bluuebunny/crossref_metadata_2025 huggingface dataset:
Chronic Conditions and Multimorbidity Among Middle-Aged and Elderly Peri-Urban Dwellers in Dar es Salaam, Tanzania
Authors: Kohler Stefan , Bärnighausen Till , Kazonda Patrick , Leyna Germana H. , Lohmann Julia , Killewo Japhet , Rohr Julia K. , Stieglitz Laura-Marie , Paul Nicolas
Published: June 2024

Objectives

Chronic conditions and multimorbidity affect care needs and prevention opportunities.

Methods

We studied 2,246 men and women aged ≥40 years within the Dar es Salaam Urban Cohort Study from June 2017 to July 2018. Seventeen chronic conditions were assessed based on self-report, body and blood pressure measurement, blood tests, and screening instruments.

Results

Hypertension (51.3%), anemia (34.1%), obesity (32.2%), diabetes (31.6%), depressive symptoms (31.5%), low grip strength (21.2%), and ischemic heart disease (11.9%) were widespread. Multimorbidity was common (73.7%). Women had higher odds of obesity, ischemic heart disease, and high cholesterol (adjusted OR: 2.08–4.16) and lower odds of underweight, low grip strength, alcohol problems, and smoking (adjusted OR: 0.04–0.45). Ten years of age were associated with higher odds of low grip strength, cognitive problems, hypertension, kidney disease, chronic cough, diabetes, high cholesterol, ischemic heart disease, and multimorbidity (adjusted OR: 1.21–1.81) and lower odds of HIV infection (adjusted OR: 0.51).

Conclusion

We found a higher prevalence of multimorbidity than previously estimated for middle-aged and elderly people in sub-Saharan Africa. The chronic conditions underlying multimorbidity differed by sex.
Development of the Anthropomorphic Arm for Collaborative and Home Service Robot CHARMIE
Authors: Syed Fawad A. , Lopes Gil , Ribeiro A. Fernando
Published: June 2024

Service robots are rapidly transitioning from concept to reality, making significant strides in development. Similarly, the field of prosthetics is evolving at an impressive pace, with both areas now being highly relevant in the industry. Advancements in these fields are continually pushing the boundaries of what is possible, leading to the increasing creation of individual arm and hand prosthetics, either as standalone units or combined packages. This trend is driven by the rise of advanced collaborative robots that seamlessly integrate with human counterparts in real-world applications. This paper presents an open-source, 3D-printed robotic arm that has been assembled and programmed using two distinct approaches. The first approach involves controlling the hand via teleoperation, utilizing a camera and machine learning-based hand pose estimation. This method details the programming techniques and processes required to capture data from the camera and convert it into hardware signals. The second approach employs kinematic control using the Denavit-Hartenbergmethod to define motion and determine the position of the end effector in 3D space. Additionally, this work discusses the assembly and modifications made to the arm and hand to create a cost-effective and practical solution. Typically, implementing teleoperation requires numerous sensors and cameras to ensure smooth and successful operation. This paper explores methods enabled by artificial intelligence (AI) that reduce the need for extensive sensor arrays and equipment. It investigates how AI-generated data can be translated into tangible hardware applications across various fields. The advancements in computer vision, combined with AI capable of accurately predicting poses, have the potential to revolutionize the way we control and interact with the world around us.
Stable Rapid Sagittal Walking Control for Bipedal Robot Using Passive Tendon
Authors: Gao Haibo , Wang Shengjun , Shan Kaizheng , Mu Changxi , Wang Xin , Su Bo , Yu Haitao
Published: June 2024

This paper presents the development, control, and experimental validation of a novel bipedal robot with a passive tendon. The robot, featuring foldable legs, coaxial actuation, and compact folded size, is endowed with a leg configuration with a five-bar mechanism. Based on biological observations of human walking, a passive artificial tendon made of emulsion is fabricated to work in conjunction with a tensioning device, providing adaptive heel touchdown and toe push-off in sync with single-leg movement. The tailored control framework for the bipedal robot is further established with the double-layer architecture. The regulation layer employs the linear inverted pendulum (LIP) model to generate reference trajectory of the center of mass (CoM) with a dead-beat style of parameter adjustment. An inverse-dynamics-based whole-body controller (WBC) is applied to enforce the full-order dynamics of the bipedal robot to reproduce the LIP model’s behavior. We carry out the experiments on the physical prototype to evaluate the walking performance of the developed bipedal robot. The results show that the robot achieves stable walking at the speed of 0.8 m/s (almost twice the leg length/s) and exhibits robustness to external push disturbance.
Research on Predictive Control Method of Pigsty Environment Based on Fuzzy Control
Authors: Zhu Fengwu , Zhang Yuqing , Zhang Weijian , Gao Tianshi , Wang Suyu , Zhou Lina
Published: June 2024

At present, most of the environmental control systems of pigsties use direct control methods; when factors, such as temperature and humidity, exceed the set threshold value, the corresponding actuator is turned on for regulation. However, such methods have problems such as low control accuracy, high energy consumption, and untimeliness. In order to save on energy consumption and improve control accuracy, this paper takes the predicted value, set value, and current detection value of the internal environment of a pigsty as input, and combines fuzzy control and direct control methods to realize the predictive control of the pigsty environment. The test results show that, compared with the direct control method, the fuzzy predictive control method can make fluctuations in the internal temperature and humidity of the pigsty less close to the set threshold value, while the ammonia concentration hardly exceeds the set threshold value. The results show that predictive control can more accurately control the internal environment of the pigsty and reduce energy costs by about 20%. Therefore, this method can provide scientific and effective environmental control methods for agricultural production processes, such as livestock breeding and greenhouse cultivation, in semi-confined spaces.
Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review
Authors: Radočaj Dorijan , Gašparović Mateo , Jurišić Mladen
Published: June 2024

This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations’ Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping.
Preparation of Nanoscale Indoxacarb by Using Star Polymer for Efficiency Pest Management
Authors: Chen Min , Zhang Jie , Wang Hongtao , Li Lingyun , Yin Meizhen , Shen Jie , Yan Shuo , Liu Baoyou
Published: June 2024

The utilization efficiency of conventional pesticides is relatively low in agricultural production, resulting in excessive application and environmental pollution. The efficient utilization of pesticides is crucial for promoting sustainable agriculture, and the development of nanopesticides presents a promising solution to the challenges associated with traditional pesticides. In order to explore an efficient application method for indendicarb (IDC), a star polymer nanocarrier (SPc) was employed to design and construct an efficient nanodelivery system for IDC. In this study, the morphology and physicochemical properties of the complex were determined, and its bioactivity and control efficacy were assessed using leaf-dipping and field spraying methods. The results show that IDC could be spontaneously incorporated into the hydrophobic core of SPc via hydrophobic association. This assembly disrupted the self-aggregated structure of IDC and significantly reduced its particle size to nanoscale. Furthermore, IDC emulsifiable concentrate (IDC EC) demonstrated improved adhesion to plant leaves with the aid of SPc, increasing retention from 8.083 to 10.418 mg/cm2. The LC50 (1d) of IDC EC against Plutella xylostella (Linnaeus) and Pieris rapae (Linnaeus) decreased by 6.784 and 1.931 times, respectively, with the addition of SPc. The inclusion of SPc increased the control effect of IDC EC by up to 8.28% (7d, 3000×) for P. xylostella and 12.53% (3d, 8000×) for P. rapae. This reveals that the IDC EC + SPc formulation exhibits superior insecticidal activity against these two highly destructive insect pests. This study successfully developed a novel nanodelivery system for the efficient application of IDC, which has the potential to reduce over-application and promote sustainable agricultural practices.
Silvopastures: Benefits, Past Efforts, Challenges, and Future Prospects in the United States
Authors: Poudel Sanjok , Pent Gabriel , Fike John
Published: June 2024

The global human population is projected to reach 9.7 billion by 2050, increasing the demand for food and fiber, but also raising concerns about the environmental impact of agricultural production scaled to meet their needs. Silvopastures—integrated tree–forage–livestock systems—have emerged as a viable practice to meet the required productivity and environmental stewardship outcomes. This review consolidates the extensive research on silvopasture practices in the United States and highlights the benefits of these systems. A comprehensive literature search across databases such as ScienceDirect and Google Scholar revealed 152 publications on silvopastures in the United States since 2000, indicating growing interest. These studies have primarily focused on the impacts of silvopastures on livestock welfare and productivity, forage production and composition, soil health and nutrient dynamics, and socio-economic factors. Geographical distribution analysis indicated that the research is more focused in the Southeastern United States, with Florida, Virginia, Alabama, Missouri, and Arkansas being the top five contributing states. The review also offers insights into the tree and forage species used across these states and discusses the challenges to silvopasture adoption among producers and land managers while exploring future prospects. This review may be used as a resource for understanding the multifaceted dimensions of silvopasture adoption, providing insights for researchers, policymakers, and practitioners alike.
Selenium Nanoparticles Boost the Drought Stress Response of Soybean by Enhancing Pigment Accumulation, Oxidative Stress Management and Ultrastructural Integrity
Authors: Zeeshan Muhammad , Wang Xin , Salam Abdul , Wu Hao , Li Shengnan , Zhu Shiqi , Chang Jinzhe , Chen Xiaoyuan , Zhang Zhixiang , Zhang Peiwen
Published: June 2024

Drought is a persistent and devastating obstacle to crop production, affecting both humanity and livestock. The application of selenium (Se) effectively mitigates various types of abiotic stresses and enhances crop yield under unfavorable conditions. However, our understanding of how nano-Se (nSe) alleviates drought stress (DS) in soybeans is still limited. To address this gap, our study focused on assessing the effectiveness of foliar nSe application during the reproductive stage of soybeans. Three concentrations of nSe were applied to plants grown in pots filled with clay loam soil, simulating DS conditions. Our findings reveal that nSe spraying significantly promoted the accumulation of above-ground dry biomass and enhanced relative water content (RWC) and photosynthetic pigment over alone-DS treatment. Furthermore, nSe application boosted the activity and contents of protective enzymes and osmolytes, resulting in a dose-dependent reduction in electrolyte leakage (EL), reactive oxygen species (ROS) accumulation, and malondialdehyde (MDA) content. Additionally, nSe improved stomatal characteristics and mesophyll cell ultrastructure, further mitigating the adverse effects of drought stress. These findings suggest the potential of nSe as an effective strategy to enhance soybean tolerance and potentially improve crop yields under drought conditions.
Influence of Replacing Soybean Meal with Nigella sativa Seed Meal on Feed Intake, Digestibility, Growth Performance, Blood Metabolites, and Antioxidant Activity of Growing Lambs
Authors: Hassan Ola G. A. , Hassaan Noha A. , Kholif Ahmed E. , Chahine Mireille , Mousa Gamal A.
Published: June 2024

The present experiment aimed to evaluate the partial or complete substitution of soybean meal (SBM) with Nigella sativa seed meal (NSM) on chemical composition, in vitro ruminal fermentation, and the growth performance and economic efficiency of growing lambs. Thirty-two male Ossimi lambs weighing 41 ± 0.4 kg at 195 ± 5 d were divided randomly into four experimental groups of eight lambs each. Lambs were fed four diets containing 40% berseem clover and 60% concentrate feed mixture. Soybean meal was replaced with NSM at 0% (NSM0; control), 50% (NSM50), 75% (NSM75), or 100% (NSM100). The experiment lasted for 105 d, consisting of 15 d for adaptation and 90 days for measurements. Higher concentrations of crude protein (CP) and nonstructural carbohydrates were observed with SBM; however, NSM contained more fibers and gross energy. Moreover, SBM contained higher concentrations of individual amino acids and lower concentrations of polyphenols. The replacement did not affect in vitro gas production and decreased (p < 0.05) methane production and CP degradability. Treatments did not affect feed intake, nutrient digestibility, and diet’s nutritive value measured as starch value, total digestible nutrient, digestible energy, and apparent digestible crude protein. The NSM50 and NSM75 treatments increased (p < 0.001) total weight gain and daily gain compared to the control treatment, with lower feed conversion values associated with the NSM75 treatment. Treatments decreased cholesterol (p = 0.028) and high-density lipoprotein (p = 0.029) and increased antioxidant activity. Higher economic efficiencies were observed with the NSM75 followed by NSM50 and then NSM100 treatments. It is concluded that replacing 75% of SBM with NSM enhanced feed conversion and economic efficiency.
Assessment of Body Condition in a Threatened Dolphin Population in an Anthropized Area in Southeastern Brazil
Authors: Silva Deyverson , Maricato Guilherme , Cezimbra Tomaz , Melo Larissa , Maciel Israel S. , Tardin Rodrigo
Published: June 2024

Sepetiba Bay, located in southwestern Rio de Janeiro state, in southeastern Brazil, is a region of extreme anthropogenic impact, and is home to a large population of Guiana dolphins, which face increasing and cumulative pressures on their physical health. Cetacean body condition provides a useful indicator for the evaluation of the conservation status of marine mammals. Given this, the present study quantified the proportion of dolphins with different body condition scores and assessed temporal variation in these scores between 2017 and 2022 through the analysis of photographic records. We analyzed the photographs and identified the individuals using FinFindR and classified each individual based on its apparent body condition. A total of 29,737 photographs were taken during the study, and 79 individuals were identified, of which 68.35% were in poor condition. The evidence suggests that the Guiana dolphins are in relatively poor condition overall, possibly reflecting the cumulative impact of human activities in Sepetiba Bay.
The Molecular Mechanisms and Therapeutic Potential of Cranberry, D-Mannose, and Flavonoids against Infectious Diseases: The Example of Urinary Tract Infections
Authors: Ioannou Petros , Baliou Stella
Published: June 2024

The treatment of infectious diseases typically includes the administration of anti-infectives; however, the increasing rates of antimicrobial resistance (AMR) have led to attempts to develop other modalities, such as antimicrobial peptides, nanotechnology, bacteriophages, and natural products. Natural products offer a viable alternative due to their potential affordability, ease of access, and diverse biological activities. Flavonoids, a class of natural polyphenols, demonstrate broad anti-infective properties against viruses, bacteria, fungi, and parasites. Their mechanisms of action include disruption of microbial membranes, inhibition of nucleic acid synthesis, and interference with bacterial enzymes. This review explores the potential of natural compounds, such as flavonoids, as an alternative therapeutic approach to combat infectious diseases. Moreover, it discusses some commonly used natural products, such as cranberry and D-mannose, to manage urinary tract infections (UTIs). Cranberry products and D-mannose both, yet differently, inhibit the adhesion of uropathogenic bacteria to the urothelium, thus reducing the likelihood of UTI occurrence. Some studies, with methodological limitations and small patient samples, provide some encouraging results suggesting the use of these substances in the prevention of recurrent UTIs. While further research is needed to determine optimal dosages, bioavailability, and potential side effects, natural compounds hold promise as a complementary or alternative therapeutic strategy in the fight against infectious diseases.
Staphylococcus aureus Is the Predominant Pathogen in Hospitalised Patients with Diabetes-Related Foot Infections: An Australian Perspective
Authors: Morton Kate E. , Coghill Sarah H.
Published: June 2024

Diabetes prevalence continues to increase worldwide, which has led to a rising incidence of diabetes-related foot infections (DFIs). There is significant local variation in the microbiology of DFIs, and Pseudomonas spp. is suggested to be more prevalent in subtropical climates. The aim of this study was to investigate the local microbiological findings in patients admitted to the hospital with DFIs. This retrospective study analysed data from all adult patients diagnosed with diabetes and admitted to the hospital for the treatment of a DFI between 1 January 2021 and 31 December 2022. Both superficial wound swabs and tissue cultures were included. The Infectious Diseases Society of America classification system was used to categorise the severity of the DFI. Patient characteristics and demographics were analysed using descriptive statistics. One hundred fifty-one episodes of care were included. Most of the DFIs were classified as moderate infections 101/151 (67%). The most commonly isolated microorganism was Staphylococcus aureus (33%) followed by normal skin flora (11%) and β-haemolytic streptococci (7%). P. aeruginosa was isolated more commonly in those with chronic DFIs (10%) compared to those with acute DFIs (2%). Despite the frequent identification of S. aureus, 83% of patients received an antipseudomonal antibiotic. The introduction of multidisciplinary DFI rounds should be considered.
Multiple Intrusion Detection Using Shapley Additive Explanations and a Heterogeneous Ensemble Model in an Unmanned Aerial Vehicle’s Controller Area Network
Authors: Hong Young-Woo , Yoo Dong-Young
Published: June 2024

Recently, methods to detect DoS and spoofing attacks on In-Vehicle Networks via the CAN protocol have been studied using deep learning models, such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. However, these studies have typically addressed studies on single-model intrusion detection verification in drone networks. This study developed an ensemble model that can detect multiple types of intrusion simultaneously. In preprocessing, the patterns within the payload using the measure of Feature Importance are distinguished from the attack and normal data. As a result, this improved the accuracy of the ensemble model. Through the experiment, both the accuracy score and the F1-score were verified for practical utility through 97% detection performance measurement.
Study on Temporal and Spatial Distribution of Landslides in the Upper Reaches of the Yellow River
Authors: Li Zongren , Wei Sailajia , Wu Kai , Sha Yonglian , Zhang Xing , Li Delin , Xin Rongfang , Guan Peng
Published: June 2024

The geological structure of the upper reaches of the Yellow River is complex, especially in the Sigouxia-Laganxia section. It has always been a high-incidence area of landslide disasters, which poses a threat to the safe operation of the upper reaches of the Yellow River. In this study, based on the high-precision remote sensing image data, the spatial distribution of each landslide was obtained by superimposing the remote sensing image and the 1:50,000 digital elevation model (DEM). Some typical landslides were selected for detailed field investigation and field verification. The results show that the remote sensing image characteristics of landslides in the upper reaches of the Yellow River are obvious. Through remote sensing interpretation and field investigation, a total of 508 landslides of various types were found, including 24 giant landslides. The spatial spreading patterns of landslides mainly include six types: dumb-bell shape, rectangle, saddle type, long arc shape, triangle, and side-by-side shape. The length and width of the landslide deposit are mainly concentrated at 550–1500 m and 600–1500 m, and the average elevation of the sliding body is mainly concentrated between 2000 and 2800 m. The average slope of the landslide is mainly distributed between 15–20°. Giant landslides are mainly distributed in the Jianzha basin area, surrounded by the Jishishan fault and the Lajishan fault in the West Qinling Mountains. The spatial distribution characteristics of giant landslides have obvious regional differences due to different factors such as lithologic differences and riverside erosion. The research results are of great significance for the early identification, prevention, and mitigation of landslide disasters in the upper reaches of the Yellow River.
Field Compaction Characteristics of Ultra-Thin Porous Friction Course Based on Laboratory Simulation
Authors: Du Xiaobo , Lin Hongwei , Sun Mutian , Liu Wenchang , Zhang Hongchao
Published: June 2024

As a preventive maintenance treatment, the ultra-thin porous friction course (UPFC) has been widely recognized and used in road maintenance because of its excellent performance and cost effectiveness. The Marshall compaction method (MCM) has been adopted to design UPFC mixtures worldwide, particularly in China. However, there are few studies concerning the field compaction properties of MCM-designed UPFCs. The laboratory test results of this study from simulating on-site compaction showed that all UPFC specimens with thicknesses of less than 20 mm barely achieved the target compaction thickness, and all UPFC specimens with different thicknesses failed to meet the air void (AV) requirements of UPFC mixes designed using the MCM. According to the results of a virtual compaction test, and using the discrete element method, the strong force chains were strengthened as the UPFC thickness decreased inside the specimen, making it difficult to evenly diffuse and transfer inside the specimen and resulting in insufficient compaction of the UPFC. Furthermore, it was demonstrated that the MCM-designed UPFC specimens showed significant differences in the AV distributions along the vertical and lateral directions from those of the UPFC specimens that simulated field compaction. The UPFCs designed using the MCM had a poor correlation with field compaction.
Multi-Step Physics-Informed Deep Operator Neural Network for Directly Solving Partial Differential Equations
Authors: Wang Jing , Li Yubo , Wu Anping , Chen Zheng , Huang Jun , Wang Qingfeng , Liu Feng
Published: June 2024

This paper establishes a method for solving partial differential equations using a multi-step physics-informed deep operator neural network. The network is trained by embedding physics-informed constraints. Different from traditional neural networks for solving partial differential equations, the proposed method uses a deep neural operator network to indirectly construct the mapping relationship between the variable functions and solution functions. This approach makes full use of the hidden information between the variable functions and independent variables. The process whereby the model captures incredibly complex and highly nonlinear relationships is simplified, thereby making network learning easier and enhancing the extraction of information about the independent variables in partial differential systems. In terms of solving partial differential equations, we verify that the multi-step physics-informed deep operator neural network markedly improves the solution accuracy compared with a traditional physics-informed deep neural operator network, especially when the problem involves complex physical phenomena with large gradient changes.
Assessing Ground Motion Intensity Measures and Structural Damage Measures in Underground Structures: A Finite Element Analysis of the Daikai Subway Station
Authors: Liu Tong , Han Yulong , Wang Qinghe , Yuan Yong
Published: June 2024

Research on the characterization of ground motion intensity and damage of underground structures is limited, while reasonable selection of ground motion intensity measures and structural damage measures is a crucial prerequisite for structural seismic performance evaluation. In this study, a two-dimensional finite element model of soil and structures was established based on the Daikai subway station in Japan. Through incremental dynamic analysis, 32 ground motion intensity measures and seven structural damage measures were comprehensively evaluated from seven properties, including efficiency, practicality, proficiency, scaling robustness, relativity, hazard computability, and sufficiency. According to the analysis results, the purpose and significance of each property during measure optimization were hierarchically sorted out. The results show that peak ground acceleration, acceleration spectrum intensity, and sustained maximum acceleration are recommended as ground motion intensity measures, while maximum inter-story drift ratio, column end displacement angle, and two-parameter measures are recommended as the structural damage measures for seismic performance evaluation of the shallow-buried subway station. Furthermore, measure optimization approaches are proposed as follows: the basic selection of IMs should satisfy scaling robustness, hazard computability, and sufficiency to site condition; the optimal selection of IMs is suggested to be evaluated mainly through efficiency, practicality and proficiency, and verified through relativity and relative sufficiency between IMs. The optimal selection of DM is suggested to be evaluated through four properties, including efficiency, practicality, proficiency, and relativity.
The Two-Parameter Bifurcation and Evolution of Hunting Motion for a Bogie System
Authors: Wang Shijun , Ma Lin , Zhang Lingyun
Published: June 2024

The complex service environment of railway vehicles leads to changes in the wheel–rail adhesion coefficient, and the decrease in critical speed may lead to hunting instability. This paper aims to reveal the diversity of periodic hunting motion patterns and the internal correlation relationship with wheel–rail impact velocities after the hunting instability of a bogie system. A nonlinear, non-smooth lateral dynamic model of a bogie system with 7 degrees of freedom is constructed. The wheel–rail contact relations and the piecewise smooth flange forces are the main nonlinear, non-smooth factors in the system. Based on Poincaré mapping and the two-parameter co-simulation theory, hunting motion modes and existence regions are obtained in the parameter plane consisting of running speed v and the wheel–rail adhesion coefficient μ. Three-dimensional cloud maps of the maximum lateral wheel–rail impact velocity are obtained, and the correlation with the hunting motion pattern is analyzed. The coexistence of periodic hunting motions is further revealed based on combined bifurcation diagrams and multi-initial value phase diagrams. The results show that grazing bifurcation causes the number of wheel–rail impacts to increase at a low-speed range. Periodic hunting motion with period number n = 1 has smaller lateral wheel–rail impact velocities, whereas chaotic motion induces more severe wheel–rail impacts. Subharmonic periodic hunting motion windows within the speed range of chaotic motion, pitchfork bifurcation, and jump bifurcation are the primary forms that induce the coexistence of periodic motion.
Virtual Sensor for Estimating the Strain-Hardening Rate of Austenitic Stainless Steels Using a Machine Learning Approach
Authors: Contreras-Fortes Julia , Rodríguez-García M. Inmaculada , Sales David L. , Sánchez-Miranda Rocío , Almagro Juan F. , Turias Ignacio
Published: June 2024

This study introduces a Multiple Linear Regression (MLR) model that functions as a virtual sensor for estimating the strain-hardening rate of austenitic stainless steels, represented by the Hardening Rate of Hot rolled and annealed Stainless steel sheet (HRHS) parameter. The model correlates tensile strength (Rm) with cold thickness reduction and chemical composition, evidencing a robust linear relationship with an R-coefficient above 0.9800 for most samples. Key variables influencing the HRHS value include Cr, Mo, Si, Ni, and Nb, with the MLR model achieving a correlation coefficient of 0.9983. The Leave-One-Out Cross-Validation confirms the model’s generalization for test examples, consistently yielding high R-values and low mean squared errors. Additionally, a simplified HRHS version is proposed for instances where complete chemical analyses are not feasible, offering a practical alternative with minimal error increase. The research demonstrates the potential of linear regression as a virtual sensor linking cold strain hardening to chemical composition, providing a cost-effective tool for assessing strain hardening behaviour across various austenitic grades. The HRHS parameter significantly aids in the understanding and optimization of steel behaviour during cold forming, offering valuable insights for the design of new steel grades and processing conditions.
3D-Printed SMC Core Alternators: Enhancing the Efficiency of Vortex-Induced Vibration (VIV) Bladeless Wind Turbines
Authors: González-González Enrique , Yáñez David J. , Hidalgo Arturo , Del Pozo Susana , Lagüela Susana
Published: June 2024

This study investigates the application of soft magnetic composite (SMC) materials in alternator core manufacturing for bladeless wind turbines operating under the principle of vortex-induced vibration (VIV), employing additive manufacturing (AM) technologies. Through a comparative analysis of alternator prototypes featuring air, SMC, and iron cores, the investigation aims to evaluate the performance of SMC materials as an alternative to the most commonly used material (iron) in VIV BWT, by assessing damping, resonance frequency, magnetic hysteresis, and energy generation. Results indicate that while alternators with iron cores exhibit superior energy generation (peaking at 3830 mV and an RMS voltage of 1019 mV), those with SMC cores offer a promising compromise with a peak voltage of 1150 mV and RMS voltage of 316 mV, mitigating eddy current losses attributed to magnetic hysteresis. Notably, SMC cores achieve a damping rate of 60%, compared to 67% for air cores and 59% for iron cores, showcasing their potential to enhance the efficiency and sustainability of bladeless wind turbines (BWTs). Furthermore, the adaptability of AM in optimizing designs and accommodating intricate shapes presents significant advantages for future advancements. This study underscores the pivotal role of innovative materials and manufacturing processes in driving progress towards more efficient and sustainable renewable energy solutions.
An Advanced Framework for Predictive Maintenance Decisions: Integrating the Proportional Hazards Model and Machine Learning Techniques under CBM Multi-Covariate Scenarios
Authors: Godoy David R. , Mavrakis Constantino , Mena Rodrigo , Kristjanpoller Fredy , Viveros Pablo
Published: June 2024

Under Condition-Based Maintenance, the Proportional Hazards Model (PHM) uses Cox’s partial regression and vital signs as covariates to estimate risk for predictive management. However, maintenance faces challenges when dealing with a multi-covariate scenario due to the impact of the conditions’ heterogeneity on the intervention decisions, especially when the combined measurement lacks a physical interpretation. Therefore, we propose an advanced framework based on a PHM-machine learning formulation integrating four key areas: covariate prioritization, covariate weight estimation, state band definition, and the generation of an enhanced predictive intervention policy. The paper validates the framework’s effectiveness through a comparative analysis of reliability metrics in a case study using real condition monitoring data from an energy company. While the traditional log-likelihood minimization may fall short in covariate weight estimation, sensitivity analyses reveal that the proposed policy using IPOPT and a non-scaler transformation results in consistent prediction quality. Given the challenge of interpreting merged covariates, the scheme yields improved results compared to expert criteria. Finally, the advanced framework strengthens the PHM modeling by coherently integrating diverse covariate scenarios for predictive maintenance purposes.
Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar Buoy
Authors: Xiong Xinquan , Fan Wei , Shi Yongchuang , Wu Zuli , Yang Shenglong , Xu Wenjie , Yu Shengchi , Dai Yang
Published: June 2024

To reduce costs associated with the detection and population assessment of Antarctic krill and diversify the single detection approach, our team designed and deployed a drifting sonar buoy for krill detection in the waters surrounding Antarctica. The experimental results indicate that the drifting sonar buoy fulfills its primary functions and meets the requirements for krill detection in designated marine areas. The initial experiment lasted seven days, during which the buoy collected 157 records of speed and location data as well as 82 records of sea surface temperature and acoustic data, demonstrating its potential for krill detection. The experiment also revealed shortcomings in the initial design of the drifting sonar buoy, leading to proposed improvements. The paper further compares the advantages and disadvantages of the drifting sonar buoy and traditional fishing vessels in krill detection with the buoy offering unique benefits in low-cost deployment, labor savings, broad monitoring range, and continuous real-time data monitoring. The drifting sonar buoy serves as an excellent complement to fishing vessels in krill detection.
Global Analysis of Adjustable Pallet Rack Down-Aisle Frames Using the Generalised Beam Theory
Authors: Bonada Jordi , Bové Oriol , Casafont Miquel , López-Rull Ignasi
Published: June 2024

A global analysis of a rack frame conducted using the Generalised Beam Theory (GBT) is presented. This study specifically focuses on a down-aisle frame, showing the particular characteristics of this type of structure, such as its perforated columns, semi-rigid joints, and significant second-order effects. The single-column simplified approach is applied, meaning that the entire rack is modelled with one single column and its adjacent half-beams. This approach substantially reduces modelling time and allows for very quick parametric studies while adequately reproducing the general 2D down-aisle behaviour. The GBT single-column model is first verified against a similar single-column shell finite element model used by the authors in previous research. Afterwards, the model is applied to study the effects of various factors influencing the results of a rack global analysis, including perforations, imperfection magnitudes and combinations, and sectional deformations. The nature of GBT allows for deriving clear conclusions on the influence of these aspects on structural behaviour. Furthermore, this article also aims to demonstrate the potential advantages of the theory for performing global analyses of these structures in both research and design.
Neuroprotective and Mental Health Benefits of Salt-Tolerant Plants: A Comprehensive Review of Traditional Uses and Biological Properties
Authors: Rodrigues Maria João , Pereira Catarina Guerreiro , Custódio Luísa
Published: June 2024

This study undertakes a thorough review of the ethnomedicinal properties of salt-tolerant plants and their potential to treat neurological disorders and enhance mental health. Aimed at bridging the gap between historical knowledge and contemporary scientific validation, our research meticulously evaluates both the traditional uses and the existing scientific evidence supporting the neuroprotective effects of these plants, leveraging in vitro and in vivo experimental findings. Through a comprehensive search of articles from 2001 to December 2023 across scientific databases, we identified sixteen species across nine plant families with demonstrated in vitro neuroprotective properties. Among these, the Chenopodiaceae and Juncaceae families emerged as the most represented, including plants such as Salicornia sp., Juncus sp., and Limonium sp., primarily recognized for their cholinesterase inhibitory activity. However, a notable disparity exists between traditional applications and scientific examination, with only six species undergoing in vivo testing. This discrepancy underscores the imperative for future research to delve deeper into validating traditional uses and elucidating the mechanisms underlying neuroprotection. Our findings highlight the need for research on salt-tolerant plants traditionally used for neurological benefits. Key steps include systematic screening, identification of active compounds through bioassay-guided fractionation, and in vivo testing. Integrating traditional knowledge with modern pharmacology, while emphasizing sustainable and ethical approaches, is essential for advancing neuroprotective drug discovery.
Design of a Technology-Based Magic Show System with Virtual User Interfacing to Enhance the Entertainment Effects
Authors: Wang Chao-Ming , Huang Qi-Jia
Published: June 2024

The integration of interactive technology into magic performances was explored in this study, with a focus on leveraging virtual user interfacing and interactive video projection techniques to enhance the entertainment effects. A thorough literature review identified transformation techniques between virtual and real forms in the magic performance, along with various digital magic effects. Design principles derived from the review were applied in constructing a magic show system named “FUI Magic”, where FUI stands for fantasy user interface. The system is based on virtual user interfacing, implemented via laser range-finding and video projection techniques. The “FUI Magic” system facilitated the development and presentation of a digital-multimedia magic show titled “Fantasy Doves”, publicly showcased in an exhibition. Statistical evaluation of the feedback from expert interviews and a questionnaire survey revealed positive audience impressions and confirmed the effectiveness of incorporating virtual user-interfacing technology for captivating entertainment. This study affirms the significance of visual design through virtual user interfacing in enhancing technological ambiance and magic effects, suggesting its practicality for further exploration in diverse applications.
Research on Time-Reversal Focusing Imaging Method to Evaluate a Multi-Layer Armor Composite Structure
Authors: Jiang Jian , Qian Linfang , Xu Yadong
Published: June 2024

Armor composite structures have attracted interest in structural health monitoring (SHM) for their applications in damage localization. The signal propagation and the frequency dispersion features of the Lamb wave signal on thick armor composite structures are more complicated than their counterparts on other composite plates. In this research, a time-reversal localization and imaging method for impact localization of armor composite structures is proposed. First, composite sandwich structures were designed that are typically composed of ballistic-resistant ceramic materials as the face panel and a composite material as the core layer, sandwiched between metal materials serving as the backplate. The results show that the proposed method can validate the position of impact efficiently, and radial error is within 4.12 mm and 5.39 mm in single-damage and multi-damage imaging localization, respectively.
Forensic Analysis on Damage of a Cable-Stayed Bridge without Backstays Using BIM Information
Authors: Xu Qi , Shi Xuefei , Xu Ziqi , Ma Haiying
Published: June 2024

Serious cracks were found in the pylon of a cable-stayed bridge without backstays. Based on forensic damage investigation, this paper uses the finite element (FE) method incorporating Building Information Modeling (BIM) to analyze the causes of cracking. The BIM model is established based on the survey of design, construction, and service information of the bridge. Then, FE analysis is conducted using BIM information. Finally, the causes of cracking in different regions of the pylon are explained in detail. The results show that the FE simulation agrees well with the inspected distribution of cracks, and the causes of cracks are closely related to the pylon construction process. The main cause of the cracks is the shrinkage difference between concrete segments of different ages. The anchorage effect of stayed cables also causes inclined cracks perpendicular to the cable direction. The combination of temperature load and concrete shrinkage results in cracks at the root of the pylon, and the reduction in cable forces exacerbates the cracking.
A Method for Processing Static Analysis Alarms Based on Deep Learning
Authors: Tan Yaodan , Tian Junfeng
Published: June 2024

Automatic static analysis tools (ASATs), also known as static analyzers, have demonstrated their significance and practicability in detecting software defects. ASATs assist developers to identify potential vulnerabilities, errors, and security hazards in source code without executing the software. As software systems grow in scale and complexity, ASATs are replacing manual security audits and becoming crucial for detecting issues in code. However, ASATs often generate numerous warnings with high false positive rates, while developers typically only take measures on a small portion of actionable alarms. To cope with this problem, we propose an innovative method that combines the pre-trained CodeBERT model and neural networks to reduce false positives detected by ASATs. Our approach was evaluated on the Defects4J dataset, which comprises 835 real-world software defects extracted from 17 open-source Java projects. The experimental results explicitly manifest the effectiveness in processing static analysis alarms. By employing a bidirectional recurrent neural network for context embeddings, our approach achieved an accuracy of 95.77% and an AUC score of 98.3%. This research enables developers to minimize false positive alarms and ensure a reasonable number of actionable warnings while guaranteeing software quality and security.
Glycine-Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations
Authors: Arifian Hanggara , Maharani Rani , Megantara Sandra , Ikram Nur Kusaira Khairul , Muchtaridi Muchtaridi
Published: June 2024

Natural compounds have demonstrated good biological activity when combined with certain amino acids. For example, a glycine-conjugated glycyrrhetinic acid exhibits heightened efficiency against MCF7 cancer cells. Consequently, a molecular modeling analysis is conducted to construct glycine-conjugated α-mangostins and investigate their potential. According to pharmacophore modeling using the ligand-based drug design technique, only two glycine-conjugated α-mangostins conform to the pharmacophore features. The docking simulation results show that the Am1Gly conjugate can interact with the estrogen receptor-α (ERα) with a binding energy of −10.91 kcal/mol. This interaction is further supported by molecular dynamics simulations performed over a 200 ns timeframe. Based on molecular dynamics modeling using the MMPBSA method, the binding affinity of Am1Gly (ΔGTotal = −48.79 kcal/mol) is determined. The results of this analysis indicate that Am1Gly might function as an antagonist to estrogen receptors.
The Role of Microorganisms and Their Antibacterial Compounds in Food Biopreservation
Authors: Sionek Barbara , Szydłowska Aleksandra , Kołożyn-Krajewska Danuta
Published: June 2024

The increase in the nutritional awareness of consumers has meant that products with high nutritional value, sensory attractiveness, and safety are currently being sought on the market. One of the aspects in which the innovativeness of a food product can be considered is the preservation method. Fermentation is considered one of the oldest methods. In practice, biopreservation is primarily a method of using non-pathogenic microorganisms and/or their metabolites to increase microbiological safety and extend food shelf life. Advances in microbiology and genetic engineering, taking into account various sources of microbiota isolation, have rediscovered the fermentation process and allowed us to obtain innovative functional products. Recently, bacteriocins have gained importance. For many years, they have been applied as biopreservatives in food manufacturing, alone or in combination with other preservatives. The most promising perspective of food preservation seems to be the development of combined systems including natural preservatives (i.e., bacteriocin and lipopeptides), emerging non-thermal technologies, and other methods such as encapsulation nanotechnology and active packaging. In this paper, a narrative review is presented to analyze the most recently published literature regarding the role of microorganisms and microbial produced antibacterial compounds in food biopreservation. New biopreservation technologies as an alternative to artificial preservatives were also discussed.