bluuebunny/crossref_metadata_2025 huggingface dataset:
The Relationship and Effects of Self-Esteem and Body Shape on Eating Disorder Behavior: A Cross-Sectional Survey of Chinese University Students
Authors: Gao Zeng , Zhao Jingyi , Peng Sanying , Yuan Han
Published: May 2024

Background: Eating disorders (EDs) have become a global public health concern among adolescents and young adults. However, Chinese university students exhibit a high prevalence of eating disorders. This study aims to investigate the effects of self-esteem (SE) and body shape (BS) on ED behaviors among Chinese university students. Methods: Using random sampling, 946 Chinese university students (aged 18 to 24, M = 19.94, SD = 1.04) participated in a survey comprising the Sick, Control, One, Fat, and Food Questionnaire (SCOFF-Q), the Body Shape Questionnaire (BS-Q), and the Rosenberg Self-Esteem Scale (RS-S) to assess their eating disorder or non-eating disorder (NED) behavior. Results: There was a significant positive correlation between body shape and eating disorder behaviors (r = 0.19, p < 0.01), while there was a significant negative correlation between self-esteem and eating disorder behaviors (r = −0.14, p = 0.001 < 0.01). Gender was a moderating factor in the relationship between body shape and eating disorder behaviors (t = 3.14, p = 0.002 < 0.01), while parents’ marital status was a moderating factor in the relationship between self-esteem and eating disorder behavior (t = 2.72, p = 0.007 < 0.01). Body shape (z = 6.47, p = 0.001 < 0.01), self-esteem (z = −2.81, p = 0.005 < 0.05), and gender (z = 3.06, p = 0.002 < 0.01) significantly influenced eating disorder behavior among Chinese university students aged 18–24 years. Conclusions: There was a direct effect between body shape and self-esteem and eating disorder behaviors among Chinese university students aged 18–24 years. Alarmingly, female university students are becoming susceptible to external influences on self-esteem and body shape, leading to eating disorder behaviors at an increasingly younger age in China.
Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status
Authors: Carella Alessandro , Bulacio Fischer Pedro Tomas , Massenti Roberto , Lo Bianco Riccardo
Published: May 2024

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment of plant water status is crucial for understanding plant physiological responses to water stress and optimizing water management practices in agriculture. Proximal and remote sensing techniques have emerged as powerful tools for the non-destructive, efficient, and spatially extensive monitoring of plant water status. This review aims to examine the recent advancements in proximal and remote sensing methodologies utilized for assessing the water status, consumption, and irrigation needs of fruit tree crops. Several proximal sensing tools have proved useful in the continuous estimation of tree water status but have strong limitations in terms of spatial variability. On the contrary, remote sensing technologies, although less precise in terms of water status estimates, can easily cover from medium to large areas with drone or satellite images. The integration of proximal and remote sensing would definitely improve plant water status assessment, resulting in higher accuracy by integrating temporal and spatial scales. This paper consists of three parts: the first part covers current plant-based proximal sensing tools, the second part covers remote sensing techniques, and the third part includes an update on the on the combined use of the two methodologies.
Notch Signaling: An Emerging Paradigm in the Pathogenesis of Reproductive Disorders and Diverse Pathological Conditions
Authors: Parambath Sreesada , Selvraj Nikhil Raj , Venugopal Parvathy , Aradhya Rajaguru
Published: May 2024

The highly conserved Notch pathway, a pillar of juxtacrine signaling, orchestrates intricate intercellular communication, governing diverse developmental and homeostatic processes through a tightly regulated cascade of proteolytic cleavages. This pathway, culminating in the migration of the Notch intracellular domain (NICD) to the nucleus and the subsequent activation of downstream target genes, exerts a profound influence on a plethora of molecular processes, including cell cycle progression, lineage specification, cell–cell adhesion, and fate determination. Accumulating evidence underscores the pivotal role of Notch dysregulation, encompassing both gain and loss-of-function mutations, in the pathogenesis of numerous human diseases. This review delves deep into the multifaceted roles of Notch signaling in cellular dynamics, encompassing proliferation, differentiation, polarity maintenance, epithelial–mesenchymal transition (EMT), tissue regeneration/remodeling, and its intricate interplay with other signaling pathways. We then focus on the emerging landscape of Notch aberrations in gynecological pathologies predisposing individuals to infertility. By highlighting the exquisite conservation of Notch signaling in Drosophila and its power as a model organism, we pave the way for further dissection of disease mechanisms and potential therapeutic interventions through targeted modulation of this master regulatory pathway.
Clinical Survival Rate and Laboratory Failure of Dental Veneers: A Narrative Literature Review
Authors: Alghazzawi Tariq F.
Published: May 2024

There is a vast amount of published literature concerning dental veneers; however, the effects of tooth preparation, aging, veneer type, and resin cement type on the failure of dental veneers in laboratory versus clinical scenarios are not clear. The purpose of the present narrative review was to determine the principal factors associated with failures of dental veneers in laboratory tests and to understand how these factors translate into clinical successes/failures. Articles were identified and screened by the lead author in January 2024 using the keywords ‘‘dental veneer”, “complication”, “survival rate”, “failure”, and “success rate” using PubMed/Medline, Scopus, Google Scholar, and Science Direct. The inclusion criteria included articles published between January 1999 and January 2024 on the topics of preparation of a tooth, aging processes of the resin cement and veneer, translucency, thickness, fabrication technique of the veneer; shade, and thickness of the resin cement. The exclusion criteria included articles that discussed marginal and internal fit, microhardness, water sorption, solubility, polishability, occlusal veneers, retention, surface treatments, and wear. The results of the present review indicated that dental veneers generally have a high survival rate (>90% for more than 10 years). The amount of preserved enamel layer plays a paramount role in the survival and success rates of veneers, and glass-ceramic veneers with minimal/no preparation showed the highest survival rates. Fracture was the primary failure mechanism associated with decreased survival rate, followed by debonding and color change. Fractures increased in the presence of parafunctional activities. Fewer endodontic complications were associated with veneer restorations. No difference was observed between the maxillary and mandibular teeth. Clinical significance: Fractures can be reduced by evaluation of occlusion immediately after cementation and through the use of high-strength veneer materials, resin cements with low moduli, and thin layers of highly polished veneers. Debonding failures can be reduced with minimal/no preparation, and immediate dentin sealing should be considered when dentin is exposed. Debonding can also be reduced by preventing contamination from blood, saliva, handpiece oil, or fluoride-containing polishing paste; through proper surface treatment (20 s of hydrofluoric acid etching for glass ceramic followed by silane for 60 s); and through use of light-cured polymerization for thin veneers. Long-term color stability may be maintained using resin cements with UDMA-based resin, glass ceramic materials, and light-cure polymerization with thin veneers.
A Novel Positional Calibration Method for an Underwater Acoustic Beacon Array Based on the Equivalent Virtual Long Baseline Positioning Model
Authors: Zhang Ge , Yi Guoxing , Wei Zhennan , Xie Yangguang , Qi Ziyang
Published: May 2024

The performance of long baseline (LBL) positioning systems is significantly impacted by the distribution and positional calibration accuracy of underwater acoustic beacon arrays. In previous calibration methods for beacon arrays based on autonomous underwater vehicle (AUV) platforms, the slant range information of each beacon was processed independently, and each beacon was calibrated one at a time. This approach not only decreases the calibration efficiency but also leaves the positional calibration accuracy of each beacon highly susceptible to the navigation trajectory of the AUV. To overcome these limitations, an equivalent virtual LBL (EVLBL) positioning model is introduced in this paper. This model operates by adjusting the positions of each beacon according to the dead reckoning increments computed during the AUV’s reception of positioning signals, effectively forming a virtual beacon array. Consequently, the AUV is capable of mitigating LBL positioning errors that arise from its motion by simultaneously receiving positioning signals from all beacons. Additionally, an overall calibration method for beacon arrays based on particle swarm optimization (PSO) is proposed. In this approach, the minimization of the deviation between the EVLBL trajectory and the dead reckoning trajectory is set as the optimization objective, and the coordinates of each beacon are iteratively optimized. The simulation results demonstrate that the proposed EVLBL-based PSO algorithm (EVPSO) significantly enhanced the calibration efficiency and positional accuracy of the beacon array. Compared with conventional methods, the estimation error of the beacon positions was reduced from 6.40 m to within 1.00 m. After compensating for the beacon array positions, the positioning error of the LBL system decreased from approximately 5.00 m (with conventional methods) to around 1.00 m (with EVPSO), demonstrating the effectiveness of the proposed approach.
Deploying a Computer Vision Model Based on YOLOv8 Suitable for Drones in the Tuna Fishing and Aquaculture Industry
Authors: Pham Duc-Anh , Han Seung-Hun
Published: May 2024

In recent years, the global tuna fishing and aquaculture industry has encountered significant challenges in balancing operational efficiency with sustainable resource management. This study introduces an innovative approach utilizing an advanced computer vision model, PA-YOLOv8, specifically adapted for drones, to enhance the monitoring and management of tuna populations. PA-YOLOv8 leverages the capabilities of YOLOv8, a state-of-the-art object detection system known for its precision and speed, tailored to address the unique demands of aerial surveillance in marine environments. Through comprehensive modifications including downsampling techniques, feature fusion enhancements, and the integration of the Global Attention Module (GAM), the model significantly improves the detection accuracy of small and juvenile tuna within complex aquatic landscapes. Experimental results using the Tuna dataset from Roboflow demonstrate marked improvements in detection metrics such as precision, recall, and mean average precision (mAP), affirming the model’s effectiveness. This study underscores the potential of integrating cutting-edge technologies like UAVs and computer vision in promoting sustainable practices in the aquaculture sector, setting a new standard for technological applications in environmental and resource management. The advancements presented here provide a scalable and efficient solution for real-time monitoring, contributing to the long-term sustainability of marine ecosystems.
The Holistic Advantage: Unified Quantitative Modeling for Less-Biased, In-Depth Insights into (Socio)Linguistic Variation
Authors: Gonzales Wilkinson Daniel Wong
Published: May 2024

What happens when recognized and diverse conditioning factors of linguistic variation are omitted from analysis and/or are not analyzed under a single analytical procedure? This paper explores the consequences of such a choice on data interpretation and, consequently, (socio)linguistic theorization. Utilizing Twitter-style English in the Philippines (EngPH) as a case study, I employ the Twitter Corpus of Philippine Englishes (TCOPE) primarily to investigate and elucidate variations in three morphosyntactic variables that have been previously examined using a piecemeal approach. I propose a holistic quantitative approach that incorporates documented linguistic, social, diachronic, and stylistic factors in a unified analysis. The paper illustrates the impacts of adopting this holistic approach through two statistical procedures: Bayesian regression modeling and Boruta feature selection with random forest modeling. In contrast to earlier research findings, my overall results reveal biases in non-unified quantitative analyses, where the confidence in the effects of certain factors diminishes in light of others during analysis. The adoption of a unified analysis or modeling also enhances the resolution at which variations have been examined in EngPH. For instance, it highlights that presumed ‘universals’, such as the hierarchy of linguistic > stylistic > diachronic > social factors in explaining variation in some domains, is contingent on the specific variable under examination. Overall, I argue that unified analyses reduce data distortion and introduce more nuanced interpretations and insights that are critical for establishing a well-grounded empirical theory of EngPH variation and language variation as a whole.
Ensuring the Abrasive Jet Machining Efficiency Using a Nozzle with a Perforated Insert
Authors: Baha Vadym , Pavlenko Ivan , Židek Kamil , Ciszak Olaf
Published: May 2024

Ejector-cleaning devices for abrasive jet machining have various practical applications. The working nozzle is one of the device’s key elements affecting the treated surface quality. There arises the necessity for new approaches to achieving an efficiency increase in abrasive jet equipment nozzles, namely their design improvement and further development of a new, relatively cheap but effective technology for their manufacturing and maintenance. This technology should allow for the high durability of nozzles without being essential for the hardness or wear resistance parameters of the material used for manufacturing. The nozzle should be designed as a long-length perforated insert to allow for radial airflow, forcing the abrasive material (river sand) from the inner walls of the nozzle’s working surface to reduce its friction with the abrasive material. This will result in new wear-out conditions, providing an essential decrease in the wear-out of a nozzle’s working surface. The article aims to develop a more effective design for the working nozzle based on the perforated insert application. The task was set to provide a more detailed experimental and theoretical study of the processes in perforated nozzles to improve their effectiveness. The research resulted in a new design for nozzles with higher efficiency.
Extracellular Vesicles from Ecklonia cava and Phlorotannin Promote Rejuvenation in Aged Skin
Authors: Batsukh Sosorburam , Oh Seyeon , Lee Ji Min , Joo Judy Hong Jin , Son Kuk Hui , Byun Kyunghee
Published: May 2024

Plant-derived extracellular vesicles (EVs) elicit diverse biological effects, including promoting skin health. EVs isolated from Ecklonia cava (EV-EC) carry heat shock protein 70 (HSP70), which inhibits key regulators such as TNF-α, MAPKs, and NF-κB, consequently downregulating matrix metalloproteinases (MMPs). Aging exacerbates oxidative stress, upregulating MAPK and NF-κB signaling and worsening extracellular matrix degradation in the skin. E. cava-derived phlorotannin (PT) mitigates MAPK and NF-κB signaling. We evaluated the impact of EV-EC and PT on skin rejuvenation using an in vitro keratinocyte senescence model and an in vivo aged-mouse model. Western blotting confirmed the presence of HSP70 in EV-EC. Treatment with EV-EC and PT in senescent keratinocytes increased HSP70 expression and decreased the expression of TNF-α, MAPK, NF-κB, activator protein-1 (AP-1), and MMPs. Oxidative stress was also reduced. Sequential treatment with PT and EV-EC (PT/EV-EC) yielded more significant results compared to individual treatments. The administration of PT/EV-EC to the back skin of aged mice mirrored the in vitro findings, resulting in increased collagen fiber accumulation and improved elasticity in the aged skin. Therefore, PT/EV-EC holds promise in promoting skin rejuvenation by increasing HSP70 expression, decreasing the expression of MMPs, and reducing oxidative stress in aged skin.
Antifungal Activities of Biogenic Silver Nanoparticles Mediated by Marine Algae: In Vitro and In Vivo Insights of Coating Tomato Fruit to Protect against Penicillium italicum Blue Mold
Authors: Hamouda Ragaa A. , Almaghrabi Fatimah Q. , Alharbi Ohoud M. , Al-Harbi Abla D. M. , Alsulami Rahaf M. , Alhumairi Abrar M.
Published: May 2024

In an attempt to reduce such decay induced by pathogenic causes, several studies investigated the effectiveness of nanoparticles (NPs) that play a vital role in saving food products, especially fruits. Current research delves into biogenic silver nanoparticles (using marine alga Turbinaria turbinata (Tt/Ag-NPs) and their characterization using FT-IR, TEM, EDS, and zeta potential. Some pathogenic fungi, which cause fruit spoilage, were isolated. We studied the impact of using Tt/Ag-NPs to protect against isolated fungi in vitro, and the influence of Tt/Ag-NPs as a coating of tomato fruit to protect against blue mold caused by Penicillium italicum (OR770486) over 17 days of storage time. Five treatments were examined: T1, healthy fruits were used as the positive control; T2, healthy fruits sprayed with Tt/Ag-NPs; T3, fruits infected with P. italicum followed by coating with Tt/Ag-NPs (pre-coating); T4, fruits coated with Tt/Ag-NPs followed by infection by P. italicum (post-coating); and T5, the negative control, fruits infected by P. italicum. The results displayed that Tt/Ag-NPs are crystalline, spherical in shape, with size ranges between 14.5 and 39.85 nm, and negative charges. Different concentrations of Tt/Ag-NPs possessed antifungal activities against Botrytis cinerea, Rhodotorula mucilaginosa, Penicillium expansum, Alternaria alternate, and Stemphylium vesicarium. After two days of tomatoes being infected with P. italicum, 55% of the fruits were spoilage. The tomato fruit coated with Tt/Ag-NPs delayed weight loss, increased titratable acidity (TA%), antioxidant%, and polyphenol contents, and decreased pH and total soluble solids (TSSs). There were no significant results between pre-coating and post-coating except in phenol contents increased in pre-coating. A particular focus is placed on the novel and promising approach of utilizing nanoparticles to combat foodborne pathogens and preserve commodities, with a spotlight on the application of nanoparticles in safeguarding tomatoes from decay.
Impact of Plant Oil Supplementation on Lipid Production and Fatty Acid Composition in Cunninghamella elegans TISTR 3370
Authors: Khankhum Surasak , Khamkaew Karnjana , Li Hua , Prakitchaiwattana Chuenjit , Siriamornpun Sirithon
Published: May 2024

The Cunninghamella genus has been utilized for the production of PUFA-rich lipids. Therefore, we investigate the impact of plant oil supplementation in the culture medium (soybean oil, rice bran oil, and perilla oil), selected based on their different fatty acid predominant, on lipid production and fatty acid composition in C. elegans (TISTR 3370). All oils significantly boosted fungal growth, each influencing distinct patterns of lipid accumulation within the cells. The cells exhibited distinct patterns of lipid accumulation, forming intracellular lipid bodies, influenced by the different oils. Monounsaturated fatty acids (MUFAs) were found to be the most abundant, followed by polyunsaturated fatty acids (PUFAs) and saturated fatty acids (SFAs) in the fungal lipid cultures. Oleic acid was identified as the primary MUFA, while palmitic acid was the predominant SFA in perilla oil supplements. Remarkably, perilla oil supplement provided the highest total lipid production with arachidonic acid being exclusively detected. The percentage of PUFAs ranged from 12% in the control to 33% in soybean oil, 32% in rice bran oil, and 61% in perilla oil supplements. These findings offer valuable opportunities for advancing biotechnological applications in lipid production and customization, with implications for food and nutrition as well as pharmaceuticals and cosmetics.
Whole Genome Sequence Analysis of Listeria monocytogenes Isolates Obtained from the Beef Production Chain in Gauteng Province, South Africa
Authors: Gana James , Gcebe Nomakorinte , Pierneef Rian Edward , Chen Yi , Moerane Rebone , Adesiyun Abiodun Adewale
Published: May 2024

The study used whole-genome sequencing (WGS) and bioinformatics analysis for the genomic characterization of 60 isolates of Listeria monocytogenes obtained from the beef production chain (cattle farms, abattoirs, and retail outlets) in Gauteng province, South Africa. The sequence types (STs), clonal complexes (CCs), and the lineages of the isolates were determined using in silico multilocus sequence typing (MLST). We used BLAST-based analyses to identify virulence and antimicrobial genes, plasmids, proviruses/prophages, and the CRISPR-Cas system. The study investigated any association of the detected genes to the origin in the beef production chain of the L. monocytogenes isolates. Overall, in 60 isolates of Listeria monocytogenes, there were seven STs, six CCs, forty-four putative virulence factors, two resistance genes, one plasmid with AMR genes, and three with conjugative genes, one CRISPR gene, and all 60 isolates were positive for proviruses/prophages. Among the seven STs detected, ST204 (46.7%) and ST2 (21.7%) were the most prominent, with ST frequency varying significantly (p < 0.001). The predominant CC detected were CC2 (21.7%) and CC204 (46.7%) in lineages I and II, respectively. Of the 44 virulence factors detected, 26 (across Listeria Pathogenicity Islands, LIPIs) were present in all the isolates. The difference in the detection frequency varied significantly (p < 0.001). The two AMR genes (fosX and vga(G)) detected were present in all 60 (100%) isolates of L. monocytogenes. The only plasmid, NF033156, was present in three (5%) isolates. A CRISPR-Cas system was detected in six (10%), and all the isolates carried proviruses/prophages. The source and sample type significantly affected the frequencies of STs and virulence factors in the isolates of L. monocytogenes. The presence of fosX and vga(G) genes in all L. monocytogenes isolates obtained from the three industries of the beef production chain can potentially cause therapeutic implications. Our study, which characterized L. monocytogenes recovered from the three levels in the beef production chain, is the first time genomics was performed on this type of data set in the country, and this provides insights into the health implications of Listeria.
Ticks and Tick-Borne Pathogens Circulating in Peri-Domestic Areas in Mainland Portugal
Authors: Moerbeck Leonardo , Parreira Ricardo , Szczotko Magdalena , Seixas Gonçalo , Velez Rita , Dmitryjuk Małgorzata , Santos Ana Sofia , Domingos Ana , Antunes Sandra
Published: May 2024

Over the years, tick-borne pathogens (TBPs) have garnered significant interest due to their medical, veterinary and economic importance. Additionally, TBPs have drawn attention to how these microorganisms interact with their own vectors, increasing the risk to human and animal infection of emerging and reemerging zoonoses. In this sense, ticks, which are obligate hematophagous ectoparasites, have a key role in maintaining and transmitting TBPs among humans and animals. The aim of this study was to assess the prevalence of neglected TBPs in mainland Portugal, namely Anaplasma spp., Babesia spp., Ehrlichia spp. and Neoehrlichia mikurensis. DNA fragments were detected in questing ticks collected from five different ecological areas under investigation. To the best of the authors’ knowledge, this study reports new worldwide findings, including B. bigemina infecting Ixodes frontalis, Ixodes ricinus and Rhipicephalus sanguineus sensu lato. Additionally, it presents new findings in Portugal of N. mikurensis infecting I. ricinus and of presumably Wolbachia endosymbionts being detected in I. ricinus. Overall, there were 208 tick samples that were negative for all screened TBPs. The results herein obtained raise concerns about the circulation of neglected TBPs in mainland Portugal, especially in anthropophilic ticks, highlighting the importance of adopting a One Health perspective.
Narrowband Organic/Inorganic Hybrid Afterglow Materials
Authors: Xia Wen , Li Xun , Li Junbo , Yan Qianqian , Wang Guangming , Piao Xixi , Zhang Kaka
Published: May 2024

Narrowband afterglow materials display interesting functions in high-quality anti-counterfeiting and multiplexed bioimaging. However, there is still a limited exploration of these afterglow materials, especially for those with a full width at half maxima (FWHM) around 30 nm. Here, we report the fabrication of narrowband organic/inorganic hybrid afterglow materials via energy transfer technology. Coronene (Cor) with a long phosphorescence feature and broad phosphorescence band is selected as the donor for energy transfer, and inorganic quantum dots (QDs) of CdSe/ZnS with a narrowband emission are used as acceptors. Upon doping into the organic matrix, the resultant three-component materials exhibit a narrowband afterglow with an afterglow lifetime of approximately 3.4 s and an FWHM of 31 nm. The afterglow wavelength of the afterglow materials can be controlled by the QDs. This work based on organic/inorganic hybrids provides a facile approach for developing multicolor and narrowband afterglow materials, as well as opens a new way for expanding the features of organic afterglow for multifunctional applications. It is expected to rely on narrowband afterglow emitters to solve the “spectrum congestion” problem of high-density information storage in optical anti-counterfeiting and information encryption.
Enhanced Chromium (VI) Removal by Micron-Scale Zero-Valent Iron Pretreated with Aluminum Chloride under Aerobic Conditions
Authors: Long Xuejun , Li Rui , Wan Jun , Zhong Zhenxing , Ye Yuxuan , Yang Jiazhi , Luo Jun , Xia Jin , Liu Yaomeng
Published: May 2024

Micron-scale zero-valent iron (ZVI)-based material has been applied for hexavalent chromium (Cr(VI)) decontamination in wastewater treatment and groundwater remediation, but the passivation problem has limited its field application. In this study, we combined aluminum chloride solution with ZVI (pcZVI-AlCl3) to enhance Cr(VI) removal behavior under aerobic conditions. The optimal pre-corrosion conditions were found to be 2.5 g/L ZVI, 0.5 mM AlCl3, and a 4 h preconditioning period. Different kinds of techniques were applied to detect the properties of preconditioned ZVI and corrosion products. The 57Fe Mössbauer spectra showed that proportions of ZVI, Fe3O4, and FeOOH in pcZVI-AlCl3 were 49.22%, 34.03%, and 16.76%, respectively. The formation of Al(OH)3 in the corrosion products improved its pHpzc (point of zero charge) for Cr(VI) adsorption. Continuous-flow experiments showed its great potential for Cr(VI) removal in field applications. The ZVI and corrosion products showed a synergistic effect in enhancing electron transfer for Cr(VI) removal. The mechanisms underlying Cr(VI) removal by pcZVI-AlCl3 included adsorption, reduction, and precipitation, and the contribution of adsorption was less. This work provides a new strategy for ZVI pre-corrosion to improve its longevity and enhance Cr(VI) removal.
Unlocking the Potential of Pepper Plants under Salt Stress: Mycorrhizal Effects on Physiological Parameters Related to Plant Growth and Gas Exchange across Tolerant and Sensitive Genotypes
Authors: Altuntas Ozlem , Dasgan Hayriye Yildiz , Akhoundnejad Yelderem , Nas Yahya
Published: May 2024

Agriculture is confronted with the challenge of ensuring global food security, yet the rapid expansion of salinity stress undoubtedly restricts plant productivity in cultivable areas, posing a significant threat to crop yields. Arbuscular mycorrhizal fungi (AMFs) have emerged as a biological tool for enhancing plant salt stress tolerance. To utilize this biological tool, this study evaluated the response in growth and physiological parameters of tolerant (Karaisali) and sensitive (Demre) pepper genotypes. The experiment involved mycorrhizal-treated (Glomus clarium) and non-mycorrhizal (control) plants of both the tolerant and sensitive pepper genotypes. The plants were subjected to two salt doses: 75 and 150 mM. The plant growth and physiological parameters were measured at 40 days after transplanting. The mycorrhizal activity was found to be significantly more effective in the sensitive genotype. We found notable differences in mycorrhizal activity between the pepper genotypes under salt stress conditions, with the sensitive genotype “Demre” showing greater responsiveness to mycorrhizal association compared with the “Karaisali” variety. Under both moderate (75 mM NaCl) and higher salt stress levels (150 mM NaCl), both the “Karaisali” and “Demre” varieties exhibited substantial increases in their shoot dry weights. However, these increases were consistently higher in the “Demre” plants. Moreover, the AMFs demonstrated significant enhancements in photosynthesis rates under both moderate and high salinity levels in both genotypes. Overall, our findings suggest that AMFs play a crucial role in improving plant growth, water status, and photosynthesis characteristics, particularly in salt-sensitive pepper genotypes, under moderate-to-high salinity levels. In conclusion, the plant growth, water status, and photosynthesis characteristics of the salt-sensitive pepper genotype were significantly improved by AMFs at medium and high salinity levels, such as 75 mM and 150 mM NaCl, respectively.
RsRbohD1 Plays a Significant Role in ROS Production during Radish Pithiness Development
Authors: Gong Qiong , Wang Chaonan , Fan Weiqiang , Li Shuiling , Zhang Hong , Huang Zhiyin , Liu Xiaohui , Ma Ziyun , Wang Yong , Zhang Bin
Published: May 2024

Pithiness is one of the physiological diseases of radishes, which is accompanied by the accumulation of reactive oxygen species (ROS) during the sponging of parenchyma tissue in the fleshy roots. A respiratory burst oxidase homolog (Rboh, also known as NADPH oxidase) is a key enzyme that catalyzes the production of ROS in plants. To understand the role of Rboh genes in radish pithiness, herein, 10 RsRboh gene families were identified in the genome of Raphanus sativus using Blastp and Hmmer searching methods and were subjected to basic functional analyses such as phylogenetic tree construction, chromosomal localization, conserved structural domain analysis, and promoter element prediction. The expression profiles of RsRbohs in five stages (Pithiness grade = 0, 1, 2, 3, 4, respectively) of radish pithiness were analyzed. The results showed that 10 RsRbohs expressed different levels during the development of radish pithiness. Except for RsRbohB and RsRbohE, the expression of other members increased and reached the peak at the P2 (Pithiness grade = 2) stage, among which RsRbohD1 showed the highest transcripts. Then, the expression of 40 genes related to RsRbohD1 and pithiness were analyzed. These results can provide a theoretical basis for improving pithiness tolerance in radishes.
Solid–Liquid Two-Phase Flowmeter Flow-Passage Wall Erosion Evolution Characteristics and Calibration of Measurement Accuracy
Authors: Han Wei , Yan Lumin , Li Rennian , Zhang Jing , Yang Xiang , Ji Lei , Qiang Yan
Published: May 2024

Solid–liquid two-phase flowmeters are widely used in critical sectors, such as petrochemicals, energy, manufacturing, the environment, and various other fields. They are indispensable devices for measuring flow. Currently, research has primarily focused on gas–liquid two-phase flow within the flowmeter, giving limited attention to the impact of solid phases. In practical applications, crude oil frequently contains solid particles and other impurities, leading to equipment deformation and a subsequent reduction in measuring accuracy. This paper investigates how particle dynamic parameters affect the erosion evolution characteristics of flowmeters operating in solid–liquid two-phase conditions, employing the dynamic boundary erosion prediction method. The results indicate that the erosion range and peak erosion position on the overcurrent wall of the solid–liquid two-phase flowmeter vary with different particle dynamic parameters. Erosion mainly occurs at the contraction section of the solid–liquid two-phase flowmeter. When the particle inflow velocity increases, the erosion range shows no significant change, but the peak erosion position shifts to the right, primarily due to the evolution of the erosion process. With an increase in particle diameter, the erosion range expands along the inlet direction due to turbulent diffusion, as particles with lower kinetic energy exhibit better followability. There is no significant change in the erosion range and peak erosion position with an increase in particle volume fraction and particle sphericity. With a particle inflow velocity of 8.4 m/s, the maximum erosion depth reaches 750 μm. In contrast, at a particle sphericity of 0.58, the minimum erosion depth is 251 μm. Furthermore, a particle volume fraction of 0.5 results in a maximum flow coefficient increase of 1.99 × 10−3.
Optimization Design of CMUT Sensors with Broadband and High Sensitivity Characteristics Based on the Genetic Algorithm
Authors: Zhang Sai , Lu Wentao , Wang Ailing , He Huizi , Wang Renxin , Zhang Wendong
Published: May 2024

In this study, we propose a method for optimizing the design of CMUT sensors using genetic algorithms. Existing CMUT sensors face frequency response and sensitivity limitations, necessitating optimization to enhance their sensing performance. Traditional optimization methods are often intricate and time-consuming and may fail to yield the optimal solution. Genetic algorithms, which simulate the biological evolution process, offer advantages in global optimization and efficiency, making them widely utilized in the optimization design of Microelectromechanical Systems (MEMS) devices. Based on the theoretical framework and finite element model of CMUT sensors, we propose a CMUT array element optimization design method based on genetic algorithms. The optimization and validation results demonstrate that we have successfully designed a broadband CMUT array element consisting of four microelements with a 1–2 MHz frequency range. Compared with a randomly arranged array element, the optimized array shows a 63.9% increase in bandwidth and a 7.5% increase in average sensitivity within the passband. Moreover, the sensitivity variance within the passband is reduced by 50.2%. Our proposed method effectively optimizes the design of high sensitivity CMUT sensors with the desired bandwidth, thereby offering significant reference value for the optimization design of CMUT sensors.
Machine Learning-Based Prediction of Cattle Activity Using Sensor-Based Data
Authors: Hernández Guillermo , González-Sánchez Carlos , González-Arrieta Angélica , Sánchez-Brizuela Guillermo , Fraile Juan-Carlos
Published: May 2024

Livestock monitoring is a task traditionally carried out through direct observation by experienced caretakers. By analyzing its behavior, it is possible to predict to a certain degree events that require human action, such as calving. However, this continuous monitoring is in many cases not feasible. In this work, we propose, develop and evaluate the accuracy of intelligent algorithms that operate on data obtained by low-cost sensors to determine the state of the animal in the terms used by the caregivers (grazing, ruminating, walking, etc.). The best results have been obtained using aggregations and averages of the time series with support vector classifiers and tree-based ensembles, reaching accuracies of 57% for the general behavior problem (4 classes) and 85% for the standing behavior problem (2 classes). This is a preliminary step to the realization of event-specific predictions.
Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional Autoencoder: A Unified Solution
Authors: Han Ming , Dang Yu , Han Jianda
Published: May 2024

Preprocessing plays a key role in Raman spectral analysis. However, classical preprocessing algorithms often have issues with reducing Raman peak intensities and changing the peak shape when processing spectra. This paper introduces a unified solution for preprocessing based on a convolutional autoencoder to enhance Raman spectroscopy data. One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder (CAE+ model). The CDAE model incorporates two additional convolutional layers in its bottleneck layer for enhanced noise reduction. The CAE+ model not only adds convolutional layers at the bottleneck but also includes a comparison function after the decoding for effective baseline correction. The proposed models were validated using both simulated spectra and experimental spectra measured with a Raman spectrometer system. Comparing their performance with that of traditional signal processing techniques, the results of the CDAE-CAE+ model show improvements in noise reduction and Raman peak preservation.
A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring
Authors: Xie Yilin , Meng Xiaolin , Nguyen Dinh Tung , Xiang Zejun , Ye George , Hu Liangliang
Published: May 2024

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors’ experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.
Rate–Distortion–Perception Optimized Neural Speech Transmission System for High-Fidelity Semantic Communications
Authors: Yao Shengshi , Xiao Zixuan , Niu Kai
Published: May 2024

We consider the problem of learned speech transmission. Existing methods have exploited joint source–channel coding (JSCC) to encode speech directly to transmitted symbols to improve the robustness over noisy channels. However, the fundamental limit of these methods is the failure of identification of content diversity across speech frames, leading to inefficient transmission. In this paper, we propose a novel neural speech transmission framework named NST. It can be optimized for superior rate–distortion–perception (RDP) performance toward the goal of high-fidelity semantic communication. Particularly, a learned entropy model assesses latent speech features to quantify the semantic content complexity, which facilitates the adaptive transmission rate allocation. NST enables a seamless integration of the source content with channel state information through variable-length joint source–channel coding, which maximizes the coding gain. Furthermore, we present a streaming variant of NST, which adopts causal coding based on sliding windows. Experimental results verify that NST outperforms existing speech transmission methods including separation-based and JSCC solutions in terms of RDP performance. Streaming NST achieves low-latency transmission with a slight quality degradation, which is tailored for real-time speech communication.
Index Air Quality Monitoring for Light and Active Mobility
Authors: Botticini Stefano , Comini Elisabetta , Dello Iacono Salvatore , Flammini Alessandra , Gaioni Luigi , Galliani Andrea , Ghislotti Luca , Lazzaroni Paolo , Re Valerio , Sisinni Emiliano , Verzeroli Matteo , Zappa Dario
Published: May 2024

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.
Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals
Authors: Parsons Ira Lloyd , Karisch Brandi B. , Stone Amanda E. , Webb Stephen L. , Norman Durham A. , Street Garrett M.
Published: May 2024

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations.
An Adaptive Control Scheme Based on Non-Interference Nonlinearity Approximation for a Class of Nonlinear Cascaded Systems and Its Application to Flexible Joint Manipulators
Authors: Liu Zhangxing , Jin Hongzhe , Zhao Jie
Published: May 2024

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications.
Optimized Design of EdgeBoard Intelligent Vehicle Based on PP-YOLOE+
Authors: Yao Chengzhang , Liu Xiangpeng , Wang Jilin , Cheng Yuhua
Published: May 2024

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system’s ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles’ operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model’s performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms.