The 2023 MDPI Annual Report has
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15 pages, 6699 KiB  
Article
Predicting the Potential Risk Area of the Invasive Plant Galinsoga parviflora in Tibet Using the MaxEnt Model
by Junwei Wang, Zhefei Zeng, Yonghao Chen and Qiong La
Sustainability 2024, 16(11), 4689; https://doi.org/10.3390/su16114689 (registering DOI) - 31 May 2024
Abstract
The Tibetan plateau, with complex and diverse ecosystems, is an important ecological security barrier to China. However, climate change and the spread of invasive plant species have imperiled the once pristine and diverse ecosystem of the region. To prevent the further spread and [...] Read more.
The Tibetan plateau, with complex and diverse ecosystems, is an important ecological security barrier to China. However, climate change and the spread of invasive plant species have imperiled the once pristine and diverse ecosystem of the region. To prevent the further spread and control of invasive plants, it is important to delineate the potential distribution patterns of alien invasive plants at the regional scale across Tibet and understand their responses to climate change. Galinsoga parviflora Cav., a member of the family Asteraceae, is an annual herbaceous plant distributed globally as an invasive weed and possesses characteristics that make it highly invasive, such as a strong ability to proliferate and disperse. The species is also known to have an allelopathic effect. There has been no report on the spatial distribution of G. parviflora in Tibet. Using field survey data, we investigated the risk of G. parviflora invasion and its impacts on the ecological safety of Tibet. We employed the MaxEnt model using the R language and SPSS software to optimize and select model parameters and data. We acquired various environmental variables along with current and future climate change scenarios (two carbon emission scenarios, SSP126 and SSP585, for the years 2050 and 2090) to predict the geographic distribution and potential risk areas in Tibet that G. parviflora can invade. The MaxEnt model accurately predicted the distribution of G. parviflora in Tibet with an average AUC of 0.985. The most suitable environmental conditions in which G. parviflora performed the best in Tibet included a mean annual temperature of 6.2–10.0 °C and an elevation range of 2672–3744 m above sea level. Our results indicate that low precipitation during the coldest quarter of the year (mean temperature −2–3 °C) was the most important variable predicting G. parviflora distribution. The results also showed that the species was hardly found when precipitation in the coldest quarter exceeded 155 mm. The current potential invasion risk areas for G. parviflora included the river valleys of central, southeastern, and eastern Tibet. With future climate change scenarios (i.e., SSP126, SSP585), the suitable habitats for G. parviflora distribution will likely shift to northwest regions from the southeast. Particularly under the highest carbon emission scenario (i.e., SSP585), the potential risk area expands more rapidly, and the center of distribution shifts to northwest regions. These findings provide useful information about the current and future changes in G. parviflora distribution in Tibet, which is crucial for the comprehensive and proactive management and control of G. parviflora under future climate change. Full article
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18 pages, 4513 KiB  
Article
Combined Analytic Hierarchy Process and Weighted Interval Method Models for the Geological Evaluation of CO2 Storage in Coal Goaf
by Dongzhuang Hou, Yifei Xiao, Lang Liu and Chao Huan
Energies 2024, 17(11), 2672; https://doi.org/10.3390/en17112672 (registering DOI) - 31 May 2024
Abstract
The increasing concentration of CO2 in the atmosphere is a major factor contributing to climate change. CO2 storage in coal goaf is a convenient, effective, and economical solution. Methods to quickly and effectively evaluate geological conditions are urgently required. The main [...] Read more.
The increasing concentration of CO2 in the atmosphere is a major factor contributing to climate change. CO2 storage in coal goaf is a convenient, effective, and economical solution. Methods to quickly and effectively evaluate geological conditions are urgently required. The main influencing factors are geological safety, storage potential, economics, and environmental protection; these include 4 aspects, 38 indexes, and 4 index levels that can be quantified using classification levels. We established a geological evaluation model, using analytic hierarchy process (AHP) and weighted interval methods. AHP was used to determine its elements, indicators, and inter-layer relationships, as well as to clarify its structural relationships. The weight interval method is used to evaluate unstable elements, reducing their difficulty, and constant values are used to assign weights of stable elements to increase accuracy. This model was applied to assess the suitability of the goaf in Yaojie mine for geological CO2 storage. The results revealed that this goaf is an above average CO2 storage space, which was consistent with previous research. This geological CO2 storage evaluation model may also be used to assess the CO2 storage suitability of other coal goafs. Full article
(This article belongs to the Special Issue Advances in Carbon Capture and Storage and Renewable Energy Systems)
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12 pages, 1097 KiB  
Article
Occupational Exposure to Metal-Based Nanomaterials: A Possible Relationship between Chemical Composition and Oxidative Stress Biomarkers
by Valeria Bellisario, Giacomo Garzaro, Giulia Squillacioti, Marco Panizzolo, Federica Ghelli, Giuseppe Mariella, Roberto Bono, Irina Guseva Canu and Enrico Bergamaschi
Antioxidants 2024, 13(6), 676; https://doi.org/10.3390/antiox13060676 (registering DOI) - 31 May 2024
Abstract
Nanomaterials (NMs) are in high demand for a wide range of practical applications; however, comprehensively understanding the toxicity of these materials is a complex challenge, due to the limited availability of epidemiological evidence on the human health effects arising from workplace exposures. The [...] Read more.
Nanomaterials (NMs) are in high demand for a wide range of practical applications; however, comprehensively understanding the toxicity of these materials is a complex challenge, due to the limited availability of epidemiological evidence on the human health effects arising from workplace exposures. The aim of this work is to assess whether and how urinary metal concentrations could be reliable and useful in NM biomonitoring. In the framework of “NanoExplore Project” [EU LIFE17 Grant ENV/GR/000285], 43 not-exposed subjects and 40 exposed workers were recruited to measure exposure to NMs (PCN and LDSA) in the proximity of the workstations and biological biomarkers (urinary metal concentrations—Aluminum (Al), Silica (Si), Titanium (Ti), and Chromium (Cr); urinary OS biomarkers—TAP, Isop, and MDA). The results showed that Si and Ti were directly associated with NM exposure (both PCN and LDSA), as well as with OS biomarkers, especially in exposed workers. Moreover, the mediation analyses showed that Si could account for about 2.8% in the relationship between LDSA and OS biomarkers, possibly by decreasing OS antioxidant defenses in exposed people. In conclusion, our study provides evidence that occupational exposure to mixtures containing NMs can represent an underestimated hazard for exposed people, increasing the body burden and the oxidative balance. Full article
(This article belongs to the Special Issue Oxidative Stress Induced by Air Pollution)
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17 pages, 5477 KiB  
Article
Comparative Analysis of Bacterial Information of Biofilms and Activated Sludge in Full-Scale MBBR-IFAS Systems
by Xiaolin Zhou, Haicheng Liu, Xing Fan, Xuyi Wang, Xuejun Bi, Lihua Cheng, Shujuan Huang, Fangchao Zhao and Tang Yang
Microorganisms 2024, 12(6), 1121; https://doi.org/10.3390/microorganisms12061121 (registering DOI) - 31 May 2024
Abstract
This study extensively analyzed the bacterial information of biofilms and activated sludge in oxic reactors of full-scale moving bed biofilm reactor-integrated fixed-film activated sludge (MBBR-IFAS) systems. The bacterial communities of biofilms and activated sludge differed statistically (R = 0.624, p < 0.01). The [...] Read more.
This study extensively analyzed the bacterial information of biofilms and activated sludge in oxic reactors of full-scale moving bed biofilm reactor-integrated fixed-film activated sludge (MBBR-IFAS) systems. The bacterial communities of biofilms and activated sludge differed statistically (R = 0.624, p < 0.01). The denitrifying genera Ignavibacterium, Phaeodactylibacter, Terrimonas, and Arcobacter were more abundant in activated sludge (p < 0.05), while comammox Nitrospira was more abundant in biofilms (p < 0.05), with an average relative abundance of 8.13%. Nitrospira and Nitrosomonas had weak co-occurrence relationships with other genera in the MBBR-IFAS systems. Potential function analysis revealed no differences in pathways at levels 1 and 2 based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) between biofilms and activated sludge. However, in terms of pathways at level 3, biofilms had more potential in 26 pathways, including various organic biodegradation and membrane and signal transportation pathways. In comparison, activated sludge had more potential in only five pathways, including glycan biosynthesis and metabolism. With respect to nitrogen metabolism, biofilms had greater potential for nitrification (ammonia oxidation) (M00528), and complete nitrification (comammox) (M00804) concretely accounted for methane/ammonia monooxygenase (K10944, K10945, and K10946) and hydroxylamine dehydrogenase (K10535). This study provides a theoretical basis for MBBR-IFAS systems from the perspective of microorganisms. Full article
(This article belongs to the Section Microbial Biotechnology)
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12 pages, 1925 KiB  
Article
Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake
by Sylwia Nowakowska, Karol Borkowski, Carlotta Ruppert, Patryk Hejduk, Alexander Ciritsis, Anna Landsmann, Magda Marcon, Nicole Berger, Andreas Boss and Cristina Rossi
Bioengineering 2024, 11(6), 556; https://doi.org/10.3390/bioengineering11060556 (registering DOI) - 31 May 2024
Abstract
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually [...] Read more.
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility of such an assessment to inter-reader variability highlights the urgent need for a standardized classification algorithm. In this retrospective study, the first post-contrast subtraction images for 27 healthy female subjects were included. The BPE was classified slice-wise by two expert radiologists. The extraction of radiomic features from segmented BPE was followed by dataset splitting and dimensionality reduction. The latent representations were then utilized as inputs to a deep neural network classifying BPE into BI-RADS classes. The network’s predictions were elucidated at the radiomic feature level with Shapley values. The deep neural network achieved a BPE classification accuracy of 84 ± 2% (p-value < 0.00001). Most of the misclassifications involved adjacent classes. Different radiomic features were decisive for the prediction of each BPE class underlying the complexity of the decision boundaries. A highly precise and explainable pipeline for BPE classification was achieved without user- or algorithm-dependent radiomic feature selection. Full article
(This article belongs to the Special Issue Advances in Breast Cancer Imaging)
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19 pages, 851 KiB  
Review
CAR-T Therapy in Multiple Myeloma: Looking Beyond
by Gianluca Maiorana, Giusy Antolino, Giacinto La Verde and Agostino Tafuri
Hemato 2024, 5(2), 180-198; https://doi.org/10.3390/hemato5020015 (registering DOI) - 31 May 2024
Abstract
Multiple Myeloma is a hematological neoplasm that, over the recent few years, has benefited from numerous therapeutic options. Among the latter, CAR-T stands out as the most recent and one of the most promising treatments currently available. Despite its recent introduction, multiple CAR-T [...] Read more.
Multiple Myeloma is a hematological neoplasm that, over the recent few years, has benefited from numerous therapeutic options. Among the latter, CAR-T stands out as the most recent and one of the most promising treatments currently available. Despite its recent introduction, multiple CAR-T products have already been approved, and research regarding cellular therapy is rapidly increasing. We conducted a comprehensive search and review of the available literature, including published studies and abstracts from recent meetings (ASH, ASCO, ASTCT, IMS), regarding Multiple Myeloma and CAR-T therapy. We describe the discovery and research regarding promising targets like the B-Cell Maturation Antigen (BCMA) and others, the origin and nature of CAR-T cells, and the recent introduction of anti-BCMA CAR-Ts Idecabtagene-vicleucel and Ciltacabtagene-autoleucel, which are currently the only approved CAR-T products for MM. Additionally, we discuss non-BCMA-targeting CAR-Ts and their clinical implications. Given the significant impact of cellular therapy, we provide an overview of its limitations and possible adverse implications, as well as related resistance mechanisms. Finally, we describe the current research aimed at improving CAR-T therapy in MM, including structural innovations and new therapeutic approaches, such as in the earlier lines of treatment and maintenance therapy. Full article
(This article belongs to the Section Plasma Cell Disorders)
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15 pages, 2741 KiB  
Systematic Review
Impact of Depression on Postoperative Medical and Surgical Outcomes in Spine Surgeries: A Systematic Review and Meta-Analysis
by Sepehr Aghajanian, Arman Shafiee, Mohammad Mobin Teymouri Athar, Fateme Mohammadifard, Saba Goodarzi, Fatemeh Esmailpur and Aladine A. Elsamadicy
J. Clin. Med. 2024, 13(11), 3247; https://doi.org/10.3390/jcm13113247 (registering DOI) - 31 May 2024
Abstract
Introduction: The relationship between psychiatric disorders, including depression, and invasive interventions has been a topic of debate in recent literature. While these conditions can impact the quality of life and subjective perceptions of surgical outcomes, the literature lacks consensus regarding the association between [...] Read more.
Introduction: The relationship between psychiatric disorders, including depression, and invasive interventions has been a topic of debate in recent literature. While these conditions can impact the quality of life and subjective perceptions of surgical outcomes, the literature lacks consensus regarding the association between depression and objective perioperative medical and surgical complications, especially in the neurosurgical domain. Methods: MEDLINE (PubMed), EMBASE, PsycINFO, and the Cochrane Library were queried in a comprehensive manner from inception until 10 November 2023, with no language restrictions, for citations investigating the association between depression and length of hospitalization, medical and surgical complications, and objective postoperative outcomes including readmission, reoperation, and non-routine discharge in patients undergoing spine surgery. Results: A total of 26 articles were considered in this systematic review. Upon pooled analysis of the primary outcome, statistically significantly higher rates were observed for several complications, including delirium (OR:1.92), deep vein thrombosis (OR:3.72), fever (OR:6.34), hematoma formation (OR:4.7), hypotension (OR:4.32), pulmonary embolism (OR:3.79), neurological injury (OR:6.02), surgical site infection (OR:1.36), urinary retention (OR:4.63), and urinary tract infection (OR:1.72). While readmission (OR:1.35) and reoperation (OR:2.22) rates, as well as non-routine discharge (OR:1.72) rates, were significantly higher in depressed patients, hospitalization length was comparable to non-depressed controls. Conclusions: The results of this review emphasize the significant increase in complications and suboptimal outcomes noted in patients with depression undergoing spinal surgery. Although a direct causal relationship may not be established, addressing psychiatric aspects in patient care is crucial for providing comprehensive medical attention. Full article
(This article belongs to the Special Issue Neurosurgery and Spine Surgery: From Up-to-Date Practitioners)
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16 pages, 4283 KiB  
Article
Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
by Qiang Sun, Jun Yan, Dongsheng Peng, Zhaokuan Lu, Xiaorui Chen and Yuxin Wang
Appl. Sci. 2024, 14(11), 4759; https://doi.org/10.3390/app14114759 (registering DOI) - 31 May 2024
Abstract
Time-domain numerical simulation is generally considered an accurate method to predict the mooring system performance, but it is also time and resource-consuming. This paper attempts to completely replace the time-domain numerical simulation with machine learning approaches, using a catenary anchor leg mooring (CALM) [...] Read more.
Time-domain numerical simulation is generally considered an accurate method to predict the mooring system performance, but it is also time and resource-consuming. This paper attempts to completely replace the time-domain numerical simulation with machine learning approaches, using a catenary anchor leg mooring (CALM) system design as an example. An adaptive sampling method is proposed to determine the dataset of various parameters in the CALM mooring system in order to train and validate the generated machine learning models. Reasonable prediction accuracy is achieved by the five assessed machine learning algorithms, namely random forest, extremely randomized trees, K-nearest neighbor, decision tree, and gradient boosting decision tree, among which random forest is found to perform the best if the sampling density is high enough. Full article
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11 pages, 6459 KiB  
Article
Assessment of the Micro-Tensile Bond Strength of a Novel Bioactive Dental Restorative Material (Surefil One)
by Abdulrahman A. Alghamdi, Smaher Athamh, Reham Alzhrani and Hanan Filemban
Polymers 2024, 16(11), 1558; https://doi.org/10.3390/polym16111558 (registering DOI) - 31 May 2024
Abstract
Objectives: The aim of this study is to assess the micro-tensile bond strength and the mode of failure of a bioactive hybrid self-adhesive composite (Surefil one) under various dentin conditions. Methods: Thirty-two extracted human molar teeth were used to test the micro-tensile bond [...] Read more.
Objectives: The aim of this study is to assess the micro-tensile bond strength and the mode of failure of a bioactive hybrid self-adhesive composite (Surefil one) under various dentin conditions. Methods: Thirty-two extracted human molar teeth were used to test the micro-tensile bond strength of Surefil one under different dentine conditions (no treatment, 37% phosphoric acid etching, and universal adhesive) in comparison with a resin-modified glass ionomer (RIVA). All restorations were light cure-bonded onto flat dentine and then sectioned into beams. Then, fractured specimens were observed under a light microscope to evaluate the mode of failure. Results: The Surefil one no-treatment group (NTG) exhibited the highest micro-tensile bond strength. Furthermore, there was no statistically significant difference observed between the Surefil one adhesive group (EAG) and the Surefil one acid etch group (EG). However, compared to other groups, the resin-modified glass ionomer (RIVA) produced the lowest results, which are statistically significant. Conclusion: Surefil one offers superior bond strength values when compared to resin-modified glass ionomers. Furthermore, Surefil one requires no dentin condition and has more straightforward clinical steps. Full article
(This article belongs to the Section Polymer Composites and Nanocomposites)
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19 pages, 1802 KiB  
Article
Research on Facial Expression Recognition Algorithm Based on Lightweight Transformer
by Bin Jiang, Nanxing Li, Xiaomei Cui, Weihua Liu, Zeqi Yu and Yongheng Xie
Information 2024, 15(6), 321; https://doi.org/10.3390/info15060321 (registering DOI) - 31 May 2024
Abstract
To avoid the overfitting problem of the network model and improve the facial expression recognition effect of partially occluded facial images, an improved facial expression recognition algorithm based on MobileViT has been proposed. Firstly, in order to obtain features that are useful and [...] Read more.
To avoid the overfitting problem of the network model and improve the facial expression recognition effect of partially occluded facial images, an improved facial expression recognition algorithm based on MobileViT has been proposed. Firstly, in order to obtain features that are useful and richer for experiments, deep convolution operations are added to the inverted residual blocks of this network, thus improving the facial expression recognition rate. Then, in the process of dimension reduction, the activation function can significantly improve the convergence speed of the model, and then quickly reduce the loss error in the training process, as well as to preserve the effective facial expression features as much as possible and reduce the overfitting problem. Experimental results on RaFD, FER2013, and FER2013Plus show that this method has significant advantages over mainstream networks and the network achieves the highest recognition rate. Full article
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18 pages, 5639 KiB  
Article
TYCOS: A Specialized Dataset for Typical Components of Satellites
by He Bian, Jianzhong Cao, Gaopeng Zhang, Zhe Zhang, Cheng Li and Junpeng Dong
Appl. Sci. 2024, 14(11), 4757; https://doi.org/10.3390/app14114757 (registering DOI) - 31 May 2024
Abstract
The successful detection of key components within satellites is a crucial prerequisite for executing on-orbit capture missions. Due to the inherent data-driven functionality, deep learning-based component detection algorithms rely heavily on the scale and quality of the dataset for their accuracy and robustness. [...] Read more.
The successful detection of key components within satellites is a crucial prerequisite for executing on-orbit capture missions. Due to the inherent data-driven functionality, deep learning-based component detection algorithms rely heavily on the scale and quality of the dataset for their accuracy and robustness. Nevertheless, existing satellite image datasets exhibit several deficiencies, such as the lack of satellite motion states, extreme illuminations, or occlusion of critical components, which severely hinder the performance of detection algorithms. In this work, we bridge the gap via the release of a novel dataset tailored for the detection of key components of satellites. Unlike the conventional datasets composed of synthetic images, the proposed Typical Components of Satellites (TYCOS) dataset comprises authentic photos captured in a simulated space environment. It encompasses three types of satellite, three types of key components, three types of illumination, and three types of motion state. Meanwhile, scenarios with occlusion in front of the satellite are also taken into consideration. On the basis of TYCOS, several state-of-the-art detection methods are employed in rigorous experiments followed by a comprehensive analysis, which further enhances the development of space scene perception and satellite safety. Full article
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25 pages, 15276 KiB  
Article
PP-ISEA: An Efficient Algorithm for High-Resolution Three-Dimensional Geometry Reconstruction of Space Targets Using Limited Inverse Synthetic Aperture Radar Images
by Rundong Wang, Weigang Zhu, Chenxuan Li, Bakun Zhu and Hongfeng Pang
Sensors 2024, 24(11), 3550; https://doi.org/10.3390/s24113550 (registering DOI) - 31 May 2024
Abstract
As the variety of space targets expands, two-dimensional (2D) ISAR images prove insufficient for target recognition, necessitating the extraction of three-dimensional (3D) information. The 3D geometry reconstruction method utilizing energy accumulation of ISAR image sequence (ISEA) facilitates superior reconstruction while circumventing the laborious [...] Read more.
As the variety of space targets expands, two-dimensional (2D) ISAR images prove insufficient for target recognition, necessitating the extraction of three-dimensional (3D) information. The 3D geometry reconstruction method utilizing energy accumulation of ISAR image sequence (ISEA) facilitates superior reconstruction while circumventing the laborious steps associated with factorization methods. Nevertheless, ISEA’s neglect of valid information necessitates a high quantity of images and elongated operation times. This paper introduces a partitioned parallel 3D reconstruction method utilizing sorted-energy semi-accumulation with ISAR image sequences (PP-ISEA) to address these limitations. The PP-ISEA innovatively incorporates a two-step search pattern—coarse and fine—that enhances search efficiency and conserves computational resources. It introduces a novel objective function ‘sorted-energy semi-accumulation’ to discern genuine scatterers from spurious ones and establishes a redundant point exclusion module. Experiments on the scatterer model and simulated electromagnetic model demonstrate that the PP-ISEA reduces the minimum image requirement from ten to four for high-quality scatterer model reconstruction, thereby offering superior reconstruction quality in less time. Full article
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16 pages, 320 KiB  
Article
Information Collection and Personalized Service Strategy of Monopoly under Consumer Misrepresentation
by Mingyue Zhong, Yan Cheng, Shu-e Mei and Weijun Zhong
J. Theor. Appl. Electron. Commer. Res. 2024, 19(2), 1321-1336; https://doi.org/10.3390/jtaer19020067 (registering DOI) - 31 May 2024
Abstract
To decrease privacy risks, consumers may choose to misrepresent themselves when they are asked to offer personal information. Using a game theoretic model, this study examines the impact of consumer misrepresentation on both a monopolistic firm and consumers. The results show that consumer [...] Read more.
To decrease privacy risks, consumers may choose to misrepresent themselves when they are asked to offer personal information. Using a game theoretic model, this study examines the impact of consumer misrepresentation on both a monopolistic firm and consumers. The results show that consumer misrepresentation may benefit the firm, but hurt consumers under certain conditions. In addition, we find that when the unit cost of personalized service is low, consumer misrepresentation may encourage the firm to provide a higher personalized service level. Moreover, when consumers misrepresent themselves and the firm only covers part of the market, a greater unit value of consumer private information will reduce the firm’s profit, while a greater unit cost of personalized service will increase the firm’s profit. The analysis reported here provides important insights regarding the application of consumer information in online personalized marketing and consumer privacy protection. Full article
(This article belongs to the Topic Online User Behavior in the Context of Big Data)
14 pages, 4129 KiB  
Article
D-Limonene Is the Active Olfactory Attractant in Orange Juice for Bactrocera dorsalis (Insecta: Diptera: Tephritidae)
by Leyuan Liu, Lang Yang, Jinxi Yuan, Jie Zhang, Chenhao Liu, Hongxu Zhou, Wei Liu and Guirong Wang
Life 2024, 14(6), 713; https://doi.org/10.3390/life14060713 (registering DOI) - 31 May 2024
Abstract
The oriental fruit fly, Bactrocera dorsalis (Hendel), poses a significant threat to the global fruit industry, causing damage to diverse fruits like citrus, mango, and guava. Chemical pesticides have limited effectiveness, and pesticide residues and pesticide resistance are pressing issues. Therefore, it is [...] Read more.
The oriental fruit fly, Bactrocera dorsalis (Hendel), poses a significant threat to the global fruit industry, causing damage to diverse fruits like citrus, mango, and guava. Chemical pesticides have limited effectiveness, and pesticide residues and pesticide resistance are pressing issues. Therefore, it is essential to develop environmentally friendly pest control methods to address this problem. Behavior-modifying chemicals, including male attractants and intersex protein baits, play a critical role in the control of B. dorsalis. The mature host fruit serves as both an oviposition site and food source under natural conditions, making it a potential attraction source for oriental fruit flies. Orange, Citrus sinensis, is a main host of B. dorsalis, and commercial orange juice is a common attractant for the egg laying of B. dorsalis. Although it can both attract and elicit oviposition behaviors in B. dorsalis adults, its active components are still unclear. This study utilized analytical chemistry, behavioral tests, and electrophysiology to identify the active components of commercial orange juice that attract B. dorsalis, with the aim of providing a reference for the development of behavior-modifying chemical-based techniques to control B. dorsalis. Five compounds with a high abundance were identified via a GC-MS, including D-Limonene, butanoic acid ethyl ester, β-myrcene, linalool, and α-terpineol. Behavioral and electrophysiological experiments uncovered that D-Limonene was the active substance that was the main attractant in the mixture of these five substances, evoking a strong electrophysiological response in adult B. dorsalis. D-Limonene strongly attracts adult B. dorsalis only when they are sexually mature, and the attraction is not rhythmic. Olfaction plays a leading role in the attraction of D-Limonene to adult B. dorsalis, and Orco−/− mediates the perception of D-Limonene by B. dorsalis. Overall, D-Limonene is one of the key attractant compounds for B. dorsalis in the volatile compounds of commercial orange juice, offering possible support for the development of behavior-modifying chemical-based technology to control B. dorsalis in the future. Full article
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14 pages, 1621 KiB  
Article
Cationic Glucan Dendrimer Gel-Mediated Local Delivery of Anti-OC-STAMP-siRNA for Treatment of Pathogenic Bone Resorption
by Kenta Yamamoto, Shin-Ichi Sawada, Satoru Shindo, Shin Nakamura, Young M. Kwon, Nazanin Kianinejad, Saynur Vardar, Maria Hernandez, Kazunari Akiyoshi and Toshihisa Kawai
Gels 2024, 10(6), 377; https://doi.org/10.3390/gels10060377 (registering DOI) - 31 May 2024
Abstract
Osteoclast stimulatory transmembrane protein (OC-STAMP) plays a pivotal role in the promotion of cell fusion during osteoclast differentiation (osteoclastogenesis) in the context of pathogenic bone resorption. Thus, it is plausible that the suppression of OC-STAMP through a bioengineering approach could lead to the [...] Read more.
Osteoclast stimulatory transmembrane protein (OC-STAMP) plays a pivotal role in the promotion of cell fusion during osteoclast differentiation (osteoclastogenesis) in the context of pathogenic bone resorption. Thus, it is plausible that the suppression of OC-STAMP through a bioengineering approach could lead to the development of an effective treatment for inflammatory bone resorptive diseases with minimum side effects. Here, we synthesized two types of spermine-bearing (Spe) cationic glucan dendrimer (GD) gels (with or without C12) as carriers of short interfering RNA (siRNA) to silence OC-STAMP. The results showed that amphiphilic C12-GD-Spe gel was more efficient in silencing OC-STAMP than GD-Spe gel and that the mixture of anti-OC-STAMP siRNA/C12-GD-Spe significantly downregulated RANKL-induced osteoclastogenesis. Also, local injection of anti-OC-STAMP-siRNA/C12-GD-Spe could attenuate bone resorption induced in a mouse model of periodontitis. These results suggest that OC-STAMP is a promising target for the development of a novel bone regenerative therapy and that C12-GD-Spe gel provides a new nanocarrier platform of gene therapies for osteolytic disease. Full article
(This article belongs to the Section Gel Applications)
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13 pages, 7709 KiB  
Article
Functional Characterization of the First Bona Fide Phytoene Synthase in Red Algae from Pyropia yezoensis
by Cheng-Ling Li, Jia-Qiu Pu, Wei Zhou, Chuan-Ming Hu, Yin-Yin Deng, Ying-Ying Sun and Li-En Yang
Mar. Drugs 2024, 22(6), 257; https://doi.org/10.3390/md22060257 (registering DOI) - 31 May 2024
Abstract
The formation of phytoene by condensing two geranylgeranyl diphosphate molecules catalyzed by phytoene synthase (PSY) is the first committed and rate-limiting step in carotenoid biosynthesis, which has been extensively investigated in bacteria, land plants and microalgae. However, this step in macroalgae remains unknown. [...] Read more.
The formation of phytoene by condensing two geranylgeranyl diphosphate molecules catalyzed by phytoene synthase (PSY) is the first committed and rate-limiting step in carotenoid biosynthesis, which has been extensively investigated in bacteria, land plants and microalgae. However, this step in macroalgae remains unknown. In the present study, a gene encoding putative phytoene synthase was cloned from the economic red alga Pyropia yezoensis—a species that has long been used in food and pharmaceuticals. The conservative motifs/domains and the tertiary structure predicted using bioinformatic tools suggested that the cloned PyPSY should encode a phytoene synthase; this was empirically confirmed by pigment complementation in E. coli. This phytoene synthase was encoded by a single copy gene, whose expression was presumably regulated by many factors. The phylogenetic relationship of PSYs from different organisms suggested that red algae are probably the progeny of primary endosymbiosis and plastid donors of secondary endosymbiosis. Full article
(This article belongs to the Special Issue Enzymes from Marine By-Products and Wastes)
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13 pages, 4045 KiB  
Article
Ni and Co Catalysts on Interactive Oxide Support for Anion Exchange Membrane Electrolysis Cell (AEMEC)
by Katerina Maksimova-Dimitrova, Borislava Mladenova, Galin Borisov and Evelina Slavcheva
Inorganics 2024, 12(6), 153; https://doi.org/10.3390/inorganics12060153 (registering DOI) - 31 May 2024
Abstract
The work presents novel composite catalytic materials—Ni and Co deposited on Magneli phase titania—and describes their complex characterization and integration into membrane electrode assemblies to produce hydrogen by electrochemical water splitting in cells with anion exchange membranes (AEMEC). Chemical composition, surface structure, and [...] Read more.
The work presents novel composite catalytic materials—Ni and Co deposited on Magneli phase titania—and describes their complex characterization and integration into membrane electrode assemblies to produce hydrogen by electrochemical water splitting in cells with anion exchange membranes (AEMEC). Chemical composition, surface structure, and morphology were characterized by XRD and SEM analysis. The activity in the evolution of the partial electrode reactions of hydrogen (HER) and oxygen (OER) was assessed in an aqueous alkaline electrolyte (25 wt.% KOH) using linear sweep voltammetry. The interactive role of the support was investigated and discussed. Among the tested samples, the sample with 30 wt.% Co (Co30/MPT) demonstrated superior performance in the OER. The reaction started at 1.65 V, and at 1.8 V, the current density reached 75 mA cm−2. The HER is most efficient on the sample containing 40 wt.% Ni (Ni40/MPT), where the current density reaches 95 mA at a potential of −0.5 V. The change in catalytic efficiency compared to that of the unsupported Ni and Co is due to synergism resulting from electronic interactions between the transition metal having a hyper-d-electron character and hypo-d-electron support. The pre-selected catalysts were integrated in membrane electrode assembly (MEA) using commercial and laboratory-prepared anion-conductive membranes and tested in a custom-made AEMEC. The performance was compared to that of MEA with a commercial carbon-supported Pt catalyst. It was found that the MEA with newly prepared catalysts demonstrated better performance in long-term operation (50 mA cm−2 at 1.8 V in a 60 h durability test), which, combined with the higher cost efficiency, gave credence to considering this combination of materials as promising for AEMEC applications. Full article
(This article belongs to the Special Issue Simulation-Aided Materials Design for Electrocatalysis)
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19 pages, 4568 KiB  
Article
Pore Water Pressure Prediction Based on Machine Learning Methods—Application to an Earth Dam Case
by Lu An, Daniel Dias, Claudio Carvajal, Laurent Peyras, Pierre Breul, Orianne Jenck and Xiangfeng Guo
Appl. Sci. 2024, 14(11), 4749; https://doi.org/10.3390/app14114749 (registering DOI) - 31 May 2024
Abstract
Pore water pressure (PWP) response is significant for evaluating the earth dams’ stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order [...] Read more.
Pore water pressure (PWP) response is significant for evaluating the earth dams’ stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R2), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R2 = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models’ robustness and the hyperparameter’s influence on the performance of the prediction model. Full article
(This article belongs to the Special Issue Data Science in Water Conservancy Engineering)
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16 pages, 13553 KiB  
Article
Evaluation of Lateral Radar Positioning for Vital Sign Monitoring: An Empirical Study
by Lars Hornig, Benedek Szmola, Wiebke Pätzold, Jan Paul Vox and Karen Insa Wolf
Sensors 2024, 24(11), 3548; https://doi.org/10.3390/s24113548 (registering DOI) - 31 May 2024
Abstract
Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral [...] Read more.
Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral radar position is a radar placement from which multiple human body zones are mapped onto different radar range sections. These body zones can be used to extract breathing and heartbeat motions independently from one another via these different range sections. Radars were positioned above the bed as a conventional approach and on a bedside table as well as at the foot end of the bed as lateral positions. These positions were evaluated based on six nights of sleep collected from healthy volunteers with polysomnography (PSG) as a reference system. For breathing extraction, comparable results were observed for all three radar positions. For heartbeat extraction, a higher level of agreement between the radar foot end position and the PSG was found. An example of the distinction between thoracic and abdominal breathing using a lateral radar position is shown. Lateral radar positions could lead to a more detailed analysis of movements along the body, with the potential for diagnostic applications. Full article
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23 pages, 5123 KiB  
Article
Direct Yaw Moment Control for Distributed Drive Electric Vehicles Based on Hierarchical Optimization Control Framework
by Jie Hu, Kefan Zhang, Pei Zhang and Fuwu Yan
Mathematics 2024, 12(11), 1715; https://doi.org/10.3390/math12111715 (registering DOI) - 31 May 2024
Abstract
Direct yaw moment control (DYC) can effectively improve the yaw stability of four-wheel distributed drive electric vehicles (4W-DDEVs) under extreme conditions, which has become an indispensable part of active safety control for 4W-DDEVs. This study proposes a novel hierarchical DYC architecture for 4W-DDEVs [...] Read more.
Direct yaw moment control (DYC) can effectively improve the yaw stability of four-wheel distributed drive electric vehicles (4W-DDEVs) under extreme conditions, which has become an indispensable part of active safety control for 4W-DDEVs. This study proposes a novel hierarchical DYC architecture for 4W-DDEVs to enhance vehicle stability during ever-changing road conditions. Firstly, a vehicle dynamics model is established, including a two-degree-of-freedom (2DOF) vehicle model for calculating the desired yaw rate and sideslip angle as the control target of the upper layer controller, a DDEV model composed of a seven-degree-of-freedom (7DOF) vehicle model, a tire model, a motor model and a driver model. Secondly, a hierarchical DYC is designed combining the upper layer yaw moment calculation and low layer torque distribution. Specifically, based on Matlab/Simulink, improved linear quadratic regulator (LQR) with weight matrix optimization based on inertia weight cosine-adjustment particle swarm optimization (IWCPSO) is employed to compute the required additional yaw moment in the upper-layer controller, while quadratic programming (QP) is used to allocate four motors’ torque with the optimization objective of minimizing the tire utilization rate. Finally, a comparative test with double-lane-change and sinusoidal conditions under a low and high adhesion road surface is conducted on Carsim and Matlab/Simulink joint simulation platform. With IWCPSO-LQR under double-lane-change (DLC) condition on a low adhesion road surface, the yaw rate and sideslip angle of the DDEV exhibits improvements of 95.2%, 96.8% in the integral sum of errors, 94.9%, 95.1% in the root mean squared error, and 78.8%, 98.5% in the peak value compared to those without control. Simulation results indicate the proposed hierarchical control method has a remarkable control effect on the yaw rate and sideslip angle, which effectively strengthens the driving stability of 4W-DDEVs. Full article
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13 pages, 692 KiB  
Article
Mitigating Identity-Related Anxiety through Humor and Immersive Storytelling with 360-Degree Video in Virtual Reality: A Study on Microaggressions’ Mental Health Effects
by Changmin Yan, Alan Eno and Adam Wagler
Int. J. Environ. Res. Public Health 2024, 21(6), 713; https://doi.org/10.3390/ijerph21060713 (registering DOI) - 31 May 2024
Abstract
Background: Microaggressions are subtle slights that can cause significant psychological distress among marginalized groups. Few studies have explored interventions that might mitigate these effects. Objective: This study aimed to investigate if and how humor-infused immersive storytelling via virtual reality (VR) could [...] Read more.
Background: Microaggressions are subtle slights that can cause significant psychological distress among marginalized groups. Few studies have explored interventions that might mitigate these effects. Objective: This study aimed to investigate if and how humor-infused immersive storytelling via virtual reality (VR) could reduce identity-related psychological distress caused by microaggressions. Methods: Using a community-based participatory research approach, we developed a 7-min 360-degree VR film depicting scenarios of microaggressions across various identities. Forty-six college students participated in a controlled study where they were exposed to this immersive VR experience. We measured identity-related psychological anxiety, character identification, perceived humor, and perceived psychological presence. Results: The findings demonstrated a significant anxiety reduction following the VR intervention, supporting the efficacy of humor-infused storytelling in alleviating the impact of microaggressions. Character identification significantly predicted anxiety reduction, while perceived humor and psychological presence did not directly influence anxiety reduction but indirectly contributed through enhanced character identification. Conclusions: Humor-infused immersive storytelling, facilitated by VR, effectively reduces identity-related psychological distress primarily through character identification. The structural equation modeling results emphasize the importance of integrating humor and psychological presence to enhance character connection, advocating for a balanced approach that combines traditional narrative elements with technological innovations in health interventions aimed at combating the adverse psychological effects of microaggressions. Full article
(This article belongs to the Special Issue The 20th Anniversary of IJERPH)
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12 pages, 932 KiB  
Article
The Effect of Critical Distance in Digital Levelling
by Jana Izvoltova, Jakub Chromcak and Dasa Bacova
Computation 2024, 12(6), 111; https://doi.org/10.3390/computation12060111 (registering DOI) - 31 May 2024
Abstract
Critical distance concerns precise digital levelling, which has inaccurate results at a certain sighting distance. The influence of critical distance on a measured height difference has been confirmed by calibrating certain digital levels and their appropriate code devices on a vertical comparator under [...] Read more.
Critical distance concerns precise digital levelling, which has inaccurate results at a certain sighting distance. The influence of critical distance on a measured height difference has been confirmed by calibrating certain digital levels and their appropriate code devices on a vertical comparator under laboratory conditions. The paper aims to explore the influence of critical distance on height differences obtained by precise digital levels of Leica NA3003 and DNA03 by experimental measurements realised in situ. The processing of the measurement results consisted of defining a random error on a station by using parameter estimation of an error model to specify a partial error on a station dependent on sighting distance. Then the processing phase continues with the finding of the relation between the sighting distance and the dispersion of height differences acquired by digital levelling under terrain conditions. The theoretical part involves the development of levelling accuracy theories that vary over time by view on random and systematic error propagation. The numerical and graphical solution of the experimental measurements involves ordering the height differences into sighting groups according to the sighting distance. The standard deviation computed in each sighting group represents a measure of the dispersion of height differences. Suppose the standard deviation in the sighting group in both independent experimental locations K1 and K2 exceeds twice the total standard deviation. In that case, it is most likely considered to be the influence of the critical distance, which is then compared with values obtained by laboratory calibration of the same digital levels. Full article
(This article belongs to the Special Issue Causal Inference, Probability Theory and Graphical Concepts)
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24 pages, 1605 KiB  
Review
Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies
by Dalton Hilovsky, Joshua Hartsell, Jamey D. Young and Xiaojing Liu
Metabolites 2024, 14(6), 318; https://doi.org/10.3390/metabo14060318 (registering DOI) - 31 May 2024
Abstract
Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new [...] Read more.
Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism. Full article
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