bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Real-Time Models for Object Detection and Instance Segmentation for Agaricus bisporus Segmentation and Localization System Using RGB-D Panoramic Stitching Images
Authors: Shi Chenbo , Mo Yuanzheng , Ren Xiangqun , Nie Jiahao , Zhang Chun , Yuan Jin , Zhu Changsheng
Published: May 2024

The segmentation and localization of Agaricus bisporus is a precondition for its automatic harvesting. A. bisporus growth clusters can present challenges for precise localization and segmentation because of adhesion and overlapping. A low-cost image stitching system is presented in this research, utilizing a quick stitching method with disparity correction to produce high-precision panoramic dual-modal fusion images. An enhanced technique called Real-Time Models for Object Detection and Instance Segmentation (RTMDet-Ins) is suggested. This approach utilizes SimAM Attention Module’s (SimAM) global attention mechanism and the lightweight feature fusion module Space-to-depth Progressive Asymmetric Feature Pyramid Network (SPD-PAFPN) to improve the detection capabilities for hidden A. bisporus. It efficiently deals with challenges related to intricate segmentation and inaccurate localization in complex obstacles and adhesion scenarios. The technology has been verified by 96 data sets collected on a self-designed fully automatic harvesting robot platform. Statistical analysis shows that the worldwide stitching error is below 2 mm in the area of 1200 mm × 400 mm. The segmentation method demonstrates an overall precision of 98.64%. The planar mean positioning error is merely 0.31%. The method promoted in this research demonstrates improved segmentation and localization accuracy in a challenging harvesting setting, enabling efficient autonomous harvesting of A. bisporus.
Recognition of Plastic Film in Terrain-Fragmented Areas Based on Drone Visible Light Images
Authors: Du Xiaoyi , Huang Denghong , Dai Li , Du Xiandan
Published: May 2024

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands.
Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot
Authors: Ma Chenyu , Liu Chaorui , Ye Zhilan
Published: May 2024

Maize yields perform differently in different environments, so the selection of suitable genotypes in diverse environments is essential for variety selection to enable better site-specific planting. Hence, the objective of the study was to estimate the productivity of 11 maize hybrids (G) in 10 different environments (E) and select high-yield and stable varieties for adaptive cultivation in 2022 and 2023. The combined analysis of variance showed that G (4%), E (50%), and their interaction (31%) had a significant effect (p < 0.01) on maize yield, with E factors contributing the most. In addition, the average yield ranged from 9398 kg/ha to 10,574 kg/ha, and ZF-2208 and DY-519 performed relatively well in both years. The AMMI model showed that the varieties DY-213, DY-605, and DY-519 had high and stable production in 2022, whereas it was ZF-2209 and LX-24 in 2023. The “W-W-W” biplot showed that DY-519 and JG-18 were the optimal varieties in 2022, and ZF-2208 and ZF-2210 were optimal in 2023. The “mean vs. stability” biplot indicated that JG-18, DY-605, and DY-213 (in 2022) and ZF-2208, LX-24, and ZF-2209 (in 2023) were the optimal varieties. Additionally, both the discrimination and representative biplot and the ranking biplot reflected that BinChuan and ShiDian (in 2022) and GengMa and YongSheng (in 2023) were the ideal test environments. In conclusion, DY-519, DY-605, ZF-2208, and LX-24 hybrids could be used for variety promotion. Moreover, BinChuan, ShiDian, GengMa, and YongSheng were the ideal test environments for selecting varieties. Therefore, the AMMI model and GGE biplot can be used to complement each other for a comprehensive evaluation of maize yield. In this way, excellent maize hybrids with high yield and stability can be selected, which could promote the selection and popularization of varieties and shorten the breeding process.
The Use of Spectroscopic Methods to Study Organic Matter in Virgin and Arable Soils: A Scoping Review
Authors: Lodygin Evgeny , Abakumov Evgeny
Published: May 2024

The use of modern spectroscopic methods of analysis, which provide extensive information on the chemical nature of substances, significantly expands our understanding of the molecular composition and properties of soil organic matter (SOM) and its transformation and stabilization processes in various ecosystems and geochemical conditions. The aim of this review is to identify and analyze studies related to the application of nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) spectroscopy techniques to study the molecular composition and transformation of organic matter in virgin and arable soils. This article is mainly based on three research questions: (1) Which NMR spectroscopy techniques are used to study SOM, and what are their disadvantages and advantages? (2) How is the NMR spectroscopy technique used to study the molecular structure of different pools of SOM? (3) How is ESR spectroscopy used in SOM chemistry, and what are its advantages and limitations? Relevant studies published between 1996 and 2024 were searched in four databases: eLIBRARY, MDPI, ScienceDirect and Springer. We excluded non-English-language articles, review articles, non-peer-reviewed articles and other non-article publications, as well as publications that were not available according to the search protocols. Exclusion criteria for articles were studies that used NMR and EPR techniques to study non-SOM and where these techniques were not the primary methods. Our scoping review found that both solid-state and solution-state NMR spectroscopy are commonly used to study the structure of soil organic matter (SOM). Solution-phase NMR is particularly useful for studying soluble SOM components of a low molecular weight, whereas solid-phase NMR offers advantages such as higher 13C atom concentration for stronger signals and faster analysis time. However, solution-phase NMR has limitations including sample insolubility, potential signal aggregation and reduced sensitivity and resolution. Solid-state NMR is better at detecting non-protonated carbon atoms and identifying heterogeneous regions within structures. EPR spectroscopy, on the other hand, offers significant advantages in experimental biochemistry due to its high sensitivity and ability to provide detailed information about substances containing free radicals (FRs), aiding in the assessment of their reactivity and transformations. Understanding the FR structure in biopolymers can help to study the formation and transformation of SOM. The integration of two- and three-dimensional NMR spectroscopy with other analytical methods, such as chromatography, mass spectrometry, etc., provides a more comprehensive approach to deciphering the complex composition of SOM than one-dimensional techniques alone.
CSN1S1, CSN3 and LPL: Three Validated Gene Polymorphisms Useful for More Sustainable Dairy Production in the Mediterranean River Buffalo
Authors: Pauciullo Alfredo , Gaspa Giustino , Zhang Yi , Liu Qingyou , Cosenza Gianfranco
Published: May 2024

The search for DNA polymorphisms useful for the genetic improvement of dairy farm animals has spanned more than 40 years, yielding relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing has been found in casein genes and in DGAT1. Nowadays, similar results have not yet been reached in river buffaloes, despite the availability of advanced genomic technologies and accurate phenotype records. The aim of the present study was to investigate and validate the effect of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes on seven milk traits in a larger buffalo population. These SNPs have previously been reported to be associated with, or affect, dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. The following traits were individually recorded, monthly, throughout each whole lactation period from 2010 to 2021: daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yield (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103 cell/mL) and urea (mg/dL). A total of 15,742 individual milk test day records (2496 lactations) were available for 680 buffalo cows, with 3.6 ± 1.7 parities (from 1 to 13) and an average of 6.1 ± 1.2 test day records per lactation. Three out four SNPs in the CSN1S1, CSN3 and LPL genes were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C>T) gave favorable associations with all yield traits (dMY, p = 0.022; dPY, p = 0.014; dFY, p = 0.029) and somatic cell score (SCS, p = 0.032). The CSN3 (HQ677596: c.536C>T) was positively associated with SCS (p = 0.005) and milk urea (p = 0.04). Favorable effects on daily milk yield (dMY, p = 0.028), fat (dFP, p = 0.027) and protein (dPP, p = 0.050) percentages were observed for the LPL. Conversely, the SCD did not show any association with milk traits. This is the first example of a confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in the dairy field, and it represents a very important indication for the preselection of young bulls destined for breeding programs aimed at more sustainable dairy production.
Veterinarians’ Self-Reported Behaviors and Attitudes toward Spectrum of Care Practices
Authors: Dolan Emily D. , Slater Margaret R.
Published: May 2024

Access to veterinary care for animal owners is an important part of keeping animals healthy and keeping pets and people together whenever that is appropriate. Insufficient financial and other resources to allocate to veterinary care are major barriers for pet owners to receiving preventative, sick, and emergency services. The veterinary community has begun to incorporate offering a range of diagnostic and treatment options more intentionally in response to clients’ inability to pay and to a lesser extent to mitigate other barriers to care. Many veterinarians are nonetheless oriented toward providing specialized and more sophisticated care based on their training. This study sought to identify the self-reported behaviors, knowledge, attitudes, and beliefs of veterinarians about offering a spectrum of care options (SoC) to clients. The finding that many reported offering SoC is encouraging. However, veterinarians who report comfort and confidence in a variety of aspects of clinical care were most likely to offer SoC. Practitioners in the field for 20 or more years were less likely to offer SoC to clients with financial limitations. Rural veterinarians were more likely to offer SoC to any client compared to urban veterinarians. These results provide a point of reference and potential focus for veterinarians who are not currently offering SoC as well as an exploration of veterinarians’ reported knowledge, behaviors, attitudes, perceptions, and concerns about SoC.
Magnetic Resonance Imaging, Computed Tomographic and Radiographic Findings in the Metacarpophalangeal Joints of 31 Warmblood Showjumpers in Full Work and Competing Regularly
Authors: Nagy Annamaria , Dyson Sue
Published: May 2024

There is a limited description of magnetic resonance imaging (MRI) and no information on computed tomographic (CT) findings in the fetlock of non-lame, non-racing sports horses. This study aimed to document comparative CT, MRI and radiographic findings in the metacarpophalangeal joints of showjumpers in full work. Clinical and gait assessments, low-field MRI, fan-beam CT and radiographic examinations of both metacarpophalangeal joints were performed on 31 showjumpers. Images were analysed descriptively. In most limbs (53/62, 85.5%), there were CT and MRI changes consistent with densification in the sagittal ridge and/or condyles of the third metacarpal bone (McIII). Hypoattenuation (subchondral bone resorption) was seen in CT reconstructions in the metacarpal condyle dorsoproximally (n = 2) and dorsodistally (n = 1), in the sagittal groove (n = 2) and medial fovea (n = 1) of the proximal phalanx. The McIII resorptive lesions were detected on MR images but not the proximal phalanx lesions. None were identified on radiographs. In conclusion, MRI and CT abnormalities previously associated with lameness were seen in the front fetlocks of showjumpers without relevant lameness. Densification in the sagittal ridge and the metacarpal condyles likely reflects an adaptive change to exercise. Subchondral bone resorption may indicate an early stage of disease; follow-up information is needed to establish its clinical significance.
Towards Personally Relevant Navigation: The Differential Effects of Cognitive Style and Map Orientation on Spatial Knowledge Development
Authors: Park Hannah , Dixit Manish K. , Pariafsai Fatemeh
Published: May 2024

Under emergencies such as floods and fires or during indoor navigation where cues from local landmarks and a Global Positioning System (GPS) are no longer available, the acquisition of comprehensive environmental representation becomes particularly important. Several studies demonstrated that individual differences in cognitive style might play an important role in creating a complete environmental representation and spatial navigation. However, this relationship between cognitive style and spatial navigation is not well researched. This study hypothesized that a specific type of map orientation (north-up vs. forward-up) might be more efficient for individuals with different cognitive styles. Forty participants were recruited to perform spatial tasks in a virtual maze environment to understand how cognitive style may relate to spatial navigation abilities, particularly the acquisition of survey and route knowledge. To measure survey knowledge, pointing direction tests and sketch map tests were employed, whereas, for route knowledge, the landmark sequencing test and route retracing test were employed. The results showed that both field-dependent and field-independent participants showed more accurate canonical organization in their sketch map task with a north-up map than with a forward-up map, with field-independent participants outperforming field-dependent participants in canonical organization scores. The map orientation did not influence the performance of Field-Independent participants on the pointing direct test, with field-dependent participants showing higher angular error with north-up maps. Regarding route knowledge, field-independent participants had more accurate responses in the landmark sequencing tests with a north-up map than with a forward-up map. On the other hand, field-dependent participants had higher accuracy in landmark sequencing tests in the forward-up map condition than in the north-up map condition. In the route retracing test, however, the map orientation had no statistically significant effect on different cognitive style groups. The results indicate that cognitive style may affect the relationship between map orientation and spatial knowledge acquisition.
Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
Authors: Sankalpa Donthi , Dhou Salam , Pasquier Michel , Sagahyroon Assim
Published: May 2024

Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities’ preparedness for future pandemics.
Identification and Control of Flexible Joint Robots Based on a Composite-Learning Optimal Bounded Ellipsoid Algorithm and Prescribe Performance Control Technique
Authors: Li Xianyan , Zheng Dongdong , Guo Kai , Ren Xuemei
Published: May 2024

This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes.
Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review
Authors: Wang Yuchen , Hayashibe Mitsuhiro , Owaki Dai
Published: May 2024

Policy learning enables agents to learn how to map states to actions, thus enabling adaptive and flexible behavioral generation in complex environments. Policy learning methods are fundamental to reinforcement learning techniques. However, as problem complexity and the requirement for motion flexibility increase, traditional methods that rely on manual design have revealed their limitations. Conversely, data-driven policy learning focuses on extracting strategies from biological behavioral data and aims to replicate these behaviors in real-world environments. This approach enhances the adaptability of agents to dynamic substrates. Furthermore, this approach has been extensively applied in autonomous driving, robot control, and interpretation of biological behavior. In this review, we survey developments in data-driven policy-learning algorithms over the past decade. We categorized them into the following three types according to the purpose of the method: (1) imitation learning (IL), (2) inverse reinforcement learning (IRL), and (3) causal policy learning (CPL). We describe the classification principles, methodologies, progress, and applications of each category in detail. In addition, we discuss the distinct features and practical applications of these methods. Finally, we explore the challenges these methods face and prospective directions for future research.
A Numerical Study of the Performance of Point Absorber Wave Energy Converters
Authors: Rathaur Ranjana , Verdin Patrick G. , Ghosh Sumana
Published: May 2024

Free-floating and submerged wave energy converters (SWECs) are regarded as promising technologies for renewable energy production. These converters rely on a heave-motion buoy to capture the kinetic energy of ocean waves and convert it into electrical energy through power conversion systems. To better understand the impact of various factors on power generation and efficiency, the effects of different buoy shapes (rectangular, circular cylinder, and trapezoidal fin), submergence depths (0, 0.1, and 0.2 m), wave heights (0.04, 0.06, and 0.1 m), and spring stiffness (50 and 100 N/m) were investigated. A 2D numerical wave tank with a buoy was simulated, and the results were validated against experimental data. Information on vorticity, vertical displacement, power absorption, and efficiency are provided. The findings indicate that the buoy shape and wave height significantly affect power absorption and efficiency. Additionally, this study reveals that increasing submergence leads to higher power absorption and lower conversion efficiency.
A Comparison of Myoelectric Control Modes for an Assistive Robotic Virtual Platform
Authors: Polo-Hortigüela Cristina , Maximo Miriam , Jara Carlos A. , Ramon Jose L. , Garcia Gabriel J. , Ubeda Andres
Published: May 2024

In this paper, we propose a daily living situation where objects in a kitchen can be grasped and stored in specific containers using a virtual robot arm operated by different myoelectric control modes. The main goal of this study is to prove the feasibility of providing virtual environments controlled through surface electromyography that can be used for the future training of people using prosthetics or with upper limb motor impairments. We propose that simple control algorithms can be a more natural and robust way to interact with prostheses and assistive robotics in general than complex multipurpose machine learning approaches. Additionally, we discuss the advantages and disadvantages of adding intelligence to the setup to automatically assist grasping activities. The results show very good performance across all participants who share similar opinions regarding the execution of each of the proposed control modes.
Long-Term Structural Changes in the Osteochondral Unit in Patients with Osteoarthritis Undergoing Corrective Osteotomy with Platelet-Rich Plasma or Stromal Vascular Fraction Post-Treatment
Authors: Prizov Aleksey , Tchetina Elena , Volkov Aleksey , Eremin Ilya , Zagorodniy Nikolay , Lazko Fedor , Pulin Andrey , Belyak Evgeniy , Kotenko Konstantin , Eshmotova Gulnora , Glukhova Svetlana , Lila Aleksandr
Published: May 2024

This pilot study examined the long-term structural changes in the osteochondral unit of 20 patients with knee osteoarthritis (KOA) who underwent high tibial osteotomy (HTO) and received post-treatment with either platelet-rich plasma (PRP) or stromal vascular fraction (SVF). Ten patients were injected with autologous PRP (PRP subgroup), while another ten patients received autologous SVF (SVF subgroup) six weeks after surgery and were monitored for 18 months. Histological samples of bone and cartilage (2 mm in diameter and 2 cm long) were taken from tibial and femoral sites during surgery and 18-month post-HTO, and morphometric analyses were conducted using Mega-Morf12 software. Both post-treatment resulted in an increase in articular cartilage height at both sites (p < 0.001 in the tibia and femur), indicating positive outcomes. Significant improvements in subchondral and trabecular bone architecture were also observed, with SVF injection showing higher reparative capacity in terms of bone volume (p < 0.001 for the tibia and p = 0.004 for the femur), subchondral bone height (p < 0.001 for the tibia and p = 0.014 for the femur), trabecular bone volume (p < 0.001 for the femur), and intertrabecular space (p = 0.009 for the tibia and p = 0.007 for the femur). This pilot study, for the first time, demonstrates that HTO surgery combined with PRP and SVF post-treatments can lead to significant enhancements in knee articular cartilage and bone architecture in KOA patients, with SVF showing higher regenerative potential. These findings may contribute to improving treatment strategies for better clinical outcomes in HTO therapy for patients with KOA.
Design, In Vitro Evaluation and In Vivo Biocompatibility of Additive Manufacturing Three-Dimensional Printing of β beta-Tricalcium Phosphate Scaffolds for Bone Regeneration
Authors: Llorente José Javier , Junquera Luis , Gallego Lorena , Pérez-Basterrechea Marcos , Suárez Luis Ignacio , Llorente Santiago
Published: May 2024

The reconstruction of bone deficiencies remains a challenge due to the limitations of autologous bone grafting. The objective of this study is to evaluate the bone regeneration efficacy of additive manufacturing of tricalcium phosphate (TCP) implants using lithography-based ceramic manufacturing (LCM). LCM uses LithaBone TCP 300 slurry for 3D printing, producing cylindrical scaffolds. Four models of internal scaffold geometry were developed and compared. The in vitro studies included cell culture, differentiation, seeding, morphological studies and detection of early osteogenesis. The in vivo studies involved 42 Wistar rats divided into four groups (control, membrane, scaffold (TCP) and membrane with TCP). In each animal, unilateral right mandibular defects with a total thickness of 5 mm were surgically performed. The animals were sacrificed 3 and 6 months after surgery. Bone neoformation was evaluated by conventional histology, radiology, and micro-CT. Model A (spheres with intersecting and aligned arrays) showed higher penetration and interconnection. Histological and radiological analysis by micro-CT revealed increased bone formation in the grafted groups, especially when combined with a membrane. Our innovative 3D printing technology, combined with precise scaffold design and efficient cleaning, shows potential for bone regeneration. However, further refinement of the technique and long-term clinical studies are crucial to establish the safety and efficacy of these advanced 3D printed scaffolds in human patients.
MicroRNA Monitoring in Human Alveolar Macrophages from Patients with Smoking-Related Lung Diseases: A Preliminary Study
Authors: Mirra Davida , Esposito Renata , Spaziano Giuseppe , Sportiello Liberata , Panico Francesca , Squillante Antonio , Falciani Maddalena , Cerqua Ida , Gallelli Luca , Cione Erika , D’Agostino Bruno
Published: May 2024

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is commonly considered to be a potent driver of non-small cell lung cancer (NSCLC) development and related mortality. A growing body of evidence supports a role of the immune system, mainly played by alveolar macrophages (AMs), in key axes regulating the development of COPD or NSCLC phenotypes in response to harmful agents. MicroRNAs (miRNAs) are small non-coding RNAs that influence most biological processes and interfere with several regulatory pathways. The purpose of this study was to assess miRNA expression patterns in patients with COPD, NSCLC, and ever- or never-smoker controls to explore their involvement in smoking-related diseases. Bronchoalveolar lavage (BAL) specimens were collected from a prospective cohort of 43 sex-matched subjects to determine the expressions of hsa-miR-223-5p, 16-5p, 20a-5p, -17-5p, 34a-5p and 106a-5p by RT-PCR. In addition, a bioinformatic analysis of miRNA target genes linked to cancer was performed. Distinct and common miRNA expression levels were identified in each pathological group, suggesting their possible role as an index of NSCLC or COPD microenvironment. Moreover, we identified miRNA targets linked to carcinogenesis using in silico analysis. In conclusion, this study identified miRNA signatures in AMs, allowing us to understand the molecular mechanisms underlying smoking-related conditions and potentially providing new insights for diagnosis or pharmacological treatment.
Development of a DC-Biased AC-Stimulated Microfluidic Device for the Electrokinetic Separation of Bacterial and Yeast Cells
Authors: Nasir Ahamed Nuzhet Nihaar , Mendiola-Escobedo Carlos A. , Perez-Gonzalez Victor H. , Lapizco-Encinas Blanca H.
Published: May 2024

Electrokinetic (EK) microsystems, which are capable of performing separations without the need for labeling analytes, are a rapidly growing area in microfluidics. The present work demonstrated three distinct binary microbial separations, computationally modeled and experimentally performed, in an insulator-based EK (iEK) system stimulated by DC-biased AC potentials. The separations had an increasing order of difficulty. First, a separation between cells of two distinct domains (Escherichia coli and Saccharomyces cerevisiae) was demonstrated. The second separation was for cells from the same domain but different species (Bacillus subtilis and Bacillus cereus). The last separation included cells from two closely related microbial strains of the same domain and the same species (two distinct S. cerevisiae strains). For each separation, a novel computational model, employing a continuous spatial and temporal function for predicting the particle velocity, was used to predict the retention time (tR,p) of each cell type, which aided the experimentation. All three cases resulted in separation resolution values Rs>1.5, indicating complete separation between the two cell species, with good reproducibility between the experimental repetitions (deviations < 6%) and good agreement (deviations < 18%) between the predicted tR,p and experimental (tR,e) retention time values. This study demonstrated the potential of DC-biased AC iEK systems for performing challenging microbial separations.
Study on Wind-Induced Dynamic Response and Statistical Parameters of Skeleton Supported Saddle Membrane Structure in Arching and Vertical Direction
Authors: Chen Ziye , Liu Changjiang , Li Dong , Liu Jian , Deng Xiaowei , Luo Chiyu , Zhou Guangen
Published: May 2024

Wind tunnel tests and numerical simulations are the mainstream methods to study the wind-induced vibration of structures. However, few articles use statistical parameters to point out the differences and errors of these two research methods in exploring the wind-induced response of membrane structures. The displacement vibration of a saddle membrane structure under the action of wind load is studied by wind tunnel tests and numerical simulation, and statistical parameters (mean, range, skewness, and kurtosis) are introduced to analyze and compare the displacement data. The most unfavorable wind direction angle is 0° (arching direction). The error between experiment and simulation is less than 10%. The probability density curve has a good coincidence degree. Both the test and simulation show a certain skewed distribution, indicating that the wind-induced vibration of the membrane does not obey the Gaussian distribution. The displacement response obtained by the test has good stability, while the simulated displacement response has strong discreteness. The difference between the two research methods is quantitatively given by introducing statistical parameters, which is helpful to improve the shortcomings of wind tunnel tests and numerical simulations.
Study on Bonding Behavior between High Toughness Resin Concrete with Steel Wire Mesh and Concrete
Authors: Yu Qu , Ren Yu , Liu Anhang , Yang Yongqing
Published: May 2024

This paper investigates the interfacial bonding behavior between high toughness resin concrete with steel wire mesh (HTRCS) and concrete. A total of five sets of fifteen double shear specimens were tested for parameters including concrete strength and material properties of HTRCS composites. The test results showed that the failure mode of DS1 specimens was partial debonding and fracture, and the rest of the specimens were the fracture of HTRCS. The concrete strength and reinforcement ratios of HTRCS composites were positively correlated with interfacial adhesion properties. When the concrete strength was increased from C30 to C40 and C50, the ultimate load increased by 43.4% and 43.2%, respectively. The ultimate load capacity increased by 32.1%, with the reinforcement ratio of HTRCS composites increasing from 1.05% to 1.83%. Moreover, the bonding slip model and the bearing capacity formula for the interface between HTRCS composites and concrete were proposed, and the calculation values were in good agreement with the test values, with an average value of 0.978.
Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus
Authors: Karimi Hirou , Adibhesami Mohammad Anvar , Hoseinzadeh Siamak , Salehi Ali , Groppi Daniele , Astiaso Garcia Davide
Published: May 2024

This study introduces a novel framework that leverages artificial intelligence (AI), specifically deep learning and reinforcement learning, to enhance energy efficiency in architectural design. The goal is to identify architectural arrangements that maximize energy efficiency. The complexity of these models is acknowledged, and an in-depth analysis of model selection, their inherent complexity, and the hyperparameters that govern their operation is conducted. This study validates the scalability of these models by comparing them with traditional optimization techniques like genetic algorithms and simulated annealing. The proposed system exhibits superior scalability, adaptability, and computational efficiency. This research study also explores the ethical and societal implications of integrating AI with architectural design, including potential impacts on human creativity, public welfare, and personal privacy. This study acknowledges it is in its preliminary stage and identifies its potential limitations, setting the stage for future research to enhance and expand the effectiveness of the proposed methodology. The findings indicate that the model can steer the architectural field towards sustainability, with a demonstrated reduction in energy usage of up to 20%. This study also conducts a thorough analysis of the ethical implications of AI in architecture, emphasizing the balance between technological advancement and human creativity. In summary, this research study presents a groundbreaking approach to energy-efficient architectural design using AI, with promising results and wide-ranging applicability. It also thoughtfully addresses the ethical considerations and potential societal impacts of this technological integration.
Research on Multi-Stage Post-Occupancy Evaluation Framework of Community Comprehensive Elderly Care Service Facilities under the Public-Private Partnership Mode—A Case Study of China
Authors: Lin Lijun , Zhang Lin , Geng Shuai , Zhao Yulin , Tian Yuanyuan
Published: May 2024

The key to whether elderly individuals in the community can enjoy their later years peacefully lies in the service capabilities of community comprehensive elderly care service facilities (CCECSF) under the Public-Private Partnership (PPP) mode. To maintain a high level of service capability in community comprehensive elderly care service facilities under PPP mode, scientific evaluation of the effectiveness of these facilities is equally crucial. This article first constructs a post-occupancy evaluation index system of community comprehensive elderly care service facilities under PPP mode that includes three evaluation attributes and fifteen evaluation criteria based on the Chinese culture and lifestyle habits. Regarding the issue of direct users being unable to directly participate in evaluations, the uncertainty in evaluation information, and the volatility of evaluation results, a multi-stage post-occupancy evaluation model is constructed based on probabilistic linguistic term set, TOPSIS model and multi-stage decision theory. The above post-occupancy evaluation index system and evaluation model together constitute a multi-stage post-occupancy evaluation framework for community comprehensive elderly care service facilities under PPP mode. The outcomes of the case study indicate that the post-occupancy evaluation index system can offer a scientifically guided approach for evaluating the service level of community comprehensive elderly care service facilities under the PPP mode; meanwhile, the multi-stage evaluation model can enable direct user participation in the post-evaluation of facility usage and improve the robustness and reduce the fluctuation of the evaluation results, so as to improve the scientificity of the evaluation results.
Machine Learning Prediction and Evaluation for Structural Damage Comfort of Suspension Footbridge
Authors: Zhao Shaojie , Tang Xing , Du Yongjun
Published: May 2024

To investigate the impact of structural damages on the comfort level of suspension footbridges under human-induced vibrations, this study addresses the limitations of traditional manual testing, which often entails significant manpower and material resources. The aim is to achieve rapid estimation and health monitoring of comfort levels during bridge operation. To accomplish this, the study combines finite-element simulation results to establish a data-driven library and introduces three distinct machine learning algorithms. Through comparative analysis, a machine learning-based method is proposed for quick evaluation of bridge comfort levels. Focusing on the Yangjiadong Suspension Bridge, the study evaluates and researches the comfort level of the structure under the influence of human-induced vibrations. The findings revealed a relatively low base frequency and high flexibility. Additionally, when considering the mass of individuals, peak acceleration decreased. The predictive performance of the Artificial Neural Network (ANN) model was found to be superior when accounting for multi-parameter damages, yielding root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2) values of 0.03, 0.02, and 0.98, respectively. Moreover, the error ratio of the generalization performance analysis was below 5%. Furthermore, the study identified a damage coefficient of 0.13 for the bridge’s main cable, hanger, and steel longitudinal beam. Under a crowd density of 0.5 people per square meter, the predicted peak acceleration was 1.098 m/s2, with a model error of less than 10% compared to the observed value of 1.004 m/s2. These results underscore the model’s effectiveness in swiftly evaluating bridge comfort levels, thereby offering valuable insights for the health monitoring of bridge comfort levels.
Analysis of Microwave-Induced Damage in Granite Aggregates Influenced by Mineral Texture
Authors: Yuan Yuan , Zhao Shuang
Published: May 2024

The use of microwave energy to recycle high-quality coarse aggregates from waste concrete or assist hard rock breakage in underground building engineering is promising. Controlling or promoting the damage of coarse aggregates, i.e., hard rocks, under microwave irradiation is a crucial issue faced by these techniques. Understanding the damage mechanisms of hard rocks exposed to microwaves is thus urgent. Fracture toughness is a significant mechanical parameter of rocks that reflects their ability to resist crack propagation and damage evolution. In this study, the fracture toughness degradation of microwave-heated granite was investigated by combining experimental investigations and numerical simulations. A three-point-bending (TPB) experiment was conducted on granite specimens after microwave irradiation. A coupled electromagnetic–thermal–mechanical model considering the actual mineral texture of the granite specimen was established. The evolution of the temperature gradient and stress field near the initial notch tip were investigated. The results suggest that the microwave-induced maximum temperature gradient and stress in granite are at the plagioclase–quartz (Pl–Qtz) interfaces or inside the Pl near the boundary. The region of cracking initiation was defined as the damage zone, which could be obtained by comparing the microwave-induced thermal stress with the critical value. The fracture toughness degradation, which corresponds to the evolution of the damage zones, can be divided into two stages. A relatively rapid decrease in fracture toughness in the first stage is primarily caused by the spread of the scattered damage zones along the Pl–Qtz interfaces; subsequently, a gentler fracture toughness degradation results mainly from the extension of the previous damage zones.
Code Requirements for the Seismic Design of Irregular Elevation RC Structures
Authors: Santos Davi , Melo José , Varum Humberto
Published: May 2024

The recent seismic activity highlights the crucial need to enhance seismic design and safety assessment methods, particularly for irregular structures, in both new and existing constructions. The present study focuses on structural irregularities in elevation for buildings, as the design of structural systems involves multiple variables that often result in irregularities in many buildings. This work aims to perform a comparative assessment of the criteria adopted for the evaluation of the structural irregularities in elevation present in European and international seismic codes. This paper is structured as follows: Firtsly, it discusses structural irregularities and more specifically the most common types of structural damage due to seismic events. Then, it shows the documented experiences of structural damages in seismic events associated with structural irregularities in China, Italy, Spain, Nepal and Mexico. Additionaly, it discusses the requirements of the standards on irregularities and their limitation in that matter. At the end of this section, the different approaches of each code in irregularities in elevation are compared. All assessed seismic codes addresses the structural irregularity issue, attributing the desired characteristics of a seismic-resistant structure. However, there are considerable development differences between norms, demonstrated on ambiguity of few codes on criteria of vertical irregularies.
Effect of Micro-Cracks on Chloride Ion Diffusion in Concrete Based on Stochastic Aggregate Approach
Authors: Yang Qianfan , Wu Yuching , Zhi Peng , Zhu Peng
Published: May 2024

For concrete structures in offshore areas, chloride ion erosion is one of the main factors affecting durability. It is crucial to evaluate the chloride ion permeability resistance of concrete structures. In this paper, a finite element simulation of the chloride ion diffusion process in concrete is conducted. A mass diffusion finite element model based on a random aggregate approach is established to investigate the influences of an aggregate, the interface transition zone, and micro-cracks on the chloride ion diffusion coefficients in concrete. The results show that the mass diffusion finite element analysis based on the exponential function model and the power function model can effectively simulate the chloride ion diffusion process in concrete. In addition, the data reveal that volume fraction and distribution aggregates considerably affect chloride ion diffusivity in concrete. Also, the interface transition zone significantly accelerates chloride ion diffusion in concrete. Moreover, this acceleration effect exceeds the barrier effect of an aggregate.
Shaking Table Tests and Numerical Analysis Conducted on an Aluminum Alloy Single-Layer Spherical Reticulated Shell with Fully Welded Connections
Authors: Lu Jiawei , Ning Qiujun , Lu Xiaosong , Yang Fan , Wang Yuanshun
Published: May 2024

Aluminum alloy offers the advantages of being lightweight, high in strength, corrosion-resistant, and easy to process. It has a promising application prospect in large-span space structures, with its primary application form being single-layer reticulated shells. In this study, shaking table tests were conducted on a 1/25 scale aluminum alloy single-layer spherical reticulated shell structure. A finite element (FE) model of the reticulated shell structure was established in Ansys. Compared with the experimental results, the deviation in natural frequency, acceleration amplitude, and displacement amplitude was less than 20%, confirming the validity of the model. An extensive analysis of the various rise–span ratios and connection constraints of a single-layer spherical reticulated shell structure was carried out using the proposed FE model. The experimental and simulation results showed that as the rise–span ratio of the aluminum alloy reticulated shell increases, the natural frequency of the reticulated shell structure also increases while the dynamic performance decreases. The connection of the circumferential members changes from a rigid connection to a hinged connection. The natural frequency of the reticulated shell structure is reduced by about 40% while the acceleration and displacement response values are decreased by approximately 15%.