bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco
Authors: Sanad Hatim , Moussadek Rachid , Mouhir Latifa , Oueld Lhaj Majda , Dakak Houria , El Azhari Hamza , Yachou Hasna , Ghanimi Ahmed , Zouahri Abdelmjid
Published: May 2024

Accurate assessment of soil quality is crucial for sustainable agriculture and soil conservation. Thus, this study aimed to assess soil quality in the agricultural ecosystem of the Mnasra region within the Gharb Plain of Morocco, employing a comprehensive approach integrating multivariate analysis and geostatistical techniques. Thirty soil samples were collected from the surface layers across thirty selected sites. The results showed significant variations in soil properties across the study area, influenced by factors such as soil texture, parent material, and agricultural practices. Pearson correlation and principal component analysis (PCA) were employed to analyze the relationships among soil properties and compute the Soil Quality Index (SQI). The SQI revealed values ranging from 0.48 to 0.74, with 46.66% of sampled soils classified as “Good” and 53.33% as “Fair”. Geostatistical analysis, particularly ordinary kriging (OK) interpolation and semivariogram modeling, highlighted the spatial variability of soil properties, aiding in mapping soil quality across the landscape. The integrated approach demonstrates the importance of combining field assessments, statistical analyses, and geospatial techniques for comprehensive soil quality evaluation and informed land management decisions. These findings offer valuable insights for decision-makers in monitoring and managing agricultural land to promote sustainable development in the Gharb region of Morocco.
Nanoparticles of Zinc Oxides Mitigated N2O Emissions in Tea Plantation Soil
Authors: Wang Jing , Guo Linfang , Yang Fengmin , Xiang Jian , Long Lizhi , Ni Kang
Published: May 2024

The excessive application of nitrogen in tea plantations leads to severe soil acidification and N2O emission boosting. To promote sustainable agriculture, nanoparticles (NPs) have emerged as alternative fertilizers, but their effects on soil nitrification and greenhouse gas emissions in tea plantations remain unclear. In this study, the effects of NP type (ZnO-NPs and Fe2O3-NPs) and dose (0, 1, 10, and 100 mg·kg−1) on soil N2O emissions were investigated via a lab incubation trial. Soil pH, ammonium, and nitrate changes were also monitored during the incubation period. The abundance of functional genes related to nitrification and denitrification processes was analyzed as well. The results showed that ZnO-NPs led to a decrease in N2O emissions. The reduction effect was stronger with increasing dose and resulted in a 33% reduction at an addition rate of 100 mg·kg−1. The cumulative N2O emissions had significantly positive correlations with NH4+-N and NO3−-N. ZnO-NP addition showed a significantly negative effect on Ammonia-Oxidizing Archaea (AOA) but a positive effect on Ammonia-Oxidizing Bacteria (AOB) gene abundance. In contrast, Fe2O3-NPs showed an insignificant impact on N2O emissions and soil N content, as well as nitrification–denitrification gene abundance, regardless of different doses. These results imply that the application of ZnO-NPs may inhibit nitrification through the retarding of AOA activity. This study provided us with a potential practice to reduce N2O emissions in tea plantations by applying ZnO-NPs, but the efficiency of this reduction needs further examination under ambient conditions before field application.
An Assessment of Some Mechanical Properties of Harvested Potato Tubers cv. Spunta
Authors: Almady Saad S. , Al-Hamed Saad A. , Marey Samy A. , Al-Sager Saleh M. , Aboukarima Abdulwahed M.
Published: May 2024

Mechanical properties of vegetables or crop materials play a noteworthy part in designing new related implements. These properties can be extracted from force–deformation curves. Several factors, such as soil preparation, irrigation, and pre- and post-harvest treatments influence them. The core objective of this investigation work was to analyze force–deformation curves obtained from compression, penetration, and shear tests of potatoes (Spunta cv.) produced with three tillage implements. The potatoes cv. Spunta were cultivated in loamy sand soil under the center-pivot irrigation system. The tillage implements used were a disc harrow plow, chisel plow, and moldboard plow. The trials were performed at a constant planting depth (15 cm) below the soil and a single plowing speed of 5.4 km/h. All data were expressed as an average of five replicates ± standard deviation. The force–deformation curves analysis showed that the modulus of elasticity for potatoes cv. Spunta ranged from 4.32 to 5.8 N/mm, the bioyield force ranged from 84.25 to 114.12 N, and rupture forces ranged from 100.90 to 139.78 N. Furthermore, the results showed that the average values of the elastic and plastic ranges were 3.0 and 2.1 mm, respectively. The mean value of hardness was 1671.53 N·mm. No significant differences were observed with respect to the two planting seasons, but tillage implements had a significant impact on the characteristics extracted from the compression tests. The mean of the maximum forces required to penetrate the potato during the penetration stage were 41.24 N, 44.86 N, and 47.16 N for potatoes produced with the disc harrow plow, chisel plow, and moldboard plow, respectively. Similarly, the means of the maximum forces required to cut the potato in the shear stage were 724.38 N, 761 N, and 773.43 N for the disc harrow plow, chisel plow, and moldboard plow, respectively. The force–deformation curves showed that additional information might be required to obtain a complete description of the potato quality necessary to harvest potatoes cv. Spunta using harvesting and handling equipment with reduced economic loss. An extensive study of the soil characteristics and the above-mentioned properties is also recommended. The results obtained about the mechanical characteristics of potatoes cv. Spunta can be useful in providing information that aids in designing potato harvesting machines and in potato products factories.
The Honey Bee Colony’s Criterion for Candidate Selection: “Ongoing” or “One-Shot”?
Authors: Pan Luxia , Zhong Shiqing , Xu Tianyu , Chen Weixuan , Zeng Zhijiang
Published: May 2024

In the honey bee, the queen’s death severely threatens the survival of the colony. In an emergency, new queens are reared from young worker larvae, where nepotism is thought to influence the choice of queen candidates by the workers. This article simulates the emergency queen-rearing process in a colony under natural conditions and records the results of colony selection (without nepotism). In queenless colonies, worker larvae aged three days or younger were preferred for queen rearing, and 1-day-old larvae were the first to be selected for the queen-cell cups. In the capping stage, the number of capped queen cells selected from the 1-day-old larvae was much higher than the 3-day-old larvae. On the first day, the number of emerging queens reared from 1-day-old larvae was significantly higher than the queens reared from 2-day-old and 3-day-old larvae. However, there was no significant difference in the birth weights of queens reared from 1-day-old, 2-day-old, or 3-day-old larvae. When the newly emerged queens were introduced into the original queenless colony, 1-day-old larval queens triggered more worker followers than 2-day-old larval queens. The expression of ovarian development-related genes (vg, hex110, and Jh) was higher in queens reared from 1-day-old larvae than those reared from 2-day-old and 3-day-old larvae, indicating that the quality of the queens reared from 1-day-old larvae is superior. This study shows that in the absence of nepotism, the colony selection of queen candidates at the larval stage, capping stage, and emerging stage is not final, but is gradually optimized to maximize colony development through a “quality control” process.
Ischemic Brain Injury: Involvement of Lipids in the Pathophysiology of Stroke and Therapeutic Strategies
Authors: Bernoud-Hubac Nathalie , Lo Van Amanda , Lazar Adina-Nicoleta , Lagarde Michel
Published: May 2024

Stroke is a devastating neurological disorder that is characterized by the sudden disruption of blood flow to the brain. Lipids are essential components of brain structure and function and play pivotal roles in stroke pathophysiology. Dysregulation of lipid signaling pathways modulates key cellular processes such as apoptosis, inflammation, and oxidative stress, exacerbating ischemic brain injury. In the present review, we summarize the roles of lipids in stroke pathology in different models (cell cultures, animal, and human studies). Additionally, the potential of lipids, especially polyunsaturated fatty acids, to promote neuroprotection and their use as biomarkers in stroke are discussed.
Design and Application of Bionic Camouflage Materials Simulating Spectral Reflection Characteristics of Plants: A Review
Authors: Lin Yanping , Ren Luquan , Yang Xiaodong , Yuan Hengyi
Published: May 2024

Hyperspectral remote sensing (RS) has rapidly developed in recent years and has been widely used in the military field. This technology not only brings huge opportunities for military reconnaissance but also poses unprecedented challenges to military camouflage, severely complicating the development of plant hyperspectral camouflage materials and technology. In this review, the spectral reflectance characteristics of plants and the application of hyperspectral RS in plant RS and military operations are reviewed. The development status of bionic camouflage materials that simulate the spectral reflection characteristics of plants is analyzed. With the existing hyperspectral camouflage materials and technology, bionic camouflage technology is limited by the inability of bionic materials to accurately imitate the characteristic absorption peaks of green vegetation, low stability and durability, and the large overall material thickness, which complicate actual large-scale application. On this basis, a future development direction and a trend of plant hyperspectral bionic camouflage materials and technology are proposed.
Effect of Sodium Laureth Sulfate on Contact Angles of High-Impact Polystyrene and Acrylonitrile–Butadiene–Styrene from Recycled Refrigeration Equipment
Authors: Malinowska Benita , Munoz Esteban Julio León , Pakhlov Evgeniy , Terpiłowski Konrad
Published: May 2024

This paper investigates the effects of sodium laureth sulfate (SLES) on the wettability of the surface of the two most common recycled plastics in refrigeration equipment: HIPS (high-impact polystyrene) and ABS (acrylonitrile–butadiene–styrene). These plastics, in the form of flakes, were identified on the basis of their FTIR spectra, and then, they were subjected to a study of contact angles using the sessile droplet method. The solutions for the angle analysis included tap water with the addition of SLES. The results of this study showed that at SLES concentrations of 0.1 g/L and 0.2 g/L, the differences in the contact angles for HIPS and ABS were 10.76° and 10.10°, respectively. This research confirmed the potential of using SLES as a support for the flotation separation of plastics with similar densities and surface characteristics, such as HIPS and ABS.
Effect of Sodium Phosphate and Cellulose Ethers on MgO/SiO2 Cements for the 3D Printing of Forsterite Bioceramics
Authors: Cheli Lorenzo , Bonini Massimo , Tonelli Monica
Published: May 2024

Magnesium silicate ceramics are promising materials for bone tissue regeneration and can be prepared through 3D printing of magnesium oxide/silica (MgO/SiO2) cement pastes followed by calcination. Despite the growing interest in these formulations, additive manufacturing technology has only recently been explored for these cements, and the effects of admixtures and additives on such printing inks remain largely unexplored. In this study, we prepared various MgO/SiO2 cement formulations with differing amounts of sodium orthophosphate, a setting retarder, and cellulose ethers, used as rheo-modifiers. The samples’ setting properties were investigated, and printing parameters were properly adjusted. The most promising formulations were then 3D printed and calcined to obtain forsterite bioceramics, which were further characterized using confocal Raman microscopy, scanning electron microscopy, atomic force microscopy, gas porosimetry, and compressive strength tests. Our results revealed that the cellulose derivatives influence the printability of the MgO/SiO2 formulations without affecting the hardening time, which can be adjusted by the addition of sodium phosphate. The use of fine-tuned formulations allowed for the preparation of 3D-printed forsterite bioceramics, potentially suitable for biological applications as cancellous bone scaffolds.
An Artificial Intelligence Approach for Estimating the Turbidity of Artisanal Wine and Dosage of Clarifying Agents
Authors: De La Cruz Rojas Erika Mishell , Nuñez-Pérez Jimmy , Lara-Fiallos Marco , Pais-Chanfrau José-Manuel , Espín-Valladares Rosario , DelaVega-Quintero Juan Carlos
Published: May 2024

Red wine is a beverage consumed worldwide and contains suspended solids that cause turbidity. The study’s purpose was to mathematically model estimated turbidity in artisanal wines concerning the dosage and types of fining agents based on previous studies presenting positive results. Burgundy grape wine (Vitis lambrusca) was made and clarified with ‘yausabara’ (Pavonia sepium) and bentonite at different concentrations. The system was modelled using several machine learning models, including MATLAB’s Neural Net Fitting and Regression Learner applications. The results showed that the validation of the neural network trained with the Levenberg–Marquardt algorithm obtained significant statistical indicators, such as the coefficient of determination (R2) of 0.985, mean square error (MSE) of 0.004, normalized root mean square error (NRSME) of 6.01 and Akaike information criterion (AIC) of −160.12, selecting it as the representative model of the system. It presents an objective and simple alternative for measuring wine turbidity that is useful for artisanal winemakers who can improve quality and consistency.
Utilization of Natural Adsorbents in the Purification of Used Sunflower and Palm Cooking Oils
Authors: Bostan Roxana , Glevitzky Mirel , Varvara Simona , Dumitrel Gabriela-Alina , Rusu Gerlinde Iuliana , Popa Maria , Glevitzky Ioana , Vică Mihaela Laura
Published: May 2024

The purification of used oils and their introduction into production cycles lead to reduction in environmental contamination. A simulation was conducted to study the thermal degradation of sunflower oil under varying temperatures over time. In the purification process of used cooking oil, an adsorption technique using zeolite and eggshell as an adsorbent (5, 10, 20, and 30 g/100 mL used oil) was applied. To optimize purification, different doses of thermally and chemically activated adsorbents were used, at different temperatures (30 and 80 °C). Therefore, this study was conducted in batch operations to determine the effect of suitable adsorption for a contact time of the adsorbent of 2 h. In comparison, the purification of used vegetable oils was achieved using a saline solution. The adsorption capacity was evaluated by determining the physicochemical parameters of the oils before and after purification. The characterization of natural adsorbents was carried out using scanning electron microscopy (SEM) and X-ray fluorescence spectrometry (XRF). The results showed that the adsorbent in a dose of 30 g of zeolite activated with NaOH and heat-treated eggshell, respectively, exhibited a larger surface area and greater adsorption capacity. Adsorption increased with contact time. The FT-IR spectra of the oils showed the IR bands at 1097, 1160, and 1237, corresponding to the presence of the ester C-O-, 1743 for C=O ester, 2853, 2922 for (CH2, CH3), and 3008 for (C=C). The acidity and peroxide values decreased with increasing dose and contact time with zeolite or eggshell. Together, our result strongly suggests that natural adsorbents contribute to the purification of used oils.
Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete
Authors: Vargas John F. , Oviedo Ana I. , Ortega Nathalia A. , Orozco Estebana , Gómez Ana , Londoño Jorge M.
Published: May 2024

The process of concrete production involves mixing cement, water, and other materials. The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength. It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than required, to ensure the performance committed to the client. This study proposes the construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the Team Data Science Process (TDSP) methodology, using a dataset generated by the Colombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performance metrics measured in the laboratory. Predictive models such as XGBoost and neural networks were trained, and hyperparameter tuning was performed using advanced techniques such as genetic algorithms to obtain three models with high performance for estimating compressive strength, flexural strength, and slump. This study concludes that it is possible to use machine learning techniques to design reliable concrete mixes that, when combined with traditional analytical methods, could reduce costs and minimize over-designed concrete mixes.
Preliminary Failure Analyses of Loaded Hot Water Bottles
Authors: Towler Joseph , Baraya Mohamed , Ran Ziying , Alshammari Adel , Arif Syead , Desai Mohammad , Palanivel Sasidharan , Readioff Rosti , Abass Ahmed
Published: May 2024

Hot water bottles are widely utilised for their therapeutic advantages, such as relieving muscle tension and imparting warmth. However, the increasing frequency and potential risks associated with bursting or failure necessitate a detailed examination of the contributing factors as their failure is not fully understood in a scientific manner. With the apparent lack of analysis of hot water bottles in the literature, this study employs, for the first time, a dual methodology involving finite-element (FE) analysis conducted in ABAQUS and experimental validation to systematically investigate the underlying mechanisms leading to failure incidents. Through FE modelling and analysis, the stress and strain distribution within typical hot water bottles is modelled under compression loading conditions, facilitating the identification of vulnerable areas prone to failure. Experimental validation encompasses uniaxial loading compression tests on distinct specimens, generating load–displacement curves that elucidate material responses to compressive forces and highlight variations in load-bearing capacities. The study explores diverse failure modes, attributing them to stress concentration at geometric transitions and contact regions. Stress–strain curves contribute valuable insights into material characteristics, with ultimate stress values as crucial indicators of resistance to deformation and rupture. The FE analysis simulation results visualise deformation patterns and stress concentration zones. The findings illustrate that the highest stress concentration areas exist in the internal boundary of hot water bottles near the neck and cap region. This is experimentally confirmed through the bursting failures of four samples, with three failures occurring in this specific region. The findings support the guidance that users should avoid sleeping with a hot water bottle as it may fail under compression if they lay on top of it. Meanwhile, this result guides manufacturers to strengthen the weak areas of hot water bottles around the nicks and edges. This study significantly enhances our understanding of hot water bottle mechanics, thereby guiding design practice to improve overall performance and user safety. In summary, hot water bottles are commonly used but have not been investigated scientifically regarding external loading conditions and their related failure, as the current study has achieved. Identifying the weak points through experiment and simulation directs manufacturers towards required improvements in particular regions, such as the bottleneck and edge reinforcement during the design and manufacturing phases.
Predictive Analyses of Traffic Level in the City of Barcelona: From ARIMA to eXtreme Gradient Boosting
Authors: Garcia Eloi , Calvet Laura , Carracedo Patricia , Serrat Carles , Miró Pau , Peyman Mohammad
Published: May 2024

This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council’s open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies—including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm—the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables.
Reaching the Heights: A Desk Study on Exploring Opportunities and Challenges for Lithuania’s Tallest Wind Turbine
Authors: Ambarcumianas Tomas , Karulyté Greta , Xydis George
Published: May 2024

This study investigates the opportunities and challenges of installing Vestas’ V172-7.2 MW wind turbine, standing at 199 m, in Lithuania. As part of the country’s commitment to sustainable growth and the European Union’s goal of achieving a Net Zero Carbon economy by 2050, Lithuania aims to achieve self-sufficiency in energy production, focusing on wind energy projects. The research addresses two key questions: the optimal geographical location for the wind turbine installation and the anticipated outcomes and impacts of the project. Hence, the turbine’s technical requirements are researched to offer a clear picture of the manufacturer’s minimal standards, such as cleared site radius, distance to residence, highways, and wind speed range. Following that, Lithuanian regulatory standards are investigated for turbine installation in terms of residential zones, protected natural regions, and locations where any construction is controlled or prohibited. Therefore, the research’s data is gathered using a multi-method that relies on secondary data techniques. The deductive approach served as a structured framework for results, leveraging theories to help the identification process of suitable wind turbine locations. Delimitations include a focus on the specific features of the turbine, predefined environmental elements, and a holistic view of potential locations.
Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
Authors: Baidya Ranjai , Lee Sang-Woong
Published: May 2024

Real-life time series datasets exhibit complications that hinder the study of time series forecasting (TSF). These datasets inherently exhibit non-stationarity as their distributions vary over time. Furthermore, the intricate inter- and intra-series relationships among data points pose challenges for modeling. Many existing TSF models overlook one or both of these issues, resulting in inaccurate forecasts. This study proposes a novel TSF model designed to address the challenges posed by real-life data, delivering accurate forecasts in both multivariate and univariate settings. First, we propose methods termed “weak-stationarizing” and “non-stationarity restoring” to mitigate distributional shift. These methods enable the removal and restoration of non-stationary components from individual data points as needed. Second, we utilize the spectral decomposition of weak-stationary time series to extract informative features for forecasting. To learn features from the spectral decomposition of weak-stationary time series, we exploit a mixer architecture to find inter- and intra-series dependencies from the unraveled representation of the overall time series. To ensure the efficacy of our model, we conduct comparative evaluations against state-of-the-art models using six real-world datasets spanning diverse fields. Across each dataset, our model consistently outperforms or yields comparable results to existing models.
Citrus Pest Identification Model Based on Improved ShuffleNet
Authors: Yu Yan-Nan , Xiong Chun-Lin , Yan Ji-Chi , Mo Yong-Bin , Dou Shi-Qing , Wu Zuo-Hua , Yang Rong-Feng
Published: May 2024

To address the current issues of complex structures and low accuracies in citrus pest identification models, a lightweight pest identification model was proposed. First, a parameterized linear rectification function was introduced to avoid neuronal death. Second, the model’s attention to pest characteristics was improved by incorporating an improved mixed attention mechanism. Subsequently, the network structure of the original model was adjusted to reduce architectural complexity. Finally, by employing transfer learning, an SCHNet model was developed. The experimental results indicated that the proposed model achieved an accuracy rate of 94.48% with a compact size of 3.84 MB. Compared to the original ShuffleNet V2 network, the SCHNet model showed a 3.12% accuracy improvement while reducing the model size by 22.7%. The SCHNet model exhibited an excellent classification performance for citrus pest identification, enabling the accurate identification of citrus pests.
The Limits of Words: Expanding a Word-Based Emotion Analysis System with Multiple Emotion Dictionaries and the Automatic Extraction of Emotive Expressions
Authors: Wang Lu , Isomura Sho , Ptaszynski Michal , Dybala Pawel , Urabe Yuki , Rzepka Rafal , Masui Fumito
Published: May 2024

Wide adoption of social media has caused an explosion of information stored online, with the majority of that information containing subjective, opinionated, and emotional content produced daily by users. The field of emotion analysis has helped effectively process such human emotional expressions expressed in daily social media posts. Unfortunately, one of the greatest limitations of popular word-based emotion analysis systems has been the limited emotion vocabulary. This paper presents an attempt to extensively expand one such word-based emotion analysis system by integrating multiple emotion dictionaries and implementing an automatic extraction mechanism for emotive expressions. We first leverage diverse emotive expression dictionaries to expand the emotion lexicon of the system. To do that, we solve numerous problems with the integration of various dictionaries collected using different standards. We demonstrate the performance improvement of the system with improved accuracy and granularity of emotion classification. Furthermore, our automatic extraction mechanism facilitates the identification of novel emotive expressions in an emotion dataset, thereby enriching the depth and breadth of emotion analysis capabilities. In particular, the automatic extraction method shows promising results for applicability in further expansion of the dictionary base in the future, thus advancing the field of emotion analysis and offering new avenues for research in sentiment analysis, affective computing, and human–computer interaction.
Study on Water Entry of a 3D Torpedo Based on the Improved Smoothed Particle Hydrodynamics Method
Authors: Cai Xiaowei , Wu Wei , Han Wenji , Li Wenjie , Zhang Jun , Jiao Yanmei
Published: May 2024

The water entry of a torpedo is a complex nonlinear problem, involving transient impact, free surface deformation, droplet splashing, and fluid–structure coupling, which poses severe challenges to traditional mesh methods. The meshless smoothed particle hydrodynamics (SPH) method shows unique advantages in capturing the complex features of the water entry of the torpedo at different entry angles. However, it still suffers from some inherent shortcomings, such as low surface discretization accuracy, poor discretization flexibility, and low calculation efficiency. In this study, an improved adaptive SPH algorithm is proposed to investigate the water entry of the torpedo accurately and efficiently. This method integrates meshless point generation and adaptive techniques simultaneously. The numerical results demonstrate that when the torpedo vertically enters the water at different velocities, the induced impact loads acting on the head of the torpedo fluctuate significantly with two peak values in the initial stage and thereafter stabilize in a later stage. The impact load acting on the torpedo, the entry depth of the torpedo, the splash height of the droplets, and the size of the cavity generated around the torpedo increase with the increment in the entry velocity. When the torpedo enters the water at different entry angles under the same initial entry velocity, both the vertical and the horizontal movements of the torpedo are observed, which results in more complex variations in parameters along the x- and y-axes. The findings and the corresponding numerical method in this study can provide a certain basis for the future designs of the entry trajectory and the structural bearing capacity of torpedoes.
The Effect of Sumac (Rhus coriaria L.) Application on Oxidation Status, Sensory Attributes, Physicochemical and Microbiological Parameters of Carp (Cyprinus carpio) Fish during Chilled Storage
Authors: Abdullah Fouad Ali Abdullah , Bursová Šárka , Bartáková Klára
Published: May 2024

The aim of this study was to evaluate the impact of sumac on the oxidation status, sensory properties, and physicochemical and microbiological parameters of common carp (Cyprinus carpio) fish during chilled storage. Fillets of carp were treated with ground sumac at percentages of 0% (T0), 0.5% (T1), 1.5% (T2) and 2.5% (T3) and analysed after 24 and 72 h of chilled storage. The fat content, oxidation parameters (thiobarbituric acid reactive substances, TBARS), antioxidant capacity, total volatile basic nitrogen (TVB-N), free fatty acids (FFA), sensory properties, colour indicators (lightness L*, redness a*, yellowness b*), water activity, pH value, aerobic plate count (APC), psychrotrophic microorganism count (PMC), and coliform bacteria count were determined. The TBARS values of T1, T2, and T3 decreased significantly (p < 0.05) after 24 h of chilled storage. The antioxidant capacity increased in a dose-dependent and time-dependent manner. As regards organoleptic properties, sumac was able to reduce the natural fishy odour, increase the sour odour and flavour, and mask the fish odour and flavour significantly. A significant effect of sumac on the redness (a*) values of muscles was observed, which increased with elevation of the sumac ratio. A decrease in fillet pH and lower APC and PMC were observed. No effect was found on water activity or the growth of coliform bacteria. The study found that sumac plays a role in the oxidation status, pH value and organoleptic properties of fish fillets, which could be beneficial for the fish and fishery products industry.
A Dynamic Detection Method for Railway Slope Falling Rocks Based on the Gaussian Mixture Model Segmentation Algorithm
Authors: Zhang Xiulin , Fu Qiang , Li Yange , Han Zheng , Jiang Nan , Li Changli
Published: May 2024

Rockfall intrusion detection is crucial for the safety management of railway operations, and video detection methods help reduce deployment costs and improve detection efficiency. Mainstream neural network-based video detection methods have rapidly evolved in recent years but struggle to adapt to complex scenarios such as existing railway slope constructions due to weak generalization ability, low accuracy, and limited information acquisition. Therefore, this paper introduces a dynamic neural network detection model and establishes a dataset for rockfall intrusions in existing railway slope scenarios. The model initially relies on the YOLOv5 neural network, adopting an activation function suitable for the target scenario and addressing overfitting to achieve precise target recognition. Based on the neural network, the model dynamically detects rolling rockfalls by integrating a background subtraction algorithm based on the Gaussian Mixture Model and captures target dimensions using monocular vision technology, thus broadening the dimensions of detection information. Trials conducted on a railway in Shandong, China, demonstrate that the model accurately identifies moving rockfalls along the railway slopes and acquires the dimensions of moving rockfalls, successfully filtering out low-risk targets in the scene.
Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors
Authors: Mehboob Fozia , Fattouh Anas , Sahoo Smruti
Published: May 2024

Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor’s thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.
Band Gap Modulation in Zn2TiO4 Spinels for Efficient UV-A Persistent Luminescence
Authors: Sharma Ankit , Chouhan Moondeep , Sharma Suchinder K.
Published: May 2024

Spinels are important materials for an application in bioimaging. The key advantage with spinel-type hosts is the presence of antisite defects, which act as charge reservoirs for trapping electrons and holes at complementary defect sites. This makes them a host system similar to a molecular system. Herein, we present a systematic approach to modulating the band gap of an inverse Zn2TiO4 spinel. With a change in ZnO concentration, the absorption band at 375 nm diminishes and disappears at a ZnO:TiO2 concentration of 1.40:1.00. The band gap of the material is modified from 3.30 to 4.40 eV. The crystal structure of the sample does not change drastically as determined using X-ray diffraction and Rietveld refinement. The Zn2TiO4 emits in the UV-A region with a lifetime in the time domain of ‘ns’. The sample also shows persistent luminescence of at least 15 min upon excitation with 254 nm with prominent emission in the UV-A region (300–390 nm). The present results open a new avenue for the synthesis of spinel hosts where the band gap can be modified with ease. The UV emission thus observed is expected to find usage in interesting applications like photocatalysis, anti-counterfeiting, water disinfecting, etc.
Three-Dimensional Path Planning for Post-Disaster Rescue UAV by Integrating Improved Grey Wolf Optimizer and Artificial Potential Field Method
Authors: Han Dan , Yu Qizhou , Jiang Hao , Chen Yaqing , Zhu Xinyu , Wang Lifang
Published: May 2024

The path planning of unmanned aerial vehicles (UAVs) is crucial in UAV search and rescue operations to ensure efficient and safe search activities. However, most existing path planning algorithms are not suitable for post-disaster mountain rescue mission scenarios. Therefore, this paper proposes the IGWO-IAPF algorithm based on the fusion of the improved grey wolf optimizer (GWO) and the improved artificial potential field (APF) algorithm. This algorithm builds upon the grey wolf optimizer and introduces several improvements. Firstly, a nonlinear adjustment strategy for control parameters is proposed to balance the global and local search capabilities of the algorithm. Secondly, an optimized individual position update strategy is employed to coordinate the algorithm’s search ability and reduce the probability of falling into local optima. Additionally, a waypoint attraction force is incorporated into the traditional artificial potential field algorithm based on the force field to fulfill the requirements of three-dimensional path planning and further reduce the probability of falling into local optima. The IGWO is used to generate an initial path, where each point is assigned an attraction force, and then the IAPF is utilized for subsequent path planning. The simulation results demonstrate that the improved IGWO exhibits approximately a 60% improvement in convergence compared to the conventional GWO. Furthermore, the integrated IGWO-IAPF algorithm shows an approximately 10% improvement in path planning effectiveness compared to other traditional algorithms. It possesses characteristics such as shorter flight distance and higher safety, making it suitable for meeting the requirements of post-disaster rescue missions.
Changes in Countermovement Jump Height in Elite Volleyball Players in Two Competitive Seasons: Consideration on the Technique of Execution of the Jump
Authors: Masel Sebastian , Maciejczyk Marcin
Published: May 2024

Monitoring athletes’ well-being during the competitive season is a common practice in team sports, and countermovement jump (CMJ) is often introduced to monitor neuromuscular fatigue of the players. In this pilot observational study, we aimed to evaluate changes in jump performance in professional volleyball players during their two competitive seasons after introducing different executions of the jump test. Different execution of the flight phase of the jumps was introduced as a differentiating factor. Nine professional volleyball players (age: 23.3 ± 1.9 years; volleyball training experience: 10.4 ± 1.9 years; body height: 192.3 ± 8.3) performed CMJ measurements 1–2 times a week on a contact mat during two competitive seasons, and a total number of 1008 jumps (540 in season 1 and 468 in season 2) was collected. Players’ CMJ height significantly decreased (p < 0.05) in season 2 by approximately 4%, and standard deviation of the data reduced by approximately 30%. Our study confirms that the testing procedures need to be rigorous as different execution of the jump may be associated with changes in CMJ height. Execution of the jump should not be interchangeable using the same measurement device, and the technique introduced in season 2 seems to be more appropriate for this measurement device.
Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots
Authors: Kwon Ji-Wook , Yun Sung-Jo , Kim Hyogon , Lee Jungwoo , Lee Na-Hyun , Choi Young-Ho , Chung Goobong
Published: May 2024

This paper proposes a coupled-error-based formation control algorithm for the rapid formation completion of multi-robot systems. We consider a multi-robot system with omni-directional robots with swerve-driving mechanisms and a communication system with minimized constraints. This paper introduces a coupled error that links the distance errors with the leading robot and the following robot through a coupling ratio. We propose a controller using the coupled error to achieve the control objectives of this paper. Unlike existing results that only use the information of the preceding robot, this algorithm couples the information of both the preceding robot and one’s follower. Using the proposed error-coupling-based formation control algorithm, multi-robot systems can quickly establish formations for collaboration, allowing tasks to commence swiftly and reducing deformations in formations due to speed variations. With stability analysis and simulation results for the practical application of the proposed algorithm, the approach has been verified to improve the speed of both the completion of the formation and overall system trajectory tracking, balancing trade-offs between them.
Fuzzy Control Systems for Power Quality Improvement—A Systematic Review Exploring Their Efficacy and Efficiency
Authors: Miron Anca , Cziker Andrei C. , Beleiu Horia G.
Published: May 2024

Fuzzy-based control systems have demonstrated a remarkable ability to control nonlinear processes, a characteristic commonly observed in power systems, particularly in the context of power quality enhancement. Despite this, an updated and comprehensive literature review on the applications of fuzzy logic in the domain of power quality control has been lacking. To address this gap, this study critically examines published research on the effective and efficient use of fuzzy logic in resolving quality issues within power systems. Data sources included the Web of Science and academic journal databases, followed by an evaluation of target articles based on predefined criteria. The information was then classified into seven categories, including control system type, features of the fuzzy logic controller, fuzzy logic inference strategy, power quality issue, control device, implementation methodology (efficacy testing), and efficiency improvement. Our study revealed that fuzzy-based control systems have evolved from simple type-1 fuzzy controllers to advanced control systems (type-2 fuzzy and hybrid) capable of effectively addressing complex power quality issues. We believe that the insights gained from this study will be useful to both experienced and inexperienced researchers and industry engineers seeking to leverage fuzzy logic to enhance power quality control.
Perceiving Design Features in New Interaction Environments: Comparing Rendered Images, 360° Rotation, AR, Immersive and Non-Immersive VR, and Real Product Interaction
Authors: Agost Maria-Jesus , Vergara Margarita , Bayarri-Porcar Vicente
Published: May 2024

The emergence of new display technologies can change the perception of product design features and their assessment. Previous studies are limited to comparisons between a few technologies; the real product is considered only occasionally. This work compares the perceptions of 10 design features in two household products, shown by five display technologies (image rendering, 360° rotation, and augmented, immersive, and non-immersive virtual reality), and also with the real product. Results show that the 360° rotation provides the best perception for the most important features. However, the perception of aesthetic features is better achieved with i_VR. Other global results vary depending on the product. Finally, interaction with the real product shows a quite different perception for many features. The results contribute to the understanding of product perceptions influenced by different displays, comparing them with perceptions generated through real interaction. It is expected that the conclusions will be used to optimize the presentation of product features.
Non-Cutting Moving Toolpath Optimization with Elitist Non-Dominated Sorting Genetic Algorithm-II
Authors: Demir Gamze , Acar Vural Revna
Published: May 2024

Path planning (PP) is fundamental in the decision-making and control processes of computer numerical control (CNC) machines, playing a critical role in smart manufacturing research. Apart from improving optimization in PP, enhancing efficiency while decreasing CNC machine cycle time is important in manufacturing. Many methods have been offered in the literature to improve the cycle time for obtaining optimal trajectories in toolpath optimization, but these methods are mostly considered for improvements in path length or machining time in optimal PP. This study demonstrates a method for creating a smoothing path. It aims to minimize both cycle time and toolpath length, while demonstrating that the non-dominated sorting genetic algorithm (NSGA-II) is efficient in addressing the multi-objective PP problems within static situations. Pareto optimality for performance comparisons with multi-objective genetic algorithms (MOGAs) is presented in order to highlight the positive features of the non-dominant solving generated by the NSGA-II. According to the comprehensive analysis results, the optimization of the path carried out with the NSGA-II emphasizes its shorter and smoother attributes, with the optimal trajectory achieving approximately 30% and 7% reductions in path length and machining cycle time, respectively.
Local Influence for the Thin-Plate Spline Generalized Linear Model
Authors: Ibacache-Pulgar Germán , Pacheco Pablo , Nicolis Orietta , Uribe-Opazo Miguel Angel
Published: May 2024

Thin-Plate Spline Generalized Linear Models (TPS-GLMs) are an extension of Semiparametric Generalized Linear Models (SGLMs), because they allow a smoothing spline to be extended to two or more dimensions. This class of models allows modeling a set of data in which it is desired to incorporate the non-linear joint effects of some covariates to explain the variability of a certain variable of interest. In the spatial context, these models are quite useful, since they allow the effects of locations to be included, both in trend and dispersion, using a smooth surface. In this work, we extend the local influence technique for the TPS-GLM model in order to evaluate the sensitivity of the maximum penalized likelihood estimators against small perturbations in the model and data. We fit our model through a joint iterative process based on Fisher Scoring and weighted backfitting algorithms. In addition, we obtained the normal curvature for the case-weight perturbation and response variable additive perturbation schemes, in order to detect influential observations on the model fit. Finally, two data sets from different areas (agronomy and environment) were used to illustrate the methodology proposed here.