bluuebunny/crossref_metadata_2025 huggingface dataset:
Resource selection by Sarda cattle in a Mediterranean silvopastoral system
Authors: Acciaro Marco , Pittarello Marco , Decandia Mauro , Sitzia Maria , Giovanetti Valeria , Lombardi Giampiero , Clark Patrick E.
Published: March 2024

Knowledge of how grazing cattle utilize heterogeneous landscapes in Mediterranean silvopastoral areas is scarce. Global positioning systems (GPS) to track animals, together with geographic information systems (GIS), can relate animal distribution to landscape features. With the aim to develop a general spatial model that provides accurate prediction of cattle resource selection patterns within a Mediterranean mountainous silvopastoral area, free-roaming Sarda cows were fitted with GPS collars to track their spatial behaviors. Resource selection function models (RSF) were developed to estimate the probability of resource use as a function of environmental variables. A set of over 500 candidate RSF models, composed of up to five environmental predictor variables, were fitted to data. To identify a final model providing a robust prediction of cattle resource selection pattern across the different seasons, the 10 best models (ranked on the basis of the AIC score) were fitted to seasonal data. Prediction performance of the models was evaluated with a Spearman correlation analysis using the GPS position data sets previously reserved for model validation. The final model emphasized that watering point, elevation, and distance to fences were important factors affecting cattle resource-selection patterns. The prediction performances (as Spearman rank correlation scores) of the final model, when fitted to each season, ranged between 0.7 and 0.94. The cows were more likely to select areas lower in elevation and farther from the watering point in winter than in summer (693 ± 1 m and 847 ± 13 m vs. 707 ± 1 m and 635 ± 21 m, respectively), and in spring opted for the areas furthest from the water (963 ± 12). Although caution should be exercised in generalizing to other silvopastoral areas, the satisfactory Spearman correlations scores from the final RSF model applied to different seasons indicate resource selection function is a powerful predictive model. The relative importance of the individual predictors within the model varied among the different seasons, demonstrating the RSF model’s ability to interpret changes in animal behavior at different times of the year. The RSF model has proven to be a useful tool to interpret the spatial behaviors of cows grazing in Mediterranean silvopastoral areas and could therefore be helpful in managing and preserving ecosystem services of these areas.
Genetic Improvement of Drought Tolerance in a Mega-Rice Variety Improved White Ponni through Marker-Assisted Backcross Breeding
Authors: Seeli F. D. Prisca , Manoharan Muthukumar , Ayyenar Bharathi , Kambale Rohit , Mohanavel Vignesh , Rajagopalan Veera Ranjani , Manickam Sudha , Muthurajan Raveendran , Swaminathan Manonmani
Published: March 2024

In the ever-changing climatic conditions, it has become important to enhance rice productivity to ensure global food security. Drought is one of the major limiting factors in rice pro- duction. Drought during the reproductive stage results in maximum or complete yield loss. Efforts have been taken to develop drought-tolerant rice lines by introgressing three major drought-effect QTLs, viz. qDTY1.1, qDTY2.1, and qDTY3.1, from Apo into a susceptible popular rice variety, Improved White Ponni (IWP). Backcross inbred lines of IWP × Apo were developed through the marker-assisted backcross breeding approach. Foreground analysis using linked markers resulted in the identification of 17 progenies carrying two or more QTLs, and the recurrent parent genome recoveries of these lines were >95.6% using 72 genome-wide SSR markers distributed throughout all chromosomes. Upon phenotypic evaluation of 17 IWP BILs, the water limited condition resulted in the identification of improved lines by recording the yield and the yield-related parameters. The promising performance of IWP BILs in terms of spikelet fertility (63.3%) and grain yield per plant (>10 g) under drought stress indicated the positive effects of introgressed qDTYs, while IWP recorded complete yield loss (94.2%). Out of the cultivars considered, the best-performing lines which truly exhibited drought tolerance, with more increased yield than the recurrent parent under water-limited conditions, and the effects of these QTLs and their interactions were examined in this research work.
Exploring Cooperative Mechanisms in the Chinese Agricultural Value Chain: A Game Model Analysis Based on Leading Enterprises and Small Farmers
Authors: Jin Qiang , Dang Hui , Wang Heng , Zhang Zhenghe
Published: March 2024

In the context of agricultural modernization in China, this paper examines the micro-level perspective of agricultural value chains. Drawing from three primary models of agricultural value chain cooperation—namely, “leading enterprises + small farmers”, “leading enterprises + cooperatives + small farmers”, and “corporate integration”—it establishes four game models: the decentralized decision-making game model, the two revenue-sharing game models, and the centralized decision-making game model. It systematically analyzes the cooperation mechanisms between leading enterprises and small farmers in upstream production links of the agricultural value chain, aiming to improve the cooperation strategy between leading enterprises and small farmers, elevate the status of small farmers in the agricultural value chain, promote increased income for farmers, and strengthen the agricultural value chain. The research findings are as follows: Firstly, the traditional contract of “leading enterprise + smallholder farmers” is incomplete, which makes it difficult to avoid opportunism and moral hazard that may arise between the two parties. By comparing multiple parameter values, it is found that this model is at a lower level of agricultural value chain development. Secondly, the model of “leading enterprise + cooperative + smallholder farmers” improves the tightness and stability of cooperation between leading enterprises and smallholder farmers. This model explains to some extent the operability of smallholder farmers sharing the value of the agricultural value chain. Compared with various parameter values, this model is at a medium level between other models. Finally, the “corporate integration” model is a fully vertical integration model. Compared with various parameter values, this model is at an advanced stage of agricultural value chain development. Therefore, agricultural value chains will ultimately develop toward the direction of corporate integration. This study has positive practical significance for enhancing the status and claim rights of small farmers, promoting increased income for farmers, enhancing the consistency of values between leading enterprises and small farmers, strengthening the stability of the agricultural value chain, and ultimately achieving common prosperity and agricultural modernization in China.
Water Footprint Assessment of Agricultural Crop Productions in the Dry Farming Region, Shanxi Province, Northern China
Authors: Wang Lu , Yan Cunjie , Zhang Wenqi , Zhang Yinghu
Published: March 2024

Exploring the crop production water footprint and their driving factors is of significant importance for management of agricultural water resources. However, how do we effectively assess the total agricultural water consumption and explore the significance of their driving factors, i.e., population, economy, and agricultural production conditions, using a backpropagation neural network (BPNN)? It is still ambiguous. Water consumption for crops during the growing season is explicitly explored by way of water footprint indicators (green water footprint, WFPg, and blue water footprint, WFPb). This study provides new insights into the factors driving the changes in crop production water footprint in Taiyuan City over the period of 2005–2021. Simulations of crop evapotranspiration using the CROPWAT model were quantified. The results showed that Taiyuan City has a low crop yield level below the average level of China, with the highest crop yield in maize. The crop production water footprint in Taiyuan City showed a non-linearly decreasing trend over time. The average annual crop production water footprint was 187.09 × 103 m3/kg in Taiyuan City, with the blue water footprint and green water footprint accounting for 63.32% and 36.68%, respectively. The crop production water footprint in the west and north of Taiyuan City was significantly higher than those in other areas, accounting for 42.92% of the total crop production water footprint. Oilseed crops contributed most to the total crop production water footprint, accounting for 47.11%. The GDP and total sown area of crops were more important for the changes in WFPb. Agricultural machinery power and agriculture-to-non-agriculture ratio were more important for the changes in WFPg. Agricultural machinery power and GDP were more important for the changes in IWFP. In-depth analysis of the factors driving the changes in crop production water footprint is dramatically important for agricultural decision makers to mitigate water resource pressure in Taiyuan City.
The Application of Knowledge-Based Clinical Decision Support Systems to Detect Antibiotic Allergy
Authors: Han Nayoung , Oh Ock Hee , Oh John , Kim Yoomi , Lee Younghee , Cha Won Chul , Yu Yun Mi
Published: March 2024

Prevention of drug allergies is important for patient safety. The objective of this study was to evaluate the outcomes of antibiotic allergy-checking clinical decision support system (CDSS), K-CDSTM. A retrospective chart review study was performed in 29 hospitals and antibiotic allergy alerts data were collected from May to August 2022. A total of 15,535 allergy alert cases from 1586 patients were reviewed. The most frequently prescribed antibiotics were cephalosporins (48.5%), and there were more alerts of potential cross-reactivity between beta-lactam antibiotics than between antibiotics with the same ingredients or of the same class. Regarding allergy symptoms, dermatological disorders were the most common (38.8%), followed by gastrointestinal disorders (28.4%). The 714 cases (4.5%) of immune system disorders included 222 cases of anaphylaxis and 61 cases of severe cutaneous adverse reactions. Alerts for severe symptoms were reported in 6.4% of all cases. This study confirmed that K-CDS can effectively detect antibiotic allergies and prevent the prescription of potentially allergy-causing antibiotics among patients with a history of antibiotic allergies. If K-CDS is expanded to medical institutions nationwide in the future, it can prevent an increase in allergy recurrence related to drug prescriptions through cloud-based allergy detection CDSSs.
Whole Genome Sequencing Reveals Antimicrobial Resistance and Virulence Genes of Both Pathogenic and Non-Pathogenic B. cereus Group Isolates from Foodstuffs in Thailand
Authors: Sornchuer Phornphan , Saninjuk Kritsakorn , Amonyingcharoen Sumet , Ruangtong Jittiporn , Thongsepee Nattaya , Martviset Pongsakorn , Chantree Pathanin , Sangpairoj Kant
Published: March 2024

Members of the Bacillus cereus group are spore-forming Gram-positive bacilli that are commonly associated with diarrheal or emetic food poisoning. They are widespread in nature and frequently present in both raw and processed food products. Here, we genetically characterized 24 B. cereus group isolates from foodstuffs. Whole-genome sequencing (WGS) revealed that most of the isolates were closely related to B. cereus sensu stricto (12 isolates), followed by B. pacificus (5 isolates), B. paranthracis (5 isolates), B. tropicus (1 isolate), and “B. bingmayongensis” (1 isolate). The most detected virulence genes were BAS_RS06430, followed by bacillibactin biosynthesis genes (dhbA, dhbB, dhbC, dhbE, and dhbF), genes encoding the three-component non-hemolytic enterotoxin (nheA, nheB, and nheC), a gene encoding an iron-regulated leucine-rich surface protein (ilsA), and a gene encoding a metalloprotease (inhA). Various biofilm-associated genes were found, with high prevalences of tasA and sipW genes (matrix protein-encoding genes); purA, purC, and purL genes (eDNA synthesis genes); lytR and ugd genes (matrix polysaccharide synthesis genes); and abrB, codY, nprR, plcR, sinR, and spo0A genes (biofilm transcription regulator genes). Genes related to fosfomycin and beta-lactam resistance were identified in most of the isolates. We therefore demonstrated that WGS analysis represents a useful tool for rapidly identifying and characterizing B. cereus group strains. Determining the genetic epidemiology, the presence of virulence and antimicrobial resistance genes, and the pathogenic potential of each strain is crucial for improving the risk assessment of foodborne B. cereus group strains.
In Vitro Microevolution and Co-Selection Assessment of Amoxicillin and Cefotaxime Impact on Escherichia coli Resistance Development
Authors: Kerek Ádám , Török Bence , Laczkó Levente , Somogyi Zoltán , Kardos Gábor , Bányai Krisztián , Kaszab Eszter , Bali Krisztina , Jerzsele Ákos
Published: March 2024

The global spread of antimicrobial resistance has become a prominent issue in both veterinary and public health in the 21st century. The extensive use of amoxicillin, a beta-lactam antibiotic, and consequent resistance development are particularly alarming in food-producing animals, with a focus on the swine and poultry sectors. Another beta-lactam, cefotaxime, is widely utilized in human medicine, where the escalating resistance to third- and fourth-generation cephalosporins is a major concern. The aim of this study was to simulate the development of phenotypic and genotypic resistance to beta-lactam antibiotics, focusing on amoxicillin and cefotaxime. The investigation of the minimal inhibitory concentrations (MIC) of antibiotics was performed at 1×, 10×, 100×, and 1000× concentrations using the modified microbial evolution and growth arena (MEGA-plate) method. Our results indicate that amoxicillin significantly increased the MIC values of several tested antibiotics, except for oxytetracycline and florfenicol. In the case of cefotaxime, this increase was observed in all classes. A total of 44 antimicrobial resistance genes were identified in all samples. Chromosomal point mutations, particularly concerning cefotaxime, revealed numerous complex mutations, deletions, insertions, and single nucleotide polymorphisms (SNPs) that were not experienced in the case of amoxicillin. The findings suggest that, regarding amoxicillin, the point mutation of the acrB gene could explain the observed MIC value increases due to the heightened activity of the acrAB-tolC efflux pump system. However, under the influence of cefotaxime, more intricate processes occurred, including complex amino acid substitutions in the ampC gene promoter region, increased enzyme production induced by amino acid substitutions and SNPs, as well as mutations in the acrR and robA repressor genes that heightened the activity of the acrAB-tolC efflux pump system. These changes may contribute to the significant MIC increases observed for all tested antibiotics. The results underscore the importance of understanding cross-resistance development between individual drugs when choosing clinical alternative drugs. The point mutations in the mdtB and emrR genes may also contribute to the increased activity of the mdtABC-tolC and emrAB-tolC pump systems against all tested antibiotics. The exceptionally high mutation rate induced by cephalosporins justifies further investigations to clarify the exact mechanism behind.
Characterization of Azorean Plant Leaves for Sustainable Valorization and Future Advanced Applications in the Food, Cosmetic, and Pharmaceutical Industries
Authors: Barros Jorge Gomes Lopes , Fernandes Raquel , Abraão Ana , Costa Rui Dias , Aires Alfredo , Gouvinhas Irene , Granato Daniel , Barros Ana Novo
Published: March 2024

The historical use of plants as sources of natural compounds has persisted over time. Increasing the intake of bioactive substances shows significant potential for promoting overall well-being and health. This study delves into the pigments, phenolic composition, and profile, along with antioxidant properties, of leaf extracts rich in bioactives from plants in the Azores region, contributing to sustainable primary food production. Analyses encompassed chlorophylls, carotenoids, total phenols, ortho-diphenols, and flavonoids, as well as antioxidant capacity assessment, polyphenolic profiling, and quantification. Psidium guajava L. and Smallanthus sonchifolius (Poepp.) H.Rob. exhibited elevated chlorophyll content, while Colocasia esculenta (L.) Schott displayed the highest carotenoid levels. Annona cherimola Mill., Eriobotrya japonica (Thunb.) Lindl, and Psidium guajava L. demonstrated pronounced total phenols, ortho-diphenols, and flavonoids. These findings align with heightened antioxidant capacity. HPLC–DAD (high-performance liquid chromatography with diode-array detection) characterization unveiled elevated hydroxycinnamic acids in E. japonica and Ipomea batatas (L.) Lam. compared to A. cherimola Mill., while C. esculenta exhibited increased flavone content. Among the quantified compounds, flavonols were the ones that predominantly demonstrated contribution to the antioxidant capacity of these leaves. This research highlights Azorean leaf plants’ antioxidant potential, fostering natural product development for better health.
Induction of Neuroinflammation and Brain Oxidative Stress by Brain-Derived Extracellular Vesicles from Hypertensive Rats
Authors: Chen Xinqian , Yan Xin , Gingerich Leah , Chen Qing-Hui , Bi Lanrong , Shan Zhiying
Published: March 2024

Neuroinflammation and brain oxidative stress are recognized as significant contributors to hypertension including salt sensitive hypertension. Extracellular vesicles (EVs) play an essential role in intercellular communication in various situations, including physiological and pathological ones. Based on this evidence, we hypothesized that EVs derived from the brains of hypertensive rats with salt sensitivity could trigger neuroinflammation and oxidative stress during hypertension development. To test this hypothesis, we compared the impact of EVs isolated from the brains of hypertensive Dahl Salt-Sensitive rats (DSS) and normotensive Sprague Dawley (SD) rats on inflammatory factors and mitochondrial reactive oxygen species (mtROS) production in primary neuronal cultures and brain cardiovascular relevant regions, including the hypothalamic paraventricular nucleus (PVN) and lamina terminalis (LT). We found that brain-derived DSS-EVs significantly increased the mRNA levels of proinflammatory cytokines (PICs) and chemokines, including TNFα, IL1β, CCL2, CCL5, and CCL12, as well as the transcriptional factor NF-κB in neuronal cultures. DSS-EVs also induced oxidative stress in neuronal cultures, as evidenced by elevated NADPH oxidase subunit CYBA coding gene mRNA levels and persistent mtROS elevation. When DSS-EVs were injected into the brains of normal SD rats, the mRNA levels of PICs, chemokines, and the chronic neuronal activity marker FOSL1 were significantly increased in the PVN and LT. Furthermore, DSS-EVs caused mtROS elevation in brain PVN and LT, particularly in neurons. Our study reveals a novel role for brain-derived EVs from hypertensive rats in triggering neuroinflammation, upregulating chemokine expression, and inducing excessive ROS production. These findings provide insight into the complex interactions between EVs and hypertension-associated processes, offering potential therapeutic targets for hypertension-linked neurological complications.
Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels
Authors: Xu Wenxiang , Shi Jianjun , Zhang Hao
Published: March 2024

Building a new tunnel adjacent to an existing tunnel has become a common means of transformation in engineering. Existing tunnels are prone to some deterioration, such as cavities and cracks under long-term traffic load. This kind of deterioration tunnel is prone to collapsing under the action of blasting. Therefore, the vibration caused by blasting should be strictly controlled. Based on the reconstruction project of the Bo Jiling Tunnel, this paper puts forward the method of mechanical cutting in a central position combined with an ordinary detonator to reduce blasting vibrations. ANSYS/LS-DYNA version 19.2, was used to simulate two conditions of full-section blasting and central mechanical cutting blasting. By comparing the stress and velocity of the existing tunnel, the damping effect of mechanical cutting blasting is analyzed. Via field experiments, the superiority of the mechanical cutting method in reducing blasting vibration is further discussed. At the same time, the relationship between the main vibration frequency and the peak velocity of the existing deterioration tunnel is obtained by wavelet packet analysis of the field experimental data. The frequency band energy distribution in each direction of vibration velocity is also obtained. The results show that the central mechanical cutting increases the blasting free surface, and the mechanical cutting method reduces the vibration velocity by 36.3%. The third frequency band (31.25~46.875 Hz) is the most concentrated, which is the dominant frequency band of the signal. The novelty of this paper is to propose mechanical cutting of the central hole instead of traditional blasting for existing deterioration tunnels. The feasibility of this method is verified by numerical simulation and field tests. The relationship between peak vibration velocity, band energy, and tunnel frequency is clarified, which can better control blasting vibration and ensure the safety of existing deterioration tunnels.
Connecting the Elderly Using VR: A Novel Art-Driven Methodology
Authors: Kosti Makrina Viola , Benayoun Maurice , Georgakopoulou Nefeli , Diplaris Sotiris , Pistola Theodora , Xefteris Vasileios-Rafail , Tsanousa Athina , Valsamidou Kalliopi , Koulali Panagiota , Shekhawat Yash , Sciama Piera , Kalisperakis Ilias , Vrochidis Stefanos , Kompatsiaris Ioannis
Published: March 2024

Demographic change confronts us with an ever-increasing number of elderly people who face isolation and socialization issues. Background: The main challenge of this study is to inject emotional and aesthetic aspects into the design process of a virtual reality (VR) social space for the elderly. In this context, we asked architects and artists to improve the perception elderly people have of their way of communicating with others. Artists, in collaboration with computer engineers, designed experiences that evoke positive cognitive and emotional feelings and memories by following design trends and aesthetic values likely to be appreciated by older people, which were integrated in VR. Methods: We approached our goal by implementing an innovative art-driven methodology, using a plethora of technologies and methods, such as VR, artificial intelligence algorithms, visual analysis, and 3D mapping, in order to make design decisions based on a detailed understanding of the users’ preferences and collective behavior. Results: A so-called virtual village “Cap de Ballon” was co-created, having a public space inspired by the villages of Santorini and Meteora and a private space inspired by the 3D scanning of an elderly person’s apartment. Conclusions: The overall concept of the VR village‘s utility, design, and interior design were appreciated by the end users and the concept was evaluated as original and stimulating for creativity.
Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory
Authors: Chen Yu , Ding Jianwan , Chen Yu , Yan Dong
Published: March 2024

The introduction of a dynamic model in robot trajectory tracking control design can significantly improve its trajectory tracking accuracy, but there are many uncertainties in the robot dynamic model which can be dealt with through robust control and adaptive control. The prevailing robust control as well as adaptive control methods require real-time computation of robot dynamics, but the extreme complexity of the robot dynamics equations makes it difficult to apply these methods in real industrial systems. To this end, this article proposes a robust adaptive control method based on the desired trajectory, which uses the desired trajectory to compute most of the control terms offline, including the robot’s nominal dynamics and regression matrices, and substantially reduces the need for real-time computation of the feedback signals. The robust term modifies the perturbation of the inertial parameters of the links, the adaptive term learns the friction coefficients of the joints online, and an additional compensation term is designed to satisfy the Lyapunov stability condition of the system. Finally, taking a universal manipulator as the experimental platform, the control performances of different control methods are compared to show the feasibility of the controller and the effective reduction in real-time computational complexity.
U-ETMVSNet: Uncertainty-Epipolar Transformer Multi-View Stereo Network for Object Stereo Reconstruction
Authors: Zhao Ning , Wang Heng , Cui Quanlong , Wu Lan
Published: March 2024

The Multi-View Stereo model (MVS), which utilizes 2D images from multiple perspectives for 3D reconstruction, is a crucial technique in the field of 3D vision. To address the poor correlation between 2D features and 3D space in existing MVS models, as well as the high sampling rate required for static sampling, we proposeU-ETMVSNet in this paper. Initially, we employ an integrated epipolar transformer module (ET) to establish 3D spatial correlations along epipolar lines, thereby enhancing the reliability of aggregated cost volumes. Subsequently, we devise a sampling module based on probability volume uncertainty to dynamically adjust the depth sampling range for the next stage. Finally, we utilize a multi-stage joint learning method based on multi-depth value classification to evaluate and optimize the model. Experimental results demonstrate that on the DTU dataset, our method achieves a relative performance improvement of 27.01% and 11.27% in terms of completeness error and overall error, respectively, compared to CasMVSNet, even at lower depth sampling rates. Moreover, our method exhibits excellent performance with a score of 58.60 on the Tanks &Temples dataset, highlighting its robustness and generalization capability.
A Comprehensive Evaluation of Features and Simple Machine Learning Algorithms for Electroencephalographic-Based Emotion Recognition
Authors: Álvarez-Jiménez Mayra , Calle-Jimenez Tania , Hernández-Álvarez Myriam
Published: March 2024

The study of electroencephalographic (EEG) signals has gained popularity in recent years because they are unlikely to intentionally fake brain activity. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. Analyzing these signals involves three main processes: feature extraction, feature selection, and classification. The present study extensively evaluates feature sets across domains and their impact on emotion recognition. Feature selection improves results across the different domains. Additionally, hybrid models combining features from various domains offer a superior performance when applying the public DEAP dataset for emotion classification using EEG signals. Time, frequency, time–frequency, and spatial domain attributes and their combinations were analyzed. The effectiveness of the input vectors for the classifiers was validated using SVM, KNN, and ANN, which are simple classification algorithms selected for their widespread use and better performance in the state of the art. The use of simple machine learning algorithms makes the findings particularly valuable for real-time emotion recognition applications where the computational resources and processing time are often limited. After the analysis stage, feature vector combinations were proposed to identify emotions in four quadrants of the valence–arousal representation space using the DEAP dataset. This research achieved a classification accuracy of 96% using hybrid features in the four domains and the ANN classifier. A lower computational cost was obtained in the frequency domain.
Analysis and Multi-Objective Optimization of the Rate of Penetration and Mechanical Specific Energy: A Case Study Applied to a Carbonate Hard Rock Reservoir Based on a Drill Rate Test Using Play-Back Methodology
Authors: Mantegazini Diunay Zuliani , Nascimento Andreas , Dornelas Vitória Felicio , Mathias Mauro Hugo
Published: March 2024

Until early 2006, in Brazil, the focus used to be on oil and gas exploration/exploitation of post-salt carbonates. This changed when the industry announced the existence of large fields in pre-salt layers across the South Atlantic Ocean from nearshore zones up to almost 350 [km] from the shore. With the discovery of pre-salt hydrocarbons reservoirs, new challenges appeared. One of the main challenges is the necessity to optimize the drilling processes due to their high operational costs. Drilling costs are considerably high, which leads the oil and gas industry to search for innovative and entrepreneurial methods. The coupling of the mechanical specific energy (MSE) and the rate of penetration (ROP) is a method that allows for the identification of ideal conditions to efficiently enhance the drilling process. In addition, the performance of the drilling process can be estimated through pre-operational tests, which consist in continuously testing the applied drilling mechanic parameters, such as the weight-on-bit (WOB) and drill string rotary speed (RPM), looking for optimum sets that would ultimately provide the most desirable ROP. Thus, the goal of this research was to analyze field data from pre-salt layer operations, using a multi-objective optimization based on the play-back methodology for pre-operational drilling tests, through the ideal combination of the highest ROP and the lowest MSE. The results showed that the new concept of pre-operational tests based on the MSE proved to be effective in the drilling process optimization. The combination of the highest ROP and the lowest MSE allows for a high-performance drilling process. For WOB intervals of 5 and 7 [klb], a good fit of the parameters was obtained. Through the parameters obtained from pre-operational tests, the eventual cost-saving and time-saving values could be estimated, respectively, ranging from USD 1,056,180 to 1,151,898 and 19.50 to 21.27 [h], respectively. In addition, the results of this research can be applied to the exploration of other natural resources, such as natural hydrogen and geothermal sources.
An Ultra-Wideband Indoor Localization Algorithm with Improved Cubature Kalman Filtering Based on Sigmoid Function
Authors: Lv Yunzhu , Liu Songlin , Gao Yipin , Dai Jun , Ren Zongbin , Liu Yang
Published: March 2024

In this paper, an improved cubature Kalman filtering (CKF) is proposed using the Sigmoid function to address the problems of positioning accuracy degradation and large deviations in ultra-wideband (UWB) indoor positioning in non-line-of-sight environments. The improved CKF is based on the squared range difference (SRD) model of the time difference of arrival (TDOA) algorithm. The inaccurate impact of model estimation under non-Gaussian noise is reduced by updating the measurement noise matrix in real time. The covariance matrix is estimated using singular value decomposition (SVD) to solve the problem of degraded state estimation performance. The filtering effect of the improved CKF algorithm is evaluated by referring to the checkpoints in the dynamic trajectory. The experimental results show that the proposed algorithm effectively mitigates the impact of UWB ranging outliers in the occluded experimental environment, which makes the dynamic positioning trajectory smoother, better fitted, and more stable. The algorithm improves the positioning accuracy by up to 39.29% compared with the SRD model used alone.
A Security-Enhanced Energy Conservation with Enhanced Random Forest Classifier for Low Execution Time Framework (S-2EC-ERF) for Wireless Sensor Networks
Authors: Ibraheem Manar Khalid Ibraheem , Al-Abadi Abdullah Ali Jawad , Mohamed Mbarka Belhaj , Fakhfakh Ahmed
Published: March 2024

Wireless sensor networks (WSNs) play a pivotal role in diverse applications such as environmental monitoring, industrial automation, healthcare, and smart cities. The motivation behind the development of WSNs stems from their impact in providing real-time data on various environmental parameters. The challenge for WSNs is to achieve strong security and efficient energy saving together. Traditional methods sought to find solutions either through security or energy. In response, this study proposed a secure and energy-efficient framework for enhancing security measures in WSNs while minimizing the impact on energy resources by using the Enhanced Consumed Energy Leach (ECP-LEACH) protocol and the Enhanced Random Forest Classifier for Low Execution Time (ERF-LET) algorithm for attack detection named Security-Enhanced Energy Conservation with ERF-LET (S-2EC-ERF). The integration of the detection algorithm at the node level played a pivotal role in fortifying the security posture of individual nodes by detecting and mitigating potential security threats. Leveraging a comprehensive dataset obtained from NS3 simulations, the ERF-LET algorithm demonstrated its proficiency in differentiating between normal and attack packets, thereby laying a strong foundation for subsequent evaluations, where it achieved an accuracy of 98.193%. The proposed methodology was further validated through real-time simulations conducted on the NS3. The results demonstrated the superiority of the proposed S-2EC-ERF in terms of the packet delivery ratio (PDR), average throughput, end-to-end delay, and mean energy consumption compared to the Security-Enhanced Energy Conservation with Logistic Regression (S-2EC-LR), Security-Enhanced Energy Conservation with Decision Tree (S-2EC-DT), and Security-Enhanced Energy Conservation with AdaBoost (S-2EC-Ada) algorithms.
Power System Integration of Electric Vehicles: A Review on Impacts and Contributions to the Smart Grid
Authors: İnci Mustafa , Çelik Özgür , Lashab Abderezak , Bayındır Kamil Çağatay , Vasquez Juan C. , Guerrero Josep M.
Published: March 2024

In recent years, electric vehicles (EVs) have become increasingly popular, bringing about fundamental shifts in transportation to reduce greenhouse effects and accelerate progress toward decarbonization. The role of EVs has also experienced a paradigm shift for future energy networks as an active player in the form of vehicle-to-grid, grid-to-vehicle, and vehicle-to-vehicle technologies. EVs spend a significant part of the day parked and have a remarkable potential to contribute to energy sustainability as backup power units. In this way, EVs can be connected to the grid as stationary power units, providing a range of services to the power grid to increase its reliability and resilience. The available systems show that EVs can be used as alternative energy sources for various network systems like smart grids, microgrids, and virtual power plants besides transportation. While the grid–EV connection offers various contributions, it also has some limitations and effects. In this context, the current study highlights the power system impacts and key contributions of EVs connected to smart grids. Regarding the power system impacts in case of EV integration into smart grids, the challenges and difficulties are categorized under the power system stability, voltage/current distortions, load profile, and power losses. Voltage/current distortions like sags, unbalances, harmonics, and supraharmonics are also detailed in the study. Subsequently, the key contributions to the smart grid in terms of energy management, grid-quality support, grid balancing, and socio-economic impacts are explained. In the energy management part, issues such as power flow, load balancing, and renewable energy integration are elaborated. Then, the fault ride-through capability, reactive power compensation, harmonic mitigation, and grid loss reduction are presented to provide information on power quality enhancement. Lastly, the socio-economic impacts in terms of employment, net billing fees, integration with renewable energy sources, and environmental effects are elucidated in the present study.
Dual-Targeting Gold Nanoparticles: Simultaneous Decoration with Ligands for Co-Transporters SGLT-1 and B0AT1
Authors: D’Orazio Giuseppe , Marradi Marco , La Ferla Barbara
Published: March 2024

Sodium–glucose co-transporter 1 (SGLT1) and sodium-dependent neutral amino acid transporter (B0AT1) are mainly expressed on the membrane of enterocytes, a type of epithelial cell found in the intestines. In addition to their physiological role in the absorption of nutrients, a protective role in the integrity of the intestinal barrier has been established. The natural ligands of SGLT1 (d-glucose) and of B0AT1 (l-glutamine) can trigger a protective anti-inflammatory effect on the intestinal epithelium. The literature suggests the activation of common intracellular pathways upon engagement of the two transporters, whose functional forms are composed of oligomers or clusters. Simultaneous activation of these two co-transporters could lead to a potential multitarget and synergistic anti-inflammatory effect. Therefore, nanoplatforms containing multiple copies of the ligands could represent chemical tools to study the potential simultaneous activation of the two co-transporters. For these reasons, in this study, a set of different gold nanoparticles decorated with derivatives of d-glucose and of l-glutamine were designed and prepared. In particular, the synthesis of suitable sulfur-ending functionalized ligand derivatives, including a C-glucoside derivative, their anchoring to gold nanoparticles and their physical–chemical characterization have been carried out. The obtained nanostructures could represent promising multifunctional platforms for further investigation of the existence of possible multitarget and synergistic effects toward the two co-transporters SGLT1 and B0AT1.
Using Kolmogorov Entropy to Verify the Description Completeness of Traffic Dynamics of Highly Autonomous Driving
Authors: Kiss Gabor , Bakucz Peter
Published: March 2024

In this paper, we outline the analysis of a fully provable traffic system based on the Kolmogorov entropy. The completeness of the traffic node dynamics is realized in the form of a nonlinear dynamical model of the participating transport objects. The goal of this study is to determine the completeness of transport nodes based on the Kolmogorov entropy of the traffic trajectories of a node with an unspecified number of actors, like cars and pedestrians. The completeness of a highly autonomous driving detection system describing a traffic node could be realized if the entropy-based error-doubling time of the trajectories of the Euler–Lagrange equation interpreted at the transport junction is less than 1.3.
High-Precision Calculation Using the Method of Analytical Regularization for the Cut-Off Wave Numbers for Waveguides of Arbitrary Cross Sections with Inner Conductors
Authors: Vinogradova Elena , Smith Paul , Shestopalov Yury
Published: March 2024

A method for the accurate calculation of the cut-off wavenumbers of a waveguide with an arbitrary cross section and a number of inner conductors is demonstrated. Concepts of integral and infinite-matrix (summation) operator-valued functions depending nonlinearly on the frequency spectral parameter provide a secure basis for formulating the spectral problem, and the Method of Analytical Regularization is employed to implement an effective algorithm. The algorithm is based on a mathematically rigorous solution of the homogeneous Dirichlet problem for the Helmholtz equation in the interior of the waveguide, excluding the regions occupied by the inner conductor boundaries. A highly efficient method of calculating the cut-off wavenumbers and the corresponding non-trivial solutions representing the modal distribution is developed. The mathematical correctness of the problem statement, the method, and the ability to calculate the cut-off wavenumbers with any prescribed and proven accuracy provide a secure basis for treating these as “benchmark solutions”. In this paper, we use this new approach to validate previously obtained results against our benchmark solutions. Furthermore, we demonstrate its universality in solving some new problems, which are barely accessible by existing methods.
Some Limiting Laws in Non-Commutative Probability
Authors: Alanzi Ayed. R. A. , Fakhfakh Raouf , Alshahrani Fatimah
Published: March 2024

In this article, we provide some new limiting laws related to the free multiplicative law of large numbers and involving free and Boolean additive convolutions. Some examples of these limiting laws are presented within the framework of non-commutative probability theory.
An Efficient Penalty Method without a Line Search for Nonlinear Optimization
Authors: Leulmi Assma
Published: March 2024

In this work, we integrate some new approximate functions using the logarithmic penalty method to solve nonlinear optimization problems. Firstly, we determine the direction by Newton’s method. Then, we establish an efficient algorithm to compute the displacement step according to the direction. Finally, we illustrate the superior performance of our new approximate function with respect to the line search one through a numerical experiment on numerous collections of test problems.
New Bounds for Three Outer-Independent Domination-Related Parameters in Cactus Graphs
Authors: Cabrera-Martínez Abel , Rueda-Vázquez Juan Manuel , Segarra Jaime
Published: March 2024

Let G be a nontrivial connected graph. For a set D⊆V(G), we define D¯=V(G)∖D. The set D is a total outer-independent dominating set of G if |N(v)∩D|≥1 for every vertex v∈V(G) and D¯ is an independent set of G. Moreover, D is a double outer-independent dominating set of G if |N[v]∩D|≥2 for every vertex v∈V(G) and D¯ is an independent set of G. In addition, D is a 2-outer-independent dominating set of G if |N(v)∩D|≥2 for every vertex v∈D¯ and D¯ is an independent set of G. The total, double or 2-outer-independent domination number of G, denoted by γtoi(G), γ×2oi(G) or γ2oi(G), is the minimum cardinality among all total, double or 2-outer-independent dominating sets of G, respectively. In this paper, we first show that for any cactus graph G of order n(G)≥4 with k(G) cycles, γ2oi(G)≤n(G)+l(G)2+k(G), γtoi(G)≤2n(G)−l(G)+s(G)3+k(G) and γ×2oi(G)≤2n(G)+l(G)+s(G)3+k(G), where l(G) and s(G) represent the number of leaves and the number of support vertices of G, respectively. These previous bounds extend three known results given for trees. In addition, we characterize the trees T with γ×2oi(T)=γtoi(T). Moreover, we show that γ2oi(T)≥n(T)+l(T)−s(T)+12 for any tree T with n(T)≥3. Finally, we give a constructive characterization of the trees T that satisfy the equality above.
Three-Dimensional Printing of Drug-Eluting Implantable PLGA Scaffolds for Bone Regeneration
Authors: Annaji Manjusha , Mita Nur , Poudel Ishwor , Boddu Sai H. S. , Fasina Oladiran , Babu R. Jayachandra
Published: March 2024

Despite rapid progress in tissue engineering, the repair and regeneration of bone defects remains challenging, especially for non-homogenous and complicated defects. We have developed and characterized biodegradable drug-eluting scaffolds for bone regeneration utilizing direct powder extrusion-based three-dimensional (3D) printing techniques. The PLGA scaffolds were fabricated using poly (lactic-co-glycolic acid) (PLGA) with inherent viscosities of 0.2 dl/g and 0.4 dl/g and ketoprofen. The effect of parameters such as the infill, geometry, and wall thickness of the drug carrier on the release kinetics of ketoprofen was studied. The release studies revealed that infill density significantly impacts the release performance, where 10% infill showed faster and almost complete release of the drug, whereas 50% infill demonstrated a sustained release. The Korsmeyer–Peppas model showed the best fit for release data irrespective of the PLGA molecular weight and infill density. It was demonstrated that printing parameters such as infill density, scaffold wall thickness, and geometry played an important role in controlling the release and, therefore, in designing customized drug-eluting scaffolds for bone regeneration.
Monte Carlo-Based Optical Simulation of Optical Distribution in Deep Brain Tissues Using Sixteen Optical Sources
Authors: Yang Xi , Chai Chengpeng , Zuo Hongzhi , Chen Yun-Hsuan , Shi Junhui , Ma Cheng , Sawan Mohamad
Published: March 2024

Optical-based imaging has improved from early single-location research to further sophisticated imaging in 2D topography and 3D tomography. These techniques have the benefit of high specificity and non-radiative safety for brain detection and therapy. However, their performance is limited by complex tissue structures. To overcome the difficulty in successful brain imaging applications, we conducted a simulation using 16 optical source types within a brain model that is based on the Monte Carlo method. In addition, we propose an evaluation method of the optical propagating depth and resolution, specifically one based on the optical distribution for brain applications. Based on the results, the best optical source types were determined in each layer. The maximum propagating depth and corresponding source were extracted. The optical source propagating field width was acquired in different depths. The maximum and minimum widths, as well as the corresponding source, were determined. This paper provides a reference for evaluating the optical propagating depth and resolution from an optical simulation aspect, and it has the potential to optimize the performance of optical-based techniques.
The Role of MUC1 in Renal Cell Carcinoma
Authors: Milella Martina , Rutigliano Monica , Lasorsa Francesco , Ferro Matteo , Bianchi Roberto , Fallara Giuseppe , Crocetto Felice , Pandolfo Savio , Barone Biagio , d’Amati Antonio , Spilotros Marco , Battaglia Michele , Ditonno Pasquale , Lucarelli Giuseppe
Published: March 2024

Mucins are a family of high-molecular-weight glycoproteins. MUC1 is widely studied for its role in distinct types of cancers. In many human epithelial malignancies, MUC1 is frequently overexpressed, and its intracellular activities are crucial for cell biology. MUC1 overexpression can enhance cancer cell proliferation by modulating cell metabolism. When epithelial cells lose their tight connections, due to the loss of polarity, the mucins become dispersed on both sides of the epithelial membrane, leading to an abnormal mucin interactome with the membrane. Tumor-related MUC1 exhibits certain features, such as loss of apical localization and aberrant glycosylation that might cause the formation of tumor-related antigen epitopes. Renal cell carcinoma (RCC) accounts for approximately 3% of adult malignancies and it is the most common kidney cancer. The exact role of MUC1 in this tumor is unknown. Evidence suggests that it may play a role in several oncogenic pathways, including proliferation, metabolic reprogramming, chemoresistance, and angiogenesis. The purpose of this review is to explore the role of MUC1 and the meaning of its overexpression in epithelial tumors and in particular in RCC.