bluuebunny/crossref_metadata_2025 huggingface dataset:
Manufacture of a 4-Degree-of-Freedom Robot to Support an IRB 120 Robot
Authors: Balcazar Ricardo , Rubio José de Jesús , Hernandez Mario Alberto , Pacheco Jaime , Guel-Cortez Adrian-Josue , Ochoa Genaro , Garcia Enrique , Zacarías Alejandro , González Gabriela Saavedra
Published: November 2024

In this work, we present the construction and control of a four-degrees-of-freedom (DOF) manipulator aimed at addressing one of the key challenges faced by the Academy-Industry Cooperation Center (CCAI): the need for mechatronic equipment to support and facilitate the development of advanced robotic cells. We begin by designing the robot’s structure and components using SolidWorks software for computer-aided design (CAD) modeling. This ensures that all the links and parts fit together properly without collisions. The robot links are then manufactured using 3D printing. Additionally, we performed kinematic modeling, dynamic analysis, and PI-V control, along with control using a trigonometric function (hyperbolic tangent). To evaluate the robot’s movement, we simulate these processes using Matlab R2019a/Simulink software, focusing on key parameters such as position, velocity, and acceleration, which inform the design of PI-V control for each link. We also present the electrical and electronic designs, followed by system implementation. The kinematics of the robot play a crucial role in the dynamics and controller design. We validate the kinematics using Peter Corke’s libraries based on the Denavit–Hartenberg parameters. The results show that the controller based on the trigonometric function improves the response time, particularly enhancing the performance of axes 2 and 3.
The Relationship Between Managers’ Emotional Intelligence and Project Management Decisions
Authors: Rodrigues Nuno J. P. , Matos Catarina I. V.
Published: November 2024

Today, effective project management goes beyond technical skills, incorporating an emotional dimension that plays a crucial role in the success and dynamics of teams. Emotional intelligence (EI), which refers to the ability to control emotions—both one’s own and those of others—has become essential for good performance in work environments, often surpassing technical skills. This study aims to explore the relationship between managers’ emotional intelligence and decision-making in the context of project management. This research was based on an extensive literature review, followed by multiple interviews with project managers. Using semi-structured interviews, it was possible to identify how EI positively influences decision-making and project success. Managers with high levels of EI are more effective in decision-making, communication, conflict resolution, and leadership, resulting in more cohesive and productive teams. EI is a critical factor for success in project management in companies with the same characteristics as this one, providing a collaborative and effective working environment. The results might clearly assess the current state of the relationship between EI in the context of project management and consequent decisions leading to the project’s success.
Resolving Visible Emission Lines in Hydrogen Diffusion Flames
Authors: Pan Muyi , Liu Xuanqi , Lai Yufeng , Zhang Yuchen , Zhang Yang
Published: November 2024

The hydrogen diffusion flame is commonly described as difficult to see in the visible range. However, even in controlled laboratory conditions with careful imaging, the flame appears reddish. Previous research has reported a variety of colours generated from hydrogen flames. Some researchers believe that the visible colour is due to sodium in airborne dust. Other studies suggest the flame colour is caused by the vibration–rotation band of water vapour. In addition, H−α emits radiance in the visible range; therefore, the visible colour of the hydrogen flame could be contributed from the H−α emission. Nevertheless, a definitive conclusion to explain the visible reddish colour of the hydrogen flame is lacking. This paper reports precisely instrumented spectroscopic imaging tests, calibration, and data processing in order to resolve the spectral lines in the red colour zone (580–700 nm). This study used a spectrograph and a DSLR camera to capture the spectrum of hydrogen diffusion flames under different co-flow conditions. The values of emission lines in this range were compared with the databases provided by HITRAN molecular spectroscopy and the National Institute of Standards and Technology (NIST). The results of this study show that H−α emission is highly likely to appear in a hydrogen diffusion flame, which contradicts the previous hypothesis. This work may provide new insight into hydrogen-based combustion.
Transient Energy Growth in a Free Cylindrical Liquid Jet
Authors: Huang Dongqi , Fu Qingfei , Yang Lijun
Published: November 2024

The stability and behavior of jet flows are critical in various engineering applications, yet many aspects remain insufficiently understood. Previous studies predominantly relied on modal methods to describe small perturbations on jet flow surfaces through the linear superposition of modal waves. However, these approaches largely neglected the interaction between different modes, which can lead to transient energy growth and significantly impact jet stability. This work addresses this gap by focusing on the transient growth of disturbances in jet flows through a comprehensive non-modal analysis, which captures the short-term energy evolution. Unlike modal analysis, which provides insights into the overall trend of energy changes over longer periods, non-modal analysis reveals the instantaneous dynamics of the disturbance energy. This approach enables the identification of transient growth mechanisms that are otherwise undetectable using modal methods, which treat disturbance waves as independent and fail to account for their coupling effects. The results demonstrate that non-modal analysis effectively quantifies the interplay between disturbance waves, capturing the nonlinearity inherent in transient energy growth. This method highlights the short-term amplification of disturbances, providing a more accurate understanding of jet flow stability. Furthermore, the impact of dimensionless parameters such as the Reynolds number, Weber number, and initial wave number on transient energy growth is systematically analyzed. Key findings reveal the optimal conditions for maximizing energy growth and elucidate the mechanisms driving these phenomena. By integrating non-modal analysis, this study advances the theoretical framework of transient energy growth, offering new insights into jet flow stability and paving the way for practical improvements in fluid dynamic systems.
Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles
Authors: Silva Gabriel Italo Novaes da , Jardim Alexandre Maniçoba da Rosa Ferraz , Santos Wagner Martins dos , Bezerra Alan Cézar , Alba Elisiane , Silva Marcos Vinícius da , Silva Jhon Lennon Bezerra da , Souza Luciana Sandra Bastos de , Marinho Gabriel Thales Barboza , Montenegro Abelardo Antônio de Assunção , Silva Thieres George Freire da
Published: November 2024

The objective of this study was to correlate the biophysical parameters of forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them with machine learning in different agricultural systems. Four experimental units were conducted. Units I and II had different plant spacings (0.10, 0.20, 0.30, 0.40, and 0.50 m) with East–West and North–South planting directions, respectively. Unit III had row spacings (1.00, 1.25, 1.50, and 1.75 m), and IV had cutting frequencies (6, 9, 12 + 6, and 18 months) with the clones “Orelha de Elefante Mexicana”, “Miúda”, and “IPA Sertânia”. Plant height and width, cladode area index, fresh and dry matter yield (FM and DM), dry matter content, and fifteen vegetation indices of the visible range were analyzed. The RGBVI and ExGR indices stood out for presenting greater correlations with FM and DM. The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha−1 in experimental units I and II, III, and IV, respectively. The results showed potential in the application of machine learning with RGB images for predictive analysis of the biophysical parameters of forage cactus.
Application of a Portable Chlorophyll Meter to Assess the Nitrogen Sufficiency Index and Nitrogen Requirements in Sweet Potatoes
Authors: Rodrigues Fabrício E. , Fernandes Adalton M. , Oliveira Arthur V. , Vargas Pablo F. , Souza Emerson F. C. , Guedes Politon T. P. , Figueiredo Ricardo T. , Guimarães Ítala T.
Published: November 2024

Balanced nitrogen (N) supply is essential for high root yield in sweet potatoes (Ipomoea potatoes [L.] Lam.). A portable chlorophyll meter can support N fertilization management. Here, we determined the appropriate N sufficiency index (NSI) for sweet potato leaves to achieve the best leaf N status, plant growth, N uptake and removal, and storage root yield and quality. Experiments were conducted at three sites (Braúna, São Manuel, and Regente Feijó) in São Paulo, Brazil, using a randomized block design with four replicates. Treatments included a control (without N application), conventional N fertilization (50 kg ha−1), reference N fertilization (150 kg ha−1), and NSI-based N fertilization (NSI: 90% or 95%, based on the chlorophyll meter readings). Plant response to N fertilization was low, with no N deficiency observed in the conventional and chlorophyll meter-managed treatments. NSI < 90% was better than NSI < 95% for N top-dressing management, reducing N application rates by 44–66%, depending on the site. In contrast, NSI < 95% increased the N application rate without any yield benefit. Thus, monitoring N fertilization using a portable chlorophyll meter with 90% NSI can reduce N fertilization rates without negatively impacting the sweet potato root yield.
Proximal Absorbance Calibration Method Using an Embedded Blank Reference RGB Sensor for Determination of Ion Concentrations
Authors: Lee Jung-Kyu , Lee Ye-Hun , Lee Dong-Hoon
Published: November 2024

Accurate analyses and management of ion concentrations are crucial in precision agriculture. Modern technology-based methods are non-destructive and do not require sample preparation, enabling fast and accurate analysis; however, they have limitations when processing multiple samples. In this study, a multi-ion analysis system was developed for the prompt and accurate analysis of concentrations of important ions such as NO3−, HnPO4−, K+, Ca2+, and Mg2+. The RGB sensitivity control was automated through calibrations by applying a reference slot-based error rate across six slots between sample measurements, facilitating sample-to-sample comparisons and enabling accurate concentration analysis. By analyzing the correlation between each ion concentration and the proximal absorbance-based concentration prediction in the simultaneous analysis system, the accuracy was verified by achieving a coefficient of determination exceeding 0.99 for most ions. This system minimizes possible deviations between slots by using an automatic calibration algorithm, thereby facilitating the simultaneous analysis of multiple samples. This is important for saving time and cost and can help in real-time nutrient analysis and monitoring in agriculture.
Coupling Coordination Analysis Between Farmland Transfer and Agricultural Eco-Efficiency in China
Authors: He Xiuli , Liu Wenxin
Published: November 2024

The transfer of farmland refers to the process of transferring land contracting and management rights, during which various changes occur in production entities, management behaviors, and management methods. This process fosters positive changes in grain yield and farmers’ income. However, there is no consensus on the ecological impacts of farmland transfer. The objective of this study is to explore the coupling and coordination relationship between the process of farmland transfer and regional agricultural eco-efficiency across 30 provincial units in China. The following conclusions can be drawn from the results: (1) The level of farmland transfer in China exhibits an overall upward trend, primarily influenced by the average farmland area per household in each region and the number of large grain producers, with Heilongjiang consistently maintaining the highest level. (2) The agricultural eco-efficiency value remains relatively stable at approximately 1.1, with high-efficiency areas concentrated in Beijing, Qinghai, Hainan, Shanghai, Fujian, and Guizhou, where lower agricultural carbon emissions are a significant influencing factor. (3) The coupling degree between farmland transfer and agricultural eco-efficiency in various provinces of China is predominantly in a state of adjustment, with the coupling coordination level improving year by year. However, the coupling coordination level does not demonstrate a clear “high-high” or “low-low” synchronous state with grain yield per unit area, indicating that the levels of farmland transfer and agricultural eco-efficiency have not yet established a robust interactive relationship with farmland production efficiency.
Design, Fabrication, and Performance Evaluation of a Food Solar Dryer
Authors: Rana Md. Suman , Rahman A. N. M. Arifur , Ahmed Rakib , Hossain Md. Pallob , Shadman Md. Salim , Majumdar Pranta Kumar , Islam Kh. Shafiqul , Colton Jonathan
Published: November 2024

One of the oldest techniques for preserving food is drying. Dehydrating foods reduces their moisture content and increases their shelf life by preventing microbiological activity. Food placed on the ground to dry in the sun is a common sight in rural areas of low- and middle-income countries but requires a large amount of land and can lead to food degradation by overexposure to the sun, insects, and vermin. This study designed, fabricated, and evaluated the performance of a solar dryer in comparison to direct sun drying for efficiency and product quality, utilizing bananas and potatoes as representative foods. The dryer was produced and tested within the context of Bangladesh, unlike other commercial devices. With its mild steel frame, fan, solar collector, and DC battery, the dryer achieved a drying efficiency of 49.2% by reaching a drying chamber temperature of 71 °C, which is 30 °C higher than ambient. Drying times were decreased, and samples of potatoes and bananas reached equilibrium moisture content in 6 h as opposed to 9 h for direct sun drying. The moisture content of solar-dried foods was between 12 and 13 percent, making them appropriate for long-term storage. Bioactive substances such as phenolic content and DPPH scavenging activity were reduced by 18% and 21%, respectively, in comparison to direct sun drying. Quality assessments showed that there was little loss in color and nutrients for solar-dried samples. With a one-year payback period, an economic attribute of 3.26, and a life cycle benefit of BDT 310,651 (USD 2597.68), economics show the dryer’s feasibility. The solar dryer functioned faster than direct sun drying due to its significantly higher heat generation. The solar dryer was more efficient, effective, and economic within the context of Bangladesh and other low- and middle-income countries.
Feasibility of Vegetable Residues Compost Used as the Growth Medium for Brassica chinensis L. Seedlings: Effects of Membrane Covering and Superphosphate Application
Authors: Fu Jingjing , Fang Yunxiao , Chen Yongsheng , Cao Jie , Wu Aibing , Wang Zhenwei
Published: November 2024

In order to evaluate the feasibility of vegetable residues compost used as the growth medium for Brassica chinensis L. seedings, the effects of membrane covering and superphosphate application on the performance of tomato and mushroom residues aerobic compost were investigated. In the composting process, four treatments (T1: non-cover with non-addition, T2: membrane-covered with non-addition, T3: non-cover with superphosphate addition, and T4: membrane-covered with superphosphate addition) were explored. The results showed that membrane covering could effectively promote the degradation of lignocellulose, it had about 70% higher degradation rate than non-covered compost. The effect of superphosphate application was not significant. The humification indexes in Fourier Transform Infrared Spectroscopy (FTIR) and the fluorescence parameters were the main factors affecting the GI and chemical properties. The results manifested the membrane covering possess a higher conversion rate of humic substances, indicating better maturity. All the compost could almost meet the requirements of seedling substrate performance standards, especially the membrane-covered sample T2 had higher germination index (GI) value more than 100% and higher nutrient concentrations. Then, the four compost products were mixed with vermicompost, perlite, and vermiculite for the Brassica chinensis L. seedling raising experiment (T1B, T2B, T3B, T4B). It has been found that the compost-based substrate T2B with the best stability and maturity showed better seedling growth, and even comparable to peat substrate.
Optimizing Genomic Selection Methods to Improve Prediction Accuracy of Sugarcane Single-Stalk Weight
Authors: Wang Zihao , Xia Chengcai , Lu Yanjie , Liu Qi , Zou Meiling , Zan Fenggang , Xia Zhiqiang
Published: November 2024

Sugarcane (Saccharum spp. Hybrids), serving as a vital sugar and energy crop, holds immense development potential on a global scale. In the process of sugarcane breeding and variety improvement, single-stalk weight stands as a crucial selection criterion. By cultivating sugarcane varieties with heavier single stalks, robust growth, high yields, and superior quality, the planting efficiency and market competitiveness of sugarcane can be further enhanced. Single-stalk weight was determined by measuring individual stalks three times in the field, calculating the average value as the phenotypic expression. The distribution of single-stalk weights in the orthogonal and reciprocal populations revealed coefficients of variation of 19.3% and 17.7%, respectively, with the reciprocal population showing greater genetic stability. After rigorous filtering of Hyper_seq_FD sequencing data from 409 sugarcane samples, we identified 31,204 high-quality single-nucleotide polymorphisms (SNPs) evenly distributed across all 32 chromosomes, providing a comprehensive representation of the sugarcane genome. In this study, we evaluated the predictive performance of various genomic selection (GS) methods for single-stalk weight in the 299 orthogonal population, with the male parent being GZ_73-204 and the female parent being GZ_P72-1210, and in the 108 reciprocal population, with the male parent being GZ_P72-1210 and the female parent being GZ_73-204. Initially, we compared the performance of five prediction approaches, including genomic best linear unbiased prediction (GBLUP), single-step genomic best linear unbiased prediction (SSBLUP), Bayes A, machine learning (ML), and deep learning (DL) approaches. The results showed that the GBLUP model had the highest prediction accuracy, at 0.35, while the deep learning model had the lowest accuracy, at 0.20. To improve prediction accuracy, we assigned different scores to various regions of the sugarcane genome based on gene annotation information, thereby giving different weights to SNPs located in these regions. Additionally, we incorporated inbred and outbred populations as fixed effects into the model. The optimized SSBLUP model achieved a prediction accuracy of 0.44, which was a 17% improvement over the original SSBLUP model and a 9% increase compared to the originally optimal GBLUP model. The research results indicate that it is crucial to fully consider genomic structural regions, population structure characteristics, and fixed effects in GS predictions.
Raffinose Priming Improves Seed Vigor by ROS Scavenging, RAFS, and α-GAL Activity in Aged Waxy Corn
Authors: Zhu Min , Xiao Ru , Yu Tong , Guo Tao , Zhong Xuemei , Qu Jianzhou , Du Wanli , Xue Wei
Published: November 2024

Raffinose family oligosaccharides (RFOs) are known to benefit plants under stress conditions; however, the role of exogenous raffinose in seed germination remains poorly understood. In this study, we investigated the potential role of raffinose in promoting seed germination and elucidated the underlying mechanisms. The results showed that artificial aging significantly reduced the germination rate and vigor of waxy corn seeds. Conversely, exogenous raffinose significantly enhanced the germination of these artificially aged seeds. Exogenous raffinose significantly reduced the levels of reactive oxygen species (O2− and H2O2) and enhanced the activity of antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). Additionally, the levels of α-galactosidase (α-GAL) and raffinose synthase (RAFS) were significantly elevated in raffinose-treated aged seeds. These findings suggest that exogenous raffinose induces the expression of α-GAL and RAFS, thereby providing energy and reducing excessive reactive oxygen species (ROS), which in turn promotes the germination of artificially aged seeds. This study provides a theoretical foundation for enhancing seed vigor and extending seed longevity in crop management.
The Effects of Lactobacillus plantarum, Bacillus subtilis, a Lignocellulolytic Enzyme System, and Their Combination on the Fermentation Profiles, Chemical Composition, Bacterial Community, and In Situ Rumen Digestion of Fresh Waxy Corn Stalk Silage
Authors: Su Jiaqi , Xue Ye , Zhang Kunlun , Liu Zihan , Lv Jingyi , Yang Qi , Li Zhongqiu , Liu Chunlong , Xin Hangshu
Published: November 2024

The objective of this study was to assess the impact of Lactobacillus plantarum, Bacillus subtilis, and a lignocellulolytic enzyme system on the nutritional value, fermentation profiles, rumen digestion, and bacterial community of fresh waxy corn stalk silage. Fresh waxy corn stalks harvested after 90 days of growth were treated with no additives (CON), compound multiple lactobacilli (ML, comprising Lactobacillus plantarum at 1.0 × 105 cfu/g fresh weight and Bacillus subtilis at 1.0 × 105 cfu/g fresh weight), a lignocellulolytic enzyme system (LE, at 500 g/t), and a combination of the lignocellulolytic enzyme system and multiple lactobacilli (MLLE). Throughout the fermentation process, the contents of dry matter (DM) and water-soluble carbohydrates (WSC), as well as the counts of lactic acid bacteria, yeast, and mold, continuously decreased with the extension of the fermentation time. In contrast, the concentrations of acetic acid, propionic acid, and ammonia N progressively increased over time. Notably, the pH initially decreased and then increased as fermentation progressed, while the lactic acid concentration first increased and then decreased with the extension of the fermentation time. After 45 days of fermentation, the DM contents of LE and MLLE were significantly lower than that of CON, while the WSC content of MLLE was significantly higher than that of CON. LE most effectively increased the ED of DM and neutral detergent fiber (NDF). Compared to ML and LE, MLLE showed a greater increase in the “a” value of DM and NDF. The DM content in LE and MLLE was lower than in CON, with a higher WSC content. Both ML and MLLE produced more lactic acid than CON and LE, resulting in a lower pH in these groups. Additionally, the acetic acid content and ammonia N content were lower in ML and MLLE compared to CON and LE. The counts of lactic acid bacteria, yeast, and mold declined over the fermentation period, dropping below detectable levels on the seventh and fifth days, respectively. Relative to CON, the three additives reduced the relative abundance of Weissella and Klebsiella and increased that of Lactobacillus. In conclusion, LE improved the nutritional value and rumen digestion of the silage, ML enhanced its fermentation profiles and bacterial community, and MLLE significantly improved the nutritional value, rumen digestion characteristics, fermentation profiles, and bacterial community of the fresh waxy corn stalk silage.
Effect of Feeding Sugarcane Bagasse-Extracted Polyphenolic Mixture on the Growth Performance, Meat Quality, and Oxidative and Inflammatory Status of Chronic Heat-Stressed Broiler Chickens
Authors: Kikusato Motoi , Namai Fu , Yamada Katsushige
Published: November 2024

Background: This study aimed to evaluate the effects of sugarcane bagasse-extracted polyphenolic mixture (SBPM) supplementation on the harmful effects of chronic heat stress (HS) in broiler chickens. Methods: Two hundred and eighty-eight day-old male Ross 308 chicks were fed an SBPM in 0, 75, 150, or 300 ppm-supplemented diets and reared under thermoneutral (TN, 22.1–24.8 °C) or chronic HS (28.3–36.2 °C) conditions from 11 d to 42 d. Results: The chronic HS treatment negatively affected body weight, feed intake, and feed conversion ratio (p < 0.05), and these changes were partially attenuated by the SBPM supplementation (p < 0.05). Plasma lipid peroxidation content, inflammatory cytokines [interleukin (IL)-6, IL-β], corticosterone, and uric acid concentrations were significantly increased by HS, and these increases were attenuated by the SBPM supplementation (p < 0.05). Intestinal permeability indicator and serum fluorescein isothiocyanate-dextran levels after oral gavage were increased by HS and were also suppressed by the supplementation (p < 0.05). The HS-decreased muscle drip loss, lipid peroxidation, and glutathione content were also suppressed by the SBPM supplementation. The abovementioned alleviating effects of the SBPM were of a dose-dependent manner in most cases. Conclusion: This study demonstrated that SBPM supplementation can improve the growth performance, meat quality, inflammation, and intestinal permeability of chronic HS-treated broiler chickens.
A Systematic Review of the Efficacy of Compression Wraps as an Anxiolytic in Domesticated Dogs
Authors: Mathis Savannah , Schoolfield Suzie , Gross Peggy , Gruen Margaret , Dorman David C.
Published: November 2024

Many domesticated dogs show signs of anxiety, negatively impacting their own and their owner’s mental and physical health. A systematic review evaluating whether pressure wraps decrease clinical signs of anxiety in dogs was completed to identify relevant studies. The main outcomes of interest included behavioral and physiological measures. A total of 229 studies were identified, of which four met the inclusion criteria. Commercially available pressure wraps and a telemetry vest were used in the reviewed studies. Three experimental studies included brief exposure to recorded firecracker or thunderstorm sounds or separation from their owner as anxiety-invoking stimuli. A non-experimental study evaluated the use of pressure wraps for dogs with thunderstorm phobia living in their home environment exposed to naturally occurring thunderstorms. The risk of bias in domains related to the blinding of investigators or study participants was generally high. Domains with moderate risk of bias often reflected incomplete reporting of research methods and imprecision due to small sample sizes. Most studies reported minimal benefits on behavior or heart rate in the absence of adverse side effects. Our review suggests there is weak and limited evidence to support the beneficial effect of pressure wraps in reducing physiological or behavioral signs of anxiety. Confidence in the available literature is low due to the moderate-to-high risks of bias and inconsistent findings. Further studies are needed regarding the efficacy of these products.
Predicting Enteric Methane Emissions from Dairy and Beef Cattle Using Nutrient Composition and Intake Variables
Authors: Wang Yaodong , Song Weitao , Wang Qian , Yang Fafa , Yan Zhengang
Published: November 2024

The objective of this study was to develop linear and nonlinear statistical models for predicting enteric methane emissions from beef and dairy cattle (EME, MJ/day). Ration nutrient composition (g/kg), nutrient (kg/day), energy (MJ/day), and energy and organic matter (OM) digestibility (g/kg) were used as predictors of CH4 production. Three databases of beef cattle, dairy cattle, and their combinations were developed using 34 published experiments to model EME predictions. Linear and nonlinear regression models were developed using a mixed-model approach to predict CH4 production (MJ/day) of individual animals based on feed composition. For the beef cattle database, Equation methane (MJ/d) = 1.6063 (±0.757) + 0.4256 (±0.0745) × DMI + 1.2213 (±0.1715) × NDFI + −0.475 (±0.446) × ADFI had the smallest RMSPE (21.99%), with 83.51% of this coming from random error and a regression bias was 16.49%. For the dairy cattle database, the RMSPE was minimized (15.99%) for methane (MJ/d) = 0.3989 (±1.1073) + 0.8685 (±0.1585) × DMI + 0.6675 (±0.4264) × NDFI, of which 85.11% was from random error and the regression deviation was 14.89%. When the beef and dairy cattle databases were combined, the RMSPE was minimized (24.4%) for methane(MJ/d) = −0.3496 (±0.723) + 0.5941 (±0.0851) × DMI + 1.388 (±0.2203) × NDFI + −0.027 (±0.4223) × ADFI, of which 85.62% was from the random error and the regression bias was 14.38%. Among the nonlinear equations for the three databases, the DMI-based exponential model outperformed the other nonlinear models, but the predictability and goodness of fit of the equations did not improve compared to the linear model. The existing equations overestimate CH4 production with low accuracy and precision. Therefore, the equations developed in this study improve the preparation of methane inventories and thus improve the estimation of methane production in beef and dairy cattle.
Integrating Diagnostic Approaches in Infant Bacterial Meningitis Caused by a Non-K1 Escherichia coli: A Case Report
Authors: Vrenna Gianluca , Agosta Marilena , Fox Valeria , Rossitto Martina , Cortazzo Venere , Raimondi Serena , Lucignano Barbara , Onori Manuela , Mancinelli Livia , Pereyra Boza Maria del Carmen , Fini Vanessa , Granaglia Annarita , Lancella Laura , Carducci Francesca Ippolita Calo’ , Tripiciano Costanza , Villani Alberto , Bernaschi Paola , Perno Carlo Federico
Published: November 2024

Background: Infant meningitis, particularly caused by Escherichia coli, remains a life-threatening condition, especially in premature and low-weight infants. Infections of the central nervous system can be fatal, necessitating prompt diagnosis and appropriate treatment. Acute infections caused by various pathogens, including E. coli, often present with similar clinical symptoms. The rapid identification of pathogens and their antimicrobial resistance mechanisms is critical for timely and effective treatment. We report the case of an 8-month-old patient who presented with fever, diarrhea, and convulsive seizures and was subsequently diagnosed with meningitis. Despite initial empirical treatment with ceftriaxone, the patient’s condition worsened. Methods: At Bambino Gesù Children’s Hospital, molecular diagnostic tools, including the FilmArray Meningitis/Encephalitis and Blood Culture Identification panels, were employed. Results: Although the Meningitis panel did not detect any pathogens due to the lack of the specific bacterial target, the off-label use of the Blood Culture Identification panel identified a non-K1 Escherichia coli strain carrying the CTX-M resistance gene, an extended-spectrum beta-lactamase (ESBL). Despite the rapid diagnostic approach and adjustment of antibiotic therapy, the patient succumbed to the infection due to the strain’s high virulence and multidrug resistance. Whole-genome sequencing further characterized the strain, revealing that it belonged to the ST131 group, a highly resistant and virulent strain associated with sepsis. Conclusions: This case highlights the importance of integrating advanced molecular diagnostics, such as whole-genome sequencing, with traditional methods to improve pathogen detection, especially in cases of emerging resistant strains that are not covered by standard diagnostic panels. It also emphasizes the need for the continuous adaptation of diagnostic tools to include non-K1 E. coli strains for more comprehensive and timely meningitis diagnosis.
Classification of Real-World Objects Using Supervised ML-Assisted Polarimetry: Cost/Benefit Analysis
Authors: Pereira Rui M. S. , Oliveira Filipe , Romanyshyn Nazar , Estevez Irene , Borges Joel , Clain Stephane , Vasilevskiy Mikhail I.
Published: November 2024

We study the problem of classification of various real-world objects using as input a database (DB) of laboratory polarimetric measures (Mueller matrix elements—MMEs). It can work as a complementary technology of surroundings’ imaging that can be used, in particular, in autonomous driving. To this end, we look for an algorithm using less input parameters without great loss of the quality of classification. We start by analyzing the data in order to understand the attributes that are more important for associating the objects with one of several predefined classes. Different sets of attributes are studied using an artificial neural network (ANN), which is optimized in terms of the number of hidden layers and the activation function. After that, an improved machine learning (ML) architecture is built using the K-nearest neighbors (KNN) classifier on each cluster generated by applying the pre-trained ANN to the training set. This article focuses on the situation wherein one may not be able to measure all MMEs or it would be too expensive or challenging to implement when the measurement time is crucial. The results obtained for a reduced set of attributes using different ML architectures are very good, especially for the proposed combined ANN-KNN approach (wherein the ANN acts as a predictor and KNN as a corrector), which can help to avoid measuring all MMEs.
Bias and Variance Analysis of Contemporary Symbolic Regression Methods
Authors: Kammerer Lukas , Kronberger Gabriel , Winkler Stephan
Published: November 2024

Symbolic regression is commonly used in domains where both high accuracy and interpretability of models is required. While symbolic regression is capable to produce highly accurate models, small changes in the training data might cause highly dissimilar solution. The implications in practice are huge, as interpretability as key-selling feature degrades when minor changes in data cause substantially different behavior of models. We analyse those perturbations caused by changes in training data for ten contemporary symbolic regression algorithms. We analyse existing machine learning models from the SRBench benchmark suite, a benchmark that compares the accuracy of several symbolic regression algorithms. We measure the bias and variance of algorithms and show how algorithms like Operon and GP-GOMEA return highly accurate models with similar behavior despite changes in training data. Our results highlight that larger model sizes do not imply different behavior when training data change. On the contrary, larger models effectively prevent systematic errors. We also show how other algorithms like ITEA or AIFeynman with the declared goal of producing consistent results meet up to their expectation of small and similar models.
Special Issue on Dynamics of Railway Vehicles
Authors: Xie Suchao
Published: November 2024

High-speed Railway Vehicle systems have become integral to modern transportation infrastructure, offering a rapid, efficient, and environmentally friendly travel option [...]
A Validated High-Performance Thin-Layer Chromatography Method for Analyzing Fat-Soluble Vitamins in Commercial Pharmaceutical Preparations
Authors: Sikdar K. M. Yasif Kayes , Islam Md Khairul , Sostaric Tomislav , Lim Lee Yong , Locher Cornelia
Published: November 2024

A simple, cost-effective, and efficient novel high-performance thin-layer chromatography (HPTLC) tool was developed and validated in accordance with International Conference on Harmonisation (ICH) guidelines for the detection and quantification of four fat-soluble vitamins, D2, D3, E, and K1, using chloroform: cyclohexane (55:45, v/v) as mobile phase. The detection and quantification limits were found to be 30.86 and 93.52 ng/band for vitamin D2, 19.44 and 58.92 ng/band for vitamin D3, 14.17 and 42.95 ng/band for vitamin E, and 0.86 and 2.61 ng/band for vitamin K1, which were similar or lower than those reported in previous methods. The advantage of the developed method is that it uses a simple mobile phase in a single development step and has low detection and quantification limits. The application of the developed HPTLC method was successfully demonstrated with the quantitative analysis of these vitamins in some commercially available pharmaceutical preparations.
Fully Metallic Additively Manufactured Monopulse Horn Array Antenna in Ka-Band
Authors: Rico-Fernández José , Vaquero Álvaro F. , Pino Marcos R. , Arrebola Manuel
Published: November 2024

The Laser Powder-Bed Fusion Additive Manufacturing (LPBF AM) technique is evaluated for the manufacturing of fully metallic monolithic microwave components. To validate the manufacturing technique, a difference pattern array of 4 × 4 horn antennas is designed to operate at mm-Wave frequencies. The antenna is based on H-plane power dividers and a complex structure to obtain a difference radiation pattern by rotating twisted sections in two different orientations. The prototype is manufactured with a monolithic piece of aluminum alloy AlSi10Mg, providing a lightweight single structure that includes both radiating elements and a feeding network consisting of twisters and power dividers in a waveguide. The prototype was experimentally evaluated in an anechoic chamber and the near-field planar acquisition range, obtaining good agreement with full-wave simulations within an operational bandwidth from 34 to 36 GHz. The results demonstrate that the LPBF AM technique is a suitable candidate to produce challenging monolithic metal-only microwave components in the Ka-band, such as monopulse antennas.
Chemical Characterization, In Vitro Analysis of Functional Properties, and Volatile Profiling of Sixteen Nutraceuticals Marketed as “Superfood”
Authors: Végh Rita , Sipiczki Gizella , Csóka Mariann , Mednyánszky Zsuzsanna , Bujna Erika , Takács Krisztina
Published: November 2024

In recent decades, there has been an increasing consumer demand for the so-called superfoods, accompanied by a growing scientific interest. In our work, sixteen commercial nutraceuticals of different product categories, including powders of cereal grasses, vegetables, fruits, microalgae, an aquatic plant, and herbs, were analyzed to compare their nutritional properties and volatile profile. Some samples, especially spirulina, chlorella, and duckweed powders, were rich in protein; however, their amino acid composition was imbalanced. Spinach, duckweed, and moringa powders exhibited very high ash contents, while sea buckthorn powder was a relatively rich source of lipids. Acerola powder had the highest total phenolic content by far and showed exceptional antioxidant and antibacterial properties in in vitro analyses as well. Aronia and rosehip powders also had high antioxidant potential, while moringa powder exhibited remarkable antibacterial activity. The headspace volatiles of the samples showed significant variations. Terpenes were the major volatiles in herbs, especially in turmeric and ginger powders. High levels of acids were detected in fruit powders. Some compounds were only characteristic of certain products, for example, dimethyl sulfide in beetroot powder, trans-3-penten-2-one in aronia powder, hexadecane in spirulina powder, and turmerones in turmeric powder. These volatiles can be considered botanical markers, which potentially help detect the adulteration of specific nutraceuticals. Overall, this study presents the nutritional and aromatic diversity among various “superfood” powders, highlights their potential application as ingredients in functional foods, and emphasizes the need for regulatory standards to ensure product safety, quality, and consistency in the growing nutraceutical market.
Occlusion Vehicle Target Recognition Method Based on Component Model
Authors: Han Haorui , Li Hanshan
Published: November 2024

As an important part of intelligent traffic, vehicle recognition plays an irreplaceable role in traffic management. Due to the complexity and occlusion of various objects in the traffic scene, the accuracy of vehicle target recognition is poor. Therefore, based on the distribution features of vehicle components, this paper proposes a two-stage VSRS-VCFM net occlusion vehicle target recognition method. Based on the U-net codec structure, combining multi-scale detection and double constraints loss to improve the visual region segmentation under complex background (VSRS) performance. At the same time, to establish the vehicle component feature mask (VCFM) module, based on the Swin Transformer backbone unit, combined with the component perception enhancement unit and the efficient attention unit, the extraction of the low-contrast component area of the vehicle target and the filtering of the irrelevant area are realized. Then, the component mask recognition unit is introduced to remove the occlusion component feature area and realize the accurate recognition of the occluded vehicle. By labeling the public data set and the collected data set, six types of vehicle component data sets are constructed for training, as well as design ablation experiments and comparison experiments to verify the trained network, which prove the superiority of the recognition algorithm. The experimental results show that the proposed recognition method effectively solves the problem of misrecognition and missing recognition caused by interference and occlusion in vehicle recognition.
Efficient Traceability Systems with Smart Contracts: Balancing On-Chain and Off-Chain Data Storage for Enhanced Scalability and Privacy
Authors: Fernández-Iglesias Manuel José , Delgado von Eitzen Christian , Anido-Rifón Luis
Published: November 2024

The growing importance of traceability in supply chains requires robust, transparent, and efficient systems to ensure the integrity and authenticity of product journeys. This paper presents a comprehensive characterisation and data model for a generic blockchain-based traceability system, highlighting its implementation using smart contracts on Ethereum-compatible networks, as the Ethereum Virtual Machine (EVM), with its pioneering implementation of smart contracts and its extensive ecosystem; it provides a robust environment for developing decentralised applications. We discuss the advantages of using blockchain technology to notarise traceability activities, ensuring immutability and transparency by exploring two main scenarios, namely one where hash keys (i.e, cryptographic digests) are stored on-chain while detailed data remain off-chain, and another where all traceability data are fully stored on-chain. Each approach is evaluated for its impact on scalability, privacy, storage efficiency, and operational costs. The hash key method offers significant advantages in reducing blockchain storage costs, enhancing privacy, and maintaining data integrity, but it depends on reliable off-chain storage. Conversely, the full on-chain approach guarantees data immutability but at a higher cost and lower scalability. By combining these strategies, a balanced solution can be achieved, leveraging the strengths of both methods to provide a reliable, efficient, and secure blockchain-based traceability system, which is illustrated with a practical implementation to support traceability in the timber sector in Galicia, Spain. This paper aims to provide valuable insights for researchers and practitioners looking to implement or enhance traceability systems using blockchain technology, demonstrating how smart contracts can be effectively utilised to meet the demanding requirements of modern supply chains.
RADH-A Reversible and Authenticable Data Hiding Scheme in Encrypted Domain Based on Image Interpolation and Substitution
Authors: Lin Chia-Chen , Chen Rong-Ze , Tai Wei-Liang , Lin Chun-Jung
Published: November 2024

The rise of the Internet has transformed communication and information access, but it also raises serious privacy concerns, especially regarding personal image sharing. This paper presents a novel reversible and authenticable data-hiding scheme for encrypted images that utilizes interpolation techniques and substitution methods. Our encryption system achieves a lower correlation among neighboring pixels, ensuring a more uniform distribution in the encrypted image. The stego images do not reveal any information about the original cover image or secret data. The embedded authentication data provides the ability to verify the authenticity of the image and localize the tampering. Experimental results show that our scheme can securely protect an image from unauthorized access and verify its authenticity.
Variation in Daily Wheelchair Mobility Metrics of Persons with Spinal Cord Injury: The Need for Individual Monitoring
Authors: Vries Wiebe de , Eriks-Hoogland Inge , Hertig-Godeschalk Anneke , Koch-Borner Sabrina , Perret Claudio , Arnet Ursina
Published: November 2024

Manual wheelchair users (MWUs) frequently report shoulder problems and have a three-times-higher likelihood of rotator cuff pathology compared to able-bodied individuals. Shoulder health is crucial for MWU independence, their social participation, and quality of life. Daily activities such as wheelchair propulsion potentially lead to fatigue and overload. Since comprehensive data are limited, this study aimed to implement a wheelchair mobility metrics (WCMM) method to examine various aspects of wheelchair use in daily life. Two inertial measurement units (IMUs) were placed on the wheelchair frame and wheel of 19 participants with a spinal cord injury (SCI). WCMMs like distance covered, number of pushes and turns, and incline were derived from real-life measurements and normalized to a period of 8 h. Large variation was observed among participants. The distance covered ranged from 0.5 to 10.7 km, with the number of pushes from 438 to 4820. The number of turns ranged from 269 to 1396, and the average distance per mobility bout from 5 to 59 m. This wide variation over participants emphasizes the importance of data-driven clinical decision making and patient education. Further studies with larger samples and duration are needed to fully understand MWUs’ mobility patterns and their implications for shoulder health.
A Cross-Sectional Study of Gastrocnemius Medialis Muscle Fascicle Length and Anatomical Cross-Sectional Area in 8- to 18-Year-Old Female Volleyball Athletes
Authors: Panidi Ioli , Bogdanis Gregory C. , Gaspari Vasiliki , Terzis Gerasimos , Konrad Andreas , Donti Anastasia , Donti Olyvia
Published: November 2024

This cross-sectional study examined gastrocnemius medialis fascicle length (FL) and anatomical cross-sectional area (ACSA) in female volleyball athletes aged 8–18 years, in an attempt to investigate morphological growth changes during childhood and adolescence. Methods: Eighty-nine athletes were assigned to five groups: 8–10, 10–12, 12–14, 14–16 and 16–18 years old. Height, body mass, leg and calf length were measured, and maturity offset was calculated from anthropometrics. FL and ACSA were assessed using ultrasonography. Results: FL was longer and ACSA was larger in the 12–14 y group compared with the 8–10 y group (4.31 ± 0.60 vs. 3.66 ± 0.40 cm, p = 0.008 and 13.08 ± 3.01 cm2 vs. 9.98 ± 2.07 cm2, p = 0.018, respectively). However, there were no differences in FL or ACSA among the groups aged between 12 and 18 years (p > 0.573 and p > 0.352). Notably the percentage increase in ACSA between the youngest (8–10 y) and the oldest group (16–18 y) was twofold greater than the percentage increase in FL (49% vs. 24%). Significant correlations were found between anthropometric characteristics FL and ACSA (r = 0.366–760, p < 0.001). Conclusions: These data indicate that there is a rapid increase in FL and ACSA from the age of 8 y until the age of 14 y, which is a crucial time frame for muscle growth.