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22 pages, 1707 KiB  
Article
Optimizing Taxi-Pooling Operations to Enhance Efficiency and Revenue: A Queuing Model Approach
by Chaojun Wang, Jingwei Wang, Yi Zhang, Jairus Odawa Malenje and Yin Han
Mathematics 2024, 12(20), 3210; https://doi.org/10.3390/math12203210 (registering DOI) - 13 Oct 2024
Abstract
This study investigates the optimization of taxi-pooling operations using the queuing model, aiming to enhance efficiency and revenue for taxi service platforms. Traditional taxi operations face challenges during peak periods, including inefficiency and increased operational costs. Taxi-pooling, by accommodating multiple passengers with similar [...] Read more.
This study investigates the optimization of taxi-pooling operations using the queuing model, aiming to enhance efficiency and revenue for taxi service platforms. Traditional taxi operations face challenges during peak periods, including inefficiency and increased operational costs. Taxi-pooling, by accommodating multiple passengers with similar travel demands, offers a solution that reduces travel costs, operational expenses, and urban congestion. The study develops an optimization model to balance operational costs and passenger waiting times, identifying the utilization rate of taxis as a critical factor in platform revenue. By modeling the taxi-pooling service as a queuing system, we derive mathematical expressions for passenger waiting times and platform revenue under varying conditions. Our findings highlight the importance of optimal vehicle investment strategies and pricing decisions to maximize revenue. The study provides theoretical support for improving taxi-pooling platforms’ efficiency and competitiveness, contributing to better urban transportation solutions. Full article
(This article belongs to the Special Issue Application of Mathematical Methods to Transportation: Modeling and Analysis)
14 pages, 1054 KiB  
Article
Antioxidant Activity and Other Characteristics of Lactic Acid Bacteria Isolated from Korean Traditional Sweet Potato Stalk Kimchi
by Jung-Min Park, Ji-Woon Moon, Bo-Zheng Zhang and Byoung-Ki An
Foods 2024, 13(20), 3261; https://doi.org/10.3390/foods13203261 (registering DOI) - 13 Oct 2024
Abstract
The aim of this study was to examine the biological activity and probiotic properties of lactic acid bacteria (LAB) isolated from sweet potato stalk kimchi (SPK). Various LAB and Bacillus spp. are active in the early stages of the fermentation of kimchi made [...] Read more.
The aim of this study was to examine the biological activity and probiotic properties of lactic acid bacteria (LAB) isolated from sweet potato stalk kimchi (SPK). Various LAB and Bacillus spp. are active in the early stages of the fermentation of kimchi made from sweet potato stalk. Four strains of LAB were identified, including SPK2 (Levilactobacillus brevis ATCC 14869), SPK3 (Latilactobacillus sakei NBRC 15893), SPK8 and SPK9 (Leuconostoc mesenteroides subsp. dextranicum NCFB 529). SPK2, SPK3, SPK8, and SPK9 showed 64.64–94.23% bile acid resistance and 78.66–82.61% pH resistance. We identified over 106 CFU/mL after heat treatment at 75 °C. Four strains showed high antimicrobial activity to Escherichia coli and Salmonella Typhimurium with a clear zone of >11 mm. SPK2 had the highest antioxidative potentials, higher than the other three bacteria, with 44.96 μg of gallic acid equivalent/mg and 63.57% DPPH scavenging activity. These results demonstrate that the four strains isolated from sweet potato kimchi stalk show potential as probiotics with excellent antibacterial effects and may be useful in developing health-promoting products. Full article
(This article belongs to the Special Issue Natural Antimicrobial Agents Utilized in Food Preservation)
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20 pages, 4849 KiB  
Article
Occlusion Removal in Light-Field Images Using CSPDarknet53 and Bidirectional Feature Pyramid Network: A Multi-Scale Fusion-Based Approach
by Mostafa Farouk Senussi and Hyun-Soo Kang
Appl. Sci. 2024, 14(20), 9332; https://doi.org/10.3390/app14209332 (registering DOI) - 13 Oct 2024
Abstract
Occlusion removal in light-field images remains a significant challenge, particularly when dealing with large occlusions. An architecture based on end-to-end learning is proposed to address this challenge that interactively combines CSPDarknet53 and the bidirectional feature pyramid network for efficient light-field occlusion removal. CSPDarknet53 [...] Read more.
Occlusion removal in light-field images remains a significant challenge, particularly when dealing with large occlusions. An architecture based on end-to-end learning is proposed to address this challenge that interactively combines CSPDarknet53 and the bidirectional feature pyramid network for efficient light-field occlusion removal. CSPDarknet53 acts as the backbone, providing robust and rich feature extraction across multiple scales, while the bidirectional feature pyramid network enhances comprehensive feature integration through an advanced multi-scale fusion mechanism. To preserve efficiency without sacrificing the quality of the extracted feature, our model uses separable convolutional blocks. A simple refinement module based on half-instance initialization blocks is integrated to explore the local details and global structures. The network’s multi-perspective approach guarantees almost total occlusion removal, enabling it to handle occlusions of varying sizes or complexity. Numerous experiments were run on sparse and dense datasets with varying degrees of occlusion severity in order to assess the performance. Significant advancements over the current cutting-edge techniques are shown in the findings for the sparse dataset, while competitive results are obtained for the dense dataset. Full article
(This article belongs to the Special Issue Advanced Pattern Recognition & Computer Vision)
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20 pages, 11570 KiB  
Article
An Experimental Study and Result Analysis on the Dynamic Effective Bond Length of a Carbon Fiber-Reinforced Polymer Sheet Attached to a Concrete Surface
by Dong Li, Xinrui Wang, Jiangxing Zhang, Liu Jin and Xiuli Du
Buildings 2024, 14(10), 3245; https://doi.org/10.3390/buildings14103245 (registering DOI) - 13 Oct 2024
Abstract
A carbon fiber-reinforced polymer (CFRP) is a common material utilized for the enhancement in reinforced concrete (RC) constructions. Previous research indicates that the bonding performance between a CFRP sheet and concrete determines whether the bonding of CFRP material is effective. However, the majority [...] Read more.
A carbon fiber-reinforced polymer (CFRP) is a common material utilized for the enhancement in reinforced concrete (RC) constructions. Previous research indicates that the bonding performance between a CFRP sheet and concrete determines whether the bonding of CFRP material is effective. However, the majority of existing research on the bonding performance of the CFRP–concrete interface is concentrated on static loading conditions. In order to clarify the effect of dynamic load on the bonding performance of the CFRP sheet–concrete interface, this study adopts the double-sided shear test method to carry out dynamic experimental research. The test findings reveal that the damage pattern of the CFRP sheet–concrete interface remains consistent across different loading rates. The ultimate bearing capacity increases as the strain rate increases. As the strain rate increases from 10−5 s−1 to 10−2 s−1, the effect of bond length on ultimate bearing capacity increases by about 7%. As the strain rate increases, both the maximum strain of CFRP and the maximum interfacial shear stress demonstrate a corresponding increase, with respective increase rates of 60% and 20%. The effective bond length decreases by about 20% when the strain rate rises from 10−5 s−1 to 10−2 s−1. Finally, a formula for calculating the dynamic effective bond length of a CFRP sheet, grounded in the Chen and Teng formula, has been proposed and verified. Full article
(This article belongs to the Special Issue Fiber-Reinforced Polymers and Fiber-Reinforced Concrete in Civil Engineering)
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19 pages, 1709 KiB  
Systematic Review
Foresight for Sustainable Water Futures in Sub-Saharan Africa: A Systematic Review
by Henrietta E. M. George-Williams, Dexter V. L. Hunt and Christopher D. F. Rogers
Sustainability 2024, 16(20), 8874; https://doi.org/10.3390/su16208874 (registering DOI) - 13 Oct 2024
Abstract
The provision of clean and potable water and sanitation services remains a critical challenge in Sub-Saharan Africa (SSA). This is exacerbated by climate change, an ever-increasing population, urbanisation, industrialisation, and an increase in water demand, not least for agriculture. A sustainable water future [...] Read more.
The provision of clean and potable water and sanitation services remains a critical challenge in Sub-Saharan Africa (SSA). This is exacerbated by climate change, an ever-increasing population, urbanisation, industrialisation, and an increase in water demand, not least for agriculture. A sustainable water future requires more strategic planning and improved decision-making processes. To accomplish this, foresight plays a critical role. Foresight is the ability to study a system and its challenges, anticipate possible future trends, and make informed decisions that foster more desired futures. This paper presents a systematic review of the literature on the strategies or methodologies of foresight utilised to enhance decision-making and future planning for ensuring equitable and sustainable access to clean water in SSA amidst uncertainty and the evolving landscape of economic, social, and environmental challenges. The findings indicate that foresight research in most countries in SSA is in its early stages, is narrowly focused, uses foresight tools or approaches in isolation, and employs siloed approaches for overall decision-making. A transdisciplinary systems approach is recommended to support improved decision-making within sustainable water futures planning. Full article
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14 pages, 299 KiB  
Article
Comparison of SF-36 and RAND-36 in Cardiovascular Diseases: A Reliability Study
by Estrella García-Sánchez, Mirian Santamaría-Peláez, Eva Benito Figuerola, María José Carballo García, Miguel Chico Hernando, Juan Marcos García García, Jerónimo J. González-Bernal and Josefa González-Santos
J. Clin. Med. 2024, 13(20), 6106; https://doi.org/10.3390/jcm13206106 (registering DOI) - 13 Oct 2024
Abstract
Background/Objectives: Cardiovascular diseases are one of the leading causes of morbidity and mortality worldwide. Health-related quality of life is crucial to assess the impact of cardiovascular diseases and to guide therapeutic strategies. The Short Form 36 Health Survey and the RAND 36-Item [...] Read more.
Background/Objectives: Cardiovascular diseases are one of the leading causes of morbidity and mortality worldwide. Health-related quality of life is crucial to assess the impact of cardiovascular diseases and to guide therapeutic strategies. The Short Form 36 Health Survey and the RAND 36-Item Health Survey questionnaires are common tools for measuring health-related quality of life in patients with cardiovascular disease, but their reliability may vary according to the population studied. The aim of this study is to compare the reliability of the SF-36 and the RAND-36 in a population with cardiac pathology, addressing the question of which of these instruments offers a more consistent and useful measurement in this specific group. Methods: A cross-sectional observational study was carried out at the University Hospital of Burgos (Spain). A total of 413 patients with cardiovascular pathology referred to the Cardiac Rehabilitation Unit were included. Patients with incomplete data or who did not participate in the program were excluded. Internal consistency (Cronbach’s alpha), item–total correlation and reliability, and a half-and-half analysis were performed. Results: Both questionnaires showed similar and adequate reliability for patients with cardiovascular pathology. Internal consistency, as measured with Cronbach’s alpha, was above 0.80 for most dimensions, supporting its robustness. Significant inter-item and inter-dimension correlations were found in both scales, except in some specific cases in the dimension ‘Physical Functioning’. The half-and-half analysis confirmed the good reliability of both scales. Conclusions: Both the SF-36 and the RAND-36 are highly reliable tools for assessing health-related quality of life in patients with cardiovascular disease. The results may have significant implications for clinical practice, helping in the selection of health-related quality of life monitoring instruments and in the evaluation of the efficacy of therapeutic interventions. Full article
(This article belongs to the Section Cardiovascular Medicine)
11 pages, 6152 KiB  
Article
Criteria for PAVM Reintervention
by Adam Fish, Elizabeth Knight, Katharine Henderson, Jeffrey Pollak and Todd Schlachter
J. Clin. Med. 2024, 13(20), 6104; https://doi.org/10.3390/jcm13206104 (registering DOI) - 13 Oct 2024
Abstract
Background/Objectives: To propose criteria for retreating previously embolized PAVMs and determining the effectiveness of the criteria to prevent paradoxical embolization. Methods: A retrospective review of patients with PAVMs treated at a single HHT center of excellence between 1 January 2013, and [...] Read more.
Background/Objectives: To propose criteria for retreating previously embolized PAVMs and determining the effectiveness of the criteria to prevent paradoxical embolization. Methods: A retrospective review of patients with PAVMs treated at a single HHT center of excellence between 1 January 2013, and 10 September 2023, was performed. Patients with PAVM recurrence were either retreated or observed based on the following criteria for PAVM reintervention: 1. Embolic device(s) not creating a sufficiently dense matrix, such that a channel through them may be >/ 2 mm; 2. Accessory feeding artery or pulmonary collateral >/ 2 mm; 3. Hemoptysis in a patient with no other explanation. Results: A total of 438 PAVMs were treated in 151 patients, including 106 patients with definite, 14 possible, and 31 doubtful HHT. Post-embolization PAVM recurrence occurred in 36 patients (36/151, 23.8%), including 15 patients (15/151, 9.9%) with 22 PAVMs (22/438, 5.0%) meeting criteria for reintervention. A total of 21 patients (21/151, 13.9%) with recurrence did not meet reintervention criteria and were therefore observed. Pre-treatment paradoxical embolization occurred in 36 patients (36/151) for a lifetime prevalence rate of 23.7%. Post-treatment paradoxical embolization did not occur in any patients following PAVM embolization (0/151). There was one case of iatrogenic paradoxical embolization in a patient being treated for systemic collateral reperfusion and hemoptysis. However, this was not included given that it was not a spontaneous event. Conclusions: Utilizing modern embolization techniques and devices, the proposed reintervention criteria, and screening intervals, paradoxical embolizations can be effectively prevented in patients with PAVMs. Full article
(This article belongs to the Special Issue Hereditary Hemorrhagic Telangiectasia: Recent Advances and Future Challenges—2nd Edition)
11 pages, 1080 KiB  
Article
Dynamics of Neutrophil-to-Lymphocyte Ratio (NLR), Lymphocyte-to-Monocyte Ratio (LMR), and Platelet-to-Lymphocyte Ratio (PLR) in Patients with Deep Neck Infection
by Jeong-Mi Kim, Huu Hoang and Jeong-Seok Choi
J. Clin. Med. 2024, 13(20), 6105; https://doi.org/10.3390/jcm13206105 (registering DOI) - 13 Oct 2024
Abstract
Background/Objectives: Inflammatory biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), have been utilized as prognostic factors in various diseases. This study aims to evaluate changes in the NLR, PLR, and LMR in patients diagnosed with a [...] Read more.
Background/Objectives: Inflammatory biomarkers, including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), have been utilized as prognostic factors in various diseases. This study aims to evaluate changes in the NLR, PLR, and LMR in patients diagnosed with a deep neck infections (DNI) to identify useful prognostic markers. Methods: This single-center, retrospective cohort study utilized data from the electronic medical records of patients admitted to the ENT department of a tertiary university hospital between January 2000 and August 2024. Patients diagnosed with a DNI during the study period were enrolled. Preoperative and postoperative inflammatory markers were measured in all patients, and NLR, LMR, and PLR values were calculated and analyzed. Results: The post-treatment NLR was significantly lower than the pre-treatment NLR. Similarly, the post-treatment LMR was significantly higher and the post-treatment PLR was significantly lower compared to pre-treatment values. Patients admitted to the ICU had higher inflammatory markers than those in general wards. Additionally, patients with elevated inflammatory markers had longer hospital stays. Inflammatory markers were also higher in older patients and those who underwent surgical treatment. Conclusions: Significant changes in the NLR, LMR, and PLR in patients diagnosed with a DNI can serve as useful prognostic markers. These findings suggest that monitoring these markers may help to assess and improve the inflammatory status of patients, highlighting their potential role in guiding treatment. Full article
(This article belongs to the Section Immunology)
15 pages, 2800 KiB  
Article
A UUV Cluster Route-Planning Method for Dynamic Target Search
by Jingxiang Feng, Weicheng Xu, Jingwei Dong, Yao Yao and Zhixing Hu
Electronics 2024, 13(20), 4033; https://doi.org/10.3390/electronics13204033 (registering DOI) - 13 Oct 2024
Abstract
Aiming to address the problem of regional dynamic target search under weak communication conditions, this paper proposes a UUV cluster search method based on cumulative probability optimization. First, by estimating the probability distribution of the initial target location, an initial probability map is [...] Read more.
Aiming to address the problem of regional dynamic target search under weak communication conditions, this paper proposes a UUV cluster search method based on cumulative probability optimization. First, by estimating the probability distribution of the initial target location, an initial probability map is established. Then, based on the Bayesian model and Markov decision model, the target probability distribution is periodically updated, and based on the cumulative detection probability optimal principle of the UUV cluster, the UUV cluster is guided to search the region with high detection probability preferentially. Finally, we implement the simulation experiment and compare with the random search method. The results verify that the proposed method has higher search efficiency in the cases of without prior information and with prior information. Full article
(This article belongs to the Special Issue Selected Papers for the 2024 4th International Conference on Autonomous Unmanned Systems (4th ICAUS 2024))
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14 pages, 793 KiB  
Article
Kilometer-Scale Precipitation Forecasting Utilizing Convolutional Neural Networks: A Case Study of Jiangsu’s Coastal Regions
by Ninghao Cai, Hongchuan Sun and Pengcheng Yan
Hydrology 2024, 11(10), 173; https://doi.org/10.3390/hydrology11100173 (registering DOI) - 13 Oct 2024
Abstract
High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, [...] Read more.
High-resolution precipitation forecasts play a pivotal role in formulating comprehensive disaster prevention and mitigation plans. As spatial resolution enhances, striking a balance between computation, storage, and simulation accuracy becomes imperative to ensure optimal cost-effectiveness. Convolutional neural networks (CNNs), a cornerstone of deep learning, are examined in this study for their downscaling capabilities in precipitation simulation. During a precipitation event on 23 June 2022, in Jiangsu Province, China, distinct rain belts emerged in both southern and northern Jiangsu, precisely captured by a numerical model (the Weather Research and Forecasting, WRF) with a 3 km spatial resolution. Specifically, precipitation was prevalent in northern Jiangsu from 00:00 to 11:00 Beijing Time (BJT), transitioning to southern Jiangsu from 12:00 to 23:00 BJT on the same day. Upon dynamic downscaling, the model reproduced precipitation in these periods with an average error of 12.35 mm at 3 km and 12.48 mm at 1 km spatial resolutions. Employing CNN technology for statistical downscaling to a 1 km spatial resolution, samples from the initial period were utilized for training, while those from the subsequent period served for validation. Following dynamic downscaling, CNNs with four, five, six, and seven layers exhibited average errors of 8.86 mm, 8.93 mm, 9.71 mm, and 9.70 mm, respectively, accompanied by correlation coefficients of 0.550, 0.570, 0.574, and 0.578, respectively. This analysis indicates that for this precipitation event, a shallower CNN depth yields a lower average error and correlation coefficient, whereas a deeper architecture enhances the correlation coefficient. By employing deep network architectures, CNNs are capable of capturing nonlinear patterns and subtle local features from complex meteorological data, thereby providing more accurate predictions during the downscaling process. Leveraging faster computation and reduced storage requirements, machine learning has demonstrated immense potential in high-resolution forecasting research. There is significant scope for advancing technologies that integrate numerical models with machine learning to achieve higher-resolution numerical forecasts. Full article
(This article belongs to the Special Issue New Perspectives in the Flood Forecasting Chain (Weather Prediction, Rainfall-Runoff Modeling, and Communication with Stakeholders))
22 pages, 2393 KiB  
Article
SmartISM 2.0: A Roadmap and System to Implement Fuzzy ISM and Fuzzy MICMAC
by Naim Ahmad
Sustainability 2024, 16(20), 8873; https://doi.org/10.3390/su16208873 (registering DOI) - 13 Oct 2024
Abstract
Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to [...] Read more.
Interpretive structural modeling (ISM) is a widely used technique to establish hierarchical relationships among a set of variables in diverse domains, including sustainability. This technique is generally coupled with MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (cross-impact matrix multiplication applied to classification)) to classify variables in four clusters, although the manual application of the technique is complex and prone to error. In one of the previous works, a novel concept of reduced conical matrix was introduced, and the SmartISM software was developed for the user-friendly implementation of ISM and MICMAC. The web-based SmartISM software has been used more than 48,123 times in 87 countries to generate ISM models and MICMAC diagrams. This work attempts to identify existing approaches to fuzzy ISM and fuzzy MICMAC and upscale the SmartISM to incorporate fuzzy approaches. The fuzzy set theory proposed by Zadeh 1965 and Goguen 1969 helps the decision makers to provide their input with the consideration of vagueness in the real environment. The systematic review of 32 studies identified five significant approaches that have used different linguistic scales, fuzzy numbers, and defuzzification methods. Further, the approaches have differences in either using single or double defuzzification, and the aggregation of inputs of decision makers either before or after defuzzification, as well as the incorporation of transitivity either before or after defuzzification. A roadmap was devised to aggregate and generalize different approaches. Further, two of the identified approaches have been implemented in SmartISM 2.0 and the results have been reported. Finally, the comparative analysis of different approaches using SmartISM 2.0 in the area of digital transformation shows that, with a wide flexibility of fuzzy scales, the results converge and improve the confidence in the final model. The roadmap and SmartISM 2.0 will help in the implementation of fuzzy ISM and fuzzy MICMAC in a more robust and informed way. Full article
17 pages, 894 KiB  
Article
Effects of Temperature and Packaging Atmosphere on Shelf Life, Biochemical, and Sensory Attributes of Glasswort (Salicornia europaea L.) Grown Hydroponically at Different Salinity Levels
by Chiara Sanmartin, Isabella Taglieri, Alessandro Bianchi, Prangthip Parichanon, Martina Puccinelli, Alberto Pardossi and Francesca Venturi
Foods 2024, 13(20), 3260; https://doi.org/10.3390/foods13203260 (registering DOI) - 13 Oct 2024
Abstract
Halophytes, such as Salicornia species, are promising new foods and are consumed for their pleasant salty taste and nutritional value. Since Salicornia is perishable, modified atmospheric packaging (MAP) can be a useful tool, in combination with proper temperature, to halt further quality degradation [...] Read more.
Halophytes, such as Salicornia species, are promising new foods and are consumed for their pleasant salty taste and nutritional value. Since Salicornia is perishable, modified atmospheric packaging (MAP) can be a useful tool, in combination with proper temperature, to halt further quality degradation in this type of product. The purpose of this study was to investigate the effect of MAP, with or without refrigeration, to extend the shelf life of glasswort (Salicornia europaea L.) grown hydroponically (floating raft system) in a greenhouse with a nutrient solution containing 0 g/L (C) or 12.5 g/L of NaCl (T). The dry matter content, weight loss, respiration rate, biochemical composition, color, antioxidant capacity, and sensorial attributes were determined in shoots after harvest and during storage in plastic bags filled with technical air or with MAP at 4 or 20 °C for 120 h. At harvest, plants supplied with salt-enriched solution (T) showed a significant improvement in nutritional value and sensory profile. Storage in air at room temperature (20 °C) accelerated weight loss and diminished color stability, particularly in non-salinity samples (C), while MAP extended the shelf life of all the samples regardless of the storage temperature adopted. Optimal storage conditions were observed when MAP was combined with refrigeration, which allowed to effectively preserve shoots sensory acceptability for a period of about seven days. Future research could further explore the long-term effects on the nutritional value and sensory quality of S. europaea under various combinations of MAP and different storage temperatures ranging between 4 °C and 20 °C. Full article
(This article belongs to the Section Food Quality and Safety)
12 pages, 954 KiB  
Article
Antibacterial Effects of Thermosonication Technology on Salmonella typhimurium Strains Identified from Swine Food Chain: An In Vitro Study
by Luca Pennisi, Gianluigi Ferri, Carlotta Lauteri, Daniele Di Clerico and Alberto Vergara
Foods 2024, 13(20), 3259; https://doi.org/10.3390/foods13203259 (registering DOI) - 13 Oct 2024
Abstract
Among innovative food technologies, ultrasounds have demonstrated physical damages (provided by frequency and intensity factors) on bacterial structures while determining the microbiological stabilization of many foodstuffs. This study tested the efficacy of the thermosonication process on 16 Salmonella typhimurium strains belonging to the [...] Read more.
Among innovative food technologies, ultrasounds have demonstrated physical damages (provided by frequency and intensity factors) on bacterial structures while determining the microbiological stabilization of many foodstuffs. This study tested the efficacy of the thermosonication process on 16 Salmonella typhimurium strains belonging to the academic biobank (isolated from swine slaughterhouses). All strains were exposed to focused ultrasounds, generated by the Waveco® system (Milan, Italy), with the following settings: 40 KHz coupled with 80 W at different 5 min intervals starting from 5 to 15 ones, and focusing on two different temperatures: 40 °C and 50 °C. After each treatment, all strains were directly plated onto count agars immediately (t0) and after 24 h (t24) of storage at refrigerated temperature. The results showed bacterial reductions by prolonging the sonication treatments until 15 min (i.e., 50 °C for 15 min reduced of 2.16 log CFU/gr the initial loads). In the present in vitro study, the most considerable decrease was observed after 24 h. It meant that Salmonella strains were lethally damaged at the wall level, confirming the ultrasound bactericidal effect on loads. The present in vitro scientific investigation demonstrates the practical bactericidal effects of thermosonication, highlighting promising applications at the industry level for food microbial stabilization and shelf-life prolongation. Full article
(This article belongs to the Section Food Microbiology)
13 pages, 1550 KiB  
Article
Synchronization Optimization of Pipeline Layout and Pipe Diameter Selection in a Drip Irrigation Network System Based on the Jaya Algorithm
by Kai Fan, Tiantian Zhao, Xingjiao Yu, Wene Wang, Xiaotao Hu, Danjie Ran, Xuefei Huo, Yafei Wang and Yingying Pi
Water 2024, 16(20), 2913; https://doi.org/10.3390/w16202913 (registering DOI) - 13 Oct 2024
Abstract
To address the complexity and high computational burden in the design of drip irrigation networks, the Jaya algorithm is utilized to study factors affecting project costs, including equipment and pipeline depreciation and the operation and management costs of the irrigation area. A mathematical [...] Read more.
To address the complexity and high computational burden in the design of drip irrigation networks, the Jaya algorithm is utilized to study factors affecting project costs, including equipment and pipeline depreciation and the operation and management costs of the irrigation area. A mathematical model of synchronization optimal design of pipe layout and pipe diameter selection in a drip irrigation network system with constraints on pipe diameter, flow velocity, and pipe pressure is established. Using an irrigation district in Xinjiang, China, as an example, the Jaya algorithm optimization design program was run independently 50 times, and the relative deviation of each optimization result from the optimal solution was calculated. The results show that the annual cost per unit area o is reduced to 635.99 RMB/hm2, a 25.34% reduction compared to the original engineering program, and the investment-saving effect is obvious. The relative deviation is controlled within 3%, which shows that the algorithm has stable convergence performance and can meet the requirements of actual engineering design. The Jaya algorithm eliminates the need for parameter tuning, and it excels in cost savings, algorithm stability, and computational accuracy, making it an effective method for the single-objective optimization design of drip irrigation networks. Full article
(This article belongs to the Section Water, Agriculture and Aquaculture)
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21 pages, 2182 KiB  
Article
A Coffee Plant Counting Method Based on Dual-Channel NMS and YOLOv9 Leveraging UAV Multispectral Imaging
by Xiaorui Wang, Chao Zhang, Zhenping Qiang, Chang Liu, Xiaojun Wei and Fengyun Cheng
Remote Sens. 2024, 16(20), 3810; https://doi.org/10.3390/rs16203810 (registering DOI) - 13 Oct 2024
Abstract
Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. [...] Read more.
Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture. Full article
(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection (Second Edition))
23 pages, 5873 KiB  
Article
The Protective Effect against Lung Injury of Phytosome Containing the Extract of Purple Waxy Corn Tassel in an Animal Model of PM2.5-Induced Lung Inflammation
by Nut Palachai, Wipawee Thukham-mee and Jintanaporn Wattanathorn
Foods 2024, 13(20), 3258; https://doi.org/10.3390/foods13203258 (registering DOI) - 13 Oct 2024
Abstract
Lung inflammation caused by fine particulate matter (PM), particularly PM2.5, poses a significant public health challenge, with oxidative stress and inflammation playing central roles in its pathophysiology. This study evaluates the protective effects of phytosome-encapsulated extract of purple waxy corn tassel (PPT) against [...] Read more.
Lung inflammation caused by fine particulate matter (PM), particularly PM2.5, poses a significant public health challenge, with oxidative stress and inflammation playing central roles in its pathophysiology. This study evaluates the protective effects of phytosome-encapsulated extract of purple waxy corn tassel (PPT) against PM2.5-induced lung inflammation. Male Wistar rats received PPT at doses of 100, 200, and 400 mg/kg BW for 21 days prior to exposure and continued to receive the same doses for 27 days during PM2.5 exposure. Significant reductions in inflammatory markers, including cyclooxygenase-2 (COX-II), various interleukins (IL-1β, IL-6, IL-8), tumor necrosis factor-alpha (TNF-α), and nuclear factor kappa B (NF-κB), were observed, indicating that PPT effectively regulates the inflammatory response. Additionally, PPT improved oxidative stress markers by reducing malondialdehyde (MDA) levels and enhancing antioxidant enzyme activities such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px), thereby restoring lung antioxidant defenses. Notably, the study revealed that PPT modulates epigenetic mechanisms, as evidenced by decreased histone deacetylase (HDAC) activity and upregulation of sirtuins in lung tissue. These epigenetic modifications likely contribute to the reduction in inflammation and oxidative stress, suggesting a multifaceted protective role of PPT that involves both direct biochemical pathways and epigenetic regulation. The interplay between reduced inflammatory signaling, enhanced antioxidant capacity, and epigenetic modulation underscores PPT’s potential as a therapeutic agent for managing respiratory inflammation-related diseases and its promise for the development of future functional food products. Full article
(This article belongs to the Special Issue Antioxidant and Nutritional Evaluations of Food and Their Applications in the Prevention and Treatment of Chronic Diseases)
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24 pages, 17306 KiB  
Article
Depth Video-Based Secondary Action Recognition in Vehicles via Convolutional Neural Network and Bidirectional Long Short-Term Memory with Spatial Enhanced Attention Mechanism
by Weirong Shao, Mondher Bouazizi and Ohtuski Tomoaki
Sensors 2024, 24(20), 6604; https://doi.org/10.3390/s24206604 (registering DOI) - 13 Oct 2024
Abstract
Secondary actions in vehicles are activities that drivers engage in while driving that are not directly related to the primary task of operating the vehicle. Secondary Action Recognition (SAR) in drivers is vital for enhancing road safety and minimizing accidents related to distracted [...] Read more.
Secondary actions in vehicles are activities that drivers engage in while driving that are not directly related to the primary task of operating the vehicle. Secondary Action Recognition (SAR) in drivers is vital for enhancing road safety and minimizing accidents related to distracted driving. It also plays an important part in modern car driving systems such as Advanced Driving Assistance Systems (ADASs), as it helps identify distractions and predict the driver’s intent. Traditional methods of action recognition in vehicles mostly rely on RGB videos, which can be significantly impacted by external conditions such as low light levels. In this research, we introduce a novel method for SAR. Our approach utilizes depth-video data obtained from a depth sensor located in a vehicle. Our methodology leverages the Convolutional Neural Network (CNN), which is enhanced by the Spatial Enhanced Attention Mechanism (SEAM) and combined with Bidirectional Long Short-Term Memory (Bi-LSTM) networks. This method significantly enhances action recognition ability in depth videos by improving both the spatial and temporal aspects. We conduct experiments using K-fold cross validation, and the experimental results show that on the public benchmark dataset Drive&Act, our proposed method shows significant improvement in SAR compared to the state-of-the-art methods, reaching an accuracy of about 84% in SAR in depth videos. Full article
(This article belongs to the Special Issue Multi-Sensor Systems for Object Tracking—2nd Edition)
18 pages, 2342 KiB  
Review
Phase Separation Mediated Sub-Nuclear Compartmentalization of Androgen Receptors
by Selçuk Yavuz, Tsion E. Abraham, Adriaan B. Houtsmuller and Martin E. van Royen
Cells 2024, 13(20), 1693; https://doi.org/10.3390/cells13201693 (registering DOI) - 13 Oct 2024
Abstract
The androgen receptor (AR), a member of the nuclear steroid hormone receptor family of transcription factors, plays a crucial role not only in the development of the male phenotype but also in the development and growth of prostate cancer. While AR structure and [...] Read more.
The androgen receptor (AR), a member of the nuclear steroid hormone receptor family of transcription factors, plays a crucial role not only in the development of the male phenotype but also in the development and growth of prostate cancer. While AR structure and AR interactions with coregulators and chromatin have been studied in detail, improving our understanding of AR function in gene transcription regulation, the spatio-temporal organization and the role of microscopically discernible AR foci in the nucleus are still underexplored. This review delves into the molecular mechanisms underlying AR foci formation, focusing on liquid–liquid phase separation and its role in spatially organizing ARs and their binding partners within the nucleus at transcription sites, as well as the influence of 3D-genome organizations on AR-mediated gene transcriptions. Full article
(This article belongs to the Collection Functions of Nuclear Receptors)
15 pages, 3228 KiB  
Article
Topic Modeling as a Tool to Identify Research Diversity: A Study across Dental Disciplines
by Maria Teresa Colangelo, Stefano Guizzardi and Carlo Galli
Metrics 2025, 1(1), 3; https://doi.org/10.3390/metrics1010003 (registering DOI) - 13 Oct 2024
Abstract
This study investigates the diversity and evolution of research topics within the dental sciences from 1994 to 2023, using Topic modeling and Shannon’s entropy as a measure of research diversity. We analyzed a dataset of 412,036 scientific articles across six dental disciplines: Orthodontics, [...] Read more.
This study investigates the diversity and evolution of research topics within the dental sciences from 1994 to 2023, using Topic modeling and Shannon’s entropy as a measure of research diversity. We analyzed a dataset of 412,036 scientific articles across six dental disciplines: Orthodontics, Prosthodontics, Periodontics, Implant Dentistry, Oral Surgery, and Restorative Dentistry. This research relies on BERTopic to identify distinct topics within each field. The study revealed significant shifts in research focus over time, with some disciplines exhibiting robust growth in article numbers, such as Periodontics and Prosthodontics. However, despite the overall increase in publications, the number of topics per discipline varied, with Restorative Dentistry increasing at a faster rate and exceeding 50 topics over the last 15 years. We observed an increasing diversification of research efforts in disciplines such as Restorative Dentistry, with entropy levels consistently above 2 and progressively increasing. In contrast, fields such as Prosthodontics, despite high publication output, maintained a more specialized research focus, reflected in entropy levels remaining below 1.5. Oral Surgery showed a steep increase in research diversification until 2000, after which it stabilized. Taken together, our findings describe the dynamic nature of dental research and highlight the balance shifts in research focus across several key areas of Dentistry. Full article
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21 pages, 3914 KiB  
Article
Asset Returns: Reimagining Generative ESG Indexes and Market Interconnectedness
by Gordon Dash, Nina Kajiji and Bruno G. Kamdem
J. Risk Financial Manag. 2024, 17(10), 463; https://doi.org/10.3390/jrfm17100463 (registering DOI) - 13 Oct 2024
Abstract
Financial economists have long studied factors related to risk premiums, pricing biases, and diversification impediments. This study examines the relationship between a firm’s commitment to environmental, social, and governance principles (ESGs) and asset market returns. We incorporate an algorithmic protocol to identify three [...] Read more.
Financial economists have long studied factors related to risk premiums, pricing biases, and diversification impediments. This study examines the relationship between a firm’s commitment to environmental, social, and governance principles (ESGs) and asset market returns. We incorporate an algorithmic protocol to identify three nonobservable but pervasive E, S, and G time-series factors to meet the study’s objectives. The novel factors were tested for information content by constructing a six-factor Fama and French model following the imposition of the isolation and disentanglement algorithm. Realizing that nonlinear relationships characterize models incorporating both observable and nonobservable factors, the Fama and French model statement was estimated using an enhanced shallow-learning neural network. Finally, as a post hoc measure, we integrated explainable AI (XAI) to simplify the machine learning outputs. Our study extends the literature on the disentanglement of investment factors across two dimensions. We first identify new time-series-based E, S, and G factors. Second, we demonstrate how machine learning can be used to model asset returns, considering the complex interconnectedness of sustainability factors. Our approach is further supported by comparing neural-network-estimated E, S, and G weights with London Stock Exchange ESG ratings. Full article
(This article belongs to the Special Issue Business, Finance, and Economic Development)
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24 pages, 6209 KiB  
Article
Evaluation of Selected Factors Affecting the Speed of Drivers at Signal-Controlled Intersections in Poland
by Damian Iwanowicz, Tomasz Krukowicz, Justyna Chadała, Michał Grabowski and Maciej Woźniak
Sustainability 2024, 16(20), 8862; https://doi.org/10.3390/su16208862 (registering DOI) - 13 Oct 2024
Abstract
In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring [...] Read more.
In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals. Full article
(This article belongs to the Special Issue Selected Problems Arising in the Development of Sustainable Transport Systems)
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9 pages, 316 KiB  
Communication
Rabies Vaccination for Sheep and Goats: Influence of Booster on Persistence of Antibody Response
by Sarah Weyl Feinstein, Shiri Novak, Marina Eyngor, Yaniv Lavon and Boris Yakobson
Vet. Sci. 2024, 11(10), 502; https://doi.org/10.3390/vetsci11100502 (registering DOI) - 13 Oct 2024
Abstract
Infrequent rabies cases occur in Israel, endangering humans and animals. While dogs receive mandatory vaccinations, farm animals are vaccinated voluntarily. However, optimal vaccination protocol for small ruminants is lacking. The aim of this study was to test the immunological responses to the rabies [...] Read more.
Infrequent rabies cases occur in Israel, endangering humans and animals. While dogs receive mandatory vaccinations, farm animals are vaccinated voluntarily. However, optimal vaccination protocol for small ruminants is lacking. The aim of this study was to test the immunological responses to the rabies vaccine, with or without a booster, in sheep and goats; 70 ewes and 49 does participated in the trial. Following the first vaccine, 88% of the ewes and 100% of the does had a sufficient level of rabies antibodies (>0.5 IU/mL) 30 days post-vaccination. A year later, 82% of the ewes that had received a booster dose remained protected, whereas 46% of the non-boosted ewes had a sufficient antibody level. For does, 83% of those receiving a booster maintained sufficient antibody levels 1 year later; 80% of the non-boosted does remained protected, demonstrating no significant contribution of the booster dose in this group of goats. However, while the initial immunological response of the does was higher, the change in response between 1 month and 12 months post-vaccination differed significantly between species, with a greater titer reduction in the does. Differential immunological responses between individuals and between species warrant longer-term studies to recommend a proper vaccine protocol for each species. Full article
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13 pages, 1904 KiB  
Perspective
Antibiotic Resistance Genes in Global Food Transformation System: Edible Insects vs. Livestock
by Rifat Nowshin Raka, Lin Zhang, Rui Chen and Xiaofeng Xue
Foods 2024, 13(20), 3257; https://doi.org/10.3390/foods13203257 (registering DOI) - 13 Oct 2024
Abstract
Antibiotic-resistant genes (ARGs) pose a significant threat to the global food transformation system. The increasing prevalence of ARGs in food has elicited apprehension about public health safety. The widespread dissemination of ARGs in food products, driven by the inappropriate use of antibiotics, presents [...] Read more.
Antibiotic-resistant genes (ARGs) pose a significant threat to the global food transformation system. The increasing prevalence of ARGs in food has elicited apprehension about public health safety. The widespread dissemination of ARGs in food products, driven by the inappropriate use of antibiotics, presents significant adversity for the safety of emerging future food sources such as edible insects. As the world faces increasing challenges related to food security, climate change, and antibiotic resistance, edible insects offer a sustainable and resilient food source. The intriguing possibility of edible insects serving as a less conducive environment for ARGs compared to livestock warrants further exploration and investigation. In this recent work, we listed ARGs from edible insects detected so far by in vitro approaches and aimed to construct a fair comparison with ARGs from livestock based on relevant genes. We also presented our argument by analyzing the factors that might be responsible for ARG abundance in livestock vs. edible insects. Livestock and edible insects have diverse gut microbiota, and their diets differ with antibiotics. Consequently, their ARG abundance may vary as well. In addition, processed edible insects have lower levels of ARGs than raw ones. We hypothesize that edible insects could potentially contain a lower abundance of ARGs and exhibit a diminished ability to disseminate ARGs relative to livestock. A regulatory framework could help intercept the increasing prevalence of ARGs. Due diligence should also be taken when marketing edible insects for consumption. Full article
(This article belongs to the Section Food Security and Sustainability)
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16 pages, 4035 KiB  
Article
Ginsenoside Re Regulates Oxidative Stress through the PI3K/Akt/Nrf2 Signaling Pathway in Mice with Scopolamine-Induced Memory Impairments
by Xin Li, Kai Zheng, Hao Chen and Wei Li
Curr. Issues Mol. Biol. 2024, 46(10), 11359-11374; https://doi.org/10.3390/cimb46100677 (registering DOI) - 13 Oct 2024
Abstract
While Ginsenoside Re has been shown to protect the central nervous system, reports of its effects on memory in the model of scopolamine-induced memory impairment are rare. The aim of this study was to investigate the effects of Ginsenoside Re on scopolamine (SCOP)-induced [...] Read more.
While Ginsenoside Re has been shown to protect the central nervous system, reports of its effects on memory in the model of scopolamine-induced memory impairment are rare. The aim of this study was to investigate the effects of Ginsenoside Re on scopolamine (SCOP)-induced memory damage and the mechanism of action. Male ICR mice were treated with SCOP (3 mg/kg) for 7 days and with or without Ginsenoside Re for 14 days. As evidenced by behavioral studies (escape latency and cross platform position), brain tissue morphology, and oxidative stress indicators after Ginsenoside Re treatment, the memory damage caused by SCOP was significantly ameliorated. Further mechanism research indicated that Ginsenoside Re inhibited cell apoptosis by regulating the PI3K/Akt/Nrf2 pathway, thereby exerting a cognitive impairment improvement effect. This research suggests that Ginsenoside Re could protect against SCOP-induced memory defects possibly through inhibiting oxidative stress and cell apoptosis. Full article
(This article belongs to the Section Bioorganic Chemistry and Medicinal Chemistry)
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