bluuebunny/crossref_metadata_2025 huggingface dataset:
Privacy threats of behaviour identity detection in VR
Authors: Kumarapeli Dilshani , Jung Sungchul , Lindeman Robert W.
Published: January 2024

This study explores the potential privacy risks associated with the use of behavioural data as an identification mechanism in immersive VR applications. With the advent of modern VR technology, tracking sensors are now able to provide a highly immersive experience with a high level of user agency, significantly increasing both the amount and richness of behavioural data being collected and recorded. However, there has been little research into the privacy risks of such approaches. In this work, we investigate the capability of machine learning algorithms to identify VR users across multiple sessions and activities, as well as their effectiveness when users deliberately change their behaviour to evade detection. We also examine how the physical characteristics of users impact the accuracy of these algorithms. Our results show that once a user is tracked in VR, they can be identified with 83% accuracy across multiple sessions of the same activity and with 80% accuracy when performing a different task. Even when users attempt to deliberately obfuscate their behaviour, they can still be recognised with 78% accuracy. These findings highlight the need for more robust technical measures to safeguard the behavioural privacy of VR users.
Biodegradation and decolorization of methylene blue, reactive Black-5, and toluidine blue-O from an aqueous solution using the polyphenol oxidase enzyme
Authors: Uddin Jalal , Idrees Muhammad , Ahmed Humam , Batool Saima , Rahman Tanzeel Ur , Mehmood Shahzil , Tanoli Asad Khan , Muhsinah Abdullatif Bin , Ullah Hidayat , Musharraf Syed Ghulam
Published: January 2024

In this study, a copper-containing polyphenol oxidase (PPO) enzyme was extracted from the selected agricultural food products, i.e., Mangifera indica (L.), Solanum tuberosum (L.), and Luffa cylindrica (L.), which has efficiently degraded the azo dyes (Methylene blue, Reactive Black-5, and Toluidine Blue-O) into less toxic compounds. After preparing the dye solution at various concentrations, the PPO enzymes extracted with phosphate buffer were incubated with it for 2 hours. The percentage of decolorization was estimated by using the unprocessed dye solution. The degraded products were characterized by UV–vis and FT-IR. All sets of experiments followed pseudo-first-order kinetics. The highest kinetic degradation rate constant was observed at kt = 0.0213 min─1 (R2 = 0.9258) and kt = 0.0244 min─1 (R2 = 0.7627) in reactive black-5 from Mangifera indica (L.) and Solanum tuberosum (L.), respectively. The enzyme extracted from the peel of Solanum tuberosum (L.) decolorized Reactive Black-5 and Toluidine Blue-O dyes to a greater extent, i.e., 83.29 and 70.84% with reaction times of 120 and 90 min, respectively.
Multi-omics reveals goose fatty liver formation from metabolic reprogramming
Authors: Wei Rongxue , Teng Yongqiang , Han Chunchun , Wei Shouhai , Li Liang , Liu Hehe , Hu Shenqiang , Kang Bo , Xu Hengyong
Published: January 2024

To comprehensively provide insight into goose fatty liver formation, we performed an integrative analysis of the liver transcriptome, lipidome, and amino acid metabolome, as well as peripheral adipose tissue transcriptome analysis using samples collected from the overfed geese and normally fed geese. Transcriptome analysis showed that liver metabolism pathways were mainly enriched in glucolipid metabolism, amino acid metabolism, inflammation response, and cell cycle; peripheral adipose tissue and the liver cooperatively regulated liver lipid accumulation during overfeeding. Liver lipidome patterns obviously changed after overfeeding, and 157 different lipids were yielded. In the liver amino acid metabolome, the level of Lys increased after overfeeding. In summary, this is the first study describing goose fatty liver formation from an integrative analysis of transcriptome, lipidome, and amino acid metabolome, which will provide a whole new dimension to understanding the mechanism of goose fatty liver formation.
Simulation and Analysis of Aerodynamic Characteristics during Parafoil Canopy Curving Process and Application by LBM
Authors: Wang Yi , Yang Chunxin
Published: January 2024

A parafoil is a crucial aerodynamic deceleration device used in the field of airdrop. The overall objective of this paper is to study the aerodynamic characteristics of the curving process of the canopy using the lattice Boltzmann method, to verify it with the experimental results, and to analyze the stalling phenomenon using the finite volume method(FVM). Simulations were conducted to analyze the aerodynamic curves of four−stage models of canopies, examining the flow field characteristics. Additionally, the influence of air chamber structures is also analyzed. The reasons for differences in the aerodynamic characteristics are discussed based on the results obtained. The reliability of utilizing the lattice Boltzmann method for aerodynamic simulations is demonstrated. Overall, the lift coefficient of models II/III/IV was increased by 30.97% compared with model I, which proved the effectiveness of the air chamber structure and curving process. Notably, different curved canopies showed significantly improved lift and drag aerodynamic characteristics to varying extents, highlighting their robustness. Also, it was observed that air chamber partitions exerted a greater influence compared to perforation. Through validation and analysis, it was determined the accuracy of the LBM improved up to 10.9% with respect to the FVM. These findings provide a valuable reference for parafoil experiments and simulation research.
Impact of Heat Stress on the In-Line Registered Milk Fat-to-Protein Ratio and Metabolic Profile in Dairy Cows
Authors: Antanaitis Ramūnas , Džermeikaitė Karina , Krištolaitytė Justina , Ribelytė Ieva , Bespalovaitė Agnė , Bulvičiūtė Deimantė , Tolkačiovaitė Kotryna , Baumgartner Walter
Published: January 2024

The aim of our study was to investigate and quantify the impact of heat stress on the milk fat-to-protein ratio (F/P) and the metabolic profile in dairy cows, utilizing in-line registration methods for accurate and real-time data collection. This study was carried out in Lithuania at coordinates 55.819156, 23.773541, from 1 June 2023 until 31 August 2023. Milk composition, including fat and protein, was measured using a BROLIS HerdLine in-line milk analyzer from Brolis Sensor Technology, Vilnius, Lithuania. During the general clinical examinations (twice per week), blood samples were collected and determined for GLU—blood glucose concentration; BHB—blood β-hydroxybutyrate concentration; AST—aspartate transaminase activity; GGT—gamma-glutamyltransferase activity; and NEFAs—non-esterified fatty acids. The parameters based on the Temperature–Humidity Index (THI) were categorized into two groups: group 1, consisting of THI values less than 72, representing the comfort zone, and group 2, with THI values of 72 or higher, indicating a greater risk of thermal stress. Specifically, group 2 exhibited an 8.6% increase in the F/P ratio compared to group 1 (p = 0.049). Additionally, there was a 4.2% decrease in glucose levels in group 2 (p = 0.056) and a notable 5.8% decrease in albumin levels compared to group 1 (p < 0.001). We found a very weak, non-significant correlation between humidity and the milk fat-to-protein ratio (r = 0.043, p = 0.447) and a similarly negligible correlation with Beta-Hydroxybutyrate (BHB; r = 0.046, p = 0.417). We observed significant changes in milk composition, particularly an increase in the milk fat-to-protein ratio, and alterations in metabolic indicators like glucose, albumin, and liver enzymes. These changes, indicative of a negative energy balance and altered metabolic processes such as gluconeogenesis and lipolysis, correspond to previous research. The adoption of advanced tools, such as the BROLIS HerdLine analyzer, is recommended for the real-time monitoring of milk composition, which assists in the early detection of negative energy balances and metabolic issues. It is also crucial to adjust feeding practices to maintain energy balance during periods of high THI and to conduct regular health checks with a special focus on cows in early lactation.
Insights into Drought Tolerance of Tetraploid Wheat Genotypes in the Germination Stage Using Machine Learning Algorithms
Authors: Benlioğlu Berk , Demirel Fatih , Türkoğlu Aras , Haliloğlu Kamil , Özaktan Hamdi , Kujawa Sebastian , Piekutowska Magdalena , Wojciechowski Tomasz , Niedbała Gniewko
Published: January 2024

Throughout germination, which represents the initial and crucial phase of the wheat life cycle, the plant is notably susceptible to the adverse effects of drought. The identification and selection of genotypes exhibiting heightened drought tolerance stand as pivotal strategies aimed at mitigating these effects. For the stated objective, this study sought to evaluate the responses of distinct wheat genotypes to diverse levels of drought stress encountered during the germination stage. The induction of drought stress was achieved using polyethylene glycol at varying concentrations, and the assessment was conducted through the application of multivariate analysis and machine learning algorithms. Statistical significance (p < 0.01) was observed in the differences among genotypes, stress levels, and their interaction. The ranking of genotypes based on tolerance indicators was evident through a principal component analysis and biplot graphs utilizing germination traits and stress tolerance indices. The drought responses of wheat genotypes were modeled using germination data. Predictions were then generated using four distinct machine learning techniques. An evaluation based on R-square, mean square error, and mean absolute deviation metrics indicated the superior performance of the elastic-net model in estimating germination speed, germination power, and water absorption capacity. Additionally, in assessing the criterion metrics, it was determined that the Gaussian processes classifier exhibited a better performance in estimating root length, while the extreme gradient boosting model demonstrated superior performance in estimating shoot length, fresh weight, and dry weight. The study’s findings underscore that drought tolerance, susceptibility levels, and parameter estimation for durum wheat and similar plants can be reliably and efficiently determined through the applied methods and analyses, offering a fast and cost-effective approach.
Effect of Subsurface Drainage in Regulating Water on Desalinization and Microbial Communities in Salinized Irrigation Soils
Authors: Zhang Jiapeng , Zhao Sha , Miao Qingfeng , Feng Liang , Chi Zhaonan , Li Zhen , Li Weiping
Published: January 2024

In order to achieve water conservation and salt control in saline irrigation areas and improve the soil ecological environment of farmland in irrigation areas, this study carried out a field trial in 2020–2021 on edible sunflowers planted in saline subsurface farmland in the Hetao Irrigation District. Three irrigation level treatments and a control setup under subsurface drainage were compared. The control was with no drainage and local conventional irrigation levels (the spring irrigation amount is 240 mm and the bud stage irrigation amount is 90 mm, CK); and the three irrigation levels were conventional irrigation (the spring irrigation amount is 240 mm and the bud stage irrigation amount is 90 mm, W1), medium water (the spring irrigation amount is 120 mm and the bud stage irrigation amount is 90 mm, W2), and low water (the spring irrigation amount is 120 mm and there is no irrigation in the bud stage, W3). The results showed that soil desalinization was best in the conventional irrigation (W1) treatment and lowest in the low-water treatment (W3) under subsurface drainage. The desalinization rate was 13.54% higher in the subsurface drainage than in the undrained treatment with the same amount of irrigation water. Under subsurface drainage, the medium-water treatment (W2) increased the diversity of soil microorganisms and the relative abundance of dominant phyla such as Ascomycetes, Chlorobacterium, Acidobacterium, and Ascomycetes among soil bacteria and Ascomycetes and Tephritobacterium amongst fungi. The average sunflower yield in the treatments under subsurface drainage increased by 32.37% compared with the undrained treatment, and the medium-water treatment (W2) was the most favorable for protein and essential amino acid synthesis. Structural equation modeling indicated that desalinization rate, irrigation water utilization efficiency, bacterial Chao1 abundance and Shannon diversity, and fungal Chao1 abundance and Shannon diversity were the major influences on sunflower yield. Based on the entropy weight method TOPSIS model, 15 indicators such as soil desalinization rate, soil microbial diversity, water and nitrogen utilization rate, and sunflower yield and quality were evaluated comprehensively for each water treatment of subsurface drainage farmland. It was found that the irrigation volume under tile drainage of 210 mm (W2) had the highest comprehensive score, which could improve the soil microenvironment of the farmland while realizing water conservation and salt control in salty farmland, increase the production of high-quality crops, and be conducive to the sustainable development of agriculture; it was the optimal irrigation treatment for the comprehensive effect. The results of this study are of great significance for the realization of efficient water conservation and salt control and the protection of food security and ecological safety in the Hetao Irrigation District.
Herbicide Applications Reduce Gaseous N Losses: A Field Study of Three Consecutive Wheat–Maize Rotation Cycles in the North China Plain
Authors: Zheng Xiangzhou , Zou Chenyi , Wang Yasa , Qin Shuping , Ding Hong , Zhang Yushu
Published: January 2024

Herbicide residues in farmland soils have attracted a great deal of attention in recent decades. Their accumulation potentially decreases the activity of microbes and related enzymes, as well as disturbs the nitrogen cycle in farmland soils. In previous studies, the influence of natural factors or nitrogen fertilization on the soil nitrogen cycle have frequently been examined, but the role of herbicides has been ignored. This study was conducted to examine the effects of herbicides on NH3 volatilization- and denitrification-related nitrogen loss through three rotation cycles from 2013 to 2016. The four treatments included no urea fertilizer (CK), urea (CN), urea+acetochlor-fenoxaprop-ethyl (AC-FE), and urea+2,4D-dicamba (2,4D-DI) approaches. The results showed that the application of nitrogen fertilizer significantly increased the nitrogen losses from ammonia volatilization and denitrification in the soil. Ammonia volatilization was the main reason for the gaseous loss of urea nitrogen in a wheat–maize rotation system in the North China Plain (NCP), which was significantly higher than the denitrification loss. In the CK treatment, the cumulative nitrogen losses from ammonia volatilization and denitrification during the three crop rotation cycles were 66.64 kg N hm−2 and 8.07 kg N hm−2, respectively. Compared with CK, the nitrogen losses from ammonia volatilization and denitrification under the CN treatment increased 52.62% and 152.88%, respectively. The application of AC-FE and 2,4D-DI significantly reduced the nitrogen gas losses from the ammonia volatilization and denitrification in the soil. Ammonia volatilization reduction mainly occurred during the maize season, and the inhibition rates of AC-FE and 2,4D-DI were 7.72% and 11.80%, respectively, when compared with CN. From the perspective of the entire wheat–maize rotation cycle, the inhibition rates were 5.41% and 7.23% over three years, respectively. Denitrification reduction also mainly occurred in the maize season, with the inhibition rates of AC-FE and 2,4D-DI being 34.12% and 30.94%, respectively, when compared with CN. From the perspective of the entire wheat–maize rotation cycle, the inhibition rates were 28.39% and 28.58% over three years, respectively. Overall, this study demonstrates that herbicides could impact the nitrogen cycle of farmland soil ecosystems via the suppression of ammonia volatilization and denitrification rates, thus reducing gaseous N losses and mitigating global climate change.
Exogenous Melatonin Affects the Morphometric Characteristics and Glucosinolates during the Initial Growth Stages of Broccoli
Authors: Arena Donata , Ben Ammar Hajer , Rodriguez Victor Manuel , Velasco Pablo , Garcia Gresheen , Calì Riccardo , Branca Ferdinando
Published: January 2024

Nowadays, there is a global surge in interest surrounding novel foods, particularly sprouts, microgreens, and baby leaves, attributed to their rich content of bioactive compounds, such as phenolic derivatives, glucosinolates, and vitamins. This study delves into the impact of exogenously applied melatonin on novel foods derived from Brassica oleracea L. Two distinct cultivars of broccoli (Brassica oleracea var. italica Plenck), namely Sicilian sprouting broccoli (Broccolo nero) and a commercial variety (Cavolo Broccolo Ramoso Calabrese), were compared across the sprouts, microgreens, and baby leaves stages, adhering to organic farming practices. Various doses of melatonin (0, 50, and 100 µM) were administered at each harvesting stage. Plantlets were collected at different growth stages and assessed for key morphometric traits, including the weight, hypocotyl length, and cotyledon dimensions during the sprouts stage. For microgreens, the number and dimensions of the true leaves were recorded, while for baby leaves, the stem length was additionally measured. The analysis of glucosinolates was carried out using a high-performance liquid chromatograph with a diode array detector (HPLC-DAD). The results revealed significant variations among the experimental factors considered. Melatonin application significantly influenced the morphometric parameters at different growth stages, exhibiting notable variations in the weight, hypocotyl length, cotyledon width, and leaf width. The GLSs profile exhibited significant variations between the different growth stages and genotypes studied. Particularly noteworthy was the tendency for the GLSs content to be higher during the sprouts stage compared to the baby leaves stage, ranging from 24.07 to 4.61 µmol g−1 d.w. from sprouts to baby leaves, respectively.
Long-Term Effects of a Stepwise, Multimodal, Non-Restrictive Antimicrobial Stewardship Programme for Reducing Broad-Spectrum Antibiotic Use in the ICU
Authors: Ronda Mar , Gumucio-Sanguino Victor Daniel , Shaw Evelyn , Granada Rosa , Tubau Fe , Santafosta Eva , Sabater Joan , Esteve Francisco , Tebé Cristian , Mañez Rafael , Carratalà Jordi , Puig-Asensio Mireia , Cobo-Sacristán Sara , Padullés Ariadna
Published: January 2024

Information on the long-term effects of non-restrictive antimicrobial stewardship (AMS) strategies is scarce. We assessed the effect of a stepwise, multimodal, non-restrictive AMS programme on broad-spectrum antibiotic use in the intensive care unit (ICU) over an 8-year period. Components of the AMS were progressively implemented. Appropriateness of antibiotic prescribing was also assessed by monthly point-prevalence surveys from 2013 onwards. A Poisson regression model was fitted to evaluate trends in the reduction of antibiotic use and in the appropriateness of their prescription. From 2011 to 2019, a total of 12,466 patients were admitted to the ICU. Antibiotic use fell from 185.4 to 141.9 DDD per 100 PD [absolute difference, −43.5 (23%), 95% CI −100.73 to 13.73; p = 0.13] and broad-spectrum antibiotic fell from 41.2 to 36.5 [absolute difference, −4.7 (11%), 95% CI −19.58 to 10.18; p = 0.5]. Appropriateness of antibiotic prescribing rose by 11% per year [IRR: 0.89, 95% CI 0.80 to 1.00; p = 0.048], while broad-spectrum antibiotic use showed a dual trend, rising by 22% until 2015 and then falling by 10% per year since 2016 [IRR: 0.90, 95% CI 0.81 to 0.99; p = 0.03]. This stepwise, multimodal, non-restrictive AMS achieved a sustained reduction in broad-spectrum antibiotic use in the ICU and significantly improved appropriateness of antibiotic prescribing.
Advancing Antibiotic Residue Analysis: LC-MS/MS Methodology for Ticarcillin Degradation Products in Tomato Leaves
Authors: Hakeem Muhammad K. , Elangovan Sampathkumar , Rafi Mohammed , George Suja , Shah Iltaf , Amiri Khaled M. A.
Published: January 2024

The indiscriminate use of antibiotics in agriculture has raised concerns about antibiotic residues in food products, necessitating robust analytical methods for detection and quantification. In this study, our primary aim was to develop a robust and advanced liquid chromatography-tandem mass spectrometry (LC-MS/MS) methodology specifically designed for the accurate quantification of ticarcillin degradation products in tomato leaves. The choice of ticarcillin as the target analyte stems from its frequent use in agriculture and the potential formation of degradation products, which can pose a threat to food safety. The use of tomatoes as the target sample matrix in this study is justified by their significance in human diets, their widespread cultivation, and their suitability as a model for assessing antibiotic residue dynamics in diverse agricultural environments. By optimizing the MS/MS parameters, the study successfully demonstrates the practicality and reliability of the employed LC-MS/MS method in accurately assessing ticarcillin degradation product (Thiophene-2-Acetic acid and Thiophene-3-Acetic acid) levels. The chromatographic separation was achieved using a specialized column, ensuring high resolution and sensitivity in detecting analytes. Multiple reaction monitoring (MRM) data acquisition was employed to enhance the selectivity and accuracy of the analysis. The developed method exhibited excellent linearity and precision, meeting the stringent requirements for antibiotic residue analysis in complex matrices. Key outcomes of this study include the successful identification and quantification of ticarcillin and its degradation products in tomato leaves, providing crucial insights into the fate of this antibiotic in agricultural settings. The methodology’s applicability was further demonstrated by analyzing real-world samples, highlighting its potential for routine monitoring and ensuring food safety compliance. In summary, our study constitutes a noteworthy advancement in the domain of antibiotic residue analysis, offering a reliable method for quantifying ticarcillin degradation products in tomato leaves. The optimized parameters and MRM-based LC-MS/MS approach enhance the precision and sensitivity of the analysis, opening up opportunities for further studies in the assessment of antibiotic residues in agricultural ecosystems.
Antioxidant Agriculture for Stress-Resilient Crop Production: Field Practice
Authors: Sun Yao , Xie Xianzhi , Jiang Chang-Jie
Published: January 2024

Oxidative stress, resulting from the excessive production of reactive oxygen species, is a common and major cause of cellular damage in plants exposed to various abiotic stresses. To address this challenge, we introduce the concept of antioxidant agriculture as a comprehensive strategy to improve stress tolerance and thus crop productivity by minimizing oxidative stress levels in the field environment. This strategy encompasses a diverse range of approaches, including genetic engineering, the exogenous application of antioxidant agents, microbial inoculation, and agronomic practices, to reinforce the plant’s intrinsic antioxidant defense system and mitigate oxidative stress. We present recent successful studies of antioxidant measures that have been validated in field conditions, along with our perspective on achieving antioxidant agriculture.
Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images
Authors: Tenguam Jaqueline J. , Longo Leonardo H. da Costa , Roberto Guilherme F. , Tosta Thaína A. A. , de Faria Paulo R. , Loyola Adriano M. , Cardoso Sérgio V. , Silva Adriano B. , do Nascimento Marcelo Z. , Neves Leandro A.
Published: January 2024

In this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin’s lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.
Survey-Based Studies of the Agility Construct in the Healthcare Sector: A Systematic Literature Review
Authors: Sarmiento Falla Jorge Flavio , Karwowski Waldemar
Published: January 2024

As a dynamic capability, agility has been extensively examined within manufacturing settings due to the industry’s dynamics and the imperative for rapid adaptation to unforeseen market changes. Similarly, service sectors such as healthcare also confront significant unpredictability, underscoring the necessity for agile capacities. Furthermore, healthcare organizations may require guidance in formulating strategies to enhance their agility. This study systematically appraised peer-reviewed empirical investigations centered on agility within the healthcare domain. This assessment delved into the various degrees of agility scrutinized in healthcare-focused research, the methodologies employed, the facets of agility assessed in each study, and their correlations with other factors. The study adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. The insights from the analysis identified four predominant categorizations of agility within healthcare entities: organizational agility, workforce agility, supply chain agility, and treatment agility. While diverse survey instruments have been used to gauge agility, certain consistent themes emerge across the dimensions and items of these tools. The reviewed empirical frameworks predominantly concentrate on organizational and supply chain agility, leaving the constructs of workforce and treatment agility as fertile grounds for further explorations.
Improved Production of Astaxanthin from Haematococcus pluvialis Using a Hybrid Open–Closed Cultivation System
Authors: An Yunji , Kim Taesoo , Byeon Huijeong , Rayamajhi Vijay , Lee Jihyun , Jung SangMok , Shin HyunWoung
Published: January 2024

Haematococcus species are rich sources of the antioxidant astaxanthin and have good potential for carbon dioxide reduction. A variety of culture systems for these microalgae are currently in development, but clearly profitable approaches have yet to be reported. Open outdoor culture is currently the only feasible culture system for producing large amounts of biomass. In this study, based on laboratory results, the cultivation of Haematococcus was divided into two stages: a green stage characterised by cell growth, and a red stage characterised by astaxanthin accumulation. For mass culture, we adopted a hybrid open–closed pond system for astaxanthin production. The open culture system was shown to produce approximately 50 kg (dry weight) of biomass per culture at an average rate of 0.51 g L−1, with 0.52 μg mL−1 of astaxanthin content in a 12 -m3 water tank. As large amounts of microalgal bioproducts are in high demand, inexpensive open outdoor culture methods should be adopted as an alternative to costly closed photobioreactors. Although the levels of biomass and astaxanthin production were found to be 30% lower in the field than in the laboratory in this study, the basic data obtained in this research may be useful for lowering astaxanthin production costs.
Experimental Study of the Soil Water Dissipation Law of Vegetated Slopes under Natural Evaporation Conditions
Authors: Xiao Henglin , Liu Zebang , Wan Juan , Chen Junyi , Shi Yunfeng
Published: January 2024

Under the combined action of soil evaporation and vegetation transpiration, the law of soil water dissipation at different depths of vegetated slopes is unknown and the related influencing factors are unclear. In this paper, six large-scale slope models were constructed for long-term dynamic monitoring of soil water. The effects of slope ratio and vegetation on the dynamic changes in soil water at different depths were analyzed. Pearson correlation analysis was used to analyze the relationship between slope conditions, meteorological factors, and soil water dissipation. The results show that under the condition of natural evaporation, slope ratio has little effect on the dynamic change in soil water in bare slopes. However, the greater the slope ratio of vegetated slopes, the faster the soil water decreases in the 40 cm depth range. Additionally, soil water dissipation follows a logarithmic functional relationship with evaporation time in both bare and vegetated slopes. The correlation between slope conditions and soil water dissipation is stronger than that of meteorological factors. The research results can provide some theoretical support for exploring the hydrological effects of vegetated slopes.
Storage and Management of Ship Position Based on Geographic Grid Coding and Its Efficiency Analysis in Neighborhood Search—A Case Study of Shipwreck Rescue and Google S2
Authors: Jiang Bohui , Zhou Weifeng , Han Haibin
Published: January 2024

The marine fishery is a high-risk industry, and when a fishing vessel is in distress at sea, it is essential to protect the lives of the fishermen by implementing a fast and effective rescue for the vessel in distress. In this process, quickly retrieving the closest neighboring vessel in distress based on an effective rescue time is critical to the vessel’s rescue. In order to improve the time efficiency of retrieving the nearest available rescue vessels in the current sea area, this paper proposes a method based on the Google S2 grid coding for ships in distress at sea. The method is divided into three parts: (1) encoding the ship’s position based on the Google S2 algorithm, (2) retrieving the set of available ships in the current sea area based on the effective rescue distance and its corresponding coding level, and (3) sorting the set of available ships in the current sea area according to the proximity to the ship in distress by using the method of “Alternating Grid Sorting Based on Neighborhoods and Different Coding Levels”. The effective rescue distance is set by the rescue time, the type of rescue vessel, and the speed. This paper sets the simulation experiment area as the East China Sea area. Different magnitudes of ship position datasets (1 × 102, 1 × 103, 1 × 104, 1 × 105, 1 × 106) are generated by simulating the scenarios where a ship’s location is reported or collected by AIS or VMS. The temporal retrieval efficiencies of querying based on the two methods, the Euclidean distance and the Google S2 grid encoding, are compared and analyzed. The experimental results show that the total time consumed by the query method based on the Google S2 grid encoding and the query method based on Euclidean distance is reduced by 37.06%, 29.83%, 72.75%, 94.43%, and 94.53%, respectively, in the process of generating the set of rescuable ships retrieved, based on the effective rescue distance. Therefore, the time retrieval efficiency of the maritime vessel search and rescue method based on the Google S2 coding is high, which can effectively improve the query efficiency of rescue vessels in the neighborhood of distressed vessels.
Deploying IIoT Systems for Long-Term Planning in Underground Mining: A Focus on the Monitoring of Explosive Atmospheres
Authors: Medina Fabian , Ruiz Hugo , Espíndola Jorge , Avendaño Eduardo
Published: January 2024

This paper presents a novel methodology for deploying wireless sensor nodes in the Industrial Internet of Things (IIoT) to address the safety and efficiency challenges in underground coal mining. The methodology is intended to support long-term planning on mitigating the risks in occupational health and safety policies. To ensure realistic and accurate deployment, we propose a software tool that generates mine models based on geolocation data or blueprints in image format, allowing precise adaptation to the specific conditions of each mine. Furthermore, the process is based on sensing and communication range values obtained through simulations and on-site experiments. The deployment strategy is articulated in two complementary steps: a deterministic deployment, where nodes are strategically placed according to the structure of the tunnels, followed by a random stage to include additional nodes that ensure optimal coverage and connectivity inside the mine by comparing different methodologies for deploying sensor networks using coverage density as a performance metric. We analyze coverage and connectivity based on the three probability density functions (PDFs) for the random deployment of nodes: uniform, normal, and exponential, evaluating both the degree of coverage (k-coverage) and the degree of connectivity (k-connectivity). The results show that our proposed methodology stands out for its lower density of sensors per square meter, which translates into a reduction of between 20.81% and 23.46% for uniform and exponential PDFs, respectively, concerning the number of sensors compared to the analyzed methodologies. In this way, it is possible to determine which distribution is suitable to cover the elongated area with the smallest number of nodes, considering the coverage and connectivity requirements, to reduce the deployment cost. The uniform PDF minimizes the number of sensors needed by 44.70% in small mines and 46.27% in medium ones compared to the exponential PDF. These findings provide valuable information to optimize node deployment regarding cost and efficiency; a uniform function is a good option depending on prices. The exponential distribution reached the highest values of k-coverage and k-connectivity for small and medium-sized mines; in addition, it has greater robustness and tolerance to faults like signal network intermittence. This methodology not only improves the collection of critical information for the mining operation but also plays a vital role in reducing the risks to the health and safety of workers by providing a more robust and adaptive monitoring system. The approach can be used to plan IIoT systems based on Wireless Sensor Networks (WSN) for underground mining exploitation, offering a more reliable and adaptable strategy for monitoring and managing complex work environments.
Do Vibrational Forces Induce an Anabolic Effect in the Alveolar Bone of Animal Models? A Systematic Review
Authors: Villegas Aguilar Julio César , García Vega María Fernanda , Salas Orozco Marco Felipe , Aguilar Madrigal Rosa Margarita , Reyes Cervantes Eric , Flores-Tochihuitl Julia , Soto Sainz Jesús Eduardo , Casillas Santana Miguel Angel
Published: January 2024

Mechanical vibrations have a biphasic effect depending on the context in which they are applied; their anabolic action has been used in medicine to increase bone density. In dental specialties such as orthodontics, their catabolic effect during mechanical compression has been widely studied, but the anabolic effect of vibrations is less investigated, so it is important to carry out research to clarify the effect of vibrations on the alveolar bone, explore a new approach to its use in orthodontics, and the increase of post-treatment bone density to prevent relapse. Hence, this work aims to systematically review the literature to evaluate the evidence regarding vibratory stimulation and its anabolic effects on alveolar bone in animal models. Methodology: A systematic review followed the PRISMA guidelines in PubMed, Scopus, and Web of Science databases. With the PICO strategy, we formulate the subsequent research question: Does the application of vibrational force induce an anabolic effect in the alveolar bone of animal models? Due to the lack of human studies, the population of interest was animal models; only articles where mechanical vibrations were the intervention method and the alveolar bone density or osteogenesis were evaluated and included. The selected studies underwent quality and risk of bias assessment through ARRIVE and SYCRLE instruments, respectively. This protocol was registered in INPLASY, under ID number: 202280103. Results: All eight articles included in this work demonstrate that applying low and high frequency vibrations increases the osteogenic effect by increasing the density and volume of bone tissue and increasing the expression of osteogenic markers. The included studies present a medium quality and risk of bias. Conclusion: It is important to highlight that, regardless of the protocol used, low or high frequency vibrations increase bone density, particularly in the alveolar bone, since this is the bone of interest in orthodontics. These promising results set an important precedent for the design of experimental protocols but now in the context of post-orthodontic treatment in humans.
Study on the Energy Evolution Law and Bursting Liability of Coal Failure with Different Joint Inclination Angles
Authors: Yin Shan , Li Zhonghui , Song Dazhao , Mu Hongwei , Niu Yue , Wang Xiaoran
Published: January 2024

Joints are the weak plane structures in coal. The existence of joints leads to coal failure, with different fracture modes and energy evolution laws. In this paper, the energy evolution and bursting liability index of coal failure with different joint inclination angles (JIAs) are analyzed. The results show that with an increase in joint inclination angle (JIA), the total energy and elastic energy of coal first decrease and then increase and the dissipation energy decreases gradually. The existence of joints changes the bursting liability of coal. With an increase in the JIA, the uniaxial compressive strength (Rc) of coal first decreases and then increases, the dynamic failure time (DT) gradually decreases, and the impact energy velocity index (WST) and the impact energy index (KE) gradually increase. With an increase in the JIA, coal went from tensile failure to shear failure and tension shear mixed failure. After coal failure, the fractal dimension was between 1.7 and 2.4, decreasing first and then increasing; the larger the JIA and the degree of fragmentation, the more energy consumed at the moment of failure and the stronger the bursting liability of coal. The results have a guiding significance for the monitoring and prevention of rock bursts in coal mines.
Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
Authors: Lim Soon-Bum , Ji Young-Seo , Ahn Byunghak , Park Jae Hong , Song Yoojeong
Published: January 2024

Rapid digital content growth demands pivotal font selection for design and communication. Our study focuses on a font recommendation system that aligns fonts with content emotions. To achieve this, we define font-emotions and quantify them. Additionally, we leverage deep learning techniques for content analysis. Understanding common emotional perceptions, we aimed to align fonts with content emotions. After evaluating diverse mapping methods, we determined a correlation analysis-based model to be most effective. Implementing this model, we verified its utility through usability evaluations. Our proposed system not only assists users with limited design knowledge in receiving contextually fitting font suggestions but also extends its application across various digital content realms.
Initial Costate Approximation for Rapid Orbit Raising with Very Low Propulsive Acceleration
Authors: Quarta Alessandro A.
Published: January 2024

The transfer between two circular, coplanar Keplerian orbits of a spacecraft equipped with a continuous thrust propulsion system is usually studied in an optimal framework by maximizing a given performance index. Using an indirect approach, the optimal trajectory and the maximum value of the performance index are obtained by numerically solving a two-point boundary value problem (TPBVP). In this context, the computation time required by the numerical solution of the TPBVP depends on the guess of unknown initial costates. The aim of this paper is to describe an analytical procedure to accurately approximate the initial costate variables in a coplanar, circle-to-circle, minimum-time transfer. In particular, this method considers a freely steerable propulsive acceleration vector, whose magnitude varies over a finite range with a sufficiently low maximum value. The effectiveness of the analytical method is tested in a set of both geocentric and heliocentric (simplified) mission scenarios, which model the classical LEO-GEO or interplanetary transfers toward Venus, Mars, Jupiter, and comet 29P/Schwassmann–Wachmann 1.
Research on Digital Forensics Analyzing Heterogeneous Internet of Things Incident Investigations
Authors: Shin Dong-Hyuk , Han Seung-Ju , Kim Yu-Bin , Euom Ieck-Chae
Published: January 2024

In the landscape of the Fourth Industrial Revolution, the integration of the Internet of Things (IoT) in smart-home technology presents intricate challenges for digital forensics. This study investigates these challenges, focusing on developing forensic methodologies suitable for the diverse and complex world of smart-home IoT devices. This research is contextualized within the rising trend of interconnected smart homes and their associated cybersecurity vulnerabilities. Methodologically, we formulate a comprehensive approach combining open-source intelligence, application, network, and hardware analyses, aiming to accommodate the operational and data storage characteristics of various IoT devices. Extensive experiments were conducted on prevalent platforms, such as Samsung SmartThings, Aqara, QNAP NAS, and Hikvision IP cameras, to validate the proposed methodology. These experiments revealed crucial insights into the complexities of forensic data acquisition in smart-home environments, emphasizing the need for customized forensic strategies tailored to the specific attributes of various IoT devices. The study significantly advances the field of IoT digital forensics and provides a foundational framework for future explorations into broader IoT scenarios. It underscores the need for evolving forensic methodologies to keep pace with rapid technological advancements in IoT.
Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
Authors: Zhao Di , Cai Guangrui , Wang Yuxing , Li Xixing
Published: January 2024

This paper proposes a golden sine grey wolf optimizer (GSGWO) that can be adapted to the obstacle-crossing function to solve the path planning problem of obstacle-crossable robot. GSGWO has been improved from the gray wolf optimizer (GWO), which provide slow convergence speed and easy to fall into local optimum, especially without obstacle-crossing function. Firstly, aiming at the defects of GWO, the chaotic map is introduced to enrich the initial population and improve the convergence factor curve. Then, the convergence strategy of the golden sine optimizer is introduced to improve the shortcomings of GWO, such as insufficient convergence speed in the later stage and the ease with which it falls into the local optimum. Finally, by adjusting the working environment model, path generation method and fitness function, the path-planning problem of the obstacle-crossing robot is adapted. In order to verify the feasibility of the algorithm, four standard test functions and three different scale environment models are selected for simulation experiments. The results show that in the performance test of the algorithm, the GSGWO has higher convergence speed and accuracy than the GWO under different test functions. In the path-planning experiment, the length, number and size of inflection points and stability of the path planned by the GSGWO are better than those of the GWO. The feasibility of the GSGWO is verified.
Investigating the Reliability of Nonlinear Static Procedures for the Seismic Assessment of Existing Masonry Buildings
Authors: Giusto Sofia , Cattari Serena , Lagomarsino Sergio
Published: January 2024

This paper presents, firstly, an overview of the nonlinear static procedures (NSPs) given in different codes and research studies available in the literature, followed by the results achieved by the authors to evaluate the reliability of the safety level that they guarantee. The latter is estimated by adopting the fragility curve concept. In particular, 125 models of a masonry building case study are generated through a Monte Carlo process to obtain numerical fragility curves by applying various NSPs. More specifically, among the NSPs, the N2 method (based on the use of inelastic response spectra) with different alternatives and the capacity spectrum method (CSM)—based on the use of overdamped response spectra—are investigated. As a reference solution to estimate the reliability of the nonlinear static approach, nonlinear dynamic analyses (NLDAs) are carried out using the cloud method and a set of 125 accelerograms; the results are post-processed to derive fragility curves under the assumption of a lognormal distribution. The focus of this investigation is to quantify the influence that the NSP method’s choices imply, such as the criteria adopted to calculate the displacement demand of a structure or those for the bilinearization of the pushover curve. The results show that the N2 methods are all non-conservative. The only method that provides a good approximation of the capacity of the analyzed URM structures as derived from NLDAs is the CSM. In particular, bilinearization is proven to have a relevant impact on the results when using the N2 method to calculate displacement capacities, whereas the CSM method is not affected at all by such an assumption. The results obtained may have a significant impact on engineering practice and in outlining future directions regarding the methods to be recommended in codes.