bluuebunny/crossref_metadata_2025 huggingface dataset:
The Synergistic Optimization of Rice Yield, Quality, and Profit by the Combined Application of Organic and Inorganic Nitrogen Fertilizers
Authors: Tao Wenli , Zhang Yajun , Gu Junfei , Zhu Kuanyu , Wang Zhiqin , Yang Jianchang
Published: November 2024

The replacement of urea with polymer-coated urea (PCU) fertilizer and the application of organic fertilizers (OFs) are effective strategies for reducing N loss in farmland and preventing soil degradation. However, limited research has been conducted on the synergistic effects of OF combined with inorganic N fertilizer, particularly PCU, on rice yield, quality, and profit. To address this issue, a two-year field experiment was conducted involving five fertilization treatments: no nitrogen fertilizer (0N), urea applied at the full local rate of 270 kg N ha−1 (CK), PCU at a reduced rate of 240 kg N ha−1 (T1), a combination of 70% PCU and 30% urea at 240 kg N ha−1 (T2), and T2 supplemented with 4500 kg ha−1 of OF (T3). The results showed that, compared with CK, the T1 treatment improved the appearance quality and taste value but slightly reduced the other quality indices. In contrast, the T2 and T3 treatments enhanced the grain yield, especially for T3, with an advantage in the tiller number, shoot dry weight, and leaf area index, which promoted the panicle number, filled grain, and grain weight, thereby significantly increasing the yield. The T2 improved the processing, appearance, and taste qualities by reducing the protein content, increasing the amylose content and gel consistency, and optimizing the starch viscosity characteristics (increasing the peak viscosity and breakdown while reducing the setback and consistency), with the addition of OF (T3) further expanding the benefits. Furthermore, the nutritional quality was also enhanced by optimizing the protein components and increasing the protein yield. Although the agricultural inputs in the T2 and T3 treatments were higher, the profit from the increased grain yield could cover these inputs, thereby maintaining profit with T3 or increasing profit with T2. In summary, the combined application of PCU with urea and OF can synergistically improve the rice yield, quality, and profit.
In Vitro Plant Regeneration of Sulla coronaria from Floral Explants as a Biotechnological Tool for Plant Breeding
Authors: Auteri Monica , Carra Angela , Di Miceli Giuseppe , Iacuzzi Nicolò , Albano Alessandro , Lala Nicoletta , Motisi Antonio , Catalano Caterina
Published: November 2024

Sulla coronaria L. Medik., a biennial forage legume typical of the Mediterranean basin, plays a significant role in foraging systems due to its high nutritional value, ability to increase ruminant live weight, and potential to reduce methane emissions. However, its allogamous nature complicates genetic improvement and the development of new varieties with desired traits. Given these challenges, this study aims to develop, for the first time, a successful protocol for the in vitro meristematic shoot regeneration of S. coronaria. The experiment utilizes four different flower explants (anther with filament, ovary, petals, and whole immature flower) collected from twenty distinct S. coronaria biotypes with three plant growth regulator (PGR) combinations and under both light and dark conditions. In terms of the regeneration response, the key factors appear to be the combinations of PGRs and the type of explant used. The interactions between all the factors do not seem to be significant.
Fusion of UAV-Acquired Visible Images and Multispectral Data by Applying Machine-Learning Methods in Crop Classification
Authors: Zheng Zuojun , Yuan Jianghao , Yao Wei , Kwan Paul , Yao Hongxun , Liu Qingzhi , Guo Leifeng
Published: November 2024

The sustainable development of agriculture is closely related to the adoption of precision agriculture techniques, and accurate crop classification is a fundamental aspect of this approach. This study explores the application of machine learning techniques to crop classification by integrating RGB images and multispectral data acquired by UAVs. The study focused on five crops: rice, soybean, red bean, wheat, and corn. To improve classification accuracy, the researchers extracted three key feature sets: band values and vegetation indices, texture features extracted from a grey-scale co-occurrence matrix, and shape features. These features were combined with five machine learning models: random forest (RF), support vector machine (SVM), k-nearest neighbour (KNN) based, classification and regression tree (CART) and artificial neural network (ANN). The results show that the Random Forest model consistently outperforms the other models, with an overall accuracy (OA) of over 97% and a significantly higher Kappa coefficient. Fusion of RGB images and multispectral data improved the accuracy by 1–4% compared to using a single data source. Our feature importance analysis showed that band values and vegetation indices had the greatest impact on classification results. This study provides a comprehensive analysis from feature extraction to model evaluation, identifying the optimal combination of features to improve crop classification and providing valuable insights for advancing precision agriculture through data fusion and machine learning techniques.
Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China
Authors: Zhu Jun , Sun Zhong-Xiu
Published: November 2024

The cation exchange capacity (CEC) of the clay fraction (<2 μm), denoted as CECclay, serves as a crucial indicator for identifying low-activity clay (LAC) soils and is an essential criterion in soil classification. Traditional methods of estimating CECclay, such as dividing the whole-soil CEC (CECsoil) by the clay content, can be problematic due to biases introduced by soil organic matter and different types of clay minerals. To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CECclay from CECsoil using experimental soil data. We conducted a study on 122 pedons in South China, focusing on highly weathered and strongly leached soils. Samples from the B horizon were used, and eight models and PTFs (four machine learning methods, multiple linear regression (MLR) and three PTFs from publication) were evaluated for their predictive performance. Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CECsoil by clay content led to significant overestimation of CECclay, with a mean error of 14.42 cmol(+) kg−1. MLR produced the most accurate predictions, with an R2 of 0.63–0.71 and root mean squared errors (RMSE) of 3.21–3.64 cmol(+) kg−1. The incorporation of environmental variables improved the accuracy by 2–10%. A linear model was fitted to enhance the current calculation method, resulting in the equation: CECclay = 15.31 + 15.90 × (CECsoil/Clay), with an R2 of 0.41 and RMSE of 4.48 cmol(+) kg−1. Therefore, given limited soil data, the MLR PTFs with explicit equations were recommended for predicting the CECclay of B horizons in humid subtropical regions.
Vibrio alginolyticus Reprograms CIK Cell Metabolism via T3SS Effector VopS to Promote Host Cell Ferroptosis
Authors: Zhang Weijie , Huang Chao , Chen Zhihang , Song Dawei , Zhang Yujia , Yang Shuai , Wang Na , Jian Jichang , Pang Huanying
Published: November 2024

Vibrio alginolyticus is a Gram-negative pathogen of both marine animals and humans, resulting in significant losses for the aquaculture industry. Emerging evidence indicates that V. alginolyticus manipulates cell death for its pathogenicity, but the underlying molecular mechanisms remain unclear. Here, a gene designated vopS in V. alginolyticus HY9901 was identified, which was predicted to encode the T3SS effector protein. To determine whether VopS contributes to the pathogenesis of V. alginolyticus, the ΔvopS mutant strain was constructed and phenotypically characterized. The deletion of VopS not only reduced the ability to secrete extracellular proteases and virulence but also affected the expression of the T3SS genes. Furthermore, VopS was cytotoxic and induced apoptosis, as confirmed by elevated LDH and the activation of caspase-3. Metabolomic analysis revealed considerable metabolomic disruptions upon V. alginolyticus infection. The VopS effector induced host cell ferroptosis by promoting the synthesis of adrenic acid, depleting cellular glutathione, and subsequently increasing the accumulation of ferrous (Fe2+). Taken together, our findings provide that the VopS effector is an essential virulence factor of V. alginolyticus, which can lead to ferroptosis.
Urban Pest or Aussie Hero? Changing Media Representations of the Australian White Ibis
Authors: Scollen Rebecca
Published: November 2024

The Australian White Ibis (Threskiornis molucca) is an Australian native bird species whose traditional habitat is inland wetlands. Environmental factors have seen the species steadily relocate to the eastern coast of Australia over the last few decades, primarily settling in cities. In 2016, McKiernan and Instone identified that 70% of Australian newspaper reports about the urban ibises from 1998 to 2012 presented the birds as either pests or victims. Since then, the ibis populations have grown, leading to the ibis being voted one of the top ten most commonly seen urban backyard birds in 2019. Media representation both reflects and shapes public perception and understanding, so it is timely for a contemporary investigation into how the ibis is represented in Australian newspapers. Has newspaper representation of the Australian White Ibis changed since 2013? If so, what new narratives have emerged? A content analysis of 68 Australian newspaper items from 2013 to 2024 was conducted with results showing a decrease in pest and victim narratives and the introduction of two new positive narratives-survivor and hero. This finding evidences changes in media representation pointing to potential changes in public understanding of the ibis and the possibility of improved relations between humans and the native bird species.
Intestinal Incarceration and Strangulation by the Median Ligament of the Urinary Bladder in a Dog
Authors: Ellis Luke , House Arthur
Published: November 2024

A 4.5-year-old female spayed, mixed-breed (Beagle X Cavalier King Charles Spaniel) presented with acute onset of vomiting and abdominal pain. The dog had undergone an exploratory celiotomy for comparable symptoms 9 months earlier, with no aetiology identified. Computed tomography (CT) was highly suggestive of mechanical obstruction of the small intestine. An exploratory celiotomy was performed and identified strangulation of the jejunum by a rent within the median ligament of the urinary bladder. It was hypothesised the rent formed secondary to previous surgical trauma. Complete excision of the median ligament resulted in resolution of clinical signs. This report emphasises the importance of minimising the formation of mesenteric rents when performing abdominal surgery.
Recognition of Sheep Feeding Behavior in Sheepfolds Using Fusion Spectrogram Depth Features and Acoustic Features
Authors: Yu Youxin , Zhu Wenbo , Ma Xiaoli , Du Jialei , Liu Yu , Gan Linhui , An Xiaoping , Li Honghui , Wang Buyu , Fu Xueliang
Published: November 2024

In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted the task of collecting sound data in actual production environments, considering noise and complex surroundings. The method included evaluating and filtering the optimal acoustic features, utilizing a customized convolutional neural network (SheepVGG-Lite) to extract Short-Time Fourier Transform (STFT) spectrograms and Constant Q Transform (CQT) spectrograms’ deep features, employing cross-spectrogram feature fusion and assessing classification performance through a support vector machine (SVM). Results indicate that the fusion of cross-spectral features significantly improved classification performance, achieving a classification accuracy of 96.47%. These findings highlight the value of integrating acoustic features with spectrogram deep features for accurately recognizing sheep feeding behavior.
Effect of Subconjunctival Injection of Canine Adipose-Derived Mesenchymal Stem Cells on Canine Spontaneous Corneal Epithelial Defects
Authors: Kengkla Pechchalee , Panyasing Yaowalak , Thayananuphat Aree , Tuntivanich Nalinee
Published: November 2024

Spontaneous chronic corneal epithelial defects (SCCEDs) are characterized by nonadherent corneal epithelium leading to poor attachment to the corneal stroma. The objective of this study was to characterize corneal outcomes concurrently with the quantification of tumor necrosis factor-alpha (TNF-α) and vascular endothelial growth factor-A (VEGF-A) in tear fluid after the subconjunctival injection of canine adipose-derived mesenchymal stem cells (cAD-MSCs) in canine SCCEDs. Ten eyes with SCCEDs, which were nonresponsive to two rounds of diamond burr debridement, were included in this study. All eyes received a single subconjunctival injection of 1 × 106 cAD-MSCs. Ophthalmic examinations were performed before treatment and on day 7, 14, and 21 after treatment. Tear samples were collected for the quantification of TNF-α and VEGF-A concentrations by a canine multiplex immunoassay. Nine out of ten eyes revealed complete healing by day 21. The mean healing time was 10.89 ± 1.7 days. All eyes showed a decreased degree of ocular discomfort, in accordance with the degree of corneal characteristics. The concentrations of VEGF-A significantly reduced from pre-treatment (4334.91 ± 1275.92 pg/mL) to day 21 post-treatment (3064.61 ± 1028.66 pg/mL). No significant difference in TNF-α concentration was observed before/after treatment. In conclusion, the single use of a subconjunctival injection of cAD-MSCs could be used as an alternative treatment for canine SCCEDs.
Genetic Characterization of Multidrug-Resistant Acinetobacter baumannii and Synergy Assessment of Antimicrobial Combinations
Authors: Luna-De-Alba Aurora , Flores-Treviño Samantha , Camacho-Ortiz Adrián , Contreras-Cordero Juan Francisco , Bocanegra-Ibarias Paola
Published: November 2024

Background/Objectives: A. baumannii is a prominent nosocomial pathogen due to its drug-resistant phenotype, representing a public health problem. In this study, the aim was to determine the effect of different antimicrobial combinations against selected multidrug-resistant (MDR) or extensive drug-resistant (XDR) isolates of A. baumannii. Methods: MDR or XDR A. baumannii isolates were characterized by assessing genes associated with drug resistance, efflux pumps, porin expression, and biofilm formation. The activities of antimicrobial combinations including tigecycline, ampicillin/sulbactam, meropenem, levofloxacin, and colistin were evaluated using checkerboard and time-to-kill assays on isolates with different susceptibility profiles and genetic characteristics. Results: Genetic characterization of MDR/XDR strains (n = 100) included analysis of OXA-24/40 gene carbapenemase (98%), genes encoding aminoglycoside-modifying enzymes (44%), and parC gene mutations (10%). AdeIJK, AdeABC, and AdeFGH efflux pumps were overexpressed in 17–34% of isolates. Omp33-36, OmpA, and CarO membrane porins were under-expressed in 50–76% of isolates; CarO was overexpressed in 22% of isolates. Isolates showed low biofilm production (11%). Synergistic activity was observed with levofloxacin-ampicillin/sulbactam and meropenem-colistin, which were able to inhibit bacterial growth. Conclusions: Genetic characteristics of A. baumannii were highly variable among the strains. Synergistic activity was observed with meropenem-colistin and levofloxacin-ampicillin/sulbactam combinations in the checkerboard method, but not in the time-to-kill assays. These discrepancies among both methods indicate that further studies are needed to determine the best therapeutic combination for treating infections by A. baumannii.
Effects of Flavonoids in Fructus Aurantii Immaturus on Carcass Traits, Meat Quality and Antioxidant Capacity in Finishing Pigs
Authors: Yang Zekun , Guo Qiuping , Kong Xiangfeng , Li Yixing , Li Fengna
Published: November 2024

This experiment aimed to explore the effects of flavonoids in Fructus Aurantii Immaturus (FFAI) on carcass traits, meat quality, and the antioxidant capacity of finishing pigs. The results indicated that the addition of an appropriate amount of FFAI into their diet could significantly reduce the backfat thickness and perirenal fat percentage of finishing pigs, as well as the drip loss, water-holding capacity, shear force, and the levels of lactate, glucose-6-phosphate, glucose, ATP, phosphofructokinase, and pyruvate in the longissimus dorsi (LD) muscle. It also elevated the levels of flavor amino acids such as glutamate, serine, and threonine, and enriched the composition of flavor substances, including benzene and octanal, which significantly contributed to the enhancement of pork flavor. Furthermore, it enhanced the expression levels of MyHC I and MyHC IIa. In summary, the appropriate addition of FFAI to the diet could improve the carcass traits, meat quality, and antioxidant capacity of finishing pigs. The optimal level of FFAI supplementation is 0.12%.
An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
Authors: Zarin Syed Shahab , Mustafa Ehzaz , Zaman Sardar Khaliq uz , Namoun Abdallah , Alanazi Meshari Huwaytim
Published: November 2024

Automatic noise-robust speaker identification is essential in various applications, including forensic analysis, e-commerce, smartphones, and security systems. Audio files containing suspect speech often include background noise, as they are typically not recorded in soundproof environments. To this end, we address the challenges of noise robustness and accuracy in speaker identification systems. An ensemble approach is proposed combining two different neural network architectures including an RNN and DNN using softmax. This approach enhances the system’s ability to identify speakers even in noisy environments accurately. Using softmax, we combine voice activity detection (VAD) with a multilayer perceptron (MLP). The VAD component aims to remove noisy frames from the recording. The softmax function addresses these residual traces by assigning a higher probability to the speaker’s voice compared to the noise. We tested our proposed solution on the Kaggle speaker recognition dataset and compared it to two baseline systems. Experimental results show that our approach outperforms the baseline systems, achieving a 3.6% and 5.8% increase in test accuracy. Additionally, we compared the proposed MLP system with Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) classifiers. The results demonstrate that the MLP with VAD and softmax outperforms the LSTM by 23.2% and the BiLSTM by 6.6% in test accuracy.
Self-Adaptive Alternating Direction Method of Multipliers for Image Denoising
Authors: Xie Mingjie , Guo Haibing
Published: November 2024

In this study, we introduce a novel self-adaptive alternating direction method of multipliers tailored for image denoising. Our approach begins by formulating a collaborative regularization model that upholds structured sparsity within images while delving into spatial correlations among pixels. To address the challenge of penalty parameter influence on convergence speed, we innovate by proposing a self-adaptive alternating direction method of multipliers. This adaptive technique autonomously adjusts variable penalty parameters to expedite algorithm convergence, thereby markedly boosting algorithmic performance. Through a fusion of simulations and empirical analyses, our research demonstrates that this novel methodology significantly amplifies the efficacy of denoising processes.
Assessing Project Resilience Through Reference Class Forecasting and Radial Basis Function Neural Network
Authors: Chen Shu , Wang Chen , Yan Kesheng
Published: November 2024

A project needs to be able to anticipate potential threats, respond effectively to adverse events, and adapt to environmental changes. This overall capability is known as project resilience. In order to make efficient project decisions when the project is subjected to disruption, such as adjusting the project budget, reformulating the work plan, and rationalizing the allocation of resources, it is necessary to quantitatively understand the level of project resilience. Therefore, this paper develops a novel approach for forecasting project performance, illustrating the changes in performance levels during the disruption and recovery phases of a project and thus quantitatively assessing project resilience. While there are several methods for assessing project resilience in existing research, the majority of assessment approaches originate from within projects and are highly subjective, which makes it difficult to objectively reflect the level of project resilience. Moreover, the availability of project samples is limited, which makes it difficult to forecast the level of project performance. In view of the fact that the Reference Class Forecasting (RCF) technique avoids subjectivity and the Radial Basis Function (RBF) neural network is known to be better at forecasting small sample datasets, this paper therefore combines the RCF technique and the RBF neural network to construct a model that forecasts the project performance of the current project after experiencing a disruption, further assessing the level of the project resilience. Specifically, this paper first presents a conceptual model of project resilience assessment; subsequently, an RBF neural network model that takes into account project budget, duration, risk level of disruption, and performance before disruption based on the RCF technique is developed to forecast project performance after experiencing disruption; and finally, the level of project resilience is assessed through calculating the ratio of recovery to loss of project performance. The model is trained and validated using 64 completed construction projects with disruptions as the datasets. The results show that the average relative errors between the forecast results of schedule performance index (SPI) and the real values are less than 5%, and the R2 of the training set and the testing set is 0.991 and 0.964, respectively, and the discrepancy between the forecasted and real values of project resilience is less than 10%. These illustrate that the model performs well and is feasible for quantifying the level of project resilience, clarifying its impact on project disruption and recovery situations, and facilitating the decision-makers of the project to make reasonable decisions.
Application and Effect Evaluation of Elastic Foundation Beam Method in Deformation Analysis of Underground Passage Underneath Excavated Tunnel
Authors: Lin Yunhong , Li Mingyu , Zhang Qirui , Yang Jinghui
Published: November 2024

Tunneling under existing underground buildings has become a common issue in densely populated urban areas. The current elastic analysis for ground displacement from new tunnel construction focuses on circular tunnels. However, theoretical analysis for non-circular chamber excavations is lacking. This paper aims to develop an elastic analysis for ground deformation from non-circular chamber excavations using the ‘equivalent radius’ theory, Verruijt’s formula, and Park’s model. It adjusts the ellipticization parameter δ and uniform radial displacement ε in Verruijt’s formula. Additionally, equivalent stiffness values for diverse existing hollow structures are considered using various approaches. A comparison of the theoretical results from field measurements and simulations shows the modified equations predict ground deformation well. Finally, parametric analysis explores the impacts of non-circular cavern excavations on existing structures under different factors.
Advancing Geotechnical Evaluation of Wellbores: A Robust and Precise Model for Predicting Uniaxial Compressive Strength (UCS) of Rocks in Oil and Gas Wells
Authors: Ahmadi Mohammadali
Published: November 2024

This study examines the efficacy of various machine learning models for predicting the uniaxial compressive strength (UCS) of rocks in oil and gas wells, which are essential for ensuring wellbore stability and optimizing drilling operations. The investigation encompasses Linear Regression, ensemble methods (including Random Forest, Gradient Boosting, XGBoost, and LightGBM), support vector machine-based regression (SVM-SVR), and multilayer perceptron artificial neural network (MLP-ANN) models. The results demonstrate that XGBoost and Gradient Boosting offer superior predictive accuracy for UCS in drillability, as indicated by low Mean Absolute Percentage Error (MAPE) values of 3.87% and 4.18%, respectively, and high R2 scores (0.8542 for XGBoost). These models emerge as optimal choices for UCS prediction focused on drillability, offering increased accuracy and reliability in practical engineering scenarios. Ensemble methods and MLP-ANN emerge as frontrunners, providing valuable tools for improving wellbore stability assessments, optimizing drilling parameter selection, and facilitating informed decision-making processes in oil and gas drilling operations. Moreover, this study lays a foundation for further research in drillability-centred predictive modelling for geotechnical parameters, advancing our understanding of rock behaviour under drilling conditions.
Pre-Impact Fall Detection for E-Scooter Riding Using an IMU: Threshold-Based, Supervised, and Unsupervised Approaches
Authors: Lee Seunghee , Koo Bummo , Kim Youngho
Published: November 2024

Pre-impact fall detection during e-scooter riding is essential for rider safety. Both threshold-based and deep learning algorithms (supervised and unsupervised models) were developed in this study. Twenty participants performed normal driving maneuvers such as straight driving, speed bumps, clockwise roundabouts, and counterclockwise roundabouts, along with falls (abnormal driving maneuvers). A 6-axis IMU sensor (Xsens DOT, The Netherlands) was positioned at the T7 location to record data at 60 Hz. The approaches included threshold-based, supervised learning, and unsupervised learning models The threshold-based approach yielded an accuracy of 98.86% with an F1 score of 0.99, while the supervised model had a slightly lower performance, reaching 86.29% accuracy and an F1 score of 0.56. The unsupervised knowledge distillation model achieved 98.86% accuracy, an F1 score of 0.99, and a memory size of only 46 kB. All models demonstrated lead times of more than 250 ms, sufficient for airbag deployment.
Comparative Analysis of Sewage Sludge Characteristics After Natural Deposition, Accelerated Aging, and Composting
Authors: Gogina Elena , Makisha Nikolay , Gulshin Igor
Published: November 2024

This study investigated treatment methods for urban wastewater sludge, specifically examining natural drying over five years, accelerated freeze–thaw–drying cycles, and composting with and without a zeolite additive. The findings reveal that composting effectively stabilized the sludge while retaining essential nutrients crucial for agriculture. Notably, with the addition of 2% zeolite by total mass, approximately 40% of the total nitrogen was preserved. Adequate aeration during composting maintained acceptable levels of phosphorus compounds, with the phosphorus content expressed as P2O5 showing significant retention compared with the natural drying methods. Composting also demonstrated a substantial reduction in petroleum hydrocarbon concentrations, which decreased from 30 mg/kg to 3 mg/kg, thereby showcasing its potential for processing contaminated sludge. The inclusion of zeolite enhanced the nitrogen retention by an additional 10–20% compared with the composting without zeolite, aligning with previous studies on its effectiveness. While composting and thermal treatments, like accelerated freeze–thaw cycles, influenced the physical properties of the sludge—such as reducing the moisture content and altering the volatile substance concentrations—they did not significantly affect the heavy metal levels. Natural drying over five years resulted in reduced metal quantities, which possibly reflected changes in the wastewater characteristics over time. Given that the heavy metal concentrations remained largely unchanged, additional treatment methods are recommended when the initial sludge contains high levels of these contaminants to ensure the safe use of the final product as fertilizer. This study underscored the significant role of biochemical and microbiological processes during composting and natural drying in transforming sludge properties. Future research should focus on establishing upper contamination thresholds and exploring microbiological safety measures to enhance the viability of sludge reuse in agriculture, balancing nutrient preservation with environmental safety.
Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller
Authors: Rajotte Kiriaki J. , Wooding Anson , McDonald Benjamin E. , Farrell Todd R. , Li Jianan , Huang Xinming , Clancy Edward A.
Published: November 2024

Concomitant improvements in wireless communication and sensor technologies have increased capabilities of wearable biosensors. These improvements have not transferred to wireless prosthesis/orthosis controllers, in part due to strict latency and power consumption requirements. We used a Bluetooth Low Energy 5.3 (BLE) network to study the influence of the connection interval (10–100 ms) and event length (2500–7500 μs), ranges appropriate for real-time myoelectric prosthesis/orthosis control on the maximum network size, power consumption, and latency. The number of connections increased from 4 to 12 as the connection interval increased from 10 to 50 ms (event length of 2500 μs). For connection intervals ≤50 ms, the number of connections reduced by ≥50% with the increasing event length. At a connection interval of 100 ms, little change was observed in the number of connections vs. event length. Across event lengths, increasing the connection interval from 10 to 100 ms decreased the average power consumed by approximately 16%. Latency measurements showed that an average of one connection interval (maximum of just over two) elapses between the application of the signal at the peripheral node ADC input and its detection on the central node. Overall, reducing the latency using shorter connection intervals reduces the maximum number of connections and increases power consumption.
Effect of Fines Content on the Compression Behavior of Calcareous Sand
Authors: Huang Suhang , Gong Xiaonan
Published: November 2024

Due to the hydraulic sorting effect in the hydraulic filling process, a fine-grained aggregate layer dominated by silty fine sand with uneven distribution is easily formed in reclamation projects, which triggers issues with the bearing capacity and nonuniform settlement of calcareous sand foundations. In this study, a series of one-dimensional compression tests were conducted to investigate the effect of different fines contents (fc) on the compression behavior of calcareous sand. The results show that at the same relative density (medium-density, Dr = 50%), the addition of fine particles leads to a reduction in the initial void ratio (for fc ≤ 40%). Furthermore, while the compressibility of the soil samples increases with the rising of fines content, it begins to decrease with further addition of fine particles beyond a threshold value of fines content (fc-th). Additionally, particle crushing contributes to the compressive deformation of calcareous sand, and the particle relative breakage of calcareous sand increases at the initial stage of adding fine particles. Moreover, a comparison of the compression test results between calcareous silty sand (fc = 10%) and clean sand reveals that the addition of fine particles accentuates the compressibility differences among calcareous sands with different relative densities. These findings provide valuable insights for addressing the challenges posed by fine-grained layers in calcareous sand foundations.
Investigation into the Suitability of AA 6061 and Ti6Al4V as Substitutes for SS 316L Use in the Paraplegic Swivel Mechanism
Authors: Ajayi Oluwaseun K. , Malomo Babafemi O. , Du Shengzhi , Owolabi Hakeem A. , Oladosu Olusola A.
Published: November 2024

SS 316L, a low-carbon 316 Stainless Steel, has been used to manufacture swivel mechanisms for paraplegic patients, but its weight is relatively high compared to a few materials in its range of properties. Aluminum alloy 6061 and Titanium alloy (Ti6Al4V) offer lightweight and incredible strength-to-weight ratio, hence their use for medical, aerospace, and automotive applications. This study, therefore, seeks a replacement for SS 316L. A 3D model of a swivel mechanism was developed to compare the performance of the swivel mechanism made with SS 316L, AA 6061, and Ti6Al4V. The kinematic analysis of the mechanism based on a range of weights: 1kN, 1.1 kN, 1.2 kN, 1.3 kN, 1.4 kN, and 1.5 kN was carried out to generate the inputs for the simulation. The 3D model was made with SolidWorks, and the results of the kinematic analysis were used to define the simulation parameters for the mechanism. Two scenarios generated depicted the full collapse of the mechanism and the full extension. The results showed that AA 6061 and Ti6Al4V outperformed SS 316L with higher yield strength and factor of safety. Therefore, swivel plates made with AA 6061 and Ti6Al4V have higher yield strength than those made with SS 316L, adding to the advantage that they have a higher strength-to-weight ratio. From this analysis and known knowledge of the cost of these materials, the optimal replacement considering cost with yield strength is AA 6061. However, Ti6Al4V is a better alternative for the strength-to-weight ratio for SS 316L.
Deterministic Trajectory Design and Attitude Maneuvers of Gradient-Index Solar Sail in Interplanetary Transfers
Authors: Bassetto Marco , Mengali Giovanni , Quarta Alessandro A.
Published: November 2024

A refractive sail is a special type of solar sail concept, whose membrane exposed to the Sun’s rays is covered with an advanced engineered film made of micro-prisms. Unlike the well-known reflective solar sail, an ideally flat refractive sail is able to generate a nonzero thrust component along the sail’s nominal plane even when the Sun’s rays strike that plane perpendicularly, that is, when the solar sail attitude is Sun-facing. This particular property of the refractive sail allows heliocentric orbital transfers between orbits with different values of the semilatus rectum while maintaining a Sun-facing attitude throughout the duration of the flight. In this case, the sail control is achieved by rotating the structure around the Sun–spacecraft line, thus reducing the size of the control vector to a single (scalar) parameter. A gradient-index solar sail (GIS) is a special type of refractive sail, in which the membrane film design is optimized though a transformation optics-based method. In this case, the membrane film is designed to achieve a desired refractive index distribution with the aid of a waveguide array to increase the sail efficiency. This paper analyzes the optimal transfer performance of a GIS with a Sun-facing attitude (SFGIS) in a series of typical heliocentric mission scenarios. In addition, this paper studies the attitude control of the Sun-facing GIS using a simplified mathematical model, in order to investigate the effective ability of the solar sail to follow the (optimal) variation law of the rotation angle around the radial direction.
Influence of Ultrasound on the Rheological Properties, Color, Carotenoid Content, and Other Physical Characteristics of Carrot Puree
Authors: Janiszewska-Turak Emilia , Sitkiewicz Iwona , Janowicz Monika
Published: November 2024

This study aimed to investigate the effect of ultrasonic frequencies (21 and 35 kHz) on the physical properties of carrot puree at different concentrations (9, 12, and 21 °Brix). The viscosity, total soluble content, density, color, and β-carotene content were tested. It was found that the viscosity of the puree, determined with respect to shear rate, concentration, and the use of ultrasonic treatment, indicates that the purees should be defined as shear thinning fluids. Moreover, a decrease in activation energy was observed with the increase in extract and ultrasonic treatment, which may cause changes in the rate of reactions occurring in the tested material. A significant effect of this may be the observed change in the color of the puree after ultrasonic treatment; the increase in frequency from 21 to 35 kHz caused an increase in redness and yellowness and a decrease in lightness, independently of concentration. The most significant color difference was noted in the puree with a 21 °Brix concentration, where a ΔE value of 21 was recorded. In contrast, the ΔE values for the other purees post-treatment remained below 5. The content of carotenoids did not change after sonication, independently of the concentration of carrot puree.
Methods of Balancing Technological Systems of Multiproduct Production
Authors: Alexandrov Islam A. , Mikhailov Maxim S. , Chervyakov Leonid M.
Published: November 2024

The functioning of the machine-building industry has its specifics, particularly periodic changes in the range (size, configuration, and others) of manufactured products. In addition, it is essential to consider the need to reduce the time spent on the production of each unit. Almost continuous changes in technology, failures in the supply of raw materials, uncoordinated logistics, and many other factors often cause significant and unproductive costs, leading to an increase in the technological stage. The most promising direction to reduce the technological time of manufacturing products by multiproduct enterprises is to reduce the waiting time owing to the uniform distribution of each technological transition according to the state of the available workshop equipment (plant, production area, enterprise). This study proposes a novel model of technological systems that enables the adaptation of technological processes for part manufacturing and comprises data structures that define their technical capabilities. The proposed algorithm facilitates a reduction in downtime and an increase in equipment utilization factor. It is possible to optimize the technological processes that change the structure of each production operation to adapt to the existing technology. Testing this methodology demonstrated a significant increase of 8% in the process utilization rate of machinery.
Impact of Wetting and Drying Cycles on the Hydromechanical Properties of Soil and Implications on Slope Stability
Authors: Shah Syed Samran Ali , Turrakheil Kanishka Sauis , Naveed Muhammad
Published: November 2024

The soil-based infrastructure is the backbone of the global economy, connecting people, enhancing quality of life, and promoting health and safety. However, its vulnerabilities are becoming apparent due to climate change, mainly through frequent wetting and drying (wd) cycles. Despite few studies in the past, research showing the stability of flood embankments in the long term, incorporating the impact of wetting and drying cycles on the hydromechanical characteristics of soil, is scarce. This study aimed to assess the impact of controlled wd cycles on the hydromechanical properties of clay and silty sand soils and its implications for the stability of a typical flood embankment. Volumetric changes were monitored during the wd cycles. The soil water characteristic curve (SWCC), saturated hydraulic conductivity (ksat), effective cohesion (c′), and effective angle of internal friction (ϕ′) were measured at 1 and 10 wd cycles. The results indicated that the 10 wd cycles decreased the saturated moisture content and resulted in a flatter SWCC compared to the 1 wd cycle for clayey soil. The ksat value was also significantly higher at 10 wd cycles than 1 wd cycle for clayey soil. An insignificant difference was found in both the SWCC and ksat at 1 and 10 wd cycles for silty sand soil. The ϕ′ value for the clayey soil decreased from 28.5 to 20.1 as the wd cycles increased from 1 to 10, while c′ remained unchanged at 10 kN/m2. On the other hand, for the silty sand soil, ϕ′ increased from 34.6 to 37.5 with an increase in wd cycles from 1 to 10, and c′ remained constant at 1 kN/m2. Numerical modelling of transient water flow coupled with a slope stability analysis revealed that the stability of a flood embankment depends on the evolution of soil hydromechanical properties due to wd cycles and the duration of flooding. These findings underscore the need for proactive measures to mitigate landslide risks in regions prone to frequent wd cycles, thereby ensuring the safety and resilience of slopes and associated infrastructure.
Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses
Authors: Imamoglu Esra
Published: November 2024

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.
SCOUT: Skull-Corrected Optimization for Ultrasound Transducers
Authors: Jiang Zheng , Hua Michelle , Li Jacqueline , Mau Hieu Le , Choi James , Gormley William B. , Amich Jose M. , Sha Raahil M.
Published: November 2024

Transcranial focused ultrasound has been studied for non-invasive and localized treatment of many brain diseases. The biggest challenge for focusing ultrasound onto the brain is the skull, which attenuates ultrasound and changes its propagation direction, leading to pressure drop, focus shift, and defocusing. We presented an optimization algorithm which automatically found the optimal location for placing a single-element focused transducer. At this optimal location, the focus shift was in an acceptable range and the ultrasound was tightly focused. The algorithm simulated the beam profiles of placing the transducer at different locations and compared the results. Locations with a normalized peak-negative pressure (PNP) above threshold were first found. Then, the optimal location was identified as the location with the smallest focal volume. The optimal location found in this study had a normalized PNP of 0.966 and a focal volume of 6.8% smaller than without the skull. A Zeta navigation system was used to automatically place the transducer and track the error caused by movement. These results demonstrated that the algorithm could find the optimal transducer location to avoid large focus shift and defocusing. With the Zeta navigation system, our algorithm can help to make transcranial focused ultrasound treatment safer and more successful.
Droplet Digital PCR: A New Molecular Method to Detect G1105S/V Mutations in Plasmopara viticola CesA3 Gene
Authors: Sánchez-Zelaia Helene , Nanni Irene Maja , Oggiano Ivano , Hernández Mónica , Díez-Navajas Ana María , Collina Marina
Published: November 2024

Plasmopara viticola is the causal agent of Grapevine Downy Mildew (GDM), which is a devastating disease of grapevines in humid temperate regions. The most employed method for protecting grapevines against GDM is the application of chemical fungicides. In Spain, Carboxylic Acid Amides (CAAs) are a fungicide group currently utilized in GDM control. In P. viticola, resistance to CAAs is conferred by G1105S and G1105V mutations in the CesA3 gene. Droplet digital polymerase chain reaction (ddPCR) is an innovative technique that combines PCR and droplet microfluidics to disperse the sample into thousands of water-in-oil droplets in which an amplification reaction is individually performed. In this study, we set up a ddPCR protocol to quantify S1105 and V1105 mutations conferring resistance to CAAs in P. viticola. The optimal PCR conditions were established, and the sensitivity and precision of the protocol were assessed. Four P. viticola populations coming from commercial vineyards in northern Spain were analyzed, and different allele frequencies were found in the analyzed samples corresponding to the different fungicide management strategies, ranging from 7.72% to 100%. Knowing the level of mutated alleles allows for designing resistance management strategies suited for each location. This suggests that similar ddPCR assays could be developed for studying mutations implicated in fungicide resistance in other fungicide groups and plant pathogens.
Lower FGFR2 mRNA Expression and Higher Levels of FGFR2 IIIc in HER2-Positive Breast Cancer
Authors: Dix-Peek Thérèse , Dickens Caroline , Valcárcel Juan , Duarte Raquel A. B.
Published: November 2024

Fibroblast growth factor receptor 2 (FGFR2) has been associated with breast cancer. We performed in silico analyses to investigate the FGFR2 mRNA expression and splice variants associated with breast cancer subtypes. Online databases, including cBioPortal and TCGA SpliceSeq, were used to examine the association between the FGFR2 expression and splice variants with breast cancer subtypes. A higher FGFR2 mRNA was significantly associated with luminal, oestrogen receptor (ER)-positive breast cancers, and invasive lobular carcinomas, whereas a lower FGFR2 was associated with human epidermal growth factor receptor 2 (HER2)-positive breast cancer and invasive ductal carcinomas. The epithelial alternatively spliced FGFR2 IIIb isoform was significantly enriched in ER+ breast cancer, while the mesenchymal FGFR2 IIIc isoform was significantly prevalent in HER2+ cancer. Increased levels of FGFR2 and IIIb splice isoforms are associated with less aggressive breast cancer phenotypes, while decreased levels of FGFR2 and increased IIIc splice isoform are associated with more aggressive phenotypes.