ruo , Wang Zhongxing , Fu Changmin
Published: May 2024

In geophysical inversion issues, the Jacobian matrix computation takes the greatest time, and it is the most significant factor limiting the inversion’s calculation speed. We think that the correctness of the inverse problem is determined by the difference between the inversion data and the real data, not the precision of the gradient solution in each iteration. Based on this, we present an approximate computation approach for the Jacobian matrix that may rapidly solve the inverse issue by estimating the gradient information. In this research, the approximate gradient information is solved in each iteration process, and the approximate gradient is utilized for computation; nevertheless, the poor fitting of the evaluation data is correctly evaluated, and the inversion model that fits the criteria is achieved. We employed this approach of estimating the Jacobian matrix to invert the 3D airborne transient electromagnetic method (ATEM) on synthetic data, and it was able to significantly minimize the time necessary for the inversion while maintaining inversion accuracy. When the model mesh is more precise, this technique outperforms the previous way of finding the exact Jacobian matrix in terms of acceleration.
Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary
Authors: Cheng Xi , Mu Ruiqi , Lin Sheng , Zhang Min , Wang Hai
Published: May 2024

In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to detect anomalies. To address this challenge, a novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection. To be specific, dual graph regularization, which combines spectral and spatial regularization, provides a new paradigm for LRR, and it can effectively preserve the local geometrical structure in the spectral and spatial information. To obtain a robust background dictionary, a novel adaptive dictionary strategy is utilized for the LRR model. In addition, extensive comparative experiments and an ablation study were conducted to demonstrate the superiority and practicality of the proposed DGRAD-LRR method.
TSFE: Two-Stage Feature Enhancement for Remote Sensing Image Captioning
Authors: Guo Jie , Li Ze , Song Bin , Chi Yuhao
Published: May 2024

In the field of remote sensing image captioning (RSIC), mainstream methods typically adopt an encoder–decoder framework. Methods based on this framework often use only simple feature fusion strategies, failing to fully mine the fine-grained features of the remote sensing image. Moreover, the lack of context information introduction in the decoder results in less accurate generated sentences. To address these problems, we propose a two-stage feature enhancement model (TSFE) for remote sensing image captioning. In the first stage, we adopt an adaptive feature fusion strategy to acquire multi-scale features. In the second stage, we further mine fine-grained features based on multi-scale features by establishing associations between different regions of the image. In addition, we introduce global features with scene information in the decoder to help generate descriptions. Experimental results on the RSICD, UCM-Captions, and Sydney-Captions datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches.
Optimizing the Numerical Simulation of the Dust Event of March 2021: Integrating Aerosol Observations through Multi-Scale 3D Variational Assimilation in the WRF-Chem Model
Authors: Mei Shuang , You Wei , Zhong Wei , Zang Zengliang , Guo Jianping , Xiang Qiangyue
Published: May 2024

The integration of high-resolution aerosol measurements into an atmospheric chemistry model can improve air quality forecasting. However, traditional data assimilation methods are challenged in effectively incorporating such detailed aerosol information. This study utilized the WRF-Chem model to conduct data assimilation and prediction experiments using the Himawari-8 satellite’s aerosol optical depth (AOD) product and ground-level particulate matter concentration (PM) measurements during a record-breaking dust event in the Beijing–Tianjin–Hebei region from 14 to 18 March 2021. Three experiments were conducted, comprising a control experiment without assimilation (CTL), a traditional three-dimensional variational (3DVAR) experiment, and a multi-scale three-dimensional variational (MS-3DVAR) experiment. The results indicated that the CTL method significantly underestimated the intensity and extent of the severe dust event, while the analysis fields and forecasting fields of PM concentration and AOD can be significantly improved in both 3DVAR and MS-3DVAR assimilation. Particularly, the MS-3DVAR assimilation approach yielded better-fitting extreme values than the 3DVAR method, mostly likely due to the multi-scale information from the observations used in the MS-3DVAR method. Compared to the CTL method, the correlation coefficient of MS-3DVAR assimilation between the assimilated PM10 analysis fields and observations increased from 0.24 to 0.93, and the positive assimilation effect persisted longer than 36 h. These findings suggest the effectiveness and prolonged influence of integrating high-resolution aerosol observations through MS-3DVAR assimilation in improving aerosol forecasting capabilities.
Exploring the Relationships between Personality and Psychological Well-Being: The Mediating Role of Pro-Environmental Behaviors
Authors: Pino Maria Chiara , Giancola Marco , Sannino Marta , D’Amico Simonetta , Palmiero Massimiliano
Published: May 2024

Background: Research in psychology has attempted to identify the main predictors and strategies that are useful to promote well-being. Although personality has been recognized as one of the main determinants of well-being, the primary mechanisms involved in this relationship are not fully disclosed. This research addressed the impact of pro-environmental behaviors in the interplay between the Big Five (openness, extraversion, agreeableness, conscientiousness, and neuroticism) and psychological well-being (eudaimonic). Methods: A total of 176 young adults (mean age = 21.55 years; SD age = 1.76 years; 114 F; mean education = 14.57 years; SD = 2.11 years) participated in this study. The participants were requested to complete a short battery of self-report questionnaires, including the Big Five Inventory-10, the Pro-environmental Behavior Questionnaire, and the Psychological Well-being Scale. Results: The results revealed that pro-environmental behaviors only mediated the association between agreeableness and eudaimonic well-being (B = 2.25, BootSE = 1.26, BootCIs 95% [0.149, 5.050]). Conclusions: These findings contributed to identifying the potential mechanisms through which personality contributes to individual eudaimonic well-being, also providing insights into the development of promoting interventions based on eco-sustainable behaviors. Limitations and future research directions are discussed.
Navigating the New Normal: The Role of Residents’ Involvement and Support in Sustainable Tourism Recovery
Authors: Erul Emrullah , Uslu Abdullah , Woosnam Kyle Maurice , Santos José António C. , Aleshinloye Kayode D. , Ribeiro Manuel Alector
Published: May 2024

This study seeks to provide a nuanced understanding of how residents’ views on tourism impacts and their awareness of the pandemic’s effects shape their attitudes and, consequently, influence their involvement and support for tourism by integrating emotional solidarity, the knowledge–attitude–practices theory, and social exchange theory. Furthermore, the study explores potential moderating factors such as gender and professional ties to tourism. Data were gathered from 545 residents of Manavgat, Turkey, following a cluster sampling scheme, confirming all ten hypotheses. The results reveal that residents’ knowledge and perceived positive impacts of tourism significantly forecast attitudes toward tourism and tourists, which explains their involvement and support. The results also demonstrate that residents with strong ties to tourism were more positive and involved than those with limited links to tourism. The complementary merger of theoretical frameworks enriches our understanding of residents’ support and involvement in tourism, shedding light on the intricacies of these relationships.
Assessing Economic Development and Quality of Life: A Management Perspective on Romania and the Republic of Moldova
Authors: Ioan Gina , Pirju Ionel Sergiu , Panaitescu Manuela Carmen , Vrabie Tincuța
Published: May 2024

This article presents a comparative analysis focusing on the economic dimensions of quality of life and explores the factors influencing economic growth and well-being between two countries located in Eastern Europe: Romania—an EU member state since 2007, and Moldova—an EU candidate status since June 2022. By examining statistical data, we assess the relationship between economic growth and development, aiming to discern patterns and dynamics within these countries’ economies. Through this comparative approach, we aim to elucidate how economic factors contribute to societal welfare and living standards. The study underscores the significance of economic management policies and structural reforms in fostering growth and enhancing the quality of life for citizens. By focusing on the economic landscapes of Romania and Moldova, we aim to offer insights into the challenges and opportunities faced by transitioning economies in Eastern Europe, shedding light on strategies for sustainable development and improved well-being.
Spatial Distribution Prediction of Soil Heavy Metals Based on Random Forest Model
Authors: Nie Shunqi , Chen Honghua , Sun Xinxin , An Yunce
Published: May 2024

Mastering the spatial distribution of soil heavy metal content and evaluating the pollution status of soil heavy metals is of great significance for ensuring agricultural production and protecting human health. This study used a machine learning model to study the spatial distribution of soil heavy metal content in a coastal city in eastern China. Having obtained six soil heavy metal contents, including Cr, Cd, Pb, As, Hg, and Ni, environmental variables such as precipitation, soil moisture, and population density were selected. Random forest (RF) was used to model the spatial distribution of soil heavy metal content. The research findings indicate that the RF model demonstrates a robust predictive capability in discerning the spatial distribution of soil heavy metals, and environmental factor variables can explain 60%, 52.3%, 53.5%, 63.1%, 61.2%, and 51.2% of the heavy metal content of Cr, Cd, Pb, As, Hg, and Ni in soil, respectively. Among the chosen environmental variables, precipitation and population density exert notable influences on the predictive outcomes of the model. Specifically, precipitation exhibits the most substantial impact on Cr and Ni, whereas population density emerges as the primary determinant for Cd, Pb, As, and Hg. The RF prediction results show that Cr and Ni in the study area are less affected by human activities, while Cd, Pb, As, and Hg are more affected by human industrial and agricultural production. Research has shown that using RF models for predicting soil heavy metal distributions has certain significance.
Navigating Passenger Satisfaction: A Structural Equation Modeling–Artificial Neural Network Approach to Intercity Bus Services
Authors: Rahnama Shaghayegh , Cortez Adriana , Monzon Andres
Published: May 2024

The phenomenon of passenger satisfaction is an important issue for public transport services and transport companies. Clarifying the relationship between influencing attributes and passenger satisfaction significantly improves service satisfaction. This study examines passenger satisfaction with intercity buses and, in particular, the role of digital information channels (websites and mobile apps) in promoting sustainable travel choices on the Madrid–Bilbao route. This study analyzed data from 459 passengers to identify the key factors influencing the bus choice for intercity bus travel. Punctuality, safety, and ticket price are the most important determinants. We use a combined structural equation modeling (SEM) and artificial neural network (ANN) approach to capture the intricate relationships between service attributes and information channels. The results show that information channels, travel experience, and ticket prices significantly impact passenger satisfaction, which bus operators should improve. Also, inserting the SEM result as input for the ANN showed that ticket price is the most significant predictor of satisfaction, followed by information channels (84%) and travel experience (65%). This approach provides valuable insights for improving the passenger experience. This study emphasizes integrating digital transformation strategies into public transport systems to promote sustainable mobility goals.
Predictive Model of Energy Consumption Using Machine Learning: A Case Study of Residential Buildings in South Africa
Authors: Moulla Donatien Koulla , Attipoe David , Mnkandla Ernest , Abran Alain
Published: May 2024

The recurrent load shedding crisis in South Africa has highlighted the need to accurately predict electricity consumption for residential buildings. This has significant ramifications for daily life and economic productivity. To address this challenge, this study leverages machine learning models to predict the hourly energy consumption of residential buildings in South Africa. This study evaluates the performance of various regression techniques, including Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) machine learning models, using a national residential dataset that contains measurements collected every hour. The objective is to determine the most effective models for predicting next-hour residential building consumption. These models use historical patterns of energy usage to capture temporal details such as seasonal variations and rolling averages. Feature engineering methods are further employed to enhance their predictive capabilities. The performance of each individual model was evaluated using criteria such as the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The results show that both RF and DT achieve the best accuracy for the prediction of residential electricity consumption (because the MSE, MAE, and MAPE for RF and DT are very close to 0). These findings offer actionable insights for households, businesses, and policymakers. By enabling more accurate and granular energy consumption forecasts, this can mitigate the effects of load shedding. This study contributes to the discourse on sustainable energy management by combining advanced machine learning models with real-world energy challenges.